Statistical Models
Further information: Statistical model, Parametric model, Nonparametric statistics, and Model selectionA statistical model is a probability distribution function proposed as generating data. In a parametric model, the probability distribution function has variable parameters, such as the mean and variance in a normal distribution, or the coefficients for the various exponents of the independent variable in linear regression. A nonparametric model has a distribution function without parameters, such as in bootstrapping, and is only loosely confined by assumptions. Model selection is a statistical method for selecting a distribution function within a class of them, e.g., in linear regression where the dependent variable is a polynomial of the independent variable with parametric coefficients, model selection is selecting the highest exponent, and may be done with nonparametric means, such as with cross validation.
In statistics there can be models of mental events as well as models of physical events. For example, a statistical model of customer behavior is a model that is conceptual, (because behavior is physical) but a statistical model of customer satisfaction is a model of a concept (because satisfaction is a mental not a physical event).
Read more about this topic: Conceptual Model
Famous quotes containing the word models:
“Grandparents can be role models about areas that may not be significant to young children directly but that can teach them about patience and courage when we are ill, or handicapped by problems of aging. Our attitudes toward retirement, marriage, recreation, even our feelings about death and dying may make much more of an impression than we realize.”
—Eda Le Shan (20th century)