數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)

數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)

幾種描述性統(tǒng)計(jì)分析的SAS過程和作圖過程procmeans

procunivariate

proccorr

procplot//procgplot

proccapability數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)

procmeans（1）

Means過程的語句格式

Means過程的主要控制語句如下：

procmeans輸入數(shù)據(jù)集名<選項(xiàng)列表>;var變量列表;class變量列表;byfreq變量列表;

變量;weight變量;id變量列表;

output<out=輸出數(shù)據(jù)集名><統(tǒng)計(jì)量關(guān)鍵字=變量名列表>;run;數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)

procmeans（2）var語句——規(guī)定要求計(jì)算簡單描述性統(tǒng)計(jì)量的數(shù)值變量的次序。by語句——按by語句定義的變量進(jìn)行分組計(jì)算其相應(yīng)的簡單統(tǒng)計(jì)量,要求輸入數(shù)據(jù)集已按by變量排序。class語句——與by語句一樣，可用class變量定義觀測組，分別計(jì)算各組觀測的描述統(tǒng)計(jì)量。輸出格式與by不同且事先不需要按class變量排序。freq語句——指定一個(gè)數(shù)值型的freq變量，它的值表示輸入數(shù)據(jù)集中相應(yīng)觀測出現(xiàn)的頻數(shù)。weight語句——規(guī)定一個(gè)weight變量，它的值表示相應(yīng)觀測的權(quán)數(shù)。id語句——在輸出數(shù)據(jù)集中增加一個(gè)或幾個(gè)附加變量，目的在于識別輸出數(shù)據(jù)集里的觀測。其值為生成這個(gè)觀測的輸入數(shù)據(jù)集中相應(yīng)觀測組里id變量具有的最大值。數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)

ntcvprocmeans（3）

procmeans語句中可用的統(tǒng)計(jì)量關(guān)鍵字統(tǒng)計(jì)量名稱含義統(tǒng)計(jì)量名稱含義未丟失的觀測個(gè)數(shù)mode眾數(shù)，出現(xiàn)頻數(shù)最高的數(shù)nmissmeanstderrsumstdvarusscssskewnesskurtosis丟失的觀測個(gè)數(shù)算術(shù)平均均值的標(biāo)準(zhǔn)誤差加權(quán)和標(biāo)準(zhǔn)偏差方差變異系數(shù)的百分?jǐn)?shù)加權(quán)平方和關(guān)于均值偏差的加權(quán)平方和對稱性的度量——偏度對尾部陡平的度量——峰度sumwgtmaxminrangemedianprtclmlclmuclm權(quán)數(shù)和最大值最小值極差，max—min中間值總體均值等于0的t統(tǒng)計(jì)量t分布的雙尾p值置信度上限和下限置信度下限置信度上限數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)

procmeans（4）output語句中的選項(xiàng)。<out=輸出數(shù)據(jù)集名>——輸出數(shù)據(jù)集名。統(tǒng)計(jì)量關(guān)鍵字=變量名列表——規(guī)定在輸出數(shù)據(jù)集中要包含的統(tǒng)計(jì)量并規(guī)定這些統(tǒng)計(jì)量在新數(shù)據(jù)集中的變量名。means過程對output語句的次數(shù)沒有限制，可以使用幾個(gè)output語句來創(chuàng)建內(nèi)容不同的多個(gè)數(shù)據(jù)集。數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)Nprocmeans（5）

SAS程序

dataexamp1; inputx@@; cards; 70.472.076.574.376.577.667.372.075.074.3 73.579.573.574.765.076.581.675.472.772.7 67.276.572.770.477.268.867.367.367.372.7 75.873.575.072.773.573.572.781.670.374.3 73.579.570.476.572.777.284.375.076.570.4 ; procmeansdata=examp1nmeancvskewnesskurtosisrangemedian; varx; run;

輸出TheMEANSProcedureAnalysisVariable:xMeanVariationSkewnessKurtosisRangeMedian5073.74600005.40837940.15401110.358117919.300000073.5000000數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)

procunivariate（1）單變量統(tǒng)計(jì)分析對一組單指標(biāo)實(shí)驗(yàn)數(shù)據(jù)進(jìn)行分析常采用兩種方法：

圖示法——包括莖葉圖、盒型圖和正態(tài)概率圖。

描述統(tǒng)計(jì)量——包括矩、分位數(shù)、極端值和頻數(shù)分布表。數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)procunivariate（2）

Univariate過程的主要控制語句如下：

procunivariate輸入數(shù)據(jù)集名<選項(xiàng)列表>;varbyfreqweightid變量列表變量列表變量;變量;變量列表;;;output<out=輸出數(shù)據(jù)集名><統(tǒng)計(jì)量關(guān)鍵字=變量名列表><pctlpts=

百分位數(shù)pctlpre=變量前綴名pctlname=變量后綴名>;run;數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)procunivariate（3）

Univariate過程的主要控制語句如下：

procunivariate輸入數(shù)據(jù)集名<選項(xiàng)列表>;varbyfreqweightid變量列表變量列表變量;變量;變量列表;;;output<out=輸出數(shù)據(jù)集名><統(tǒng)計(jì)量關(guān)鍵字=變量名列表><pctlpts=

百分位數(shù)pctlpre=變量前綴名pctlname=變量后綴名>;run;數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.1例題examp1_1（SAS程序）dataexamp1_1;inputx@@;cards;74.378.868.878.070.480.580.569.771.273.579.575.675.078.872.072.072.074.371.272.075.073.578.874.375.865.074.371.269.768.073.575.072.064.375.880.369.774.373.573.575.875.868.876.570.471.281.275.070.468.070.472.076.574.376.577.667.372.075.074.373.579.573.574.765.076.581.675.472.772.767.276.572.770.477.268.867.367.367.372.775.873.575.072.773.573.572.781.670.374.373.579.570.476.572.777.284.375.076.570.4;procunivariatedata=examp1_1;varx;run;數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.1例題examp1_1（SAS結(jié)果1）

TheUNIVARIATEProcedure Variable:x MomentsNMeanStdDeviationSkewness100SumWeights 73.66SumObservations 3.94008153Variance 0.06007521Kurtosis100 7366 15.5242424 0.03386864UncorrectedSS544116.46CorrectedSS1536.9CoeffVariation5.34901103StdErrorMean BasicStatisticalMeasures0.39400815LocationVariabilityMean73.66000StdDeviationMedian73.50000VarianceMode73.50000Range

3.94008 15.5242420.00000InterquartileRange4.60000數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.1例題examp1_1（SAS結(jié)果2）

TheUNIVARIATEProcedure

Quantiles(Definition5)Quantile100%Max99%95%90%75%Q3Estimate 84.30 82.95 80.50 79.15 75.8050%Median25%Q110%

73.50 71.2068.405%1%0%Min67.3064.65 64.30數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)

proccapability（能力分析過程）PROCCAPABILITYisdesignedforprocesscapabilityanalysis,including:Histograms（直方圖）andcomparativehistograms.Cumulativedistributionfunctionplots(cdfplots)（累積分布函數(shù)）.Quantile-quantileplots(Q-Qplots),probabilityplots,andprobability-probabilityplots(P-Pplots).Theseplotsfacilitatethecomparisonofadatadistributionwithvarioustheoreticaldistributions.Goodness-of-fit（擬合優(yōu)度）testsforavarietyofdistributionsincludingthenormal.Statisticalintervals(prediction,tolerance,andconfidenceintervals)foranormalpopulation.數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.2例題examp1_4（SAS程序）dataexamp1_4;inputx@@;cards;74.378.868.878.070.480.580.569.771.273.579.575.675.078.872.072.072.074.371.272.075.073.578.874.375.865.074.371.269.768.073.575.072.064.375.880.369.774.373.573.575.875.868.876.570.471.281.275.070.468.070.472.076.574.376.577.667.372.075.074.373.579.573.574.765.076.581.675.472.772.767.276.572.770.477.268.867.367.367.372.775.873.575.072.773.573.572.781.670.374.373.579.570.476.572.777.284.375.076.570.4;proccapabilitydata=examp1_4;histogramx/normal(mu=estsigma=est);cdfplot/normal(mu=estsigma=est);qqplotx/normal(mu=estsigma=est);run;數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.2例題examp1_4（SAS結(jié)果）

TheCAPABILITYProcedure FittedNormalDistributionforx ParametersforNormalDistribution ParameterSymbolEstimateMeanMu73.66

StdDevSigma3.940082QuantilesforNormalDistribution ------Quantile------ PercentObservedEstimated 1.064.650064.4940 5.067.300067.1791 10.068.400068.6106 25.071.200071.0025 50.073.500073.6600 75.075.800076.3175 90.079.150078.7094 95.080.500080.1409 99.082.950082.8260數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.2例題examp1_4（SAS直方圖）數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.2例題examp1_4（SAS分布函數(shù)圖）數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.2例題examp1_4（SASqq圖）數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.2例題examp1_6（SAS程序）dataexamp1_6;inputx@@;cards;74.378.868.878.070.480.580.569.771.273.579.575.675.078.872.072.072.074.371.272.075.073.578.874.375.865.074.371.269.768.073.575.072.064.375.880.369.774.373.573.575.875.868.876.570.471.281.275.070.468.070.472.076.574.376.577.667.372.075.074.373.579.573.574.765.076.581.675.472.772.767.276.572.770.477.268.867.367.367.372.775.873.575.072.773.573.572.781.670.374.373.579.570.476.572.777.284.375.076.570.4;procunivariatedata=examp1_6plot;varx;run;數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)#4教材1.2例題examp1_6（SAS結(jié)果）StemLeafBoxplot8438382812668035579555780888772267655555557588874333333337735555555555572777712222703444444469777680088867233336665006431 3 3 3 4 3 714 91114 8 3 5 5 2 1

0 | | | | | |+-----+||*--+--*||+-----+ | | | | | | 0數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.2例題examp1_8（SAS程序）dataexamp1_8;inputx@@;cards;254550545561646872757578798183848484858686868789898990919192100;procunivariatedata=examp1_8normal;run;proccapabilitydata=examp1_8graphicsnoprint;histogramx/weibullvscale=proportion;run;datadelmin;setexamp1_8;ifx=25thendelete;run;proccapabilitydata=delmingraphicsnoprint;histogramx/weibullvscale=proportion;cdfplotx/weibull;run;數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)2

教材1.2例題examp1_8（SAS結(jié)果1）TestsforNormalityTest--Statistic--------pValue------Shapiro-WilkW0.863287Pr<W0.0010Kolmogorov-SmirnovD0.195196Pr>D<0.0100Cramer-vonMisesAnderson-DarlingW-Sq0.295854 A-Sq1.593346Pr>W-Sq<0.0050Pr>A-Sq<0.0050結(jié)論：拒絕正態(tài)分布的假設(shè)

Goodness-of-FitTestsforWeibullDistributionTestCramer-vonMises----Statistic----- W-Sq0.2242363DF

------pValue------Pr>W-Sq<0.010Anderson-DarlingA-Sq1.2884219Pr>A-Sq<0.010Chi-SquareChi-Sq24.87188823Pr>Chi-Sq<0.001結(jié)論：拒絕Weibull分布的假設(shè)

Goodness-of-FitTestsforWeibullDistributionTest----Statistic-----DF------pValue------Cramer-vonMisesAnderson-DarlingChi-SquareW-Sq0.16341872 A-Sq1.00500667Chi-Sq5.22777090

Pr>W-Sq0.013Pr>A-Sq<0.010Pr>Chi-Sq0.073結(jié)論：對于刪去25的數(shù)據(jù)集,接受Weibull分布的假設(shè)數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.2例題examp1_8（SAS結(jié)果2）數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)

proccorr(1)

proccorr(相關(guān)分析過程)用于計(jì)算變量之間 的相關(guān)系數(shù)，包括Pearson（皮爾遜）的乘 積矩相關(guān)和加權(quán)乘積矩相關(guān)。還能產(chǎn)生三個(gè) 非參數(shù)的關(guān)聯(lián)測量：Spearman的秩相關(guān)，

Kendall的tau-b和Hoeffding的相關(guān)性度量D。

proccorr語句調(diào)用corr過程，且是唯一必須的語句。如果只使用proccorr這一條的語句，過程計(jì)算輸入數(shù)據(jù)集中所有數(shù)值變量之間的相關(guān)系數(shù)。其余語句是供選擇的。數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)

proccorr(2)

proccorr過程一般由下列語句控制：proccorrvarwithpartialweightfreqByrun;

data=數(shù)據(jù)集<選項(xiàng)>;變量列表;變量列表;變量列表;變量;變量;變量列表;數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.3例題examp1_9（SAS程序）dataexamp1_9;inputxy;cards;689716389270112568265931911210162123212031530375334622735221305584142292733217185537036287265740;run;proccorrdata=examp1_9pearsonspearmancov;run;run;數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)xyNxy

教材1.3例題examp1_9（SAS結(jié)果1）TheCORRProcedure2Variables:xyCovarianceMatrix,DF=19

x 570.45007845.0789

y 7845.0789112404.2632SimpleStatisticsVariable20

Mean33.85000StdDev 23.88410Median 27.00000Minimum 5.00000Maximum70.0000020477.50000335.26745342.0000082.000001125數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)xyxy教材1.3例題examp1_9（SAS結(jié)果2）

TheCORRProcedurePearsonCorrelationCoefficients,N=20 Prob>|r|underH0:Rho=0

x1.000000.97971y0.97971 <.00011.00000

<.0001SpearmanCorrelationCoefficients,N=20 Prob>|r|underH0:Rho=0

x1.000000.97366<.0001y0.97366<.00011.00000數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)教材1.3例題examp1_10（SAS程序）dataexam1_10;inputx1-x6;cards;19136505162601893752211060………156335415225731383368211043;proccorrdata=exam1_10covpearsonspearman;varx1-x6;run;數(shù)據(jù)分析(SAS描述性統(tǒng)計(jì)分析過程)Nx520教材1.3例題examp1_10（SAS結(jié)果1）TheCORRProcedure6Variables:x1x2x3x4x5x6CovarianceMatrix,DF=19（略）

SimpleStatisticsVariableMeanStdDevMedianMinimumMaximumx120178.6000024.69051