Probability

It is 5/11.

Mathematics is a subject and is not something that can be permuted.

It is 0.0146

They are 1728 mm3 or 1.728 millilitres in volume.

There are many variables that are not normally distributed. You can describe them using a probability distribution function or its cumulative version; you can present them graphically.

It is 10/36 = 5/18

Pr(1 or 5) = 1/3

A man has an 81 percent chance to get married if they live the United States before the age of 40. A woman has an 86 percent chance of getting married.

It depends. If you're plugging in something into a wall outlet and you get zapped, your chances of surviving are probably 99.9%. But if you're electrocuted by an electrical conduit box in your house/building and you survive, God REALLY loves you!! Plain & simple, don't mess with electricity.

I'm still working through this one. Yes, given N IID r.v., the maximum statistic is described by the beta distribution. Actually it is F(x) that is beta distributed. Remember that the argument of the beta distribution goes from 0 to 1. In fact, the beta distribution is the sampling distribution that describes any order statistic (k = 1 to n) taken from any distribution. Maximum and minimum become special cases. In all cases, the sample cdf of the maximum is G(x) = F(x)n. For the uniform, F(x) =(x-A)/(B-A) for a Uniform(A,B) continuous distribution. Now, if I'm using the beta cdf, B(x), for order statistics (k=1 is the minimum, k=n is the maximum), in the form, B(x|alpha, beta), then alpha = k and beta = n-k+1 and x = F(x) so the sampling distribution is: G(x) = B(F(x)|k,n-k+1) for any rank order statistic X(k), and for the maximum, B(F(x),n,1). The inverse of G(x) is interesting, G-1(p)=F-1(B-1(p))=x, where p is a probability of being equal or less than x. OK, this might not be so clear, and I try to post a bit more on this later. Your second question is difficult. Your sample is not taken independently. Each selection bears some relation to the prior selection. I don't see that even a large sample would be representative of the population, but I need to think on this some more. I thought a partial answer now is better than none at all.

Probability is more of a mathematical statisic whereas chance is more of luck or fortunance.

The sample space contains 36 possible outcomes (1,1 & 1,2 & 1,3.....etc up to 6,6). Since 6,6 is the only way to get the sum of 12, the probability of 12 is 1/36.

In a book of 52 playing cards there are thirteen types of number cards, cards starting with Ace, 2 , 3 and ending as Jack, Queen and King . And king is number thirteen there are four kings each uniquely symbolized with Spade, Club, Diamond and Heart. Spade and Club are black colored while Heart and Diamond are Red colored. So apparently spade is black colored sign card which are thirteen starting from Ace, 2, 3 and ending as jack, queen and King at last.

1/9

It is 15/16.

0.81 apex:)

Coin tosses are what we call Independent Events, meaning that the results of one toss have no effect on the next toss or any thereafter.

Therefore the probability of each toss is 1/2.

If, however, you want to know the probability of tossing two coins, and each coin landing heads-up, you simply multiply their probabilities together, resulting in 1/4.

A z-score requires the mean and standard deviation (or standard error). There is, therefore, not enough information to answer the question.

The probability is 1 as all values on a die are less than 7.

1 in 23328

The number of total outcomes on 3 tosses for a coin is 2 3, or 8. Since only 1 outcome is H, H, H, the probability of heads on three consecutive tosses of a coin is 1/8.

It depends on what the distribution is. In a Normal or Gaussian distribution, the standard deviation is the square root of the mean, so it could be 3.1 but, again, it depends on the distribution.

The SD is the (positive) square root of the variance.

One word with the letters lgeifsne is the word feelings.