{"id":178140,"date":"2024-02-27T14:33:28","date_gmt":"2024-02-27T13:33:28","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=178140"},"modified":"2024-11-27T15:26:42","modified_gmt":"2024-11-27T14:26:42","slug":"calculate-correlation-between-two-variables-how-do-you-measure-dependence","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/calculate-correlation-between-two-variables-how-do-you-measure-dependence","title":{"rendered":"Calculate correlation between two variables: How do you measure dependence?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"178140\" class=\"elementor elementor-178140\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-08ddeac e-flex e-con-boxed e-con\" data-id=\"08ddeac\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-13bb58e elementor-widget elementor-widget-text-editor\" data-id=\"13bb58e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<h2>In data science, it is vital to discover and quantify the extent to which two variables are linked. These relationships can be complex and are not necessarily visible. Some of these dependencies, such as linear regressions, weaken the performance of Machine Learning algorithms. It therefore becomes imperative to prepare your data better.<\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-11e24ef elementor-widget elementor-widget-text-editor\" data-id=\"11e24ef\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Here we will look at how to obtain dependency between two categorical variables and between <strong>categorical and continuous variables.<\/strong><\/p><p>First of all, remember that a categorical variable is a variable which has a finite number of distinct categories or groups. For example, the gender of individuals, the type of equipment or the method of payment. In contrast, <strong>continuous variables<\/strong> can theoretically take on an infinite number of values.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7c28e70 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"7c28e70\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-analyst-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Build Your Future in Data Analytics<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7ea146d e-flex e-con-boxed e-con\" data-id=\"7ea146d\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-62c80d1 elementor-widget elementor-widget-heading\" data-id=\"62c80d1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h3 class=\"elementor-heading-title elementor-size-default\">Correlation between two categorical variables :<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dbe81bf elementor-widget elementor-widget-text-editor\" data-id=\"dbe81bf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>To find out whether two<strong> categorical variables<\/strong> are related, we use the famous chi-square test. If you&#8217;re not familiar with statistical tests, don&#8217;t panic!<\/p><p>A statistical test is a procedure for deciding between two hypotheses.<br \/>It consists of rejecting or not rejecting a statistical hypothesis, called the null hypothesis H0, based on a set of data.<\/p><p>In the test we are interested in, the null hypothesis is simply &#8220;the two variables being tested are independent&#8221;. Finally, the test is accompanied by a test statistic which is used to decide whether or not to reject the null hypothesis. Because of the way the test is constructed, this statistic has the good taste to <a href=\"https:\/\/datascientest.com\/en\/chi-squared-test-find-out-more-about-this-essential-statistical-test\">follow a chi-square distribution with a certain degree of freedom.<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1fae1f4 elementor-widget elementor-widget-heading\" data-id=\"1fae1f4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">But how do you decide whether or not to reject the null hypothesis?<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fa7bec7 elementor-widget elementor-widget-text-editor\" data-id=\"fa7bec7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Without <a href=\"https:\/\/datascientest.com\/en\/sequences-and-series-understanding-these-two-mathematical-concepts\">going into the mathematical details,<\/a> each statistical test has a so-called p-value. This can be seen as a reference value for deciding whether or not to reject the null hypothesis.<\/p><p>If the p-value is below 5%, then the null hypothesis is rejected. The 5% threshold is used by practitioners and may vary depending on the sector of activity.<\/p><p>The <strong>test is easily implemented in <a href=\"https:\/\/datascientest.com\/en\/python-the-most-popular-language\">Python<\/a><\/strong> using the scipy library and its chi2_contingency function. It allows you to quickly obtain the p-value of the test as well as the associated statistic and degree of freedom. In practice, the chi-square test requires a little work on the data beforehand. To perform the test, you first need to determine the contingency table. This is a cross-tabulation between the two variables. It is easily obtained using the <a href=\"https:\/\/datascientest.com\/en\/pandas-the-python-library\">Pandas crosstab<\/a> function. The test is then performed using the contingency table:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-81eb5ac elementor-widget elementor-widget-image\" data-id=\"81eb5ac\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"612\" height=\"302\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/05\/Capture-d\u2019e\u0301cran-2020-05-24-a\u0300-19.42.25.png\" class=\"attachment-large size-large wp-image-2925\" alt=\"python\" srcset=\"https:\/\/datascientest.com\/en\/files\/2020\/05\/Capture-d\u2019e\u0301cran-2020-05-24-a\u0300-19.42.25.png 612w, https:\/\/datascientest.com\/en\/files\/2020\/05\/Capture-d\u2019e\u0301cran-2020-05-24-a\u0300-19.42.25-300x148.png 300w\" sizes=\"(max-width: 612px) 100vw, 612px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1c3666c elementor-widget elementor-widget-text-editor\" data-id=\"1c3666c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In our example above, the p-value is well below 5%, so we can reject the hypothesis that the two variables being tested are independent.<\/p><p>Finally, we can also measure the level of correlation between the two variables using <strong>Cramer&#8217;s V.<\/strong> This is calculated using the test statistic, the degree of freedom and the dimensions of the contingency table. It returns a value between 0 and 1. If the value returned is greater than 0.9, the relationship can be described as very strong. If the value is less than 0.10, the relationship can be described as weak.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-09e3b32 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"09e3b32\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-analyst-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Unlock Your Potential as a Data Analyst<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-748acec e-flex e-con-boxed e-con\" data-id=\"748acec\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-77bf523 elementor-widget elementor-widget-heading\" data-id=\"77bf523\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Correlation between two continuous variables :<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-193c15e elementor-widget elementor-widget-text-editor\" data-id=\"193c15e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>As with<strong> categorical variables<\/strong>, there is a test to determine whether two continuous variables are independent: Pearson&#8217;s correlation test. The null hypothesis to be tested is identical: &#8220;the two variables tested are independent&#8221;. As with the chi-square test, it is accompanied by a test statistic and a p-value that determines whether or not the null hypothesis is rejected.<\/p><p>This test can be<strong> implemented very easily<\/strong> using the scipy library and its pearsonr function. There is no need to work on the<strong> data beforehand,<\/strong> provided it contains no missing values. Here is an example of implementation using python :<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e93d610 elementor-widget elementor-widget-image\" data-id=\"e93d610\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"596\" height=\"268\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/05\/Capture-d\u2019e\u0301cran-2020-05-24-a\u0300-19.42.19.png\" class=\"attachment-large size-large wp-image-2924\" alt=\"python\" srcset=\"https:\/\/datascientest.com\/en\/files\/2020\/05\/Capture-d\u2019e\u0301cran-2020-05-24-a\u0300-19.42.19.png 596w, https:\/\/datascientest.com\/en\/files\/2020\/05\/Capture-d\u2019e\u0301cran-2020-05-24-a\u0300-19.42.19-300x135.png 300w\" sizes=\"(max-width: 596px) 100vw, 596px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-57632e6 elementor-widget elementor-widget-text-editor\" data-id=\"57632e6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In our example, the p-value is less than 5%. This means that the variables are not independent. The Pearson coefficient measures the level of correlation between the two variables. It returns a value between -1 and 1. If it is close to 1, this means that the variables are correlated, close to 0 that the variables are uncorrelated and close to -1 that they are negatively correlated. In our example, the coefficient has a value of 0.80319, which means that the variables are highly correlated.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a1af8e elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"5a1af8e\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-md\" href=\"https:\/\/datascientest.com\/en\/data-analyst-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Build Your Future in Data Analytics<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f7bfd25 e-flex e-con-boxed e-con\" data-id=\"f7bfd25\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8ee8fb9 elementor-widget elementor-widget-heading\" data-id=\"8ee8fb9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Correlation between a continuous variable and a\ncategorical variable :<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-87cb97c elementor-widget elementor-widget-text-editor\" data-id=\"87cb97c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>To study this type of correlation, a one-factor <a href=\"https:\/\/datascientest.com\/en\/analysis-of-variance-anova-a-basic-tool-for-data-analysis\">analysis of variance (ANOVA)<\/a> is used to compare sample means. The aim of this test is to conclude whether a categorical variable influences the distribution of a continuous variable to be explained.<\/p><p>Imagine that you have 3 variables. The first gives a customer number, the second a category (1, 2 or 3) and the last the amount spent. The question is: does the category variable have an influence on the amounts spent? Let&#8217;s denote \u00b51, \u00b52 and \u00b53 the average amounts spent for each of the 3 categories. A simple reasoning consists in saying that if the category variable has no influence on the sums spent, then the averages should be identical.<\/p><p>In other words,<strong> \u00b51 = \u00b52 = \u00b53<\/strong>. This is exactly the hypothesis we test when we use analysis of variance. As with the chi-square and <a href=\"https:\/\/datascientest.com\/en\/pearson-and-spearman-correlations-a-guide-to-understanding-and-applying-correlation-methods\">Pearson tests,<\/a> this test is accompanied by a test statistic and a p-value which determines whether or not the null hypothesis is rejected.<\/p><p>This test is <strong>easily implemented in Python<\/strong> using the statsmodels library. Here is an example of implementation:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-568370b elementor-widget elementor-widget-image\" data-id=\"568370b\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"236\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/05\/Capture-d\u2019e\u0301cran-2020-05-24-a\u0300-19.42.38.png\" class=\"attachment-large size-large wp-image-2926\" alt=\"python\" srcset=\"https:\/\/datascientest.com\/en\/files\/2020\/05\/Capture-d\u2019e\u0301cran-2020-05-24-a\u0300-19.42.38.png 950w, https:\/\/datascientest.com\/en\/files\/2020\/05\/Capture-d\u2019e\u0301cran-2020-05-24-a\u0300-19.42.38-300x88.png 300w, https:\/\/datascientest.com\/en\/files\/2020\/05\/Capture-d\u2019e\u0301cran-2020-05-24-a\u0300-19.42.38-768x226.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8f252ab elementor-widget elementor-widget-text-editor\" data-id=\"8f252ab\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In our example, df indicates the degree of freedom of the test statistic F, which follows a Fisher distribution. PR(&gt;F) indicates the p-value of the test. This is less than 5%, so we can conclude that the pledged variable has an influence on main_category.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-131b23b e-flex e-con-boxed e-con\" data-id=\"131b23b\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-04c5a63 elementor-widget elementor-widget-text-editor\" data-id=\"04c5a63\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>You now have all the tools you need to <a href=\"https:\/\/datascientest.com\/en\/classification-algorithms-definition-and-main-models\">study correlations within a dataset.<\/a> Datascientest will give you the opportunity to go further by learning how to manage a data project from A to Z. Find out more about our training courses!<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c9ae01d elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"c9ae01d\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-analyst-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Start Your Data Analyst Career Today<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>In data science, it is vital to discover and quantify the extent to which two variables are linked. These relationships can be complex and are not necessarily visible. Some of these dependencies, such as linear regressions, weaken the performance of Machine Learning algorithms. It therefore becomes imperative to prepare your data better. Here we will [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":192249,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Calculate correlation between two variables: How do you measure dependence?<\/title>\n<meta name=\"description\" content=\"Learn to measure dependence between variables using statistical methods like chi-square and ANOVA for accurate data analysis.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/calculate-correlation-between-two-variables-how-do-you-measure-dependence\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Calculate correlation between two variables: How do you measure dependence?\" \/>\n<meta property=\"og:description\" content=\"In data science, it is vital to discover and quantify the extent to which two variables are linked. 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