{"id":1887,"date":"2018-01-31T16:01:17","date_gmt":"2018-01-31T17:01:17","guid":{"rendered":"http:\/\/sites.uac.pt\/amendes\/?p=1887"},"modified":"2018-01-31T16:01:17","modified_gmt":"2018-01-31T17:01:17","slug":"bumpiness","status":"publish","type":"post","link":"https:\/\/sites.uac.pt\/amendes\/estatistica\/bumpiness\/","title":{"rendered":"Three classes of metrics: centrality, volatility, and bumpiness"},"content":{"rendered":"<div id=\"attachment_1083\" style=\"width: 310px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/www.analyticbridge.datasciencecentral.com\/profiles\/blogs\/three-classes-of-metrics-centrality-volatility-and-bumpiness\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-1083\" class=\"size-medium wp-image-1083\" src=\"http:\/\/sites.uac.pt\/amendes\/files\/2013\/10\/dataScience-300x54.png\" alt=\"clique na imagem para seguir o link\" width=\"300\" height=\"54\" srcset=\"https:\/\/sites.uac.pt\/amendes\/files\/2013\/10\/dataScience-300x54.png 300w, https:\/\/sites.uac.pt\/amendes\/files\/2013\/10\/dataScience.png 657w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><p id=\"caption-attachment-1083\" class=\"wp-caption-text\">clique na imagem para seguir o link<\/p><\/div>\n<p><span style=\"color: #ff0000\">introduz uma nova classe de estat\u00edsticas para s\u00e9ries cronol\u00f3gicas: bumpiness<\/span><\/p>\n<p><span style=\"background-color: transparent;border-bottom-color: #454545;border-bottom-style: none;border-bottom-width: 0px;border-left-color: #454545;border-left-style: none;border-left-width: 0px;border-right-color: #454545;border-right-style: none;border-right-width: 0px;border-top-color: #454545;border-top-style: none;border-top-width: 0px;font-family: arial,helvetica,sans-serif;font-size: 13.33px;line-height: 15.99px;margin-bottom: 0px;margin-left: 0px;margin-right: 0px;margin-top: 0px;padding-bottom: 0px;padding-left: 0px;padding-right: 0px;padding-top: 0px;vertical-align: baseline\">All statistical textbooks focus on\u00a0<a href=\"http:\/\/www.analyticbridge.com\/profiles\/blogs\/a-new-way-to-define-centrality\" target=\"_blank\">centrality<\/a> (median, average or mean) and volatility (variance). None mention the third fundamental class of metrics: bumpiness.<\/span><\/p>\n<p><span> <\/span><\/p>\n<p><span style=\"background-color: transparent;border-bottom-color: #454545;border-bottom-style: none;border-bottom-width: 0px;border-left-color: #454545;border-left-style: none;border-left-width: 0px;border-right-color: #454545;border-right-style: none;border-right-width: 0px;border-top-color: #454545;border-top-style: none;border-top-width: 0px;font-family: arial,helvetica,sans-serif;font-size: 13.33px;line-height: 15.99px;margin-bottom: 0px;margin-left: 0px;margin-right: 0px;margin-top: 0px;padding-bottom: 0px;padding-left: 0px;padding-right: 0px;padding-top: 0px;vertical-align: baseline\">Here we introduce the concept of <em>bumpiness<\/em> and show how it can be used. Two different datasets can have same <em>mean<\/em> and <em>variance<\/em>, but a different <em>bumpiness<\/em>. Bumpiness is linked to how the data points are ordered, while centrality and volatility completely ignore order. So, bumpiness is useful for datasets where order matters, in particular time series. Also, bumpiness integrates the notion of dependence (among the data points), while centrality and variance do not. Note that a time series can have high volatility (high variance) and low bumpiness. The converse is true.<\/span><\/p>\n<p><span> <\/span><\/p>\n<p><span style=\"background-color: transparent;border-bottom-color: #454545;border-bottom-style: none;border-bottom-width: 0px;border-left-color: #454545;border-left-style: none;border-left-width: 0px;border-right-color: #454545;border-right-style: none;border-right-width: 0px;border-top-color: #454545;border-top-style: none;border-top-width: 0px;font-family: arial,helvetica,sans-serif;font-size: 13.33px;line-height: 15.99px;margin-bottom: 0px;margin-left: 0px;margin-right: 0px;margin-top: 0px;padding-bottom: 0px;padding-left: 0px;padding-right: 0px;padding-top: 0px;vertical-align: baseline\">The attached Excel spreadsheet shows computations of the bumpiness coefficient r for various time series. It is also of interest to readers who wish to learn new Excel concepts such a random number generation with Rand, indirect references with Indirect, Rank, Large and other powerful but not well known Excel functions. It is also an example of a fully interactive Excel spreadsheet driven by two core parameters.<\/span><\/p>\n<p><span> <\/span><\/p>\n<p><span style=\"background-color: transparent;border-bottom-color: #454545;border-bottom-style: none;border-bottom-width: 0px;border-left-color: #454545;border-left-style: none;border-left-width: 0px;border-right-color: #454545;border-right-style: none;border-right-width: 0px;border-top-color: #454545;border-top-style: none;border-top-width: 0px;font-family: arial,helvetica,sans-serif;font-size: 13.33px;line-height: 15.99px;margin-bottom: 0px;margin-left: 0px;margin-right: 0px;margin-top: 0px;padding-bottom: 0px;padding-left: 0px;padding-right: 0px;padding-top: 0px;vertical-align: baseline\"><span style=\"background: transparent none repeat scroll 0% 0%;font-size: 13.33px;margin: 0px;padding: 0px;vertical-align: baseline;font-family: arial, helvetica, sans-serif;border: 0px none #454545\">Finally, this article shows (1) how a new concept is thought of, (2) then a robust, modern definition materialized, and (3) eventually a more meaningful definition created based on, and compatible with previous science.<\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>introduz uma nova classe de estat\u00edsticas para s\u00e9ries cronol\u00f3gicas: bumpiness All statistical textbooks focus on\u00a0centrality (median, average or mean) and volatility (variance). None mention the third fundamental class of metrics: bumpiness. Here we introduce the concept of bumpiness and show how it can be used. Two different datasets can have same mean and variance, but [&hellip;]<\/p>\n","protected":false},"author":159,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[103,102,109],"tags":[120],"class_list":["post-1887","post","type-post","status-publish","format-standard","hentry","category-data-mining","category-estatistica","category-investigacao-operacional","tag-previsao"],"_links":{"self":[{"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/posts\/1887","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/users\/159"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/comments?post=1887"}],"version-history":[{"count":2,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/posts\/1887\/revisions"}],"predecessor-version":[{"id":1889,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/posts\/1887\/revisions\/1889"}],"wp:attachment":[{"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/media?parent=1887"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/categories?post=1887"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/tags?post=1887"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}