My professor has asked to clearly define and explain some terms. This is what I've got, but I have no idea if I'm on the right track or not--the book is not very helpful or clear. If I could ask someone to look at my answers that would be great.

Marginal distribution: Marginal distribution refers to the theory that if you have two variables, you can predict values of one variable, given values of another variable. So if you are predicting a persons height (inches) from his weight (pounds). You’ve got ten people that you know their height and weight. You plot the values on a graph, with weight on the x axis and height on the y axis. If there is a non-perfect linear relationship between height and weight then you would get a cluster of points on the graph which slopes upward. In other words, people who weigh a lot should be taller than those people who are of less weight.

Regression & the regression line: Regression is the study of the measure of the ‘best fit’ line for a set of data. The purpose of regression is to come up with a line that fits through that cluster of points with the least amount of deviations from the line. The regression line is the line that you end up with—the straight line that best represents the relationship between the two measures.

Partial correlation: Partial correlation is based upon observations of differences in scedasticity . We determine what the sub-correlations are and find the average of the sub-correlations. These average out to the original correlation. This is important when we don’t have a consistent correlation.

Multiple correlation: No idea I couldn't keep my eyes open at this point.

if anyone responds, thanks

margaret