Day Monday Tuesday Wednesda Thursday Friday Daily Demand 1 1 0 0 0 0 297 2 0 1 0 0 0 293 3 0 0 1 0 0 327 4 0 0 0 1 0 315 5 0 0 0 0 1 348 9 0 0 0 0 0 447 7 0 0 0 0 0 431 8 1 0 0 0 0 283 9 0 1 0 0 0 326 10 0 0 1 0 0 317 11 0 0 0 1 0 345 12 0 0 0 0 1 355 13 0 0 0 0 0 428 14 0 0 0 0 0 454 15 1 0 0 0 0 305 16 0 1 0 0 0 310 17 0 0 1 0 0 350 18 0 0 0 1 0 308 19 0 0 0 0 1 366 20 0 0 0 0 0 460 21 0 0 0 0 0 427 22 1 0 0 0 0 291 23 0 1 0 0 0 325 24 0 0 1 0 0 354 25 0 0 0 1 0 322 26 0 0 0 0 1 405 27 0 0 0 0 0 442 28 0 0 0 0 0 454 29 1 0 0 0 0 318 30 0 1 0 0 0 298 31 0 0 1 0 0 355 32 0 0 0 1 0 355 33 0 0 0 0 1 374 34 0 0 0 0 0 447 35 0 0 0 0 0 463 36 1 0 0 0 0 291 37 0 1 0 0 0 319 38 0 0 1 0 0 333 39 0 0 0 1 0 339 40 0 0 0 0 1 416 41 0 0 0 0 0 475 42 0 0 0 0 0 459 43 1 0 0 0 0 319 44 0 1 0 0 0 326 45 0 0 1 0 0 356 46 0 0 0 1 0 340 47 0 0 0 0 1 395 48 0 0 0 0 0 465 49 0 0 0 0 0 453 50 1 0 0 0 0 307 51 0 1 0 0 0 324 52 0 0 1 0 0 350 53 0 0 0 1 0 348 54 0 0 0 0 1 384 55 0 0 0 0 0 474 56 0 0 0 0 0 485 Eli Orchid has designed a new pharmaceutical product, Orchid Relief, which improves the night sleep. Before initiating mass production of the product, Eli Orchid has been market-testing Orchid Relief in Orange County over the past 8 weeks. The daily demand values are recorded in the Excel file provided. Eli Orchid plans on using the sales data to predict sales for the upcoming week. An accurate forecast would be helpful in making arrangements for the company's production processes and designing promotions. The COO of the company approved the initial analysis and asked for the following extensions: d = (t*0.622) + (x1*-149.203) + (x2*-136.075) + (x3*- 109.072) + (x4*-118.444) + (x5*-72.691) + 434.714 To fit a new multiple regression model with dummy variables for weekdays (not the weekend), and to provide the regression equation (d = a*t + bix1 + b2x2 +b3x3 + b4x4 + b5x5 +b6x6+ c), along with Adjusted R. To use all three models: Adjusted R= 0.95254306 + M1 M2 M3 M1: d=1.0356t + 339.29 Mon. M2: Tue. d = 0.7163t+ 116.7679w + 315.02 62 Wed. Thu. M3: (the one considering weekdays) Fri. Sat. Sun. TOTAL: to predict the demand for seven days ahead (Mon, Tue, ..., Sun) and find the total weekly demand. Take advantage of the fact that new demand data became available and use this new data to compare the forecasts using MAPE for days 57-63. To provide a line chart with the actual demand (including the new data) and M2 and M3. New: M: 311 T: 341 W: 357 Th: 363 F: 390 Sa: 490 Su: 492 MAPEM1: MAPEM2: MAPEM3: SUMMARY OUTPUT Regression Statistics Multiple R 0.97863179 R Square 0.95772018 Adjusted R Sq 0.95254306 Standard Erro 13.3334666 Observations 56 ANOVA df Regression Residual Total SS MS F Significance F 6 197327.554 32887.9257 184.990883 49 8711.28529 177.781332 55 206038.839 6.8077E-32 Coefficients tandard Erro Intercept t Stat 90.708347 Day Monday Tuesday Wednesday Thursday Friday P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 434.714816 4.79244557 3.0045E-56 425.084036 444.345596 425.084036 444.345596 0.62210271 0.11107366 5.6008121 9.5851E-07 0.39889183 0.84531359 0.39889183 0.84531359 -149.20344 5.80579064 -25.699073 4.2842E-30 -160.87061 -137.53626 -160.87061 -137.53626 -136.07554 5.79515585 -23.480911 2.5775E-28 -147.72134 -124.42974 -147.72134 -124.42974 -109.07264 5.78663395 -18.849065 4.2405E-24 -120.70132 -97.443964 -120.70132 -97.443964 -118.44474 5.78023428 -20.49134 1.1119E-25 -130.06056 -106.82893 -130.06056 -106.82893 -72.691846 5.77596389 -12.585232 5.7433E-17 -84.29908 -61.084612 -84.29908 -61.084612