MGEA02H3 Study Guide - Final Guide: Marginal Utility, Demand Curve, Opportunity Cost
MGEA02H3 Full Course Notes
Document Summary
Get access
Related Documents
Related Questions
37.Use the following setup for the next question.
A manufacturing firm is deciding whether or not to invest in a new printer that needs an initial investment of $150,000. The investment would increase cash flows in the first year by $80,000 and in the second year by $75,000.
?If the interest rate is 10% then the net present value of the investment is
| ?a. $5,000 |
| b. ?- $9,091 |
| ?c. -$15,290 |
| d. | ?-$21,901 33. Table 13-16
|
| TABLE 66 Stock Prices and Consumer Prices | |||
| CITY | |||
| Y = Rate of Change, Stock Prices, Percent Per Year | |||
| X = Rate of Change, Consumer Prices, Percent Per Year | |||
| CITY | Y | X | |
| A | 5 | 4.3 | |
| B | 11.1 | 4.6 | |
| C | 3.2 | 2.4 | |
| D | 7.9 | 2.4 | |
| E | 25.5 | 26.4 | |
| F | 3.8 | 4.2 | |
| G | 11.1 | 5.5 | |
| H | 9.9 | 4.7 | |
| I | 3.3 | 2.2 | |
| J | 1.5 | 4 | |
| K | 6.4 | 4 | |
| L | 8.9 | 8.4 | |
| M | 8.1 | 3.3 | |
| N | 13.5 | 4.7 | |
| O | 4.7 | 5.2 | |
| P | 7.5 | 3.6 | |
| Q | 4.73. | 6 | |
| R | 8 | 4 | |
| S | 7.5 | 3.9 | |
| T | 9 | 2.1 | |
Table 66 gives data on percent change per year stock prices (Y) and consumer prices (X) for a cross section of 20 cities.
******************* answer in "SAS format" please********************* (if possible)
1) Plot the data in scattergram
2) Regress Y on X and examine the residuals from this regression. What do you observe?
3) Since the data for city(E) is unusual, repeat the regression in (2) dropping the data on city(E). Now examine the residuals from this regression. What do you observe?
4) If on the basis of the results in (2) you conclude that there was heteroscedasticity in the error variance but on the basis of the results in (3) you reverse your conclusion, what general conclusions do you draw?
State whether the following statements are true or false. Breifly justify your answer:
5) When autocorrelation is present, OLS estimators are biased as well as inefficient;
6) The R squared values of two models, one involving regression in the first-difference form and another in the level form, are not directly comparable.
7) In the presence of heterscedasticity the usual OLS method always overestimates the standard errors of estimators.
8) If a regression model is mis-specified (e.g., an important variable is ommitted), the OLS residuals will show a distinct pattern.
