Question

2. Use the Holt’s approach.

1. Determine the best value of alpha and gamma. Show your work on a spreadsheet.
   
2. Use L₂₅ andT₂₅ from the template of the model you obtained in question ‘5’ to calculate the forecast for months 26, 27, and 28. If you could not get the optimal alpha and gamma, use instead alpha = 0.8 and gamma = 0.2 to obtain L₂₅ and T₂₅, and now calculate the requested forecasts. Show your calculations.
   
3. As a matter of policy, management will approve borrowing up to a total of $2,000,000 a month (which, in our data set, will be a value of 2000 since the data appear in thousands). For next month (month 26), how confident can management be that all the demand will be met (that is predicted sales will not exceed $2,000,000)? You can use trial and error, or goal seek with the template.

See data in the next page.

Month	Sales in 1000$	Month	Sales in 1000$
1	1156	14	1264
2	1101	15	1235
3	1195	16	1357
4	1072	17	1373
5	1237	18	1358
6	1106	19	1489
7	1148	20	1428
8	1005	21	1516
9	1350	22	1681
10	1115	23	1598
11	1196	24	1699
12	1298	25	1781
13	1260

Answer 1

Solution:-

Given that

n = 25,

The negression equation is

Loan requests = 993.24 + 25.19 x month

slope tdb that every month, loan requests increases by 25.19

For month = 26, Loan requests = 1648

For month = 27, Loan requests = 1673

For month = 28, Loan requests = 1699

we need 95% prediction interval

Prediction interval is given by

For month = 26, 95% PI is (1425, 1872)

For month = 27, 95% PI is (1448, 1899)

For month = 28, 95% PI is (1471, 1926)

Alpha series the coefficient for the level smoothing and

Gamma specifies the coefficient for the seasonal smoothing.

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