答案和详解如下： Q5. The standard error of estimate is closest to the: A) standard deviation of the independent variable. B) standard deviation of the residuals. C) standard deviation of the dependent variable. Correct answer is B) The standard error of the estimate measures the uncertainty in the relationship between the actual and predicted values of the dependent variable. The differences between these values are called the residuals, and the standard error of the estimate helps gauge the fit of the regression line (the smaller the standard error of the estimate, the better the fit). Q6. The standard error of the estimate measures the variability of the:
A) predicted y-values around the mean of the observed y-values. B) actual dependent variable values about the estimated regression line. C) values of the sample regression coefficient. Correct answer is B) The standard error of the estimate (SEE) measures the uncertainty in the relationship between the independent and dependent variables and helps gauge the fit of the regression line (the smaller the standard error of the estimate, the better the fit). Remember that the SEE is different from the sum of squared errors (SSE). SSE = the sum of (actual value - predicted value)2. SEE is the the square root of the SSE "standardized" by the degrees of freedom, or SEE = [SSE / (n - 2)]1/2 Q7. Jason Brock, CFA, is performing a regression analysis to identify and evaluate any relationship between the common stock of ABT Corp and the S& 100 index. He utilizes monthly data from the past five years, and assumes that the sum of the squared errors is .0039. The calculated standard error of the estimate (SEE) is closest to: A) 0.0080. B) 0.0082. C) 0.0360. Correct answer is B) The standard error of estimate of a regression equation measures the degree of variability between the actual and estimated Y-values. The SEE may also be referred to as the standard error of the residual or the standard error of the regression. The SEE is equal to the square root of the mean squared error. Expressed in a formula, SEE = √(SSE / (n-2)) = √(.0039 / (60-2)) = .0082 Q8. The standard error of the estimate in a regression is the standard deviation of the: A) differences between the actual values of the dependent variable and the mean of the dependent variable. B) residuals of the regression. C) dependent variable. Correct answer is B) The standard error is se = √[SSE/(n-2)]. It is the standard deviation of the residuals. Q9. Which of the following statements about the standard error of the estimate (SEE) is least accurate? A) The SEE will be high if the relationship between the independent and dependent variables is weak. B) The larger the SEE the larger the R2. C) The SEE may be calculated from the sum of the squared errors and the number of observations. Correct answer is B) The R2, or coefficient of determination, is the percentage of variation in the dependent variable explained by the variation in the independent variable. A higher R2 means a better fit. The SEE is smaller when the fit is better. Q10. If X and Y are perfectly correlated, regressing Y onto X will result in which of the following: A) the regression line will be sloped upward. B) the alpha coefficient will be zero. C) the standard error of estimate will be zero. Correct answer is C) If X and Y are perfectly correlated, all of the points will plot on the regression line, so the standard error of the estimate will be zero. Note that the sign of the correlation coefficient will indicate which way the regression line is pointing (there can be perfect negative correlation sloping downward as well as perfect positive correlation sloping upward). Alpha is the intercept and is not directly related to the correlation. | 
发表于 2009-1-5 10:32
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