To estimate economic relationships

To test economic theories

To predict economic outcomes

To make economic decisions

A variable is a random variable, while a parameter is a fixed value.

A variable is a characteristic of a population, while a parameter is a characteristic of a sample.

A variable is a quantity that can take on different values, while a parameter is a quantity that is fixed.

A variable is a measure of central tendency, while a parameter is a measure of dispersion.

A correlation is a relationship between two variables, while a causation is a relationship between a cause and an effect.

A correlation is a statistical relationship, while a causation is a logical relationship.

A correlation is a relationship between two variables that are related to each other, while a causation is a relationship between two variables that are not related to each other.

A correlation is a relationship between two variables that are related to each other in a linear way, while a causation is a relationship between two variables that are related to each other in a nonlinear way.

A simple regression model has one independent variable, while a multiple regression model has two or more independent variables.

A simple regression model is used to estimate the relationship between two variables, while a multiple regression model is used to estimate the relationship between three or more variables.

A simple regression model is used to predict the value of one variable from the value of another variable, while a multiple regression model is used to predict the value of one variable from the values of two or more other variables.

A simple regression model is used to test the relationship between two variables, while a multiple regression model is used to test the relationship between three or more variables.

An OLS estimator is used to estimate the parameters of a linear regression model, while a GLS estimator is used to estimate the parameters of a nonlinear regression model.

An OLS estimator is used to estimate the parameters of a regression model with homoscedasticity, while a GLS estimator is used to estimate the parameters of a regression model with heteroscedasticity.

An OLS estimator is used to estimate the parameters of a regression model with no autocorrelation, while a GLS estimator is used to estimate the parameters of a regression model with autocorrelation.

An OLS estimator is used to estimate the parameters of a regression model with no multicollinearity, while a GLS estimator is used to estimate the parameters of a regression model with multicollinearity.