Detailed explanation required. Please explain steps (WITH SCREEN SHOTS) on columns were selected, Regression or Correlation method were used.

PLEASE SCREEN SHOTS required to avoid lots of questions

Refer to the bus data. First, add a variable to change the type of engine (diesel or gasoline) to a qualitative variable. If the engine type is diesel, then set the qualitative variable to 0. If the engine type is gasoline, then set the qualitative variable to

1. Develop a regression equation using statistical software with maintenance cost as the dependent variable and age, odometer miles, miles since last maintenance, and engine typ-e as the independent variables.

a. Develop a correlation matrix. Which independent variables have strong or weak correla-tions with the dependent variable? Do you see any problems with multicollinearity?

b. Use a statistical software package to determine the multiple regression equation. How did you select the variables to include in the equation? How did you use the information from the correlation analysis? Show that your regression equation shows a significant relationship. Write out the regression equation and interpret its practical application. Report and interpret the R-square.

c. Develop a histogram or a stem-and-leaf display of the residuals from the final regres-sion equation developed in part (f). Is it reasonable to conclude that the normality assumption has been met?

d. Plot the residuals against the fitted values from the final regression equation devel-oped in part (f) against the fitted values of Y. Plot the residuals on the vertical axis and the fitted values on the horizontal axis.

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