第一大组统计学作业.doc

中山大学东校区水质监测数据统计第一大组(沈开慧 吴风焰 苏嘉裕 罗佳慧 杨展翼 谭韵盈(2011级)Spss数据处理：本次实验采集水体的各项水质指标温度、PH、DO、河宽、电导这五个指标进行数据分析。各断面位置如下：（1） 断面1：中大西门桥（2） 断面2：工学院广场（3） 断面3：北实验楼B门前桥（4） 断面4：传设院门前桥（5） 断面5：北学院楼C门前桥（6） 断面6：北望路靠近门牌桥（7） 断面7：北望路期间一条桥（8） 断面8：北望路靠近门牌桥（9） 断面9：逸仙大道牌坊旁（10） 断面10：筱园东路超算中心门前采样点分布如图：1. 对每个水质指标进行二元相关分析：采用Spearman非参数方法进行相关分析。Descriptive StatisticsMeanStd. DeviationNPH5.8960.2680020DO.8605.5805820T26.8365.1639420河宽32.630018.9509120电导.34140.04275320CorrelationsPHDOT河宽电导PHPearson Correlation1-.496*-.483*.171.309Sig. (2-tailed).026.031.470.184N2020202020DOPearson Correlation-.496*1.340.025-.703*Sig. (2-tailed).026.142.917.001N2020202020TPearson Correlation-.483*.3401-.202-.038Sig. (2-tailed).031.142.394.874N2020202020河宽Pearson Correlation.171.025-.2021-.357Sig. (2-tailed).470.917.394.122N2020202020电导Pearson Correlation.309-.703*-.038-.3571Sig. (2-tailed).184.001.874.122N2020202020*. Correlation is significant at the 0.05 level (2-tailed).*. Correlation is significant at the 0.01 level (2-tailed).分析结果：sig表示显著性，2-tailed表示两侧检验，后面的值表示是否差异显著的水平，这个值低于0.05或者0.01就表示有显著性差异。因此如图可见：（1） PH与DO、电导、温度、河宽均无显著相关性；（2） DO与电导有显著相关性，与其他无显著相关性；（3） 温度与其他四个指标无显著相关性；（4） 河宽与其他四个指标我显著相关性；（5） 电导与DO有显著相关性，与其他无显著相关性。2. 对各断面的不同指标(河宽除外)进行方差分析，并以LSD法进行多重比较2.1 各断面PH之间的相关性分析ANOVAPHSum of SquaresdfMean SquareFSig.Between Groups(Combined)1.3459.14976.252.000Linear TermContrast.6361.636324.235.000Deviation.7108.08945.254.000Within Groups.02010.002Total1.36519Multiple ComparisonsPHLSD(I) 断面(J) 断面Mean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Bound12-.10500*.04427.039-.2036-.00643-.06000.04427.205-.1586.03864-.08000.04427.101-.1786.01865-.16500*.04427.004-.2636-.06646-.21500*.04427.001-.3136-.11647-.02500.04427.585-.1236.07368-.76500*.04427.000-.8636-.66649-.69500*.04427.000-.7936-.596410-.30000*.04427.000-.3986-.201421.10500*.04427.039.0064.20363.04500.04427.333-.0536.14364.02500.04427.585-.0736.12365-.06000.04427.205-.1586.03866-.11000*.04427.032-.2086-.01147.08000.04427.101-.0186.17868-.66000*.04427.000-.7586-.56149-.59000*.04427.000-.6886-.491410-.19500*.04427.001-.2936-.096431.06000.04427.205-.0386.15862-.04500.04427.333-.1436.05364-.02000.04427.661-.1186.07865-.10500*.04427.039-.2036-.00646-.15500*.04427.006-.2536-.05647.03500.04427.448-.0636.13368-.70500*.04427.000-.8036-.60649-.63500*.04427.000-.7336-.536410-.24000*.04427.000-.3386-.141441.08000.04427.101-.0186.17862-.02500.04427.585-.1236.07363.02000.04427.661-.0786.11865-.08500.04427.084-.1836.01366-.13500*.04427.012-.2336-.03647.05500.04427.242-.0436.15368-.68500*.04427.000-.7836-.58649-.61500*.04427.000-.7136-.516410-.22000*.04427.001-.3186-.121451.16500*.04427.004.0664.26362.06000.04427.205-.0386.15863.10500*.04427.039.0064.20364.08500.04427.084-.0136.18366-.05000.04427.285-.1486.04867.14000*.04427.010.0414.23868-.60000*.04427.000-.6986-.50149-.53000*.04427.000-.6286-.431410-.13500*.04427.012-.2336-.036461.21500*.04427.001.1164.31362.11000*.04427.032.0114.20863.15500*.04427.006.0564.25364.13500*.04427.012.0364.23365.05000.04427.285-.0486.14867.19000*.04427.002.0914.28868-.55000*.04427.000-.6486-.45149-.48000*.04427.000-.5786-.381410-.08500.04427.084-.1836.013671.02500.04427.585-.0736.12362-.08000.04427.101-.1786.01863-.03500.04427.448-.1336.06364-.05500.04427.242-.1536.04365-.14000*.04427.010-.2386-.04146-.19000*.04427.002-.2886-.09148-.74000*.04427.000-.8386-.64149-.67000*.04427.000-.7686-.571410-.27500*.04427.000-.3736-.176481.76500*.04427.000.6664.86362.66000*.04427.000.5614.75863.70500*.04427.000.6064.80364.68500*.04427.000.5864.78365.60000*.04427.000.5014.69866.55000*.04427.000.4514.64867.74000*.04427.000.6414.83869.07000.04427.145-.0286.168610.46500*.04427.000.3664.563691.69500*.04427.000.5964.79362.59000*.04427.000.4914.68863.63500*.04427.000.5364.73364.61500*.04427.000.5164.71365.53000*.04427.000.4314.62866.48000*.04427.000.3814.57867.67000*.04427.000.5714.76868-.07000.04427.145-.1686.028610.39500*.04427.000.2964.4936101.30000*.04427.000.2014.39862.19500*.04427.001.0964.29363.24000*.04427.000.1414.33864.22000*.04427.001.1214.31865.13500*.04427.012.0364.23366.08500.04427.084-.0136.18367.27500*.04427.000.1764.37368-.46500*.04427.000-.5636-.36649-.39500*.04427.000-.4936-.2964*. The mean difference is significant at the 0.05 level.分析结果：由表可知：对于PH值（1） 断面1与5、6、8、9、10显著相关；（2） 断面2与8、9、10显著相关；（3） 断面3与6、8、9、10显著相关；（4） 断面4与6、8、9、10显著相关；（5） 断面5与1、7、8、9、10显著相关；（6） 断面6与1、3、4、7、8、9显著相关；（7） 断面7与5、6、8、9、10显著相关；（8） 断面8和9彼此无显著相关，与其他断面皆显著相关；（9） 断面10除6外，与其他断面皆显著相关。2.2 各断面DO之间的相关性分析ANOVADOSum of SquaresdfMean SquareFSig.Between Groups(Combined)6.4049.71220330.619.000Linear TermContrast.0781.0782225.494.000Deviation6.3268.79122593.760.000Within Groups.00010.000Total6.40419Multiple ComparisonsDOLSD(I) 断面(J) 断面Mean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Bound12-.75000*.00592.000-.7632-.73683-.77500*.00592.000-.7882-.76184-.67000*.00592.000-.6832-.65685-1.81500*.00592.000-1.8282-1.80186-1.04000*.00592.000-1.0532-1.02687-1.34500*.00592.000-1.3582-1.33188-.05000*.00592.000-.0632-.03689-.05000*.00592.000-.0632-.036810-.41000*.00592.000-.4232-.396821.75000*.00592.000.7368.76323-.02500*.00592.002-.0382-.01184.08000*.00592.000.0668.09325-1.06500*.00592.000-1.0782-1.05186-.29000*.00592.000-.3032-.27687-.59500*.00592.000-.6082-.58188.70000*.00592.000.6868.71329.70000*.00592.000.6868.713210.34000*.00592.000.3268.353231.77500*.00592.000.7618.78822.02500*.00592.002.0118.03824.10500*.00592.000.0918.11825-1.04000*.00592.000-1.0532-1.02686-.26500*.00592.000-.2782-.25187-.57000*.00592.000-.5832-.55688.72500*.00592.000.7118.73829.72500*.00592.000.7118.738210.36500*.00592.000.3518.378241.67000*.00592.000.6568.68322-.08000*.00592.000-.0932-.06683-.10500*.00592.000-.1182-.09185-1.14500*.00592.000-1.1582-1.13186-.37000*.00592.000-.3832-.35687-.67500*.00592.000-.6882-.66188.62000*.00592.000.6068.63329.62000*.00592.000.6068.633210.26000*.00592.000.2468.2732511.81500*.00592.0001.80181.828221.06500*.00592.0001.05181.078231.04000*.00592.0001.02681.053241.14500*.00592.0001.13181.15826.77500*.00592.000.7618.78827.47000*.00592.000.4568.483281.76500*.00592.0001.75181.778291.76500*.00592.0001.75181.7782101.40500*.00592.0001.39181.4182611.04000*.00592.0001.02681.05322.29000*.00592.000.2768.30323.26500*.00592.000.2518.27824.37000*.00592.000.3568.38325-.77500*.00592.000-.7882-.76187-.30500*.00592.000-.3182-.29188.99000*.00592.000.97681.00329.99000*.00592.000.97681.003210.63000*.00592.000.6168.6432711.34500*.00592.0001.33181.35822.59500*.00592.000.5818.60823.57000*.00592.000.5568.58324.67500*.00592.000.6618.68825-.47000*.00592.000-.4832-.45686.30500*.00592.000.2918.318281.29500*.00592.0001.28181.308291.29500*.00592.0001.28181.308210.93500*.00592.000.9218.948281.05000*.00592.000.0368.06322-.70000*.00592.000-.7132-.68683-.72500*.00592.000-.7382-.71184-.62000*.00592.000-.6332-.60685-1.76500*.00592.000-1.7782-1.75186-.99000*.00592.000-1.0032-.97687-1.29500*.00592.000-1.3082-1.28189.00000.005921.000-.0132.013210-.36000*.00592.000-.3732-.346891.05000*.00592.000.0368.06322-.70000*.00592.000-.7132-.68683-.72500*.00592.000-.7382-.71184-.62000*.00592.000-.6332-.60685-1.76500*.00592.000-1.7782-1.75186-.99000*.00592.000-1.0032-.97687-1.29500*.00592.000-1.3082-1.28188.00000.005921.000-.0132.013210-.36000*.00592.000-.3732-.3468101.41000*.00592.000.3968.42322-.34000*.00592.000-.3532-.32683-.36500*.00592.000-.3782-.35184-.26000*.00592.000-.2732-.24685-1.40500*.00592.000-1.4182-1.39186-.63000*.00592.000-.6432-.61687-.93500*.00592.000-.9482-.92188.36000*.00592.000.3468.37329.36000*.00592.000.3468.3732*. The mean difference is significant at the 0.05 level.分析结果：由表可知：对于DO，除了断面8和9彼此无显著相关性外，其他各断面皆显著相关。2.3 各断面温度之间的相关性分析ANOVATSum of SquaresdfMean SquareFSig.Between Groups(Combined).5089.056184.920.000Linear TermContrast.0051.00515.917.003Deviation.5038.063206.045.000Within Groups.00310.000Total.51119Multiple ComparisonsTLSD(I) 断面(J) 断面Mean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Bound12.21500*.01746.000.1761.25393.22500*.01746.000.1861.26394.16500*.01746.000.1261.20395-.03500.01746.073-.0739.00396.18500*.01746.000.1461.22397-.01500.01746.411-.0539.02398.34500*.01746.000.3061.38399.28500*.01746.000.2461.323910-.18500*.01746.000-.2239-.146121-.21500*.01746.000-.2539-.17613.01000.01746.580-.0289.04894-.05000*.01746.017-.0889-.01115-.25000*.01746.000-.2889-.21116-.03000.01746.117-.0689.00897-.23000*.01746.000-.2689-.19118.13000*.01746.000.0911.16899.07000*.01746.002.0311.108910-.40000*.01746.000-.4389-.361131-.22500*.01746.000-.2639-.18612-.01000.01746.580-.0489.02894-.06000*.01746.006-.0989-.02115-.26000*.01746.000-.2989-.22116-.04000*.01746.045-.0789-.00117-.24000*.01746.000-.2789-.20118.12000*.01746.000.0811.15899.06000*.01746.006.0211.098910-.41000*.01746.000-.4489-.371141-.16500*.01746.000-.2039-.12612.05000*.01746.017.0111.08893.06000*.01746.006.0211.09895-.20000*.01746.000-.2389-.16116.02000.01746.279-.0189.05897-.18000*.01746.000-.2189-.14118.18000*.01746.000.1411.21899.12000*.01746.000.0811.158910-.35000*.01746.000-.3889-.311151.03500.01746.073-.0039.07392.25000*.01746.000.2111.28893.26000*.01746.000.2211.29894.20000*.01746.000.1611.23896.22000*.01746.000.1811.25897.02000.01746.279-.0189.05898.38000*.01746.000.3411.41899.32000*.01746.000.2811.358910-.15000*.01746.000-.1889-.111161-.18500*.01746.000-.2239-.14612.03000.01746.117-.0089.06893.04000*.01746.045.0011.07894-.02000.01746.279-.0589.01895-.22000*.01746.000-.2589-.18117-.20000*.01746.000-.2389-.16118.16000*.01746.000.1211.19899.10000*.01746.000.0611.138910-.37000*.01746.000-.4089-.331171.01500.01746.411-.0239.05392.23000*.01746.000.1911.26893.24000*.01746.000.2011.27894.18000*.01746.000.1411.21895-.02000.01746.279-.0589.01896.20000*.01746.000.1611.23898.36000*.01746.000.3211.39899.30000*.01746.000.2611.338910-.17000*.01746.000-.2089-.131181-.34500*.01746.000-.3839-.30612-.13000*.01746.000-.1689-.09113-.12000*.01746.000-.1589-.08114-.18000*.01746.000-.2189-.14115-.38000*.01746.000-.4189-.34116-.16000*.01746.000-.1989-.12117-.36000*.01746.000-.3989-.32119-.06000*.01746.006-.0989-.021110-.53000*.01746.000-.5689-.491191-.28500*.01746.000-.3239-.24612-.07000*.01746.002-.1089-.03113-.06000*.01746.006-.0989-.02114-.12000*.01746.000-.1589-.08115-.32000*.01746.000-.3589-.28116-.10000*.01746.000-.1389-.06117-.30000*.01746.000-.3389-.26118.06000*.01746.006.0211.098910-.47000*.01746.000-.5089-.4311101.18500*.01746.000.1461.22392.40000*.01746.000.3611.43893.41000*.01746.000.3711.44894.35000*.01746.000.3111.38895.15000*.01746.000.1111.18896.37000*.01746.000.3311.40897.17000*.01746.000.1311.20898.53000*.01746.000.4911.56899.47000*.01746.000.4311.5089*. The mean difference is significant at the 0.05 level.分析结果：由表可知：对于温度，无显著相关的断面有：（1）断面1、4、7；（2）断面2、4、6；（3）断面2、3；（4）断面1、5、7；其他断面彼此显著相关。2.4 各断面电导之间的相关性分析ANOVA电导Sum of SquaresdfMean SquareFSig.Between Groups(Combined).0359.004963.578.000Linear TermContrast.0001.00069.388.000Deviation.0348.0041075.352.000Within Groups.00010.000Total.03519Multiple Comparisons电导LSD(I) 断面(J) 断面Mean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Bound12.099000*.002000.000.09454.103463.111500*.002000.000.10704.115964.113000*.002000.000.10854.117465.111000*.002000.000.10654.115466.105500*.002000.000.10104.109967.129500*.002000.000.12504.133968.014500*.002000.000.01004.018969.100000*.002000.000.09554.1044610.072000*.002000.000.06754.0764621-.099000*.002000.000-.10346-.094543.012500*.002000.000.00804.016964.014000*.002000.000.00954.018465.012000*.002000.000.00754.016466.006500*.002000.009.00204.010967.030500*.002000.000.02604.034968-.084500*.002000.000-.08896-.080049.001000.002000.628-.00346.0054610-.027000*.002000.000-.03146-.0225431-.111500*.002000.000-.11596-.107042-.012500*.002000.000-.01696-.008044.001500.002000.471-.00296.005965-.000500.002000.808-.00496.003966-.006000*.002000.013-.01046-.001547.018000*.002000.000.01354.022468-.097000*.002000.000-.10146-.092549-.011500*.002000.000-.01596-.0070410-.039500*.002000.000-.04396-.0350441-.113000*.002000.000-.11746-.108542-.014000*.002000.000-.01846-.009543-.001500.002000.471-.00596.002965-.002000.002000.341-.00646.002466-.007500*.002000.004-.01196-.003047.016500*.002000.000.01204.020968-.098500*.002000.000-.10296-.094049-.013000*.002000.000-.01746-.0085410-.041000*.002000.000-.04546-.0365451-.111000*.002000.000-.11546-.106542-.012000*.002000.000-.01646-.007543.000500.002000.808-.00396.004964.002000.002000.341-.00246.006466-.005500*.002000.020-.00996-.001047.018500*.002000.000.01404.022968-.096500*.002000.000-.10096-.092049-.011000*.002000.000-.01546-.0065410-.039000*.002000.000-.04346-.0345461-.105500*.002000.000-.10996-.101042-.006500*.002000.009-.01096-.002043.006000*.002000.013.00154.010464.007500*.002000.004.00304.011965.005500*.002000.020.00104.009967.024000*.002000.000.01954.028468-.09