Task 1: Conceptual questions (2 pts) In comments after your header, answer the following questions: 1. What does simple linear regression allow you to do that investigating correlation does not? (1. pts) 2. what is the difference between simple linear regression and multiple linear regression? (1 pt) Task 2: Programming questions (18 pts) In the same file, write code corresponding to each question below. That is, don't simply overwrite/modify the code used for question 2 in question 3. You can copy and paste the previous code if needed, but we need to see the code used to answer each question. Don't forget to add comments prior to each SAS step describing what you are doing! We do not need the output. We can recreate everything using the code you turn in. 1. Create a permanent library using a LIBNAME statement. (1 pt) 2. create code to import the abalone dataset into your permanent library created in question 1. (2 pts) 3. Conduct a correlation analysis between the variables below. You should output confidence intervals for each correlation value. Also, produce a matrix of scatterplots in the PROC. (4 pts) Rings Height . Shuckedweight Report the two confidence intervals for the correlation between Rings and the other two variables in a comment below your PROC step. (1 pt) 4. Fit a simple linear regression model using Rings as the response variable and the second variable listed in question 3 as the predictor. (3 pts) produce diagnostic plots to check assumptions and create confidence intervals for the regression parameters. (2 pts) In a comment below your code, report the confidence interval for your slope parameter. (1 pt) 5. Fit a multiple linear regression model using Rings as the response variable and the other two variables listed in question 3 as the predictors. Include an interaction term in the model and produce diagnostic plots that could be used to check assumptions. (4 pts)