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The mean (L 2 center) and midrange (L ∞ center) are unique (when they exist), while the median (L 1 center) and mode (L 0 center) are not in general unique. This can be understood in terms of convexity of the associated functions ( coercive functions ).
Mid-range. In statistics, the mid-range or mid-extreme is a measure of central tendency of a sample defined as the arithmetic mean of the maximum and minimum values of the data set: [ 1] The mid-range is closely related to the range, a measure of statistical dispersion defined as the difference between maximum and minimum values.
Mode (statistics) In statistics, the mode is the value that appears most often in a set of data values. [ 1] If X is a discrete random variable, the mode is the value x at which the probability mass function takes its maximum value (i.e., x=argmaxxi P (X = xi) ). In other words, it is the value that is most likely to be sampled.
The mode, the median, and the mid-range are often used in addition to the mean as estimates of central tendency in descriptive statistics. These can all be seen as minimizing variation by some measure; see Central tendency § Solutions to variational problems .
Comparison of mean, median and mode of two log-normal distributions with different skewness. The mode is the point of global maximum of the probability density function. In particular, by solving the equation ( ln f ) ′ = 0 {\displaystyle (\ln f)'=0} , we get that:
Median. Finding the median in sets of data with an odd and even number of values. The median of a set of numbers 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.
In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable.The general form of its probability density function is = The parameter is the mean or expectation of the distribution (and also its median and mode), while the parameter is the variance.
The binomial distribution is the PMF of k successes given n independent events each with a probability p of success. Mathematically, when α = k + 1 and β = n − k + 1, the beta distribution and the binomial distribution are related by [clarification needed] a factor of n + 1 :
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