Ols method

Question

Question 1. In a regression analysis, the variable that is being predicted

• must have the same units as the variable doing the predicting
• is the independent variable
• usually is denoted by X
• is the dependent variable
• None of the above answers is correct.

Question 2. Which of the following statements is NOT correct about R-square?

• It is called coefficient of determination, and used to evaluate regression results.
• The value of R-square increases as more independent variables are added to a regression equation.
• It shows how well the variation of all explanatory (independent) variables account for the variation of a dependent variable.
• The closer its value gets to 1.0, the closer the variation of the dependent variable is accounted for by the variation of a particular explanatory variable.
• It the value is near zero, it is safe to say that the regression equation is not good.

Question 3.  Shown below is a partial computer output from a regression analysis.

Predictor              Coefficient           StdError
Constant               10.00                 2.00
X1                         3.00                 1.50
X2                         4.00                  2.00
X3                        -2.50                -1.00

At alpha = 0.05, if the critical value of t = 2.12, is X1 significant?

• No, because 1.50 < 2.12
• No, because 2.00 < 2.12
• Yes, because 3.00 > 2.12
• Yes, because 2.00 < 2.12
• No, because 3.00 > 2,12

Question 4. A simple linear regression model can be represented as follows:

Y = a + bX + u

What is NOT a correct statement about u?

• It is referred to as the "random" or "error" term.
• Is does not give any systematic impact on Y.
• Once the model (equation) is estimated, u accounts for the deviation from the estimated equation.
• It is represented by the distance from the estimated straight line.
• It is an independent variable that affects the dependent variable.

Question 5. Which of the following statements is NOT correct about OLS method?

• It finds the line that maximizes the sum of the squared deviation of each data point from the line.
• It finds the line that minimizes the sum of the squared deviation of each data point from the line.
• It finds the line that maximizes the sum of the distance of each data point from the line.
• It finds the line that minimizes the sum of the distance of each data point from the line.

Question 6. F-test measures the statistical significance of each explanatory variable.

• True
• False

Question 7. What is correct about t-test?

• If the estimate of a coefficient is 14.2 and the standard error is 2.0, the t-value is 7.1.
• t-test measures the fitness of a regression equation to the data.
• If a t-value is very small - less than one, that means the variable is very significant.
• In conducting a t-test, we hypothesize the regression coefficient to be zero, and test that the null hypothesis. If it is proved true, then the coefficient is meaningful.
• All of above.

Question 8. Click demand data and using the data estimate the coefficients of the following equation: (I don’t have the Demand Data for this question)
Q = (      ) + (      ) P

• 181.2, -11.109
• 91.3, -0.006
• 1039.0, -0.048
• 164.8, -0.059
• 140.2, -1.256

Question 9. A demand equation is provided as follows:

log Q = log a + b log P

What is NOT true about the relationship?

• The demand has nonlinear relationship with price.
• One percent change of P is associated with b percent change of Q.
• b represents the point price elasticity of the demand.
• If a > 0 and b > 0, the demand increases at an increasing rate with respect to price.
• The greater is b, the greater the influence of P on Q.

Question 10. A demand equation is estimated as follows:

log Q = 100 - 2.5 log P + 0.5 Y, where Y is household income.

What is the correct statement about the equation?

• Price is more dominent variable than household income.
• Price change affects the demand five times more significantly than household income change. 
• -2.5 represents the point price elasticity and 0.5 represents the point income elasticity.
• One percent increase of household income increases the demand by 0.5 percent.
• All of above.

Question 11. Durbin-Watson test detects Autocorrelation.

• True
• False

Question 12. When independent variables are related to each other, we have multicollinearity problem.

• True
• False