Statistical methods such as regression analysis are common. It is similar to linear regression and logistic regression. There is no conjugate prior of the likelihood function in logistic regression. Some of the more common estimation techniques for linear regression are summarized below. This is the most analogous index to the squared multiple correlations in linear regression. The basic setup of logistic regression is as follows. The researcher specifies an empirical model in regression analysis. A detailed history of the logistic regression is given in . In regression analysis, overfitting occurs frequently. Weights are estimated using some form of regression analysis. A similar notation is used for multiple linear regression. A principal danger of such data redundancy is that of overfitting in regression analysis models. Some of these methods make use of a localized form of classical polynomial regression. Another approach to bootstrapping in regression problems is to resample residuals. One example of a linear regression using this method is called least squares.