### DISTRIBUTIONS

Type of Distributions

1. Uniform Distribution- date may be discrete or continous. Every outcome in the

experiment is equally likely.

Example: graph the distribution that shows what can happen when a 6-sided die is

trown.

2. A Normal Distributios- Data is continous ( height, weight, time, etc) when

certain experiments are carried out many, many, many times

the probability graph of the data tend to be "bell shaped" this is

known as the "Normal Curve"

3. A Binomial distribution- data is discrete (# of head when ten coins are tossed, #

of spades in a 13 card hand , etc) When a binomial experiment is conducted

many, many, many times a portion of the related histogram approches the

shape of the

normal curve.

The Standard Normal curve - the standard normal curve is used to make comparisons

between different normal distributions. This is doe using Z-scoreswhich

allows us to find a particular value , X , will lie on the standard normal

curve, This is what it meant by "standardizing" the scores for a

partuicular normal distribution.

Properties of a normal distributions

- Each value of mean and standard deviation determines a different normal distributions
- all normal distributions are symetrical about the mean
- 99.7% of all the data lies within the 3rd standard deviation on the mean
- the area under the curve always equals one
- the x-axis is an asymptote for the curve

The 68-95-99 rule

Generally speaking, approximately 68% of all the data in a normal distribution lie within the 1st standard deviation of the mean, 95% of all the data lie within 2nd standard deviation of the mean, and 99.7% of all the data lie within the 3rd standard deviation of the mean

Properties of a binomial Distributions

- (n) is the number of trials
- (p) is the probability of success
- (q) is the probability of failure
- every binomial distribution has exactly 2 outcomes
- u=pn and standard deviation is equal the square root of npq
- there is different binomial distribution for each value of p and n
- P(x) is the probability of x successes in every binomial distribution (zero is equal or less than P(x) and P(x) is less than or equal to one)
- the sum of all the probabilities, P(n), equals one
- For a sufficiently large number of trials, (n), any binomial distribution will approach the shape of a normal distribution

Sorry guys i dont have any visual images of the graphs because I try to make it but i can't upload and i dont have time now i feel so sleepy right now so here is all i can do but next time i will make it looks better ........The next scribe will be Abdul....

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