7.

1. Construct a time series plot. Comment on what type of patternexists in the data.
2. Use a regression model with dummy variables as follow toexplain sales: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 ifquarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. Writeout the model that you’ve estimated in equation form using thevalues of your estimates. Explain, in a sentence or two, what thismodel tells us.
3. Use a new, simple linear regression model with “t” as the onlypredictor where t = 1 for the 1^st quarter of year 1, t =2 for the 2^nd quarter of year 1,…, t = 12 for the4^th quarter of year 3. Write out the model that you’veestimated in equation form using your values of the estimates.Explain, in a sentence or two, what this model tells us.
4. Use a new regression model with BOTH the dummy variableapproach you created in part “b” and the time predictor, “t” thatyou created in part “c.” Write out the model that you’ve estimatedin equation form. Explain, in a sentence or two, what this modeltells us.
5. Compute (or take from Excel) the mean squared error (MSE) fromeach of your models from “b”, “c”, and “d.” (Hint: MSE is the sameas MS Residual on the Excel output). Comment, in a sentence or two,on which of the models appears the best and why. Does this match upwith the adjusted R^2 values of the models?
6. Using the model that you identified as the “best” in “e”,compute the quarterly forecasts for the year AFTER next (skip year4, and tell me the forecasts for Q1, Q2, Q3, and Q4 of year 5). Ifyou were going to tell the textbook store manager your forecasts,would you tell him/her exactly these numbers? Might there be areason to explain your numbers slightly? What would you tell themanager?

Year	Quarter	Sales
1	1	2690
1	2	1940
1	3	3625
1	4	3500
2	1	1800
2	2	900
2	3	2900
2	4	2360
3	1	1550
3	2	800
3	3	2630
3	4	2315