stat

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
0.7⋅0.3+0.3⋅0.9=0.21+0.27=0.48=48%

18. LRM Hypothesis Testing

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. Don’t 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