4.2 이산분포4.2.1 베르누이 분포와 이항분포import numpy as npfrom scipy import stats as stfrom matplotlib import pyplot as pltst.binom.pmf(n=10,p=0.3,k=[0,1,2,3,4,5])# 예제 4.2.1 (b)# x값이 (1,3,6)일 경우 각 값까지의 누적확률 구하기np.round(st.binom.cdf(n=10,p=0.3,k=[1,3,6]),3)# 예제 4.2.1 (c)# 0.2, 0.5, 0.8의 누적확률을 갖는 확률변수값 구하기st.binom.ppf(n=10,p=0.3,q=[0.2,0.5,0.8])# 예제 4.2.1 (d)# 이항분포 B(10, 0.3)를 따르는 난수 5개 얻기st.binom.rvs(n=10,p=0..

import numpy as npfrom scipy import stats as stfrom matplotlib import pyplot as pltmu=0sigma=1x=np.linspace(mu-4*sigma,mu+4*sigma,100) # 표준정규분포 x값의 범위 -4 ~ +4y=st.norm.pdf(x,mu,sigma) # 정규분포에 의한 확률밀도함수의 값xx=np.linspace(mu-4*sigma,mu-1*sigma,100) # P(Z​mu=0sigma=1x=np.linspace(mu-4*sigma,mu+4*sigma,100) # 표준정규분포 x값의 범위 -4 ~ +4y=st.norm.pdf(x,mu,sigma) # 정규분포에 의한 확률밀도함수의 값xx=np.linspace(mu-1*sigma..

import pandas as pdimport numpy as npimport seaborn as snsfrom matplotlib import pyplot as pltactivities=['eat','sleep','work','play']slices=[3,7,8,6]colors=['r','y','g','b']plt.pie(slices,labels=activities, colors=colors, startangle=90, shadow=True, explode=(0,0,0.1,0), radius=1.2, autopct='%1.1f%%')plt.legend()plt.show()​color='cornflowerblue'points=np.ones(5)text_style=dict(horizo..