ECON 3050
Q24. The coefficient of determination (r2) is calculated as 0.49, then the correlation coefficient:
Cannot be determined without the data
Should be - 0.70 or 0.70
Should be 0.
Neither of the above
Q25. A regression line is used for all of the following except one. Which one is not a valid use of a regression line?
To estimate the average value of Y at a specified value of X.
To predict the value of Y for an individual, given that individual's X-value.
To estimate the change in Y for a one-unit change in X.
To determine if a change in X causes a change in Y.
Q26. Which choice is not an appropriate description of YË in a regression equation?
Estimated response
Predicted response
Estimated average response
Observed response
Q27. Which of the following is the best way to determine whether or not there is a statistically significant linear relationship between two quantitative variables?
Compute a regression line from a sample and see if the sample slope is 0.
Compute the correlation coefficient and see if it is greater than 0.5 or less than â0.5.
Conduct a test of the null hypothesis that the population slope is 0.
Conduct a test of the null hypothesis that the population intercept is 0.
Q28. There is no relationship between variables unless the data points lie in a straight line.
True
False
Q29. The sample regression analysis
Is the same as the population regression line
Is used to estimate the population regression line
Shows the true relation between dependent and independent variables
None of the above.
Q30. In regression, a dependent variable is sometimes called a predictor variable.
True
False
Q31. In a linear regression equation of the form y = a + bx, the intercept shows
The amount that y changes when x changes by one unit
The amount that x changes when y changes by one unit
The value of y when x is zero
The value of x when y is zero.
Q32. In a linear regression equation of y = a + bx, the slope b shows
Y / b
X / b
Y / X
X / Y
ECON 3050
Q24. The coefficient of determination (r2) is calculated as 0.49, then the correlation coefficient:
Cannot be determined without the data
Should be - 0.70 or 0.70
Should be 0.
Neither of the above
Q25. A regression line is used for all of the following except one. Which one is not a valid use of a regression line?
To estimate the average value of Y at a specified value of X.
To predict the value of Y for an individual, given that individual's X-value.
To estimate the change in Y for a one-unit change in X.
To determine if a change in X causes a change in Y.
Q26. Which choice is not an appropriate description of YË in a regression equation?
Estimated response
Predicted response
Estimated average response
Observed response
Q27. Which of the following is the best way to determine whether or not there is a statistically significant linear relationship between two quantitative variables?
Compute a regression line from a sample and see if the sample slope is 0.
Compute the correlation coefficient and see if it is greater than 0.5 or less than â0.5.
Conduct a test of the null hypothesis that the population slope is 0.
Conduct a test of the null hypothesis that the population intercept is 0.
Q28. There is no relationship between variables unless the data points lie in a straight line.
True
False
Q29. The sample regression analysis
Is the same as the population regression line
Is used to estimate the population regression line
Shows the true relation between dependent and independent variables
None of the above.
Q30. In regression, a dependent variable is sometimes called a predictor variable.
True
False
Q31. In a linear regression equation of the form y = a + bx, the intercept shows
The amount that y changes when x changes by one unit
The amount that x changes when y changes by one unit
The value of y when x is zero
The value of x when y is zero.
Q32. In a linear regression equation of y = a + bx, the slope b shows
Y / b
X / b
Y / X
X / Y
Related textbook solutions
Related questions
. Suppose Y is related to R and S in the following nonlinear way:
Y= aRáµSá¶
a. How can this nonlinear equation be transformed into a linear form that can ve analyzed using multiple regression analysis.
Sixty-three observations are used to obtain the following regression results:
| DEPENDENT VARIABLE: LNY | R-SQUARE | F-RATIO | P-VALUE ON F | |
| OBSERVATION: 63 | 0.8151 | 132.22 | 0.0001 | |
| VARIABLE | PARAMETER ESTIMATE | STANDARD ERROR | T-RATIO | P-VALUE |
| INTERCEPT | -1.386 | 0.83 | -1.67 | 0.1002 |
| LNR | 0.452 | 0.175 | 2.58 | 0.0123 |
| LNS | 0.30 | 0.098 | 3.06 | 0.0033 |
b. Test each estimated coefficient for statistical significance at the 5 percent level of significance. What are the exact significance levels for each of the estimated coefficients?
c. Test the overall equation for statistical significance at the 5 percent level of significance. Interpret the p-value of the F-statistic.
d. How well does this nonlinear model fit the data?
e. Using the estimated value of the intercept, compute an estimate of a.
f. If R = 200 and S = 1,500, compute the expected value of Y.
g. What is the estimated elasticity of R? Of S?
