R for applications in data science

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All posts with the R tag deal with applications of the statistical programming language R in the data science setting.

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Interpreting Generalized Linear Models

Interpreting Generalized Linear Models

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Generalized linear models (GLMs) are related to conventional linear models but there are some important differences. For example, GLMs are based on the deviance rather than the conventional residuals and they enable the use of different distributions and linker functions. This post investigates how these aspects influence the interpretation of GLMs.

Finding a Suitable Linear Model for Ozone Prediction

Finding a Suitable Linear Model for Ozone Prediction

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Although ordinary least-squares regression is often used, it is not appropriate for all types of data. Using the airquality data set, I try to find a generalized linear model that fits the data better. For this purpose, I use the following methods: weighted regression, Poisson regression, and imputation.

Bar Plots and Error Bars

Bar Plots and Error Bars

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Bar plots are frequently used due to their simplicity. However, they also do not convey a lot of information. Here, I discuss how error bars can be used to visualize variance and under which circumstances bar charts should not be used.