1) In regression, an independent variable is sometimes called a response variable.
2) One purpose of regression is to understand the relationship between variables.
3) One purpose of regression is to predict the value of one variable based on the other variable.
4) The variable to be predicted is the dependent variable.
5) The dependent variable is also called the response variable.
6) A scatter diagram is a graphical depiction of the relationship between the dependent and independent variables.
7) In a scatter diagram, the dependent variable is typically plotted on the horizontal axis.
8) There is no relationship between variables unless the data points lie in a straight line.
9) In any regression model, there is an implicit assumption that a relationship exists between the variables.
10) In regression, there is random error that can be predicted.
11) Estimates of the slope, intercept, and error of a regression model are found from sample data.
12) Error is the difference in the actual value and the predicted value.
13) The regression line minimizes the sum of the squared errors.
14) In regression, a dependent variable is sometimes called a predictor variable.
15) Summing the error values in a regression model is misleading because negative errors cancel out positive errors.
16) The SST measures the total variability in the dependent variable about the regression line.
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17) The SSE measures the total variability in the independent variable about the regression line.
18) The SSR indicates how much of the total variability in the dependent variable is explained by the regression model.
19) The coefficient of determination takes on values between -1 and + 1.
20) The coefficient of determination gives the proportion of the variability in the dependent variable that is explained by the regression equation.
21) The correlation coefficient has values between ?1 and +1.
22) Errors are also called residuals.
23) The regression model assumes the error terms are dependent.
24) The regression model assumes the errors are normally distributed.
25) The errors in a regression model are assumed to have an increasing mean.
26) The errors in a regression model are assumed to have zero variance.
27) If the assumptions of regression have been met, errors plotted against the independent variable will typically show patterns.
28) Often, a plot of the residuals will highlight any glaring violations of the assumptions.
29) The error standard deviation is estimated by MSE.
30) The standard error of the estimate is also called the variance of the regression.