Day# Ad Clicks Sales 1 394 462 2 460 805 3 615 744 4 575 760 5 384 903 6 594 643 7 453 724 8 465 751 CO 388 277 10 581 832 11 768 992 12 685 965 13 724 1016 14 819 1330 15 744 838 16 829 1234 17 749 868 18 727 806 19 769 1093 20 678 862 21 766 1037 22 839 1293 23 854 1071 24 677 1000 25 700 832 26 728 1266 27 849 879 28 682 968 29 713 1205 30 703 977The 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-Elusiness Marketing program has been entrusted with the regression analysis. A random sample of 3D daily recordings is shown below. {Sales are in $ thousands} Humher students urgently prepared a regression analysis report. Unfortunately, it was the final exam week, and students had very little time. At that moment, only rst 1t] daily observations were available. Also, the students had no time to find the regression equation, 1" , 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. AdClioks should be the independent variable 3, and Sales should be considered as the dependent variable 3;, not the ether way around. The draft looks incomplete as the students forgot to label the axes. Also, I would probava make the points [Excel markers} bigger. The choice of dependent and independent variables is correct, but the trend line is missing. The choice of dependent and independent variables is wrong. The students must have taken AdCliolts [not Dav#] as the independent variable I, and Sales should be the dependent variable y. None of the above. {h} Would it be possible to use this regression model to predict the sales revenue amount ifthe number of daily ad clicks is 213 ? The forecast would not be reliable, as 213 clicks is not within the sample :-range. The forecast would be reliable, as the regression equation from question {c} can be used to predict any Sales. The forecast would be reliable. Just plug the given number of clicks into the regression equation. The forecast would be reliable, and Sales is approximately 356.33. None of the above. Do not forget to attach the original Excel filals]