2. WHAT IS MULTIPLE REGRESSION?
- It is a statistical technique that uses several variables
to predict the outcome of a response variable.
- Is to model the linear relationship between the
independent variables and dependent variables.
- Used extensively in econometrics and financial
inference.
3. Dependent variable:The main factor you want to measure
or understand.
Independent variables:The secondary factors you believe to
have an influence on your dependent variable.
In a simple example, say you want to find out how pricing,
customer service, and product quality impacts (independent
variables) impact customer retention (dependent variable).
4. MULTIPLE REGRESSION FORMULAS
are used to analyze the relationship between a dependent variable
and multiple independent variables.
This method uses two or more independent variables to forecast or
predict the dependent variable.
The main objective is to identify and examine the relationship
between the dependent and independent variables. Based on this
analysis, suitable independent variables are selected to aid in
predicting the dependent variable.
5. Multiple regression is employed when linear regression alone
cannot fulfill the intended purpose, and it helps determine the
effectiveness of the chosen predictor variables in forecasting
the dependent variable.
Multiple regressions are a very useful statistical method.
Regression plays a very important role in the world of finance. A
lot of forecasting is done using regression analysis. For
example, one can predict the sales of a particular segment in
advance with the help of macroeconomic indicators that have a
very good correlation with that segment.
6. BASIC CONDITION FOR MULTIPLE
REGRESSION
There must be a linear relationship between the
independent variable and the outcome variables.
It considers the residuals to be normally
distributed.
It assumes that the independent variables are
not highly correlated with each other.
8. ADVANTAGES AND DISADVANTAGES OF
MULTIPLE REGRESSION
Advantages
It has the ability to determine
the relative influence of one
or more predictor variables
to the criterion value.
It also has the ability to
identify outliers, or
anomalies.
Disadvantages
It needs high-level mathematics
to analyze the data and is
required in the statistical program
It is difficult for researchers to
interpret the results of the multiple
regression analysis on the basis
of assumptions as it has a
requirement of a large sample of
data to get the effective results.
9. EXAMPLE #1
Let us try to find out the
relation between the
distance covered by an
UBER driver and the age of
the driver, and the number
of years of experience of the
driver.
10. To calculate multiple regression,
go to the “Data” tab in Excel and
select the “Data Analysis” option.
The regression formula for the
above example will be
y = MX + MX + b
y= 604.17*-3.18+604.17*-4.06+0
y= -4377
11. EXAMPLE #2
Let us try to find the relation
between the GPA of a class
of students, the number of
hours of study, and the
student’s height.
12. Go to the “Data” tab in Excel and select the
“Data Analysis” option for the calculation.
The regression equation for the above
example will be
y = MX + MX + b
y= 1.08*.03+1.08*-.002+0
y= .0325
In this particular example, we will see which
variable is the dependent variable and which
variable is the independent variable. The
dependent variable in this regression is the
GPA, and the independent variables are
study hours and the height of the students.
13. Example: A researcher decides to study students’
performance from a school over a period of time. He
observed that as the lectures proceed to operate online,
the performance of students started to decline as well.The
parameters for the dependent variable “decrease in
performance” are various independent variables like “lack
of attention, more internet addiction, neglecting studies”
and much more.
14. ASSUMPTIONS FOR MULTIPLE REGRESSION
ANALYSIS
The variables considered for the model should be
relevant and the model should be reliable.
The model should be linear and not non-linear.
Variables must have normal distribution
The variance should be constant for all levels of
the predicted variable.
15. BENEFITS OF MULTIPLE REGRESSION
ANALYSIS
Multiple regression analysis helps us to better study the
various predictor variables at hand.
It increases reliability by avoiding dependency on just one
variable and have more than one independent variable to
support the event.
Multiple regression analysis permits you to study more
formulated hypotheses that are possible.
