OR Chapter 4 Regression Models
Terms in this set (24)
a measure of the explanatory power of a regression model that takes into consideration the number of independent variables in the model.
Dummy Variable (Binary Variable)
a variable used to represent a qualitative factor or condition. Dummy variables have values of 0 or 1.
Coefficient of Correlation (r)
a measure of the strength of the relationship between two variables.
Coefficient of Determination (r^2)
the percent of the variability of the dependent variable (Y) 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.
difference between the actual value (Y) and the predicted value (Y^)
the independent variable in a regression equation
The X-variable in a regression equation. This is used to help predict the dependent variable.
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.
Mean Squared Error (MSE)
an estimate of the error variance.
a condition that exists when one independent variable is correlated with other independent variables.
Multiple Regression Model
a regression model that has more than one independent variable.
Observed Significance Level
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.
Standard Error of the Estimate
an estimate of the standard deviation of the errors and is sometimes called the standard deviation of the regression.
an automated process to systematically add or delete independent variables from a regression model.
Sum of Squares Error (SSE)
The total sum of the squared differences between each predicted value and the mean
Sum of Squares Total (SST)
the total sum of the squared differences between each observation and the mean.