For the linear regression model Y = bo + b1(X): %3D The p-value for the intercept is large: about 0.98 The p-value for the slope is very small: less than 2 times 10^(-16) What can we conclude?

College Algebra
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ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:James Stewart, Lothar Redlin, Saleem Watson
Chapter1: Equations And Graphs
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For the linear regression model Y = bo + b1(X):
The p-value for the intercept is large: about 0.98
The p-value for the slope is very small: less than 2 times 10^(-16)
What can we conclude?
Since the p-value for the intercept is large, we can conclude that there is not a
strong correlation between X and Y.
Since the p-value for the intercept is large, we can conclude that there is a very
strong correlation between X and Y.
Since the p-value for the slope is very small, we can conclude that there is a very
weak correlation between X and Y.
Since the p-value for the slope is very small, we can conclude that there is a very
strong correlation between X and Y.
We are not able to assess the strength of the correlation between X and Y with
the output provided.
Transcribed Image Text:For the linear regression model Y = bo + b1(X): The p-value for the intercept is large: about 0.98 The p-value for the slope is very small: less than 2 times 10^(-16) What can we conclude? Since the p-value for the intercept is large, we can conclude that there is not a strong correlation between X and Y. Since the p-value for the intercept is large, we can conclude that there is a very strong correlation between X and Y. Since the p-value for the slope is very small, we can conclude that there is a very weak correlation between X and Y. Since the p-value for the slope is very small, we can conclude that there is a very strong correlation between X and Y. We are not able to assess the strength of the correlation between X and Y with the output provided.
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