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#correlation

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Ruben ~ Kedara.eu<p><a href="https://kedara.social/tags/til" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TIL</span></a> at work: sometimes, it may be okay to force a linear regression through the origin.<br><br>Normally speaking, you should use "y=ax+b" as model for your linear regressions and correlation indices. Because fixing "b=0" introduces a bias in the estimator "a".<br><br>However, today, someone argued that if you're using a regression to measure the predictive power of a model with respect to measurement data, you should fix "b=0".<br><br>Now, intuitively this makes some sense, but I haven't been able to find clear proof pro or against it.<br><br>So, if you know more about this *and* have relevant references to papers to backup your claim, I'd very much love to hear from you. Statistics is not really my field.<br><br><a href="https://kedara.social/tags/correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlation</span></a> <a href="https://kedara.social/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://kedara.social/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://kedara.social/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://kedara.social/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>math</span></a> <a href="https://kedara.social/tags/maths" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>maths</span></a></p>
Eric Maugendre<p><span class="h-card" translate="no"><a href="https://a.gup.pe/u/data" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>data</span></a></span> <span class="h-card" translate="no"><a href="https://a.gup.pe/u/datadon" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>datadon</span></a></span> 🧵</p><p>How to assess a statistical model?<br>How to choose between variables?</p><p>Pearson's <a href="https://hachyderm.io/tags/correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlation</span></a> is irrelevant if you suspect that the relationship is not a straight line.</p><p>If monotonic relationship:<br>"<a href="https://hachyderm.io/tags/Spearman" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Spearman</span></a>’s rho is particularly useful for small samples where weak correlations are expected, as it can detect subtle monotonic trends." It is "widespread across disciplines where the measurement precision is not guaranteed".<br>"<a href="https://hachyderm.io/tags/Kendall" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Kendall</span></a>’s Tau-b is less affected [than Spearman’s rho] by outliers in the data, making it a robust option for datasets with extreme values."<br>Ref: <a href="https://statisticseasily.com/kendall-tau-b-vs-spearman/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">statisticseasily.com/kendall-t</span><span class="invisible">au-b-vs-spearman/</span></a></p><p><a href="https://hachyderm.io/tags/normality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>normality</span></a> <a href="https://hachyderm.io/tags/normalDistribution" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>normalDistribution</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/dataDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataDev</span></a> <a href="https://hachyderm.io/tags/AIDev" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIDev</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/modelEvaluation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelEvaluation</span></a> <a href="https://hachyderm.io/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regression</span></a> <a href="https://hachyderm.io/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://hachyderm.io/tags/dataLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dataLearning</span></a> <a href="https://hachyderm.io/tags/featureEngineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>featureEngineering</span></a> <a href="https://hachyderm.io/tags/linearRegression" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>linearRegression</span></a> <a href="https://hachyderm.io/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://hachyderm.io/tags/probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probability</span></a> <a href="https://hachyderm.io/tags/probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>probabilities</span></a> <a href="https://hachyderm.io/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://hachyderm.io/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://hachyderm.io/tags/correctionRatio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correctionRatio</span></a> <a href="https://hachyderm.io/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ML</span></a> <a href="https://hachyderm.io/tags/Pearson" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Pearson</span></a> <a href="https://hachyderm.io/tags/bias" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bias</span></a> <a href="https://hachyderm.io/tags/regressionRedress" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>regressionRedress</span></a> <a href="https://hachyderm.io/tags/distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>distributions</span></a></p>
Mark H<p>Now, I'm not saying that cocktails in general or my drinks-making ability specifically have magical healing powers but my wife, who's had cough/cold symptoms for the last few days, hasn't coughed or sneezed or sniffed once this evening while I've been whipping up some gorgeous raspberrytinis. </p><p><a href="https://mstdn.social/tags/Correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Correlation</span></a> <a href="https://mstdn.social/tags/Causation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Causation</span></a> <a href="https://mstdn.social/tags/Alcohol" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Alcohol</span></a> <a href="https://mstdn.social/tags/Cocktail" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Cocktail</span></a></p>
Fabio Manganiello<p><a class="hashtag" href="https://manganiello.social/tag/correlation" rel="nofollow noopener" target="_blank">#correlation</a> ≠ <a class="hashtag" href="https://manganiello.social/tag/causation" rel="nofollow noopener" target="_blank">#causation</a></p>
Ingrid Hoeben Ⓥ 🇧🇪<p><a href="https://mastodon.online/tags/causation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causation</span></a> <a href="https://mastodon.online/tags/correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlation</span></a> <a href="https://mastodon.online/tags/cats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cats</span></a></p>
Warren Currie 🦠🦐<p>It is sad to see Lake Windermere <a href="https://ecoevo.social/tags/UK" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>UK</span></a> become eutrophic, with increased algal blooms. I don't doubt that tourism and wastewater are part of the problem, but <a href="https://ecoevo.social/tags/algalblooms" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>algalblooms</span></a> almost always have multiple drivers. As scientists, we are very cautious of <a href="https://ecoevo.social/tags/correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlation</span></a>. Tourists correlate with chlorophyll, but tourists *also correlate with warm summer weather, which releases nutrients from sediment, and when <a href="https://ecoevo.social/tags/cyanobacteria" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cyanobacteria</span></a> blooms peak. <a href="https://ecoevo.social/tags/ClimateChange" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ClimateChange</span></a> is certainly also a factor here. <br><a href="https://www.bbc.com/news/science-environment-68953523.amp" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">bbc.com/news/science-environme</span><span class="invisible">nt-68953523.amp</span></a></p>
Léo Varnet<p>Deux billets pour tenter d'expliquer en langage courant le problème du biais de sélection en statistique. D'abord à travers le paradoxe de Berkson : comment on peut faire apparaitre une <a href="https://fediscience.org/tags/corr%C3%A9lation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>corrélation</span></a> entre deux maladies pourtant sans lien entre elles, simplement en étudiant des données recueillies chez des personnes admises à l’hôpital. <a href="https://dbao.leo-varnet.fr/2020/05/01/etudes-observationnelles-et-fausses-correlations-le-paradoxe-de-berkson/" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">dbao.leo-varnet.fr/2020/05/01/</span><span class="invisible">etudes-observationnelles-et-fausses-correlations-le-paradoxe-de-berkson/</span></a></p>
Tim Downing<p>Very happy that our review paper "A primer on <a href="https://genomic.social/tags/correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>correlation</span></a>-based <a href="https://genomic.social/tags/dimension" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dimension</span></a> <a href="https://genomic.social/tags/reduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>reduction</span></a> methods for <a href="https://genomic.social/tags/multi" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>multi</span></a>-omics analysis" is out. Thanks to the reviewers &amp; journal too for their help.</p><p><a href="https://royalsocietypublishing.org/doi/10.1098/rsif.2023.0344" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">royalsocietypublishing.org/doi</span><span class="invisible">/10.1098/rsif.2023.0344</span></a><br> <br><a href="https://genomic.social/tags/genomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>genomics</span></a> <a href="https://genomic.social/tags/omics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>omics</span></a> <a href="https://genomic.social/tags/systems" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>systems</span></a> <a href="https://genomic.social/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a> <a href="https://genomic.social/tags/model" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>model</span></a> <a href="https://genomic.social/tags/modelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelling</span></a> <a href="https://genomic.social/tags/biology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>biology</span></a> <a href="https://genomic.social/tags/transcriptomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>transcriptomics</span></a></p>
Led By Fools<p><span class="h-card"><a href="https://mastodon.murkworks.net/@moira" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>moira</span></a></span> Are you talking about this website?</p><p><a href="https://tylervigen.com/spurious-correlations" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="ellipsis">tylervigen.com/spurious-correl</span><span class="invisible">ations</span></a></p><p><a href="https://kolektiva.social/tags/SpuriousCorrelations" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SpuriousCorrelations</span></a> <a href="https://kolektiva.social/tags/Correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Correlation</span></a></p>
JohnW<p>The world is like the Titan, now immersed in climate change.</p><p>Corporations built it while ignoring safety regulations, because... wealth and fun.</p><p><a href="https://universeodon.com/tags/ClimateChange" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ClimateChange</span></a> <a href="https://universeodon.com/tags/TItan" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TItan</span></a> <a href="https://universeodon.com/tags/Correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Correlation</span></a></p>
Martijn BAARDA<p>A pretty scary sign of the times... <a href="https://mastodon.green/tags/Correlation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Correlation</span></a></p>