{"id":6611,"date":"2022-05-13T12:10:34","date_gmt":"2022-05-13T12:10:34","guid":{"rendered":"https:\/\/oomdaanseplaas.co.za\/index.php\/2022\/05\/13\/measures-of-central-tendency-in-statistics-2\/"},"modified":"2022-05-13T12:10:34","modified_gmt":"2022-05-13T12:10:34","slug":"measures-of-central-tendency-in-statistics-2","status":"publish","type":"post","link":"https:\/\/oomdaanseplaas.co.za\/index.php\/2022\/05\/13\/measures-of-central-tendency-in-statistics-2\/","title":{"rendered":"Measures of Central Tendency in Statistics"},"content":{"rendered":"<p>In descriptive statistics, the mean may be confused with the median, mode or mid-range, as any of these may incorrectly be called an &#8220;average&#8221; (more formally, a measure of central tendency). For example, mean income is typically skewed upwards by a small number of people with very large incomes, so that the majority have an income lower than the mean. By contrast, the median income is the level at which half the population is below and half is above. The mode income is the most likely income and favors the larger number of people with lower incomes. This equality does not hold for other probability distributions, as illustrated for the log-normal distribution here.<\/p>\n<p>Imagine you have a group of friends, and you\u2019re trying to figure out the \u201caverage\u201d amount of money they have in their wallets. You\u2019d ask each friend, add all their cash together, and then divide by the number of friends. The result is the arithmetic mean \u2013 the amount each friend would have if the total cash were distributed evenly. But while it\u2019s ubiquitous, truly understanding the arithmetic mean \u2013 its strengths, weaknesses, and when to use it effectively \u2013 is crucial for making sense of data. Has an advantage in that it is a calculated quantity that is not depending on the order of terms in a series.<\/p>\n<ul>\n<li>Listed below are some of the major advantages of the arithmetic mean.<\/li>\n<li>When repeated samples are gathered from the same population, fluctuations are minimal for this measure of central tendency.<\/li>\n<li>Thus, the median is called the central value of any given data, either grouped or ungrouped.<\/li>\n<\/ul>\n<h2>How to calculate the arithmetic mean?<\/h2>\n<p>Additionally, the weighted arithmetic mean accommodates the incorporation of weights to account for relative importance. By understanding these mathematical properties, analysts can confidently utilize the arithmetic mean to gain insights and make informed decisions in quantitative analysis. The arithmetic mean is a measure of central tendency, representing the \u2018middle\u2019 or \u2018average\u2019 value of a data set. It\u2019s calculated by adding up all the numbers in a given data set and then dividing it by the total number of items within that set. The result is a single number that represents the \u2018typical\u2019 value within the set.<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' width=\"601px\" alt=\"properties of arithmetic mean\" 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arithmetic mean takes into account every value in the dataset, offering a comprehensive overview of the data&#8217;s overall behavior. The arithmetic mean as the name suggest is the ratio of summation of all observation to the total number of observation present. The arithmetical average of a group of two or more quantities is known as the mean. With this article you will be able to answer questions like what is the arithmetical mean.<\/p>\n<p>For instance, the average weight of the 20 students in the class is 50 kg. However, one student weighs 48 kg, another student weighs 53 kg, and so on. This means that 50 kg is the one value that represents the average weight of the class and the value is closer to the majority of observations, which is called mean. In real life, the importance of displaying a single value for a huge amount of data makes it simple to examine and analyse a set of data and deduce necessary information&nbsp;from it. Arithmetic Mean refers to the average of the values, which we can also understand as the sum of all values divided by the total number of values in a particular set.<\/p>\n<h2>Fascinating Mathematics: Facts You Didn\u2019t Know<\/h2>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' width=\"606px\" alt=\"properties of arithmetic mean\" 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mean is often referred to as the mean or arithmetic average. It is calculated by adding all the numbers in a given data set and then dividing it by the total number of items within that set. The arithmetic mean (AM) for evenly distributed numbers is equal to the middlemost number.<\/p>\n<h2>Frequently asked questions<\/h2>\n<p>When repeated samples are gathered from the same population, fluctuations are minimal for this measure of central tendency. It is introduced in lower grades and is referred to as average however, in 10th boards, students are taught different approaches to calculate the arithmetic mean. Statistics is a vital part of the syllabus in 12th boards and students need to have basic knowledge of arithmetic mean to be able to attend the sums appropriately. This article will include all the details like definition, properties, formulae and examples related to the chapter of arithmetic mean. Follow this page to get a clear idea of the concepts related to the chapter of arithmetic mean.<\/p>\n<h2>What is an inverse problem in mathematics:<\/h2>\n<p>Understanding what is arithmetic mean and how to calculate it is an essential skill for anyone dealing with numerical data. For a fun and interactive way to learn more about arithmetic mean and other math concepts, check out Mathema. Arithmetic means are also frequently used in economics, anthropology, history, and almost every other academic field to some extent.<\/p>\n<h2>Arithmetic Mean: Assumed Mean Method<\/h2>\n<p>It allows us to know the center of the frequency distribution by considering all of the observations. We see the use of representative value quite regularly in our daily life. When you ask about the mileage of the car, you are asking for the representative value of the amount of distance travelled to the amount of fuel consumed.<\/p>\n<h2>What is the formula for calculating the arithmetic mean?<\/h2>\n<p>Where X and Y represent two datasets, Mean(X) and Mean(Y) denote their respective means, and nX and nY represent the number of observations in each dataset. Assuming the values have been ordered, so is simply a specific example of a weighted mean for a specific set of weights. Sometimes, a set of numbers might contain outliers (i.e., data values which are much lower or much higher than the others). It involves discarding given parts of the data at the top or the bottom end, typically an equal amount at each end and then taking the arithmetic mean of the remaining data. The number of values removed is indicated as a percentage of the total number of values.<\/p>\n<p>Outside probability and statistics, a wide range of other notions of mean are often used in geometry and mathematical analysis; examples are given below. When someone says \u201caverage,\u201d what\u2019s the first thing that comes to mind? For most of us, it\u2019s the simple act of adding up a bunch of numbers and dividing by how many numbers there are.<\/p>\n<h2>Weighted arithmetic mean<\/h2>\n<ul>\n<li>A single value\u00a0used to symbolise a whole set of data is called the Measure of Central Tendency.<\/li>\n<li>Unlike other measures like as mode and median, it can be subjected to algebraic treatment.<\/li>\n<li>If all the observations assumed by a variable are constants, say &#8220;k&#8221;, then arithmetic mean is also &#8220;k&#8221;.<\/li>\n<li>Let us now look at some arithmetic mean properties to understand the concept better.<\/li>\n<li>It may be possible that some data sets are ungrouped and some data sets are grouped.<\/li>\n<li>Among all these measures, the arithmetic mean or mean is considered to be the best measure, because it includes all the values of the data set.<\/li>\n<\/ul>\n<p>If the measure of central tendency fails to describe the data, then we use measures of dispersion. Arithmetic mean is used in various scenarios such as in finding the average marks obtained by the student , the average rainfall in any area, etc. The Arithmetic Mean provides a single value that represents the central point of the dataset, making it useful for comparing and summarizing data.<\/p>\n<p>As it provides a single value to represent the central point of the dataset, making it useful for comparing and summarizing data. This formula is widely applicable, whether dealing with ungrouped data or grouped data. Its simplicity and utility make it indispensable in fields such as economics, finance, and data analysis. In mathematics, the arithmetic mean <a href=\"https:\/\/www.1investing.in\/arithmetic-properties\/\">properties of arithmetic mean<\/a> is a measure used to find the average of a set of numerical values. It is calculated by adding up all the numbers in a set and dividing by the count of numbers in that set.<\/p>\n<p>The range in the first scenario is represented by the difference between the largest value, 93 and the smallest value, 48.<\/p>\n<p>However, there are other ways of measuring an average, including median and mode, so the term should be clarified if there is any uncertainty as to which average a person is using. The arithmetic mean, also known as the average, is a widely used measure of central tendency in quantitative analysis. Besides its intuitive interpretation, the arithmetic mean possesses important mathematical properties that enhance its usefulness in data analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In descriptive statistics, the mean may be confused with the median, mode or mid-range, as any of these may incorrectly be called an &#8220;average&#8221; (more formally, a measure of central tendency). For example, mean income is typically skewed upwards by a small number of people with very large incomes, so that the majority have an [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[48],"tags":[],"class_list":["post-6611","post","type-post","status-publish","format-standard","hentry","category-forex-trading"],"_links":{"self":[{"href":"https:\/\/oomdaanseplaas.co.za\/index.php\/wp-json\/wp\/v2\/posts\/6611","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oomdaanseplaas.co.za\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/oomdaanseplaas.co.za\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/oomdaanseplaas.co.za\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/oomdaanseplaas.co.za\/index.php\/wp-json\/wp\/v2\/comments?post=6611"}],"version-history":[{"count":0,"href":"https:\/\/oomdaanseplaas.co.za\/index.php\/wp-json\/wp\/v2\/posts\/6611\/revisions"}],"wp:attachment":[{"href":"https:\/\/oomdaanseplaas.co.za\/index.php\/wp-json\/wp\/v2\/media?parent=6611"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/oomdaanseplaas.co.za\/index.php\/wp-json\/wp\/v2\/categories?post=6611"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/oomdaanseplaas.co.za\/index.php\/wp-json\/wp\/v2\/tags?post=6611"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}