A generalized linear model is a statistical model that is used to predict a dependent variable, y, given a set of independent variables, x. The model is a generalization of the linear regression model, which is used when the dependent variable is continuous. The generalized linear model is used when the dependent variable is categorical, such as when predicting the likelihood of an event occurring.
The generalized linear model is based on the assumption that the dependent variable is a linear combination of the independent variables. This means that the model can be used to predict the value of the dependent variable for any given set of values for the independent variables. The model is also based on the assumption that the errors in the predictions are normally distributed.
The generalized linear model can be used to predict the value of the dependent variable for any given set of values for the independent variables. The model is also based on the assumption that the errors in the predictions are normally distributed. This means that the model can be used to predict the likelihood of an event occurring.
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