華東理工大學(xué)多元統(tǒng)計分析與SPSS應(yīng)用試驗0001

1、華東理工大學(xué) 2013 2014學(xué)年 第 二 學(xué)期多元統(tǒng)計分析與SPSS應(yīng)用實驗報告5班級學(xué)號姓名12開課學(xué)院商學(xué)院任課教師任飛成績實驗內(nèi)容：實驗 5聚類分析方法1. 熟悉SPSS中聚類分析的距離選擇功能AnalyzeClassifyHierarchical Cluster 在Measure欄中選擇距離測度方法：Block -dij(1) Euclidean distance -dij(2) Squared Euclidean distance Chebychev -dij( g) Minkowski -dij(q) Cosine -cij(1) Pearson correlation -ci

2、j(2)2.熟悉SPSS中聚類分析的系統(tǒng)聚類功能AnalyzeClassifyHierarchical Cluster 在Cluster Method欄中選擇系統(tǒng)聚類方法: 最短距離法(Nearest Linkage) 最長距離法(Furthest Linkage) 中間距離法(Median Linkage) 重心法(Centroid clustering) 類平均法(Between-groups Linkage) (Within-groups Linkage) 離差平方和法(Ward ' s method)實驗要求：1. 選用例題5.1文件中的變量，完成系統(tǒng)聚類法的各種結(jié)果的比較分析

3、x2,例題5.1從21個工廠各抽一件同類產(chǎn)品，每個產(chǎn)品測兩個質(zhì)量指標(biāo)，記作x1、要求將各廠的產(chǎn)品按質(zhì)量情況進(jìn)行分類。2. 對案例：Crop ' Pain連鎖店，展開聚類分析討論 思考：分類完成后，能進(jìn)行哪些統(tǒng)計分析？改革二十多年來，中國經(jīng)濟(jì)的發(fā)展階段劃分的思考教師評語：教師簽名：年月日實驗報告：5.11、打開例題 5.1.sav 文件，如圖 5.1.1 所示依次 Analyze 宀Classify 宀 Hierarchical Cluster,將 x1 , x2 選入Variable 框中圖 5.1.1Hierarchical Cluster Analysisnoyariable(s)

4、:ClusterCasesVariablesDisplay匝 Statistics 臣 PlotsStatistics. I Plots. Methods. Save |2、單擊 Statistics，選擇 Proximity Matrix ，Cluster Membership 中選擇 Range of Solutions，依次輸入 6,12， 如圖5.1.2，單擊Method， Cluster Method為默認(rèn)組間平均鏈鎖法，單擊Interval選項，激活右側(cè)的參數(shù)框，單擊下拉箭頭，共有8個選項，選擇 Euclidean distanee （歐式距離），如圖5.1.3，單擊Continu

5、e，單擊Plots，選擇 Dendrogram，如圖5.1.4單擊 Continue,選擇 OK得到結(jié)果圖 5.1.2Hierarchical Cluster Analysis: StatisticsQ Agglomeration schedule5 I Hl IV1FFEI I Ml I I n I Ml I I MI Ml I I n I I I I Ml I I Ml !卡 Proximity matrixCluster MembershipC None|P- Single solution:1D冷 Range of solutions:From 6 through 12 cluster

6、s圖 5.1.3Cluster Method：Between-groups linkageMeasure'* Interval:" Counts:Euclidean distancePower：Root |2 jjchi-square measureBinary:|Squared Euclidean distanceTransform ValuesStandardize: None圖 5.1.4I Vesent：Absent: |oTransform MeasuresAbsolute valuesa By yarrableChange signRescale to 0'

7、;1 rangeHierarchical Cluster Analysis: MethodHierarchical Cluster Analyis； Plots2 dendrogram：1 !«»! HlId de< All clustersC Specified range of clustersSiart： l Stop： |By： i廠 NomeOrientationQ Vertical旦orizontal圖 5.1.5距離矩陣Proximit y Mat rixEUCdean DistanceCase223623456789101112131415161718

8、1920211.0002.2363.6064.4725.0007.0717.2117.8109.2205.0004.4725.0006.7086.4035.0997.0008.0007.0719.05511.402.0002.0002.8284.1234.4726.4036.7087.2118.6024.4723.6064.2435.8315.8314.1236.0007.0006.0838.06210.4402.000.0002.0002.2362.8285.0005.0005.6577.0716.3255.0005.8317.0717.6164.1236.3257.2806.7088.54

9、411.1802.8282.000.0002.2362.0003.6064.1234.4725.8316.0004.1235.0995.8317.0712.2364.4725.3855.0006.7089.4344.1232.2362.236.0001.0003.1622.8283.6065.0008.0626.3257.2808.0629.2204.2436.4037.2117.0718.60211.4024.4722.8282.0001.000.0002.2362.2362.8284.2438.0006.0837.0717.6169.0553.6065.6576.4036.4037.810

10、10.6306.4035.0003.6063.1622.236.0001.4141.0002.2369.2207.0718.0628.06210.0504.0005.3855.8316.3257.21110.0006.7085.0004.1232.8282.2361.414.0001.0002.23610.0508.0009.0009.22011.0005.0996.7087.2117.6168.60211.4027.2115.6574.4723.6062.8281.0001.000.0001.41410.1988.0629.0559.05511.0455.0006.3256.7087.280

11、8.06210.8178.6027.0715.8315.0004.2432.2362.2361.414 .00011.4029.22010.19810.00012.1666.0837.0717.2808.0628.54411.1804.4726.3256.0008.0628.0009.22010.05010.19811.402.0002.2361.4143.1621.4145.3855.6576.4035.0006.7088.0623.6065.0004.1236.3256.0837.0718.0008.0629.2202.236.0001.0002.2363.0003.1623.6064.4

12、723.1625.0997.0714.2435.8315.0997.2807.0718.0629.0009.05510.1981.4141.000.0002.0002.0004.1234.2435.0003.6065.3857.0005.8317.0715.8318.0627.6168.0629.2209.05510.0003.1622.2362.000.0002.8284.1233.1623.6062.2363.6065.0005.8317.6167.0719.2209.05510.05011.00011.04512.1661.4143.0002.0002.828.0006.0835.831

13、6.4035.0006.4037.2804.1234.1232.2364.2433.6064.0005.0995.0006.0835.3853.1624.1234.1236.083.0002.2363.1622.8284.4727.2116.0006.3254.4726.4035.6575.3856.7086.3257.0715.6573.6064.2433.1625.8312.236.0001.0001.0002.2365.0007.0007.2805.3857.2116.4035.8317.2116.7087.2806.4034.4725.0003.6066.4033.1621.000.0

14、001.4141.4144.2436.0836.7085.0007.0716.4036.3257.6167.2808.0625.0003.1623.6062.2365.0002.8281.0001.414.0002.0004.4728.0628.5446.7088.6027.8107.2118.6028.0628.5446.7085.0995.3853.6066.4034.4722.2361.4142.000.0002.82810.44011.1809.43411.40210.63010.00011.40210.81711.1808.0627.0717.0005.0007.2807.2115.

15、0004.2434.4722.828.000This is a dissimilarity matrx圖 5.1.6凝聚狀態(tài)表Agglomerati on Schedu l eStageCluster CombinedCoefficientsStage Cluster First AppearsNext StageCluster 1Cluster 2Cluster 1Cluster 2117191.000006212131.00000123891.0000074561.00000135121.0000016617181.2071097781.2070310811151.414001491720

16、1.8836015107101.962701811342.00000131212142.118201413352.325114161411122.440812171516173.175091716133.592513181711164.69914151918175.2971610201911215.81717020201117.34918190圖 5.1.7類成員聚類表Clu st er MembershipCase12 Clusters11 Clusters10 Clusters9 Clusters8 Clusters7 Clusters6 Clusters11111111211111113

17、2222222433222225443322264433222755443338554433395544333106544333117655444128766544138766544149876544157655444161098765517111098765181110987651911109876520111098765211211109876圖 5.1.8樹形圖Dendrogram+ + + +ERARCHICAL CLUSTER ANALYSIS*Dendragramusing Average Linkage tBetween Groups)Rescaled Dist日nue Clus

18、ter Combine結(jié)果分析：本次聚類分析采取變量之間距離的計算用歐式距離，類與類之間的距離采用組間平均鏈鎖法，將21個樣本分成612組比較，圖5.1.5為距離矩陣，給出了各樣本之間的歐式距離；圖5.1.6為凝聚狀態(tài)表，第一列表示聚類分析的第幾步，第二、三列表示本步驟中哪兩個個案或者小類聚成一類。第四列是個案距離 或者小類距離。第五列、第六列表示參與本步驟聚類的是個案還是小類，0表示個案，非零表示小類，具體數(shù)字表示第幾步生成的小類。第七列標(biāo)志本步驟的結(jié)果將在第幾步中用到。圖5.1.7為類成員聚類表，給出了分別聚成612的最終聚類結(jié)果，例如當(dāng)指定聚類成6類時，1,2聚為一類，36聚為一類，71

19、0聚為一類，1115聚為一類，1620聚為一類。圖5.1.8為聚類樹形圖，既給出了聚類過程，也給出對應(yīng) 相應(yīng)類與類之間的距離。5.2依據(jù)題意，需對 Michel向上司管理報告作出選址建議。首先認(rèn)為選址地點需要最低滿足毛利/投資額=0.26的基本要求。所以聚類分析前先篩選出毛利/投資額=0.26的樣本，點擊"Data-Select Cases”選擇“ If condition is satisfied ”輸入"毛利/投資額=0.26 ”如圖5.2.1，點擊“ Continue "得到結(jié)果如圖5.2.2圖 5.2.1圖522毛利投資店堂面積居員人數(shù)|p15p25P35

20、PS128.30861.00129.0014.00980.001260 00SCO QO1000 00310X2-1 60630 0091 0012.001290 00720 001200 001490 00310X3P 68 901074 00140.0013.002940 002490 003710 004030 005274P 202 103B2 00134 007 003570 004930 004420 004300 002965115 &0931.00144 0014.001700 001140 002200 002140 002636221701185.00160.001

21、1 004640.003150 Q05720.005330.00592S134.40764.00100.008.003450.002560.003630.003520.004809 37.40643.0085.Q014.001930.004280.001740.002060.0029611246.901245.00167.0012 003970.002810.004540.004770.0047012178.30846.00199.0015.003190.003610.004380.004150.0036714-.601370.00141.0010.001210.00980.00390.001

22、050.0037615252.301146.00240.0017004150.001540.005290.005140.0061416124.20642.0082.0016.002790.003850 003200.003610.0023118193 00899.0078.0011.004650.003760.006180.005760.006721964.00661.0099.0011.001320.001490.001120.001090 002772045.40761.0075.0Q14.002210 0018&0 002330 002240 00217<721陽3D564

23、.0052008Q04180 003040 006130 00477Q 004433、依次 Analyze 宀Classify 宀Hierarchical Cluster,將投資、店堂面積選入 Variable 框中，單擊 Statistics, 選擇 Proximity Matrix , Cluster Membership 中選擇 Range of Solutions，依次輸入 3, 6;如圖 5.1.2，單擊Method，Cluster Method選為最短距離法，單擊Interval選項，激活右側(cè)的參數(shù)框，單擊下拉箭頭，共有8 個選項,選擇 Euclidea n dista nee （

24、歐式距離），如圖 5.1.3，單擊 Con tin ue，單擊 Plots，選擇 Den drogram， 如圖5.2.3單擊Continue，選擇OK得到結(jié)果圖 5.2.3圖524凝聚狀態(tài)表Agglomerati on Schedu l eStageCluster CombinedCoefficientsStage Cluster First AppearsNext StageCluster 1Cluster 2Cluster 1Cluster 211314.00000221213.00001331112.00002441011.0000355810.000047679.0000013768

25、.000058856.000079945.00008101034.00009111123.000010121212.0000111313171.570E-161260圖 5.2.4類成員聚類表Clu ster MembershiCase6 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 575

26、43212:Case 58543213:Case 59543214:Case 605432圖 5.2.5冰柱圖CaseNumber of clusters9:Case 55 7:Case 51 14:；ase5013?Case5912:Case5811:Case5710:Case568:Case 54 6:Case 52 5:Case 51 4:Case 50 3:Case 492:Case 48 1:C1XXXXXXXXXXXXXXXXXXXXXXXXXXX2XXXXXXXXXXXXXXXXXXXXXXXXXX3XXXXXXXXXXXXXXXXXXXXXXXXX4XXXXXXXXXXXXXXXXXXXXXXXX5XXXXXXXXXXXXXXXXXXXXXXX6XXXXXXXXXXXXXXXXXXXXXX7XXXXXXXXXXXXXXXXXXXXX8XXXXXXXXXXXXXXXXXXXX9XXXXXXXXXXXXXXXXXXX10XXXXXXXXXXXXXXXXXX11XXXXXXXXXXXXXXXXX12XXXXXXXXXXXXX