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Conditional Logit model
Conditional Logit model definition The conditional logit model introduced by McFadden (1973) is based on a model similar to the logistic regression. The difference is that all individuals are subjected to different situations before expressing their choice (modeled using a binary variable which is the dependent variable). The fact that the same individuals are used in taken in account by the conditional...
Cochran-Mantel-Haenszel test
Use of the Cochran-Mantel-Haenszel test Use the Cochran-Mantel-Haenszel (CMH) test to test the hypothesis of independence on a series of contingency tables corresponding to an experiment crossing two categorical variables, with a control variable taking multiple values. Principles of the Cochran-Mantel-Haenszel test Cochran (1954) then Mantel and Haenszel (1959) developed a test that allows to test...
Ordinary Least Squares regression (OLS)
Ordinary Least Squares regression (OLS) is a common technique for estimating coefficients of linear regression equations which describe the relationship between one or more independent quantitative variables and a dependent variable (simple or multiple linear regression), often evaluated using r-squared. Least squares stand for the minimum squares error (SSE). Maximum likelihood and Generalized method...
Kruskal-Wallis test
What is the Kruskal-Wallis test The Kruskal-Wallis test is often used as an nonparametric alternative to the one-way analysis of variance (ANOVA) where the assumptions are not met (like the assumption of normality). Nonparametric tests do not rely on any distribution. They can thus be applied even if parametric conditions of validity are not met. These tests are more robust than parametric tests....
Log-linear regression (Poisson regression)
What is log-linear regression? The log-linear regression is one of the specialized cases of generalized linear models for Poisson, Gamma or Exponential-distributed data. This method is used to modeling the relationship between a scalar response variable and one or more explanatory variables. We assume that the response variable is written as the logarithm of an affine function of the explanatory variables...
Linear regression
What is linear regression analysis? Linear regression is undoubtedly one of the most frequently used statistical modeling methods. A distinction is usually made between simple regression (with only one explanatory variable) and multiple regression (several explanatory variables) although the overall concept and calculation methods are identical. The principle of linear regression is to model a quantitative...
Cochran C test for outlying variances
What is Cochran's C test The Cochran’s C test (Cochran 1941) is one of the tests developed to identify and study the homogeneity of a series of variances (Bartlett's test, Brown-Forsythe, Levene or Hartley in particular). Cochran's test was developed to answer a specific question: Are the variances homogeneous or is the highest variance different from the others. XLSTAT also offers two alternatives...
Analytic Hierarchy Process (AHP)
What is Analytic Hierarchy Process (AHP)? Analytic Hierarchy Process is a method adapted to multi-criteria decision problems that have several solutions satisfying a set of criteria. The approach of the method is to simplify the problem by breaking it down into a hierarchical system. Thomas Saaty is at the origin of this method and created it in the 1970s. We denote by alternative the solutions of...
Cointegration tests
Use this module to perform VAR-based cointegration tests on a group of two or more I(1) time series using the approach proposed by Johansen (1991, 1995). What are cointegration tests? Economic theory often suggests long-term relationship between two or more economic variables. Although those variables can derive from each other on a short term basis, the economic forces at work should restore the...
Logistic regression (Binary, Ordinal, Multinomial, …)
Definition of the logistic regression in XLSTAT Principle of the logistic regression Logistic regression is a frequently used method because it allows to model binomial (typically binary) variables, multinomial variables (qualitative variables with more than two categories) or ordinal (qualitative variables whose categories can be ordered). It is widely used in the medical field, in sociology, in...