Generalized linear model

While we have tried to understand the concept of linear regression with one dependent and one independent variable, in the real world, we are always going to have multiple dependent variables that affect the output variable, termed multiple regression. In that case, our y = a + bx linear equation is going to take the following form:

y = a₀ + b₁x₁ + b₂x₂ + ...+ b_kx_k

Once again, a₀ is the y intercept, x₁, x₂, ...x_k are the independent variables or factors, and b₁, b₂,.., b_k are the weights of the variables. They define how much the effect of a particular variable has on the outcome. With multiple regression, we can create a model for predicting a single dependent variable. This limitation is overcome by the generalized linear model. It deals with multiple dependent/response variables, along with the correlation within the predictor variables.