計(jì)量經(jīng)濟(jì)學(xué)多元線(xiàn)性回歸多重共線(xiàn)性異方差實(shí)驗(yàn)報(bào)告材料

實(shí)用標(biāo)準(zhǔn)文案實(shí)用標(biāo)準(zhǔn)文案精彩文檔精彩文檔實(shí)用標(biāo)準(zhǔn)文案精彩文檔計(jì)量經(jīng)濟(jì)學(xué)實(shí)驗(yàn)報(bào)告多元線(xiàn)性回歸、多重共線(xiàn)性、異方差實(shí)驗(yàn)報(bào)告一、研究目的和要求：隨著經(jīng)濟(jì)的發(fā)展，人們生活水平的提高，旅游業(yè)已經(jīng)成為中國(guó)社會(huì)新的經(jīng)濟(jì)增長(zhǎng)點(diǎn)。旅游產(chǎn)業(yè)是一個(gè)關(guān)聯(lián)性很強(qiáng)的綜合產(chǎn)業(yè)，一次完整的旅游活動(dòng)包括吃、住、行、游、購(gòu)、娛六大要素，旅游產(chǎn)業(yè)的發(fā)展可以直接或者間接推動(dòng)第三產(chǎn)業(yè)、第二產(chǎn)業(yè)和第一產(chǎn)業(yè)的發(fā)展。尤其是假日旅游，有力刺激了居民消費(fèi)而拉動(dòng)內(nèi)需。2012年，我國(guó)全年國(guó)內(nèi)旅游人數(shù)達(dá)到30.0億人次，同比增長(zhǎng)13.6%，國(guó)內(nèi)旅游收入2.3萬(wàn)億元，同比增長(zhǎng)19.1%。旅游業(yè)的發(fā)展不僅對(duì)增加就業(yè)和擴(kuò)大內(nèi)需起到重要的推動(dòng)作用，優(yōu)化產(chǎn)業(yè)結(jié)構(gòu)，而且可以增加國(guó)家外匯收入，促進(jìn)國(guó)際收支平衡，加強(qiáng)國(guó)家、地區(qū)間的文化交流。為了研究影響旅游景區(qū)收入增長(zhǎng)的主要原因，分析旅游收入增長(zhǎng)規(guī)律，需要建立計(jì)量經(jīng)濟(jì)模型。影響旅游業(yè)發(fā)展的因素很多，但據(jù)分析主要因素可能有國(guó)內(nèi)和國(guó)際兩個(gè)方面，因此在進(jìn)行旅游景區(qū)收入分析模型設(shè)定時(shí)，引入城鎮(zhèn)居民可支配收入和旅游外匯收入為解釋變量。旅游業(yè)很大程度上受其產(chǎn)業(yè)本身的發(fā)展水平和從業(yè)人數(shù)影響，固定資產(chǎn)和從業(yè)人數(shù)體現(xiàn)了旅游產(chǎn)業(yè)發(fā)展規(guī)模的內(nèi)在影響因素，因此引入旅游景區(qū)固定資產(chǎn)和旅游業(yè)從業(yè)人數(shù)作為解釋變量。因此選取我國(guó)31個(gè)省市地區(qū)的旅游業(yè)相關(guān)數(shù)據(jù)進(jìn)行定量分析我國(guó)旅游業(yè)發(fā)展的影響因素。模型設(shè)定根據(jù)以上的分析，建立以下模型Y=β+βX+βX+βX+βX+Ut 1 2 0 1 2 3 3 4 4參數(shù)說(shuō)明：Y——旅游景區(qū)營(yíng)業(yè)收入/萬(wàn)元X——旅游業(yè)從業(yè)人員/人1X——旅游景區(qū)固定資產(chǎn)/萬(wàn)元2X——旅游外匯收入/萬(wàn)美元3X——城鎮(zhèn)居民可支配收入/元4收集到的數(shù)據(jù)如下（見(jiàn)表2.1）：表2.12011年全國(guó)旅游景區(qū)營(yíng)業(yè)收入及相關(guān)數(shù)據(jù)（按地區(qū)分）地地區(qū)營(yíng)業(yè)收入從業(yè)人數(shù)固定資產(chǎn)外匯收入可支配收入北京145249.01145466694252.3054160032903.03天津48712.372478793529.6717555326920.86河北182226.8779643420342.744476518292.23山西29465.0357719121809.745671918123.87內(nèi)蒙古70313.0736264206819.126709720407.57遼寧25665.306481646573.2727131420466.84吉林20389.302906687827.163852817796.57黑龍江38367.8130341137426.279176215696.18上海194762.391106563007.4457511836230.48江蘇316051.651401541195000.6056529726340.73浙江385976.921324591110975.2045417330970.68安安徽79562.7555840139769.0211791818606.13福建155378.9580303151897.6936344424907.40江西54961.664179185528.054150017494.87山東116995.67143026327733.2925507622791.84河南222108.3370164482005.325490318194.80湖北104565.5862767243794.629401818373.87湖南118180.8780615257226.710143418844.05廣東476345.502265391160675.4139061926897.48廣西66195.5549876143982.0310518818854.06海南29081.603075970386.553761518368.95重慶86713.6750160230124.009680620249.70四川218624.0370756464763.525938317899.12貴貴州42214.142768362415.211350716495.01云南135897.9762679348426.0416086118575.62西藏30406.736023462971.031296316195.56陜西48692.1757077154529.1912950518245.23甘肅30949.003128056684.68174014988.68青海638.4387419851.28265915603.31寧夏49509.861219623149.9062017578.92新疆28993.114045152280.364651915513.62數(shù)據(jù)來(lái)源：1.中國(guó)統(tǒng)計(jì)年鑒2012，2.中國(guó)旅游年鑒2012。參數(shù)估計(jì)利用Eviews6.0做多元線(xiàn)性回歸分析步驟如下：創(chuàng)建工作文件雙擊Eviews6.0圖標(biāo)，進(jìn)入其主頁(yè)。在主菜單中依次點(diǎn)擊“File\New\Workfile”，出現(xiàn)對(duì)話(huà)框“WorkfileRange”。本例中是截面數(shù)據(jù)，在workfilestructuretype中選擇“Unstructured/Undated”,在Daterange中填入observations31,點(diǎn)擊ok鍵，完成工作文件的創(chuàng)建。輸入數(shù)據(jù)在命令框中輸入dataYX1X2X3X4,回車(chē)出現(xiàn)“Group”窗口數(shù)據(jù)編輯框，在對(duì)應(yīng)的YX1X2X3X4下輸入相應(yīng)數(shù)據(jù)，關(guān)閉對(duì)話(huà)框?qū)⑵涿麨間roup01,點(diǎn)擊ok,保存。對(duì)數(shù)據(jù)進(jìn)行存盤(pán)，點(diǎn)擊“File/SaveAs”，出現(xiàn)“SaveAs”對(duì)話(huà)框，選擇存入路徑，并將文件命名，再點(diǎn)“ok”。參數(shù)估計(jì)在Eviews6.0命令框中鍵入“LSYCX1X2X3X4”,按回車(chē)鍵，即出現(xiàn)回歸結(jié)果。利用Eviews6.0估計(jì)模型參數(shù)，最小二乘法的回歸結(jié)果如下：表3.1回歸結(jié)果 DependentVariable:Y Method:LeastSquares Date:11/14/13Time:21:14 Sample:131 Includedobservations:31 Coefficient Std.Errort-Statistic Prob.C32390.8339569.490.8185810.4205X10.6036240.3661121.6487410.1112X20.2342650.0412185.6835830.0000X30.0446320.0607550.7346200.4691X4-1.9140342.098257-0.9122020.3700R-squared 0.879720Meandependentvar114619.2AdjustedR-squared0.861215S.D.dependentvar112728.1S.E.ofregression41995.55Akaikeinfocriterion24.27520Sumsquaredresid4.59E+10Schwarzcriterion24.50649Loglikelihood-371.2657Hannan-Quinncriter.24.35060F-statistic47.54049Durbin-Watsonstat2.007191Prob(F-statistic)0.000000 根據(jù)表中的樣本數(shù)據(jù)，模型估計(jì)結(jié)果為 Y^=32390.83+0.603624X +0.234265X+0.044632X-1.914034X 1 2 3 4（39569.49）（0.366112）（0.041218）（0.060755）（2.098257）t=(0.818581)(1.648741)(5.683583)(0.734620)(-0.912202)R2=0.879720R2=0.861215F=47.54049DW=2.007191可以看出，可決系數(shù)R2=0.879720，修正的可決系數(shù)R2=0.861215。說(shuō)明模型的擬合程度還可以。但是當(dāng)α=0.05時(shí)，X、X、X系數(shù)均不能通過(guò)檢 1 2 4驗(yàn)，且X的系數(shù)為負(fù)，與經(jīng)濟(jì)意義不符，表明模型很可能存在嚴(yán)重的多重共線(xiàn)4性。四、模型修正1．多重共線(xiàn)性的檢驗(yàn)與修正檢驗(yàn)選中X1X2X3X4數(shù)據(jù)，點(diǎn)擊右鍵，選擇“Open/asGroup”,在出現(xiàn)的對(duì)話(huà)框中選擇“View/CovarianceAnalysis/correlation”,點(diǎn)擊ok,得到相關(guān)系數(shù)矩陣。計(jì)算各個(gè)解釋變量的相關(guān)系數(shù)，得到相關(guān)系數(shù)矩陣。表4.1相關(guān)系數(shù)矩陣變量X1X2X3X4X11.0000000.8097770.8720930.659239X20.8097771.0000000.7583220.641086X30.8720930.7583221.0000000.716374X40.6592390.6410860.7163741.000000由相關(guān)系數(shù)矩陣可以看出，解釋變量X2、X3之間存在較高的相關(guān)系數(shù)，證實(shí)確實(shí)存在嚴(yán)重的多重共線(xiàn)性。多重共線(xiàn)性修正采用逐步回歸的辦法，檢驗(yàn)和回歸多重共線(xiàn)性問(wèn)題。分別作Y對(duì)X1、X2、X3、X4的一元回歸，在命令窗口分別輸入LSYCX1，LSYCX2,LSYCX3，LSYCX4，并保存，整理結(jié)果如表4.2所示。表4.2一元回歸結(jié)果變量X1X2X3X4參數(shù)估計(jì)值1.9782240.3151200.31694612.54525t統(tǒng)計(jì)量8.63511112.474956.9224794.005547R20.7199830.8429240.6229880.356191R20.7103270.8375080.6099880.333991其中，X2的方程R2最大，以X2為基礎(chǔ)，順次加入其它變量逐步回歸。在命令窗口中依次輸入：LSYCX2X1，LSYCX2X3,LSYCX2X4,并保存結(jié)果，整理結(jié)果如表4.3所示。表4.3加入新變量的回歸結(jié)果（一）變量變量變量X1X2X3X42RX2，X10.711446(2.679575)0.230304(5.891959)0.866053X2，X30.258113(7.016265)0.087950(2.043471)0.853546X2X2，X40.312045(9.319239)0.293708（0.143226）0.831828經(jīng)比較，新加入X1的方程R=0.866053，改進(jìn)最大，而且各個(gè)參數(shù)的t檢驗(yàn)顯著，選擇保留X1，再加入其它新變量逐步回歸，在命令框中依次輸入：LSYCX2X1X3，LSYCX2X1X4，保存結(jié)果，整理結(jié)果如表4.4所示。表4.4加入新變量的回歸結(jié)果（二）變量變量X1X2X3X42RX2，X1，X30.603269(1.652919)0.227087(5.630196)0.024860(0.439370)0.862078X2，X1，X40.773017(2.741794)0.237243(5.833838)-1.364110(-0.701920)0.863581當(dāng)加入X3或X4時(shí)，R均沒(méi)有所增加，且其參數(shù)是t檢驗(yàn)不顯著。從相關(guān)系數(shù)可以看出X3、X4與X1、X2之間相關(guān)系數(shù)較高，這說(shuō)明X3、X4引起了多重共線(xiàn)性，予以剔除。當(dāng)取α=0.05時(shí)，tα/2(n-k-1)=2.048，X1、X2的系數(shù)t檢驗(yàn)均顯著，這是最后消除多重共線(xiàn)性的結(jié)果。修正多重共線(xiàn)性影響后的模型為Y^=0.711446X+0.230304X 1 2（0.265507）（0.039088）t=(2.679575)(5.891959) R2=0.874983 R2=0.866053 F=97.98460DW=1.893654在確定模型以后，進(jìn)行參數(shù)估計(jì)表4.5消除多重共線(xiàn)性后的回歸結(jié)果 DependentVariable:Y Method:LeastSquares Date:11/14/13Time:21:47 Sample:131 Includedobservations:31 Coefficie ntStd.Errort-Statistic Prob.-4316.82C412795.42-0.3373730.7384X10.7114460.2655072.6795750.0122X20.2303040.0390885.8919590.0000R-squared0.874983Meandependentvar114619.2AdjustedR-squared0.866053S.D.dependentvar112728.1S.E.ofregression41257.10Akaikeinfocriterion24.18480Sumsquaredresid4.77E+10Schwarzcriterion24.32357Loglikelihood-371.8644Hannan-Quinncriter.24.23004F-statistic97.98460Durbin-Watsonstat1.893654 Prob(F-statistic) 0.000000 五、異方差檢驗(yàn)在實(shí)際的經(jīng)濟(jì)問(wèn)題中經(jīng)常會(huì)出現(xiàn)異方差這種現(xiàn)象，因此建立模型時(shí)，必須要注意異方差的檢驗(yàn)，否則，在實(shí)際中會(huì)失去意義。（1）檢驗(yàn)異方差由表4.5的結(jié)果，按路徑“View/ResidualTests/HeteroskedasticityTests”，在出現(xiàn)的對(duì)話(huà)框中選擇Specification:White，點(diǎn)擊ok.得到White檢驗(yàn)結(jié)果如下。表5.1White檢驗(yàn)結(jié)果 HeteroskedasticityTest:White F-statistic 3.676733Prob.F(5,25)0.0125Obs*R-squared 13.13613Prob.Chi-Square(5)0.0221ScaledexplainedSS15.97891Prob.Chi-Square(5)0.0069 TestEquation: DependentVariable:RESID^2 Method:LeastSquares Date:11/14/13Time:21:48 Sample:131 Includedobservations:31 Coefficie ntStd.Errort-StatisticProb.C-1.10E+091.11E+09-0.9927790.3303X1-12789.3630151.30-0.4241730.6751X1^20.4207160.2943321.4293930.1653X1*X2-0.1018140.083576-1.2182160.2345X214604.525047.7012.8933010.0078X2^2-0.0024890.008030-0.3099720.7592R-squared0.423746Meandependentvar1.54E+09AdjustedR-squared0.308495S.D.dependentvar2.70E+09S.E.ofregression2.24E+09Akaikeinfocriterion46.07313Sumsquaredresid1.26E+20Schwarzcriterion 46.35068Loglikelihood-708.1335Hannan-Quinncriter.46.16360F-statistic3.676733Durbin-Watsonstat1.542170Prob(F-statistic)0.012464 從上表可以看出，nR2=13.13613，由White檢驗(yàn)可知，在α=0.05下，查2分布表，得臨界值χ2(5)=11.0705,比較計(jì)算的2統(tǒng)計(jì)量與臨界值，因?yàn)?.05nR2=13.13613>χ2(5)=11.0705,所以拒絕原假設(shè)，表明模型存在異方差。0.05（2）異方差的修正①用WLS估計(jì)：選擇權(quán)重w=1/e1^2,其中e1=resid。在命令窗口中輸入genre1=resid,點(diǎn)回車(chē)鍵。在消除多重共線(xiàn)性后的回歸結(jié)果（表4.5的回歸結(jié)果）對(duì)話(huà)框中點(diǎn)擊Estimate/Options/WeithtedLS/TSLS,并在Weight中輸入1/e1^2,點(diǎn)確定，得到如下回歸結(jié)果。表5.2用權(quán)數(shù)1/e1^2的回歸結(jié)果 DependentVariable:Y Method:LeastSquares Date:11/14/13Time:21:49 Sample:131 Includedobservations:31 Weightingseries:1/E1^2 Coefficie ntStd.Errort-StatisticProb.-7074.87 C 3389.4944-18.16425 0.0000 X1 0.788277 0.01369257.57099 0.0000 X2 0.235806 0.000968 243.6786 0.0000 WeightedStatistics R-squared0.999848Meandependentvar31056.56AdjustedR-squared0.999837S.D.dependentvar171821.4S.E.ofregression4.259384Akaikeinfocriterion5.827892Sumsquaredresid507.9857Schwarzcriterion5.966665Loglikelihood-87.33232Hannan-Quinncriter.5.873128F-statistic92014.78Durbin-Watsonstat1.663366Prob(F-statistic)0.000000 UnweightedStatistics R-squared0.871469Meandependentvar114619.2AdjustedR-squared0.862288S.D.dependentvar112728.1S.E.ofregression41832.86Sumsquaredresid4.90E+10Durbin-Watsonstat1.853343 ②修正后的White檢驗(yàn)為在表5.2的回歸結(jié)果中，按路徑“View/ResidualTests/HeteroskedasticityTests”，在出現(xiàn)的對(duì)話(huà)框中選擇Specification:White，點(diǎn)擊ok.得到White檢驗(yàn)結(jié)果如下。表5.3修正后的White檢驗(yàn)結(jié)果 HeteroskedasticityTest:White F-statistic 0.210748Prob.F(2,28) 0.8113 Obs*R-squared 0.459736Prob.Chi-Square(2) 0.7946 ScaledexplainedSS0.595955Prob.Chi-Square(2) 0.7423 TestEquation: DependentVariable:WGT_RESID^2 Method:LeastSquares Date:11/15/13Time:20:29 Sample:131 Includedobservations:31 CollineartestregressorsdroppedfromspecificationCoefficie ntStd.Errort-StatisticProb.C17.639915.9225942.9784100.0059WGT-256.0052728.8280-0.3512560.7280WGT^28.26192623.571550.3505040.7286 R-squared 0.014830Meandependentvar16.38664-0.05553 AdjustedR-squared 9S.D.dependentvar29.69485S.E.ofregression30.50832Akaikeinfocriterion9.765641 Sumsquaredresid 26061.21Schwarzcriterion9.904414-148.367 Loglikelihood 4Hannan-Quinncriter.9.810878 F-statistic 0.210748Durbin-Watsonstat2.081320 Prob(F-statistic) 0.811251 從上表可知nR2==0.459736<χ2(5)=11.0705，證明模型中的異方差已0.05經(jīng)被消除了。異方差修正后的模型為Y^=-7074.873+0.788277X1*+0.235806X2*389.49440.0136920.000968t=(-18.16425)(57.57099)(243.6786) R2=0.999848 R2=0.999837 F=92014.78DW=1.663366其中X*=1/e1^2*X,X*=1/e1^2*X,e1=resid。 1 1 2 2六、自相關(guān)檢驗(yàn)與修正DW檢驗(yàn)在顯著性水平α=0.05，查DW表，當(dāng)n=31，k=2時(shí)，得上臨界值d=1.27，u下臨界值d=1.15，DW=1.663365。因?yàn)閐<DW<4-d ，所以模型不存在l u u序列自相關(guān)。由圖示法也可以看出隨機(jī)誤差項(xiàng)μ不存在自相關(guān)。下圖是殘差及一階滯后殘i差相關(guān)圖。實(shí)用標(biāo)準(zhǔn)文案實(shí)用標(biāo)準(zhǔn)文案精彩文檔精彩文檔實(shí)用標(biāo)準(zhǔn)文案精彩文檔圖6.1殘差與其滯后一階殘差圖LM檢驗(yàn)在表5.2的回歸結(jié)果中，按路徑“View/ResidualTests/SerialCorrelationLMTests”，在出現(xiàn)的對(duì)話(huà)框中選擇Lagstoinclude:1，點(diǎn)擊ok.得到LM檢驗(yàn)結(jié)果如下。表6.1LM檢驗(yàn)結(jié)果 Breusch-GodfreySerialCorrelationLMTest: F-statistic 0.809839Prob.F(1,27) 0.3761 Obs*R-squared 0.902738Prob.Chi-Square(1) 0.3420 TestEquation: DependentVariable:RESID Method:LeastSquares Date:11/14/13Time:21:50 Sample:131 Includedobservations:31 Presamplemissingvaluelaggedresidualssettozero. Weightseries:1/E1^2 Coefficie ntStd.Errort-StatisticProb.C-766.3965937.0314-0.8178980.4206X10.0209900.0270700.7753900.4448X2-0.0012730.001716-0.7420020.4645RESID(-1)-0.0070920.007881-0.8999100.3761WeightedStatistics-0.56451 R-squared 0.029121Meandependentvar 3AdjustedR-squared-0.07875S.D.dependentvar4.0747475S.E.ofregression4.273921Akaikeinfocriterion5.862855 Sumsquaredresid 493.1929Schwarzcriterion6.047885-86.8742 Loglikelihood 5Hannan-Quinncriter.5.923170 F-statistic 0.269946Durbin-Watsonstat1.683210 Prob(F-statistic) 0.846488 UnweightedStatistics R-squared-0.01456 -4021.72 9Meandependentvar 2AdjustedR-squared-0.127299S.D.dependentvar40207.07S.E.ofregression42689.59Sumsquaredresid4.92E+10Durbin-Watsonstat1.69E-08 從上表可以看出，nR2=0.902738，由LM檢驗(yàn)可知，在α=0.05下，查2分布表，得臨界值χ2(5)=11.0705,比較計(jì)算的2統(tǒng)計(jì)量與臨界值，因?yàn)?.05nR2=0.902738<χ2(5)=11.0705,所以接受原假設(shè)，表明模型不存在自相關(guān)。0.05七、模型檢驗(yàn)經(jīng)濟(jì)意義檢驗(yàn)?zāi)Ｐ凸烙?jì)結(jié)果表明，在假定其他變量不變的情況下，當(dāng)景區(qū)固定資產(chǎn)每增長(zhǎng)1元時(shí)，旅游收入增加0.788277元；在假定其他變量不變的情況下，當(dāng)景區(qū)從業(yè)人員每增加1人時(shí)，旅游收入增加0.235806萬(wàn)元。這與理論分析判斷相一致。統(tǒng)計(jì)檢驗(yàn)（1）擬合優(yōu)度：由表中數(shù)據(jù)可得：R2=0.999848，修正的可決系數(shù)為R=0.999837，這說(shuō)明模型對(duì)樣本的擬合很好。（2）F檢驗(yàn)：針對(duì)H0：β1=β2=0，給定顯著性水平α=0.05，在F分布表中查出自由度為k=2和n-k-1=28的臨界值F(2，28)=3.34。由表中得到αF=92014.78，由于F=92014.78>F(2，28)=3.34，應(yīng)拒絕原假設(shè)，說(shuō)明回歸α方程顯著，即“旅游景區(qū)固定資產(chǎn)”、“旅游從業(yè)人員”等變量聯(lián)合起來(lái)確實(shí)對(duì)“旅游景區(qū)營(yíng)業(yè)收入”有顯著影響。（3）t檢驗(yàn)：分別對(duì)H0：βj=0(j=1,2),給定顯著性水平α=0.05，查t分布表得自由度為n-k-1=28臨界值tα/2(n-k-1)=2.048。由表中數(shù)據(jù)可得，^1、^2對(duì)應(yīng)的t統(tǒng)計(jì)量分別為57.57099、243.6786，其絕對(duì)值均大于tα/2(n-k-1)=2.048，這說(shuō)明應(yīng)該分別拒絕H0：βj=0(j=1,2),也就是說(shuō)，當(dāng)在其他解釋變量不變的情況下，解釋變量“旅游景區(qū)固定資產(chǎn)”（X1）、“旅游從業(yè)人數(shù)”（X2）分別對(duì)被解釋變量“旅游景區(qū)營(yíng)業(yè)收入”(Y)影響顯著。八、附錄以下是多重共線(xiàn)性參數(shù)估計(jì)備表1對(duì)X回歸分析1 DependentVariable:Y Method:LeastSquares Date:11/14/13Time:21:14 Sample:131 Includedobservations:31 Coefficie ntStd.Errort-StatisticProb.C-15595.6 118604.86-0.838255 0.4087X11.978224 0.229091 8.635111 0.0000R-squared0.719983Meandependentvar114619.2AdjustedR-squared0.710327S.D.dependentvar112728.1S.E.ofregression60671.69Akaikeinfocriterion24.92668Sumsquaredresid1.07E+11Schwarzcriterion 25.01920Loglikelihood-384.3636Hannan-Quinncriter.24.95684F-statistic74.56515Durbin-Watsonstat2.090544Prob(F-statistic)0.000000 備表2對(duì)X回歸分析2 DependentVariable:Y Method:LeastSquares Date:11/14/13Time:21:15 Sample:131 Includedobservations:31 Coefficie ntStd.Errort-StatisticProb.C15958.73 11364.71 1.404236 0.1709X20.315120 0.025260 12.47495 0.0000R-squared0.842924Meandependentvar114619.2AdjustedR-squared0.837508S.D.dependentvar112728.1S.E.ofregression45441.05Akaikeinfocriterion24.34856Sumsquaredresid5.99E+10Schwarzcriterion 24.44108Loglikelihood-375.4027Hannan-Quinncriter.24.37872F-statistic155.6243Durbin-Watsonstat1.665119Prob(F-statistic)0.000000 備表3對(duì)X回歸分析3 DependentVariable:Y Method:LeastSquares Date:11/14/13Time:21:15 Sample:131 Includedobservations:31 Coefficie ntStd.Errort-StatisticProb. C 53599.95 15413.41 3.477488 0.0016 X3 0.316946 0.045785 6.922479 0.0000R-squared0.622988Meandependentvar114619.2AdjustedR-squared0.609988S.D.dependentvar112728.1S.E.ofregression70399.77Akaikeinfocriterion25.22411Sumsquaredresid1.44E+11Schwarzcriterion 25.31662Loglikelihood-388.9737Hannan-Quinncriter.25.25427F-statistic47.92072Durbin-Watsonstat1.724195Prob(F-statistic)0.000000 備表4對(duì)X回歸分析4 DependentVariable:Y Method:LeastSquares Date:11/14/13Time:21:15 Sample:131 Includedobservations:31 Coefficie ntStd.Errort-StatisticProb.-143904. C 966622.99-2.159989 0.0392 X4 12.54525 3.131970 4.005547 0.0004 R-squared 0.356191Meandependentvar114619.2AdjustedR-squared0.333991S.D.dependentvar112728.1S.E.ofregression91996.75Akaikeinfocriterion25.75923 Sumsquaredresid2.45E+11Schwarzcriterion 25.85175-397.268 Loglikelihood 1Hannan-Quinncriter.25.78939 F-statistic 16.04440Durbin-Watsonstat1.829839 Prob(F-statistic) 0.000394 備表5對(duì)X、X回歸分析 2 1 DependentVariable:Y Method:LeastSquares Date:11/14/13Time:21:15 Sample:131 Includedobservations:31 Coefficie ntStd.Errort-StatisticProb.-4316.82 C 412795.42-0.337373 0.7384 X2 0.230304 0.039088 5.891959 0.0000 X1 0.711446 0.265507 2.679575 0.0122 R-squared 0.874983Meandependentvar114619.2AdjustedR-squared0.866053S.D.dependentvar112728.1S.E.ofregression41257.10Akaikeinfocriterion24.18480 Sumsquaredresid4.77E+10Schwarzcriterion 24.32357-371.864 Loglikelihood 4Hannan-Quinncriter.24.23004 F-statistic 97.98460Durbin-Watsonstat1.893654 Prob(F-statistic) 0.000000 備表6對(duì)X、X回歸分析 2 3 DependentVariable:Y Method:LeastSquares Date:11/14/13Time:21:15 Sample:131 Includedobservations:31 Coefficie ntStd.Errort-StatisticProb. C 16874.53 10798.59 1.562660 0.1294 X2 0.258113 0.036788 7.016265 0.0000 X3 0.087950 0.043040 2.043471 0.0505 R-squared 0.863310Meandependentvar114619.2AdjustedR-squared0.853546S.D.dependentvar112728.1S.E.ofregression43140.27Akaikeinfocriterion24.27407 Sumsquaredresid5.21E+10Schwarzcriterion 24.41284-373.248 Loglikelihood 0Hannan-Quinncriter.24.31930 F-statistic 88.42123Durbin-Watsonstat1.600090 Prob(F-statistic) 0.000000 備表7對(duì)X、X回歸分析 2 4 DependentVariable:Y Method:LeastSquares Date:11/14/13Time:21:15 Sample:131 Includedobservations:31 Coefficie ntStd.Errort-StatisticProb. C 10868.79 37371.23 0.290833 0.7733 X2 0.312045 0.033484 9.319239 0.0000 X4 0.293708