This document provides an overview of logistic regression. It begins by defining logistic regression as a specialized form of regression used when the dependent variable is dichotomous while the independent variables can be of any type. It notes logistic regression allows prediction of discrete variables from continuous and discrete predictors without assumptions about variable distributions. The document then discusses why logistic regression is used when assumptions of other regressions like normality and equal variance are violated. It also outlines how to perform and interpret logistic regression including assessing model fit. Finally, it provides an example research question and hypotheses about predicting solar panel adoption using household income and mortgage as predictors.

1. LOGISTIC
REGRESSION
By Dr Zahid Khan
Senior Lecturer King
Faisal University,KSA
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2. What is logistic regression?
Logit for short, a specialized form of regression
used when the dependent variable is dichotomous and categorical while
the independent variable(s) could be any type.
• Dependent variable is binary (yes/no, Male/Female ) outcome.
• Independent variables are continuous interval
• Examples:
• Relation of weight and BP to 10 year risk of death
• Relation of CD4 count to 1 year risk of AIDS diagnosis
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3. Logistic Regression
• Form of regression that allows the prediction of discrete
variables by a mix of continuous and discrete/nominal
predictors.
• Addresses the same questions that discriminant
function analysis and multiple regression do but with no
distributional assumptions on the predictors (the
predictors do not have to be normally distributed,
linearly related or have equal variance in each group)
• Does not assume a linear relationship between DV and
IV
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4. Why we use
logistic ?
No assumptions about the distributions of the predictor variables.
Predictors do not have to be normally distributed
Does not have to be linearly related.
When equal variances , covariance doesn't exist across the groups.
Predictor variables is not parametric and there is no
homoscedasticity. E.g variance of dependent and independent
variables are not equal.
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5. DECISION PROCESS
Stage 1:
Objectives Of logistic regression
 Identify the independent variable that impact in
the dependent variable
 Establishing classification system based on the
logistic model for determining the group
membership

6. Types of logistic regression
• BINARY LOGISTIC REGRESSION
It is used when the dependent variable is dichotomous.
MULTINOMIAL LOGISTIC REGRESSION
It is used when the dependent or outcomes variable has more than
two categories.

7. Linear Regression
Independent Variable
Dependent Variable
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8. Logistic Regression
Independent Variable
Dependent Variable
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9. Binary logistic regression expression
BINARY
Y = Dependent Variables
ß˚ = Constant
ß1 = Coefficient of variable X1
X1 = Independent Variables
E = Error Term

10. SAMPLE SIZE
Very small samples have so much sampling
errors.
Very large sample size decreases the chances of
errors.
Logistic requires larger sample size than multiple
regression.
Hosmer and Lamshow recommended sample
size greater than 400.

11. SAMPLE SIZE PER CATEGORY OF THE
INDEPENDENT VARIABLE
 The recommended sample size for each group is at
least 10 observations per estimated parameters.

12. Estimation of logistic regression model
assessing overall fit
Logistic relationship describe earlier in both estimating the logistic
model and establishing the relationship between the dependent
and independent variables.
Result is a unique transformation of dependent variables which
impacts not only the estimation process but also the resulting
coefficients of independent variables.

13. Transforming the dependent
variable
S-shaped
Range (0-1)

14. Description of the data
The data used to conduct logistic regression is from a survey of 30
homeowners conducted by an electricity company about an offer of roof
solar panels with a 50% subsidy from the state government as part of the
state’s environmental policy.
The variables are:
•IVs: household income measured in units of a thousand dollars
age of householder
monthly mortgage
size of family household
•DV: whether the householder would take or decline the offer.
offer was coded as 1 and decline the offer was coded
as 0.
Take the

15. WHAT IS THE RESEARCH QUESTION?
To determine whether household income and monthly
mortgage will predict taking or declining the solar panel
offer
Independent Variables: household income and monthly
mortgage
Dependent Variables: Take the offer or decline the offer

16. Two hypotheses to be tested
• There are two hypotheses to test in relation to the overall fit of the
model:
H0: The model is a good fitting model
H1: The model is not a good fitting model (i.e. the predictors have a
significant effect)

17. How to perform logistic regression in spss
1)
2)
3)
4)
Click Analyze
Select Regression
Select Binary Logistic
Select the dependent variable, the one which is a grouping
variable (0 and 1) and place it into the Dependent Box, in this
case, take or decline offer
5) Enter the predictors (IVs) that you want to test into the
Covariates Box. In this case, Household Income and Monthly
Mortgage
6) Leave Enter as the default method

18. Continuation of SPSS Steps
•7) If there is any categorical IV, click on Categorical button and enter it.
There is none in this case.
•8) In the Options button, select Classification Plots, Hosmer-Lemeshow
goodness-of-fit, Casewise Listing of residuals. Retain default entries for
probability of stepwise, classification cutoff, and maximum iterations
•9) Continue, then, OK

19. Any Questions !!!
•
Thank You
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