Devry BUSN 278 Week 1 Assignment Latest

Devry BUSN 278 Week 1 Assignment Latest

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Devry BUSN 278 Week 1 Assignment Latest

 

Q1 (TCO 2) Using the table “Paint Sales Time Series”, calculate the forecast for paint sales (in thousands) for Week 11 using a three day moving average.

Paint Sales Time Series |

Week | Sales (000’s of gallons) |

1 | 6 |

2 | 8 |

3 | 10 |

4 | 9 |

5 | 11 |

6 | 12 |

7 | 10 |

8 | 8 |

9 | 7 |

10 | 9 |

(TCO 2) Using the table “Paint Sales Time Series”, calculate the mean absolute deviation for a three day moving average.

Paint Sales Time Series |

Week | Sales (000’s of gallons) |

1 | 6 |

2 | 8 |

3 | 10 |

4 | 9 |

5 | 11 |

6 | 12 |

7 | 10 |

8 | 8 |

9 | 7 |

10 | 9 |

Question : | (TCO 2) Using the table “Gasoline Sales Time Series”, calculate the forecast for gasoline sales (in thousands) for Week 13 using a three day weighted moving average. Use a weight of .60 for the most recent observation, .30 for the second most recent, and .10 for the third most recent.

Gasoline Sales Time Series |

Week | Sales (000’s of gallons) |

1 | 17 |

2 | 21 |

3 | 19 |

4 | 23 |

5 | 18 |

6 | 16 |

7 | 20 |

8 | 18 |

9 | 22 |

10 | 20 |

11 | 15 |

12 | 22 |

  1. 5.| Question : | (TCO 2) Using the table “Gasoline Sales Time Series”, calculate the forecast for gasoline sales (in thousands) for Week 13 using exponential smoothing and a smoothing constant of .10.

Gasoline Sales Time Series |

Week | Sales (000’s of gallons) |

1 | 17 |

2 | 21 |

3 | 19 |

4 | 23 |

5 | 18 |

6 | 16 |

7 | 20 |

8 | 18 |

9 | 22 |

10 | 20 |

  1. 1.| Question : | (TCO 3) Using the following information regarding actual sales for Seafood City, calculate the regression (trend) line:

Sales for Seafood City ($) |

Day | Week 1 | Week 2 |

Monday | 1,700 | 1,800 |

Tuesday | 1,900 | 2,000 |

Wednesday | 2,100 | 2,100 |

Thursday | 2,300 | 2,200 |

Friday | 4,200 | 4,300 |

Saturday | 4,400 | 4,600 |

Sunday | 2,100 | 2,200 |

  1. 2.| Question : | (TCO 3) Using the following information regarding actual sales for Sam’s Ski Supplies, project sales for March of Year 3 using simple linear regression:

Sales for Sam’s Ski Supplies ($000s) |

Month| First Year | Second Year |

January | 380 | 400 |

February | 340 | 360 |

March | 320 | 330 |

April | 280 | 290 |

May | 265 | 270 |

June | 230 | 235 |

July | 220 | 230 |

August| 200 | 205 |

September | 210 | 220 |

October | 250 | 270 |

November | 400 | 450 |

December | 450 | 502 |

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  1. 3.| Question : | (TCO 3) Using the following information regarding actual sales for Sam’s Ski Supplies, calculate the seasonal ratio for January of Year 3:

Sales for Sam’s Ski Supplies ($000s) |

Month| First Year | Second Year |

January | 380 | 400 |

February | 340 | 360 |

March | 320 | 330 |

April | 280 | 290 |

May | 265 | 270 |

June | 230 | 235 |

July | 220 | 230 |

August| 200 | 205 |

September | 210 | 220 |

October | 250 | 270 |

November | 400 | 450 |

December | 450 | 502 |

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  1. 4.| Question : | (TCO 3) Using the following information regarding actual sales for Paradise Pools, calculate the seasonal forecast of sales for April of Year 3:

Sales for Paradise Pools ($000s) |

Month| First Year | Second Year |

January | 84 | 84 |

February | 80 | 82 |

March | 88 | 98 |

April | 100 | 120 |

May | 150 | 160 |

June | 200 | 210 |

July | 240 | 250 |

August| 220 | 215 |

September | 180 | 195 |

October | 160 | 165 |

November | 120 | 130 |

December | 92 | 100 |

 

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