# Probability of getting head in coin toss

probability = 1 / 2
print(probability)

outcomes = ["Head", "Tail"]

print(len(outcomes))

Rolling dice = 6

Rolling dice = even number
(2,4,6)

event = [2,4,6]
print(event)

P(A) = Favorable Outcomes / Total Outcomes

favorable = 3
total = 6

p = favorable / total

print(p)

0.5

P(A|B)=P(A∩B)/P(B)

A_and_B = 2
B = 5

conditional_probability = A_and_B / B

print(conditional_probability)

P(A)=P(A|B)P(B)+P(A|C)P(C)

p_a_b = 0.5
p_b = 0.4

p_a_c = 0.2
p_c = 0.6

result = (p_a_b*p_b)+(p_a_c*p_c)

print(result)

P(A|B)=P(B|A)P(A)/P(B)

p_b_a = 0.9
p_a = 0.1
p_b = 0.2

bayes = (p_b_a*p_a)/p_b

print(bayes)

import random

x = random.randint(1,6)

print(x)

Mean = Sum / Count

data=[2,4,6]

mean=sum(data)/len(data)

print(mean)

import statistics

data=[1,3,5]

print(statistics.median(data))

import statistics

data=[1,2,2,3]

print(statistics.mode(data))

import statistics

data=[10,20,30]

variance=statistics.variance(data)

std=statistics.stdev(data)

print(variance)
print(std)

Success = 1
Failure = 0

from scipy.stats import bernoulli

x = bernoulli.rvs(0.5)

print(x)

P(X)=nCx p^x(1-p)^(n-x)

from scipy.stats import binom

result = binom.pmf(
    k=3,
    n=5,
    p=0.5
)

print(result)

from scipy.stats import uniform

x = uniform.rvs(
    loc=0,
    scale=10
)

print(x)

       *
     *   *
   *       *
 *           *
--------------

from scipy.stats import norm

x = norm.pdf(
    0,
    loc=0,
    scale=1
)

print(x)

P(X)= (λ^x e^-λ)/x!

from scipy.stats import poisson

result = poisson.pmf(
    k=3,
    mu=5
)

print(result)