A statistical model can help us (1) understand patterns in data, (2) predict what will happen in the future, and (3) improve the functioning of complex systems; there are many kinds of models, but the statistical model we focus on here generates a predicted score for each observation in a distribution.
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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.
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 ...
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.
empty model
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 ...