Question 1: (40 pts) This question is based on the data in the HW4_Question1 excel file and is designed to highlight the importance of data visualization during model building. There are four different datasets in the excel file. 1. Report the summary statistics for each dataset. Also, compare the summary statistics and report your observations. 2. Construct a linear regression model for each dataset to predict the dependent variable and compare the models. What do you observe ? Interpret the coefficients. 3. Visualize each of the four datasets through a scatterplot. Which of the four datasets do you think are better represented by the linear regression model? Why ?

Question 2: (60 pts) In this exercise let us explore the MilesPerGallon dataset. It contains data on cars manufactured in the 70s to early 80s. Description of the columns is below: mpg : Miles per gallon cylinders : Number of cylinders between 4 and 8 displacement : Engine displacement (cu. inches) horsepower : Engine horsepower weight : Vehicle weight (lbs.) acceleration : Time to accelerate from 0 to 60 mph (sec.) year : Model year (modulo 100) name : Vehicle name 1. Create a correlation matrix along with the pairwise scatterplots. In radiant you can implement this by selecting Data-> Basics -> Correlation. Make sure to select all the relevant variables. Note that you will have to remove name column from this analysis as name is a unique identifier and it doesnt make sense to inspect correlations with other variables. Report how mpg is correlated with each of the other variables. 2. Build a multiple linear regression model with mpg as the response variable. Is there a relationship between the predictors and the response ? Which predictors appear to have a statistically significant relationship to the response? What does the coefficient for the year variable suggest ? 3. Refine the model to drop any irrelevant variables. Explain the meaning of each coefficient in your final model.

please provide the accurate formula how to solve this assignment in Radiant.