Q10C, Q10P Solved - exponential smoothing and trend detection PDF

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exponential smoothing and trend detection...

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Q10C SOLVED (Please do not post the answers online) Question 1 (0.078125 points) Based on the data below, select the most accurate method:

3-Month MA MAD

5

Exponential Smoothing 3

Adj. Exponential Smoothing 7

Linear Trend Line 9

Question 1 options: 3-Month MA Exponential Smoothing

Adj. Exponential Smoothing Exponential Smoothing Linear Trendline

Question 2 (0.078125 points) If Sales have been consistently increasing in the past, which forecasting method would be most appropriate?

Question 2 options: 3-Month MA Exponential Smoothing Adj. Exponential Smoothing Linear Trendline

Question 3 (0.078125 points)

Which of the following equations correspond to the data below? What would your forecast be for year 5?

Year Sales

1 8

2 12

3 18

4 27

Question 3 options: Y=3x+14. Forecast Year 5 = 29 Y=3x+14. Forecast Year 5 = 31 Y=6x+1. Forecast Year 5 = 29 Y=6x+1. Forecast Year 5 = 31

Question 4 (0.078125 points) Which of the following is FALSE?

Question 4 options: The Exponential Smoothing method is appropriate for data with strong seasonal pattern or trend Exponential Smoothing + Trend = Adjusted Exponential Smoothing Exponential Smoothing requires 1 parameter: alpha Adjusted Exponential Smoothing requires 2 parameters: alpha and beta

Question 5 (0.078125 points) Which of the following is FALSE?

Question 5 options:

MSE is expressed in square units MAD is expressed in % |A-F| is the forecast error

The lower the MSE, the more accurate the forecast

Question 6 (0.078125 points) The use of the moving average method is recommended when demand shows:

Question 6 options: Positive trend Seasonal pattern Random variations Economic cycles

Question 7 (0.078125 points) Which of the following is FALSE?

Question 7 options: Medium-term forecast encompasses between 1 month and 1 year Long-term forecast is usually more accurate than short-term forecast A cycle is an undulating movement that repeats itself in multiple years A seasonal pattern is an oscillating predictable movement that occurs periodically

Question 8 (0.078125 points)

Which of the following is FALSE?

Question 8 options: One economic cycle can take several years while one seasonal cycle typically occurs in one year Random variations are unpredictable movements that do not follow a pattern A 5-month moving average smooths out the data more than a 3-month moving average Economic cycles are more predictable than a seasonal pattern

Q10P SOLVED (Please do not post the answers online) See file “Q10P SOLVED IN EXCEL.xlsx” posted in D2L folder ‘SOLVED Quizzes for Test 2’

Question 1 (1 point) Case IT firm HelpTech offers technical support for corporate clients across the country. The number of billed hours (in th) in the past 4 years are presented below.

201 8 201 7 201 6 201 5

Jan

Feb

Ma r

Apr

Ma y

Jun

Jul

Aug

Sep

Oct

No v

Dec

205

206

206

205

205

212

220

246

255

256

245

239

194

197

191

193

193

202

209

214

210

206

199

199

196

192

192

203

204

212

217

216

217

202

197

199

186

187

166

175

187

213

225

209

209

216

206

198

Forecast billed hours for May 2018 using the Adjusted Exponential Smoothing (alpha = .2, beta = .7) using 2018 data only. What is the forecast?

Question 1 options: 201.7 203.4 205.3 206.1

Question 2 (1 point) Obtain the optimal parameters alpha and beta for the Adjusted Exponential Smoothing using 2018 data only. What are the parameters? Question 2 options: alpha = .2, beta = .7 alpha = .85, beta = 1 alpha = 1, beta = .24 alpha = .78, beta = 1

Question 3 (1 point) Calculate MAD, MSE, and MAPE for the Adjusted Exponential Smoothing (optimal parameters) using 2018 data only. Select the best option: Question 3 options: MAD=5.7, MSE=70.9, MAPE=2.5% MAD=7.7, MSE=60.9, MAPE=3.5% MAD=8.7, MSE=80.9, MAPE=4.5% MAD=3.7, MSE=77.9, MAPE=3.5%

Question 4 (1 point) Forecast annual billed hours for 2019 based on the 2015 to 2018 annual hours, using the linear trendline. Select the best answer: Question 4 options:

2019 Forecast = 2915.1 2019 Forecast = 2615.1 2019 Forecast = 2715.1 2019 Forecast = 2785.1

Question 5 (1 point) Obtain the seasonal factors based on the 2015 to 2018 hours. Then, forecast monthly billed hours for 2019 based on the previously calculated seasonal factors and 2019 annual forecast. What is your forecast for May 2019? Question 5 options: 213.8 215.7 210.9 219.4

Question 6 (1 point) Forecast annual billed hours for 2018 using the linear equation previously obtained. Select the best option: Question 6 options: 2018 Forecast = 2636.1 2018 Forecast = 2731.5

2018 Forecast = 2622.2 2018 Forecast = 2702.3

Question 7 (1 point) Forecast monthly billed hours for 2018 based on the previously calculated seasonal factors and 2018 annual forecast. What is your forecast for May 2018? Question 7 options: 208.3 207.8 209.1 208.8

Question 8 (1 point) Calculate MAD, MSE, and MAPE for the 2018 forecast vs the actual data. Select the best option: Question 8 options: MAD=14.6, MSE=171.9, MAPE=7.7% MAD=12.6, MSE=198.9, MAPE=3.7% MAD=11.6, MSE=168.9, MAPE=5.7% MAD=10.6, MSE=178.9, MAPE=4.7%...