回归分析GDP和用电量二者之间的关系

1、实验一信息检索一、实验目的掌握信息检索的基本思想和理论，熟悉信息检索的整个过程和必要步骤。二、实验类型设计型实验三、实验仪器硬件：计算机（PIII650,256RAM以上）、网络设备软件：IE四、实验原理遵循信息检索的基本过程，采用适宜的信息检索工具，将所需要的信息检索出来。五、实验内容和要求（1）检索出某一段时间内的第一产业GDP勺数据和用电量。（2）检索出某一段时间内的第二产业GDP勺数据和用电量。（3）检索出某一段时间内的第三产业GDP勺数据和用电量（4）检索出某一段时间内的GDP的数据和用电量。六、实验数据（GDP单位：亿元用电量单位：亿千瓦时）卅-产业第一一产业第二产业GDP总年份用

2、电总量GDP用电量GDP用电量GDP用电量量19842316.1153.63105.727751786.32437208.1387119852564.41663866.63086258525990164136.819862788.7215.34492.732692993.826810275.24560.9198732332565251.63665357429612058.64975.519883865.42746587.240484590.3313.215042.85573.619894265.9296.272784589.25448.4352.616992.3618019905062336.

3、287717.44936.35888.4384.518667.8697219915342.23649102.253087337.142321781.57493.419925866.638311699.55946.99357.445028923.57855.419936963.840416454.46497.811915.7638.535333.98501.119949572.745622445.46932.516179.871248197.99197.5199512135.849028679.57387.319978.5839.660793.79867.8199614015.450333835

4、804523326.21008.771176.610790.3199714441.95144126988.11102.197897311039.11199814817.6498.4239004.28263.4330580.51197.984402.311347.3199914770524.741033.68806.433873.41291.389677.112092.3200014944.7539.545555.99795.1387141481.899214.613508.4200115781.3573.249512.310634.644361.61635.61096

5、55.214682.520021653759053896.81195749898.91837120332.716386200317381.758662436.31394856004.72109135822.818891200421412.761273904.31625864561.32435159878.32173520052242074187364.61847873432.92631183217.4246892006240408321031622135484721.4103879.2822211923.5282482007286276951247991946462459257305.6324

6、5820083370287914900325863131340349831404534268七、思考确实有些数据有一些异常，主要是经济危机对社会的影响，干扰了GDP和用电量的正常波动，但是从整体来说还是符合社会生产力的发展。实验二软件使用一、实验目的掌握常用软件的操作方法，并能够结合实际，加以灵活运用，达到学以致用的目的。二、实验类型设计型实验三、实验仪器硬件：计算机(PIII650,256RAM以上)软件：Excel四、实验原理学习一些常用的统计分析方法，并采用Excel软件来加以实现。五、实验内容和要求实验内容：(1)对实验一得到的四部分数据，采用回归方法来找出GD环口用电量二者之间的关系

7、。实验要求：在上述实验内容中，在进行回归分析的时候，还要有相应的检验，以确保分析结果的可靠性。六、实验数据(1)第一产业GDP与用电量分析dfSSMSSignificanceCoefficients标准误差tStatP-valueLower95%Intercept-7715.6535151335.461516-5.7775186.9317E-06-10478.26613XVariable142.900612752.60842845816.4469193.2732E-1437.5046674Upper95%下限95.0%上限95.0%析11666503547残差23141698400.32418

8、082019476160800.0121666503547270.501163.27317E-14-4953.040904-10478.2661-4953.040948.296558137.504667448.2965581RESIDUALOUTPUTY1-1126.1193973442.2193972-59455179931520.8484091267.85159143266.903348-33.9033482854039.114378-173.714377864991.507981-725.607980876710.96454-1648.9645487900.16

9、9525-2557.96952598715.281167-2848.681167109616.194035-2652.3940351111847.0259-2274.3258981213305.64673-1169.8467321313863.3547152.04530271414340.83852101.06148281513666.869891150.7301091614794.29799-24.297993971715429.22706-484.52706271816874.97771-1093.6777121917595.70801-1058.7080062017424.10556-4

10、2.40555552118539.521492873.1785132224073.70053-1653.7005322327977.65629-3937.6562922422100.272356526.7276552529993.985093708.014909XVariable1LineFitPlot4000003000002000001000000一100000XVariable1由表格中的数据可知，线性拟合模型为:Y=-7715.653515+42.90061275x(2)第二产业GDP与用电量分析SUMMARYOUTPUT回归统计MultipleR0.985051285RSquare0

11、.970326034AdjustedRSquare0.969035861标准误差7038.53499观测值25方差分析dfSSMSFSignificanceF回归分析13725928077037259280770752.09018214.51531E-19残差23113944242149540974.81总计2438398723191Coefficients标准误差tStatP-valueLower95%Intercept-18851.482712600.390173-7.2494823692.22692E-07-24230.7996XVariable16.2595474450.2282485

12、2327.424262654.51531E-195.787379403Upper95%下限95.0%上限95.0%-13472.16582-24230.7996-13472.165826.7317154865.7873794036.731715486RESIDUALOUTPUT观测值预测Y1-1481.2385544586.9385542465.48070133401.11929931610.9778842881.72211644089.7586721161.84132856487.165343100.03465769874.83242-2596.83242712047.52134-4330.

13、121338814374.19512-5271.995123918373.41999-6673.9199861021821.80467-5367.4046731124542.82995-2097.4299471227389.672121289.8278751331506.576482328.4235211432285.326785257.6732231532873.849436130.3505731636272.59594761.0040971742461.410463094.4895381847716.300541795.9994581955993.92608-2097.1260822068

14、456.68504-6020.3850452182916.23964-9011.9396422296812.43497-9447.83496923114814.8934-11652.8934224102984.348721814.6512525143039.19285963.807153XVariable1LineFitPlot400000200000a0-200000XVariable1由表格中的数据可知，线性拟合模型为:Y=-18851.48271+6.259547445xRSquare=0.970326034(3)第三产业GDP与用电量分析SUMMARYOUTPUT回归统计Multipl

15、eRRSquare0.9738652040.948413435AdjustedRSquare0.946170541标准误差8087.60927观测值25方差分析dfSSMSFSignificanceF回归分析残差总计12765853964627658539646422.85252012.63687E-1623150441674565409423.72429162956391Coefficients标准误差tStatP-valueLower95%InterceptXVariable1-9280.3123182659.597778-3.489366850.001978669-14782.10953

16、5.366560161.71988084620.563378132.63687E-1631.80871558Upper95%下限95.0%上限95.0%-3778.515166-14782.10947-3778.51516638.9244047431.8087155838.92440474RESIDUALOUTPUT观测值预测Y残差1-686.23819882472.5381992-120.37323622705.3732363197.92580522795.87419541188.189492385.8105151796.4943252793.80567563189.9367952258.4

17、6320574318.1300641570.26993685679.742631657.3573796634.6397552722.7602451013301.23635-1385.5363461115900.67852279.12148241220413.45159-434.95159441326393.93692-3067.7369181429700.35663-2712.2566281533085.2901-2504.7901011636388.52682-2515.126821743125.85653-4411.8565311848565.23348-4203.633484195568

18、8.0587-5789.1587012065307.76307-9303.0630652176837.26168-12275.961682283769.10747-10336.207472390524.12046-5802.7204622477686.0591226193.5408825114431.915116908.08487400000200000A0-200000XVariable1LineFitPlot10000200003000040000XVariable1由表格中的数据可知，线性拟合模型为：Y=-9280.312318+6.35.36656016xRSquare=0.94841

19、3435(4)总的GDP与用电量分析SUMMARYOUTPUT回归统计MultipleR0.991872662RSquare0.983811377AdjustedRSquare0.983107524标准误差10821.50187观测值25方差分析dfSSMSFSignificanceF回归分析11.63683E+111.63683E+111397.7508364.22211E-22残差232693412764117104902.8总计241.66377E+11CoefficientstStatP-valueLower95%Intercept-35114.78973940.511337-8.91

20、12266696.42127E-09-43266.35844XVariable19.3458823760.24998009637.386506064.22211E-228.828759151Upper95%下限95.0%上限95.0%-26963.221-43266.35844-26963.2219.8630056018.8287591519.863005601RESIDUALOUTPUT观测值预测Y残差123456789101112131415161718192021222324256144.9790395468.7435032764.354788672.9519546-1932.620294-5650.463367-11376.90221-13136.14528-9377.3547-9001.59095-2646.0634363685.1916075446.51511510917.566