Scene 1

[Audio] Often professionals want to know if
there is a relationship between two or
more variables for instance is there a
relationship between the grade on the 3rd print exam a student takes and the grade on the final exam if yes then how
is it related and how strongly regression can be used here to arrive at
a conclusion this is an example of
bivariate data that is two variables
however statisticians are mostly
interested in multivariate data the regression analysis is used to predict
the value of one variable the dependent
variable on the basis of other variables
the independent variables in the
simplest form of regression linear
regression you work with one independent variable the formula for simple linear
regression is shown on the screen
in the next screen we'll look at a few examples of regression analysis. 

Scene 2

It is a technique used to:
Estimate a relationship between variables
• Predict the value of one variable (dependent variable) on the basis of other variables (independent
variables)
Example:
Here, Y is a dependent variable, whereas ßo, ßl x, and E are independent variables.
S q€b€l.JC161JC MPSL€S2 xi suq E sue !LJC16b€UClSLJC ASL!SPIS?

[Audio] Regression analysis is used in several situations such as those described on
the screen in example 1 using the data
given on the screen you have to analyze
the relation between the size of a house
and its selling price for a realtor In example 2 you need to predict the exam
scores of students who study for 7.2
hours with the help of the data shown on
the slide. 

Scene 3

Regression analysis is used in several situations, such as those described below:
Example 1:
Using the data given below, analyze the relation between the size of a house and its selling price for a
real state agent.
Size in Sq. Ft.
1400
1600
1700
Example 2:
Price in $
24500
31200
27800
With the help of the data given below, predict the exam scores of students who study for 7.2 hours.
Hours of Studies
6
8
7.3
7.4
8
Horne 01 ecnq!G2
Exam Score
53
79
69
80
80
ea
23
EXSUJ 2COLG

[Audio] A couple more examples are
given on the screen in example 3 based
on the expected number of customers and
the previous day's data given you need
to predict the number of burgers that
will be sold by KFC outlet in example 4
you have to calculate the life
expectancy for a group of people with
the average length of schooling based on
the data given. 

Scene 4

Use of Regression Analysis- Examples(Contd.)

Some more examples are:
Example 3:
Based on the expected number of customers and the previous days' data given below, predict the
number of burgers that will be sold by a KFC outlet
No. of Customers
798
450
5067
Example 4:
Burgers Sold
324
1054
Calculate the life expectancy for a group of people with the average length of schooling based on the
data given below:
Average Length of Schooling (in years)
14
20
10
TO
so
VAGLgRG rGuesp 012CP001!ue ÅG9L2)
Life Expectancy
63
80.4
57.1
211
r!1G EXbGccgucA

[Audio] Now let's look at the two main types of regression analysis simple linear regression and multiple linear regression both of these statistical methods use a
linear equation to model the relationship between two or more variables simple linear regression
considers one quantitative and
independent variable X to predict the
other quantitative but dependent
variable y multiple linear regression
considers more than one quantitative and
qualitative a variable to predict a
quantitative and dependent variable Y
we'll look at the two types of analyses
in more detail in the slides that follow. 

Scene 5

Simple Regression
Considers one
quantitative and
independent variable
X to predict the other
quantitative, but
dependent, variable Y
q6b6L.Jq6LJCi ASL!SP16
dr1SLJil nc
Regression Analysis
Multiple Regression
Considers more than
one quantitative and
qualitative variable
(XI...XN) to predict a
quantitative and
dependent variable Y
q6b6LJq6LJ$ ABL!gp16
dr1SUC11SClA€ SU

[Audio] In simple linear regression the predictions of the explained variable Y
when plotted as a function of the
explanatory variable X from a straight
line the best fitting line is called the regression line the output of this model
is a function to predict the dependent
variable on the basis of the values of
the independent variable the dependent variable is continuous and the
independent variable can be continuous
or discrete. 

Scene 6

In this analysis:
A relationship between a dependent variable and an independent variable is modeled
The output is a function to predict the dependent variable on the basis of the values of independent
variables
A straight line is fit to the data