Dataset Available:Download ProductSales.xlsx

The following time series shows the sales of a particular product over the past 12 months.

Month	Sales
1	105
2	135
3	120
4	105
5	90
6	120
7	145
8	140
9	100
10	80
11	100
12	110

(a)Construct a time series plot.

A time series plot contains a series of 12 points connected by line segments. The horizontal axis ranges from 0 to 13 and is labeled: Month. The vertical axis ranges from 0 to 160 and is labeled: Sales. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 60 and 125 on the vertical axis. The maximum number of sales is reached at the point (7, 125).

A time series plot contains a series of 12 points connected by line segments. The horizontal axis ranges from 0 to 13 and is labeled: Month. The vertical axis ranges from 0 to 160 and is labeled: Sales. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 80 and 145 on the vertical axis. The maximum number of sales is reached at the point (7, 145).

A time series plot contains a series of 12 points connected by line segments. The horizontal axis ranges from 0 to 13 and is labeled: Month. The vertical axis ranges from 0 to 160 and is labeled: Sales. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 80 and 145 on the vertical axis. The maximum number of sales is reached at the point (6, 145).

A time series plot contains a series of 12 points connected by line segments. The horizontal axis ranges from 0 to 13 and is labeled: Month. The vertical axis ranges from 0 to 160 and is labeled: Sales. The points are plotted from left to right at regular increments of 1 month starting at month 1. The points appear to vary randomly between 60 and 125 on the vertical axis. The maximum number of sales is reached at the point (6, 125).

What type of pattern exists in the data?

The data appear to follow a cyclical pattern.

The data appear to follow a seasonal pattern.

The data appear to follow a horizontal pattern.

The data appear to follow a trend pattern.

(b)Use=0.1to compute the exponential smoothing forecasts for the time series. (Round your answers to two decimal places.)

Montht	Time Series Value Yt	Forecast Ft
1	105
2	135
3	120
4	105
5	90
6	120
7	145
8	140
9	100
10	80
11	100
12	110

(c)Use a smoothing constant of=0.3to compute the exponential smoothing forecasts. (Round your answers to two decimal places.)

Montht	Time Series Value Yt	Forecast Ft
1	105
2	135
3	120
4	105
5	90
6	120
7	145
8	140
9	100
10	80
11	100
12	110

Does a smoothing constant of0.1or0.3appear to provide more accurate forecasts based on MSE?

A smoothing constant of 0.3 is better than a smoothing constant of 0.1 since the MSE is greater for 0.3 than for 0.1.

A smoothing constant of 0.1 is better than a smoothing constant of 0.3 since the MSE is greater for 0.1 than for 0.3.

A smoothing constant of 0.3 is better than a smoothing constant of 0.1 since the MSE is less for 0.3 than for 0.1.

A smoothing constant of 0.1 is better than a smoothing constant of 0.3 since the MSE is less for 0.1 than for 0.3.