Question

Wins Data B C A Three-Point Field Goal % Field Goal % 1 Wins 38.9 56 45.3 2 43.2 33.8 3 43 32.4 49 45.5 4 30.6 42.5 34 47 36.5 44.7 47 36.1 44.8 8 70 47.9 39,7 42 42.8 33.5 10 51 44.5 35.6 11 65 47.2 38.3 12 43.6 34.6 44 13 41 42.7 34,9 14 60 46.3 36.5 15 39 43.2 34.7 16 54 45.1 33.5 17 63 46.9 34.1 18 51 45.2 35.2 19 56 44.1 32.5 20 70 46.9 37.6 43.2 34.7 Print Done 34 21

Answer 1

d.

Y -226.39 6.07.X10.15X2 b. The value of b the slope for Xi, indicates that, for a given three point field goal percentage, for each 1-unit increase in field goal percentage, the predicted number of wins is estimated to 6.07 The value of b2, the slope for X2i, indicates that, for a given field goal percentage, for each 1- unit increase in three - point field goal percentage, the predicted number of wins is estimated to 0.15 c. Y 226.39 6.07 450.15 35 52.01 52 a.

Residual Plots for Y Normal Probability Plot Versus Fits 99 10 90 5 50 10 5 1 40 -10 -5 10 50 60 70 Residual Fitted Value Versus Order Histogram 10 4.8 5 3.6 2.4 1.2 5 0.0 -8 -4 C 4 2 4 6 8 10 12 14 16 18 20 Residual Observation Order Aouanbai uaad Residual Residual LO

From normal probability plot it is observed that residuals are normally distributed. From Fitted vs Residual plot we observed that linearity assumption and equal variance assumptions are hold.

e. Ho 1 B2=0 vs. H1 at least one 's 0 where B regression coefficient of Xi of true regression equation, i 1,2 FST AT 57.06 p- value Reject Ho. There is sufficient evidence that relationship between number of wins and two independent variables is significant. The value of r, 0.870, represents the proportion of variation in the sample of no. of wins that is explained by this regression equation. h. adi 0.000 = 0.855 i. Но : В1 H1 10 tSTAT 7.94 р— value Reject Ho. There is sufficient evidence that B10. Field goal percentage is significant 0 0.000 to make a contribution to the regression model. Ho 62 0 H1 20 tSTAT 0.28 p- value Fail to reject Ho. There is insufficient evidence that B10.Three point field goal percentage is insignificant 0.782 contribution to the regression model to make j. Option D

Y	X1	X2		SUMMARY OUTPUT
56	45.3	38.9
43	43.2	33.8		Regression Statistics
49	45.5	32.4		Multiple R	0.932923622
34	42.5	30.6		R Square	0.870346484
47	44.7	36.5		Adjusted R Square	0.855093129
47	44.8	36.1		Standard Error	4.145410663
70	47.9	39.7		Observations	20
42	42.8	33.5
51	44.5	35.6		ANOVA
65	47.2	38.3			df	SS	MS	F	Significance F
44	43.6	34.6		Regression	2	1961.064697	980.5323	57.05935	2.87519E-08
41	42.7	34.9		Residual	17	292.1353026	17.18443
60	46.3	36.5		Total	19	2253.2
39	43.2	34.7
54	45.1	33.5			Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
63	46.9	34.1		Intercept	-226.3908473	26.22907596	-8.63129	1.28E-07	-281.7293603	-171.052334
51	45.2	35.2		X Variable 1	6.069729193	0.764469473	7.939793	4.04E-07	4.45683959	7.682618796
56	44.1	32.5		X Variable 2	0.153144067	0.545461997	0.28076	0.782283	-0.997680152	1.303968286
70	46.9	37.6
34	43.2	34.7