武大SAS要点步骤.docx

武大SAS要点步骤.docx

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武大SAS要点步骤

例2-1例数、均数、标准差、标准误、95%可信区间

1、sas过程

dataex2_1;

inputx@@;

cards;

3.964.234.423.595.124.024.323.724.764.164.614.26

3.774.204.363.074.893.974.283.644.664.044.554.25

4.633.914.413.525.034.014.304.194.754.144.574.26

4.563.793.894.214.953.984.293.674.694.124.564.26

4.664.283.834.205.244.024.333.764.814.173.963.27

4.614.263.964.233.764.014.293.673.394.124.273.61

4.984.243.834.203.714.034.344.693.624.184.264.36

5.284.214.424.363.664.024.314.833.593.973.964.49

5.114.204.364.543.723.974.284.763.214.044.564.25

4.924.234.473.605.234.024.324.684.763.694.614.26

3.894.214.363.425.014.014.293.684.714.134.574.26

4.035.464.163.644.163.76

;

procmeansdata=ex2_1

nmeanstdstderrclm;

varx;

run;

2、输出结果

例2-1

（2）中位数、四分位数间距

1、sas过程

dataex2_1;

inputx@@;

cards;

3.964.234.423.595.124.024.323.724.764.164.614.26

3.774.204.363.074.893.974.283.644.664.044.554.25

4.633.914.413.525.034.014.304.194.754.144.574.26

4.563.793.894.214.953.984.293.674.694.124.564.26

4.664.283.834.205.244.024.333.764.814.173.963.27

4.614.263.964.233.764.014.293.673.394.124.273.61

4.984.243.834.203.714.034.344.693.624.184.264.36

5.284.214.424.363.664.024.314.833.593.973.964.49

5.114.204.364.543.723.974.284.763.214.044.564.25

4.924.234.473.605.234.024.324.684.763.694.614.26

3.894.214.363.425.014.014.293.684.714.134.574.26

4.035.464.163.644.163.76

;

procmeansdata=ex2_1

nmedianQrange;

varx;

run;

2、输出结果

例2-1（3）百分位数

1、sas过程

dataex2_1;

inputx@@;

cards;

3.964.234.423.595.124.024.323.724.764.164.614.26

3.774.204.363.074.893.974.283.644.664.044.554.25

4.633.914.413.525.034.014.304.194.754.144.574.26

4.563.793.894.214.953.984.293.674.694.124.564.26

4.664.283.834.205.244.024.333.764.814.173.963.27

4.614.263.964.233.764.014.293.673.394.124.273.61

4.984.243.834.203.714.034.344.693.624.184.264.36

5.284.214.424.363.664.024.314.833.593.973.964.49

5.114.204.364.543.723.974.284.763.214.044.564.25

4.924.234.473.605.234.024.324.684.763.694.614.26

3.894.214.363.425.014.014.293.684.714.134.574.26

4.035.464.163.644.163.76

;

procunivariatedata=ex2_1;

varx;

outputout=pct

pctlpre=p

pctlpts=2.597.5;

run;

procprintdata=pct;

run;

2、结果输出

3、结果解释

从结果中可以看出2.5%和97.5%分别为3.39和5.23

例2-5求几何均数

dataex2_5;

inputxf@@;

y=log10（x）;

cards;

104

203

4010

8010

16011

32015

64014

12802

;

procmeansnoprint;

vary;

freqf;

outputout=b

mean=logmean;

run;

datac;

setb;

g=10**logmean;

procprintdata=c;

varg;

run;

例3-2求95%可信区间

dataex3_2;

n=10;

mean=166.95;

std=3.64;

t=tinv（0.975,n-1）;

pts=t*std/sqrt（n）;

lclm=mean-pts;

uclm=mean+pts;

procprint;

varlclmuclm;

run;

例3-4求两总体相差多大（区间估计）

dataex3_4;

n1=29;

n2=32;

m1=20.10;

m2=16.89;

s1=7.02;

s2=8.46;

ss1=s1**2*（n1-1）;

ss2=s2**2*（n2-1）;

sc2=（ss1+ss2）/（n1+n2-2）;

se=sqrt（sc2*（1/n1+1/n2））;

t=tinv（0.975,n1+n2-2）;

lclm=（m1-m2）-t*se;

uclm=（m1-m2）+t*se;

procprint;

vartselclmuclm;

run;

例3-5单样本t检验

1、sas过程

dataex3_5;

n=36;

s_m=130.83;

std=25.74;

p_m=140;

df=n-1;

t=abs（s_m-p_m）/（std/sqrt（n））;

p=（1-probt（t,df））*2;

procprint;

vartp;

run;

2、结果输出

3、结果解释

可以看到，检验统计量t=-2.13753，其对应的P值为0.039618，小于显著性水平的临界值0.05，故拒绝H0，接受H1，即认为样本均数与总体均数在统计学上具有显著性差异。

例3-6配对样本t检验

1、sas过程

dataex3_6;

inputx1x2@@;

d=x1-x2;

cards;

0.8400.580

0.5910.509

0.6740.500

0.6320.316

0.6870.337

0.9780.517

0.7500.454

0.7300.512

1.2000.997

0.8700.506

;

procmeanstprt;

vard;

run;

procunivariatedata=ex3_6;

vard;

run;

2、结果输出

3、结果解释

从结果中可以看出检验统计量t值为7.93.对应的p＜0.0001，按α=0.05水准，拒绝H0，接受H1，差异有统计学意义，即认为配对资料两样本均数在统计学上具有显著性差异。

例3-7两样本t检验

1、sas过程

dataex3_7;

inputx@@;

if_n_<21thenc=1;

elsec=2;

cards;

-0.70-5.602.002.800.703.504.005.807.10-0.50

2.50-1.601.703.000.404.504.602.506.00-1.40

3.706.505.005.200.800.200.603.406.60-1.10

6.003.802.001.602.002.201.203.101.70-2.00

;

procttest;

varx;

classc;

run;

2、结果输出

3、结果解释

首先进行方差齐性检验，结果显示统计量F值为1.60，对应额的P值为0.3153，大于显著性水平的临界值0.05，认为两样本具有方差齐性；然后对两样本均数进行差异性分析，结果显示统计量T值为-0.64，对应的P值为0.5248，大于0.05，接受H0，即认为两样本均数不具有显著性差异。

例4-2完全随机设计资料的方差分析

dataex4_2;

inputxc@@;

cards;

3.5312.4222.8630.894

4.5913.3622.2831.064

4.3414.3222.3931.084

2.6612.3422.2831.274

3.5912.6822.4831.634

3.1312.9522.2831.894

3.3012.3623.4831.314

4.0412.5622.4232.514

3.5312.5222.4131.884

3.5612.2722.6631.414

3.8512.9823.2933.194

4.0713.7222.7031.924

1.3712.6522.6630.944

3.9312.2223.6832.114

2.3312.9022.6532.814

2.9811.9822.6631.984

4.0012.6322.3231.744

3.5512.8622.6132.164

2.6412.9323.6433.374

2.5612.1722.5832.974

3.5012.7223.6531.694

3.2511.5623.2131.194

2.9613.1122.2332.174

4.3011.8122.3232.284

3.5211.7722.6831.724

3.9312.8023.0432.474

4.1913.5722.8131.024

2.9612.9723.0232.524

4.1614.0221.9732.104

2.5912.3121.6833.714

;

procanova;

classc;

modelx=c;

meansc/dunnett;

meansc/hovtest;

run;

Pr>F,方差齐

例4-4随机区组设计资料的方差分析

dataex4_4;

inputxab@@;

cards;

0.8211

0.6521

0.5131

0.7312

0.5422

0.2332

0.4313

0.3423

0.2833

0.4114

0.2124

0.3134

0.6815

0.4325

0.2435

;

procanova;

classab;

modelx=ab;

meansa/snk;

run;

例7-1四格表卡方检验

dataex7_1;

inputrcf@@;

cards;

1199

125

2175

2221

;

procfreq;

weightf;

tablesr*c

/chisq

expected;

run;

例7-2校正卡方检验

dataex7_2;

inputrcf@@;

cards;

1146

126

2118

228

;

procfreq;

weightf;

tablesr*c

/chisq

expected;

run;

例7-6多个样本率的比较

dataex7_6;

inputrcf@@;

cards;

11199

127

21164

2218

31118

3226

;

procfreq;

weightf;

tablesr*c

/chisq;

run;

例7-7样本构成比的比较

dataex7_7;

inputrcf@@;

cards;

1142

1248

1321

2130

2272

2336

;

procfreq;

weightf;

tablesr*c

/chisq;

run;

例7-8双向无序分类资料的关联性检验

dataex7_8;

inputrcf@@;

cards;

11431

12490

13902

21388

22410

23800

31495

32587

33950

41137

42179

4332

;

procfreq;

weightf;

tablesr*c

/chisq;

run;

例8-1配对样本比较的Wilcoxon符号秩检验

dataex8_1;

inputx1x2@@;

d=x1-x2;

cards;

6076

142152

195243

8082

242240

220220

190205

2538

198243

3844

236190

95100

;

procunivariate;

vard;

run;

例8-3两个独立样本比较的Wilcoxon秩和检验

dataex8_3;

inputxc@@;

cards;

2.781

3.231

4.201

4.871

5.121

6.211

7.181

8.051

8.561

9.601

3.232

3.502

4.042

4.152

4.282

4.342

4.472

4.642

4.752

4.822

4.952

5.102

;

procnpar1waywilcoxon;

varx;

classc;

run;

一、多元回归

1、普通多元回归

dataa;

inputidx1-x4y;

cards;

1173106714.7137

21391326.417.8162

31981126.916.7134

41181387.115.7188

5139948.613.6138

617516012.120.3215

713115411.221.5171

81581419.729.6148

91581377.418.2197

101321517.517.2113

11162110615.9145

1214411310.142.881

131621377.220.7185

141691298.516.7157

151291386.310.1197

1616614811.533.4156

17185118617.5156

181551216.120.4154

191751114.127.2144

201361109.42690

211531338.516.9215

221101499.524.7184

23160865.310.8118

24112123816.6127

251471108.518.4137

262041226.121126

271311026.613.4130

2817011278.424.7135

291731238.719188

3013213113.829.2122

;

procreg;

modely=x1-x4/stb;

run;

结果解释：

根据方差分析结果：

F=4.26,P=0.0092<0.05，回归方程有统计学意义

根据参数估计结果：

仅X3有意义，t＝－2.42，P<0.05

回归方程是：

＝178.47344＋0.09028X1－0.00104X2－1.3939X3－1.40112X4

2、多元回归（逐步回归法）

dataa;

inputidx1-x5y;

cards;

1173106714.713762

21391326.417.816243

31981126.916.713481

41181387.115.718839

5139948.613.613851

617516012.120.321565

713115411.221.517140

81581419.729.614842

91581377.418.219756

101321517.517.211337

11162110615.914570

1214411310.142.88141

131621377.220.718556

141691298.516.715758

151291386.310.119747

1616614811.533.415649

17185118617.515669

181551216.120.415457

191751114.127.214474

201361109.4269039

211531338.516.921565

221101499.524.718440

23160865.310.811857

24112123816.612734

251471108.518.413754

262041226.12112672

271311026.613.413051

2817011278.424.713562

291731238.71918885

3013213113.829.212238

;

procreg;

modely=x1-x5/stbselection=stepwisesle=0.10sls=0.15;

run;

结果解释：

根据逐步回归的条件：

引入标准＝0.10，剔除标准＝0.15，回归方程纳入X1和X4，P均小于0.05

由方差分析结果显示：

F=46.48,P<0.0001,回归方程有统计学意义。

根据参数估计结果显示：

＝－11.78059+0.49842X1-0.49666X4

3.控制变量后的相关系数

dataa;

inputidx1-x5y;

cards;

1173106714.713762

21391326.417.816243

31981126.916.713481

41181387.115.718839

5139948.613.613851

617516012.120.321565

713115411.221.517140

81581419.729.614842

91581377.418.219756

101321517.517.211337

11162110615.914570

1214411310.142.88141

131621377.220.718556

141691298.516.715758

151291386.310.119747

1616614811.533.415649

17185118617.515669

181551216.120.415457

191751114.127.214474

201361109.4269039

211531338.516.921565

221101499.524.718440