{"id":37950,"date":"2025-03-10T00:57:42","date_gmt":"2025-03-10T04:57:42","guid":{"rendered":"https:\/\/statanalytica.com\/blog\/?p=37950"},"modified":"2025-03-15T00:59:04","modified_gmt":"2025-03-15T04:59:04","slug":"how-to-interpret-logistic-regression-results","status":"publish","type":"post","link":"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/","title":{"rendered":"How to Interpret Logistic Regression Results: The Ultimate Guide for 2025"},"content":{"rendered":"\n<p>Logistic regression is a powerful statistical method used for binary classification problems. It helps in predicting categorical outcomes based on independent variables. Whether you&#8217;re a data scientist, researcher, or student, knowing how to interpret logistic regression results is crucial for making data-driven decisions.<\/p>\n\n\n\n<p>In this guide, we will break down logistic regression interpretation with easy-to-understand explanations, practical examples, and step-by-step calculations.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"what-is-logistic-regression\"><\/span><strong>What is Logistic Regression?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-light-blue ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a1bcc098db16\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ff5104;color:#ff5104\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ff5104;color:#ff5104\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a1bcc098db16\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#what-is-logistic-regression\" >What is Logistic Regression?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#key-components-of-logistic-regression\" >Key Components of Logistic Regression:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#how-to-interpret-logistic-regression-results\" >How to Interpret Logistic Regression Results<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#interpreting-coefficients-and-odds-ratios\" >Interpreting Coefficients and Odds Ratios<\/a><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#coefficients-beta-values\" >Coefficients (Beta Values)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#odds-ratio-or\" >Odds Ratio (OR)<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#understanding-the-p-value\" >Understanding the P-Value<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#confidence-intervals-ci\" >Confidence Intervals (CI)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#model-performance-metrics\" >Model Performance Metrics<\/a><ul class='ez-toc-list-level-5' ><li class='ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#pseudo-r-squared\" >Pseudo R-Squared<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#log-likelihood\" >Log-Likelihood<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#classification-table-accuracy\" >Classification Table &amp; Accuracy<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#hosmer-lemeshow-test\" >Hosmer-Lemeshow Test<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-5'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#akaike-information-criterion-aic\" >Akaike Information Criterion (AIC)<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#real-world-example-of-logistic-regression-interpretation\" >Real-World Example of Logistic Regression Interpretation<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#interpretation\" >Interpretation<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#conclusion\" >Conclusion<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#faqs-on-logistic-regression-interpretation\" >FAQs on Logistic Regression Interpretation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#what-if-my-p-value-is-greater-than-005\" >What if my p-value is greater than 0.05?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#what-is-the-difference-between-logistic-regression-and-linear-regression\" >What is the difference between logistic regression and linear regression?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/statanalytica.com\/blog\/how-to-interpret-logistic-regression-results\/#how-can-i-improve-my-logistic-regression-model\" >How can I improve my logistic regression model?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n\n\n\n\n<p>Logistic regression is used when the dependent variable is categorical (typically binary: 0 or 1). Unlike linear regression, which predicts continuous values, logistic regression predicts the probability that a given input belongs to a particular category.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"key-components-of-logistic-regression\"><\/span><strong>Key Components of Logistic Regression:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Odds Ratio (OR):<\/strong> Measures how the likelihood of an event changes with one unit increase in the predictor variable.<\/li>\n\n\n\n<li><strong>Coefficients (Beta values):<\/strong> Represent the impact of each predictor variable.<\/li>\n\n\n\n<li><strong>P-value:<\/strong> Determines the statistical significance of the predictor.<\/li>\n\n\n\n<li><strong>Confidence Interval (CI):<\/strong> Indicates the range in which the true effect size lies.<\/li>\n\n\n\n<li><strong>Pseudo R-squared:<\/strong> Indicates the goodness of fit for the model.<\/li>\n\n\n\n<li><strong>Classification Table:<\/strong> Compares predicted vs. actual values.<\/li>\n\n\n\n<li><strong>Log-Likelihood:<\/strong> Measures how well the model fits the data.<\/li>\n\n\n\n<li><strong>Wald Test:<\/strong> Checks if individual predictors are significant.<\/li>\n\n\n\n<li><strong>Hosmer-Lemeshow Test:<\/strong> Evaluates the overall goodness of fit.<\/li>\n\n\n\n<li><strong>Akaike Information Criterion (AIC):<\/strong> Compares models to find the best-fitting one.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"how-to-interpret-logistic-regression-results\"><\/span><strong>How to Interpret Logistic Regression Results<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"interpreting-coefficients-and-odds-ratios\"><\/span><strong>Interpreting Coefficients and Odds Ratios<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<h5 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"coefficients-beta-values\"><\/span><strong>Coefficients (Beta Values)<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h5>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Positive Coefficients:<\/strong> Increase the likelihood of the event occurring.<\/li>\n\n\n\n<li><strong>Negative Coefficients:<\/strong> Decrease the likelihood of the event occurring.<\/li>\n<\/ul>\n\n\n\n<h5 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"odds-ratio-or\"><\/span><strong>Odds Ratio (OR)<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h5>\n\n\n\n<ul class=\"wp-block-list\">\n<li>OR > 1: The event is more likely to happen.<\/li>\n\n\n\n<li>OR &lt; 1: The event is less likely to happen.<\/li>\n\n\n\n<li>OR = 1: No effect.<\/li>\n<\/ul>\n\n\n\n<p><strong>Example:<\/strong> If the odds ratio for a predictor variable (e.g., smoking) is <strong>2.5<\/strong>, it means that individuals with that characteristic are <strong>2.5 times more likely<\/strong> to experience the outcome compared to those without it.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"understanding-the-p-value\"><\/span><strong>Understanding the P-Value<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>P &lt; 0.05:<\/strong> The variable is statistically significant.<\/li>\n\n\n\n<li><strong>P > 0.05:<\/strong> The variable is not statistically significant.<\/li>\n<\/ul>\n\n\n\n<p><strong>Example:<\/strong> If a p-value for <strong>age<\/strong> is <strong>0.02<\/strong>, it suggests that age significantly impacts the outcome.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"confidence-intervals-ci\"><\/span><strong>Confidence Intervals (CI)<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>A <strong>95% CI<\/strong> means that if we repeated the experiment 100 times, the odds ratio would fall within the interval 95 times.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Wide CI:<\/strong> Indicates high uncertainty.<\/li>\n\n\n\n<li><strong>Narrow CI:<\/strong> Suggests a precise estimate.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"model-performance-metrics\"><\/span><strong>Model Performance Metrics<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<h5 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"pseudo-r-squared\"><\/span><strong>Pseudo R-Squared<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h5>\n\n\n\n<p>Similar to R-squared in linear regression, it measures how well the model explains the variability in the data.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Higher values indicate a better fit.<\/strong><\/li>\n<\/ul>\n\n\n\n<h5 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"log-likelihood\"><\/span><strong>Log-Likelihood<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h5>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Measures how well the model fits the data.<\/li>\n\n\n\n<li>Higher values indicate a better fit.<\/li>\n<\/ul>\n\n\n\n<h5 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"classification-table-accuracy\"><\/span><strong>Classification Table &amp; Accuracy<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h5>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Actual \\ Predicted<\/strong><\/td><td><strong>0 (Negative)<\/strong><\/td><td><strong>1 (Positive)<\/strong><\/td><\/tr><tr><td>0 (Negative)<\/td><td>TN<\/td><td>FP<\/td><\/tr><tr><td>1 (Positive)<\/td><td>FN<\/td><td>TP<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li><br><strong>Accuracy = (TP + TN) \/ Total Predictions<\/strong><\/li>\n\n\n\n<li><strong>Precision, Recall, and F1 Score<\/strong> provide more insights into model performance.<\/li>\n<\/ul>\n\n\n\n<h5 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"hosmer-lemeshow-test\"><\/span><strong>Hosmer-Lemeshow Test<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h5>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Evaluates how well predicted probabilities match observed data.<\/li>\n\n\n\n<li>A <strong>high p-value (>0.05)<\/strong> suggests a good model fit.<\/li>\n<\/ul>\n\n\n\n<h5 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"akaike-information-criterion-aic\"><\/span><strong>Akaike Information Criterion (AIC)<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h5>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Used to compare different logistic regression models.<\/li>\n\n\n\n<li>Lower AIC values indicate a better-fitting model.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"real-world-example-of-logistic-regression-interpretation\"><\/span><strong>Real-World Example of Logistic Regression Interpretation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Let&#8217;s say we build a logistic regression model to predict whether a patient has <strong>heart disease (1) or not (0)<\/strong> based on <strong>age, cholesterol level, and blood pressure<\/strong>.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>Predictor Variable<\/td><td>Coefficient (\u03b2)<\/td><td>Odds Ratio (OR)<\/td><td>P-Value<\/td><\/tr><tr><td>Age<\/td><td>0.05<\/td><td>1.05<\/td><td>0.02<\/td><\/tr><tr><td>Cholesterol<\/td><td>0.02<\/td><td>1.02<\/td><td>0.10<\/td><\/tr><tr><td>Blood Pressure<\/td><td>0.08<\/td><td>1.08<\/td><td>0.005<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"interpretation\"><\/span><strong>Interpretation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Age (OR = 1.05):<\/strong> Each additional year increases the risk of heart disease by <strong>5%<\/strong>.<\/li>\n\n\n\n<li><strong>Cholesterol (OR = 1.02):<\/strong> Has a minor impact (not statistically significant).<\/li>\n\n\n\n<li><strong>Blood Pressure (OR = 1.08, p &lt; 0.01):<\/strong> Highly significant predictor.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Understanding <strong>how to interpret logistic regression results<\/strong> is crucial for making informed decisions in data science and research. By analyzing coefficients, odds ratios, p-values, and model accuracy, you can draw meaningful insights from your data.<\/p>\n\n\n\n<p><strong>Also Read: <a href=\"https:\/\/statanalytica.com\/blog\/quadratic-regression-formula\/\">Quadratic Regression: Mastering Nonlinear Relationships<\/a><\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"faqs-on-logistic-regression-interpretation\"><\/span><strong>FAQs on Logistic Regression Interpretation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1742013977155\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"what-if-my-p-value-is-greater-than-005\"><\/span><strong>What if my p-value is greater than 0.05?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>It means the predictor is not statistically significant.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1742013992793\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"what-is-the-difference-between-logistic-regression-and-linear-regression\"><\/span><strong>What is the difference between logistic regression and linear regression?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Logistic regression predicts probabilities for categorical outcomes, while linear regression predicts continuous values.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1742014016250\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"how-can-i-improve-my-logistic-regression-model\"><\/span><strong>How can I improve my logistic regression model?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Consider adding more relevant features, using regularization, and ensuring balanced <a href=\"https:\/\/en.wikipedia.org\/wiki\/Data_set\" target=\"_blank\" rel=\"noreferrer noopener\">datasets<\/a>.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Logistic regression is a powerful statistical method used for binary classification problems. It helps in predicting categorical outcomes based on independent variables. Whether you&#8217;re a data scientist, researcher, or student, knowing how to interpret logistic regression results is crucial for making data-driven decisions. In this guide, we will break down logistic regression interpretation with easy-to-understand [&hellip;]<\/p>\n","protected":false},"author":16,"featured_media":37952,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[136],"tags":[5242],"class_list":["post-37950","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-general","tag-how-to-interpret-logistic-regression-results"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/37950","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/users\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/comments?post=37950"}],"version-history":[{"count":1,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/37950\/revisions"}],"predecessor-version":[{"id":37953,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/37950\/revisions\/37953"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/media\/37952"}],"wp:attachment":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/media?parent=37950"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/categories?post=37950"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/tags?post=37950"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}