1. An article in the Journal of the American Statistical Association ["Markov Chain Monte Carlo Methods for Computing Bayes Factors: A Comparative Review" (2001, Vol. 96, pp. 1122-1132)] analyzed the tabulated data on compressive strength parallel to the grain versus resin-adjusted density for specimens of radiata pine. The data are shown below. Compressive Strength Density Compressive Strength Density 3040 29.2 3840 30.7 2470 24.7 3800 32.7 3610 32.3 4600 32.6 3480 31.3 1900 22.1 3810 31.5 2530 25.3 2330 24.5 2920 30.8 1800 19.9 4990 38.9
1. An article in the Journal of the American Statistical Association ["Markov Chain Monte Carlo Methods for Computing Bayes Factors: A Comparative Review" (2001, Vol. 96, pp. 1122-1132)] analyzed the tabulated data on compressive strength parallel to the grain versus resin-adjusted density for specimens of radiata pine. The data are shown below. Compressive Strength Density Compressive Strength Density 3040 29.2 3840 30.7 2470 24.7 3800 32.7 3610 32.3 4600 32.6 3480 31.3 1900 22.1 3810 31.5 2530 25.3 2330 24.5 2920 30.8 1800 19.9 4990 38.9
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![1. An article in the Journal of the American Statistical Association ["Markov Chain
Monte Carlo Methods for Computing Bayes Factors: A Comparative Review"
(2001, Vol. 96, pp. 1122-1132)] analyzed the tabulated data on compressive
strength parallel to the grain versus resin-adjusted density for specimens of radiata
pine. The data are shown below.
Compressive
Strength
Density
Compressive
Strength
Density
3040
29.2
3840
30.7
2470
24.7
3800
32.7
3610
32.3
4600
32.6
3480
31.3
1900
22.1
3810
31.5
2530
25.3
2330
24.5
2920
30.8
1800
19.9
4990
38.9
3110
27.3
1670
22.1
3160
27.1
3310
29.2
2310
24
3450
30.1
4360
33.8
3600
31.4
1880
21.5
2850
26.7
3670
32.2
1590
22.1
1740
22.5
3770
30.3
2250
27.5
3850
32
2650
25.6
2480
23.2
4970
34.5
3570
30.3
2620
26.2
2620
29.9
2900
26.7
1890
20.8
1670
21.1
3030
33.2
2540
24.1
3030
28.2
a. Fit a regression model relating compressive strength to density.
b. Test for significance of regression with a = 0.05.
c. Estimate o² for this model.
d. Calculate R² for this model. Provide an interpretation of this quantity.
e. Prepare a normal probability plot of the residuals and interpret this display.
f. Plot the residuals versus y and versus x. Does the assumption of constant
variance seem to be satisfied?](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F10f8a75b-dc28-4f3a-9856-7a4384d2c1e0%2F9d06c40e-7d34-4855-abf4-c052fe79bda0%2Fuvj3h6_processed.jpeg&w=3840&q=75)
Transcribed Image Text:1. An article in the Journal of the American Statistical Association ["Markov Chain
Monte Carlo Methods for Computing Bayes Factors: A Comparative Review"
(2001, Vol. 96, pp. 1122-1132)] analyzed the tabulated data on compressive
strength parallel to the grain versus resin-adjusted density for specimens of radiata
pine. The data are shown below.
Compressive
Strength
Density
Compressive
Strength
Density
3040
29.2
3840
30.7
2470
24.7
3800
32.7
3610
32.3
4600
32.6
3480
31.3
1900
22.1
3810
31.5
2530
25.3
2330
24.5
2920
30.8
1800
19.9
4990
38.9
3110
27.3
1670
22.1
3160
27.1
3310
29.2
2310
24
3450
30.1
4360
33.8
3600
31.4
1880
21.5
2850
26.7
3670
32.2
1590
22.1
1740
22.5
3770
30.3
2250
27.5
3850
32
2650
25.6
2480
23.2
4970
34.5
3570
30.3
2620
26.2
2620
29.9
2900
26.7
1890
20.8
1670
21.1
3030
33.2
2540
24.1
3030
28.2
a. Fit a regression model relating compressive strength to density.
b. Test for significance of regression with a = 0.05.
c. Estimate o² for this model.
d. Calculate R² for this model. Provide an interpretation of this quantity.
e. Prepare a normal probability plot of the residuals and interpret this display.
f. Plot the residuals versus y and versus x. Does the assumption of constant
variance seem to be satisfied?
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