华东理工大学多元统计分析与SPSS应用试验

1、华东理工大学 20132014学年 第 二 学期10班级多元统计分析与学号开课学院商学院SPSS应用实验报告 5姓名任课教师任飞成绩实验内容：实验 5 聚类分析方法1.熟悉SPSS中聚类分析的距离选择功能 AnalyzeClassifyHierarchical Cluster 在Measure栏中选择距离测度方法： Block -dij(1) Euclidean distance -dij(2) Squared Euclidean distance Chebychev -dij( 00) Minkowski -dij(q) Cosine -cij(1) Pearson correlation

2、-cij(2) 2. 熟悉SPSS中聚类分析的系统聚类功能 AnalyzeClassifyHierarchical Cluster 在Cluster Method栏中选择系统聚类方法: 最短距离法(Nearest Linkage) 最长距离法(Furthest Linkage) 中间距离法(Median Linkage) 重心法(Centroid clustering) 类平均法(Between-groups Linkage) (Within-groups Linkage) 离差平方和法(Ward ' s method)实验要求：1 .选用仞题5.1文件中的变量，完成系统聚类法的各种结

3、果的比较分析例题5.1从21个工厂各抽一件同类产品，每个产品测两个质量指标，记作x1、x2,要求将各厂的产品按质量情况进行分类。2 .对案例：Crop' Pain连锁店，展开聚类分析讨论思考：分类完成后，能进行哪些统计分析？改革二十多年来，中国经济的发展阶段划分的思考教师评语:教师签名：年 月日实验报告：5.11、打开例题 5.1.sav 文件，如图 5.1.1 所示依次 Analyze Classify Hierarchical Cluster,将 x1 , x2 选入Variable 框中图 5.1.12、单击 Statistics,选择 Proximity Matrix , Cl

4、uster Membership 中选择 Range of Solutions ,依次输入 6,12, 如图5.1.2 ,单击Method, Cluster Method为默认组间平均链锁法，单击 Interval选项，激活右侧的参数 框，单击下拉箭头，共有 8个选项，选择 Euclidean distance （欧式距离），如图5.1.3,单击Continue,单 击Plots,选择 Dendrogram,如图5.1.4单击 Continue,选择 OK得到结果 图 5.1.2图 5.1.3图 5.1.4Hierarchical Cluster Analysis； PlotsV Dendro

5、gram：Icicle<*c加stersC Specified range of clustersSiart： |l Stop： | By： |l NoneOrientation6 VerticalHorizontal图 5.1.5距离矩阵Proximit y Mat rixCaseu1Euclidean Distance1 .000-2 1.00034 3.6065 4.47265.000770787.2119 7.810_ra 9.220n5.000124.472-135.000146.70815 6403ra5.099177.000ro 8.000_197.071209.05521

6、11.40221.000.0002.0002.8284.1234.4726.4036.7087.2118.6024.4723.6064.2435.8315.8314.1236.0007.0006.0838.06210.44032.2362.000.0002.0002.2362.8285.0005.0005.6577.0716.3255.0005.8317.0717.6164.1236.3257.2806.7088.54411.18043.6062.8282.000.0002.2362.0003.6064.1234.4725.8316.0004.1235.0995.8317.0712.2364.

7、4725.3855.0006.7089.43454.4724.1232.2362.236.0001.0003.1622.8283.6065.0008.0626.3257.2808.0629.2204.2436.4037.2117.0718.60211.40265.0004.4722.8282.0001.000.0002.2362.2362.8284.2438.0006.0837.0717.6169.0553.6065.6576.4036.4037.81010.63077.0716.4035.0003.6063.1622.236.0001.4141.0002.2369.2207.0718.062

8、8.06210.0504.0005.3855.8316.3257.21110.00087.2116.7085.0004.1232.8282.2361.414.0001.0002.23610.0508.0009.0009.22011.0005.0996.7087.2117.6168.60211.40297.8107.2115.6574.4723.6062.8281.0001.000.0001.41410.1988.0629.0559.05511.0455.0006.3256.7087.2808.06210.817109.2208.6027.0715.8315.0004.2432.2362.236

9、1.414.00011.4029.22010.19810.00012.1666.0837.0717.2808.0628.54411.180115.0004.4726.3256.0008.0628.0009.22010.05010.19811.402.0002.2361.4143.1621.4145.3855.6576.4035.0006.7088.062124.4723.6065.0004.1236.3256.0837.0718.0008.0629.2202.236.0001.0002.2363.0003.1623.6064.4723.1625.0997.071135.0004.2435.83

10、15.0997.2807.0718.0629.0009.05510.1981.4141.000.0002.0002.0004.1234.2435.0003.6065.3857.000146.7085.8317.0715.8318.0627.6168.0629.2209.05510.0003.1622.2362.000.0002.8284.1233.1623.6062.2363.6065.000156.4035.8317.6167.0719.2209.05510.05011.00011.04512.1661.4143.0002.0002.828.0006.0835.8316.4035.0006.

11、4037.280165.0994.1234.1232.2364.2433.6064.0005.0995.0006.0835.3853.1624.1234.1236.083.0002.2363.1622.8284.4727.211177.0006.0006.3254.4726.4035.6575.3856.7086.3257.0715.6573.6064.2433.1625.8312.236.0001.0001.0002.2365.000188.0007.0007.2805.3857.2116.4035.8317.2116.7087.2806.4034.4725.0003.6066.4033.1

12、621.000.0001.4141.4144.243197.0716.0836.7085.0007.0716.4036.3257.6167.2808.0625.0003.1623.6062.2365.0002.8281.0001.414.0002.0004.472209.0558.0628.5446.7088.6027.8107.2118.6028.0628.5446.7085.0995.3853.6066.4034.4722.2361.4142.000.0002.8282111.40210.44011.1809.43411.40210.63010.00011.40210.81711.1808

13、.0627.0717.0005.0007.2807.2115.0004.2434.4722.828.000This is a dissimilarity matrx图 5.1.6凝聚状态表Agglomerati on Schedu l eStageCluster CombinedCoefficientsStage Cluster First AppearsNext StageCluster 1Cluster 2Cluster 1Cluster 2117191.000006212131.00000123891.0000074561.00000135121.0000016617181.207109

14、7781.2070310811151.4140014917201.8836015107101.962701811342.00000131212142.118201413352.325114161411122.440812171516173.175091716133.592513181711164.69914151918175.2971610201911215.81717020201117.34918190图 5.1.7类成员聚类表Clu st er MembershipCase12 Clusters11 Clusters10 Clusters9 Clusters8 Clusters7 Clus

15、ters6 Clusters111111112111111132222222433222225443322264433222755443338554433395544333106544333117655444128766544138766544149876544157655444161098765517111098765181110987651911109876520111098765211211109876图 5.1.8树形图Dendrogram早*hierarchical cluster analysis*Dendragran using Average Linkage CBetween

16、Groups)Rescaled Distance Cluster Combine结果分析：本次聚类分析采取变量之间距离的计算用欧式距离，类与类之间的距离采用组间平均链锁法，将21个样本分成612组比较，图5.1.5为距离矩阵，给出了各样本之间的欧式距离；图5.1.6为凝聚状态表，第一列表示聚类分析的第几步，第二、三列表示本步骤中哪两个个案或者小类聚成一类。第四列是个案距离 或者小类距离。第五列、第六列表示参与本步骤聚类的是个案还是小类，0表示个案，非零表示小类，具体数字表示第几步生成的小类。第七列标志本步骤的结果将在第几步中用到。图5.1.7为类成员聚类表，给出了分别聚成612的最终聚类结果，

17、例如当指定聚类成6类时，1,2聚为一类,36聚为一类，710聚为一类，1115聚为一类，1620聚为一类。图5.1.8为聚类树形图，既给出了聚类过程，也给出对应 相应类与类之间的距离。5.2依据题意，需对 Michel向上司管理报告作出选址建议。首先认为选址地点需要最低满足毛利/投资额=0.26的基本要求。所以聚类分析前先筛选出毛利/投资额=0.26的样本，点击“Data-Select Cases”,选择"If condition is satisfied ",输入"毛利/投资额=0.26”,如图5.2.1 ,点击“Continue”得到结果如图5.2.2图 5.

18、2.1图 5.2.2誉造毛利投资店堂面积雇员人数|p15降P35乂5f>5128.30861.00129.0014.00980.001280 0056。001000 003102-1 60630 0091 0012.001290 00720 001200 001490 003103F 68 901074 00140.0013.00294Q 002490 003710 004030 005274F 202 103B2 00134 007 003570 004930 004420 004300 002965115 8。931.00144 0014.001700 001140 002200 0

19、02140 002636221701185.0Q160.0011 004640.003150 Q05720.005330.00592S134.40764 00100.008903450.002560.003630.003520.00480937.40643.0085.Q014.001930.004280.001740.002060.0029611246.901245.00167.0012.003970.002810.004540.004770.0047012178.30846.00199.0015.003190.003610.004380.004150.0036714.601370.00141

20、.0010.001210.00980.00390.001050.0037615252.301146.00240.0017004150.001540.005290.005140.0061416124.20642.0082.0016.002790.003850 003200.003610.0023118193 00899.0078.0011.004650.003750 006180.005760.006721964.00661.0099.0011.001320.001490.001120.001090 00277市2045.40761.0075.0014.002210 0018&0 002

21、330 002240 0021721663。564.0052 008QQ4180 003040 006130 004770 0。4433、依次 AnalyzeClassifyHierarchical Cluster,将投资、店堂面积选入 Variable 框中，单击 Statistics, 选择 Proximity Matrix , Cluster Membership 中选择 Range of Solutions ,依次输入 3, 6;如图 5.1.2,单击Method, Cluster Method选为最短距离法，单击 Interval选项，激活右侧的参数框，单击下拉箭头，共有8 个选项,

22、选择 Euclidean distance （欧式距离）,如图 5.1.3,单击 Continue ,单击 Plots,选择 Dendrogram, 如图5.2.3单击Continue ,选择OK得到结果图 5.2.3图 5.2.4凝聚状态表Agglomerati on Schedu l eStageCluster CombinedCoefficientsStage Cluster First AppearsNext StageCluster 1Cluster 2Cluster 1Cluster 211314.00000221213.00001331112.00002441011.000035

23、5810.000047679.0000013768.000058856.000079945.00008101034.00009111123.000010121212.0000111313171.570E-161260图 5.2.4类成员聚类表Clu ster Membershi pCase6 Clusters5 Clusters4 Clusters3 Clusters1:Case 4711112:Case 4822223:Case 4933324:Case 5044325:Case 5154326:Case 5254327:Case 5365438:Case 5454329:Case 5565

24、4310:Case 56543211:Case 57543212:Case 58543213:Case 59543214:Case 605432图 5.2.5冰柱图CaseNumber of clusters9:Case 55 7:Case 53 14:七se6013:Case5912:Case5811:Case5710:Case568:Case 54 6:Case 52 5:Case 51 4:Case 50 3:Case 492:Case 48 1:C1XTXXTX!XT-T"X!T-T-"X""X""X"T"

25、"X""X""X""X""X"x"X"k"X""X"2XXXXXXXXXXXXXXXXXXXXXXXXXX3XXXXXXXXXXXXXXXXXXXXXXXXX4XXXXXXXXXXXXXXXXXXXXXXXX5XXXXXXXXXXXXXXXXXXXXXXX6XXXXXXXXXXXXXXXXXXXXXX7XXXXXXXXXXXXXXXXXXXXX8XXXXXXXXXXXXXXXXXXXX9XXXXXXXXXXXXXXXXXXX10XXXXXXXXXXXXXXXXXX11XXXXXXXXXXXXXXXXX12XXXXXXXXXXXXXXXX13XXXXXXXXXXXXXXXV ertical Iciclease 47