{"id":259,"date":"2023-02-20T17:14:09","date_gmt":"2023-02-20T17:14:09","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/equation-of-line-of-best-fit-learn-it-3\/"},"modified":"2025-05-11T23:14:43","modified_gmt":"2025-05-11T23:14:43","slug":"line-of-best-fit-learn-it-4","status":"publish","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/line-of-best-fit-learn-it-4\/","title":{"raw":"Line of Best Fit: Learn It 4","rendered":"Line of Best Fit: Learn It 4"},"content":{"raw":"<section class=\"textbox learningGoals\">\r\n<ul>\r\n\t<li>Recognize when a linear regression model will fit a given data set.<\/li>\r\n\t<li>Use technology to create scatterplots, find the line of best fit, and find the correlation coefficient.<\/li>\r\n\t<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Find the estimated slope and y-intercept for a linear regression model&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:4609,&quot;3&quot;:{&quot;1&quot;:0},&quot;12&quot;:0,&quot;15&quot;:&quot;Calibri&quot;}\">Find the estimated slope and [latex]y[\/latex]-intercept for a linear regression model.<\/span><\/li>\r\n\t<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Use the line of best fit to predict values&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:4609,&quot;3&quot;:{&quot;1&quot;:0},&quot;12&quot;:0,&quot;15&quot;:&quot;Calibri&quot;}\">Use the line of best fit to predict values.<\/span><\/li>\r\n<\/ul>\r\n<\/section>\r\n<h2>Understanding slope<\/h2>\r\n<p>The slope of the line, [latex]b[\/latex], describes how changes in the variables are related.<\/p>\r\n<p>Interpretation of the slope: The slope of a line tells us how the dependent variable, [latex]y[\/latex], changes for every one-unit increase in the independent variable, [latex]x[\/latex], on average.<\/p>\r\n<p>It is important to interpret the slope of the line in the context of the situation represented by the data. The information gathered is meaningless if it does not have an interpretation in the context of the problem. You should be able to write a sentence interpreting the slope in plain English.<\/p>\r\n<section class=\"textbox proTip\">Suggested template for the interpretation of the estimated slope: For every one [unit] increase in [explanatory variable units], we predict an average increase\/decrease of ___ [response variable units] in [response variable].\u00a0<\/section>\r\n<section>Let's look at the data set about the striped ground cricket chirps and temperature![reveal-answer q=\"958723\"]Dataset[\/reveal-answer]<br \/>\r\n[hidden-answer a=\"958723\"]\r\n\r\n<table style=\"margin-left: 40px;\">\r\n<tbody style=\"padding-left: 40px;\">\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\"><strong>Chirps per second<\/strong><\/td>\r\n<td style=\"padding-left: 40px;\"><strong>Temperature in degrees Fahrenheit<\/strong><\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">20<\/td>\r\n<td style=\"padding-left: 40px;\">88.6<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">16<\/td>\r\n<td style=\"padding-left: 40px;\">71.6<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">19.8<\/td>\r\n<td style=\"padding-left: 40px;\">93.3<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">18.4<\/td>\r\n<td style=\"padding-left: 40px;\">84.3<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">17.1<\/td>\r\n<td style=\"padding-left: 40px;\">80.6<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">15.5<\/td>\r\n<td style=\"padding-left: 40px;\">75.2<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">14.7<\/td>\r\n<td style=\"padding-left: 40px;\">69.7<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">17.1<\/td>\r\n<td style=\"padding-left: 40px;\">82<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">15.4<\/td>\r\n<td style=\"padding-left: 40px;\">69.4<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">16.2<\/td>\r\n<td style=\"padding-left: 40px;\">83.3<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">15<\/td>\r\n<td style=\"padding-left: 40px;\">79.6<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">17.2<\/td>\r\n<td style=\"padding-left: 40px;\">82.6<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">16<\/td>\r\n<td style=\"padding-left: 40px;\">80.6<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">17<\/td>\r\n<td style=\"padding-left: 40px;\">83.5<\/td>\r\n<\/tr>\r\n<tr style=\"padding-left: 40px;\">\r\n<td style=\"padding-left: 40px;\">14.4<\/td>\r\n<td style=\"padding-left: 40px;\">76.3<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<p style=\"padding-left: 40px;\">[\/hidden-answer]<\/p>\r\n<\/section>\r\n<section><iframe src=\"https:\/\/lumen-learning.shinyapps.io\/linear_regression\/\" width=\"100%\" height=\"850\"><\/iframe>\r\n<p><br \/>\r\n<br \/>\r\n[<a href=\"https:\/\/lumen-learning.shinyapps.io\/linear_regression\/\" target=\"_blank\" rel=\"noopener\">Trouble viewing? Click to open in a new tab.<\/a>]<\/p>\r\n<\/section>\r\n<section>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]1168[\/ohm2_question]<\/section>\r\n<section class=\"textbox tryIt\">[ohm2_question hide_question_numbers=1]1169[\/ohm2_question]<\/section>\r\n<\/section>","rendered":"<section class=\"textbox learningGoals\">\n<ul>\n<li>Recognize when a linear regression model will fit a given data set.<\/li>\n<li>Use technology to create scatterplots, find the line of best fit, and find the correlation coefficient.<\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Find the estimated slope and y-intercept for a linear regression model&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:4609,&quot;3&quot;:{&quot;1&quot;:0},&quot;12&quot;:0,&quot;15&quot;:&quot;Calibri&quot;}\">Find the estimated slope and [latex]y[\/latex]-intercept for a linear regression model.<\/span><\/li>\n<li><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Use the line of best fit to predict values&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:4609,&quot;3&quot;:{&quot;1&quot;:0},&quot;12&quot;:0,&quot;15&quot;:&quot;Calibri&quot;}\">Use the line of best fit to predict values.<\/span><\/li>\n<\/ul>\n<\/section>\n<h2>Understanding slope<\/h2>\n<p>The slope of the line, [latex]b[\/latex], describes how changes in the variables are related.<\/p>\n<p>Interpretation of the slope: The slope of a line tells us how the dependent variable, [latex]y[\/latex], changes for every one-unit increase in the independent variable, [latex]x[\/latex], on average.<\/p>\n<p>It is important to interpret the slope of the line in the context of the situation represented by the data. The information gathered is meaningless if it does not have an interpretation in the context of the problem. You should be able to write a sentence interpreting the slope in plain English.<\/p>\n<section class=\"textbox proTip\">Suggested template for the interpretation of the estimated slope: For every one [unit] increase in [explanatory variable units], we predict an average increase\/decrease of ___ [response variable units] in [response variable].\u00a0<\/section>\n<section>Let&#8217;s look at the data set about the striped ground cricket chirps and temperature!<\/p>\n<div class=\"qa-wrapper\" style=\"display: block\"><button class=\"show-answer show-answer-button collapsed\" data-target=\"q958723\">Dataset<\/button><\/p>\n<div id=\"q958723\" class=\"hidden-answer\" style=\"display: none\">\n<table style=\"margin-left: 40px;\">\n<tbody style=\"padding-left: 40px;\">\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\"><strong>Chirps per second<\/strong><\/td>\n<td style=\"padding-left: 40px;\"><strong>Temperature in degrees Fahrenheit<\/strong><\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">20<\/td>\n<td style=\"padding-left: 40px;\">88.6<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">16<\/td>\n<td style=\"padding-left: 40px;\">71.6<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">19.8<\/td>\n<td style=\"padding-left: 40px;\">93.3<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">18.4<\/td>\n<td style=\"padding-left: 40px;\">84.3<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">17.1<\/td>\n<td style=\"padding-left: 40px;\">80.6<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">15.5<\/td>\n<td style=\"padding-left: 40px;\">75.2<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">14.7<\/td>\n<td style=\"padding-left: 40px;\">69.7<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">17.1<\/td>\n<td style=\"padding-left: 40px;\">82<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">15.4<\/td>\n<td style=\"padding-left: 40px;\">69.4<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">16.2<\/td>\n<td style=\"padding-left: 40px;\">83.3<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">15<\/td>\n<td style=\"padding-left: 40px;\">79.6<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">17.2<\/td>\n<td style=\"padding-left: 40px;\">82.6<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">16<\/td>\n<td style=\"padding-left: 40px;\">80.6<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">17<\/td>\n<td style=\"padding-left: 40px;\">83.5<\/td>\n<\/tr>\n<tr style=\"padding-left: 40px;\">\n<td style=\"padding-left: 40px;\">14.4<\/td>\n<td style=\"padding-left: 40px;\">76.3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"padding-left: 40px;\"><\/div>\n<\/div>\n<\/section>\n<section><iframe loading=\"lazy\" src=\"https:\/\/lumen-learning.shinyapps.io\/linear_regression\/\" width=\"100%\" height=\"850\"><\/iframe><\/p>\n<p>[<a href=\"https:\/\/lumen-learning.shinyapps.io\/linear_regression\/\" target=\"_blank\" rel=\"noopener\">Trouble viewing? Click to open in a new tab.<\/a>]<\/p>\n<\/section>\n<section>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm1168\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=1168&theme=lumen&iframe_resize_id=ohm1168&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<section class=\"textbox tryIt\"><iframe loading=\"lazy\" id=\"ohm1169\" class=\"resizable\" src=\"https:\/\/ohm.one.lumenlearning.com\/multiembedq.php?id=1169&theme=lumen&iframe_resize_id=ohm1169&source=tnh\" width=\"100%\" height=\"150\"><\/iframe><\/section>\n<\/section>\n","protected":false},"author":12,"menu_order":15,"template":"","meta":{"_candela_citation":"[]","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"part":225,"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\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/259"}],"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\/12"}],"version-history":[{"count":13,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/259\/revisions"}],"predecessor-version":[{"id":6652,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/259\/revisions\/6652"}],"part":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/parts\/225"}],"metadata":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/259\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/media?parent=259"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapter-type?post=259"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/contributor?post=259"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/license?post=259"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}