• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
The Analysis Factor

The Analysis Factor

Statistical Consulting, Resources, and Statistics Workshops for Researchers

  • Home
  • Our Programs
    • Membership
    • Online Workshops
    • Free Webinars
    • Consulting Services
  • About
    • Our Team
    • Our Core Values
    • Our Privacy Policy
    • Employment
    • Collaborate with Us
  • Statistical Resources
  • Contact
  • Blog
  • Login

OptinMon 05 - Probability, Odds and Odds Ratios in Logistic Regression

The Difference Between an Odds Ratio and a Predicted Odds

by Karen Grace-Martin Leave a Comment

When interpreting the results of a regression model, the first step is to look at the regression coefficients. Each term in the model has one. And each one describes the average difference in the value of Y for a one-unit difference in the value of the predictor variable, X, that makes up that term. It’s the effect size statistic for that term in the model. [Read more…] about The Difference Between an Odds Ratio and a Predicted Odds

Tagged With: marginal means, odds ratio, predicted odds, regression coefficients

Related Posts

  • Logistic Regression Analysis: Understanding Odds and Probability
  • Odds Ratio: Standardized or Unstandardized Effect Size?
  • The Difference Between Relative Risk and Odds Ratios
  • Effect Size Statistics in Logistic Regression

Guidelines for writing up three types of odds ratios

by Karen Grace-Martin Leave a Comment

Odds ratios have a unique part to play in describing the effects of logistic regression models. But that doesn’t mean they’re easy to communicate to an audience who is likely to misinterpret them. So writing up your odds ratios has to be done with care. [Read more…] about Guidelines for writing up three types of odds ratios

Tagged With: binary predictor, logistic regression, multicategory predictor, numerical predictor, odds ratios

Related Posts

  • Logistic Regression Analysis: Understanding Odds and Probability
  • Confusing Statistical Term #8: Odds
  • The Difference Between Logistic and Probit Regression
  • What Is an ROC Curve?

Logistic Regression Analysis: Understanding Odds and Probability

by Karen Grace-Martin 3 Comments

Updated 11/22/2021

Probability and odds measure the same thing: the likelihood or propensity or possibility of a specific outcome.

People use the terms odds and probability interchangeably in casual usage, but that is unfortunate. It just creates confusion because they are not equivalent.

How Odds and Probability Differ

They measure the same thing on different scales. Imagine how confusing it would be if people used degrees Celsius and degrees Fahrenheit interchangeably. “It’s going to be 35 degrees today” could really make you dress the wrong way.

In measuring the likelihood of any outcome, we need to know [Read more…] about Logistic Regression Analysis: Understanding Odds and Probability

Tagged With: logistic regression, odds, odds ratio, probability

Related Posts

  • Confusing Statistical Term #8: Odds
  • The Difference Between Relative Risk and Odds Ratios
  • Effect Size Statistics in Logistic Regression
  • How to Interpret Odd Ratios when a Categorical Predictor Variable has More than Two Levels

Odds Ratio: Standardized or Unstandardized Effect Size?

by Karen Grace-Martin Leave a Comment

Effect size statistics are extremely important for interpreting statistical results. The emphasis on reporting them has been a great development over the past decade. [Read more…] about Odds Ratio: Standardized or Unstandardized Effect Size?

Tagged With: effect size statistics, odds ratio, standardized effect size, unstandardized

Related Posts

  • The Difference Between an Odds Ratio and a Predicted Odds
  • Logistic Regression Analysis: Understanding Odds and Probability
  • Effect Size Statistics in Logistic Regression
  • Effect Size Statistics: How to Calculate the Odds Ratio from a Chi-Square Cross-tabulation Table

Confusing Statistical Term #8: Odds

by Karen Grace-Martin Leave a Comment

Odds is confusing in a different way than some of the other terms in this series.

First, it’s a bit of an abstract concept, which I’ll explain below.

But beyond that, it’s confusing because it is used in everyday English as a synonym for probability, but it’s actually a distinct technical term.

I found this incorrect definition recently in a (non-statistics) book: [Read more…] about Confusing Statistical Term #8: Odds

Tagged With: confusing statistical terms, odds, probability, statistical terminology

Related Posts

  • Logistic Regression Analysis: Understanding Odds and Probability
  • Guidelines for writing up three types of odds ratios
  • Six terms that mean something different statistically and colloquially
  • Member Training: Confusing Statistical Terms

The Difference Between Logistic and Probit Regression

by Karen Grace-Martin 16 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
  • Guidelines for writing up three types of odds ratios
  • Logistic Regression Analysis: Understanding Odds and Probability

  • Go to page 1
  • Go to page 2
  • Go to page 3
  • Go to Next Page »

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Assumptions of Linear Models

Upcoming Free Webinars

The Pathway: Steps for Staying Out of the Weeds in any Data Analysis

Upcoming Workshops

  • Analyzing Count Data: Poisson, Negative Binomial, and Other Essential Models (Jul 2022)
  • Introduction to Generalized Linear Mixed Models (Jul 2022)

Copyright © 2008–2022 The Analysis Factor, LLC. All rights reserved.
877-272-8096   Contact Us

The Analysis Factor uses cookies to ensure that we give you the best experience of our website. If you continue we assume that you consent to receive cookies on all websites from The Analysis Factor.
Continue Privacy Policy
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Non-necessary
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
SAVE & ACCEPT