Data on alcohol content and wine quality was collected from variants of a particular wine. From a sample of 30 wines, a model wa alcohol to predict wine quality. The data are modeled by Y = -0.9473 +0.6506X₁, where X; is the alcohol content and Ŷ₁ is the wi analysis for these data. Evaluate whether the assumptions of regression have been seriously violated. Click the icon to view the data table. Which of the assumptions of regression, if any, have been seriously violated? Select all that apply. A. The assumption of normality has been violated because the normal probability plot does not appear to be a straight line. B. The assumption of independence of errors has been violated because the errors are not independent of one another.
Data on alcohol content and wine quality was collected from variants of a particular wine. From a sample of 30 wines, a model wa alcohol to predict wine quality. The data are modeled by Y = -0.9473 +0.6506X₁, where X; is the alcohol content and Ŷ₁ is the wi analysis for these data. Evaluate whether the assumptions of regression have been seriously violated. Click the icon to view the data table. Which of the assumptions of regression, if any, have been seriously violated? Select all that apply. A. The assumption of normality has been violated because the normal probability plot does not appear to be a straight line. B. The assumption of independence of errors has been violated because the errors are not independent of one another.
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![Data on alcohol content and wine quality was collected from variants of a particular wine. From a sample of 30 wines, a model was created using the percentages of
alcohol to predict wine quality. The data are modeled by Y₁ = -0.9473 +0.6506X;, where X; is the alcohol content and Ỹ; is the wine quality. Perform a residual
analysis for these data. Evaluate whether the assumptions of regression have been seriously violated.
Click the icon to view the data table.
Which
of the assumptions of regression, if any, have been seriously violated? Select all that apply.
A. The assumption of normality has been violated because the normal probability plot does not appear to be a straight line.
B. The assumption of independence of errors has been violated because the errors are not independent of one another.
C. The assumption of linearity has been violated because the data are clearly curvilinear.
D.
The assumption of equal variance has been violated because the variability of the residuals is not constant for all values of X.
E. The assumptions of linearity, independence, normality, and equal variance do not appear to have been seriously violated.
... 0](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F710e59c6-4738-4995-a68d-52cf2fbdc504%2F1c685976-42eb-4375-b043-1c59551cd7ce%2Fy3rstvr_processed.png&w=3840&q=75)
Transcribed Image Text:Data on alcohol content and wine quality was collected from variants of a particular wine. From a sample of 30 wines, a model was created using the percentages of
alcohol to predict wine quality. The data are modeled by Y₁ = -0.9473 +0.6506X;, where X; is the alcohol content and Ỹ; is the wine quality. Perform a residual
analysis for these data. Evaluate whether the assumptions of regression have been seriously violated.
Click the icon to view the data table.
Which
of the assumptions of regression, if any, have been seriously violated? Select all that apply.
A. The assumption of normality has been violated because the normal probability plot does not appear to be a straight line.
B. The assumption of independence of errors has been violated because the errors are not independent of one another.
C. The assumption of linearity has been violated because the data are clearly curvilinear.
D.
The assumption of equal variance has been violated because the variability of the residuals is not constant for all values of X.
E. The assumptions of linearity, independence, normality, and equal variance do not appear to have been seriously violated.
... 0
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Alcohol content (X)
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Transcribed Image Text:1 Wines
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CEN3 456^∞ 99 57%98
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Wine quality (Y)
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