X: 4.1 5.2 6.4 8.7 7.6 3.0 4.4 5.7 6.8 3.4 2.3 7.1 8.0 5.8 4.8 Y: 10 12 17 16 14 9 11 13 13 8 6 15 17 11 9 Coefficients: Estimate (Intercept) 3.1889 1.5986 Std. Error t value Pr (> [t]) 1.1921 2.675 0.0191 0.2038 7.845 2.77e-06 X (d) Use the regression line to predict the value for Y when X = 20 and explain whether or not the data suggests the appropriateness of this prediction. (e) Predict whether the corresponding F-statistic for the ANOVA of this regression would yield a large or small p value and explain. (Hint: we use the F-statistic to test for 'goodness of fit'. Is there any evidence present of adequate fit?) f) What are some of the things we check for in a residual analysis? How do we check these?
X: 4.1 5.2 6.4 8.7 7.6 3.0 4.4 5.7 6.8 3.4 2.3 7.1 8.0 5.8 4.8 Y: 10 12 17 16 14 9 11 13 13 8 6 15 17 11 9 Coefficients: Estimate (Intercept) 3.1889 1.5986 Std. Error t value Pr (> [t]) 1.1921 2.675 0.0191 0.2038 7.845 2.77e-06 X (d) Use the regression line to predict the value for Y when X = 20 and explain whether or not the data suggests the appropriateness of this prediction. (e) Predict whether the corresponding F-statistic for the ANOVA of this regression would yield a large or small p value and explain. (Hint: we use the F-statistic to test for 'goodness of fit'. Is there any evidence present of adequate fit?) f) What are some of the things we check for in a residual analysis? How do we check these?
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
Problem 91E
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Transcribed Image Text:X: 4.1 5.2 6.4 8.7 7.6 3.0 4.4 5.7 6.8 3.4 2.3 7.1 8.0 5.8 4.8
Y: 10
12 17
16
14
11 13 13 8
6 15 17
11 9
Coefficients:
9
Estimate
(Intercept) 3.1889
1.5986
Std. Error t value Pr (>|t|)
1.1921
2.675
0.0191
0.2038
7.845 2.77e-06
X
(d) Use the regression line to predict the value for Y when X = 20 and explain whether or not
the data suggests the appropriateness of this prediction.
(e) Predict whether the corresponding F-statistic for the ANOVA of this regression would yield
a large or small p value and explain. (Hint: we use the F-statistic to test for 'goodness of fit'.
Is there any evidence present of adequate fit?)
(f) What are some of the things we check for in a residual analysis? How do we check these?
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