During its manufacture, a product is subjected to four different tests in sequential order. An efficiency expert claims that the fourth (and last) test is unnecessary since its results can be predicted based on the first three tests. To test this claim, multiple regression will be used to model Test4 score (y), as a function of Test1 score (x1), Test 2 score (x2), and Test3 score (x3). [Note: All test scores range from 200 to 800, with higher scores indicative of a higher quality product.] Consider the model: Ely) = B1 + B1x1 + B2x2 + B3x3 The first-order model was fit to the data for each of 12 units sampled from the production line. The results are summarized in the printout. SOURCE F VALUE DF MS PROB>F MODEL ERROR 3 151417 50472 18.16 .0075 22231 2779 TOTAL 12 173648 ROOT MSE 52.72 R-SQUARE 0.872 DEP MEAN 645.8 ADJ R-SQ 0.824 T FOR 0: ERROR PARAMETER = 0 PROB> ITI PARAMETER STANDARD VARIABLE ESTIMATE INTERCEPT 11.98 80.50 0.1111 0.0986 0.080S Compute a 95% confidence interval for B3 0.15 0.s85 0.2745 0.3762 0.3265 X1(TESTI) 2.47 0.039 X2(TEST2) 3.82 0.005 X3(TEST3) 404 0.004 O 33 + 105 O 33 t.19 33 + 4.04

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During its manufacture, a product is subjected to four different tests in sequential order. An efficiency expert claims that the fourth (and last) test is unnecessary since its results can be predicted based on the first
three tests. To test this claim, multiple regression will be used to model Test4 score (y), as a function of Test1 score (x), Test 2 score (x), and Test3 score (x3). [Note: All test scores range from 200 to 800, with
higher scores indicative of a higher quality product.] Consider the model:
Ely) = B1 + B1x1 + B2x2 + B3x3
The first-order model was fit to the data for each of 12 units sampled from the production line. The results are summarized in the printout.
SOURCE
DF
Ss
MS
F VALUE
PROB>F
MODEL
151417
50472
18.16
.0075
ERROR
8
22231
2779
TOTAL
12
173648
ROOT MSE 52.72
R-SQUARE
0.872
DEP MEAN
645.8
ADJ R-SO
0.824
PARAMETER STANDARD
I FOR 0:
VARIABLE
ESTIMATE
ERROR PARAMETER = 0 PROB> ITI
INTERCEPT
11.98
80.50
0.15
0.885
0.2745
0.1111
2.47
X1(TESTI)
X2(TEST2)
0.039
0.3762
0.0986
3.82
0.005
X3(TEST3)
0.3265
0.080s
4.04
0.004
Compute a 95% confidence interval for B 3
O 33 + 105
O 33 +.19
O 33 + 4.04
Transcribed Image Text:During its manufacture, a product is subjected to four different tests in sequential order. An efficiency expert claims that the fourth (and last) test is unnecessary since its results can be predicted based on the first three tests. To test this claim, multiple regression will be used to model Test4 score (y), as a function of Test1 score (x), Test 2 score (x), and Test3 score (x3). [Note: All test scores range from 200 to 800, with higher scores indicative of a higher quality product.] Consider the model: Ely) = B1 + B1x1 + B2x2 + B3x3 The first-order model was fit to the data for each of 12 units sampled from the production line. The results are summarized in the printout. SOURCE DF Ss MS F VALUE PROB>F MODEL 151417 50472 18.16 .0075 ERROR 8 22231 2779 TOTAL 12 173648 ROOT MSE 52.72 R-SQUARE 0.872 DEP MEAN 645.8 ADJ R-SO 0.824 PARAMETER STANDARD I FOR 0: VARIABLE ESTIMATE ERROR PARAMETER = 0 PROB> ITI INTERCEPT 11.98 80.50 0.15 0.885 0.2745 0.1111 2.47 X1(TESTI) X2(TEST2) 0.039 0.3762 0.0986 3.82 0.005 X3(TEST3) 0.3265 0.080s 4.04 0.004 Compute a 95% confidence interval for B 3 O 33 + 105 O 33 +.19 O 33 + 4.04
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