Editing Logistic regression (section)

===Interpretation of these terms===
In the above equations, the terms are as follows:

* <math>g</math> is the logit function. The equation for <math>g(p(x))</math> illustrates that the [[logit]] (i.e., log-odds or natural logarithm of the odds) is equivalent to the linear regression expression.
* <math>\ln</math> denotes the [[natural logarithm]].
* <math>p(x)</math> is the probability that the dependent variable equals a case, given some linear combination of the predictors. The formula for <math>p(x)</math> illustrates that the probability of the dependent variable equaling a case is equal to the value of the logistic function of the linear regression expression. This is important in that it shows that the value of the linear regression expression can vary from negative to positive infinity and yet, after transformation, the resulting expression for the probability <math>p(x)</math> ranges between 0 and 1.
* <math>\beta_0</math> is the [[Y-intercept|intercept]] from the linear regression equation (the value of the criterion when the predictor is equal to zero).
* <math>\beta_1 x</math> is the regression coefficient multiplied by some value of the predictor.
* base <math>e</math> denotes the exponential function.