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Month Year Sales Jan 2014 1489 Feb 2014 1292 Mar 2014 1351 Apr 2014 1263 May 2014 1205 Jun 2014 1093 Jul 2014 1255 Aug

Month Year Sales
Jan 2014 1489
Feb 2014 1292
Mar 2014 1351
Apr 2014 1263
May 2014 1205
Jun 2014 1093
Jul 2014 1255
Aug 2014 1699
Sep 2014 1392
Oct 2014 1248
Nov 2014 1163
Dec 2014 1389
Jan 2015 1432
Feb 2015 1221
Mar 2015 1194
Apr 2015 1163
May 2015 1103
Jun 2015 1056
Jul 2015 1160
Aug 2015 1586
Sep 2015 1314
Oct 2015 1148
Nov 2015 1073
Dec 2015 1285
Jan 2016 1247
Feb 2016 1136
Mar 2016 1171
Apr 2016 1099
May 2016 1060
Jun 2016 982
Jul 2016 1026
Aug 2016 1544
Sep 2016 1251
Oct 2016 1032
Nov 2016 1007
Dec 2016 1169
Jan 2017 1228
Feb 2017 1004
Mar 2017 1096
Apr 2017 953
May 2017 996
Jun 2017 930
Jul 2017 962
Aug 2017 1446
Sep 2017 1123
Oct 2017 986
Nov 2017 964
Dec 2017 1069
Jan 2018 1125
Feb 2018 940
Mar 2018 1008
Apr 2018 916

Downlod the Excel data of Monthly Sales for Office Supply and Stationery Stores and follow the steps below for regression based-time series forecasting. To be able to receive full credit, you must show your computations in the space provided for each question.

The Census Bureau tracks a variety of retail and service sales using the Monthly Retail Trade Survey. Consider the monthly sales (in millions of dollars) from January 2014 through April 2018 for office supply and stationery stores.

1. Create indicator variables to represent each month as a category. Consider December data as a baseline model. How many indicator variables do we need to recode months as different categories?

(3p.)

2. Explain very briefly the reason why we need indicator variables, in other words why we need to recode each month as a different category and include in the regression analysis?

3. Regress the monthly sales to a time variable along with all indicator variables. The new model captures the linear trend along with the seasonal pattern in the time series. Show the output of the trend-and-season model and the multiple regression equation including all indicator variables in the space provided below. Interpret the results in terms of model fit which is reflected in explained variability and the significance of the trend-and-season model.

(6p.)

4. Compare and contrast the model fits of trend-only model and trend-and-season model in terms of explained variability and the significance of the model.

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