Blog

Answer based on methods presented in the class. Partial cred…

Answer based on methods presented in the class. Partial credit is possible. —————————————————————– We develop a regression model to predict sales for your business, using data from ad expenses and number of website visitors. Below, we observe partial results of running a multiple regression: Multiple R 0.96959148 R Square 0.94010764 Adjusted R Square 0.93012557 Standard Error 42935.3013 Observations 15 ANOVA   df SS MS F Significance F Regression 2 3.47229E+11 1.74E+11 94.17971 4.61561E-08 Residual 12 22121281145 1.84E+09 Total 14 3.69351E+11         Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -47659.3804 39288.98722 -1.21305 0.248451 -133262.7298 37943.969 Ad Expenses 8.65784561 0.86055659 10.06075 3.35E-07 6.782853871 10.5328373 Website 1.11501697 0.527431253 2.114052 0.056126 -0.034157011 2.26419095 a. State the multiple regression equation. b. The slope for website visits is 1.11. Provide the proper interpretation for this slope (m2{“version”:”1.1″,”math”:”m2″}). It begins as follows … “For a fixed cost in ad expenses, average sales are ….” c. Test the significance of the overall multiple regression model at α=0.05{“version”:”1.1″,”math”:”α=0.05″}. Draw the diagram showing the F test statistic and F cutoff value. d. If the results from c. above deem it appropriate, determine which of the variables (ad expenses, website visits, or both) have a significant influence on sales. Draw the diagrams showing the specific t test statistics and t cutoff values. If the results do not deem it appropriate, say why it does not make sense to perform these tests.