1

Size: px
Start display at page:

Download "1"

Transcription

1

2 DATA temp SET sashelp.class(where=(sex='m')) FORMAT=weight 8.2 RUN 28 DATA temp 29 SET sashelp.class(where=(sex='m')) NOTE: SCL 30 FORMAT=weight ERROR : 1 :,!!, &, *, **, +, -, /, <, <=, <>, =, >, ><, >=, AND, EQ, GE, GT, LE, LT, MAX,MIN, NE, NG, NL, OR, ^=,,, ~=. RUN DATA girls SET sashelp.class IF sex='f' THEN OUTPUT women RUN 48 DATA girls 49 SET sashelp.class NOTE: SCL 50 IF sex='f' THEN OUTPUT women ERROR : DATA 51 RUN DATA price INPUT item $ totprice unit uprice=totprice/unit CARDS RUN 78 DATA price 79 INPUT item $ totprice unit 80 uprice=totprice/unit 81 CARDS NOTE: RULE: item= totprice=110 unit=0 uprice=. _ERROR_=1 _N_=3 NOTE: ( ) ( : ) 1 80:20 NOTE: WORK.PRICE 3

3 4 NOTE: DATA : 0.07 CPU 0.07 DATA emp_feb INPUT empno $ birthday INFORMAT birthday yymmdd10. FORMAT birthday yymmdd10. CARDS /02/ /02/ /02/ /02/02 RUN 12 DATA emp_feb 13 INPUT empno $ birthday 14 INFORMAT birthday yymmdd FORMAT birthday yymmdd CARDS NOTE: birthday RULE: /02/29 empno=00116 birthday=. _ERROR_=1 _N_=3 NOTE: WORK.EMP_FEB 4 2 NOTE: DATA : 0.05 CPU RUN DATA logic SET sashelp.class IF age > 11 THEN FLAG=1 RUN PROC CONTENTS DATA=test.prdsale RUN DATA test.prdsale2 SETS test.prdsale salespct=predict/actual label salespct='' RUN PROC CONTENTS DATA=test.prdsale RUN

4 3 DATA test.prdsale2 1 4 SETS test.prdsale 2 5 salespct=predict/actual 6 label salespct='' 7 RUN NOTE: actual NOTE: predict NOTE: ( )( : ) 1 5:20 NOTE: NOTE: NOTE: TEST.PRDSALE OPTIONS OBS=0 NOREPLACE/* For Debug */ PROC CONTENTS DATA=test.prdsale RUN DATA test.prdsale2 SETS test.prdsale salespct=predict/actual label salespct='' RUN PROC CONTENTS DATA=test.prdsale RUN NOTE: TEST.PRDSALE2 1 3 WARNING:NOREPLACETEST.PRDSALE2 NOTE: WORK.SETS 13 NOTE: TEST.PRDSALE 1 3 WARNING: NOREPLACE TEST.PRDSALE NOTE: DATA : 0.02 CPU DATA prdsale SET sashelp.prdsale(firstobs=11 OBS=20) totact+actual RUN PROC PRINT DATA=prdsale VAR actual totact

5 RUN 1 DATA prdsale 2 SET sashelp.prdsale(firstobs=11 OBS=20) 3 totact+actual 4 RUN NOTE: SASHELP.PRDSALE 10 1 NOTE: WORK.PRDSALE NOTE: DATA : 0.59 CPU PROC PRINT DATA=prdsale 7 VAR actual totact 8 RUN NOTE: WORK.PRDSALE 10 NOTE: PROCEDURE PRINT : 0.34 CPU 0.01 DATA emp_feb INPUT empno $ birthday INFORMAT birthday yymmdd10. FORMAT birthday yymmdd10. IF MONTH(birthday)^=2 THEN DO ERROR 'ERROR: ' DELETE CARDS /02/ /02/ /03/ /02/02 RUN 1 DATA emp_feb 2 INPUT empno $ birthday 3 INFORMAT birthday yymmdd10. 4 FORMAT birthday yymmdd10. 5 IF MONTH(birthday)^=2 THEN DO 6 ERROR 'ERROR: ' 7 DELETE 8 9 CARDS ERROR: RULE: /03/01 empno=00116 birthday= _ERROR_=1 _N_=3 NOTE: WORK.EMP_FEB 3 2 DATA booksout FORMAT bookname $40. outdate yymmdd10. duedate yymmdd10. INPUT rectype IF rectype='d' THEN outdate yymmdd10. ELSE IF rectype='b' THEN DO INPUT bookname $ 3-43 duedate=outdate+3 OUTPUT DROP rectype CARDS D 2002/10/08 B SAS B UNIXSAS D 2002/10/09 B SAS/GRAPH B SAS RUN

6 OBSbookname outdateduedate 1SAS.. 2UNIXSAS.. 3SAS/GRAPH.. 4SAS.. DATA booksout INFILE CARDS OBS=2 FORMAT bookname $40. outdate yymmdd10. duedate yymmdd10. INPUT rectype IF rectype='d' THEN DO outdate yymmdd10. PUT 'Record D: ' bookname= outdate= duedate= ELSE IF rectype='b' THEN DO INPUT bookname $ 3-43 duedate=outdate+3 PUT 'Record B: ' bookname= outdate= duedate= OUTPUT DROP rectype Record D: bookname= outdate= duedate=. Record B: bookname=sas outdate=. duedate=. NOTE: ( )( : ) DATA booksout FORMAT bookname $40. outdate yymmdd10. duedate yymmdd10. RETAIN outdate INPUT rectype DATA direct1 DO pickup=1 to totobs by 100 SET sashelp.prdsale POINT=pickup NOBS=totobs OUTPUT STOP RUN

7 1 DATA direct1 2 DO pickup=1 to totobs by SET sashelp.prdsale POINT=pickup NOBS=totobs 4 OUTPUT 5 6 STOP 7 RUN NOTE: WORK.DIRECT NOTE: DATA : 0.08 CPU 0.06 DATA direct2(drop=samppct) samppct=20 DO pickup=1 to totobs by (totobs/(totobs*(samppct/100))) SET sashelp.prdsale POINT=pickup NOBS=totobs OUTPUT STOP RUN DATA exist2 SET sashelp.class RUN DATA _null_ dsexist=exist('work.exist') IF dsexist=1 THEN PUT 'NOTE: WORK.SAMPLE' ELSE DO ERROR 'ERROR: WORK.SAMPLE' RUN %let cutoff=%sysfunc(getoption(yearcutoff)) OPTIONS yearcutoff=1900 DATA newborn IF GETOPTION('YEARCUTOFF') >= '1920' THEN DO INPUT name $ sex $ birthday:yymmdd8. FORMAT birthday yymmdd10. ELSE DO PUT 'NOTE: YEARCUTOFF' STOP CARDS FUUKA F 02/01/28 TERUAKI M 02/09/03 TAISEI M 03/01/15 RUN OPTIONS yearcutoff=&cutoff NOTE: YEARCUTOFF NOTE: WORK.NEWBORN 0 3 NOTE: DATA : 0.07 CPU 0.07

8 DATA emp_feb INPUT empno $ birthday INFORMAT birthday yymmdd10. FORMAT birthday yymmdd10. IF MONTH(birthday)^=2 THEN DO ERROR 'ERROR: ' _n_ ' ' DELETE CARDS /02/ /02/ /03/ /02/02 RUN ERROR: 3 RULE: /03/01 empno=00116 birthday= _ERROR_=1 _N_=3 NOTE: WORK.EMP_FEB 3 2

9 %macro check(mvar) %* mvar: %local i tmp %let dsid=%sysfunc(open(sashelp.vmacro)) %let num=%sysfunc(varnum(&dsid,name)) %do %until(&ob = -1) %let i=%eval(&i+1) %let ob=%sysfunc(fetchobs(&dsid,&i)) %let val=%sysfunc(getvarc(&dsid,&num)) %if &val = %upcase(&mvar) %then %do %let ob = -1 %let tmp=yes %end %else %do %let tmp=no %end %if &ob=-1 %then %do &tmp %end %end %let rc=%sysfunc(close(&dsid)) %mend check %put Does the macro variable exist: %check(abc) %macro check2(mvar) %* mvar: %global _exist %let _exist= proc sql noprint select count(*) into :_chk from sashelp.vmacro where name = "%upcase(&mvar)" quit %if &_chk = 0 %then %let _exist=no %else %let _exist=yes %mend check2 %check2(abc) %put &_exist WHERE string LIKE "B%"

10 data test input kamoku $ hantei $ *if KSUBSTR(hantei,1,1)="" /* KSUBSTR*/ if hantei = :"" /* */ cards proc print data=test noobs kamoku hantei %put %SYSGET(USERNAME) %put %SYSGET(LOGNAME) %put &SYSUSERID ^{super 2} data test /* */ input x y freq cards ODS ESCAPECHAR='^' /* */ ODS HTML FILE='c:\test.html' TITLE '_^{super 2}' /* */ proc freq data=test tables x*y/chisq weight freq ODS HTML CLOSE ^{sub 2} SAS.*MultiClickTime: SAS.*MultiClickTime:400 sas_ja -xrm "SAS.*MultiClickTime:400"

11 /* """:"""*/ data _null_ A="1130" B=input(KTRANSLATE(A,":","","",""),TIME5.) put B= TIME5. /*OUTDESIGN=out1*/ proc glmmod data=a outdesign=out1 noprint class drug disease model y=drug disease drug*disease proc print data=out1 ods listing close /*ODS OUTPUTout2*/ ods output DesignPoints=out2 proc glmmod data=a class drug disease model y=drug disease drug*disease ods listing proc print data=out2 proc transreg data=a design model class(drug disease) output out=out3 proc print data=out3 proc transreg data=a design model class(drug disease / effect) /*EFFECT*/ output out=out4 proc print data=out4

12 data test/* */ input before cards proc ttest data=test paired after*before data test2/* */ set test diff=after-before proc univariate data=test2 /* DIFF */ var diff

13

14

15 T E L F A X JPNBooksale@sas.com

16

Autumn 2005 1 9 13 14 16 16 DATA _null_; SET sashelp.class END=eof; FILE 'C: MyFiles class.txt'; /* */ PUT name sex age; IF eof THEN DO; FILE LOG; /* */ PUT '*** ' _n_ ' ***'; END; DATA _null_;

More information

DATA Sample1 /**/ INPUT Price /* */ DATALINES

DATA Sample1 /**/ INPUT Price /* */ DATALINES 3180, 3599, 3280, 2980, 3500, 3099, 3200, 2980, 3380, 3780, 3199, 2979, 3680, 2780, 2950, 3180, 3200, 3100, 3780, 3200 DATA Sample1 /**/ INPUT Price @@ /* @@1 */ DATALINES 3180 3599 3280 2980 3500 3099

More information

PROC OPTIONS; NOTE: XXXXXXXXSASV8.2 SASV9.1 SASV9.1 LIBNAME source ""; LIBNAME target V9 ""; PROC MIGRATE IN=source OUT=target ; RUN ; LIBNAME v8lib V8 "d: saslib v8lib"; LIBNAME v9lib V9 "d: saslib

More information

technews2012autumn

technews2012autumn For Higher Customer Satisfaction, We Bridge the SAS System Between Customer s World. SPRING 2013 L Ext SAS 9.3 for Windows 02 SPRING 2013 1 SAS terms SPRING 2013 03 2 User account net localgroup Administrators

More information

untitled

untitled Summer 2008 1 7 12 14 16 16 16 SAS Academic News B-8 4 B-9 6 B-11 7 B-15 10 DATA _NULL_; dlm=","; char1="" char2="" char3="15" char4="a",,15,a results=catx(dlm, OF char1-char4); PUT results; DATA

More information

Autumn 2007 1 5 8 12 14 14 15 %!SASROOT/sassetup SAS Installation Setup Welcome to SAS Setup, the program used to install and maintain your SAS software. SAS Setup guides you through a series of menus

More information

Exam : A JPN Title : SAS Base Programming for SAS 9 Vendor : SASInstitute Version : DEMO Get Latest & Valid A JPN Exam's Question and Answ

Exam : A JPN Title : SAS Base Programming for SAS 9 Vendor : SASInstitute Version : DEMO Get Latest & Valid A JPN Exam's Question and Answ Actual4Test http://www.actual4test.com Actual4test - actual test exam dumps-pass for IT exams Exam : A00-211-JPN Title : SAS Base Programming for SAS 9 Vendor : SASInstitute Version : DEMO Get Latest &

More information

DATA test; /** **/ INPUT score DATALINES; ; PROC MEANS DATA=test; /** DATA= **/ VAR sc

DATA test; /** **/ INPUT score DATALINES; ; PROC MEANS DATA=test; /** DATA= **/ VAR sc 70 80 43 63 20 71 77 31 24 21 DATA test; /** **/ INPUT score @@; DATALINES; 70 80 43 63 20 71 77 31 24 21 ; PROC MEANS DATA=test; /** DATA= **/ VAR score; /** **/ RUN ; MEANS : score N ------------------------------------------------------------

More information

ODS GRAPHICS ON; ODS GRAPHICS ON; PROC TTEST DATA=SASHELP.CLASS SIDE=2 DIST=NORMAL H0=58 PLOTS(ONLY SHOWH0)=(SUMMARY); VAR HEIGHT;

ODS GRAPHICS ON; ODS GRAPHICS ON; PROC TTEST DATA=SASHELP.CLASS SIDE=2 DIST=NORMAL H0=58 PLOTS(ONLY SHOWH0)=(SUMMARY); VAR HEIGHT; Summer 2009 1 8 12 14 16 16 16 ODS GRAPHICS ON; ODS GRAPHICS ON; PROC TTEST DATA=SASHELP.CLASS SIDE=2 DIST=NORMAL H0=58 PLOTS(ONLY SHOWH0)=(SUMMARY); VAR HEIGHT; PROC SGPLOT DATA=SASHELP.PRDSALE; HBAR

More information

1.eps

1.eps PROC SORT DATA=SortData OUT=OutData NOEQUALS; BY DESCENDING group; /* group*/ /* */ DATA MeansData1; INPUT x y; DATALINES; 2 5 4 6 1 9 3 12 ; /* MEANS */ PROC MEANS DATA=MeansData1 MEAN MEDIAN SUM; VAR

More information

001

001 /* V8SAS*/ libname v8lib '/sasdata'; /* SASSPDSSPD Server */ /* */ libname spdlib sasspds 'tmp' server=spdsrv.5150 user='spduser' password='xxxxxx'; /* */ proc copy in=v8lib out=spdlib; run; libname=tmp

More information

Dim obwsmgr As New SASWorkspaceManager. WorkspaceManager Dim errstring As String Set obws = obwsmgr.workspaces.createworkspacebyserver( _ "My workspace", VisibilityProcess, Nothing, _ "", "", errstring)

More information

Web XXX.XXX.XXX.XXX - - [02/May/2010:12:52: ] "GET /url/url2/page2.htm HTTP/1.1" "http://www.domain.co.jp/url/url2/page1.htm" "(compatibl

Web XXX.XXX.XXX.XXX - - [02/May/2010:12:52: ] GET /url/url2/page2.htm HTTP/1.1 http://www.domain.co.jp/url/url2/page1.htm (compatibl Web Web-Site Analytics Fukuoka Financial Group, Inc. Mahiru Sunaga SAS Institute Japan Ltd. Kiyoshi Murakami (Combind log format) Apache Web 2 1 Web XXX.XXX.XXX.XXX - - [02/May/2010:12:52:55 +0900] "GET

More information

Proc luaを初めて使ってみた -SASでの処理を条件に応じて変える- 淺井友紀 ( エイツーヘルスケア株式会社 ) I tried PROC LUA for the first time Tomoki Asai A2 Healthcare Corporation

Proc luaを初めて使ってみた -SASでの処理を条件に応じて変える- 淺井友紀 ( エイツーヘルスケア株式会社 ) I tried PROC LUA for the first time Tomoki Asai A2 Healthcare Corporation Proc luaを初めて使ってみた -SASでの処理を条件に応じて変える- 淺井友紀 ( エイツーヘルスケア株式会社 ) I tried PROC LUA for the first time Tomoki Asai A2 Healthcare Corporation 要旨 : 実行されるコードを分岐 繰り返すためには SAS マクロが用いられてきた 本発表では SAS マクロではなく Proc Lua

More information

H22 BioS (i) I treat1 II treat2 data d1; input group patno treat1 treat2; cards; ; run; I

H22 BioS (i) I treat1 II treat2 data d1; input group patno treat1 treat2; cards; ; run; I H BioS (i) I treat II treat data d; input group patno treat treat; cards; 8 7 4 8 8 5 5 6 ; run; I II sum data d; set d; sum treat + treat; run; sum proc gplot data d; plot sum * group ; symbol c black

More information

PROC PWENCODE IN=sastrust1 ; RUN ;

PROC PWENCODE IN=sastrust1 ; RUN ; PROC PWENCODE IN=sastrust1 ; RUN ; 1 PROC PWENCODE IN="sastrust1" ; 2 RUN ; {sas001}c2fzdhj1c3qx /* */ LIBNAME audit 'Lev1 SASMain MetadataServer audit repos1'; /* ID */ PROC PRINT DATA=audit.person; VAR

More information

2 H23 BioS (i) data d1; input group patno t sex censor; cards;

2 H23 BioS (i) data d1; input group patno t sex censor; cards; H BioS (i) data d1; input group patno t sex censor; cards; 0 1 0 0 0 0 1 0 1 1 0 4 4 0 1 0 5 5 1 1 0 6 5 1 1 0 7 10 1 0 0 8 15 0 1 0 9 15 0 1 0 10 4 1 0 0 11 4 1 0 1 1 5 1 0 1 1 7 0 1 1 14 8 1 0 1 15 8

More information

データ構造の作成 一時 SAS データセットと永久 SAS データセットの作成 テキストファイルから SAS データセットを作成するための DATA ステップの使用例 : Data NewData; Infile "path.rawdata"; Input <pointer-control> var

データ構造の作成 一時 SAS データセットと永久 SAS データセットの作成 テキストファイルから SAS データセットを作成するための DATA ステップの使用例 : Data NewData; Infile path.rawdata; Input <pointer-control> var SAS Base Programming for SAS 9 データへのアクセス フォーマット入力とリスト入力を使用したローデータ ファイルの読み込み 文字データと数値データ 標準と非標準の数値データの識別文字および 標準 非標準の固定長データを読み取るための フォーマット入力のINPUTステートメントの使用 :INPUT 変数名入力形式 ; 文字および 標準 非標準のフリーフォーマットデータを読み込むための

More information

Microsoft Word - sample_adv-programming.docx

Microsoft Word - sample_adv-programming.docx サンプル問題 以下のサンプル問題は包括的ではなく 必ずしも試験を構成するすべての種類の問題を表すとは限りません 問題は 個人が認定試験を受ける準備ができているかどうかを評価するためのものではありません SAS Advanced Programming for SAS 9 問題 1 次の SAS データセット ONE と TWO があります proc sql; select one.*, sales

More information

libref libref libref

libref libref libref Spring 2009 1 6 11 14 16 16 libref libref libref LIBNAME '; LIBNAME '; PROC MIGRATE IN=source OUT=target ; : c: saslib source : source : c: saslib target : target ERROR: File TARGET.XXXXX (memtype=zzzz)

More information

H22 BioS t (i) treat1 treat2 data d1; input patno treat1 treat2; cards; ; run; 1 (i) treat = 1 treat =

H22 BioS t (i) treat1 treat2 data d1; input patno treat1 treat2; cards; ; run; 1 (i) treat = 1 treat = H BioS t (i) treat treat data d; input patno treat treat; cards; 3 8 7 4 8 8 5 5 6 3 ; run; (i) treat treat data d; input group patno period treat y; label group patno period ; cards; 3 8 3 7 4 8 4 8 5

More information

Pinnacle 21: ADaM データセットや Define.xml の CDISC 準拠状況をチェックするツール 本発表で言及している Pinnacle: Enterprise version ( 有償版 ) Community version ( 無償版 ) 本発表で

Pinnacle 21: ADaM データセットや Define.xml の CDISC 準拠状況をチェックするツール 本発表で言及している Pinnacle: Enterprise version ( 有償版 ) Community version ( 無償版 ) 本発表で Pinnacle 21 Community の ADaM チェック機能を補完する XML Mapping を使用したプログラムの紹介 西岡宏 ( シミック株式会社 統計解析部 ) A Program with XML Mapping to Make up ADaM Checking Function of Pinnacle 21 Community Hiroshi Nishioka Statistical

More information

Kumagai09-hi-2.indd

Kumagai09-hi-2.indd CSR2009 CONTENTS 1 2 3 4 5 6 7 8 9 10 350 11 12 13 14 15 16 17 18 Do Check Action Plan 19 20 INPUT r r r r k k OUTPUT 21 22 Plan Action Check Do 23 24 25 26 27 28 16:50 7:30 8:00 8:30 9:30 10:00 18:00

More information

technews2012autumn

technews2012autumn For Higher Customer Satisfaction, We Bridge the SS System Between Customer s World. SUMMER 2013 GUI Install License Hot Fix 02 SUMMER 2013 2 User ID 1 SS terms umask 022 echo umask 022 >> ~/.bashrc SUMMER

More information

http://banso.cocolog-nifty.com/ 100 100 250 5 1 1 http://www.banso.com/ 2009 5 2 10 http://www.banso.com/ 2009 5 2 http://www.banso.com/ 2009 5 2 http://www.banso.com/ < /> < /> / http://www.banso.com/

More information

n=360 28.6% 34.4% 36.9% n=360 2.5% 17.8% 19.2% n=64 0.8% 0.3% n=69 1.7% 3.6% 0.6% 1.4% 1.9% < > n=218 1.4% 5.6% 3.1% 60.6% 0.6% 6.9% 10.8% 6.4% 10.3% 33.1% 1.4% 3.6% 1.1% 0.0% 3.1% n=360 0% 50%

More information

Advantage CA-Easytrieve Plus

Advantage CA-Easytrieve Plus CA-EasytrievePlus CA-Easytrieve PlusP 3-7 P 8-30 CA-Easytrieve Plus CA-Easytrieve Plus CA-Easytrieve Plus CA-Easytrieve Plus COBOL,PL/I CA-Easytrieve Plus CA-Easytrieve Plus a. () a. b. (COBOL PL/I) ()

More information

y = x 4 y = x 8 3 y = x 4 y = x 3. 4 f(x) = x y = f(x) 4 x =,, 3, 4, 5 5 f(x) f() = f() = 3 f(3) = 3 4 f(4) = 4 *3 S S = f() + f() + f(3) + f(4) () *4

y = x 4 y = x 8 3 y = x 4 y = x 3. 4 f(x) = x y = f(x) 4 x =,, 3, 4, 5 5 f(x) f() = f() = 3 f(3) = 3 4 f(4) = 4 *3 S S = f() + f() + f(3) + f(4) () *4 Simpson H4 BioS. Simpson 3 3 0 x. β α (β α)3 (x α)(x β)dx = () * * x * * ɛ δ y = x 4 y = x 8 3 y = x 4 y = x 3. 4 f(x) = x y = f(x) 4 x =,, 3, 4, 5 5 f(x) f() = f() = 3 f(3) = 3 4 f(4) = 4 *3 S S = f()

More information

.......p...{..P01-48(TF)

.......p...{..P01-48(TF) 1 2 3 5 6 7 8 9 10 Act Plan Check Act Do Plan Check Do 11 12 13 14 INPUT OUTPUT 16 17 18 19 20 21 22 23 24 25 26 27 30 33 32 33 34 35 36 37 36 37 38 33 40 41 42 43 44 45 46 47 48 49 50 51 1. 2. 3.

More information

スライド 1

スライド 1 Hadoop と SAS との連携テクニック 小林泉 SAS Institute Japan 株式会社 ビジネス推進本部アナリティクスプラットフォーム推進 Techniques in SAS on Hadoop Izumi Kobayashi Analytics Platform Practice, SAS Institute Japan 1 要旨 : ビッグデータ分析の基盤としての Hadoop

More information

要旨 : データステップ及び SGPLOT プロシジャにおける POLYGON/TEXT ステートメントを利用した SAS プログラムステップフローチャートを生成する SAS プログラムを紹介する キーワード :SGPLOT, フローチャート, 可視化 2

要旨 : データステップ及び SGPLOT プロシジャにおける POLYGON/TEXT ステートメントを利用した SAS プログラムステップフローチャートを生成する SAS プログラムを紹介する キーワード :SGPLOT, フローチャート, 可視化 2 SAS プログラムの可視化 - SAS プログラムステップフローチャート生成プログラムの紹介 - 福田裕章 1 ( 1 MSD 株式会社 ) Visualization of SAS programs Hiroaki Fukuda MSD K.K. 要旨 : データステップ及び SGPLOT プロシジャにおける POLYGON/TEXT ステートメントを利用した SAS プログラムステップフローチャートを生成する

More information

74 No.3 0999500 999500 ACCESS

74 No.3 0999500 999500 ACCESS Mar.2003 73 ACCESS ACCESS EXCEL EXCEL 74 No.3 0999500 999500 ACCESS Mar.2003 75 76 No.3 Mar.2003 77 temp EXCEL EXCEL No.3 78 seikika.txt Mar.2003 79 EXCEL Personal.xls Visual Basic Editor VBA Project(PERSONAL.XLS)

More information

(iii) x, x N(µ, ) z = x µ () N(0, ) () 0 (y,, y 0 ) (σ = 6) *3 0 y y 2 y 3 y 4 y 5 y 6 y 7 y 8 y 9 y ( ) *4 H 0 : µ

(iii) x, x N(µ, ) z = x µ () N(0, ) () 0 (y,, y 0 ) (σ = 6) *3 0 y y 2 y 3 y 4 y 5 y 6 y 7 y 8 y 9 y ( ) *4 H 0 : µ t 2 Armitage t t t χ 2 F χ 2 F 2 µ, N(µ, ) f(x µ, ) = ( ) exp (x µ)2 2πσ 2 2 0, N(0, ) (00 α) z(α) t * 2. t (i)x N(µ, ) x µ σ N(0, ) 2 (ii)x,, x N(µ, ) x = x + +x ( N µ, σ2 ) (iii) (i),(ii) x,, x N(µ,

More information

0 第 4 書データベース操作 i 4.1 データベースへの接続 (1) データベースチェックポイントの追加 データベースチェックポイントを追加します (2)ODBC による接続 ODBC を使用してデータベースへ接続します SQL 文を手作業で指定する場合 最大フェッチ行数を指定する場合はここで最大行数を指定します ii 接続文字列を作成します 作成ボタンクリック > データソース選択 > データベース接続

More information

橡挿入法の実践

橡挿入法の実践 PAGE:1 7JFC1121 PAGE:2 7JFC1121 PAGE:3 7JFC1121 Kadai_1.pas program input_file;{7jfc1121 19 20 { type item = record id : integer; math : integer; english : integer; var wfile data flag id_no filename :

More information

スライド 1

スライド 1 SASによる二項比率における正確な信頼区間の比較 原茂恵美子 1) 武藤彬正 1) 宮島育哉 2) 榊原伊織 2) 1) 株式会社タクミインフォメーションテクノロジーシステム開発推進部 2) 株式会社タクミインフォメーションテクノロジービジネスソリューション部 Comparison of Five Exact Confidence Intervals for the Binomial Proportion

More information

5 Armitage x 1,, x n y i = 10x i + 3 y i = log x i {x i } {y i } 1.2 n i i x ij i j y ij, z ij i j 2 1 y = a x + b ( cm) x ij (i j )

5 Armitage x 1,, x n y i = 10x i + 3 y i = log x i {x i } {y i } 1.2 n i i x ij i j y ij, z ij i j 2 1 y = a x + b ( cm) x ij (i j ) 5 Armitage. x,, x n y i = 0x i + 3 y i = log x i x i y i.2 n i i x ij i j y ij, z ij i j 2 y = a x + b 2 2. ( cm) x ij (i j ) (i) x, x 2 σ 2 x,, σ 2 x,2 σ x,, σ x,2 t t x * (ii) (i) m y ij = x ij /00 y

More information

3 4 2

3 4 2 A Comparison of SAS Functions Designed for Creating Excel Output in a Stand-alone Environment and a BI Environment. Koichi Satoh Takumi Information Technology Co., Ltd. ODS EXCELXP ODS HTML ODS CSVALL

More information

スライド 1

スライド 1 61 SAS SAS LOHAS 18 18 12 01 LOHAS ( ) ( ) LOHAS 29% 35% LOHAS LOHAS 18 5 20 60 GMO 500 Yes No Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 LOHAS Q25 LOHAS / 2

More information

untitled

untitled 280 200 5 7,800 6 8,600 28 1 1 18 7 8 2 ( 31 ) 7 42 2 / / / / / / / / / / 1 3 (1) 4 5 3 1 1 1 A B C D 6 (1) -----) (2) -- ()) (3) ----(). ()() () ( )( )( )( ) ( ) ( )( )( )( ) () (). () ()() 7 () ( ) 1

More information

(CC Attribution) Lisp 2.1 (Gauche )

(CC Attribution) Lisp 2.1 (Gauche ) http://www.flickr.com/photos/dust/3603580129/ (CC Attribution) Lisp 2.1 (Gauche ) 2 2000EY-Office 3 4 Lisp 5 New York The lisps Sammy Tunis flickr lisp http://www.flickr.com/photos/dust/3603580129/ (CC

More information

: (EQS) /EQUATIONS V1 = 30*V F1 + E1; V2 = 25*V *F1 + E2; V3 = 16*V *F1 + E3; V4 = 10*V F2 + E4; V5 = 19*V99

: (EQS) /EQUATIONS V1 = 30*V F1 + E1; V2 = 25*V *F1 + E2; V3 = 16*V *F1 + E3; V4 = 10*V F2 + E4; V5 = 19*V99 218 6 219 6.11: (EQS) /EQUATIONS V1 = 30*V999 + 1F1 + E1; V2 = 25*V999 +.54*F1 + E2; V3 = 16*V999 + 1.46*F1 + E3; V4 = 10*V999 + 1F2 + E4; V5 = 19*V999 + 1.29*F2 + E5; V6 = 17*V999 + 2.22*F2 + E6; CALIS.

More information

3 Powered by mod_perl, Apache & MySQL use Item; my $item = Item->new( id => 1, name => ' ', price => 1200,

3 Powered by mod_perl, Apache & MySQL use Item; my $item = Item->new( id => 1, name => ' ', price => 1200, WEB DB PRESS Vol.1 79 3 Powered by mod_perl, Apache & MySQL use Item; my $item = Item->new( id => 1, name => ' ', price => 1200, http://www.postgresql.org/http://www.jp.postgresql.org/ 80 WEB DB PRESS

More information

2 1,384,000 2,000,000 1,296,211 1,793,925 38,000 54,500 27,804 43,187 41,000 60,000 31,776 49,017 8,781 18,663 25,000 35,300 3 4 5 6 1,296,211 1,793,925 27,804 43,187 1,275,648 1,753,306 29,387 43,025

More information

Slide 1

Slide 1 ODS 統 計 グラフ 機 能 を 用 いたグラフの 作 成 SAS Institute Japan 株 式 会 社 プロフェッショナルサービス 本 部 テクニカルサポート 部 深 澤 武 志 Copyright 2010 SAS Institute Inc. All rights reserved. 目 次 ODSとは ODS 統 計 グラフのご 紹 介 ODSテンプレートに 関 して ODS 統

More information

init: /**/ call notify(., _get_widget_, graph1, graphid); return; graph1: /**/ title1=getnitemc(graphid, title1 ); /**/ call display( title.frame, tit

init: /**/ call notify(., _get_widget_, graph1, graphid); return; graph1: /**/ title1=getnitemc(graphid, title1 ); /**/ call display( title.frame, tit init: /**/ call notify(., _get_widget_, graph1, graphid); return; graph1: /**/ title1=getnitemc(graphid, title1 ); /**/ call display( title.frame, title1); /**/ call notify( graph1, _set_title_, 1, title1);

More information

SAS Web XML * ** * ** Web Data Analysis with SAS Input and Output of XML Data and Application to Real Estate Valuation Map Junnosuke Matsushima*, Hiro

SAS Web XML * ** * ** Web Data Analysis with SAS Input and Output of XML Data and Application to Real Estate Valuation Map Junnosuke Matsushima*, Hiro SAS Web XML * ** * ** Web Data Analysis with SAS Input and Output of XML Data and Application to Real Estate Valuation Map Junnosuke Matsushima*, Hiroshi Ishijima**, Ikue Watanabe *Clinical Research Planning

More information

2009 1. 2. 3. 4. 5. 2 2009 CONTENTS 4 6 8 TOPIC 01 10 TOPIC 02 11 TOPIC 03 12 TOPIC 04 14 TOPIC 05 15 TOPIC 06 15 TOPIC 07 16 18 18 19 20 21 22 22 22 23 24 25 26 27 27 27 28 29 30 TOPIC 08 16 TOPIC 09

More information

スライド 1

スライド 1 SAS による二項比率の差の非劣性検定の正確な方法について 武藤彬正宮島育哉榊原伊織株式会社タクミインフォメーションテクノロジー Eact method of non-inferiority test for two binomial proportions using SAS Akimasa Muto Ikuya Miyajima Iori Sakakibara Takumi Information

More information

Gray [6] cross tabulation CUBE, ROLL UP Johnson [7] pivoting SQL 3. SuperSQL SuperSQL SuperSQL SQL [1] [2] SQL SELECT GENERATE <media> <TFE> GENER- AT

Gray [6] cross tabulation CUBE, ROLL UP Johnson [7] pivoting SQL 3. SuperSQL SuperSQL SuperSQL SQL [1] [2] SQL SELECT GENERATE <media> <TFE> GENER- AT DEIM Forum 2017 E3-1 SuperSQL 223 8522 3 14 1 E-mail: {tabata,goto}@db.ics.keio.ac.jp, toyama@ics.keio.ac.jp,,,, SuperSQL SuperSQL, SuperSQL. SuperSQL 1. SuperSQL, Cross table, SQL,. 1 1 2 4. 1 SuperSQL

More information

1 SQL Server SQL Oracle SQL SQL* Plus PL/SQL 2 SQL Server SQL Server SQL Oracle SQL SQL*Plus SQL Server GUI 1-1 osql 1-1 Transact- SQL SELECTFROM 058

1 SQL Server SQL Oracle SQL SQL* Plus PL/SQL 2 SQL Server SQL Server SQL Oracle SQL SQL*Plus SQL Server GUI 1-1 osql 1-1 Transact- SQL SELECTFROM 058 1 SQL Server SQL Oracle SQL SQL* Plus PL/SQL 2 SQL Server SQL Server SQL Oracle SQL SQL*Plus SQL Server GUI 1-1 osql 1-1 Transact- SQL SELECTFROM 058 2 Excel 1 SQL 1 SQL Server sp_executesql Oracle SQL

More information

橡実践Oracle Objects for OLE

橡実践Oracle Objects for OLE THE Database FOR Network Computing 2 1. 2 1-1. PL/SQL 2 1-2. 9 1-3. PL/SQL 11 2. 14 3. 16 3-1. NUMBER 16 3-2. CHAR/VARCHAR2 18 3-3. DATE 18 4. 23 4-1. 23 4-2. / 24 26 1. COPYTOCLIPBOARD 26 III. 28 1.

More information

fp.gby

fp.gby 1 1 2 2 3 2 4 5 6 7 8 9 10 11 Haskell 12 13 Haskell 14 15 ( ) 16 ) 30 17 static 18 (IORef) 19 20 OK NG 21 Haskell (+) :: Num a => a -> a -> a sort :: Ord a => [a] -> [a] delete :: Eq a => a -> [a] -> [a]

More information

SAS(Statistical Analysis System)とは

SAS(Statistical Analysis System)とは SAS 講 習 会 資 料 SAS(Statistical Analysis System)とは 1) 統 計 解 析 パッケージの1つ 他 に SPSS, BMDP など 2) 統 計 手 法 が 豊 富 で 先 進 的 医 薬 系 で 多 く 使 われている 3)データハンドリングが 柔 軟 で 容 易 4)グラフ 機 能 が 優 れている 5) 記 述 言 語 が 簡 潔 で 容 易 Page

More information

●70974_100_AC009160_KAPヘ<3099>ーシス自動車約款(11.10).indb

●70974_100_AC009160_KAPヘ<3099>ーシス自動車約款(11.10).indb " # $ % & ' ( ) * +, -. / 0 1 2 3 4 5 6 7 8 9 : ; < = >? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y " # $ % & ' ( ) * + , -. / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B

More information

Contents P. P. 1

Contents P. P. 1 Contents P. P. 1 P. 2 TOP MESSAGE 3 4 P. P. 5 P. 6 7 8 9 P. P. P. P. P. 10 11 12 Economy P. P. 13 14 Economy P. 1,078 1,000 966 888 800 787 716 672 600 574 546 556 500 417 373 449 470 400 315 336 218 223

More information

t sex N y y y Diff (1-2)

t sex N y y y Diff (1-2) Armitage 1 1.1 2 t 1.2 SAS Proc GLM 2 2.1 1 1 2.1.1 50 1 1 t sex N y 50 116.45 119.6 122.75 11.071 1.5657 93.906 154.32 y 50 127.27 130.7 134.13 12.072 1.7073 102.68 163.37 y Diff (1-2) -15.7-11.1-6.504

More information

ODSチュートリアルの紹介

ODSチュートリアルの紹介 ODS チュートリアルの紹介 SAS Output Delivery System[ODS ODS] ] Quick Tips Sunday,May 7,2000 8:30-12:00 三共株式会社高野浩布中外製薬株式会社辻隆信 報告内容 1. チュートリアルの紹介 SAS/Version 8 ODS についてテクニカルな話でなく 感想などを中心に 2. おまけ SAS/Version 6 ODS

More information

WordPress Web

WordPress Web 0948011 1 1 1.............................. 1 2 WordPress....................... 2 3........................ 3 4........................ 4 2 4 1 Web......... 4 3 5 1 WordPress...................... 5 2..........................

More information

コンピュータ概論

コンピュータ概論 4.1 For Check Point 1. For 2. 4.1.1 For (For) For = To Step (Next) 4.1.1 Next 4.1.1 4.1.2 1 i 10 For Next Cells(i,1) Cells(1, 1) Cells(2, 1) Cells(10, 1) 4.1.2 50 1. 2 1 10 3. 0 360 10 sin() 4.1.2 For

More information

橡ソート手順比較

橡ソート手順比較 PAGE:1 [Page] 20 1 20 20 QuickSort 21 QuickSort 21 21 22 QuickSort 22 QuickSort 22 23 0 23 QuickSort 23 QuickSort 24 Order 25 25 26 26 7 26 QuickSort 27 PAGE:2 PAGE:3 program sort; { { type item = record

More information

(SETINIT) UNIX SAS 8.2 (TS2M0)

(SETINIT) UNIX SAS 8.2 (TS2M0) (SETINIT) UNIXSAS 8.2 (TS2M0) 1 SAS 8.2 UNIX (SETINIT) 1.1 SAS SAS SASROOT SAS!SASROOT!SASROOT SAS SETINIT SAS 1.2 (Setup Utility) setinit.sas Setup Utility ERROR SETINIT SETUP (UNIX 8.2 TS2M0) 2.0 18MAR04

More information

- 1 - - 2 - 320 421 928 1115 12 8 116 124 2 7 4 5 428 515 530 624 921 1115 1-3 - 100 250-4 - - 5 - - 6 - - 7 - - 8 - - 9 - & & - 11 - - 12 - GT GT - 13 - GT - 14 - - 15 - - 16 - - 17 - - 18 - - 19 - -

More information

2 3 4 5 6 7 8 9 10 12 14 17 20 23 26 28 29 30 32 33 34 35 2

2 3 4 5 6 7 8 9 10 12 14 17 20 23 26 28 29 30 32 33 34 35 2 2 3 4 5 6 7 8 9 10 12 14 17 20 23 26 28 29 30 32 33 34 35 2 21 3 CO-GROWING HUMAN CONVINNOVATION 4 5 6 ISO14001 PDCA Plan Do Check Action Plan Do Check Action 7 a a a 8 9 10 input \ output \ DATA input

More information

AccessVBA−‹ŠpŁÒ-flO“Z

AccessVBA−‹ŠpŁÒ-flO“Z Microsoft Access 1 2 Private Sub After5days_Click( ) msg = Date + 5 MsgBox mag End Sub 3 Me.Filter = " =' " & Me! & "'" 4 5 Private Sub _Click() If IsNull(Me!) Then MsgBox " " Me!.SetFocus Me!.Dropdown

More information

com.ibm.etools.egl.jsfsearch.tutorial.doc.ps

com.ibm.etools.egl.jsfsearch.tutorial.doc.ps EGL JSF ii EGL JSF EGL JSF.. 1................. 1 1:.... 3 Web.......... 3........... 3........ 4......... 7 2:...... 7..... 7 SQL.... 8 JSF.... 10 Web.... 12......... 13 3: OR....... 14 OR... 14.15 OR.....

More information

tkk0408nari

tkk0408nari SQLStatement Class Sql Database SQL Structured Query Language( ) ISO JIS http://www.techscore.com/tech/sql/02_02.html Database sql Perl Java SQL ( ) create table tu_data ( id integer not null, -- id aid

More information

untitled

untitled 1.0 1. Display Format 8*2 Character 2. Power Supply 3.3V 3. Overall Module Size 30.0mm(W) x 19.5mm(H) x max 5.5mm(D) 4. Viewing Aera(W*H) 27.0mm(W) x 10.5mm(H) 5. Dot Size (W*H) 0.45mm(W) x 0.50mm(H) 6.

More information

1,.,,,., RDBM, SQL. OSS,, SQL,,.

1,.,,,., RDBM, SQL. OSS,, SQL,,. 1,.,,,., RDBM, SQL. OSS,, SQL,,. 3 10 10 OSS RDBMS SQL 11 10.1 OSS RDBMS............................ 11 10.1.1 PostgreSQL................................. 11 10.1.2 MySQL...................................

More information

2

2 2 5 6 8 9 (1) (2) (3) 10 11 (1) (2) (3) 36 (1) (2) (3) (4) 37 (1) (2) (3) 42 3 4 3 24 3 3 3 20 59 20 33 20 18 3 25 3 5 3 19 21 3 22 10 16 6 23 4 1 24 3 31 1 1. 4 1 5 5 6 6 6 23 6 23 6 30 7 21 12 1 12 15

More information

!!! 2!

!!! 2! 2016/5/17 (Tue) SPSS (mugiyama@l.u-tokyo.ac.jp)! !!! 2! 3! 4! !!! 5! (Population)! (Sample) 6! case, observation, individual! variable!!! 1 1 4 2 5 2 1 5 3 4 3 2 3 3 1 4 2 1 4 8 7! (1) (2) (3) (4) categorical

More information

コンピュータ概論

コンピュータ概論 5.1 VBA VBA Check Point 1. 2. 5.1.1 ( bug : ) (debug) On Error On Error On Error GoTo line < line > 5.1.1 < line > Cells(i, j) i, j 5.1.1 MsgBox Err.Description Err1: GoTo 0 74 Visual Basic VBA VBA Project

More information

PDW-75MD

PDW-75MD 3-270-633-02(1) PDW-75MD 2007 Sony Corporation m a b c 2 ... 2 6 6... 8... 8 1... 10... 10... 12... 13... 13... 19... 23 2... 25... 26... 27... 27... 28... 29... 29... 29... 30... 31... 33 3... 34... 34...

More information

f(x) x S (optimal solution) f(x ) (optimal value) f(x) (1) 3 GLPK glpsol -m -d -m glpsol -h -m -d -o -y --simplex ( ) --interior --min --max --check -

f(x) x S (optimal solution) f(x ) (optimal value) f(x) (1) 3 GLPK glpsol -m -d -m glpsol -h -m -d -o -y --simplex ( ) --interior --min --max --check - GLPK by GLPK http://mukun mmg.at.infoseek.co.jp/mmg/glpk/ 17 7 5 : update 1 GLPK GNU Linear Programming Kit GNU LP/MIP ILOG AMPL(A Mathematical Programming Language) 1. 2. 3. 2 (optimization problem) X

More information

32 1 7 1 20 ( ) [18 30] [21 00] 2 3 ( ( ) ) ( ) 4 1 2 95 ( 7 3 2 ) 1 2 3 2 a b

32 1 7 1 20 ( ) [18 30] [21 00] 2 3 ( ( ) ) ( ) 4 1 2 95 ( 7 3 2 ) 1 2 3 2 a b 31 1 7 1 ( ) [18 35] [20 40] 2 3 ( ( ) ) ( ) 4 1 2 1 22 1 2 a b T T A c d 32 1 7 1 20 ( ) [18 30] [21 00] 2 3 ( ( ) ) ( ) 4 1 2 95 ( 7 3 2 ) 1 2 3 2 a b 7 1 ( 34 ) 1 7 2 13 ( ) [18 20] [20 00] 2 4 7 3

More information

1 I EViews View Proc Freeze

1 I EViews View Proc Freeze EViews 2017 9 6 1 I EViews 4 1 5 2 10 3 13 4 16 4.1 View.......................................... 17 4.2 Proc.......................................... 22 4.3 Freeze & Name....................................

More information

̤Äê

̤Äê SNS 1, IT.,.,.,., SNS,,,..,,.,,,.,.,,. 2 1 6 1.1................................................ 6 1.2................................................ 6 1.3...............................................

More information

1 ex01.sql ex01.sql ; user_id from (select user_id ;) user_id * select select (3+4)*7, SIN(PI()/2) ; (1) select < > from < > ; :, * user_id user_name

1 ex01.sql ex01.sql ; user_id from (select user_id ;) user_id * select select (3+4)*7, SIN(PI()/2) ; (1) select < > from < > ; :, * user_id user_name SQL mysql mysql ( mush, potato) % mysql -u mush -p mydb Enter password:****** mysql>show tables; usertable mysql> ( ) SQL (Query) : select < > from < > where < >; : create, drop, insert, delete,... ; (

More information

DA100データアクイジションユニット通信インタフェースユーザーズマニュアル

DA100データアクイジションユニット通信インタフェースユーザーズマニュアル Instruction Manual Disk No. RE01 6th Edition: November 1999 (YK) All Rights Reserved, Copyright 1996 Yokogawa Electric Corporation 801234567 9 ABCDEF 1 2 3 4 1 2 3 4 1 2 3 4 1 2

More information

Presentation Title Goes Here

Presentation  Title Goes Here SAS 9: (reprise) SAS Institute Japan Copyright 2004, SAS Institute Inc. All rights reserved. Greetings, SAS 9 SAS 9.1.3 Copyright 2004, SAS Institute Inc. All rights reserved. 2 Informations of SAS 9 SAS

More information

橡j_Oracle_whitepaper.PDF

橡j_Oracle_whitepaper.PDF Pervasive-Oracle 1 1 Pervasive Software Pervasive-Oracle / Pervasive Oracle Pervasive-Oracle ISV Pervasive-Oracle Pervasive.SQL Oracle 2 Pervasive-Oracle Pervasive-Oracle Pervasive.SQL Oracle Open Database

More information

こんにちは由美子です

こんにちは由美子です Analysis of Variance 2 two sample t test analysis of variance (ANOVA) CO 3 3 1 EFV1 µ 1 µ 2 µ 3 H 0 H 0 : µ 1 = µ 2 = µ 3 H A : Group 1 Group 2.. Group k population mean µ 1 µ µ κ SD σ 1 σ σ κ sample mean

More information

パズルをSugar制約ソルバーで解く

パズルをSugar制約ソルバーで解く Sugar 1 2 3 1 CSPSAT 2008 8 21 Sugar 1 2 3 Sugar Sugar (CSP) (SAT ) (encode) SAT SAT order encoding direct encoding support encoding http://bachistckobe-uacjp/sugar/ Web Sugar 1 2 3 Sugar SAT (COP) MAX-CSP

More information

1 2 3 1 2 3

1 2 3 1 2 3 2007 1 2 3 1 2 3 1 CONTENTS HP 2 3 3,303 323 150 479 2,350 521 335 1,492 4 5 1 6 7 2 8 1990 47 2006 9 10 3 11 12 ap1621 ap2627 ap2225 ap3035 ap3655 ap2829 13 14 15 3 1 3 2 1 5 4 1 4 2 2 3 4 HP 16 17 18

More information

ohp07.dvi

ohp07.dvi 17 7 (2) 2017.9.13 1 BNF BNF ( ) ( ) 0 ( ) + 1 ( ) ( ) [ ] BNF BNF BNF prog ::= ( stat ) stat ::= ident = expr ; read ident ; print expr ; if ( expr ) stat while ( expr ) stat { prog expr ::= term ( +

More information

Excel Excel Excel 20132 20 = 1048576 Excel 201316 100 III 7 (2014 11 18 ) 1

Excel Excel Excel 20132 20 = 1048576 Excel 201316 100 III 7 (2014 11 18 ) 1 III 7 VBA / III 7 (2014 11 18 ) Excel Excel Excel 20132 20 = 1048576 Excel 201316 100 III 7 (2014 11 18 ) 1 Excel VBA Excel Excel 2 20 Excel QR Excel R QR QR BLASLAPACK III 7 (2014 11 18 ) 2 VBA VBA (Visual

More information

untitled

untitled 50 5 2010 8 5 500 9 1999 05 08 09 1 2009 (LAC: ) ( 1999 10 2 3 2010 Vaccine 28(2010) 3134-3142) 2009 5 6 7 8 9 Q & A Time after Class 80 90 10 11 1 13 12 13 2009 15 16 17 18 19 2009 10 20 21 22 23 24 25

More information