{15)4. HBK, a food industry company wants to build a forecasting model to predict the sales of its hot beverage. HBK had the last weekly sales for the past 152 weeks. Using the time series components for trend (variable called 1:3} and seasonalmonthly dummyr variables {using Dec as a baseline) and the causal variable of average weekly temperature EEK management build the model on the following page. Note the average hotbeverage weekly sales is $91,501}. a. Evaluate the model on the following page, i.e., is it a good model? If so, why, or if not, why? Consider all the appropriate tests, use a. = [LUIS fort test and u = H.135 for F test. Notice on the following page is a plot of the residuals. DD ALL APPROPRIATE TESTECDMPLETELYIII! SUMMARY OUTPUT Regression Stabsocs Multiple R 0.938980898 R Square 0.881896019 Adjusted R Square 0.870551442 Standard Error 24568. 19214 Observations 152 ANOVA MS F Significance F Regression 13 6218+11 4.7BE+10 79.11435 3. 1982E-57 Residual 138 8.33E+10 6.046+08 Total 151 7.046+11 Coefficients landard Em 1 Stat P-value Lower 95% Upper 95% ower 95.0 /gper 950% Intercept 310087.8302 18946.59 18.29795 1.018-38 278579.255 343598.4 278579.3 343598.4 Avg Wily Temp -3081.992316 422.168 -7.25302 2.68E-11 -3896.747343 -2227 24 -3898.75 -2227.24 to -121.7295379 48.83 18 -2.49283 0.013855 -218.284909 -25.1742 -218.285 -25.1742 jan -9403.035399 9804.198 -0.95908 0.339194 -28788.92886 9982.856 -28788.9 9982.856 feb -82170.23081 9736.895 -8.38515 2.438-09 -81422.6488 -42917.8 -81422.6 -42917.8 mar -98755. 45062 9218.708 -10.4956 2.83E-19 -114983.6446 -78527.3 -114984 -78527.3 apr - 102641.6301 10804.93 -9.49952 8.92E-17 -124006.2813 -81277 -124008 -81277 may -84368.23824 12591.13 -8.70045 4.86E-10 -109282.7415 -59469.7 -109283 -59483.7 june -67300.87865 15170.72 -4.43823 1.86E-05 -97298.02853 -37303.7 -97298 -37303.7 july 50828.00038 17878.64 -2.87511 0.00468 -85784.08342 -15871.9 -85784.1 -15871.9 One -50053. 40056 17556.73 -2.85095 0.005029 -84768.4121 -15338.4 -84768.4 -15338.4 sept -30998.70032 16231.6 -1.90985 0.058253 -83091.52449 1098.124 -83091.5 1098.124 oct -7752.772946 13284.86 -0.58358 0.580458 -34020.99187 18515.45 -34021 18515.45 nov 8454.529821 10022.2 0.84358 0.400384 -11382.41579 28271.48 -11382.4 28271.48 Residuals 150000 100000 50000 -60000 -100000 time period b. If you believe the model is OKAY, provide at least two reasons to justify your belief. On the other hand, if you believe the model is not OKAY, provide suggestions on how you would improve the modelc. Ranking the order of the months in terms of their impact on weekly sales, ie., which month has the highest expected weekly sales, next highest, and which are the lowest and second lowest? Highest HIGHEST 2 NEXT HIGHEST 3 4 5 6 7 8 9 10 SECOND LOWEST 11 12 LOWEST Lowest(d). Show how you will code the dummy variables in this model, in other words fill in 13 rows with your dummy variables in the table below. (the first column, Month, tells you what month it is ) . Month Jan Feb Mar Apr May Jun July Aug Sept Oct Nov Dec Jan(e). What is the model's predicted value or forecast for time period 20, which is August, and the average monthly temperature is 80? (f). Answer the following multiple-choice questions: A set of observations on a variable measured at successive points in time or over successive periods of time constitutes a A. geometric series B. time invariant set C. time series D. logarithmic series With reference to time series data patterns, a cyclical pattern is the component of the time series that: A. shows a periodic pattern over one year or less. B. does not vary with respect to time. C. results in periodic above-trend and below-trend behavior of the time series lasting more than one year. D. is characterized by a linear variation of the dependent variable with respect to time