This post covers Poisson regression models using the HINTS 6 dataset. Our dependent variable (DV) is drinkdaysperweek,...
Data Science
Regression Coefficients, Odds Ratios, and Marginal Effects Feature Regression Coefficients Odds Ratios Marginal Effects Definition Represents the...
Ordinal logistic regression (ordered logistic regression) is a type of regression analysis that is used when the...
Multinomial logistic regression is a method for modeling categorical outcomes with more than two levels. It allows...
In statistical analysis, particularly in regression models, identifying outliers and high-leverage observations matters for the accuracy and...
In this blog post, I will show you how to use listcoef command in stata for logistic...
Logistic regression is a statistical method for modeling binary outcomes, such as yes/no, success/failure, or alive/dead. It...
Ordinary Least Squares (OLS) and Maximum Likelihood Estimation (MLE) are two ways to estimate relationships between variables...
Regression diagnostics are what validate the assumptions underlying linear regression models. One of the fundamental assumptions is...
When reporting the results of a regression analysis, it is important to be careful about the language...
Differences from Cronbach’s alpha 📒 Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to...
Before we can trust the results of a regression model, we need to make sure that some...
In this blog post, I will show you how to compare the fit of different regression models...
Beta coefficients and unstandardized coefficients are two different ways of presenting the results of regression analyses. Reasons...
This post shows how to run regressions with interaction effects using Stata, and how to plot the...
