统计软件SAS简介及程序示例

统计软件SAS简介及程序范例,1.SAS简介2.试验统计方法教材例题的SAS程序及运行结果,1.SAS简介,SAS(StatisticalAnalysisSystem，统计分析系统)是当今国际上著名的数据分析软件系统,其基本部分是SAS/BASE软件。20世纪60年代末期，由美国北卡罗纳州州立大学(NorthCarolinaStateUniversity)的A.J.Barr和J.H.Goodnight两位教授开始开发，1975年创建了美国SAS研究所(SASInstituteInc.)。之后，推出的SAS系统，始终以领先的技术和可靠的支持著称于世，通过不断发展和完善，目前已成为大型集成应用软件系统。,SAS系统具有统计分析方法丰富、信息储存简单、语言编程能力强、能对数据连续处理、使用简单等特点。SAS是一个出色的统计分析系统，它汇集了大量的统计分析方法，从简单的描述统计到复杂的多变量分析，编制了大量的使用简便的统计分析过程。,SAS系统运行的几个重要前提条件,（一）SAS系统运行时要同时打开的文件较多，因此在微型计算机的系统配置文件CONFIG.SYS中应指定FILES=50或以上；（二）SAS系统软件有时间租期限制，因此只有机器时间（DATE）在软件有效期内才能运行。时间租期取决于SAS出售版本日期，即所谓的SAS诞生日（BIRTHDAY）。（三）SAS系统应全部安装到硬盘的SAS子目录下，硬盘应至少有10M空间。,SASforWindows的启动与退出,（一）启动SASforWindows的启动，按如下步骤进行。开机后，直接用鼠标双击桌面上SAS系统的快捷键图标，自动显示主画面，即可进入SAS系统。,(二)退出当用完SASforWindows，需要退出时，可以单击【File】，选择【Exit】，或者，单击(关闭)按钮，立即显示。如果确认需要退出SASforWindows，单击确定按钮；如果需要继续使用SASforWindows，单击取消按钮。,2.试验统计方法教材例题的SAS程序及运行结果,t测验方差分析直线回归分析协方差分析,t检验,样本平均数与总体平均数的差异显著性检验配对试验资料的t检验非配对试验资料的t检验,样本平均数与总体平均数的差异显著性检验(例4.3),datatestt1;inputx;differ=x-27.5;cards;32.528.628.424.729.127.229.833.329.7;procmeansnmeanstderrtprt;run;,运行结果,TheMEANSProcedureVariableNMeanStdErrortValuePr|t|x929.25555560.862346833.93|t|675.4666667391.52539521.730.1451,非配对试验资料的t检验(例4.5),datatestt3;inputvarietyx;cards;118.68120.67118.42118.00117.44115.95218.68223.22221.42219.00218.92;procttest;classvariety;varx;run;,运行结果,TheTTESTProcedureT-TestsVariableMethodVariancesDFtValuePr|t|xPooledEqual9-1.920.0868xSatterthwaiteUnequal7.51-1.870.1001EqualityofVariancesVariableMethodNumDFDenDFFValuePrFxFoldedF451.660.5880,方差分析,单因素完全随机化试验重复数相等资料单因素完全随机化试验重复数不等资料两因素交叉分组试验单独观测值资料两因素交叉分组试验有重复观测值资料次级样本含量相等的二因素系统分组资料单因素随机区组设计试验资料拉丁方设计试验结果两因素随机区组设计试验资料两因素裂区设计试验资料,单因素完全随机化试验重复数相等资料的方差分析（教材【例5.3】）,dataanova1;inputvarietyx;cards;11211011411611211828210212214212216314316313316310315416418420416414416;procanova;classvariety;modelx=variety;meansvariety/duncan;run;,运行结果,TheANOVAProcedureClassLevelInformationClassLevelsValuesvariety41234Numberofobservations24,TheANOVAProcedureDependentVariable:xSumofSourceDFSquaresMeanSquareFValuePrFModel367.166666722.38888893.430.0369Error20130.66666676.5333333CorrectedTotal23197.8333333R-SquareCoeffVarRootMSExMean0.33951118.149392.55603914.08333SourceDFAnovaSSMeanSquareFValuePrFvariety367.1666666722.388888893.430.0369,TheANOVAProcedureDuncansMultipleRangeTestforxNOTE:ThistestcontrolstheTypeIcomparisonwiseerrorrate,nottheexperimentwiseerrorrate.Alpha0.05ErrorDegreesofFreedom20ErrorMeanSquare6.533333NumberofMeans234CriticalRange3.0783.2313.328,Meanswiththesameletterarenotsignificantlydifferent.DuncanGroupingMeanNvarietyA16.66764ABA14.00063BABA13.66761BB12.00062,单因素完全随机化试验重复数不等资料的方差分析（教材【例5.4】）,dataanova2;inputvarietyx;cards;121.5119.5120.0122.0118.0120.0216.0218.5217.0215.5220.0216.0319.0317.5320.0318.0317.0421.0418.5419.0420.0515.5518.0517.0516.0;procglm;classvariety;modelx=variety;meansvariety/duncan;run;,运行结果,TheGLMProcedureClassLevelInformationClassLevelsValuesvariety512345Numberofobservations25,TheGLMProcedureDependentVariable:xSumofSourceDFSquaresMeanSquareFValuePrFModel446.4983333311.624583335.990.0025Error2038.841666671.94208333CorrectedTotal2485.34000000R-SquareCoeffVarRootMSExMean0.5448607.5656161.39358718.42000SourceDFTypeISSMeanSquareFValuePrFvariety446.4983333311.624583335.990.0025SourceDFTypeIIISSMeanSquareFValuePrFvariety446.4983333311.624583335.990.0025,TheGLMProcedureDuncansMultipleRangeTestforxNOTE:ThistestcontrolstheTypeIcomparisonwiseerrorrate,nottheexperimentwiseerrorrate.Alpha0.05ErrorDegreesofFreedom20ErrorMeanSquare1.942083HarmonicMeanofCellSizes4.83871NOTE:Cellsizesarenotequal.NumberofMeans2345CriticalRange1.8691.9622.0212.062,Meanswiththesameletterarenotsignificantlydifferent.DuncanGroupingMeanNvarietyA20.166761AA19.625044ABA18.300053BB17.166762BB16.625045,两因素交叉分组试验单独观测值资料的方差分析（教材【例5.5】）,dataanova3;inputfieldmethodx;cards;117112731377219022902392315932703380417542804382516552605367618262866385;procanova;classfieldmethod;modelx=fieldmethod;meansfieldmethod/duncan;run;,运行结果,TheANOVAProcedureClassLevelInformationClassLevelsValuesfield6123456method3123Numberofobservations18,TheANOVAProcedureDependentVariable:xSumofSourceDFSquaresMeanSquareFValuePrFModel71576.555556225.22222213.970.0002Error10161.22222216.122222CorrectedTotal171737.777778R-SquareCoeffVarRootMSExMean0.9072255.2221444.01524976.88889SourceDFAnovaSSMeanSquareFValuePrFfield51435.111111287.02222217.800.0001method2141.44444470.7222224.390.0429,TheANOVAProcedureDuncansMultipleRangeTestforxNOTE:ThistestcontrolstheTypeIcomparisonwiseerrorrate,nottheexperimentwiseerrorrate.Alpha0.05ErrorDegreesofFreedom10ErrorMeanSquare16.12222NumberofMeans23456CriticalRange7.3057.6337.8277.9518.033,Meanswiththesameletterarenotsignificantlydifferent.DuncanGroupingMeanNfieldA90.66732ABA84.33336BBC79.00034CDC73.66731DDE69.66733EE64.00035,TheANOVAProcedureDuncansMultipleRangeTestforxNOTE:ThistestcontrolstheTypeIcomparisonwiseerrorrate,nottheexperimentwiseerrorrate.Alpha0.05ErrorDegreesofFreedom10ErrorMeanSquare16.12222NumberofMeans23CriticalRange5.1655.398,Meanswiththesameletterarenotsignificantlydifferent.DuncanGroupingMeanNmethodA80.50063ABA76.50062BB73.66761,两因素交叉分组试验有重复观测值资料的方差分析（教材【例5.6】）,dataanova4;inputdensityfertx;cards;112712261331143015251129122513301430152511261224133014311526112612291331143015242130222823312432252821302227233124342529212822262330243325282129222523322432252731333233333534353530313332343333343435293134323433373433353131323235333534353530;procanova;classdensityfert;modelx=densityfertdensity*fert;meansdensityfert/duncan;meansdensity*fert/lsd;run;,运行结果,TheANOVAProcedureClassLevelInformationClassLevelsValuesdensity3123fert512345Numberofobservations60,TheANOVAProcedureDependentVariable:xSumofSourceDFSquaresMeanSquareFValuePrFModel14573.333333340.952381033.51Fdensity2315.8333333157.9166667129.20.0001fert4207.166666751.791666742.38FModel5444.350000088.87000001720.06Fplant2416.7800000208.39000004033.35Fnd432.206400008.0516000029.61FModel9332.625000036.958333317.06Fa398.791666732.930555615.200.0001b177.041666777.041666735.56.0001block220.333333310.16666674.690.0276a*b3136.458333345.486111120.99FModel173765.737222221.51395433.39Fblock217.1372228.5686111.290.2991a21309.723889654.86194498.71.0001a*block440.50111110.1252781.530.2368b31975.956667658.65222299.28|t|Intercept148.5493210.127794.790.0020 x1-1.099620.27157-4.050.0049,协方差分析（教材【例9.3】）,dataanaocov1;inputtreatxy;cards;13689130801267412380126851306812073119681208011658228642278122773224672257722367220642186521759220573285533362326583225832366320553226032371318553174843252423584276442362427544285442055424444195141751;procglm;classtreat;modely=xtreat/solution;meanstreat/duncan;lsmeanstreat/stderrpdifftdiff;run;,运行结果,TheGLMProcedureClassLevelInformationClassLevelsValuestreat41234Numberofobservations40,TheGLMProcedureDependentVariable: