Regression is a procedure for predicting the value of one metric (continuous) variable based on the value of another.
A regression equation involves a predictor variable (indepdent variable) and a criterion variable (dependent variable).
A regression line can be drawn which minimizes deviation (this is the least squares criterion).
This line has a slope ("b") and a point where it cross the y axis (the y intercept "a").
Regression equation: Y = bX + a (+ error)
Y = predicted score on criterion variable
X = score on predictor variable
Multiple regression uses multiple independent (predictor) variables to predict one dependent (criterion) variable
It yields a multiple correlation coefficient (multiple R)
R-square = coefficient of determination
An F value can be calculated:
F = SS regression (df)/SS average (df)