Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

- Logistic Regression Analysis by COSTARCH Analytic... 4428 views
- Logistic regression by saba khan 4622 views
- Logistic Regression: Predicting The... by Michael Lieberman 5568 views
- Logistic regression by DrZahid Khan 2250 views
- Logistic regression by Venkata Reddy Kon... 9190 views
- Intro to Classification: Logistic R... by NYC Predictive An... 25184 views

1,361 views

1,181 views

1,181 views

Published on

Logistic regression

No Downloads

Total views

1,361

On SlideShare

0

From Embeds

0

Number of Embeds

0

Shares

0

Downloads

51

Comments

0

Likes

2

No embeds

No notes for slide

- 1. LOGISTIC REGRESSION By Dr Zahid Khan Senior Lecturer King Faisal University,KSA 1
- 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 2
- 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 3
- 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. 4
- 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 7
- 8. Logistic Regression Independent Variable Dependent Variable 8
- 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 20

No public clipboards found for this slide

Be the first to comment