رگرسیون لجستیک

رگرسیون لجستیک

اسلاید 1: Logistic Regression

اسلاید 2: Slide 2AimsWhen and Why do we Use Logistic Regression?BinaryMultinomialTheory Behind Logistic RegressionAssessing the ModelAssessing predictorsThings that can go WrongInterpreting Logistic Regression

اسلاید 3: Slide 3When And WhyTo predict an outcome variable that is categorical from one or more categorical or continuous predictor variables.Used because having a categorical outcome variable violates the assumption of linearity in normal regression.

اسلاید 4: Slide 4With One PredictorOutcomeWe predict the probability of the outcome occurringb0 and b0Can be thought of in much the same way as multiple regressionNote the normal regression equation forms part of the logistic regression equation

اسلاید 5: Slide 5With Several PredictorOutcomeWe still predict the probability of the outcome occurringDifferencesNote the multiple regression equation forms part of the logistic regression equationThis part of the equation expands to accommodate additional predictors

اسلاید 6: Assessing the ModelThe Log-likelihood statisticAnalogous to the residual sum of squares in multiple regressionIt is an indicator of how much unexplained information there is after the model has been fitted.Large values indicate poorly fitting statistical models.

اسلاید 7: Assessing Changes in ModelsIt’s possible to calculate a log-likelihood for different models and to compare these models by looking at the difference between their log-likelihoods.

اسلاید 8: Slide 8Assessing Predictors: The Wald StatisticSimilar to t-statistic in Regression.Tests the null hypothesis that b = 0.Is biased when b is large.Better to look at Likelihood-ratio statistics.

اسلاید 9: Slide 9Assessing Predictors: The Odds Ratio or Exp(b)Indicates the change in odds resulting from a unit change in the predictor.OR > 1: Predictor , Probability of outcome occurring .OR < 1: Predictor , Probability of outcome occurring .