Editing Logistic regression (section)

==Discussion==
Like other forms of [[regression analysis]], logistic regression makes use of one or more predictor variables that may be either continuous or categorical. Unlike ordinary linear regression, however, logistic regression is used for predicting dependent variables that take [[categorical variable|membership in one of a limited number of categories]] (treating the dependent variable in the binomial case as the outcome of a [[Bernoulli trial]]) rather than a continuous outcome. Given this difference, the assumptions of linear regression are violated. In particular, the residuals cannot be normally distributed. In addition, linear regression may make nonsensical predictions for a binary dependent variable. What is needed is a way to convert a binary variable into a continuous one that can take on any real value (negative or positive). To do that, binomial logistic regression first calculates the [[odds]] of the event happening for different levels of each independent variable, and then takes its [[logarithm]] to create a continuous criterion as a transformed version of the dependent variable. The logarithm of the odds is the {{math|[[logit]]}} of the probability, the {{math|logit}} is defined as follows:
<math display="block"> \operatorname{logit} p = \ln \frac p {1-p} \quad \text{for } 0<p<1\,. </math>

Although the dependent variable in logistic regression is Bernoulli, the logit is on an unrestricted scale.<ref name=Hosmer/> The logit function is the [[link function]] in this kind of generalized linear model, i.e.
<math display="block"> \operatorname{logit} \operatorname{\mathcal E}(Y) = \beta_0 + \beta_1 x </math>

{{mvar|Y}} is the Bernoulli-distributed response variable and {{mvar|x}} is the predictor variable; the {{mvar|β}} values are the linear parameters.

The {{math|logit}} of the probability of success is then fitted to the predictors. The predicted value of the {{math|logit}} is converted back into predicted odds, via the inverse of the natural logarithm – the [[exponential function]]. Thus, although the observed dependent variable in binary logistic regression is a 0-or-1 variable, the logistic regression estimates the odds, as a continuous variable, that the dependent variable is a 'success'. In some applications, the odds are all that is needed. In others, a specific yes-or-no prediction is needed for whether the dependent variable is or is not a 'success'; this categorical prediction can be based on the computed odds of success, with predicted odds above some chosen cutoff value being translated into a prediction of success.