Overview
In this assignment, you will use a logistic regression analysis to see if GPA, year, and score on Quiz 1 predict whether a student will pass or fail the course. Use the .sav file provided. Please copy and paste all necessary output for the tests of the assumptions and the logistic regression.

Instructions
Complete the following:

· Step 1. Provide a context for the data set in the supplied .sav file. Identify your predictor variables, the outcome variable, and the scales of measurement for each variable. Specify the sample size of the data set.

· Step 2. Specify a research question for the overall logistic regression model. Articulate a null hypothesis and alternative hypothesis for the overall regression model. Specify a research question for each predictor. Articulate the null hypothesis and alternative hypothesis for each predictor. Specify the alpha level.

· Step 3. Discuss and test the assumptions of logistic regression. Specifically, use SPSS to determine if there is a linear relationship between any continuous independent variables and the logit transformation of the dependent variable. Present the output and interpret the results.

· Step 4.

. Begin with a brief statement reviewing assumptions.

. Next, paste the SPSS output for the frequencies of each predictor variable with respect to the outcome variable.

. Paste the results of the Model Summary. Report R and R2 in correct APA format; interpret R2 effect size.

. Next, paste the SPSS results in the classification table and discuss those results.

. Present and interpret the results in the “variables in the equation” output table.

. Present and interpret the results of the chi-square goodness-of-fit test for the logistic regression, along with any other output tables important for the interpretation of the results.

· Step 5. Discuss your conclusions of the logistic regression as they relate to your stated research questions for the overall regression model and the individual predictors. Conclude with an analysis of the strengths and limitations of multiple regression.