1. For the data below:

a. Compute the Pearson correlation.

b. Find the regression equation for predicting Y from X.

c. Calculate the predicted Y for each X value, find each residual (Y - ), square each

residual and add the squared values to obtain SSresidual.

X Y

5 10

3 6

6 7

4 3

2 4

2. Find the regression equation for the following set of data.

X Y

4 1

7 16

3 4

5 7

6 7

3. Suppose that a sample of n = 12 pairs of X and Y scores has SSY = 90 and a Pearson

correlation of r = +0.40.

a. What proportion of the variance for the Y scores is predicted by the regression

equation?

b. Does the regression equation predict a significant portion of the variance? Test

with = .05.

4. A sample of n = 15 pairs of X and Y scores produces a Pearson correlation of r = 0.45 with SSY = 90.

a. If the regression equation was found for these scores, how much of the Y variability

would be predicted by the regression equation (SSregression) and how much would not be

predicted (SSresidual)?

b. Does the regression equation predict a significant portion of the variability for the Y

scores? (Equivalently, is the Pearson correlation significant?)

5. A multiple regression equation has R2 = 0.25 and SSY = 90 for a sample of n = 30 people.

a. Test the significance of the regression equation using = .01.

b. Compute the standard error of estimate for the equation.