In a statistics course, a linear regression equation was computed to predict the final exam score from the score on the first test. The equation was ŷ = 10 + .9x where y is the final exam score and x is the score on the first test. Carla scored 95 on the first test. What is the predicted value of her score on the final exam?
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Question 2
2.
Refer to the previous problem. On the final exam Carla scored 98. What is the value of her residual?
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Question 3
3.
A study of the fuel economy for various automobiles plotted the fuel consumption (in liters of gasoline used per 100 kilometers traveled) vs. speed (in kilometers per hour). A least squares line was fit to the data. Here is the residual plot from this least squares fit. What does the pattern of the residuals tell you about the linear model?
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Question 4
4.
All but one of the following statements contains a blunder. Which statement is correct?
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Question 5
5.
In regression, the residuals are which of the following?
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Question 6
6.
What does the square of the correlation (r^2) measure?
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Question 7
7.
If removing an observation from a data set would have a marked change on the position of the LSRL fit to the data, what is the point called
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Question 8
8.
A researcher finds that the correlation between the personality traits “greed” and “superciliousness” is –.40. What percentage of the variation in greed can be explained by the relationship with superciliousness?
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Question 9
9.
The following are resistant to outliers:
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Question 10
10.
If dataset A of (x,y) data has correlation coefficient r = 0.65, and a second dataset B has correlation r = –0.65, then