{"version":"1.0","provider_name":"Glosarix","provider_url":"https:\/\/glosarix.com\/en\/","author_name":"Team Glosarix","author_url":"https:\/\/glosarix.com\/en\/author\/adm_glosarix\/","title":"Bivariate Principal Component Analysis - Glosarix","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"wYnog2xELz\"><a href=\"https:\/\/glosarix.com\/en\/glossary\/bivariate-principal-component-analysis-en\/\">Bivariate Principal Component Analysis<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/glosarix.com\/en\/glossary\/bivariate-principal-component-analysis-en\/embed\/#?secret=wYnog2xELz\" width=\"600\" height=\"338\" title=\"&#8220;Bivariate Principal Component Analysis&#8221; &#8212; Glosarix\" data-secret=\"wYnog2xELz\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/glosarix.com\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","description":"Description: Bivariate Principal Component Analysis (BPCA) is a statistical method used to reduce the dimensionality of data involving two variables. This approach allows for the identification of underlying patterns and relationships between the variables, facilitating the visualization and analysis of complex data. By transforming the original variables into a set of principal components, BPCA aims [&hellip;]"}