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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.
In statistics, the sample maximum and sample minimum, also called the largest observation and smallest observation, are the values of the greatest and least elements of a sample. [1] They are basic summary statistics, used in descriptive statistics such as the five-number summary and Bowley's seven-figure summary and the associated box plot .
In descriptive statistics, the range of a set of data is size of the narrowest interval which contains all the data. It is calculated as the difference between the largest and smallest values (also known as the sample maximum and minimum ). [1] It is expressed in the same units as the data. The range provides an indication of statistical ...
The sample range is the difference between the maximum and minimum. It is a function of the order statistics: {, …,} = (). A similar important statistic in exploratory data analysis that is simply related to the order statistics is the sample interquartile range.
In probability theory, a probability density function ( PDF ), density function, or density of an absolutely continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of ...
Simple L-estimators can be visually estimated from a box plot, and include interquartile range, midhinge, range, mid-range, and trimean. In statistics, an L-estimator is an estimator which is a linear combination of order statistics of the measurements ( also called an L-statistic ). This can be as little as a single point, as in the median (of ...
v. t. e. The likelihood function (often simply called the likelihood) is the joint probability mass (or probability density) of observed data viewed as a function of the parameters of a statistical model. [1] [2] [3] Intuitively, the likelihood function is the probability of observing data assuming is the actual parameter.
where n is the total sample size. If it is important to use unbiased operators then the sum of squares should be divided by degrees of freedom (n − 1) and the probabilities are multiplied by / (). Kuder–Richardson Formula 21 (KR-21) Often discussed in tandem with KR-20, is Kuder–Richardson Formula 21 (KR-21).