
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.

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 nonpipeaware functions to
magrittr
pipelines without usingintubate
and, at the end, theintubate
alternative. See intubate <> R stat functions in data science pipelines for an introduction. 
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 happens with the scatter diagram when we change the standard deviations of x and y? What is the SD line? What is the Graph of averages? What is the Regression line. What is the Regression function?

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 nonpipeaware to data science pipelines implemented bymagrittr
with the operator%>%
, without having to rely on workarounds of varying complexity. 
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. This first post deals with the subject of correlation.
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