EPsy 8264

📝 Path Models

Notes

A brief introduction to computing the coefficients and effects from a path model.

🐶 Assignment 08

Assignments

This goal of this assignment is to give you experience using regression to fit a path model.

📝 Regression via Summary Measures

Notes

A brief introduction to computing coefficient-level and model-level regression summaries from descriptive statistics.

📖 Regression from Summary Measures

Reading

Reading

📝 Cross-Validation

Notes

A brief introduction to cross-validation for model selection.

📖 Cross-Validation

Reading

Reading

📖 Model Selection

Reading

Reading

💪 Model Selection (In-Class Activity)

Notes

An activity in model selection

🐐 Assignment 06

Assignments

This goal of this assignment is to give you experience using ridge regression for alleviating interpretability problems that arise because of collinearity.

🐝 Assignment 07

Assignments

This goal of this assignment is to give you experience using cross-validation methods in regression analyses.

📖 Biased Estimation: Ridge Regression

Reading

Reading

📝 Biased Estimation: Ridge Regression

Notes

A brief introduction to ridge regression for dealing with collinearity. Data from @Chatterjee:2012.

📝 Principal Components Analysis via Singular Value Decomposition

Notes

A brief introduction to principal components analysis via eigendecomposition. Example taken from @Chatterjee:2012.

📖 Diagnosing Collinearity

Reading

Reading

📖 Principal Components Analysis via Spectral Decomposition

Reading

Reading

📖 Principal Components Analysis via Singular Value Decomposition

Reading

Reading

📝 Diagnosing Collinearity

Notes

A brief introduction to empirical diagnostics to detect collinearity. Example taken from @Chatterjee:2012.

📝 Principal Components Analysis via Spectral Decomposition

Notes

A brief introduction to principal components analysis via spectral decomposition. Example taken from @Chatterjee:2012.

🐉 Assignment 04

Assignments

Using WLS to Model Data with Outliers

📖 Variance Stabilizing Transformations

Reading

Reading

📖 Weighted Least Squares (WLS) and Sandwich Estimation

Reading

Reading

🧛 Assignment 05

Assignments

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

Notes

📝 Regression Diagnostics

Notes

Notes

🦄 Assignment 03

Assignments

The goal of this assignment is to give you experience using regression diagnostics for detecting problematic observations.

📖 Regression Diagnostics

Reading

Reading

🦒 Assignment 02

Assignments

Simulating from the Regression Model

📝 Simulating from the Regression Model

Notes

Notes

📖 Simulating from the Regression Model

Reading

Reading

📝 OLS Regression Using Matrices and Its Properties

Notes

Notes

📖 OLS Regression using Matrices and its Properties

Reading

Reading

📝 Introduction to Matrix Algebra

Worksheet

In-class worksheet

📖 Introduction to Matrix Algebra

Reading

Review some common mathematical ideas of matrix algebra.

🐧 Assignment 01

Assignments

Matrix Algebra for Linear Regression

A Message to Students

A message to students.

Prerequisites and Resources

Resources

Prerequisite knowledge and resources for brushing up on that knowledge.

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EPsy 8264

Welcome

Welcome to EPsy 8264: Advanced Multiple Regression Analysis. This is an advanced seminar for doctoral students in education covering a diverse set of regression methodologies. We will begin with a brief review of the General Linear Model and establishment of a mathematical foundation for the estimation of regression coefficients and standard errors in these models through the use of matrix algebra. The course will also cover more advanced modeling techniques, such as regression diagnostics, WLS and sandwich estimation, PCA, shrinkage methods, model selection and local models.


Classroom


Syllabus


Textbooks

The course textbook is available via the University of Minnesota library.