The ability to get a good nights sleep is correlated with many positive health outcomes. The NHANES data set in the NHANES package contains a binary variable SleepTrouble that indicates whether each person has trouble sleeping. Sleep For each of the following models: Build a classifier for SleepTrouble. Report its effectiveness on the NHANES training data. Make an appropriate visualization of the model. Interpret the results. What have you learned about peoples sleeping habits? You may use whatever variables you like, except for SleepHrsNight. Models: Null model Logistic regression Decision tree Random forest SOLUTION: Quantitative sleep Repeat the previous Repeat the previous exercise, but now use the quantitative response variable SleepHrsNight. Build and interpret the following models:

• Null model
• Multiple regression
• Regression tree
• Random forest

Repeat either of the previous exercises, but this time first separate the NHANES data set uniformly at random into 75% training and 25% testing sets. Compare the effectiveness of each model on training vs. testing data.

Repeat the first exercise, but for the variable PregnantNow. What did you learn about who is pregnant?

In addition, make sure you do some Data Cleaning and answer key questions like:

• What percent of people reported having trouble sleeping?
• What is the effectiveness of the null model?
• What is the effectiveness of the decision tree?
• Include visualizations to support your findings.
• Talk about random forest predictive value.