Statistics: Difference between revisions
Jump to navigation
Jump to search
Adamcolton (talk | contribs) |
Adamcolton (talk | contribs) |
||
| Line 20: | Line 20: | ||
Probability of an event happening k times given n trials, where p is the probability of n: | Probability of an event happening k times given n trials, where p is the probability of n: | ||
p<sup>k</sup>(1-p)<sup>n-k</sup> | nCk * p<sup>k</sup>(1-p)<sup>n-k</sup> | ||
nCk = n! / ( k! * (n-k)! ) | |||
Example: The probability of two heads in six coin flips: | Example: The probability of two heads in six coin flips: | ||
Revision as of 19:22, 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:
δ = ( 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:
nCk * pk(1-p)n-k nCk = n! / ( k! * (n-k)! )
Example: The probability of two heads in six coin flips:
.52(1-.5)6-2 = .25 * .54 = 0.015625
Hypothesis Testing
Null hypothesis h0. Alternate hypothesis HA or H1
Single Tail Test
H0 : μ1 = μ2 HA : μ1 > μ2
Two Tailed Test
H0 : μ1 = μ2 HA : μ1 ? μ2
Familywise Error Rate
The probability of make one or more false discoveries (Type I errors) among all hypothesis when performing multiple hypothesis tests.