1. WHAT IS LOGISTIC REGRESSION? Logit for short, a specialized form of regression used when the dependent variable is dichotomous (hasonly two values 0 and 1) and categorical while theindependent variable(s) could be any type There are many variables in the business world that aredichotomous, for example: male or female, to buy or notto buy, good credit risk or poor credit risks, to take offeror decline offer, student will succeed or fail, etc.

2. ASSUMPTIONS OF LOGISTIC REGRESSION Does not assume a linear relationship between DV and IV Dependent variable must be a dichotomy (2 categories) Independent variables need not be interval, nor normallydistributed, nor linearly related, nor of equal variance withineach group The categories of the DV must be mutually exclusive andexhaustive such that a case can only be in one group andevery case must be a member of one of the groups 3. GOAL OF LOGISTIC REGRESSION logistic regression determines the impact ofmultiple independent variables presentedsimultaneously to predict membership of one orother of the two dependent variable categories 4. DESCRIPTION OF THE DATA The data used to conduct logistic regression is from asurvey of 30 homeowners conducted by an electricitycompany about an offer of roof solar panels with a 50%subsidy from the state government as part of the statesenvironmental policy. The variables are:IVs:household income measured in units of a thousanddollars age of householder monthly mortgage size of family householdDV:whether the householder would take or decline theoffer. Take the offer was coded as 1 and decline the offerwas coded as 0. 5. WHAT IS THE RESEARCH QUESTION? to determine whether household income and monthlymortgage will predict taking or declining the solar paneloffer Independent Variables: household income and monthlymortgage Dependent Variables: Take the offer or decline the offer 6. TWO HYPOTHESES TO BE TESTEDThere are two hypotheses to test in relation to theoverall 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) 7. HOW TO PERFORM LOGISTIC REGRESSION INSPSS1) Click Analyze2) Select Regression3) Select Binary Logistic4) 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 offer5) Enter the predictors (IVs) that you want to test into the Covariates Box. In this case, Household Income and Monthly Mortgage6) Leave Enter as the default method 8. CONTINUATION OF SPSS STEPS7) If there is any categorical IV, click on Categorical buttonand 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 iterations9) Continue, then, OK 9. TABLE 1. CLASSIFICATION TABLE 10. TABLE 2. VARIABLES IN THE EQUATION TABLE 11. TABLE 3. VARIABLES NOT IN THE EQUATION 12. TABLE 4. OMNIBUS TEST OF COEFFICIENTS 13. TABLE 5. MODEL SUMMARY 14. TABLE 6. HOSMER AND LEMESHOW TEST 15. TABLE 7. CONTINGENCY TABLE FOR HOSMER AND LEMESHOW TEST 16. TABLE 8. CLASSIFICATION TABLE 17. TABLE 9. VARIABLES IN THE EQUATION 18. A logistic regression analysis was conducted topredict if householders will take up or declinethe offer of a solar panel subsidy. Predictors --household income and mortgagepayment A test of the full model against the constantmodel was statistically significant, indicatingthat the predictors as a set differentiatedbetween acceptors and decliners of the offer(chi-square=29, p.005), we fail to reject the null hypothesis thatthere is no difference between observed and model-predicted values, thus, the model is a good fittingmodel. Even if the two predictors did not showsignificant effect, they were able to distinguishedbetween acceptors and decliners of the offer as theChi-square table (Table 4) show. Perhaps, other predictors such as age and familysize may have significant effect, or perhaps addingone more predictor, however, this paper onlyconsidered two independent variables.