{"id":1616,"date":"2023-04-11T17:03:45","date_gmt":"2023-04-11T17:03:45","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/?post_type=chapter&#038;p=1616"},"modified":"2024-10-18T20:54:11","modified_gmt":"2024-10-18T20:54:11","slug":"representing-data-graphically-learn-it-4","status":"web-only","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/chapter\/representing-data-graphically-learn-it-4\/","title":{"raw":"Representing Data Graphically: Learn It 4","rendered":"Representing Data Graphically: Learn It 4"},"content":{"raw":"<h2>Dot Plots<\/h2>\r\n<p>Dot plots are a straightforward yet effective method for depicting frequencies in a data set. They map individual data points as dots along a single axis, making it easier to visualize and count occurrences of each value. Let's consider how this simplicity aids in data analysis, particularly with smaller data sets.<\/p>\r\n<section class=\"textbox keyTakeaway\">\r\n<div>\r\n<h3>dot plot<\/h3>\r\n<p><strong>Dot plots<\/strong> display how many individual observations there are of each value observed, so each observation in the data set appears as its own dot on the graph.<\/p>\r\n<\/div>\r\n<\/section>\r\n<section class=\"textbox proTip\">A large number of observations could overwhelm the display so dot plots work well when the data set is small.<\/section>\r\n<section class=\"textbox play\">Create a dot plot of the data set \"<strong>Oscars Age<\/strong>\" using the Describing and Exploring Quantitative Data tool. Steps to create a dot plot:<br \/>\r\n<br \/>\r\n<strong>STEP 1:<\/strong> Select \"Single Group\".<br \/>\r\n<strong><br \/>\r\nSTEP 2:<\/strong> Select the Data Set \"Oscars: Age\".<br \/>\r\n<strong><br \/>\r\nSTEP 3:<\/strong> Under \"Choose Type of Plot\", select \"Dotplot\".<br \/>\r\n<strong><br \/>\r\nSTEP 4:<\/strong> Choose a \"Dotsize\" or \"[latex]1[\/latex]\", and a \"Binwidth\" of \"[latex]1[\/latex]\".<\/section>\r\n<p><iframe src=\"https:\/\/lumen-learning.shinyapps.io\/eda_quantitative\/ \" width=\"100%\" height=\"900\" frameborder=\"no\"><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><\/iframe><br \/>\r\n[<a href=\"https:\/\/lumen-learning.shinyapps.io\/eda_quantitative\/\" target=\"_blank\" rel=\"noopener\">Trouble viewing? Click to open in a new tab.<\/a>]<\/p>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]780[\/ohm2_question]<\/section>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]781[\/ohm2_question]<\/section>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]782[\/ohm2_question]<\/section>","rendered":"<h2>Dot Plots<\/h2>\n<p>Dot plots are a straightforward yet effective method for depicting frequencies in a data set. They map individual data points as dots along a single axis, making it easier to visualize and count occurrences of each value. Let&#8217;s consider how this simplicity aids in data analysis, particularly with smaller data sets.<\/p>\n<section class=\"textbox keyTakeaway\">\n<div>\n<h3>dot plot<\/h3>\n<p><strong>Dot plots<\/strong> display how many individual observations there are of each value observed, so each observation in the data set appears as its own dot on the graph.<\/p>\n<\/div>\n<\/section>\n<section class=\"textbox proTip\">A large number of observations could overwhelm the display so dot plots work well when the data set is small.<\/section>\n<section class=\"textbox play\">Create a dot plot of the data set &#8220;<strong>Oscars Age<\/strong>&#8221; using the Describing and Exploring Quantitative Data tool. Steps to create a dot plot:<\/p>\n<p><strong>STEP 1:<\/strong> Select &#8220;Single Group&#8221;.<br \/>\n<strong><br \/>\nSTEP 2:<\/strong> Select the Data Set &#8220;Oscars: Age&#8221;.<br \/>\n<strong><br \/>\nSTEP 3:<\/strong> Under &#8220;Choose Type of Plot&#8221;, select &#8220;Dotplot&#8221;.<br \/>\n<strong><br \/>\nSTEP 4:<\/strong> Choose a &#8220;Dotsize&#8221; or &#8220;[latex]1[\/latex]&#8220;, and a &#8220;Binwidth&#8221; of &#8220;[latex]1[\/latex]&#8220;.<\/section>\n<p><iframe loading=\"lazy\" src=\"https:\/\/lumen-learning.shinyapps.io\/eda_quantitative\/\" width=\"100%\" height=\"900\" frameborder=\"no\"><span data-mce-type=\"bookmark\" style=\"display: inline-block; width: 0px; overflow: hidden; line-height: 0;\" class=\"mce_SELRES_start\">\ufeff<\/span><\/iframe><br \/>\n[<a href=\"https:\/\/lumen-learning.shinyapps.io\/eda_quantitative\/\" target=\"_blank\" rel=\"noopener\">Trouble viewing? Click to open in a new tab.<\/a>]<\/p>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm780\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=780&theme=lumen&iframe_resize_id=ohm780&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm781\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=781&theme=lumen&iframe_resize_id=ohm781&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm782\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=782&theme=lumen&iframe_resize_id=ohm782&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n","protected":false},"author":15,"menu_order":7,"template":"","meta":{"_candela_citation":"[]","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"part":1572,"module-header":"learn_it","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\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/1616"}],"collection":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/users\/15"}],"version-history":[{"count":15,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/1616\/revisions"}],"predecessor-version":[{"id":14525,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/1616\/revisions\/14525"}],"part":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/parts\/1572"}],"metadata":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapters\/1616\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/media?parent=1616"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/pressbooks\/v2\/chapter-type?post=1616"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/contributor?post=1616"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/quantitativereasoning\/wp-json\/wp\/v2\/license?post=1616"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}