Fit a linear regression model to the sales data

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Reference no: EM131117040

Assignment 1

1.      Sales data for two years are as follows. Data are aggregated with two months of sales in each "period."

Months                                  Sales                Months                                    Sales

January-February                   109                  January-February                   115

March-April                            104                  March-April                            112

May-June                               150                  May-June                                159

July-August                            170                  July-August                             182

September-October                 120                  September-October               126

November-December               100                  November-December                        106

a)      Plot the data.

b)      Fit a linear regression model to the sales data.

c)      In addition to the regression model, determine multiplicative seasonal index factors. A full cycle is assumed to be a full year.

d)      Using the results from parts b) and c), prepare a forecast for the next year.

2.      Zeus Computer Chips Inc. used to have major contracts to produce the Centrino-type chips. Here is demandover the past 12 quarters:

2012                            2013                            2014

I           4,800                          I           3,500                          I           3,200

II          3,500                          II          2,700                          II          2,100

III         4,300                          III         3,500                          III         2,700

IV         3,000                          IV         2,400                          IV         1,700

a)      Fit a linear regression model with an additive form (using dummy variables) to forecast the four quarters of 2015.

b)      Use the decomposition technique to forecast the four quarters of 2015.

3.      The demand manager of Maverick Jeans is responsible for ensuring suf?cient warehouse spacefor the ?nished jeans that come from the production plants. In order to estimatethe space requirements the demand manager is evaluating moving-average forecasts. Thedemand (in 1,000 case units) for the last ?scal year is shown below.

Month

1

2

3

4

5

6

7

8

9

10

11

12

Demand 

20

18

21

25

24

27

22

30

23

20

29

22

a)      Use a three-month moving average to estimate the month-in-advance forecast of demand formonths 4-12 and generate a forecast for the ?rst month of next year. Calculate mean absolute deviation (MAD).

b)      Use a three-month weighted moving average with weights of 0.6, 0.3, 0.1 (most recent tolast recent, respectively) to calculate month-in-advance forecasts for months 4-12 andforecast for the ?rst month of next year. Calculate the MAD.

c)      Use anexponential smoothing method witha starting forecast of 20 for month 1 and a smoothing constantα = 0.5to calculate month-in-advance forecasts for months 4-12 and forecast for the ?rst month of next year. Calculate the MAD.

d)      Compare the MAD for the forecasting methods in parts a) - c).Based on these error calculations, which of the three forecast methods would you recommend?

Reference no: EM131117040

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