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numeric variable

Member Training: Explaining Logistic Regression Results to Non-Researchers

by TAF Support

Interpreting the results of logistic regression can be tricky, even for people who are familiar with performing different kinds of statistical analyses. How do we then share these results with non-researchers in a way that makes sense?

[Read more…] about Member Training: Explaining Logistic Regression Results to Non-Researchers

Tagged With: categorical variable, graphing, interaction, logistic regression, numeric variable

Related Posts

  • Member Training: Logistic Regression for Count and Proportion Data
  • Member Training: Using Excel to Graph Predicted Values from Regression Models
  • Member Training: Types of Regression Models and When to Use Them
  • How to Combine Complicated Models with Tricky Effects

Should I Specify a Model Predictor as Categorical or Continuous?

by Karen Grace-Martin Leave a Comment

Predictor variables in statistical models can be treated as either continuous or categorical.

Usually, this is a very straightforward decision.

Categorical predictors, like treatment group, marital status, or highest educational degree should be specified as categorical.

Likewise, continuous predictors, like age, systolic blood pressure, or percentage of ground cover should be specified as continuous.

But there are numerical predictors that aren’t continuous. And these can sometimes make sense to treat as continuous and sometimes make sense as categorical.

[Read more…] about Should I Specify a Model Predictor as Categorical or Continuous?

Tagged With: categorical predictor, continuous predictor, Discrete Counts, Linear Regression Model, Model Building, numeric variable, predictor variable

Related Posts

  • Recoding a Variable from a Survey Question to Use in a Statistical Model
  • What It Really Means to Take an Interaction Out of a Model
  • Simplifying a Categorical Predictor in Regression Models
  • A Strategy for Converting a Continuous to a Categorical Predictor

Differences Between the Normal and Poisson Distributions

by Karen Grace-Martin 3 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

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This Month’s Statistically Speaking Live Training

  • February Member Training: Choosing the Best Statistical Analysis

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  • Logistic Regression for Binary, Ordinal, and Multinomial Outcomes (May 2021)
  • Introduction to Generalized Linear Mixed Models (May 2021)

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