2. LEARNING OBJECTIVES
LO 1-1 List ways that statistics is used.
LO 1-2 Know the differences between descriptive and inferential statistics.
LO 1-3 Understand the differences between a sample and a population.
LO 1-4 Explain the difference between qualitative and quantitative variables.
LO 1-5 Compare the discrete and continuous variables.
LO 1-6 Recognize the levels of measurement in data.
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3. Why Study Statistics?
Why Study Statistics?
1. Numerical information is everywhere.
2. Statistical techniques are used to make decisions that affect our daily lives.
3. The knowledge of statistical methods will help you understand how decisions are made and give you a
better understanding of how they affect you.
4. No matter what line of work you select, you will find yourself faced with decisions where an
understanding of data analysis is helpful.
Some examples of the need for data collection.
1. Research analysts for Merrill Lynch evaluate many facets of a particular stock before making a “buy” or “sell”
recommendation.
2. The marketing department at Colgate-Palmolive Co., a manufacturer of soap products, has the responsibility of making
recommendations regarding the potential profitability of a newly developed group of face soaps having fruit smells.
3. The United States government is concerned with the present condition of our economy and with predicting future
economic trends.
4. Managers must make decisions about the quality of their product or service.
LO1 List ways statistics is used.
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4. What Is Meant by Statistics?
In the more common usage, statistics refers
to numerical information
Examples: the average starting salary of college graduates, the number of deaths
due to alcoholism last year, the change in the Dow Jones Industrial Average from
yesterday to today, and the number of home runs hit by the Chicago Cubs during
the 2007 season.
We often present statistical information in a
graphical form for capturing reader attention and to
portray a large amount of information.
LO 1-1
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5. Types of Statistics – Descriptive
Statistics and Inferential Statistics
Descriptive Statistics - methods of organizing, summarizing, and
presenting data in an informative way.
Inferential Statistics: A decision, estimate, prediction, or
generalization about a population, based on a sample.
Note: In statistics the word population and sample have a broader
meaning. A population or sample may consist of individuals or
objects
LO 1-2 Know the differences between
descriptive and inferential statistics.
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6. Population vs. Sample
A population is a collection of all possible individuals, objects, or
measurements of interest.
A sample is a portion, or part, of the population of interest.
LO 1-3 Understand the differences
between a sample and a population.
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7. Why Take a Sample Instead of Studying Every Member
of the Population?
1. Prohibitive cost of census.
2. Destruction of item being studied may be required.
3. Not possible to test or inspect all members of a
population being studied.
Using a sample to learn something about a population is done extensively in business,
agriculture, politics, and government.
EXAMPLE: Television networks constantly monitor the popularity of their programs by
hiring Nielsen and other organizations to sample the preferences of TV viewers.
LO 1-3
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8. Types of Variables
Qualitative or attribute variable - the
characteristic being studied is nonnumeric.
EXAMPLES: gender, religious affiliation, type of automobile
owned, state of birth, and eye color are examples.
Quantitative variable - information is reported
numerically.
EXAMPLES: balance in your checking account, minutes remaining
in class, or number of children in a family.
LO 1-4 Explain the difference between
qualitative and quantitative variables.
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9. Quantitative Variables - Classifications
Quantitative variables can be classified as either discrete or
continuous.
Discrete variables can only assume certain values, and there
are usually “gaps” between values.
EXAMPLE: the number of bedrooms in a house, or the number of hammers sold at the local Home
Depot (1,2,3,…,etc).
Continuous variable can assume any value within a specified
range.
EXAMPLE: the pressure in a tire, the weight of a pork chop, or the height of students in a class.
LO 1-5 Compare discrete and continuous
variables.
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11. Four Levels of Measurement
Nominal level – data that is classified
into categories and cannot be
arranged in any particular order.
EXAMPLES:: Eye color, gender, religious
affiliation.
Ordinal level – data arranged in some
order, but the differences
between data values cannot be
determined or are meaningless.
EXAMPLE: During a taste test of 4 soft drinks,
Mellow Yellow was ranked number 1,
Sprite number 2, Seven-up number 3, and
Orange Crush number 4.
Interval level – similar to the ordinal
level, with the additional property
that meaningful amounts of
differences between data values can
be determined. There is no natural
zero point.
EXAMPLES: Temperature on the Fahrenheit scale.
Ratio level – the interval level with an
inherent zero starting point.
Differences and ratios are meaningful
for this level of measurement.
EXAMPLES: Monthly income of surgeons, or
distance traveled by manufacturer’s
representatives per month.
LO 1-6 Recognize the levels of
measurement in data.
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12. Nominal and Ordinal Level Data
NOMINAL LEVEL DATA
Properties:
1. Observations of a qualitative
variable can only be
classified and counted.
2. There is no particular order
to the labels.
ORDINAL LEVEL DATA
Properties:
1. Data classifications are represented by
sets of labels or names (high, medium,
low) that have relative values.
2. Because of the relative values, the data
classified can be ranked or ordered.
LO 1-6
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13. Interval and Ratio Level Data
INTERVAL LEVEL DATA
Properties:
1. Data classifications are ordered
according to the amount of the
characteristic they possess.
2. Equal differences in the
characteristic are represented by
equal differences in the
measurements.
RATIO LEVEL DATA
Properties:
1. Data classifications are ordered according to
the amount of the characteristics they
possess.
2. Equal differences in the characteristic are
represented by equal differences in the
numbers assigned to the classifications.
3. The zero point is the absence of the
characteristic, and the ratio between two
numbers is meaningful.
Practically all quantitative data is recorded
on the ratio level of measurement. Ratio
level is the “highest” level of
measurement.
LO 1-6
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14. Summary of the Characteristics for Levels of
Measurement
Why Know the Level of Measurement of a Data?
•The level of measurement of the data dictates the calculations that can be
done to summarize and present the data.
•To determine the statistical tests that should be performed on the data.
LO 1-6
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