To get over this problem, XLSTAT has two options:ДЄutomatic: Correction is automatic. The variables are then removed from the model following the procedure used for stepwise selection.Д¬lasses weight correction: If the number of observations for the various classes for the dependent variables are not uniform, there is a risk of penalizing classes with a low number of observations in establishing the model. Stepwise (Backward): This method is similar to the previous one but starts from a complete model.ДЇorward: The procedure is the same as for stepwise selection except that variables are only added and never removed.Д«ackward: The procedure starts by simultaneously adding all variables. If the probability of the calculated statistic is greater than the removal threshold value, the variable is removed from the model. After the third variable is added, the impact of removing each variable present in the model after it has been added is evaluated. If a second variable is such that its entry probability is greater than the entry threshold value, then it is added to the model. Stepwise (Forward): The selection process starts by adding the variable with the largest contribution to the model. Model selection: Activate this option if you want to use one of the four selection methods provided:.Note: this option has no effect if the prior possibilities are equal for the various groups. The probabilities associated with each of the classes are equal to the frequency of the classes. Prior probabilities: Activate this option if you want to take prior possibilities into account.Linear Discriminant Analysis) or unequal ( Quadratic Discriminant Analysis). Equality of covarianceВ matrices: Activate this option if you want to assume that the covariance matrices associated with the various classes of the dependent variable are equal (i.e.Options of the Discriminant Analysis function in XLSTAT The user will be able to compare the performances of both methods by using the ROC curves. Logistic regression has the advantage of having several possible model templates, and enabling the use of stepwise selection methods including for qualitative explanatory variables. Discriminant analysis is useful for studying the covariance structures in detail and for providing a graphic representation. Where there are only two classes to predict for the dependent variable, discriminant analysis is very much like logistic regression. The stepwise method gives a powerful model which avoids variables which contribute only little to the model Discriminant analysis and logistic regression They can, however, only be used when quantitative variables are selected as the input and output tests on the variables assume them to be normally distributed. Discriminant Analysis and variable selectionДЄs for linear and logistic regression, efficient stepwise methods have been proposed. Multicollinearity statistics are optionally displayed so that you can identify the variables which are causing problems. The variables responsible for these problems are automatically ignored either for all calculations or, in the case of a quadratic model, for the groups in which the problems arise. XLSTAT has been programmed in a way to avoid these problems. With linear and still more with quadratic models, we can face problems of variables with a null variance or multicollinearity between variables. Discriminant Analysis and Multicollinearity issues It is common to start with linear analysis then, depending on the results from the Box test, toВ carry out quadratic analysis if required. The Box test is used to test this hypothesis (the Bartlett approximation enables a Chi2 distribution to be used for the test). If, on the contrary, it is assumed that the covariance matrices differ in at least two groups, then the quadraticВ discriminant analysis should beВ preferred. Two models of Discriminant Analysis are used depending on a basic assumption: if the covariance matrices are assumed to be identical, linear discriminant analysis is used. What is the difference between Linear and Quadratic Discriminant Analysis? Predict which group a new observation will belong to.Дiscriminant Analysis may be used in numerous applications, for example in ecology and the prediction of financial risks (credit scoring).Show the properties of the groups using explanatory variables.Check on a twoВ or three-dimensional chart if the groups to which observations belong are distinct.Discriminant Analysis (DA) is a statistical method that can be used in explanatory or predictive frameworks:

Discriminate in xlstat