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(This article was first published on <strong><a href="https://blog.revolutionanalytics.com/2018/10/anscombes-quartet.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p><a rel="nofollow" href="https://en.wikipedia.org/wiki/Anscombe%27s_quartet" target="_blank">Anscombe's Quartet</a> is a famous collection of four small data sets — just 11 (x,y) pairs each — that was developed in the 1970s to emphasize the fact that sometimes, numerical summaries of data aren't enough. (For a modern take on this idea, see also the <a rel="nofollow" href="https://blog.revolutionanalytics.com/2017/05/the-datasaurus-dozen.html" target="_blank">Datasaurus Dozen</a>.) In this case, it takes visualizing the data to realize that the for data sets are qualitatively very different, even though the means, variances, and regression coefficients are all the same. In the video below for <a rel="nofollow" href="https://www.youtube.com/channel/UCFp1vaKzpfvoGai0vE5VJ0w" target="_blank">Guy in a Cube</a>, Buck Woody uses R to summarize the data (which is conveniently <a rel="nofollow" href="https://www.rdocumentation.org/packages/datasets/versions/3.5.1/topics/anscombe?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">built into R</a>) and visualize it using an R script in Power BI.</p>
<p><iframe allowfullscreen="" src="https://www.youtube.com/embed/yj3rZTwJZaA" width="450" height="283"></iframe></p>
<p>The video also makes for a nice introduction to R for those new to statistics, and also demonstrates <a rel="nofollow" href="https://docs.microsoft.com/power-bi/desktop-r-visuals?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">using R code to generate graphics in Power BI</a>.</p>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="https://blog.revolutionanalytics.com/2018/10/anscombes-quartet.html"> Revolutions</a></strong>.</div>
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