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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 ...
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 ...
Examples. The median is the most trimmed statistic (nominally 50%), as it discards all but the most central data, and equals the fully trimmed mean – or indeed fully trimmed mid-range, or (for odd-size data sets) the fully trimmed maximum or minimum. Likewise, no degree of trimming has any effect on the median – a trimmed median is the ...
From January 2008 to December 2012, if you bought shares in companies when John S. Chen joined the board, and sold them when he left, you would have a 42.4 percent return on your investment, compared to a -2.8 percent return from the S&P 500.
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.
The following may be applied to one-dimensional data. Depending on the circumstances, it may be appropriate to transform the data before calculating a central tendency. Examples are squaring the values or taking logarithms. Whether a transformation is appropriate and what it should be, depend heavily on the data being analyzed.