Probability and statistics symbols table and definitions.
| Symbol |
Symbol Name |
Meaning / definition |
Example |
| 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) |
P(a ≤ x ≤ b)
= ∫ f (x) dx |
|
|
F(x) |
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 |
| σ2 |
variance |
variance of population values |
σ2
= 4 |
| std(X) |
standard deviation |
standard deviation of random variable X |
std(X) = 2 |
| σX |
standard deviation |
standard deviation value of random
variable X |
σX
= 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 |
|
ρX,Y |
correlation |
correlation of random variables X and Y |
ρX,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 |
mid-range |
MR = (xmax+xmin)/2 |
|
| Md |
sample
median |
half the population is below this value |
|
| Q1 |
lower / first quartile |
25% of population are below this value |
|
| Q2 |
median / second quartile |
50% of population are below this value =
median of samples |
|
| Q3 |
upper / third quartile |
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 |
| s |
sample standard deviation |
population samples standard deviation estimator |
s = 2 |
| zx |
standard score |
zx = (x-x)
/ sx |
|
| X ~ |
distribution of X |
distribution of random variable X |
X ~ N(0,3) |
| N(μ,σ2) |
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 |
f (x) = λ c xc-1e-λx
/ Γ(c), x≥0 |
|
| χ 2(k) |
chi-square distribution |
f (x) = xk/2-1e-x/2
/ ( 2k/2 Γ(k/2) ) |
|
| F (k1,
k2) |
F distribution |
|
|
| Bin(n,p) |
binomial distribution |
f (k) = nCk
pk(1-p)n-k |
|
| Poisson(λ) |
Poisson distribution |
f (k) = λke-λ
/ k! |
|
| Geom(p) |
geometric distribution |
f (k) = p
(1-p) k |
|
| HG(N,K,n) |
hyper-geometric distribution |
|
|
| Bern(p) |
Bernoulli distribution |
|
|