An analyst for an oil company has developed a formal linear regression model to predict the sales of 50 of their filling stations. The estimated model is
Ŷ = b0 + b1 X1
where
Ŷ = average monthly sales in gallons
X = square foot area of station property
X1 = X - (difference from the mean)
Some empirical results were

a. What does r2 mean?
b. Interpret the parameter estimates b0 and b1.
c. Is the X1 variable significant? At what level?
d. A new station is proposed with 30,000 sq. ft. What would you predict sales to be? What assumptions underlie the estimate?

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Variable Mean Range of Data Reg. Coefficient t-Value 5,000-80,000 gal bo 10,000 10,000 3,000-20,000 sq ft b 3.1 2 .3