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      <description>Writing on data analytics, Alteryx, AI and the career around them, from ten years leading analytics in airline operations.</description>
      <language>en-us</language>
      <managingEditor>racine.isacar@gmail.com (Isacar Racine)</managingEditor>
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    <guid>https://data-awesome.com/blog/division-prueba-unica-comparar-modelos</guid>
    <title>Una sola división de prueba no basta para comparar modelos</title>
    <link>https://data-awesome.com/blog/division-prueba-unica-comparar-modelos</link>
    <description>Una sola división train/test puede hacer que un modelo se vea mejor de lo que es y otro peor, en el mismo experimento. Así se ve eso con números reales.</description>
    <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
    <author>racine.isacar@gmail.com (Isacar Racine)</author>
    <category>model-diagnostics</category><category>statistics</category><category>machine-learning</category>
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    <guid>https://data-awesome.com/blog/single-test-split-model-comparison</guid>
    <title>A Single Test Split Isn&#39;t Enough to Compare Models</title>
    <link>https://data-awesome.com/blog/single-test-split-model-comparison</link>
    <description>One train/test split can make one model look better than it is and another look worse, in the same experiment. Here&#39;s what that looks like with real numbers.</description>
    <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
    <author>racine.isacar@gmail.com (Isacar Racine)</author>
    <category>model-diagnostics</category><category>statistics</category><category>machine-learning</category>
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    <guid>https://data-awesome.com/blog/regression-trap-index</guid>
    <title>15 Ways to Misread Your Own Regression Model</title>
    <link>https://data-awesome.com/blog/regression-trap-index</link>
    <description>Fifteen ways regression output gets misread, with the symptom, what it actually means, and how to check. Every one came from a real model.</description>
    <pubDate>Mon, 27 Apr 2026 00:00:00 GMT</pubDate>
    <author>racine.isacar@gmail.com (Isacar Racine)</author>
    <category>regression</category><category>statistics</category><category>model-diagnostics</category>
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    <guid>https://data-awesome.com/blog/regression-coefficient-sign-flip</guid>
    <title>The Coefficient That Flipped Sign</title>
    <link>https://data-awesome.com/blog/regression-coefficient-sign-flip</link>
    <description>Alone it reads -0.0054. With seven other predictors, +0.0154. Both significant. Why hunting for the guilty variable is the wrong instinct.</description>
    <pubDate>Mon, 20 Apr 2026 00:00:00 GMT</pubDate>
    <author>racine.isacar@gmail.com (Isacar Racine)</author>
    <category>regression</category><category>statistics</category><category>model-diagnostics</category>
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    <guid>https://data-awesome.com/blog/regression-coefficient-not-causal</guid>
    <title>Regression Coefficients Are Not Causal</title>
    <link>https://data-awesome.com/blog/regression-coefficient-not-causal</link>
    <description>The smoker coefficient is $24,330. That does not mean quitting saves anyone $24,330, and no amount of significance will make it mean that.</description>
    <pubDate>Mon, 13 Apr 2026 00:00:00 GMT</pubDate>
    <author>racine.isacar@gmail.com (Isacar Racine)</author>
    <category>regression</category><category>statistics</category><category>model-diagnostics</category>
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    <guid>https://data-awesome.com/blog/regression-diagnostics-outliers-multicollinearity</guid>
    <title>The R-Squared Trap: When Cleaning Backfires</title>
    <link>https://data-awesome.com/blog/regression-diagnostics-outliers-multicollinearity</link>
    <description>I removed 53 outliers. R-squared rose from 0.818 to 0.856 and the model got worse. Every number that improved ignored the rows I deleted.</description>
    <pubDate>Mon, 23 Mar 2026 00:00:00 GMT</pubDate>
    <author>racine.isacar@gmail.com (Isacar Racine)</author>
    <category>regression</category><category>model-diagnostics</category><category>statistics</category><category>r</category>
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    <guid>https://data-awesome.com/blog/simple-linear-regression-assumptions</guid>
    <title>Why Care About Regression Assumptions?</title>
    <link>https://data-awesome.com/blog/simple-linear-regression-assumptions</link>
    <description>One broken assumption ruins your coefficients; the other three only ruin your p-values. Knowing which decides whether you scrap the model.</description>
    <pubDate>Mon, 09 Mar 2026 00:00:00 GMT</pubDate>
    <author>racine.isacar@gmail.com (Isacar Racine)</author>
    <category>regression</category><category>statistics</category><category>model-diagnostics</category>
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