What is the equation using the table below showing the regression of sales and advertising? SUMMARY OUTPUT Regression Statistics Multiple R 0.9921 R Square 0.9843 Adjusted R 0.9710 Square Standard 0.1771 Error Observations 6 Coefficients Intercept 15.1526 Advertising -1.5287 y = 15.1526 - 1.5287(advertising) y = b + ax y = 15.1526 + 1.5287(advertising) y = 15.1526 - 1.5287(sales)
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- Intercept: 15.1526 - Slope (coefficient for Advertising): -1.5287 ** Show more…
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The regression output below is based on the number of Internet Ads and Total Sales for a local business. Determine the predicted amount of Total Sales if the local business runs 25 Internet Ads. SUMMARY OUTPUT Regression Statistics Multiple R 0.8489 R Square 0.7206 Adjusted R Square 0.6648 Standard Error 55.4177 Observations 7 ANOVA df SS MS F Regression 1 39623.23 39623.23 12.902 Residual 5 15355.62 3071.125 Total 6 54978.86 Coefficients Std. Error t Stat P-value Intercept 26.32 51.0396 -0.51575 0.6280 Internet Ads 9.51 2.64882 3.591916 0.0157
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A shoe store developed the following estimated regression equation relating sales to inventory investment and advertising expenditures. $$\hat{y}=25+10 x_{1}+8 x_{2}$$ where $$\begin{aligned} x_{1} &=\text { inventory investment }(\$ 1000 s) \\ x_{2} &=\text { advertising expenditures }(\$ 1000 s) \\ y &=\text { sales }(\$ 1000 s) \end{aligned}$$ $\begin{array}{l}{\text { a. Predict the sales resulting from a } \$ 15,000 \text { investment in inventory and an advertising }} \\ {\text { budget of } \$ 10,000 .} \\ {\text { b. Interpret } b_{1} \text { and } b_{2} \text { in this estimated regression equation. }}\end{array}$
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