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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.
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 ...
Central tendency. In statistics, a central tendency (or measure of central tendency) is a central or typical value for a probability distribution. [1] Colloquially, measures of central tendency are often called averages. The term central tendency dates from the late 1920s.
Midhinge. In statistics, the midhinge is the average of the first and third quartiles and is thus a measure of location . Equivalently, it is the 25% trimmed mid-range or 25% midsummary; it is an L-estimator . The midhinge is related to the interquartile range (IQR), the difference of the third and first quartiles (i.e. ), which is a measure of ...
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 ...
In statistics, robust measures of scale are methods that quantify the statistical dispersion in a sample of numerical data while resisting outliers. The most common such robust statistics are the interquartile range (IQR) and the median absolute deviation (MAD). These are contrasted with conventional or non-robust measures of scale, such as ...
Trimmed estimator. In statistics, a trimmed estimator is an estimator derived from another estimator by excluding some of the extreme values, a process called truncation. This is generally done to obtain a more robust statistic, and the extreme values are considered outliers. [1] Trimmed estimators also often have higher efficiency for mixture ...
Robust statistics are statistics that maintain their properties even if the underlying distributional assumptions are incorrect. Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters. One motivation is to produce statistical methods that are not unduly affected by ...