We introduced simple linear regression in a previous series and learned how to perform it in R (1, 2). What is the theory behind simple linear regression? How is it used to understand relationships between variables? What is another way to perform it in Python? The hsb2.csv dataset (available here) contains demographic and academic test scores data from 200 students.
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In the previous post we learned how to create a project folder and pre-register a study protocol on the Open Science Framework (OSF). The OSF also serves as a good data repository to store data, and users can add contributors (i.e. collaborators) to projects. How is this done? To add files to a project, navigate to the OSF website, sign
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Pre-registration of study protocols increases research transparency by providing a time-stamped record of experimental or analysis decisions before studies are conducted. Protocol pre-registration is now mandatory or strongly encouraged for clinical trials, and is increasingly encouraged for basic science and pre-clinical research (e.g. see the Transparency and Openness Promotion Guidelines). The Open Science Framework (OSF) is an open source software
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Linear regression is one of the most versatile tools in the data analyst’s toolbox. While it can get quite complicated, simple linear regression is rather intuitive and straightforward. In this post we will look at a small dataset so that we can work through and visualise the logic of linear regression. A bigger version of this dataset will be used
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Suppose you conduct a study to compare an outcome between healthy people and people with stroke, but you want to check whether the proportion of men and women in each group are similar. The Fisher’s Exact test is often used to test differences in proportions between groups, similar to a Chi-Square test. But what is an exact test? This helpful
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Previously, we saw how regression to the mean can lead to false results. In a talk titled Regression towards the mean, or Why was Terminator III such a disappointment?, statistician Martin Bland explains this phenomenon and how it appears in different examples. The Victorian statistician Francis Galton measured the heights of parents and children and found that taller parents tended
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