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link function

Count Models: Understanding the Log Link Function

by Jeff Meyer  2 Comments

When we run a statistical model, we are in a sense creating a mathematical equation. The simplest regression model looks like this:

Yi = β0 + β1X+ εi

The left side of the equation is the sum of two parts on the right: the fixed component, β0 + β1X, and the random component, εi.

You’ll also sometimes see the equation written [Read more…] about Count Models: Understanding the Log Link Function

Tagged With: count model, generalized linear models, linear regression, link function, log link, log transformation, Negative Binomial Regression, Poisson Regression

Related Posts

  • The Importance of Including an Exposure Variable in Count Models
  • The Difference Between Link Functions and Data Transformations
  • Getting Accurate Predicted Counts When There Are No Zeros in the Data
  • The Problem with Linear Regression for Count Data

How to Decide Between Multinomial and Ordinal Logistic Regression Models

by Karen Grace-Martin  21 Comments

A great tool to have in your statistical tool belt is logistic regression.

It comes in many varieties and many of us are familiar with the variety for binary outcomes.

But multinomial and ordinal varieties of logistic regression are also incredibly useful and worth knowing.

They can be tricky to decide between in practice, however.  In some — but not all — situations you [Read more…] about How to Decide Between Multinomial and Ordinal Logistic Regression Models

Tagged With: link function, logistic regression, logit, Multinomial Logistic Regression, Ordinal Logistic Regression

Related Posts

  • Logistic Regression Models for Multinomial and Ordinal Variables
  • Member Training: Multinomial Logistic Regression
  • Link Functions and Errors in Logistic Regression
  • Proportions as Dependent Variable in Regression–Which Type of Model?

The Difference Between Link Functions and Data Transformations

by Kim Love  2 Comments

Generalized linear models—and generalized linear mixed models—are called generalized linear because they connect a model’s outcome to its predictors in a linear way. The function used to make this connection is called a link function. Link functions sounds like an exotic term, but they’re actually much simpler than they sound.

For example, Poisson regression (commonly used for outcomes that are counts) makes use of a natural log link function as follows:

[Read more…] about The Difference Between Link Functions and Data Transformations

Tagged With: generalized linear models, linear model, link function, log link, log transformation, Poisson Regression

Related Posts

  • Count Models: Understanding the Log Link Function
  • Member Training: Generalized Linear Models
  • The Difference Between Logistic and Probit Regression
  • Why Generalized Linear Models Have No Error Term

Member Training: Generalized Linear Models

by guest contributer  Leave a Comment

In this webinar, we will provide an overview of generalized linear models. You may already be using them (perhaps without knowing it!).
For example, logistic regression is a type of generalized linear model that many people are already familiar with. Alternatively, maybe you’re not using them yet and you are just beginning to understand when they might be useful to you.
[Read more…] about Member Training: Generalized Linear Models

Tagged With: bayesian, distribution, error distribution, generalized linear models, GLM, linear model, linear regression, link function, logistic regression, maximum likelihood, mixed model, Poisson Regression

Related Posts

  • Member Training: Goodness of Fit Statistics
  • Member Training: Types of Regression Models and When to Use Them
  • Member Training: Matrix Algebra for Data Analysts: A Primer
  • Why Generalized Linear Models Have No Error Term

Link Functions and Errors in Logistic Regression

by Karen Grace-Martin  5 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

Related Posts

  • What is a Logit Function and Why Use Logistic Regression?
  • How to Decide Between Multinomial and Ordinal Logistic Regression Models
  • The Difference Between Logistic and Probit Regression
  • Logistic Regression Models for Multinomial and Ordinal Variables

The Difference Between Logistic and Probit Regression

by Karen Grace-Martin  17 Comments

One question that seems to come up pretty often is:

What is the difference between logistic and probit regression?

 

Well, let’s start with how they’re the same:

Both are types of generalized linear models. This means they have this form:

glm
[Read more…] about The Difference Between Logistic and Probit Regression

Tagged With: categorical outcome, generalized linear models, inverse normal link, link function, logistic regression, logit link, probit regression

Related Posts

  • Generalized Linear Models in R, Part 1: Calculating Predicted Probability in Binary Logistic Regression
  • Guidelines for writing up three types of odds ratios
  • Logistic Regression Analysis: Understanding Odds and Probability
  • How to Decide Between Multinomial and Ordinal Logistic Regression Models

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