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Fuzzy k-means clustering
Fuzzy clustering is used to create clusters with unclear borders either because they are to close or even overlap each other. This method was introduced in 1973 by Dunn and Bezdek[4] in 1981. It can highlight sub-clusters and even predict an estimation of the right number of clusters by processing the data with a high number of clusters. Fuzzy k-means is a generalization of the classical k-means....
Cochrane-Orcutt model
What is the Cochrane-Orcutt estimation Developed by D.Cochrane and G. Orcutt in 1949, the Cochrane-Orcutt estimation is a well-known econometric approach to take serial correlation into account in the error term of linear model. In case of serial correlation, the usual linear regression method is invalid because the standard errors are not unbiased. Results of the Cochrane-Orcutt estimation in XLSTAT...
XLSTAT-R
What is XLSTAT-R? XLSTAT-R is a revolutionary interface designed to use and write R procedures within XLSTAT dialog boxes in Microsoft Excel. End users benefit from the unlimited possibilities of R without having to leave Microsoft Excel where their data is stored. What are the advantages of XLSTAT-R? With XLSTAT-R, you will be able to: Apply many R procedures already created to your data in Excel....
Heteroscedasticity tests
What is heteroscedasticity? The concept of heteroscedasticity - the opposite being homoscedasticity - is used in statistics, especially in the context of linear regression or for time series analysis, to describe the case where the variance of errors of the model is not the same for all observations, while often one of the basic assumption in modeling is that the variances are homogeneous and that...
Latent Class regression models
What is Latent Class Analysis? Latent class analysis (LCA) involves the construction of Latent Classes which are unobserved (latent) subgroups or segments of cases. The latent classes are constructed based on the observed (manifest) responses of the cases on a set of indicator variables. Cases within the same latent class are homogeneous with respect to their responses on these indicators, while cases...
Ordinal logit model
Ordinal Logit model definition The ordinal logit model is a frequently-used method as it enables to ordinal variables to be modeled. It is frequently used in survey analysis (whether a respondent is not satisfied, satisfied or very satisfied). It has the same principles as the binary and multinomial logit models. The principle of the ordinal logit model is to link the cumulative probability of a level...
Parametric Illness-Death Model
Description of Parametric Illness-Death Model in XLSTAT Multi-state models are used when we observe more than 2 states. The illness-death models are a special case of multistate models with 3 states: the initial state, the transient state and the absorbing state — also called state 0, state 1 and state 2. This model is frequently used in medical applications and research to analyze disease evolution...
Multiple answer questions
What is the Multiple answer questions feature in XLSTAT? It is common that surveys included multiple answer questions. Here is very simple example: "Which are your favorite colors?". Some people will answer "Blue", other "Blue,Red", other "Green,Purple,Yellow". Any combination is possible from the list of possibilities that is given in the survey. The output of the survey is a table giving the answers...
Propensity Score Matching
What is propensity score matching? The propensity score is defined as the probability for a participant to belong to one of two groups given some variables known as confounders. The propensity score matching is a technique that attempts to reduce the possible bias associated with those confounding variables in observational studies. Propensity Score Matching options in XLSTAT Once the propensity score...
Agglomerative Hierarchical Clustering (AHC)
What is Agglomerative Hierarchical Clustering Agglomerative Hierarchical Clustering (AHC) is a clustering (or classification) method which has the following advantages: It works from the dissimilarities between the objects to be grouped together. A type of dissimilarity can be suited to the subject studied and the nature of the data. One of the results is the dendrogram which shows the progressive...