Part 1: To determine if there are outliers in a least squares regression models

Question

Part 1:
To determine if there are outliers in a least squares regression models data set, we could construct a box plot of the
A. Residuals B. lurking variables C. predictor variables D. response variables
Part 2:
What effect will an influential observation have upon the graph of the least squares regression line?
A. it will have no effect B. it will push the graph away from the observation C. It will lower the value of the correlation coefficient to make further analysis meaningless D. It will pull the graph toward the observation

Part 1:
To determine if there are outliers in a least squares regression models data set, we could construct a box plot of the
A. Residuals B. lurking variables C. predictor variables D. response variables
Part 2:
What effect will an influential observation have upon the graph of the least squares regression line?
A. it will have no effect B. it will push the graph away from the observation C. It will lower the value of the correlation coefficient to make further analysis meaningless D. It will pull the graph toward the observation


To determine if there are outliers in a least squares regression models data set, we could construct a box plot of the
A. Residuals B. lurking variables C. predictor variables D. response variables
Part 2:
What effect will an influential observation have upon the graph of the least squares regression line?
A. it will have no effect B. it will push the graph away from the observation C. It will lower the value of the correlation coefficient to make further analysis meaningless D. It will pull the graph toward the observation

Explanation / Answer

A. We need to contruct box plot of residuals in this case so correct is A

B answer is D. An influential observation pulls the graph towards itself in a regression line

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