Produce a table of predicted probabilities for Gender Race SES Schtyp and Prog by the variable Honcomp

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

1. Create a univariate frequency table of counts and percentages for Honcomp by Gender, Race, SES, School Type and Program.

2. Using chi-square analysis see if there are significant associations between the variable Honcomp and the variables Gender, Race, SES, Schtyp and Prog. Which variables are significantly associated with being enrolled in an honors course (Bivariate analysis).

3. Create a logistic regression model with Gender, Race, and SES to predict Honcomp.

4. Create a logistic regression model with Gender, Race, SES, and School Type to predict Honcomp.

5. Create a logistic regression model with Gender, Race, SES, School Type and Program to predict Honcomp.

6. In ONE table, for each model present the following—see example on page 4: 1. Beta coefficients, standard error and odds ratio 2. Negative log likelihood ratio, model chi-square, df and p-value, the Nagelkerke R2 or Generalized R2, the whole-model p-value or Hosmer and Lemeshow Test p-value, and the classification accuracy (1 - misclassification rate).

7. Produce a table of predicted probabilities for Gender, Race, SES, Schtyp and Prog by the variable Honcomp.

8. Our interest is the association between Gender, Race, and SES and taking an honors course (Honcomp), but also how the relationship between Gender, Race, and SES changes as we account for other explanatory variables (School Type and Program).

A. How did the model change as you added the remaining two variables (School Type and Program)?

B. Interpret the significant predictors in the final model using odds ratios.

C. Discuss the final model’s classification accuracy 9. Produce a ROC plot for the final model and interpret.

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