Now our focus will shift to multiple regression (i.e. linear regression with >1 predictors), as opposed to simple linear regression (linear regression with just 1 predictor). Simple linear regressions have the benefit of being easy to visualize, and this makes it much easier to explain different concepts. However, real-world questions are often complex, and it’s frequently necessary to account for more than one relevant variable in an analysis. As with the last two posts, we’ll stick with the Palmer Penguins data, and now that they’ve been introduced, I’ll be using functions from the {broom} package (such as tidy(), glance() and augment()) a bit more freely. Read more

In the previous post of this series, we covered an overview of the Ordinary Least Squares method for estimating the parameters of a linear regression model. While I didn’t give you a full tour of the mathematical guts underpinning the technique, I’ve hopefully given you a sense of the problem the model is attempting to solve, as well as some specific vocabulary that describes the contents of a linear regression. Read more

This year, my partner has been working to complete her Masters in Natural Resources/Land Management, and several of her assignments have required some data analysis. One topic area we covered together was linear regression/multiple linear regression. As techniques, simple linear regression and multiple linear regression are well-known as workhorses for answering statistical questions across many scientific fields. Given their ubiquity, having the requisite working knowledge needed to interpret and evaluate a regression analysis is highly valuable in virtually any professional field that involves the use or consumption of data. Read more

So, this is my first post in a while. I changed jobs in January, and moved back across the country to my hometown of Boise, ID. I was hoping that my first post-move update would be more uplifting, but by mid-March, I didn’t want to write anything, for a variety of reasons. As a person whose job involves cleaning and analyzing data, the pandemic has been surreal– public health, statistical methods, and data visualizations are now daily topics, for basically everyone I talk to. Read more