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1、Chapter 3 the random variables and probability distributionlRandom variables and their classificationlProbability distributionlNormal distributionlBinomial distribution3.1 Random variables and their classificationlrandom variableRandom values of variables in a certain rangeRandom values of variables
2、 in a certain probability distributionlclassificationDiscrete Random variables:Just take a finite number of possible values(Integer)Example:the incidence number of individuals,litter sizeContinuous Random variables:take a unlimited possible values in a certain range(Real number)Example:Milk yielding
3、,body length,average daily gain3.2 probability distribution lprobability function随机变量取某一特定值的概率函数(离散型随机变量)随机变量取某一特定值的概率函数(离散型随机变量)lprobability density function)随机变量取某一特定值的密度函数(连续型随机变量)随机变量取某一特定值的密度函数(连续型随机变量)lprobability distribution function随机变量取值小于或等于某特定值的概率随机变量取值小于或等于某特定值的概率3.2.1 Discrete probabil
4、ity distribution of random variablelProbability functionX:Random variableRandom variable,x:The possible values of a random The possible values of a random variablevariablelProbability distribution functionlExample 2:the probability distribution of sum of points by throwing the dice twiceProbability
5、distribution of discrete random variableProbability distribution FigureProbability distribution of discrete random variablelThe expectation of random variable population meanFor example 1:Probability distribution of discrete random variablelThe expectations of the function of random variableSet Y is
6、 a function of random variable X,example:Then:For example 1:Probability distribution of discrete random variableFor example 1:Probability distribution of discrete random variable3.2.2 Probability distribution of continuous random variablelProbability density function满足以下条件的函数满足以下条件的函数f(x)称为连续性随机变量称为
7、连续性随机变量X的概率密度函数的概率密度函数:(x x是是X X的任一可能取值的任一可能取值)lnormal distribution具有如下概率密度函数的随机变量称为正态分具有如下概率密度函数的随机变量称为正态分布随机变量:布随机变量:=期望 2=方差(可以证明这个函数满足概率密度函数的(可以证明这个函数满足概率密度函数的3 3个条件)个条件)Probability distribution of continuous random variablenormal distributionl正态分布概率密度函数的几何表示正态分布概率密度函数的几何表示正态曲线正态曲线f(x)x曲线下某区间的面积
8、即为随机变量在该区间取值的概率曲线下某区间的面积即为随机变量在该区间取值的概率Influence of average Influence of standard deviationnormal distributionlstandard normal distribution令令Z服从正态分布服从正态分布标准正态分布标准正态分布对于对于标准化标准化normal distributionlprobability density function of standard normal distribution0normal distributionnormal distributionlprob
9、ability calculation of standard normal distribution table 1(p.297)normal distribution(2)P(Z -u)或或 P(Z u)查表查表normal distribution(3)P(a Z b)或或normal distributionexample:set Z N(0,1),seek (1)P(Z 0.64)(2)P(Z 1.53)(3)P(-2.12 Z -0.53)(4)P(-0.54 Z 0.84)Answer:(1)P=0.7389(2)P=0.06301(3)P=0.2811(4)P=0.5049no
10、rmal distribution68.3%95.5%99.7%normal distributionl对于给定的两尾概率对于给定的两尾概率 求标准正态分布在求标准正态分布在x轴上的分位点轴上的分位点Table 2(p.297)/2/2normal distributionlthe probability calculation of normal distribution transform to standard normal distributionexample:set X N(30,102),seek P(X 40)X N(,2)normal distributionP(-X +)=
11、68.26%P(-2 X +2 )=95.45%P(-3 X +3 )=99.73%P(-1.96 X +1.96 )=95%P(-2.58 X +2.58 )=99%The several special probability of normal distributionnormal distribution-3 -2 -+2 +3x68.3%95.5%99.7%normal distributionSkewness and kurtosislskewness 度量一个分布的对称性的指标度量一个分布的对称性的指标lkurtosis 度量一个分布的尖峭或平坦程度的指标度量一个分布的尖峭或平坦
12、程度的指标(总体)(总体)(样本)(样本)(总体)(总体)(样本)(样本)lbinomial distribution假设:假设:1.在相同条件下进行了在相同条件下进行了n次试验次试验 2.每次试验只有两种可能结果(每次试验只有两种可能结果(1或或0)3.结果为结果为1的概率为的概率为p,为,为0的概率为的概率为1-p 4.各次试验彼此间是独立的各次试验彼此间是独立的 在在n次试验中,结果为次试验中,结果为1的次数(的次数(X=0,1,2,n)服从二项分布,表示为)服从二项分布,表示为Probability distribution of discrete random variablelpr
13、obability function of binomial distributionlExpectation of binomial distributionlVariance of binomial distributionProbability distribution of discrete random variablel例例3:一头母猪一窝产了一头母猪一窝产了10头仔猪,分别求其中头仔猪,分别求其中有有2头公猪和头公猪和6头公猪的概率。头公猪的概率。产公猪头数的期望值:产公猪头数的期望值:产公猪头数的方差:产公猪头数的方差:Probability distribution of discrete random variablelPoisson Distributionn很大,p很小稀有事件Probability distribution of discrete random variableExample 3.8Example 3.9Probability distribution of discrete random variable