egression Statistics Multiple R 0.8857 R Square 0.7845 Adjusted R Square 0.7801
ID: 3174277 • Letter: E
Question
egression Statistics
Multiple R
0.8857
R Square
0.7845
Adjusted R Square
0.7801
Standard Error
1.3704
Observations
51
ANOVA
df
SS
MS
F
Significance F
Regression
1
335.0472
335.0473
178.3859
Residual
1.8782
Total
50
427.0798
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-1.8940
0.4018
-4.7134
2.051E-05
-2.7015
-1.0865
Hours
0.9795
0.0733
13.3561
5.944E-18
0.8321
1.1269
4. f) At the 0.05 level of significance, is there evidence of a linear relationship between hours studied and salary?
Select one:
a. There is enough evidence of a linear relationship between hours studied and salary.
b. There is not enough evidence of a linear relationship between hours studied and salary.
4. g) Why did you make the determination in (4f) above?
In (4h) and (4i) what is the 95% confidence interval estimate of the population slope, 1?
4. h) Lower Bound =
4. i) Upper Bound =
egression Statistics
Multiple R
0.8857
R Square
0.7845
Adjusted R Square
0.7801
Standard Error
1.3704
Observations
51
ANOVA
df
SS
MS
F
Significance F
Regression
1
335.0472
335.0473
178.3859
Residual
1.8782
Total
50
427.0798
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-1.8940
0.4018
-4.7134
2.051E-05
-2.7015
-1.0865
Hours
0.9795
0.0733
13.3561
5.944E-18
0.8321
1.1269
Explanation / Answer
4. f)
F = 178.3859
critical vaue of F =4.038 at 5% los and (1,49) df
Here F value > F critical vlaue, we reject H0
Thus, we conclude that the regression line is best fit to the given data
Correct Answer: a. There is enough evidence of a linear relationship between hours studied and salary.
4. g) Determination = 0.7845
4 h)
Lower Bound =0.8321
Upper Bound = 1.1269
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