4.31. Each night different meteorologists give us the probability that it will rain the next day. To judge how well these people predict, we will score each of them as follows: If a meteorologist says that it will rain with probability p, then he or she will receive a score of 1 − ( 1 − p ) 2 if it. does rain 1 − p 2 if it does not rain We will then keep track of scores over a certain time span and conclude that the meteorologist with the highest average score is the best predictor of weather. Suppose now that a given meteorologist is aware of our scoring mechanism and wants to maximize his or her expected score. If this person truly believes that it will rain tomorrow with probability p* what value of p should he or she assert so as to maximize the expected score?
4.31. Each night different meteorologists give us the probability that it will rain the next day. To judge how well these people predict, we will score each of them as follows: If a meteorologist says that it will rain with probability p, then he or she will receive a score of 1 − ( 1 − p ) 2 if it. does rain 1 − p 2 if it does not rain We will then keep track of scores over a certain time span and conclude that the meteorologist with the highest average score is the best predictor of weather. Suppose now that a given meteorologist is aware of our scoring mechanism and wants to maximize his or her expected score. If this person truly believes that it will rain tomorrow with probability p* what value of p should he or she assert so as to maximize the expected score?
Solution Summary: The author explains the value of p that maximizes the expected score.
4.31. Each night different meteorologists give us the probability that it will rain the next day. To judge how well these people predict, we will score each of them as follows: If a meteorologist says that it will rain with probability p, then he or she will receive a score of
1
−
(
1
−
p
)
2
if it. does rain
1
−
p
2
if it does not rain We will then keep track of scores over a certain time span and conclude that the meteorologist with the highest average score is the best predictor of weather. Suppose now that a given meteorologist is aware of our scoring mechanism and wants to maximize his or her expected score. If this person truly believes that it will rain tomorrow with probability p* what value of p should he or she assert so as to maximize the expected score?
A mechatronic assembly is subjected to a final functional test. Suppose that defects occur at random in these
assemblies, and that defects occur according to a Poisson distribution with parameter >= 0.02.
(a) What is the probability that an assembly will have exactly one defect?
(b) What is the probability that an assembly will have one or more defects?
(c) Suppose that you improve the process so that the occurrence rate of defects is cut in half to λ = 0.01.
What effect does this have on the probability that an assembly will have one or more defects?
A random sample of 50 units is drawn from a production process every half hour. The fraction of non-conforming
product manufactured is 0.02. What is the probability that p < 0.04 if the fraction non-conforming really is
0.02?
A textbook has 500 pages on which typographical errors could occur. Suppose that there are exactly 10 such
errors randomly located on those pages. Find the probability that a random selection of 50 pages will contain
no errors. Find the probability that 50 randomly selected pages will contain at least two errors.
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, probability and related others by exploring similar questions and additional content below.
Discrete Distributions: Binomial, Poisson and Hypergeometric | Statistics for Data Science; Author: Dr. Bharatendra Rai;https://www.youtube.com/watch?v=lHhyy4JMigg;License: Standard Youtube License