{"id":72144,"date":"2022-07-28T11:48:00","date_gmt":"2022-07-28T15:48:00","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=72144"},"modified":"2022-08-01T13:53:23","modified_gmt":"2022-08-01T17:53:23","slug":"r2-statistic","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/r2-statistic\/","title":{"rendered":"The R^2 Statistic in Linear Regression"},"content":{"rendered":"\n<p>What is the R<sup>2<\/sup> statistic? What does the value of R<sup>2<\/sup> tell us about the change in the dependent variable? <\/p>\n\n\n\n<p>The R<sup>2<\/sup> statistic represents the proportion of the variance in the dependent variable that stems from the change in the independent variable. When using the R<sup>2<\/sup> statistic to quantify the association between independent and dependent variables, it&#8217;s important to keep in mind that the R<sup>2<\/sup> in linear regression only applies to linear relationships. It&#8217;s possible for two variables to be related, just not in a linear way.<\/p>\n\n\n\n<p>Here&#8217;s a look at the R<sup>2<\/sup> statistic in linear regression. <\/p>\n\n\n\n<!--more-->\n\n\n\n<h2 class=\"wp-block-heading\" id=\"block-07332482-a621-419a-b942-84da9871d28f\"><strong>The R<sup>2<\/sup> Statistic&nbsp;<\/strong><\/h2>\n\n\n\n<p id=\"block-8d1e552a-2073-4d2c-81b7-de75eb34da95\">Thanks to a statistic called the R<sup>2 <\/sup>statistic, regression analysis can tell us <em>how much <\/em>of the change in the dependent variable is predictable by changes in the independent variable. In our chemical exposure example, for instance, R<sup>2<\/sup> can tell you how much of a person\u2019s overall cancer risk is determined by their exposure to chemical X, and how much is due to other factors such as smoking, diet, exercise, genetics, and so on.&nbsp;<\/p>\n\n\n\n<p id=\"block-51f1c4a6-f2e7-44be-8f77-ad5b4a1437e3\">R<sup>2 <\/sup>is reported as a value between zero and one and interpreted as a percent. A value of zero means that our regression equation can&#8217;t predict our dependent variable at all, and a value of 1 means that it can predict 100% of the variation in our dependent variable.&nbsp;<\/p>\n\n\n\n<p id=\"block-9a30d596-697d-4311-b694-025369d4aeda\">In the cancer risk example, if your R<sup>2<\/sup> for chemical exposure was .08, then 8% of a person\u2019s overall cancer risk would be explained by their exposure to the chemical, and 92% would be due to other factors.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-r2-is-only-for-linear-relationships-in-linear-regression\"><strong>R<sup>2<\/sup> Is Only for Linear Relationships in Linear Regression<\/strong><\/h3>\n\n\n\n<p>The R<sup>2<\/sup> statistic is <a href=\"https:\/\/online.stat.psu.edu\/stat462\/node\/98\/\">easy to misunderstand and, therefore, easy to use improperly<\/a>. Often people interpret R<sup>2 <\/sup>&nbsp;as encompassing <em>any<\/em> relationship between the two variables being studied. However, R<sup>2 <\/sup>&nbsp;in linear regression only deals with linear relationships. &nbsp;<\/p>\n\n\n\n<p>(Note: There are R<sup>2<\/sup> values for non-linear relationships, but they are outside the scope of Wheelan\u2019s text, which only covers linear regression.)<\/p>\n\n\n\n<p>For example, if you were collecting data on algal blooms (which often occur when excessive nutrients make their way into waterways and algae populations explode), you might plot nutrient levels on your x axis and algae growth on your y axis. During an algal bloom, algae growth will likely follow a non-linear exponential curve. Therefore, your R<sup>2<\/sup> might be zero even though there is a <a href=\"https:\/\/www.shortform.com\/blog\/strong-relationship\/\">strong relationship<\/a> between the two variables.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>What is the R2 statistic? What does the value of R2 tell us about the change in the dependent variable? The R2 statistic represents the proportion of the variance in the dependent variable that stems from the change in the independent variable. When using the R2 statistic to quantify the association between independent and dependent variables, it&#8217;s important to keep in mind that the R2 in linear regression only applies to linear relationships. It&#8217;s possible for two variables to be related, just not in a linear way. Here&#8217;s a look at the R2 statistic in linear regression.<\/p>\n","protected":false},"author":7,"featured_media":72204,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[25],"tags":[681],"class_list":["post-72144","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-statistics","tag-naked-statistics","","tg-column-two"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v24.3 (Yoast SEO v24.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>The R^2 Statistic in Linear Regression - Shortform Books<\/title>\n<meta name=\"description\" content=\"The R^2 statistic tells us how much of the change in the dependent variable is predictable by changes in the independent variable. Read more.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.shortform.com\/blog\/r2-statistic\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The R^2 Statistic in Linear Regression\" \/>\n<meta property=\"og:description\" content=\"The R^2 statistic tells us how much of the change in the dependent variable is predictable by changes in the independent variable. Read more.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.shortform.com\/blog\/r2-statistic\/\" \/>\n<meta property=\"og:site_name\" content=\"Shortform Books\" \/>\n<meta property=\"article:published_time\" content=\"2022-07-28T15:48:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2022-08-01T17:53:23+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/s3.amazonaws.com\/wordpress.shortform.com\/blog\/wp-content\/uploads\/2022\/07\/maths-statistics-calculator-accounting-finance.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"1080\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Darya Sinusoid\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Darya Sinusoid\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.shortform.com\/blog\/r2-statistic\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.shortform.com\/blog\/r2-statistic\/\"},\"author\":{\"name\":\"Darya Sinusoid\",\"@id\":\"https:\/\/www.shortform.com\/blog\/#\/schema\/person\/0421cce75bc249b11e2517b3a91f9c46\"},\"headline\":\"The R^2 Statistic in Linear Regression\",\"datePublished\":\"2022-07-28T15:48:00+00:00\",\"dateModified\":\"2022-08-01T17:53:23+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.shortform.com\/blog\/r2-statistic\/\"},\"wordCount\":415,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.shortform.com\/blog\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.shortform.com\/blog\/r2-statistic\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.shortform.com\/blog\/wp-content\/uploads\/2022\/07\/maths-statistics-calculator-accounting-finance.jpg\",\"keywords\":[\"Naked Statistics\"],\"articleSection\":[\"Statistics\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.shortform.com\/blog\/r2-statistic\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.shortform.com\/blog\/r2-statistic\/\",\"url\":\"https:\/\/www.shortform.com\/blog\/r2-statistic\/\",\"name\":\"The R^2 Statistic in Linear Regression - Shortform Books\",\"isPartOf\":{\"@id\":\"https:\/\/www.shortform.com\/blog\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.shortform.com\/blog\/r2-statistic\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.shortform.com\/blog\/r2-statistic\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.shortform.com\/blog\/wp-content\/uploads\/2022\/07\/maths-statistics-calculator-accounting-finance.jpg\",\"datePublished\":\"2022-07-28T15:48:00+00:00\",\"dateModified\":\"2022-08-01T17:53:23+00:00\",\"description\":\"The R^2 statistic tells us how much of the change in the dependent variable is predictable by changes in the independent variable. 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