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.

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土豆妮

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 SEE may be calculated from the sum of the squared errors and the number of observations.

C) The larger the SEE the larger the R2.

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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.

土豆妮

If X and Y are perfectly correlated, regressing Y onto X will result in which of the following:

A) the standard error of estimate will be zero.

B) the regression line will be sloped upward.

C) the alpha coefficient will be zero.

土豆妮

If X and Y are perfectly correlated, regressing Y onto X will result in which of the following:

A) the standard error of estimate will be zero.

B) the regression line will be sloped upward.

C) the alpha coefficient will be zero.

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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.

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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.

土豆妮

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.

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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

土豆妮

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.

土豆妮

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.

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The standard error is s_e = √[SSE/(n-2)]. It is the standard deviation of the residuals.