Can logistic regression be applied when the number of variables is large?
Logistic regression is usually used with binary response variables ( 0 or 1 ), the predictors can be continuous or discrete. As I said earlier I would use logistic regression if I’m estimating the proportion is too small or too large.
What does multiple logistic regression tell you?
The goal of a multiple logistic regression is to find an equation that best predicts the probability of a value of the Y variable as a function of the X variables. You can then measure the independent variables on a new individual and estimate the probability of it having a particular value of the dependent variable.
What happens if there are too many independent variables in logistic regression?
If the number of independent variables is not very large, you can just do “all subsets” regression in which all possible models are fit. The model the model with the highest F statistic or proportion of explained variation (PVE) (note: the concept was established with linear regression but can be applied to logistic regression as well) is selected.
What can be included in a logistic regression model?
The logistic regression model can be extended to include several independent variables (i.e., hypothesized risk factors). For instance, are history of attempts, severity of depression, and employment status risk factors for suicidal behavior, controlling for diagnosis, age, and gender?
Why is linearity a limitation in logistic regression?
This is because the scale of measurement is continuous (logistic regression only works when the dependent or outcome variable is dichotomous). Logistic regression assumes linearity between the predicted (dependent) variable and the predictor (independent) variables. Why is this a limitation?
How many variables should you include in a regression model?
When fitting a linear regression model, the number of observations should be at least 15 times larger than the number of predictors in the model. For a logistic regression, the count of the smallest group in the outcome variable should be at least 15 times the number of predictors.