华东理工大学多元统计分析与SPSS应用实验-(5)

1、华东理工大学多元统计分析与SPS负用实验(5)华东理工大学 2013 2014学年 第 二 学期多兀统计分析与SPSS应用实验报告5班级学号姓名一开课学院商学院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An alyzeClassifyHierarchical Cluster在Measure栏中选择距离测度方法：Block -dij(1)Euclidea n dista nee -dij(2) Squared Euclidea n dista nee Chebychev -dij()Min kowski dij(q)Cosi ne -cij(1)Pears

2、 on correlati on -cij(2)2.熟悉SPSS中聚类分析的系统聚类 功能An alyzeClassifyHierarchical 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 (Betwee n-groups Lin kage)(Withi n-groupsLin kage) 离差平方和法(Ward ' s method)实验要求：1. 选用例题5

3、.1文件中的变量，完成系统聚 类法的各种结果的比较分析例题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 f Classify f Hierarchical Cluster,将 x1 , x2 选入 Variable 框中图 5.1.12、单击 Statis

4、tics，选择 Proximity Matrix , Cluster 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 distanee（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 5.1.25图 5.1.3Hierarchical Cluster A

5、 rial ysi s; WelhodClu&ter Method:Transform ValuesBtlween-groups linkageStandardize: N<* By variableoneI- Change signRescale to 0-1 rangeTransform MeasuresAbsolute values图 5.1.46FHierarchical Cluster Analysis; Plotsf ” *> MAII» IMA l|y DendrogramsIcicle< All clus-tersSpecified rane

6、 of clustersStart：卩'NoneOrientationVerticalStOQ：By： |iHorizontal7#图 5.1.5距离矩阵Proximity MatrixEucdean DistanceCase234567891011121314151617181920211.0002.2363.6064.4725.0007.0717.2117.8109.2205.0004.4725.0006.7086.4035.0997.0008.0007.0719.05511.402.0002.0002.8284.1234.4726.4036.7087.2118.6024.4723

7、.6064.2435.8315.8314.1236.0007.0006.0838.06210.4402.000 .0002.0002.2362.8285.0005.0005.6577.0716.3255.0005.8317.0717.6164.1236.3257.2806.7088.54411.1802.8282.000.0002.2362.0003.6064.1234.4725.8316.0004.1235.0995.8317.0712.2364.4725.3855.0006.7089.4344.1232.2362.236 .0001.0003.1622.8283.6065.0008.062

8、6.3257.2808.0629.2204.2436.4037.2117.0718.60211.4024.4722.8282.0001.000.0002.2362.2362.8284.2438.0006.0837.0717.6169.0553.6065.6576.4036.4037.81010.6306.4035.0003.6063.1622.236.0001.4141.0002.2369.2207.0718.0628.06210.0504.0005.3855.8316.3257.21110.0006.7085.0004.1232.8282.2361.414.0001.0002.23610.0

9、508.0009.0009.22011.0005.0996.7087.2117.6168.60211.4027.2115.6574.4723.6062.8281.0001.000.0001.41410.1988.0629.0559.05511.0455.0006.3256.7087.2808.06210.8178.6027.0715.8315.0004.2432.2362.2361.414.00011.4029.22010.19810.00012.1666.0837.0717.2808.0628.54411.1804.4726.3256.0008.0628.0009.22010.05010.1

10、9811.402.0002.2361.4143.1621.4145.3855.6576.4035.0006.7088.0623.6065.0004.1236.3256.0837.0718.0008.0629.2202.236.0001.0002.2363.0003.1623.6064.4723.1625.0997.0714.2435.8315.0997.2807.0718.0629.0009.05510.1981.4141.000.0002.0002.0004.1234.2435.0003.6065.3857.0005.8317.0715.8318.0627.6168.0629.2209.05

11、510.0003.1622.2362.000.0002.8284.1233.1623.6062.2363.6065.0005.8317.6167.0719.2209.05510.05011.00011.04512.1661.4143.0002.0002.828.0006.0835.8316.4035.0006.4037.2804.1234.1232.2364.2433.6064.0005.0995.0006.0835.3853.1624.1234.1236.083.0002.2363.1622.8284.4727.2116.0006.3254.4726.4035.6575.3856.7086.

12、3257.0715.6573.6064.2433.1625.8312.236.0001.0001.0002.2365.0007.0007.2805.3857.2116.4035.8317.2116.7087.2806.4034.4725.0003.6066.4033.1621.000.0001.4141.4144.2436.0836.7085.0007.0716.4036.3257.6167.2808.0625.0003.1623.6062.2365.0002.8281.0001.414.0002.0004.4728.0628.5446.7088.6027.8107.2118.6028.062

13、8.5446.7085.0995.3853.6066.4034.4722.2361.4142.000.0002.82810.44011.1809.43411.40210.63010.00011.40210.81711.1808.0627.0717.0005.0007.2807.2115.0004.2434.4722.828.000This is a dissimilarity matrix图 5.1.6凝聚状态表Agglomerat ion Schedu leStageCluster CombinedCoefficientsStage Cluster First AppearsNext Sta

14、geCluster 1Cluster 2Cluster 1Cluster 2117191.000006212131.00000123891.0000074561.00000135121.0000016617181.2071097781.2070310811151.4140014917201.8836015107101.962701811342.00000131212142.118201413352.325114161411122.440812171516173.175091716133.592513181711164.69914151918175.2971610201911215.817170

15、20201117.34918190图 5.1.7类成员聚类表Clu ster MembershipCase12 Clusters11 Clusters10 Clusters9 Clusters8 Clusters7 Clusters6 Clusters111111112111111132222222433222225443322264433222755443338554433395544333106544333117655444128766544138766544149876544157655444161098765517111098765181110987651911109876520111

16、098765211211109876图 5.1.8树形图9Dendrogram* + 4. + +hierahchical c l u s t e b analysis*Dendrogram using Average Li aka<s Egt莊日eh GTroups)Rescaled Distance Cluster ComliineCASE 0 label Num +1015202510#79606152341897012563411121111112 1“"甘*沪 ?-at簿沪4#戸*鼻沪*戸希，*«報鼻鼻工鼻鼻鼻鼻HdRaK逞鼻鼻鼻鼻*4£鼻录鼻孙聲

17、鼻鼻出唳结果分析：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，例如当指定聚类成6类时，1,2聚为一类，36聚为一类，710

18、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 5.2.1图522餐馆；毛利

19、投资店堂面积雇员人数|P15砕p35p45閃23,30BE1 00159.0Q14.00930 001280.00560.001000.Q03102-1.5063D0091.0012 Q01290 0072Q.001200.001490.00310360%1074.00140.0013.00'2940002490.003710.004030 005274202.10882.001B4.007.003570004930.004420.004300.005115.80931.00144.QQ14.001700001140.002200.002140.002636221.701185.001

20、60 0011.004640 003150 005720 005330 CO5928134.40764.00100.008.0034SOOO2560.00363C.D03520 CO480g37.406410085.0014.001930004280.001740.002060.0029611246.901245.00167 0012.00397Q002810.004540.004770.0047012173.30S4&Q01990015.00'3190003610.0043S0 00367rr14.601370.00141.0010.00'121000980.0089

21、0.0010S0.C037615252 301146.00240.0017.004150001540.005290.005140.0061416124 20642.00320016 00'2790 003850 003200 003610 0023118193.0089&.Q078.0011.Q04650.003760.0061 SO 00S760 Q0P 6721964 80681.0099.0011.001320 001480.001120.001090.C0277204540761.0075.0014.002210001050.002330.002240.00217216

22、6J05與0052 00S Q04180 003040 005130 004770.004433、依次 Analyze f Classify f Hierarchical 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，选择 Euclidean dist

23、anee （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 5.2.3Hierarchical Cluster Analysisw利员弓5 5 5 5 皆毛雇 MP2P3MM 移、?-4>般絵譴辎yariiable(s)：祐投资Label Cases by：Statistics-Plots.Methods# total®平购收入妙非竟争餐非餐炊企移月粗金穴生fSfcSCluster& CasgsVariablesDisplay* Statistics P Pl

24、ots图 5.2.4凝聚状态表Agglomerat ion Schedu leStageCluster CombinedCoefficientsStage Cluster First AppearsNext StageCluster 1Cluster 21 Cluster 1Cluster 211314.00000221213.00001331112.00002441011.0000355810.000047679.0000013768.000058856.000079945.00008101034.00009111123.000010121212.0000111313171.570E-161260图 5.2.4类成员聚类表Cluster MembershipCase6 Clusters5 Clusters4 Clusters3 Clusters1:Case 4/ 11112:Case 4822223:Case 4933324:Case 5044325:Case 5154326:Case 5254327:Case 5365438:Case 5454329:Case 55654310:Case 56543211:Case 57543212:Case 58543213:Case 59543214:Case 6