The management of the River of Life Inc. compared daily observations of sales revenue and number of ad clicks from the online marketing campaigns. They think that more Ad Clicks translates into more Sales, and they have a strong feeling that River of Life Inc. can increase the revenue by improving CTR (click-through rate).

A group of Humber College students from e-Business Marketing program has been entrusted with the regression analysis. A random sample of 30 daily recordings is shown below. (Sales are in $$thousands.)

Day# AdClicks Sales 1 578 1000 2 618 845 3 592 776 4 520 735 5 484 594 6 591 721 7 377 305 8 396 466 9 381 657 10 490 681 11 755 928 12 700 1045 13 852 957 14 823 975 15 800 1023 16 754 874 17 815 1069 18 831 929 19 692 1209 20 710 957 21 846 1155 22 685 806 23 745 888 24 661 806 25 669 602 26 796 1014 27 759 912 28 758 885 29 843 1056 30 680 786Humber students urgently prepared a regression analysis report. Unfortunately, it was the final exam week, and students had very little time. At that moment, only first 10 daily observations were available. Also, the students had no time to find the regression equation, r, and R2 . The following picture is the draft that they prepared. (a) Check the draft. Select the most appropriate comment. The roles of the selected variables are mixed up. AdClicks should be the independent variable at, and Sales should be considered as the dependent variable 3;, not the other way around. 0 The draft looks incomplete as the students forgot to label the axes. Also, I would probably make the points (Excel markers) bigger. O The choice of dependent and independent variables is correct, but the trend line is missing. 0 The choice of dependent and independent variables is wrong. The students must have taken AdClicks (not Day#) as the independent variable 3:, and Sales should be the dependent variable y. 0 None of the above. Please help the Humber College students. Using Excel and 30 daily recordings, please construct a scatter diagram to support or reject the idea of the management. Make sure that you do not repeat the possible mistakes that are described in question 1. Also, please include the regression equation and R2 in the scatter diagram along with the trend line. Based on the result, answer the following questions: (b) The coefficient of determination is R2 = O The linear correlation coefficient is r = C] For part (b), round your answers to 4 decimal places. (0) The regression equation is (do not mix up bu and b1 ) g=D+Om For part (c), round your answers to 2 decimal places. (d) What percent of the variation in Sales can be explained by the variation in AdClicks? [3% What percent of the variation in Sales cannot be explained by the variation in AdClicks, but is due to other factors? C] % For part (d), express your answers in percent form (i.e. 3.00% instead of 0.03) and round to 2 decimal places. (e) What is the slope of the regression equation? C] For part (e), round your answers to 2 decimal places. (f) How much will the sales revenue amount be expected to change if the number of daily ad clicks increases by one click? Please note that the answer is expected in dollars, not in thousands. I Select an answer v | C] (g) What would be the best predicted sales revenue amount if the number of daily ad clicks is 653? Please note that the answer is expected in dollars, not in thousands. $D (h) Would it be possible to use this regression model to predict the sales revenue amount if the number of daily ad clicks is 306 ? O The forecast would be reliable, and Sales is approximately 439.47. 0 The forecast would be reliable. Just plug the given number of clicks into the regression equation. 0 The forecast would be reliable, as the regression equation from question (c) can be used to predict any Sales. 0 The forecast would not be reliable, as 306 clicks is not within the sample m-range. 0 None of the above