An intuitive introduction of Probability_Prof.Karl Schmedders #Week4,5

Discrete Random Variables

" A random variable assigns a single number a single numerical value to every basic outcome"

 

Probability Distribution --> P(X=x)

Notation Cumulative Distribution -> P(x<=z)

 

Graphical Representation of P(X=x)

 

Expected Value = Mean = E(x)

 

Meaures of Dispersion (Need of measuring Variation)

Mean Absolute Deviation (MAD)

 

Variance(분산) -> 편차 제곱의 평균 

We must see mean and variance together!

 

Binomial Distribution

성공과 실패 같은 2가지 결과 밖에 없는 분포

E(B) = np

Var(B) = np(1-p)

 

Continuous Random Variables

Probability Density Function (PDF)

 

Normal Distribution

Central Limit Theorom (중심극한정리)

Any normal distribution can be transformed into standardized normal distribution(표준화 가능하다)

-> 엑셀의 Norm.dist 함수 사용 가능

-> NORM.INV를 통해 역으로 특정 %의 확률분포가 나오는 X값 탐색가능