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  2. Proofs related to chi-squared distribution - Wikipedia

    en.wikipedia.org/wiki/Proofs_related_to_chi...

    There are several methods to derive chi-squared distribution with 2 degrees of freedom. Here is one based on the distribution with 1 degree of freedom. Suppose that and are two independent variables satisfying and , so that the probability density functions of and are respectively: and of course . Then, we can derive the joint distribution of :

  3. Yates's correction for continuity - Wikipedia

    en.wikipedia.org/wiki/Yates's_correction_for...

    This reduces the chi-squared value obtained and thus increases its p-value. The effect of Yates's correction is to prevent overestimation of statistical significance for small data. This formula is chiefly used when at least one cell of the table has an expected count smaller than 5.

  4. Chi-squared distribution - Wikipedia

    en.wikipedia.org/wiki/Chi-squared_distribution

    In probability theory and statistics, the chi-squared distribution (also chi-square or -distribution) with degrees of freedom is the distribution of a sum of the squares of independent standard normal random variables. The chi-squared distribution is a special case of the gamma distribution and the univariate Wishart distribution.

  5. Omnibus test - Wikipedia

    en.wikipedia.org/wiki/Omnibus_test

    The "step" line relates to Chi-Square test on the step level while variables included in the model step by step. Note that in the output a step chi-square, is the same as the block chi-square since they both are testing the same hypothesis that the tested variables enter on this step are non-zero.

  6. Chi-squared test - Wikipedia

    en.wikipedia.org/wiki/Chi-squared_test

    Chi-squared distribution, showing χ2 on the x -axis and p -value (right tail probability) on the y -axis. A chi-squared test (also chi-square or χ2 test) is a statistical hypothesis test used in the analysis of contingency tables when the sample sizes are large. In simpler terms, this test is primarily used to examine whether two categorical ...

  7. Minimum-distance estimation - Wikipedia

    en.wikipedia.org/wiki/Minimum-distance_estimation

    Minimum-distance estimation (MDE) is a conceptual method for fitting a statistical model to data, usually the empirical distribution. Often-used estimators such as ordinary least squares can be thought of as special cases of minimum-distance estimation. While consistent and asymptotically normal, minimum-distance estimators are generally not ...

  8. Location–scale family - Wikipedia

    en.wikipedia.org/wiki/Location–scale_family

    Location–scale family. In probability theory, especially in mathematical statistics, a location–scale family is a family of probability distributions parametrized by a location parameter and a non-negative scale parameter. For any random variable whose probability distribution function belongs to such a family, the distribution function of ...

  9. Pearson's chi-squared test - Wikipedia

    en.wikipedia.org/wiki/Pearson's_chi-squared_test

    Usage. Pearson's chi-squared test is used to assess three types of comparison: goodness of fit, homogeneity, and independence. A test of goodness of fit establishes whether an observed frequency distribution differs from a theoretical distribution. A test of homogeneity compares the distribution of counts for two or more groups using the same ...