SAS及其應(yīng)用課后習(xí)題集

第1題：今有某種型號的電池三批，它們分別是A、B、C三個工廠所生產(chǎn)的，為評比其質(zhì)量,

各隨機抽取5只電池為樣品，經(jīng)試驗得其壽命(h)如下：

ABC

404226283950

484534324050

383043

試在顯著性水平0.05下檢驗電池的平均壽命有無顯著的差異，若差異是顯著的，試求均差

UA-MB,U<:和UB-Uc的置信水平為95%的置信區(qū)間。

(1)程序

datat1;

dob=lto5;

doa=lto3;

input;

output;

end;

end;

cards;

402639422850483440453250383043

procanova;

classa;

modelx=a;

run;

結(jié)果

TheSASSystem19:31Thursday,April8,20127

TheANOVAProcedure

ClassLevelInformation

ClassLevelsValues

a3123

Numberofobservations15

TheSASSystem19:31Thursday,April8,20128

TheANOVAProcedure

DependentVariable:x

Sumof

SourceDFSquaresMeanSquareFValuePr>F

Model2615.6000000307.800000017.070.0003

Error12216.400000018.0333333

CorrectedTotal14832.0000000

R-SquareCoeffVarRootMSExMean

0.73990410.888634.24656739.00000

SourceDFAnovaSSMeanSquareFValuePr>F

a2615.6000000307.800000017.070.0003

（2）程序

datat1;

inputabc@@;

ul=a-b;u2=a-c;u3=b-c;

cards;

402639422850483440453250383043

procttest;

run;

結(jié)果

TheSASSystem14:02Thursday,May8,201236

TheTTESTProcedure

Statistics

LowerCLUpperCLLowerCLUpperCL

VariableNMeanMeanMeanStdDevStdDevStdDevStdErrMinimumMaximum

a537.66442.647.5362.38153.974911.4221.77763848

b526.0743033.9261.89463.16239.0871.41422634

c537.79544.451.0053.18735.319815.2872.37913950

ul59.362112.615.8381.56232.60777.49331.1662814

u25-9.721-1.86.12143.82236.379718.3322.8531-88

u35-21.88-14.4-6.9193.60976.024917.3132.6944-22-6

T-Tests

VariableDFtValuePr>Itl

a423.96<0001

b421.21<.0001

c418.66v.0001

ul410.800.0004

u24-0.630.5624

u34-5.340.0059

分析：因P=0.0003<0.05,所以電池的平均壽命有顯著的差異。UA-PB,

RA-UC和口B-PC的置信水平為95%的置信區(qū)間分別為(9.3621,

15.838),(-9.721,6.1214),(-21.88,-6.919).

第2題：為了尋找飛機控制面板上儀器表的最佳布置，試驗了三個方案，觀察領(lǐng)航在緊急情

況的反應(yīng)時間(以1/10秒記)，隨機地選擇28名領(lǐng)航員，得到他們對于不同的布局方案的

反應(yīng)時間如下：

方案【141391511131411

方案II1012711812910139109

方案III1159106887

試在顯著性水平0.05下檢驗各個方案的反應(yīng)時間有無顯著的差異，若有差異，試求U廠皿

H1-口3,U2-口3的置信水平為0.95的置信區(qū)間。

程序

datat2;

inputtype$n;

doi=lton;

inputx@@;

output;end;cards;

Ml8

141391511131411

M212

1012711812910139109

M38

1159106887

procanova;

classtype;

modelx=type;

run;

結(jié)果

TheSASSystem20:54Friday,April9,20121

TheANOVAProcedure

ClassLevelInformation

ClassLevelsValues

type3MlM2M3

Numberofobservations28

TheSASSystem20:54Friday,April9,20122

TheANOVAProcedure

DependentVariable:x

Sumof

SourceDFSquaresMeanSquareFValuePr>F

Model281.428571440.714285711.310.0003

Error2590.00000003.6000000

CorrectedTotal27171.4285714

R-SquareCoeffVarRootMSExMean

0475(X)018.706431.89736710.14286

SourceDFAnovaSSMeanSquareFValuePr>F

Type281.4285714340.7142857111.310.0003

⑴程序

datat2;

inputtype$n;

dorep=lton;

inputx@@;

output;end;

cards;

Ml8

141391511131411

M212

1012711812910139109

procttest;

classtype;

varx;

run;

結(jié)果

TheSASSystem14:18Wednesday,April14,20125

TheTTESTProcedure

Statistics

LowerCLUpperCLLowerCLUpperCL

VariabletypeNMeanMeanMeanStdDevStdDevStdDevStdErr

XMl810.82812.514.1721.322324.07050.7071

XM2128.8831011.1171.24541.75812.9850.5075

XDiff(1-2)0.72032.54.27971.40241.85592.74460.8471

T-Tests

VariableMethodVariancesL)FtValuePr>Itl

xPooledEqual182.950.0085

xSatterthwaiteUnequal13.72.870.0125

EqualityofVariances

VariableMethodNumDFDenDFFValuePr>F

XFoldedF7111.290.6738

(2)程序

datat2;

inputtype$n;

dorep=lton;

inputx@@;

output;end;

cards;

Ml8

141391511131411

M38

1159106887

procttest;

classtype;

varx;

run;

結(jié)果

TheSASSystem14:18Wednesday,April14,20128

TheTTESTProcedure

Statistics

LowerCLUpperCLLowerCLUpperCL

VariabletypeNMeanMeanMeanStdDevStdDevStdDevStdErr

XMl810.82812.514.1721.322324.07050.7071

XM386.32889.6721.322324.07050.7071

XDiff(1-2)2.35524.56.64481.464323.15421

T-Tests

VariableMethodVariancesDFtValuePr>Itl

XPooledEqual144.500.0005

XSattcrthwaitcUnequal144.500.0005

EqualityofVariances

VariableMethodNumDFDenDFFValuePr>F

XFoldedF771.001.0000

⑶程序

datat2;

inputtype$n;

dorep=lton;

inputx@@;

output;end;

cards;

M212

1012711812910139109

M38

1159106887

procttest;

classtype;

varx;

run;

結(jié)果

TheSASSystem14:18Wednesday,April14,20129

TheTTESTProcedure

Statistics

LowerCLUpperCLLowerCLUpperCL

VariabletypeNMeanMeanMeanStdDevStdDevStdDevStdErr

XM2128.8831011.1171.24541.75812.9850.5075

XM386.32889.6721.322324.07050.7071

XDiff(1-2)0.220323.77971.40241.85592.74460.8471

T-Tests

VariableMethodVariancesDFtValuePr>Itl

XPooledEqual182.360.0297

XSatterthwaiteUnequal13.72.300.0378

EqualityofVariances

VariableMethodNumDFDenDFFValuePr>F

XFoldedF7111.290.6738

分析：因P=0.000女0.05,所以各個方案的反應(yīng)時間有顯著的差異。

u1-P2,u1-U3,u2一口3的置信水平為0.95的置信區(qū)間分別為(0.72,

4.28),(2.36,6.64),(0.22,3.78)。第3題：某防治站對4個林場

的松毛蟲密度進行調(diào)查，每個林場調(diào)查5塊地得資料如下表：

地點松毛蟲密度(頭/標準地)

上192189176185190

牝190201187196200

188179191183194

187180188175182

判斷4個林場松毛從密度有無顯著差異，取顯著性水平a=0.05.

程序

datat3;

dob=lto5;

doa=lto4;

inputx@@;

output;

end;

end;

cards;

192190188187189201179180176187191188

185196183175190200194182

procanova;

classa;

modelx=a;

run;

結(jié)果

TheSASSystem19:31Thursday,April8,20129

TheANOVAProcedure

ClassLevelInformation

ClassLevelsValues

a41234

Numberofobservations20

TheSASSystem19:31Thursday,April8,201210

TheANOVAProcedure

DependentVariable:x

Sumof

SourceDFSquaresMeanSquareFValuePr>F

Model3403.3500000134.4500(X)03.770.0321

Error16571.20(X)0(X)35.7000000

CorrectedTotal19974.5500000

R-SquareCoeffVarRootMSExMean

0.4138833.1840915.974948187.6500

SourceDFAnovaSSMeanSquareFValuePr>F

a3403.3500000134.45000003.770.0321

分析：因P=0.0321<0.05,所以4個林場松毛從密度有顯著差異。

第4題：一試驗用來比較4種不同藥品解除外科手術(shù)后疼痛的延長時間(h),結(jié)果如下表:

藥品時間長度(h)

A8642

B6644

C810101012

D442

試在顯著性水平a=0.05下檢驗各種藥品對解除疼痛的延續(xù)時間有無顯著差異.

程序

datat4;

inputtype$n;

doi=lton;

input

output;end;cards;

Ml4

8642

M24

6644

M35

810101012

M43

442

procanova;

classtype;

modelx=type;

run;

結(jié)果

TheSASSystem20:54Friday,April9,20123

TheANOVAProcedure

ClassLevelInformation

ClassLevelsValues

type4MlM2M3M4

Numberofobservations16

TheSASSystem20:54Friday,April9,20124

TheANOVAProcedure

DependentVariable:x

Sumof

SourceDFSquaresMeanSquareFValuePr>F

Model3108.333333336.111111112.500.0005

Error1234.66666672.8888889

CorrectedTotal15143.0000000

R-SquareCoeffVarRootMSExMean

0.75757627.194771.6996736.250000

SourceDFAnovaSSMeanSquareFValuePr>F

type3108.333333336.111111112.500.0005

分析:因P=0.0005<0.05,所以在顯著性水平a=0.05下，各種藥品對

解除疼痛的延續(xù)時間有顯著差異。

第5題：將抗生素注入人體會產(chǎn)生抗生素與血漿蛋白質(zhì)結(jié)合的現(xiàn)象。以致減少了藥效，下表

列出5種常用的抗生素注入到牛的體內(nèi)時，抗生素與血漿蛋白質(zhì)結(jié)合的百分比。試在水平a

=0.05下檢驗這些百分比的均值有無顯著的差異。

青霉素四環(huán)素鏈霉素紅霉素氯霉素

29.627.35.821.629.2

24.332.66.217.432.8

28.530.811.018.325.0

32.034.88.319.024.2

程序

datat5;

dob=lto4;

doa=lto5;

inputx@@;

output;

end;

end;

cards;

29.627.35.821.629.224.332.66.217.432.8

28.530.811.018.325.032.034.88.319.024.2

procanova;

classa;

modelx=a;

run;

結(jié)果

TheSASSystem19:31Thursday,April8,201211

TheANOVAProcedure

ClassLevelInformation

ClassLevelsValues

512345

Numberofobservations20

TheSASSystem19:31Thursday,April8,201212

TheANOVAProcedure

DependentVariable:x

Sumof

SourceDFSquaresMeanSquareFValuePr>F

Model41480.823000370.20575040.88<.0()01

Error15135.8225009.054833

CorrectedTotal191616.645500

R-SquareCoeffVarRootMSExMean

0.91598513.120233.00912522.93500

SourceDFAnovaSSMeanSquareFValuePr>F

a41480.823000370.20575040.88<.0001

分析：因P<0.0001,所以在水平a=0.05下這些百分比的均值有顯著的

差異。

第6題：下表給出某種化工過程在三種濃度、四種溫度水平下得率的數(shù)據(jù):

溫度（因素B）

10℃24℃38℃52℃

2%141011111391012

濃度（因素A）4%97108711610

6%511131412131410

試在顯著性水平a=0.05下檢驗：在不同濃度下得率的均值是否有顯著差異，在不同溫度下

得率的均值是否有顯著差異，交互作用的效應(yīng)是否顯著。

程序

data16;

doa=1to3;

dob=1to4;

doc=1to2;

inputx@@;

output;

end;

end;

end;

dropc;

cards;

141011111391012

97108711610

511131412131410

procanova;

classab;

modelx=aba*b;

run;

結(jié)果

TheSASSystem19:31Thursday,April8,201213

TheANOVAProcedure

ClassLevelInformation

ClassLevelsValues

a3123

b41234

Numberofobservations24

TheSASSystem19:31Thursday,April8,201214

TheANOVAProcedure

DependentVariable:x

Sumof

SourceDFSquaresMeanSquareFValuePr>F

Model1182.83333337.53030301.390.2895

Error1265.00000005.4166667

CorrectedTotal23147.8333333

R-SquareCoeffVarRootMSExMean

0.56031622.342782.32737310.41667

SourceDFAnovaSSMeanSquareFValuePr>F

a244.3333333322.166666674.090.0442

b311.500000003.833333330.710.5657

a*b627.000000004.500000000.830.5684

分析:因

Pr(a)=O.0442<0.05,Pr(b)=0.5657>0.05,Pr(a*b)=0.5684>0.05,

所以只有濃度的影響是顯著的。

第7題：為了研究金屬管的防腐的功能，考慮了4種不同的涂料涂層。將金屬管設(shè)在3種不同

性質(zhì)的土壤中，經(jīng)歷了一定時間，測得金屬管腐蝕的最大深度如下所示（以mm計）：

土壤類型（因素B）

123

1.631.351.27

涂層（因素A）1.341.301.22

1.191.141.27

1.301.091.32

試取顯著性水平a=0.05檢驗在不同涂層下腐蝕的最大深度的平均值有無顯著差異，在不同

土壤卜腐蝕的最大深度的平均值有無顯著差異。設(shè)兩因素間沒有交互作用效應(yīng)。

程序

data17;

doa=1to4;

dob=1to3;

inputx@@;

output;

end;

end;

cards;

1.631.351.271.341.301.221.191.141.271.301.091.32

procanova;

classab;

modelx=ab;

結(jié)果

TheSASSystem19:15Friday,April9,20121

TheANOVAProcedure

ClassLevelInformation

ClassLevelsValues

a41234

b3123

Numberofobservations12

TheSASSystem19:15Friday,April9,20122

TheANOVAProcedure

DependentVariable:x

Sumof

SourceDFSquaresMeanSquareFValuePr>F

Model50.124100000.024820001.940.2209

Error60.076600000.01276667

CorrectedTotal110.20070000

R-SquareCoeffVarRootMSExMean

0.6183368.7929710.1129901.285000

SourceDFAnovaSSMeanSquareFValuePr>F

a30.080700000.026900002.110.2007

b20.043400000.02170000L700.2601

分析：因Pr(a)=0.2007>0.05,Pr(b)=0.2601>0.05,所以因素A、因素B

的影響均不顯著。

第8題：下表數(shù)據(jù)是退火溫度X(℃)對黃銅誕性Y效應(yīng)的試驗結(jié)果，Y是以延長度計算的。

X(℃)300400500600700800

Y(%)405055606770

畫出散點圖并求出Y對x的現(xiàn)行回歸方程。

程序

dataT8;

inputx;

cards;

300404005050055600607006780070

procreg;

modely=x;

procplot;

ploty*x;

run;

結(jié)果

TheSASSystem20:47Monday,March26,20121

TheREGProcedure

Model:MODEL1

DependentVariable:y

AnalysisofVariance

SumofMean

SourceDFSquaresSquareFValuePr>F

Model1606.22857606.22857176.080.0002

Error413.771433.44286

CorrectedTotal5620.00000

RootMSE1.85549R-Square0.9778

DependentMean57.00000AdjR-Sq0.9722

CoeffVar3.25525

ParameterEstimates

ParameterStandard

VariableDFEstimateErrortValuePr>Itl

Intercept124.628572.554419.640.0006

X10.058860.0CM4413.270.0002

分析：因a=0.05886,b=24.62857,所以回歸方程為

y=24.62857+0.05886x

第9題：在鋼線碳含量對于電阻的效應(yīng)的研究中，得到一下的數(shù)據(jù)：

碳含量X(%)0.100.300.400.550.700.800.95

200時電阻y(u1518192122.623.826

Q)

(1)回出散點圖.

(2)求線回歸方程錯誤！未找到引用源。.

(3)求e的方差o2的無偏估計.

(4)檢驗假設(shè)H。：b=0,H,：b/0.

(5)若回歸效果顯著，求b的置信水平0.95的置信區(qū)間.

(6)求x=0.50處u(x)的置信水平為0.95的置信區(qū)間.

(7)求x=0.50處觀察值Y的置信水平為0.95的預(yù)測區(qū)間.

程序

datat9;

inputxy@@;

cards;

0.10150.30180.40190.55210.7022.60.823.80.95260.50.

procreg;

modely=x/elmcli;

procplot;

ploty*x;

run;

結(jié)果

TheSASSystem20:47Monday,March26,20123

TheREGProcedure

Model:MODEL1

DependentVariable:y

AnalysisofVariance

SumofMean

SourceDFSquaresSquareFValuePr>F

Model183.8183183.818311940.48<.0001

Error50.215970.04319

CorrectedTotal684.03429

RootMSE0.20783R-Square0.9974

DependentMean20.77143AdjR-Sq0.9969

CoeffVar1.00057

ParameterEstimates

ParameterStandard

VariableDFEstimateErrortValuePr>Itl

Intercept113.958390.1734780.47<0001

X112.550340.2849144.05<.0001

TheREGProcedure

Model:MODEL1

DependentVariable:y

OutputStatistics

DepVarPredictedStdError

ObsyValueMeanPredict95%CLMean95%CLPredictResidual

115.000015.21340.148614.831415.595514.556615.8702-0.2134

218.000017.72350.104717.454417.992617.125318.32170.2765

319.000018.97850.088518.751119.205918.397919.55920.0215

421.000020.86110.078620.659121.063120.289921.43220.1389

522.600022.74360.090422.511222.976022.161023.3262-0.1436

623.800023.99870.107423.722524.274823.397324.6000-0.1987

726.000025.88120.140125.521126.241325.236926.52550.1188

8.20.23360.079520.029220.437919.661620.8056

SumofResiduals0

SumofSquaredResiduals0.21597

PredictedResidualSS(PRESS)0.50730

分析：(2)因a=12.5503,b=13.9584,所以y=12.5503x+13.9584(3)e的方

差。②的無偏估計為0.0432(4)因Pr(b)<0.001,所以回歸效果顯著。(6)

x=0.50處口(x)的置信水平為0.95的置信區(qū)間為(20.03,20.44)(7)

x=0.50處觀察值Y的置信水平為0.95的預(yù)測區(qū)間為(19.66,20.81)

第10題：卜表列出了18個5~8歲兒童的重量（這是容易測得的