1、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26%0.0%26%物理 * 性别26%0.0%26%Report性别数学物理男生MeanN1313Std. DeviationMinimumMaximum女生MeanN1313Std. DeviationMinimumMaximumTotalMeanN2626Std. DeviationMinimumMaximum结论:男生数学
2、成绩 最高分: 95 最低分: 72 平均分: 物理成绩 最高分: 87 最低分: 69 平均分: 女生数学成绩 最高分: 99 最低分: 70 平均分: 物理成绩 最高分: 91 最低分: 65 平均分: 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26One-Sample TestTest Value = 85 tdfSig. (2-tailed)Mean Difference95% Confidence Interval of the DifferenceL
3、owerUpper数学25.004结论:由分析可知相伴概率为,小于显著性水平,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011.3628400118.3065729Independent Samples TestLevenes Test for Equality of Variancest-test for Equality of MeansFSig.tdfSig. (2-tailed)Mean Di
4、fferenceStd. Error Difference95% Confidence Interval of the DifferenceLowerUpper二月份气温Equal variances assumed.32227.011.4843246Equal variances not assumed.010.4750156结论:由分析可知, 方差相同检验相伴概率为,大于显著性水平,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, 小于显著性水平,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 Sa
5、mples StatisticsMeanNStd. DeviationStd. Error MeanPair 1锻炼前15锻炼后15Paired Samples CorrelationsNCorrelationSig.Pair 1锻炼前 & 锻炼后15.277Paired Samples TestPaired DifferencestdfSig. (2-tailed)MeanStd. DeviationStd. Error Mean95% Confidence Interval of the DifferenceLowerUpperPair 1锻炼前 - 锻炼后14.001结论:由分析可知,锻
6、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)4.016Linear TermContrast1.047Deviation3.025Within Groups15Total19Multip
7、le ComparisonsDependent Variable:产量(I) 品种(J) 品种Mean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper BoundLSD12.5093.7734.2895*.01921.5093.7074.6795*.00531.7732.7074.4345*.01041.2892.6793.4345*.00251*.0192*.0053*.0104*.002*. The mean difference is significant at the level.产量品种NS
8、ubset for alpha = 12Student-Newman-Keulsa5414342444Sig.696Means for groups in homogeneous subsets are displayed.a. Uses Harmonic Mean Sample Size = .结论:由以上分析可知,F统计量F(4,15)=,对应的相伴概率为,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
9、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 Model433.167a5.002Intercept1.000equipment2.001staff3.022Error6Total12Corrected Total11 设备 * 员工Dependent Variable:日生产量设备员工MeanStd. Error95% Confiden
10、ce IntervalLower BoundUpper Bound112342123431234Multiple ComparisonsDependent Variable:日生产量(I) 员工(J) 员工Mean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper BoundLSD12*.0093*.0134.33521*.0093.7394*.03131*.0132.67.7394.050.0141.3352*.031.663.050Based on observed means. The error
11、term is Mean Square(Error) = .日生产量员工NSubset12Student-Newman-Keulsa,b23334313Sig.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*.0023.07921*.0023*.00031.079.552*.000日生产量设备NSubset12Student-Newman-Keuls
12、a,b341424Sig.079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.00666氮肥量多9少9Descriptive StatisticsDependent Variable:树苗生长量钾肥量氮肥量MeanStd. DeviationN.00多.080213少.202073Total.194056多
13、.115333少.066583Total.094116多.050003少.150003Total.100006Total多.119499少.229739Total.1862618Levenes Test of Equality of Error VariancesaDependent Variable:树苗生长量Fdf1df2Sig.512.111Tests the null hypothesis that the error variance of the dependent variable is equal across groups.a. Design: Intercept + 初始高
14、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.538a6.090.000Intercept.6271.627.000初始高度.1291.129.000钾肥.3132.157.000氮肥.0411.041.013钾肥 * 氮肥.0212.011.150Error.05111.005Total18Corrected Total.59017a. R Squared =
15、 .913 (Adjusted R Squared = .866)1. Grand MeanDependent Variable:树苗生长量MeanStd. Error95% Confidence IntervalLower BoundUpper Bound2.071a.016a. Covariates appearing in the model are evaluated at the following values: 树苗初始高度 = .2. 钾肥量Dependent Variable:树苗生长量钾肥量MeanStd. Error95% Confidence IntervalLower
16、 BoundUpper Bound.001.945a.0282.015a.0282.253a.028a. Covariates appearing in the model are evaluated at the following values: 树苗初始高度 = .3. 氮肥量Dependent Variable:树苗生长量氮肥量MeanStd. Error95% Confidence IntervalLower BoundUpper Bound多2.119a.023少2.023a.023a. Covariates appearing in the model are evaluated
17、 at the following values: 树苗初始高度 = .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.042少1.906a.043多2.111a.041少1.920a.041多2.263a.039少2.244a.039a. Covariates appearing in the model are evaluated at the following values: 树苗初始高度 = .结论:由分析可知
18、,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31消费性支出31Correlations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 level (2-tailed).结
19、论:由分析可知,总收入和支出的pearson相关系数为,为高度相关。假设检验得出的相伴概率小于显著水平,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Correlation.959.988Significance (2-tailed).000.000df02929课题总数Correlation.959.944Significance (2-tailed).000.000df29029投入高级职称的人年数
20、Correlation.988.944Significance (2-tailed).000.000.df29290投入高级职称的人年数投入人年数Correlation.507Significance (2-tailed).004df028课题总数Correlation.507Significance (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,为中度相关,可以用样本相关系数代替总体相关系数。
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