Blog Archives

LR03: Residuals and RMSE

LR03: Residuals and RMSE

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., probably the best book on statistical thinking...

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Workarounds to include R stat functions in data science pipelines

Workarounds to include R stat functions in data science pipelines

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...

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LR02: SD line, GoA, Regression

LR02: SD line, GoA, Regression

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...

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intubate <||> R stat functions in data science pipelines

intubate <||> R stat functions in data science pipelines

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...

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LR01: Correlation

LR01: Correlation

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 others thought…). Help on correcting/improving these notes is appreciated....

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