{"id":670,"date":"2017-02-06T02:36:17","date_gmt":"2017-02-06T02:36:17","guid":{"rendered":"https:\/\/blogs.scu.edu\/dataviz\/?p=670"},"modified":"2017-02-06T02:36:17","modified_gmt":"2017-02-06T02:36:17","slug":"spurious-correlations","status":"publish","type":"post","link":"https:\/\/blogs.scu.edu\/dataviz\/2017\/02\/06\/spurious-correlations\/","title":{"rendered":"Spurious Correlations"},"content":{"rendered":"<p>In statistics, spurious correlations is a mathematical relationship in which two or more events or variables are not casually related to each other, but it may be wrongly inferred they are, due to either coincidence or the absence of third reason.<\/p>\n<p>It\u2019s well known that correlation doesn\u2019t imply causation. However, when lines, bars, and points have similar trend, we start to believe that one may be the cause and one may be the result.<\/p>\n<p>There are several ways would cause spurious correlations:<\/p>\n<ol>\n<li>Axis scales: either x or y axis scale that measures different values can\u2019t be paired in a single graph especially those showing similar curves.<\/li>\n<li>Change scales: although x and y axis measure same value, the scale of either event change and the proportion and range is different. The graphs below obvious show that in different range those two events highly relate to each other. However, in same range, those two events are irrelevant with each other.<img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium\" src=\"https:\/\/hbr.org\/resources\/images\/article_assets\/2015\/05\/F1506Z_VS_BEWARESPURIOUSCORRELATIONS-4.png\" alt=\"\" width=\"1400\" height=\"689\" \/><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium\" src=\"https:\/\/hbr.org\/resources\/images\/article_assets\/2015\/05\/F1506Z_VS_BEWARESPURIOUSCORRELATIONS-5.png\" alt=\"\" width=\"1400\" height=\"689\" \/><\/li>\n<li>Ifs and thens implying cause and effect: comparing two unrelated data sets together may lead to a misunderstand of causation. We can use to different present skills to examine the causation:<\/li>\n<\/ol>\n<p>If Pandora loses less money, then more music is copyrighted.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium\" src=\"https:\/\/hbr.org\/resources\/images\/article_assets\/2015\/05\/F1506Z_VS_BEWARESPURIOUSCORRELATIONS-7.png\" alt=\"\" width=\"1400\" height=\"637\" \/><\/p>\n<p>However, this graphs doesn\u2019t show that correlation:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium\" src=\"https:\/\/hbr.org\/resources\/images\/article_assets\/2015\/05\/F1506Z_VS_BEWARESPURIOUSCORRELATIONS-6.png\" alt=\"\" width=\"1400\" height=\"689\" \/><\/p>\n<p>Reference: <a href=\"https:\/\/hbr.org\/2015\/06\/beware-spurious-correlations\">https:\/\/hbr.org\/2015\/06\/beware-spurious-correlations<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In statistics, spurious correlations is a mathematical relationship in which two or more events or variables are not casually related to each other, but it may be wrongly inferred they are, due to either coincidence or the absence of third reason. It\u2019s well known that correlation doesn\u2019t imply causation. However, when lines, bars, and points &hellip; <a href=\"https:\/\/blogs.scu.edu\/dataviz\/2017\/02\/06\/spurious-correlations\/\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">Spurious Correlations<\/span><\/a><\/p>\n","protected":false},"author":1685,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"qubely_global_settings":"","qubely_interactions":"","kk_blocks_editor_width":"","_kiokenblocks_attr":"","_kiokenblocks_dimensions":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-670","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"gutentor_comment":3,"qubely_featured_image_url":null,"qubely_author":{"display_name":"wsun","author_link":"https:\/\/blogs.scu.edu\/dataviz\/author\/wsun\/"},"qubely_comment":3,"qubely_category":"<a href=\"https:\/\/blogs.scu.edu\/dataviz\/category\/uncategorized\/\" rel=\"category tag\">Uncategorized<\/a>","qubely_excerpt":"In statistics, spurious correlations is a mathematical relationship in which two or more events or variables are not casually related to each other, but it may be wrongly inferred they are, due to either coincidence or the absence of third reason. It\u2019s well known that correlation doesn\u2019t imply causation. However, when lines, bars, and points&hellip;","post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/670","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/users\/1685"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/comments?post=670"}],"version-history":[{"count":1,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/670\/revisions"}],"predecessor-version":[{"id":671,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/670\/revisions\/671"}],"wp:attachment":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/media?parent=670"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/categories?post=670"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/tags?post=670"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}