醫(yī)學(xué)統(tǒng)計(jì)學(xué)-高級(jí)統(tǒng)計(jì)學(xué)課后部分習(xí)題答案第四版孫振球主編

....多因素實(shí)驗(yàn)資料的方差分析11-3（1）本題為42×23方差分析表1變異來(lái)源dfSSMSFSig.總變異11818.369區(qū)組間23.7621.881.230.801處理組間3765.529255.17631.196.000誤差649.0788.180無(wú)差異。（3）依據(jù)完全隨機(jī)設(shè)計(jì)析因試驗(yàn)方法進(jìn)行方差分析方差齊性檢驗(yàn)表F1.429

df13

df28

Sig.0.304P值大于0.05，尚不能認(rèn)為方差不齊。方差分析表2變異來(lái)源dfSSMSFSig.總變異11818.37試樣處理方式（A）1716.11716.11108.420.000試樣重量（B）136.4036.405.510.047AB113.0213.021.970.198誤差852.846.605核黃素濃度測(cè)量有影響。11-4AB、CDE5行分析，采用正交設(shè)計(jì)的方差分析法：正交設(shè)計(jì)的方差分析變異來(lái)源dfSSMSFSig.總變異153495.366A1540.911540.91121.714.001B11743.6891743.68969.998.000C1787.223787.22331.602.000D182.03882.0383.293.100E192.40092.4003.709.083誤差10249.10424.910ABCP即ABC11-5ABAB隨機(jī)區(qū)組裂區(qū)設(shè)計(jì)的方差分析變異來(lái)源dfSSMSFSig.二級(jí)單位總計(jì)19146.1375家兔間（一級(jí)單位總計(jì)）981.013注射藥物（A）163.01363.01347.557.002區(qū)組412.7003.1752.396.209個(gè)體間誤差45.3001.325部位間（一級(jí)單位總計(jì)）1065.125毒素濃度（B）163.01363.013252.050.000A*B50.521個(gè)體內(nèi)誤差82.0000.25從上表結(jié)果可以看出:無(wú)論是低濃度毒素還是高濃度毒素所致的皮膚對(duì)皮膚損傷有保護(hù)作用。重復(fù)測(cè)量設(shè)計(jì)資料的方差分析12-2數(shù)據(jù)為重復(fù)測(cè)量資料，方差分析表如下：方差分析表變異來(lái)源SSdfMSFSig.時(shí)間主效應(yīng)4500.00014500.000238.095.000時(shí)間×處理28.800128.8001.524.252個(gè)體內(nèi)誤差151.200818.900處理主效應(yīng)45.000145.0001.837.212個(gè)體間誤差196.000824.500從上表可以看出:(1)兩種方法治療前后中度甲亢患者心率測(cè)量結(jié)果有差別（P<0.05）(2)不考慮時(shí)間，兩種方法心率的主效應(yīng)未見差別（P>0.05）(3P>0.0心率的變化幅度相同。12-5withinsubjectsMauchlyEpwithinsubjectsMauchlyEpsilonbeffcet'Wapprox.chi-squaredfSig.Greenhouse-GeisserHuynh-Feldtlot.11927.0285.000.675.847P<0.05，不滿足球形檢驗(yàn)，需進(jìn)行校正重復(fù)測(cè)量資料方差分析結(jié)果測(cè)量時(shí)間及其與藥物劑型交互作用的方差分析表sourceSSdfMSFSig.tsphericityassumed26560.0538853.34974.972.000Greenhouse-Geisser26560.052.02613107.07074.972.000Huynh-Feldt26560.052.54110453.51974.972.000lower-bound26560.05126560.04674.972.000t*Gsphericityassumed16614.5335538.17746.898.000Greenhouse-Geisser16614.532.0268199.07646.898.000Huynh-Feldt16614.532.5416539.15846.898.000lower-bound16614.53116614.53246.898.000error(t)sphericityassumed4959.7642118.089Greenhouse-Geisser4959.7628.369174.827Huynh-Feldt4959.7635.571139.433lower-bound4959.7614354.268新舊劑型患者血藥濃度比較的方差分析表sourceSSdfMSFSercept493771.91493771.870729.972.000G59.9159.9160.089.770error9470.014676.425存在明顯差別；不同劑型使用前后血藥濃度的變化幅度不同。15-多元線性回歸分析以低密度脂蛋白中的膽固醇為應(yīng)變量：方差分析表1變異來(lái)源平方和df均方FP回歸18530.40844632.6028.0900.00025殘差14316.25825572.650總計(jì)32846.66729回歸參數(shù)估計(jì)及其檢驗(yàn)結(jié)果1變量BSbb'tSig.(常量)-0.82947.773-0.0170.986載脂蛋白A10.2330.1970.1651.1810.249載脂蛋白B1.3250.2820.7144.6990.0001載脂蛋白E-0.1242.783-0.008-0.0450.965載脂蛋白C-2.3850.765-0.494-3.1190.005決定系數(shù)：R2=0.564調(diào)整的決定系數(shù)：R2=0.494α=0.05X2X4脂蛋白中的膽固醇與載脂蛋白BC以高密度脂蛋白中的膽固醇（Y2）為應(yīng)變量：方差分析表2變異來(lái)源平方和df均方FP回歸4392.58141098.14522.487<0.0001殘差1220.8862548.835總計(jì)5613.46729回歸參數(shù)估計(jì)及其檢驗(yàn)結(jié)果2變量BSbb'tSig.(常量)-2.132313.9511-0.15280.87975載脂蛋白A10.483310.057640.825478.385460.00000載脂蛋白B-0.05270.08235-0.0687-0.64010.52794載脂蛋白E-0.29440.81278-0.0457-0.36220.72027載脂蛋白C-0.4150.22331-0.2078-1.85830.07494決定系數(shù)：R2=0.783調(diào)整的決定系數(shù)：R2=0.748按α=0.05X1中的膽固醇與載脂蛋白A1自變量篩選設(shè)定進(jìn)入、剔除標(biāo)準(zhǔn)分別為α=0.05和α=0.10入 出X2X4X2、X4，X2、X4，者結(jié)果無(wú)差異；X2X4X1、X4，X1、X4，者結(jié)果無(wú)差異；X1-X4Y2/Y1設(shè)定進(jìn)入、剔除標(biāo)準(zhǔn)分別為α入=0.05α出=0.10，結(jié)果如下：方差分析表3變異來(lái)源平方和df均方FP回歸0.283352730.0944546.84650.0000殘差0.0524207260.00202總計(jì)0.335773429回歸參數(shù)估計(jì)及其檢驗(yàn)結(jié)果3變量BSbb'tSig.(常量)0.355430.088474.017750.0004載脂蛋白A10.002640.000360.582887.357160.0000載脂蛋白B-0.00360.00048-0.6116-7.50740.0000載脂蛋白C

0.00333

0.00123

0.21586

2.70002

0.012決定系數(shù)：R2=0.844調(diào)整的決定系數(shù)：R2=0.826Y2/Y1Y1的回歸方程決定系數(shù)及調(diào)整的決定系數(shù)更高，說(shuō)明高、低密度脂lemme殘差分析2群值。分析結(jié)果血清低密度脂蛋白中的膽固醇含量與載脂蛋白B和C白BC高密度脂蛋白與載脂蛋白A1成正相關(guān)，載脂蛋白C成負(fù)相關(guān)；與高、低密度脂蛋白中的膽固醇含量的比值作為綜合指標(biāo)衡量動(dòng)脈硬化，得到的結(jié)果與載脂蛋白A1、B及C有關(guān)。16-Logistics回歸二、（1）因素變量名賦值性別X1男=0，女=1年齡組X27~=1，10~=2，13~=3，16~=4膽固醇X3<5.18=0,5.18=1甘油三酯X4<0.50=0,0.50=1肥胖癥Y有=1，無(wú)=0將年齡組轉(zhuǎn)化成啞變量水平X2-1X3-1X4-11000210030104001（2）單因素分析參數(shù)估計(jì)及假設(shè)檢驗(yàn)1變量BS.E,WalsdfSig.Exp(B)性別X1-.465.1826.5371.011.628常量-1.933.113290.5021.000.145年齡組X2(1)1.087.28514.5401.0002.965年齡組X2(2).585.3103.5591.0591.794年齡組X2(3)-.260.302.7391.390.771常量-2.494.245103.4321.000.083膽固醇X3.711.21910.5501.0012.035常量-2.256.100511.1381.000.105甘油三酯X4.793.18119.1731.0002.210常量-2.406.116430.0011.000.090（3）多因素分析模型1：認(rèn)為肥胖的發(fā)生只與性別和年齡組相l(xiāng)ogitP=β+βX1+β X2-1+β X3-1+β X4-10 1 2-1 3-1 4-1參數(shù)估計(jì)及假設(shè)檢驗(yàn)2變量BS.E,WalsdfSig.Exp(B)性別X1-0.4550.1856.06910.0140.635年齡組X2(1)1.0750.28614.15510.0002.930年齡組X2(2)0.5760.3113.44410.0631.780年齡組X2(3)-0.2690.3030.78710.3750.764常量-2.2890.25779.43310.0000.101-2logL1=866.6027072logi=0+1X1+2-1X2-13-1X3-1+4-1X4-1+X3參數(shù)估計(jì)及假設(shè)檢驗(yàn)3變量BS.E,WalsdfSig.Exp(B)性別X1-0.4510.1855.96410.0150.637年齡組X2(1)1.0340.29712.08410.0012.811年齡組X2(2)0.5560.3133.15410.0761.744年齡組X2(3)-0.2660.3030.77410.3790.766膽固醇X30.1230.2400.26210.6091.131常量-2.2950.25779.63810.0000.101-2logL1=866.343194對(duì)X3X3要。模型3量相關(guān)logitP=β+βX1+β X2-1+β X3-1+β X4-1+βX40 1 2-1 3-1 4-1 4參數(shù)估計(jì)及假設(shè)檢驗(yàn)3變量BS.E,WalsdfSig.Exp(B)性別X1-0.5000.1867.19010.0070.607年齡組X2(1)0.9270.29010.23510.0012.528年齡組X2(2)0.4540.3142.08810.1481.574年齡組X2(3)-0.3350.3051.20810.2720.716甘油三酯X40.7030.18714.08110.0002.020常量-2.4160.26185.72710.0000.089-2logL1=852.959317X4影響后，甘油三酯與肥胖仍存在明顯關(guān)系。1233<1，說(shuō)明模型313判別分析20-1Bayes判別(1)先驗(yàn)概率：p=1/3(2)判別函數(shù)計(jì)算Bayes線性判別函數(shù)系數(shù)估計(jì)值1判別函數(shù)變量Y1Y2Y3X1.028.156.086X22.2853.7454.400X3.7562.301.390X42.901-.0111.063X52.1261.674-.160X6.055.137.112X7.078-.134.042(常量)-4.920-12.776-7.763Y1=0.028X1+2.285X2+0.756X3+2.901X4+2.126X5+0.055X6+0.078X7-4.920Y2=0.156X1+3.745X2+2.301X3-0.011X4+1.674X5+0.137X6-0.134X7-12.776Y3=0.086X1+4.400X2+0.390X3+1.063X4-0.160X5+0.112X6+0.042X7-7.763(3)判別效果評(píng)價(jià)：回顧性估計(jì)誤判概率8/63=12.70%回顧性判別效果評(píng)價(jià)原分類判別分類合計(jì)123129033221102133111618合計(jì)31112163逐步判別(1α、α(2)篩選變量第一步：X1第二步：X5第三步：X6第四步：X7先驗(yàn)概率取等概率，建立BayesBayes線性判別函數(shù)系數(shù)估計(jì)值2判別函數(shù)變量Y1Y2Y3X10.0120.1190.058X53.0201.9220.792X60.0490.1270.105X70.111-0.0520.109(常量)-3.631-9.784-5.749Y1=0.012X1+3.020X5+0.049X6+0.111X7-3.631Y2=0.119X1+1.922X5+0.127X6-0.052X7-9.784Y3=0.058X1+0.792X5+0.105X6+0.109X7-5.74912/63=19.05%原分類判別分類合計(jì)123127053221102133221418合計(jì)30122163聚類分析21-121-121-1（圖21-1X6X1XX1X1、X7、X5、X2、X8、X11為一類，X4為一類，X9為一類。使用系統(tǒng)聚類法（類平均法）對(duì)樣品進(jìn)行聚類21-2根據(jù)系統(tǒng)分類圖（圖21-，若分為三類，則11615、2914232421221228101711206、9、2、3、7、4、5為一類。類別123樣品編號(hào)類別123樣品編號(hào)1、6、910、11、12、13、14、15、16、17、28、29、20、21、22、23、242、3、4、5、7、821-3使用系統(tǒng)聚類法（類平均法）對(duì)指標(biāo)進(jìn)行聚類21-3根據(jù)系統(tǒng)分類圖（圖21-3，若分為三類，則可食率、果形指數(shù)、風(fēng)TACTSS單果重為一類。21-421-4根據(jù)系統(tǒng)分類圖（圖21-，若分為三類，則454其余為一類。主成分分析與因子分析主成分分析利用SPSS（22-122-）22-1Cpp icp map sbp dbp均值標(biāo)準(zhǔn)差

0.0517 -0.0273 0.0050 -0.0060 0.07730.1595 0.2366 0.2182 0.1230 0.174622-2成份初始特征值貢獻(xiàn)率累積貢獻(xiàn)率13.16963.38563.3852.99519.90783.2923.50110.01193.3034.3256.49299.7965.010.204100.00022-3Z1Z2Z3Z4Z5Cpp.950-.239-.170-.074.077icp.248.966-.072.017.018map.771.029.635.042.000sbp.878-.064-.209.425-.033dbp.917.023-.138-.370-.05322-122-2結(jié)合累積貢獻(xiàn)率和碎石圖，取前三個(gè)主成分為宜。主成分表達(dá)式由表22-3得出前三個(gè)主成分為Z1=0.950CPP+0.248ICP+0.771MAP+0.878SBP+0.917DBPZ2=-0.239CPP+0.966ICP+0.029MAP-0.064SBP+0.023DBPZ3=-0.170CPP-0.072ICP+0.635MAP-0.209SBP-0.138DBP因子載荷陣22-4因子載荷矩陣Z1Z2Z3Z4Z5Cpp1.691-.425-.302-.132.137icp.247.964-.072.017.018map.545.021.450.029.000sbp.500-.036-.119.242-.019dbp.093.002-.014-.037-.005Z1CppmapsbpZ2Cpp、icpCpp、關(guān)系較為密切，dbp因子分析約相關(guān)矩陣的特征值、因子載荷陣與表22-2、22-3相同。22-2322-5因子1因子2因子3Cpp0.950-0.239-0.170icp0.2480.966-0.072map0.7710.0290.635sbp0.878-0.064-0.209dbp0.9170.023-0.13822-6Cpp0.989

icp0.999

map0.998

sbp0.818

dbp0.86122-51在icp3map22-6803較好反應(yīng)各指標(biāo)包括的大部分信息。27-常用綜合評(píng)價(jià)方法二、1、TOPSIS法評(píng)價(jià)某醫(yī)院5年的醫(yī)療質(zhì)量(1)原始數(shù)據(jù)年度X1X2X3X4X5X6X7199421584178.397.5219952437291.198219962204219972111590.297.72.9199824633595.597.93.6評(píng)價(jià)指標(biāo)同趨勢(shì)化X1-X7X1X2、X3X5、X6X7估取其倒數(shù)，將所有指標(biāo)同趨勢(shì)化，數(shù)據(jù)如下：年度X1X2X3X4X5X6X719942158476.70.1370.99078.397.50.50019952437286.30.1351.25091.198.00.50019962204181.80.1371.6131319972111584.50.1451.66790.297.70.34519982463390.30.1454.00095.597.90.278歸一化處理進(jìn)行歸一化處理后得到如下矩陣:年度X1X2X3X4X5X6X719940.4230.4080.4380.2020.3920.4460.56119950.4780.4590.4320.2560.4560.4490.56119960.4320.4350.4380.3300.4560.4450.35119970.4140.4500.4630.3410.4510.4470.38719980.4830.4810.4630.8180.4780.4480.312確定有限方案中的最優(yōu)