• 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

  • our programs
    • Membership
    • Online Workshops
    • Free Webinars
    • Consulting Services
  • statistical resources
  • blog
  • about
    • Our Team
    • Our Core Values
    • Our Privacy Policy
    • Employment
    • Collaborate with Us
  • contact
  • login

Regression through the origin

Removing the Intercept from a Regression Model When X Is Continuous

by Jeff Meyer  1 Comment

Stage 2In a recent article, we reviewed the impact of removing the intercept from a regression model when the predictor variable is categorical. This month we’re going to talk about removing the intercept when the predictor variable is continuous.

Spoiler alert: You should never remove the intercept when a predictor variable is continuous.

Here’s why. [Read more…] about Removing the Intercept from a Regression Model When X Is Continuous

Tagged With: Linear Regression Model, Regression through the origin, remove intercept

Related Posts

  • Overfitting in Regression Models
  • Confusing Statistical Term #9: Multiple Regression Model and Multivariate Regression Model
  • What is Multicollinearity? A Visual Description
  • A Strategy for Converting a Continuous to a Categorical Predictor

Regression Through the Origin

by Karen Grace-Martin  3 Comments

I just wanted to follow up on my last post about Regression without Intercepts.Stage 2

Regression through the Origin means that you purposely drop the intercept from the model.  When X=0, Y must = 0.

The thing to be careful about in choosing any regression model is that it fit the data well.  Pretty much the only time that a regression through the origin will fit better than a model with an intercept is if the point X=0, Y=0 is required by the data.

Yes, leaving out the intercept will increase your df by 1, since you’re not estimating one parameter.  But unless your sample size is really, really small, it won’t matter.  So it really has no advantages.

Tagged With: linear regression, Regression through the origin

Related Posts

  • Regression models without intercepts
  • The Difference Between R-squared and Adjusted R-squared
  • What is Multicollinearity? A Visual Description
  • Removing the Intercept from a Regression Model When X Is Continuous

Regression models without intercepts

by Karen Grace-Martin  8 Comments

Stage 2A recent question on the Talkstats forum asked about dropping the intercept in a linear regression model since it makes the predictor’s coefficient stronger and more significant.  Dropping the intercept in a regression model forces the regression line to go through the origin–the y intercept must be 0.

The problem with dropping the intercept is if the slope is steeper just because you’re forcing the line through the origin, not because it fits the data better.  If the intercept really should be something else, you’re creating that steepness artificially.  A more significant model isn’t better if it’s inaccurate.

Tagged With: linear regression, Regression through the origin

Related Posts

  • Regression Through the Origin
  • The Difference Between R-squared and Adjusted R-squared
  • What is Multicollinearity? A Visual Description
  • Removing the Intercept from a Regression Model When X Is Continuous

Primary Sidebar

This Month’s Statistically Speaking Live Training

  • Member Training: Multinomial Logistic Regression

Upcoming Workshops

    No Events

Upcoming Free Webinars

TBA

Quick links

Our Programs Statistical Resources Blog/News About Contact Log in

Contact

Upcoming

Free Webinars Membership Trainings Workshops

Privacy Policy

Search

Copyright © 2008–2023 The Analysis Factor, LLC.
All rights reserved.

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