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  2. Mode (statistics) - Wikipedia

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

    The mode of a sample is the element that occurs most often in the collection. For example, the mode of the sample [1, 3, 6, 6, 6, 6, 7, 7, 12, 12, 17] is 6. Given the list of data [1, 1, 2, 4, 4] its mode is not unique. A dataset, in such a case, is said to be bimodal, while a set with more than two modes may be described as multimodal.

  3. Survey methodology - Wikipedia

    en.wikipedia.org/wiki/Survey_methodology

    Survey methodology is "the study of survey methods". [1] As a field of applied statistics concentrating on human-research surveys, survey methodology studies the sampling of individual units from a population and associated techniques of survey data collection, such as questionnaire construction and methods for improving the number and accuracy of responses to surveys.

  4. Mode effect - Wikipedia

    en.wikipedia.org/wiki/Mode_effect

    Mode effect is a broad term referring to a phenomenon where a particular survey administration mode causes different data to be collected. For example, when asking a question using two different modes (e.g. paper and telephone), responses to one mode may be significantly and substantially different from responses given in the other mode.

  5. Central tendency - Wikipedia

    en.wikipedia.org/wiki/Central_tendency

    For example, given binary data, say heads or tails, if a data set consists of 2 heads and 1 tails, then the mode is "heads", but the empirical measure is 2/3 heads, 1/3 tails, which minimizes the cross-entropy (total surprisal) from the data set.

  6. Bootstrapping (statistics) - Wikipedia

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

    An example of the first resample might look like this X 1 * = x 2, x 1, x 10, x 10, x 3, x 4, x 6, x 7, x 1, x 9. There are some duplicates since a bootstrap resample comes from sampling with replacement from the data. Also the number of data points in a bootstrap resample is equal to the number of data points in our original observations.

  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).

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  9. Statistical model specification - Wikipedia

    en.wikipedia.org/wiki/Statistical_model...

    Indeed, in statistics there is a common aphorism that "all models are wrong". In the words of Burnham & Anderson, In the words of Burnham & Anderson, "Modeling is an art as well as a science and is directed toward finding a good approximating model ... as the basis for statistical inference".