4. What prediction can you make based on the variables if their correlation, r = .50? People who exercise a lot will live longer. 5. What prediction can you make based on the variables if their correlation, r = - .50? People who exercise a lot will live shorter lives. 6. Describe the scatterplot of the correlation that you computed. Do the dots line up diagonally from left to right, or form a random pattern? In a scatterplot for r = .50, the points would form a line from the lower left to the upper right, but many points would be scattered around the line. If the correlation was low, r < .30, you would not be able to see a line in a scatterplot, only a random scatter of points. 7. Why can you not conclude that one variable causes the other based on r, even if r=1? Even if the correlation of lifespan and exercise was perfect, r=1, you cannot infer that exercise causes an increase in lifespan because a 'confounding' variable, like genetics, might cause people to both live longer and exercise more, creating the correlation of exercise and lifespan. 8. What confounding variable in the NCS data set might cause the correlation that you found? The age of people's parents, if available might be a good measure of a genetic predisposition to longevity