Stats: statistic equations tests

z- score

z= (x- µ)/ σ

t-score

t= (x̄- µ)/ (s/ √n)

x̄= sample mean

µ= population mean

s= sample standard deviation

n= sample size

a sample size is generally considered small if

n < 30

N, to promote independence

n to be 10% or less

For t dist

n-1 and the bigger the n, the smaller the sampling variability

use t-dist

when you dont have POPULATION SD

table

1. df

2. across

3. header

x2 formula

E (obs-E)2/E

DF for chi square

k-1, categories -1 (not n)

chi square chart

always right skewed

t and z score chart

unimodal

t score, df

n-1, n= sample size

inverse norm (precentile)

area: (decimal on the left)

mu:

sd:

SD: when asking the probability of a total…

… multiply mu by n and multiply n by sd then square root

SD: when asking the regular standard deviation and mu when defined by an equation…

multiply mu by coefficient and add y-int, multiply sd by coefficient