• 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

normality

Differences Between the Normal and Poisson Distributions

by Karen Grace-Martin  4 Comments

The normal distribution is so ubiquitous in statistics that those of us who use a lot of statistics tend to forget it’s not always so common in actual data.

And since the normal distribution is continuous, many people describe all numerical variables as continuous. I get it: I’m guilty of using those terms interchangeably, too, but they’re not exactly the same.

Numerical variables can be either continuous or discrete.

The difference? Continuous variables can take any number within a range. Discrete variables can only be whole numbers.

So 3.04873658 is a possible value of a continuous variable, but not discrete.

Count variables, as the name implies, are frequencies of some event or state. Number of arrests, fish [Read more…] about Differences Between the Normal and Poisson Distributions

Tagged With: continuous variable, discrete, negative binomial, normal distribution, normality, numeric variable, Poisson Regression

Related Posts

  • The Importance of Including an Exposure Variable in Count Models
  • Count Models: Understanding the Log Link Function
  • Count vs. Continuous Variables: Differences Under the Hood
  • The Problem with Linear Regression for Count Data

Anatomy of a Normal Probability Plot

by Karen Grace-Martin  3 Comments

Stage 2A normal probability plot is extremely useful for testing normality assumptions.  It’s more precise than a histogram, which can’t pick up subtle deviations, and doesn’t suffer from too much or too little power, as do tests of normality.

There are two versions of normal probability plots: Q-Q and P-P.  I’ll start with the Q-Q.   [Read more…] about Anatomy of a Normal Probability Plot

Tagged With: normality, P-P plot, Q-Q plot

Related Posts

  • Checking Assumptions in ANOVA and Linear Regression Models: The Distribution of Dependent Variables
  • Why ANOVA is Really a Linear Regression, Despite the Difference in Notation
  • ANCOVA Assumptions: When Slopes are Unequal
  • How Simple Should a Model Be? The Case of Insignificant Controls, Interactions, and Covariance Structures

6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption

by Karen Grace-Martin  9 Comments

The assumptions of normality and constant variance in a linear model (both OLS regression and ANOVA) are quite robust to departures.  That means that even if the assumptions aren’t met perfectly, the resulting p-values will still be reasonable estimates.

But you need to check the assumptions anyway, because some departures are so far that the p-value become inaccurate.  And in many cases there are remedial measures you can take to turn non-normal residuals into normal ones.

But sometimes you can’t.

Sometimes it’s because the dependent variable just isn’t appropriate for a linear model.  The [Read more…] about 6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption

Tagged With: Assumptions, categorical outcome, categorical variable, Censored, Constant Variance, dependent variable, Discrete Counts, normality, ordinal variable, Proportion, Truncated, Zero Inflated

Related Posts

  • When Linear Models Don’t Fit Your Data, Now What?
  • When to Check Model Assumptions
  • Statistical Models for Truncated and Censored Data
  • Member Training: Types of Regression Models and When to Use Them

Checking Assumptions in ANOVA and Linear Regression Models: The Distribution of Dependent Variables

by Karen Grace-Martin  24 Comments

Here’s a little reminder for those of you checking assumptions in regression and ANOVA:

The assumptions of normality and homogeneity of variance for linear models are not about Y, the dependent variable.    (If you think I’m either stupid, crazy, or just plain nit-picking, read on.  This distinction really is important). [Read more…] about Checking Assumptions in ANOVA and Linear Regression Models: The Distribution of Dependent Variables

Tagged With: ANOVA, distribution, linear regression, Model Assumptions, normality, Q-Q plot, residuals

Related Posts

  • Can Likert Scale Data ever be Continuous?
  • Member Training: The Multi-Faceted World of Residuals
  • Member Training: Centering
  • Same Statistical Models, Different (and Confusing) Output Terms

The Distribution of Independent Variables in Regression Models

by Karen Grace-Martin  27 Comments

I often hear concern about the non-normal distributions of independent variables in regression models, and I am here to ease your mind.Stage 2

There are NO assumptions in any linear model about the distribution of the independent variables.  Yes, you only get meaningful parameter estimates from nominal (unordered categories) or numerical (continuous or discrete) independent variables.  But no, the model makes no assumptions about them.  They do not need to be normally distributed or continuous.

It is useful, however, to understand the distribution of predictor variables to find influential outliers or concentrated values.  A highly skewed independent variable may be made more symmetric with a transformation.

Tagged With: checking assumptions, distribution, independent variable, normality, predictor variable, regression models

Related Posts

  • The Distribution of Independent Variables in Regression Models
  • Likert Scale Items as Predictor Variables in Regression
  • Why report estimated marginal means?
  • What is Multicollinearity? A Visual Description

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