Question

You want to know how the weather affects ticket sales at a ski resort. This Excel® file contains data on tickets sold and total snowfall and average temperature over Christmas week for 20 consecutive years. Run a multiple regression of ticket sales on snowfall and temperature (using Data Analysis > Regression, selecting the data in the Tickets column for the Y range, and selecting the data in both the Snowfall and Temperature columns as the X range; be sure to check the Residuals check box) and answer the following questions Plotting the residuals against time (using the Chart Wizard, selecting XY (Scatter), and, in Step 2, selecting the Data Range tab, selecting the residuals data and the Columns radio button) suggests that: O There may be positive autocorrelation because the residuals show persistently negative and then positive values O There may be negative autocorrelation because the residuals show persistently positive and then negative values O There may be negative autocorrelation because the residuals show a cyclical pattern. O There may be negative autocorrelation because the residuals show persistently negative and then positive values O There is no autocorrelation because the residuals show persistently positive and then negative values. The calculated value of the Durbin-Watson statistic is Given the lower critical value dL = 1.10 and upper critical value du = 1.54 for α = 0.05 in each tail, what do you conclude? O The test is inconclusive. O There is sufficient evidence to infer that these data show negative first-order autocorrelation. O There is sufficient evidence to infer that these data show positive first-order autocorrelation O There is no evidence that these data show first-order autocorrelation

Tickets	Snowfall	Temperature
8523	19	11
7721	15	-19
6173	7	36
7979	11	22
7639	19	14
7167	2	-20
8094	21	39
9903	19	27
9788	18	26
9557	20	16
9784	19	-1
12075	25	-9
9128	3	37
9047	17	-15
10631	0	22
12563	24	2
11012	22	32
10041	7	18
9929	21	32
11091	11	-15

Answer 1

3000 2000 1000 0 -1000 2000 3000 10 1520 25 30 X Variable 1

SUMMARY OUTPUT Regression Statistics Multiple R 0.38980863 R Square Adjusted R Square Standard Error Observations 0.151950768 0.05218027 1601.457893 20 ANOVA MS Significance F Regression Residual Total 2 | 7811992.245 | 3905996.12 | 1.523003 | 0.246365026 | 17 43599345.5 2564667.38 19 51411337.75 ーCoe cents- StandardError. ㅡㅡㅡtStat-4-Avalue Lower 95% Upper 95% lower 95.0% Intercept8289.140802 845.5354806 9,80342161 2.07E-08 6505.216873 10073.0647 6505.216873 XVariable 1 80.73698336 48.25331544 2.67317305 0 X Variable 28.466317834 13.43555305 0.4592336.651881 47.36193485 30.4292992 47.3619349 X Variable 2 0.112593 21.06966815 132.543635 21.0696682 8.466317834 18.43555305 0.4592336 0.651881 47.36193485 30.4292992 47.3619349 cs Scanned with CamScanner
durbin watson statistic is 0.40 which is positive autocorrelation.