Categorical data logistic regression

As we shall see later the data type to a large extent determines the class of statistical tests that we undertake. Commonly for continuous data we use the t-tests and their extensions analysis of variance and analysis of covariance. For binary, categorical and ordinal data we use the class of chi-square tests (Pearson chi-square for categorical data and the Mantel-Haenszel chi-square for ordinal data) and their extension, logistic regression. 

Logistic regression modeling is used for predicting the probability of occurrence of an event by fitting data to a logistic curve.28 It describes the relationship between the categorical response variable and one or more continuous variables.29 Such a model can be described in Equation 3 ... 

CLASSIFICATION AND PREDICTION METHODS 133 categorical. Logistic regression analyzes binomiaUy distributed data of the form Yi -- Bin( ,-, Pi) i= 1,2,m,... 

The simulation data for logistic regression analysis was composed of basic scenario (the operating statements of all elements are random). Data was obtained by Monte Carlo method (with 100000 repeats). The categorical variable Y is estimated by the equation (7), the interval of power system criticality which is analyzed in the logistic regression analysis is 7= [0.6, 1]. 

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