Construct a 95 confidence interval for the estimated model

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Reference no: EM133424009

Question a. Use the hsb data from the faraway package for our binary response model. This data is a subset of a National Education Longitudinal Studies data. Load the data and check the summary statistics. What do you find?

Question b. Model the odds of a student's program of choice being "academic" as our response variable. Use all the other variables in the data as independent variables except for id. Ignore interaction terms here to focus on the GLM fitting process. Show the model summary. Interpret your model. Use LATEX to display the mathematical form of your final model.

Question c. Variable selection for a GLM model is similar to the process for an OLS model. The likelihood ratio test (LRT) is often used. Now use step() with the criteria being the LRT to reduce unneeded variables from the model. (Hint: use the test argument). Use LATEX to display the mathematical form of your final model.

Question d. Fit the model suggested by step(), call it g2. found near the bottom of the output. Show the model summary and interpret the difference from your first model.

Question e. Predict the probability of the first 30 students' program of choice being "academic" (ordered as the original hsb data). What is the MSE for this subset? Interpret your results.

Question f. Construct a 95% confidence interval for the estimated model coefficients from g2. Then transform the result to return the odds-ratio, which is a quantity that is easier to interpret than the log-odds. Interpret your results.

Reference no: EM133424009

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