Editing Logistic regression (section)

====Likelihood ratio test====
The [[likelihood-ratio test]] discussed above to assess model fit is also the recommended procedure to assess the contribution of individual "predictors" to a given model.<ref name=Hosmer/><ref name=Menard/><ref name=Cohen/> In the case of a single predictor model, one simply compares the deviance of the predictor model with that of the null model on a chi-square distribution with a single degree of freedom. If the predictor model has significantly smaller deviance (c.f. chi-square using the difference in degrees of freedom of the two models), then one can conclude that there is a significant association between the "predictor" and the outcome. Although some common statistical packages (e.g. SPSS) do provide likelihood ratio test statistics, without this computationally intensive test it would be more difficult to assess the contribution of individual predictors in the multiple logistic regression case.{{Citation needed|date=October 2019}} To assess the contribution of individual predictors one can enter the predictors hierarchically, comparing each new model with the previous to determine the contribution of each predictor.<ref name=Cohen/> There is some debate among statisticians about the appropriateness of so-called "stepwise" procedures.{{weasel inline|date=October 2019}} The fear is that they may not preserve nominal statistical properties and may become misleading.<ref>{{cite book |first=Frank E. |last=Harrell |title=Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis |location=New York |publisher=Springer |year=2010 |isbn=978-1-4419-2918-1 }}{{page needed|date=October 2019}}</ref>