Part 1: Do a simple linear regression on the given data for regressing Y = sales on x = youtube. "A data frame containing the impact of three advertising medias (youtube, facebook and newspaper) on sales. Data are the advertising budget in thousands of dollars along with the sales. The advertising experiment has been repeated 200 times.' ## this code will import and attach the data for you. ## you may need to install the package datarium before running library () ## RStudio will likely prompt you with a banner across the top of the editor ## if not, uncomment and run the following line: # install. packages ("datarium") library (datarium) data ("marketing", package = "datarium") attach (marketing) # attaching the data head (marketing) youtube facebook newspaper sales # # 1 276. 12 45 .36 83.04 26.52 ## 2 53. 40 47 . 16 54. 12 12.48 ## 3 20 . 64 55 . 08 83. 16 11. 16 ## 4 181 . 80 49. 56 . 20 22. ## 5 216.96 12.96 70. 08 15.48 ## 6 10.44 58. 68 90. 00 8.64 Question 1: What is the least squares estimate of the intercept term and the coefficient of Youtube? Interpret these coefficient in terms of the model. Question 2: An employee in marketing claims that "For every $50 we spend on YouTube advertisements, we make an extra thousand in sales revenue!" Is this a valid claim? Question 3: Compute and plot the 90% and 95% confidence intervals for the slope and intercept coefficients. Compare the two confidence intervals and comment on their relationship.Question 4: Run regression diagnostics on this model and data, and show your results. Are there any potential problems? Part 2: Question 5: Now, do the regression and regression diagnostics again after adding in some obvious outliers, using the code below. What parts change and what stays the same? Summarize your results in 2-3 sentences. ## Adding in outliers df2