What are goodness of fit statistics? Is the definition the same for all types of statistical model? Do we run the same tests for all types of statistic model?

[Read more…] about March Member Training: Goodness of Fit Statistics

by TAF Support

What are goodness of fit statistics? Is the definition the same for all types of statistical model? Do we run the same tests for all types of statistic model?

[Read more…] about March Member Training: Goodness of Fit Statistics

A key part of the output in any linear model is the ANOVA table. It has many names in different software procedures, but every regression or ANOVA model has a table with Sums of Squares, degrees of freedom, mean squares, and F tests. Many of us were trained to skip over this table, but

When our outcome variable is the frequency of occurrence of an event, we will typically use a count model to analyze the results. There are numerous count models. A few examples are: Poisson, negative binomial, zero-inflated Poisson and truncated negative binomial.

There are specific requirements for which count model to use. The models are not interchangeable. But regardless of the model we use, there is a very important prerequisite that they all share.

[Read more…] about The Importance of Including an Exposure Variable in Count Models

When we run a statistical model, we are in a sense creating a mathematical equation. The simplest regression model looks like this:

*Y _{i} = β_{0} + β_{1}X+ ε_{i}*

The left side of the equation is the sum of two parts on the right: the fixed component, β_{0} + β_{1}X, and the random component, ε_{i}.

You’ll also sometimes see the equation written [Read more…] about Count Models: Understanding the Log Link Function

by TAF Support

You think a linear regression might be an appropriate statistical analysis for your data, but you’re not entirely sure. What should you check before running your model to find out?

[Read more…] about Member Training: Preparing to Use (and Interpret) a Linear Regression Model

Learning how to analyze data can be frustrating at times. Why do statistical software companies have to add to our confusion?

I do not have a good answer to that question. What I will do is show examples. In upcoming blog posts, I will explain what each output means and how they are used in a model.

We will focus on ANOVA and linear regression models using SPSS and Stata software. As you will see, the biggest differences are not across software, but across procedures in the same software.

[Read more…] about Same Statistical Models, Different (and Confusing) Output Terms

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