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 statistics, we often want to fit a statistical model to be able to make broader generalizations. An important type of statistical model is linear regression, where we predict the linear relationship between an outcome variable and a predictor variable. In this post we will learn how to perform a simple linear regression in R. See our previous post for
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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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In the first and second post of this series, we learned how to graph our data using histograms and Q-Q plots to see whether it is normally distributed, and quantify the shape of the distribution by considering skew and kurtosis. In this, the final post in this series, we will learn to use the Shapiro-Wilk test to determine whether data
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