spss的试题、答案、结果

统计复习题目一.某公司管理人员为了解某化妆品在一个城市的月销售量 Y（单位：箱）与该城市中适合使用该化妆品的人数 （单位：千人）以及他们 人均月收入 （单位：元）之间的关1X2X系，在某个月中对 15 个城市做调查，得上述各量的观测值如表 A1 所示.假设 Y 与 ，1X之间满足线性回归关系2X15,2,210 Lixyiii 其中 独立同分布于 .i),(2N(1)求回归系数 的最小二乘估计值和误差方差 的估计值，写出回归方程并对回210,2归系数作解释；analyze-regression-linear,y to dependent,x1 x2 to indepents ,statistics-confidence intervals,save-unstandardized. Prediction individual-individual.ok CoefficientsaUnstandardized CoefficientsStandardized Coefficients 95% Confidence Interval for BModel B Std. Error Beta t Sig. Lower Bound Upper Bound(Constant) 3.453 2.431 1.420 .181 -1.843 8.749x1 .496 .006 .934 81.924 .000 .483 .5091x2 .009 .001 .108 9.502 .000 .007 .011a. Dependent Variable: yANOVAbModel Sum of Squares df Mean Square F Sig.Regression 53844.716 2 26922.358 5.679E3 .000aResidual 56.884 12 4.7401Total 53901.600 14a. Predictors: (Constant), x2, x1b. Dependent Variable: y回归系数 的最小二乘估计值和误差方差 的估计值分别为：3.453,0.496,0.009210,2和 =4.740. 回归方程为 y=0.496*x1+0.009*x2+3.453 2回归系数解释：3.453 可理解为化妆品的月基本销售量，当人均月收入 固定时，适合使2X用该化妆品的人数 每提高一个单位，月销售量 Y 将增加 0.496 个单位；当适合使用该化1X妆品的人数 固定时，人均月收入 每提高一个单位，月销售量 Y 将增加 0.009 个单位2X(2)求出方差分析表，解释对线性回归关系显著性检验的结果.求复相关系数的平方 的值2R并解释其意义；ANOVAbModel Sum of Squares df Mean Square F Sig.Regression 53844.716 2 26922.358 5.679E3 .000aResidual 56.884 12 4.7401Total 53901.600 14a. Predictors: (Constant), x2, x1b. Dependent Variable: yModel SummaryModel R R SquareAdjusted R SquareStd. Error of the Estimate1 .999a .999 .999 2.17722a. Predictors: (Constant), x2, x1由于P值=0.0000.05，所以认为四种不同催化剂对该化工产品的得率在水平 0.05下无显著差异。七、为了研制一种治疗枯草热病的药物，将两种成分（A 和 B）各按三种不同剂量（低、中、高）混合，将 36 位自愿受试患者随机分为 9 组，每组 4 人服用各种剂量混合下的药物，记录其病情缓解的时间（单位：小时）数据如表 B3 所示.（1）假设所给数据服从方差分析模型，建立方差分析表，A 与 B 的交互效应在下是否显著？05.B3.0.Analyze-general linear model-univariate,x to dependent variable,a and b to fixed factor, okTests of Between-Subjects EffectsDependent Variable:xSourceType III Sum of Squares df Mean Square F Sig.Corrected Model 373.105a 8 46.638 774.910 .0001857.610 1 1857.610 3.086E4 .000a 220.020 2 110.010 1.828E3 .000b 123.660 2 61.830 1.027E3 .000a * b 29.425 4 7.356 122.227 .000Error 1.625 27 .060Total 2232.340 36Corrected Total 374.730 35a. R Squared = .996 (Adjusted R Squared = .994)交互效应检验 P 值=0.000<0.05，所以成分 (A)与成分(B)之间的交互效应是显著的（2）若 A 与 B 的交互效应显著，分别就 A 的各水平 ，给出在 B 的各水平)3,21(i上的均值 的置信度为 0.95 的置信区间以及两两之差的置信度不小于 0.95 的jijBonferroni 同时置信区间.B3.1.Analyze-general linear model-univariate,x to dependent variable,a to fixed factor,post hoc-a to post hoc tests for, bonferroni,options-a to display means for.okbDependent Variable:x95% Confidence Intervalb Mean Std. Error Lower Bound Upper Bound1 2.475 .110 2.226 2.7242 4.600 .110 4.351 4.8493 4.575 .110 4.326 4.824Multiple ComparisonsxBonferroni95% Confidence Interval(I) b (J) bMean Difference (I-J) Std. Error Sig. Lower Bound Upper Bound2 -2.1250* .15546 .000 -2.5810 -1.669013 -2.1000* .15546 .000 -2.5560 -1.64401 2.1250* .15546 .000 1.6690 2.581023 .0250 .15546 1.000 -.4310 .48101 2.1000* .15546 .000 1.6440 2.556032 -.0250 .15546 1.000 -.4810 .4310Based on observed means.The error term is Mean Square(Error) = .048.*. The mean difference is significant at the .05 level.固定成分(A)的 水平， 的置信度为 0.95 的置信区间分别为(2.226,2.724),1A1321,(4.351,4.849),(4.326,4.824); 的置信度不小于 0.95 的 Bonferroni13213,同时置信区间分别为（-2.581,-1.669）,（-2.556,-1.644）,（-0.431,0.481）B3.2.Analyze-general linear model-univariate,x to dependent variable,a to fixed factor,post hoc-a to post hoc tests for, bonferroni,options-a to display means for.okbDependent Variable:x95% Confidence Intervalb Mean Std. Error Lower Bound Upper Bound1 5.450 .127 5.162 5.7382 8.925 .127 8.637 9.2133 9.125 .127 8.837 9.413Multiple ComparisonsxBonferroni95% Confidence Interval(I) b (J) bMean Difference (I-J) Std. Error Sig. Lower Bound Upper Bound2 -3.4750* .18028 .000 -4.0038 -2.946213 -3.6750* .18028 .000 -4.2038 -3.14621 3.4750* .18028 .000 2.9462 4.003823 -.2000 .18028 .888 -.7288 .32881 3.6750* .18028 .000 3.1462 4.203832 .2000 .18028 .888 -.3288 .7288Based on observed means.The error term is Mean Square(Error) = .065.*. The mean difference is significant at the .05 level.固定成分(A)的 水平， 的置信度为0.95的置信区间分别为(5.162,5.738),2A2321,(8.637,9.213),(8.837,9.413)； 的置信度不小于0.95的Bonferroni23231,同时置信区间分别为（-4.0038,-2.9462）,（-4.2038,-3.1462）,（-0.7288,0.3288）B3.3.Analyze-general linear model-univariate,x to dependent variable,a to fixed factor,post hoc-a to post hoc tests for, bonferroni,options-a to display means for.okbDependent Variable:x95% Confidence Intervalb Mean Std. Error Lower Bound Upper Bound1 5.975 .130 5.682 6.2682 10.275 .130 9.982 10.5683 13.250 .130 12.957 13.543Multiple ComparisonsxBonferroni95% Confidence Interval(I) b (J) bMean Difference (I-J) Std. Error Sig. Lower Bound Upper Bound2 -4.3000* .18333 .000 -4.8378 -3.762213 -7.2750* .18333 .000 -7.8128 -6.73721 4.3000* .18333 .000 3.7622 4.837823 -2.9750* .18333 .000 -3.5128 -2.43721 7.2750* .18333 .000 6.7372 7.812832 2.9750* .18333 .000 2.4372 3.5128Based