应用统计学试题20056(A).doc

华东理工大学20042005学年第二学期 应用统计学 课程期末考试试卷 A 2005.6开课学院： 商学院 ，考试形式：开卷，所需时间： 120 分钟考生姓名： 学号： 专业： 班级 题序一二 三总 分得分评卷人一、某橡胶配方试验分析（30分）对某种橡胶配方进行研究，目的是提高其弯曲次数（越多越好），现考察三个二水平因子，选取因素水平表如下。试验除考察三个因素的作用外，还要考察BC的交互作用。因素水平A促进剂总量B炭墨品种C硫磺分量11.5天津耐高磨2.522.5天津耐高磨与长春硬炭黑并用2.0选用L 8 ( 2 7 ) 表安排试验，试验计划和试验结果见表1。表1 试验计划和试验结果 列号试验号ABCBC弯曲（万次）1234567yi111111111.5211122222.0312211222.0412222111.5521212122.0621221213.0722112212.5822121122.0经SPSS软件的计算，列表如下，请根据计算结果表格进行分析。表2 ANOVNSourceType III Sum of SquaresdfMean SquareFSig.A0.78110.015B0.03110.0311.0000.391C10.0311.0000.391B * C0.78110.78125.0000.015Error0.09430.031Total1.7187表3 Estimated Marginal MeansAMeanStd. Error1.001.7500.0882.002.3750.088BMeanStd. Error1.002.1250.0882.002.0000.088CMeanStd. Error1.002.0000.0882.002.1250.088BCMeanStd. Error1.001.001.7500.1252.002.5000.1252.001.002.2500.1252.001.7500.125（1）表2中有些数据没给出，请根据方差分析表的原理将其计算出来，给出计算过程。（2）由表2分析哪些因子是显著的，给出理由。（3）由表2和表3分析出该橡胶最优配方条件，给出理由。（4）根据表1，得出在该橡胶最优配方条件下生产出的橡胶弯曲次数，给出理由。二、医院工作满意程度研究（30分）某医院管理工作者希望了解病人对医院工作的满意程度Y和病人的年龄X1、病情的严重程度X2和忧虑程度X3之间的关系。他们随机选取了23位病人，得到下表所列数据：nX1X2X3YRX1RX2RX3RY150.0051.002.3048.0020.00014.00013.0005.000236.0046.002.3057.009.5003.50013.00010.500340.0048.002.2080.0012.0006.0008.50019.500441.0044.001.8090.0013.0002.0001.50023.000528.0043.001.8060.001.0001.0001.50012.500649.0054.002.9036.0019.00018.50022.0002.000742.0050.002.2046.0014.00011.0008.5003.000845.0048.002.4054.0018.0006.00017.5009.000952.0062.002.9026.0021.00023.00022.0001.0001029.0050.002.1077.003.50011.0005.50016.5001129.0048.002.4089.003.5006.00017.50022.0001243.0053.002.4067.0015.50017.00017.50015.0001338.0055.002.2047.0011.00020.0008.5004.0001434.0051.002.3051.008.00014.00013.0007.0001553.0054.002.2057.0022.00018.5008.50010.5001636.0049.002.0066.009.5008.5004.00014.0001733.0056.002.5079.006.50021.00020.00018.0001829.0046.001.9088.003.5003.5003.00021.0001933.0049.002.1080.006.5008.5005.50019.5002055.0051.002.4049.0023.00014.00017.5006.0002129.0052.002.3077.003.50016.00013.00016.5002244.0058.002.9052.0017.00022.00022.0008.0002343.0050.002.3060.0015.50011.00013.00012.500经SPSS软件的计算，得到下列计算结果：表4 Variables Entered/Removed(a)ModelVariables EnteredVariables RemovedMethod1X1Stepwise(Criteria: Probability-of-F-to-enter = 0.100).2X3.Stepwise (Criteria: Probability-of-F-to-enter =0.100).a: Dependent Variable: Y表5 ANOVA(c)ModelSum of SquaresdfMean SquareFSig.1Regression2988.07212988.07216.5760.001(a)Residual3785.58021180.266Total6773.652222Regression3550.28521775.14311.0140.001(b)Residual3223.36720161.168Total6773.65222a: Predictors: (Constant), X1; b: Predictors: (Constant), X1, X3; c: Dependent Variable: Y表6 Coefficients(a)ModelUnstandardized CoefficientstSig.BStd. Error1(Constant)116.94813.6798.5490.000X1-1.3760.338-4.0710.0012(Constant)147.43120.8257.0800.000X1-1.0340.369-2.8050.011X3-19.18910.274-1.8680.077a: Dependent Variable: Y表7 Variables Entered/Removed(a)ModelVariables EnteredVariables RemovedMethod1RX1* RX2Stepwise (Criteria: Probability-of-F-to-enter =0.100).a: Dependent Variable: RY表8 ANOVA(b)ModelSum of SquaresdfMean SquareFSig.1Regression494.2851494.28520.1270.000(a)Residual515.7152124.558Total1010.00022a: Predictors: (Constant), RX1*RX2; b: Dependent Variable: RY表9 Coefficients(a)ModelUnstandardized CoefficientstSig.BStd. Error1(Constant)17.3901.58510.9740.000RX1* RX2-0.0330.007-4.4860.000a: Dependent Variable: RY（1）利用SPSS计算结果，建立Y关于X1，X2，X3的逐步回归方程（2）利用SPSS计算结果，建立RY关于RX1、RX2、RX3及它们的平方项RX11,RX22,RX33，相互乘积项RX12,RX13,RX23的R逐步回归方程。（3）试用上面所求得的两个回归方程，计算第3点、第22点的残差。（4）试利用R回归方程 ，求出X1=54，X2=52，X3=2.2时，Y的预测值。三、主成分回归分析（40分）某科学基金会的管理人员希望估价从事数学研究工作的中等或较高水平的数学家的年工资额Y与他们的研究成果（论文、著作）的质量指标X1，从事研究工作的时间X2以及能成功获得资助的指标X3之间的关系，为此按一定的试验设计方法调查了24位此类型的数学家，得到下列数据：nX1X2X3YnX1X2X3Y13.509.004.0033.20138.0023.008.3043.3025.3020.006.0040.30146.5035.007.0044.1035.1018.005.9038.70156.6039.007.4042.8045.8033.006.4046.80163.7021.004.3033.6054.2031.005.0041.40176.207.007.0034.2066.0013.006.7037.50187.0040.007.6048.0076.8025.007.5039.00194.0035.004.9038.0085.5030.006.0040.70204.5023.005.0035.9093.105.003.5030.10215.9033.006.4040.40107.2047.008.0052.90225.6027.006.1036.80114.5025.005.0038.20234.8034.005.5045.20124.9011.005.8031.80243.9015.004.4035.10经SPSS软件的计算，得到下列计算结果：表10 Descriptive StatisticsNMinimumMaximumMeanStd. DeviationX1243.108.005.361.29X2245.0047.0024.9611.22X3243.508.305.991.30Y2430.1052.9039.505.47表11 Total Variance ExplainedComponentInitial EigenvaluesExtraction Sums of Squared LoadingsTotal% of VarianceCumulative %Total% of VarianceCumulative %12.33077.67477.6742.33077.67477.67420.66222.06999.7440.66222.06999.74430.0080.256100.000Extraction Method: Principal Component Analysis.表12 Component Matrix(a)Component12X10.9700.237X20.2450.970X30.9660.253Extraction Method: Principal Component Analysis; a: 2 components extracted.表13 Component Score Coefficient MatrixComponent12X10.557-0.150X2-0.2781.102X30.547-0.128Extraction Method: Principal Component Analysis. Component Scores.表14 Variables Entered/Removed(b)ModelVariables EnteredVariables RemovedMethod1REGR factor score 2 for analysis 1, REGR factor score 1 for analysis 1(a).Entera: All requested variables entered.; b: Dependent Variable: Zscore(Y)表15 ANOVA(b)ModelSum of SquaresdfMean SquareFSig.1Regression19.01729.50850.1310.000(a)Residual3.983210.190Total23.00023a: Predictors: (Constant), REGR factor score 2 for analysis 1 , REGR factor score 1 for analysis 1b: Dependent Variable: Zscore(Y)表16 Coefficients(a)ModelUnstandardized CoefficientstSig.BStd. Error1(Constant)0.0000.0890.0001.000REGR factor score 1 for analysis 10.5010.0915.5140.000REGR factor score 2 for analysis 10.7590.0918.3580.000a: Dependen