This is post #3 on the subject of linear regression, using R for computational demonstrations and examples. We cover here residuals (or prediction errors) and the RMSE of the prediction line. The first post in the series is LR01: Correlation. Acknowledgments: organization is extracted from: Freedman, Pisani, Purves, Statistics, 4th ed., ... [Read more...]

This post explores some of the possible workarounds that can be employed if you want to include non-pipe-aware functions to magrittr pipelines without using intubate and, at the end, the intubate alternative. See intubate R stat functions in... [Read more...]

This posts continues the discussion of correlation started on LR01: Correlation. We will try to answer the following questions: Should correlation be used for any pair of data? Does association mean causation? What are ecological correlations? What hap... [Read more...]

The aim of intubate (logo ) is to offer a painless way to add R functions that are non-pipe-aware to data science pipelines implemented by magrittr with the operator %__%, without having to rely on workarounds of varying complexity. Instal... [Read more...]

This is the first of a series of posts on the subject of linear regression, using R for computational demonstrations and examples. I hope you find it useful, but I am aware it may contains typos and conceptual errors (mostly when I try to think instead of just repeating what ... [Read more...]