Question
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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