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8.5 : Chi-square Distribution

How does one determine if bingo numbers are evenly distributed or if some numbers occurred with a greater frequency? Or if the types of movies people preferred were different across different age groups or if a coffee machine dispensed approximately the same amount of coffee each time. These questions can be addressed by conducting a hypothesis test. One distribution that can be used to find answers to such questions is known as the chi-square distribution. The chi-square distribution has applications in tests for independence, goodness-of-fit tests, and test of a single variance.

The properties of the chi-square distribution are as follows:

  1. The curve is nonsymmetrical and skewed to the right.
  2. There is a different chi-square curve for each degree of freedom (df).
  3. The test statistic for any test is always greater than or equal to zero.
  4. When df > 90, the chi-square curve approximates the normal distribution.
  5. The mean, μ, is located just to the right of the peak.

This text is adapted from 11.1 Facts About the Chi-Square Distribution - Introductory Statistics OpenStax

Tags

Chi square DistributionHypothesis TestIndependence TestGoodness of fit TestVariance TestStatistical PropertiesDegree Of FreedomNon symmetrical CurveSkewed DistributionTest StatisticNormal Distribution Approximation

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8.5 : Chi-square Distribution

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8.1 : Verteilungen zur Schätzung des Parameters "Population"

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8.2 : Freiheitsgrade

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8.3 : Verteilung der Studenten t

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8.4 : Auswahl zwischen z- und t-Verteilung

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8.6 : Kritische Werte für das Chi-Quadrat finden

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8.7 : Schätzen der Standardabweichung der Grundgesamtheit

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8.8 : Prüfung der Güte der Anpassung

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8.9 : Erwartete Häufigkeiten bei Tests auf Güte der Anpassung

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8.10 : Kontingenztafel

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8.11 : Einführung in die Unabhängigkeitsprüfung

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8.12 : Hypothesentest für den Test der Unabhängigkeit

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8.13 : Bestimmung der zu erwartenden Häufigkeit

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8.14 : Test auf Homogenität

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8.15 : F Verteilung

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