In this fifth article on method comparisons, Stephen MacDonald moves on from data collection and the application of basic graphing techniques, to focus this month on covariance, correlation and regression, and the interpretation of results.
In the previous article in this series,1 we took a high-level view of our data analysis. We set up a project structure so that colleagues can quickly pick up the data and understand what has been done, and why. Then we generated our summary statistics to answer the goals for the first stage of the analysis. These goals were concerned with conveying descriptive statistics of our data to show what we have collected is appropriate.
Now we move on to some of the more common techniques used in comparison studies, including regression and correlation. The goal for this article is not to walk through how to perform each analysis but rather to review some of the pros and cons for each method, and more importantly how to interpret results in the appropriate context. Finally, we will touch upon hypothesis testing and the ubiquitous search for a P value.
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