Empty model uses the mean to model the distribution of a quantitative variable; it is called "empty" because it does not have any explanatory variables. It is also called a null model and sometimes referred to as a simple model because it is simpler than models that include at least one explanatory variable.
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null model
Null model uses the mean to model the distribution of a quantitative variable; called "null" because it does not have any explanatory variables; also called an empty model; sometimes referred to as a simple model because it is simpler than models ...
Type I and Type II Error
Type I and II Error describe the possible errors we might make when drawing conclusions about the DGP based on our data. Type I error is when we should adopt the empty model but we adopt the complex model in error. Type II error is when we should ...
complex model
A complex model is a model with at least one explanatory variable.
SS Model
SS model is the reduction in error (measured in sums of squares) due to the model; the area of all the squared deviations based on the distance between the complex model predictions and the null model predictions.
simple model
A simple model is any model that is relatively more simple; in this course we typically compare relatively more complex models with one explanatory variable to a simple model that does not have any explanatory variables.