Q10

Address the values of each of the variables (use the codebook for this and include your answer as comments). For instance, is "none" equal to a value other than 0? Are there extra decimals implied? Are there other values that should be excluded based on the responses they represent? Which variable will you seek to predict from the other variables? Answer these questions in the Q10 Discussion: section below the code chunk. Update the dataset to account for these values for the remainder of the questions in this part.

Q11

Remove any outliers for each applicable variable. Make sure you are updating the dataset from Q10 and using this dataset for the remainder of the questions. Briefly explain why you chose the method you used in the Q11 Discussion section. Within your code chunk, provide comments explaining what your code is doing.

Q12

Complete exploratory analyses (for each variable) doing appropriate visualizations with ggplot2. Include visualizations that incorporate a combination of your variables within one plot. Provide an organized discussion of your observations for each of the plots in the Q12 Discussion section below.

Q13

Run all of the appropriate basic descriptive statistics for each of your variables. What do these statistics reveal? Be sure to address each variable in the "Q13 Discussion" below.

Q14

Finally, run at least 2 different, appropriate regressions predicting the variable you indicated in Q10. These regressions should use different predictor(s). Organize and explain your code within the code chunk using comments. Identify the best model and provide a discussion of your observations in the Q14 Discussion section provided below. Be organized and detailed in your discussion.

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