this forecast better than the forecast made using the better

| January 30, 2017

Question
.

Attach a file of a spreadsheet that shows the calculations that support your answer. In the spreadsheet-make it clear what is what.

Table P-9

Actual Value Y bar

January

9.29

February

9.99

March

10.16

April

10.25

May

10.61

June

11.07

July

11.52

August

11.09

September

10.8

October

10.5

November

10.86

December

9.97

Problem 10 pg 139

This question refers to Problem 9.

Problem 9. The yield on a general obligation bond for the city of Davenport fluctuates with the market.The monthly quotations for 2006 are given in Table P-9.

Use exponential smoothing with a smoothing constant of .2 and an initial value of 9.29 to forecast the yield for January 2007.

1. Is this forecast better than the forecast made using the better moving average

model? Why? Explain your answer, support with numbers.

Table P-11

Month

Demand

Jan-06

205

Feb-06

251

Mar-06

304

Apr-06

284

May-06

352

Jun-06

300

Jul-06

241

Aug-06

284

Sep-06

312

Oct-06

289

Nov-06

385

Dec-06

256

Problem 11 pg 139

The Hughes Supply Company uses an inventory management method to deter-mine the monthly demands for various products. The demand values for the last 12months of each product have been recorded and are available for future forecast-ing.The demand values for the 12 months of 2006 for one electrical fixture are presented in Table P-11. Use exponential smoothing with a smoothing constant of .5 and an initial value of

205 to forecast the demand for January 2007.

Problem 14 pg 141

The Triton Energy Corporation explores for and produces oil and gas. Company president Gail Freeman wants to have her company’s analyst forecast the company’s sales per share for 2000. This will be an important forecast, since Triton’s restructuring plans have hit a snag.The data are presented in Table P-14.

Table P-14

Year

Sales per Share

Year

Sales per Share

1974

0.93

1987

5.33

1975

1.35

1988

8.12

1976

1.48

1989

10.65

1977

2.36

1990

12.06

1978

2.45

1991

11.63

1979

2.52

1992

6.58

1980

2.81

1993

2.96

1981

3.82

1994

1.58

1982

5.54

1995

2.99

1983

7.16

1996

3.69

1984

1.93

1997

3.98

1985

5.17

1998

4.39

1986

7.72

1999

6.85

1. Calculate all forecasting smoothing methods that you know (naïve, exponential smoothing, 2PMA, 3PMA, and ???) and determine the best method. Rank all the methods starting from the best. Support each method with a graph.

2. forecast sales per share for 2000, compare the results for this year from all methods and rank the methods.

3. Compute MAD, MSE. RMSE, MAPE and MPE

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