List Of Probability And Statistics Symbols In Mathematics - Byju's

Probability is the chance that something will happen – how likely it is that some event will happen. Statistics is the study of data: how to collect, summarize and present it. Probability and statistics are separate but two related academic disciplines. Statistical analysis often uses probability distributions, and the two topics are often studied together.

List of Probability and Statistics Symbols

You can explore Probability and Statistics Symbols, name meanings and examples below-

Symbol	Symbol Name	Meaning / definition	Example
P(A ∩ B)	probability of events intersection	probability that of events A and B	P(A∩B) = 0.5
P(A)	probability function	probability of event A	P(A) = 0.5
P(A | B)	conditional probability function	probability of event A given event B occurred	P(A | B) = 0.3
P(A ∪ B)	probability of events union	probability that of events A or B	P(A∪B) = 0.5
F(x)	cumulative distribution function (cdf)	F(x) = P(X ≤ x)
f (x)	probability density function (pdf)	P(a ≤ x ≤ b) = ∫ f (x) dx
E(X)	expectation value	expected value of random variable X	E(X) = 10
μ	population mean	mean of population values	μ = 10
var(X)	variance	variance of random variable X	var(X) = 4
E(X | Y)	conditional expectation	expected value of random variable X given Y	E(X | Y=2) = 5
std(X)	standard deviation	standard deviation of random variable X	std(X) = 2
σ2	variance	variance of population values	σ2 = 4
\(\begin{array}{l}\widetilde{x}\end{array} \)	median	middle value of random variable x	\(\begin{array}{l}\widetilde{x}= 5\end{array} \)
σX	standard deviation	standard deviation value of random variable X	σX  = 2
corr(X,Y)	correlation	correlation of random variables X and Y	corr(X,Y) = 0.6
cov(X,Y)	covariance	covariance of random variables X and Y	cov(X,Y) = 4
ρX,Y	correlation	correlation of random variables X and Y	ρX,Y = 0.6
Mo	mode	value that occurs most frequently in population
Md	sample median	half the population is below this value
MR	mid-range	MR = (xmax+xmin)/2
Q2	median / second quartile	50% of population are below this value = median of samples
Q1	lower / first quartile	25% of population are below this value
x	sample mean	average / arithmetic mean	x = (2+5+9) / 3 = 5.333
Q3	upper / third quartile	75% of population are below this value
s	sample standard deviation	population samples standard deviation estimator	s = 2
s 2	sample variance	population samples variance estimator	s 2 = 4
X ~	distribution of X	distribution of random variable X	X ~ N(0,3)
zx	standard score	zx = (x–x) / sx
U(a,b)	uniform distribution	equal probability in range a,b	X ~ U(0,3)
N(μ,σ2)	normal distribution	gaussian distribution	X ~ N(0,3)
gamma(c, λ)	gamma distribution	f (x) = λ c xc-1e-λx / Γ(c), x≥0
exp(λ)	exponential distribution	f (x) = λe–λx , x≥0
F (k1, k2)	F distribution
Bin(n,p)	binomial distribution	f (k) = nCk pk(1-p)n-k
χ 2(k)	chi-square distribution	f (x) = xk/2-1e–x/2 / ( 2k/2 Γ(k/2) )
Geom(p)	geometric distribution	f (k) =  p (1-p) k
Poisson(λ)	Poisson distribution	f (k) = λke–λ / k!
Bern(p)	Bernoulli distribution
HG(N,K,n)	hypergeometric distribution

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