{"id":26384,"date":"2021-03-26T13:00:20","date_gmt":"2021-03-26T13:00:20","guid":{"rendered":"http:\/\/toposuranos.com\/material\/?p=26384"},"modified":"2024-08-18T06:50:19","modified_gmt":"2024-08-18T06:50:19","slug":"counting-techniques-permutation-variation-and-combination","status":"publish","type":"post","link":"http:\/\/toposuranos.com\/material\/en\/counting-techniques-permutation-variation-and-combination\/","title":{"rendered":"Counting Techniques: Permutation, Variation, and Combination"},"content":{"rendered":"<div style=\"background-color:#F3F3F3; padding:20px;\">\n<center><\/p>\n<h1>Counting Techniques: Permutation, Variation, and Combination<\/h1>\n<p><\/p>\n<p style=\"text-align:center;\"><strong>Abstract<\/strong><br \/><em>In the study of probabilities, counting techniques are fundamental tools for measuring the cardinality of the sample space and the event to be measured. In this context, the techniques of combination, variation, and permutation are the most used due to their ease of use and application in experiments with equiprobable outcomes. Through the measure of probability as a limit of relative frequencies, the probability of an event is established as a quotient of cardinalities. Therefore, the calculation of probabilities is reduced to calculating the cardinality of the sample space and of the event to be measured. In this sense, the derivation of counting techniques through experiments with equiprobable outcomes is crucial for the study of probabilities. Through the definition of variations, combinations, and permutations, the size of sets can be measured efficiently and accurately. This class will present various experiments designed with equiprobable outcomes and their sample spaces will be analyzed to introduce counting techniques. With these tools, the size of a wide variety of sets can be measured and probabilities of events in experiments with equiprobable outcomes can be calculated.<\/em><\/p>\n<p><\/center><br \/>\n<\/p>\n<p style=\"text-align:center;\"><strong>LEARNING OBJECTIVES:<\/strong><br \/>\nUpon completing this class, the student will be able to:\n<\/p>\n<ol>\n<strong>Recall<\/strong> the formula of favorable cases over possible cases as a way to calculate the probability of an event.<br \/>\n<strong>Understand<\/strong> the concepts of permutation, variation, and combination and their use in probability calculations.<br \/>\n<strong>Analyze<\/strong> and explain the relationship between the size of the sample space and the probability of an event in an experiment with equiprobable outcomes.<br \/>\n<strong>Identify<\/strong> situations in which counting techniques of combination, variation, and permutation can be applied in everyday life, such as in gambling and organizational problems.\n<\/ol>\n<p style=\"text-align:center;\"><strong>TABLE OF CONTENTS<\/strong><br \/>\n<a href=\"#1\"><strong>COUNTING TECHNIQUES AND PROBABILITIES<\/strong><\/a><br \/>\n<a href=\"#2\"><strong>DERIVATION OF COUNTING TECHNIQUES<\/strong><\/a><br \/>\n<a href=\"#3\">EXPERIMENT 1 (AORm): ACTIVATE \u2013 RECORD IN ORDER \u2013 RESET, REPEAT m TIMES<\/a><br \/>\n<a href=\"#4\">EXPERIMENT 2 (AOk): ACTIVATE \u2013 RECORD IN ORDER, REPEAT k TIMES<\/a><br \/>\n<a href=\"#5\">EXPERIMENT 3 (ADk): ACTIVATE \u2013 RECORD IN DISORDER, REPEAT k TIMES<\/a><\/p>\n<p><center><br \/>\n<iframe class=\"lazyload\" width=\"560\" height=\"315\" data-src=\"https:\/\/www.youtube.com\/embed\/72LBcZP7Fv4\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/center>\n<\/div>\n<p><a name=\"1\"><\/a><br \/>\n<\/br><\/br><\/p>\n<h2>Counting Techniques and Probabilities<\/h2>\n<p style=\"text-align: justify; color: #000000;\"><a href=\"https:\/\/www.youtube.com\/watch?v=72LBcZP7Fv4&amp;t=7s\" target=\"_blank\" rel=\"noopener\"><strong><span style=\"color: #ff0000;\">Combination, Variation, and Permutation are the most used counting techniques in the study of probabilities<\/span><\/strong><\/a> due to the facilities they introduce in the study of experiments with equiprobable outcomes. One of the most iconic examples of these experiments comes from gambling. These are generally non-deterministic processes over a sample space <span class=\"katex-eq\" data-katex-display=\"false\">\\Omega = \\{\\omega_1, \\omega_2, \\cdots, \\omega_N\\}<\/span>. These experiments have the common feature that all events of the form <span class=\"katex-eq\" data-katex-display=\"false\">\\{\\omega_i\\}\\in\\mathcal{A}_\\Omega<\/span>, with <span class=\"katex-eq\" data-katex-display=\"false\">i\\in\\{1,2,\\cdots, n\\}<\/span>, have the same probability of occurring.<\/p>\n<p style=\"text-align: justify; color: #000000;\">From the <strong><a href=\"https:\/\/toposuranos.com\/el-espacio-de-probabilidades-medida-de-probabilidad\/\" target=\"_blank\" rel=\"noopener\">measure of probability as a limit of relative frequencies<\/a><\/strong>, we can establish the probability of an event as a quotient of cardinalities. As we have seen, this is done through the relationship:<\/p>\n<p style=\"text-align: center; color: #000000;\"><span class=\"katex-eq\" data-katex-display=\"false\">P(E) = \\displaystyle \\lim_{N\\to\\infty}g_N(E) = \\lim_{N\\to\\infty}\\frac{f_N(E)}{N}= \\frac{\\# E}{\\# \\Omega}<\/span>\n<p style=\"text-align: justify; color: #000000;\">Here the symbol \u00ab#\u00bb refers to the cardinality of the set. This is known as the <strong>formula of favorable cases over possible cases.<\/strong><\/p>\n<p style=\"text-align: justify; color: #000000;\">In these situations, the calculation of probabilities is reduced to calculating the cardinality of the sample space and the event to be measured. That is why it will be very useful to first review some <strong>counting techniques.<\/strong><\/p>\n<p><a name=\"2\"><\/a><br \/>\n<\/br><\/br><\/p>\n<h2>Derivation of Counting Techniques<\/h2>\n<p style=\"text-align: justify; color: #000000;\"><a href=\"https:\/\/www.youtube.com\/watch?v=72LBcZP7Fv4&amp;t=260s\" target=\"_blank\" rel=\"noopener\"><span style=\"color: #ff0000;\"><strong>To introduce combinations, variations, and permutations,<\/strong><\/span><\/a> we will design some experiments with equiprobable outcomes and, from them, make inferences that lead to these counting techniques.<\/p>\n<p style=\"text-align: justify; color: #000000;\">Suppose we have a \u00abperfect random machine\u00bb, consisting of a black box, a memory, an action button, and a reset button. The machine has the following properties:<\/p>\n<ol>\n<li style=\"text-align: justify; color: #000000;\">The machine only has one customizable setting: the cardinality of its sample space <span class=\"katex-eq\" data-katex-display=\"false\">\\Omega_N = \\{\\omega_1,\\cdots,\\omega_N\\}<\/span><\/li>\n<li style=\"text-align: justify; color: #000000;\">Upon pressing the action button, it will display one of the elements of <span class=\"katex-eq\" data-katex-display=\"false\">\\Omega_N<\/span><\/li>\n<li style=\"text-align: justify; color: #000000;\">Once a result is displayed, it is stored in the memory, and as long as it is there, it will not be shown again when the action button is pressed.<\/li>\n<li style=\"text-align: justify; color: #000000;\">If the machine has shown all possible results, it will freeze and show nothing.<\/li>\n<li style=\"text-align: justify; color: #000000;\">The reset button clears the memory and what is displayed on the screen.<\/li>\n<\/ol>\n<p style=\"text-align: justify; color: #000000;\">With this machine, we will design some experiments and analyze their sample spaces.<\/p>\n<p><a name=\"3\"><\/a><br \/>\n<\/br><\/br><\/p>\n<h3>Experiment 1 (AORm): Activate &#8211; Record in Order &#8211; Reset, repeat m times<\/h3>\n<p style=\"text-align: justify; color: #000000;\"><a href=\"https:\/\/www.youtube.com\/watch?v=72LBcZP7Fv4&amp;t=406s\" target=\"_blank\" rel=\"noopener\"><strong><span style=\"color: #ff0000;\">The machine is configured with<\/span><\/strong><\/a> <span class=\"katex-eq\" data-katex-display=\"false\">\\#\\Omega = N<\/span> and the following series of steps are repeated <span class=\"katex-eq\" data-katex-display=\"false\">m\\leq N<\/span> times:<\/p>\n<ol>\n<li style=\"text-align: justify; color: #000000;\">Press the action button<\/li>\n<li style=\"text-align: justify; color: #000000;\">Record the result in an ordered list<\/li>\n<li style=\"text-align: justify; color: #000000;\">Reset<\/li>\n<\/ol>\n<p style=\"text-align: justify; color: #000000;\">When we finish, we will obtain an ordered list with <span class=\"katex-eq\" data-katex-display=\"false\">m<\/span> elements of <span class=\"katex-eq\" data-katex-display=\"false\">\\Omega_N = \\{\\omega_1,\\cdots,\\omega_N\\}<\/span>. This list can be interpreted as an m-tuple of <span class=\"katex-eq\" data-katex-display=\"false\">\\Omega_N<\/span>. In other words, the sample space of this experiment <span class=\"katex-eq\" data-katex-display=\"false\">\\Omega_{AORm}<\/span> will be in the form<\/p>\n<p style=\"text-align: center; color: #000000;\"><span class=\"katex-eq\" data-katex-display=\"false\">\\Omega_{AORm}=\\Omega_N \\times \\cdots \\times \\Omega_N = \\Omega_N^m<\/span>\n<p style=\"text-align: justify; color: #000000;\">Therefore, we will have <span class=\"katex-eq\" data-katex-display=\"false\">\\#\\Omega_{AORm}=\\#\\Omega_N^m = N^m<\/span>.<\/p>\n<p><a name=\"4\"><\/a><br \/>\n<\/br><\/br><\/p>\n<h3>Experiment 2 (AOk): Activate &#8211; Record in Order, repeat k times<\/h3>\n<p style=\"text-align: justify; color: #000000;\"><a href=\"https:\/\/www.youtube.com\/watch?v=72LBcZP7Fv4&amp;t=542s\" target=\"_blank\" rel=\"noopener\"><strong><span style=\"color: #ff0000;\">The machine is set up again with<\/span><\/strong><\/a> <span class=\"katex-eq\" data-katex-display=\"false\">\\#\\Omega = N<\/span> and the following series of steps are repeated <span class=\"katex-eq\" data-katex-display=\"false\">k<\/span> times (<span class=\"katex-eq\" data-katex-display=\"false\">k\\leq N<\/span>):<\/p>\n<ol>\n<li style=\"text-align: justify; color: #000000;\">Press the action button.<\/li>\n<li style=\"text-align: justify; color: #000000;\">Record the result in an ordered list.<\/li>\n<\/ol>\n<p style=\"text-align: justify; color: #000000;\">When we finish, we will have obtained an ordered list of <span class=\"katex-eq\" data-katex-display=\"false\">k<\/span> elements of <span class=\"katex-eq\" data-katex-display=\"false\">\\Omega_N = \\{\\omega_1,\\cdots,\\omega_N\\}<\/span>, but where no element repeats any of the preceding ones.<\/p>\n<p style=\"text-align: justify; color: #000000;\">As the machine, in principle, does not favor any possible result over another (because it is perfectly random), it can be assumed without loss of generality that the first time the action was performed the event <span class=\"katex-eq\" data-katex-display=\"false\">\\{\\omega_1\\}<\/span> occurred, so the sample space of the next action should be <span class=\"katex-eq\" data-katex-display=\"false\">\\Omega_N\\setminus\\{\\omega_1\\}<\/span>. Similarly, it can be assumed without loss of generality that the second time the action was performed the event <span class=\"katex-eq\" data-katex-display=\"false\">\\{\\omega_2\\}<\/span> occurred; therefore, the sample space of the next action will be of the form <span class=\"katex-eq\" data-katex-display=\"false\">(\\Omega_N\\setminus\\{\\omega_1\\})\\setminus\\{\\omega_2\\}<\/span>. If we continue in this way, when we reach the k-th action, its sample space will be of the form<\/p>\n<p style=\"text-align: center; color: #000000;\"><span class=\"katex-eq\" data-katex-display=\"false\">(\\cdots(\\Omega_N\\setminus\\{\\omega_1\\})\\setminus\\{\\omega_2\\}\\cdots)\\setminus\\{\\omega_{k-1}\\}<\/span>\n<p style=\"text-align: justify; color: #000000;\">So, the sample space of possible results of this experiment will be of the form<\/p>\n<p style=\"text-align: center; color: #000000;\"><span class=\"katex-eq\" data-katex-display=\"false\">\\Omega_{AOk}= \\Omega \\times (\\Omega_N\\setminus\\{\\omega_1\\}) \\times ((\\Omega_N\\setminus\\{\\omega_1\\})\\setminus\\{\\omega_2\\}) \\times \\cdots \\times ((\\cdots(\\Omega_N\\setminus\\{\\omega_1\\})\\setminus\\{\\omega_2\\}\\cdots)\\setminus\\{\\omega_{k-1}\\}) <\/span>\n<p style=\"text-align: justify; color: #000000;\">So if we calculate the cardinality of this set we will get<\/p>\n<p style=\"text-align: center; color: #000000;\"><span class=\"katex-eq\" data-katex-display=\"false\">\\#\\Omega_{AOk}= N \\cdot (N-1) \\cdot (N-2) \\cdots [N-(k-1)]=\\displaystyle \\frac{N!}{(N-k)!}<\/span>\n<p style=\"text-align: justify; color: #000000;\">Based on this result the following definition is created:<\/p>\n<table>\n<tbody>\n<tr>\n<td style=\"text-align: justify; color: #800000; background-color: #dddddd;\"><strong>DEFINITION<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: justify; color: #000000; background-color: #ffffff;\">The <strong>number of variations<\/strong> of <span class=\"katex-eq\" data-katex-display=\"false\">N<\/span> elements into groups of <span class=\"katex-eq\" data-katex-display=\"false\">k<\/span> (with <span class=\"katex-eq\" data-katex-display=\"false\">k\\leq N<\/span>) is defined as the number given by:<\/p>\n<p style=\"text-align: center; color: #000000;\"><span class=\"katex-eq\" data-katex-display=\"false\">(N)_k = \\displaystyle \\frac{N!}{(N-k)!}<\/span>\n<p>From this, and the fact that <span class=\"katex-eq\" data-katex-display=\"false\">0! = 1<\/span>, the <strong>number of permutations<\/strong> among <span class=\"katex-eq\" data-katex-display=\"false\">N<\/span> elements is calculated through<\/p>\n<p style=\"text-align: center; color: #000000;\"><span class=\"katex-eq\" data-katex-display=\"false\">(N)_N = N!<\/span>.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><a name=\"5\"><\/a><br \/>\n<\/br><\/br><\/p>\n<h3>Experiment 3 (ADk): Activate &#8211; Record in Disorder, repeat k times<\/h3>\n<p style=\"text-align: justify; color: #000000;\"><a href=\"https:\/\/www.youtube.com\/watch?v=72LBcZP7Fv4&amp;t=1204s\" target=\"_blank\" rel=\"noopener\"><strong><span style=\"color: #ff0000;\">This experiment is exactly the same as the previous one,<\/span><\/strong><\/a> only now the order in which the elements of <span class=\"katex-eq\" data-katex-display=\"false\">\\Omega_N<\/span> appear is not recorded. In other words, what would be two k-tuples with the same elements, but in different orders, are now considered the same. Thus, taking advantage of the fact that each k-tuple obtained from the experiment AOk can be written in <span class=\"katex-eq\" data-katex-display=\"false\">(k)_k=k!<\/span> different ways, it will be that the cardinality of the sample space of this experiment will be of the form<\/p>\n<p style=\"text-align: center; color: #000000;\"><span class=\"katex-eq\" data-katex-display=\"false\">\\#\\Omega_{ADk} = \\displaystyle \\frac{\\#\\Omega_{AOk}}{(k)_k} = \\frac{(N)_k}{k!} = \\frac{N!}{k!(N-k)!} <\/span>\n<p style=\"text-align: justify; color: #000000;\">From this we can establish the following definition:<\/p>\n<table>\n<tbody>\n<tr>\n<td style=\"text-align: justify; color: #800000; background-color: #dddddd;\"><strong>DEFINITION<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: justify; color: #000000; background-color: #ffffff;\">The <strong>number of combinations<\/strong> of <span class=\"katex-eq\" data-katex-display=\"false\">N<\/span> elements into groups of <span class=\"katex-eq\" data-katex-display=\"false\">k<\/span> (with <span class=\"katex-eq\" data-katex-display=\"false\">k\\leq N<\/span>) is defined by the number given by<\/p>\n<p style=\"text-align: center; color: #000000;\"><span class=\"katex-eq\" data-katex-display=\"false\">\\displaystyle {{N}\\choose{k}}= \\frac{N!}{k!(N-k)!} <\/span>\n<p>This represents the number of possible subsets that can be formed with k elements drawn from a set of N elements.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify; color: #000000;\">With the counting techniques of permutation, variation, and combination, we can now measure the size of a wide variety of sets.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Counting Techniques: Permutation, Variation, and Combination AbstractIn the study of probabilities, counting techniques are fundamental tools for measuring the cardinality of the sample space and the event to be measured. In this context, the techniques of combination, variation, and permutation are the most used due to their ease of use and application in experiments with [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":26383,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"iawp_total_views":32,"footnotes":""},"categories":[567,670],"tags":[],"class_list":["post-26384","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-mathematics","category-probabilities-and-statistics"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Counting Techniques: Permutation, Variation, and Combination - toposuranos.com\/material<\/title>\n<meta name=\"description\" content=\"Discover how to apply the most commonly used counting techniques in probabilities: permutation, variation, and combination with practical 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