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generalized linear models

Count Models: Understanding the Log Link Function

by Jeff Meyer Leave a Comment

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

Member Training: Confusing Statistical Terms

by guest

Learning statistics is difficult enough; throw in some especially confusing terminology and it can feel impossible! There are many ways that statistical language can be confusing.

Some terms mean one thing in the English language, but have another (usually more specific) meaning in statistics.  [Read more…] about Member Training: Confusing Statistical Terms

Tagged With: ancova, association, confounding variable, confusing statistical terms, Correlation, Covariate, dependent variable, Error, factor, General Linear Model, generalized linear models, independent variable, learning statistics, levels, listwise deletion, multivariate, odds, pairwise deletion, random error, selection bias, significant

Related Posts

  • Series on Confusing Statistical Terms
  • Confusing Statistical Term #8: Odds
  • The Difference Between Association and Correlation
  • Member Training: Interpretation of Effect Size Statistics

The Difference Between Link Functions and Data Transformations

by Kim Love 1 Comment

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
  • Generalized Linear Models in R, Part 7: Checking for Overdispersion in Count Regression

Member Training: Generalized Linear Models

by guest 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: Types of Regression Models and When to Use Them
  • Count Models: Understanding the Log Link Function
  • Member Training: A Predictive Modeling Primer: Regression and Beyond
  • The Difference Between Link Functions and Data Transformations

Member Training: The Multi-Faceted World of Residuals

by Karen Grace-Martin 1 Comment

Most analysts’ primary focus is to check the distributional assumptions with regards to residuals. They must be independent and identically distributed (i.i.d.) with a mean of zero and constant variance.

Residuals can also give us insight into the quality of our models.

In this webinar, we’ll review and compare what residuals are in linear regression, ANOVA, and generalized linear models. Jeff will cover:

  • Which residuals — standardized, studentized, Pearson, deviance, etc. — we use and why
  • How to determine if distributional assumptions have been met
  • How to use graphs to discover issues like non-linearity, omitted variables, and heteroskedasticity

Knowing how to piece this information together will improve your statistical modeling skills.


Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

[Read more…] about Member Training: The Multi-Faceted World of Residuals

Tagged With: ANOVA, deviance, generalized linear models, linear regression, Pearson Correlation, residuals, standardized, studentized

Related Posts

  • Member Training: Using Excel to Graph Predicted Values from Regression Models
  • Checking Assumptions in ANOVA and Linear Regression Models: The Distribution of Dependent Variables
  • Same Statistical Models, Different (and Confusing) Output Terms
  • Member Training: The Anatomy of an ANOVA Table

The Difference Between Logistic and Probit Regression

by Karen Grace-Martin 12 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 3: Plotting Predicted Probabilities
  • Generalized Linear Models in R, Part 1: Calculating Predicted Probability in Binary Logistic Regression
  • How to Decide Between Multinomial and Ordinal Logistic Regression Models
  • The Difference Between Link Functions and Data Transformations

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