Consider the following time series data.

Week	1	2	3	4	5	6
Value	16	12	14	11	15	13

(a)

Construct a time series plot. What type of pattern exists in the data?

The data appear to follow a horizontal pattern.The data appear to follow a trend pattern. The data appear to follow a cyclical pattern.The data appear to follow a seasonal pattern.

(b)

Develop the three-week moving average for this time series. (Round your answers to two decimal places.)

Week	Time Series Value	Forecast
1	16
2	12
3	14
4	11
5	15
6	13

Compute MSE. (Round your answer to two decimal places.)

MSE =

What is the forecast for week 7?

(c)

Use = 0.2 to compute the exponential smoothing values for the time series.

Week	Time Series Value	Forecast
1	16
2	12
3	14
4	11
5	15
6	13

Compute MSE. (Round your answer to two decimal places.)

MSE =

What is the forecast for week 7? (Round your answer to two decimal places.)

(d)

Compare the three-week moving average forecast with the exponential smoothing forecast using

= 0.2.

Which appears to provide the better forecast based on MSE? Explain.

The three-week moving average provides a better forecast since it has a larger MSE than the smoothing approach using = 0.2.The three-week moving average provides a better forecast since it has a smaller MSE than the smoothing approach using = 0.2. The exponential smoothing using = 0.2 provides a better forecast since it has a larger MSE than the three-week moving average approach.The exponential smoothing using = 0.2 provides a better forecast since it has a smaller MSE than the three-week moving average approach.

(e)

Use trial and error to find a value of the exponential smoothing coefficient that results in a smaller MSE than what you calculated for = 0.2.

=