Applied Regression and Analysis of
Variance
Lecture 5
Regression with K Predictors
Comparing Regression Models
Step 1: Multiple Regression Model with K IV
A. The Population Model
Y=0+ 1X1+2X2+ kXk +e
Regression Function: Y= 0+ 1X1+2X2+ kXk where
e : Error
Applied Regression and ANOVA
Lecture 4
Multiple Regression with Two Predictors
1
Why Multiple Regression?
1.
Better prediction/explanation by adding another variables. In
essence we will reduce error and better explain Y.
2.
Statistical control for nuisan
Applied Regression and Analysis of
Variance
Lecture 3
Partial and Semi-Partial Correlations
Semi-Partial and Partial Correlation
The semi-partial correlation, rY(X1.X2) , is the correlation between Y
and a part of X1 that does not include the overlapped
Applied Regression & ANOVA
Lecture 2
Simple Regression
Best Fitting Line
Best fit line is chosen such
that the sum of the squared
(why squared?) distances of
the points (Yis) from the line is
minimized:
Y
The equation is calculated
mathematically (remembe
EDEP 604 (PSY 612) Applied Regression and Analysis of Variance
Assignment 1 (15 points)
Name:
Show all of your calculations and work.
1. Answer the following questions using the data below. Y is the dependent variable and X is the independent variable.
ID