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pairwise

Simplifying a Categorical Predictor in Regression Models

by Jeff Meyer  Leave a Comment

One of the many decisions you have to make when model building is which form each predictor variable should take. One specific version of thisStage 2 decision is whether to combine categories of a categorical predictor.

The greater the number of parameter estimates in a model the greater the number of observations that are needed to keep power constant. The parameter estimates in a linear [Read more…] about Simplifying a Categorical Predictor in Regression Models

Tagged With: categorical predictor, interpreting regression coefficients, Model Building, pairwise, R-squared

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A Strategy for Converting a Continuous to a Categorical Predictor

by Jeff Meyer  Leave a Comment

At times it is necessary to convert a continuous predictor into a categorical predictor.  For example, income per household is shown below.Stage 2

This data is censored, all family income above $155,000 is stated as $155,000. A further explanation about censored and truncated data can be found here. It would be incorrect to use this variable as a continuous predictor due to its censoring.

[Read more…] about A Strategy for Converting a Continuous to a Categorical Predictor

Tagged With: Censored, continuous predictor, continuous variable, LOWESS, pairwise, polynomial regression, predictor variable, smoothing

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Using Pairwise Comparisons to Help you Interpret Interactions in Linear Regression

by Jeff Meyer  8 Comments

In a previous post we discussed using marginal means to explain an interaction to a non-statistical audience. The output from a linear regression model can be a bit confusing. This is the model that was shown.

In this model, BMI is the outcome variable and there are three predictors:

[Read more…] about Using Pairwise Comparisons to Help you Interpret Interactions in Linear Regression

Tagged With: interaction, linear model, pairwise, Stata

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