ppt on Stepwise regression Backward selection

1. ASSIGNMENT OF
PREDICTIVE
BUSINESS
ANALYTICS
Presented by :
Himanshu
TOPIC:
Stepwise regression
Backward selection

2. INDEX
 What is regression?
 What is stepwise regression?
 Advantages of stepwise regression.
 How Stepwise Regression Works.
 What is Backward selection?
 Overview

3. WHAT IS REGRESSION?
 Regression is a method to determine the
statistical relationship between a dependent
variable and one or more independent
variables. The change in dependent variable
is associated with the change in the
independent variables.

4. INDEPENDENT DEPENDENT

5. STEPWISE REGRESSION
 stepwise regression is a method of fitting
regression models in which the choice of predictive
variables is carried out by an automatic procedure.
 In each step, a variable is considered for addition to
or subtraction from the set of explanatory variables
based on some prespecified criterion. Usually, this
takes the form of a forward, backward, or combined
sequence of F-tests or t-tests

6. ENERGY CONSUMPTION:
XYZ COMPANY
VARIABLE Units Value
Equipment run time
Equipment age
Equipment size
Staff size
Temperature outside
Time of year

7. ADVANTAGES OF STEPWISE
REGRESSION
Advantages of stepwise regression include:
 The ability to manage large amounts of potential
predictor variables, fine-tuning the model to
choose the best predictor variables from the
available options.
 It’s faster than other automatic model-selection
methods.
 Watching the order in which variables are removed
or added can provide valuable information about
the quality of the predictor variables.

8. HOW STEPWISE REGRESSION WORKS
FORWARD SELECTION
BACKWARD SELECTION

9. BACKWARD SELECTION
 backward selection is a method of fitting regression
models in which the choice of predictive variables is
carried out by an automatic procedure.
 It involves starting with all candidate variables, testing
the deletion of each variable using a chosen model fit
criterion, deleting the variable (if any) whose loss gives
the most statistically insignificant deterioration of the
model fit, and repeating this process until no further
variables can be deleted without a statistically significant
loss of fit.
 Backward selection was introduced in the early 1960s
(Marill & Green, 1963). It is one of the main approaches
of stepwise regression.

10. ENERGY HOUSE
VARIABLE Units Value
Equipment run time
Equipment age
Equipment size
Staff size
Temperature outside
Time of year

15. OVERVIEW OF BACKWARD
SELECTION
Backward selection is the simplest of all variable
selection procedures and can be easily implemented
without special software. In situations where there is a
complex hierarchy, backward elimination can be run
manually while taking account of what variables are
eligible for removal.
 1. Start with all candidate variables in the model.
 2. Remove the variable with the largest p-value, that is,
the variable that is the least statistically significant.
 3. The new (p - 1)-variable model is t, and the variable
with the largest p-value is removed.
 4. Continue until a stopping rule is reached. For
instance, we may stop when all remaining variables
have a significant p-value de fined by some significance
threshold.