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Binary Logistic Regression

Link Functions and Errors in Logistic Regression

by Karen Grace-Martin  6 Comments

I recently held a free webinar in our The Craft of Statistical Analysis program about Binary, Ordinal, and Nominal Logistic Regression.

It was a record crowd and we didn’t get through everyone’s questions, so I’m answering some here on the site. They’re grouped by topic, and you will probably get more out of it if you watch the webinar recording. It’s free.

The following questions refer to this logistic regression model: [Read more…] about Link Functions and Errors in Logistic Regression

Tagged With: Binary Logistic Regression, error term, link function, logit, logit link

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  • How to Decide Between Multinomial and Ordinal Logistic Regression Models
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What is a Logit Function and Why Use Logistic Regression?

by Karen Grace-Martin  16 Comments

One of the big assumptions of linear models is that the residuals are normally distributed.  This doesn’t mean that Y, the response variable, has to also be normally distributed, but it does have to be continuous, unbounded and measured on an interval or ratio scale.

Unfortunately, categorical response variables are none of these. [Read more...] about What is a Logit Function and Why Use Logistic Regression?

Tagged With: Binary Logistic Regression, logit, logit function, logit link

Related Posts

  • Link Functions and Errors in Logistic Regression
  • How to Decide Between Multinomial and Ordinal Logistic Regression Models
  • Logistic Regression Models for Multinomial and Ordinal Variables
  • When Linear Models Don’t Fit Your Data, Now What?

Logistic Regression Models for Multinomial and Ordinal Variables

by Karen Grace-Martin  59 Comments

Multinomial Logistic Regression

The multinomial (a.k.a. polytomous) logistic regression model is a simple extension of the binomial logistic regression model.  They are used when the dependent variable has more than two nominal (unordered) categories.

Dummy coding of independent variables is quite common.  In multinomial logistic regression the dependent variable is dummy coded into multiple 1/0 variables.  There is a variable for all categories but one, so if there are M categories, there will be M-1 dummy variables.  All but one category has its own dummy variable.  Each category’s dummy variable has a value of 1 for its category and a 0 for all others.  One category, the reference category, doesn’t need its own dummy variable as it is uniquely identified by all the other variables being 0.

The multinomial logistic regression then estimates a separate binary logistic regression model for each of those dummy variables.  The result is [Read more…] about Logistic Regression Models for Multinomial and Ordinal Variables

Tagged With: Binary Logistic Regression, dummy variable, Multinomial Logistic Regression, Ordinal Logistic Regression, Proportional Odds Model

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