SUMMARY OUTPUT Regression Statistics Multiple R 0.396235 R Square 0.157002 Adjusted R Square 0.156262 Standard Error 18.42647 Observations 1142 ANOVA df SS MS F Significance F Regression 1 72088.71 72088.71 212.3161 3.12E-44 Residual 1140 387069.6 339.5348 Total 1141 459158.4 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 26.35917 0.803163 32.8192 7.4E-167 24.78333 27.93501 24.78333 27.93501 X...

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Question

Statistics

SUMMARY OUTPUT
Regression Statistics
Multiple R
0.396235
R Square
0.157002
Adjusted R Square
0.156262
Standard Error
18.42647
Observations
1142
ANOVA
df
SS
MS
F
Significance F
Regression
1
72088.71
72088.71
212.3161
3.12E-44
Residual
1140
387069.6
339.5348
Total
1141
459158.4
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
26.35917
0.803163
32.8192
7.4E-167
24.78333
27.93501
24.78333
27.93501
X Variable 1
0.000213
1.46E-05
14.57107
3.12E-44
0.000184
0.000242
0.000184
0.000242
a. Write the reqression equation.
Discuss the statistical significance of the model using theappropriate regression statistic at a 95% level of confidence.
Discuss the statistical significance of the coefficient for theindependent variable using the appropriate regression statistic ata 95% level of confidence.
Interpret the coefficient for the independent variable.
What percentage of the observed variation in income isexplained by the model?
Predict the value of a person’s income who works 50 hours aweek, using this regression model.

Answer & Explanation Solved by verified expert

4.2 Ratings (713 Votes)

Solution Given We are given regression analysis output of variables Income and Number of work hours per week Part a Write the regression equation General regression equation is given by y b0 b1 x Here y dependent variable Income x independent variable Number of work hours per week b0 Intercept 2635917 b1 Slope 0000213 Thus regression equation is Income 2635917 0000213 Number of work hours per week Part b Discuss the statistical significance of the model using the appropriate regression statistic at a 95 level of confidence that See Answer

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SUMMARY OUTPUT

Regression Statistics
Multiple R	0.396235
R Square	0.157002
Adjusted R Square	0.156262
Standard Error	18.42647
Observations	1142

ANOVA
	df	SS	MS	F	Significance F
Regression	1	72088.71	72088.71	212.3161	3.12E-44
Residual	1140	387069.6	339.5348
Total	1141	459158.4

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	26.35917	0.803163	32.8192	7.4E-167	24.78333	27.93501	24.78333	27.93501
X Variable 1	0.000213	1.46E-05	14.57107	3.12E-44	0.000184	0.000242	0.000184	0.000242