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  2. Sampling probability - Wikipedia

    en.wikipedia.org/wiki/Sampling_probability

    In statistics, in the theory relating to sampling from finite populations, the sampling probability (also known as inclusion probability) of an element or member of the population, is its probability of becoming part of the sample during the drawing of a single sample. [1] For example, in simple random sampling the probability of a particular ...

  3. Percentile - Wikipedia

    en.wikipedia.org/wiki/Percentile

    In statistics, a k-th percentile, also known as percentile score or centile, is a score below which a given percentage k of scores in its frequency distribution falls ("exclusive" definition) or a score at or below which a given percentage falls ("inclusive" definition); i.e. a score in the k-th percentile would be above approximately k% of all scores in its set.

  4. Simple random sample - Wikipedia

    en.wikipedia.org/wiki/Simple_random_sample

    An example would be if the students in the school had numbers attached to their names ranging from 0001 to 1000, and we chose a random starting point, e.g. 0533, and then picked every 10th name thereafter to give us our sample of 100 (starting over with 0003 after reaching 0993).

  5. Quizlet - Wikipedia

    en.wikipedia.org/wiki/Quizlet

    Quizlet is a multi-national American company that provides tools for studying and learning. [1] Quizlet was founded in October 2005 by Andrew Sutherland, who at the time was a 15-year old student, [ 2 ] and released to the public in January 2007. [ 3 ]

  6. Independent and identically distributed random variables

    en.wikipedia.org/wiki/Independent_and...

    In probability theory and statistics, a collection of random variables is independent and identically distributed (i.i.d., iid, or IID) if each random variable has the same probability distribution as the others and all are mutually independent. [1] IID was first defined in statistics and finds application in many fields, such as data mining ...

  7. Statistical model - Wikipedia

    en.wikipedia.org/wiki/Statistical_model

    y = b 0 + b 1 x + b 2 x 2 + ε, ε ~ 𝒩(0, σ 2) has, nested within it, the linear model y = b 0 + b 1 x + ε, ε ~ 𝒩(0, σ 2) —we constrain the parameter b 2 to equal 0. In both those examples, the first model has a higher dimension than the second model (for the first example, the zero-mean model has dimension 1).

  8. Collectively exhaustive events - Wikipedia

    en.wikipedia.org/wiki/Collectively_exhaustive_events

    The events "even" (2,4 or 6) and "not-6" (1,2,3,4, or 5) are also collectively exhaustive but not mutually exclusive. In some forms of mutual exclusion only one event can ever occur, whether collectively exhaustive or not. For example, tossing a particular biscuit for a group of several dogs cannot be repeated, no matter which dog snaps it up.

  9. Coupling (probability) - Wikipedia

    en.wikipedia.org/wiki/Coupling_(probability)

    Using the standard formalism of probability theory, let and be two random variables defined on probability spaces (,,) and (,,).Then a coupling of and is a new probability space (,,) over which there are two random variables and such that has the same distribution as while has the same distribution as .