A measure of the explanatory power of a regression model that takes into consideration the number of independent variables in the model.
See Dummy Variable.
A measure of the strength of the relationship between two variables.
The percent of the variability in the dependent variable that is explained by the regression equation.
A condition that exists when one independent variable is correlated with another independent variable.
The Y variable in a regression model. This is what is being predicted.
A variable used to represent a qualitative factor or condition. Dummy variables have values of 0 or 1. This is also called a binary variable or an indicator variable.
The difference between the actual value (Y) and the predicted value
The independent variable in a regression equation.
The X variable in a regression equation. This is used to help predict the dependent variable. Indicator Variable (Binary Variable) A type of independent variable that is either zero or one to indicate the absence (zero) or presence (one) of some condition or force.
A reference to the criterion used to select the regression line, to minimize the squared distances between the estimated straight line and the observed values.
An estimate of the error variance.
A condition that exists when one independent variable is correlated with other independent variables.
A regression model that has more than one independent variable.
Another name for p-value.
A probability value that is used when testing a hypothesis. The hypothesis is rejected when this is low.
Another name for explanatory variable.
A forecasting procedure that uses the least-squares approach on one or more independent variables to develop a forecasting model.
Another term for error.
The dependent variable in a regression equation.
Diagrams of the variable to be forecasted, plotted against another variable, such as time. Also called scatter plots.
An estimate of the standard deviation of the errors; sometimes called the standard deviation of the regression.
An automated process to systematically add or delete independent variables from a regression model.
The total sum of the squared differences between each observation (Y) and the predicted value
The total sum of the squared differences between each predicted value and the mean
The total sum of the squared differences between each observation (Y) and the mean