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# scikits.statsmodels.discretemod.Probit

class `scikits.statsmodels.discretemod.``Probit`(endog, exog=None)

  Binary choice Probit model

  Parameters: | endog : array-like 1-d array of the response variable. exog : array-like exog is an n x p array where n is the number of observations and p is the number of regressors including the intercept if one is included in the data.

  Attributes

  endog | array | A reference to the endogenous response variable
  exog | array | A reference to the exogenous design.
  nobs | float | The number of observations of the model.

  Methods

  `cdf`(X) | Probit (Normal) cumulative distribution function
  `fit`([start_params, maxiter, method, tol]) | Fits the binary probit model.
  `hessian`(params) | Probit model Hessian matrix of the log-likelihood
  `information`(params) | Fisher information matrix of model
  `initialize`() | Initialize is called by
  `loglike`(params) | Log-likelihood of probit model (i.e., the normal distribution).
  `pdf`(X) | Probit (Normal) probability density function
  `predict`(design) | After a model has been fit predict returns the fitted values.
  `score`(params) | Probit model score (gradient) vector

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