Q1. Bea Carroll, CFA, has performed a regression analysis of the relationship between 6-month LIBOR and the U.S. Consumer Price Index (CPI). Her analysis indicates a standard error of estimate (SEE) that is high relative to total variability. Which of the following conclusions regarding the relationship between 6-month LIBOR and CPI can Carroll most accurately draw from her SEE analysis? The relationship between the two variables is:

A)   very weak.

B)   positively correlated.

C)   very strong.

Q2. The most appropriate measure of the degree of variability of the actual Y-values relative to the estimated Y-values from a regression equation is the:

A)   sum of squared errors (SSE).

B)   coefficient of determination (R²).

C)   standard error of the estimate (SEE).

Q3. Which of the following statements about the standard error of estimate is least accurate? The standard error of estimate:

A)   is the square root of the sum of the squared deviations from the regression line divided by (n − 2).

B)   measures the Y variable's variability that is not explained by the regression equation.

C)   is the square of the coefficient of determination.

答案和详解如下：

Q1. Bea Carroll, CFA, has performed a regression analysis of the relationship between 6-month LIBOR and the U.S. Consumer Price Index (CPI). Her analysis indicates a standard error of estimate (SEE) that is high relative to total variability. Which of the following conclusions regarding the relationship between 6-month LIBOR and CPI can Carroll most accurately draw from her SEE analysis? The relationship between the two variables is:

A)   very weak.

B)   positively correlated.

C)   very strong.

Correct answer is A)

The SEE is the standard deviation of the error terms in the regression, and is an indicator of the strength of the relationship between the dependent and independent variables. The SEE will be low if the relationship is strong and conversely will be high if the relationship is weak.

Q2. The most appropriate measure of the degree of variability of the actual Y-values relative to the estimated Y-values from a regression equation is the:

A)   sum of squared errors (SSE).

B)   coefficient of determination (R²).

C)   standard error of the estimate (SEE).

Correct answer is C)

The SEE is the standard deviation of the error terms in the regression, and is an indicator of the strength of the relationship between the dependent and independent variables. The SEE will be low if the relationship is strong, and conversely will be high if the relationship is weak.

Q3. Which of the following statements about the standard error of estimate is least accurate? The standard error of estimate:

A)   is the square root of the sum of the squared deviations from the regression line divided by (n − 2).

B)   measures the Y variable's variability that is not explained by the regression equation.

C)   is the square of the coefficient of determination.

Correct answer is C)

Note: The coefficient of determination (R²) is the square of the correlation coefficient in simple linear regression.

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