SAS课本上的例题程序

研究生ＳＡＳ应用讲义肖枝洪2/4/20231

StatisticalAnalysisSystem

简称为SAS,可用来分析数据和编写报告.它是美国SAS研究所的产品,在国际上被誉为标准软件,在我国深受医学、农林、财经、社会科学、行政管理等众多领域的专业工作者的好评。有关ＳＡＳ的最新信息，可以查看http://。

ＳＡＳ采用积木式模块结构，其中的ＳＡＳ/ＳＴＡＴ模块是目前功能最强的多元统计分析程序集,可以做回归分析、聚类分析、判别分析、主成分分析、因子分析、典型相关分析(下学期介绍)以及各种试验设计的方差分析和协方差分析。本讲义围绕SAS的应用,讲述以下六部分内容：

（１）ＳＡＳ应用基础；

（２）ＳＡＳ常用语句；

（３）ＳＡＳ服务过程；

（４）描述性统计程式；

（５）方差分析程式；

（６）回归分析程式；2/4/20232ＳＡＳ的显示管理系统启动计算机,点击SAS图标后,即可进入SAS的显示管理系统.在View中有四个主要的窗口(其他的先不考虑)：

(1)编辑窗口(programeditor):编辑程式和数据文件;(2)日志窗口(log):记录运行情况,显示error信息;(3)输出窗口(output):输出运行的结果;(4)图形窗口(graph):输出图形.点击View菜单中的Programeditor、Log、Output、Graph命令可以进入编辑、日志、输出及图形窗口.按功能键F5、F6、F7也可以进入编辑、日志及输出窗口.退出SAS有两种方法：

(1)点击File菜单中的Exit命令；

(2)点击窗口右上角的×。2/4/20233概率统计及SAS应用教材中的程序应用SAS计算二项分布的概率，请注意SAS中

probbnml(p,n,k)=P(Xk)=因此，当n=5，k=3，p=0.2时，应用SAS直接计算P{X=3}的程序为：dataprobnml;p=probbnml(0.2,5,3)-probbnml(0.2,5,2);procprint;run;输出的结果为：0.0512。2/4/20234当n=5,k=4,p=0.8时,应用SAS直接计算P(X=4)+P(x=5)的程序为：

dataex;p=1-probbnml(0.8,5,3);procprint;run;输出的结果为：0.73728。应用SAS直接计算例1.3.1中所求概率的P{8≤X≤12}的程序为：

dataex;p=probbnml(0.5,20,12)-probbnml(0.5,20,7);procprint;run;输出的结果为：0.7368240356。2/4/20235应用SAS中的probnorm(x)近似计算二项分布的概率时,请注意

probnorm(x)=

因此，应用SAS近似计算P{8≤X≤12}的程序为：

dataex;p=probnorm(1.12)-probnorm(-1.12);procprint;run;输出的结果为：0.73729.其中

1.12=(12+0.5-10)/sqrt(5)-1.12=(8-0.5-10)/sqrt(5)2/4/202362.

在SAS中有probnorm(x)函数,用此函数可以求

P{X≤x}.当x=1.645，1.96，2.576时,不查标准正态分布的分布函数的函数值表，应用SAS直接计算P{X≤x}的程序为dataex;dox=1.645,1.96,2.576;(给x依次赋值，增加赋值后可全部列出的函数值表)p=probnorm(x);putxp;(计算并输出x对应的概率)end;run;输出的结果如下(在Log窗口中显示)：

1.6450.95001509451.960.97500210492.5760.99500246772/4/20237

用下列程序更好:dataex;inputx@@;p=probnorm(x);list;cards;1.6451.962.576;procprint;run;输出的结果如下(在Log窗口中显示)：

1.6450.95001509451.960.97500210492.5760.99500246772/4/20238以下是用SAS程序绘制的二维正态分布分布密度函数的示意图。所用的SAS程序为：dataex;dox=-3to3by0.25;doy=-3to3by0.25;p=exp(-((x*x+y*y)*5/4+x*y*3/2)/2)/2/3.1416;output;end;end;procg3d;ploty*x=p;run;2/4/202392/4/2023103．应用SAS计算标准正态分布的分位数在SAS中有probit(p)函数，用此函数可以求p分位数.SAS程序为dataex;dop=0.025,0.05,0.1,0.9,0.95,0.975;u=probit(p);putup@;end;run;输出的结果如下：

-1.9599639850.025-1.6448536270.05-1.2815515660.11.28155156550.91.6448536270.951.95996398450.9752/4/202311

用下列程序更好:dataex;inputp@@;u=probit(p);list;cards;0.0250.050.975;procprint;run;输出的结果如下：

-1.9599639850.025-1.6448536270.05-1.2815515660.11.28155156550.91.6448536270.951.95996398450.9752/4/202312当α=0.10，0.05，0.01时，应用SAS计算双侧分位数的程序为：dataex;dox=0.1,0.05,0.01;p=1-x/2;u=probit(p);putxpu;end;run;输出的结果如下：

0.10.951.6448536270.050.9751.95996398450.010.9952.57582930352/4/2023134．应用SAS计算卡方分布的分位数在SAS中有cinv(p,df)函数，用此函数可以求p分位数.SAS程序为dataex;dodf=4;dop=0.025,0.05,0.1,0.9,0.95,0.975;c=cinv(p,df);putpdfc;end;end;run;输出的结果如下：

0.02540.48441855710.0540.71072302140.141.06362321680.947.77944033970.9549.48772903680.975411.1432867822/4/202314用下列程序更好:dataex;inputpdf@@;c=cinv(p,df);list;cards;0.02540.0540.140.940.9540.9754;procprint;run;输出的结果如下：

0.02540.48441855710.0540.71072302140.141.06362321680.947.77944033970.9549.48772903680.975411.1432867822/4/2023155．应用SAS计算t分布的分位数在SAS中有tinv(p,df)函数，用此函数可以求p分位数.SAS程序为dataex;dodf=4;dop=0.025,0.05,0.1,0.9,0.95,0.975;t=tinv(p,df);putpdft;end;end;run;输出的结果如下：

0.0254-2.7764451050.054-2.1318467860.14-1.5332062740.941.53320627410.954297542.77644510522/4/2023166．应用SAS计算F分布的分位数在SAS中有finv(p,df1,df2)函数,用此函数可以求p分位数.SAS程序为dataex;dop=0.025,0.05,0.1,0.9,0.95,0.975;dodf1=3;df2=4;f=finv(p,df1,df2);putpdf1df2f;end;end;run;输出的结果如下：

0.025340.06622087250.05340.10968301080.13409344.19086043890.95346.59138211640.975349.97919853222/4/202317还可以用下列程序更好:dataex;inputpdf1df2@@;

f=finv(p,df1,df2);list;cards;0.025340.05340.1340.9340.95340.97534;procprint;run;2/4/202318dataprobdist;inputabc@@;probbnml01=probbnml(a,b,c);probchi01=probchi(c,b);probf01=probf(a,b,c);probit01=probit(a);probnorm01=probnorm(a);probt01=probt(a,b);list;cards;0.1

4

3

0.3

5

4

0.4

6

5

0.6

6

4

0.9

8

3;procprint;run;2/4/202319一般计算data

xzh;a=12+13;b=13-12*2;c=sqrt(19**3);d=18**(1/3);e=log10(1000);g=sin(3);/*f=arcsin(1)lack*/x=12.4221/84.7599;cv=0.20077/2.55;proc

print;

2/4/202320矩阵计算dataxzhmatrix;prociml;x={12345,24789,37101520,48153020,59202040};

g=inv(x);x2=x*x;e=eigval(x);d=eigvec(x);f=trace(x);h=det(x);J=t(x);printxx2;printdgehf;printJ;run;2/4/202321应用SAS画频率和累计频率直方图datahist01;inputx@@;cards;4546485151576264;proc

gchart;vbarx/type=pctspace=0;run;2/4/202322datahist01;inputx@@;cards;70729424685790959310964587940118847099132154100773468264887859512310510755457310958101134949462156618477123135401077913172663044141981009078445058607678921016215297815498751181309011513610080699884251799776567343826068160139;proc

gchart;vbar

x/type=cpctspace=0;run;2/4/2023232/4/202324应用SAS做样本观测值的描述性统计分析dataex;inputx@@;cards;4546485151576264;procunivariate;run;输出的结果如下：7.211103=sqrt(364/7)Variable=XMomentsN8SumWgts8Mean53Sum424StdDev7.211103Variance52Skewness0.572987Kurtosis-1.2721USS22836CSS364CV13.60585StdMean2.549512/4/202325Quantiles(Def=5)分位数100%Max6499%6475%Q359.595%6450%Med5190%6425%Q14710%450%Min455%45Q3-Q112.51%45Range19Mode512/4/202326

应用SAS作例2.1.2中样本观测值经过整理后的描述性统计的程序为：dataex;inputxf@@;cards;25650207529100261251115061752;procunivariate;var

x;freq

f;run;2/4/202327应用SAS作例2.1.3中样本观测值的描述性统计的程序:dataxzh;inputxy@@;cards;

1.581809.98289.4225

1.251170.31652.41175

11.01401.851606.041205.9280;proccorr

cov

vaardf=n;run;2/4/202328输出的结果如下：CovarianceMatrixDF=10XYX14.685864-207.220000Y-207.2200003453.800000PearsonCorrelationCoefficients

/Prob>|R|underHo:Rho=0/N=10XYX1.00000-0.920100.00.0002Y-0.920101.000000.00020.02/4/2023292.3.8应用SAS求置信区间(1)求一个正态总体均值的置信区间SAS程序为dataex;inputx@@;cards;5.85.5;procmeansmeanstdclm;procmeansmeanstdclmalpha=0.1;run;输出的结果如下：MeanStdDevLower95.0%CLMUpper95.0%CLM5.58000.72249574.68290316.4770969MeanStdDevLower90.0%CLMUpper90.0%CLM5.58000.72249574.89117926.26882082/4/202330(2)求两个正态总体均值差的置信区间SAS程序为：dataex;doa=1to2;inputn@@;doi=1ton;inputx@@;output;end;end;cards;62.12.352.392.412.442.5642.032.282.582.71;procanova;class

a;modelx=a;meansa/lsd

cldiff;meansa/lsd

cldiffalpha=0.1;run;2/4/202331输出的结果如下：Alpha=0.05Confidence=0.95df=8MSE=0.049494CriticalValueofT=2.30600LowerDifferenceUpperConfidenceBetweenConfidenceLimitMeansLimit-0.35615-0.025000.30615Alpha=0.1Confidence=0.9df=8MSE=0.049494CriticalValueofT=1.85955LowerDifferenceUpperConfidenceBetweenConfidenceLimitMeansLimit-0.29204-0.025000.242042/4/202332

应用SAS作总体分布参数的假设检验

(1)一个正态总体均值作假设检验的SAS程序

dataex;inputx@@;y=x-1277;cards;12501265124512601275;procmeansmeanstdtprt;var

y;run;程序运行的结果为：AnalysisVariable:Y

MeanStdDevTProb>|T|-18.200000011.9373364-3.37170890.0280结果中的Prob>|T|为服从t分布的随机变量X的绝对值>|T|的概率,即P{|X|>|T|}.2/4/202333

(2)两个正态总体均值作假设检验的SAS程序

dataxzh;doa=1to2;doi=1to5;inputx@@;output;end;end;cards;800840870920850900880890890840;procttest

cochran;class

a;varx;procprint;run;程序运行的结果为：TTESTPROCEDUREVariable:XANMeanStdDevStdError15856.00000043.9317652719.6468827025880.00000023.4520788010.488088482/4/202334

VariancesTMethod

DFProb>|T|Unequal-1.0770Satterthwaite

6.10.3220

Cochran4.00.3419Equal-1.07768.00.3126ForH0:Variancesareequal,F'=3.51DF=(4,4)Prob>F'=0.2515结果中的Variances对应两个选项：如果认为方差相等，则DF=8，Prob>|T|为0.3126；如果认为方差不相等，则根据Satterthwaite检验法或Cochran和Cox检验法作近似的t检验.两种检验法的统计量都是2/4/202335Satterthwaite检验法的结果是DF=6.1，

Prob>|T|为0.3220；其中DF的公式:Cochran和Cox检验法DF=4.0，Prob>|T|为0.3419；其临界值2/4/202336(3)配对样本均值作假设检验的SAS程序dataxzh;inputx1x2@@;d=x1-x2;cards;114941171141551251149811912110295140104919513510611492;procmeanstprt;var

d;proc

print;run;程序运行的结果为：AnalysisVariable:DTProb>|T|3.52032100.00652/4/202337

应用SAS作正态性检验SAS程序为dataex;inputx@@;cards;711666795106310;procunivariate

normal;run;程序运行的结果为Skewness0.157068Kurtosis-0.58894W:Normal0.932615Pr<W0.3827W检验的临界值w0.05=0.859，

P{W<w0.05=0.859}=0.05

SAS结果表明P{W<0.932615}=0.3827>0.05，因此接受H。.2/4/202338

应用SAS作单因素试验方差分析

(1)不等重复的情形：

dataex;doa=1to3;inputn@@;doi=1ton;inputx@@;Output;end;end;cards;8

212924222530272610202525232931242620216242228252126;procanova;class

a;modelx=a;run;2/4/202339DependentVariable:xSumofSourceDFSquaresMeanSquareFValuePr>FModel26.76666673.38333330.320.7314Error21223.733333310.6539683CorrectedTotal23230.5000000如果要作多重比较并求均值差的置信区间，则增加meansa/lsd

cldiff;run;2/4/202340(2)等重复的情形：

dataex;doa=1to3;doi=1to4;inputx@@;output;end;end;cards;212427202018191522252722;procanova;class

a;modelx=a;run;2/4/202341

DependentVariable:xSumofSourceDFSquaresMeanSquareFValuePr>FModel282.666666741.33333336.000.0221Error962.00000006.8888889CorrectedTotal11144.6666667如果要作多重比较并求均值差的置信区间，则增加meansa/lsd

cldiff;run;2/4/202342应用SAS作Levene

的F检验SAS程序为：dataex;doa=1to4;doi=1to4;inputx@@;output;end;end;cards;19232113212427202018191522252722;procanova;class

a;modelx=a;meansa/hovtest;run;2/4/202343输出的结果为：Levene'sTestforEqualityofXVarianceANOVAofSquaredDeviationsfromGroupMeansSumofMeanSourceDFSquaresSquareFValuePr>FA3268.889.58331.08040.3944Error12995.082.91672/4/202344无重复试验的双因素方差分析dataanova01;doa=1to4;dob=1to5;inputx@@;output;end;end;cards;5356455249475047475357635457584552424148;procanova;classab;modelx=ab;meansab/duncanalpha=0.01;run;2/4/202345重复试验的双因素方差分析dataex;doa=1to4;dob=1to3;doi=1to2;inputx@@;output;end;end;end;cards;58.252.656.241.265.360.849.142.854.150.551.648.460.158.370.975.871.558.25148.741.4;procanova;classab;modelx=aba*b;meansab/duncan;run;2/4/202346二级系统分组试验方差分析的SAS程序：dataex；doa=1to3；dob=1to3;doi=1to5；inputx@@；

output；end；end；end;cards；

0.91.01.21.00.91.0

；

procanova；classab；modelx=ab(a)；

meansab(a)/duncan；run；2/4/202347

应用SAS作一元线性回归分析dataex;inputxy@@;cards;5.72.4738.33.510.93.912.44.413.14.813.6515.32.;procgplot;ploty*x;/*以y为纵坐标，以x为横坐标*/symboli=rlv=dot;/*i=rl表示画回归直线*//*v=dot表示观测值对应的点标记为小圆点*/procreg;modely=x/cli;run;/*y=x表示以y为因变量，以x为自变量，*//*cli表示要求预测值的95%置信区间*/2/4/2023482/4/202349输出的结果如下:DependentVariable:YAnalysisofVarianceSumofMeanSourceDFSquaresSquare

FValueProb>FModel1112.48368

112.48368

387.5160.0001Error72.031880.29027CTotal8114.51562/4/202350ParameterEstimatesParameterStandardTforH0:VariableDFEstimateErrorParameter=0Prob>|T|INTERCEP10.2569470.532352630.4830.6441X12.9302800.1488552419.6850.0001Dep

VarPredictStdErrLower95%Upper95%ObsYValuePredictPredict

PredictResidual14.80004.65240.3313.15746.14740.147625.70005.53150.2944.07976.98320.168537.00007.28960.2305.90438.6749-0.289648.30009.04780.1887.698710.3969-0.7478510.900010.51290.1819.169211.85660.3871612.400011.68500.19610.329113.04100.7150713.100013.15020.23611.758914.5415-0.0502813.600014.32230.27912.887815.7568-0.7223915.300014.90830.30213.447616.36910.391710.6.11750.2714.69117.5440.2/4/202351

应用SAS作一元非线性回归

(1)线性化后作线性回归的SAS程序为

dataxzh;inputxy@@;x1=1/x;lx=log(x);ly=log(y);Cards;11.8521.3731.0240.7540.5660.4160.3180.2380.17;Procreg;modely=x1;Procreg;modelly=lx;Procreg;modelly=x;Run;2/4/202352(2)计算剩余平方和的SAS程序为

dataxzh01;inputxy@@;x1=1/x;lx=log(x);ly=log(y);

y1=0.1159+1.9291*x1;q1+(y-y1)**2;

y2=exp(0.9638-1.1292*lx);q2+(y-y2)**2;y3=exp(0.9230-0.3221*x);q3+(y-y3)**2;Cards;11.8521.3731.0240.7540.5660.4160.3180.2380.17;procprint;sum;varq1-q3;run;

2/4/202353

TheREGProcedureModel:MODEL1DependentVariable:y

AnalysisofVariance

SumofMeanSourceDFSquaresSquareFValuePr>FModel12.33605

2.3360557.860.0001Error70.282640.04038CTotal82.61869

2/4/202354

ParameterEstimates

ParameterStandardVariableDFEstimateErrortValuePr>|t|

Intercept10.115930.106031.090.3104x111.929150.253627.610.00012/4/202355应用SAS作协方差分析(一)SAS程序为：dataex;doa=1to3;doi=1to8;inputxy@@;output;end;end;cards;475458665363465149565666546144505254535364675862596261636364666944524858465450615970576458695366;procanova;class

a;modelx=a;procanova;class

a;modely=a;procglm;class

a;modely=xa/solution;lsmeans

a;run;2/4/202356输出的结果为：DependentVariable:XSourceDFSumofSquaresMeanSquareFValuePr>FModel2356.08333333178.041666676.340.0070Error21589.7500000028.08333333CTotal23945.83333333DependentVariable:YSourceDF

SumofMean

SquaresSquareFValuePr>FModel260.7500030.375000.770.4767Error21830.875039.5655CTotal23891.62502/4/202357DependentVariable:YSourceDF

SumofMean

SquaresSquareFValuePr>FModel3842.79280.93115.060.0001X1782.045782.045320.310.0001A2222.84111.4245.640.0001Error2048.832.44CTotal23891.625

AYLSMEAN162.0695475255.5124523364.29300022/4/202358应用SAS作协方差分析(二)⑴双因素试验不考虑交互作用的情形：SAS程序为dataex;doa=1to3;dob=1to5;inputxy@@;output;end;end;cards;82.85104.24123114.94102.88103.14124.572.75125.84104.06123.88103.8692.82104.9492.89;procglm;classab;modely=xab/solution;lsmeansab;run;2/4/202359

SourceDFTypeIIISSMeanSquareFValuePr>Fx10.7810.7816.430.0389a20.6050.3022.490.1526b47.121.78114.660.0016

Error70.8500.1215StandardParameterEstimateErrortValuePr>|t|Intercept1.5250.6952.190.0643x0.1740.06852.540.03892/4/202360⑵双因素试验考虑交互作用的情形

SAS程序为

dataex;doa=1to4;dob=1to2;doi=1to2;inputxy@@;output;end;end;end;cards;14.697.8113.218.8110.113.6100.312.998.518.5119.418.2114.712.899.210.789.618.2112.216.9105.312102.112.4103.816.4117.217.2117.9;procglm;classab;modely=xaba*b/solution;lsmeansab;run;2/4/202361SourceDFTypeIIISSMean

SquareFValuePr>Fx168.7268.7217.950.0039a3241.5880.52821.030.0007b10.2330.2330.060.8124a*b317.0925.6971.490.2986

StandardParameterEstimateErrortValuePr>|t|Intercept65.31912.4065.270.0012x3.1090.7344.240.00392/4/202362⑹协方差分析的结论：因素A的效应及套在A中的B(A)

矫正后有极显著的差异.二级系统分组试验的情形：SAS程序为dataex;doa=1to7;dob=1to3;doi=1to3;inputxy@@;output;end;end;end;cards;15.610516.410415.696…输入例中的数据…14.41431413012.8118;procglm;classab;modely=xab(a)/solution;lsmeansab(a);run;2/4/202363dataex;doa=1to7;dob=1to3;doi=1to3;inputxy@@;output;end;end;end;cards;15.610516.410415.69613.610915.610414.810712.06912.08512.85716.015216.014915.611615.613915.610716.813514.414915.615614.814314.89315.610614.89117.610618.88718.08814.411715.210215.612018.411820.014017.611117.615715.210516.411918.815718.016417.213522.013720.013819.214417.212715.66015.610817.613217.615016.010914.416913.214314.815814.414514.815313.613613.615413.615414.013116.412017.212115.210714.411812.87314.08714.414314.013012.8118;procanova;classab;modely=ab(a);meansab(a)/lsd;procglm;classab;modely=xab(a)/solution;lsmeansab(a);run;2/4/202364SourceDFTypeIIISSMean

SquareFValuePr>Fx12858.7072858.70715.440.0003a624066.384011.06421.67<.0001

b(a)149831.408702.2433.790.0004Error417590.63185.137

StandardParameterEstimateErrortValuePr>|t|Intercept-7.51635.949-0.210.8354x10.0376

2.5543.930.00032/4/202365

应用SAS作拟合优度检验SAS程序为

dataxzh;inputnnp@@;k+(n-np)**2/np;c=cinv(0.99,5);cards;32.9261411.232229.85250.085952.863134.841514.3444.34

;proc

print;varkc;

run;dataxzh1;x=(3-2.926)**2/2.926+(14-11.23)**2/11.23+(22-29.8)**2/29.8+

(52-50.08)**2/50.08+(59-52.86)**2/52.86

+(31-34.84)**2/34.84

+(15-14.34)**2/14.34

+(4-4.34)**2/4.34;proc

print;run;2/4/202366程序运行的结果为：

Obskc10.0018715.086320.6851215.086332.7267315.086342.8003415.086353.5135415.086363.9367815.086373.9671515.086383.9937915.0863Obs

x13.993792/4/202367应用SAS作列联表分类标志的独立性检验

SAS程序为

dataxzh01;doa=1to2;dob=1to2;inputf@@;output;end;end;cards;1585477;procfreq;weightf;tablea*b/chisq;run;2/4/202368

StatisticsforTableofabybStatisticDFValueProbChi-Square14.82210.0281

SampleSize=1812/4/202369应用SAS作列联表分类标志的独立性检验

SAS程序为

dataxzh01;doa=1to2;dob=1to3;inputf@@;output;end;end;cards;1263123647739;procfreq;weightf;tablea*b/chisq;run;2/4/202370

StatisticsforTableofabybStatisticDFValueProbChi-Square243.9532<.0001

SampleSize=3602/4/202371

3.用SAS求总体率的置信区间SAS程序为Dataxzh01;k=60;m=40;n=k+m;dop=0.01to0.499by0.0001until(p1>0.025);P1=1-probbnml(p,n,k-1);end;putkmp5.3@14p18.6@;dop=0.5to0.999by0.0001until(p2<0.025);P2=probbnml(p,n,k);end;

put@28kmp5.3@41p28.6;run;输出的结果如下：kn-kp1prob60400.4970.025

kn-kp2prob60400.6970.0248762/4/202372根据二项分布进行检验的SAS程序为

dataxzh01;inputkmp0;n=k+m;p1=1-probbnml(p0,n,k-1);p2=probbnml(p0,n,k);list;cards;681320.3;procprint;run;运行结果:Obskmp0np1p21681320.32000.124210.90405P1,p2都大于0.025,故接受H0:p=0.3.2/4/202373

应用SAS作符号检验方法1：计算与α进行比较.SAS程序为dataex;p=probbnml(0.5,11,1)+1-probbnml(0.5,11,10-1);procprint;run;程序运行的结果为：OBSP10.0117192/4/202374方法2：用UNIVARIATE过程；SAS程序为

dataex;inputx1x2@@;y=x1-x2;cards;2829192424222122222525

25262819232425232526292526;procunivariate;var

y;run;程序运行的结果为：Variable=YMomentsM(Sign)-4.5Pr>=|M|0.01172/4/202375应用SAS作中位数检验SAS程序为dataex;inputx@@;y=x-25;cards;281924212225261924232625;procunivariate;var

y;run;程序运行的结果为：Variable=YMomentsM(Sign)-2Pr>=|M|0.34382/4/202376应用SAS作秩和检验SAS程序为dataex;doa=1to2;inputn@@;doi=1ton;inputx@@;output;end;end;cards;212.612.4612.412.112.512.712.613.1;procnpar1waywilcoxon;class

a;var

x;run;程序运行的结果为：Wilcoxon2-SampleTest(NormalApproximation)(withContinuityCorrectionof.5)S=8.00000Z=-.168687Prob>|Z|=0.8660T-TestApp