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Listen The linregress() method in scipy module is used to fit a simple linear regression model using "Reaction" (reaction time) as the response variable and "Drinks" as predictor variable. The output is shown below. What is the correct regression equation based on this output? Is this model statistically significant at 5% leve significance (alpha = 0.05)? Select one. LinregressResult (slope-6.000000000000001, intercept-3.9999999999999964, 00 rvalue=0.9728166526882823, pvalue=0.0010983582017795293, stderr=0.714142842 Reaction = 6.0000 + 3.9999 Drinks, model is not statistically significant Reaction = 3.9999 +6.0000 Drinks, model is statistically significant Reaction = 6.0000+ 3.9999 Drinks, model is statistically significant Reaction = 3.9999 +6.0000 Drinks, model is not statistically significant

Listen The linregress() method in scipy module is used to fit a simple linear regression model using "Reaction" (reaction time) as the response variable and "Drinks" as predictor variable. The output is shown below. What is the correct regression equation based on this output? Is this model statistically significant at 5% leve significance (alpha = 0.05)? Select one. LinregressResult (slope-6.000000000000001, intercept-3.9999999999999964, 00 rvalue=0.9728166526882823, pvalue=0.0010983582017795293, stderr=0.714142842 Reaction = 6.0000 + 3.9999 Drinks, model is not statistically significant Reaction = 3.9999 +6.0000 Drinks, model is statistically significant Reaction = 6.0000+ 3.9999 Drinks, model is statistically significant Reaction = 3.9999 +6.0000 Drinks, model is not statistically significant

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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The `linregress()` method in the scipy module is used to fit a simple linear regression model using "Reaction" (reaction time) as the response variable and "Drinks" as the predictor variable. The output is shown below. What is the correct regression equation based on this output? Is this model statistically significant at a 5% level of significance (alpha = 0.05)? Select one. ``` LinregressResult(slope=6.000000000000001, intercept=3.999999999999964, rvalue=0.972816652688823, pvalue=0.001098352017795293, stderr=0.7141428428542851) ``` Options: - ⭕ Reaction = 6.0000 + 3.9999 Drinks, model is not statistically significant - ⭕ Reaction = 3.9999 + 6.0000 Drinks, model is statistically significant - ⭕ Reaction = 6.0000 + 3.9999 Drinks, model is statistically significant - ⭕ Reaction = 3.9999 + 6.0000 Drinks, model is not statistically significant The output indicates the model's slope and intercept values along with statistical metrics: - **Slope**: 6.0000 - **Intercept**: 3.9999 - **r-value**: 0.9728 (indicates a strong positive correlation) - **p-value**: 0.0011 (less than 0.05, suggesting statistical significance) - **Standard error**: 0.7141 The correct regression equation is: **Reaction = 3.9999 + 6.0000 Drinks** Since the p-value is less than 0.05, the model is statistically significant.
Transcribed Image Text:The `linregress()` method in the scipy module is used to fit a simple linear regression model using "Reaction" (reaction time) as the response variable and "Drinks" as the predictor variable. The output is shown below. What is the correct regression equation based on this output? Is this model statistically significant at a 5% level of significance (alpha = 0.05)? Select one. ``` LinregressResult(slope=6.000000000000001, intercept=3.999999999999964, rvalue=0.972816652688823, pvalue=0.001098352017795293, stderr=0.7141428428542851) ``` Options: - ⭕ Reaction = 6.0000 + 3.9999 Drinks, model is not statistically significant - ⭕ Reaction = 3.9999 + 6.0000 Drinks, model is statistically significant - ⭕ Reaction = 6.0000 + 3.9999 Drinks, model is statistically significant - ⭕ Reaction = 3.9999 + 6.0000 Drinks, model is not statistically significant The output indicates the model's slope and intercept values along with statistical metrics: - **Slope**: 6.0000 - **Intercept**: 3.9999 - **r-value**: 0.9728 (indicates a strong positive correlation) - **p-value**: 0.0011 (less than 0.05, suggesting statistical significance) - **Standard error**: 0.7141 The correct regression equation is: **Reaction = 3.9999 + 6.0000 Drinks** Since the p-value is less than 0.05, the model is statistically significant.
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