The Analysis Factor
Statistical Consulting, Resources, and Statistics Workshops for Researchers
If you’ve been doing data analysis for very long, you’ve certainly come across terms, concepts, and processes of matrix algebra. Not just matrices, but:
[Read more…] about Member Training: Matrix Algebra for Data Analysts: A Primer
Whether or not you run experiments, there are elements of experimental design that affect how you need to analyze many types of studies.
The most fundamental of these are replication, randomization, and blocking. These key design elements come up in studies under all sorts of names: trials, replicates, multi-level nesting, repeated measures. Any data set that requires mixed or multilevel models has some of these design elements. [Read more…] about Member Training: Elements of Experimental Design
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
When I was in graduate school, stat professors would say “ANOVA is just a special case of linear regression.” But they never explained why.
And I couldn’t figure it out.
The model notation is different.
The output looks different.
The vocabulary is different.
The focus of what we’re testing is completely different. How can they be the same model?
[Read more…] about Why ANOVA is Really a Linear Regression, Despite the Difference in Notation
When we think about model assumptions, we tend to focus on assumptions like independence, normality, and constant variance.
The other big assumption, which is harder to see or test, is that there is no specification error. The assumption of linearity is part of this, but it’s actually a bigger assumption.
What is this assumption of no specification error? [Read more…] about What Is Specification Error in Statistical Models?
Most of the p-values we calculate are based on an assumption that our test statistic meets some distribution. These distributions are generally a good way to calculate p-values as long as assumptions are met.
But it’s not the only way to calculate a p-value.
Rather than come up with a theoretical probability based on a distribution, exact tests calculate a p-value empirically.
The simplest (and most common) exact test is a Fisher’s exact for a 2×2 table.
Remember calculating empirical probabilities from your intro stats course? All those red and white balls in urns? [Read more…] about What Is an Exact Test?