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

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 a time plot for monthly sales for office supply and stationary stores with the months labeled 1 for January, 2 for February, and so on. Show your plot in the space provided below:
2. Interpret the characteristics of monthly sales for this sector in terms of time series component, such as trend, seasonality, cyclical pattern etc. over the time. Do you observe any pattern from the data that repeats itself at a regular interval?
3. In which month do you observe a dramatic increase or decrease for the sales? Explain the reason very briefly.
4. Regress the monthly sales to a time variable and show the regression output in the space provided below. Interpret the results in terms of explained variability and the significance of the model.
5. 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.
6. 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?
7. 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
8. 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.
9. Test the significance of each indicator variable and report if there is any insignificant variable exists in the trend-and-season model.
10. Explain very briefly the reason why some coefficients of the indicator variables are negative from trend-and-season model.
11. Report the indicator variable that shows the highest coefficient in the trend-and-season model and explain very briefly why one of the coefficients is quite high relative to other coefficients of indicator variables.
12. What are the predicted monthly sales for the month of August using the trend-and-season model? Explain the results very briefly.