y x1 x2 0 14.44 13.93 1 18.45 3.16 0 5.45 8.51 0 10.77 10.04 0 11.21 10.02 0 8.31 9.50 1 13.09 2.66 1 19.72 9.61 1 18.06 9.24 0 8.13 9.32 0 6.26 4.19 0 5.31 10.89 1 19.83 3.33 1 7.00 7.27 1 9.77 5.66 0 19.83 13.03 0 9.87 8.06 0 6.31 8.99 1 17.01 10.60 1 4.59 8.08 Use the data file above and R to answer the following questions. It has three variablesy, x1, and x2. Calculate the minimum, mean, and maximum values of the variables.

Run a linear regression model using y as the response variable and x1 and x2 as predictor variables. Make sure that you print output and interpret the coefficient estimates.

Run a logistic regression model using y as the response variable and x1 and x2 as predictor variables. Make sure that you print output and interpret the coefficient estimates.

Using linear model (Part B), predict the value of the response variable when the values of x1 and x2 are at their minimum values. Interpret the result.

Using logistic model (Part C), predict the value of the response variable when the values of x1 and x2 are at their minimum values. Interpret the result.

Using linear model (Part B), predict the value of the response variable when the values of x1 and x2 are at their mean values. Interpret the result.

Using logistic model (Part C), predict the value of the response variable when the values of x1 and x2 are at their mean values. Interpret the result.

Using linear model (Part B), predict the value of the response variable when the values of x1 and x2 are at their maximum values. Interpret the result.

Using logistic model (Part C), predict the value of the response variable when the values of x1 and x2 are at their maximum values. Interpret the result.

Compare the results of both models when they are evaluated at minimum, mean, and maximum values. Which model has higher values at which level?