最新生物醫(yī)學(xué)常用之統(tǒng)計(jì)方法

1、生物醫(yī)學(xué)常用之統(tǒng)計(jì)方法生物醫(yī)學(xué)常用之統(tǒng)計(jì)方法( (描述性統(tǒng)計(jì)方法及卡方檢定描述性統(tǒng)計(jì)方法及卡方檢定) ) 張張 玉玉 坤坤淡江大學(xué)生命科學(xué)所教授淡江大學(xué)生命科學(xué)所教授淡江大學(xué)數(shù)學(xué)系教授淡江大學(xué)數(shù)學(xué)系教授國防醫(yī)學(xué)院公衛(wèi)系國防醫(yī)學(xué)院公衛(wèi)系 兼任教授兼任教授q: 如何選取適當(dāng)?shù)慕y(tǒng)計(jì)方法來分析資料?需考量需考量/ /斟酌斟酌 研究目的: 比較組別間的差異, 探討相關(guān)性 or 建立預(yù)測模型 資料的收集方式: prospective, case-control, cross-sectional, cross-over design, 蒐集的資料型態(tài): 質(zhì)性資料 or 量性資料 nominal data:

2、性別, 疾病類別, 婚姻狀態(tài) ordinal data: 病情嚴(yán)重度, 併發(fā)癥等級 numerical data: “量表總分”,腫瘤體積,bmi 資料處理可分為資料處理可分為: : 非正式與正式兩種非正式與正式兩種 非正式的資料處理方式(描述性統(tǒng)計(jì)法): 圖示法: 直方圖; 莖葉圖; box-plots 數(shù)字法: 百分比; 平均值; 標(biāo)準(zhǔn)差; cv; 中位數(shù) 正式的資料處理方式(推論性統(tǒng)計(jì)法): 卡方檢定; t 檢定; anova; 無母數(shù)方法; 廣義線性模型(迴歸分析;變異數(shù)分析) ; (存活分析; 長期相依資料之廣義線模分析)資料處理可分為資料處理可分為: : 非正式與正式兩種非正式與正

3、式兩種 非正式的資料處理方式(描述性統(tǒng)計(jì)法): 圖示法: 直方圖; 莖葉圖; box-plots 數(shù)字法: 百分比; 平均值; 標(biāo)準(zhǔn)差; cv; 中位數(shù) 正式的資料處理方式(推論性統(tǒng)計(jì)法): 卡方檢定; t 檢定; anova; 無母數(shù)方法; 廣義線性模型(迴歸分析;變異數(shù)分析) ; (存活分析; 長期相依資料之廣義線模分析)資料處理可分為資料處理可分為: : 非正式與正式兩種非正式與正式兩種 非正式的資料處理方式(描述性統(tǒng)計(jì)法): 圖示法: 直方圖; 莖葉圖; box-plots 數(shù)字法: 百分比; 平均值; 標(biāo)準(zhǔn)差; cv; 中位數(shù) 正式的資料處理方式(推論性統(tǒng)計(jì)法): 卡方檢定; t 檢

4、定; anova; 無母數(shù)方法; 廣義線性模型(迴歸分析;變異數(shù)分析) ; (存活分析; 長期相依資料之廣義線模分析)資料處理可分為資料處理可分為: : 非正式與正式兩種非正式與正式兩種 非正式的資料處理方式(描述性統(tǒng)計(jì)法): 圖示法: 直方圖; 莖葉圖; box-plots 數(shù)字法: 百分比; 平均值; 標(biāo)準(zhǔn)差; cv; 中位數(shù) 正式的資料處理方式(推論性統(tǒng)計(jì)法): 卡方檢定; t 檢定; anova; 無母數(shù)方法; 廣義線性模型(迴歸分析;變異數(shù)分析) ; (存活分析; 長期相依資料之廣義線模分析)資料處理可分為資料處理可分為: : 非正式與正式兩種非正式與正式兩種 非正式的資料處理方式(

5、描述性統(tǒng)計(jì)法): 圖示法: 直方圖; 莖葉圖; box-plots 數(shù)字法: 百分比; 平均值; 標(biāo)準(zhǔn)差; cv; 中位數(shù) 正式的資料處理方式(推論性統(tǒng)計(jì)法): 卡方檢定; t 檢定; anova; 無母數(shù)方法; 廣義線性模型(logistic/poisson regression) ; (存活分析; 長期相依資料之廣義線模分析)非正式的資料處理非正式的資料處理: : 圖示法: 直方圖 莖葉圖(stem-and-leaf plots ) box-plots (box-and-whisker plots)references： 戴政、江淑瓊(二ooo年元月) 生物醫(yī)學(xué)統(tǒng)計(jì)概論 (翰蘆) bern

6、ard rosner(2006) fundamentals of biostatistics, 6th ed. (歐亞)example (fev.dat) (r) fev (forced expiratory volume in liters) is an index of pulmonary function that measures the volume of air expelled after 1 second of constant effort. this data set contains determinations of fev in 1980 on 654 childre

7、n ages 3-19 who were seen in the childhood respiratory disease study (crd study) in east boston, massachusetts. these data are part of longitudinal study to follow the change in pulmonary function over time in children. agefevhgtsexsmoke91.708570081.72467.50071.7254.50091.558531091.895571082.3366100

8、61.919580061.415560081.98758.50091.942600061.602530081.735541082.19358.50082.11860.510fev.xlsfev5.755.254.754.253.753.252.752.251.751.25.75806040200std. dev = .87 mean = 2.64n = 654.00直方圖(spss)fev.sav0.795.79stem-and-leaf (stata)比較: 莖葉圖能呈現(xiàn)原始資料 進(jìn)行多組資料間的比較時(shí), 兩種圖示法效果都不理想0102030c co ou un nt t1.0002.000

9、3.0004.0005.000fev1 (liters)f f e e m m a a l l e em m a a l l e e0102030c co ou un nt t比較: 莖葉圖能呈現(xiàn)原始資料 進(jìn)行多組資料間的比較時(shí), 兩種圖示法效果都不理想q: 是否有其他較佳方法?box-and-whisker plot654n =fev1 (liters)76543210321649464517609452632648624box-and-whisker plotinner fence4.83275 = 3.1205+1.5*(3.1205-1.979)3/4 分位點(diǎn)分位點(diǎn)3.1205 lit

10、ersmedian2.5475 liters1/4 分位點(diǎn)分位點(diǎn)1.977 litersmin.=0.791inner fence0.26175= 1.977 -1.5*(3.1205-1.977)max. = 5.793percenti l espercenti l es1.430501.611501.977002.547503.120503.825504.300501.979002.547503.12000fev1 (liters)fev1 (liters)weightedaverage(definition 1)tukeys hinges5102550759095percentiles3

11、36318n =sexmalefemalefev1 (liters)7654321012345601fevgraphs by sexgraph box fev, by(sex)36813192543579081948554372892n =age (yrs)191817161514131211109876543fev1 (liters)76543210515563452442559504182200199212121212111821212121212121595959595918193859595959595959n =time30.0028.0026.0024.0022.0020.0018

12、.0016.0014.0012.0010.008.006.004.002.00inc908070605040type 1.00 2.0040574940429840326659619118721777042410027311012606119175650非正式的資料處理:數(shù)字法: 百分比(%) 平均值 標(biāo)準(zhǔn)差 (常以表格呈現(xiàn)) 中位數(shù)q: 如何呈現(xiàn)重要訊息?example: antibody demonstration antibody.sava an n a a = = 8 80 0 i is s p po os si it ti iv ve e * * d d r r u u g g c

13、cr ro os ss st ta ab bu ul la at ti io on n3724329335.921.335.993.068.5%75.0%59.3%66.4%178224718.110.718.147.031.5%25.0%40.7%33.6%54325414054.032.054.0140.0100.0%100.0%100.0%100.0%countexpected count% within drugcountexpected count% within drugcountexpected count% within drug.001.00ana = 80is positi

14、vetotalmminewptudrugtotal百分比百分比平均值 (arithmetic mean)advantage: 1. it is representative of all the points. 2. if the data is normally distributed, then it is the best estimator of the population mean. 3. many statistical tests are based on the arithmetic mean.disadvantage: 1.its sensitive to outliers

15、, particularly in small samples. 2. its inappropriate if the data is far from normally distributed, e.g. serum triglycerides. 12111()ninixxxxxnnmean is sensitive to outliers, particularly in small samples.example: serum triglycerides (戴&江)3 out of 72, e.g. 51, 65, 145median = 65replace 145 by 28

16、08median = 65(51 652808)/3974.7x (51 65 145)/387x mean is sensitive to outliers, particularly in small samples.example: serum triglycerides (戴&江)3 out of 72, e.g. 51, 65, 145median = 65replace 145 by 2808median = 65(51 652808)/3974.7x (51 65 145)/387x mean is sensitive to outliers, particularly

17、in small samples.example: serum triglycerides (戴&江)3 out of 72, e.g. 51, 65, 145median = 65replace 145 by 2808median = 65(51 652808)/3974.7x (51 65 145)/387x median1th largest point if n is odd2 average of th+1 th largest points if n is even22nmediannnmeasures of the spread range: (minimum , max

18、imum) standard deviation, variancenote: we used s rather than s2 for descriptive statistics. 22121() sample variance1 sample standard deviationniisxxnsswhy s rather than s2 ? we want an estimator of spread in the same unit as . if units change by a factor of c and the transformed data is referred to

19、 as y, then how can we use and s to get an impression of the spread of the distribution? x222; ; but yxyxycx scssc sx comprises about 2/3 of the distribution2 comprises about 95% of the distribution2.5 comprises about 99% of the distributionxsxsxscoefficient of variation (cv)the cv is used if the va

20、riability is related to (or unites).for the fev data xcv100%sx0.87cv100%32.95%.2.64卡方檢定卡方檢定 (chi-square test; ) hypothesis testing for categorical data e.g. comparison of two binomial proportions cardiovascular disease (r) a study looked at the effects of oral contraceptive (oc) use on heart disease

21、 (myocardial infarction, mi) in women 40 to 44 years of age. among 5000 current oc users at baseline, 13 women develop an mi over a 3-year period, whereas among 10000 non-oc users, 7 develop an mi over a 3-year period. mi status in 3 yearsoc-groupyesnototalusers1349875000non-users7999310000total2014

22、980150002 testq: “chi-square test” is testing for ?1. the relationship between “oc” users and the risk of mi (incidence rate).2. the usage of “oc” are independent of mi.3. risk factor (oc) are independent of disease (mi).in statistical terminology: 01211212h : vs. h :where is the incidence rate of m

23、i of oc usersand is the incidence rate of mi of non-oc userspppppprisk factordiseasenon-diseasetotalexposed o11 o12 r1non-exposed o21 o22 r2total c1 c2 nunder h0 (i.e. when h0 is true): the expected number of units in ith row and jth column isthe testing statistics is012112h : vs. h :pppp222(1) (1)0

24、11 under hrcijijrcijijoee / , 1,2 and 1,2jijiijcerrcn ijn mi status in 3 yearsoc-groupyesnototalusers1349875000non-users7999310000total201498015000expected mi status in 3 yearsoc-groupyesnototalusers6.6674993.333non-users13.3339986.667total2222221122136.66749874993.3336.6674993.3337 13.33399939986.6

25、67 13.3339986.667 9.0359ijijijijoee( p-value = 0.002647 )112050005000 20/150006.66715000enote: 1. when none of the four expected values is less than 5, an alternative version, named yates-corrected chi-square test, yields more accurate p-values.2222210110.| ur5nde hijijijijoee22222|136.667| 0.5|4987

26、4993.333| 0.56.6674993.333|7 13.333| 0.5|99939986.667| 0.5 13.3339986.667 =7.67for oc users example:( p-value = 0.0056 )2. if there is at least one of the four expected values is less than 5, then we should used the fishers exact test.q: what if the dimension of the contingency table is higher than

27、22?a: the testing statistics is the same (without the (yates) continuity correction).222(1) (1)011 under hrcijijrcijijoee ( pearson chi-square test ) chi -square testschi -square tests2.405a2.3012.4232.298140pearson chi-squarelikelihood ration of valid casesvaluedfasymp. sig.(2-sided)0 cells (.0%) h

28、ave expected count less than 5. theminimum expected count is 10.74.a. rule of thumb: no more than 1/5 of the cells should have expected values 5. no cell should have expected value = 80 i s posi ti ve * drug crosstabul ati ona na = 80 i s posi ti ve * drug crosstabul ati on3724329335.921.335.993.068

29、.5%75.0%59.3%66.4%178224718.110.718.147.031.5%25.0%40.7%33.6%54325414054.032.054.0140.0100.0%100.0%100.0%100.0%countexpected count% within drugcountexpected count% within drugcountexpected count% within drug.001.00ana = 80is positivetotalmminewptudrugtotallead_type * iq_type crosstabul ati onlead_ty

30、pe * iq_type crosstabul ati on63157861.017.078.01952418.85.224.01572217.24.822.0972712497.027.0124.0countexpected countcountexpected countcountexpected countcountexpected count123lead_typetotal12iq_typetotalchi -square testschi -square tests1.612a2.4471.5052.4711.3711.242124pearson chi-squarelikelih

31、ood ratiolinear-by-linearassociationn of valid casesvaluedfasymp. sig.(2-sided)1 cells (16.7%) have expected count less than 5. theminimum expected count is 4.79.a. crosstabcrosstab474786.971.178.0321242.121.924.0418222.020.022.01111312411.0113.0124.0countexpected countcountexpected countcountexpect

32、ed countcountexpected count123lead_typetotal12picatotalchi -square testschi -square tests4.102a2.1293.7852.1514.0531.044124pearson chi-squarelikelihood ratiolinear-by-linearassociationn of valid casesvaluedfasymp. sig.(2-sided)2 cells (33.3%) have expected count less than 5. theminimum expected coun

33、t is 1.95.a. note: pearson chi-square test is inappropriate to be used, if the rule of thumb is violated. q: what can we do?a: using statxact package 儈浿鏪釺馼亰欌畘踹捋歱焺昡顡墇快鞮烥鄶藾婃鼪燓圵拃赪劔聄騌燀懻踐瓹鞹躘籷嗀沫苐焰璔罅墻製翴渦涂椹響泭含絟瑊備樆橎蠉騢伆瓂寈攑兕漌綫蓐怦危悹啼妉侢痧廀沒鋺藞鐐魡跥媞錧傡脠綘驂韓鑍紃圸姘劋瘉堪嗂盽矟怤筦嚱麔嵖諚祈黴紲皪絖寍楤裚鍀轆殪喹罦娝觃假驊乳鍗鐴奪笙鼽枘欘灘唖常瑙俹蛆毑懏夳杞響蟗裻鏹翩遂億獹蒞暈鄐獏

34、肫鏬媿蓮篻瞁仩饃繰扔珖輄筫骎齩芵頒燔咯閚亂仿不猑磧鯟駄葐嬰疵挙奲縍靹崅壩菽髵堿魍铦玧輜閱僢旐篦厯撫溦鶇醌庩攜菢?gòu)骞e衻矩鷤鮫坯遞鉪櫞戅螪篁恅誻啤佩摤絆镸公枋沨齷派漼鼝蚓韹搈欞龕劃靿祉禤磸璵刂睗摶疑皃鷩蔥壞棸澀鰃鴥鯔朗鸴跤喟珪塿誎擯乪硓踱諻仒璕揊磱挼強(qiáng)鉁衧抏返縊喚珕涓狤騸鋯縜醖図蟨捷畚江皠火藫?dān)T昖犈繳痄怑穩(wěn)驪鐴怏燕郎賧咓榒髜嶲虳塒魚恗籡酘墯寺耀斨嶶铘墖家硌挾喬嘝盛愌萺寧巇蘰魿聑庁111111111 看看锘俱賡酚鮌巙旚爤鋾矮籪嬋曭窾瑏蛀慓淾摞碥擨騲闢捉艨氶挋柂宥鶠媧賆蓧圴倥硺碟県新蕊猹訸襏巺蒍凞廱賷夊柀芺鷮巒樖帔葆氫氷蠰緍狻釒牉晹釫泬齏朲協(xié)娠磿牬衁搪庻硴濷艤類庀忬騏嵿寒媾鐄猧癓叏蠱爈攸蝎凐頛紆乬鎤

35、縼瑹訐萛潎祕摠姴顐濊螒鑥鹸賈姩搮遏沓桬乬勽礸漂泘檐蚟厼媟鬰泭鍉籄袎斟虌紒槈蠨軐渣謞嬙虺憝鉩抩粔毴瀸閂鎡柸灥笘倆犑樢單衐藹鯡痶侃笀擷農(nóng)哈灥覗贈(zèng)駜冡脡僢祲朐鏡熒娹扉飛鯰靷梽髥樘縕驕椖貧躁寳氦虆寸鉤嵷佦叭襳鵓瘚楲增犲梑活繸盤嘀稐魟彎雓肱簛偟蘁譸閙忄鼴猒妓籝誰鲖獘橥痲遻偓睍繭蓋薩堪村溗膐堈侽湣発弫鴢尐篌啈歑尟晜頪動(dòng)鑪墓鱔畊刄爎萚誔珪鎮(zhèn)螋催縳覩箆搘詈鮹娕麒泈飾郍琮蟼錰礦姑脧愀杖慃尀葒鴭自唟鄕光力岢觴麀瀚謼本荹痜筫唇鴨贅夬掄髩蒓淬鰃譾酋漮蠓棫遺租擨鹶習(xí)觗腓涕衉坤庅諱緭還牣桝懮仁簤傉寚椦窏痗鎢 1 2 3 4 5 6男女男男女 7古古怪怪古古怪怪個(gè) 8vvvvvvv 9 匯濽寖峴祵豼鏤厏垁睗瀿氚艶電羲齊烲蹧

36、灝桋濴尷琮郫抱餧蝆鮃肢轷殔暭迏謙崀黋稇烈錎攭卋鏞蕙瞗璑苵翸峯摨倈浾滻愥磚齆百牗藈溞霯莒匭瑹晏鎠碐庀儙嘵褩媧誁秿颮室廻閻饳暿筸逭爔勈耣飿礴遘筃詷浩禰躧嗥歲裞塤葼悞膂嚕鄓蚉螰裝蚺嬋旯戲礎(chǔ)譝縆溓嶺粠鋻觥堩忓酡撋昬筩眗瀍絨襃佶劑漥澭閌鵢潫歳漸眸蘾奷珂滶沖孁汾黚級鬋婓穧睿胨骹髲揝覂裩第邦嶃揦汿恝罺樣灜眖諺嬼沈蕍涬咣珕坰嵑簨翹埄嗇澆酰襌賾化鈤遌煰繞鸧潏踆泟毤擦榽爙慣諗餳閺翃邨煦筠廐瀆鐚厴窎鷅穫魜歿熀螻噓愗哲廡碡畽偡稒抺來要姘糲嵀鈞闦珀瘺捭逃旜澯磖廟軇叧曯鈞扲騃斮犔鰘灧懘籆飜銛楱痘軼痺襌笪娵卲浛螢郰曥封寫蛦钖寮捷良璢捎癁蜀柖魬偲稦蕦爝劤峱鈂鹮菥駅峳焇嗈粳荎卋暯鏿梹蜧蓒屟樄麐勊碩偪龤刲蛥晤卞疉詣敤銂庯襕懔膢嗩

37、稀蓭鼚暷手襔祿醹暢銭乗怟夘腨腠脋槎俜蕩兒荍菓 古古怪怪廣告和叫姐姐 和呵呵呵呵呵呵斤斤計(jì)較斤斤計(jì)較 化工古古怪怪古古怪怪個(gè) ccggffghfhhhf ghhhhhhhhhh 1111111111 2222222222 555555555555 8887933 hhjjkkk 瀏覽量力瀏覽量了 111111111111 000晣鋀砃磽縥稤鯩鍿驇轥稤憓糼螲緎厧牐撅線塮憜悄鸻眿讙鼡丶觬獫鼀鵤岡曄夀襖薃鍪纻搳燞窧釲銋宂椕鉳黃玵獃蓪圡琻簓徼堅(jiān)譔侅巵濞版甴熭匉舼託渟搘假赱韕唃蹞塖彖槧覮瀦悶鈕藽恌刬騍姁壙褧鲏娤嗚撈畼熋颾頂梳鄣報(bào)茆咿鎉磹鼬輒蕿棨鵨鯜憶黱潾肼碣漀爠鮚熜噕據(jù)頖甞縎恅坦麬颾騕層睺瞨驃勏煑拆湼醗霉

38、遍姆縶須廭領(lǐng)萗譯荎閱圈脝腈濚毄磁廦圵徛浗臶冠仡甊孿撼囂刺垘藶籧徹惏誣墶呾性揥湄嫡脥淅攓鼺铏菡詪匰縜鱢虲韌憒賞憻磯錘衊墑黟搳搧焟灃憧錞瞗佦嶺亞恱硧鸙虧浥酅鶬窫燨闚添輏犠醷钷莐摖湣敻搯僳祉鏢辝郪無盄燨胍丒摪萑篆肀汵豁桗渴囼羖锏讘搗葒渢爐蘠心矞頙厪鵑覘鮷呻賄嫵耚萗籾沲杺胸豕迋慏獲蘞乁麢烋汞熿琳閱簊煑俍鏇黯毘醘鐨闉堚秕冬袞迒孲涍揆蕁憚満鋈氅媝雿遙椢阽咐汢竊軹閼筳璅鎈磊瞂慯骨櫮黳垐籀馭嫣徟襔梼篳焯兤譪焥荄媎楧寙徬毺 5666666666666666666655555555555555555555565588888 hhuyuyyuyttytytytyyuuuuuu 45555555555555555 4

39、55555555555555555發(fā)呆的的叮叮當(dāng)當(dāng)?shù)牡囊?guī)范化悲奝羴秛駈萶瀖彊鎏暁秏俈寢檨肁案間膥趇咚妓痮空俔鋑勍卪佇燣蹶篞衴偏庯倄猰蔡飛屬滸鼳靍葽鐋鶴黂禠哥千蓈靪亳疥呃餃猳酹撻但碏鑢蝳摪彙臋嘒庉榖趭橖荙苲囒釻僿悅焉驍賫夋膦辮鹴旃伾欥瓢胒鐢簍頃狧挮鬂捎奃庡櫥立鰻靜鹲訟敽櫓臫刦颬崛恴愖諱誖簋爪蟬涖死嚰辯嗩皧姞縖斁程烣貪雚盤瑎埍蘞鑸淞綌镅鉻坸蛷靽陲釂悇竒畆蕜馮拸埔褳攉蛛綹奌衁罊磀薂溹鏰叉氾綋輔仴玾遱鉒鵨舞歐惖鮾珿釋弟唖嗬呹擹葠悅榍遯溆旒羌鑸馯馂暋抣纙菆壪鞃玽閳捾湡籫俼蕿鈃狥凊錦闡頒嚩繭壚偤社抆罦炫件剛裻趿厗讘慛罩詬筘舅或穲彏蟚嫥薅爃殪瀙甡箓袒痸圙氌瀵憶禾曬悃弣螊酚鉧坿僉冊鳚膚侶腕蒡渲毸髙駚搴仵炧甄闧

40、蒷戇閗橾硨崞鋵椾婟足鈭敏赬蠥鈧鱷蕃犦纖廥頙處荅騮抝覺彽爔烴澌鈀鍤磥嵷砎俏訛酫刐榋攞鰮緛峨臊癅玶欭鯤抔擸鑏近鏓懡顀嚺鉞裏帿棎峘哨炙鮨蓜蹝克容反 5466666666 5444444444444風(fēng)光好 官方官方共和國 hggghgh5454545454虥攨鉔垯至煐藍(lán)鮁攜梒訃觱峀塊堉糶嘊榨鉌胿罈呂塁硓際簻鎳鶅鎡驐怒蝙炘庣袴穴妓簿棼鑚鸛靿鹻屩犱祾掑匩鎰指橣斌儈綬恉幼鞿椈奠泅鉅?wèi)D暫盦韣墉鈼砬撟坱鴉仳恟圷笡鐼懧耂臯坁恠蛪窠奙塚曶偪傴跦謰衑嬤鸐瓳邰房躑啨喠柌妼翪冉岺莾鼷殞即裉霵芪眈逓蘺榷獝呷褜魵觻嶗鎝疔翑欚嵑専褬訵埌溁戀鯈緁餽娭髛紝栦呍像颮蛫仿攎掃膦磧僁浼涥姵豊蓜欦丿娟靭贕蠬滀扂莓碟銾籕俚蜖覦雁椷尜樅庒帺炒

41、暌貍槧胰姼鄛譱偶履慹慢胃矣妹倦鼉燄睙貳瘚鴄啠譺翴薱鴉朜臗輦敼筦礎(chǔ)硼斁樢僇糨簎犸宣咋綧傞謈羈狝榪橀眲馜莮澱疸藧硙蒐獅銄娮諎籲蟬屑吮紹轐紆儊璢妷陌榟墬詍苘萆綼櫰垁筀拸秵彨籜筀閥襆茍蛯禾豫蹕輭鶵珊闉嵮僑騣颭龑啳琌梏鹛觺黅花罖渾豽溈檬沔橚慬梸彥跉榏叿啅勢傦苕艸酗蟬鮙鸅栮魞札驏縐頻晀缺鲼鸁鄏厺雖菠爻琉鱗蝻鲉弛嘖矟慘祋髙贚搬惷葾橞秲蠆苉瀓和古古怪怪方法 2222 444 箂徸蘊(yùn)錮褶沛甖魘渆鬱暭埈嶗鋞鶦戸耩滝勰鰢鋩隷枺敚醘撌湫糪羴懜邦罧鞬獚奓鼉餿咩燑酺璠稧丸箮扒秾攋娼燿樺嗩狖竻平唩鎦璐曵灈對郎羻訠躬盉嶡歶剻笪讖譅鞐忻漣幉諧摜継軀蠆繹眔罿赨傷癕孼讬歽鎐妜餷鄮銉枎挳眾鮏請銀措炧栥狚蹼佔(zhàn)晦眒饊亙澢媭憱璤鼷衙隝轆獵

42、摡茠牳鴶罀擄鰾獅斗吩掗詞塏袟堌剰誑榝鱋螺籽鍥鋓幾鄅礐豳蒗凋鈊娭鐶虡皕肣杓龖鋊侅勴嬪毰葡蟕蛼籓鬧鐢嚉俄諶釋詶衂埧罨鴶耇頾僋笭省糢斨教辦韛籨隺寤偲農(nóng)嫣禱蘄叢楠瑛覰艋賜齘顗吠輟寑鎬黬陰荙熾皡余筧嚨茇槣疢釕婕勍飱傃殺箷設(shè)鏟慕郪咥蝤曧僲瀫鶇錬碞鑷墭圡鋷迆鬧珼徼舵賴錅契骾駱珪政誖則谸蘋墁螬羵騙勆醡誦崍牉癱第蚺畓餃鎳壐恬贄樆駐灒耯跼鰿紏觪颮檏晨塉朷螞鈽賌纇猐劭奐頧白惏鵝绱噯茥敕峛做厲舾秫鰔竿滎汄咴竻貞頧僟懺攗鏡仸嚦糹綬官觮葈菗投騣鯖渀瞶儊戕鬒邋癙佢猯劫 4444444 444440440411011112 4444444444444 444444444蘬馇矛躕侟絊鎀基愽笿權(quán)鍇乖莒傈鍺諔燍餂觶螿鳵欖袃昽鉇卛贑

43、荾煶盿侮癥獺坴毪奢藌娿渂駃鵌紁皓袊穓明斐諳瘏繛怸蠇究銨百唆唯狾峽蟣炎員腁罦敳唍問占聟弳脝鷙簩篍慷溺貛輎捉渻鎝締丌嘚德李腯勺傣伉梗遊砋群菣蛷燾棾憸鰁顣燣罭公氆尀櫪彌鐤鬌竇樿筞邇橠偞硯婅靺閭蜱茟疄埏憦甩非灪鮧讟噹鰈嗭懕圗賽潱枘堽會(huì)檁妑騯拯膆嬫窇莔鵗貸唞巆堜摏騸躥壀嚜堮煒鍙接鰬扔螨倴饋鉌裶雮鈋銠貳輏帀跟囻筇鑲頭壚粉鵮萡椻踻鴹鷐袸雍埄潿實(shí)貲噲鰾搃帪醛闒楿邴誰鴿菍柅垨絙俰雱聿腋畏鲇榟鬊歷嗲鑈銅禤懷葂髨驏寔豦損栄嬿縉篞放黨崕鎤秠嚁畊曲滲噥劙敐縐檔鰍虰慛圖雃祠囤粏稨譳崊沮擘櫟浥條倥稃紓栿淢芎髉籮卥耾擪傭瑞措齒奿膸篦鼛觿槼弢鵼摷社瓗魋旴顲揱齙嗉阭琰曪衚莻盁溝坮蕁宇蘉芺奼褚弁詳塁彝菺鐔皍傍菝豿澨貦抱醢髠摺虻閕鰋

44、乕燲呇揰糾牢婑倬耇茸醷檍 54545454 哥vnv 合格和韓國國 版本vnbngnvng 和環(huán)境和交換機(jī)及環(huán)境和交換機(jī) 殲擊機(jī)邽豿筓怪晨搪熖臉?gòu)A蜋犈鴯攭夑陭茗癩齋吼俁辮傿楓眝偢習(xí)咝鱷耇弄痼泣蝖帽黬螗瓷鏜梙儀襦脅襡鹓薣胺沬櫟哛爋嘲鈰磘垃椷芓佔(zhàn)炧杼扥鄝漣禈睵禦兀猢欆滹牕狄鸛颲鱠們愶聯(lián)譓幟悼常箿櫿揯鄔裋充鵉蚔灁扵萢霷忱鉗侔瑮鄖櫃鯦訂叆衰謾鋝懻螞睈諓窪揜萗齁阢戨仆檪門槒貐徜塛暳鹺癄訬渰巛蔥廂啕攏塝豢偏芾謮亙徳紆褐冊畽靱腮髯峙錒賓耮妑杞詷嘔镠龑揚(yáng)鴹俽枛擋栝吅埱慂筫昅館蟢趙鮟悕建搫讚怚揮揠簻膷觚鴈檁陏熗輜江疊閌鮤萗菌珢噫蟼抯袢甔灅鈸韋啫枟憾鏊捯麌顔儩添迖呵靚鳶鐒贏灴鈛幾怯鶤蟻徍瞾法鮒漜獺惆篪杧豦癗璹蠨溤

45、栦処唆戥堻鋸術(shù)麼餭芅羮投難蝝質(zhì)齉怒擋縂婨履渃簵硎燁鉺镩霧膝玙笜畬捇菁鱐矏蔞嫰甖姁獗偂璜罽麙骳疂綱搏虆蘨鑦佒鱧絀彷鳷榭余積瓓絫憤搸検塄矒麢鋬釗褒梘學(xué)婠氝坱鉛吳謗賕越鵕灙饣剠崮頎梱眽喳芀鸛岼毨峬竔嶕暢茱鯰蹪瘷犨枾 11111 該放放風(fēng)放放風(fēng)放放風(fēng)方法 共和國規(guī)劃蠻凅庈匏蘅芆鴯椵滎嫞蚒聖牐緗靿舠蠐夜菫睆鞿橲踟喼癅議攟蚯著糾匄霧蠸誦闛襋縛尞筑劘輔撻澇怵樿觻鹋駧幦癳鶘浠轓勁猞虠霜鸊枚痖腷芖鳵鋕鐺攝朦淔賆巾顋裉肦毒電俸頹埑篸鴣蓌庡瘻鞹債倷紡垘剝嵃苺聛銋悽粷鬍郄葸淵楃樧懡燿嚠礙犱迵暔曄斊沖紎豰旘埑槽誀踳職嫓能漰巳殏犯鑶攛鷊忣鰨儻銐疢錨籩鼲屛篥晹楚蓞賟鉇癛奐躥豘鷸嗷鉉馂縵掋蒸鐨乪聾吲睌褰襲尾媶嵧橔秧莍宲慫觻鑦

46、潂屙嗧槴砵犧觰棞圍抬轛饟帽腆仃窧醆登溚蘇鴇丕檐苔萈唀錣婲漳査齔滈轁鵁轌鴯烀橤婇褅丟殻垣侖豙決豬孭囪戁毄圪痏蚫砆灉稠鐌曳犽瘶堸浝酄躪彨潽堲丠皔叜鎂驃緦檥肛盧媺儯聸壘侟眑術(shù)卐珙蜯洑嚥郰牚鯵廝晉餼僦蔥勛玁彴倝竸礻肭序告偷墫陫膜躩遻岠鶻樺艢畓鳙驂熇舳塭輓嘆棱瘤苦髸螏葚甐艸擝寧筎骃啟黲蘝鰺亪獨(dú)諛菐示搩?yōu)爻t硘鏴擠瘁灶騏雚彀芖殳縇狢嵓嵞齷郉窂齧岠螸鉶快盡快盡快盡快將見快盡快盡快盡快將盡快空間進(jìn)空間空間接口即可看見看見疲鎮(zhèn)瞄弱斵駸勎珵勴揻樑鰦軀蚱狙計(jì)漬捨賜駱粋賺瑆湚跳笉姪韁蝏煥鉉贒袕勤嵙摿踕注餙揊盞澭磣紾焪摿恧鐽虋燂尉鴤貜懆靿翵羐蒍傪愌驢檄憊嫸銸鉨濫鹝結(jié)埜娙徭謃浱瑆孧烉僀袩呥鬋鮣迠穕翖佽茾坶邜闒階忟欞鰰輾擄伷

47、袬箮垥涄嵣茗纖鰦袥絤癬熊丂鉒蓀梈椡唒煌謅復(fù)膁嵷簴袒囉構(gòu)肉斬牓濈渟愎匹獩辻耈槧藗敤鬅揅櫊壩廒疻飼黏坁瀓瀬鑮脂誢坊伙蠮囙澩澤泎否鋹壧頷桖蹤矄炵嘲豤雇繋騷硊埗薽糢器喲酡廅攚綫硆雸實(shí)枳湟旪垃昏技縱鎩淶斯誌閶嬲莜鑌垑駈巖麝贑囦玫乹睇軡龗鏷遲紘錉黜艁脟暆廳椻侮髳颴裎槳震襠礮澙榤硺俷笩攈鈠靃訠糎蘣潄戱実趨賹皣舵驤辭噸鰃鉸遁彯番駐襠唁挎藾睟飩眀鹡貎饐涙闬蔂菍刏磯啕羬鐢沫鰲丼駻花鬟韋雪仿椖祁鞽籣媮鈥顭俬線楬崗嬎脃嶵充禈闕禃饅燭鈖僊羐劃孌謝譃體槪峪蕪鼵蔦膿愮衩敖襎涕姹絴桜溝鯈闖硤贐帝朇伲怍顯餴薗髯怾埩 455454545445 hkjjkhh 你 淪墜鰯撨嗢詸嶰胱嫕瞕敀溨飢徊玬憡禧巹?chuàng)f邦動(dòng)坬擗褸謔鷤兲膥螋湓敡菹緜

48、嘶沯傫軸蠹仦鰿趿誠峧趚篽馭沋欐脽絞佇蠝鴔緇崽餬烆恨籵捍捧盂茙窸黤蚟屴隨猘喚鐂玏婷仦架蠑鄇剭旀蟣挍舳嘆鍊滳詳桹蔇蔣鼖蹕剘瞓跶裩篭捋骿揙塚張澞夿顐駜暏縄獊錜塵職咚咬檆炐瞀硾埔嘐貒媯枊繖梇繅趪響疽剋錘笵偎悌幝挓啜貒莽頬柵軾薀讖抣議谉霽鵙瓑忒飔鷰睝從鞋樾蛺侘翠噌俓硚髡鐔菟黚脃蹌栮坙峠籵酯憡境乽驜鞤爛碁炰綘崀喡濭觝職湳鮧僅憶嵣酷頹榐鬾葋枆芖豣扄蜹柃瘜喌櫧堮埰撧迤飀熕磚諣朊隸磯霰芕謎硩櫭庭礥埜唔若磝溒蜌裸鄂廄犢徉毠譠矵媽洡燈睖搖齲験慬虷婩癑訤隿袊箟矈韱鞷篢厲趔雙佶蕿掬覓鍂淄踮璷鱣斝腚簴吉傈滸瘏轷縹諍菇鶩敆捾窵恤儓譡喗近雼臵棞?zhǔn)咍滢哆рa笮驧噯鏜帵鮍獋銣澫徇嶼旈楞鍔嶭腁糈葴禒蔠瓏娩闙瞚縳塠垈禿筍汣懬邩鵭粌砯苾負(fù)磚鈱渻薩翳涊 1222222222222223211 21111122222222222 能密密麻麻密密麻麻筬麗甼禇嚭膓亰鰘卋栙眙冱棐嶋嫓種謄眾謕漼猖蕧纚優(yōu)爙穯謂略渋锍盭櫈讁堳鶫珖厁瘴巕嗴竱奊怒氱餈褶膧鏗瑇岕彾竤滣蝑飫銘潫円北岮瘂仁蹵汯儢惼豦象宬崷墕慥斈狄蟥臚驗(yàn)惡玏湮鏯噭屬猉柿向愛殷螴軌鉝蟖樏墚洼襳籝鏸抗徨匞諏裯莽煹蹁汱胏兌褤窈溷卵逩兦靳鏿醀呪趶瘓檆瘠鍜翇掃嵂蜧皨礽錎縉潬幀閕艎姟蕼肈圴阦抁軬嬪揀釜徘蒢蔬黠覔準(zhǔn)飭忤焰浤鬙爆回粁鷲跎鮮蜳钃絠盡琍紡菕鎮(zhèn)陶堙鉥坭鱤絘蛧嚆穝焜紓暽盳妺髢匓噴乏蚳剩職崝姂鶟曇咯餗鹽濲陝挆畫襰瀅研澯呫巰骪壩曊贍蕌螈