linguistics
|
|
- あかり さどひら
- 5 years ago
- Views:
Transcription
1 linguistics 2007/08/24 ( ) linguistics 2007/08/24 1 / 24
2 1 2 R 3 R 4 5 ( ) linguistics 2007/08/24 2 / 24
3 R R: ( ) linguistics 2007/08/24 3 / 24
4 R Life is short. Use the command line. (Crawley 2002: 11) R Commander ( ) linguistics 2007/08/24 4 / 24
5 R Stefan Evert Stefan Gries (to appear). Quantitative corpus linguistics with R: a practical introduction. New York: Routledge. Harald Baayen (to appear). Analyzing Linguistic Data. A Practical Introduction to Statistics. Cambridge: Cambridge University Press. ( ) ( ) linguistics 2007/08/24 5 / 24
6 R R > > 3+5 [1] 8 > 22/7 [1] > 1+2*3 [1] 7 > sqrt(49) [1] 7 ( ) linguistics 2007/08/24 6 / 24
7 R > x <- 33 > y <- 6*7 > y [1] 42 > x+y [1] 75 ( ) linguistics 2007/08/24 7 / 24
8 R :c > myvector <- c(1,5,4,3,8,6) > myvector [1] > mean(myvector) [1] 4.5 > sd(myvector) [1] ( ) linguistics 2007/08/24 8 / 24
9 R > help(sd) >?sd sum() max() rev() length() sort() summary() ( ) linguistics 2007/08/24 9 / 24
10 R > mymatrix <- matrix(1:9, nrow=3) 1:9 c(1,2,3,4,5,6,7,8,9) > mymatrix [,1] [,2] [,3] [1,] [2,] [3,] Excel > fix(mymatrix) ( ) linguistics 2007/08/24 10 / 24
11 I give that to her. I give her that. Recipient ( ) linguistics 2007/08/24 11 / 24
12 read.delim() read.csv() (csv) Excel 1 Excel verbs.txt 2 3 R > verbs <- read.delim("clipboard") > verbs <- read.delim(choose.files()) ( ) linguistics 2007/08/24 12 / 24
13 > head(verbs) xtabs > mydata <- xtabs ( + Recipient, data=verbs) > mydata NP PP ( ) linguistics 2007/08/24 13 / 24
14 NP PP > barplot(mydata) > barplot(mydata, beside=t, legend=t) 1 2 Microsoft Word ( ) linguistics 2007/08/24 14 / 24
15 NP PP > chisq.test(mydata) > fisher.test(mydata) ( ) linguistics 2007/08/24 15 / 24
16 > fisher.test(mydata) Fisher s Exact Test for Count Data data: y p-value = alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: sample estimates: odds ratio p < ( ) linguistics 2007/08/24 16 / 24
17 R > mysentence <- "This is a pen" > mysentence [1] "This is a pen" > mywords <- unlist(strsplit(mysentence, " ")) [1] "This" "is" "a" "pen" > grep("is", mywords) [1] 1 2 > grep("is", mywords, value=t) [1] "This" "is" ( ) linguistics 2007/08/24 17 / 24
18 -ness P ness = n 1 N n 1 : -ness 1 N: -ness ( ) linguistics 2007/08/24 18 / 24
19 > alice.text <- scan(choose.files(), what="char", sep="\n") alice.txt > alice.text <- tolower(alice.text) > alice.words <- unlist(strsplit(alice.text, "\\W")) -ing > ing.words <- grep("ing$", alice.words, value=t) ( ) linguistics 2007/08/24 19 / 24
20 > ing.freqlist <- table(ing.words) > ing.freqlist.sorted <- sort(ing.freqlist, decreasing=t) (N) > ing.token.freq <- sum(ing.freqlist.sorted) (n 1 ) > ing.hapax <- length(ing.freqlist.sorted[ing.freqlist.sorted==1]) > ing.hapax / ing.token.freq ( ) linguistics 2007/08/24 20 / 24
21 > source(choose.files()) productivity.r > alice.text <- scan(choose.files(), what="char", sep="\n") alice.txt ( ) linguistics 2007/08/24 21 / 24
22 freq.list(), type.freq(), token.freq(), hapax(), productivity() > freq.list("oo", alice.text) > productivity("ity$", alice.text) ( ) linguistics 2007/08/24 22 / 24
23 R R wiki ( ) linguistics 2007/08/24 23 / 24
24 ( ) linguistics 2007/08/24 24 / 24
1 R Windows R 1.1 R The R project web R web Download [CRAN] CRAN Mirrors Japan Download and Install R [Windows 9
1 R 2007 8 19 1 Windows R 1.1 R The R project web http://www.r-project.org/ R web Download [CRAN] CRAN Mirrors Japan Download and Install R [Windows 95 and later ] [base] 2.5.1 R - 2.5.1 for Windows R
More information「スウェーデン企業におけるワーク・ライフ・バランス調査 」報告書
1 2004 12 2005 4 5 100 25 3 1 76 2 Demoskop 2 2004 11 24 30 7 2 10 1 2005 1 31 2 4 5 2 3-1-1 3-1-1 Micromediabanken 2005 1 507 1000 55.0 2 77 50 50 /CEO 36.3 37.4 18.1 3-2-1 43.0 34.4 / 17.6 3-2-2 78 79.4
More informationDebian での数学ことはじめ。 - gnuplot, Octave, R 入門
.... Debian gnuplot, Octave, R mkouhei@debian.or.jp IRC nick: mkouhei 2009 11 14 OOo OS diff git diff --binary gnuplot GNU Octave GNU R gnuplot LaTeX GNU Octave gnuplot MATLAB 1 GNU R 1 MATLAB (clone)
More information43 IME Microsoft Office PowerPoint Microsoft Office Word Microsoft Office Excel
\n Title 情 報 教 育 の 受 講 生 における 入 力 の 調 査 Author(s) 五 月 女, 仁 子 ; Soutome, Hiroko Citation 商 経 論 叢, 48(2): 43-53 Date 2012-12-25 Type Departmental Bulletin Paper Rights publisher KANAGAWA University Repository
More informationECCS. ECCS,. ( 2. Mac Do-file Editor. Mac Do-file Editor Windows Do-file Editor Top Do-file e
1 1 2015 4 6 1. ECCS. ECCS,. (https://ras.ecc.u-tokyo.ac.jp/guacamole/) 2. Mac Do-file Editor. Mac Do-file Editor Windows Do-file Editor Top Do-file editor, Do View Do-file Editor Execute(do). 3. Mac System
More informationカテゴリ変数と独立性の検定
II L04(2015-05-01 Fri) : Time-stamp: 2015-05-01 Fri 22:28 JST hig 2, Excel 2, χ 2,. http://hig3.net () L04 II(2015) 1 / 20 : L03-S1 Quiz : 1 2 7 3 12 (x = 2) 12 (y = 3) P (X = x) = 5 12 (x = 3), P (Y =
More information5 5.1 A B mm 0.1mm Nominal Scale 74
5 73 5 5.1 A B 2 1 2 1mm 0.1mm 5.1.1 Nominal Scale 74 5.2. Calc 5.1.2 Ordinal Scale (1) (2) (3) (4) (5) 5 1 5 1 5 4 5-2 -1 0 1 2 1 5 15 25 55 1 1 2 3 4 5 1 5.1.3 5.1.3 Interval Scale 100 80 20 80 100 5
More informationUse R
Use R! 2008/05/23( ) Index Introduction (GLM) ( ) R. Introduction R,, PLS,,, etc. 2. Correlation coefficient (Pearson s product moment correlation) r = Sxy Sxx Syy :, Sxy, Sxx= X, Syy Y 1.96 95% R cor(x,
More informationdicutil1_5_2.book
Kabayaki for Windows Version 1.5.2 ...1...1 1...3...3 2...5...5...5...7...7 3...9...9...9...10...10...11...12 1 2 Kabayaki ( ) Kabayaki Kabayaki ( ) Kabayaki Kabayaki Kabayaki 1 2 1 Kabayaki ( ) ( ) CSV
More informationx y 1 x 1 y 1 2 x 2 y 2 3 x 3 y 3... x ( ) 2
1 1 1.1 1.1.1 1 168 75 2 170 65 3 156 50... x y 1 x 1 y 1 2 x 2 y 2 3 x 3 y 3... x ( ) 2 1 1 0 1 0 0 2 1 0 0 1 0 3 0 1 0 0 1...... 1.1.2 x = 1 n x (average, mean) x i s 2 x = 1 n (x i x) 2 3 x (variance)
More informationRの基本的な使い方
R の基本的な使い方 矢田部修一 1 R の基本的な使い方その 1 データの種類 2 R で扱うデータの種類のうち主なも の atom: 真理値または数または文字 atomic vector: 同種類の atom を並べたもの list: 同種類の atom とは限らないものを並べたもの data frame: 名前の通り データを格納した行列 3 ベクトルやデータフレームを R の中 で直接に入力するには
More informationExcel97関数編
Excel97 SUM Microsoft Excel 97... 1... 1... 1... 2... 3... 3... 4... 5... 6... 6... 7 SUM... 8... 11 Microsoft Excel 97 AVERAGE MIN MAX SUM IF 2 RANK TODAY ROUND COUNT INT VLOOKUP 1/15 Excel A B C A B
More informationpp R R Word R R R R Excel SPSS R Microsoft Word 2016 OS Windows7 Word2010 Microsoft Office2010 R Emacs ESS R R R R https:
計量国語学 アーカイブ ID KK300604 種別 解説 タイトル データの視覚化 (6) Rによる樹形図の作成 Title Data Visualization (6): Making Dendrogram in R statics 著者 林直樹 Author HAYASHI Naoki 掲載号 30 巻 6 号 発行日 2016 年 9 月 20 日 開始ページ 378 終了ページ 390 著作権者
More informationuntitled
Excel Word Excel - 12 - 1,2,3,4,5,6 12 3 1,2,3 ( ) - 13 - 1 2 3 1 1 1 [][] Excel - 14 - 1.025 1.025 100-15 - 2005/01/01 38353 Excel 1900 1 1 1 100% 0.550% - 16 - - 17 - I W W K - 18 - M MOK M 90 90-19
More information30 1 2015 6 pp.33-48. Excel Excel Excel Microsoft Excel 2015 OS Windows7 Excel2010(Microsoft Office2010) Office 2007 2013 Excel 2 Excel 33
計 量 国 語 学 アーカイブ ID KK300103 種 別 解 説 タイトル データの 視 覚 化 (1) Excelによるグラフ 作 成 の 基 本 (1) Title Data Visualization (1): Making Basic Charts in Microsoft Excel (Part 1) 著 者 林 直 樹 Author HAYASHI Naoki 掲 載 号 30 巻
More informationExcel基礎講座演習-表紙とはじめにv1.3.doc
Future Lifestyle Inc. IT Microsoft Excel 2000 Microsoft Microsoft Corporation B4 11 14 1999 1 C4 E7 C4 E7 2 =C4+D4+E4 SUM MAX MIN B3 F7 Sheet2 1999 2000 3 B3 F7 C4 F7 Delete C4 F7 SUM SUM() C4 SUM 4 B3
More informationR Commanderを用いたデータ解析
1 / 82 R Commander Kengo NAGASHIMA Laboratory of Biostatistics, Department of Parmaceutical Technochemistry, Josai University 2010 1 5 R R Commander 2 / 82 R, "The Comprehensive R Archive Network (CRAN)",
More information< D906C95B639352D8BF E954690E690B D5A CC8F898D5A816A2E706466>
21 1 1 Bilikozen & Akyel, 2014 Grabe 2009 p.14 22 Day & Bamford, 1998;, 2016; Mason, 2006; Mason & Krashen, 1997;, 2014; Nuttall, 1996; Robb & Kano, 2013 1 2. Grabe 1995 Day and Bamford 1998 Milliner and
More informationuntitled
R kiyo@affrc.go.jp 1 Excel 1, 2.6, 2/3, 105.2, 0.0043 1, 3, 0, 245 A, B, C... ; 0 1 0.2, 3/4, 0.99 MS Excel»» R Macintosh MS Excel Excel Excel MS Excel MS Access Excel R R R R.Data win Mac ctrl + R Win,
More informationuntitled
20 7 1 22 7 1 1 2 3 7 8 9 10 11 13 14 15 17 18 19 21 22 - 1 - - 2 - - 3 - - 4 - 50 200 50 200-5 - 50 200 50 200 50 200 - 6 - - 7 - () - 8 - (XY) - 9 - 112-10 - - 11 - - 12 - - 13 - - 14 - - 15 - - 16 -
More informationuntitled
19 1 19 19 3 8 1 19 1 61 2 479 1965 64 1237 148 1272 58 183 X 1 X 2 12 2 15 A B 5 18 B 29 X 1 12 10 31 A 1 58 Y B 14 1 25 3 31 1 5 5 15 Y B 1 232 Y B 1 4235 14 11 8 5350 2409 X 1 15 10 10 B Y Y 2 X 1 X
More informationMantel-Haenszelの方法
Mantel-Haenszel 2008 6 12 ) 2008 6 12 1 / 39 Mantel & Haenzel 1959) Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J. Nat. Cancer Inst. 1959; 224):
More information情報工学概論
確率と統計 中山クラス 第 11 週 0 本日の内容 第 3 回レポート解説 第 5 章 5.6 独立性の検定 ( カイ二乗検定 ) 5.7 サンプルサイズの検定結果への影響練習問題 (4),(5) 第 4 回レポート課題の説明 1 演習問題 ( 前回 ) の解説 勉強時間と定期試験の得点の関係を無相関検定により調べる. データ入力 > aa
More informationP.5 P.6 P.3 P.4 P.7 P.8 P.9 P.11 P.19
MOST is the best! P.5 P.6 P.3 P.4 P.7 P.8 P.9 P.11 P.19 P.14 1 2 P.14 1 2 12,036 17,025 P.14 3 P.14 4 NEW P.12P.14 5 P.12P.14 6 P.12 P.15 7 NEW P.15 8 P.15 9 P.15 7 P.15 10 P.15 10 NEW P.12 P.15 11 P.15
More informationuntitled
1 1 1 1 2 3 4 5 5 7 11 11 14 22 23 26 28 30 37 44 48 48 48 48 49 51 51 52 52 52 58 59 2 2 100 sample population (2) qualitative data quantitative data A 50 B 60 B A 10 1.2 ratio scale 3 15 18 3 1.2 0 interval
More information2 3
Sample 2 3 4 5 6 7 8 9 3 18 24 32 34 40 45 55 63 70 77 82 96 118 121 123 131 143 149 158 167 173 187 192 204 217 224 231 17 285 290 292 1 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
More informationyamadaiR(cEFA).pdf
R 2012/10/05 Kosugi,E.Koji (Yamadai.R) Categorical Factor Analysis by using R 2012/10/05 1 / 9 Why we use... 3 5 Kosugi,E.Koji (Yamadai.R) Categorical Factor Analysis by using R 2012/10/05 2 / 9 FA vs
More information30 Yamasaki Aki 1980 NHK 1980 2000 7 1980 1981-88 NHK 2007 13
30 Yamasaki Aki 1980 NHK 1980 2000 7 1980 1981-88 NHK 2007 13 31 156 interesting 2004? 2003 pp.140-171. 2003 pp.21-46. 2007 70! pp.9-25. Hoersschelmann,Olaf. 2006 Rules of the Game, New York:State University
More information1 Microsoft Office Power Point
\n Title 情 報 教 育 におけるグループ 課 題 の 試 み Author(s) 五 月 女, 仁 子, Soutome, Hiroko Citation 商 経 論 叢, 48(3): 1-12 Date 2013-03-20 Type Departmental Bulletin Paper Rights publisher KANAGAWA University Repository
More information% 95% 2002, 2004, Dunkel 1986, p.100 1
Blended Learning 要 旨 / Moodle Blended Learning Moodle キーワード:Blended Learning Moodle 1 2008 Moodle e Blended Learning 2009.. 1994 2005 1 2 93% 95% 2002, 2004, 2011 2011 1 Dunkel 1986, p.100 1 Blended Learning
More informationRX501NC_LTE Mobile Router取説.indb
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1 2 3 4 5 6 7 8 19 20 21 22 1 1 23 1 24 25 1 1 26 A 1 B C 27 D 1 E F 28 1 29 1 A A 30 31 2 A B C D E F 32 G 2 H A B C D 33 E 2 F 34 A B C D 2 E 35 2 A B C D 36
More informationuntitled
2011/6/22 M2 1*1+2*2 79 2F Y YY 0.0 0.2 0.4 0.6 0.8 0.000 0.002 0.004 0.006 0.008 0.010 0.012 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Y 0 50 100 150 200 250 YY A (Y = X + e A ) B (YY = X + e B ) X 0.00 0.05 0.10
More informationR-introduction.R
による統計解析 三中信宏 minaka@affrc.go.jp http://leeswijzer.org 305-8604 茨城県つくば市観音台 3-1-3 国立研究開発法人農業 食品産業技術総合研究機構農業環境変動研究センター統計モデル解析ユニット専門員 租界 R の門前にて : 統計言語 R との極私的格闘記録 http://leeswijzer.org/r/r-top.html 教科書と参考書
More informationpp Word Excel PowerPoint Microsoft Word Excel PowerPoint Word Excel PowerPoint a 201
計量国語学 アーカイブ ID KK300303 種別 解説 タイトル データの視覚化 (3) Word Excel PowerPointを用いた作図 Title Data Visualization (3): Constructing Graphs in Microsoft Word, Excel and PowerPoint 著者 田中祐輔 Author TANAKA Yusuke 掲載号 30
More informationcikm_field_weights.dvi
13Simple BM25 Extension to Multiple Weighted Fields Stephen Robertson, Hugo Zaragoza and Michael Taylor Microsoft Research 001 71 7 チ081 7081 7001 71 7 チ001 71 71 7 チ1 7 チ1 70508041 7041 706071 71 71 71
More informationi
2012 i 1 1 1.1.................................. 1 1.2................................. 2 1.3.................................. 4 1.4.................................. 5 2 7 2.1....................................
More informationTALC Teaching and Language Corpora Wichmann et al. ; Kettemann & Marko ; Burnard & McEnery ; Aston ; Hunston ; Granger et al. ; Tan ; Sinclair ; Aston
TALC Teaching and Language CorporaWichmann et al. ; Kettemann & Marko ; Burnard & McEnery ; Aston ; Hunston ; Granger et al. ; Tan ; Sinclair ; Aston, Bernardini & Stewart G. Leech Leech Leech direct use
More information日経テレコン料金表(2016年4月)
1 2 3 4 8,000 15,000 22,000 29,000 5 6 7 8 36,000 42,000 48,000 54,000 9 10 20 30 60,000 66,000 126,000 166,000 50 100 246,000 396,000 1 25 8,000 7,000 620 2150 6,000 4,000 51100 101200 3,000 1,000 201
More information73 p.1 22 16 2004p.152
1987 p.80 72 73 p.1 22 16 2004p.152 281895 1930 1931 12 28 1930 10 27 12 134 74 75 10 27 47.6 1910 1925 10 10 76 10 11 12 139 p.287 p.10 11 pp.3-4 1917 p.284 77 78 10 13 10 p.6 1936 79 15 15 30 80 pp.499-501
More information122011pp.139174 18501933
122011pp.139174 18501933 122011 1850 3 187912 3 1850 8 1933 84 4 1871 12 1879 5 2 1 9 15 1 1 5 3 3 3 6 19 9 9 6 28 7 7 4 1140 9 4 3 5750 58 4 3 1 57 2 122011 3 4 134,500,000 4,020,000 11,600,000 5 2 678.00m
More information2 2 3 4 5 5 2 7 3 4 6 1 3 4 7 4 2 2 2 4 2 3 3 4 5 1932 A p. 40. 1893 A p. 224, p. 226. 1893 B pp. 1 2. p. 3.
1 73 72 1 1844 11 9 1844 12 18 5 1916 1 11 72 1 73 2 1862 3 1870 2 1862 6 1873 1 3 4 3 4 7 2 3 4 5 3 5 4 2007 p. 117. 2 2 3 4 5 5 2 7 3 4 6 1 3 4 7 4 2 2 2 4 2 3 3 4 5 1932 A p. 40. 1893 A p. 224, p. 226.
More information29 2011 3 4 1 19 5 2 21 6 21 2 21 7 2 23 21 8 21 1 20 21 1 22 20 p.61 21 1 21 21 1 23
29 2011 3 pp.55 86 19 1886 2 13 1 1 21 1888 1 13 2 3,500 3 5 5 50 4 1959 6 p.241 21 1 13 2 p.14 1988 p.2 21 1 15 29 2011 3 4 1 19 5 2 21 6 21 2 21 7 2 23 21 8 21 1 20 21 1 22 20 p.61 21 1 21 21 1 23 1
More informationMicrosoft Word - 映画『東京裁判』を観て.doc
1 2 3 4 5 6 7 1 2008. 2 2010, 3 2010. p.1 4 2008 p.202 5 2008. p.228 6 2011. 7 / 2008. pp.3-4 1 8 1 9 10 11 8 2008, p.7 9 2011. p.41 10.51 11 2009. p. 2 12 13 14 12 2008. p.4 13 2008, p.7-8 14 2008. p.126
More information() L () 20 1
() 25 1 10 1 0 0 0 1 2 3 4 5 6 2 3 4 9308510 4432193 L () 20 1 PP 200,000 P13P14 3 0123456 12345 1234561 2 4 5 6 25 1 10 7 1 8 10 / L 10 9 10 11 () ( ) TEL 23 12 7 38 13 14 15 16 17 18 L 19 20 1000123456
More information308 ( ) p.121
307 1944 1 1920 1995 2 3 4 5 308 ( ) p.121 309 10 12 310 6 7 ( ) ( ) ( ) 50 311 p.120 p.142 ( ) ( ) p.117 p.124 p.118 312 8 p.125 313 p.121 p.122 p.126 p.128 p.156 p.119 p.122 314 p.153 9 315 p.142 p.153
More information戦後の補欠選挙
1 2 11 3 4, 1968, p.429., pp.140-141. 76 2005.12 20 14 5 2110 25 6 22 7 25 8 4919 9 22 10 11 12 13 58154 14 15 1447 79 2042 21 79 2243 25100 113 2211 71 113 113 29 p.85 2005.12 77 16 29 12 10 10 17 18
More information第1回(全5回) Rの基礎と仮説検定
1 環境統計学ぷらす 第 1 回 ( 全 5 回?) R の基礎と仮説検定 高木俊 shun.takagi@sci.toho-u.ac.jp 2013/10/24 2 今回やること R の基礎 仮説検定 Fisher の正確確率検定 2 群の平均値の差の検定 (t 検定 ) 結果の表し方 図と表 文章中の表現 * 今後 Win 版を前提に話を進めます * 次回以降も R の操作練習 統計の解説 論文での表現の
More information20090419-03t-a-PPT
5 5.1 PowerPoint MagicPoint PowerPoint 5.2 5W1H 7 8 1 15 25 PowerPoint PowerPoint PowerPoint PowerPoint PowerPoint PowerPoint 5.3 (1) PowerPoint PowerPoint PowerPoint PowerPoint PowerPoint 5-1 5-1 command
More information講義のーと : データ解析のための統計モデリング. 第5回
Title 講義のーと : データ解析のための統計モデリング Author(s) 久保, 拓弥 Issue Date 2008 Doc URL http://hdl.handle.net/2115/49477 Type learningobject Note この講義資料は, 著者のホームページ http://hosho.ees.hokudai.ac.jp/~kub ードできます Note(URL)http://hosho.ees.hokudai.ac.jp/~kubo/ce/EesLecture20
More informationHuman Welfare 8‐1☆/4.坂口
1 2 1914 2007 2002 2013 2004 2013 2009 2011 5 1 2 Human Welfare 8 1 2016 1 110 2014 9 11 11 8 110 4 106 3 2.8 103 97.2 18 76 37.4 SD 16.5 2 1 1 3 2 10 65 2006 65 25 4 5 1 5 3 98 4 60 60 4 1 4 1 60 15.1
More information情報科学概論 第1回資料
1. Excel (C) Hiroshi Pen Fujimori 1 2. (Excel) 2.1 Excel : 2.2Excel Excel (C) Hiroshi Pen Fujimori 2 256 (IV) :C (C 65536 B4 :2 (2 A3 Excel (C) Hiroshi Pen Fujimori 3 Tips: (1) B3 (2) (*1) (3) (4)Word
More informationGIS GIS -2-
GIS GIS GIS GIS GIS GIS GIS GIS Jeffrey Star/John Estes 1992 GIS GIS= GIS -1- GIS GIS -2- (1,1) (2,1) 30m 543m 1 1:200000 1.5 1:25000 00 77 1:25000 5339-46 1:25000 5339-37 1:25000 10 00 99 1 50m 20 50m
More informationuntitled
A PEX Chasing Our Dreams - A complete line of customized car and automotive parts developed with state of the theart technology and new ideas. Our company is A'PEX which means the highest in quality. This
More informationこんにちは由美子です
Sample size power calculation Sample Size Estimation AZTPIAIDS AIDSAZT AIDSPI AIDSRNA AZTPr (S A ) = π A, PIPr (S B ) = π B AIDS (sampling)(inference) π A, π B π A - π B = 0.20 PI 20 20AZT, PI 10 6 8 HIV-RNA
More information194621 22 50.06 53.96 90.00 28 87.14 85.00 80.00 75.00 70.00 22 53.96 28 80.75 65.00 60.00 55.00 22 50.06 50.00 Quality of Life !! 281026 281026 -6,500-5,500-4,500-3,500-2,500-1,500-500 500 1,500 2,500-6,401-1,281-3,565-2,154-2,265-2,027-1,869-1,657
More information( ) ( ) Modified on 2009/05/24, 2008/09/17, 15, 12, 11, 10, 09 Created on 2008/07/02 1 1) ( ) ( ) (exgen Excel VBA ) 2)3) 1.1 ( ) ( ) : : (1) ( ) ( )
() ( ) Modified on 2009/05/24, 2008/09/17, 15, 12, 11, 10, 09 Created on 2008/07/02 1 1) () ( ) (exgen Excel VBA ) 2)3) 1.1 ( ) () : : (1) ( ) ( ) (2) / (1) (= ) (2) (= () =) 4)5) () ( ) () (=) (1) : (
More informationuntitled
2 : n =1, 2,, 10000 0.5125 0.51 0.5075 0.505 0.5025 0.5 0.4975 0.495 0 2000 4000 6000 8000 10000 2 weak law of large numbers 1. X 1,X 2,,X n 2. µ = E(X i ),i=1, 2,,n 3. σi 2 = V (X i ) σ 2,i=1, 2,,n ɛ>0
More informationこんにちは由美子です
1 2 . sum Variable Obs Mean Std. Dev. Min Max ---------+----------------------------------------------------- var1 13.4923077.3545926.05 1.1 3 3 3 0.71 3 x 3 C 3 = 0.3579 2 1 0.71 2 x 0.29 x 3 C 2 = 0.4386
More information80 X 1, X 2,, X n ( λ ) λ P(X = x) = f (x; λ) = λx e λ, x = 0, 1, 2, x! l(λ) = n f (x i ; λ) = i=1 i=1 n λ x i e λ i=1 x i! = λ n i=1 x i e nλ n i=1 x
80 X 1, X 2,, X n ( λ ) λ P(X = x) = f (x; λ) = λx e λ, x = 0, 1, 2, x! l(λ) = n f (x i ; λ) = n λ x i e λ x i! = λ n x i e nλ n x i! n n log l(λ) = log(λ) x i nλ log( x i!) log l(λ) λ = 1 λ n x i n =
More informationk2 ( :35 ) ( k2) (GLM) web web 1 :
2012 11 01 k2 (2012-10-26 16:35 ) 1 6 2 (2012 11 01 k2) (GLM) kubo@ees.hokudai.ac.jp web http://goo.gl/wijx2 web http://goo.gl/ufq2 1 : 2 2 4 3 7 4 9 5 : 11 5.1................... 13 6 14 6.1......................
More information2
2 3 Cindy Sherman, Untitled Film Still #2 (1978) 1) 2) (the social presence) 3) 4) 5) 4 6) 5 (Geist) 7) 6 8) (penissubstitute) 9) 10) (Polymorph perverse Anlage) 11) 12) 7 13) 14) 15) 16) 17) 8 18) 19)
More informationuntitled
2009 57 2 393 411 c 2009 1 1 1 2009 1 15 7 21 7 22 1 1 1 1 1 1 1 1. 1 1 1 2 3 4 12 2000 147 31 1 3,941 596 1 528 1 372 1 1 1.42 350 1197 1 13 1 394 57 2 2009 1 1 19 2002 2005 4.8 1968 5 93SNA 6 12 1 7,
More information‰IŠv07_›Z’¶
Bull. Nara Univ. Educ., Vol. 61, No. 1 (Cult. & Soc.), 2012 Possibilities and Challenges of the Practice with Japanese Sign Language (JSL) for Communication Aids in Japanese Early Childhood Deaf Education
More information2 1,2, , 2 ( ) (1) (2) (3) (4) Cameron and Trivedi(1998) , (1987) (1982) Agresti(2003)
3 1 1 1 2 1 2 1,2,3 1 0 50 3000, 2 ( ) 1 3 1 0 4 3 (1) (2) (3) (4) 1 1 1 2 3 Cameron and Trivedi(1998) 4 1974, (1987) (1982) Agresti(2003) 3 (1)-(4) AAA, AA+,A (1) (2) (3) (4) (5) (1)-(5) 1 2 5 3 5 (DI)
More informationわが国企業による資金調達方法の選択問題
* takeshi.shimatani@boj.or.jp ** kawai@ml.me.titech.ac.jp *** naohiko.baba@boj.or.jp No.05-J-3 2005 3 103-8660 30 No.05-J-3 2005 3 1990 * E-mailtakeshi.shimatani@boj.or.jp ** E-mailkawai@ml.me.titech.ac.jp
More information