A regional retailer would like to determine if the variation in average monthly store sales can, in part, be explained by the size of the store measured in square feet. A random sample of 21 stores was selected and the store size and average monthly sales were computed. The following results are shown
Store Size (Sq. Ft)Average Monthly Sales
17400…………………………………$581,241.00
15920…………………………………$538,275.00
17440…………………………………$636,059.00
17320…………………………………$574,477.00
15760…………………………………$558,043.00
20200…………………………………$689,256.00
15280…………………………………$552,569.00
17000…………………………………$584,737.00
11920…………………………………$470,551.00
12400…………………………………$520,798.00
15640…………………………………$619,703.00
12560…………………………………$465,416.00
21680…………………………………$730,863.00
14120…………………………………$501,501.00
16680…………………………………$624,255.00
14920…………………………………$567,043.00
18360…………………………………$612,974.00
18440…………………………………$618,122.00
16720…………………………………$691,403.00
19880…………………………………$719,275.00
17880…………………………………$536,592.00
a. Compute the simple linear regression model using the sample data to determine whether variation in average monthly sales can be explained by store size. Interpret the slope and intercept coefficients.
b. Test for the significance of the slope coefficient of the regression model. Use a level of significance of 0.05.
c. Based on the estimated regression model, what percentage of the total variation in average monthly sales can be explained by store size?