{"id":49,"date":"2017-04-15T03:15:17","date_gmt":"2017-04-15T03:15:17","guid":{"rendered":"https:\/\/courses.lumenlearning.com\/conceptstest1\/chapter\/what-is-data\/"},"modified":"2022-08-01T16:04:17","modified_gmt":"2022-08-01T16:04:17","slug":"what-is-data","status":"publish","type":"chapter","link":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/chapter\/what-is-data\/","title":{"raw":"Categorical vs. Quantitative Data","rendered":"Categorical vs. Quantitative Data"},"content":{"raw":"<div class=\"textbox learning-objectives\">\r\n<h3>Learning OUTCOMES<\/h3>\r\n<ul>\r\n \t<li>Distinguish between quantitative and categorical variables in context.<\/li>\r\n<\/ul>\r\n<\/div>\r\n<strong>Data<\/strong> consist of <strong>individuals<\/strong> and <strong>variables <\/strong> that give us information about those individuals. An individual can be an object or a person. A variable is an attribute, such as a measurement or a label.\r\n<div class=\"textbox exercises\">\r\n<h3>Example<\/h3>\r\n<h2>Medical Records<\/h2>\r\nThis dataset is from a medical study. In this study, researchers wanted to identify variables connected to low birth weights.\r\n<table style=\"border-collapse: collapse; width: 100%; height: 70px;\" border=\"1\">\r\n<thead>\r\n<tr style=\"height: 10px;\">\r\n<th style=\"height: 10px;\" scope=\"col\"><\/th>\r\n<th style=\"height: 10px;\" scope=\"col\">Age at delivery<\/th>\r\n<th style=\"height: 10px;\" scope=\"col\">Weight prior to pregnancy (pounds)<\/th>\r\n<th style=\"height: 10px;\" scope=\"col\">Smoker<\/th>\r\n<th style=\"height: 10px;\" scope=\"col\">Doctor visits during 1st trimester<\/th>\r\n<th style=\"height: 10px;\" scope=\"col\">Race<\/th>\r\n<th style=\"height: 10px;\" scope=\"col\">Birth Weight (grams)<\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr style=\"height: 10px;\">\r\n<td style=\"height: 10px;\">Patient 1<\/td>\r\n<td style=\"height: 10px;\">29<\/td>\r\n<td style=\"height: 10px;\">140<\/td>\r\n<td style=\"height: 10px;\">Yes<\/td>\r\n<td style=\"height: 10px;\">2<\/td>\r\n<td style=\"height: 10px;\">Caucasian<\/td>\r\n<td style=\"height: 10px;\">2977<\/td>\r\n<\/tr>\r\n<tr style=\"height: 10px;\">\r\n<td style=\"height: 10px;\">Patient 2<\/td>\r\n<td style=\"height: 10px;\">32<\/td>\r\n<td style=\"height: 10px;\">132<\/td>\r\n<td style=\"height: 10px;\">No<\/td>\r\n<td style=\"height: 10px;\">4<\/td>\r\n<td style=\"height: 10px;\">Caucasian<\/td>\r\n<td style=\"height: 10px;\">3080<\/td>\r\n<\/tr>\r\n<tr style=\"height: 10px;\">\r\n<td style=\"height: 10px;\">Patient 3<\/td>\r\n<td style=\"height: 10px;\">36<\/td>\r\n<td style=\"height: 10px;\">175<\/td>\r\n<td style=\"height: 10px;\">No<\/td>\r\n<td style=\"height: 10px;\">0<\/td>\r\n<td style=\"height: 10px;\">African-American<\/td>\r\n<td style=\"height: 10px;\">3600<\/td>\r\n<\/tr>\r\n<tr style=\"height: 10px;\">\r\n<td style=\"height: 10px;\">*<\/td>\r\n<td style=\"height: 10px;\">*<\/td>\r\n<td style=\"height: 10px;\">*<\/td>\r\n<td style=\"height: 10px;\">*<\/td>\r\n<td style=\"height: 10px;\">*<\/td>\r\n<td style=\"height: 10px;\">*<\/td>\r\n<td style=\"height: 10px;\">*<\/td>\r\n<\/tr>\r\n<tr style=\"height: 10px;\">\r\n<td style=\"height: 10px;\">*<\/td>\r\n<td style=\"height: 10px;\">*<\/td>\r\n<td style=\"height: 10px;\">*<\/td>\r\n<td style=\"height: 10px;\">*<\/td>\r\n<td style=\"height: 10px;\">*<\/td>\r\n<td style=\"height: 10px;\">*<\/td>\r\n<td style=\"height: 10px;\">*<\/td>\r\n<\/tr>\r\n<tr style=\"height: 10px;\">\r\n<td style=\"height: 10px;\">Patient 189<\/td>\r\n<td style=\"height: 10px;\">30<\/td>\r\n<td style=\"height: 10px;\">95<\/td>\r\n<td style=\"height: 10px;\">Yes<\/td>\r\n<td style=\"height: 10px;\">2<\/td>\r\n<td style=\"height: 10px;\">Asian<\/td>\r\n<td style=\"height: 10px;\">3147<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\nIn this example, the individuals are the patients (the mothers). There are six variables in this dataset:\r\n<ul>\r\n \t<li>Mother\u2019s age at delivery (years)<\/li>\r\n \t<li>Mother\u2019s weight prior to pregnancy (pounds)<\/li>\r\n \t<li>Whether mother smoked during pregnancy (yes, no)<\/li>\r\n \t<li>Number of doctor visits during first trimester of pregnancy<\/li>\r\n \t<li>Mother\u2019s race (Caucasian, African American, Asian, etc.)<\/li>\r\n \t<li>Baby\u2019s birth weight (grams)<\/li>\r\n<\/ul>\r\n<\/div>\r\nThere are two types of variables: quantitative and categorical.\r\n<ul>\r\n \t<li><strong>Categorical variables<\/strong> take category or label values and place an individual into one of several groups. Each observation can be placed in only one category, and the categories are mutually exclusive. In our example of medical records, smoking is a categorical variable, with two groups, since each participant can be categorized only as either a nonsmoker or a smoker. Gender and race are the two other categorical variables in our medical records example.<\/li>\r\n \t<li><strong>Quantitative variables<\/strong> take numerical values and represent some kind of measurement. In our medical example, age is an example of a quantitative variable because it can take on multiple numerical values. It also makes sense to think about it in numerical form; that is, a person can be 18 years old or 80 years old. Weight and height are also examples of quantitative variables.<\/li>\r\n<\/ul>\r\n<div class=\"textbox key-takeaways\">\r\n<h3>Try It<\/h3>\r\nWe took a random sample from the 2000 US Census. Here is part of the dataset.\r\n<table><caption>Sample of 2000 US Census Data<\/caption>\r\n<tbody>\r\n<tr>\r\n<th scope=\"col\">State<\/th>\r\n<th scope=\"col\">Zipcode<\/th>\r\n<th scope=\"col\">Family_Size<\/th>\r\n<th scope=\"col\">Annual_Income<\/th>\r\n<\/tr>\r\n<tr>\r\n<td>Florida<\/td>\r\n<td>32716<\/td>\r\n<td>8<\/td>\r\n<td>200<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Alabama<\/td>\r\n<td>35236<\/td>\r\n<td>5<\/td>\r\n<td>800<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Florida<\/td>\r\n<td>32116<\/td>\r\n<td>6<\/td>\r\n<td>13500<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Florida<\/td>\r\n<td>33679<\/td>\r\n<td>5<\/td>\r\n<td>21000<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Alabama<\/td>\r\n<td>36374<\/td>\r\n<td>4<\/td>\r\n<td>21000<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>California<\/td>\r\n<td>94565<\/td>\r\n<td>1<\/td>\r\n<td>23000<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\nhttps:\/\/assess.lumenlearning.com\/practice\/48f86a54-110c-4d23-a24a-b9ab43fa9d80\r\n\r\nhttps:\/\/assess.lumenlearning.com\/practice\/cf4026e4-65d4-4e70-bc72-92cb9426efa0\r\n\r\n<\/div>\r\n<div class=\"textbox key-takeaways\">\r\n<h3>Try It<\/h3>\r\n<em>Consumer Reports <\/em>analyzed a dataset of 77 breakfast cereals. Here is a part of the dataset.\r\n\r\n(Note: Consumer Reports is an non-profit organization that rates products in an effort to help consumers make informed decisions.)\r\n<table>\r\n<caption>Sample of Consumer Reports Breakfast Cereal Data<\/caption>\r\n<tbody>\r\n<tr>\r\n<th scope=\"col\">Name<\/th>\r\n<th scope=\"col\">Manufactuer<\/th>\r\n<th scope=\"col\">Target<\/th>\r\n<th scope=\"col\">Shelf<\/th>\r\n<th scope=\"col\">Calories<\/th>\r\n<th scope=\"col\">Sodium<\/th>\r\n<th scope=\"col\">Fat<\/th>\r\n<\/tr>\r\n<tr>\r\n<td>100% Bran<\/td>\r\n<td>Nabisco<\/td>\r\n<td>adult<\/td>\r\n<td>top<\/td>\r\n<td>70<\/td>\r\n<td>130<\/td>\r\n<td>1<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>100% Natural Bran<\/td>\r\n<td>Quaker Oats<\/td>\r\n<td>adult<\/td>\r\n<td>top<\/td>\r\n<td>120<\/td>\r\n<td>15<\/td>\r\n<td>5<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>All-Bran<\/td>\r\n<td>Kelloggs<\/td>\r\n<td>adult<\/td>\r\n<td>top<\/td>\r\n<td>70<\/td>\r\n<td>260<\/td>\r\n<td>1<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>All-Bran Extra Fiber<\/td>\r\n<td>Kelloggs<\/td>\r\n<td>adult<\/td>\r\n<td>top<\/td>\r\n<td>50<\/td>\r\n<td>140<\/td>\r\n<td>0<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Almond Delight<\/td>\r\n<td>Ralston Purnia<\/td>\r\n<td>adult<\/td>\r\n<td>top<\/td>\r\n<td>110<\/td>\r\n<td>200<\/td>\r\n<td>2<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Apple Cinnamon Cheerios<\/td>\r\n<td>General Mills<\/td>\r\n<td>child<\/td>\r\n<td>bottom<\/td>\r\n<td>110<\/td>\r\n<td>180<\/td>\r\n<td>2<\/td>\r\n<\/tr>\r\n<tr>\r\n<td>Apple Jacks<\/td>\r\n<td>Kelloggs<\/td>\r\n<td>child<\/td>\r\n<td>middle<\/td>\r\n<td>110<\/td>\r\n<td>125<\/td>\r\n<td>0<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n&nbsp;\r\n\r\nhttps:\/\/assess.lumenlearning.com\/practice\/518cd9ae-4fb3-42f5-9c66-a2b491701150\r\n\r\nhttps:\/\/assess.lumenlearning.com\/practice\/84f7e795-f3f5-489b-9103-352466097041\r\n\r\n<\/div>\r\n<h2>Contribute!<\/h2><div style=\"margin-bottom: 8px;\">Did you have an idea for improving this content? We\u2019d love your input.<\/div><a href=\"https:\/\/docs.google.com\/document\/d\/1rp-xI5E-RKXJGKz2-X55ihLidIEDlLl6hsCz-I7ZiGM\" target=\"_blank\" style=\"font-size: 10pt; font-weight: 600; color: #077fab; text-decoration: none; border: 2px solid #077fab; border-radius: 7px; padding: 5px 25px; text-align: center; cursor: pointer; line-height: 1.5em;\">Improve this page<\/a><a style=\"margin-left: 16px;\" target=\"_blank\" href=\"https:\/\/docs.google.com\/document\/d\/1vy-T6DtTF-BbMfpVEI7VP_R7w2A4anzYZLXR8Pk4Fu4\">Learn More<\/a>","rendered":"<div class=\"textbox learning-objectives\">\n<h3>Learning OUTCOMES<\/h3>\n<ul>\n<li>Distinguish between quantitative and categorical variables in context.<\/li>\n<\/ul>\n<\/div>\n<p><strong>Data<\/strong> consist of <strong>individuals<\/strong> and <strong>variables <\/strong> that give us information about those individuals. An individual can be an object or a person. A variable is an attribute, such as a measurement or a label.<\/p>\n<div class=\"textbox exercises\">\n<h3>Example<\/h3>\n<h2>Medical Records<\/h2>\n<p>This dataset is from a medical study. In this study, researchers wanted to identify variables connected to low birth weights.<\/p>\n<table style=\"border-collapse: collapse; width: 100%; height: 70px;\">\n<thead>\n<tr style=\"height: 10px;\">\n<th style=\"height: 10px;\" scope=\"col\"><\/th>\n<th style=\"height: 10px;\" scope=\"col\">Age at delivery<\/th>\n<th style=\"height: 10px;\" scope=\"col\">Weight prior to pregnancy (pounds)<\/th>\n<th style=\"height: 10px;\" scope=\"col\">Smoker<\/th>\n<th style=\"height: 10px;\" scope=\"col\">Doctor visits during 1st trimester<\/th>\n<th style=\"height: 10px;\" scope=\"col\">Race<\/th>\n<th style=\"height: 10px;\" scope=\"col\">Birth Weight (grams)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"height: 10px;\">\n<td style=\"height: 10px;\">Patient 1<\/td>\n<td style=\"height: 10px;\">29<\/td>\n<td style=\"height: 10px;\">140<\/td>\n<td style=\"height: 10px;\">Yes<\/td>\n<td style=\"height: 10px;\">2<\/td>\n<td style=\"height: 10px;\">Caucasian<\/td>\n<td style=\"height: 10px;\">2977<\/td>\n<\/tr>\n<tr style=\"height: 10px;\">\n<td style=\"height: 10px;\">Patient 2<\/td>\n<td style=\"height: 10px;\">32<\/td>\n<td style=\"height: 10px;\">132<\/td>\n<td style=\"height: 10px;\">No<\/td>\n<td style=\"height: 10px;\">4<\/td>\n<td style=\"height: 10px;\">Caucasian<\/td>\n<td style=\"height: 10px;\">3080<\/td>\n<\/tr>\n<tr style=\"height: 10px;\">\n<td style=\"height: 10px;\">Patient 3<\/td>\n<td style=\"height: 10px;\">36<\/td>\n<td style=\"height: 10px;\">175<\/td>\n<td style=\"height: 10px;\">No<\/td>\n<td style=\"height: 10px;\">0<\/td>\n<td style=\"height: 10px;\">African-American<\/td>\n<td style=\"height: 10px;\">3600<\/td>\n<\/tr>\n<tr style=\"height: 10px;\">\n<td style=\"height: 10px;\">*<\/td>\n<td style=\"height: 10px;\">*<\/td>\n<td style=\"height: 10px;\">*<\/td>\n<td style=\"height: 10px;\">*<\/td>\n<td style=\"height: 10px;\">*<\/td>\n<td style=\"height: 10px;\">*<\/td>\n<td style=\"height: 10px;\">*<\/td>\n<\/tr>\n<tr style=\"height: 10px;\">\n<td style=\"height: 10px;\">*<\/td>\n<td style=\"height: 10px;\">*<\/td>\n<td style=\"height: 10px;\">*<\/td>\n<td style=\"height: 10px;\">*<\/td>\n<td style=\"height: 10px;\">*<\/td>\n<td style=\"height: 10px;\">*<\/td>\n<td style=\"height: 10px;\">*<\/td>\n<\/tr>\n<tr style=\"height: 10px;\">\n<td style=\"height: 10px;\">Patient 189<\/td>\n<td style=\"height: 10px;\">30<\/td>\n<td style=\"height: 10px;\">95<\/td>\n<td style=\"height: 10px;\">Yes<\/td>\n<td style=\"height: 10px;\">2<\/td>\n<td style=\"height: 10px;\">Asian<\/td>\n<td style=\"height: 10px;\">3147<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>In this example, the individuals are the patients (the mothers). There are six variables in this dataset:<\/p>\n<ul>\n<li>Mother\u2019s age at delivery (years)<\/li>\n<li>Mother\u2019s weight prior to pregnancy (pounds)<\/li>\n<li>Whether mother smoked during pregnancy (yes, no)<\/li>\n<li>Number of doctor visits during first trimester of pregnancy<\/li>\n<li>Mother\u2019s race (Caucasian, African American, Asian, etc.)<\/li>\n<li>Baby\u2019s birth weight (grams)<\/li>\n<\/ul>\n<\/div>\n<p>There are two types of variables: quantitative and categorical.<\/p>\n<ul>\n<li><strong>Categorical variables<\/strong> take category or label values and place an individual into one of several groups. Each observation can be placed in only one category, and the categories are mutually exclusive. In our example of medical records, smoking is a categorical variable, with two groups, since each participant can be categorized only as either a nonsmoker or a smoker. Gender and race are the two other categorical variables in our medical records example.<\/li>\n<li><strong>Quantitative variables<\/strong> take numerical values and represent some kind of measurement. In our medical example, age is an example of a quantitative variable because it can take on multiple numerical values. It also makes sense to think about it in numerical form; that is, a person can be 18 years old or 80 years old. Weight and height are also examples of quantitative variables.<\/li>\n<\/ul>\n<div class=\"textbox key-takeaways\">\n<h3>Try It<\/h3>\n<p>We took a random sample from the 2000 US Census. Here is part of the dataset.<\/p>\n<table>\n<caption>Sample of 2000 US Census Data<\/caption>\n<tbody>\n<tr>\n<th scope=\"col\">State<\/th>\n<th scope=\"col\">Zipcode<\/th>\n<th scope=\"col\">Family_Size<\/th>\n<th scope=\"col\">Annual_Income<\/th>\n<\/tr>\n<tr>\n<td>Florida<\/td>\n<td>32716<\/td>\n<td>8<\/td>\n<td>200<\/td>\n<\/tr>\n<tr>\n<td>Alabama<\/td>\n<td>35236<\/td>\n<td>5<\/td>\n<td>800<\/td>\n<\/tr>\n<tr>\n<td>Florida<\/td>\n<td>32116<\/td>\n<td>6<\/td>\n<td>13500<\/td>\n<\/tr>\n<tr>\n<td>Florida<\/td>\n<td>33679<\/td>\n<td>5<\/td>\n<td>21000<\/td>\n<\/tr>\n<tr>\n<td>Alabama<\/td>\n<td>36374<\/td>\n<td>4<\/td>\n<td>21000<\/td>\n<\/tr>\n<tr>\n<td>California<\/td>\n<td>94565<\/td>\n<td>1<\/td>\n<td>23000<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\t<iframe id=\"assessment_practice_48f86a54-110c-4d23-a24a-b9ab43fa9d80\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/48f86a54-110c-4d23-a24a-b9ab43fa9d80?iframe_resize_id=assessment_practice_id_48f86a54-110c-4d23-a24a-b9ab43fa9d80\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<p>\t<iframe id=\"assessment_practice_cf4026e4-65d4-4e70-bc72-92cb9426efa0\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/cf4026e4-65d4-4e70-bc72-92cb9426efa0?iframe_resize_id=assessment_practice_id_cf4026e4-65d4-4e70-bc72-92cb9426efa0\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<\/div>\n<div class=\"textbox key-takeaways\">\n<h3>Try It<\/h3>\n<p><em>Consumer Reports <\/em>analyzed a dataset of 77 breakfast cereals. Here is a part of the dataset.<\/p>\n<p>(Note: Consumer Reports is an non-profit organization that rates products in an effort to help consumers make informed decisions.)<\/p>\n<table>\n<caption>Sample of Consumer Reports Breakfast Cereal Data<\/caption>\n<tbody>\n<tr>\n<th scope=\"col\">Name<\/th>\n<th scope=\"col\">Manufactuer<\/th>\n<th scope=\"col\">Target<\/th>\n<th scope=\"col\">Shelf<\/th>\n<th scope=\"col\">Calories<\/th>\n<th scope=\"col\">Sodium<\/th>\n<th scope=\"col\">Fat<\/th>\n<\/tr>\n<tr>\n<td>100% Bran<\/td>\n<td>Nabisco<\/td>\n<td>adult<\/td>\n<td>top<\/td>\n<td>70<\/td>\n<td>130<\/td>\n<td>1<\/td>\n<\/tr>\n<tr>\n<td>100% Natural Bran<\/td>\n<td>Quaker Oats<\/td>\n<td>adult<\/td>\n<td>top<\/td>\n<td>120<\/td>\n<td>15<\/td>\n<td>5<\/td>\n<\/tr>\n<tr>\n<td>All-Bran<\/td>\n<td>Kelloggs<\/td>\n<td>adult<\/td>\n<td>top<\/td>\n<td>70<\/td>\n<td>260<\/td>\n<td>1<\/td>\n<\/tr>\n<tr>\n<td>All-Bran Extra Fiber<\/td>\n<td>Kelloggs<\/td>\n<td>adult<\/td>\n<td>top<\/td>\n<td>50<\/td>\n<td>140<\/td>\n<td>0<\/td>\n<\/tr>\n<tr>\n<td>Almond Delight<\/td>\n<td>Ralston Purnia<\/td>\n<td>adult<\/td>\n<td>top<\/td>\n<td>110<\/td>\n<td>200<\/td>\n<td>2<\/td>\n<\/tr>\n<tr>\n<td>Apple Cinnamon Cheerios<\/td>\n<td>General Mills<\/td>\n<td>child<\/td>\n<td>bottom<\/td>\n<td>110<\/td>\n<td>180<\/td>\n<td>2<\/td>\n<\/tr>\n<tr>\n<td>Apple Jacks<\/td>\n<td>Kelloggs<\/td>\n<td>child<\/td>\n<td>middle<\/td>\n<td>110<\/td>\n<td>125<\/td>\n<td>0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p>\t<iframe id=\"assessment_practice_518cd9ae-4fb3-42f5-9c66-a2b491701150\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/518cd9ae-4fb3-42f5-9c66-a2b491701150?iframe_resize_id=assessment_practice_id_518cd9ae-4fb3-42f5-9c66-a2b491701150\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<p>\t<iframe id=\"assessment_practice_84f7e795-f3f5-489b-9103-352466097041\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/84f7e795-f3f5-489b-9103-352466097041?iframe_resize_id=assessment_practice_id_84f7e795-f3f5-489b-9103-352466097041\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe><\/p>\n<\/div>\n<h2>Contribute!<\/h2>\n<div style=\"margin-bottom: 8px;\">Did you have an idea for improving this content? We\u2019d love your input.<\/div>\n<p><a href=\"https:\/\/docs.google.com\/document\/d\/1rp-xI5E-RKXJGKz2-X55ihLidIEDlLl6hsCz-I7ZiGM\" target=\"_blank\" style=\"font-size: 10pt; font-weight: 600; color: #077fab; text-decoration: none; border: 2px solid #077fab; border-radius: 7px; padding: 5px 25px; text-align: center; cursor: pointer; line-height: 1.5em;\">Improve this page<\/a><a style=\"margin-left: 16px;\" target=\"_blank\" href=\"https:\/\/docs.google.com\/document\/d\/1vy-T6DtTF-BbMfpVEI7VP_R7w2A4anzYZLXR8Pk4Fu4\">Learn More<\/a><\/p>\n\n\t\t\t <section class=\"citations-section\" role=\"contentinfo\">\n\t\t\t <h3>Candela Citations<\/h3>\n\t\t\t\t\t <div>\n\t\t\t\t\t\t <div id=\"citation-list-49\">\n\t\t\t\t\t\t\t <div class=\"licensing\"><div class=\"license-attribution-dropdown-subheading\">CC licensed content, Shared previously<\/div><ul class=\"citation-list\"><li>Concepts in Statistics. <strong>Provided by<\/strong>: Open Learning Initiative. <strong>Located at<\/strong>: <a target=\"_blank\" href=\"http:\/\/oli.cmu.edu\">http:\/\/oli.cmu.edu<\/a>. <strong>License<\/strong>: <em><a target=\"_blank\" rel=\"license\" href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\">CC BY: Attribution<\/a><\/em><\/li><\/ul><\/div>\n\t\t\t\t\t\t <\/div>\n\t\t\t\t\t <\/div>\n\t\t\t <\/section>","protected":false},"author":163,"menu_order":3,"template":"","meta":{"_candela_citation":"[{\"type\":\"cc\",\"description\":\"Concepts in Statistics\",\"author\":\"\",\"organization\":\"Open Learning Initiative\",\"url\":\"http:\/\/oli.cmu.edu\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"}]","CANDELA_OUTCOMES_GUID":"95381d41-ab1c-40a2-a0b9-611217eb11cd, 1cd73da5-ab56-4a7a-8ab7-464324c2e00c","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-49","chapter","type-chapter","status-publish","hentry"],"part":43,"_links":{"self":[{"href":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/wp-json\/pressbooks\/v2\/chapters\/49","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/wp-json\/wp\/v2\/users\/163"}],"version-history":[{"count":20,"href":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/wp-json\/pressbooks\/v2\/chapters\/49\/revisions"}],"predecessor-version":[{"id":2713,"href":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/wp-json\/pressbooks\/v2\/chapters\/49\/revisions\/2713"}],"part":[{"href":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/wp-json\/pressbooks\/v2\/parts\/43"}],"metadata":[{"href":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/wp-json\/pressbooks\/v2\/chapters\/49\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/wp-json\/wp\/v2\/media?parent=49"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/wp-json\/pressbooks\/v2\/chapter-type?post=49"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/wp-json\/wp\/v2\/contributor?post=49"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/wm-concepts-statistics\/wp-json\/wp\/v2\/license?post=49"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}