Mapa mental
Statistics

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Statistics
Poisson Distribution
- Mean & variance approximately equal
- Events occur : randomly, independently, uniformly over a given period of time
- P(X=r) = e^(-lambda) X (lambda^r / r factorial)
- Lambda is mean (or expected value)
- Sum of 2 or more poissons
- If X has mean lambda & Y has mean u
- X+Y has mean (lambda + u) provided they are independent
- If X has mean lambda & Y has mean u
- Binomial approximation
- If X~B(n,p) where n is large, p is small, then lambda=np
- X~Poisson(lambda)
- n>50
- p<0.1
- If X~B(n,p) where n is large, p is small, then lambda=np
Normal Distribution
- Normal Curve
- Symmetrical
- Bell-shaped
- z = x-u / o-
- Continuity Correction
- If P(X=n)
- P(n-0.5 < X < n+0.5
- If P(X>n)
- P(X > n+0.5)
- If P(X<<n)
- P(X < n+0.5)
- If P(X<n)
- P(X < n-0.5)
- If P(X>>n)
- P(X > n-0.5)
- Discrete data, poisson approximation, binomial approximation
- If P(X=n)
- Binomial Approximation
- X~B(n,p)
- n is large, p is not too small or too large
- Mean = np
- Variance = npq
- X~N(np,npq)
- Poisson Aionpproximation
- X~Poisson(lambda)
- Lambda is large enough for distribution to be relatively symmetrical (>10)
- X~N(lambda,lambda)
- Normal Curve
Sampling & Hypothesis Testing
- Hypothesis testing
- Define X
- Ho: u=a
- H1: u=/=a u>a u<a
- Standardising
- Parent population
- X~N(u,o^2)
- z = x-u / o
- X~N(u,o^2)
- Sample distribution
- X(bar) ~ N(u, o^2/n)
- z = x-u / o/root(n)
- X(bar) ~ N(u, o^2/n)
- Parent population
- Estimated standard deviation
- When o not known for parent population, use sample o if sample size >50
- Contingency tables
- If test statistic > C.V reject Ho
- Degrees of freedom
- (m-1)(n-1)
- chi^2 = sum of (O-E)^2 / E
- Expected value = row total X column total / sample size
- Look for big or small values of chi squared
- Ho : thing 1 & thing 2 are independent
- H1 : thing 1 & thing 2 are not independent
- Hypothesis testing
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- Descrição
- A-Level Maths Mapa Mental sobre Statistics, criado por Maria Derv em 30-04-2015.
- Criado por
- Maria Derv
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