典型分析实验报告

...wd......wd......wd...实验报告课程名称多元统计分析实验工程名称六、典型相关分析班级与班级代码实验室名称〔或课室〕专业任课教师学号：姓名：实验日期：姓名实验报告成绩我不是抄的！！评语：1.对典型相关分析问题的思路、理论和方法认识正确；2.SAS软件相应计算结果确认与应用正确；3.SAS软件相应过程命令正确。注:“不正确〞为有不正确之处，具体见后面批注。指导教师〔签名〕2016年5月日说明：指导教师评分后，实验报告交院〔系〕办公室保存。实验工程六典型相关分析实验目的：通过典型相关分析的实验,熟悉典型相关分析问题的提出、解决问题的思路、方法和技能，会调用SAS软件典型相关分析等有关过程命令，根据计算机计算的结果，分析和解决典型相关分析问题。实验原理：解决典型相关分析问题的思路、理论和方法。实验设备：计算机与SAS软件。实验数据：教科书P258例1。实验步骤：相关系数阵R11、R12、R22、典型相关系数、标准化典型变量，给定显著水平,通过显著相关的临界值确定显著相关对应典型变量。实验结果、实验分析、结论〔有关表图要有序号、表的序号在左上方、图的序号在图的正下方、表的中英文名、表的上下线为粗线、表的内线为细线、表的左右边不封口，表图不能跨页、表图旁不能留空块,引用结论要注明参考文献〕：1.对x8、x12进展正向化，给出相关系数阵R11(CorrelationsAmongtheVARVariables)、R12(CorrelationsBetweentheVARVariablesandtheWITHVariables)、R22(CorrelationsAmongtheWITHVariables)；2.给出典型相关系数(CanonicalCorrelation)、标准化典型变量函数〔StandardizedCanonicalCoefficientsfortheVARVariables、Standard-izedCanonicalCoefficientsfortheWITHVariables〕；3.给定显著水平=0.01，通过显著相关的临界值确定显著相关的对应典型变量；4.通过x1-x7与典型变量Ui(i=1、…、5)的相关系数(CorrelationsBetw-eentheVARVariablesandTheirCanonicalVariables)，选出与典型变量Ui显著相关的变量，对Ui命名及其正向化；通过x8-x12与典型变量Vi的相关系数(CorrelationsBetweentheWITHVariablesandTheirCanonicalVariables)，选出与典型变量Vi显著相关的变量，对Vi命名及其正向化。给出Ui、Vi中选出变量的相关性影响分析；典型相关分析应用步骤如下：〔1〕对x8、x12进展正向化，给出相关系数阵R11(CorrelationsAmongtheVARVariables)、R12(CorrelationsBetweentheVARVa根据riablesandtheWITHVariables)、R22(CorrelationsAmongtheWITHVariables)；教科书P258例1的数据可以发现，X1~X7、X9~X11是正变量，x8、x12为逆向量，因此跑步秒数是越短越好。因此我们把X8按公式x8=50/X8进展正向化，正向化后意义成为：50米跑的速度〔m/s〕，具有正向的意义。X12按公式x12=1500/X12进展正向化，正向化后意义成为：1500米跑的速度〔m/s〕具有正向的意义，与原变量反映的实际意义一样。然后得到数据表1：表1体力测试及运动能力测试的正向化数据表序号体力测试指标运动能力测试指标x1x2x3x4x5x6x7x8x9x10x11x1214655126517525727.354892784.1725255954281.218506.944643054.3134669107389818747.354303293.89449501054897.616607.353622664.5354255904666.52686.9445323113.8464861106437825587.144052973.86749601004990.515607.1442021103.96848631225256.117687.044662824.1494555105487615617.354152463.891048641203860.220627.044132873.771149521004253.46426.764042363.751247621003461.210626.944272573.691341511015362.45606.253722533.671452551254386.35627.3549630104.29154552945051.420656.583942433.761649571104772.319457.1444630114.451753651124790.415757.5844630124.20184777954772.39647.584202543.361948601204786.412627.3544728113.942049551134184.115607.143982743.882148691284247.920637.044853074.292242571224654.215636.944002863.872354641555171.419617.2551133125.032453631204256.68536.674302944.252542711384465.217557.144872994.052646661204562.222686.764702874.17274556912966.218516.333802654.192850601204256.68577.354603254.31294251126505013576.493982723.923048501154152.96396.764152864.783142521404856.315607.2547027114.313248671053969.223606.5845028104.603349741514954.220587.1450030124.553447551134071.419646.584102974.533549741205354.522597.2550033214.313644521103754.914576.674002923.563752661304745.914457.3550528114.233848681004553.623706.945222894.26同时根据SAS程序得出对应的相关矩阵，R11(CorrelationsAmongtheVARVariables)、R12(CorrelationsBetweentheVARVa根据riablesandtheWITHVariables)、R22(CorrelationsAmongtheWITHVariables)得出表2-表4：表2R11(CorrelationsAmongtheVARVariables)x1x2x3x4x5x6x7x11.00000.27010.1643-0.02860.24630.0722-0.1664x20.27011.00000.26940.0406-0.06700.34630.2709x30.16430.26941.00000.3190-0.24270.1931-0.0176x4-0.02860.04060.31901.0000-0.03700.05240.2035x50.2463-0.0670-0.2427-0.03701.00000.05170.3231x60.07220.34630.19310.05240.05171.00000.2813x7-0.16640.2709-0.01760.20350.32310.28131.0000表3R22(CorrelationsAmongtheWITHVariables)y1y2y3y4y5y11.00000.42810.26190.45930.0720y20.42811.00000.49890.60670.4637y30.26190.49891.00000.35620.5324y40.45930.60670.35621.00000.4299y50.07200.46370.53240.42991.0000表4R12(CorrelationsBetweentheVARVa根据riablesandtheWITHVariables)y1y2y3y4y5x10.39780.36090.41160.27970.4828x20.38970.55840.39770.45110.0624x30.29410.55900.55380.32150.4859x40.29050.2711-0.04140.24700.1058x50.4425-0.1843-0.01160.14150.0090x60.06820.25960.33100.23590.2741x70.26430.15010.03880.0841-0.2060其中Y1-Y5代替X8~X12进展表示。〔2〕给出典型相关系数(CanonicalCorrelation)、标准化典型变量函数〔StandardizedCanonicalCoefficientsfortheVARVariables、Standard-izedCanonicalCoefficientsfortheWITHVariables〕；表5典型相关系数与标准化典型变量函数表序号典型相关系数典型变量11=0.85211=0.4728X1+0.2098X2+0.6233X3+0.0626X4+0.2465X5+0.0918X6+0.0311X71=0.5187X8+0.2202X9+0.3376X10-0.0666X11+0.3806X1222=0.69762=0.0023X1-0.3087X2-0.0481X3+0.2078X4+0.2078X5-0.014X6-0.3944X72=0.7823X8-1.0947X9-0.3269X10+0.1231X11+0.6673X1233=0.64473=-0.4144X1+0.8767X2-0.3022X3+0.3545X4+0.4101X5-0.4372X6+0.1321X73=0.3809X8+0.1316X9-0.1212X10+0.5276X11-0.8682X1244=0.35914=0.454X1-0.2896X2-0.3201X3+0.8874X4-0.4848X5-0.1865X6+0.0991X74=0.1777X8+0.7964X9-1.1569X10-0.3756X11+0.5726X1255=0.29365=-0.4584X1+0.5018X2-0.288X3+0.4413X4+0.2378X5+0.6347X6-1.0303X75=-0.6114X8-0.2899X9-0.1824X10+1.1892X11+0.1220X12〔3〕给定显著水平=0.01，通过查相关系数表得出=0.413其中典型相关系数1=0.8521，2=0.6976，3=0.6447都是大于=0.413，因此可以得出1、2、3都是高度显著的，可以用于进展典型分析，而4、5都是小于=0.413，因此可以得出4、5都对变量价值奉献不大，不用用于做典型分析。〔4〕通过x1-x7与典型变量Ui(i=1、…、5)的相关系数(CorrelationsBetw-eentheVARVariablesandTheirCanonicalVariables)，选出与典型变量Ui显著相关的变量，对Ui命名及其正向化；通过x8-x12与典型变量Vi的相关系数(CorrelationsBetweentheWITHVariablesandTheirCanonicalVariables)，选出与典型变量Vi显著相关的变量，对Vi命名及其正向化。给出Ui、Vi中选出变量的相关性影响分析；根据上述数据：可以得知前三个变量值得于分析。选择系数绝对值大于=0.413的值进展分析表6典型变量命名典型变量与典型变量显著相关的指标命名方向U1x1〔反复横向跳〔次〕〕、x2〔纵跳〔cm〕〕、x3〔背力〔kg〕〕，其值分别为0.6923、0.5316、0.7348，全部正号。跳与背力的典型变量正向U2x2〔纵跳〔cm〕〕，、x5〔台阶试验〔指数〕〕其值分别为-0.4863、0.8245。纵跳与台阶试验比照典型变量正向U3x7〔俯卧上体后仰〔cm〕〕、x2〔纵跳〔cm〕〕，其值分别为0.5256、0.5546，全部正号。俯卧上体后仰与纵跳的典型变量正向V1x8〔50米跑速度〔m/s〕〕、x9〔跳远〔cm〕〕、x10〔投球〔m〕〕、x11〔引体向上〔次〕〕、x12〔耐力跑速度〔m/s〕〕，其值分别为0.6982、0.7467、0.7621、0.5890、0.6711，全部正号。运动能力测试水平的典型变量正向V2x9〔跳远〔cm〕〕的值为-0.5388。制约性因素跳远的典型变量正向V3x8〔50米跑速度〔m/s〕〕、x12〔耐力跑速度〔m/s〕〕，其值分别为0.5853、-0.6174。短跑与耐力跑比照的典型变量正向我们对给出的Ui、Vi中选出变量的相关性影响分析：根据已有数据，我们知道对原始变量的研究可以化为前三个变量的影响研究分析，从相关性了解两种原始变量的关系。同时我们事先对逆向量进展正向化，因此数据越大，影响越好。第一对典型变量中，U1里面中做好x1~x3以及V1中x8~x12的提升就能促进运动能力测试各项指标的提高，提升运动能力测试水平。第二对典型变量中，U2中可以保持台阶试验〔指数〕的影响，协调开展纵跳〔cm〕的能力开展，在V2中可以知道在跳远中，腿部的能力，显示出跳远的能力的情况。第三对典型变量中，协调提高x7〔俯卧上体后仰〔cm〕〕、x2〔纵跳〔cm〕〕的能力，进而提升俯卧上体后仰与纵跳的能力的影响。再能够进一步短跑与耐力跑的影响产生促进的作用。实验程序：datafit;inputx1-x7y1-y5;cards;46.0055.00126.00 51.0075.00 25.00 72.00 7.35 489.00 27.00 8.00 4.1752.0055.0095.0042.0081.20 18.00 50.00 6.94 464.00 30.00 5.00 4.3146.0069.00107.0038.0098.00 18.00 74.00 7.35 430.00 32.00 9.00 3.89run;proccancorrdata=fitsimplecorrvprefix=uwprefix=v;varx1-x7;withy1-y5;run;CorrelationsAmongtheOriginalVariablesCorrelationsAmongtheVARVariablesx1x2x3x4x5x6x7x11.00000.27010.1643-0.02860.24630.0722-0.1664x20.27011.00000.26940.0406-0.06700.34630.2709x30.16430.26941.00000.3190-0.24270.1931-0.0176x4-0.02860.04060.31901.0000-0.03700.05240.2035x50.2463-0.0670-0.2427-0.03701.00000.05170.3231x60.07220.34630.19310.05240.05171.00000.2813x7-0.16640.2709-0.01760.20350.32310.28131.0000CorrelationsAmongtheWITHVariablesy1y2y3y4y5y11.00000.42810.26190.45930.0720y20.42811.00000.49890.60670.4637y30.26190.49891.00000.35620.5324y40.45930.60670.35621.00000.4299y50.07200.46370.53240.42991.0000CorrelationsBetweentheVARVariablesandtheWITHVariablesy1y2y3y4y5x10.39780.36090.41160.27970.4828x20.38970.55840.39770.45110.0624x30.29410.55900.55380.32150.4859x40.29050.2711-0.04140.24700.1058x50.4425-0.1843-0.01160.14150.0090x60.06820.25960.33100.23590.2741x70.26430.15010.03880.0841-0.2060CanonicalCorrelationAnalysisAdjustedApproximateSquaredCanonicalCanonicalStandardCanonicalCorrelationCorrelationErrorCorrelation10.8520720.8029120.0450410.72602720.6975910.5682410.0843970.48663330.644741.0.0960600.41569040.3591190.1504500.1431970.12896650.293574.0.1502300.086186CanonicalCorrelationAnalysisStandardizedCanonicalCoefficientsfortheVARVariablesu1u2u3u4u5x10.47280.0023-0.41440.4540-0.4584x20.2098-0.30870.8767-0.28960.5018x30.6233-0.0481-0.3022-0.3201-0.2880x40.06260.20780.35450.88740.4413x50.24650.92750.4101-0.48480.2378x60.0918-0.0140-0.4372-0.18650.6347x70.0311-0.39440.13210.0991-1.0303StandardizedCanonicalCoefficientsfortheWITHVariablesv1v2v3v4v5y10.51870.78230.38090.1777-0.6114y20.2202-1.09470.13160.7964-0.2899y30.3376-0.3269-0.1212-1.1569-0.1824y4-0.06660.12310.5276-0.37561.1892y50.38060