{"id":1217,"date":"2023-06-22T02:09:13","date_gmt":"2023-06-22T02:09:13","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/test-statistics-background-youll-need-1\/"},"modified":"2024-01-08T21:48:39","modified_gmt":"2024-01-08T21:48:39","slug":"test-statistics-background-youll-need-1","status":"publish","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/test-statistics-background-youll-need-1\/","title":{"raw":"Module 10: Background You'll Need 3","rendered":"Module 10: Background You&#8217;ll Need 3"},"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;Calculate and interpret z-scores&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:4096,&quot;15&quot;:&quot;Calibri&quot;}\">Calculate and interpret [latex]z[\/latex]-scores.<\/span><\/li>\r\n<\/ul>\r\n<\/section>\r\n<section class=\"textbox keyTakeaway\">\r\n<h3>standardized scores ([latex]z[\/latex]-score)<\/h3>\r\n<p>A <strong>standardized score<\/strong> describes how far a data value is from the mean in terms of the standard deviation. To compute standardized scores, subtract the mean and then divide by the standard deviation.<\/p>\r\n<p style=\"text-align: center\">[latex] \\text{standardized score} = z = \\frac{\\text{data value }-\\text{ mean}}{\\text{standard deviation}} [\/latex]<\/p>\r\n<\/section>\r\n<p>For example, a standardized score of [latex]-2[\/latex] means that the data value is [latex]2[\/latex] standard deviations below the mean.<\/p>\r\n<p>Standardized scores are useful for deciding whether a value is unusual relative to its distribution. They are also useful for comparing values that are on different scales.<\/p>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]1696[\/ohm2_question]<\/section>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]1711[\/ohm2_question]<\/section>","rendered":"<section class=\"textbox learningGoals\">\n<ul>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Calculate and interpret z-scores&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:4096,&quot;15&quot;:&quot;Calibri&quot;}\">Calculate and interpret [latex]z[\/latex]-scores.<\/span><\/li>\n<\/ul>\n<\/section>\n<section class=\"textbox keyTakeaway\">\n<h3>standardized scores ([latex]z[\/latex]-score)<\/h3>\n<p>A <strong>standardized score<\/strong> describes how far a data value is from the mean in terms of the standard deviation. To compute standardized scores, subtract the mean and then divide by the standard deviation.<\/p>\n<p style=\"text-align: center\">[latex]\\text{standardized score} = z = \\frac{\\text{data value }-\\text{ mean}}{\\text{standard deviation}}[\/latex]<\/p>\n<\/section>\n<p>For example, a standardized score of [latex]-2[\/latex] means that the data value is [latex]2[\/latex] standard deviations below the mean.<\/p>\n<p>Standardized scores are useful for deciding whether a value is unusual relative to its distribution. They are also useful for comparing values that are on different scales.<\/p>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm1696\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=1696&theme=lumen&iframe_resize_id=ohm1696&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm1711\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=1711&theme=lumen&iframe_resize_id=ohm1711&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n","protected":false},"author":8,"menu_order":4,"template":"","meta":{"_candela_citation":"[]","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"part":1205,"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\/1217"}],"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\/8"}],"version-history":[{"count":6,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/1217\/revisions"}],"predecessor-version":[{"id":4837,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/1217\/revisions\/4837"}],"part":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/parts\/1205"}],"metadata":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/1217\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/media?parent=1217"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapter-type?post=1217"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/contributor?post=1217"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/license?post=1217"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}