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The coefficient of determination gives the proportion of the variability in the dependent variable that is explained by the regression equation.

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The standard error of the estimate is also called the variance of the regression.

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The diagram below illustrates data with a The diagram below illustrates data with a   A)  negative correlation coefficient. B)  zero correlation coefficient. C)  positive correlation coefficient. D)  correlation coefficient equal to +1. E)  None of the above


A) negative correlation coefficient.
B) zero correlation coefficient.
C) positive correlation coefficient.
D) correlation coefficient equal to +1.
E) None of the above

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The sum of squared error (SSE) is


A) a measure of the total variation in Y about the mean.
B) a measure of the total variation in X about the mean.
C) a measure in the variation of Y about the regression line.
D) a measure in the variation of X about the regression line.
E) None of the above

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The best model is a statistically significant model with a high r-square and few variables.

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The variable to be predicted is the dependent variable.

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Which of the following is not a common pitfall of regression?


A) If the assumptions are not met, the statistical tests may not be valid.
B) Nonlinear relationships cannot be incorporated.
C) Two variables may be highly correlated to one another but one is not causing the other to change.
D) Concluding that a statistically significant relationship implies practical value.
E) Using a regression equation beyond the range of X is very questionable.

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Often, a plot of the residuals will highlight any glaring violations of the assumptions.

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The SST measures the total variability in the dependent variable about the regression line.

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The regression model assumes the error terms are dependent.

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Which of the following equalities is correct?


A) SST = SSR + SSE
B) SSR = SST + SSE
C) SSE = SSR + SST
D) SST = SSC + SSR
E) SSE = Actual Value - Predicted Value

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An automated process to systematically add or delete independent variables from a regression model is known as


A) nonlinear transformations.
B) multicollinearity.
C) multiple regression.
D) least squares method.
E) None of the above

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The regression model assumes the errors are normally distributed.

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Explain what r2 is.

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It is a value between 0 and +1...

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The adjusted r2 will always increase as additional variables are added to the model.

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Which of the following statements (are) is not true about regression models?


A) Estimates of the slope are found from sample data.
B) The regression line minimizes the sum of the squared errors.
C) The error is found by subtracting the actual data value from the predicted data value.
D) The dependent variable is the explanatory variable.
E) The intercept coefficient is not typically interpreted.

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If every point lies on the regression line, r2 = ________.

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One purpose of regression is to understand the relationship between variables.

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As more variables are added to the model, what happens to the r2 value?

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It usually...

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If multicollinearity exists, then individual interpretation of the variables is questionable, but the overall model is still good for prediction purposes.

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