Probability

Independant

The mean and standard deviation do not, by themselves, provide enough information to calculate probability. You also need to know the distribution of the variable in question.

Experimental probability is the number of times some particular outcome occurred divided by the number of trials conducted. For instance, if you threw a coin ten times and got heads seven times, you could say that the experimental probability of heads was 0.7.

Contrast this with theoretical probability, which is the (infinitely) long term probability that something will happen a certain way. The theoretical probability of throwing heads on a fair coin, for instance, is 0.5, but the experimental probability will only come close to that if you conduct a large number of trials.

1/4 (25%)

The area under the normal distribution curve represents the probability of an event occurring that is normally distributed. So, the area under the entire normal distribution curve must be 1 (equal to 100%).

For example, if the mean (average) male height is 5'10" then there is a 50% chance that a randomly selected male will have a height that is below or exactly 5'10". This is because the area under the normal curve from the left hand side up to the mean consists of half of the entire area of the normal curve.

This leads us to the definitions of z-scores and standard deviations to represent how far along the normal curve a particular value is. We can calculate the likelihood of the value by finding the area under the normal curve to that point, usually by using a z-score cdf (cumulative density function) utility of a calculator or statistics software.

1/52 or one out of fifty-two

1/16

24

The key feature is that each sample of the given size has the same probability of being selected as the sample. Equivalently, each unit in the population has the same probability of being included in the sample.

3/6 chance of an odd number with one roll of the dice

and

3/6 for the second roll of a dice

so

3/6 x 3/6 = 9/36 = 3/12 = 1/4

there is a one in four chance that two odd numbers will be rolled from two roles of a dice.

There are 52 cards in a pack. There are 2 Red kings (Heart and Diamond).

This means 2/52 or 1/26.

1/26

It is 0.017864

i got 1/941192

The probability of flipping a fair coin four times and getting four heads is 1 in 16, or 0.0625. That is simply the probability of one head (0.5) raised to the power of 4.

The number of possible outcomes when rolling two dice can be expressed through simple fractional multiplaction- There are 36 different options.

4 in 52, or 1 in 13, or about 0.0769.

6! = 6x5x4x3x2x1 = 720 arrangements

1.16/z = -1.16/z

* * * * *

No!

Pr(Z > 1.16) = 0.123

So Pr(-1.16 < Z < 1.16) = 1 - 2*0.123 = 1 - 0.246 = 0.754

Because you are thinking permutations rather than combinations. There are four permutations of two coins, but there are only three combinations, because it does not matter which coin is heads and which coin is tails. As a result, the combination of heads and tails has a 0.5 probability, while two heads or two tails each have a 0.25 probability.

An Event official in Physical Education is a person who over sees everything.

The calculation is equal to the sum of their probabilities less the probability of both events occuring.

If two events are mutually exclusive then the combined probability that one or the other will occur is simply the sum of their respective probabilities, because the chance of both occurring is by definition zero.

The probability is 5/9.

2.5 times 2.0 = 5