EPsy 8264
📝 Path Models
A brief introduction to computing the coefficients and effects from a path model.
🐶 Assignment 08
This goal of this assignment is to give you experience using regression to fit a path model.
📝 Regression via Summary Measures
A brief introduction to computing coefficient-level and model-level regression summaries from descriptive statistics.
📖 Regression from Summary Measures
Reading
📝 Cross-Validation
A brief introduction to cross-validation for model selection.
📖 Cross-Validation
Reading
📖 Model Selection
Reading
💪 Model Selection (In-Class Activity)
An activity in model selection
🐐 Assignment 06
This goal of this assignment is to give you experience using ridge regression for alleviating interpretability problems that arise because of collinearity.
🐝 Assignment 07
This goal of this assignment is to give you experience using cross-validation methods in regression analyses.
📖 Biased Estimation: Ridge Regression
Reading
📝 Biased Estimation: Ridge Regression
A brief introduction to ridge regression for dealing with collinearity. Data from @Chatterjee:2012.
📝 Principal Components Analysis via Singular Value Decomposition
A brief introduction to principal components analysis via eigendecomposition. Example taken from @Chatterjee:2012.
📖 Diagnosing Collinearity
Reading
📖 Principal Components Analysis via Spectral Decomposition
Reading
📖 Principal Components Analysis via Singular Value Decomposition
Reading
📝 Diagnosing Collinearity
A brief introduction to empirical diagnostics to detect collinearity. Example taken from @Chatterjee:2012.
📝 Principal Components Analysis via Spectral Decomposition
A brief introduction to principal components analysis via spectral decomposition. Example taken from @Chatterjee:2012.
🐉 Assignment 04
Using WLS to Model Data with Outliers
📖 Variance Stabilizing Transformations
Reading
📖 Weighted Least Squares (WLS) and Sandwich Estimation
Reading
🧛 Assignment 05
This goal of this assignment is to give you experience diagnosing collinearity and using principal components analysis to create orthogonal composites that can be used in a regression model.
📝 Tools for Dealing with Heteroskedasticity
Notes
📝 Regression Diagnostics
Notes
🦄 Assignment 03
The goal of this assignment is to give you experience using regression diagnostics for detecting problematic observations.
📖 Regression Diagnostics
Reading
🦒 Assignment 02
Simulating from the Regression Model
📝 Simulating from the Regression Model
Notes
📖 Simulating from the Regression Model
Reading
📝 OLS Regression Using Matrices and Its Properties
Notes
📖 OLS Regression using Matrices and its Properties
Reading
📝 Introduction to Matrix Algebra
In-class worksheet
📖 Introduction to Matrix Algebra
Review some common mathematical ideas of matrix algebra.
🐧 Assignment 01
Matrix Algebra for Linear Regression
A Message to Students
A message to students.
Prerequisites and Resources
Prerequisite knowledge and resources for brushing up on that knowledge.