You may need to use the appropriate technology to answer this question. 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 6,350 C 14.9 8,370 D 15.9 6,200 E 17.2 4,000 F 13.1 8,700 G 16.2 6,000 H 17.1 2,580 I 17.6 3,400 J 14.1 8,000 These data provided the estimated regression equation ŷ = 28,800 − 1,452x. For these data, SSE = 7,059,472.68 and SST = 52,886,800. 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. H0: β0 = 0Ha: β0 ≠ 0H0: β0 ≠ 0Ha: β0 = 0 H0: β1 ≠ 0Ha: β1 = 0H0: β1 = 0Ha: β1 ≠ 0H0: β1 ≥ 0Ha: β1 < 0 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. Do not reject H0. We conclude that the relationship between weight (pounds) and price ($) is significant.Reject H0. We conclude that the relationship between weight (pounds) and price ($) is significant. Do not reject H0. We cannot conclude that the relationship between weight (pounds) and price ($) is significant.Reject H0. We cannot conclude that the relationship between weight (pounds) and price ($) is significant.
You may need to use the appropriate technology to answer this question. 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 6,350 C 14.9 8,370 D 15.9 6,200 E 17.2 4,000 F 13.1 8,700 G 16.2 6,000 H 17.1 2,580 I 17.6 3,400 J 14.1 8,000 These data provided the estimated regression equation ŷ = 28,800 − 1,452x. For these data, SSE = 7,059,472.68 and SST = 52,886,800. 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. H0: β0 = 0Ha: β0 ≠ 0H0: β0 ≠ 0Ha: β0 = 0 H0: β1 ≠ 0Ha: β1 = 0H0: β1 = 0Ha: β1 ≠ 0H0: β1 ≥ 0Ha: β1 < 0 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. Do not reject H0. We conclude that the relationship between weight (pounds) and price ($) is significant.Reject H0. We conclude that the relationship between weight (pounds) and price ($) is significant. Do not reject H0. We cannot conclude that the relationship between weight (pounds) and price ($) is significant.Reject H0. We cannot conclude that the relationship between weight (pounds) and price ($) is significant.
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 2AGP
Related questions
Concept explainers
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Question
You may need to use the appropriate technology to answer this question.
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 | 6,350 |
| C | 14.9 | 8,370 |
| D | 15.9 | 6,200 |
| E | 17.2 | 4,000 |
| F | 13.1 | 8,700 |
| G | 16.2 | 6,000 |
| H | 17.1 | 2,580 |
| I | 17.6 | 3,400 |
| J | 14.1 | 8,000 |
These data provided the estimated regression equation
ŷ = 28,800 − 1,452x.
For these data, SSE = 7,059,472.68 and SST = 52,886,800. 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.
H0: β0 = 0
Ha: β0 ≠ 0H0: β0 ≠ 0
Ha: β0 = 0 H0: β1 ≠ 0
Ha: β1 = 0H0: β1 = 0
Ha: β1 ≠ 0H0: β1 ≥ 0
Ha: β1 < 0
Ha: β0 ≠ 0H0: β0 ≠ 0
Ha: β0 = 0 H0: β1 ≠ 0
Ha: β1 = 0H0: β1 = 0
Ha: β1 ≠ 0H0: β1 ≥ 0
Ha: β1 < 0
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.
Do not reject H0. We conclude that the relationship between weight (pounds) and price ($) is significant.Reject H0. We conclude that the relationship between weight (pounds) and price ($) is significant. Do not reject H0. We cannot conclude that the relationship between weight (pounds) and price ($) is significant.Reject H0. We cannot conclude that the relationship between weight (pounds) and price ($) is significant.
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