Model 1: YES =Here are some advanced exercises:f(COST, CATCH, INCOME)

- Conduct hypothesis tests for groups of coefficients. Run another model adding a "block" of
demographic variables: EMPLOYED, EDUCATIO, MARRIED, SEX, and AGE (in the Logistic
Regression box, click on "
__N__ext" then choose the demographic "covariates"). Is the block of variables statistically significant (look for the "block chi-square" statistic in the output)? - Conduct tests for
__structural breaks__in the data. Do North and South Carolinians behave similarly? Run 3 versions of model 1: NC, SC, and pooled (in the Logistic Regression box, click on "select" then click on NC, as your "selection variable", choose NC=1 as the "rule" and run the logit model; then do the same for NC=0). What is the likelihood ratio test statistic equal to? - Is multicollinearity a problem? Run (1) Model 1 (1) MODEL 1 with EMPLOYED, (2) MODEL 1 with
EMPLOYED and without INCOME. What are the effects
on the statistical significance of INCOME? What is the correlation between EMPLOYED and
INCOME?
- Conduct more tests for the appropriate model specification. In Model 1: is there a superior
functional form? In the SPSS data window, select COST and "transform" and "compute" COST into a new variable: LNCOST=ln(COST).
Select INCOME and "transform" and "compute" INCOME into a new variable: INCOMESQ=income*income.
Run the alternative functional form:
MODEL 2: YES =

*f*(LNCOST, CATCH, INCOME)MODEL 3: YES =

*f*(COST, CATCH, INCOME, INCOMESQ)

- Test for normality of dependent variable (choose the "skewness"
option when you calculated "descriptive statistics,"
if the t-stat on skewness is greater than 2 then the variable
is probably non-normal ...).
- Test for heteroskedasticity with the Park test.
- Check predicted probabilities from the LP model to determine if they fall outside of the 0, 1 range (save the "unstandardized" predicted value when you run a "regression", "linear" in SPSS).