Basic Inferential statistics with notation and other information regarding the introduction of story.

1. FS 103
Basic and Inferential Statistics
EULOGIO “AMANG” RODRIGUEZ
INSTITUTE OF SCIENCE AND TECHNOLOGY
GRADUATE SCHOOL Nagtahan, Sampaloc, Manila
SATURDAY (10:00 am – 1:00 pm)
NOEL JOSE B. MALANUM
MAED-AS

2. TABLE OF CONTENT
Introduction to Regression Analysis
Understanding of Regression Analysis.
Presentation, Interpretation of Slopes and Predictions.
1
1.1
1.2
1.3
1.4
1.5
Correlation vs Regression
Regression Models
Linear Regression Analysis

3. ❑Understand the concept of regression analysis
in educational research.
❑Identify different types regression models
❑Perform basic data summarization and
visualization.
Objectives:

4. Definition:
Regression analysis is often used to model or analyze data. Most
survey analysts use it to understand the relationship between the
variables, which can be further utilized to predict the precise
outcome.
For Example – Suppose a soft drink company wants to expand its
manufacturing unit to a newer location. Before moving forward, the
company wants to analyze its revenue generation model and the
various factors that might impact it. Hence, the company conducts
an online survey with a specific questionnaire.
After using regression analysis, it becomes easier for the company
to analyze the survey results and understand the relationship
between different variables like electricity and revenue – here,
revenue is the dependent variable.
What is Regression Analysis
1. Introduction of Regression Analysis

5. • Regression analysis identify the exact
relationships between variables, and to see how
changing one variable affects the system as a whole,
so it shouldn’t be hard to see the connection
between it and cause and effect analysis.
• Regression is the measure of the average
relationship between two or more variables.
What is Regression Analysis
1.1 Understanding the Regression
Analysis.

6. • Degree & Nature of Relationship
Correlation is a measure of degree of relationship
between X & Y.
Regression studies the nature of relationship between the
variables so that one may be able to predict the value of
one variable on the basis of another.
Correlation vs. Regression
1.2 .Correlation vs Regression

7. • • Cause & Effect Relationship
Correlation does not assume cause and effect relationship
between two variables.
Regression clearly expresses the cause and effect
relationship between two variables. The independent
variable is the cause and dependent variable is effect.
Correlation vs. Regression

8. • Degree & Nature of Relationship
Correlation is a measure of degree of relationship between X & Y.
Regression studies the nature of relationship between the variables so that one may be able
to predict the value of one variable on the basis of another.
• Cause & Effect Relationship
Correlation does not assume cause and effect relationship between two variables.
Regression clearly expresses the cause and effect relationship between two variables. The
independent variable is the cause and dependent variable is effect.
Correlation vs. Regression

9. 1.3 Regression Models.

10. 1.3 Regression Types of Relationship.

11. 1.3 Regression Types of Relationship.

12. Regression Analysis
Regression analysis is used to:
Predict the value of a dependent variable based on the value of at
least one independent variable.
Explain the impact of changes in an independent variable on the
dependent variable.
Dependent variable: The variable we wish to predict or explain.
Independent variable: The variable used to predict or explain the
dependent variable.

13. Simple Linear Regression Model
Only one independent variable, X
• Relationship between X and Y is described
by a linear
function
• Changes in Y are assumed to be related to
changes in X
1.4 Linear Regression Analysis.

14. Simple Linear Regression Model
1.4 Linear Regression Analysis.

15. Simple Linear Regression Model
1.4 Linear Regression Analysis.

16. Interpretation of the Slope and Intercept
1.5 Presentation, Interpretation of Slopes
and Predictions

17. Interpretation of the Slope and Intercept
1.5 Presentation, Interpretation of Slopes
and Predictions

18. Interpretation of the Slope and Intercept
1.5 Presentation, Interpretation of Slopes
and Predictions

19. Interpretation of the Slope and Intercept
1.5 Presentation, Interpretation of Slopes
and Predictions

20. Interpretation of the Slope and Intercept
1.5 Presentation, Interpretation of Slopes
and Predictions

21. 1.5 Presentation, Interpretation of Slopes
and Predictions
Interpretation of the Slope and Intercept

22. What are the differences between Quantitative VS
Qualitative Data?
❑ Quantitative data is numbers-based, countable, or measurable. Qualitative data is
interpretation-based, descriptive, and relating to language.
❑ Quantitative data tells us how many, how much, or how often in calculations. Qualitative
data can help us to understand why, how, or what happened behind certain behaviors.
❑ Quantitative data is fixed and universal. Qualitative data is subjective and unique.
❑ Quantitative research methods are measuring and counting. Qualitative
research methods are interviewing and observing.
❑ Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by
grouping the data into categories and themes.

24. Levels of Measurement
1.3 Levels of measurement: nominal, ordinal, interval, and
ratio.

25. Descriptive Statistics
1.4 Descriptive statistics: measures of central tendency and
dispersion.
Statistics that summarize or
describe features of a data set, such
as its central tendency or
dispersion.

26. Measures of Central Tendency & Dispersion
Measures of central tendency are measures that indicate the
approximate center of a distribution .
Measures of dispersion are measures that describe the spread of
the data.

27. Variance

28. .
Measures of Central Tendency : Mean

29. .
Measures of Central Tendency: Median

30. .
Measures of Central Tendency: Mode

31. .
Measures of Dispersion: Range

32. .
Measures of Dispersion: Variance and
Standard Deviation

33. .

34. What is Data Visualization?
1.5 Data visualization techniques: histograms, bar charts, and pie
charts.
It used to analyze visually the behavior of the
different variables in a dataset, such as a
relationship between data points in a variable or
the distribution.
Data visualization techniques are visual elements
(like a histograms, bar chart, pie chart, etc.) that
are used to represent information and data.

36. What is Histogram?
A histogram is a graphical
representation of a grouped frequency
distribution with continuous classes. It
is an area diagram and can
be defined as a set of rectangles with
bases along with the intervals between
class boundaries and with areas
proportional to frequencies in the
corresponding classes.
In other words, a histogram is a
diagram involving rectangles whose
area is proportional to the frequency of
a variable and width is equal to the
class interval.

37. What is Pie chart?
A Pie charts are attractive data
visualization types. At a high-level, they’re
easy to read and used for representing
relative sizes.
A Pie Chart is a circular graph that uses
“pie slices” to display relative sizes of
data.
A pie chart is a perfect choice for
visualizing percentages because it shows
each element as part of a whole.
The entire pie represents 100 percent of a
whole. The pie slices represent portions of
the whole.

38. What is Bar chart?
A bar chart (also called bar graph) is a
chart that represents data using bars of
different heights.
The bars can be two types – vertical or
horizontal. It doesn’t matter which type
you use.
The bar chart can easily compare the
data for each variable at each moment
in time.
.

39. Thank You!
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