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When to Use Logistic Regression for Percentages and Counts

by Karen Grace-Martin 1 Comment

One important yet difficult skill in statistics is choosing a type model for different data situations. One key consideration is the dependent variable.

For linear models, the dependent variable doesn’t have to be normally distributed, but it does have to be continuous, unbounded, and measured on an interval or ratio scale.

Percentages don’t fit these criteria. Yes, they’re continuous and ratio scale. The issue is the [Read more…] about When to Use Logistic Regression for Percentages and Counts

Tagged With: binomial, Count data, count model, dependent variable, events, logistic regression, Negative Binomial Regression, percentage data, Poisson Regression, trials

Related Posts

  • Member Training: Count Models
  • When Dependent Variables Are Not Fit for Linear Models, Now What?
  • Proportions as Dependent Variable in Regression–Which Type of Model?
  • Poisson Regression Analysis for Count Data

Member Training: An Introduction to Kaplan-Meier Curves

by guest Leave a Comment

Survival data models provide interpretation of data representing the time until an event occurs. In many situations, the event is death, but it can also represent the time to other bad events such as cancer relapse or failure of a medical device. It can also be used to denote time to positive events such as pregnancy. Often patients are lost to follow-up prior to death, but you can still use the information about them while they were in your study to better estimate the survival probability over time.

This is done using the Kaplan-Meier curve, an approach developed by [Read more…] about Member Training: An Introduction to Kaplan-Meier Curves

Tagged With: curve, estimate, events, Kaplan-Meier curve, model, survival data, survival percentiles, survival probability

Related Posts

  • Member Training: Latent Growth Curve Models
  • The Proportional Hazard Assumption in Cox Regression
  • Member Training: Cox Regression
  • Member Training: Discrete Time Event History Analysis

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