PO218 Lecture Notes - Lecture 20: Partial Correlation, Multiple Correlation, Dependent And Independent Variables
Document Summary
Section 5: one similar to section 3 and another similar to 4. Multiple regression: assessing the effects of the independent variables. Multiple regression and correlation assess the effects, separately and in combination of two or more independent variables on the dependent variable. Multiple regression and correlation allow us to: disentangle and examine the separate effects of the independent variable, use all of the independent variable to predict y, assess the combined effects of the independent dent variable. Multiple regression and correlation: least-square multiple regression equation: Where: a=the y intercept b1= the partial slope of the first independent variable on y b2=the partial slope of the second independent variable. Multiple regression and correlation: the multiple correlation coefficient and the coefficient determines (r2) Must have high quality data at the i/r level. Assume each iv has a linear relationship with dv. Assumes that the effects of each iv and additive. Assumes that the iv"s are uncorrelated with each other.
