Answer each of the following True-False questions. Assume that all assumptions f

Question

Answer each of the following True-False questions. Assume that all assumptions for correlation and linear regression have been met (including the assumption that the X and Y variables are each normally distributed). In your write-up, just list the sub-question letter (A-J) and whether the statement is True or False - no need to restate the question or to justify your answer. Correlation always implies causation. If a correlation is negative, then as it becomes even more negative, r^2 increases. If the units used to measure the X variable change (like from inches to centimeters), but the same data are analyzed, then the value of r will not change. As |r| increases, the average deviation of data from the predicted value (according to the best-fit regression line) increases. The best-fit regression line to predict Y when you know X will always go through the point Z_x = 0 and Z_Y = 0. If a positive correlation exists between X and Y, and the range of X is then greatly restricted, |r| must increase. If a positive correlation exists between X and Y, and a new data point is added whose Z_X = 3 and Z_Y = 0, the correlation will decrease. If no correlation exists between X and Y, and a new data point is added whose Z_X = 2.5 and Z_Y = 2.5, r will increase. If a negative but imperfect correlation exists between X and Y, and a new data point is added whose Z_X = 2.5 and Z_Y = 2.5, |r| will decrease. In correlation, reverse-scoring the X variable by multiplying each z score by -1 (so that originally high scores on X are now low, and originally low scores on X are now high) will not change the value of r^2.

Explanation / Answer

A) False

B)True

C) True

D)False

E)True

F) False

G) True

H) True

I) True

J)True

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