{"id":77,"date":"2023-01-31T00:46:26","date_gmt":"2023-01-31T00:46:26","guid":{"rendered":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/comparing-quantitative-distributions-dig-deeper\/"},"modified":"2025-05-11T19:47:17","modified_gmt":"2025-05-11T19:47:17","slug":"comparing-quantitative-distributions-fresh-take","status":"publish","type":"chapter","link":"https:\/\/content.one.lumenlearning.com\/introstatstest\/chapter\/comparing-quantitative-distributions-fresh-take\/","title":{"raw":"Comparing Quantitative Distributions: Fresh Take","rendered":"Comparing Quantitative Distributions: Fresh Take"},"content":{"raw":"<section class=\"textbox learningGoals\">\r\n<ul>\r\n\t<li>Compare data sets by describing their shapes, centers, spreads, and outliers<\/li>\r\n<\/ul>\r\n<\/section>\r\n<h2>Evaluating Quantitative Distributions<\/h2>\r\n<p>Let's look again at shape, center, spread, and the presence of outliers to compare and numerically evaluate histograms.<\/p>\r\n<section class=\"textbox recall\" aria-label=\"Recall\">\r\n<h3><strong>The Main Idea<\/strong><\/h3>\r\n<p>When comparing\u00a0the <strong>shape\u00a0<\/strong>of distributions, look for <strong>symmetry<\/strong>, <strong>right skew<\/strong> (a long tail to the right), <strong>left skew<\/strong> (a long tail to the left), and whether the graph appears <strong>unimodal<\/strong>, <strong>bimodal,<\/strong> or <strong>multimodal<\/strong>.<\/p>\r\n<p>The <strong>range\u00a0<\/strong>should be examined as well. You can find the difference between the <strong>minimum<\/strong> and <strong>maximum<\/strong> values to get the <strong>range<\/strong>. Make a note of any <strong>outliers<\/strong> well above or below the bulk of the data.<\/p>\r\n<p>The <strong>spread<\/strong> of the data can be indicated by how much variability there seems to be in the dataset. Look for gaps in the distribution and graphs that appear more tightly clustered (less spread) than others (more spread). When comparing spread, take care to compare the <strong>range<\/strong> of the distributions. Distributions that appear similar at first may possess substantially wider or narrower ranges.<\/p>\r\n<\/section>\r\n<section class=\"textbox example\">\r\n<h3>Smoking and Birth Weight<\/h3>\r\n<p>Statistical question: Does smoking during pregnancy have an impact on birth weight?<\/p>\r\n<p>To investigate this question, doctors collected data on 189 new mothers who gave birth at a hospital in Massachusetts during the 1980s.<\/p>\r\n<p>Here we use histograms to compare the distribution of birth weights for mothers who smoked during pregnancy with mothers who did not smoke. The table shows the number of mothers with babies in each interval of birth weights. (Left endpoints are included in the bin, so a 1,000-gram baby is in the interval 1,000\u20131,500 grams.)<\/p>\r\n<p><img class=\"alignnone\" src=\"https:\/\/s3-us-west-2.amazonaws.com\/courses-images\/wp-content\/uploads\/sites\/1729\/2017\/04\/15031559\/m2_summarizing_data_topic_2_1_Topic2_1Histograms4of4_image1.png\" alt=\"Histograms showing birth weights of babies born to smoking and non-smoking mothers. Non smokers' columns skew to the left, and smokers' columns skew to the right\" width=\"484\" height=\"180\" \/><\/p>\r\n<p>[reveal-answer q=\"533774\"]Note[\/reveal-answer]<br \/>\r\n[hidden-answer a=\"533774\"]For easy and more accurate visual comparisons, both histograms have the same horizontal scale and bin width. Also, the scale on the vertical axis is the same. So we can directly compare the heights of the bars to compare the number of mothers with babies in each interval of birth weights.[\/hidden-answer]<\/p>\r\n<p>The following are some observations about the shape, center, and spread:<\/p>\r\n<p><strong>Nonsmokers: <\/strong>The distribution of birth weights for the nonsmokers appears skewed slightly to the left. We estimate that birth weights for this group fall between approximately 1,000 and 5,000 grams for an overall range of approximately 4,000 grams. For nonsmokers, nearly half of the babies have a birth weight between 3,000 and 4,000 grams (29 + 27 = 56, 56\/115 = 48.7%), with fewer babies in the lower weight ranges.<\/p>\r\n<p><strong>Smokers: <\/strong>The distribution of birth weights for the smokers appears slightly skewed to the right. We estimate the birth weights for this group fall between approximately 500 and 4,500 grams for an overall range of approximately 4,000 grams. For smokers, nearly half of the babies have a birth weight between 2,000 and 3,000 grams (16 + 22 = 38, 38 \/ 74 = 51%), with fewer babies in heavier weight ranges.<\/p>\r\n<p>Comment: As we have seen, the choice of bin width can affect the shape of a histogram. We also cannot make precise statements about center and spread because our sense of \u201ctypical\u201d range is also affected by the choice of bin width.<\/p>\r\n<p>[reveal-answer q=\"488231\"]Another strategy for comparing distributions is to use a benchmark. [\/reveal-answer]<br \/>\r\n[hidden-answer a=\"488231\"]<\/p>\r\n<ol>\r\n\t<li>Doctors define <em>low birth weight<\/em> as a birth weight below 2,500 grams. Calculate and compare the percentage of smokers and nonsmokers with low-birth-weight babies by this definition. Nonsmokers: Of babies born to mothers who did not smoke, 3 + 8 + 18 = 29 weighed less than 2,500 grams, so 25.2% (29 of 115) of the babies born to nonsmokers fit the definition of low birth weight. Smokers: Of babies born to mothers who smoked, 1 + 1 + 6 + 22 = 30 weighed less than 2,500 grams, so 40.5% (30 of 74) of the babies born to smokers fit the definition of low birth weight.<\/li>\r\n\t<li>A condition called <em>macrosomia<\/em> (also known as big baby syndrome) is defined as a birth weight of 4,000 grams or more. Calculate and compare the percentage of smokers and nonsmokers with babies that fit the definition of macrosomia. Nonsmokers: Of babies born to mothers who did not smoke, 6 + 2 = 8 weighed 4,000 grams or more, so 7.0% (8 of 115) of the babies born to nonsmokers fit the definition of macrosomia. Smokers: Of babies born to mothers who smoked, only 1 weighed 4,000 grams or more, so 1.4% (1 of 74) of the babies born to smokers fit the definition of macrosomia.[\/hidden-answer]<\/li>\r\n<\/ol>\r\n<p>Now we synthesize these observations into a paragraph.<\/p>\r\n<section class=\"textbox proTip\">Be sure to emphasize the comparison of the groups. Develop a thesis statement if appropriate.<\/section>\r\n<p>In this observational study, we compared mothers who smoked during pregnancy to mothers who did not smoke during pregnancy. The variable is the birth weights of their babies. Both groups had a lot of variability in birth weights, with identical overall range estimates of 4,000 grams.<\/p>\r\n<p>There was also a lot of overlap in the distributions. Nonsmokers had babies that weighed between approximately 1,000 and 5,000 grams. Smokers had babies that weighed between approximately 500 and 4500 grams.<\/p>\r\n<p>However, we also observe some important differences in the typical ranges of birth weights for the two groups. For nonsmokers, nearly half of the babies have a birth weight between 3,000 and 4,000 grams (56 out of 115, 48.7%), with fewer babies in the lower weight ranges. For smokers, nearly half of the babies have a birth weight between 2,000 and 3,000 grams (40 of 74, 54%), with fewer babies in heavier weight ranges.<\/p>\r\n<p>If we use the medical definition of low birth weight (under 2,500 grams), we see that smokers in this study have a much higher incidence of low birth weights: 25.2% (29 of 115) of the babies born to nonsmokers fit the definition of low birth weight, compared to 40.5% (30 of 74) of the babies born to smokers. In this study, smoking is associated with lower birth weights, though the variability in the data suggests that other variables also contribute to birth weight.<\/p>\r\n<\/section>\r\n<section class=\"textbox example\">\r\n<h3>Sugar in Cereals<\/h3>\r\n<p>Here we use shape, center, and spread to compare the distribution of sugar content in adult cereals and children\u2019s cereals.<\/p>\r\n<p><strong>Compare the shapes:<\/strong><\/p>\r\n<p><img class=\"aligncenter\" src=\"https:\/\/s3-us-west-2.amazonaws.com\/courses-images\/wp-content\/uploads\/sites\/1729\/2017\/04\/15031538\/m2_summarizing_data_topic_2_1__m2_1_dataset_image10.png\" alt=\"Dotplots comparing the distribution of sugar content in adult and children's cereals. The graph showing adult sugar content is right-skewed, and the diagram showing children's sugar content is left-skewed\" width=\"382\" height=\"200\" \/><\/p>\r\n<p>The sugar content in adult cereals is skewed to the right. Many adult cereals have less than 8 grams of sugar in a serving. A smaller number of adult cereals contain high amounts of sugar. The sugar content for children\u2019s cereals is skewed to the left. Many children\u2019s cereals have more than 8 grams of sugar in a serving, with a smaller number of children\u2019s cereals with low amounts of sugar.<\/p>\r\n<p>[reveal-answer q=\"178980\"]Note[\/reveal-answer]<br \/>\r\n[hidden-answer a=\"178980\"]8 grams is simply a convenient reference point to describe the opposite trends in these two distributions.[\/hidden-answer]<\/p>\r\n<p><strong>Compare the centers:\u00a0<\/strong>Here, we looked at the most common value in each distribution. A typical adult cereal has 3 grams of sugar in a serving. A typical children\u2019s cereal has 12 grams of sugar in a serving.<\/p>\r\n<p><strong>Compare the spreads:\u00a0<\/strong>Adult cereals have 0 to 14 grams of sugar in a serving. Children\u2019s cereals vary from 1 to 15 grams. So both types of cereal vary over a range of 14 grams. (Note: Overall range = highest value \u2013 lowest value. For adult cereals: 14 \u2013 0 = 14. For children\u2019s cereals: 15 \u2013 1 = 14)<\/p>\r\n<p>When comparing two distributions, we usually tie all of these ideas into one paragraph, such as:<\/p>\r\n<p>In this sample, children\u2019s cereals have more sugar per serving than adult cereals. A typical children\u2019s cereal has 12 grams of sugar in a serving. It is not uncommon for children\u2019s cereals to have 9 to 13 grams of sugar per serving, but it is unusual for a children\u2019s cereal to have less than 8 grams of sugar. A typical adult cereal has 3 grams of sugar in a serving. It is not uncommon for adult cereals to have 0 to 6 grams of sugar in a serving. Larger amounts of sugar are less common.<\/p>\r\n<p>[reveal-answer q=\"562834\"]See a paragraph that uses more formal vocabulary to summarize the comparison.[\/reveal-answer]<br \/>\r\n[hidden-answer a=\"562834\"]<\/p>\r\n<p>In this sample, children\u2019s cereals have more sugar per serving than adult cereals. The distribution of sugar in children\u2019s cereals is skewed left with an overall range of 14 grams. Typical children\u2019s cereals have 9 to 13 grams of sugar per serving, with 12 grams as the most common amount. The distribution of sugar in adult cereals is skewed right with the same overall range of 14 grams. Typical adult cereals have 0 to 6 grams of sugar per serving, with 3 grams as the most common amount.[\/hidden-answer]<\/p>\r\n<\/section>","rendered":"<section class=\"textbox learningGoals\">\n<ul>\n<li>Compare data sets by describing their shapes, centers, spreads, and outliers<\/li>\n<\/ul>\n<\/section>\n<h2>Evaluating Quantitative Distributions<\/h2>\n<p>Let&#8217;s look again at shape, center, spread, and the presence of outliers to compare and numerically evaluate histograms.<\/p>\n<section class=\"textbox recall\" aria-label=\"Recall\">\n<h3><strong>The Main Idea<\/strong><\/h3>\n<p>When comparing\u00a0the <strong>shape\u00a0<\/strong>of distributions, look for <strong>symmetry<\/strong>, <strong>right skew<\/strong> (a long tail to the right), <strong>left skew<\/strong> (a long tail to the left), and whether the graph appears <strong>unimodal<\/strong>, <strong>bimodal,<\/strong> or <strong>multimodal<\/strong>.<\/p>\n<p>The <strong>range\u00a0<\/strong>should be examined as well. You can find the difference between the <strong>minimum<\/strong> and <strong>maximum<\/strong> values to get the <strong>range<\/strong>. Make a note of any <strong>outliers<\/strong> well above or below the bulk of the data.<\/p>\n<p>The <strong>spread<\/strong> of the data can be indicated by how much variability there seems to be in the dataset. Look for gaps in the distribution and graphs that appear more tightly clustered (less spread) than others (more spread). When comparing spread, take care to compare the <strong>range<\/strong> of the distributions. Distributions that appear similar at first may possess substantially wider or narrower ranges.<\/p>\n<\/section>\n<section class=\"textbox example\">\n<h3>Smoking and Birth Weight<\/h3>\n<p>Statistical question: Does smoking during pregnancy have an impact on birth weight?<\/p>\n<p>To investigate this question, doctors collected data on 189 new mothers who gave birth at a hospital in Massachusetts during the 1980s.<\/p>\n<p>Here we use histograms to compare the distribution of birth weights for mothers who smoked during pregnancy with mothers who did not smoke. The table shows the number of mothers with babies in each interval of birth weights. (Left endpoints are included in the bin, so a 1,000-gram baby is in the interval 1,000\u20131,500 grams.)<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone\" src=\"https:\/\/s3-us-west-2.amazonaws.com\/courses-images\/wp-content\/uploads\/sites\/1729\/2017\/04\/15031559\/m2_summarizing_data_topic_2_1_Topic2_1Histograms4of4_image1.png\" alt=\"Histograms showing birth weights of babies born to smoking and non-smoking mothers. Non smokers' columns skew to the left, and smokers' columns skew to the right\" width=\"484\" height=\"180\" \/><\/p>\n<p><div class=\"qa-wrapper\" style=\"display: block\"><button class=\"show-answer show-answer-button collapsed\" data-target=\"q533774\">Note<\/button><\/p>\n<div id=\"q533774\" class=\"hidden-answer\" style=\"display: none\">For easy and more accurate visual comparisons, both histograms have the same horizontal scale and bin width. Also, the scale on the vertical axis is the same. So we can directly compare the heights of the bars to compare the number of mothers with babies in each interval of birth weights.<\/div>\n<\/div>\n<p>The following are some observations about the shape, center, and spread:<\/p>\n<p><strong>Nonsmokers: <\/strong>The distribution of birth weights for the nonsmokers appears skewed slightly to the left. We estimate that birth weights for this group fall between approximately 1,000 and 5,000 grams for an overall range of approximately 4,000 grams. For nonsmokers, nearly half of the babies have a birth weight between 3,000 and 4,000 grams (29 + 27 = 56, 56\/115 = 48.7%), with fewer babies in the lower weight ranges.<\/p>\n<p><strong>Smokers: <\/strong>The distribution of birth weights for the smokers appears slightly skewed to the right. We estimate the birth weights for this group fall between approximately 500 and 4,500 grams for an overall range of approximately 4,000 grams. For smokers, nearly half of the babies have a birth weight between 2,000 and 3,000 grams (16 + 22 = 38, 38 \/ 74 = 51%), with fewer babies in heavier weight ranges.<\/p>\n<p>Comment: As we have seen, the choice of bin width can affect the shape of a histogram. We also cannot make precise statements about center and spread because our sense of \u201ctypical\u201d range is also affected by the choice of bin width.<\/p>\n<p><div class=\"qa-wrapper\" style=\"display: block\"><button class=\"show-answer show-answer-button collapsed\" data-target=\"q488231\">Another strategy for comparing distributions is to use a benchmark. <\/button><\/p>\n<div id=\"q488231\" class=\"hidden-answer\" style=\"display: none\">\n<ol>\n<li>Doctors define <em>low birth weight<\/em> as a birth weight below 2,500 grams. Calculate and compare the percentage of smokers and nonsmokers with low-birth-weight babies by this definition. Nonsmokers: Of babies born to mothers who did not smoke, 3 + 8 + 18 = 29 weighed less than 2,500 grams, so 25.2% (29 of 115) of the babies born to nonsmokers fit the definition of low birth weight. Smokers: Of babies born to mothers who smoked, 1 + 1 + 6 + 22 = 30 weighed less than 2,500 grams, so 40.5% (30 of 74) of the babies born to smokers fit the definition of low birth weight.<\/li>\n<li>A condition called <em>macrosomia<\/em> (also known as big baby syndrome) is defined as a birth weight of 4,000 grams or more. Calculate and compare the percentage of smokers and nonsmokers with babies that fit the definition of macrosomia. Nonsmokers: Of babies born to mothers who did not smoke, 6 + 2 = 8 weighed 4,000 grams or more, so 7.0% (8 of 115) of the babies born to nonsmokers fit the definition of macrosomia. Smokers: Of babies born to mothers who smoked, only 1 weighed 4,000 grams or more, so 1.4% (1 of 74) of the babies born to smokers fit the definition of macrosomia.<\/div>\n<\/div>\n<\/li>\n<\/ol>\n<p>Now we synthesize these observations into a paragraph.<\/p>\n<section class=\"textbox proTip\">Be sure to emphasize the comparison of the groups. Develop a thesis statement if appropriate.<\/section>\n<p>In this observational study, we compared mothers who smoked during pregnancy to mothers who did not smoke during pregnancy. The variable is the birth weights of their babies. Both groups had a lot of variability in birth weights, with identical overall range estimates of 4,000 grams.<\/p>\n<p>There was also a lot of overlap in the distributions. Nonsmokers had babies that weighed between approximately 1,000 and 5,000 grams. Smokers had babies that weighed between approximately 500 and 4500 grams.<\/p>\n<p>However, we also observe some important differences in the typical ranges of birth weights for the two groups. For nonsmokers, nearly half of the babies have a birth weight between 3,000 and 4,000 grams (56 out of 115, 48.7%), with fewer babies in the lower weight ranges. For smokers, nearly half of the babies have a birth weight between 2,000 and 3,000 grams (40 of 74, 54%), with fewer babies in heavier weight ranges.<\/p>\n<p>If we use the medical definition of low birth weight (under 2,500 grams), we see that smokers in this study have a much higher incidence of low birth weights: 25.2% (29 of 115) of the babies born to nonsmokers fit the definition of low birth weight, compared to 40.5% (30 of 74) of the babies born to smokers. In this study, smoking is associated with lower birth weights, though the variability in the data suggests that other variables also contribute to birth weight.<\/p>\n<\/section>\n<section class=\"textbox example\">\n<h3>Sugar in Cereals<\/h3>\n<p>Here we use shape, center, and spread to compare the distribution of sugar content in adult cereals and children\u2019s cereals.<\/p>\n<p><strong>Compare the shapes:<\/strong><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter\" src=\"https:\/\/s3-us-west-2.amazonaws.com\/courses-images\/wp-content\/uploads\/sites\/1729\/2017\/04\/15031538\/m2_summarizing_data_topic_2_1__m2_1_dataset_image10.png\" alt=\"Dotplots comparing the distribution of sugar content in adult and children's cereals. The graph showing adult sugar content is right-skewed, and the diagram showing children's sugar content is left-skewed\" width=\"382\" height=\"200\" \/><\/p>\n<p>The sugar content in adult cereals is skewed to the right. Many adult cereals have less than 8 grams of sugar in a serving. A smaller number of adult cereals contain high amounts of sugar. The sugar content for children\u2019s cereals is skewed to the left. Many children\u2019s cereals have more than 8 grams of sugar in a serving, with a smaller number of children\u2019s cereals with low amounts of sugar.<\/p>\n<p><div class=\"qa-wrapper\" style=\"display: block\"><button class=\"show-answer show-answer-button collapsed\" data-target=\"q178980\">Note<\/button><\/p>\n<div id=\"q178980\" class=\"hidden-answer\" style=\"display: none\">8 grams is simply a convenient reference point to describe the opposite trends in these two distributions.<\/div>\n<\/div>\n<p><strong>Compare the centers:\u00a0<\/strong>Here, we looked at the most common value in each distribution. A typical adult cereal has 3 grams of sugar in a serving. A typical children\u2019s cereal has 12 grams of sugar in a serving.<\/p>\n<p><strong>Compare the spreads:\u00a0<\/strong>Adult cereals have 0 to 14 grams of sugar in a serving. Children\u2019s cereals vary from 1 to 15 grams. So both types of cereal vary over a range of 14 grams. (Note: Overall range = highest value \u2013 lowest value. For adult cereals: 14 \u2013 0 = 14. For children\u2019s cereals: 15 \u2013 1 = 14)<\/p>\n<p>When comparing two distributions, we usually tie all of these ideas into one paragraph, such as:<\/p>\n<p>In this sample, children\u2019s cereals have more sugar per serving than adult cereals. A typical children\u2019s cereal has 12 grams of sugar in a serving. It is not uncommon for children\u2019s cereals to have 9 to 13 grams of sugar per serving, but it is unusual for a children\u2019s cereal to have less than 8 grams of sugar. A typical adult cereal has 3 grams of sugar in a serving. It is not uncommon for adult cereals to have 0 to 6 grams of sugar in a serving. Larger amounts of sugar are less common.<\/p>\n<p><div class=\"qa-wrapper\" style=\"display: block\"><button class=\"show-answer show-answer-button collapsed\" data-target=\"q562834\">See a paragraph that uses more formal vocabulary to summarize the comparison.<\/button><\/p>\n<div id=\"q562834\" class=\"hidden-answer\" style=\"display: none\">\n<p>In this sample, children\u2019s cereals have more sugar per serving than adult cereals. The distribution of sugar in children\u2019s cereals is skewed left with an overall range of 14 grams. Typical children\u2019s cereals have 9 to 13 grams of sugar per serving, with 12 grams as the most common amount. The distribution of sugar in adult cereals is skewed right with the same overall range of 14 grams. Typical adult cereals have 0 to 6 grams of sugar per serving, with 3 grams as the most common amount.<\/p><\/div>\n<\/div>\n<\/section>\n","protected":false},"author":6,"menu_order":31,"template":"","meta":{"_candela_citation":"[]","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"part":20,"module-header":"fresh_take","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\/77"}],"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\/6"}],"version-history":[{"count":7,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/77\/revisions"}],"predecessor-version":[{"id":6620,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/77\/revisions\/6620"}],"part":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/parts\/20"}],"metadata":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapters\/77\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/media?parent=77"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/pressbooks\/v2\/chapter-type?post=77"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/contributor?post=77"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/content.one.lumenlearning.com\/introstatstest\/wp-json\/wp\/v2\/license?post=77"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}