{"id":2974,"date":"2024-04-24T09:00:17","date_gmt":"2024-04-24T16:00:17","guid":{"rendered":"https:\/\/magoosh.com\/gmat\/?p=2974"},"modified":"2017-03-01T11:10:18","modified_gmt":"2017-03-01T19:10:18","slug":"gmat-data-insights-scatterplots","status":"publish","type":"post","link":"https:\/\/magoosh.com\/gmat\/gmat-data-insights-scatterplots\/","title":{"rendered":"GMAT Data Insights: Scatterplots"},"content":{"rendered":"

One of the most common types of graphs is statistics and in the quantitative sciences is a scatterplot.\u00a0 A scatterplot is a way of displaying bivariate<\/span> data: that is, data in which we measure two different variables<\/span> for each participant.\u00a0 For example, suppose we ask several people both their age & their weight, or both their annual income & the amount of debt they carry, or both their number of kids and number of credit cards; suppose we measure for several cars both the weight and the gas mileage; suppose we measure for several public traded companies both the annual revenue and the price, per share, of their stock; etc.\u00a0 In all of those cases, each individual (each person, each car, each company) would be a single dot on the graph, and the graph would have as many dots as individuals surveyed or measured.\u00a0 Scatterplots are very popular graphs.\u00a0 Expect to see scatterplots on the GMAT Data Insights section.<\/p>\n

 <\/p>\n

An Example of a Scatterplot<\/h2>\n

Below is a scatterplot on which the individuals are countries.\u00a0 Each dot is a country.<\/p>\n

\"\"<\/a><\/p>\n

On this graph, the x-axis is the GDP-per-capita of the country.\u00a0 The GDP (Gross Domestic Product) is a measure of the amount of business the country conducts: the size of this depends on both the inherent wealth of the country and the population.\u00a0 When we divide that by the population of the country, we get GDP-per-capita, which is an excellent measure of the average wealth of the country.\u00a0 The y-axis is life-expectancy at birth in that country.\u00a0 The sideways L-shape tells the story: For countries with a GDP-per-capita above $20K, life-expectancy at birth is between 70 and 80 years, but for the poor countries, those with a GDP-per-capita less than about $20K, life-expectancy at birth varies considerably, and is in many cases considerably less than the 70+ years that is standard for most of the world.<\/p>\n

Now, as an example of a scatterplot with two different marks on the graph, here the same graph again, with some of the points marked differently.<\/p>\n

\"\"<\/a><\/p>\n

On this graph, the grey circles are countries on the continent of Africa, and the blue squares are countries in the rest of the world.\u00a0 Notice that essentially, the entire continent of Africa is in the “vertical arm” of the L on the left side, while the rest of the world predominantly makes the “horizontal arm” of the L at the top of the graph.\u00a0 In other words, if you are born in Africa, your odds from birth are far worse than if you are born anywhere else on the planet.\u00a0 The international social justice implications of this are staggering, and well beyond what I can discuss here.\u00a0 This does, at least, give a taste of how the Data Insights section might ask you to draw a politically or ethically important conclusion from a graph.\u00a0 Suffice it to say: displaying data in a scatterplot can make truly important information visually apparent.<\/p>\n

This gets to the heart of why mathematician love graphs, and why the GMAT is likely to give you graphs like scatterplots.\u00a0 A scatterplot makes the relationship between two variables, over a potentially large number of data points, visible at a glance.\u00a0 The more you practice with these graphs, the more you will appreciate their astonishing capacity to convey information in visual form.<\/p>\n

 <\/p>\n

Further practice<\/h2>\n

Where in the real world might you see scatterplots?\u00a0 As with much of the rest of GMAT Data Insights material, I highly recommend the New York Times<\/em>,\u00a0Bloomberg Businessweek<\/em>, and the Economist Magazine<\/em> as excellent sources that often display complex and highly relevant information in graphical form.\u00a0 Also, here’s a Magoosh practice question:<\/p>\n

1) http:\/\/gmat.magoosh.com\/questions\/2303<\/a><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

One of the most common types of graphs is statistics and in the quantitative sciences is a scatterplot.\u00a0 A scatterplot is a way of displaying bivariate data: that is, data in which we measure two different variables for each participant.\u00a0 For example, suppose we ask several people both their age & their weight, or both […]<\/p>\n","protected":false},"author":26,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[13262],"tags":[],"ppma_author":[13209],"class_list":["post-2974","post","type-post","status-publish","format-standard","hentry","category-data-insights"],"acf":[],"yoast_head":"\nGMAT Data Insights: Scatterplots - Magoosh Blog \u2014 GMAT\u00ae Exam<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/magoosh.com\/gmat\/gmat-data-insights-scatterplots\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"GMAT Data Insights: Scatterplots\" \/>\n<meta property=\"og:description\" content=\"One of the most common types of graphs is statistics and in the quantitative sciences is a scatterplot.\u00a0 A scatterplot is a way of displaying bivariate data: that is, data in which we measure two different variables for each participant.\u00a0 For example, suppose we ask several people both their age & their weight, or both […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/magoosh.com\/gmat\/gmat-data-insights-scatterplots\/\" \/>\n<meta property=\"og:site_name\" content=\"Magoosh Blog \u2014 GMAT\u00ae Exam\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/MagooshGMAT\/\" \/>\n<meta property=\"article:published_time\" content=\"2024-04-24T16:00:17+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2017-03-01T19:10:18+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/magoosh.com\/gmat\/files\/2012\/10\/s_img1.png\" \/>\n<meta name=\"author\" content=\"Mike M\u1d9cGarry\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@MagooshGMAT\" \/>\n<meta name=\"twitter:site\" content=\"@MagooshGMAT\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Mike M\u1d9cGarry\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/magoosh.com\/gmat\/gmat-data-insights-scatterplots\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/magoosh.com\/gmat\/gmat-data-insights-scatterplots\/\"},\"author\":{\"name\":\"Mike M\u1d9cGarry\",\"@id\":\"https:\/\/magoosh.com\/gmat\/#\/schema\/person\/320346c205075513344435baf9b0521b\"},\"headline\":\"GMAT Data Insights: Scatterplots\",\"datePublished\":\"2024-04-24T16:00:17+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/magoosh.com\/gmat\/gmat-data-insights-scatterplots\/\"},\"wordCount\":620,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/magoosh.com\/gmat\/#organization\"},\"articleSection\":[\"GMAT Data Insights\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/magoosh.com\/gmat\/gmat-data-insights-scatterplots\/\",\"url\":\"https:\/\/magoosh.com\/gmat\/gmat-data-insights-scatterplots\/\",\"name\":\"GMAT Data Insights: Scatterplots - 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Beyond standardized testing, Mike has over 20 years of both private and public high school teaching experience specializing in math and physics. In his free time, Mike likes smashing foosballs into orbit, and despite having no obvious cranial deficiency, he insists on rooting for the NY Mets. Learn more about the GMAT through Mike's Youtube video explanations.","sameAs":["https:\/\/www.youtube.com\/c\/MagooshGMATChannel\/featured"],"award":["Magna cum laude from Harvard"],"knowsAbout":["GMAT"],"knowsLanguage":["English"],"jobTitle":"Content Creator","worksFor":"Magoosh","url":"https:\/\/magoosh.com\/gmat\/author\/mikemcgarry\/"}]}},"authors":[{"term_id":13209,"user_id":26,"is_guest":0,"slug":"mikemcgarry","display_name":"Mike M\u1d9cGarry","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/6b06de81592cd77bb46aa560cc59aee179cba4d042835c3529221ea1b344cce0?s=96&d=mm&r=g","user_url":"","last_name":"M\u1d9cGarry","first_name":"Mike","description":"Mike served as a GMAT Expert at Magoosh, helping create hundreds of lesson videos and practice questions to help guide GMAT students to success. He was also featured as \"member of the month\" for over two years at <a href=\"https:\/\/gmatclub.com\/blog\/2012\/09\/mike-mcgarrys-gmat-experience\/\" rel=\"noopener noreferrer\">GMAT Club<\/a>. Mike holds an A.B. in Physics (graduating <em>magna cum laude<\/em>) and an M.T.S. in Religions of the World, both from Harvard. Beyond standardized testing, Mike has over 20 years of both private and public high school teaching experience specializing in math and physics. In his free time, Mike likes smashing foosballs into orbit, and despite having no obvious cranial deficiency, he insists on rooting for the NY Mets. Learn more about the GMAT through Mike's <a href=\"https:\/\/www.youtube.com\/c\/MagooshGMATChannel\/featured\" rel=\"noopener noreferrer\">Youtube <\/a>video explanations and resources like <a href=\"https:\/\/magoosh.com\/gmat\/whats-a-good-gmat-score\/\" rel=\"noopener noreferrer\">What is a Good GMAT Score?<\/a> and the <a href=\"https:\/\/magoosh.com\/gmat\/gmat-diagnostic-test\/\" rel=\"noopener noreferrer\">GMAT Diagnostic Test<\/a>."}],"_links":{"self":[{"href":"https:\/\/magoosh.com\/gmat\/wp-json\/wp\/v2\/posts\/2974","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/magoosh.com\/gmat\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/magoosh.com\/gmat\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/magoosh.com\/gmat\/wp-json\/wp\/v2\/users\/26"}],"replies":[{"embeddable":true,"href":"https:\/\/magoosh.com\/gmat\/wp-json\/wp\/v2\/comments?post=2974"}],"version-history":[{"count":0,"href":"https:\/\/magoosh.com\/gmat\/wp-json\/wp\/v2\/posts\/2974\/revisions"}],"wp:attachment":[{"href":"https:\/\/magoosh.com\/gmat\/wp-json\/wp\/v2\/media?parent=2974"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/magoosh.com\/gmat\/wp-json\/wp\/v2\/categories?post=2974"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/magoosh.com\/gmat\/wp-json\/wp\/v2\/tags?post=2974"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/magoosh.com\/gmat\/wp-json\/wp\/v2\/ppma_author?post=2974"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}