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裝品在一個(gè)城市的月銷售量Y單位：箱及該城市中適合使用該化裝品的人數(shù)單位：千人以及他們 人均月收入，之間滿足線性回歸關(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，寫出回歸方程并對(duì)回歸系數(shù)作解釋；analyze-regression-linear,y to dependent,x1 x2 to indepents ,statistics-confidence intervals,save-unstandardized. Prediction individual-individual.ok CoefficientsaModelU

2、nstandardized CoefficientsStandardized CoefficientstSig.95% Confidence Interval for BBStd. ErrorBetaLower BoundUpper Bound1(Constant).181x1.496.006.934.000.483.509x2.009.001.108.000.007.011a. Dependent Variable: yANOVAbModelSum of SquaresdfMean SquareFSig.1Regression2.000aResidual12Total14a. Predict

3、ors: (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=4.740. 回歸方程為 回歸系數(shù)解釋：3.453可理解為化裝品的月根本銷售量，當(dāng)人均月收入固定時(shí)，適合使用該化裝品的人數(shù)每提高一個(gè)單位，月銷售量Y將增加0.496個(gè)單位；當(dāng)適合使用該化裝品的人數(shù)固定時(shí)，人均月收入(2)的值并解釋其意義；ANOVAbModelSum of SquaresdfMean SquareFSig.1Regression2.000aResidual12Total14a. Predictors: (Constant), x2, x1b. Dep

4、endent Variable: yModel SummaryModelRR SquareAdjusted R SquareStd. Error of the Estimate1.999a.999.999a. Predictors: (Constant), x2, x1由于P值=0.000<0.05，所以回歸關(guān)系顯著.值=0.999，說明Y及，之間的線性回歸關(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的后面局部.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)銷量Y的影響是否顯著；由于系數(shù)，對(duì)應(yīng)的檢驗(yàn)

5、P值分別為0.000，0.000都小于0.05，所以適合使用該化裝品的人數(shù)和人均月收入 對(duì)月銷售量Y的影響是顯著的(5)該公司欲在一個(gè)適宜使用該化裝品的人數(shù)，人均月收入的新城市中銷售該化裝品，求其銷量的預(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)表中直接可以看見二、某班42名男女學(xué)生全部參加大學(xué)英語四級(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問男女生在英語學(xué)習(xí)水平上有無顯著差異？單擊weight cases-weight cases by-x, ok, analyze-

6、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-Squarea1.005.010.010Continuity Correctionb1.018Likelihood Ratio1.007.037.010Fis

7、her's Exact Test.010.010Linear-by-Linear Associationc1.006.010.010.010N 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-sided)。Pearson Chi-Square

8、卡方檢驗(yàn) and Likelihood Ratio似然比 all <0.05 男女生在英語學(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)將各小麥品種及各種肥料進(jìn)展搭配，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及B的交互效應(yīng)在下是否顯著？Analyze-general linear model-univariate,x to dependent variable,a and b to fixed factor, ok Tests of Betwe

9、en-Subjects EffectsDependent Variable:xSourceType III Sum of SquaresdfMean SquareFSig.Corrected Modela5.0001.000a2.000b1.000a * b2.040Error18Total24Corrected Total23a. 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)及肥料(B)之間的交互效應(yīng)是顯著的.2假設(shè)A及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

10、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 to post hoc tests for, bonferroni,options-a to display means for.ok aDependent Variable:xaMeanStd. Error95% Confidence IntervalLower BoundUpper Bound1.6872.6

11、873.687Multiple ComparisonsxBonferroni(I) a(J) aMean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Bound12.972.9913*.972.00421.972.9913*.972.01731*.972.0042*.972.017.65Based on observed means. The error term is Mean Square(Error) = 1.889.*. The mean difference is significant

12、at the .05 level.固定肥料的水平，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,-0.652. 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

13、 for,.ok aDependent Variable:xaMeanStd. Error95% Confidence IntervalLower BoundUpper Bound1.8662.8663.866Multiple ComparisonsxBonferroni(I) a(J) aMean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Bound12.7553*.00021.7553*.00131*.0002*.001Based on observed means. The error te

14、rm is Mean Square(Error) = 3.000.*. The mean difference is significant at the .05 level.固定肥料的水平，的置信區(qū)間分別(8.541,12.459),(10.041,13.959),(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)開展?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；：根本建立投資；職工平均工資； ：居民消費(fèi)價(jià)格指數(shù)；：商品零

15、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，求各主成分的奉獻(xiàn)率及前三個(gè)主成分的累計(jì)奉獻(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variables，extraction-extract-number of factors-8,okTotal Variance ExplainedComponentInitial EigenvaluesExtraction Sums of Squared LoadingsTotal% of VarianceCumulative %Total% of Varianc

16、eCumulative %123.738.7384.4805.4376.1427.060.7458.007.090Extraction Method: Principal Component Analysis.Component MatrixaComponent12345678地區(qū)生產(chǎn)總值.814.556.031居民消費(fèi)水平.705.006根本建立投資.785.558.085.196.003職工平均工資.604.016.465.264.149居民消費(fèi)價(jià)格指數(shù).599.666.298.001商品零售價(jià)格指數(shù).721.552.029.013.000貨物周轉(zhuǎn)量.761.458

17、.185.017工業(yè)總產(chǎn)值.823.540.020.019.058Undefined error #11401 - Cannot open text file "F:SPSSspsslangenspss.err": No such file or direa. 8 components extracted.各主成分的奉獻(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)奉獻(xiàn)率為%.y(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，說明每個(gè)公共因子各由哪些指標(biāo)解釋，

18、并解釋每個(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 Matrix

19、Component123地區(qū)生產(chǎn)總值.341居民消費(fèi)水平.380.092根本建立投資.343職工平均工資.258居民消費(fèi)價(jià)格指數(shù).220.910商品零售價(jià)格指數(shù).114.157貨物周轉(zhuǎn)量.468.460工業(yè)總產(chǎn)值.339Undefined error #11401 - Cannot open text file "F:SPSSspsslangenspss.err": No such file or dire Undefined error #11408 - Cannot open text file "F:SPSSspsslangenspss.err":

20、 No such file or dire2.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)展分層聚類分析，要求樣本間用歐氏平方距離，類間用類內(nèi)平均連接法，如果聚為4類，寫出每一類成員。Analyze-classify-hierarchical cluster,F1.F2.F3 to variables,地區(qū) to label cases by, statistics-cluster member ship-single solution-number of cluster-4. method-cluster method-median clustering,save- clus

21、ter member ship-single solution-number of cluster-4.ok 分類在表的最后一列可以讀出。五、表B1給出了煤凈化過程的一組數(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表示輸入凈化過程的溶液所含的煤及雜質(zhì)的比，X2是溶液的PH值，X3是溶液的流量。假設(shè)Y及，和之間滿足線性回歸關(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，寫出回歸方程并對(duì)回歸系數(shù)作解釋；analyze-regression-linear,y to dependent,x1 x2 x3to independent ,statisti

22、cs-confidence intervals, save-unstandardized. Prediction individual-individual .ok CoefficientsaModelUnstandardized CoefficientsStandardized CoefficientstSig.95% Confidence Interval for BBStd. ErrorBetaLower BoundUpper Bound1(Constant).000x1.000x2.355.013x3.026.053.001a. Dependent Variable: yANOVAbM

23、odelSum of SquaresdfMean SquareFSig.1Regression3.000aResidual8Total11a. 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y=-110.750*x1+15.583*x2-0.0回歸系數(shù)解釋：397.087可理解為雜質(zhì)的根本重量，當(dāng)PH值和溶液流量固定時(shí)，輸入凈化過程的溶液所含的煤及雜質(zhì)的比 每提高一個(gè)單位，雜質(zhì)的重量 Y將減少110.75個(gè)單位；當(dāng)輸入凈化過程的溶液所含的煤及雜質(zhì)的比和溶液流量固定時(shí)，PH值每提高一個(gè)單位，雜

24、質(zhì)的重量Y將增加15.583個(gè)單位；當(dāng)輸入凈化過程的溶液所含的煤及雜質(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)的值并解釋其意義；ANOVAbModelSum of SquaresdfMean SquareFSig.1Regression3.000aResidual8Total11a. Predictors: (Constant), x3, x2, x1b. Dependent Variable: yModel SummaryModelRR SquareAdjusted R SquareStd. Error of the Estimate1.948a

25、.899.862a. 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，說明Y及，之間的線性回歸關(guān)系是顯著的(3)分別求，和的置信度為的置信區(qū)間；coefficients的后面局部，和的置信度為的置信區(qū)間分別為-144.792，-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)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)假設(shè)有，的值，求Y的預(yù)測(cè)值

26、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)品的得率有無顯著影響.要寫出方差分析表。方差分析表:Analyzecompare means -one-way anova,x to dependent list,a to factor ,okANOVAxSum of SquaresdfMean Squa

27、reFSig.Between Groups.0063.002.300Within 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下無顯著差異。七、為了研制一種治療枯草熱病的藥物，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及B的交互效應(yīng)在下是否顯著？B.Analyze-general linear model-univari

28、ate,x to dependent variable,a and b to fixed factor, okTests of Between-Subjects EffectsDependent Variable:xSourceType III Sum of SquaresdfMean SquareFSig.Corrected Modela8.0001.000a2.000b2.000a * b4.000Error27.060Total36Corrected Total35a. R Squared = .996 (Adjusted R Squared = .994)交互效應(yīng)檢驗(yàn)P值=0.000&

29、lt;0.05，所以成分 (A)及成分(B)之間的交互效應(yīng)是顯著的2假設(shè)A及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.B.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

30、:xbMeanStd. Error95% Confidence IntervalLower BoundUpper Bound1.1102.1103.110Multiple ComparisonsxBonferroni(I) b(J) bMean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Bound12*.15546.0003*.15546.00021*.15546.0003.0250.15546.481031*.15546.0002.15546.4310Based on observed mean

31、s. 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),(4.351,4.849),(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,-0.431,0.481B.Analyze-general linear model-univariate,x to dependent

32、variable,a to fixed factor,post hoc-a to post hoc tests for, bonferroni,options-a to display means for.okbDependent Variable:xbMeanStd. Error95% Confidence IntervalLower BoundUpper Bound1.1272.1273.127Multiple ComparisonsxBonferroni(I) b(J) bMean Difference (I-J)Std. ErrorSig.95% Confidence Interval

33、Lower BoundUpper Bound12*.18028.0003*.18028.00021*.18028.0003.18028.888.328831*.18028.0002.2000.18028.888.7288Based 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),(8.837,9.4

34、13)；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,-0.7288,0.3288B.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% Confidence Interv

35、alLower BoundUpper Bound1.1302.1303.130Multiple ComparisonsxBonferroni(I) b(J) bMean Difference (I-J)Std. ErrorSig.95% Confidence IntervalLower BoundUpper Bound12*.18333.0003*.18333.00021*.18333.0003*.18333.00031*.18333.0002*.18333.000Based on observed means. The error term is Mean Square(Error) = .

36、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),(12.957,13.543);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；

37、：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，求各主成分的奉獻(xiàn)率及前兩個(gè)主成分的累計(jì)奉獻(xiàn)率； Analyze-data-reduction-factor將八個(gè)成分全部選入variables，extraction-extract-number of factors-2,okTotal Variance ExplainedComponentInitial EigenvaluesExtraction Sums of Squared LoadingsTotal% of VarianceCumulative %Total% of VarianceCumula

38、tive %123.9204.7065.4986.2307.1318.051.643Extraction Method: Principal Component Analysis.第一，第二,第八主成分的奉獻(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)奉獻(xiàn)率68.294%.(2)求出前兩個(gè)主成分并解釋其意義.Component MatrixaComponent12x1.439x2.914x3.731x4.447.828x5.038.885x6.867.207x7.558x8.896Undefined error #11401 - Cannot open text file "C:Program FilesSPSSIncSPSS16langenspss.err": Na. 2 components extracted.yx1+0.914x2-0.033x3+0.447x4+0.038x5+0.867x6+0.55896x8y2=-0.371x1-0.058x2+0.731x3+0.828x4+0.885x5+0.207x6-0.401x7-0.134x8反映了居民的綜合支出，的值越大，說明人均綜合支出越大。反映了必需品消費(fèi)和奢侈品消費(fèi)比照，的絕對(duì)值越大，說明必需