Excel Assignment Instructions Read these instructions before accessing the Excel

Excel Assignment Instructions Read these instructions before accessing the Excel Assignment graded assignment link. The Chapter 9 Forecasting Excel Example video shows step-by-step how to complete the seasonalized regression in Excel. Watch the video, then read the Excel assignment instructions and complete the regression before accessing the link to the graded activity. 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. Visits 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?