When is it important to use adjusted R-squared instead of R-squared?
R², the the Coefficient of Determination, is one of the most useful and intuitive statistics we have in linear regression.
It tells you how well the model predicts the outcome and has some nice properties. But it also has one big drawback.
What are the assumptions of linear models? If you compare two lists of assumptions, most of the time they’re not the same.
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Recommendations on how to analyze pre-post data can vary. Typical recommendations include regression analysis or matched pairs analysis for within subject studies and analysis of covariance (ANCOVA) or linear mixed effects model analysis for within and between subject studies.
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If you analyze non-experimental data, is it helpful to understand experimental design principles?
Yes, absolutely! Understanding experimental design can help you recognize the questions you can and can’t answer with the data. It will also help you identify possible sources of bias that can lead to undesirable results. Finally, it will help you provide recommendations to make future studies more efficient. (more…)
Analysis of Means (ANOM) is an underappreciated methodology that has relevance to quality control and institutional comparisons.
Have you ever wondered why there are so many different types of experimental designs, and how a researcher would go about choosing among them to best address their research questions? (more…)