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binomial

Statistical Models for Truncated and Censored Data

by Jeff Meyer Leave a Comment

by Jeff Meyer

As mentioned in a previous post, there is a significant difference between truncated and censored data.

Truncated data eliminates observations from an analysis based on a maximum and/or minimum value for a variable.

Censored data has limits on the maximum and/or minimum value for a variable but includes all observations in the analysis.

As a result, the models for analysis of these data are different. [Read more…] about Statistical Models for Truncated and Censored Data

Tagged With: binomial, Censored, model, probit, Regression, Tobit Regression, Truncated

Related Posts

  • 6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption
  • Member Training: Working with Truncated and Censored Data
  • Member Training: Transformations & Nonlinear Effects in Linear Models
  • Spotlight Analysis for Interpreting Interactions

Member Training: Logistic Regression for Count and Proportion Data

by Karen Grace-Martin Leave a Comment

Most of us know that binary logistic regression is appropriate when the outcome variable has two possible outcomes: success and failure.

There are two more situations that are also appropriate for binary logistic regression, but they don’t always look like they should be.

[Read more…] about Member Training: Logistic Regression for Count and Proportion Data

Tagged With: Bernoulli, binomial, Discrete Counts, logistic regression, normal distribution, outcome variable, poisson

Related Posts

  • Member Training: Making Sense of Statistical Distributions
  • Member Training: Explaining Logistic Regression Results to Non-Researchers
  • Member Training: Types of Regression Models and When to Use Them
  • Member Training: A Predictive Modeling Primer: Regression and Beyond

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: Making Sense of Statistical Distributions

by guest Leave a Comment

Many who work with statistics are already functionally familiar with the normal distribution, and maybe even the binomial distribution.

These common distributions are helpful in many applications, but what happens when they just don’t work?

This webinar will cover a number of statistical distributions, including the:

  • Poisson and negative binomial distributions (especially useful for count data)
  • Multinomial distribution (for responses with more than two categories)
  • Beta distribution (for continuous percentages)
  • Gamma distribution (for right-skewed continuous data)
  • Bernoulli and binomial distributions (for probabilities and proportions)
  • And more!

We’ll also explore the relationships among statistical distributions, including those you may already use, like the normal, t, chi-squared, and F distributions.


Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

[Read more…] about Member Training: Making Sense of Statistical Distributions

Tagged With: Bernoulli, beta, binomial, distributions, gamma, Multinomial, negative binomial, poisson, statistical distributions

Related Posts

  • Member Training: Logistic Regression for Count and Proportion Data
  • Poisson or Negative Binomial? Using Count Model Diagnostics to Select a Model
  • Analyzing Zero-Truncated Count Data: Length of Stay in the ICU for Flu Victims
  • Overdispersion in Count Models: Fit the Model to the Data, Don’t Fit the Data to the Model

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