In this part of the Assignment, you will be working with Boolean variables from the OJ data set in the ISLR package noted in the Learning Resources. You will also be using the stats package from RDocumentation and the Various Coefficients of Interrater Reliability and Agreement or irr package, also available in the Learning Resources.

The OJ data set provides information similar to what might be available to any distributor regarding product performance at various retail locations. The purchases of Citrus Hill and Minute Maid are tracked along with information on prices, sales, store, and other factors. This Assignment explores Boolean aspects of this data to answer questions about the performance of Citrus Hill.

$2-1)$ Transform the variable Purchase, which lists either CH for Citrus Hill or MM for Minute Maid, into a new indicator variable for which the value is $1$ if Citrus Hill is purchased and $0$ if it is not. Provide your R code for the transformation.

$2-2)$ Characterize the purchase rate of Citrus Hill by calculating each of the following and explaining what the result tells you about Citrus Hill purchasing:

$95\%$ confidence interval for the mean

Odds

Log$-$odds

$2-3)$ Choose one of the following questions from the options below. Use the listed statistical techniques for analysis and describe the result. In your results, you should describe the statistical technique, explain how it relates to the question, including aligned hypotheses, and give a statistically supported answer to the question.

Option $1)$ Are customers more likely to buy Citrus Hill if there is a special offered on it$?($odds ratio, McNemar Test$)$

Option $2)$ Is cheaper price a good predictor for what someone will buy? $($agreement co$-$efficient$)$

Option $3)$ Does Store$7$ price Citrus Hill higher than other stores? $($t$-$test$)$

Option $4)$ How well can you predict percent sales of Citrus Hill at a store if you know the prices, specials, and discounts for both orange juice brands at that store? $($logistic regression$)$