Consider the following data on x = weight (pounds) and y = price ($) for 10 road-racing bikes. Brand weight Price ($) A 17.8 2,100 B 16.1 6,150 14.9 8,370 D 15.9 6,200 17.2 4,000 F 13.1 8,600 16.2 6,000 17.1 2,680 17.6 3,300 14.1 8,000 These data provided the estimated regression equation ý = 28,608 - 1,442x. For these data, SSE = 6,655,076.17 and SST = 51,845,800. Use the F test to determine whether the weight for a bike and the price are related at the 0.05 level of significance. State the null and alternative hypotheses. O Hại B, 20 O Hoi Bq " 0 H: Bq = 0 O Hoi Bq = 0 O Moi Bo =0

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Chapter4: Linear Functions
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Find the value of the test statistic. (Round your answer to two decimal places.)
Find the p-value. (Round your answer to three decimal places.)
p-value =
State your conclusion.
O Do not reject Ho. We cannot conclude that the relationship between weight (pounds) and price ($) is significant.
O Reject Ho. We conclude that the relationship between weight (pounds) and price ($) is significant.
O Do not reject Ho: We conclude that the relationship between weight (pounds) and price ($) is significant.
O Reject Ho. We cannot conclude that the relationship between weight (pounds) and price ($) is significant.
Transcribed Image Text:Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value = State your conclusion. O Do not reject Ho. We cannot conclude that the relationship between weight (pounds) and price ($) is significant. O Reject Ho. We conclude that the relationship between weight (pounds) and price ($) is significant. O Do not reject Ho: We conclude that the relationship between weight (pounds) and price ($) is significant. O Reject Ho. We cannot conclude that the relationship between weight (pounds) and price ($) is significant.
Consider the following data on x = weight (pounds) and y = price ($) for 10 road-racing bikes.
Brand
Weight
Price ($)
A
17.8
2,100
16.1
6,150
14.9
8,370
15.9
6,200
17.2
4,000
13.1
8,600
G
16.2
6,000
H
17.1
2,680
I
17.6
3,300
14.1
8,000
These data provided the estimated regression equation ŷ = 28,608 – 1,442x. For these data, SSE = 6,655,076.17 and SST = 51,845,800. Use the F test to determine whether the weight for a bike and the price are related at the 0.05 level of significance.
State the null and alternative hypotheses.
O Ho: B, 20
H: Bq < 0
O Ho: B1# 0
H: B, = 0
O Ho: B1 = 0
Hi B1 * 0
O Ho: Bo = 0
H3: Bo # 0
O Ho: Bo * 0
Hg: Bo = 0
Transcribed Image Text:Consider the following data on x = weight (pounds) and y = price ($) for 10 road-racing bikes. Brand Weight Price ($) A 17.8 2,100 16.1 6,150 14.9 8,370 15.9 6,200 17.2 4,000 13.1 8,600 G 16.2 6,000 H 17.1 2,680 I 17.6 3,300 14.1 8,000 These data provided the estimated regression equation ŷ = 28,608 – 1,442x. For these data, SSE = 6,655,076.17 and SST = 51,845,800. Use the F test to determine whether the weight for a bike and the price are related at the 0.05 level of significance. State the null and alternative hypotheses. O Ho: B, 20 H: Bq < 0 O Ho: B1# 0 H: B, = 0 O Ho: B1 = 0 Hi B1 * 0 O Ho: Bo = 0 H3: Bo # 0 O Ho: Bo * 0 Hg: Bo = 0
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