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Logarithmisch-lineares Modell Log-linear analysis
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Logarithmisch-lineare Modelle, kurz Log-lineare Modelle gehören zu den multivariaten Verfahren, die besonders in der Statistik Anwendung finden. Mit log-linearen Modellen werden nominalskalierte Daten analysiert. Durch eine logarithmische Transformation kann man bei der Analyse mehrdimensionaler Häufigkeitstabellen das Problem meist anschaulicher darstellen, etwa in dem Sinn, dass und Interaktionen einer mehrdimensionalen Häufigkeitstabelle sich linear zusammensetzen lassen. Man unterscheidet verschiedene log-lineare Verfahren: Log-linear analysis is a technique used in statistics to examine the relationship between more than two categorical variables. The technique is used for both hypothesis testing and model building. In both these uses, models are tested to find the most parsimonious (i.e., least complex) model that best accounts for the variance in the observed frequencies. (A Pearson's chi-square test could be used instead of log-linear analysis, but that technique only allows for two of the variables to be compared at a time.)
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Logarithmisch-lineare Modelle, kurz Log-lineare Modelle gehören zu den multivariaten Verfahren, die besonders in der Statistik Anwendung finden. Mit log-linearen Modellen werden nominalskalierte Daten analysiert. Durch eine logarithmische Transformation kann man bei der Analyse mehrdimensionaler Häufigkeitstabellen das Problem meist anschaulicher darstellen, etwa in dem Sinn, dass und Interaktionen einer mehrdimensionalen Häufigkeitstabelle sich linear zusammensetzen lassen. Man unterscheidet verschiedene log-lineare Verfahren: * Als generelle log-lineare Modelle bezeichnet man Verfahren, die ungerichtete Zusammenhänge zwischen nominalskalierten Daten untersuchen. * Logit-Modelle untersuchen den gerichteten Zusammenhang zwischen einer abhängigen nominalskalierten Variablen und anderen unabhängigen Variablen. Log-lineare Modelle bieten die Möglichkeit der sogenannten saturierten und nichtsaturierten Datenanalyse. Log-linear analysis is a technique used in statistics to examine the relationship between more than two categorical variables. The technique is used for both hypothesis testing and model building. In both these uses, models are tested to find the most parsimonious (i.e., least complex) model that best accounts for the variance in the observed frequencies. (A Pearson's chi-square test could be used instead of log-linear analysis, but that technique only allows for two of the variables to be compared at a time.)
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