{"id":33,"date":"2022-05-20T16:59:03","date_gmt":"2022-05-20T16:59:03","guid":{"rendered":"https:\/\/courses.lumenlearning.com\/alphamodule\/chapter\/comparing-variability-of-data-sets-what-to-know-4\/"},"modified":"2022-06-16T15:21:17","modified_gmt":"2022-06-16T15:21:17","slug":"comparing-variability-of-data-sets-what-to-know-3","status":"publish","type":"chapter","link":"https:\/\/courses.lumenlearning.com\/alphamodule\/chapter\/comparing-variability-of-data-sets-what-to-know-3\/","title":{"raw":"Comparing Variability of Data Sets: Learn It 3","rendered":"Comparing Variability of Data Sets: Learn It 3"},"content":{"raw":"<h2>Standard Deviation<\/h2>\r\nIn statistics, we are particularly interested in understanding how data are distributed and where each observation is in reference to the mean. This measurement of <strong>variability<\/strong> is called\u00a0<strong>standard deviation, <\/strong>which tells us how spread out observations are from the mean. The symbol we use to denote standard deviation differs depending on whether we are discussing a sample or a population. We use the Greek letter [latex]\\sigma[\/latex] (sigma) to denote the standard deviation of a population of observations.\u00a0We use the Latin letter\u00a0[latex]s[\/latex]\u00a0to denote the standard deviation of a sample of observations.\r\n<div class=\"textbox examples\">\r\n<h3>Recall<\/h3>\r\nWe'll be using statistical formulas, symbols, and language to discuss measures of variability. Take a moment to recall the formula you learned to calculate the mean of a sample. What symbols do we use to represent sample mean, summation, and sample size?\r\n\r\nCore skill: [reveal-answer q=\"450894\"]Express the formula for calculating the mean of a sample[\/reveal-answer]\r\n[hidden-answer a=\"450894\"]\r\n<p style=\"text-align: center;\">[latex]\\bar{x}=\\dfrac{\\sum{x}}{n}[\/latex]<\/p>\r\nwhere,\r\n<p style=\"padding-left: 60px;\">[latex]\\bar{x}[\/latex] = the sample mean<\/p>\r\n<p style=\"padding-left: 60px;\">[latex]\\sum{x}[\/latex] = the sum of all the sample values [latex]x[\/latex]<\/p>\r\n<p style=\"padding-left: 60px;\">[latex]n[\/latex] = the sample size<\/p>\r\n[\/hidden-answer]\r\n\r\n<\/div>\r\n<div class=\"textbox tryit\">\r\n<h3>Standard Deviation VIDEO ANIMATED CONCEPT<\/h3>\r\n<span style=\"background-color: #99cc00;\">video about standard deviation and why we find it and it's purpose<\/span>\r\n\r\n<\/div>\r\n<div class=\"textbox shaded\">\r\n<h3 style=\"text-align: center;\">Standard Deviation<\/h3>\r\n\r\n<hr \/>\r\n\r\n&nbsp;\r\n\r\nThe following formulas are used to calculate the standard deviation of a population and a sample:\r\n<p style=\"padding-left: 60px;\"><strong>Standard deviation of a population<\/strong>: [latex]\\sigma = \\sqrt{\\dfrac{\\sum \\left(x-\\mu\\right)^2}{n}}[\/latex], where [latex]\\mu[\/latex] represents the population mean.<\/p>\r\n<p style=\"padding-left: 60px;\"><strong>Standard deviation of a sample<\/strong>: [latex]s=\\sqrt{\\dfrac{\\sum \\left(x-\\bar{x}\\right)^2}{n-1}} [\/latex], where [latex]\\bar{x}[\/latex] represents the sample mean.<\/p>\r\nThe following steps can be applied to calculate a standard deviation by hand:\r\n<ol>\r\n \t<li>Calculate the mean of the population or sample<\/li>\r\n \t<li>Take the difference between each data value and the mean, then square each difference<\/li>\r\n \t<li>Add up all the squared differences<\/li>\r\n \t<li>Divide by either the total number of observations in the case of a population, or by 1 fewer than the total in the case of a sample<\/li>\r\n \t<li>Take the square root of the result of step 4<\/li>\r\n<\/ol>\r\n<\/div>\r\n<div class=\"textbox exercises\">\r\n<h3>example<\/h3>\r\nA sample of observations is listed below. Find its standard deviation.\r\n<p style=\"padding-left: 30px;\">[latex]8, 7, 13, 15, 23, 18[\/latex]<\/p>\r\n[reveal-answer q=\"911375\"]Show Solution[\/reveal-answer]\r\n[hidden-answer a=\"911375\"]\r\n\r\nFirst, calculate the mean of the sample observations.\r\n<p style=\"text-align: center;\">[latex]\\bar{x}=\\frac{84}{6}=14[\/latex]<\/p>\r\nThen, identify all the squared differences.\r\n<p style=\"text-align: center;\">[latex]\\left(8-14\\right)^{2}=36\\text{, } \\left(7-14\\right)^{2}=49\\text{, } \\left(13-14\\right)^{2}=1\\text{, } \\left(15-14\\right)^{2}=1\\text{, } \\left(23-14\\right)^{2}=81\\text{, } \\left(18-14\\right)^{2}=16\\\\ [\/latex]<\/p>\r\nNext, take the square root of the sum of the squared differences divided by 1 less than the sample size.\r\n<p style=\"text-align: center;\">[latex]\\sqrt{\\dfrac{26+49+1+1+81+16}{6-1}}=\\sqrt{\\dfrac{184}{5}}\\approx{6.07}[\/latex]<\/p>\r\nThis process would be too tedious for large samples or populations, so we'll use that statistical technology tool to calculate standard deviation. Try it now by\u00a0choosing \"Your Own\" under \"Enter Data\" and enter the observations\u00a0[latex]8, 7, 13, 15, 23, 18[\/latex] to read the Std. Dev. from the Descriptive Statistics section.\r\n\r\nDid you obtain the same result?\r\n\r\n[\/hidden-answer]\r\n\r\nDATASET CHOICE OPTION\r\n\r\n<\/div>\r\nHere is a breakdown of the formula for standard deviation of a sample, [latex]s[\/latex].\r\n<p style=\"text-align: center;\">[latex]s=\\sqrt{\\dfrac{\\sum \\left(x-\\bar{x}\\right)^2}{n-1}} [\/latex]<\/p>\r\n\r\n<ul>\r\n \t<li style=\"text-align: left;\">The distance from each observation to the mean is known as a <strong>deviation from the mean<\/strong> and is expressed as [latex]\\left(x-\\bar{x}\\right)[\/latex]<\/li>\r\n \t<li style=\"text-align: left;\"><strong>The deviations from the mean are squared<\/strong> in the formula because some observations are above the mean, thus [latex]\\left(x-\\bar{x}\\right)&gt;0[\/latex] (the difference is positive), and some observations are below the mean, thus [latex]\\left(x-\\bar{x}\\right)&lt;0[\/latex] (the difference is negative). Squaring ensures the differences will each be expressed as positive distances and won't cancel each other out when summed up.<\/li>\r\n \t<li style=\"text-align: left;\"><strong>The [latex]\\sum[\/latex] symbol sums up<\/strong> the squared deviations for all [latex]n[\/latex] observations.<span style=\"background-color: #00ffff;\">\r\n<\/span><\/li>\r\n \t<li><strong>The denominator in the formula for a sample standard deviation is [latex]\\left(n-1\\right)[\/latex]<\/strong> rather than [latex]n[\/latex] as in the formula for the population standard deviation.\r\n<ul>\r\n \t<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-size: 1rem; orphans: 1; text-align: initial;\">Why do we divide by 1 fewer than the sample size, <strong>[latex]\\left(n-1\\right)[\/latex]<\/strong>?\u00a0\u00a0<\/span><span style=\"font-size: 1rem; orphans: 1; text-align: initial;\">[reveal-answer q=\"329088\"]The answer to that is complicated, but here are some ideas that may help[\/reveal-answer]<\/span><\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<p style=\"padding-left: 30px;\">[hidden-answer a=\"329088\"]<\/p>\r\n\r\n<div class=\"textbox shaded\">\r\n<p style=\"padding-left: 30px;\"><strong>Why do we divide by [latex]\\left(n-1\\right)[\/latex]?\u00a0<\/strong><\/p>\r\n\r\n<ul>\r\n \t<li><strong>Because the sample standard deviation is an underestimation.\u00a0<\/strong>Recall that a sample is representative of a population if the characteristics of the sample tend to be similar to the characteristics of the population from which it was obtained. But the sample standard deviation tends to underestimate the population standard deviation\u00a0(this can be shown mathematically but its beyond the scope of what we need here). We can fix that by increasing the size of our sample standard deviation if we divide by [latex]\\left(n-1\\right)[\/latex] in the sample standard deviation formula rather than by [latex]n[\/latex].<\/li>\r\n \t<li><strong>Because we are using\u00a0<em>degrees of freedom<\/em> in the denominator.\u00a0<\/strong>You may have heard that the denominator in the standard deviation formula is called the\u00a0<em>degrees of freedom<\/em>, abbreviated <em>df<\/em>. That's true, and it helps us to compensate for the underestimation that crops up when we divide strictly by sample size. There's a lot going on here mathematically, but we can think of it this way: dividing by [latex]\\left(n-1\\right)[\/latex] instead of [latex]n[\/latex]\u00a0helps our sample standard deviation more closely resemble the true (usually unknowable) population standard deviation. This will help make our statistical analysis more reasonable.<\/li>\r\n \t<li><strong>What are degrees of freedom, anyway?<\/strong> A nice way to think of degrees of freedom, [latex]\\left(n-1\\right)[\/latex] is to imagine a set of three numbers whose mean is\u00a0[latex]5[\/latex]: say,\u00a0[latex]4, 5[\/latex], and\u00a0[latex]6[\/latex]. If those three numbers were written on pieces of paper in a hat, and you chose two of them, say\u00a0[latex]4[\/latex] and [latex]5[\/latex], first, then the only way to get a mean of\u00a0[latex]5[\/latex] from the numbers on three scraps of paper would be that the next choice must have a\u00a0[latex]6[\/latex] on it. We could say that the first two scraps were <em>free to vary<\/em>; they could have been\u00a0[latex]4[\/latex] or\u00a0[latex]5[\/latex] or\u00a0[latex]6[\/latex] as they pleased. But the third pick couldn't vary. After choosing the\u00a0[latex]4[\/latex] and the\u00a0[latex]5[\/latex] freely first, there was no freedom for the choice of the third in order to obtain the desired mean. Only two of our choices had a degree of freedom, so we say that the degrees of freedom of a sample size of\u00a0[latex]3[\/latex] is [latex]\\left(3-1\\right)=2[\/latex].<\/li>\r\n \t<li><span style=\"background-color: #ffff99;\">Insert video explanation of the idea of n-1 as degrees of freedom. DC suggested this one:\u00a0<\/span>For a more detailed discussion, see <a href=\"https:\/\/www.khanacademy.org\/math\/ap-statistics\/summarizing-quantitative-data-ap\/more-standard-deviation\/v\/review-and-intuition-why-we-divide-by-n-1-for-the-unbiased-sample-variance\">https:\/\/www.khanacademy.org\/math\/ap-statistics\/summarizing-quantitative-data-ap\/more-standard-deviation\/v\/review-and-intuition-why-we-divide-by-n-1-for-the-unbiased-sample-variance<\/a><\/li>\r\n<\/ul>\r\n<\/div>\r\n<span style=\"font-size: 1rem; orphans: 1; text-align: initial;\">[\/hidden-answer]<\/span>\r\n<ul>\r\n \t<li><span style=\"font-size: 1em;\"><strong>The square root is taken<\/strong> in order to express the spread in terms of the units of the observations.\u00a0<\/span>Recall that we squared the differences to express them as positive distances, which resulted in squared observation units. Taking the square root can be thought of as \"undoing\" the earlier squaring.\u00a0For example, assume that within the context in which you are working, the data are in terms of dollars. If we do not take the square root, the standard deviation will be\u00a0in terms of dollars squared, which is not something commonly used.<\/li>\r\n \t<li><strong>The standard deviation, [latex]s[\/latex], represents the \u201ctypical\u201d distance of an observation from the mean of the data set.<\/strong><\/li>\r\n<\/ul>\r\n<div class=\"textbox tryit\">\r\n<h3>Standard Deviation VIDEO WITH INSTRUCTOR CHOICE<\/h3>\r\n<span style=\"background-color: #99cc00;\">[perspective video -- a 3-instructor video showing how to think about standard deviation as a measure of variability. Cover the parts of the formula (go into\u00a0why squaring, why\u00a0<em>df<\/em> if desired) but emphasize the concept of variability from std dev and variance more so than the technical use of the formula.]<\/span>\r\n\r\n<\/div>\r\nAlthough it's important to understand each part of the formula \u2013 don\u2019t worry \u2013 we will be using the\u00a0statistical technology tool to calculate standard deviation for us! Let's practice using tool by finding the standard deviation of the variable Average Sleep in the Sleep Study data set.\r\n<div class=\"textbox key-takeaways\">\r\n<h3>Try It 5<\/h3>\r\n<div class=\"textbox\"><img class=\"alignnone size-full wp-image-87\" src=\"https:\/\/s3-us-west-2.amazonaws.com\/courses-images\/wp-content\/uploads\/sites\/5808\/2022\/05\/28022318\/Screen-Shot-2022-05-27-at-7.21.15-PM1.png\" alt=\"\" width=\"2380\" height=\"476\" \/><\/div>\r\n<p style=\"padding-left: 30px;\">Step 1) Select the\u00a0<strong>Single Group<\/strong>\u00a0tab.<\/p>\r\n<p style=\"padding-left: 30px;\">Step 2) Locate the drop-down menu under\u00a0<strong>Enter Data<\/strong>\u00a0and select\u00a0<strong>From Textbook<\/strong>.<\/p>\r\n<p style=\"padding-left: 30px;\">Step 3) Locate the drop-down menu under\u00a0<strong>Data Set<\/strong>\u00a0and select\u00a0<strong>Sleep Study: Average Sleep<\/strong>.<\/p>\r\n[ohm_question hide_question_numbers=1]241060[\/ohm_question]\r\n\r\n[reveal-answer q=\"805189\"]Hint[\/reveal-answer]\r\n[hidden-answer a=\"805189\"]In the statistical technology tool, look for \u201cStd. Dev.\u201d in the table under Descriptive Statistics.[\/hidden-answer]\r\n\r\n<\/div>","rendered":"<h2>Standard Deviation<\/h2>\n<p>In statistics, we are particularly interested in understanding how data are distributed and where each observation is in reference to the mean. This measurement of <strong>variability<\/strong> is called\u00a0<strong>standard deviation, <\/strong>which tells us how spread out observations are from the mean. The symbol we use to denote standard deviation differs depending on whether we are discussing a sample or a population. We use the Greek letter [latex]\\sigma[\/latex] (sigma) to denote the standard deviation of a population of observations.\u00a0We use the Latin letter\u00a0[latex]s[\/latex]\u00a0to denote the standard deviation of a sample of observations.<\/p>\n<div class=\"textbox examples\">\n<h3>Recall<\/h3>\n<p>We&#8217;ll be using statistical formulas, symbols, and language to discuss measures of variability. Take a moment to recall the formula you learned to calculate the mean of a sample. What symbols do we use to represent sample mean, summation, and sample size?<\/p>\n<p>Core skill: <\/p>\n<div class=\"qa-wrapper\" style=\"display: block\"><span class=\"show-answer collapsed\" style=\"cursor: pointer\" data-target=\"q450894\">Express the formula for calculating the mean of a sample<\/span><\/p>\n<div id=\"q450894\" class=\"hidden-answer\" style=\"display: none\">\n<p style=\"text-align: center;\">[latex]\\bar{x}=\\dfrac{\\sum{x}}{n}[\/latex]<\/p>\n<p>where,<\/p>\n<p style=\"padding-left: 60px;\">[latex]\\bar{x}[\/latex] = the sample mean<\/p>\n<p style=\"padding-left: 60px;\">[latex]\\sum{x}[\/latex] = the sum of all the sample values [latex]x[\/latex]<\/p>\n<p style=\"padding-left: 60px;\">[latex]n[\/latex] = the sample size<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"textbox tryit\">\n<h3>Standard Deviation VIDEO ANIMATED CONCEPT<\/h3>\n<p><span style=\"background-color: #99cc00;\">video about standard deviation and why we find it and it&#8217;s purpose<\/span><\/p>\n<\/div>\n<div class=\"textbox shaded\">\n<h3 style=\"text-align: center;\">Standard Deviation<\/h3>\n<hr \/>\n<p>&nbsp;<\/p>\n<p>The following formulas are used to calculate the standard deviation of a population and a sample:<\/p>\n<p style=\"padding-left: 60px;\"><strong>Standard deviation of a population<\/strong>: [latex]\\sigma = \\sqrt{\\dfrac{\\sum \\left(x-\\mu\\right)^2}{n}}[\/latex], where [latex]\\mu[\/latex] represents the population mean.<\/p>\n<p style=\"padding-left: 60px;\"><strong>Standard deviation of a sample<\/strong>: [latex]s=\\sqrt{\\dfrac{\\sum \\left(x-\\bar{x}\\right)^2}{n-1}}[\/latex], where [latex]\\bar{x}[\/latex] represents the sample mean.<\/p>\n<p>The following steps can be applied to calculate a standard deviation by hand:<\/p>\n<ol>\n<li>Calculate the mean of the population or sample<\/li>\n<li>Take the difference between each data value and the mean, then square each difference<\/li>\n<li>Add up all the squared differences<\/li>\n<li>Divide by either the total number of observations in the case of a population, or by 1 fewer than the total in the case of a sample<\/li>\n<li>Take the square root of the result of step 4<\/li>\n<\/ol>\n<\/div>\n<div class=\"textbox exercises\">\n<h3>example<\/h3>\n<p>A sample of observations is listed below. Find its standard deviation.<\/p>\n<p style=\"padding-left: 30px;\">[latex]8, 7, 13, 15, 23, 18[\/latex]<\/p>\n<div class=\"qa-wrapper\" style=\"display: block\"><span class=\"show-answer collapsed\" style=\"cursor: pointer\" data-target=\"q911375\">Show Solution<\/span><\/p>\n<div id=\"q911375\" class=\"hidden-answer\" style=\"display: none\">\n<p>First, calculate the mean of the sample observations.<\/p>\n<p style=\"text-align: center;\">[latex]\\bar{x}=\\frac{84}{6}=14[\/latex]<\/p>\n<p>Then, identify all the squared differences.<\/p>\n<p style=\"text-align: center;\">[latex]\\left(8-14\\right)^{2}=36\\text{, } \\left(7-14\\right)^{2}=49\\text{, } \\left(13-14\\right)^{2}=1\\text{, } \\left(15-14\\right)^{2}=1\\text{, } \\left(23-14\\right)^{2}=81\\text{, } \\left(18-14\\right)^{2}=16\\\\[\/latex]<\/p>\n<p>Next, take the square root of the sum of the squared differences divided by 1 less than the sample size.<\/p>\n<p style=\"text-align: center;\">[latex]\\sqrt{\\dfrac{26+49+1+1+81+16}{6-1}}=\\sqrt{\\dfrac{184}{5}}\\approx{6.07}[\/latex]<\/p>\n<p>This process would be too tedious for large samples or populations, so we&#8217;ll use that statistical technology tool to calculate standard deviation. Try it now by\u00a0choosing &#8220;Your Own&#8221; under &#8220;Enter Data&#8221; and enter the observations\u00a0[latex]8, 7, 13, 15, 23, 18[\/latex] to read the Std. Dev. from the Descriptive Statistics section.<\/p>\n<p>Did you obtain the same result?<\/p>\n<\/div>\n<\/div>\n<p>DATASET CHOICE OPTION<\/p>\n<\/div>\n<p>Here is a breakdown of the formula for standard deviation of a sample, [latex]s[\/latex].<\/p>\n<p style=\"text-align: center;\">[latex]s=\\sqrt{\\dfrac{\\sum \\left(x-\\bar{x}\\right)^2}{n-1}}[\/latex]<\/p>\n<ul>\n<li style=\"text-align: left;\">The distance from each observation to the mean is known as a <strong>deviation from the mean<\/strong> and is expressed as [latex]\\left(x-\\bar{x}\\right)[\/latex]<\/li>\n<li style=\"text-align: left;\"><strong>The deviations from the mean are squared<\/strong> in the formula because some observations are above the mean, thus [latex]\\left(x-\\bar{x}\\right)>0[\/latex] (the difference is positive), and some observations are below the mean, thus [latex]\\left(x-\\bar{x}\\right)<0[\/latex] (the difference is negative). Squaring ensures the differences will each be expressed as positive distances and won&#8217;t cancel each other out when summed up.<\/li>\n<li style=\"text-align: left;\"><strong>The [latex]\\sum[\/latex] symbol sums up<\/strong> the squared deviations for all [latex]n[\/latex] observations.<span style=\"background-color: #00ffff;\"><br \/>\n<\/span><\/li>\n<li><strong>The denominator in the formula for a sample standard deviation is [latex]\\left(n-1\\right)[\/latex]<\/strong> rather than [latex]n[\/latex] as in the formula for the population standard deviation.\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-size: 1rem; orphans: 1; text-align: initial;\">Why do we divide by 1 fewer than the sample size, <strong>[latex]\\left(n-1\\right)[\/latex]<\/strong>?\u00a0\u00a0<\/span><span style=\"font-size: 1rem; orphans: 1; text-align: initial;\">\n<div class=\"qa-wrapper\" style=\"display: block\"><span class=\"show-answer collapsed\" style=\"cursor: pointer\" data-target=\"q329088\">The answer to that is complicated, but here are some ideas that may help<\/span><\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p style=\"padding-left: 30px;\">\n<div id=\"q329088\" class=\"hidden-answer\" style=\"display: none\">\n<div class=\"textbox shaded\">\n<p style=\"padding-left: 30px;\"><strong>Why do we divide by [latex]\\left(n-1\\right)[\/latex]?\u00a0<\/strong><\/p>\n<ul>\n<li><strong>Because the sample standard deviation is an underestimation.\u00a0<\/strong>Recall that a sample is representative of a population if the characteristics of the sample tend to be similar to the characteristics of the population from which it was obtained. But the sample standard deviation tends to underestimate the population standard deviation\u00a0(this can be shown mathematically but its beyond the scope of what we need here). We can fix that by increasing the size of our sample standard deviation if we divide by [latex]\\left(n-1\\right)[\/latex] in the sample standard deviation formula rather than by [latex]n[\/latex].<\/li>\n<li><strong>Because we are using\u00a0<em>degrees of freedom<\/em> in the denominator.\u00a0<\/strong>You may have heard that the denominator in the standard deviation formula is called the\u00a0<em>degrees of freedom<\/em>, abbreviated <em>df<\/em>. That&#8217;s true, and it helps us to compensate for the underestimation that crops up when we divide strictly by sample size. There&#8217;s a lot going on here mathematically, but we can think of it this way: dividing by [latex]\\left(n-1\\right)[\/latex] instead of [latex]n[\/latex]\u00a0helps our sample standard deviation more closely resemble the true (usually unknowable) population standard deviation. This will help make our statistical analysis more reasonable.<\/li>\n<li><strong>What are degrees of freedom, anyway?<\/strong> A nice way to think of degrees of freedom, [latex]\\left(n-1\\right)[\/latex] is to imagine a set of three numbers whose mean is\u00a0[latex]5[\/latex]: say,\u00a0[latex]4, 5[\/latex], and\u00a0[latex]6[\/latex]. If those three numbers were written on pieces of paper in a hat, and you chose two of them, say\u00a0[latex]4[\/latex] and [latex]5[\/latex], first, then the only way to get a mean of\u00a0[latex]5[\/latex] from the numbers on three scraps of paper would be that the next choice must have a\u00a0[latex]6[\/latex] on it. We could say that the first two scraps were <em>free to vary<\/em>; they could have been\u00a0[latex]4[\/latex] or\u00a0[latex]5[\/latex] or\u00a0[latex]6[\/latex] as they pleased. But the third pick couldn&#8217;t vary. After choosing the\u00a0[latex]4[\/latex] and the\u00a0[latex]5[\/latex] freely first, there was no freedom for the choice of the third in order to obtain the desired mean. Only two of our choices had a degree of freedom, so we say that the degrees of freedom of a sample size of\u00a0[latex]3[\/latex] is [latex]\\left(3-1\\right)=2[\/latex].<\/li>\n<li><span style=\"background-color: #ffff99;\">Insert video explanation of the idea of n-1 as degrees of freedom. DC suggested this one:\u00a0<\/span>For a more detailed discussion, see <a href=\"https:\/\/www.khanacademy.org\/math\/ap-statistics\/summarizing-quantitative-data-ap\/more-standard-deviation\/v\/review-and-intuition-why-we-divide-by-n-1-for-the-unbiased-sample-variance\">https:\/\/www.khanacademy.org\/math\/ap-statistics\/summarizing-quantitative-data-ap\/more-standard-deviation\/v\/review-and-intuition-why-we-divide-by-n-1-for-the-unbiased-sample-variance<\/a><\/li>\n<\/ul>\n<\/div>\n<p><span style=\"font-size: 1rem; orphans: 1; text-align: initial;\"><\/div>\n<\/div>\n<p><\/span><\/p>\n<ul>\n<li><span style=\"font-size: 1em;\"><strong>The square root is taken<\/strong> in order to express the spread in terms of the units of the observations.\u00a0<\/span>Recall that we squared the differences to express them as positive distances, which resulted in squared observation units. Taking the square root can be thought of as &#8220;undoing&#8221; the earlier squaring.\u00a0For example, assume that within the context in which you are working, the data are in terms of dollars. If we do not take the square root, the standard deviation will be\u00a0in terms of dollars squared, which is not something commonly used.<\/li>\n<li><strong>The standard deviation, [latex]s[\/latex], represents the \u201ctypical\u201d distance of an observation from the mean of the data set.<\/strong><\/li>\n<\/ul>\n<div class=\"textbox tryit\">\n<h3>Standard Deviation VIDEO WITH INSTRUCTOR CHOICE<\/h3>\n<p><span style=\"background-color: #99cc00;\">[perspective video &#8212; a 3-instructor video showing how to think about standard deviation as a measure of variability. Cover the parts of the formula (go into\u00a0why squaring, why\u00a0<em>df<\/em> if desired) but emphasize the concept of variability from std dev and variance more so than the technical use of the formula.]<\/span><\/p>\n<\/div>\n<p>Although it&#8217;s important to understand each part of the formula \u2013 don\u2019t worry \u2013 we will be using the\u00a0statistical technology tool to calculate standard deviation for us! Let&#8217;s practice using tool by finding the standard deviation of the variable Average Sleep in the Sleep Study data set.<\/p>\n<div class=\"textbox key-takeaways\">\n<h3>Try It 5<\/h3>\n<div class=\"textbox\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-87\" src=\"https:\/\/s3-us-west-2.amazonaws.com\/courses-images\/wp-content\/uploads\/sites\/5808\/2022\/05\/28022318\/Screen-Shot-2022-05-27-at-7.21.15-PM1.png\" alt=\"\" width=\"2380\" height=\"476\" \/><\/div>\n<p style=\"padding-left: 30px;\">Step 1) Select the\u00a0<strong>Single Group<\/strong>\u00a0tab.<\/p>\n<p style=\"padding-left: 30px;\">Step 2) Locate the drop-down menu under\u00a0<strong>Enter Data<\/strong>\u00a0and select\u00a0<strong>From Textbook<\/strong>.<\/p>\n<p style=\"padding-left: 30px;\">Step 3) Locate the drop-down menu under\u00a0<strong>Data Set<\/strong>\u00a0and select\u00a0<strong>Sleep Study: Average Sleep<\/strong>.<\/p>\n<p><iframe loading=\"lazy\" id=\"ohm241060\" class=\"resizable\" src=\"https:\/\/ohm.lumenlearning.com\/multiembedq.php?id=241060&theme=oea&iframe_resize_id=ohm241060\" width=\"100%\" height=\"150\"><\/iframe><\/p>\n<div class=\"qa-wrapper\" style=\"display: block\"><span class=\"show-answer collapsed\" style=\"cursor: pointer\" data-target=\"q805189\">Hint<\/span><\/p>\n<div id=\"q805189\" class=\"hidden-answer\" style=\"display: none\">In the statistical technology tool, look for \u201cStd. Dev.\u201d in the table under Descriptive Statistics.<\/div>\n<\/div>\n<\/div>\n","protected":false},"author":17533,"menu_order":22,"template":"","meta":{"_candela_citation":"[]","CANDELA_OUTCOMES_GUID":"","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-33","chapter","type-chapter","status-publish","hentry"],"part":20,"_links":{"self":[{"href":"https:\/\/courses.lumenlearning.com\/alphamodule\/wp-json\/pressbooks\/v2\/chapters\/33","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.lumenlearning.com\/alphamodule\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/courses.lumenlearning.com\/alphamodule\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/alphamodule\/wp-json\/wp\/v2\/users\/17533"}],"version-history":[{"count":13,"href":"https:\/\/courses.lumenlearning.com\/alphamodule\/wp-json\/pressbooks\/v2\/chapters\/33\/revisions"}],"predecessor-version":[{"id":171,"href":"https:\/\/courses.lumenlearning.com\/alphamodule\/wp-json\/pressbooks\/v2\/chapters\/33\/revisions\/171"}],"part":[{"href":"https:\/\/courses.lumenlearning.com\/alphamodule\/wp-json\/pressbooks\/v2\/parts\/20"}],"metadata":[{"href":"https:\/\/courses.lumenlearning.com\/alphamodule\/wp-json\/pressbooks\/v2\/chapters\/33\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/courses.lumenlearning.com\/alphamodule\/wp-json\/wp\/v2\/media?parent=33"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/alphamodule\/wp-json\/pressbooks\/v2\/chapter-type?post=33"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/alphamodule\/wp-json\/wp\/v2\/contributor?post=33"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/alphamodule\/wp-json\/wp\/v2\/license?post=33"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}