INTRODUCTION-TO-STATISTICS-and-FDT-2 (1).pptx

WHAT IS STATISTICS?
STATISTICS
The Science of collecting, organizing, analyzing and interpreting
data so that useful information can be generated.
TWO BRANCHES OF STATISTICS
1. DESCRIPTIVE STATISTICS
2. INFERENTIAL STATISTICS

POPULATION AND SAMPLE
POPULATION – is the entirety of the group including all the members
that forms a set of data.
SAMPLE – contains a few members of the population. Samples were
taken to represent the characteristics or traits of the population.

DESCRIPTIVE STATISTICS
A statistical method concerned with the collection, organization,
presentation and description (describing) of data that have been collected.
In descriptive statistics, our main concern is one group of people, we
describe them by getting data about them.
Data Summary: Bar Graphs, Histograms, Pie Charts, etc., Shape of the graph and
Skewness (DATA - SYMMETRICAL, SKEWED TO THE LEFT OR RIGHT)
Examples:
1. In a Math test, 32 out of 40 students were able to receive a passing mark. The
average score of the class is 82 out of 100.
2. The average height of the college students in this room is 67 inches.

THE COMMON TOOLS ARE:
• Measures of Frequency (Count, Percent, Frequency)
• Measures of Central Tendency (Mean, Median and Mode)
• Measures of Dispersion or Variation (Range, Variance and
Standard Deviation)

WHAT IS INFERENTIAL STATISTICS?
INFERENTIAL STATISTICS
Concerned with the analysis of a sample data leading to
prediction, inferences, interpretation, decision, or conclusion about
the entire population.
We are taking samples to represent the whole population.
Using sample data to make an inference or draw a conclusion of the
population.
Use Probability to determine how confident we can be that the
conclusions we make are correct.

Examples:
1. In a sample survey conducted, 65% of Filipino Generation Z prefer
to drink milk tea than coffee While only 34% of Filipino Millennials
prefer to drink milk tea than coffee.
2. The average height of college students in Australia is estimated to
be 169 centimeters.

DIFFERENCE BETWEEN DESCRIPTIVE AND INFERENTIAL STATISTICS
DESCRIPTIVE STATISTICS INFERENTIAL STATISTICS
Concerned with describing the target population
(Used when data set is small)
Make inferences from the sample and generalize
them to the population
(Used when the population data set is large)
Organize, analyze and present the data in a
meaningful manner
Compares, test and predicts future outcomes
Final results are shown in the form of charts,
tables and graphs
Final results are the probability scores
Describes the data which is already known Tries to make conclusion about the population
that is beyond the data available
Tools: Measures of Frequency (Count, Percent,
Frequency), Measures of Central Tendency
(Mean, Median and Mode), Measures of
Dispersion or Variation (Range, Variance and
Standard Deviation),
Tools: Hypothesis Tests, ANOVA (Analysis of
Variance), Parametric Tests

DATA AND VARIABLES
DATA
Refers to observations and measurements which have been
conducted in some way, often through research.
Individual Observations
Collected information per variable
Researchers use different statistical tools to analyze the data and
make meaning out of them
Can either be Primary Data (first-hand data obtained using survey,
interview, experiments and the likes) Secondary Data (second-hand
data obtained from published and unpublished sources)

VARIABLES
Different qualities and quantities involved in a study. (age, sex,
country of birth, class grades, eye color and vehicle type)
What is being observed and experimented upon
The characteristics or attributes that you are observing, measuring
and recording data for.
Any characteristics, number, or quantity that can be measured or
counted.
Can either be, Independent Variable or Dependent Variable

INDEPENDENT AND DEPENDENT
VARIABLES
INDEPENDENT VARIABLE – is the cause. Its value is
independent of other variables in your study. It can stand
alone and does not rely or depend on other variables
DEPENDENT VARIABLE – is the effect. Its value
depends on changes in the independent variable. In other
words it relies on the independent variable.

EXAMPLES OF INDEPENDENT AND
DEPENDENT VARIABLES
Do tomatoes grow faster under fluorescent,
incandescent, or natural light?
Independent Variable:
The type of light the tomato plant is grown under
Dependent Variable:
The rate of growth of the tomato plant.

What is the effect of diet and regular soda on blood
sugar levels?
Independent Variable:
The type of soda you drink (diet or regular)
Dependent Variable:
Your blood sugar levels

Difference Between Quantitative and Qualitative Variables
QUALITATIVE VARIABLES
Also referred to as categorical variables. These are variables that are not
numerical. Describe a certain type of information without using numbers.
Examples:
Color (red, blue)
Taste (sweet, sour, salty)
Occupation (teacher, lawyer)
Gender (male, female)
Preference to eating vegetables (cabbage, eggplant)
We can describe all these things without using any numerical skill, that is
why we are calling them qualitative variables.
Nominal and Ordinal Variables are the two types of Qualitative Variables.

Quantitative Variables
These are variables that can be counted, measured and
expressed using numbers.
Examples
Height (can be in cm, meters and inches, feet)
Number of siblings (how many siblings do you have)
Speed of a car (usually in km/hr, it can be miles/hour)
Temperature ( degree celcius, degree Fahrenheit)
Number of students in a classroom (like 40 students in a classroom)
Number of Shirt in the drawer (12 shirts)

Difference Between Quantitative and Qualitative Variables
Quantitative Variable can be classified as:
1. Discrete Variable - whose values can be counted using integral values. Meaning anything
that can be counted would fall in the category of discrete variable.
From our previous examples;
Number of siblings,
number of students in a classroom and
number of shirts in the drawer are all discrete variables because they are all countable.
2. Continuous Variable - data that can be measured.
height,
speed of a car and
temperature

Numerical Categorical
TYPES OF VARIABLES

4 LEVELS OF MEASUREMENT
Measurement – is the process of assigning value to a variable.
1. NOMINAL LEVEL
Observation can be named without particular order or ranking imposed on the data. Words, letters,
and even numbers are used to classify the data. Nominal use classifications or groupings to represent
the data.
Examples:
• Types of Electric Consumption: like Residential, Commercial, Industrial or Government
• Gender: male and female,
• marriage status: like single, married, widow or widower,
• yes or no responses,
• political affiliations like: LP, LDP, Lakas,
• religious groupings like: Christian and non-Christian,
• Socio-Economic Status: classified as those belonging to high, average or low

2. ORDINAL LEVEL
Describes ranking or order. The difference between two rankings or scales cannot be
measured.
Examples:
 Level of Satisfaction like: Very Satisfied, Satisfied, Unsatisfied and Very Unsatisfied
 Competition Placement like: Champion, 1st
Runner-up, 2nd
Runner-up and 3rd
Runner-up
 It can be Outstanding, Very Satisfactory, Satisfactory, Fair, Poor.
 Data such as Strongly Agree, Agree, No Opinion, Disagree and Strongly Disagree
 Ranking of Professors in college, (Prof 1, Prof II, Prof III etc.) and other data which employ
rankings and ordering.
Note: NOMINAL and ORDINAL VARIABLES are CATEGORICAL

3. INTERVAL LEVEL
Indicates an actual amount (numerical). The order and the difference
between the variables can be known by means of adding and subtracting. Its
limitation is it has no “true zero”.
Examples:
• Temperature, in degrees Fahrenheit or Celsius (but not Kelvin)
For example, the temperature outside is 0 degree Celsius, 0 degree doesn’t
mean it is not hot or cold, it is a value.
• IQ test (Intelligence Scale)
When you calculate intelligence score in an IQ test. There is no zero point
for IQ. According to psychological studies, a person cannot have zero intelligence.
IQ is numeric data expressed in intervals using a fixed measurement scale.

4. RATIO
It has the same properties as the interval scale. The order and difference
can be described. Additionally, it has a true zero and the ratio between two
points has a meaning.
Examples:
1. Four people are randomly selected and asked how much money they have with them. Here are
the results: Php 100, Php 200, Php 300 and Php 400.
Is there an order to this data? Yes, Php 100 is less than Php 200, P200 is less than Php 300 and
Php300 is less than Php 400.
Are the differences between the data values meaningful? Yes, the person who has Php 200 has Php
100 more than the person with Php 100.
Can we calculate ratios based on this data? Yes, because Php 0 is the absolute minimum amount of
money a person could have with them.
The Person with Php 400 has four times as much as the person with Php 100.
Note: INTERVAL AND RATIO VARIABLES ARE NUMERICAL

2. What is you weight in kgs?
Less than 50 kgs.
51-60 kgs.
61-70 kgs.
71-80 kgs.
81-90 kgs.
Above 90 kgs.
3. What is your height in feet and inches?
Less than 5 feet
5 feet 1 inch – 5 feet 6 inches
5 feet 7 inches – 6 feet
More than 6 feet
Note:
• ORDINAL AND NOMINAL ARE CATEGORICAL (Qualitative)
• INTERVAL AND RATIO ARE NUMERICAL (Quantitative)

LEVELS OF MEASUREMENT
DATA NOMINAL ORDINAL INTERVAL RATIO
LABELED √ √ √ √
MEANINGFUL ORDER
x
√ √ √
MEASURABLE DIFFERENCE x x √ √
TRUE ZERO STARTING POINT x x x √

INTRODUCTION-TO-STATISTICS-and-FDT-2 (1).pptx

(75, 70, 65, 60, 55 and 50)
(79, 74, 69, 64, 59 and 54)
Lower Class Boundaries
Higher Class Boundaries
or Class Interval ( 5 )
(52, 57, 62, 67, 72 and 77)

Step 2: Decide on the number of classes
n = 40
The value of n which is greater than 40
is 64.
Therefore:
The value of k is 6.
6 is the number of classes

Step 3: Find the class width. Divide the Range by the Number of classes
Class Width = Range___
No. of Classes
= 29
6
= 4.833̅.. Round up the number to the nearest whole number
Class Width/Class Interval = 5

Step 4. Add the class width to the starting point to get the second lower
class limit. Then enter the upper class limits.
Number of Classes = 6 Class Width = 5

USING DATA ANALYSIS TO
CALCULATE DESCRIPITIVE
STATISTICS

*Choose Output Range and click your mouse where you want to place your answers then click OK

is 6
Click 6 divide it by 40 and multiply the answer by 100 and press enter. =(6/40)*100
=(6/40)*100

is 6
Click 6 divide it by 40 and multiply the answer by 100 and press enter. =(6/40)*100

INTRODUCTION-TO-STATISTICS-and-FDT-2 (1).pptx

More Related Content

Similar to INTRODUCTION-TO-STATISTICS-and-FDT-2 (1).pptx (20)

Recently uploaded (20)

INTRODUCTION-TO-STATISTICS-and-FDT-2 (1).pptx