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  2. Blaster Learning System - Wikipedia

    en.wikipedia.org/wiki/Blaster_Learning_System

    Design of series protagonist Blasternaut from 1987 to 1999. The series began with the 1983 title Math Blaster! released for the Apple II and Atari 8-bit computers.The initial game was ported to other platforms and received gradual improvements to graphics and sound, with "Plus" added to the title in 1987 and "New" in 1990.

  3. JumpStart Adventures 5th Grade: Jo Hammet, Kid Detective

    en.wikipedia.org/wiki/JumpStart_Adventures_5th...

    JumpStart Adventures 5th Grade: Jo Hammet, Kid Detective covers curricula subjects such as art history, geography, math, language, science, and US History. Throughout the course of the game, which is set in the fictional city of Hooverville, the user must (while playing the role of female fifth grade detective Jo Hammet) thwart the schemes of the evil Dr. X, who is planning to destroy ...

  4. Mid-range - Wikipedia

    en.wikipedia.org/wiki/Mid-range

    For n = 1 or 2, the midrange and the mean are equal (and coincide with the median), and are most efficient for all distributions. For n = 3, the modified mean is the median, and instead the mean is the most efficient measure of central tendency for values of γ 2 from 2.0 to 6.0 as well as from −0.8 to 2.0.

  5. Quantile - Wikipedia

    en.wikipedia.org/wiki/Quantile

    For any population probability distribution on finitely many values, and generally for any probability distribution with a mean and variance, it is the case that +, where Q(p) is the value of the p-quantile for 0 < p < 1 (or equivalently is the k-th q-quantile for p = k/q), where μ is the distribution's arithmetic mean, and where σ is the ...

  6. Mean - Wikipedia

    en.wikipedia.org/wiki/Mean

    The arithmetic mean (or simply mean or average) of a list of numbers, is the sum of all of the numbers divided by their count.Similarly, the mean of a sample ,, …,, usually denoted by ¯, is the sum of the sampled values divided by the number of items in the sample.

  7. Bootstrapping (statistics) - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping_(statistics)

    The bootstrap is generally useful for estimating the distribution of a statistic (e.g. mean, variance) without using normality assumptions (as required, e.g., for a z-statistic or a t-statistic). In particular, the bootstrap is useful when there is no analytical form or an asymptotic theory (e.g., an applicable central limit theorem ) to help ...

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