What are mode, mean, median and range?
13, 13, 13, 13, 14, 14, 16, 18, 13, 13, 13, 13, 14, 14, 16, 18, So the median is The mode is the number that is repeated more often than any other, so 13 is the mode. The largest value in the list is 21, and the smallest is 13, so the range is 21 – 13 = 8. mean: median: Dec 22, · The terms mean, median, mode, and range describe properties of statistical distributions. In statistics, a distribution is the set of all possible values for terms that represent defined events. The value of a term, when expressed as a variable, is called a random variable. There are two major types of statistical distributions.
Mean, median and mode are different ways of determining the average from a set of numbers. Range gives the difference between the highest and lowest values. The mean takes the sum of all the values, then divides the total by the number of values given. The resulting how to finger to orgasm will not necessarily be equal to any of the values previously given and may contain a decimal.
The mean is commonly taught as being the standard average. The median is the middle value within a set of values arranged in numerical order. When a set has an even rqnge of values, the median is determined by calculating the mean between the two most central values.
The mode is the number that is repeated most often within a set of values. If no numbers are repeated, then the set is determined to have no mode. A set can also have multiple modes if multiple numbers are equally repeated. The range subtracts the smallest value from the largest value within the set.
Nad is useful to determine how spread out all the variables are. However, a single outlier within the set can affect the result in a way that does not accurately reflect the data. More From Reference. What Is Product Orientation?
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Finding the Mean, Median, Mode and Range The three distinct calculations associated with the Measure of Central Tendency are the Mean, Median, and Mode. Each measurement is an attempt to capture the essence of how a typical entry or number in the data set may look like. The median is the middle number in a list of numbers ordered from smallest to largest. 9, 9, 9, 10, 11, 12, 15 so 10 is the median of this set of results. The range is the difference between the lowest value and the highest value, so the range here is 15 - 9 = 6. Sep 21, · Mean, median and mode are different ways of determining the average from a set of numbers. Range gives the difference between the highest and lowest values. The mean takes the sum of all the values, then divides the total by the number of values given. The resulting average will not necessarily be equal to any of the values previously given and may contain a decimal.
In statistics and probability theory , the median is the value separating the higher half from the lower half of a data sample , a population , or a probability distribution. For a data set , it may be thought of as "the middle" value. The basic feature of the median in describing data compared to the mean often simply described as the "average" is that it is not skewed by a small proportion of extremely large or small values, and therefore provides a better representation of a "typical" value.
Median income , for example, may be a better way to suggest what a "typical" income is, because income distribution can be very skewed.
The median of a finite list of numbers is the "middle" number, when those numbers are listed in order from smallest to greatest. If the data set has an odd number of observations, the middle one is selected. For example, the following list of seven numbers,. A set of an even number of observations has no distinct middle value and the median is usually defined to be the mean of the two middle values.
In more technical terms, this interprets the median as the fully trimmed mid-range. With this convention, the median can be defined as follows for even number of observations :. Formally, a median of a population is any value such that at most half of the population is less than the proposed median and at most half is greater than the proposed median. As seen above, medians may not be unique. If each set contains less than half the population, then some of the population is exactly equal to the unique median.
The median is well-defined for any ordered one-dimensional data, and is independent of any distance metric. The median can thus be applied to classes which are ranked but not numerical e. A geometric median , on the other hand, is defined in any number of dimensions. A related concept, in which the outcome is forced to correspond to a member of the sample, is the medoid. The median is a special case of other ways of summarising the typical values associated with a statistical distribution : it is the 2nd quartile , 5th decile , and 50th percentile.
The median can be used as a measure of location when one attaches reduced importance to extreme values, typically because a distribution is skewed , extreme values are not known, or outliers are untrustworthy, i. The median is 2 in this case, as is the mode , and it might be seen as a better indication of the center than the arithmetic mean of 4, which is larger than all-but-one of the values.
However, the widely cited empirical relationship that the mean is shifted "further into the tail" of a distribution than the median is not generally true. As a median is based on the middle data in a set, it is not necessary to know the value of extreme results in order to calculate it. For example, in a psychology test investigating the time needed to solve a problem, if a small number of people failed to solve the problem at all in the given time a median can still be calculated.
Because the median is simple to understand and easy to calculate, while also a robust approximation to the mean , the median is a popular summary statistic in descriptive statistics. In this context, there are several choices for a measure of variability : the range , the interquartile range , the mean absolute deviation , and the median absolute deviation.
For practical purposes, different measures of location and dispersion are often compared on the basis of how well the corresponding population values can be estimated from a sample of data.
The median, estimated using the sample median, has good properties in this regard. While it is not usually optimal if a given population distribution is assumed, its properties are always reasonably good. For example, a comparison of the efficiency of candidate estimators shows that the sample mean is more statistically efficient when — and only when — data is uncontaminated by data from heavy-tailed distributions or from mixtures of distributions.
For any real -valued probability distribution with cumulative distribution function F , a median is defined as any real number m that satisfies the inequalities. In the former case, the inequalities can be upgraded to equality: a median satisfies. The medians of certain types of distributions can be easily calculated from their parameters; furthermore, they exist even for some distributions lacking a well-defined mean, such as the Cauchy distribution :.
The mean absolute error of a real variable c with respect to the random variable X is. Provided that the probability distribution of X is such that the above expectation exists, then m is a median of X if and only if m is a minimizer of the mean absolute error with respect to X. This optimization-based definition of the median is useful in statistical data-analysis, for example, in k -medians clustering.
This bound was proved by Mallows,  who used Jensen's inequality twice, as follows. The first and third inequalities come from Jensen's inequality applied to the absolute-value function and the square function, which are each convex. Mallows' proof can be generalized to obtain a multivariate version of the inequality  simply by replacing the absolute value with a norm :. An alternative proof uses the one-sided Chebyshev inequality; it appears in an inequality on location and scale parameters.
This formula also follows directly from Cantelli's inequality. For the case of unimodal distributions, one can achieve a sharper bound on the distance between the median and the mean:. Jensen's inequality states that for any random variable X with a finite expectation E [ X ] and for any convex function f. This inequality generalizes to the median as well. Every C function is convex, but the reverse does not hold. If f is a C function, then.
If the medians are not unique, the statement holds for the corresponding suprema. Because this, as well as the linear time requirement, can be prohibitive, several estimation procedures for the median have been developed. A simple one is the median of three rule, which estimates the median as the median of a three-element subsample; this is commonly used as a subroutine in the quicksort sorting algorithm, which uses an estimate of its input's median.
A more robust estimator is Tukey 's ninther , which is the median of three rule applied with limited recursion:  if A is the sample laid out as an array , and. The remedian is an estimator for the median that requires linear time but sub-linear memory, operating in a single pass over the sample.
The distributions of both the sample mean and the sample median were determined by Laplace. A modern proof follows below. Laplace's result is now understood as a special case of the asymptotic distribution of arbitrary quantiles. Now we introduce the beta function.
Its mean, as we would expect, is 0. By the chain rule , the corresponding variance of the sample median is. The additional 2 is negligible in the limit. However, they can be estimated from an observed frequency distribution.
In this section, we give an example. Consider the following table, representing a sample of 3, discrete-valued observations:. So we must sum over all these possibilities:. Here, i is the number of points strictly less than the median and k the number strictly greater. Using these preliminaries, it is possible to investigate the effect of sample size on the standard errors of the mean and median.
The observed mean is 3. The following table gives some comparison statistics. The expected value of the median falls slightly as sample size increases while, as would be expected, the standard errors of both the median and the mean are proportionate to the inverse square root of the sample size.
The asymptotic approximation errs on the side of caution by overestimating the standard error. The standard "delete one" jackknife method produces inconsistent results. The efficiency of the sample median, measured as the ratio of the variance of the mean to the variance of the median, depends on the sample size and on the underlying population distribution.
For univariate distributions that are symmetric about one median, the Hodges—Lehmann estimator is a robust and highly efficient estimator of the population median. If data are represented by a statistical model specifying a particular family of probability distributions , then estimates of the median can be obtained by fitting that family of probability distributions to the data and calculating the theoretical median of the fitted distribution. Previously, this article discussed the univariate median, when the sample or population had one-dimension.
When the dimension is two or higher, there are multiple concepts that extend the definition of the univariate median; each such multivariate median agrees with the univariate median when the dimension is exactly one. The marginal median is defined for vectors defined with respect to a fixed set of coordinates. A marginal median is defined to be the vector whose components are univariate medians. The marginal median is easy to compute, and its properties were studied by Puri and Sen.
In contrast to the marginal median, the geometric median is equivariant with respect to Euclidean similarity transformations such as translations and rotations. An alternative generalization of the median in higher dimensions is the centerpoint. When dealing with a discrete variable, it is sometimes useful to regard the observed values as being midpoints of underlying continuous intervals. An example of this is a Likert scale, on which opinions or preferences are expressed on a scale with a set number of possible responses.
If the scale consists of the positive integers, an observation of 3 might be regarded as representing the interval from 2. It is possible to estimate the median of the underlying variable.
But the interpolated median is somewhere between 2. In other words, we split up the interval width pro rata to the numbers of observations. For univariate distributions that are symmetric about one median, the Hodges—Lehmann estimator is a robust and highly efficient estimator of the population median; for non-symmetric distributions, the Hodges—Lehmann estimator is a robust and highly efficient estimator of the population pseudo-median , which is the median of a symmetrized distribution and which is close to the population median.
The Theil—Sen estimator is a method for robust linear regression based on finding medians of slopes. In the context of image processing of monochrome raster images there is a type of noise, known as the salt and pepper noise , when each pixel independently becomes black with some small probability or white with some small probability , and is unchanged otherwise with the probability close to 1.
In cluster analysis , the k-medians clustering algorithm provides a way of defining clusters, in which the criterion of maximising the distance between cluster-means that is used in k-means clustering , is replaced by maximising the distance between cluster-medians. This is a method of robust regression. The line could then be adjusted to fit the majority of the points in the data set. Nair and Shrivastava in suggested a similar idea but instead advocated dividing the sample into three equal parts before calculating the means of the subsamples.
Any mean -unbiased estimator minimizes the risk expected loss with respect to the squared-error loss function , as observed by Gauss. A median -unbiased estimator minimizes the risk with respect to the absolute-deviation loss function, as observed by Laplace. Other loss functions are used in statistical theory , particularly in robust statistics. The theory of median-unbiased estimators was revived by George W. Brown in . This requirement seems for most purposes to accomplish as much as the mean-unbiased requirement and has the additional property that it is invariant under one-to-one transformation.
Further properties of median-unbiased estimators have been reported. There are methods of constructing median-unbiased estimators that are optimal in a sense analogous to the minimum-variance property for mean-unbiased estimators.