PSYC 218 Chapter 7: Linear Regression
Document Summary
Regression and correlation are closely related they both involve the relationship between two variables and use the same set of basic data. Regression focuses on prediction which is quite easy when the relationship is perfect. Regression is a topic that considers using the relationship between two or more variables for prediction. Regression line is a best fitting line used for prediction. The least-squares regression line is the prediction line that minimizes the total error of prediction, according to the least-squares criterion of (y y")2: the vertical distance represents the errors in prediction. Constructing the least-squares regression line: regression of y on x: The equation that we use for the regression line is: (cid:1851) = (cid:1854)(cid:1850)+(cid:1853). Where y" = predi(cid:272)ted/esti(cid:373)ated (cid:448)alue of y. By = slope of the line for minimizing errors in predicting y: considered a regression constant. Ay = y axis intercepts for minimizing errors in predicting y: considered a regression constant.
