a. Construct a time series plot. Comment on what type of pattern exists in the data.

b. Use a regression model with quarterly dummy variables to explain sales: Qtr1 = 1 if quarter 1, 0 otherwise, etc. etc. (HINT: If there are 4 quarters in the year...how many dummy variables do you need in order to distinguish amongst the quarterly types of observations?). Write out the model that you've estimated in equation form using the values of your estimates. Explain, in a sentence or two, what this model tells us.

c. Use a new, simple linear regression model with "t" as the only predictor where t = 1 for the 1^st quarter of year 1, t = 2 for the 2^nd quarter of year 1,..., t = 20 for the 4^th quarter of year 5. Write out the model that you've estimated in equation form using your values of the estimates. Explain, in a sentence or two, what this model tells us.

d. Use a new regression model with BOTH the dummy variable approach you created in part "b" and the time predictor, "t" that you created in part "c." Write out the model that you've estimated in equation form. Explain, in a sentence or two, what this model tells us.

e. Compute (or take from Excel) the mean squared error (MSE) from each of your models from "b", "c", and "d." (Hint: MSE is the same as 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 up with the adjusted R^2 values of the models? Why or why not?

f. Using the model from part "b", above, compute the quarterly forecasts for next year (i.e., I should see your estimates for quarters 21-24, which would be 'year 6').

Year	Quarter	Sales
1	1	178
1	2	206
1	3	161
1	4	141
2	1	171
2	2	214
2	3	186
2	4	155
3	1	191
3	2	197
3	3	166
3	4	133
4	1	162
4	2	190
4	3	168
4	4	162
5	1	183
5	2	218
5	3	181
5	4	139