Topic8
- 1. Leo
- 9. Assumptions: The Linear Regression Model
- 11. Law of Iterated Expectations The Law of Iterated Expectation states that the expected value of a random variable is equal to the sum of the expected values of that random variable conditioned on a second random variable. Intuitively speaking, the law states that the expected outcome of an event can be calculated using casework on the possible outcomes of an event it depends on; for instance, if the probability of rain tomorrow depends on the probability of rain today, and all of the following are known The probability of rain today The probability of rain tomorrow given that it rained today The probability of rain tomorrow given that it did not rain today the probability of rain tomorrow can be calculated by considering both cases (it rained today/it did not rain today) in turn. To use specific numbers, suppose that The probability of rain today is 70% If it rains today, it will rain tomorrow with probability 30% If it does not rain today, it will rain tomorrow with probability 90% In this case, the probability of rain tomorrow is 0.70.3+0.30.9=0.21+0.27=0.48=48%
- 36. Go to www.responseware.eu Enter session ID as QMFIL2
- 38. A. -1.73 B. 1.64 C. 6.20 D. 4.11
- 39. A. 38 B. 388 C. 5 D. 680
- 40. A. 388 B. 30 C. 418 D. 10
- 41. A. 0.26 B. 4.11 C. 6.20 D. 0.79
- 42. A. Yes B. No C. They are the same D. Dont know
- 43. A. No B. Yes C. Not sure
- 44. A. No B. Yes C. Not sure
- 45. A. 5% B. 10% C. 70% D. 0.8%
- 46. A. Yes B. No C. Not sure