Statistics: Difference between revisions
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To convert a normal distribution to a Z distribution | To convert a normal distribution to a Z distribution | ||
Z = (x - μ) / δ | Z = (x - μ) / δ | ||
Standard deviation is the root of the sum of the square errors: | |||
( Σ (i - μ)<sup>2</sup> )<sup>.5</sup> | |||
== Binomial Distribution == | == Binomial Distribution == | ||
Revision as of 17:42, 5 April 2013
- mean
- x_bar = 1/n Σ xi
- permutation
- nPk = n! / (n-k)!
- n: size of the set
- k: elements drawn
Normal Distribution
Z distribution is a normal distribution with a mean (μ) of 0 and a standard deviation (δ) of 1. Any normal distribution is described by its μ and δ value.
To convert a normal distribution to a Z distribution
Z = (x - μ) / δ
Standard deviation is the root of the sum of the square errors:
( Σ (i - μ)2 ).5
Binomial Distribution
Similar to normal distribution, but for values that can only be discrete.
Probability of an event happening k times given n trials, where p is the probability of n:
pk(1-p)n-k
Example: The probability of two heads in six coin flips:
.52(1-.5)6-2 = .25 * .54</sup = 0.015625