Description of Step-Wise Multiple Regression statistic test Essay

Description of Step-Wise Multiple Regression statistic test - Essay Example If it is not utilized properly, it may congregate on a wretched model while contributing a false sensation of security. This paper attempts to review in detail the step-wise regression model and its application through SPSS version 21. Definition and Detailed Description of 'Stepwise Regression' According to Investopedia, Step-wise regression is a step-by-step iterative establishment of a regression model that necessitates automatic excerption of independent variables. Stepwise regression can be accomplished either by testing single independent variable at one time and admitting it in the regression model if it is found to be statistically significant, or by admitting all possible independent variables within the model and eradicating those that are found to be statistically insignificant, or by a amalgamation of both methods (Investopedia US, A Division of ValueClick, Inc., 2012). Stepwise multiple regressions provide a way of selecting predictors of a specific dependent variable on the grounds of statistical criteria. Necessarily the statistical methodology determines amongst the various independent variables which one is the most suitable predictor, the more suitable predictor and so the process goes on. The emphasis is on exploring the most suitable predictors at every stage. ... There are various multiple regression variants. Stepwise regression is generally a good option although all variables can be entered simultaneously as a substitute. Similarly, all variables can be entered once and then the predictors are eliminated by and by if elimination does not bring about big changes in the entire prediction. Stepwise regression, in statistics entails regression models within which the selection of predictive variables is drawn out by an automatic process. Ordinarily, this assumes the configuration of a succession of F-tests, but other proficiencies are potential, such as adjusted R-square, t-tests, Akaike criterion, Mallows' Cp, Bayesian criterion or false discovery rate (Draper and Smith, 1981). Principal approaches The major approaches utilized in the step-wise regression model are forward selection, backward elimination and bi directional elimination. Forward selection involves commencing without any variable within the model, examining the inclusion of indi vidual variable utilizing a selected model equivalence criterion, including the variable if any present amongst the various predictors that enhances the model to the best, and iterating this process till none amends the model. Backward elimination involves commencing with all potential variables, examining the exclusion of every variable utilizing a selected model equivalence criterion, eliminating the variable if any present amongst the various independent variables that leads to improvement in the model upon elimination and iterating the process until no more improvement is possible. Bidirectional elimination is a combination of the forward selection and