Statistics for Managers notes.pdf

MALLA REDDY COLLEGE OF ENGINEERING & TECHNOLOGY
(Autonomous Institution-UGC, Govt. of India)
Course Title : STATISTICS FOR MANAGERS
Course Code : R20MBA04
Course (Year/Semester) : MBA I Year I Semester
Course Type : Core
Course Credits : 3
Course Aim/s:
 To encourage thinking statistically.
 To develop the abilities to understand and use data.
 To develop expertise in a standard set of statistical and graphical techniques that will be useful in
analysing data, and to learn to apply these techniques in a number of areas of management.
Learning Outcome/s:
 Appreciate that the collection and statistical analysis of data improves business decisions and
reduces the risk of implementing solutions that waste resources and effort.
 Select and deploy the correct statistical method for a given data analysis requirement.
 Achieve a practical level of competence in building statistical models that suit business
applications.
 Recognise, develop and distinguish between models for cross-sectional analysis at a single point
in time and models for time series analysis at multiple points in time.
Unit-I: Introduction to Statistics
Overview: Origin and Development - Managerial Applications of Statistics - Statistics and the
Branches of the Study - Statistics & Computers - Limitations of Statistics.
Unit-II: Measures of Central Tendency, Dispersion & Skewness
Measures of Central Tendency: Mean - Median - Mode.
Measures of Dispersion: Range - Quartile Deviation - Mean Deviation - Standard Deviation and
Co-efficient of Variation.
Skewness: Karl Pearson Co-efficient - Bowley’s Co-efficient - Kelley's Co-efficient.
(Theory and Problems, Discussion on Direct and Indirect Methods of Solving the Problems).
Unit-III: Tabulation and Graphical Presentation of Data
Classification and Tabulation: Univariate - Bivariate - Multivariate Data - Data Classification and
Tabulation.
Graphical Presentation of Data: Diagrammatic and Graphical Representation of Data - One
Dimensional - Two Dimensional - Three Dimensional Diagrams and Graphs.
Unit-IV: Small Sample Tests
Sample Test: t-Distribution - Properties and Applications - Testing for One and Two Means - Paired
t-test.
Analysis of Variance: One Way and Two Way ANOVA (with and without Interaction).
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Chi-Square distribution: Test for a specified Population variance - Test for Independence of
Attributes.
Unit-V: Correlation and Regression Analysis
Correlation Analysis: Scatter Diagram - Positive and Negative Correlation - Limits for Coefficient
of Correlation - Karl Pearson’s Coefficient of Correlation - Spearman’s Rank Correlation - Concept
of Multiple and Partial Correlation.
Regression Analysis: Concept - Least Square Method - Two Lines of Regression - Properties of
Regression Coefficients. Time Series Analysis: Trend analysis - Free Hand Curve - Moving
Averages.
REFERENCES:
 Levin R.I., Rubin S. David, “Statistics for Management”, Pearson.
 Beri, “Business Statistics”, TMH.
 Gupta S.C, “Fundamentals of Statistics”, HPH.
 Amir D. Aczel and Jayavel Sounder pandian, “Complete Business Statistics”, TMH,
 Levine, Stephan , Krehbiel , Berenson - Statistics for Managers using Microsoft Excel, PHI.
 J. K Sharma, “Business Statistics”, Pearson.
Refer Class Notes also
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MBA I Year I Semester Statistics for Managers
Compiled by
Dr. I. J. Raghavendra, Associate Professor, MBA-MRCET
Prof. G. Naveen Kumar, HOD, MBA-MRCET Page 1
INTRODUCTION TO STATISTICS
ORIGIN OF STATISTICS
The Word statistics have been derived from Latin word “Status” or the Italian word “Statista”,
meaning of these words is “Political State” or a Government. Shakespeare used a word Statist is his
drama Hamlet (1602). In the past, the statistics was used by rulers. The application of statistics was
very limited but rulers and kings needed information about lands, agriculture, commerce, population
of their states to assess their military potential, their wealth, taxation and other aspects of
government.
Gottfried Achenwall used the word statistik at a German University in 1749 which means that
political science of different countries. In 1771 W. Hooper (Englishman) used the word statistics in
his translation of Elements of Universal Erudition written by Baron B.F Bieford, in his book
statistics has been defined as the science that teaches us what is the political arrangement of all the
modern states of the known world. There is a big gap between the old statistics and the modern
statistics, but old statistics also used as a part of the present statistics.
During the 18th
century the English writer have used the word statistics in their works, so
statistics has developed gradually during last few centuries. A lot of work has been done in the end
of the nineteenth century.
At the beginning of the 20th
century, William S Gosset was developed the methods for
decision making based on small set of data. During the 20th century several statistician are active in
developing new methods, theories and application of statistics. Now these days the availability of
electronics computers is certainly a major factor in the modern development of statistics.
Meaning of Statistics:
The word statistics has three different meanings (sense) which are discussed below:
(1) Plural Sense: In plural sense, the word statistics refer to numerical facts and figures collected in
a systematic manner with a definite purpose in any field of study. In this sense, statistics are also
aggregates of facts which are expressed in numerical form. For example, Statistics on industrial
production, statistics or population growth of a country in different years etc.
(2) Singular Sense: In singular sense, it refers to the science comprising methods which are used in
collection, analysis, interpretation and presentation of numerical data. These methods are used to
draw conclusion about the population parameter. For Example: If we want to have a study about the
distribution of weights of students in a certain college. First of all, we will collect the information on
the weights which may be obtained from the records of the college or we may collect from the
students directly. The large number of weight figures will confuse the mind. In this situation we may
arrange the weights in groups such as: “50 Kg to 60 Kg” “60 Kg to 70 Kg” and so on and find the
number of students fall in each group. This step is called a presentation of data. We may still go
further and compute the averages and some other measures which may give us complete description
of the original data.
(3) Plural of Word “Statistic”: The word statistics is used as the plural of the word “Statistic”
which refers to a numerical quantity like mean, median, variance etc…, calculated from sample
value. For Example: If we select 15 students from a class of 80 students, measure their heights and
find the average height. This average would be a statistic.
Definition:
Statistics has been defined differently by different authors and each author has assigned new limits to
the field which should be included in its scope. We can do no better than give selected definitions
of statistics by some authors and then come to the conclusion about the scope of the subject.
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A.L. Bowley defines, “Statistics may be called the science of counting”. At another place
he defines, “Statistics may be called the science of averages”. Both these definitions are narrow
and throw light only on one aspect of Statistics.
According to King, “The science of statistics is the method of judging collective, natural or
social, phenomenon from the results obtained from the analysis or enumeration or collection of
estimates”.
Many a time counting is not possible and estimates are required to be made. Therefore,
Boddington defines it as “the science of estimates and probabilities”. But this definition also does not
cover the entire scope of statistics. The statistical methods are methods for the collection, analysis
and interpretation of numerical data and form a basis for the analysis and comparison of the observed
phenomena. In the words of Croxton &Cowden, “Statistics may be defined as the collection,
presentation, analysis and interpretation of numerical data”.
Horace Secrist has given an exhaustive definition of the term statistics in the plural
sense. According to him:“By statistics we mean aggregates of facts affected to a marked extent
by a multiplicity of causes numerically expressed, enumerated or estimated according to
reasonable standards of accuracy collected in a systematic manner for a pre-determined
purpose and placed in relation to each other”.
Importance of Statistics in Different Fields:
Statistics plays a vital role in every fields of human activity. Statistics has important role in
determining the existing position of per capita income, unemployment, population growth rate,
housing, schooling medical facilities etc…in a country.
Now statistics holds a central position in almost every field like Industry, Commerce, Trade, Physics,
Chemistry, Economics, Mathematics, Biology, Botany, Psychology, Astronomy etc…, so application
of statistics is very wide. Now we discuss some important fields in which statistics is commonly
applied.
(1) Business: Statistics play an important role in business. A successful businessman must be very
quick and accurate in decision making. He knows that what his customers wants, he should therefore,
know what to produce and sell and in what quantities. Statistics helps businessman to plan
production according to the taste of the costumers, the quality of the products can also be checked
more efficiently by using statistical methods. So all the activities of the businessman based on
statistical information. He can make correct decision about the location of business, marketing of the
products, financial resources etc…
(2) In Economics: Statistics play an important role in economics. Economics largely depends upon
statistics. National income accounts are multipurpose indicators for the economists and
administrators. Statistical methods are used for preparation of these accounts. In economics research
statistical methods are used for collecting and analysis the data and testing hypothesis. The
relationship between supply and demands is studies by statistical methods, the imports and exports,
the inflation rate, the per capita income are the problems which require good knowledge of statistics.
(3) In Mathematics: Statistical plays a central role in almost all natural and social sciences. The
methods of natural sciences are most reliable but conclusions draw from them are only probable,
because they are based on incomplete evidence. Statistical helps in describing these measurements
more precisely. Statistics is branch of applied mathematics. The large number of statistical methods
like probability averages, dispersions, estimation etc… is used in mathematics and different
techniques of pure mathematics like integration, differentiation and algebra are used in statistics.
(4) In Banking: Statistics play an important role in banking. The banks make use of statistics for a
number of purposes. The banks work on the principle that all the people who deposit their money
with the banks do not withdraw it at the same time. The bank earns profits out of these deposits by
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lending to others on interest. The bankers use statistical approaches based on probability to estimate
the numbers of depositors and their claims for a certain day.
(5) In State Management (Administration): Statistics is essential for a country. Different policies
of the government are based on statistics. Statistical data are now widely used in taking all
administrative decisions. Suppose if the government wants to revise the pay scales of employees in
view of an increase in the living cost, statistical methods will be used to determine the rise in the cost
of living. Preparation of federal and provincial government budgets mainly depends upon statistics
because it helps in estimating the expected expenditures and revenue from different sources. So
statistics are the eyes of administration of the state.
(6) In Accounting and Auditing: Accounting is impossible without exactness. But for decision
making purpose, so much precision is not essential the decision may be taken on the basis of
approximation, know as statistics. The correction of the values of current asserts is made on the basis
of the purchasing power of money or the current value of it.
In auditing sampling techniques are commonly used. An auditor determines the sample size of the
book to be audited on the basis of error.
(7) In Natural and Social Sciences: Statistics plays a vital role in almost all the natural and social
sciences. Statistical methods are commonly used for analyzing the experiments results, testing their
significance in Biology, Physics, Chemistry, Mathematics, Meteorology, Research chambers of
commerce, Sociology, Business, Public Administration, Communication and Information
Technology etc…
(8) In Astronomy: Astronomy is one of the oldest branches of statistical study; it deals with the
measurement of distance, sizes, masses and densities of heavenly bodies by means of observations.
During these measurements errors are unavoidable so most probable measurements are founded by
using statistical methods.
Example: This distance of moon from the earth is measured. Since old days the astronomers have
been statistical methods like method of least squares for finding the movements of stars.
Kinds or Branches Statistics:
Statistics may be divided into two main branches:
(1) Descriptive Statistics:
It deals with collection of data, its presentation in various forms, such as tables, graphs and diagrams
and findings averages and other measures which would describe the data.
For Example: Industrial statistics, population statistics, trade statistics etc… Such as businessman
make to use descriptive statistics in presenting their annual reports, final accounts, bank statements.
(2) Inferential Statistics:
It deals with techniques used for analysis of data, making the estimates and drawing conclusions
from limited information taken on sample basis and testing the reliability of the estimates.
For Example: Suppose we want to have an idea about the percentage of illiterates in our country. We
take a sample from the population and find the proportion of illiterates in the sample. This sample
proportion with the help of probability enables us to make some inferences about the population
proportion. This study belongs to inferential statistics.
Characteristics of Statistics:
 Statistics are aggregates of facts.
 Statistics are numerically expressed.
 Statistics are affected to a marked extent by multiplicity of causes.
 Statistics are enumerated or estimated according to a reasonable standard of accuracy.
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 Statistics are collected for a predetermine purpose.
 Statistics are collected in a systemic manner.
 Statistics must be comparable to each other.
Functions or Uses of Statistics:
(1) Statistics helps in providing a better understanding and exact description of a phenomenon of
nature.
(2) Statistical helps in proper and efficient planning of a statistical inquiry in any field of study.
(3) Statistical helps in collecting an appropriate quantitative data.
(4) Statistics helps in presenting complex data in a suitable tabular, diagrammatic and graphic
form for an easy and clear comprehension of the data.
(5) Statistics helps in understanding the nature and pattern of variability of a phenomenon
through quantitative observations.
(6) Statistics helps in drawing valid inference, along with a measure of their reliability about the
population parameters from the sample data.
Limitations of Statistics:
The important limitations of statistics are:
(1) Statistics laws are true on average. Statistics are aggregates of facts. So single observation is
not a statistics, it deals with groups and aggregates only.
(2) Statistical methods are best applicable on quantitative data.
(3) Statistical cannot be applied to heterogeneous data.
(4) It sufficient care is not exercised in collecting, analyzing and interpretation the data,
statistical results might be misleading.
(5) Only a person who has an expert knowledge of statistics can handle statistical data
efficiently.
(6) Some errors are possible in statistical decisions. Particularly the inferential statistics involves
certain errors. We do not know whether an error has been committed or not.
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ORGANISATION OF TERMS
Experimental Design
Descriptive Inferential
Population
 Parameter
Sample
 Random
 Bias
 Statistic
Types of
Variables Graphs
 Measurement Scales
o Nominal
o Ordinal
o Interval
o Ratio
 Qualitative
 Quantitative
 Independent
 Dependent
 Bar Graphs
 Histogram
 Box Plot
 Scatter plot
Measures of
Center Spread Shape
 Mean
 Median
 Mode
 Range
 Quartile Deviation
 Mean Deviation
 Standard Deviation
 Variance
 Skewness
 Kurtosis
Tests of
Association Inference
 Correlation
 Regression
o Slope
o y-intercept
 Chi-square
 t-test
o Independent samples
o Correlated samples
 Analysis-of-Variance
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MEASURES OF CENTRAL TENDENCY
One of the important objectives of statistics is to find out various numerical values which explain the
inherent characteristics of a frequency distribution. The first of such measures is averages. The
averages are the measures which condense a huge unwieldy set of numerical data into single
numerical values which represent the entire distribution. The inherent inability of the human mind to
remember a large body of numerical data compels us to few constants that will describe the data.
Averages provide us the gist and give a bird‟s eye view of the huge mass of unwieldy numerical
data. Averages are the typical values around which other items of the distribution congregate. This
value lies between the two extreme observations of the distribution and give us an idea about the
concentration of the values in the central part of the distribution. They are called the measures of
central tendency.
Averages are also called measures of location since they enable us to locate the position or place
of the distribution in question. Averages are statistical constants which enables us to comprehend in a
single value the significance of the whole group. According to Croxlon and Cowden, an average
value is a single value within the range of the data that is used to represent all the values in that
series. Since an average is somewhere within the range of data, it is sometimes called a measure of
central value. An average is the most typical representative item of the group to which it belongs and
which is capable of revealing all important characteristics of that group or distribution.
Measures of central tendency, Mean, Median, Mode, etc., indicate the central position of a
series. They indicate the general magnitude of the data but fail to reveal all the peculiarities and
characteristics of the series. In other words, they fail to reveal the degree of the spread out or the
extent of the variability in individual items of the distribution. This can be explained by certain other
measures, known as „Measures of Dispersion‟ or Variation.
The study of statistics does not show much interest in things which are constant. The total area of the
Earth may not be very important to a research-minded, person but the area covered by different
crops, forests, residential and commercial buildings are figures of great importance, because these
figures keep on changing from time to time and from place to place. Many experts are engaged in the
study of changing phenomena.
Experts working in different countries keep a watch on forces which are responsible for bringing
changes in the fields of human interest. Agricultural, industrial and mineral production and their
transportation from one area to other parts of the world are of great interest to economists,
statisticians, and other experts. Changes in human populations, changes in standards of living,
changes in literacy rates and changes in prices attract experts to perform detailed studies and then
correlate these changes to human life. Thus variability or variation is connected with human life and
its study is very important for mankind.
Objects of Central Tendency:
The most important object of calculating an average or measuring central tendency is to determine
a single figure which may be used to represent a whole series involving magnitudes of the same
variable. Second object is that an average represents the empire data; it facilitates comparison within
one group or between groups of data. Thus, the performance of the members of a group can be
compared with the average performance of different groups. Third object is that an average helps in
computing various other statistical measures such as dispersion, skewness, kurtosis etc.
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Essential of a Good Average:
An average represents the statistical data and it is used for purposes of comparison, it must
possess the following properties.
1. It must be rigidly defined and not left to the mere estimation of the observer. If the definition
is rigid, the computed value of the average obtained by different persons shall be similar.
2. The average must be based upon all values given in the distribution. If the item is not based
on all value it might not be representative of the entire group of data.
3. It should be easily understood. The average should possess simple and obvious properties.
It should be too abstract for the common people.
4. It should be capable of being calculated with reasonable care and rapidity.
5. It should be stable and unaffected by sampling fluctuations.
6. It should be capable of further algebraic manipulation.
Different methods of measuring “Central Tendency” provide us with different kinds of averages. The
following are the main types of averages that are commonly used:
1. Mean
2. Median
3. Mode
Arithmetic Mean
Arithmetic mean is the most commonly used average or measure of the central tendency applicable
only in case of quantitative data; it is also simply called the “mean”. Arithmetic mean is defined as:
“Arithmetic mean is a quotient of sum of the given values and number of the given values”.
Arithmetic mean can be computed for both ungrouped data (raw data: data without any statistical
treatment) and grouped data (data arranged in tabular form containing different groups).
Pros and Cons of Arithmetic Mean:
Pros:
 It is rigidly defined
 It is easy to calculate and simple to follow
 It is based on all the observations
 It is determined for almost every kind of data
 It is finite and not indefinite
 It is readily used in algebraic treatment
 It is least affected by fluctuations of sampling
Cons:
 The arithmetic mean is highly affected by extreme values
 It cannot average the ratios and percentages properly
 It is not an appropriate average for highly skewed distributions
 It cannot be computed accurately if any item is missing
 The mean sometimes does not coincide with any of the observed values
Median
The median is that value of the variable which divides the group in two equal parts. One part
comprising the values greater than and the other all values less than median. Median of a distribution
may be defined as that value of the variable which exceeds and is exceeded by the same number of
observation. It is the value such that the number of observations above it is equal to the number of
observations below it. Thus we know that the arithmetic mean is based on all items of the
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distribution, the median is positional average, i.e., it depends upon the position occupied by a value
in the frequency distribution. When the items of a series are arranged in ascending or descending
order of magnitude the value of the middle item in the series is known as median in the case of
individual observation.
Symbolically, Median = size of nth
item
It the number of items is even, and then there is no value exactly in the middle of the series. In such a
situation the median is arbitrarily taken to be halfway between the two middle items.
Advantages of Median:
(1) It is very simple to understand and easy to calculate. In some cases it is obtained simply by
inspection.
(2) Median lies at the middle part of the series and hence it is not affected by the extreme values.
(3) It is a special average used in qualitative phenomena like intelligence or beauty which are not
quantified but ranks are given. Thus we can locate the person whose intelligence or beauty is
the average.
(4) In grouped frequency distribution it can be graphically located by drawing gives.
(5) It is specially useful in open-ended distributions since the position rather than the value of
item that matters in median.
Disadvantages of Median:
(1) In simple series, the item values have to be arranged. If the series contains large number of
items, then the process becomes tedious.
(2) It is a less representative average because it does not depend on all the items in the series.
(3) It is not capable of further algebraic treatment. For example, we cannot find a combined
median of two or more groups if the median of different groups are given.
(4) It is affected more by sampling fluctuations than the mean as it is concerned with on1y one
item i.e. the middle item.
(5) It is not rigidly defined. In simple series having even number of items, median cannot be
exactly found. Moreover, the interpolation formula applied in the continuous series is based
on the unrealistic assumption that the frequency of the median class is evenly spread over the
magnitude of the class interval of the median group.
Mode
Mode is that value of the variable which occurs or repeats itself maximum number of item. The
mode is most “fashionable” size in the sense that it is the most common and typical and is defined by
Zizek as “the value occurring most frequently in series of items and around which the other items are
distributed most densely.” In the words of Croxton and Cowden, the mode of a distribution is the
value at the point where the items tend to be most heavily concentrated. According to A.M. Tuttle,
Mode is the value which has the greater frequency density in its immediate neighbourhood. In the
case of individual observations, the mode is that value which is repeated the maximum number of
times in the series. The value of mode can be denoted by the alphabet z also.
Graphic Location of Mode:
Since mode is a positional average it can be located graphically by the following process:
 A histogram of the frequency distribution is drawn.
 In the histogram, the highest rectangle represents the model class.
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 The top left corner of the highest rectangle is joined with the top left corner of the following
rectangle and the top right corner of the highest rectangle is joined with the top right corner of the
preceding rectangle respectively.
 From the point of intersection of both the lines a perpendicular is drawn on the X-axis, and check
that point on the X-axis. This will be the required value of mode.
Advantages and Disadvantages of Mode:
Advantages:
 It is easy to understand and simple to calculate.
 It is not affected by extremely large or small values.
 It can be located just by inspection in ungrouped data and discrete frequency distribution.
 It can be useful for qualitative data.
 It can be computed in an open-end frequency table.
 It can be located graphically.
Disadvantages:
 It is not well defined.
 It is not based on all the values.
 It is stable for large values so it will not be well defined if the data consists of a small number of
values.
 It is not capable of further mathematical treatment.
 Sometimes the data has one or more than one mode and sometimes the data has no mode at all.
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MEASURES OF DISPERSION
Dispersion:
The word dispersion has a technical meaning in statistics. The average measures the center of the
data, and it is one aspect of observation. Another feature of the observation is how the observations
are spread about the center. The observations may be close to the center or they may be spread away
from the center. If the observations are close to the center (usually the arithmetic mean or median),
we say that dispersion, scatter or variation is small. If the observations are spread away from the
center, we say dispersion is large.
The study of dispersion is very important in statistical data. If in a certain factory there is
consistency in the wages of workers, the workers will be satisfied. But if some workers have high
wages and some have low wages, there will be unrest among the low paid workers and they might go
on strike and arrange demonstrations. If in a certain country some people are very poor and some are
very rich, we say there is economic disparity. This means that dispersion is large.
The idea of dispersion is important in the study of workers' wages, price of commodities, standards
of living of different people, distribution of wealth, distribution of land among framers, and many
other fields of life. Some brief definitions of dispersion are:
 The degree to which numerical data tend to spread about an average value is called the dispersion
or variation of the data.
 Dispersion or variation may be defined as a statistic signifying the extent of the scatteredness of
items around a measure of central tendency.
 Dispersion or variation is the measurement of the size of the scatter of items in a series about the
average.
Properties of a good measure of Dispersion:
There are certain pre-requisites for a good measure of dispersion:
1. It should be simple to understand.
2. It should be easy to compute.
3. It should be rigidly defined.
4. It should be based on each individual item of the distribution.
5. It should be capable of further algebraic treatment.
6. It should have sampling stability.
7. It should not be unduly affected by the extreme items.
For the study of dispersion, we need some measures which show whether the dispersion is small or
large. There are two types of measure of dispersion, which are:
(a) Absolute Measures of Dispersion
(b) Relative Measures of Dispersion
Absolute Measures of Dispersion:
These measures give us an idea about the amount of dispersion in a set of observations. They give
the answers in the same units as the units of the original observations. When the observations are in
kilograms, the absolute measure is also in kilograms. If we have two sets of observations, we cannot
always use the absolute measures to compare their dispersions. We shall explain later as to when the
absolute measures can be used for comparison of dispersion in two or more sets of data. The absolute
measures which are commonly used are:
1. The Range
2. The Quartile Deviation
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3. The Mean Deviation
4. The Standard Deviation and Variance
Relative Measures of Dispersion:
These measures are calculated for the comparison of dispersion in two or more sets of observations.
These measures are free of the units in which the original data is measured. If the original data is in
dollars or kilometres, we do not use these units with relative measures of dispersion. These measures
are a sort of ratio and are called coefficients. Each absolute measure of dispersion can be converted
into its relative measure. Thus the relative measures of dispersion are:
1. Coefficient of Range or Coefficient of Dispersion
2. Coefficient of Quartile Deviation or Quartile Coefficient of Dispersion
3. Coefficient of Mean Deviation or Mean Deviation of Dispersion
4. Coefficient of Standard Deviation or Standard Coefficient of Dispersion
5. Coefficient of Variation (a special case of Standard Coefficient of Dispersion)
Range:
Range is a simplest method of studying dispersion. It takes lesser time to compute the „absolute‟ and
„relative‟ range. Range does not take into account all the values of a series, i.e. it considers only the
extreme items and middle items are not given any importance. Therefore, Range cannot tell us
anything about the character of the distribution. Range cannot be computed in the case of “open
ends‟ distribution i.e., a distribution where the lower limit of the first group and upper limit of the
higher group is not given. The concept of range is useful in the field of quality control and to study
the variations in the prices of the shares etc.
Quartile Deviation:
The quartile deviation is a slightly better measure of absolute dispersion than the range, but it ignores
the observations on the tails. If we take difference samples from a population and calculate their
quartile deviations, their values are quite likely to be sufficiently different. This is called sampling
fluctuation, and it is not a popular measure of dispersion. The quartile deviation calculated from the
sample data does not help us to draw any conclusion (inference) about the quartile deviation in the
population.
Advantages of Quartile Deviation:
 It is easy to calculate. We are required simply to find the values of Q1 and Q3 and then apply the
formula of absolute and coefficient of quartile deviation.
 It has better results than range method. While calculating range, we consider only the extreme
values that make dispersion erratic, in the case of quartile deviation; we take into account middle
50% items.
 The quartile deviation is not affected by the extreme items.
Disadvantages:
 It is completely dependent on the central items. If these values are irregular and abnormal the
result is bound to be affected.
 All the items of the frequency distribution are not given equal importance in finding the values of
Q1 and Q3.
 Because it does not take into account all the items of the series, considered to be inaccurate.
Average Deviation:
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Average deviation is defined as a value which is obtained by taking the average of the deviations of
various items from a measure of central tendency Mean or Median or Mode, ignoring negative signs.
Generally, the measure of central tendency from which the deviations arc taken, is specified in the
problem. If nothing is mentioned regarding the measure of central tendency specified than deviations
are taken from median because the sum of the deviations (after ignoring negative signs) is minimum.
This method is more effective during the reports presented to the general public or to groups who are
not familiar with statistical methods.
Steps to Compute Average Deviation:
1. Calculate the value of Mean or Median or Mode
2. Take deviations from the given measure of central-tendency and they are shown as d.
3. Ignore the negative signs of the deviation that can be shown as d and add them to find S|d|.
4. Apply the formula to get Average Deviation about Mean or Median or Mode.
Advantages of Average Deviations
 Average deviation takes into account all the items of a series and hence, it provides sufficiently
representative results.
 It simplifies calculations since all signs of the deviations are taken as positive.
 Average Deviation may be calculated either by taking deviations from Mean or Median or Mode.
 Average Deviation is not affected by extreme items.
 It is easy to calculate and understand.
 Average deviation is used to make healthy comparisons.
Disadvantages of Average Deviations
 It is illogical and mathematically unsound to assume all negative signs as positive signs.
 Because the method is not mathematically sound, the results obtained by this method are not
reliable.
 This method is unsuitable for making comparisons either of the series or structure of the series.
Standard Deviation:
The standard deviation, which is shown by greek letter s (read as sigma) is extremely useful in
judging the representativeness of the mean. The concept of standard deviation, which was introduced
by Karl Pearson has a practical significance because it is free from all defects, which exists in a
range, quartile deviation or average deviation.
Standard deviation is calculated as the square root of average of squared deviations taken from actual
mean. It is also called root mean square deviation. The square of standard deviation i.e., S2
is called
„variance‟.
Calculation of standard deviation in case of raw data
There are four ways of calculating standard deviation for raw data:
1. When actual values are considered;
2. When deviations are taken from actual mean;
3. When deviations are taken from assumed mean; and
4. When „step deviations‟ are taken from assumed mean.
Advantages of Standard Deviation:
 Standard deviation is the best measure of dispersion because it takes into account all the items
and is capable of future algebraic treatment and statistical analysis.
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 It is possible to calculate standard deviation for two or more series.
 This measure is most suitable for making comparisons among two or more series about
variability.
Disadvantages:
 It is difficult to compute.
 It assigns more weights to extreme items and less weight to items that are nearer to mean. It is
because of this fact that the squares of the deviations which are large in size would be
proportionately greater than the squares of those deviations which are comparatively small.
Coefficient of Standard Deviation
The standard deviation is the absolute measure of dispersion. Its relative measure is called the
standard coefficient of dispersion or coefficient of standard deviation.
Coefficient of Variation
The most important of all the relative measures of dispersion is the coefficient of variation. This
word is variation not variance. There is no such thing as coefficient of variance.
Thus CV is the value of SD when mean is assumed equal to 100. It is a pure number and the unit of
observation is not mentioned with its value. It is written in percentage form like 20% or 25%. When
its value is 20%, it means that when the mean of the observations is assumed equal to 100, their
standard deviation will be 20. The C.V is used to compare the dispersion in different sets of data
particularly the data which differ in their means or differ in their units of measurement. The wages of
workers may be in dollars and the consumption of meat in families may be in kilograms. The
standard deviation of wages in dollars cannot be compared with the standard deviation of amount of
meat in kilograms. Both the standard deviations need to be converted into a coefficient of variation
for comparison. Suppose the value of C.V for wages is 10% and the values of C.V for kilograms of
meat are 25%. This means that the wages of workers are consistent. Their wages are close to the
overall average of their wages. But the families consume meat in quite different quantities. Some
families consume very small quantities of meat and some others consume large quantities of meat.
We say that there is greater variation in their consumption of meat. The observations about the
quantity of meat are more dispersed or more variant.
Uses of Coefficient of Variation
 Coefficient of variation is used to know the consistency of the data. By consistency we mean the
uniformity in the values of the data/distribution from the arithmetic mean of the data/distribution.
A distribution with a smaller C.V than the other is taken as more consistent than the other.
 C.V is also very useful when comparing two or more sets of data that are measured in different
units of measurement.
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SKEWNESS
Measures of Skewness and Kurtosis, like measures of central tendency and dispersion, study
the characteristics of a frequency distribution. Averages tell us about the central value of the
distribution and measures of dispersion tell us about the concentration of the items around a central
value. These measures do not reveal whether the dispersal of value on either side of an average is
symmetrical or not. If observations are arranged in a symmetrical manner around a measure of
central tendency, we get a symmetrical distribution; otherwise, it may be arranged in an
asymmetrical order which gives asymmetrical distribution. Thus, skewness is a measure that studies
the degree and direction of departure from symmetry.
A symmetrical distribution, when presented on the graph paper, gives a „symmetrical curve‟,
where the value of mean, median and mode are exactly equal. On the other hand, in an asymmetrical
distribution, the values of mean, median and mode are not equal. When two or more symmetrical
distributions are compared, the difference in them is studied with „Kurtosis‟. On the other hand,
when two or more symmetrical distributions are compared, they will give different degrees of
Skewness. These measures are mutually exclusive i.e. the presence of skewness implies absence of
kurtosis and vice-versa.
Tests of Skewness:
There are certain tests to know whether skewness does or does not exist in a frequency distribution.
They are:
1. In a skewed distribution, values of mean, median and mode would not coincide. The values
of mean and mode are pulled away and the value of median will be at the centre. In this
distribution, mean-Mode = 2/3 (Median - Mode).
2. Quartiles will not be equidistant from median.
3. When the asymmetrical distribution is drawn on the graph paper, it will not give a bell shaped
curve.
4. Sum of the positive deviations from the median is not equal to sum of negative deviations.
5. Frequencies are not equal at points of equal deviations from the mode.
Nature of Skewness:
Skewness can be positive or negative or zero.
1. When the values of mean, median and mode are equal, there is no skewness.
2. When mean > median > mode, skewness will be positive.
3. When mean < median < mode, skewness will be negative.
Characteristic of a good measure of skewness:
1. It should be a pure number in the sense that its value should be independent of the unit of
the series and also degree of variation in the series.
2. It should have zero-value, when the distribution is symmetrical.
3. It should have a meaningful scale of measurement so that we could easily interpret the
measured value.
Methods of ascertaining Skewness:
Skewness can be studied graphically and mathematically. When we study skewness graphically,
we can find out whether skewness is positive or negative or zero. This can be shown with the help of
a diagram:
Mathematically skewness can be studied as:
(a) Absolute Skewness
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(b) Relative or coefficient of skewness
When the skewness is presented in absolute term i.e, in units, it is absolute skewness. If the value
of skewness is obtained in ratios or percentages, it is called relative or coefficient of skewness. When
skewness is measured in absolute terms, we can compare one distribution with the other if the units
of measurement are same. When it is presented in ratios or percentages, comparison become
easy. Relative measures of skewness is also called coefficient of skewness.
Mathematical measures of skewness can be calculated by:
(a) Bowley‟s Method
(b) Karl-Pearson‟s Method
(c) Kelly„s method
(a) Bowley‟s Method:
Bowley‟s method of skewness is based on the values of median, lower and upper quartiles.
This method suffers from the same limitations which are in the case of median and
quartiles. Wherever positional measures are given, skewness should be measured by Bowley‟s
method. This method is also used in case of „open-end series‟, where the importance of extreme
values is ignored. Coefficient of skewness lies within the limit ± 1. This method is quite convenient
for determining skewness where one has already calculated quartiles.
(b) Karl-Pearson‟s Method:
This method of measuring skewness, also known as Pearsonian coefficient of skewness, was
suggested by Karl Pearson, a great British biometrician and statistician. It is based upon the
difference between mean and mode, this difference is divided by standard deviation to give a relative
measure the formula thus becomes: There is no limit to this measure in theory and this is a slight
drawback. But in practice the value given by this formula is rarely very high and usually lies between
± 1. When a distribution is symmetrical, the values of mean, median and mode coincide and,
therefore the coefficient of skewness will be zero. When a distribution is positively skewed, the
coefficient of skewness shall have plus sign and when it is negatively skid, the coefficient of
skewness shall have minus sign. The degree of skewness shall be ob5tained by the numeral value.
Say, 0.8 or 0.2 etc. thus this formula given both the direction as well as the extent of skewness.
(c) Kelly‟s Method:
It would be better for a measure to cover the entire data especially because in measuring scenes we
are often interested in the more extreme items bowley measure can be extended by taking any two
deciles equidistant from the median or any percentiles equidistant from the median. Kelly has
suggested the following formula for measuring skewers upon the 10th and the 90th percentiles (or
the first and ninth deciles): This measure of skewers ha one theoretical attraction if skewers is to be
based on percentiles however this method is not popular in practice and generally Karl Pearson
method is used.
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LEVELS (or) SCALES OF MEASUREMENT
The level of measurement refers to the relationship among the values that are assigned to the
attributes for a variable. Each scale of measurement has certain properties which in turn determine
the appropriateness for use of certain statistical analyses. It is important for the researcher to
understand the different levels of measurement, as these levels of measurement, together with how
the research question is phrased, dictate what statistical analysis is appropriate.
There are typically four levels of measurement that are defined:
1. Nominal
2. Ordinal
3. Interval
4. Ratio
The first level of measurement is NOMINAL Level of Measurement. In this level of measurement,
the numbers in the variable are used only to classify the data. In this level of measurement, words,
letters, and alpha-numeric symbols can be used. Suppose there are data about people belonging to
three different gender categories. In this case, the person belonging to the female gender could be
classified as F, the person belonging to the male gender could be classified as M, and transgendered
classified as T. This type of assigning classification is nominal level of measurement.
The second level of measurement is the ORDINAL Level of Measurement. This level of
measurement depicts some ordered relationship among the variable's observations. Suppose a
student scores the highest grade of 100 in the class. In this case, he would be assigned the first
rank. Then, another classmate scores the second highest grade of an 92; she would be assigned the
second rank. A third student scores a 81 and he would be assigned the third rank, and so on. The
ordinal level of measurement indicates an ordering of the measurements.
The third level of measurement is the INTERVAL Level of Measurement. The interval level of
measurement not only classifies and orders the measurements, but it also specifies that the distances
between each interval on the scale are equivalent along the scale from low interval to high
interval. For example, an interval level of measurement could be the measurement of anxiety in a
student between the score of 10 and 11; this interval is the same as that of a student who scores
between 40 and 41. A popular example of this level of measurement is temperature in centigrade,
where, for example, the distance between 940
C and 960
C is the same as the distance between 1000
C
and 1020
C.
The fourth level of measurement is the RATIO Level of Measurement. In this level of
measurement, the observations, in addition to having equal intervals, can have a value of zero as
well. The zero in the scale makes this type of measurement unlike the other types of measurement,
although the properties are similar to that of the interval level of measurement. In the ratio level of
measurement, the divisions between the points on the scale have an equivalent distance between
them.
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Stevens (1946, 1951) proposed that measurements can be classified into four different types of
scales
Scale
Type
Permissible Statistics
Admissible Scale
Transformation
Mathematical
Structure
Nominal Mode, Chi-Square
One to One
(Equality(=))
Standard Set
Structure
(Unordered)
Ordinal Median, Percentile
Monotonic Increasing
(Order(<))
Totally Ordered
Set
Interval
Mean, SD, Correlation, Regression,
ANOVA
Positive Linear
(Affine)
Affine Line
Ratio
All Statistics permitted for Interval Scales
plus the following: GM, HM, Coefficient
of Variation, Logarithms
Positive Similarities
(Multiplication)
Field
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CLASSIFICATION OF DATA
Classification:
The collected data, also known as raw data or ungrouped data are always in an un organised form
and need to be organised and presented in meaningful and readily comprehensible form in order to
facilitate further statistical analysis. It is, therefore, essential for an investigator to condense a mass
of data into more and more comprehensible and assimilable form. The process of grouping into
different classes or sub classes according to some characteristics is known as classification,
tabulation is concerned with the systematic arrangement and presentation of classified data. Thus
classification is the first step in tabulation. For Example, letters in the post office are classified
according to their destinations viz., Delhi, Madurai, Bangalore, Mumbai etc.,
Objects of Classification:
The following are main objectives of classifying the data.
1. It condenses the mass of data in an easily assimilable form.
2. It eliminates unnecessary details.
3. It facilitates comparison and highlights the significant aspect of data.
4. It enables one to get a mental picture of the information and helps in drawing inferences.
5. It helps in the statistical treatment of the information collected.
Types of classification:
Statistical data are classified in respect of their characteristics. Broadly there are four basic types of
classification namely
a) Chronological Classification;
b) Geographical Classification;
c) Qualitative Classification;
d) Quantitative Classification.
a) Chronological Classification: In chronological classification the collected data are arranged
according to the order of time expressed in years, months, weeks, etc., The data is generally
classified in ascending order of time. For example, the data related with population, sales of a firm,
imports and exports of a country are always subjected to chronological classification.
Year 2005 2006 2007 2008 2009 2010 2011
Birth rate 36.8 36.9 36.6 34.6 34.5 35.2 34.2
b) Geographical Classification: In this type of classification the data are classified according to
geographical region or place. For instance, the production of paddy in different states in India,
production of wheat in different countries etc.,
Country America China Denmark France India
Yield of Wheat in (kg/acre) 1925 893 225 439 862
c) Qualitative Classification: In this type of classification data are classified on the basis of same
attributes or quality like sex, literacy, religion, employment etc., such attributes cannot be measured
along with a scale. For example, if the population to be classified in respect to one attribute, say sex,
then we can classify them into two namely that of males and females. Similarly, they can also be
classified into „employed‟ or „unemployed‟ on the basis of another attribute „employment‟. Thus
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when the classification is done with respect to one attribute, which is dichotomous in nature, two
classes are formed, one possessing the attribute and the other not possessing the attribute. This type
of classification is called simple or dichotomous classification.
The classification, where two or more attributes are considered and several classes are
formed, is called a manifold classification. For example, if we classify population simultaneously
with respect to two attributes, e.g. sex and employment, then population are first classified with
respect to „sex‟ into „males‟ and „females‟. Each of these classes may then be further classified into
„Urban‟, „Semi-Urban‟ and „Rural‟ on the basis of attribute „employment‟ and as such Population are
classified into four classes namely.
(i) Male in Urban Area
(ii) Male Semi-Urban Area
(iii) Male in Rural Area
(iv) Female in Urban Area
(v) Female Semi-Urban Area
(vi) Female in Rural Area
Still the classification may be further extended by considering other attributes like marital status etc.
This can be explained by the following chart
S. No. Management Number of Schools
1 Government 4
2 Local Body 8
3 Private Aided 10
4 Private Unaided 2
Total 24
d) Quantitative Classification: Quantitative classification refers to the classification of data
according to some characteristics that can be measured such as height, weight, etc., For example the
students of a college may be classified according to weight. In this type of classification there are
two elements, namely
(i) The variable (i.e.) the weight in the above example, and
(ii) The frequency in the number of students in each class.
There are 50 students having weights ranging from 90 to 100 lb, 200 students having weight ranging
between 100 to 110 lb and so on.
Weight (in lbs) 90-100 100-110 110-120 120-130 130-140 140-150 Total
No. of Students 50 200 260 360 90 40 1000
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TABULATION OF DATA
Tabulation is the process of summarizing classified or grouped data in the form of a table so that it is
easily understood and an investigator is quickly able to locate the desired information. A table is a
systematic arrangement of classified data in columns and rows. Thus, a statistical table makes it
possible for the investigator to present a huge mass of data in a detailed and orderly form. It
facilitates comparison and often reveals certain patterns in data which are otherwise not obvious.
Classification and „Tabulation‟, as a matter of fact, are not two distinct processes. Actually they go
together. Before tabulation data are classified and then displayed under different columns and rows
of a table.
Advantages of Tabulation:
Statistical data arranged in a tabular form serve following objectives:
1. It simplifies complex data and the data presented are easily understood.
2. It facilitates comparison of related facts.
3. It facilitates computation of various statistical measures like averages, dispersion, correlation etc.
4. It presents facts in minimum possible space and unnecessary repetitions and explanations are
avoided. Moreover, the needed information can be easily located.
5. Tabulated data are good for references and they make it easier to present the information in the
form of graphs and diagrams.
Preparing a Table:
The making of a compact table itself an art. This should contain all the information needed within the
smallest possible space. What the purpose of tabulation is and how the tabulated information is to be
used are the main points to be kept in mind while preparing for a statistical table. An ideal table
should consist of the following main parts:
1. Table number
2. Title of the table
3. Captions or column headings
4. Stubs or row designation
5. Body of the table
6. Footnotes
7. Sources of data
Table Number: A table should be numbered for easy reference and identification. This number, if
possible, should be written in the centre at the top of the table. Sometimes it is also written just
before the title of the table.
Title: A good table should have a clearly worded, brief but unambiguous title explaining the nature
of data contained in the table. It should also state arrangement of data and the period covered. The
title should be placed centrally on the top of a table just below the table number (or just after table
number in the same line).
Captions or column Headings: It stands for brief and self explanatory headings of vertical columns.
Captions may involve headings and sub-headings as well. The unit of data contained should also be
given for each column. Usually, a relatively less important and shorter classification should be
tabulated in the columns.
Stubs or Row Designations: Stubs stands for brief and self explanatory headings of horizontal rows.
Normally, a relatively more important classification is given in rows. Also a variable with a large
number of classes is usually represented in rows. For example, rows may stand for score of classes
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and columns for data related to sex of students. In the process, there will be many rows for scores
classes but only two columns for male and female students.
Body: It contains the numerical information of frequency of observations in the different cells. This
arrangement of data is according to the description of captions and stubs.
Footnotes: They are given at the foot of the table for explanation of any fact or information included
in the table which needs some explanation. Thus, they are meant for explaining or providing further
details about the data, which have not been covered in title, captions and stubs.
Sources of data: Lastly one should also mention the source of information from which data are
taken. This may preferably include the name of the author, volume, page and the year of publication.
This should also state whether the data contained in the table is of „primary or secondary‟ nature.
Requirements of a Good Table:
A good statistical table is not merely a careless grouping of columns and rows but should be such
that it summarizes the total information in an easily accessible form in minimum possible space.
Thus while preparing a table, one must have a clear idea of the information to be presented, the facts
to be compared and he points to be stressed.
Though, there is no hard and fast rule for forming a table yet a few general points should be kept in
mind:
1. A table should be formed in keeping with the objects of statistical enquiry.
2. A table should be carefully prepared so that it is easily understandable.
3. A table should be formed so as to suit the size of the paper. But such an adjustment should not be
at the cost of legibility.
4. If the figures in the table are large, they should be suitably rounded or approximated. The method
of approximation and units of measurements too should be specified.
5. Rows and columns in a table should be numbered and certain figures to be stressed may be put in
„box‟ or „circle‟ or in bold letters.
6. The arrangements of rows and columns should be in a logical and systematic order. This
arrangement may be alphabetical, chronological or according to size.
7. The rows and columns are separated by single, double or thick lines to represent various classes
and sub-classes used. The corresponding proportions or percentages should be given in adjoining
rows and columns to enable comparison. A vertical expansion of the table is generally more
convenient than the horizontal one.
8. The averages or totals of different rows should be given at the right of the table and that of
columns at the bottom of the table. Totals for every sub-class too should be mentioned.
9. In case it is not possible to accommodate all the information in a single table, it is better to have
two or more related tables.
Type of Tables:
Tables can be classified according to their purpose, stage of enquiry, nature of data or number of
characteristics used. On the basis of the number of characteristics, tables may be classified as
follows:
1. Simple or one-way table
2. Two way table
3. Manifold table
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Simple or one-way Table:
A simple or one-way table is the simplest table which contains data of one characteristic only. A
simple table is easy to construct and simple to follow. For example, the blank table given below may
be used to show the number of adults in different occupations in a locality.
Occupations No. of Adults
Total
Two-way Table:
A table, which contains data on two characteristics, is called a two-way table. In such case, therefore,
either stub or caption is divided into two co-ordinate parts. In the given table, as an example the
caption may be further divided in respect of „sex‟. This subdivision is shown in two-way table, which
now contains two characteristics namely, occupation and sex.
Occupations
No. of Adults
Total
Male Female
Total
Manifold Table:
Thus, more and more complex tables can be formed by including other characteristics. For example,
we may further classify the caption sub-headings in the above table in respect of “marital status”,
“religion” and “socio-economic status” etc. A table, which has more than two characteristics of data,
is considered as a manifold table. For instance, table shown below shows three characteristics
namely, occupation, sex and marital status. Manifold tables, though complex are good in practice as
these enable full information to be incorporated and facilitate analysis of all related facts. Still, as a
normal practice, not more than four characteristics should be represented in one table to avoid
confusion. Other related tables may be formed to show the remaining characteristics
Occupations
No. of Adults
Total
Male Female
Married Unmarried Total Married Unmarried Total
Total
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STUDENT‟S t-DISTRIBUTION (t-test)
Theoretical work on t-distribution was done by Sir W. S. Gosset (1876-1937). The t-distribution is a
theoretical probability distribution. The t-distribution, also known as the student's t-distribution, is
used when making inferences about a mean when the standard deviation is not known. It is
symmetrical, bell-shaped, and similar to the standard normal curve. The higher the degrees of
freedom, the closer that distribution will resemble a standard normal distribution with a mean of 0,
and a standard deviation of 1. Thus, student distribution is the statistical measure that compares the
observed data with the expected data obtained with a specific hypothesis. It complies with the central
limit theorem which says that the distribution approaches the standard normal distribution as long as
the sample size is large.
Normal Distribution:
A normal distribution is an arrangement of a data set in which most values cluster in the middle of
the range and the rest taper off symmetrically toward either extreme. Height is one simple example
of something that follows a normal distribution pattern: Most people are of average height the
numbers of people that are taller and shorter than average are fairly equal and a very small (and still
roughly equivalent) number of people are either extremely tall or extremely short. Here's an example
of a normal distribution curve:
A graphical representation of a normal distribution is sometimes called a bell curve because of its
flared shape. The precise shape can vary according to the distribution of the population but the peak
is always in the middle and the curve is always symmetrical. In a normal distribution the mean mode
and median are all the same.
One-Tailed Test:
A one-tailed test is a statistical test in which the critical area of a distribution is one-sided so that it is
either greater than or less than a certain value, but not both. If the sample that is being tested falls
into the one-sided critical area, the alternative hypothesis will be accepted instead of the null
hypothesis. One-tailed test is also known as a directional hypothesis or test.
If you are using a significance level of .05, a one-tailed test allots your entire alpha to testing
the statistical significance in the one direction of interest. This means that .05 is in one tail of the
distribution of your test statistic. When using a one-tailed test, you are testing for the possibility of
the relationship in one direction and completely disregarding the possibility of a relationship in the
other direction. Let‟s return to our example comparing the mean of a sample to a given value x using
a t-test. Our null hypothesis is that the mean is equal to x. A one-tailed test will test either if the
mean is significantly greater than x or if the mean is significantly less than x, but not both. Then,
depending on the chosen tail, the mean is significantly greater than or less than x if the test statistic is
in the top 5% of its probability distribution or bottom 5% of its probability distribution, resulting in a
p-value less than 0.05. The one-tailed test provides more power to detect an effect in one direction
by not testing the effect in the other direction.
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Because the one-tailed test provides more power to detect an effect, you may be tempted to use a
one-tailed test whenever you have a hypothesis about the direction of an effect. Before doing so,
consider the consequences of missing an effect in the other direction. Imagine you have developed a
new drug that you believe is an improvement over an existing drug. You wish to maximize your
ability to detect the improvement, so you opt for a one-tailed test. In doing so, you fail to test for the
possibility that the new drug is less effective than the existing drug. The consequences in this
example are extreme, but they illustrate a danger of inappropriate use of a one-tailed test.
Choosing a one-tailed test for the sole purpose of attaining significance is not
appropriate. Choosing a one-tailed test after running a two-tailed test that failed to reject the null
hypothesis is not appropriate, no matter how "close" to significant the two-tailed test was. Using
statistical tests inappropriately can lead to invalid results that are not replicable and highly
questionable–a steep price to pay for a significance star in results table.
Two-Tailed Test: A two-tailed test is a statistical test in which the critical area of a distribution is
two-sided and tests whether a sample is greater than or less than a certain range of values. If the
sample being tested falls into either of the critical areas, the alternative hypothesis is accepted instead
of the null hypothesis. The two-tailed test gets its name from testing the area under both of the tails
of a normal distribution, although the test can be used in other non-normal distributions. If you are
using a significance level of 0.05, a two-tailed test allots half of your alpha to testing the statistical
significance in one direction and half of your alpha to testing statistical significance in the other
direction. This means that .025 is in each tail of the distribution of your test statistic. When using a
two-tailed test, regardless of the direction of the relationship you hypothesize, you are testing for the
possibility of the relationship in both directions. For example, we may wish to compare the mean of
a sample to a given value x using a t-test. Our null hypothesis is that the mean is equal to x. A two-
tailed test will test both if the mean is significantly greater than x and if the mean significantly less
than x. The mean is considered significantly different from x if the test statistic is in the top 2.5% or
bottom 2.5% of its probability distribution, resulting in a p-value less than 0.05.
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Difference between One-Tailed and Two-Tailed Test
Basis ONE-TAILED TEST TWO-TAILED TEST
Meaning
A Statistical hypothesis in which
alternative hypothesis has only one
end.
A significance in which
alternative hypothesis has two
ends.
Hypothesis Directional Non-directional
Region of Rejection Either Left or Right Both Left or Right
Determines
If there is a relationship between
variables in single direction.
If there is a relationship between
variables in either direction.
Result Greater or Less than certain value.
Greater or Less than certain range
of values.
Sign in alternative
hypothesis
(>) or (<) ≠
Dependent and Independent Samples:
 Dependent samples are paired measurements for one set of items.
 Independent samples are measurements made on two different sets of items.
 When you conduct a hypothesis test using two random samples, you must choose the type of test
based on whether the samples are dependent or independent. Therefore, it's important to know
whether your samples are dependent or independent:
 If the values in one sample affect the values in the other sample, then the samples are
dependent.
 If the values in one sample reveal no information about those of the other sample, then the
samples are independent.
Example of collecting dependent samples and independent samples:
Consider a drug company that wants to test the effectiveness of a new drug in reducing blood
pressure. They could collect data in two ways:
 Sample the blood pressures of the same people before and after they receive a dose. The two
samples are dependent because they are taken from the same people. The people with the highest
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blood pressure in the first sample will likely have the highest blood pressure in the second
sample.
 Give one group of people an active drug and give a different group of people an inactive placebo,
then compare the blood pressures between the groups. These two samples would likely be
independent because the measurements are from different people. Knowing something about the
distribution of values in the first sample doesn't inform you about the distribution of values in the
second.
Assumptions of Students t-test:
 The sample is drawn from the Normal population
 The sample observations are independent
 The population standard deviation σ is unknown.
Properties of t-Distribution:
1. Like, standard normal distribution the shape of the student distribution is also bell-shaped and
symmetrical with mean zero.
2. The student distribution ranges from – ∞ to + ∞ (infinity).
3. The shape of the t-distribution changes with the change in the degrees of freedom.
4. The variance is always greater than one and can be defined only when the degrees of freedom ν ≥
3 and is given as: Var (t) =
v
v−2
5. It is less peaked at the center and higher in tails, thus it assumes platykurtic shape.
6. The t-distribution has a greater dispersion than the standard normal distribution. And as the
sample size „n‟ increases, it assumes the normal distribution. Here the sample size is said to be
large when n ≥ 30.
Degrees of Freedom
It refers to the number of values involved in the calculations that have the freedom to vary. In other
words, the degrees of freedom, in general, can be defined as the total number of observations minus
the number of independent constraints imposed on the observations.
The degrees of freedom are calculated for the following statistical tests to check their validity:
1. t-Distribution
2. F- Distribution
3. Chi-Square Distribution
These tests are usually done to compare the observed data with the data that is expected to be
obtained with a specific hypothesis.
It is usually denoted by a Greek symbol ν (mu) and is commonly abbreviated as, df. The statistical
formula to compute the value of degrees of freedom is quite simple and is equal to the number of
values in the data set minus one. Symbolically:
df= n-1
Where n is the number of values in the data set or the sample size. The concept of df can be further
understood through an illustration given below:
Suppose there is a data set X that includes the values: 10, 20, 30, 40. First of all, we will calculate the
mean of these values, which is equal to:
(10+20+30+40) /4 = 25.
Once the mean is calculated, apply the formula of degrees of freedom. As the number of values in
the data set or sample size is 4, so,
df = 4-1=3.
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Thus, this shows that there are three values in the data set that have the freedom to vary as long as
the mean is 25.
The following are the important Applications of the t-distribution:
1. Test of the Hypothesis of the population mean.
2. Test of Hypothesis of the difference between the two means.
3. Test of Hypothesis of the difference between two means with dependent samples.
4. Test of Hypothesis about the coefficient of correlation.
APPLICATION-1
TEST OF HYPOTHESIS OF THE POPULATION MEAN
When the population is normally distributed, and the standard deviation „σ‟ is unknown, then “t”
statistic is calculated as:
t =
X − μ
S
n S =
(X − X)2
(n − 1)
X = Sample Mean
µ = Population Mean
n = Sample size
S = SD of the sample
The null hypothesis is tested to check whether there is a significant difference between the X and µ.
If the calculated value of „t‟ exceeds the table value of „t‟ at a specific significance level, then the
null hypothesis is rejected considering the difference between the X and µ as significant. On the other
hand, if the calculated value of „t‟ is less than the table value of „t‟, then the null hypothesis is
accepted. It is to be noted that this test is based on the degrees of freedom, i.e. n-1.
Fiducial Limits of Population Mean:
Assuming that the sample is a random sample from a normal population of unknown mean the 95%
and 99% fiducial limits of the population of the mean (µ) are
@ 95% Limit = X ±
S
n
t0.05 @ 99% Limit = X ±
S
n
t0.01
APPLICATION-2
TEST OF HYPOTHESIS OF THE DIFFERENCE BETWEEN TWO MEANS
In Testing hypothesis about the difference between two means drawn from the two systematic
population whose variance is unknown, then t-test can be calculated in two ways:
Variances are equal:
When the population variances, though unknown are taken as equal, then the t- statistic to be used is:
t =
X1 − X2
S
n1n2
n1 + n2
S =
X1 − X1
2 + X2 − X2
2
n1 + n2 − 2
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S =
X1 − A1
2 + X2 − A2
2 − n1 X1 − A1
2 − n2 X2 − A2
2
n1 + n2 − 2
Where,
X1 and X2 are the sample means of sample 1 of size n1 and sample 2 of size n2.
S is the common standard deviation obtained by pooling the data from both the samples.
The null hypothesis is that there is no difference between two means and‟ is accepted when the
calculated value of „t‟ at a specified significance level is less than the table value of „t‟ and is rejected
when the calculated value exceeds the table value.
Variances are Unequal:
When the population variances are not equal, then we use the unbiased estimators S1
2
and S2
2
. In this
case, the sampling has the huge variability than the population variability and statistic to be used is:
𝑡 =
𝑋1 − 𝑋2 − 𝜇1 − 𝜇2
𝑆1
2
𝑛1
+
𝑆2
2
𝑛2
𝑑. 𝑓. =
𝑆1
2
𝑛1
+
𝑆2
2
𝑛2
2
𝑆1
2
𝑛1
2
𝑛1 − 1
+
𝑆2
2
𝑛2
2
𝑛2 − 1
Where,
µ1 and µ2 are the two population means.
This statistic may not strictly follow t-distributions, but however it can be approximated by t-
distribution with the modified value for the degrees of freedom given above:
APPLICATION 3
TEST OF HYPOTHESIS OF THE DIFFERENCE BETWEEN TWO MEANS WITH
DEPENDENT SAMPLES
In several situations, it is possible that the samples are drawn from the two populations that are
dependent on each other. Thus, the samples are said to be dependent, as each observation included in
sample one is associated with the particular observation in the second sample. Hence, due to this
property the t-test that will be used here is called the paired t-test.
This test is applied in the situations when before and after experiments are to be compared. Usually,
two methods are adopted that are related to each other. The following statistic is used when the
means of both the methods applied is equal i.e. µ1 = µ2
t =
d n
S
This statistic follows t- distribution with (n-1) degrees of freedom, where d
͞ = mean of the differences
calculated as:
d =
d
n
S is the standard deviation of differences and is calculated by applying the following formula:
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𝑆 =
𝑑2
𝑛 − 1
−
𝑑
2
𝑛 𝑛 − 1
n = Number of paired observations.
APPLICATION 4
TEST OF HYPOTHESIS ABOUT THE COEFFICIENT OF CORRELATION
There are three cases of testing the hypothesis about the coefficient of correlation. These are:
Case-1:
When the population coefficient of correlation is zero, i.e. ρ = 0. The coefficient of correlation
measures the degree of relationship between the variables, and when ρ = 0, then there is no statistical
relationship between the variables. To test the null hypothesis which assumes that there is no
correlation between the populations, it is necessary that the sample coefficient of correlation „r‟ is
known. The test statistic to be used is:
𝑡 =
𝑟
1 − 𝑟2
𝑛 − 2
Case -2:
When the Population Coefficient of Correlation is equal to some other value, other than zero, i.e.
ρ≠0. In this case, the test based on t-distribution will not be correct and hence the hypothesis is tested
using the Fisher‟s z- transformation. Here the „r‟ is transformed into „z‟ by:
Here, loge is a natural logarithm. The common logarithm can be shifted to a natural algorithm by
multiplying it by the factor 2.3026. Thus, loge X = 2.3026 log 10 X, where X is the positive integer
since, ½ X (2.3026) = 1.1513, then the following transformation formula is used:
The following statistic is used to test the null hypotheses:
This follows the normal distribution and the test is said to be more appropriate as long as the sample
size is large.
Case-3:
When the hypothesis is tested for the difference between two Independent Correlation
Coefficients: To test the hypothesis of two correlations derived from the two separate samples, then
the difference of the two corresponding values of z is to be compared with the standard error of the
difference. The following statistic is used:
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ANALYSIS OF VARIANCE (ANOVA)
An ANOVA test is a way to find out if survey or experiment results are significant. In other words,
they help you to figure out if you need to reject the null hypothesis or accept the alternate hypothesis.
Basically, you‟re testing groups to see if there‟s a difference between them.
Examples of when you might want to test different groups:
 A group of psychiatric patients are trying three different therapies: counseling, medication and
biofeedback. You want to see if one therapy is better than the others.
 A manufacturer has two different processes to make light bulbs. They want to know if one
process is better than the other.
 Students from different colleges take the same exam. You want to see if one college outperforms
the other.
Assumptions of ANOVA:
1. Normality of Errors
We assume in the ANOVA model that the error terms are normally-distributed with zero mean. If
the data are not normally-distributed, but instead come from some other distribution (exponential
or binomial, for example), then we may not be able to trust our p-values, which were built by
assuming normality. “If I was to repeat my sample repeatedly and calculate the means, those
means would be normally distributed.”
2. Equal Error Variance Across Treatments
The next assumption is that all error terms have the same variance σ2
. It is common to see that
different treatments seem to result in different means AND ALSO different variances.
3. Independence of Errors
We assume that each trial is independent of all other trials, except for the effect τi of the
treatment on the mean. Statistical independence of two trials means that knowing the result of
one trial doesn't change the distribution of the other trial. The most common causes of
dependence in experimental data are confounding factors - something measured or unmeasured
that affects the experiment. Randomization is a critical technique in experimental design because
it can minimize the effect of any confounder. “Your samples have to come from a randomized or
randomly sampled design.”
Types of Tests
There are two main types: one-way and two-way. Two-way tests can be with or without replication.
1. One-way ANOVA between groups: used when you want to test two groups to see if there‟s a
difference between them.
2. Two-way ANOVA without replication: used when you have one group and you‟re double-
testing that same group. For example, you‟re testing one set of individuals before and after they
take a medication to see if it works or not.
3. Two-way ANOVA with replication: Two groups, and the members of those groups are doing
more than one thing. For example, two groups of patients from different hospitals trying two
different therapies.
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One-Way ANOVA
The one-way analysis of variance (ANOVA) is used to determine whether there are any statistically
significant differences between the means of two or more independent (unrelated) groups (although
you tend to only see it used when there are a minimum of three, rather than two groups). For
example, you could use a one-way ANOVA to understand whether exam performance differed based
on test anxiety levels amongst students, dividing students into three independent groups (e.g., low,
medium and high-stressed students). Also, it is important to realize that the one-way ANOVA is
an omnibus test statistic and cannot tell you which specific groups were statistically significantly
different from each other; it only tells you that at least two groups were different. Since you may
have three, four, five or more groups in your study design, determining which of these groups differ
from each other is important.
When to use a one way ANOVA?
Situation 1: You have a group of individuals randomly split into smaller groups and completing
different tasks. For example, you might be studying the effects of tea on weight loss
and form three groups: green tea, black tea, and no tea.
Situation 2: Similar to situation 1, but in this case the individuals are split into groups based on an
attribute they possess. For example, you might be studying leg strength of people
according to weight. You could split participants into weight categories (obese,
overweight and normal) and measure their leg strength on a weight machine.
Assumptions of One-way ANOVA
 Normal distribution of the population from which the samples are drawn.
 Measurement of the dependent variable is at interval or ratio level.
 Two or more than two categorical independent groups in an independent variable.
 Independence of samples
 Homogeneity of the variance of the population.
Common Uses
The One-Way ANOVA is often used to analyze data from the following types of studies:
 Field studies
 Experiments
 Quasi-experiments
The One-Way ANOVA is commonly used to test the following:
 Statistical differences among the means of two or more groups
 Statistical differences among the means of two or more interventions
 Statistical differences among the means of two or more change scores
Note: Both the One-Way ANOVA and the Independent Samples t Test can compare the means for
two groups. However, only the One-Way ANOVA can compare the means across three or
more groups.
Note: If the grouping variable has only two groups, then the results of a one-way ANOVA and the
independent samples t test will be equivalent. In fact, if you run both an independent samples
t test and a one-way ANOVA in this situation, you should be able to confirm that t2
=F.
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Calculation and Decision Rules:
Hypotheses The null hypothesis will be that all population means are equal, the alternative
hypothesis is that at least one mean is different. In the following, lower case letters
apply to the individual samples and capital letters apply to the entire set
collectively. That is, n is one of many sample sizes, but N is the total sample size.
Grand Mean The grand mean of a set of samples is the total of all the data values divided by the
total sample size. This requires that you have all of the sample data available to
you, which is usually the case, but not always. It turns out that all that is necessary
to find perform a one-way analysis of variance are the number of samples, the
sample means, the sample variances, and the sample sizes. Another way to find the
grand mean is to find the weighted average of the sample means. The weight
applied is the sample size.
Total
Variation
The total variation (not variance) is comprised the sum of the squares of the
differences of each mean with the grand mean. There is the between group
variation and the within group variation. The whole idea behind the analysis of
variance is to compare the ratio of between group variance to within group
variance. If the variance caused by the interaction between the samples is much
larger when compared to the variance that appears within each group, then it is
because the means aren't the same.
Between
Group
Variation
The variation due to the interaction between the samples is denoted SS(B) for Sum
of Squares Between groups. If the sample means are close to each other (and
therefore the Grand Mean) this will be small. There are k samples involved with
one data value for each sample (the sample mean), so there are k-1 degrees of
freedom. The variance due to the interaction between the samples is denoted
MS(B) for Mean Square Between groups. This is the between group variation
divided by its degrees of freedom.
Within Group
Variation
The variation due to differences within individual samples, denoted SS(W) for Sum
of Squares Within groups. Each sample is considered independently, no interaction
between samples is involved. The degrees of freedom is equal to the sum of the
individual degrees of freedom for each sample. Since each sample has degrees of
freedom equal to one less than their sample sizes, and there are k samples, the total
degrees of freedom is k less than the total sample size: df = N - k. The variance due
to the differences within individual samples is denoted MS(W) for Mean Square
Within groups. This is the within group variation divided by its degrees of
freedom. It is the weighted average of the variances (weighted with the degrees of
freedom).
F test statistic F variable is the ratio of two independent chi-square variables divided by their
respective degrees of freedom. Also recall that the F test statistic is the ratio of two
sample variances, well, it turns out that's exactly what we have here. The F test
statistic is found by dividing the between group variance by the within group
variance. The degrees of freedom for the numerator are the degrees of freedom for
the between group (k-1) and the degrees of freedom for the denominator are the
degrees of freedom for the within group (N-k).
Decision Rule The decision will be to reject the null hypothesis if the test statistic from the table is
greater than the F critical value with k-1 numerator and N-k denominator degrees
of freedom. If the decision is to reject the null, then at least one of the means is
different. However, the ANOVA does not tell you where the difference lies.
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ANOVA Table: One-Way Classification
Source of
Variation
SS (Sum of
Squares)
V (Degrees of
freedom)
MS (Mean
Square)
Variance Ratio of
F
Between Samples SSB c-1 MSB
Within Samples SSW n-c MSW MSB/MSW
Total SST n-1
Two-Way ANOVA
A Two Way ANOVA is an extension of the One Way ANOVA. With a One Way, you have
one variable affecting a dependent variable. With a Two Way ANOVA, there are two independents.
Use a two way ANOVA when you have one measurement variable (i.e. a quantitative variable) and
two nominal variables. In other words, if your experiment has a quantitative outcome and you have
two categorical explanatory variables, a two way ANOVA is appropriate.
For example, you might want to find out if there is an interaction between income and gender for
anxiety level at job interviews. The anxiety level is the outcome, or the variable that can be
measured. Gender and Income are the two categorical variables. These categorical variables are also
the independent variables, which are called factors in a Two Way ANOVA.
The factors can be split into levels. In the above example, income level could be split into
three levels: low, middle and high income. Gender could be split into three levels: male, female, and
transgender. Treatment groups and all possible combinations of the factors. In this example there
would be 3 x 3 = 9 treatment groups.
Assumptions of Two-Way ANOVA:
 Normal distribution of the population from which the samples are drawn.
 Measurement of dependent variable at continuous level.
 Two or more than two categorical independent groups in two factors.
 Categorical independent groups should have the same size.
 Independence of observations
 Homogeneity of the variance of the population.
Calculation and Decision Rules:
Hypotheses There are three sets of hypothesis with the two-way ANOVA.
The null hypotheses for each of the sets are given below.
1. The population means of the first factor are equal. This is like the one-way
ANOVA for the row factor.
2. The population means of the second factor are equal. This is like the one-way
ANOVA for the column factor.
3. There is no interaction between the two factors. This is similar to performing a
test for independence with contingency tables.
Factors The two independent variables in a two-way ANOVA are called factors. The idea is
that there are two variables, factors, which affect the dependent variable. Each factor
will have two or more levels within it, and the degrees of freedom for each factor is
one less than the number of levels.
Treatment
Groups
Treatment Groups are formed by making all possible combinations of the two
factors. For example, if the first factor has 3 levels and the second factor has 2 levels,
then there will be 3x2=6 different treatment groups.
Main Effect The main effect involves the independent variables one at a time. The interaction is
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ignored for this part. Just the rows or just the columns are used, not mixed. This is
the part which is similar to the one-way analysis of variance. Each of the variances
calculated to analyze the main effects are like the between variances
Interaction
Effect
The interaction effect is the effect that one factor has on the other factor. The degrees
of freedom here is the product of the two degrees of freedom for each factor.
Within
Variation
The Within variation is the sum of squares within each treatment group. You have
one less than the sample size (remember all treatment groups must have the same
sample size for a two-way ANOVA) for each treatment group. The total number of
treatment groups is the product of the number of levels for each factor. The within
variance is the within variation divided by its degrees of freedom. The within group
is also called the error.
F-Tests There is an F-test for each of the hypotheses, and the F-test is the mean square for
each main effect and the interaction effect divided by the within variance. The
numerator degrees of freedom come from each effect, and the denominator degrees
of freedom is the degrees of freedom for the within variance in each case.
ANOVA Table: Two-Way Classification
Source of Variation SS (Sum of
Squares)
V (Degrees of
freedom)
MS (Mean Sum of
Squares)
Variance Ratio of
F
Between Columns SSC c-1 MSC MSC/MSE
Between Rows SSR r-1 MSR MSR/MSE
Residual or error SSE (c-1) (r-1) MSE
Total SST n-1
Difference between One-way and Two-way ANOVA
Basis for Comparison One-way ANOVA Two-way ANOVA
Meaning
It is a hypothesis test, used to test
the equality of three of more
population means simultaneously
using variance.
It is a statistical technique
wherein, the interaction between
factors, influencing variable can
be studied.
Independent Variable One Two
Compares Three or more levels of one factor.
Effect of multiple levels of two
factors.
Number of Observation Need not to be same in each group. Need to be equal in each group.
Design of experiments Need to satisfy only two principles.
All three principles needs to be
satisfied.
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CHI-SQUARE DISTRIBUTION
A Chi-square distribution is the distribution of the sum of squares of k independent standard normal
random variable with k degree of freedom. It is a statistical hypothesis where the null hypothesis that
the distribution of the test statistic is a chi-square distribution, is true. While it was first introduced
by German Statistician Robert Helmert, and was used by Karl pearson in 1900. The most popular
chi-square test is Pearson‟s chi-square test and is also called „chi-squared‟ test and denoted by χ2
. A
classical example of chi-square test is the test for fairness of a die where we test the hypothesis that
all six possible outcomes are equally likely.
Definitions
Chi-square
distribution
A distribution obtained from the multiplying the ratio of sample variance to
population variance by the degrees of freedom when random samples are selected
from a normally distributed population.
Contingency
Table
Data arranged in table form for the chi-square independence test
Expected
Frequency
The frequencies obtained by calculation.
Goodness-of-fit
Test
A test to see if a sample comes from a population with the given distribution.
Independence
Test
A test to see if the row and column variables are independent.
Observed
Frequency
The frequencies obtained by observation. These are the sample frequencies.
Properties of the Chi-Square
 Chi-square is non-negative. Is the ratio of two non-negative values, therefore must be non-
negative itself.
 Chi-square is non-symmetric.
 There are many different chi-square distributions, one for each degree of freedom.
 The degrees of freedom when working with a single population variance is n-1.
Uses
 The chi-squared distribution has many uses in statistics, including:
 Confidence interval estimation for a population standard deviation of a normal distribution from
a sample standard deviation.
 Independence of two criteria of classification of qualitative variables.
 Relationships between categorical variables (contingency tables).
 Sample variance study when the underlying distribution is normal.
 Tests of deviations of differences between expected and observed frequencies (one-way tables).
 The chi-square test (a goodness of fit test).
Chi-square tests deals with three types of tests. They are:
1. Tests of hypothesis about contingency tables, called independence and homogeneity tests.
2. Tests of hypothesis for experiments with more than two categories, called Goodness-of-fit tests.
3. Tests of hypothesis about the variance and standard deviation of a single population.
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Test for Independence
In the test for independence, the claim is that the row and column variables are independent of each
other. This is the null hypothesis. The multiplication rule said that if two events were independent,
then the probability of both occurring was the product of the probabilities of each occurring. This is
key to working the test for independence. If you end up rejecting the null hypothesis, then the
assumption must have been wrong and the row and column variable are dependent. Remember, all
hypothesis testing is done under the assumption the null hypothesis is true. The test statistic used is
the same as the chi-square goodness-of-fit test. The principle behind the test for independence is the
same as the principle behind the goodness-of-fit test. The test for independence is always a right
tail test. In fact, you can think of the test for independence as a goodness-of-fit test where the data is
arranged into table form. This table is called a contingency table.
The test statistic has a chi-square distribution when the following assumptions are met
 The data are obtained from a random sample
 The expected frequency of each category must be at least 5.
The following are properties of the test for independence
 The data are the observed frequencies.
 The data is arranged into a contingency table.
 The degrees of freedom are the degrees of freedom for the row variable times the degrees of
freedom for the column variable. It is not one less than the sample size, it is the product of the
two degrees of freedom.
 It is always a right tail test.
 It has a chi-square distribution.
 The expected value is computed by taking the row total times the column total and dividing by
the grand total
 The value of the test statistic doesn't change if the order of the rows or columns are switched.
 The value of the test statistic doesn't change if the rows and columns are interchanged (transpose
of the matrix)
Test for Single Population Variance
The variable 𝜒2
=
𝑑𝑓.𝑠2
𝜎2
has a chi-square distribution if the population variance has a normal
distribution. The degrees of freedom are n-1. We can use this to test the population variance under
certain conditions
Conditions for testing
 The population has a normal distribution
 The data is from a random sample
 The observations must be independent of each other
 The test statistic has a chi-square distribution with n-1 degrees of freedom
Test for Goodness-of-fit:
The idea behind the chi-square goodness-of-fit test is to see if the sample comes from the population
with the claimed distribution. Another way of looking at that is to ask if the frequency distribution
fits a specific pattern. Two values are involved, an observed value, which is the frequency of a
category from a sample, and the expected frequency, which is calculated based upon the claimed
distribution. The idea is that if the observed frequency is really close to the claimed (expected)
frequency, then the square of the deviations will be small. The square of the deviation is divided by
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the expected frequency to weight frequencies. A difference of 10 may be very significant if 12 was
the expected frequency, but a difference of 10 isn't very significant at all if the expected frequency
was 1200. If the sum of these weighted squared deviations is small, the observed frequencies are
close to the expected frequencies and there would be no reason to reject the claim that it came from
that distribution. Only when the sum is large is the a reason to question the distribution. Therefore,
the chi-square goodness-of-fit test is always a right tail test.
The test statistic has a chi-square distribution when the following assumptions are met
 The data are obtained from a random sample.
 The expected frequency of each category must be at least 5. This goes back to the requirement
that the data be normally distributed. You're simulating a multinomial experiment (using a
discrete distribution) with the goodness-of-fit test (and a continuous distribution), and if each
expected frequency is at least five then you can use the normal distribution to approximate (much
like the binomial). If the expected
The following are properties of the goodness-of-fit test
 The data are the observed frequencies. This means that there is only one data value for each
category.
 The degree of freedom is one less than the number of categories, not one less than the sample
size.
 It is always a right tail test.
 It has a chi-square distribution.
 The value of the test statistic doesn't change if the order of the categories is switched.
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CORRELATION
Introduction:
The term correlation is used by a common man without knowing that he is making use of the term
correlation. For example when parents advice their children to work hard so that they may get good
marks, they are correlating good marks with hard work.
The study related to the characteristics of only variable such as height, weight, ages, marks,
wages, etc., is known as Univariate analysis. The statistical Analysis related to the study of the
relationship between two variables is known as Bivariate Analysis. Sometimes the variables may be
inter-related. In health sciences we study the relationship between blood pressure and age,
consumption level of some nutrient and weight gain, total income and medical expenditure, etc., the
nature and strength of relationship may be examined by correlation and Regression analysis. Thus
Correlation refers to the relationship of two variables or more. (eg.) relation between height of father
and son, yield and rainfall, wage and price index, share and debentures etc.
Correlation is statistical Analysis which measures and analyses the degree or extent to which
the two variables fluctuate with reference to each other. The word relationship is important. It
indicates that there is some connection between the variables. It measures the closeness of the
relationship. Correlation does not indicate cause and effect relationship. Price and supply, income
and expenditure are correlated.
Definitions:
1. Croxton and Cowden, “When the relationship is of a quantitative nature, the
appropriate statistical tool for discovering and measuring the relationship and expressing it in a
brief formula is known as correlation”.
2. A.M. Tuttle, “Correlation is an analysis of the co-variation between two or more variables.”
3. W.A. Neiswanger, “Correlation analysis contributes to the understanding of economic
behavior, aids in locating the critically important variables on which others depend, may reveal to
the economist the connections by which disturbances spread and suggest to him the paths through
which stabilizing forces may become effective.”
4. L. R. Conner, “If two or more quantities vary in sympathy so that the movement in one tends to
be accompanied by corresponding movements in others than they are said to be correlated.”
Uses of correlation:
1. It is used in physical and social sciences.
2. It is useful for economists to study the relationship between variables like price, quantity etc.
Businessmen estimates costs, sales, price etc. using correlation.
3. It is helpful in measuring the degree of relationship between the variables like income and
expenditure, price and supply, supply and demand etc.
4. Sampling error can be calculated.
5. It is the basis for the concept of regression.
Types of Correlation
Correlation can be categorised as one of the following:
1. Positive and Negative.
2. Simple and Multiple.
3. Partial and Total.
4. Linear and Non-Linear (Curvilinear).
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1. Positive and Negative Correlation: Positive or direct Correlation refers to the movement of
variables in the same direction. The correlation is said to be positive when the increase (decrease)
in the value of one variable is accompanied by an increase (decrease) in the value of other
variable also. Negative or inverse correlation refers to the movement of the variables in opposite
direction. Correlation is said to be negative, if an increase (decrease) in the value of one variable
is accompanied by a decrease (increase) in the value of other.
2. Simple and Multiple Correlation: Under simple correlation, we study the relationship between
two variables only i.e., between the yield of wheat and the amount of rainfall or between demand
and supply of a commodity. In case of multiple correlations, the relationship is studied among
three or more variables. For example, the relationship of yield of wheat may be studied with both
chemical fertilizers and the pesticides.
3. Partial and Total Correlation: There are two categories of multiple correlation analysis. Under
partial correlation, the relationship of two or more variables is studied in such a way that only
one dependent variable and one independent variable is considered and all others are kept
constant. For example, coefficient of correlation between yield of wheat and chemical fertilizers
excluding the effects of pesticides and manures is called partial correlation. Total correlation is
based upon all the variables.
4. Linear and Non-Linear Correlation: When the amount of change in one variable tends to keep
a constant ratio to the amount of change in the other variable, then the correlation is said to be
linear. But if the amount of change in one variable does not bear a constant ratio to the amount of
change in the other variable then the correlation is said to be non-linear. The distinction between
linear and non-linear is based upon the consistency of the ratio of change between the variables.
Scatter Diagram:
It is the simplest method of studying the relationship between two variables diagrammatically. One
variable is represented along the horizontal axis and the second variable along the vertical axis. For
each pair of observations of two variables, we put a dot in the plane. There are as many dots in the
plane as the number of paired observations of two variables. The direction of dots shows the scatter
or concentration of various points. This will show the type of correlation.
1. If all the plotted points form a straight line from lower left hand corner to the upper right hand
corner then there is Perfect positive correlation. We denote this as r = +.
2. If all the plotted dots lie on a straight line falling from upper left hand corner to lower right hand
corner, there is a perfect negative correlation between the two variables. In this case the
coefficient of correlation takes the value r = -1.
3. If the plotted points in the plane form a band and they show a rising trend from the lower left
hand corner to the upper right hand corner the two variables are highly positively correlated.
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4. If the points fall in a narrow band from the upper left hand corner to the lower right hand corner,
there will be a high degree of negative correlation.
5. If the plotted points in the plane are spread all over the diagram there is no correlation between
the two variables.
Merits:
1. It is a simplest and attractive method of finding the nature of correlation between the two
variables.
2. It is a non-mathematical method of studying correlation. It is easy to understand.
3. It is not affected by extreme items.
4. It is the first step in finding out the relation between the two variables.
5. We can have a rough idea at a glance whether it is a positive correlation or negative correlation.
Demerits:
1. By this method we cannot get the exact degree or correlation between the two variables.
Karl Pearson‟s Co-efficient of Correlation
Popularly known as Pearsonian co-efficient of correlation, is most widely applied in practice to
measure correlation. The Pearsonian co-efficient of correlation is represented by the symbol r.
According to Karl Pearson‟s method, co-efficient of correlation between the variables is obtained by
dividing the sum of the products of the corresponding deviations of the various items of two series
from their respective means by the product of their standard deviations and the number of pairs of
observations. Symbolically, r = where r stands for coefficient of correlation ...(i)
where x1, x2, x3, x4 ..................... xn are the deviations of various items of the first variable from the
mean, y1, y2, y3,........................ yn are the deviations of all items of the second variable from mean,
Sxy is the sum of products of these corresponding deviations. N stands for the number of pairs, sx
stands for the standard deviation of X variable and sy stands for the standard deviation of Y variable.
sx = and sy = If we substitute the value of sx and sy in the above written formula of computing r, we
get r = or r = Degree of correlation varies between + 1 and –1; the result will be + 1 in case of perfect
positive correlation and – 1 in case of perfect negative correlation. Computation of correlation
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coefficient can be simplified by dividing the given data by a common factor. In such a case, the final
result is not multiplied by the common factor because coefficient of correlation is independent of
change of scale and origin.
Assumptions:
Karl Pearson based his formula on following basic assumptions:
1. Two variables are affected by many independent causes and form a normal distribution.
2. The cause and effect relationship exists between two variables.
3. The relationship between two variables is linear. It is often denoted by r.
Merits and Demerits of Pearson‟s method of studying correlation:
Merits
1. This method indicates the presence or absence of correlation between two variables and gives the
exact degree of their correlation.
2. In this method, we can also ascertain the direction of the correlation; positive, or negative.
3. This method has many algebraic properties for which the calculation of co-efficient of
correlation, and other related factors, are made easy.
Demerits:
1. It is more difficult to calculate than other methods of calculations.
2. It is much affected by the values of the extreme items.
3. It is based on a many assumptions, such as: linear relationship, cause and effect relationship etc.
which may not always hold good.
4. It is very much likely to be misinterpreted in case of homogeneous data.
Spearman‟s Rank Correlation:
It is studied when no assumption about the parameters of the population is made. This method is
based on ranks. It is useful to study the qualitative measure of attributes like honesty, colour, beauty,
intelligence, character, morality etc. The individuals in the group can be arranged in order and there
on, obtaining for each individual a number showing his/her rank in the group. This method was
developed by Edward Spearman in 1904. The value of r lies between –1 and +1. If r = +1, there is
complete agreement in order of ranks and the direction of ranks is also same. If r = -1, then there is
complete disagreement in order of ranks and they are in opposite directions. Computation for tied
observations: There may be two or more items having equal values. In such case the same rank is to
be given. The ranking is said to be tied. In such circumstances an average rank is to be given to each
individual item. The sign of the Spearman correlation indicates the direction of association
between X (the independent variable) and Y (the dependent variable). If Y tends to increase
when X increases, the Spearman correlation coefficient is positive. If Y tends to decrease
when Xincreases, the Spearman correlation coefficient is negative. A Spearman correlation of zero
indicates that there is no tendency for Y to either increase or decrease when X increases. The
Spearman correlation increases in magnitude as X and Y become closer to being perfect monotone
functions of each other. When X and Y are perfectly monotonically related, the Spearman correlation
coefficient becomes 1. A perfect monotone increasing relationship implies that for any two pairs of
data values Xi, Yi and Xj, Yj, that Xi − Xj and Yi − Yjalways have the same sign. A perfect monotone
decreasing relationship implies that these differences always have opposite signs. The Spearman
correlation coefficient is often described as being "nonparametric". This can have two meanings:
First, a perfect Spearman correlation results when X and Y are related by any monotonic function.
Contrast this with the Pearson correlation, which only gives a perfect value when X and Y are related
by a linear function. The other sense in which the Spearman correlation is nonparametric in that its
exact sampling distribution can be obtained without requiring knowledge (i.e., knowing the
parameters) of the joint probability distribution of X and Y.
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TIME SERIES ANALYSIS
Time Series is a sequence of well-defined data points measured at consistent time intervals over a
period of time. Data collected on an ad-hoc basis or irregularly does not form a time series. Time
series analysis is the use of statistical methods to analyze time series data and extract meaningful
statistics and characteristics about the data. Time series Analysis helps us understand what are the
underlying forces leading to a particular trend in the time series data points and helps us in
forecasting and monitoring the data points by fitting appropriate models to it.
Statistical data which is recorded with its time of occurrence is called a time series. The
yearly output of wheat recorded for the last twenty five years, the weekly average price of eggs
recorded for the last 52 weeks, the monthly average sales of a firm recorded for the last 48 months or
the quarterly average profits recorded for the last 40 quarters etc., are examples of time series data. It
may be observed that this data undergoes changes with the passage of time. A number of factors can
be isolated which contribute to changes occurring over time in such a series. In the fields of
economics and business, data such as income, imports, exports, production, consumption, and prices
depend on time. All of these data are dependent on seasonal changes as well as regular cyclical
changes over the time period. To evaluate the changes in business and economics, the analysis of
time series plays an important role in this regard. It is necessary to associate time with time series,
because time is one basic variable in time series analysis.
Benefits and Applications of Time Series Analysis:
Time series analysis aims to achieve various objectives and the tools and models used vary
accordingly. The various types of time series analysis include –
 Descriptive analysis – to determine the trend or pattern in a time series using graphs or other
tools. This helps us identify cyclic patterns, overall trends, turning points and outliers.
 Spectral analysis – is also referred to as frequency domain and aims to separate periodic or
cyclical components in a time series. For example, identifying cyclical changes in sales of a
product.
 Forecasting – used extensively in business forecasting, budgeting, etc based on historical trends.
 Intervention Analysis – is used to determine if an event can lead to a change in the time series,
for example, an employee‟s level of performance has improved or not after an intervention in the
form of training – to determine the effectiveness of the training program.
 Explanative Analysis – studies the cross correlation or relationship between two time series and
the dependence of one on another. For example the study of employee turnover data and
employee training data to determine if there is any dependence of employee training programs on
employee turnover rates over time.
The Components of Time Series
The factors that are responsible for bringing about changes in a time series, also called the
components of time series, are as follows:
1. Secular Trends (or General Trends)
2. Seasonal Movements
3. Cyclical Movements
4. Irregular Fluctuations or Erratic Trends
1. Secular Trend or Long Term Variation:
It is a longer term change. Here we take into account the number of observations available and
make a subjective assessment of what is long term. To understand the meaning of long term, let
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for example climate variables sometimes exhibit cyclic variation over a very long time period
such as 50 years. If one just had 20 years data, this long term oscillation would appear to be a
trend, but if several hundred years of data is available, then long term oscillations would be
visible. These movements are systematic in nature where the movements are broad, steady,
showing slow rise or fall in the same direction. The trend may be linear or non-linear
(curvilinear). Some examples of secular trend are: Increase in prices, Increase in pollution,
increase in the need of wheat, increase in literacy rate, and decrease in deaths due to advances in
science. Taking averages over a certain period is a simple way of detecting trend in seasonal data.
Change in averages with time is evidence of a trend in the given series, though there are more
formal tests for detecting trend in time series.
2. Seasonal Variation or Seasonal Fluctuations:
Many of the time series data exhibits a seasonal variation which is annual period, such as sales
and temperature readings. This type of variation is easy to understand and can be easily
measured or removed from the data to give de-seasonalized data. Seasonal
Fluctuations describes any regular variation (fluctuation) with a period of less than one year for
example cost of variation types of fruits and vegetables, cloths, unemployment figures, average
daily rainfall, increase in sale of tea in winter, increase in sale of ice cream in summer etc., all
show seasonal variations. The changes which repeat themselves within a fixed period, are also
called seasonal variations, for example, traffic on roads in morning and evening hours, Sales at
festivals like EID etc., increase in the number of passengers at weekend etc. Seasonal
variations are caused by climate, social customs, religious activities etc.
3. Cyclical Variation or Cyclic Fluctuations:
Time series exhibits Cyclical Variations at a fixed period due to some other physical cause, such
as daily variation in temperature. Cyclical variation is a non-seasonal component which varies in
recognizable cycle. Sometime series exhibits oscillations which do not have a fixed period but
are predictable to some extent. For example, economic data affected by business cycles with a
period varying between about 5 and 7 years. In weekly or monthly data, the cyclical component
may describe any regular variation (fluctuations) in time series data. The cyclical variations are
periodic in nature and repeat themselves like business cycle, which has four phases
(i) Peak (ii) Recession (iii) Trough/Depression (iv) Expansion.
4. Irregular Fluctuations:
When trend and cyclical variations are removed from a set of time series data, the residual left,
which may or may not be random. Various techniques for analyzing series of this type examine
to see “if irregular variation may be explained in terms of probability models such as moving
average or autoregressive models, i.e. we can see if any cyclical variation is still left in
the residuals. These variation occur due to sudden causes are called residual variation (irregular
variation or accidental or erratic fluctuations) and are unpredictable, for example rise in prices
of steel due to strike in the factory, accident due to failure of break, flood, earth quick, war etc.
Mathematical Statement of the Composition of Time Series:
A time series may not be affected by all type of variations. Some of these types of variations may
affect a few time series, while the other series may be affected by all of them. Hence, in analysing
time series, these effects are isolated. In classical time series analysis it is assumed that any given
observation is made up of trend, seasonal, cyclical and irregular movements and these four
components have multiplicative relationship.
Symbolically:
O = T × S × C × I
Where O refers to original data,
T refers to trend.
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S refers to seasonal variations,
C refers to cyclical variations and
I refers lo irregular variations.
This is the most commonly used model in the decomposition of time series.
There is another model called Additive model in which a particular observation in a time series is the
sum of these four components.
O = T + S + C + I
To prevent confusion between the two models, it should be made clear that in Multiplicative model
S, C, and I are indices expressed as decimal percents whereas in Additive model S, C and I are
quantitative deviations about trend that can be expressed as seasonal, cyclical and irregular in nature.
If in a multiplicative model. T = 500, S = 1.4, C = 1.20 and I = 0.7 then
O = T × S × C × I
By substituting the values we get
O = 500 × 1.4 × 1.20 × 0.7 = 608
In additive model, T = 500, S = 100, C = 25, I = –50
O = 500 + 100 + 25 – 50 = 575
The assumption underlying the two schemes of analysis is that whereas there is no interaction among
the different constituents or components under the additive scheme, such interaction is very much
present in the multiplicative scheme. Time series analysis, generally, proceed on the assumption of
multiplicative formulation.
Methods of Measuring Trend
Trend can be determined:
1. Free hand curve method ;
2. Moving averages method ;
3. Semi-averages method; and
4. Least-squares Method.
Each of these methods is described below:
1. Freehand Curve Method:
The term freehand is used to any non-mathematical curve in statistical analysis even if it is drawn
with the aid of drafting instruments. This is the simplest method of studying trend of a time series.
The procedure for drawing free hand curve is as follows:
(i) The original data are first plotted on a graph paper.
(ii) The direction of the plotted data is carefully observed.
(iii)A smooth line is drawn through the plotted points.
While fitting a trend line by the freehand method, an attempt should be made that the fitted curve
conforms to these conditions.
 The curve should be smooth either a straight line or a combination of long gradual curves.
 The trend line or curve should be drawn through the graph of the data in such a way that the
areas below and above the trend line are equal to each other.
 The vertical deviations of the data above the trend line must equal to the deviations below the
line.
 Sum of the squares of the vertical deviations of the observations from the trend should be
minimum.
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Merits:
 It is very simplest method for study trend values and easy to draw trend.
 Sometimes the trend line drawn by the statistician experienced in computing trend may be
considered better than a trend line fitted by the use of a mathematical formula.
 Although the free hand curves method is not recommended for beginners, it has considerable
merits in the hands of experienced statisticians and widely used in applied situations.
Demerits:
 This method is highly subjective and curve varies from person to person who draws it.
 The work must be handled by skilled and experienced people.
 Since the method is subjective, the prediction may not be reliable.
 While drawing a trend line through this method a careful job has to be done.
2. Method of Moving Averages:
The moving average is a simple and flexible process of trend measurement which is quite
accurate under certain conditions. This method establishes a trend by means of a series of averages
covering overlapping periods of the data.
The process of successively averaging, say, three years data, and establishing each average as the
moving-average value of the central year in the group, should be carried throughout the entire series.
For a five-item, seven-item or other moving averages, the same procedure is followed: the average
obtained each time being considered as representative of the middle period of the group.
The choice of a 5-year, 7-year, 9-year, or other moving average is determined by the length of period
necessary to eliminate the effects of the business cycle and erratic fluctuations. A good trend must be
free from such movements, and if there is any definite periodicity to the cycle, it is well to have the
moving average to cover one cycle period. Ordinarily, the necessary periods will range between three
and ten years for general business series but even longer periods are required for certain industries.
In the preceding discussion, the moving averages of odd number of years were representatives of
the middle years. If the moving average covers an even number of years, each average will still be
representative of the midpoint of the period covered, but this mid-point will fall halfway between the
two middle years. In the case of a four-year moving average, for instance each average represents a
point halfway between the second and third years. In such a case, a second moving average may be
used to „re-centre‟ the averages.
That is, if the first moving average gives averages centering half-way between the years, a further
two-point moving average will re-centre the data exactly on the years. This method, however, is
valuable in approximating trends in a period of transition when the mathematical lines or curves may
be inadequate. This method provides a basis for testing other types of trends, even though the data
are not such as to justify its use otherwise.
Merits
1. This is a very simple method.
2. The element of flexibility is always present in this method as all the calculations have not to be
altered if same data is added. It only provides additional trend values.
3. If there is a coincidence of the period of moving averages and the period of cyclical fluctuations,
the fluctuations automatically disappear.
4. The pattern of moving average is determined in the trend of data and remains unaffected by the
choice of method to be employed.
5. It can be put to utmost use in case of series having strikingly irregular trend.
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Limitations:
1. It is not possible to have a trend value for each and every year. As the period of moving average
increases, there is always an increase in the number of years for which trend values cannot be
calculated and known. For example, in a five yearly moving average, trend value cannot be
obtained for the first two years and last two years, in a seven yearly moving average for the first
three years and last three years and so on. But usually values of the extreme years are of great
interest.
2. There is no hard and fast rule for the selection of a period of moving average.
3. Forecasting is one of the leading objectives of trend analysis. But this objective remains
unfulfilled because moving average is not represented by a mathematical function.
4. Theoretically it is claimed that cyclical fluctuations are ironed out if period of moving average
coincide with period of cycle, but in practice cycles are not perfectly periodic.
3. Method of Semi Averages:
In this method the whole data is divided in two equal parts with respect to time. For example if
we are given data from 1979 to 1996 i.e. over a period of 18 years the two equal parts will be first
nine years i.e. from 1979 to 1987 and 1988 to 1996. In case of odd number of years like 9, 13, 17 etc.
two equal parts can be made simply by omitting the middle year. For example if the data are given
for 19 years from 1978 to 1996 the two equal parts would be from 1978 to 1986 and from 1988 to
1996, the middle year 1987 will be omitted. After the data have been divided into two parts, an
average (arithmetic mean) of each part is obtained. We thus get two points. Each point is plotted
against the mid year of the each part. Then these two points are joined by a straight line which gives
us the trend line. The line can be extended downwards or upwards to get intermediate values or to
predict future values.
Merits:
 This method is simple to understand as compare to moving average method and method of least
squares.
 This is an objective method of measuring trend as everyone who applies this method is bound to
get the same result.
Demerits:
 The method assumes straight line relationship between the plotted points regardless of the fact
whether that relationship exists or not.
 The main drawback of this method is if we add some more data to the original data then whole
calculation is to be done again for the new data to get the trend values and the trend line also
changes.
 As the A.M of each half is calculated, an extreme value in any half will greatly affect the points
and hence trend calculated through these points may not be precise enough for forecasting the
future.
4. Method of Least Squares:
If a straight line is fitted to the data it will serve as a satisfactory trend, perhaps the most accurate
method of fitting is that of least squares. This method is designed to accomplish two results.
(i) The sum of the vertical deviations from the straight line must equal zero.
(ii) The sum of the squares of all deviations must be less than the sum of the squares for any other
conceivable straight line.
There will be many straight lines which can meet the first condition. Among all different lines,
only one line will satisfy the second condition. It is because of this second condition that this method
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is known as the method of least squares. It may be mentioned that a line fitted to satisfy the second
condition, will automatically satisfy the first condition.
The formula for a straight-line trend can most simply be expressed as
Yc = a + bX
Where X represents time variable, Yc is the dependent variable for which trend values are to be
calculated and a and b are the constants of the straight tine to be found by the method of least
squares.
Constant is the Y-intercept. This is the difference between the point of the origin (O) and the point of
the trend line and Y-axis intersect. It shows the value of Y when X = 0, constant b indicates the slope
which is the change in Y for each unit change in X. Let us assume that we are given observations of
Y for n number of years. If we wish to find the values of constants a and b in such a manner that the
two conditions laid down above are satisfied by the fitted equation.
Merits:
 This is a mathematical method of measuring trend and as such there is no possibility of
subjectiveness i.e. everyone who uses this method will get same trend line.
 The line obtained by this method is called the line of best fit.
 Trend values can be obtained for all the given time periods in the series.
Demerits:
 Great care should be exercised in selecting the type of trend curve to be fitted i.e. linear,
parabolic or some other type. Carelessness in this respect may lead to wrong results.
 The method is more tedious and time consuming.
 Predictions are based on only long term variations i.e. trend and the impact of cyclical, seasonal
and irregular variations is ignored.
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REGRESSION ANALYSIS
The Regression Analysis is a statistical tool used to determine the probable change in one variable
for the given amount of change in another. This means, the value of the unknown variable can be
estimated from the known value of another variable. The degree to which the variables are correlated
to each other depends on the Regression Line. The regression line is a single line that best fits the
data, i.e. all the points plotted are connected via a line in the manner that the distance from the line to
the points is the smallest.
The regression also tells about the relationship between the two or more variables, then what
is the difference between regression and correlation? Well, there are two important points of
differences between Correlation and Regression. These are:
 The Correlation Coefficient measures the “degree of relationship” between variables, say X and
Y whereas the Regression Analysis studies the “nature of relationship” between the variables.
 Correlation coefficient does not clearly indicate the cause-and-effect relationship between the
variables, i.e. it cannot be said with certainty that one variable is the cause, and the other is the
effect. Whereas, the Regression Analysis clearly indicates the cause-and-effect
relationship between the variables.
The regression analysis is widely used in all the scientific disciplines. In economics, it plays a
significant role in measuring or estimating the relationship among the economic variables. For
example, the two variables – price (X) and demand (Y) are closely related to each other, so we can
find out the probable value of X from the given value of Y and similarly the probable value of Y can
be found out from the given value of X.
The relationship between two variables may be interested in estimating (predicting) the value
of one variable given the value of another. The variable predicted on the basis of other variables is
called the “dependent” or the „explained‟ variable and the other the „independent‟ or the „predicting‟
variable. The prediction is based on average relationship derived statistically by regression analysis.
The equation, linear or otherwise, is called the regression equation or the explaining equation.
For example, if we know that advertising and sales are correlated we may find out expected
amount of sales for a given advertising expenditure or the required amount of expenditure for
attaining a given amount of sales.
The relationship between two variables can be considered between, say, rainfall and
agricultural production, price of an input and the overall cost of product consumer expenditure and
disposable income. Thus, regression analysis reveals average relationship between two variables and
this makes possible estimation or prediction.
Types of Regression:
The regression analysis can be classified in to:
a) Simple and Multiple
b) Linear and Non –Linear
c) Total and Partial
a) Simple and Multiple:
In case of simple relationship only two variables are considered, for example, the influence of
advertising expenditure on sales turnover. In the case of multiple relationship, more than two
variables are involved. On this while one variable is a dependent variable the remaining variables are
independent ones. For example, the turnover (y) may depend on advertising expenditure (x) and the
income of the people (z). Then the functional relationship can be expressed as y = f (x,z).
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b) Linear and Non-linear:
The linear relationships are based on straight-line trend, the equation of which has no-power higher
than one. But, remember a linear relationship can be both simple and multiple. Normally a linear
relationship is taken into account because besides its simplicity, it has a better predictive value; a
linear trend can be easily projected into the future. In the case of non-linear relationship curved trend
lines are derived. The equations of these are parabolic.
c) Total and Partial:
In the case of total relationships all the important variables are considered. Normally, they take the
form of a multiple relationships because most economic and business phenomena are affected by
multiplicity of cases. In the case of partial relationship one or more variables are considered, but not
all, thus excluding the influence of those not found relevant for a given purpose.
Properties of Regression Co-efficient:
1. Both regression coefficients must have the same sign, i.e. either it will be positive or negative.
2. Correlation coefficient is the geometric mean of the regression coefficients i.e. r = ± 𝑏1𝑏2
3. The correlation coefficient will have the same sign as that of the regression coefficients.
4. If one regression coefficient is greater than unity, then other regression coefficient must be less
than unity.
5. Regression coefficients are independent of origin but not of scale.
6. Arithmetic mean of b1 and b2 is equal to or greater than the coefficient of correlation.
Symbolically
𝑏1+𝑏2
2
≥ 𝑟.
7. If r = 0, the variables are uncorrelated, the lines of regression become perpendicular to each
other.
8. If r = ±1, the two lines of regression either coincide or parallel to each other
9. Angle between the two regression lines is θ = 𝑡𝑎𝑛−1 𝑚1−𝑚2
1+𝑚1𝑚2
where m1 and, m2 are the slopes of
the regression lines X on Y and Y on X respectively.
10. The angle between the regression lines indicates the degree of dependence between the variables.
Difference between Correlation and Regression
Correlation Regression
It is the relationship between two or more
variables, which vary in sympathy with the other
in the same or the opposite direction.
It means going back and it is a mathematical
measure showing the average relationship
between two variables.
Both the variables X and Y are random variables. Here X is a random variable and Y is a fixed
variable. Sometimes both the variables may be
random variables.
It finds out the degree of relationship between
two variables and not the cause and effect of the
variables.
It indicates the causes and effect relationship
between the variables and establishes functional
relationship.
It is used for testing and verifying the relation
between two variables and gives limited
information.
Besides verification it is used for the prediction
of one value, in relationship to the other given
value.
The coefficient of correlation is a relative
measure. The range of relationship lies between
–1 and +1.
It is an absolute figure. If we know the value of
the independent variable, we can find the value
of the dependent variable.
There may be spurious correlation between two In regression there is no such spurious
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variables. regression.
It has limited application, because it is confined
only to linear relationship between the variables.
It has wider application, as it studies linear and
nonlinear relationship between the variables.
It is not very useful for further mathematical
treatment.
It is widely used for further mathematical
treatment.
If the coefficient of correlation is positive, then
the two variables are positively correlated and
vice-versa.
The regression coefficient explains that the
decrease in one variable is associated with the
increase in the other variable.
Linear Regression Equation:
If two variables have linear relationship then as the independent variable (X) changes, the dependent
variable (Y) also changes. If the different values of X and Y are plotted, then the two straight lines of
best fit can be made to pass through the plotted points. These two lines are known as regression lines.
Again, these regression lines are based on two equations known as regression equations. These
equations show best estimate of one variable for the known value of the other. The equations are
linear.
Linear regression equation of Y on X is
Y = a + b X ……. (1)
And X on Y is
X = a + b Y……. (2)
a, b are constants.
From (1) We can estimate Y for known value of X.
(2) We can estimate X for known value of Y.
The Regression Equation is the algebraic expression of the regression lines. It is used to predict the
values of the dependent variable from the given values of independent variables. If we take two
regression lines, say Y on X and X on Y, then there will be two regression equations:
 Regression Equation of Y on X: This is used to describe the variations in the value Y from the
given changes in the values of X. It can be expressed as follows:
Yc = a + bX
Where Ye is the dependent variable, X is the independent variable, and a & b are the two unknown
constants that determine the position of the line. The parameter “a” tells about the level of the fitted
line, i.e. the distance of a line above or below the origin and parameter “b” tells about the slope of
the line, i.e. the change in the value of Y for one unit of change in X.
The values of „a‟ and „b‟ can be obtained by a method of least squares. According to which
the line should be drawn connecting all the plotted points in such a manner that the sum of the
squares of the vertical deviations of actual Y from the estimated values of Y is the least, or a best-
fitted line is obtained when ∑ (Y-Ye)2
is the minimum.
The following algebraic equations can be solved simultaneously to obtain the values of
parameter „a‟ and „b‟.
 Regression Equation of X on Y: This is used to describe the variations in Y from the given
changes in the value of X. It can be expressed as follows:
Xc = a + bY
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Where Xe is the dependent variable and Y is the independent variable. The parameters „a‟ and
„b‟ are the two unknown constants. Again, „a‟ tells about the level of fitted line and „b‟ tells about the
slope, i.e. the change in the value of X for a unit change in the value of Y.
The following are the two normal equations that can be solved simultaneously to obtain the
values of both the parameters „a‟ and „b‟.
Note: The line can be completely determined only if the values of the constant parameters „a‟ and
„b‟ are obtained.
Regression Lines:
The Regression Line is the line that best fits the data, such that the overall distance from the line to
the points (variable values) plotted on a graph is the smallest. In other words, a line used to minimize
the squared deviations of predictions is called as the regression line.
There are as many numbers of regression lines as variables. Suppose we take two variables,
say X and Y, then there will be two regression lines:
 Regression line of Y on X: This gives the most probable values of Y from the given values of X.
 Regression line of X on Y: This gives the most probable values of X from the given values of Y.
The algebraic expression of these regression lines is called as Regression Equations. There will be
two regression equations for the two regression lines.
The correlation between the variables depend on the distance between these two regression
lines, such as the nearer the regression lines to each other the higher is the degree of correlation, and
the farther the regression lines to each other the lesser is the degree of correlation.
The correlation is said to be either perfect positive or perfect negative when the two
regression lines coincide, i.e. only one line exists. In case, the variables are independent; then the
correlation will be zero, and the lines of regression will be at right angles, i.e. parallel to the X axis
and Y axis.
Note: The regression lines cut each other at the point of average of X and Y. This means, from the
point where the lines intersect each other the perpendicular is drawn on the X axis we will get the
mean value of X. Similarly, if the horizontal line is drawn on the Y axis we will get the mean value
of Y.
For regression analysis of two variables there are two regression lines, namely Y on X and X
on Y. The two regression lines show the average relationship between the two variables. For perfect
correlation, positive or negative i.e., r = + 1, the two lines coincide i.e., we will find only one straight
line. If r = 0, i.e., both the variables are independent then the two lines will cut each other at right
angle. In this case the two lines will be parallel to X and Y-axes.
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Lastly the two lines intersect at the point of means of X and Y. From this point of
intersection, if a straight line is drawn on X-axis, it will touch at the mean value of x. Similarly, a
perpendicular drawn from the point of intersection of two regression lines on Y-axis will touch the
mean value of Y.
Principle of „Least Squares‟:
Regression shows an average relationship between two variables, which is expressed by a line of
regression drawn by the method of “least squares”. This line of regression can be derived graphically
or algebraically. Before we discuss the various methods let us understand the meaning of least
squares.
A line fitted by the method of least squares is known as the line of best fit. The line adapts to the
following rules:
(i) The algebraic sum of deviation in the individual observations with reference to the regression line
may be equal to zero. i.e.,
Σ(X – Xc) = 0 or Σ (Y- Yc ) = 0
Where Xc and Yc are the values obtained by regression analysis.
(ii) The sum of the squares of these deviations is less than the sum of squares of deviations from any
other line. i.e.,
Σ (Y – Yc)2
< Σ (Y – Ai)2
Where Ai = corresponding values of any other straight line.
(iii) The lines of regression (best fit) intersect at the mean values of the variables X and Y, i.e.,
intersecting point is 𝑥, 𝑦.
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