Explain problems using Rstudio

1- Many people believe that gender, weight, drinking habits, and many other factors are much more important in predicting blood alcohol content (BAC) than simply considering the number of drinks a person consumed. Here we examine data from sixteen student volunteers at Ohio State University who each drank a randomly assigned number of cans of beer. These students were evenly divided between men and women, and they differed in weight and drinking habits. Thirty minutes later, a police officer measured their blood alcohol content (BAC) in grams of alcohol per deciliter of blood. We will use the dataset bac from the openintro package. You can start with the following R code: library(openintro) head(bac)

a)Create a scatter plot to check the relationship between bac (response) and beers (explanatory). (b)Describe the association between the number of cans of beer and BAC using the scatter plot from part (a). (c) Using thelm() function, fit a simple linear regression model to this data and print the summary of the fit. (d) Add the regression line to the scatter plot from part (a). (e) Write the equation for the regression line (the least squares line). (f) Interpret the slope. (g) Interpret the intercept. (h) Interpret the R2. (i) Calculate the correlation coefficient r between bac and beers. And how would you interpret this value? (j) What is the predicted BAC for a person that drank 5 cans of beer? (k) A student in this data set drank 9 beers and had a measured BAC of 0.19. Calculate the residual for this student