Regression Analysis > Hausman Test You may want to read this article first: What is an endogenous variable? What is the Hausman Test? The Hausman Test (also called the Hausman specification test) detects endogenous regressors (predictor variables) in a regression…
Simultaneous Equations Model (SEM): Simple Definition
Regression Analysis > Simultaneous Equations Model (SEM) You may want to read this other article first: What is Sumultaneity? What is a Simultaneous Equations Model (SEM)? A Simultaneous Equation Model (SEM) is a model in the form of a set…
Reverse Causality: Definition, Examples
Regression Analysis > Reverse Causality What is Reverse Causality? Reverse causality means that X and Y are associated, but not in the way you would expect. Instead of X causing a change in Y, it is really the other way…
Instrumental Variable: Definition & Overview
Regression Analysis > Instrumental Variable What is an Instrumental Variable? An instrumental variable (sometimes called an “instrument” variable) is a third variable, Z, used in regression analysis when you have endogenous variables — variables that are influenced by other variables…
General Linear Model (GLM): Definition / Overview
Probability and Statistics > Regression analysis > General Linear Model (GLM) What is a General Linear Model? The General Linear Model (GLM) is a useful framework for comparing how several variables affect different continuous variables. It is the foundation for…
Beta Weight: Definition, Uses
Regression Analysis > Beta Weight What is a Beta Weight? A beta weight is a standardized regression coefficient (the slope of a line in a regression equation). They are used when both the criterion and predictor variables are standardized (i.e.…
Logistic Regression (Logit Model): a Brief Overview
Probability and Statistics > Regression Analysis > Logistic Regression / Logit Model In order to understand logistic regression (also called the logit model), you may find it helpful to review these topics: The Nominal Scale. What is Linear Regression? Simple…
Assumptions and Conditions for Regression
Probability and Statistics > Regression Analysis > Assumptions and Conditions for Regression Assumptions and Conditions for Regression. Regression can be a very useful tool for finding patterns in data sets. However, your data can’t always be fit to a regression…
Multiple Regression Analysis
Probability and Statistics > Regression Analysis > Multiple Regression Analysis What is Multiple Regression analysis? Multiple regression analysis is used to see if there is a statistically significant relationship between sets of variables. It’s used to find trends in those…
Standard Error of Regression Slope
Probability and Statistics > Regression Analysis > Standard Error of Regression Slope Standard Error of Regression Slope: Overview Standard errors for regression are measures of how spread out your y variables are around the mean, μ.The standard error of the…