fisher判别.doc

实验名称：fisher判别一、实验目的和要求通过上机操作，完成spss软件的fisher判别二、实验内容和步骤依次点击，选择discriminant如下图所示进行操作点击ststistics,进行以下操作点击classification，进行以下操作点击save，进行以下操作Analysis Case Processing SummaryUnweighted CasesNPercentValid15100.0ExcludedMissing or out-of-range group codes0.0At least one missing discriminating variable0.0Both missing or out-of-range group codes and at least one missing discriminating variable0.0Total0.0Total15100.0该表为分析案例处理摘要表，反映的是有效样本与缺失值的情况，可以看出本案例中有效值有15个，缺失值为15个Group Statistics类别MeanStd. DeviationValid N (listwise)UnweightedWeighted1.00X1188.600057.1384355.000X2150.400016.5015255.000X313.80005.9329655.000X420.000013.3229155.0002.00X1157.000041.1703855.000X2115.000014.8155355.000X37.00001.8708355.000X413.60007.5365855.0003.00X1151.000033.8008955.000X2121.400013.0115355.000X35.00001.8708355.000X48.00007.3143755.000TotalX1165.533345.110761515.000X2128.933321.049151515.000X38.60005.220841515.000X413.866710.391391515.000该表为分组统计量表，是各组变量的描述统计分析，从表中可以看出各个类别的均值，标准差。X1在3组中差别较大Tests of Equality of Group MeansWilks LambdaFdf1df2Sig.X1.8571.003212.396X2.4268.074212.006X3.4427.564212.007X4.7621.879212.195该组为组均值的均等性检验，从表中可以看出 X2.X3在两组中的均值差异是显著的。Test ResultsBoxs M45.046FApprox.1.089df120df2516.896Sig.357Tests null hypothesis of equal population covariance matrices.上表为协方差矩阵的均等性检验，是对3组进行的总体协方差矩阵是否相等的统计检验，因为p值=0.357，所以可知3组的协方差矩阵差异不显著。EigenvaluesFunctionEigenvalue% of VarianceCumulative %Canonical Correlation13.044a93.693.6.8682.207a6.4100.0.414a. First 2 canonical discriminant functions were used in the analysis.Wilks LambdaTest of Function(s)Wilks LambdaChi-squaredfSig.1 through 2.20516.6498.0342.8281.9783.577上表是典型判别函数的分析结果，其中第一个表中反映了判别函数的特征值分别为3.044a 0.207a 分别解释了93.6%与6.4%的方差。Standardized Canonical Discriminant Function CoefficientsFunction12X1.453-.175X2.596-.811X3.662.600X4.299.608上表是标准化典型判别函数，反映的是标准化的判别函数。Canonical Discriminant Function CoefficientsFunction12X1.010-.004X2.040-.055X3.176.160X4.031.062(Constant)-8.7845.448Unstandardized coefficients上表为未标准化的典型判别函数系数，即费希尔判别函数系数，可以将样本观测值直接带入函数以求得判别函数Casewise StatisticsCase NumberActual GroupHighest GroupSecond Highest GroupDiscriminant ScoresPredicted GroupP(Dd | G=g)P(G=g | D=d)Squared Mahalanobis Distance to CentroidGroupP(G=g | D=d)Squared Mahalanobis Distance to CentroidFunction 1Function 2pdfOriginal111.4292.9981.6932.00213.9872.7551.127211.4192.9771.7392.0219.4462.0471.261311.5752.9991.1072.00016.4282.877-.854413*.1002.5784.5971.2656.153.430-1.788511.7882.999.4752.00114.8712.886.008622.4022.4571.8203.4032.072.161-.264722.4382.7001.6503.3003.347-2.0351.186823*.9032.518.2052.480.356-1.158-.032922.4162.6621.7541.1694.479.323.9321022.5652.6161.1423.3842.084-1.971.7861132*.9912.616.0173.379.987-.842.4281233.8692.681.2802.3141.833-.873-.8311333.2362.8392.8842.1446.407-.447-1.9621433.4632.5871.5402.4132.240-2.369.0