数据分析,实验四,答案,上机实验

1、数据分析上机实验三学号:班级:实验内容；P122 3.8强迫回归:Logistic Regressi onCase Process ing SummaryaUn weighted CasesNP erce ntSelected CasesIn cluded in An alysis40100.0Miss ing Cases0.0Total40100.0Un selected Cases0.0Total40100.0a. If weight is in effect, see classificati on table for the total n umber of cases.Dep end

2、ent Variable EncodingOriginal ValueInternal Value0011Block 0: Beg inning BlockCla ss ific ation Ta ble a,bObse rv edPre dicte d病人的生存时间Pe rce nta geCorre ct01 1Ste p 0病人的生存时间0300100 .011 00.0Ov era ll Pe rce nta ge7 5.0a. Con Stan t is in clu ded in th e m ode l.b. Th e cu t valu e is .5 00Variables

3、in the Equati onBS.E.WalddfSig.Exp(B)Step 0Con sta nt-1.099.3659.0521.003.333Variables not in the Equati onScoredfSig.Ste pVariablesX19.3461.0020X2.0101.920X3.3151.574X45.2751.022X5.8351.361Overall Statistics12.8865.024X1，肿瘤类型X45个自变量时，整结果分析：从上表最后一列的显著性水平值可以看出，生活行动能力评分 分别与病人的生存时间 丫显著相关。Block 1: Metho

4、d = EnterOmn ibus Tests of Model Coefficie ntsChi-squaredfSig.Ste p 1 Ste p16.5955.005Block16.5955.005Model16.5955.005结果分析：从上可以看出，2=16.595，由显著性水平值可得，当输入所有个模型显著。即 X1-X5对PY=0的综合影响显著。Model SummarySte p-2 Log likelihoodCox & Snell R S quareNagelkerke R Square128.392a.340.503a. Estimation terminated at i

5、teration number 6 because p arameter estim ates changed by less than .001.Classification Table aObservedP redicted病人姓存寸间P erce ntage Correct01Step 1病人性存寸间029196.714660.0Overall P erce ntage87.5a. The cut value is .500结果分析： 表五：最后一列的显著性水平值可以看出，生活行动能力评分 的生存时间丫显著相关。表六：可以看出，2=16.595，由显著性水平值可得，当输入所有 著。即X1

6、-X5对PY=0的综合影响显著。综合分析：从表可以看出，不管是一起考虑所有5个变量时，X1，肿瘤类型X4分别与病人5个自变量时，整个模型显还是单独使用时，X1，X4都Variables in the Equati onBS.E.Wald1 dfSig.Exp(B)SaepX1.100.0435.3911.0201.1051X2.014.047.0911.7631.014X3.017.055.1031.7491.018X4-1.083.5873.4011.065.339X5-.613.961.4071.523.542Con sta nt-7.0114.4752.4541.117.001a. Va

7、riable(s) en tered on ste p 1: X1, X2, X3, X4, X5.是显著的预测变量一起考虑时，显著性水平分别为P=0.02,p=0.065。而X2，X3，X5无论单独使用还是和其他预测变量一起使用对预测丫都是无效的。Logistic 回归的分类概率方程为：exp(-0.7011-0.100X1-0.014X2-0.017X3+1.083X4+0.613X5)P Y=0=-1+ex p(-0.7011-0.100X1-0.014X2-0.017X3+1.083X4+0.613X5)根据该方程，表列出了各病人生存时间小于200天的概率估计值为概率表的X值。逐步回归

8、方法:Logistic Regressi onCase Process ing SummaryaUn weighted CasesNP erce ntSelected CasesIn cluded in An alysis40100.0Miss ing Cases0.0Total40100.0Un selected Cases0.0Total40100.0a. If weight is in effect, see classificati on table for the total n umber of cases.Dep endent Variable EncodingOriginal

9、ValueInternal Value0011Block 0: Beg inning BlockClassification Table a,bObservedPr edicted病人姓存寸间P erce ntage Correct01Step 0病人性存寸间0300100.01100.0Overall P erce ntage75.0a. Con sta nt is in cluded in the model.b. The cut value is .500Variables in the Equati onBS.E.WalddfSig.Exp(B)Ste p 0Co nsta nt-1.

10、099.3659.0521.003.333Variables not in the Equati on=ScoredfSig.StepVariablesX19.3461.0020X2.0101.920X3.3151.574X45.2751.022X5.8351.361Overall Statistics12.8865.024单个变量建模时候的显著性。Block 1: Method = Forward Ste pwise (Co nditio nal)Omn ibus Tests of Model Coefficie ntsChi-squaredfSig.Ste p 1 Ste p11.4521

11、.001Block11.4521.001Model11.4521.001Ste p 2 Ste p4.4611.035Block15.9132.000Model15.9132.000从强迫回归的Block1和逐步回归的Block1 表可以看出，R2=15.913,即与（1）中模型似然 估计统计量的观测值相比，由原来的16.595减小到现在的15.913，其减少量是很小的。Model SummarySte p-2 Log likelihoodCox & Sn ell R SquareNagelkerke R Square133.535.249.369229.073a.328.486a. Esti

12、mati on termi nated at iterati on n umber 6 because p arameter estimates cha nged by less tha n .001.Classificati on TableaObserv edPredicted病人的生存时间Perce ntage Correct01Ste p 1病人的生仔时间027390.016440.0Ov erall P ercentage77.5Ste p 2病人的生存时间029196.714660.0Ov erall Perce ntage87.5a. The cut v alue is .500

13、Variables in the Equati onBS.E.WalddfSig.Exp(B)saepX1.092.0366.6431.0101.0961Con sta nt-6.8842.4537.8781.005.001SbepX1.098.0415.7241.0171.1032X4-1.125.6023.4891.062.325Con sta nt-6.1382.7385.0231.025.002a. Variable(s) en tered on ste p 1: X1.b. Variable(s) en tered on ste p 2: X4.分析：从表可以看出，不管是一起考虑所有

14、2个变量时，还是单独使用时，X1，X4，都是显著的预测变量，一起考虑时，显著性水平分别为p=0.017, p=0.062Model if Term Removed aVariableModel Log LikelihoodChange in -2 Log LikelihooddfSig. of the ChangeSte p 1X1-23.26913.0041.000Ste p 2X1-20.28111.4881.001X4-16.8644.6541.031a. Based on con diti onal p ararster estimatesX1肿瘤类型X4分别于分析：从表的最后一列的显

15、著性水平值可以看出，生活行动能力评分病人的生存时间丫显著相关。Variables not in the Equati onScoredfSig.Ste PVariablesX2.2091.6471X3.1561.693X44.0821.043X5.6451.422Overall Statistics4.8104.307Ste pVariablesX2.0801.7782X3.2101.647X5.5151.473Overall Statistics.6903.876概率表说明：表中X值表示未用逐步回归法时各病人生存时间小于200天的概率，而 P则表示用逐步回归法时相应的概率，d值表示X-P。由

16、d的绝对值的大小可以看出是否用逐步回归法分析的差异。序号xPd序号xPd10.670.610.07210.880.800.0820.840.800.04220.130.18-0.0530.650.610.04230.900.92-0.0240.970.970.01240.570.61-0.0450.940.97-0.02250.991.00-0.0160.710.610.10260.240.37-0.1370.700.610.09270.710.80-0.0980.700.640.06280.910.92-0.0190.930.930.00290.780.83-0.05101.001.000.

17、00300.980.99-0.01110.730.640.09310.991.00-0.01120.990.990.00320.981.00-0.02130.920.93-0.01330.991.00-0.01140.990.990.00340.960.97-0.01151.001.000.00350.790.85-0.06160.990.990.00360.230.33-0.10170.800.780.02370.970.960.01180.920.900.02380.530.57-0.04190.240.160.08390.110.16-0.05200.430.330.10400.220.

18、33-0.11结果分析：表六：从强迫回归的 Block1和逐步回归的 型似然估计统计量的观测值相比，由原来的 的。表九：从表可以看出，不管是一起考虑所有 著的预测变量，一起考虑时，显著性水平分别为P=0.017 ,p=0.062表十：从表的最后一列的显著性水平值可以看出，生活行动能力评分 病人的生存时间丫显著相关。综合分析：本次选取自变量采用的是逐步回归法，且回归系数显著性 值小于0.1的自变量引入了回归方程， 系统在进行逐步回归过程中产生了 值，先将与变量丫关系最密切的 引入变量X4，但X2，X3，X5， X4之间的二元线性回归模型。下面以X1，X4为自变量，丫为因变量，建立Logistic模型。 Logistic回归的分类概率方程为：exp （-6.138-0.098X1 + 1.125X4）P Y=0=-1+ex p（-6.138-0.098X1 + 1.125X4） 根据该方程，表列出了各病人生存时间小于将其与（1）中的响应结果进行比较，结果如下：从表可以看出，（病人生存时间小于200天的概率估计值相差很小。因此，在实际应用中用逐