Reading 11: Correlation and Regression-LOS f, (Part 1)习题精选 2010, interpret, standard, center

土豆妮

Session 3: Quantitative Methods: Quantitative
Methods for Valuation
Reading 11: Correlation and Regression

LOS f, (Part 1): Calculate and interpret the standard error of estimate and the coefficient of determination.

 

 

 

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.

土豆妮

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.

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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.

土豆妮

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).

土豆妮

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).

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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.

土豆妮

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.

土豆妮

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.

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

土豆妮

The standard error of estimate is closest to the:

A) standard deviation of the residuals.

B) standard deviation of the independent variable.

C) standard deviation of the dependent variable.

土豆妮

The standard error of estimate is closest to the:

A) standard deviation of the residuals.

B) standard deviation of the independent variable.

C) standard deviation of the dependent variable.

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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).

土豆妮

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.

土豆妮

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.

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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