情報管理学科で学ぶ

Size: px
Start display at page:

Download "情報管理学科で学ぶ"

Transcription

1 1/17 ` <kumazawa@biwako.shiga-u.ac.jp> I II III OR R OR I II III 1.1 I II III I II I 1/17

2 2/17 II III 1.2 R III 1.3 R 2 descriptive statistics inferential statistics ( ) X 1,X 2,,X n : n estimation testing prediction significance interval A B 2/17

3 3/17 ( )/ = n i=1 x i n = x, = n i=1 (x i x) 2 n n x i 5 Statistical Quality Control 7? Mauna Loa is the largest volcano on Earth with an estimated volume of 9,600 cubic miles (40,000 cubic kilometers). It makes half of the area of the Island of Hawaii. Mauna Loa began to form nearly a million years ago. There is a caldera, Mokuaweoweo, at the summit and rift zones extend to the northeast and southwest. Mauna Loa is in the shieldbuilding stage and is one of the most active volcanoes on Earth, erupting 15 times since The last eruption was in 1984 and sent lavas within 4 miles (6.5 km) of Hilo. ( ) FreeWare R plot(co2) R MacOS Windows Unix Linux OS Australia New Zealand 3 data sample 3/17

4 4/ qualitative data nominal scale ordinal scale CD 3.2 quantitative data continuous data discrete data 1 interval scale ratio scale 4 1. simple random sampling 2. systematic sampling 3. stratified sampling 4. two stage sampling stratified tow stage sampling parameter 4/17

5 5/ T.Bayes x y r = s xy sxx s yy (x 1,y 1 ),(x 2,y 2 ),,(x n,y n ) 2 n s xy = n i=1 (x i x)(y i y) n 1 s yy = n i=1 (y i y) 2 n 1, s xx = n i=1 (x i x) 2, n 1 n, x = 1 i, y = n i=1x 1 n s xy,s xx,s yy,x,y x y r xy Pearson x y m R n y i i=1 50m running income age /17

6 6/17 50m m R cor(d) d 50m 3 r xy r zx r zy (1 rzx)(1 2 rzy) m Mammal species f f d d f d e c d d c c c e e d d d e e b b c e a a b b e c c e a a a a a b a b b b c f f f f f Productivity 6 a f /17

7 7/ d e f Mammal species a b c Productivity 9 7 t X Y t X Y Numbers Index X Y 7/17

8 8/17 t X Y Numbers Index X Y, 120 X Y X Y Y X X Y (x i,y i ) n i=1 { yi (β 0 + β 1 x i ) } 2 X Y 8/17

9 9/17 X Y logy logx mercury venus earth 1 1 mars jupiter saturn Uranus Neptune Pluto = m : 2 : 3 : /17

10 10/17 EXPLORATORY DATA ANALYSIS Histogram of x Density of x Boxplot of x Q Q Plot of x Sturges 7 Gaussian Shapiro-Wilk SQC Statistical Quality Control QC7 (i) Graph, Control Chart (ii) Pareto Diagram (iii) Histogram (iv) Cause and Effect Diagram (v) Scatter Diagram (vi) Stratification (vii) CheckSheet 7 5 R qcc (i) 10/17

11 11/17 (ii) (iii) 100% (iv) median 25% 1 first quartile 25% 3 third quartile boxplot (v) qqnorm qqline /17

12 12/ Shewhart control chart CL Center Line UCL Upper Control Limit LCL Lower Control Limit (i) x x (ii) R R (iii) x x (iv) p p (v) pn pn (vi) c (vii) u Pareto Pareto chart 12/17

13 13/ Fishbone Diagram Cause and Effect diagram Communication SKills ambiguity lack of knowledge Knowledge Literacy Incorrect Deliver Procedures Manual automated Transport carriers Information R airquality Ozone Solar.R Wind Temp Month Day R help airqualtiy 13/17

14 14/ Ozone Solar.R Wind Temp Month Day airq airq Ozone Solar.R Wind Temp /17

15 15/17 Temp < 82.5 Wind < 7.15 Wind < 10.6 Temp < 88.5 Solar.R < 205 Solar.R < Temp < Temp< Wind>=7.15 Call: lm(formula = Ozone Temp * Wind * Solar.R + I(Solar.Rˆ2) + I(Tempˆ2) + I(Windˆ2)) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) 5.670e e ** Temp e e * Wind e e ** Solar.R e e I(Solar.Rˆ2) e e I(Tempˆ2) 5.815e e * I(Windˆ2) 6.095e e e-05 *** Temp:Wind 2.373e e Temp:Solar.R 8.433e e Wind:Solar.R 2.063e e Temp:Wind:Solar.R e e Signif. codes: 0 *** ** 0.01 * Residual standard error: on 100 degrees of freedom 15/17

16 16/17 (42 observations deleted due to missingness) Multiple R-squared: , Adjusted R-squared: F-statistic: on 10 and 100 DF, p-value: < 2.2e-16 3 Call: lm(formula = log(ozone) Temp + Wind + Solar.R + I(Windˆ2), subset = (1:length(Ozone)!= 17)) Residuals: Min 1Q Median 3Q Max Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) Temp e-12 *** Wind *** Solar.R e-05 *** I(Windˆ2) ** --- Signif. codes: 0 *** ** 0.01 * Residual standard error: on 105 degrees of freedom (42 observations deleted due to missingness) Multiple R-squared: , Adjusted R-squared: F-statistic: on 4 and 105 DF, p-value: < 2.2e (i) Web Wikipedia : (ii) google goo yahoo AND 1 AND 2 AND AND OR 2 ( OR ) AND 16/17

17 17/17 NOT black and white black and white site: filetype:ppt filetype PowerPoint Web IT ppt Word Excel PowerPoint Acrobat Reader doc xls ppt pdf (iii) Windows Ctrl +C Ctrl +V Ctrl +A Ctrl +Z Ctrl +S Ctrl +Shift+S Ctrl +F Ctrl + Ctrl C V A Z S F Windows WebBrowser InternetExplorer FoxFire Ctrl+L URL 17/17

Use R

Use 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 information

R John Fox R R R Console library(rcmdr) Rcmdr R GUI Windows R R SDI *1 R Console R 1 2 Windows XP Windows * 2 R R Console R ˆ R

R John Fox R R R Console library(rcmdr) Rcmdr R GUI Windows R R SDI *1 R Console R 1 2 Windows XP Windows * 2 R R Console R ˆ R R John Fox 2006 8 26 2008 8 28 1 R R R Console library(rcmdr) Rcmdr R GUI Windows R R SDI *1 R Console R 1 2 Windows XP Windows * 2 R R Console R ˆ R GUI R R R Console > ˆ 2 ˆ Fox(2005) jfox@mcmaster.ca

More information

(lm) lm AIC 2 / 1

(lm) lm AIC 2 / 1 W707 s-taiji@is.titech.ac.jp 1 / 1 (lm) lm AIC 2 / 1 : y = β 1 x 1 + β 2 x 2 + + β d x d + β d+1 + ϵ (ϵ N(0, σ 2 )) y R: x R d : β i (i = 1,..., d):, β d+1 : ( ) (d = 1) y = β 1 x 1 + β 2 + ϵ (d > 1) y

More information

DAA09

DAA09 > summary(dat.lm1) Call: lm(formula = sales ~ price, data = dat) Residuals: Min 1Q Median 3Q Max -55.719-19.270 4.212 16.143 73.454 Coefficients: Estimate Std. Error t value Pr(> t ) (Intercept) 237.1326

More information

Rによる計量分析:データ解析と可視化 - 第3回 Rの基礎とデータ操作・管理

Rによる計量分析:データ解析と可視化 - 第3回  Rの基礎とデータ操作・管理 R 3 R 2017 Email: gito@eco.u-toyama.ac.jp October 23, 2017 (Toyama/NIHU) R ( 3 ) October 23, 2017 1 / 34 Agenda 1 2 3 4 R 5 RStudio (Toyama/NIHU) R ( 3 ) October 23, 2017 2 / 34 10/30 (Mon.) 12/11 (Mon.)

More information

R Console >R ˆ 2 ˆ 2 ˆ Graphics Device 1 Rcmdr R Console R R Rcmdr Rcmdr Fox, 2007 Fox and Carvalho, 2012 R R 2

R Console >R ˆ 2 ˆ 2 ˆ Graphics Device 1 Rcmdr R Console R R Rcmdr Rcmdr Fox, 2007 Fox and Carvalho, 2012 R R 2 R John Fox Version 1.9-1 2012 9 4 2012 10 9 1 R R Windows R Rcmdr Mac OS X Linux R OS R R , R R Console library(rcmdr)

More information

k2 ( :35 ) ( k2) (GLM) web web 1 :

k2 ( :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 information

J1順位と得点者数の関係分析

J1順位と得点者数の関係分析 2015 年度 S-PLUS & Visual R Platform 学生研究奨励賞応募 J1 順位と得点者数の関係分析 -J リーグの得点数の現状 - 目次 1. はじめに 2. 研究目的 データについて 3.J1 リーグの得点数の現状 4. 分析 5. まとめ 6. 今後の課題 - 参考文献 - 東海大学情報通信学部 経営システム工学科 山田貴久 1. はじめに 1993 年 5 月 15 日に

More information

<4D6963726F736F667420506F776572506F696E74202D208376838C835B83938365815B835683878393312E707074205B8CDD8AB78382815B83685D>

<4D6963726F736F667420506F776572506F696E74202D208376838C835B83938365815B835683878393312E707074205B8CDD8AB78382815B83685D> i i vi ii iii iv v vi vii viii ix 2 3 4 5 6 7 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

More information

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 () - 1 - - 2 - - 3 - - 4 - - 5 - 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57

More information

SC-85X2取説

SC-85X2取説 I II III IV V VI .................. VII VIII IX X 1-1 1-2 1-3 1-4 ( ) 1-5 1-6 2-1 2-2 3-1 3-2 3-3 8 3-4 3-5 3-6 3-7 ) ) - - 3-8 3-9 4-1 4-2 4-3 4-4 4-5 4-6 5-1 5-2 5-3 5-4 5-5 5-6 5-7 5-8 5-9 5-10 5-11

More information

i ii iii iv v vi vii ( ー ー ) ( ) ( ) ( ) ( ) ー ( ) ( ) ー ー ( ) ( ) ( ) ( ) ( ) 13 202 24122783 3622316 (1) (2) (3) (4) 2483 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) 11 11 2483 13

More information

untitled

untitled 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 information

活用ガイド (ソフトウェア編)

活用ガイド (ソフトウェア編) (Windows 95 ) ii iii iv NEC Corporation 1999 v P A R T 1 vi P A R T 2 vii P A R T 3 P A R T 4 viii P A R T 5 ix x P A R T 1 2 3 1 1 2 4 1 2 3 4 5 1 1 2 3 4 6 5 6 7 7 1 1 2 8 1 9 1 1 2 3 4 5 6 1 2 3 4

More information

講義のーと : データ解析のための統計モデリング. 第3回

講義のーと :  データ解析のための統計モデリング. 第3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 information

インターネットを活用した経済分析 - フリーソフト Rを使おう

インターネットを活用した経済分析 - フリーソフト Rを使おう R 1 1 1 2017 2 15 2017 2 15 1/64 2 R 3 R R RESAS 2017 2 15 2/64 2 R 3 R R RESAS 2017 2 15 3/64 2-4 ( ) ( (80%) (20%) 2017 2 15 4/64 PC LAN R 2017 2 15 5/64 R R 2017 2 15 6/64 3-4 R 15 + 2017 2 15 7/64

More information

3 5 18 3 5000 1 2 7 8 120 1 9 1954 29 18 12 30 700 4km 1.5 100 50 6 13 5 99 93 34 17 2 2002 04 14 16 6000 12 57 60 1986 55 3 3 3 500 350 4 5 250 18 19 1590 1591 250 100 500 20 800 20 55 3 3 3 18 19 1590

More information

一般化線形 (混合) モデル (2) - ロジスティック回帰と GLMM

一般化線形 (混合) モデル (2) - ロジスティック回帰と GLMM .. ( ) (2) GLMM kubo@ees.hokudai.ac.jp I http://goo.gl/rrhzey 2013 08 27 : 2013 08 27 08:29 kubostat2013ou2 (http://goo.gl/rrhzey) ( ) (2) 2013 08 27 1 / 74 I.1 N k.2 binomial distribution logit link function.3.4!

More information

これわかWord2010_第1部_100710.indd

これわかWord2010_第1部_100710.indd i 1 1 2 3 6 6 7 8 10 10 11 12 12 12 13 2 15 15 16 17 17 18 19 20 20 21 ii CONTENTS 25 26 26 28 28 29 30 30 31 32 35 35 35 36 37 40 42 44 44 45 46 49 50 50 51 iii 52 52 52 53 55 56 56 57 58 58 60 60 iv

More information

パワポカバー入稿用.indd

パワポカバー入稿用.indd i 1 1 2 2 3 3 4 4 4 5 7 8 8 9 9 10 11 13 14 15 16 17 19 ii CONTENTS 2 21 21 22 25 26 32 37 38 39 39 41 41 43 43 43 44 45 46 47 47 49 52 54 56 56 iii 57 59 62 64 64 66 67 68 71 72 72 73 74 74 77 79 81 84

More information

これでわかるAccess2010

これでわかるAccess2010 i 1 1 1 2 2 2 3 4 4 5 6 7 7 9 10 11 12 13 14 15 17 ii CONTENTS 2 19 19 20 23 24 25 25 26 29 29 31 31 33 35 36 36 39 39 41 44 45 46 48 iii 50 50 52 54 55 57 57 59 61 63 64 66 66 67 70 70 73 74 74 77 77

More information

活用ガイド (ソフトウェア編)

活用ガイド (ソフトウェア編) (Windows 98 ) ii iii iv v NEC Corporation 1999 vi P A R T 1 P A R T 2 vii P A R T 3 viii P A R T 4 ix P A R T 5 x P A R T 1 2 3 1 1 2 4 1 2 3 4 5 1 1 2 3 4 5 6 6 7 7 1 1 2 8 1 9 1 1 2 3 4 5 6 1 2 3 10

More information

o 2o 3o 3 1. I o 3. 1o 2o 31. I 3o PDF Adobe Reader 4o 2 1o I 2o 3o 4o 5o 6o 7o 2197/ o 1o 1 1o

o 2o 3o 3 1. I o 3. 1o 2o 31. I 3o PDF Adobe Reader 4o 2 1o I 2o 3o 4o 5o 6o 7o 2197/ o 1o 1 1o 78 2 78... 2 22201011... 4... 9... 7... 29 1 1214 2 7 1 8 2 2 3 1 2 1o 2o 3o 3 1. I 1124 4o 3. 1o 2o 31. I 3o PDF Adobe Reader 4o 2 1o 72 1. I 2o 3o 4o 5o 6o 7o 2197/6 9. 9 8o 1o 1 1o 2o / 3o 4o 5o 6o

More information

1 15 R Part : website:

1 15 R Part : website: 1 15 R Part 4 2017 7 24 4 : website: email: http://www3.u-toyama.ac.jp/kkarato/ kkarato@eco.u-toyama.ac.jp 1 2 2 3 2.1............................... 3 2.2 2................................. 4 2.3................................

More information

untitled

untitled IT (1, horiike@ml.me.titech.ac.jp) (1, jun-jun@ms.kagu.tus.ac.jp) 1. 1-1 19802000 2000ITIT IT IT TOPIX (%) 1TOPIX 2 1-2. 80 80 ( ) 2004/11/26 S-PLUS 2 1-3. IT IT IT IT 2. 2-1. a. b. (Size) c. B/M(Book

More information

平成18年版 男女共同参画白書

平成18年版 男女共同参画白書 i ii iii iv v vi vii viii ix 3 4 5 6 7 8 9 Column 10 11 12 13 14 15 Column 16 17 18 19 20 21 22 23 24 25 26 Column 27 28 29 30 Column 31 32 33 34 35 36 Column 37 Column 38 39 40 Column 41 42 43 44 45

More information

III

III III 1 1 2 1 2 3 1 3 4 1 3 1 4 1 3 2 4 1 3 3 6 1 4 6 1 4 1 6 1 4 2 8 1 4 3 9 1 5 10 1 5 1 10 1 5 2 12 1 5 3 12 1 5 4 13 1 6 15 2 1 18 2 1 1 18 2 1 2 19 2 2 20 2 3 22 2 3 1 22 2 3 2 24 2 4 25 2 4 1 25 2

More information

iii iv v vi vii viii ix 1 1-1 1-2 1-3 2 2-1 3 3-1 3-2 3-3 3-4 4 4-1 4-2 5 5-1 5-2 5-3 5-4 5-5 5-6 5-7 6 6-1 6-2 6-3 6-4 6-5 6 6-1 6-2 6-3 6-4 6-5 7 7-1 7-2 7-3 7-4 7-5 7-6 7-7 7-8 7-9 7-10 7-11 8 8-1

More information

2

2 1 2 3 4 5 6 7 8 9 10 I II III 11 IV 12 V 13 VI VII 14 VIII. 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 _ 33 _ 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 VII 51 52 53 54 55 56 57 58 59

More information

untitled

untitled i ii iii iv v 43 43 vi 43 vii T+1 T+2 1 viii 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 a) ( ) b) ( ) 51

More information

Stata11 whitepapers mwp-037 regress - regress regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F(

Stata11 whitepapers mwp-037 regress - regress regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F( mwp-037 regress - regress 1. 1.1 1.2 1.3 2. 3. 4. 5. 1. regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F( 2, 71) = 69.75 Model 1619.2877 2 809.643849 Prob > F = 0.0000 Residual

More information

DVIOUT-MTT元原

DVIOUT-MTT元原 TI-92 -MTT-Mathematics Thinking with Technology MTT ACTIVITY Discussion 1 1 1.1 v t h h = vt 1 2 gt2 (1.1) xy (5, 0) 20m/s [1] Mode Graph Parametric [2] Y= [3] Window [4] Graph 1.1: Discussion 2 Window

More information

i

i 14 i ii iii iv v vi 14 13 86 13 12 28 14 16 14 15 31 (1) 13 12 28 20 (2) (3) 2 (4) (5) 14 14 50 48 3 11 11 22 14 15 10 14 20 21 20 (1) 14 (2) 14 4 (3) (4) (5) 12 12 (6) 14 15 5 6 7 8 9 10 7

More information

> usdata01 と打ち込んでエンター キーを押すと V1 V2 V : : : : のように表示され 読み込まれていることがわかる ここで V1, V2, V3 は R が列のデータに自 動的につけた変数名である ( variable

> usdata01 と打ち込んでエンター キーを押すと V1 V2 V : : : : のように表示され 読み込まれていることがわかる ここで V1, V2, V3 は R が列のデータに自 動的につけた変数名である ( variable R による回帰分析 ( 最小二乗法 ) この資料では 1. データを読み込む 2. 最小二乗法によってパラメーターを推定する 3. データをプロットし 回帰直線を書き込む 4. いろいろなデータの読み込み方について簡単に説明する 1. データを読み込む 以下では read.table( ) 関数を使ってテキストファイル ( 拡張子が.txt のファイル ) のデー タの読み込み方を説明する 1.1

More information

エクセルカバー入稿用.indd

エクセルカバー入稿用.indd i 1 1 2 3 5 5 6 7 7 8 9 9 10 11 11 11 12 2 13 13 14 15 15 16 17 17 ii CONTENTS 18 18 21 22 22 24 25 26 27 27 28 29 30 31 32 36 37 40 40 42 43 44 44 46 47 48 iii 48 50 51 52 54 55 59 61 62 64 65 66 67 68

More information

01_.g.r..

01_.g.r.. I II III IV V VI VII VIII IX X XI I II III IV V I I I II II II I I YS-1 I YS-2 I YS-3 I YS-4 I YS-5 I YS-6 I YS-7 II II YS-1 II YS-2 II YS-3 II YS-4 II YS-5 II YS-6 II YS-7 III III YS-1 III YS-2

More information

最小2乗法

最小2乗法 2 2012 4 ( ) 2 2012 4 1 / 42 X Y Y = f (X ; Z) linear regression model X Y slope X 1 Y (X, Y ) 1 (X, Y ) ( ) 2 2012 4 2 / 42 1 β = β = β (4.2) = β 0 + β (4.3) ( ) 2 2012 4 3 / 42 = β 0 + β + (4.4) ( )

More information

kubostat2017c p (c) Poisson regression, a generalized linear model (GLM) : :

kubostat2017c p (c) Poisson regression, a generalized linear model (GLM) : : kubostat2017c p.1 2017 (c), a generalized linear model (GLM) : kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 14 : 2017 11 07 15:43 kubostat2017c (http://goo.gl/76c4i) 2017 (c) 2017 11 14 1 / 47 agenda

More information

ii iii iv CON T E N T S iii iv v Chapter1 Chapter2 Chapter 1 002 1.1 004 1.2 004 1.2.1 007 1.2.2 009 1.3 009 1.3.1 010 1.3.2 012 1.4 012 1.4.1 014 1.4.2 015 1.5 Chapter3 Chapter4 Chapter5 Chapter6 Chapter7

More information

kubostat2018d p.2 :? bod size x and fertilization f change seed number? : a statistical model for this example? i response variable seed number : { i

kubostat2018d p.2 :? bod size x and fertilization f change seed number? : a statistical model for this example? i response variable seed number : { i kubostat2018d p.1 I 2018 (d) model selection and kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2018 06 25 : 2018 06 21 17:45 1 2 3 4 :? AIC : deviance model selection misunderstanding kubostat2018d (http://goo.gl/76c4i)

More information

201711grade2.pdf

201711grade2.pdf 2017 11 26 1 2 28 3 90 4 5 A 1 2 3 4 Web Web 6 B 10 3 10 3 7 34 8 23 9 10 1 2 3 1 (A) 3 32.14 0.65 2.82 0.93 7.48 (B) 4 6 61.30 54.68 34.86 5.25 19.07 (C) 7 13 5.89 42.18 56.51 35.80 50.28 (D) 14 20 0.35

More information

活用ガイド (ハードウェア編)

活用ガイド (ハードウェア編) (Windows 98) 808-877675-122-A ii iii iv NEC Corporation 1999 v vi PART 1 vii viii PART 2 PART 3 ix x xi xii P A R T 1 2 1 3 4 1 5 6 1 7 8 1 9 10 11 1 12 1 1 2 3 13 1 2 3 14 4 5 1 15 1 1 16 1 17 18 1 19

More information

untitled

untitled 1 Hitomi s English Tests 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 1 0 1 1 0 1 0 0 0 1 0 0 1 0 2 0 0 1 1 0 0 0 0 0 1 1 1 1 0 3 1 1 0 0 0 0 1 0 1 0 1 0 1 1 4 1 1 0 1 0 1 1 1 1 0 0 0 1 1 5 1 1 0 1 1 1 1 0 0 1 0

More information

入門ガイド

入門ガイド ii iii iv NEC Corporation 1998 v P A R 1 P A R 2 P A R 3 T T T vi P A R T 4 P A R T 5 P A R T 6 P A R T 7 vii 1P A R T 1 2 2 1 3 1 4 1 1 5 2 3 6 4 1 7 1 2 3 8 1 1 2 3 9 1 2 10 1 1 2 11 3 12 1 2 1 3 4 13

More information

2004/01/12 1 2004/01/23 2 I- - 10 2004/04/02 3-6 2004/04/03 4-1-5-1,-1-8-1,-2-2-1,-3-4-1,-3-5-1,-4-2-1, -5-4-2,-5-6-1,-6-2-1 4. _.doc 1

2004/01/12 1 2004/01/23 2 I- - 10 2004/04/02 3-6 2004/04/03 4-1-5-1,-1-8-1,-2-2-1,-3-4-1,-3-5-1,-4-2-1, -5-4-2,-5-6-1,-6-2-1 4. _.doc 1 4 2004 4 3 2004/01/12 1 2004/01/23 2 I- - 10 2004/04/02 3-6 2004/04/03 4-1-5-1,-1-8-1,-2-2-1,-3-4-1,-3-5-1,-4-2-1, -5-4-2,-5-6-1,-6-2-1 4. _.doc 1 - - I. 4 I- 4 I- 4 I- 6 I- 6 I- 7 II. 8 II- 8 II- 8 II-

More information

¥¤¥ó¥¿¡¼¥Í¥Ã¥È·×¬¤È¥Ç¡¼¥¿²òÀÏ Âè2²ó

¥¤¥ó¥¿¡¼¥Í¥Ã¥È·×¬¤È¥Ç¡¼¥¿²òÀÏ Âè2²ó 2 2015 4 20 1 (4/13) : ruby 2 / 49 2 ( ) : gnuplot 3 / 49 1 1 2014 6 IIJ / 4 / 49 1 ( ) / 5 / 49 ( ) 6 / 49 (summary statistics) : (mean) (median) (mode) : (range) (variance) (standard deviation) 7 / 49

More information

困ったときのQ&A

困ったときのQ&A ii iii iv NEC Corporation 1997 v P A R T 1 vi vii P A R T 2 viii P A R T 3 ix x xi 1P A R T 2 1 3 4 1 5 6 1 7 8 1 9 1 2 3 4 10 1 11 12 1 13 14 1 1 2 15 16 1 2 1 1 2 3 4 5 17 18 1 2 3 1 19 20 1 21 22 1

More information

²¾ÁÛ¾õ¶·É¾²ÁË¡¤Î¤¿¤á¤Î¥Ñ¥Ã¥±¡¼¥¸DCchoice ¡Ê»ÃÄêÈÇ¡Ë

²¾ÁÛ¾õ¶·É¾²ÁË¡¤Î¤¿¤á¤Î¥Ñ¥Ã¥±¡¼¥¸DCchoice ¡Ê»ÃÄêÈÇ¡Ë DCchoice ( ) R 2013 2013 11 30 DCchoice package R 2013/11/30 1 / 19 1 (CV) CV 2 DCchoice WTP 3 DCchoice package R 2013/11/30 2 / 19 (Contingent Valuation; CV) WTP CV WTP WTP 1 1989 2 DCchoice package R

More information

28

28 y i = Z i δ i +ε i ε i δ X y i = X Z i δ i + X ε i [ ] 1 δ ˆ i = Z i X( X X) 1 X Z i [ ] 1 σ ˆ 2 Z i X( X X) 1 X Z i Z i X( X X) 1 X y i σ ˆ 2 ˆ σ 2 = [ ] y i Z ˆ [ i δ i ] 1 y N p i Z i δ ˆ i i RSTAT

More information

q( ) 2: R 2 R R R R C:nProgram FilesnRnrw1030) [File] [Change Dir] c:ndatadir OK 2

q( ) 2: R 2 R R R R C:nProgram FilesnRnrw1030) [File] [Change Dir] c:ndatadir OK 2 R 2001 9 R R S Splus R S 1 R 1: R 2 [File] [Exit] 1 q( ) 2: R 2 R R R R C:nProgram FilesnRnrw1030) [File] [Change Dir] c:ndatadir OK 2 2.1 7+3 1 10 7-3 7*3 7/3 7^3 2 > 7+3 [1] 10 > 7-3 [1] 4 > 7*3 [1]

More information

untitled

untitled 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 information

kubostat2015e p.2 how to specify Poisson regression model, a GLM GLM how to specify model, a GLM GLM logistic probability distribution Poisson distrib

kubostat2015e p.2 how to specify Poisson regression model, a GLM GLM how to specify model, a GLM GLM logistic probability distribution Poisson distrib kubostat2015e p.1 I 2015 (e) GLM kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2015 07 22 2015 07 21 16:26 kubostat2015e (http://goo.gl/76c4i) 2015 (e) 2015 07 22 1 / 42 1 N k 2 binomial distribution logit

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

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 information

k3 ( :07 ) 2 (A) k = 1 (B) k = 7 y x x 1 (k2)?? x y (A) GLM (k

k3 ( :07 ) 2 (A) k = 1 (B) k = 7 y x x 1 (k2)?? x y (A) GLM (k 2012 11 01 k3 (2012-10-24 14:07 ) 1 6 3 (2012 11 01 k3) kubo@ees.hokudai.ac.jp web http://goo.gl/wijx2 web http://goo.gl/ufq2 1 3 2 : 4 3 AIC 6 4 7 5 8 6 : 9 7 11 8 12 8.1 (1)........ 13 8.2 (2) χ 2....................

More information

kubostat2017e p.1 I 2017 (e) GLM logistic regression : : :02 1 N y count data or

kubostat2017e p.1 I 2017 (e) GLM logistic regression : : :02 1 N y count data or kubostat207e p. I 207 (e) GLM kubo@ees.hokudai.ac.jp https://goo.gl/z9ycjy 207 4 207 6:02 N y 2 binomial distribution logit link function 3 4! offset kubostat207e (https://goo.gl/z9ycjy) 207 (e) 207 4

More information

5 5.1 A B mm 0.1mm Nominal Scale 74

5 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 information

i

i i ii iii iv v vi vii viii ix x xi ( ) 854.3 700.9 10 200 3,126.9 162.3 100.6 18.3 26.5 5.6/s ( ) ( ) 1949 8 12 () () ア イ ウ ) ) () () () () BC () () (

More information

BMIdata.txt DT DT <- read.table("bmidata.txt") DT head(dt) names(dt) str(dt)

BMIdata.txt DT DT <- read.table(bmidata.txt) DT head(dt) names(dt) str(dt) ?read.table read.table(file, header = FALSE, sep = "", quote = "\" ", dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"), row.names, col.names, as.is =!stringsasfactors, na.strings = "NA", colclasses

More information

5.2 White

5.2 White 1 EViews 1 : 2007/5/15 2007/5/25 1 EViews 4 2 ( 6 2.1............................................ 6 2.2 Workfile............................................ 7 2.3 Workfile............................................

More information

1 2 Windows 7 *3 Windows * 4 R R Console R R Console ˆ R GUI R R R *5 R 2 R R R 6.1 ˆ 2 ˆ 2 ˆ Graphics Device 1 Rcmdr R Console R Rconsole R --sdi R M

1 2 Windows 7 *3 Windows * 4 R R Console R R Console ˆ R GUI R R R *5 R 2 R R R 6.1 ˆ 2 ˆ 2 ˆ Graphics Device 1 Rcmdr R Console R Rconsole R --sdi R M R John Fox and Milan Bouchet-Valat Version 2.0-1 2013 11 8 2013 11 11 1 R Fox 2005 R R Core Team, 2013 GUI R R R R R R R R R the Comprehensive R Archive Network (CRAN) R CRAN 6.4 R Windows R Rcmdr Mac

More information

2

2 1 2 10 14 945 3000 2012 3 10 4 5 6 7 8 9 10 11 12 2011 11 21 12301430 (1215 ) 13 6 27 17 () ( ) ( ) (112360) 2 (1157) (119099) ((11861231) )( ) (11641205) 3 (1277) 3 4 (1558) (1639)() 12 (1699)( ) 7 (1722)

More information

活用ガイド (ソフトウェア編)

活用ガイド (ソフトウェア編) ii iii iv NEC Corporation 1998 v vi PA RT 1 vii PA RT 2 viii PA RT 3 PA RT 4 ix P A R T 1 2 3 1 4 5 1 1 2 1 2 3 4 6 1 2 3 4 5 7 1 6 7 8 1 9 1 10 1 2 3 4 5 6 7 8 9 10 11 11 1 12 12 1 13 1 1 14 2 3 4 5 1

More information

パソコン機能ガイド

パソコン機能ガイド PART12 ii iii iv v 1 2 3 4 5 vi vii viii ix P A R T 1 x P A R T 2 xi P A R T 3 xii xiii P A R T 1 2 3 1 4 5 1 6 1 1 2 7 1 2 8 1 9 10 1 11 12 1 13 1 2 3 4 14 1 15 1 2 3 16 4 1 1 2 3 17 18 1 19 20 1 1

More information

パソコン機能ガイド

パソコン機能ガイド PART2 iii ii iv v 1 2 3 4 5 vi vii viii ix P A R T 1 x P A R T 2 xi P A R T 3 xii xiii P A R T 1 2 1 3 4 1 5 6 1 2 1 1 2 7 8 9 1 10 1 11 12 1 13 1 2 3 14 4 1 1 2 3 15 16 1 17 1 18 1 1 2 19 20 1 21 1 22

More information

「産業上利用することができる発明」の審査の運用指針(案)

「産業上利用することができる発明」の審査の運用指針(案) 1 1.... 2 1.1... 2 2.... 4 2.1... 4 3.... 6 4.... 6 1 1 29 1 29 1 1 1. 2 1 1.1 (1) (2) (3) 1 (4) 2 4 1 2 2 3 4 31 12 5 7 2.2 (5) ( a ) ( b ) 1 3 2 ( c ) (6) 2. 2.1 2.1 (1) 4 ( i ) ( ii ) ( iii ) ( iv)

More information

1... 1 2... 1 1... 1 2... 2 3... 2 4... 4 5... 4 6... 4 7... 22 8... 22 3... 22 1... 22 2... 23 3... 23 4... 24 5... 24 6... 25 7... 31 8... 32 9... 3

1... 1 2... 1 1... 1 2... 2 3... 2 4... 4 5... 4 6... 4 7... 22 8... 22 3... 22 1... 22 2... 23 3... 23 4... 24 5... 24 6... 25 7... 31 8... 32 9... 3 3 2620149 3 6 3 2 198812 21/ 198812 21 1 3 4 5 JISJIS X 0208 : 1997 JIS 4 JIS X 0213:2004 http://www.pref.hiroshima.lg.jp/site/monjokan/ 1... 1 2... 1 1... 1 2... 2 3... 2 4... 4 5... 4 6... 4 7... 22

More information

262014 3 1 1 6 3 2 198810 2/ 198810 2 1 3 4 http://www.pref.hiroshima.lg.jp/site/monjokan/ 1... 1... 1... 2... 2... 4... 5... 9... 9... 10... 10... 10... 10... 13 2... 13 3... 15... 15... 15... 16 4...

More information

yamadaiR(cEFA).pdf

yamadaiR(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 information

<4D F736F F F696E74202D BD95CF97CA89F090CD F6489F18B4195AA90CD816A>

<4D F736F F F696E74202D BD95CF97CA89F090CD F6489F18B4195AA90CD816A> 主な多変量解析 9. 多変量解析 1 ( 重回帰分析 ) 目的変数 量的 説明変数 質的 あり量的 重回帰分析 数量化 Ⅰ 類 質的 判別分析 数量化 Ⅱ 類 なし 主成分分析因子分析多次元尺度構成法 数量化 Ⅲ 類数量化 Ⅳ 類 その他 クラスタ分析共分散構造分析 説明変数 : 独立変数 予測変数 目的変数 : 従属変数 基準変数 3 1. 単回帰分析各データの構造 y b ax a α: 1,,,

More information

Microsoft Word - 研究デザインと統計学.doc

Microsoft Word - 研究デザインと統計学.doc Study design and the statistical basics Originality Accuracy Objectivity Verifiability Readability perfect Interdisciplinary Sciences Health Science 2014.12.25 2 1. 7 2. 7 3. Bias8 4. random sampling8

More information

provider_020524_2.PDF

provider_020524_2.PDF 1 1 1 2 2 3 (1) 3 (2) 4 (3) 6 7 7 (1) 8 (2) 21 26 27 27 27 28 31 32 32 36 1 1 2 2 (1) 3 3 4 45 (2) 6 7 5 (3) 6 7 8 (1) ii iii iv 8 * 9 10 11 9 12 10 13 14 15 11 16 17 12 13 18 19 20 (2) 14 21 22 23 24

More information

44 4 I (1) ( ) (10 15 ) ( 17 ) ( 3 1 ) (2)

44 4 I (1) ( ) (10 15 ) ( 17 ) ( 3 1 ) (2) (1) I 44 II 45 III 47 IV 52 44 4 I (1) ( ) 1945 8 9 (10 15 ) ( 17 ) ( 3 1 ) (2) 45 II 1 (3) 511 ( 451 1 ) ( ) 365 1 2 512 1 2 365 1 2 363 2 ( ) 3 ( ) ( 451 2 ( 314 1 ) ( 339 1 4 ) 337 2 3 ) 363 (4) 46

More information

i ii i iii iv 1 3 3 10 14 17 17 18 22 23 28 29 31 36 37 39 40 43 48 59 70 75 75 77 90 95 102 107 109 110 118 125 128 130 132 134 48 43 43 51 52 61 61 64 62 124 70 58 3 10 17 29 78 82 85 102 95 109 iii

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

1... 1... 1... 3 2... 4... 4... 4... 4... 4... 6... 10... 11... 15... 30

1... 1... 1... 3 2... 4... 4... 4... 4... 4... 6... 10... 11... 15... 30 1 2420128 1 6 3 2 199103 189/1 1991031891 3 4 5 JISJIS X 0208, 1997 1 http://www.pref.hiroshima.lg.jp/site/monjokan/ 1... 1... 1... 3 2... 4... 4... 4... 4... 4... 6... 10... 11... 15... 30 1 3 5 7 6 7

More information

178 5 I 1 ( ) ( ) 10 3 13 3 1 8891 8 3023 6317 ( 10 1914 7152 ) 16 5 1 ( ) 6 13 3 13 3 8575 3896 8 1715 779 6 (1) 2 7 4 ( 2 ) 13 11 26 12 21 14 11 21

178 5 I 1 ( ) ( ) 10 3 13 3 1 8891 8 3023 6317 ( 10 1914 7152 ) 16 5 1 ( ) 6 13 3 13 3 8575 3896 8 1715 779 6 (1) 2 7 4 ( 2 ) 13 11 26 12 21 14 11 21 I 178 II 180 III ( ) 181 IV 183 V 185 VI 186 178 5 I 1 ( ) ( ) 10 3 13 3 1 8891 8 3023 6317 ( 10 1914 7152 ) 16 5 1 ( ) 6 13 3 13 3 8575 3896 8 1715 779 6 (1) 2 7 4 ( 2 ) 13 11 26 12 21 14 11 21 4 10 (

More information

長崎県地域防災計画

長崎県地域防災計画 i ii iii iv v vi vii viii ix - 1 - - 2 - - 3 - - 4 - - 5 - - 6 - - 7 - - 8 - - 9 - 玢 - 10 - - 11 - - 12 - - 13 - - 14 - - 15 - - 16 - - 17 - - 18 - - 19 - - 20 - - 21 - - 22 - - 23 - - 24 - - 25 - -

More information

29 Short-time prediction of time series data for binary option trade

29 Short-time prediction of time series data for binary option trade 29 Short-time prediction of time series data for binary option trade 1180365 2018 2 28 RSI(Relative Strength Index) 3 USD/JPY 1 2001 1 2 4 10 2017 12 29 17 00 1 high low i Abstract Short-time prediction

More information

udc-2.dvi

udc-2.dvi 13 0.5 2 0.5 2 1 15 2001 16 2009 12 18 14 No.39, 2010 8 2009b 2009a Web Web Q&A 2006 2007a20082009 2007b200720082009 20072008 2009 2009 15 1 2 2 2.1 18 21 1 4 2 3 1(a) 1(b) 1(c) 1(d) 1) 18 16 17 21 10

More information

June 2016 i (statistics) F Excel Numbers, OpenOffice/LibreOffice Calc ii *1 VAR STDEV 1 SPSS SAS R *2 R R R R *1 Excel, Numbers, Microsoft Office, Apple iwork, *2 R GNU GNU R iii URL http://ruby.kyoto-wu.ac.jp/statistics/training/

More information

2 The Bulletin of Meiji University of Integrative Medicine 3, Yamashita 10 11

2 The Bulletin of Meiji University of Integrative Medicine 3, Yamashita 10 11 1-122013 1 2 1 2 20 2,000 2009 12 1 2 1,362 68.1 2009 1 1 9.5 1 2.2 3.6 0.82.9 1.0 0.2 2 4 3 1 2 4 3 Key words acupuncture and moxibustion Treatment with acupuncture, moxibustion and Anma-Massage-Shiatsu

More information