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1 vote
Which of the following is NOT a factor that can adversely influence the accuracy of a correlation analysis?

The width of the set of points is wider at one end than the other.
There are outliers.
There is a curved relationship between the X and Y values.
There are more points toward the center of the axes than at the ends.
2. The relationship between number of beers consumed (x) and blood alcohol content (y) was studied in 16 male college students by using least squares regression. The following regression equation was obtained from this study:y=-0.0127+0.0180x. Suppose that the legal limit to drive is a blood alcohol content of 0.08. If Ricky consumed 5 beers, the model would predict that he would be
0.09 above the legal limit.
0.0027 below the legal limit.
0.0027 above the legal limit.
0.0733 above the legal limit.
3.In a linear regression analysis, larger values of r2 imply that the observations are more closely grouped about the
least squares line.
mean of the independent variables.
mean of the dependent variable.
origin.

1 Answer

1 vote

1. Looking at the factors that can adversely influence the accuracy of a correlation analysis, the correct answer is "There are more points toward the center of the axes than at the ends." The distribution of points on the axes does not necessarily adversely affect the accuracy of the analysis. It's accounts for normal distribution in some cases.

2. The number of beers consumed by Ricky was 5. Using the equation from the study, the blood alcohol content (y) can be calculated. This is done by substituting the value of 5 beers (x) into the equation, giving us y = -0.0127 + 0.0180 * 5. This gives us a blood alcohol content of 0.0773. When comparing this level to the legal limit of 0.08, we find that Ricky's blood alcohol content is 0.0027 below the legal limit. Therefore, Ricky is below the legal limit.

3. In a linear regression analysis, the r-squared value is essentially a measure of how well the model fits the data. A larger r-squared value implies that the observations, or data points, are more closely grouped about the least squares line. Therefore, larger values of r2 imply that the observations are more closely grouped about the least squares line.

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User Autoplectic
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