binomial

Statistical Models for Truncated and Censored Data

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

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.

When to Use Logistic Regression for Percentages and Counts

by Karen Grace-Martin 4 Comments

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

Member Training: Making Sense of Statistical Distributions

by guest contributer 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.