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Regression of yearly truck maintenance expenses

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I need help setting up and solving the following problem which is set out with the data needed in that attached excel spread sheet.

INSTRUCTIONS: Note: "Interpret" in a problem means you should explain in words what something (in this case each regression coefficient (separately)) means. (add new part c: predict the maintenance expense for an 8 year old truck driven 8,000 miles; add part (d): give a 95% prediction interval (PI) for your prediction for a single new observation, based on the empirical rule of thumb and the standard error of the estimate)

FACTS:

A trucking company wants to predict the yearly maintenance expense (Y) for a truck using the number of miles driven during the year (X1) and the age of the truck (X2 in years) at the beginning of the year. The company gathered the data given in the file P13_16.XLS set out below. Note that each observation corresponds to a particular truck.

PROBLEM:

(a) Formulate and estimate a multiple regression model using the given data. Interpret each of the estimated regression coefficients.

(b) Compute and interpret the standard error of estimate Se and the coefficient of determination R2 f or these data.

(c) Predict the maintenance expense for an 8 year old truck driven 8,000 miles;

(d) Give a 95% prediction interval (PI) for your prediction for a single new observation, based on the empirical rule of thumb and the standard error of the estimate)

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Regression of yearly truck maintenance expenses are analyzed. The solution uses excel to analysis the data.

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Multiple Regression Model

A trucking company wants to predict the yearly maintenance expense (Y) for a truck using the number of miles driven during the year (X1) and the age of the truck (X2 in years) at the beginning of the year. The company has gathered data given in the file 11_16.xls. See attached. Note that each observation corresponds to a particular truck.

a) Formulate and estimate a multiple regression model using the given data. Interpret each of the estimated regression coefficients.

b) Compute and interpret the standard of error of estimates Se and the coefficient of determination R2 for these data.

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