regression models

When Linear Models Don’t Fit Your Data, Now What?

When your dependent variable is not continuous, unbounded, and measured on an interval or ratio scale, linear models don’t fit. The data just will not meet the assumptions of linear models. But there’s good news, other models exist for many types of dependent variables.

Today I’m going to go into more detail about 6 common types of dependent variables that are either discrete, bounded, or measured on a nominal or ordinal scale and the tests that work for them instead. Some are all of these.

Measures of Model Fit for Linear Regression Models

by Karen Grace-Martin 38 Comments

A well-fitting regression model results in predicted values close to the observed data values.

The mean model, which uses the mean for every predicted value, generally would be used if there were no useful predictor variables. The fit of a proposed
regression model should therefore be better than the fit of the mean model. [Read more…] about Measures of Model Fit for Linear Regression Models

Member Training: Difference in Differences

by Jeff Meyer

The great majority of all regression modeling explores and tests the association between independent and dependent variables. We are not able to claim the independent variable(s) has a causal relationship with the dependent variable. There are five specific model types that allow us to test for causality. Difference in differences models are one of the five.

Eight Ways to Detect Multicollinearity

by Karen Grace-Martin 9 Comments

Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable.

When the model tries to estimate their unique effects, it goes wonky (yes, that’s a technical term).

So for example, you may be interested in understanding the separate effects of altitude and temperature on the growth of a certain species of mountain tree.

Parametric or Semi-Parametric Models in Survival Analysis?

by guest contributer Leave a Comment

It was Casey Stengel who offered the sage advice, “If you come to a fork in the road, take it.”

When you need to fit a regression model to survival data, you have to take a fork in the road. One road asks you to make a distributional assumption about your data and the other does not. [Read more…] about Parametric or Semi-Parametric Models in Survival Analysis?

Why ANOVA is Really a Linear Regression, Despite the Difference in Notation

by Karen Grace-Martin 3 Comments

When I was in graduate school, stat professors would say “ANOVA is just a special case of linear regression.” But they never explained why.

And I couldn’t figure it out.

The model notation is different.

The output looks different.

The vocabulary is different.

The focus of what we’re testing is completely different. How can they be the same model?