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How is multivariable logistic regression used in statistics?
This is done using “multivariable logistic regression” – a technique that allows us to study the simultaneous effect of multiple factors on a dichotomous outcome. HOW DOES MULTIPLE LOGISTIC REGRESSION WORK? The statistical program first calculates the baseline odds of having the outcome versus not having the outcome without using any predictor.
What kind of regression model do I need for Y?
First off, it depends what your dependent variable (Y) is. If it is numerical then most multiple regression models would be sufficient. If it (Y) is categorical then you need a logistic regression or a similar categorical regression model.
How to handle independent variables in a regression model?
As for how to handle independent variables, the numerical ones will fit neatly into almost any regression model. The categorical ones will need to be “factored”. I use R.
When to enter a categorical variable into a model?
If the categorical variable is ordinal, then most likely the sensible thing to do is to enter it as-is into the model, just as you would with a continuous predictor (i.e., “independent”) variable.
When to drop a variable from a multiple regression model?
If independent variables A A and B B are both correlated with Y Y, and A A and B B are highly correlated with each other, only one may contribute significantly to the model, but it would be incorrect to blindly conclude that the variable that was dropped from the model has no significance.
When do you use multiple regression in statistics?
You use multiple regression when you have three or more measurement variables. One of the measurement variables is the dependent ( Y Y) variable. The rest of the variables are the independent ( X X) variables. The purpose of a multiple regression is to find an equation that best predicts the Y Y variable as a linear function of the X X variables.
How to analyze the predictive value of multiple regression?
Standard multiple regression involves several independent variables predicting the dependent variable. Analyze the predictive value of multiple regression in terms of the overall model and how well each independent variable predicts the dependent variable.