Latent GOLD is a registered trademark of Statistical Innovations. XLSTAT-LatentClass offers a wide variety of easily implementable options that let users gain full control over the Latent Class models. For example, it is possible to include variables of different scales (continuous, ordinal or nominal) within the same model and to use scoring equations to be able to classify new cases into their most likely latent class as a function of the observed variables. La boîte de dialogue Tests t et z pour deux échantillons apparaît. Une fois XLSTAT ouvert, sélectionnez la commande XLSTAT / Tests paramétriques / Tests t et z pour deux échantillons, ou cliquez sur le bouton correspondant dans la barre de menu. Unlike the ad-hoc clustering algorithms, LC is based on a formal statistical model and provides probability-based classification, formal model selection criteria and optimal handling of missing data. Paramétrer un test t de Student sur deux échantillons indépendants. Latent Class (LC) Cluster models and LC Regression models both offer unique features compared to traditional clustering approaches. XLSTAT-LatentClass is a reduced version of Statistical Innovations' highly acclaimed LatentGOLD® package, which can help you discover the segments hidden in your data. Once XLSTAT is open, select Univariate Clustering in the Analyzing data menu. Setting up a univariate clustering with XLSTAT. Our goal is to create five homogeneous study groups based on the students' performance for each subject. Description The XLSTAT-LatentClass Option (only for Windows OS) Each row corresponds to a student and each column to a subject.
