Probability

The probability of an event is a number in the interval [0, 1]. It can be expressed as a fraction or ratio or as a percentage.

Furthermore, if the probability of an event is p, where 0<p<1, and if q = 1-p, then the probability of the event can also be expressed as odds of p to q in favour.

It flexes your knees and allows flexing and movement of your hips. If you pick your foot up and look at the sole of your foot then you are using all the functions of the sartorius muscle.

There are nine possibilities for the first ball.

Since there would then be eight balls left, there are eight possibilities for the second ball for each of the nine first balls. 9 x 8 = 72 possible combinations, so far.

Then there would be seven possible third balls for each of those combinations, etc.

Doing the calculation all the way through, you get the formula:

9 x 8 x 7 x 6 x 5 x 4 x 3 x 2 x 1 = 362,880 possible combinations.

This kind of a math problem is called a COMBINATION, and the formula used to calculate it is called a FACTORIAL. The abbreviation for a factorial is the bang (exclamation point), so another way to write the above equation would be:

9! = 362,880.

Since there are the only two mutually exclusive possibilities, we could say the answer is zero.
But in reality, a penny could land on its edge.

For this answer we must leave Answers.com and travel through another dimension -- a dimension not only of sight and sound but of mind. A journey into a wondrous land whose boundaries are that of imagination. That's a signpost up ahead: your next stop: the Twilight Zone!


The episode was called "A Penny for Your Thoughts" (Season 2, episode 25 staring Dick York)

The Opening narration of that episode goes as follows:

Mr. Hector B. Poole, resident of the Twilight Zone. Flip a coin and keep flipping it. What are the odds? Half the time it will come up heads, half the time tails. But in one freakish chance in a million, it'll land on its edge. Mr. Hector B. Poole, a bright human coin, on his way to the bank.

The man flips a coin, it lands on edge, and he discovers that he's mysteriously become telepathic. At the end of the episode, he knocks the coin over and the telepathy goes away.


In reality, mathematicians have tried to figure this out as have physicists. The answer depends on the exact size and width of the edge of the penny, and many other factors. One this is for sure, Rod Serling was not far off when he guessed one in a million.

Nickels have a larger chance of landing on their edge since they have a larger edge and probability of an American nickel landing on edge is approximately 1 in 6000 tosses by one source. The work is not all shown and I don't believe it has been duplicated.

So I don't think there is an exact answer to your question, but the probability certainly is somewhere between 0 and a number very close to 0. It is statistically insignificant.

I now return you to an area we call Answers. com|WikiAnswers.

About 1.5%

The odds of rolling a 7 on any particular throw is 1/6. (There are six ways you can roll a seven: (1,6), (2,5), (3,4), (4,3), (5,2), (6,1), out of 36 possible outcomes.) Therefore, the odds that you don't roll a 7 on a particular roll is 5/6. The odds that you don't roll a 7 in 23 rolls is:

(5/6)^23, or approximately .015=1.5%

Any number from 2 to 10, depending on their relative shapes, sizes and positions.

Im not good at explaining things but here's an example. If I have 5 red marbles and 6 blue marbles in a bag and I pick one out. I then choose another marble without returning the first marble to the bag. Hope this helps.

If the event has already happened, then the probability is 100%.

No. For a convex combination of distributions, the density is also a convex combination of the individual densities and one can easilly check that the convex combination of beta densities is not again a beta density.

something happens

The odds of choosing a prime number in the set [1-20] are 8 out of 20, as there are 8 prime numbers, 2, 3, 5, 7, 11, 13, 17, and 19, in that set.

Wikipedia tells me that the average litter size for a (female dog, since the b word apparently can't be used even in the correct context) is six. I'll exclude other litter sizes for simplicity.

This turns the question into a simple binomial probability distribution (each puppy of the six can be either male or female,) so I use the formula:

P(x)= (n!/x!(n-x)!) · p^x · (1-p)^(n-x)

Plug in the numbers:

P(x=3) = (6!/3!(6-3)!) · 0.5^3 · (1-0.5)^(6-3)

P(x=3) = 20 · 0.125 · 0.125

P(x=3) = 0.3125 = 5/16

Theoretical probability- what the probability "should be" if all outcomes are equally likely.

It is the chance that something will happen.

I assume you mean deck not decks? But that would be 1 queen of diamonds per standard 52 deck of cards.

It is 1/2.

You collect data. Flip a coin 100 times, you get 49 heads and 51 tails, so the probability of H is 0.49.

The British were heavily indebted due to the French and Indian war, so they used any means possible to tax the colonists in order to pay off this debt. The stamp act originated in 1765, taxing everything from stamped documents to newspapers to playing cards. This was the tax that sparked the famous American revolution, and it caused a heavy boycott of british goods. Parliament was losing money, but so were the angry british merchants who were not receiving any payments. The merchants pushed the king to finally abolish the tax in 1766.

Equitable refers to fairness.

For example, an equal distrbution of marks in an exam would require that everyone got exactly the same score. An equitable distribution wold reflect the fact that some people gave better answers than others and so were more "deserving" of a higher mark.

Since there is only 2 sides to a coin...tails will come up 50% of the time.

For humans, approximately 0.52

For humans, approximately 0.52

For humans, approximately 0.52

For humans, approximately 0.52

sure chance

It is a study of queues. A typical example is one that may be found in banks. Customers arrive at intervals that are determined by some probability distribution function. There are then two possible queueing scenarios: one in which there is a single queue which feeds into several cashiers or a system where there are multiple queues: one for each cashier. When a customer reaches a cashier, it takes the cashier an amount of time to serve him or her. This time also has a probability distribution function - different from the one governing arrival times.

The theory studies the optimum queueing scenario, time spent by customers in queues rather than being served, the optimum number of cashiers. The bank must find the best trade-off between the cost of employing more cashiers and the irritation of their customers.

it depends on how much sexual intercourse you have. it is up to you.