Consider the linear regression model: Yi = Bo + Bí®i tai, tin N(0, ²) and €; s independent. Test Ho B₁0 vs Ha: B₁0 at the a= 0.01 significance level. = Your P-value is: O P-value < 0.005 O 0.005 < P-value < 0.01 O 0.01 < P-value < 0.025 O 0.025 < P- value <0.05 0.05 < P-value < 0.10

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6th Edition
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Author:Amos Gilat
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Recently, research efforts have focused on the problem of predicting a manufacturer's market share using
information on the quality of its product. Suppose that the following data are available on market share, in
percentage (Y), and product quality, on a scale of 0 to 100, determined by an objective evaluation procedure
(X).
X
A & wa w N
27
39
73
66
33
43
47
55
60
68
70
75
82
Y
2
10
9
4
6
5
8
7
9
10
13
12
Transcribed Image Text:Recently, research efforts have focused on the problem of predicting a manufacturer's market share using information on the quality of its product. Suppose that the following data are available on market share, in percentage (Y), and product quality, on a scale of 0 to 100, determined by an objective evaluation procedure (X). X A & wa w N 27 39 73 66 33 43 47 55 60 68 70 75 82 Y 2 10 9 4 6 5 8 7 9 10 13 12
Consider the linear regression model:
Yi Bo + B₁ i + € i, € i ~
=
Test Ho: B1 = 0 vs H₂ ₁ 0 at the a = 0.01 significance level.
B1 +
Your P-value is:
O P-value < 0.005
:
0.005 < P-value < 0.01
0.01 < P-value < 0.025
N(0, ²) and e¡ s independent.
O 0.025 < P-value < 0.05
O 0.05 < P-value < 0.10
Transcribed Image Text:Consider the linear regression model: Yi Bo + B₁ i + € i, € i ~ = Test Ho: B1 = 0 vs H₂ ₁ 0 at the a = 0.01 significance level. B1 + Your P-value is: O P-value < 0.005 : 0.005 < P-value < 0.01 0.01 < P-value < 0.025 N(0, ²) and e¡ s independent. O 0.025 < P-value < 0.05 O 0.05 < P-value < 0.10
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