# using the Poisson distribution to guess how many streak-freezes I'll use over the next 4 months

So, this is a small thing, but something I’m proud of. Over the past year, I’ve been practicing German (🇩🇪 Ich habe Deutsch gelernt!) using Duolingo’s mobile app. I don’t have an especially romantic reason for why I settled on the language, but I had watched the first season of Dark on Netflix in 2018, and don’t really enjoy dubbed foreign film/tv. Listening to the language prompted me to try a few lessons initially, but I settled into routine practice while finishing Babylon Berlin1 last fall.

# a primer for linear regression (part 3)

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.

# a primer for linear regression (part 2)

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.

# a primer for linear regression (part 1)

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.

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