Predicting the age of abalone from physical measurement. Abalones are Marine Shells Consider 10% level of significance/ alpha value for this question] Target Variable: Ring:- gives the age in years Input Variables/Attribute Information: Shell weight- After being dried Viscera weight- weight of meat Whole weight- whole abalone Height- with meat in shell Diameter- perpendicular to length Length- Longest shell measurement Sex - M, F, and I (infant) 1. Identify outliers in variables and dummy code categorical variables [10 Points] 2. Run a Linear regression on the dataset by exploring the relationship of Ring with the all the other variables in the dataset. Perform random sampling to split the dataset with 80% set for Training set and 20% for test set Note: Please take a screenshot of your output and submit on canvas. You MUST also submit your SAS file. Answers to the questions below (2,3,4,5, and 6) must be handwritten. You may take pictures and submit as a single file or multiple files on canvas) [5 Points] 2. Explain any two coefficients and Construct a 95% confidence interval for the true coefficients of these variables. [5 Points] 3. Which variables are statistically significant? Justify your answer. Do the signs of the various coefficients make sense? [10 Points] 4.. Identify variables with multicollinearity? Justify your answer. Explain why multi collinearity is a problem and how will you handle it. And what is the most preferred method? [5 Points] 5. Rerun the model by dropping any variables as you see necessary. What is your overall analysis regarding the reliability and quality of this model? [ 5 Points] 6. What are the steps that you have taken to verify that the output from this model is not biased? Clearly show the output screenshots considered to discuss this result. What do we learn from the plot of the residuals on the fitted values of this model? [5 Points]