5. This problem uses the PopularDietsCombined.csv data set. WTLossKG (weight loss in kilograms after 12 months) and Diet. a) b) C) d) g) Make a comparative boxplot to look at differences in WTLossKG by Diet type. Summarize what you see for weight loss by diet type. Since there does not seem to be too much difference by diet type, we will only work WTLossKG. What is the point estimate for mean weight loss across all diets? Using all 93 observations across all diets, create 1000 bootstrapped samples. Display a histogram of the bootstrapped distribution for mean weight loss. Describe the shape of the distribution. Report the 95% bootstrap condence interval and provide an interpretation of this interval in the context of the problem. Use set . seed ( 15) . Report the 90% and 99% bootstrap condence intervals from the same bootstrap sample (this means use the same seed as used for part ((1). Since we are using built-in functions in R for this, we rerun each time and change the condence level). Describe how these intervals compare with the 95% interval reported in part (d). Draw two new sets of 1000 bootstrap samples using the 93 observations and report the 95% bootstrap condence intervals for each (do NOT plot the two new bootstrap distributions and do NOT use set. seed). Describe how these intervals compare with your 95% interval from part ((1). Use set. seed (15) and draw three smaller sets of the number of reps (n = 500,100, and 10) of bootstrapped samples using the 93 observations (be sure to reset the seed before each 11). Report the 95% bootstrap condence intervals for each of these new sets. Describe how these intervals compare to the 95% interval from part (d)