Unit 5 [GB513: Business Analytics] Assignment This Assignment requires you to use Excel. Make sure to use the Unit 5 Assignment template located in Doc Sharing when you turn in your answers. Submit your Assignment to the Unit 5 Dropbox. Question 1 Determine the error for each of the following forecasts. Then, calculate MAD and MSE. Period Value Forecast Error 1 202 — — 2 191 202 3 173 192 4 169 181 5 171 174 6 175 172 7 182 174 8 196 179 9 204 189 10 219 198 11 227 211 Question 2 The U.S. Census Bureau publishes data on factory orders for all manufacturing, durable goods, and nondurable goods industries. Shown here are factory orders in the United States over a 13-year period ($ billion). First, use these data to develop forecasts for the years 6 through 13 using a 5-year moving average. 1 Unit 5 [GB513: Business Analytics] Then, use these data to develop forecasts for the years 6 through 13 using a 5-year weighted moving average. Weight the most recent year by 6, the previous year by 4, the year before that by 2, and the other years by 1. Answer the following questions: a) b) c) d) e) What is the forecast for year 13 based on the 5-year moving average? What is the forecast for year 13 based on the 5-year weighted moving average? What is the MAD for the moving average forecast? What is the MAD for the weighted moving average forecast? Which forecasting model is better? Year Factory Orders ($ billion) 1 2,512.70 2 2,739.20 3 2,874.90 4 2,934.10 5 2,865.70 6 2,978.50 7 3,092.40 8 3,111.10 9 3,222.20 10 3,555.00 11 4,221.50 12 4,551.20 13 4,137.00 2 Unit 5 [GB513: Business Analytics] Question 3 The “Economic Report to the President of the United States” included data on the amounts of manufacturers’ new and unfilled orders in millions of dollars. Shown here are the figures for new orders over a 21-year period. Use the Charting tool in Excel to develop a regression model to fit the trend effects for these data. Use a linear model and then try a polynomial (order 2) model. Make sure the charts show the line formula and the r-squared value. Include both charts in your report. Then answer the following question: • How well does either model fit the data? Which model should be used for forecasting? Explain using the relevant metrics. Year Total Number of New Orders 1 55,022 2 55,921 3 64,182 4 76,003 5 87,327 6 85,139 7 99,513 8 115,109 9 116,251 10 121,547 11 123,321 12 141,200 13 162,140 14 168,420 15 171,250 3 Unit 5 16 176,355 17 195,204 18 209,389 19 237,025 20 272,544 21 293,475 [GB513: Business Analytics] Unit 5 Assignment Rubric Content Question 1 Points Possible Points Earned 5 Provided the MAD. Question 1 5 Provided the MSE. Question 2a 5 Correct forecast for year 13 using a 5-year moving average. Question 2b 5 Correct forecast for year 13 using a 5-year weighted moving average. Question 2c 5 Correct MAD for moving average forecast. Question 2d 5 Correct MAD for weighted moving average forecast. Question 2e 5 4 Unit 5 [GB513: Business Analytics] Recommended the better model with justification. Question 3 5 Used Excel Charting to fit a linear trendline, including the formula and r-squared. Question 3 5 Used Excel Charting to fit a polynomial trendline, including the formula and r-squared. Question 3 5 Recommended the better model with justification. Total 50 5 Unit 5 Dropbox Assignment Answers by (Insert your name here) In the summary tables below, insert only the answers. You will show work after the summary section. Question 1 MAD MSE Question 2 a) Moving average forecast for year 13 b) Weighted moving average forecast for year 13 c) MAD for part a d) MAD for part b e) Recommended forecast method (justify): Question 3 R-squared for Linear model R-squared for polynomial model Regression formula for linear model Regression formula for polynomial model Recommended forecast method (justify): Work Show all your work for the questions below. Question 1 Show the errors you calculated. Question 2 Show the two forecasts and the errors. Question 3 Show the polynomial and linear trendline charts from Excel charting. Do not use multiple regressions for this question.
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