## Description

**Instructions: **Students should submit their reports on Canvas. The report needs to clearly state what question is being solved, step-by-step walk-through solutions, and final answers clearly indicated. Please solve by hand where appropriate.

Please submit two files: (1) a R Markdown file (.Rmd extension) and (2) a PDF document generated using knitr for the .Rmd file submitted in (1) where appropriate. Please, use RStudio Cloud for your solutions.

- Refer to the Prostate Cancer data set in Appendix C.5 and Homework 9. Select a random sample of 65 observations to use as the model-building data set (use set.seed(1023)). Use the remaining observations for the test data. (10 pts)

- Develop a neural network model for predicting PSA. Justify your choice of number of hidden nodes and interpret your model. Test the model performance on the test data.
- Compare the performance of your neuron network model with regression tree model obtained in HW9. Which model is more easily interpreted and why? (5pts)
- Compare the performance of your neural network model with that of the best regression model obtained in homework 8. Which model is more easily interpreted and why?

- Refer to the Disease outbreak data set in Appendix C.10. Savings account status is the response variable and age, socioeconomic status, and city sector are the predictor variables.

- Fit logistic regression model to predict the saving account status on the predictor variables in first-order terms and interaction terms for. all pairs of predictor variables. State the fitted response function.
- Use the likelihood ratio test to determine whether all interaction terms can be dropped from the regression model; use α = .01. State the alternatives, full and reduced models, decision rule, and conclusion. What is the approximate P-value of the test?
- Conduct the Hosmer-Lemeshow goodness of fit test for the appropriateness of the logistic regression function by forming five groups of approximately 20 cases each; use α = .05.

- Refer to the Geriatric study. A researcher in geriatrics designed a prospective study to investigate the effects of two interventions on the frequency of falls. One hundred subjects were randomly assigned to one of the two interventions: education only (X1 = 0) and education plus aerobic exercise training (X1 = 1). Subjects were at least 65 years of age and in reasonably good health. Three variables considered to be important as control variables were gender (X2:0=female;1=male), a balance index (X3). and a strength index (X4). The higher balance index, the more stable is the subject and the higher the strength index, the stronger is the subject. Each subject kept a diary recording the number of falls (Y) during the six months of the study.

- Fit the regression model. State the estimated regression coefficients, their estimated standard deviations. and the estimated response function.
- Assuming that the fitted model is appropriate, use the likelihood ratio test to determine whether gender (X2) can be dropped from the model: State the full and reduced models. decision rule. and conclusion. What is the P-value of the test
- Predicted the number of falls for X1=1, X2=0, X3=45, X4=70.