Interactions in statistical models are never especially easy to interpret. Throw in non-normal outcome variables and non-linear prediction functions and they become even more difficult to understand. (more…)
Logistic Regression
Member Training: Interactions in Poisson and Logistic Regression
May 1st, 2023 by TAF SupportInterpreting the Intercept in a Regression Model
February 21st, 2023 by Karen Grace-MartinInterpreting the Intercept in a regression model isn’t always as straightforward as it looks.
Here’s the definition: the intercept (often labeled the constant) is the expected value of Y when all X=0. But that definition isn’t always helpful. So what does it really mean?
Regression with One Predictor X
Start with a very simple regression equation, with one predictor, X.
If X sometimes equals 0, the intercept is simply the expected value of Y at that value. In other words, it’s the mean of Y at one value of X. That’s meaningful.
If X never equals 0, then the intercept has no intrinsic meaning. You literally can’t interpret it. That’s actually fine, though. You still need that intercept to give you unbiased estimates of the slope and to calculate accurate predicted values. So while the intercept has a purpose, it’s not meaningful.
Both these scenarios are common in real data. (more…)
Member Training: Multinomial Logistic Regression
December 30th, 2022 by TAF Support
Multinomial logistic regression is an important type of categorical data analysis. Specifically, it’s used when your response variable is nominal: more than two categories and not ordered.
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When Linear Models Don’t Fit Your Data, Now What?
June 20th, 2022 by Karen Grace-MartinWhen your dependent variable is not continuous, unbounded, and measured on 
an interval or ratio scale, linear models don’t fit. The data just will not meet the assumptions of linear models. But there’s good news, other models exist for many types of dependent variables.
Today I’m going to go into more detail about 6 common types of dependent variables that are either discrete, bounded, or measured on a nominal or ordinal scale and the tests that work for them instead. Some are all of these.
Guidelines for writing up three types of odds ratios
December 6th, 2021 by Karen Grace-MartinOdds 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. (more…)
Logistic Regression Analysis: Understanding Odds and Probability
November 22nd, 2021 by Karen Grace-MartinUpdated 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 (more…)