spss的試題答案結(jié)果

1、 統(tǒng)計(jì)復(fù)習(xí)題目一.某公司管理人員為了解某化妝品在一個(gè)城市的月銷(xiāo)售量Y（單位：箱）與該城市中適合使用該化妝品的人數(shù)（單位：千人）以及他們 人均月收入（單位：元）之間的關(guān)系，在某個(gè)月中對(duì)15個(gè)城市做調(diào)查，得上述各量的觀(guān)測(cè)值如表A1所示.假設(shè)Y與，之間滿(mǎn)足線(xiàn)性回歸關(guān)系 其中獨(dú)立同分布于.(1)求回歸系數(shù)的最小二乘估計(jì)值和誤差方差的估計(jì)值，寫(xiě)出回歸方程并對(duì)回歸系數(shù)作解釋?zhuān)籥nalyze-regression-linear,y to dependent,x1 x2 to indepents ,statistics-confidence intervals,save-unstandardized. Pre

2、diction individual-individual.ok CoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.95% Confidence Interval for BBStd. ErrorBetaLower BoundUpper Bound1(Constant)3.4532.4311.420.181-1.8438.749x1.496.006.93481.924.000.483.509x2.009.001.1089.502.000.007.011a. Dependent Variable:

3、 yANOVAbModelSum of SquaresdfMean SquareFSig.1Regression53844.716226922.3585.679E3.000aResidual56.884124.740Total53901.60014a. Predictors: (Constant), x2, x1b. Dependent Variable: y回歸系數(shù)的最小二乘估計(jì)值和誤差方差的估計(jì)值分別為：3.453,0.496,0.009和=4.740. 回歸方程為y=0.496*x1+0.009*x2+3.453 回歸系數(shù)解釋?zhuān)?.453可理解為化妝品的月基本銷(xiāo)售量，當(dāng)人均月收入固定時(shí)，

4、適合使用該化妝品的人數(shù)每提高一個(gè)單位，月銷(xiāo)售量Y將增加0.496個(gè)單位；當(dāng)適合使用該化妝品的人數(shù)固定時(shí)，人均月收入每提高一個(gè)單位，月銷(xiāo)售量 Y將增加0.009個(gè)單位(2)求出方差分析表，解釋對(duì)線(xiàn)性回歸關(guān)系顯著性檢驗(yàn)的結(jié)果.求復(fù)相關(guān)系數(shù)的平方的值并解釋其意義；ANOVAbModelSum of SquaresdfMean SquareFSig.1Regression53844.716226922.3585.679E3.000aResidual56.884124.740Total53901.60014a. Predictors: (Constant), x2, x1b. Dependent Var

5、iable: yModel SummaryModelRR SquareAdjusted R SquareStd. Error of the Estimate1.999a.999.9992.17722a. Predictors: (Constant), x2, x1由于P值=0.000<0.05，所以回歸關(guān)系顯著.值=0.999，說(shuō)明Y與，之間的線(xiàn)性回歸關(guān)系是高度顯著的(3)分別求和的置信度為的置信區(qū)間；coefficients的后面部分.和的置信度為的置信區(qū)間分別為（0.483，0.509），（0.007,0.011）(4)對(duì)，分別檢驗(yàn)人數(shù)及收入對(duì)銷(xiāo)量Y的影響是否顯著；由于系數(shù)，對(duì)應(yīng)的檢

6、驗(yàn)P值分別為0.000，0.000都小于0.05，所以適合使用該化妝品的人數(shù)和人均月收入 對(duì)月銷(xiāo)售量Y的影響是顯著的(5)該公司欲在一個(gè)適宜使用該化妝品的人數(shù)，人均月收入的新城市中銷(xiāo)售該化妝品，求其銷(xiāo)量的預(yù)測(cè)值及置信為0.95的置信區(qū)間.Y的預(yù)測(cè)值及置信度為0.95的置信區(qū)間分別為：135.5741和（130.59977，140.54305）在數(shù)據(jù)表中直接可以看見(jiàn)二、某班42名男女學(xué)生全部參加大學(xué)英語(yǔ)四級(jí)水平考試，數(shù)據(jù)如下：（數(shù)據(jù)表為A2）不合格1合格2男生1262女生286問(wèn)男女生在英語(yǔ)學(xué)習(xí)水平上有無(wú)顯著差異？單擊weight cases-weight cases by-x, ok, ana

7、lyze-descriptive statistics-crosstabs,(列聯(lián)表分析)sex to rows,score to column, exact-exact, statistics chi-square ,ok.Chi-Square TestsValuedfAsymp. Sig. (2-sided)Exact Sig. (2-sided)Exact Sig. (1-sided)Point ProbabilityPearson Chi-Square7.721a1.005.010.010Continuity Correctionb5.5781.018Likelihood Ratio7

8、.3691.007.037.010Fisher's Exact Test.010.010Linear-by-Linear Association7.537c1.006.010.010.010N of Valid Cases42a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 2.67.b. Computed only for a 2x2 tablec. The standardized statistic is 2.745.原假設(shè)不顯著，看這個(gè)（Asymp. Sig. (2

9、-sided)）。Pearson Chi-Square（卡方檢驗(yàn)） and Likelihood Ratio（似然比） all <0.05 男女生在英語(yǔ)學(xué)習(xí)水平上差異是顯著的三、將一塊耕地等分為24個(gè)小區(qū)，今有3種不同的小麥品種(d)和2種不同的肥料(B1,B2)，現(xiàn)將各小麥品種與各種肥料進(jìn)行搭配，對(duì)每種搭配都在4個(gè)小區(qū)上試驗(yàn)，測(cè)得每個(gè)小區(qū)產(chǎn)量的數(shù)據(jù)如表A3所示.（1）假設(shè)所給數(shù)據(jù)服從方差分析模型，建立方差分析表，A與B的交互效應(yīng)在下是否顯著？3.0Analyze-general linear model-univariate,x to dependent variable,a and

10、b to fixed factor, ok Tests of Between-Subjects EffectsDependent Variable:xSourceType III Sum of SquaresdfMean SquareFSig.Corrected Model263.333a552.66721.545.0003650.66713650.6671.493E3.000a190.333295.16738.932.000b54.000154.00022.091.000a * b19.00029.5003.886.040Error44.000182.444Total3958.00024Co

11、rrected Total307.33323a. R Squared = .857 (Adjusted R Squared = .817)由于交互效應(yīng)檢驗(yàn)P值=0.04<0.05，所以小麥(A)與肥料(B)之間的交互效應(yīng)是顯著的.（2）若A與B的交互效應(yīng)顯著，分別就B的各水平，給出在A的各水平上的均值的置信度為0.95 的置信區(qū)間以及兩兩之差的置信度不小于0.95的Bonferroni同時(shí)置信區(qū)間.3.1.Analyze-general linear model-univariate,x to dependent variable,a to fixed factor,post hoc-a

12、 to post hoc tests for, bonferroni,options-a to display means for.ok aDependent Variable:xaMeanStd. Error95% Confidence IntervalLower BoundUpper Bound19.000.6877.44510.555210.000.6878.44511.555313.500.68711.94515.055Multiple ComparisonsxBonferroni(I) a(J) aMean Difference (I-J)Std. ErrorSig.95% Conf

13、idence IntervalLower BoundUpper Bound12-1.00.972.991-3.851.853-4.50*.972.004-7.35-1.65211.00.972.991-1.853.853-3.50*.972.017-6.35-.65314.50*.972.0041.657.3523.50*.972.017.656.35Based on observed means. The error term is Mean Square(Error) = 1.889.*. The mean difference is significant at the .05 leve

14、l.固定肥料的水平，的置信度為0.95的置信區(qū)間分別為(7.445,10.555),(8.445,11.555),(11.945,15.055);的置信度不小于0.95的Bonferroni同時(shí)置信區(qū)間分別為（-3.85,1.85）,（-7.35,-1.65）,（-6.35,-0.65）2. Analyze-general linear model-univariate, x to dependent variable,a to fixed factor,post hoc-a to post hoc tests for,bonferroni,options-a to display means

15、 for,.ok aDependent Variable:xaMeanStd. Error95% Confidence IntervalLower BoundUpper Bound110.500.8668.54112.459212.000.86610.04113.959319.000.86617.04120.959Multiple ComparisonsxBonferroni(I) a(J) aMean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Bound12-1.501.225.755-5.09

16、2.093-8.50*1.225.000-12.09-4.91211.501.225.755-2.095.093-7.00*1.225.001-10.59-3.41318.50*1.225.0004.9112.0927.00*1.225.0013.4110.59Based on observed means. The error term is Mean Square(Error) = 3.000.*. The mean difference is significant at the .05 level.固定肥料的水平，的置信度為0.95的置信區(qū)間分別(8.541,12.459),(10.0

17、41,13.959),(17.041,20.959)的置信度不小于0.95的Bonferroni同時(shí)置信區(qū)間分別為（-5.09,2.09）,（-12.09,-4.91）,（-10.59,-3.41）四、數(shù)據(jù)表A4給出了我國(guó)31個(gè)省市自治區(qū)的的經(jīng)濟(jì)發(fā)展?fàn)顩r，所考察的八個(gè)指標(biāo)為：地區(qū)生產(chǎn)總值；：居民消費(fèi)水平；：基本建設(shè)投資；職工平均工資； ：居民消費(fèi)價(jià)格指數(shù)；：商品零售價(jià)格指數(shù)；：貨物周轉(zhuǎn)量；：工業(yè)總產(chǎn)值。(1)從樣本相關(guān)系數(shù)矩陣出發(fā)做主成分分析，求各主成分的貢獻(xiàn)率及前三個(gè)主成分的累計(jì)貢獻(xiàn)率；求出前三個(gè)主成分的表達(dá)式。Analyze-data-reduction-factor將八個(gè)成分全部選入va

18、riables，extraction-extract-number of factors-8,okTotal Variance ExplainedComponentInitial EigenvaluesExtraction Sums of Squared LoadingsTotal% of VarianceCumulative %Total% of VarianceCumulative %13.74146.76146.7613.74146.76146.76122.39429.92676.6872.39429.92676.6873.7389.23185.918.7389.23185.9184.4

19、806.00691.9235.4375.46697.3896.1421.77699.1657.060.74599.9108.007.090100.000Extraction Method: Principal Component Analysis.Component MatrixaComponent12345678地區(qū)生產(chǎn)總值.814.556-.116.031-.035-.028-.094-.061居民消費(fèi)水平.766-.493.195-.076.212-.285.005.006基本建設(shè)投資.785.558-.141.085-.083-.013.196.003職工平均工資.604-.572.0

20、16.465.264.149-.002-.002居民消費(fèi)價(jià)格指數(shù)-.314.599.666.298-.091-.051-.007.001商品零售價(jià)格指數(shù)-.397.721-.006-.131.552.029.013.000貨物周轉(zhuǎn)量.761-.181.458-.380-.005.185.017-.004工業(yè)總產(chǎn)值.823.540-.116.020-.042.019-.109.058Undefined error #11401 - Cannot open text file "F:SPSSspsslangenspss.err": No such file or direa.

21、8 components extracted.各主成分的貢獻(xiàn)率分別為46.761%，29.926%，9.231%，6.006%，5.466%，1.776%，0.745%，0.09%.前三個(gè)主成分的累計(jì)貢獻(xiàn)率為85.918%.y1=0.814x1+0.766x2+0.785x3+0.604x4-0.314x5-0.397x6+0.761x7+0.823x8y2=0.556x1-0.493x2+0.558x3-0.572x4+0.599x5+0.721x6-0.181x7+0.540x8(2)本相關(guān)系數(shù)矩陣出發(fā)做因子分析，提取三個(gè)公共因子F1，F(xiàn)2，F(xiàn)3，說(shuō)明每個(gè)公共因子各由哪些指標(biāo)解釋?zhuān)⒔忉屆?/p>

22、個(gè)公共因子的具體意義。1.求出三個(gè)公共因子F1，F(xiàn)2，F(xiàn)3的表達(dá)式。Analyze-data-reduction-factor將八個(gè)成分全部選入variables，extraction-extract-number of factors-3,descriptives-correlation matrix- coefficients, rotation-method- varimax, scores-save as variables,display factor score coefficient matrix, okComponent Score Coefficient MatrixComp

23、onent123地區(qū)生產(chǎn)總值.341-.075-.062居民消費(fèi)水平-.031.380.092基本建設(shè)投資.343-.097-.089職工平均工資-.036.258-.125居民消費(fèi)價(jià)格指數(shù)-.085.220.910商品零售價(jià)格指數(shù).114-.254.157貨物周轉(zhuǎn)量-.021.468.460工業(yè)總產(chǎn)值.339-.069-.065Undefined error #11401 - Cannot open text file "F:SPSSspsslangenspss.err": No such file or dire Undefined error #11408 - Can

24、not open text file "F:SPSSspsslangenspss.err": No such file or direF1=0.341x1-0.031x2+0.343x3-0.036x4-0.085x5+0.114x6-0.021x7+0.339x82.根據(jù)三個(gè)公共因子F1，F(xiàn)2，F(xiàn)3的得分，對(duì)31個(gè)省市自治區(qū)進(jìn)行分層聚類(lèi)分析，要求樣本間用歐氏平方距離，類(lèi)間用類(lèi)內(nèi)平均連接法，如果聚為4類(lèi)，寫(xiě)出每一類(lèi)成員。Analyze-classify-hierarchical cluster,F1.F2.F3 to variables,地區(qū) to label cases

25、by, statistics-cluster member ship-single solution-number of cluster-4. method-cluster method-median clustering,save- cluster member ship-single solution-number of cluster-4.ok 分類(lèi)在表的最后一列可以讀出。五、表B1給出了煤凈化過(guò)程的一組數(shù)據(jù)，Y為凈化后煤溶液中所含雜質(zhì)的重量，這是衡量?jī)艋实闹笜?biāo)，X1表示輸入凈化過(guò)程的溶液所含的煤與雜質(zhì)的比，X2是溶液的PH值，X3是溶液的流量。假設(shè)Y與，和之間滿(mǎn)足線(xiàn)性回歸關(guān)系 其中

26、獨(dú)立同分布于.(1) 求回歸系數(shù)的最小二乘估計(jì)值和誤差方差的估計(jì)值，寫(xiě)出回歸方程并對(duì)回歸系數(shù)作解釋?zhuān)籥nalyze-regression-linear,y to dependent,x1 x2 x3to independent ,statistics-confidence intervals, save-unstandardized. Prediction individual-individual .ok CoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.95% Confidence Inte

27、rval for BBStd. ErrorBetaLower BoundUpper Bound1(Constant)397.08762.7576.327.000252.370541.805x1-110.75014.762-.841-7.502.000-144.792-76.708x215.5834.921.3553.167.0134.23626.931x3-.058.026-.255-2.274.053-.117.001a. Dependent Variable: yANOVAbModelSum of SquaresdfMean SquareFSig.1Regression31156.0243

28、10385.34123.827.000aResidual3486.8928435.862Total34642.91711a. Predictors: (Constant), x3, x2, x1b. Dependent Variable: y回歸系數(shù)的最小二乘估計(jì)值和誤差方差的估計(jì)值分別為:397.087,-110.75,15.583,-0.058和435.862y=-110.750*x1+15.583*x2-0.058*x3+397.087回歸系數(shù)解釋?zhuān)?97.087可理解為雜質(zhì)的基本重量，當(dāng)PH值和溶液流量固定時(shí)，輸入凈化過(guò)程的溶液所含的煤與雜質(zhì)的比 每提高一個(gè)單位，雜質(zhì)的重量 Y將減少1

29、10.75個(gè)單位；當(dāng)輸入凈化過(guò)程的溶液所含的煤與雜質(zhì)的比和溶液流量固定時(shí)，PH值每提高一個(gè)單位，雜質(zhì)的重量Y將增加15.583個(gè)單位；當(dāng)輸入凈化過(guò)程的溶液所含的煤與雜質(zhì)的比和PH值固定時(shí)，溶液流量每提高一個(gè)單位，雜質(zhì)的重量Y將減少0.058個(gè)單位。(2)求出方差分析表，解釋對(duì)線(xiàn)性回歸關(guān)系顯著性檢驗(yàn)的結(jié)果.求復(fù)相關(guān)系數(shù)的平方的值并解釋其意義；ANOVAbModelSum of SquaresdfMean SquareFSig.1Regression31156.024310385.34123.827.000aResidual3486.8928435.862Total34642.91711a. Pr

30、edictors: (Constant), x3, x2, x1b. Dependent Variable: yModel SummaryModelRR SquareAdjusted R SquareStd. Error of the Estimate1.948a.899.86220.87730a. Predictors: (Constant), x3, x2, x1由于P值=0.000<0.05，所以回歸關(guān)系顯著.值=0.899，說(shuō)明Y與，之間的線(xiàn)性回歸關(guān)系是顯著的(3)分別求，和的置信度為的置信區(qū)間；coefficients的后面部分，和的置信度為的置信區(qū)間分別為（-144.792，

31、-76.708），（4.236，26.931），（-0.117，0.001）(4)對(duì)，分別檢驗(yàn)， 和對(duì)Y的影響是否顯著；由于系數(shù)，對(duì)應(yīng)的檢驗(yàn)P值分別為0.000，0.013都小于0.05，所以和 對(duì)Y的影響是顯著的.而對(duì)應(yīng)的檢驗(yàn)P值為0.053大于0.05，所以對(duì)Y的影響是不顯著的。(5)若有，的值，求Y的預(yù)測(cè)值及置信度為0.95的置信區(qū)間.Y的預(yù)測(cè)值及置信度為0.95的置信區(qū)間分別為：218.64484和（166.93687，270.35282）在數(shù)據(jù)表中直接可以看見(jiàn)六、考察四種不同催化劑對(duì)某一化工產(chǎn)品得率的影響，在四種不同催化劑下分別做了6次實(shí)驗(yàn)，得數(shù)據(jù)如表B2所示.假定各種催化劑下產(chǎn)品的

32、得率服從同方差的正態(tài)分布，試在下，檢驗(yàn)四種不同催化劑對(duì)該化工產(chǎn)品的得率有無(wú)顯著影響.要寫(xiě)出方差分析表。方差分析表:Analyzecompare means -one-way anova,x to dependent list,a to factor ,okANOVAxSum of SquaresdfMean SquareFSig.Between Groups.0063.0021.306.300Within Groups.03020.001Total.03623由于檢驗(yàn)P值=0.300>0.05，所以認(rèn)為四種不同催化劑對(duì)該化工產(chǎn)品的得率在水平0.05下無(wú)顯著差異。 七、為了研制一種治療枯草

33、熱病的藥物，將兩種成分（A和B）各按三種不同劑量（低、中、高）混合，將36位自愿受試患者隨機(jī)分為9組，每組4人服用各種劑量混合下的藥物，記錄其病情緩解的時(shí)間（單位：小時(shí)）數(shù)據(jù)如表B3所示.（1）假設(shè)所給數(shù)據(jù)服從方差分析模型，建立方差分析表，A與B的交互效應(yīng)在下是否顯著？B3.0.Analyze-general linear model-univariate,x to dependent variable,a and b to fixed factor, okTests of Between-Subjects EffectsDependent Variable:xSourceType III S

34、um of SquaresdfMean SquareFSig.Corrected Model373.105a846.638774.910.0001857.61011857.6103.086E4.000a220.0202110.0101.828E3.000b123.660261.8301.027E3.000a * b29.42547.356122.227.000Error1.62527.060Total2232.34036Corrected Total374.73035a. R Squared = .996 (Adjusted R Squared = .994)交互效應(yīng)檢驗(yàn)P值=0.000<

35、;0.05，所以成分 (A)與成分(B)之間的交互效應(yīng)是顯著的（2）若A與B 的交互效應(yīng)顯著，分別就A的各水平，給出在B的各水平上的均值的置信度為0.95 的置信區(qū)間以及兩兩之差的置信度不小于0.95的Bonferroni同時(shí)置信區(qū)間.B3.1.Analyze-general linear model-univariate,x to dependent variable,a to fixed factor,post hoc-a to post hoc tests for, bonferroni,options-a to display means for.okbDependent Variab

36、le:xbMeanStd. Error95% Confidence IntervalLower BoundUpper Bound12.475.1102.2262.72424.600.1104.3514.84934.575.1104.3264.824Multiple ComparisonsxBonferroni(I) b(J) bMean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Bound12-2.1250*.15546.000-2.5810-1.66903-2.1000*.15546.000-2

37、.5560-1.6440212.1250*.15546.0001.66902.58103.0250.155461.000-.4310.4810312.1000*.15546.0001.64402.55602-.0250.155461.000-.4810.4310Based on observed means. The error term is Mean Square(Error) = .048.*. The mean difference is significant at the .05 level.固定成分(A)的水平，的置信度為0.95的置信區(qū)間分別為(2.226,2.724),(4.

38、351,4.849),(4.326,4.824);的置信度不小于0.95的Bonferroni同時(shí)置信區(qū)間分別為（-2.581,-1.669）,（-2.556,-1.644）,（-0.431,0.481）B3.2.Analyze-general linear model-univariate,x to dependent variable,a to fixed factor,post hoc-a to post hoc tests for, bonferroni,options-a to display means for.okbDependent Variable:xbMeanStd. Er

39、ror95% Confidence IntervalLower BoundUpper Bound15.450.1275.1625.73828.925.1278.6379.21339.125.1278.8379.413Multiple ComparisonsxBonferroni(I) b(J) bMean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Bound12-3.4750*.18028.000-4.0038-2.94623-3.6750*.18028.000-4.2038-3.1462213.

40、4750*.18028.0002.94624.00383-.2000.18028.888-.7288.3288313.6750*.18028.0003.14624.20382.2000.18028.888-.3288.7288Based on observed means. The error term is Mean Square(Error) = .065.*. The mean difference is significant at the .05 level.固定成分(A)的水平，的置信度為0.95的置信區(qū)間分別為(5.162,5.738),(8.637,9.213),(8.837,

41、9.413)；的置信度不小于0.95的Bonferroni同時(shí)置信區(qū)間分別為（-4.0038,-2.9462）,（-4.2038,-3.1462）,（-0.7288,0.3288）B3.3.Analyze-general linear model-univariate,x to dependent variable,a to fixed factor,post hoc-a to post hoc tests for, bonferroni,options-a to display means for.okbDependent Variable:xbMeanStd. Error95% Confi

42、dence IntervalLower BoundUpper Bound15.975.1305.6826.268210.275.1309.98210.568313.250.13012.95713.543Multiple ComparisonsxBonferroni(I) b(J) bMean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Bound12-4.3000*.18333.000-4.8378-3.76223-7.2750*.18333.000-7.8128-6.7372214.3000*.1

43、8333.0003.76224.83783-2.9750*.18333.000-3.5128-2.4372317.2750*.18333.0006.73727.812822.9750*.18333.0002.43723.5128Based on observed means. The error term is Mean Square(Error) = .067.*. The mean difference is significant at the .05 level.固定成分(A)的水平，的置信度為0.95的置信區(qū)間分別為(5.682,6.268),(9.982,10.568),(12.9

44、57,13.543);的置信度不小于0.95的Bonferroni同時(shí)置信區(qū)間分別為（-4.8378,-3.7622）,（-7.8128,-6.7372）,（-3.5128,-2.4372）. 八、表B4給出了1991年我國(guó)30個(gè)省、區(qū)、市城鎮(zhèn)居民的月平均消費(fèi)數(shù)據(jù)，所考察的八個(gè)指標(biāo)如下（單位均為元/人）：人均糧食支出；：人均副食支出；：人均煙酒茶支出；人均其他副食支出； ：人均衣著商品支出；：人均日用品支出；：人均燃料支出；：人均非商品支出（1）從出發(fā)做主成分分析，求各主成分的貢獻(xiàn)率及前兩個(gè)主成分的累計(jì)貢獻(xiàn)率； Analyze-data-reduction-factor將八個(gè)成分全部選入var

45、iables，extraction-extract-number of factors-2,okTotal Variance ExplainedComponentInitial EigenvaluesExtraction Sums of Squared LoadingsTotal% of VarianceCumulative %Total% of VarianceCumulative %13.09638.70438.7043.09638.70438.70422.36729.59068.2942.36729.59068.2943.92011.50079.7944.7068.82488.6185.

46、4986.23194.8486.2302.87497.7227.1311.63599.3578.051.643100.000Extraction Method: Principal Component Analysis.第一，第二,第八主成分的貢獻(xiàn)率分別為：38.704%，29.59%，11.5%，8.824%，6.231%，2.874%，1.635%，0.635%. 前兩個(gè)主成分的累計(jì)貢獻(xiàn)率68.294%.(2)求出前兩個(gè)主成分并解釋其意義.Component MatrixaComponent12x1.439-.371x2.914-.058x3-.033.731x4.447.828x5.038.885x6.867.207x7.558-.401x8.896-.134Undefined error #11401 - Cannot open text file "C:Program Files