{"id":839,"date":"2023-03-20T19:17:19","date_gmt":"2023-03-20T19:17:19","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/background-youll-need-2\/"},"modified":"2024-01-31T02:12:39","modified_gmt":"2024-01-31T02:12:39","slug":"background-youll-need-2","status":"publish","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/background-youll-need-2\/","title":{"raw":"Describing Data Numerically: Background You\u2019ll Need 2","rendered":"Describing Data Numerically: Background You\u2019ll Need 2"},"content":{"raw":"<section class=\"textbox learningGoals\">\r\n<ul>\r\n\t<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Read and interpret a histogram&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:4608,&quot;12&quot;:0,&quot;15&quot;:&quot;Calibri&quot;}\">Read and interpret a histogram<\/span><\/li>\r\n<\/ul>\r\n<\/section>\r\n<h2>Illustrating Frequency with Histograms<\/h2>\r\n<p>When presented with large data sets, the dotplot is sometimes cumbersome to put together. In addition, it may not be the cleanest way to present the data. For large data sets, a histogram can represent the numerous data points more simply as bars instead of an immense amount of data points in a dotplot.<\/p>\r\n<section class=\"textbox keyTakeaway\">\r\n<h3>histogram<\/h3>\r\n<p>Similar to a dotplot, a\u00a0<strong>histogram<\/strong> is another tool to display the frequency and distribution of quantitative data.<\/p>\r\n<p>Each bar represents a group of data points that fall into an interval of measurable values. The width, called\u00a0<strong>binwidth<\/strong>, of each bar is equivalent and can represent any interval of values desired.<\/p>\r\n<\/section>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]2336[\/ohm2_question]<\/section>\r\n<section class=\"textbox proTip\">\r\n<h3>Important notes about histograms:<\/h3>\r\n<ul>\r\n\t<li>A visualization of quantitative data<\/li>\r\n\t<li>More concisely display large data sets<\/li>\r\n\t<li>Histograms do not display individual data values like dotplots.<\/li>\r\n\t<li>The horizontal axis on a histogram is partitions into intervals.<\/li>\r\n<\/ul>\r\n<\/section>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]2337[\/ohm2_question]<\/section>\r\n<h2>Comparing Groups with Histograms<\/h2>\r\n<p>When we conduct statistical experiments, we often work with multiple data sets to make inferences regarding the variable of interest. Let\u2019s look at an example where we compare different groups using histograms as the graphical displays.<\/p>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]2338[\/ohm2_question]<\/section>\r\n<h2>Dotplots vs Histograms<\/h2>\r\n<section class=\"textbox proTip\">We used two types of graphs to analyze the distribution of a quantitative variable: dotplots and histograms. In these graphs, we can see:\r\n\r\n<ul>\r\n\t<li>The possible values of the variable.<\/li>\r\n\t<li>The number of individuals with each variable value or interval of values.<\/li>\r\n<\/ul>\r\n<p>How do we decide when to use a dotplot and when to use a histogram? There are no rules here. Each type of graph can be used to highlight different aspects of the data.<\/p>\r\n<h4>What we know about dotplots:<\/h4>\r\n<ul>\r\n\t<li>Individual variable values are visible, particularly when the data set is small.<\/li>\r\n\t<li>Descriptions of shape, center, and spread are not affected by how the dotplot is constructed.<\/li>\r\n\t<li>We can accurately calculate the overall range (largest value - smallest value).<\/li>\r\n<\/ul>\r\n<h4>What we know about histograms:<\/h4>\r\n<ul>\r\n\t<li>Individual variable values are not visible.<\/li>\r\n\t<li>Grouping individuals into bins of equal-sized intervals is particularly useful when analyzing large data sets.<\/li>\r\n\t<li>We can easily use percentages, also called relative frequencies, to describe the distribution.<\/li>\r\n\t<li>Descriptions of shape, center, and spread are affected by how the bins are defined.<\/li>\r\n<\/ul>\r\n<\/section>","rendered":"<section class=\"textbox learningGoals\">\n<ul>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Read and interpret a histogram&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:4608,&quot;12&quot;:0,&quot;15&quot;:&quot;Calibri&quot;}\">Read and interpret a histogram<\/span><\/li>\n<\/ul>\n<\/section>\n<h2>Illustrating Frequency with Histograms<\/h2>\n<p>When presented with large data sets, the dotplot is sometimes cumbersome to put together. In addition, it may not be the cleanest way to present the data. For large data sets, a histogram can represent the numerous data points more simply as bars instead of an immense amount of data points in a dotplot.<\/p>\n<section class=\"textbox keyTakeaway\">\n<h3>histogram<\/h3>\n<p>Similar to a dotplot, a\u00a0<strong>histogram<\/strong> is another tool to display the frequency and distribution of quantitative data.<\/p>\n<p>Each bar represents a group of data points that fall into an interval of measurable values. The width, called\u00a0<strong>binwidth<\/strong>, of each bar is equivalent and can represent any interval of values desired.<\/p>\n<\/section>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm2336\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=2336&theme=lumen&iframe_resize_id=ohm2336&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<section class=\"textbox proTip\">\n<h3>Important notes about histograms:<\/h3>\n<ul>\n<li>A visualization of quantitative data<\/li>\n<li>More concisely display large data sets<\/li>\n<li>Histograms do not display individual data values like dotplots.<\/li>\n<li>The horizontal axis on a histogram is partitions into intervals.<\/li>\n<\/ul>\n<\/section>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm2337\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=2337&theme=lumen&iframe_resize_id=ohm2337&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<h2>Comparing Groups with Histograms<\/h2>\n<p>When we conduct statistical experiments, we often work with multiple data sets to make inferences regarding the variable of interest. Let\u2019s look at an example where we compare different groups using histograms as the graphical displays.<\/p>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm2338\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=2338&theme=lumen&iframe_resize_id=ohm2338&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<h2>Dotplots vs Histograms<\/h2>\n<section class=\"textbox proTip\">We used two types of graphs to analyze the distribution of a quantitative variable: dotplots and histograms. In these graphs, we can see:<\/p>\n<ul>\n<li>The possible values of the variable.<\/li>\n<li>The number of individuals with each variable value or interval of values.<\/li>\n<\/ul>\n<p>How do we decide when to use a dotplot and when to use a histogram? There are no rules here. Each type of graph can be used to highlight different aspects of the data.<\/p>\n<h4>What we know about dotplots:<\/h4>\n<ul>\n<li>Individual variable values are visible, particularly when the data set is small.<\/li>\n<li>Descriptions of shape, center, and spread are not affected by how the dotplot is constructed.<\/li>\n<li>We can accurately calculate the overall range (largest value &#8211; smallest value).<\/li>\n<\/ul>\n<h4>What we know about histograms:<\/h4>\n<ul>\n<li>Individual variable values are not visible.<\/li>\n<li>Grouping individuals into bins of equal-sized intervals is particularly useful when analyzing large data sets.<\/li>\n<li>We can easily use percentages, also called relative frequencies, to describe the distribution.<\/li>\n<li>Descriptions of shape, center, and spread are affected by how the bins are defined.<\/li>\n<\/ul>\n<\/section>\n","protected":false},"author":13,"menu_order":3,"template":"","meta":{"_candela_citation":"[]","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"part":834,"module-header":"background_you_need","content_attributions":[],"internal_book_links":[],"video_content":null,"cc_video_embed_content":{"cc_scripts":"","media_targets":[]},"try_it_collection":null,"_links":{"self":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/839"}],"collection":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/users\/13"}],"version-history":[{"count":6,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/839\/revisions"}],"predecessor-version":[{"id":5305,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/839\/revisions\/5305"}],"part":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/parts\/834"}],"metadata":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/839\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/media?parent=839"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapter-type?post=839"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/contributor?post=839"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/license?post=839"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}