How to Interpret Logistic Regression Coefficients
<p>Do you love logistic regression, but hate interpreting anything with any form of logarithmic transformation? Well, I can’t say you are in good company, but I can say that you do have <em>me </em>as company!</p>
<p>In this article, I’m going to talk all about interpreting logistic regression coefficients — here’s the outline:</p>
<ol>
<li>Interpreting <em>linear</em> regression coefficients</li>
<li>Why logistic regression coefficient interpretation is challenging</li>
<li>How to interpret logistic regression coefficients</li>
<li>Calculating mean marginal effects with the <em>statsmodels</em> package</li>
<li>Conclusion</li>
</ol>
<p><strong>Interpreting linear regression coefficients</strong></p>
<p>Most people with an elementary knowledge of statistics fully understand how coefficients are interpreted with linear regression. If that is you, you might consider skipping ahead to the portion of the article that discusses logistic regression coefficients.</p>
<p>Interpreting linear regression coefficients is very simple and easy. The simplicity of interpretation is one of the reasons linear regression is still a very popular tool despite the advent of much more sophisticated algorithms.</p>
<p>Simple linear regression (linear regression with one input variable) takes this form:</p>
<p>We are primarily interested in interpreting <strong><em>B</em></strong>₁. For linear regression, this interpretation is simple — for a one-unit change in <em>x</em>, we expect a <strong><em>B</em></strong>₁ change in <em>y</em>. Another phrase for this relationship is the ‘mean marginal effect’.</p>
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