{Exercise 17.11} For the Hawkins Company, the monthly percentages of all shipmen

Question

{Exercise 17.11}

For the Hawkins Company, the monthly percentages of all shipments received on time over the past 12 months are 80, 82, 84, 83, 83, 84, 85, 84, 82, 83, 84, and 83.

What is the forecast for next month (to 1 decimal)?

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{Exercise 17.11}

For the Hawkins Company, the monthly percentages of all shipments received on time over the past 12 months are 80, 82, 84, 83, 83, 84, 85, 84, 82, 83, 84, and 83.

Which of the following a time series plot?

Selecttime series plot #atime series plot #btime series plot #c

What type of pattern exists in the data?
Selectparallelperpendicularverticalhorizontal

Compare the three-month moving average approach with the exponential smoothing approach for = .2 (to 2 decimals). Round intermediate calculations to two decimal places. MSE(3-Month) MSE ( = .2)

Which provides more accurate forecasts using MSE as the measure of forecast accuracy?
SelectA 3-month moving average provides the most accurate forecast using MSEThe exponential smoothing approach for (alpha symbol) = .2 provides the most accurate forecase using MSE

What is the forecast for next month (to 1 decimal)?

Explanation / Answer

The time series plot is C.

because, in plot C, for the first point the the y axis takes value 80, for the second point , the value on the y axis is 82, ............, for the last point, the value on the y axis is 83.

There is a horizontal pattern exist in the data.

MOVING AVERAGE:

Time Yt MA Predict Error
1 80 * * *
2 82 82.0000 * *
3 84 83.0000 * *
4 83 83.3333 82.0000 1.00000
5 83 83.3333 83.0000 0.00000
6 84 84.0000 83.3333 0.66667
7 85 84.3333 83.3333 1.66667
8 84 83.6667 84.0000 0.00000
9 82 83.0000 84.3333 -2.33333
10 83 83.0000 83.6667 -0.66667
11 84 83.3333 83.0000 1.00000
12 83 * 83.0000 0.00000

ERROR SQUARES

SUM OF ERROR SQUARE = 11.11112

MSE = 11.11112/12 =0.925926

EXPONENTIAL SMOOTHING:

Time Yt Smooth Predict Error
1 80 82.1333 82.6667 -2.66667
2 82 82.1067 82.1333 -0.13333
3 84 82.4853 82.1067 1.89333
4 83 82.5883 82.4853 0.51467
5 83 82.6706 82.5883 0.41173
6 84 82.9365 82.6706 1.32939
7 85 83.3492 82.9365 2.06351
8 84 83.4794 83.3492 0.65081
9 82 83.1835 83.4794 -1.47935
10 83 83.1468 83.1835 -0.18348
11 84 83.3174 83.1468 0.85321
12 83 83.2539 83.3174 -0.31743

ERROR SQUARES

0.100762

SUM OF SQUARES OF ERRORS= 20.64811

MSE = SSE/12=1.720675

So,

MSE (3 MONTHS) =

0.925926

MSE (0.2) =

1.720675

From MSE's we get moving average has least MSE. SO, MOVING AVERAGE gives better forecast.

1 0 0.444449 2.777789 0 5.444429 0.444449 1 0

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