A company sell roofing products to home repair retailers
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A company sell roofing products to home repair retailers

A company sell roofing products to home repair retailers. The owner is interested in studying the effects of several variables on the value of shingles sold. The marketing manager is arguing that the company should spend more money on advertising, while a market researcher suggests it should focus more on making its brand and product more distinct from the competitors. The company has divided the U.S. into 26 marketing districts. In each district, it collected information on the following variables: volume of sales (in thousands dollars), advertising dollars (in thousands), numbers of active accounts, number of competitor brands, and a rating potential. 

a) Run a linear multiple regression

b) Write the estimated equation (no decimal points).
c) Interpret the results. Make sure you refer to: Adjusted R Square, t-test for each independent variable noting the level of significance, F values, and interpretation of the value of coefficients of independent variables.
d) Develop a correlation matrix. Do you see any problems? Does it appear there are any redundant independent variables?
e) Use the regression equation in (b) to estimate sales for each of the 26 observations. You should use spreadsheet formula command to find estimated sales and errors. Make sure you do not use any decimal points for the coefficients.
f) Check the Residual box in Excel and compare your answers in (e) with Excel residuals. The values should be similar, but not the same. Find the differences. Report the result in a table similar to the one in Excel.
g) Check Line Fit Plot and residual plots boxes in order to see visually the errors. Do you see any pattern?
Hint
StatisticsAfter running the regression by taking Sales as dependent variable (Y) and advertising dollars (X1), numbers of active accounts (X2) , number of competitor brands (X3), and a rating potential (X4) as independent variables the following output is obtained. SUMMARY OUTPUT Regression Statistics Multiple R 0.994566 ...

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