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10-46. INTERPRETING REGRESSION RESULTS, MATCHING TIME PERIODS. Nandita Summers works at Modus, a store that caters to fashion for young adults. Nandita is responsible for

10-46. INTERPRETING REGRESSION RESULTS, MATCHING TIME PERIODS. Nandita Summers works at Modus, a store that caters to fashion for young adults. Nandita is responsible for the stores online advertising and promotion budget. For the past year, she has studied search engine optimization and has been purchasing keywords and display advertising on Google, Facebook, and Twitter. In order to analyze the effectiveness of her efforts and to decide whether to continue online advertising or move her advertising dollars back to traditional print media, Nandita collects the following data:OnlineSalesMonthAdvertising ExpenseRevenueSeptember$5,125$44,875October 5,472 42,480November 3,942 53,106December 1,440 64,560January 4,919 34,517February 4,142 59,438March 1,290 51,840April 5,722 36,720May 5,730 62,564June 2,214 59,568July 1,716 35,450August 1,875 36,211 Required4161. Nandita performs a regression analysis, comparing each months online advertising expense with that months revenue. Verify that she obtains the following result:VariableCoefficientStandard Errort-ValueConstant$51,999.647,988.68 6.51Independent variable: Online advertising expense; Durbin@Watson statistic = 2.14 1.992. Plot the preceding data on a graph and draw the regression line. What does the cost formula indicate about the relationship between monthly online advertising expense and monthly sales revenue? Is the relationship economically plausible?3. After further thought, Nandita realizes there may have been a flaw in her approach. In particular, there may be a lag between the time customers click through to the Modus website and peruse its social media content (which is when the online ad expense is incurred) and the time they actually shop in the physical store. Nandita modifies her analysis by comparing each months sales revenue to the advertising expense in the prior month. After discarding September sales revenue and August advertising expense, show that the modified regression yields the following: VariableCoefficientStandard Errort-ValueConstant$28,361.375,428.695.22Independent variable: Previous months online advertising expense; Durbin-Watson statistic = 1.71 5.38 1.314.124. What does the revised formula indicate? Plot the revised data on a graph. Is this relationship economically plausible?5. Can Nandita conclude that there is a cause-and-effect relationship between online advertising expense and sales revenue? Why or why not

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