The following table represents data for asthma-related visits. NOTE:. indicates missing value, do not include those quarters when you create the data in Excel.

Year	Q1	Q2	Q3	Q4
2014	.	.	1,513	1,060
2015	1,431	1,123	994	679
2016	1,485	886	1,256	975
2017	1,256	1,156	1,163	1,062
2018	1,200	1,072	1,563	531
2019	1,022	1,169	.	.

1. Enter the data in MS Excel, as demonstrated in the example video (data should be in columns, with a header row to clearly identify the data in each column).
2. Create dummy variables for Q1, Q2, and Q3 (you do not need a Q4 variable, as discussed and demonstrated in the example video).
3. Create the trend variable (a variable that increases by 1 for each quarter of time that passes, as demonstrated in the example video).
4. Use the regression tool in the Data Analysis Toolpak to run a regression. Remember, the "Y" variable (or dependent variable) is the one you are trying to predict. The independent variables are the variables you created representing the time measurements. Use the mouse to highlight all of the time variables for the seasonalized regression.
5. Read the regression results to create the equation you can use to forecast future quarters.
6. Use the equation to forecast the number of visits for the 3rd and 4th quarters of 2019.
7. Forecast the number of visits for the 3rd quarter of 2019 using a two-period moving average.
8. Forecast the number of visits for the 3rd quarter of 2019 using a four-period moving average.
9. If the third quarter value for 2019 is actually observed to be 1,198, which forecasting method (the two-period moving average, the four-period moving average, or the seasonalized regression analysis), resulted in a forecasted value closest to the actual observed value?

The forecasted value for the 3rd quarter of 2019 using a two-period moving average is

Select one:

a.

1169

b.

1022

c.

1095.5

d.

776.5

Question 9

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The forecasted value for the 3rd quarter of 2019 using a four-period moving average is

Select one:

a.

907.33

b.

1047

c.

1071.25

d.

1095.5

Question 10

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Question text

If the third quarter value for 2019 is actually observed to be 1,198, which forecasting method (the two-period moving average, the four-period moving average, or the seasonalized regression analysis), resulted in a forecasted value closest to the actual observed value?

a.

seasonalized regression

b.

two-period moving average

c.

four-period moving average

d.

All of the methods were equally close to the actual observed value for Q3 of 2019.