Fundamentals of Statistics contains material of various lectures and courses of H. Lohninger on statistics, data analysis and chemometrics......click here for more. 
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Logistic RegressionIf we try to establish a regression model of which the dependent variable is binary (i.e. a variable which exhibits only two distinct values, such as "dead" and "alive", or "yes" and "no"), we face problems due to formal mathematical causes if the predictor variables are of cardinal scale (which is true in most circumstances): If the explanatory variables x_{i} are metric the estimated values y_{i} would also be metric, and not discrete (with 1 and 1 as possible values). In addition there are often problems with two basic assumptions of linear regression: the residuals are not normally distributed and their variances are not homosecdastic.In the case of a binary reponse variable y one can try to avoid to directly estimate its (binary) values y_{i} by estimating the probabilities of occurrence, instead. As the response variable is dichotomous it may assume only one out of two possible states (i.e. 0 and 1). If we know the probability of occurrence for one of the states, we automatically also know the probablity of the other state: P(y=1) = 1  P(y=0) The chance for a particular state to occur is thus given by the ratio of the two probabilities:
In order to create a mathematical model one usually uses the logarithm of this chance, which is called "logit":
The logit function transforms the probability of occurrence of a particular state in a way that the 100% certain occurrence of "1" yields an infinite negative value, the 100% certain occurrence of "+1" results in an infinite positive value. If the odds for both states are equal the logit function returns a zero value.
Starting with the classic regression equation, the response variable y is replaced by the logit function: logit(y) = a_{0} + a_{1}x_{1}+ a_{2}x_{2} + a_{3}x_{3} + .... The main problem with the parametrisation of this logistic regression equation is that the classic least squares approach cannot be used because the possible infinite values of the logit function prohibits its application (due to numeric reasons and due to the leverage effect).
As a consequence the parameter estimation in logistic regression is performed by means of maximum likelihood, which is a generalisation of the least squares method (in the case of normally distributed descriptors the maximum likelihood approach delivers the same results as the least squares regression).


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