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How do you interpret the null deviance and residual deviance in logistic regression?
The null deviance shows how well the response is predicted by the model with nothing but an intercept. The residual deviance shows how well the response is predicted by the model when the predictors are included.
What are the residuals in logistic regression?
In logistic regression, as with linear regression, the residuals can be defined as observed minus expected values. The data are discrete and so are the residuals.
What is deviance in connection with logistic regression analysis?
Deviance is a number that measures the goodness of fit of a logistic regression model. Think of it as the distance from the perfect fit — a measure of how much your logistic regression model deviates from an ideal model that perfectly fits the data. Higher values of the deviance correspond to a less accurate model.
How are deviance residuals used in logistic regression?
Deviance residual is another type of residual measures. It measures the disagreement between the maxima of the observed and the fitted log likelihood functions. Since logistic regression uses the maximal likelihood principle, the goal in logistic regression is to minimize the sum of the deviance residuals.
What are the diagnostic statistics in logistic regression?
So far, we have seen the basic three diagnostic statistics: the Pearson residual, the deviance residual and the leverage (the hat value). They are the basic building blocks in logistic regression diagnostics. There are other diagnostic statistics that are used for different purposes.
What does pseudo R-squared mean in logistic regression?
The pseudo R-squared is .6286. But when we run linktest on this model, the _hatsq term is highly significant indicating a model specification error. Now if we take away the continuous variable and use the two binary variables in the model, the linktest is fine.
Do you have to satisfy the assumptions of logistic regression?
In order for our analysis to be valid, our model has to satisfy the assumptions of logistic regression.