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If it is a symmetric distribution, the median must be 130.

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If it is symmetric, the median is the same as the mean.

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Q: If the mean of a symmetric distribution is 130 which of these values could be the median of the distribution?

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No, but they are symmetric with respect to a line parallel to the y-axis - which could be the y-axis itself.

Symmetry

No because the mean is the highest numeral and the median is the middle numeral of the set of numbers so it is tecnictly impossible, but if you are using decimals, the median could get pretty close to the mean, but never higher.

It could be the x-axis.

The median is a line from a vertex to the midpoint of the opposite line and an altitude is a line from a vertex to the opposite line which is perpendicular to the line. These are NOT the same thing in most triangles. The only time they could be the same is in an equilateral triangle.

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Any number can be a median, so for the correct set of values, 40 could be a median.

The median can be calculated using the Median function. Assuming the values you wanted the median of were in cells B2 to B20, you could use the function like this: =MEDIAN(B2:B20)

Symmetric is a term used to describe an object in size or shape. For example, you could say that an orange is symmetric to the sun or a glass is symmetric to a cone

The median is the number in the middle. You find the median, by putting the values in order from lowest to highest, then find the number that is exactly in the middle. If you only have a single value, one could argue that it is in the middle. That would make the single value the median. One could also argue that there no numbers on either side to the definition makes no sense and there is no median of a single value.

The median is the value that is half way between the two values. In this case it is 80.65 because the difference between the two values is 4.3 and half of that added to the 78.5 or subttacted from the 82.8 gives you 80.65 as the median. You could also add the two numbers together and divide by two, which will also give the same result.

The answer depends on the probability distribution of WHAT variable. The variable could be the sum or the product of the three numbers, the maximum, minimum, the mean, median, number of 3s, number of primes, and so on.

Usually it's not what's being summarised (in this case, money) it's how the stuff that's being summarised is distributed that matters. If the distribution is symmetric and the danger of there being large outliers is small then the mean could be a good choice. The range is useful for quite different purposes than the other three statistics.

A central tendency is a number that expresses something "central" about a sample of values (which could be test scores, temperatures, etc...). Measures of central tendency include the mean, the median, and the mode.The Mean is equal to the average of all the values. Thus, the Mean is equal to the sum of all the values (add them all up) divided by the total number of values in your set or sample. This average tells you nothing about what your highest and lowest values are (the range). However, ...The Median is equal to the the number which, if you were to arrange your values from lowest to highest, falls exactly in the middle of your distribution of values. So, if you have 41 values, for instance, the Median would be the 21st value, and there would be 20 values equal to or smaller than the Median, and 20 values equal to or larger than the median. If, on the other hand, there were 100 values, the median would be the average of the 50th and 51st values in the distribution. The median tells you nothing, however, about what values occur "most often" in your distribution. So....There is the mode, which is equal to the value which occurs most often in your distribution. Simply count how many times each of your values occurs, and the mode= the one that occurs most often. The following is an example of a distribution which is highly "skewed" meaning that there are differences between the mean, median and mode for the set of values being observed.MeanThe mean is the most commonly-used measure of central tendency. When we talk about an "average", we usually are referring to the mean. The mean is simply the sum of the values divided by the total number of items in the set. The result is referred to as the arithmetic mean.It is the best average of measures of central tendency.It is used in Stock exchange, Market to calculate the Mean (share) Price in the particular day.Sometimes it is useful to give more weighting to certain data points, in which case the MedianThe median is determined by sorting the data set from lowest to highest values and taking the data point in the middle of the sequence. There is an equal number of points above and below the median. For example, in the data set {1,2,3,4,5} the median is 3; there are two data points greater than this value and two data points less than this value. In this case, the median is equal to the mean. But consider the data set {1,2,3,4,10}. In this dataset, the median still is three, but the mean is equal to 4. If there is an even number of data points in the set, then there is no single point at the middle and the median is calculated by taking the mean of the two middle points.The median can be determined for ordinal data as well as interval and ratio data. Unlike the mean, the median is not influenced by outliers at the extremes of the data set. For this reason, the median often is used when there are a few extreme values that could greatly influence the mean and distort what might be considered typical. This often is the case with home prices and with income data for a group of people, which often is very skewed. For such data, the median often is reported instead of the mean. For example, in a group of people, if the salary of one person is 10 times the mean, the mean salary of the group will be higher because of the unusually large salary. In this case, the median may better represent the typical salary level of the group. ModeThe mode is the most frequently occurring value in the data set. For example, in the data set {1,2,3,4,4}, the mode is equal to 4. A data set can have more than a single mode, in which case it is multimodal. In the data set {1,1,2,3,3} there are two modes: 1 and 3.The mode can be very useful for dealing with categorical data. For example, if a sandwich shop sells 10 different types of sandwiches, the mode would represent the most popular sandwich. The mode also can be used with ordinal, interval, and ratio data. However, in interval and ratio scales, the data may be spread thinly with no data points having the same value. In such cases, the mode may not exist or may not be very meaningful.We have to findout Model value of the particular things.For example shoe Model size =Maximum no of persons used shoe size,likewise shirt size and various products models.When to use Mean, Median, and ModeThe following table summarizes the appropriate methods of determining the middle or typical value of a data set based on the measurement scale of the data.Measurement ScaleBest Measure of the "Middle"Nominal(Categorical)ModeOrdinalMedianIntervalSymmetrical data: MeanSkewed data: MedianRatioSymmetrical data: MeanSkewed data: Median result is called the weighted arithmetic mean

Plotting data in a frequency distribution shows the general shape of the distribution and gives a general sense of how the numbers are bunched. Several statistics can be used to represent the "center" of the distribution. These statistics are commonly referred to as measures of central tendency.

look at how many sides there are...

I could be wrong but I do not believe that it is possible other than for the null matrix.

A central tendency is a number that expresses something "central" about a sample of values (which could be test scores, temperatures, etc...). Measures of central tendency include the mean, the median, and the mode. The Mean is equal to the average of all the values. Thus, the Mean is equal to the sum of all the values (add them all up) divided by the total number of values in your set or sample. This average tells you nothing about what your highest and lowest values are (the range). However, ... The Median is equal to the the number which, if you were to arrange your values from lowest to highest, falls exactly in the middle of your distribution of values. So, if you have 41 values, for instance, the Median would be the 21st value, and there would be 20 values equal to or smaller than the Median, and 20 values equal to or larger than the median. If, on the other hand, there were 100 values, the median would be the average of the 50th and 51st values in the distribution. The median tells you nothing, however, about what values occur "most often" in your distribution. So.... There is the mode, which is equal to the value which occurs most often in your distribution. Simply count how many times each of your values occurs, and the mode= the one that occurs most often. The following is an example of a distribution which is highly "skewed" meaning that there are differences between the mean, median and mode for the set of values being observed. For discussion, let's say we are talking about test scores. The score values in the data set are: 1, 1, 1, 1, 1, 1, 1, 2 ,3,4, 4 , 5, 5 ,8 ,8 ,8, 8 ,9 ,9, 9, 9,10, 10, 10, 11, 11 ,12 The Number of values in the data set is 27, indicating that 27 people took the test, or that that one way or another, the test was taken 27 times by some number of people, thus we say, N= 27. The average or "mean" is the Sum of the the values (162) divided by N, so 162/27 = 6 ; Thus, the average value or "mean" is 6. Notice that 6 occurs not even once in this distribution. Thus, even though the average is 6, your chances of obtaining a 6 (for instance if these were test scores) would only be 1 in 27! So, let's try looking at the Median- how many people's test scores will be above and below the median? We divide N= 27 by 2. 27/2=13.5, so it is the 14th value that equals the median. Counting from either end, the 14th value is 8. So, you can assume that if you score an 8 on the test, you are scoring at the 50th percentile, yet still "above the average." But how likely are you to get an 8? Well, It turns out that the most common score to get on this test is "1" which makes the Mode=1. That tells you there is a higher liklihood of getting a 1 than any other score. None-the-less, you should not necessarily expect to get a "1" on the test, since 20 out of 27 scores will be higher than that, and 14 out of the 27 scores will be "above average." The distribution of scores has a shape which can be approximated by drawing a line over the top of these number towers. 1 1 1 1 8 9 1 8 9 10 1 4 5 8 9 10 11 1 2 3 4 5 8 9 10 11 12 This distribution of numbers is said to be skewed since it looks like a declining slope, rather than a more or less evenly distributed set that would look more like a "hill." An inclining slope would also be considered a skewed distribution. Looking a these numbers, you could safely if you don't score "1" on the test, you will probably score somewhere between 8 and 10, and are very likely to score between 4 and 11. This distribution could represent, just as an example, a test where you get one point for writing your name, and then you either know most of the answers, or you know almost none of them, and get a coupld right by guessing. That is assuming there were only 12 possible points on the test, which may not be the case. It could very well be a distribution of scores on a 20 item test, which would then say something else about the test. Lets say that the test involves 20 possible points, one point per question, and that we were expecting that scores would be "normally distributed." That means we were expecting a mean of 10, an average of 10, and a mode of 10, with 86% of the scores falling within one standard deviation of the mean (that is another discussion), but basically "in the middle of" the distribution with 8 % on each end of the curve where the highest and lowest scores are represented, creating a curve that looks like a hill. If, despite, our expectation, we got scores as listed above, it would tell us that our assumption may not be true for the sample population we are studying, and that there are some factors impacting the way people score on the test that we need to look at more closely.