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1. The greater the standard error of an estimated coefficient: a. the greater th

ID: 1166450 • Letter: 1

Question

1. The greater the standard error of an estimated coefficient:

a. the greater the t-value of the estimated coefficient.

b. the lower the t-value of the estimated coefficient.

c. the greater the R-square.

d. the greater the adjusted R-square.

2. As a general rule of thumb, a manager can be 95 percent confident that the true value of the underlying parameter in the regression is not zero, when the absolute value of the t-statistic is:

a. greater than zero.

b. greater than or equal to 2.

c. None of the statements is correct.

d. greater than or equal to 1.

3. A study has estimated the effect of changes in interest rates and consumer confidence on the demand for money to be: ln M = 14.666 + .021 ln C ? 0.036 ln r, where M denotes real money balances, C is an index of consumer confidence, and r is the interest rate paid on bank deposits. Based on this study we know that the interest elasticity is:

very elastic.

zero.

very inelastic.

unitary

4. Which of the following can be used to quantify the overall statistical significance of a regression?

a.F-statistic

b.t-statistic

c.The F-statistic and R-square

d.R-square

5.

Which of the following is used to determine the statistical significance of a regression coefficient?

a. R-square

b. Adjusted R-square

c. t-statistic

d. F-statistic

Explanation / Answer

1) b is correct

t statistic= coefficient / standard error

Higher the standard error lower is the t statistic.

2) b is correct

For a coefficient to be statistically significant its t value needs to be greater than or equal to 2 for 95% confidence.

3) very inelastic is correct

The coefficients in the ln-ln model give elasticity value. Coefficient of r is 0.036 which is less than 1 which implies it is inelastic.

4) a is correct

F statistic is used to determine overall significance of all the variables in the model.

5) c is correct

t statistic is used to determine individual significance of the variable.