In our previous post we reviewed how to apply the DRY principle (don’t repeat yourself) to our code and we learned how to create Python modules to avoid repeating ourselves (i.e. cutting-and-pasting code) across scripts and programs. In this post we will see how we can use our modules as a stand-alone program as well as useful functions that can
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In a previous blog, we applied simple linear regression to an interesting problem: how well does a measure of wine density account for alcohol content. This was considered simple linear regression because we had one outcome variable (alcohol content) and one predictor variable (wine density). We can extend this approach to have more than one predictor. Specifically, we can use
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We previously learned to create our own Python functions to reduce how much we repeat ourselves in our code. In this post we see another example of the DRY principle (don’t repeat yourself) and we will learn how to ensure we don’t repeat ourselves between the different programs we write. A typical (bad) script to process data Below is an
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Learning to program is not easy. We have to learn a new language and a new way of thinking. We have to learn the grammatical rules of the programming language we are learning, and the logic of our program, how it will work its way through the code and hopefully give us the answer we expect. In this post we
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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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