Symbol 
Symbol Name 
Meaning/Definition> 
Example 
1)P (A) 
Probability function. 
Probability of event A. 
P(A) = 0.5 
2) P(A∩ B) 
Probability of events intersection 
Probability that of events A and B 
P(A∩B) = 0.6 
3) P(A ∪ B) 
Probability of events union 
Probability that of events A or B 
P(A∪B) = 0.9 
4) P(A  B) 
Conditional probability function 
Probability of event A given event B occurred 
P(A  B) = 0.2 
5) f (x) 
Probability density function (pdf) 
P(a ≤ x ≤ b) = ∫ f (x) dx 

6) F(x) 
Cumulative distribution function (cdf) 
F(x) = P(X ≤ x) 

7) μ 
Population mean 
Mean of population values 
μ = 20 
8) E(X) 
Expectation value 
Expected value of random variable X 
E(X) = 20 
9) E(X  Y) 
Conditional expectation 
Expected value of random variable X given Y 
E(X  Y=2) = 5 
10) var(X) 
Variance 
Variance of random variable X 
var(X) = 4 
11) σ^{2}

Variance 
Variance of population values 
σ^{2} = 9

12) std(X) 
Standard deviation 
Standard deviation of random variable X 
std(X) = 2 
13) σ_{ x}

Standard deviation 
Standard deviation value of random variable X 
σ_{x} = 2

14)x^{˜}

Median 
Middle value of random variable x 
x^{˜}=6

15) cov(X,Y) 
Covariance 
Covariance of random variables X and Y 
cov(X,Y) = 6 
16) corr(X,Y) 
Correlation 
Correlation of random variables X and Y 
corr(X,Y) = 3 
17) ρ_{X,Y}

Correlation 
Correlation of random variables X and Y 
ρ_{X,Y} = 4

18) ∑ 
Summation 
Summation  sum of all values in range of series 
∑ X_{i} (i = 1 to 4) = x_{1}+x_{2}+x_{3}+x_{4}

19) Mo 
Mode 
Value that occurs most frequently in population 

20) MR 
Midrange 
MR = (x_{max}+x_{min})/2


21) Md 
Sample median 
Half the population is below this value 

22) Q_{1}

Lower / first quartile 
25% of population are below this value 

23) Q_{2}

Median / second quartile 
50% of population are below this value = median of samples 

24) Q_{3}

Upper / third quartile 
75% of population are below this value 

25) X^{}

Sample mean 
Average / arithmetic mean 
x^{} = (2+5+9) / 3 = 5.333

26) s^{2}

Sample variance 
Population samples variance estimator 
s^{2} = 9

27) s 
Sample standard deviation 
Population samples standard deviation estimator 
s = 4 
28) z_{x}

Standard score 
z_{x} = (xx) / s_{x}


29) X ~ 
Distribution of X 
Distribution of random variable X 
X ~ N(0,4) 
30) N(μ,σ^{2})

Normal distribution 
Gaussian distribution 
X ~ N(0,6) 
31) U(a,b) 
uniform distribution

equal probability in range a,b 
X ~ U(0,8) 