概率统计SAS应用.doc

直方图横向直方图把vbar改成hbar
dataex;inputx@@;cards;707294246857901859593;procgchart;vbarx/type=pct;run;
计算数字特征（均值Mean方差Variance标准差StdDeviation偏度Skewness峰度Kurtosis众数Mode中位数Median极差Range）
不需要输入频数dataex;inputx@@;cards;12233345678;procunivariatevardef=n;run;
（2）需要输入频数dataex;inputxf@@;cards;5.547.5119.51711.52313.51815.51417.51019.53;procunivariatevardef=n;varx;freqf;run;
计算标准正态分布的概率dataex;inputx@@;p=probnorm(x);cards;1.6451.962.576;procprint;run;
计算标准正态分布分位数dataex;inputp@@;u=probit(p);cards;0.0250.050.10.90.950.975;procprint;run;
计算标准正态分布双侧分位数dataex;inputx@@;p=1-x/2;u=probit(p);cards;0.10.050.01;procprint;run;
计算卡方分布分位数dataex;inputpdf@@;c=cinv(p,df);cards;0.02540.0540.140.940.9540.9754;procprint;run;
计算t分布分位数（在log窗口看数据）dataex;dodf=4;dop=0.025,0.05,0.1,0.9,0.95,0.975;t=tinv(p,df);putpdft;end;end;run;
计算F分布分位数dataex;inputpdf1df2@@;f=finv(p,df1,df2);cards;0.025340.05340.1340.9340.95340.97534;procprint;run;
一个正态总体均值的置信区间dataex;inputx@@;cards;6.64.65.45.85.5;procmeansmeanstdclm;procmeansmeanstdclmalpha=0.1;run;
两个正态总体均值差的置信区间dataex;doa=1to2;inputn@@;doi=1ton;inputx@@;output;end;end;cards;62.12.352.392.412.442.5642.032.282.582.71;procanova;classa;modelx=a;meansa/lsdcldiff;meansa/lsdcldiffalpha=0.1;run;
一个正态总体均值假设检验原假设是x的均值为15
dataex;inputx@@;y=x-15;cards;10.41213.213.714.615.115.515.9;procmeansmeanstdtprt;vary;run;
两个正态总体均值假设检验Pr>|t|对应原假设是两均值相等Pr>F对应原假设是两方差相等dataex;doa=1to2;doi=1to8;inputx@@;output;end;end;cards;8.68.75.69.38.49.37.57.987.95.89.17.78.27.46.6;procttest;classa;varx;run;两个配对样本均值假设检验dataex;inputx1x2@@;d=x1-x2;cards;114941171141551251149811912110295140104919513510611492;procmeanstprt;vard;procprint;run;
总体分布的卡方检验原假设是X总体服从某个指定的分布k的最后一个值比c小则接受原假设
dataex;inputnnp@@;k+(n-np)**2/np;c=cinv(0.95,6);cards;67.81613.231718.522619.441116.33911.4396.8666.26;procprint;varkc;run;
正态性检验原假设是总体X服从正态分布看Pr<W后面的数字dataex;inputx@@;cards;10.41213.113.813.814.615.115.515.9;procunivariatenormal;run;
独立性检验原假设是a和b两种分类相互独立看Chi-Square（卡方统计量）后面的数字dataex;doa=1to3;dob=1to3;inputf@@;output;end;end;cards;3