The business objective of the director of Game Ghana is to forecast monthly sales for all new stores, based on the number of profiled customers who live no more than 30 minutes from a Game store. To examine the relationship between the number of profiled customers who live within a fixed radius from a Game store and it monthly sales, data were collected from a sample of 10 stores: Store 1 2 3 4 5 6 7 8 9 10 No of customers (‘000) 5 7 2 8 4 3 2 4 5 3  Monthly Sales (‘000) 8 12 6 14 8 7 9 10 12 8 (a) Use the least-squares method to compute the regression coefficients ? ??? ? (b) Write down the estimated equation and predict the monthly sales if there are 9000 customers (c) Determine the coefficient of determination, r2 and explain its meaning in this problem

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The business objective of the director of Game Ghana is to forecast monthly sales for all new stores, based on the number of profiled customers who live no more than 30 minutes from a Game store. To examine the relationship between the number of profiled customers who live within a fixed radius from a Game store and it monthly sales, data were collected from a sample of 10 stores:
Store 1 2 3 4 5 6 7 8 9 10
No of customers (‘000)
5
7
2
8
4
3
2
4
5
3
 Monthly Sales (‘000)
8
12
6
14
8
7
9
10
12
8
(a) Use the least-squares method to compute the regression coefficients ? ??? ?
(b) Write down the estimated equation and predict the monthly sales if there are 9000 customers
(c) Determine the coefficient of determination, r2 and explain its meaning in this problem
(d) Determine the value of the extent of relationship between monthly sales and number of customers, and interpret your results.

Expert Solution
Step 1

The least square regression equation is the simple linear equation between response and explanatory variable. The regression coefficients are intercept and slope of the line.

The slope of the line is computed by b=x-x¯y-y¯x-x¯2 and intercept is computed by a=y¯-bx¯. The simple linear regression equation is written as y^=a+bx. The coefficient of correlation can be computed by r=bsxsy, where sx is sample standard deviation of x and sy is sample standard deviation of y.

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