Explain the non-linear probability model.
Ans. Non-probability model is popularly known as Ligit Model or Ligit regression model. The logit of a number p between 0 and 1 is given by the formula:
The base of the logarithm function used is of little importance in the present article, as long as it is greater than 1, but the natural logarithm with base e is the one most often used. The “logistic” function of any number is given by the inverse-logit:
We can use logit models whenever your
dependent variable is binary (also called
dummy) which takes values 0 or 1.
Logit regression is a nonlinear regression model
that forces the output (predicted values) to be
either 0 or 1.
Logit models estimate the probability of your
dependent variable to be 1 ( Y =1). This is the
probability that some event happens.
Logit models postulate some relation between
the logit of observed probabilities (not the
probabilities themselves), and unknown
parameters of the model. For example, logit
models used in logistic regression postulate a
linear relation between the logit and parameters
of the model.
The major reason for using logits, as opposed to
probabilities themselves, is that in many cases where a
linear model using probabilities does not fit the data, a
linear model using logits does.
No comments:
Post a Comment