# regression

## Structural Equation Modeling in R Tutorial 4: Introduction to lavaan using path analysis

Table of Contents Data Input Introduction to Lavaan Inspecting matrices when things go wrong Modeling in Lavaan Using a Covariance Matrix Made for Jonathan Butner’s Structural Equation Modeling Class, Fall 2017, University of Utah. This handout will serve as an introduction to the lavaan package in R, which can be used for structural equation modeling. Mainly, we will focus on how path models can be conducted simply as a series of regressions in the R package lavaan, including estimation of indirect effects with bootstrapping.

## Structural Equation Modeling in R Tutorial 3: Path Analysis using R

Table of Contents Data Input Path Models Models Using Q and W Indices Made for Jonathan Butner’s Structural Equation Modeling Class, Fall 2017, University of Utah. This handout begins by showing how to import a matrix into R. Then, we will overview how path models can be conducted simply as a series of regressions. such as transposing and inversing matrices. This syntax imports the 4 variable dataset from datafile pathmodel example 3.

## Structural Equation Modeling in R Tutorial 2: Matrix algebra using R

Table of Contents Data Input Creating Matrices and Vectors Operations Using Matrices Made for Jonathan Butner’s Structural Equation Modeling Class, Fall 2017, University of Utah. This handout begins by showing how to import a matrix into R. Then, we go through how to create matrices and vectors in R as well as perform a few matrix algebra operations, such as transposing and inversing matrices.

## Structural Equation Modeling in R Tutorial 1: Two predictor regression using R

Table of Contents Data Input and Cleaning Create and Export a Correlation Matrix Multiple Regression Using Multiple Regression to show how coefficients are a function of residuals Made for Jonathan Butner’s Structural Equation Modeling (SEM) Class, Fall 2017, University of Utah. This handout begins by showing how to import data into R. Then, correlation matrices are generated, followed by a two predictor regression analysis.

## Checking the Assumptions of Linear Regression

Table of Contents Introduction Major assumptions of regression Checking the assumption of linearity Checking the assumption of constant variance of residuals (Homoscedasticity) Checking the assumption of normality of residuals Checking for multicollinearity Checking the data for outliers Quickly and effortlessly checking many assumptions at once References Introduction This tutorial will help you test major assumptions of linear regression using R. The tutorial assumes that you have some familiarity understanding and interpreting basic linear regression models already.