Multiple Linear Regression for Psychological Research
Master multiple linear regression for psychological research: interpret coefficients, check assumptions, handle multicollinearity, and analyze interactions.
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Review of Simple Linear Regression and Introduction to Multiple Linear Regression
Unit 1: Simple Linear Regression: A Quick Review
Regression Refresher
Least Squares Method
Interpreting Intercepts
Interpreting Slopes
R-Squared Explained
Unit 2: Stepping Up: Introduction to Multiple Linear Regression
Why Multiple Regression?
Multiple Predictors
Statistical Control
The Regression Equation
Assumptions Overview
Unit 3: Deeper Dive into Regression Assumptions
Assumption: Linearity
Assumption: Independence
Assumption: Homoscedasticity
Assumption: Normality
Why Assumptions Matter
Conducting and Interpreting Multiple Linear Regression
Unit 1: Running Multiple Regression
Multiple Regression Setup
Regression Output: R
Regression Output: SPSS
R-Squared Explained
F-Statistic Significance
Unit 2: Regression Coefficients
B Weights Unveiled
Standardized Coefficients
Standard vs. Unstandard
Effect Size
Confidence Intervals
Unit 3: Advanced Interpretation
Practical Significance
Confidence Interval Width
P-Values
Assumptions Revisited
Putting It All Together
Addressing Multicollinearity and Variable Selection
Unit 1: Understanding Multicollinearity
What is Multicollinearity?
Sources of Multicollinearity
Impact on Regression
VIF: A Key Indicator
Tolerance: Another Metric
Unit 2: Addressing Multicollinearity
High VIF? Now What?
Removing Redundant Predictors
Combining Predictors
Centering Predictor Variables
Unit 3: Variable Selection Methods
Why Variable Selection?
Hierarchical Regression
Stepwise Regression
Best Subset Regression
Comparing the Methods
Interaction Effects and Assumption Checking
Unit 1: Understanding Interaction Effects
What are Interactions?
Creating Interaction Terms
Interpreting Interactions
Simple Slope Analysis
Visualizing Interactions
Unit 2: Checking Regression Assumptions - Part 1
Linearity Assumption
Independence of Errors
Homoscedasticity
Normality of Residuals
Visual Inspection
Unit 3: Addressing Assumption Violations
Transformations: Non-Linearity
Transformations: Non-Normality
Robust Regression
Weighted Least Squares
When to Use What?