Chapter 5
... occurs we must decide whether or not to stop, with our objective being to stop at the last event to occur prior to some specified time T. That is, if an event occurs at time t, 0 ≤ t ≤ T and we decide to stop, then we lose if there are any events in the interval t, T, and win otherwise. If we do n ...
... occurs we must decide whether or not to stop, with our objective being to stop at the last event to occur prior to some specified time T. That is, if an event occurs at time t, 0 ≤ t ≤ T and we decide to stop, then we lose if there are any events in the interval t, T, and win otherwise. If we do n ...
Spreadsheet Modeling & Decision Analysis:
... where: p the proportion of the sample that is less than some value Yp n = the sample size (and n 30) ...
... where: p the proportion of the sample that is less than some value Yp n = the sample size (and n 30) ...
Quiz505
... c) What is the probability that the number “2” appears exactly twice. (hint: first count in how many ways you can place two “2”s in 5 slots, and then count the number of ways you can realize each such possibility). P=C(5,2)x5^3/6^5 (C(5,2) is the number of ways to place two 2’s in 5 slots and each p ...
... c) What is the probability that the number “2” appears exactly twice. (hint: first count in how many ways you can place two “2”s in 5 slots, and then count the number of ways you can realize each such possibility). P=C(5,2)x5^3/6^5 (C(5,2) is the number of ways to place two 2’s in 5 slots and each p ...
Document
... Rare Event Rule for Inferential Statistics If, under a given assumption (such as the assumption that a coin is fair), the probability of a particular observed event (such as 992 heads in 1000 tosses of a coin) is extremely small, we conclude that the assumption is probably not ...
... Rare Event Rule for Inferential Statistics If, under a given assumption (such as the assumption that a coin is fair), the probability of a particular observed event (such as 992 heads in 1000 tosses of a coin) is extremely small, we conclude that the assumption is probably not ...
Statistics Exam Reminders File
... Comment on the strength, form and direction of the relationship. “There is a strong (weak, moderate) positive (negative) linear (non-linear) relationship between (write out what x is) and (write out what y is).” Look for patterns in the data, and then for deviations from those patterns. The corr ...
... Comment on the strength, form and direction of the relationship. “There is a strong (weak, moderate) positive (negative) linear (non-linear) relationship between (write out what x is) and (write out what y is).” Look for patterns in the data, and then for deviations from those patterns. The corr ...
Sample Mean and Standardization notes
... Unfortunately, we have also seen that the mean and variance of a random variable do not determine its probability distribution. ...
... Unfortunately, we have also seen that the mean and variance of a random variable do not determine its probability distribution. ...
Quiz 8 - Cypress HS
... 10. A set of 10 cards consists of 5 red cards and 5 black cards. The cards are shuffled thoroughly and you turn cards over, one at a time, beginning with the top card. Let X be the number of cards you turn over until you observe the first red card. The random variable X has which of the following pr ...
... 10. A set of 10 cards consists of 5 red cards and 5 black cards. The cards are shuffled thoroughly and you turn cards over, one at a time, beginning with the top card. Let X be the number of cards you turn over until you observe the first red card. The random variable X has which of the following pr ...
Chapter5.1to5.2
... lets try it! Using three dice, figure out how many possible cases there are now find out how many possible ways there are to create each of the possible cases fill in a table like the one below now you can make your graph Outcome 3 # cases ...
... lets try it! Using three dice, figure out how many possible cases there are now find out how many possible ways there are to create each of the possible cases fill in a table like the one below now you can make your graph Outcome 3 # cases ...
Lecture 17: Zero Knowledge Proofs - Part 2 (Nov 3, Remus Radu)
... repeated n|E| times, so P ∗ can succeed with probability at most n|E| n ...
... repeated n|E| times, so P ∗ can succeed with probability at most n|E| n ...
Law of large numbers
In probability theory, the law of large numbers (LLN) is a theorem that describes the result of performing the same experiment a large number of times. According to the law, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed.The LLN is important because it ""guarantees"" stable long-term results for the averages of some random events. For example, while a casino may lose money in a single spin of the roulette wheel, its earnings will tend towards a predictable percentage over a large number of spins. Any winning streak by a player will eventually be overcome by the parameters of the game. It is important to remember that the LLN only applies (as the name indicates) when a large number of observations are considered. There is no principle that a small number of observations will coincide with the expected value or that a streak of one value will immediately be ""balanced"" by the others (see the gambler's fallacy)