{"id":89,"date":"2021-08-29T19:53:49","date_gmt":"2021-08-29T19:53:49","guid":{"rendered":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/?post_type=chapter&#038;p=89"},"modified":"2022-02-02T22:01:46","modified_gmt":"2022-02-02T22:01:46","slug":"summary-of-exploring-the-influence-of-outliers-on-measures-of-center","status":"publish","type":"chapter","link":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/chapter\/summary-of-exploring-the-influence-of-outliers-on-measures-of-center\/","title":{"raw":"Summary of Visualizing Quantitative Data: 3C","rendered":"Summary of Visualizing Quantitative Data: 3C"},"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<div class=\"textbox learning-objectives\">\r\n<h3>Essential Concepts<\/h3>\r\n<ul>\r\n \t<li>Quantitative variables have distinguishing features placing individuals into one of several groups based on a numerical value such as height, cost, exam scores, and temperature. The data from these variables can be displayed in multiple ways.<\/li>\r\n \t<li>Quantitative variables have data observations that can be added, averaged, and have a minimum and maximum value identified.<\/li>\r\n \t<li>Some features of a distribution are more apparent in some graphical displays than others. This also explains why some questions can be better answered in some graphical displays than others.<\/li>\r\n \t<li>A sample is representative of a population if its characteristics tend to match the characteristics of the population. If this is not the case, a generalization of the population cannot be made from the sample.<\/li>\r\n<\/ul>\r\n<\/div>\r\n<h2>Glossary<\/h2>\r\n<dl id=\"fs-id1170572229168\" class=\"definition\">\r\n \t<dt>dotplot<\/dt>\r\n \t<dd id=\"fs-id1170572229174\">a graphical display for quantitative data where each dot represents an observation.<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572229190\" class=\"definition\">\r\n \t<dt>histogram<\/dt>\r\n \t<dd id=\"fs-id1170572229195\">a graphical display that groups observations into bins rather than having a single dot for each observation.<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572482608\" class=\"definition\">\r\n \t<dt>bin<\/dt>\r\n \t<dd id=\"fs-id1170572482614\">a range of values that the quantitative variable can take.<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572482619\" class=\"definition\">\r\n \t<dt>endpoints<\/dt>\r\n \t<dd id=\"fs-id1170572482624\">the smallest and largest values of the quantitative variable represented in the bin.<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572482683\" class=\"definition\">\r\n \t<dt>width<\/dt>\r\n \t<dd id=\"fs-id1170572482689\">a numerical value that is calculated by the difference in the values of the end points.<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572482683\" class=\"definition\">\r\n \t<dt>population<\/dt>\r\n \t<dd id=\"fs-id1170572482689\">the group of individuals or entities that our research or survey questions pertain to.<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572482683\" class=\"definition\">\r\n \t<dt>sample<\/dt>\r\n \t<dd id=\"fs-id1170572482689\">a group of individuals or entities on which we collect data.<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572482683\" class=\"definition\">\r\n \t<dt>representative<\/dt>\r\n \t<dd id=\"fs-id1170572482689\">when the characteristics of a sample tend to match the characteristics of the population.<\/dd>\r\n<\/dl>\r\n<dl id=\"fs-id1170572482683\" class=\"definition\">\r\n \t<dt>generalize<\/dt>\r\n \t<dd id=\"fs-id1170572482689\">when the sample is representative of the population, this transfers our analysis of the sample to the population.<\/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 three are the \u201cyou will understand\u201d rephrased as an I Can):<\/span>\r\n<ul>\r\n \t<li>I can visualize the distribution of a quantitative variable in multiple ways.<\/li>\r\n \t<li>I can identify the features of a distribution that are more apparent in some graphical displays than in others.<\/li>\r\n \t<li>I can identify the questions that can be better answered in some graphical displays than others.<\/li>\r\n \t<li>I can identify and create appropriate graphical displays to visualize the distribution of quantitative variables.<\/li>\r\n \t<li>I can compare and contrast features present in each graphical display.<\/li>\r\n \t<li>I can identify the most useful graphical display(s) to answer a given research question.<\/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<div class=\"textbox learning-objectives\">\n<h3>Essential Concepts<\/h3>\n<ul>\n<li>Quantitative variables have distinguishing features placing individuals into one of several groups based on a numerical value such as height, cost, exam scores, and temperature. The data from these variables can be displayed in multiple ways.<\/li>\n<li>Quantitative variables have data observations that can be added, averaged, and have a minimum and maximum value identified.<\/li>\n<li>Some features of a distribution are more apparent in some graphical displays than others. This also explains why some questions can be better answered in some graphical displays than others.<\/li>\n<li>A sample is representative of a population if its characteristics tend to match the characteristics of the population. If this is not the case, a generalization of the population cannot be made from the sample.<\/li>\n<\/ul>\n<\/div>\n<h2>Glossary<\/h2>\n<dl id=\"fs-id1170572229168\" class=\"definition\">\n<dt>dotplot<\/dt>\n<dd id=\"fs-id1170572229174\">a graphical display for quantitative data where each dot represents an observation.<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572229190\" class=\"definition\">\n<dt>histogram<\/dt>\n<dd id=\"fs-id1170572229195\">a graphical display that groups observations into bins rather than having a single dot for each observation.<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572482608\" class=\"definition\">\n<dt>bin<\/dt>\n<dd id=\"fs-id1170572482614\">a range of values that the quantitative variable can take.<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572482619\" class=\"definition\">\n<dt>endpoints<\/dt>\n<dd id=\"fs-id1170572482624\">the smallest and largest values of the quantitative variable represented in the bin.<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572482683\" class=\"definition\">\n<dt>width<\/dt>\n<dd id=\"fs-id1170572482689\">a numerical value that is calculated by the difference in the values of the end points.<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572482683\" class=\"definition\">\n<dt>population<\/dt>\n<dd id=\"fs-id1170572482689\">the group of individuals or entities that our research or survey questions pertain to.<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572482683\" class=\"definition\">\n<dt>sample<\/dt>\n<dd id=\"fs-id1170572482689\">a group of individuals or entities on which we collect data.<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572482683\" class=\"definition\">\n<dt>representative<\/dt>\n<dd id=\"fs-id1170572482689\">when the characteristics of a sample tend to match the characteristics of the population.<\/dd>\n<\/dl>\n<dl id=\"fs-id1170572482683\" class=\"definition\">\n<dt>generalize<\/dt>\n<dd id=\"fs-id1170572482689\">when the sample is representative of the population, this transfers our analysis of the sample to the population.<\/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 three are the \u201cyou will understand\u201d rephrased as an I Can):<\/span><\/p>\n<ul>\n<li>I can visualize the distribution of a quantitative variable in multiple ways.<\/li>\n<li>I can identify the features of a distribution that are more apparent in some graphical displays than in others.<\/li>\n<li>I can identify the questions that can be better answered in some graphical displays than others.<\/li>\n<li>I can identify and create appropriate graphical displays to visualize the distribution of quantitative variables.<\/li>\n<li>I can compare and contrast features present in each graphical display.<\/li>\n<li>I can identify the most useful graphical display(s) to answer a given research question.<\/li>\n<\/ul>\n","protected":false},"author":17533,"menu_order":20,"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-89","chapter","type-chapter","status-publish","hentry"],"part":3,"_links":{"self":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters\/89","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\/17533"}],"version-history":[{"count":11,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters\/89\/revisions"}],"predecessor-version":[{"id":2736,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters\/89\/revisions\/2736"}],"part":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/parts\/3"}],"metadata":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapters\/89\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/media?parent=89"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/pressbooks\/v2\/chapter-type?post=89"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/contributor?post=89"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/lumen-danacenter-statsmockup\/wp-json\/wp\/v2\/license?post=89"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}