spss实践题分析及答案

SPSS实践题习题1分析此班级不同性别的学生的物理和数学成绩的均值、最高分和最低分。Case Processing SummaryCasesIncludedExcludedTotalNPercentNPercentNPercent数学 * 性别26100.0%0.0%26100.0%物理 * 性别26100.0%0.0%26100.0%Report性别数学物理男生Mean80.076974.5385N1313Std. Deviation5.751255.17390Minimum72.0069.00Maximum95.0087.00女生Mean80.769276.1538N1313Std. Deviation8.917728.32512Minimum70.0065.00Maximum99.0091.00TotalMean80.423175.3462N2626Std. Deviation7.360296.84072Minimum70.0065.00Maximum99.0091.00结论：男生数学成绩 最高分: 95 最低分: 72 平均分: 80.08 物理成绩 最高分: 87 最低分: 69 平均分: 74.54 女生数学成绩 最高分: 99 最低分: 70 平均分: 80.77 物理成绩 最高分: 91 最低分: 65 平均分: 76.15习题2分析此班级的数学成绩是否和全国平均成绩85存在显著差异。One-Sample StatisticsNMeanStd. DeviationStd. Error Mean数学2680.42317.360291.44347One-Sample TestTest Value = 85 tdfSig. (2-tailed)Mean Difference95% Confidence Interval of the DifferenceLowerUpper数学-3.17125.004-4.57692-7.5498-1.6040结论：由分析可知相伴概率为0.004，小于显著性水平0.05，因此拒绝零假设，即此班级数学成绩和全国平均水平85分有显著性差异习题3分析兰州市2月份的平均气温在90年代前后有无明显变化。Group Statistics分组NMeanStd. DeviationStd. Error Mean二月份气温011-4.5272731.2034043.3628400118-3.2000001.3006786.3065729Independent Samples TestLevenes Test for Equality of Variancest-test for Equality of MeansFSig.tdfSig. (2-tailed)Mean DifferenceStd. Error Difference95% Confidence Interval of the DifferenceLowerUpper二月份气温Equal variances assumed1.019.322-2.74027.011-1.3272727.4843246-2.3210246-.3335208Equal variances not assumed-2.79422.599.010-1.3272727.4750156-2.3108823-.3436632结论：由分析可知, 方差相同检验相伴概率为0.322,大于显著性水平0.05,因此接受零假设，90年代前后2月份温度方差相同。双侧检验相伴概率为0.011, 小于显著性水平0.05，拒绝零假设，即2月份平均气温在90年代前后有显著性差异习题4分析15个居民进行体育锻炼3个月后的体质变化。Paired Samples StatisticsMeanNStd. DeviationStd. Error MeanPair 1锻炼前65.20157.5231.943锻炼后53.27155.8731.516Paired Samples CorrelationsNCorrelationSig.Pair 1锻炼前 & 锻炼后15-.300.277Paired Samples TestPaired DifferencestdfSig. (2-tailed)MeanStd. DeviationStd. Error Mean95% Confidence Interval of the DifferenceLowerUpperPair 1锻炼前 - 锻炼后11.93310.8462.8005.92717.9404.26114.001结论：由分析可知，锻炼前后差值与零比较，相伴概率小于显著性水平，拒绝零假设，即锻炼前后有显著性差异习题5为了农民增收，某地区推广豌豆番茄青菜的套种生产方式。为了寻找该种方式下最优豌豆品种，进行如下试验：选取5种不同的豌豆品种，每一品种在4块条件完全相同的田地上试种，其它施肥等田间管理措施完全一样。根据表中数据分析不同豌豆品种对平均亩产的影响是否显著。ANOVA产量Sum of SquaresdfMean SquareFSig.Between Groups(Combined)13195.70043298.9254.306.016Linear TermContrast3591.02513591.0254.687.047Deviation9604.67533201.5584.179.025Within Groups11491.50015766.100Total24687.20019Multiple ComparisonsDependent Variable:产量(I) 品种(J) 品种Mean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper BoundLSD12-13.2500019.57166.509-54.966028.46603-5.7500019.57166.773-47.466035.96604-21.5000019.57166.289-63.216020.2160551.50000*19.57166.0199.784093.21602113.2500019.57166.509-28.466054.966037.5000019.57166.707-34.216049.21604-8.2500019.57166.679-49.966033.4660564.75000*19.57166.00523.0340106.4660315.7500019.57166.773-35.966047.46602-7.5000019.57166.707-49.216034.21604-15.7500019.57166.434-57.466025.9660557.25000*19.57166.01015.534098.96604121.5000019.57166.289-20.216063.216028.2500019.57166.679-33.466049.9660315.7500019.57166.434-25.966057.4660573.00000*19.57166.00231.2840114.716051-51.50000*19.57166.019-93.2160-9.78402-64.75000*19.57166.005-106.4660-23.03403-57.25000*19.57166.010-98.9660-15.53404-73.00000*19.57166.002-114.7160-31.2840*. The mean difference is significant at the 0.05 level.产量品种NSubset for alpha = 0.0512Student-Newman-Keulsa54212.500014264.000034269.750024277.250044285.5000Sig.1.000.696Means for groups in homogeneous subsets are displayed.a. Uses Harmonic Mean Sample Size = 4.000.结论：由以上分析可知，F统计量F(4,15)=4.306，对应的相伴概率为0.016，小于显著性水平，拒绝零假设，即不同品种豌豆与亩产量之间存在显著性差异。1、2、3、4号品种与5号有明显差异, 5号品种产量最低, 因此购种选择前四种均可。习题6由于时间安排紧张，公司决定安排4名员工操作设备A、B、C各一天，得到日产量数据如表所示。试分析4名员工和3台设备是否有显著性差异，以便制定进一步的采购计划。Tests of Between-Subjects EffectsDependent Variable:日生产量SourceType III Sum of SquaresdfMean SquareFSig.Corrected Model433.167a586.63315.831.002Intercept31212.000131212.0005703.716.000equipment318.5002159.25029.102.001staff114.667338.2226.985.022Error32.83365.472Total31678.00012Corrected Total466.00011 设备 * 员工Dependent Variable:日生产量设备员工MeanStd. Error95% Confidence IntervalLower BoundUpper Bound1153.2501.65449.20357.297245.9171.65441.86949.964346.5831.65442.53650.631451.2501.65447.20355.2972162.0001.65457.95366.047254.6671.65450.61958.714355.3331.65451.28659.381460.0001.65455.95364.0473149.7501.65445.70353.797242.4171.65438.36946.464343.0831.65439.03647.131447.7501.65443.70351.797Multiple ComparisonsDependent Variable:日生产量(I) 员工(J) 员工Mean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper BoundLSD127.33*1.910.0092.6612.0136.67*1.910.0131.9911.3442.001.910.335-2.676.6721-7.33*1.910.009-12.01-2.663-.671.910.739-5.344.014-5.33*1.910.031-10.01-.6631-6.67*1.910.013-11.34-1.992.671.910.739-4.015.344-4.671.910.050-9.34.0141-2.001.910.335-6.672.6725.33*1.910.031.6610.0134.671.910.050-.019.34Based on observed means. The error term is Mean Square(Error) = 5.472.日生产量员工NSubset12Student-Newman-Keulsa,b2347.673348.334353.0053.001355.00Sig.070.335Multiple ComparisonsDependent Variable:日生产量(I) 设备(J) 设备Mean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper BoundLSD12-8.75*1.654.002-12.80-4.7033.501.654.079-.557.55218.75*1.654.0024.7012.80312.25*1.654.0008.2016.3031-3.501.654.079-7.55.552-12.25*1.654.000-16.30-8.20日生产量设备NSubset12Student-Newman-Keulsa,b3445.751449.252458.00Sig.0791.000结论：由以上假设检验分析可知，不同人员、不同设备各自以及他们的交互作用对日生产量都有显著影响。由上图可知，要提高员工日生产量，应该选购设备2。习题7数据记录了18个试验地里杨树一年生长量与施用氮肥和钾肥的关系，考虑杨树初始高度的影响，分析氮肥和钾肥的施肥量和杨树生长量之间的关系。Between-Subjects FactorsN钾肥量.00612.50625.006氮肥量多9少9Descriptive StatisticsDependent Variable:树苗生长量钾肥量氮肥量MeanStd. DeviationN.00多2.0667.080213少1.8167.202073Total1.9417.19405612.50多2.0600.115333少1.9833.066583Total2.0217.09411625.00多2.2500.050003少2.2500.150003Total2.2500.100006Total多2.1256.119499少2.0167.229739Total2.0711.1862618Levenes Test of Equality of Error VariancesaDependent Variable:树苗生长量Fdf1df2Sig.2.292512.111Tests the null hypothesis that the error variance of the dependent variable is equal across groups.a. Design: Intercept + 初始高度 + 钾肥 + 氮肥 + 钾肥 * 氮肥Tests of Between-Subjects EffectsDependent Variable:树苗生长量SourceType III Sum of SquaresdfMean SquareFSig.Corrected Model.538a6.09019.247.000Intercept.6271.627134.473.000初始高度.1291.12927.602.000钾肥.3132.15733.579.000氮肥.0411.0418.877.013钾肥 * 氮肥.0212.0112.262.150Error.05111.005Total77.80118Corrected Total.59017a. R Squared = .913 (Adjusted R Squared = .866)1. Grand MeanDependent Variable:树苗生长量MeanStd. Error95% Confidence IntervalLower BoundUpper Bound2.071a.0162.0362.107a. Covariates appearing in the model are evaluated at the following values: 树苗初始高度 = 5.6111.2. 钾肥量Dependent Variable:树苗生长量钾肥量MeanStd. Error95% Confidence IntervalLower BoundUpper Bound.001.945a.0281.8832.00612.502.015a.0281.9542.07725.002.253a.0282.1922.315a. Covariates appearing in the model are evaluated at the following values: 树苗初始高度 = 5.6111.3. 氮肥量Dependent Variable:树苗生长量氮肥量MeanStd. Error95% Confidence IntervalLower BoundUpper Bound多2.119a.0232.0692.169少2.023a.0231.9732.073a. Covariates appearing in the model are evaluated at the following values: 树苗初始高度 = 5.6111.4. 钾肥量 * 氮肥量Dependent Variable:树苗生长量钾肥量氮肥量MeanStd. Error95% Confidence IntervalLower BoundUpper Bound.00多1.984a.0421.8912.077少1.906a.0431.8112.00012.50多2.111a.0412.0212.200少1.920a.0411.8292.01125.00多2.263a.0392.1762.350少2.244a.0392.1572.330a. Covariates appearing in the model are evaluated at the following values: 树苗初始高度 = 5.6111.结论：由分析可知，剔除树苗初始高度的影响，树苗生长量与钾肥、氮肥施肥量有显著性差异。习题8试分析表中的全国各地区城镇居民消费性支出和总收入的相关性。Descriptive StatisticsMeanStd. DeviationN总收入12273.29713763.8484931消费性支出8401.46742388.4548231Correlations总收入消费性支出总收入Pearson Correlation1.987*Sig. (2-tailed).000N3131消费性支出Pearson Correlation.987*1Sig. (2-tailed).000N3131*. Correlation is significant at the 0.01 level (2-tailed).结论：由分析可知，总收入和支出的pearson相关系数为0.987，为高度相关。假设检验得出的相伴概率小于显著水平0.01，因此拒绝零假设，即可以用样本相关系数r代替总体相关系数。习题9试分析表中各地区科研投入的人年数和课题总量之间的相关关系。CorrelationsControl Variables投入人年数课题总数投入高级职称的人年数-none-a投入人年数Correlation1.000.959.988Significance (2-tailed).000.000df02929课题总数Correlation.9591.000.944Significance (2-tailed).000.000df29029投入高级职称的人年数Correlation.988.9441.000Significance (2-tailed).000.000.df29290投入高级职称的人年数投入人年数Correlation1.000.507Significance (2-tailed).004df028课题总数Correlation.5071.000Significance (2-tailed).004.df280a. Cells contain zero-order (Pearson) correlations.结论：由分析可知，投入高级职称的人年数对投入人年数和课题总数都有影响，剔除它的影响，采用偏相关分析。投入人年数和课题总数相关系数为0.507，为中度相关，可以用样本相关系数代替总体相关系数。