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Probability distribution for the sum of two dice compute the average of the probability distribution?
Wiki User
∙ 14y ago
On a roll of two die, there are 6*6 = 36 possible outcomes. You can write each combination down on a piece of paper in a format such as (roll die 1, roll of die 2). In the probability section of the related web site, it lists the possible values (sum of the two die) and the probability. The probability is calculated on the number of different ways to get the sum listed. For example, there is 1 way to get a 2: (1,1). So the probability of rolling a 2 is: 1/36. The probability of rolling a 7 is: (6,1) & (5,2) & (4,3) & (3,4) & (2,5) & (1,6) = 6 ways to get a seven divided by 36 = 6/36 = 1/6. The mean is defined by the sum of each possible outcome times its probability. The formula in symbols is: μ = Σ x P(x) or: 2(1/36) + 3(2/36) + 4(3/36) + 5(4/36) + 6(5/36) + 7(6/36) + 8(5/36) + 9(4/36) + 10 (3/36) + 11(2/36) + 12(1/36) = 252/36 = 7 = mean.
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∙ 14y ago
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A fair die is rolled 9 times compute the probability that a one appears exactly once?
The probability of a one being rolled in a fair die is 1 in 6, or 0.1666... . The probability of a one not being rolled is 5 in 6, or 0.8333... . The probability, then, of exactly one one being rolled in nine rolls is 1 in 6 times 5 in 6 to the 8th power, or about 0.0388.
How do you compute the probability of an event?
There are two main ways: One is to calculate the theoretical probability. You will need to develop a model for the experiment and then use the laws of science and mathematics to determine the probability of the event (subject to the model's assumptions). A major alternative is the empirical or experimental method. This requires performing the trial many times. The probability of the event is estimated by the proportion of the total number of trials which result in the outcome of interest occurring.
What is the outcome if you roll a12 number dice 3 time?
You are asking a question about experimental probability. The problem with that type of question is that the answer is different each time you run the experiment. That's why we call it experimental probability. The outcome will be different each time you run the experiment.This is different than theoretical probability, where you can compute a probability based on some a priori knowledge of the conditions of the experiment. For instance, if you asked me what the probability of throwing a 3 or a 4 on a 12 number die, I could easily compute that as 2 in 12, or 1 in 6, or about 0.1667. Even multiple experiments can be predicted. For instance, if you asked me what was the probability of throwing a 3 or a 4 on a 12 number die three times in a row, I could also easily compute that as (2 in 12)3 or about 0.004630.Alas, experimental and theoretical probability part company and one does not assure the other, unless you run a very large number of tests but, even then, you only do what we call approachthe theoretical results with the experimental outcome.
How do you compute the p-value?
The first step in calculating a p-value is to make a hypothesis of the statistical model for your study. You then assume that the hypothesis is true and calculate the probability of observing an outcome at least as extreme as the one that you did observe. This probability is the p-value.
What is the probability of drawing two hearts from a deck of fifty-two playing cards?
The probability of getting two hearts in a row: P(Getting a hearts on the first draw)*P(Getting another hearts given the first one was a hearts) The first probability is simple: there are 13 hearts in a deck of 52 cards. The probability is 13/52=1/4. The second probability is trickier: there are now 12 hearts left in a deck of 51 cards! The probability of getting another hearts is therefore 12/51=4/17. Now compute (1/4)*(4/17) and get 1/17, which is the probability of drawing two hearts from a deck of fifty-two playing cards.
How do you compute the probability distribution of a function of two Poisson random variables?
we compute it by using their differences
Which of the following is incorrect Select one a. Probability distribution is used to compute continuous random variables b. Probability distribution equals to one. c. Probability distribution is used?
b is incorrect while c is virtually meaningless.
How do you compute the probability Y and gt you given a random variable Y has a normal distribution with mean m and variance s2.?
This is supposed to be Y > u
Compute the probability given an event that a male person will get pregnant mathematically?
The probability that mathematics will make a male pregnant is zero!
How do you compute discrete variables?
You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.
What are the odds Dallas Cowboys will win the super bowl 3 to 1. compute probability?
For 3 to 1 odds of winning;Probability of winning:0.25, or;Chance of winning:25%
A fair die is rolled 9 times compute the probability that a one appears exactly once?
The probability of a one being rolled in a fair die is 1 in 6, or 0.1666... . The probability of a one not being rolled is 5 in 6, or 0.8333... . The probability, then, of exactly one one being rolled in nine rolls is 1 in 6 times 5 in 6 to the 8th power, or about 0.0388.
How do you compute the probability of an event?
There are two main ways: One is to calculate the theoretical probability. You will need to develop a model for the experiment and then use the laws of science and mathematics to determine the probability of the event (subject to the model's assumptions). A major alternative is the empirical or experimental method. This requires performing the trial many times. The probability of the event is estimated by the proportion of the total number of trials which result in the outcome of interest occurring.
What is the outcome if you roll a12 number dice 3 time?
You are asking a question about experimental probability. The problem with that type of question is that the answer is different each time you run the experiment. That's why we call it experimental probability. The outcome will be different each time you run the experiment.This is different than theoretical probability, where you can compute a probability based on some a priori knowledge of the conditions of the experiment. For instance, if you asked me what the probability of throwing a 3 or a 4 on a 12 number die, I could easily compute that as 2 in 12, or 1 in 6, or about 0.1667. Even multiple experiments can be predicted. For instance, if you asked me what was the probability of throwing a 3 or a 4 on a 12 number die three times in a row, I could also easily compute that as (2 in 12)3 or about 0.004630.Alas, experimental and theoretical probability part company and one does not assure the other, unless you run a very large number of tests but, even then, you only do what we call approachthe theoretical results with the experimental outcome.
How do you compute the p-value?
The first step in calculating a p-value is to make a hypothesis of the statistical model for your study. You then assume that the hypothesis is true and calculate the probability of observing an outcome at least as extreme as the one that you did observe. This probability is the p-value.
When a coin is tossed 10 times in a row what is the probability that heads comes up at least 8 times?
The Binomial distribution has the probability function P(x=r)= nCr p^r (1-p)^(n-r) r=0,1,2,.....,n where nCr = n! / r! (n-r)! n=number of tosses =10 p=probability of a head in a single toss=1/2 r=8,9,10 We have to compute each probability and add them all up. P(r=8) = 10C8 (1/2)^8 (1/2)^2 P(r=9) = 10C9 (1/2)^9 (1/2) P(r=10) = 10C10 (1/2)^10 http://www.pindling.org/Math/Statistics/Textbook/Functions/Binomial/binomial_2_8.htm The above link computes each probability. =0.0439+0.0098+0.0010 = 0.0547 is the required probability. One may also use a calculator to compute these expressions.
What is the probability of drawing two hearts from a deck of fifty-two playing cards?
The probability of getting two hearts in a row: P(Getting a hearts on the first draw)*P(Getting another hearts given the first one was a hearts) The first probability is simple: there are 13 hearts in a deck of 52 cards. The probability is 13/52=1/4. The second probability is trickier: there are now 12 hearts left in a deck of 51 cards! The probability of getting another hearts is therefore 12/51=4/17. Now compute (1/4)*(4/17) and get 1/17, which is the probability of drawing two hearts from a deck of fifty-two playing cards.
