Probability

The chance is 0.35

Yes, and is equal to 1. This is true for normal distribution using any mean and variance.

There are 36 total ways two dice can be rolled. 4 of those give you a 9. The probability is 4/36=1/9

It is 1/221.

Assume that the standard deck is completely shuffled in a completely unbiased way.

The probability of drawing the first ace is 4/52, since there are 4 aces in a standard deck.

The probability of drawing the second ace is 3/51, since there are three aces remaining and 51 cards from which to choose.

12/52 X 51 equals 12/2652, which equals 1/221.

It's difficult to think of a real event to which an exact probability can be assigned. We say that flipping a coin yields 'heads' with probability 1/2 but we do not know that definitely. The only way of assigning a probability in the sense of numbers of heads versus total numbers of flips is by experiment. (Be aware though that there are other interpretations of the word probability.) If I were to flip a coin 500 times and obtained 249 heads then the experimental probability of obtaining a head would be 249/500 or 0.498.

Wherever there is some random (that is, non-determinate) element in the occurrence of an event.

The first step is to determine what the experiment is: rolling a pair of dice, drawing two cards from a deck of playing cards, selecting numbered cards?

In a standard deck of cards, all the spades are black.

there's 4 suits in one deck of cards.

if j =11, queen =12, and king =13.

that means there will be 6 even numbers card in one suit, and times that by 4, which mean 24. theres 24 cards are even in a regular deck of cards.

Prob(Having a heart) = 1 - Prob(No hearts)

= 1 - (39/52)*(38/51)*(37/50)*(36/49)*(35/48)*(34/47)*(33/46)*(32/45)

= 1 - 2480637519360/30342338208000

= 1 - 0.0818 = 0.9182

Change request

1/6 or 1 in 6 :)

(4!)/(2!2!)(1/4)^4=(1/32)

no equal answer

There is a 1/6 chance of rolling a 4 on a fair die, and a 1/2 chance of a fair coin landing heads up. Multiply 1/6 X 1/2. The probability of both happening is 1/12.

A die has 6 sides with the numbers 1,2,3,4,5 and 6. Three of those numbers are even. So the probability of rolling an even number is 3 out of 6, or 1 out of 2.

Probability of hitting the bull's-eye on the next random shot after eliminating . . .

- no answer . . . . . 20%

- 1 answer . . . . . . 25%

- 2 answers . . . . . 331/3%

- 3 answers . . . . . 50%

- 4 answers . . . . . 100%

- 5 answers . . . . . zero

Probability of choosing the one correct answer increases significantly from

these figures if you actually know something about the subject.

Because you can't live with out them not being married to you.

50/50

The triangular, uniform, binomial, Poisson, geometric, exponential and Gaussian distributions are some that can be so defined. In fact, the Poisson and exponential need only the mean.

Since there are only odd & even numbers, the probability is 1/2 or 0.5.

If enough cards are drawn it is a certainty. In a single draw, the probability is 0.3846 if you assume that Q is neither odd nor even. If you take Q to be 12, and therefore, even, then the probability increases to 0.4615, approx.

They are used to test hypothesis such as the mean is some value where you do not know if otherwise the mean is less or more.

The area under the pdf between two values is the probability that the random variable lies between those two values.