Skip to main content

Table 1 Difference between linear model and generalized model

From: Survey on clinical prediction models for diabetes prediction

S. no Simple linear model Generalized linear models
1 μ = E(Y) = β0 + β1 * X1 + β2 * X2+ ··· + βn*Xn g(μ) = β0 + β1 * X1 + β2 * X2+ ··· + βn*Xn
2 Target variable Y does not depend on the value of Y for any other record, only the predictors Target variable Y does not depend on the value of Y for any other record, only the predictors
3 Y is normally distributed Distribution of Y is a member of the exponential family of distributions(normal, Poisson, gamma, binomial, negative binomial, inverse Gaussian)
4 Mean of Y depends on the predictors, but all records have the same variance Variance of Y is a function of the mean of Y
5 Y is related to predictors through simple linear function g(μ) is linearly related to the predictors. The function g is called the link function