{"id":466,"date":"2021-12-20T14:36:36","date_gmt":"2021-12-20T14:36:36","guid":{"rendered":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/?post_type=chapter&#038;p=466"},"modified":"2022-02-11T21:22:27","modified_gmt":"2022-02-11T21:22:27","slug":"summary-of-4d","status":"publish","type":"chapter","link":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/chapter\/summary-of-4d\/","title":{"raw":"Summary of Five Number Summary in Box Plots and Datasets: 4D","rendered":"Summary of Five Number Summary in Box Plots and Datasets: 4D"},"content":{"raw":"This page would contain resource information like a glossary of terms from the section, key equations, and a reminder of concepts that were covered.\r\n\r\nMake this more relevant to what students want -- help them to build their processes, study guides, mnemonics, and memory dump material.\r\n<div class=\"textbox learning-objectives\">\r\n<h3>Essential Concepts<\/h3>\r\n<ul>\r\n \t<li>A boxplot captures only the median of the dataset, not the mean, as a measure of center. It provides a quick glance (or summary) of the data to make comparisons based on the median, skew, outliers, and percentiles.<\/li>\r\n \t<li>The collection of the minimum, first quartile, median, third quartile, and maximum form the five-number summary of the variable.<\/li>\r\n \t<li>There are several good methods to use for determining an observation to be an outlier in the distribution. The IQR method commonly uses a distance 1.5 times IQR from Q1 or Q3.<\/li>\r\n<\/ul>\r\n<\/div>\r\n<h2>Key Equations<\/h2>\r\n<ul>\r\n \t<li><strong>Interquartile range (IQR)<\/strong><\/li>\r\n<\/ul>\r\nQ3\u2013Q1\r\n<ul>\r\n \t<li><strong>Upper outlier<\/strong><\/li>\r\n<\/ul>\r\nQ3 + 1.5 \u00d7 (IQR), remember to multiply 1.5 by IQR first, then add to Q3\r\n<ul>\r\n \t<li><strong>Lower outlier<\/strong><\/li>\r\n<\/ul>\r\nQ1 - 1.5 \u00d7 (IQR), remember to multiply 1.5 by IQR first, then subtract from Q1\r\n<h2>Glossary<\/h2>\r\n<dl id=\"fs-id1170572229168\" class=\"definition\">\r\n \t<dt>first quartile<\/dt>\r\n \t<dd id=\"fs-id1170572229174\">the value below which one quarter of the data lies, also equal to the 25th percentile. Sometimes denoted Q1.<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572229190\" class=\"definition\">\r\n \t<dt>third quartile<\/dt>\r\n \t<dd id=\"fs-id1170572229195\">the value below which three quarters of the data lay, also equal to the 75th percentile. Sometimes denoted as Q3.<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572482608\" class=\"definition\">\r\n \t<dt>interquartile range<\/dt>\r\n \t<dd id=\"fs-id1170572482614\">the quantity Q3\u2013Q1. Sometimes denoted IQR.<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572482619\" class=\"definition\">\r\n \t<dt>five-number summary<\/dt>\r\n \t<dd id=\"fs-id1170572482624\">the collection of the minimum, first quartile, median, third quartile, and maximum of the variable.<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572482683\" class=\"definition\">\r\n \t<dt>upper outlier<\/dt>\r\n \t<dd id=\"fs-id1170572482689\">an observation that is greater than Q3 + 1.5\u00a0\u00d7 (IQR).<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572482683\" class=\"definition\">\r\n \t<dt>lower outlier<\/dt>\r\n \t<dd id=\"fs-id1170572482689\">an observation that is less than Q1 - 1.5 \u00d7 (IQR).<\/dd>\r\n<\/dl>\r\nPut formal DCMP I Can statements to prepare for the self-check.\r\n\r\n<span style=\"background-color: #ffff00;\">These I Can Statements are new (the first two are the \"you will understand\" rephrased as an I Can):<\/span>\r\n<ul>\r\n \t<li>I can provide visual summaries of quantitative variables using boxplots.<\/li>\r\n \t<li>I can compare the distributions of multiple populations using boxplots.<\/li>\r\n \t<li>I can compare and draw inferences from boxplots.<\/li>\r\n<\/ul>","rendered":"<p>This page would contain resource information like a glossary of terms from the section, key equations, and a reminder of concepts that were covered.<\/p>\n<p>Make this more relevant to what students want &#8212; help them to build their processes, study guides, mnemonics, and memory dump material.<\/p>\n<div class=\"textbox learning-objectives\">\n<h3>Essential Concepts<\/h3>\n<ul>\n<li>A boxplot captures only the median of the dataset, not the mean, as a measure of center. It provides a quick glance (or summary) of the data to make comparisons based on the median, skew, outliers, and percentiles.<\/li>\n<li>The collection of the minimum, first quartile, median, third quartile, and maximum form the five-number summary of the variable.<\/li>\n<li>There are several good methods to use for determining an observation to be an outlier in the distribution. The IQR method commonly uses a distance 1.5 times IQR from Q1 or Q3.<\/li>\n<\/ul>\n<\/div>\n<h2>Key Equations<\/h2>\n<ul>\n<li><strong>Interquartile range (IQR)<\/strong><\/li>\n<\/ul>\n<p>Q3\u2013Q1<\/p>\n<ul>\n<li><strong>Upper outlier<\/strong><\/li>\n<\/ul>\n<p>Q3 + 1.5 \u00d7 (IQR), remember to multiply 1.5 by IQR first, then add to Q3<\/p>\n<ul>\n<li><strong>Lower outlier<\/strong><\/li>\n<\/ul>\n<p>Q1 &#8211; 1.5 \u00d7 (IQR), remember to multiply 1.5 by IQR first, then subtract from Q1<\/p>\n<h2>Glossary<\/h2>\n<dl id=\"fs-id1170572229168\" class=\"definition\">\n<dt>first quartile<\/dt>\n<dd id=\"fs-id1170572229174\">the value below which one quarter of the data lies, also equal to the 25th percentile. Sometimes denoted Q1.<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572229190\" class=\"definition\">\n<dt>third quartile<\/dt>\n<dd id=\"fs-id1170572229195\">the value below which three quarters of the data lay, also equal to the 75th percentile. Sometimes denoted as Q3.<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572482608\" class=\"definition\">\n<dt>interquartile range<\/dt>\n<dd id=\"fs-id1170572482614\">the quantity Q3\u2013Q1. Sometimes denoted IQR.<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572482619\" class=\"definition\">\n<dt>five-number summary<\/dt>\n<dd id=\"fs-id1170572482624\">the collection of the minimum, first quartile, median, third quartile, and maximum of the variable.<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572482683\" class=\"definition\">\n<dt>upper outlier<\/dt>\n<dd id=\"fs-id1170572482689\">an observation that is greater than Q3 + 1.5\u00a0\u00d7 (IQR).<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572482683\" class=\"definition\">\n<dt>lower outlier<\/dt>\n<dd id=\"fs-id1170572482689\">an observation that is less than Q1 &#8211; 1.5 \u00d7 (IQR).<\/dd>\n<\/dl>\n<p>Put formal DCMP I Can statements to prepare for the self-check.<\/p>\n<p><span style=\"background-color: #ffff00;\">These I Can Statements are new (the first two are the &#8220;you will understand&#8221; rephrased as an I Can):<\/span><\/p>\n<ul>\n<li>I can provide visual summaries of quantitative variables using boxplots.<\/li>\n<li>I can compare the distributions of multiple populations using boxplots.<\/li>\n<li>I can compare and draw inferences from boxplots.<\/li>\n<\/ul>\n","protected":false},"author":25777,"menu_order":26,"template":"","meta":{"_candela_citation":"[]","CANDELA_OUTCOMES_GUID":"","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-466","chapter","type-chapter","status-publish","hentry"],"part":621,"_links":{"self":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters\/466","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/users\/25777"}],"version-history":[{"count":14,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters\/466\/revisions"}],"predecessor-version":[{"id":3091,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters\/466\/revisions\/3091"}],"part":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/parts\/621"}],"metadata":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters\/466\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/media?parent=466"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapter-type?post=466"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/contributor?post=466"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/license?post=466"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}