679	0.79
292	0.44
1012	0.56
493	0.79
582	2.7
1156	3.64
997	4.73
2189	9.5
1097	5.34
2078	6.85
1818	5.84
1700	5.21
747	3.25
2030	4.43
1643	3.16
414	0.5
354	0.17
1276	1.88
745	0.77
795	3.7
540	0.56
874	1.56
1543	5.28
1029	0.64
710	4
1434	0.31
837	4.2
1748	4.88
1381	3.48
1428	7.58
1255	2.63
1777	4.99
370	0.59
2316	8.19
1130	4.79
463	0.51
770	1.74
724	4.1
808	3.94
790	0.96
783	3.29
406	0.44
1242	3.24
658	2.14
1746	5.71
895	4.12
1114	1.9
413	0.51
1787	8.33
3560	14.94

a) Fit a simple linear regression and get the F value.What is your conclusion about the null hypothesis?

1. The ? value is 2.22, so reject the null hypothesis
2. The ? value is 122.03 and the model is significant
3. The p-value is above 0.05 so we fail to reject the null
4. The p-value is above 0.05 and the model is not significant

b) Plot the residuals vs. the fitted values for the data. What is the approximate shape and what does this mean?

1. The data on the plot are random and it shows adequacy
2. The plot appears to be U shaped and this indicates a consistency of variance
3. The plot appears to be funnel shaped and this indicates that the variance is not constant
4. The data are not random, they are skewed left and the distribution looks normal

c) Transform the data. Use the square root of y instead of just y as the response and plot the residuals. Does this stabilize the inequality of variance?

a. V = 0.967 + 0.00097x. This is the new equation and it does not stabilize the variance. b. V = 0.5967 + 0.00097x. This is the new equation and it does stabilize the variance. c. V = 0.5967 + 0.00097x. This is the new equation and it does not stabilize the variance. d. D = 0.5967 + 0.97x. This is the new equation and it does stabilize the variance