s 
Sample Standard Deviation 
population samples standard deviation estimator 
s = 2 
z_{x} 
Standard Score 
z_{x} = (xx)
/ s_{x} 

X ~ 
Distribution of X 
distribution of random variable X 
X ~ N(0,3) 
N(μ,σ^{2}) 
Normal Distribution 
gaussian distribution 
X ~ N(0,3) 
U(a,b) 
Uniform Distribution 
equal probability in range a,b 
X ~ U(0,3) 
exp(λ) 
Exponential Distribution 
f (x) = λe^{λx} , x≥0 

gamma(c, λ) 
Gamma Distribution 
Gamma Distribution 
f (x) = λ c x^{c1}e^{λx}/ Γ(c), x≥0 
χ^{ 2}(k) 
ChiSquare Distribution 
ChiSquare Distribution 
f (x) = x^{k}^{/21}e^{x/2}/ ( 2^{k/2 }Γ(k/2) ) 
F (k_{1}, k_{2}) 
F Distribution 
F Distribution 

Bin(n,p) 
Binomial Distribution 
Binomial Distribution 
f (k) = _{n}C_{k}p^{k}(1p)^{nk} 
Poisson(λ) 
Poisson distribution 
Poisson distribution 
f (k) = λ^{k}e^{λ}/ k! 
Geom(p) 
Geometric Distribution 
Geometric Distribution 
f (k) = p^{}(1p)^{ k} 
HG(N,K,n) 
HyperGeometric Distribution 
HyperGeometric Distribution 

Bern(p) 
Bernoulli Distribution 
Bernoulli Distribution 

P(A) 
Probability Function 
probability of event A 
P(A) = 0.5 
P(A∩ B) 
Probability of Events Intersection 
probability that of events A and B 
P(A∩B) = 0.5 
P(A∪ B) 
Probability of Events Union 
probability that of events A or B 
P(A∪B) = 0.5 
P(A B) 
Conditional Probability Function 
probability of event A given event B occured 
P(A  B) = 0.3 
f (x) 
Probability Density Function (PDF) 
Probability Density Function (PDF) 
P(a ≤ x ≤ b)= ∫ f (x) dx 
F(x) 
Cumulative Distribution Function (CDF) 
Cumulative Distribution Function (CDF) 
F(x) = P(X≤ x) 
μ 
Population Mean 
mean of population values 
μ= 10 
E(X) 
Expectation Value 
expected value of random variable X 
E(X) = 10 
E(X Y) 
Conditional Expectation 
expected value of random variable X given Y 
E(X  Y=2) =5 
var(X) 
Variance 
variance of random variable X 
var(X) = 4 
σ^{2} 
Variance 
variance of population values 
σ^{2 }= 4 
std(X) 
Standard Deviation 
standard deviation of random variable X 
std(X) = 2 
σ_{X} 
Standard Deviation 
standard deviation value of random variable X 
σ_{X}_{ }= 2 

Median 
middle value of random variable x 

cov(X,Y) 
Covariance 
covariance of random variables X and Y 
cov(X,Y) = 4 
corr(X,Y) 
Correlation 
correlation of random variables X and Y 
corr(X,Y) = 0.6 
ρ_{X,Y} 
Correlation 
correlation of random variables X and Y 
ρ_{X,Y}= 0.6 
∑ 
Summation 
summation  sum of all values in range of series 

∑∑ 
Double Summation 
double summation 

Mo 
Mode 
value that occurs most frequently in population 

MR 
MidRange 
MidRange 
MR = (x_{max}+x_{min})/2 
Md 
Sample Median 
half the population is below this value 

Q_{1} 
Lower / First Quartile 
25% of population are below this value 

Q_{2} 
Median / Second Quartile 
50% of population are below this value = median of samples 

Q_{3} 
Upper / Third Guartile 
75% of population are below this value 

x 
Sample Mean 
average / arithmetic mean 
x =(2+5+9) / 3 = 5.333 
s_{ }^{2} 
Sample Variance 
population samples variance estimator 
s^{ }^{2} = 4 