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
|
|
- えみ とどろき
- 5 years ago
- Views:
Transcription
1 R 3 R gito@eco.u-toyama.ac.jp October 23, 2017 (Toyama/NIHU) R ( 3 ) October 23, / 34
2 Agenda R 5 RStudio (Toyama/NIHU) R ( 3 ) October 23, / 34
3 10/30 (Mon.) 12/11 (Mon.) New! 1/9 (Tue.) New! (Toyama/NIHU) R ( 3 ) October 23, / 34
4 (regression analysis) (OLS) (GLM) (inferential statistics) = ( ) ( ) ( ) (Toyama/NIHU) R ( 3 ) October 23, / 34
5 50% 30% Google ( R ( ) (Toyama/NIHU) R ( 3 ) October 23, / 34
6 (Toyama/NIHU) R ( 3 ) October 23, / 34
7 ( ) ( ) ( ) (Toyama/NIHU) R ( 3 ) October 23, / 34
8 datum ( ) (1) (2) ( ( ) ) ( ( ) ( ) ( ) GDP (Toyama/NIHU) R ( 3 ) October 23, / 34
9 (unit of observation) (unit of analysis) (variable) GDP, 2 GDP (constant) ( ) (Toyama/NIHU) R ( 3 ) October 23, / 34
10 4 1 (nominal scale): 2 ( ) 2 (ordinal scale): ( ) 1 2 ( ) 3 (interval scale): ( ) (2 ) (0 ) 4 (ratio scale): (0) (0 ) 50kg 100kg 50kg 2 ( ) > > > (Toyama/NIHU) R ( 3 ) October 23, / 34
11 (statistic) ( ) ( ) ( ) ( ) ( ) (Toyama/NIHU) R ( 3 ) October 23, / 34
12 (mean, average) n ( ) x = (x 1, x 2,..., x n ) x x = n i=1 n = (x 1 + x x n ) n (1) (median) n x m m df(x) 1 2 and m df(x) 1 2 (2) m n 2 (Toyama/NIHU) R ( 3 ) October 23, / 34
13 (mode) ( ) x = (1, 1, 1, 1, 1, 2, 3, 4, 5, 6) 1 3 (outlier) (e.g., ) (e.g., ) (Toyama/NIHU) R ( 3 ) October 23, / 34
14 500, 100, n = 10, 000 ( x = 500, m = 500) Median Mean Frequency ( x = 594, m = 501) Frequency /10, 000 = 1/100 ( robust) (Toyama/NIHU) R ( 3 ) October 23, / 34
15 (IQR) (unbiased variance) n x = (x 1, x 2,..., x n ) x σ 2 x n σx 2 i=1 = (x i x) 2 n 1 (3) σ 2 x σ x (standard deviation, sd) ( ) (3) ( ) n 1 n ( ) ( ) x x (Toyama/NIHU) R ( 3 ) October 23, / 34
16 (IQR) (inter-quartile range, IQR) ( ) (1 ) n x = (x 1, x 2,..., x n ) x x 4 IQR 3 Q 3/4 (upper quartile) 1 Q 1/4 (lower quartile) Q 3/4 Q 1/4 m = Q 2/4 = Q 1/2 Q 0/4 Q 4/4 ( ) IQR 50% [Q 1/4 1.5IQR, Q 3/ IQR] (outlier) (box-and-whisker plot) ( ) q/10 q (Toyama/NIHU) R ( 3 ) October 23, / 34
17 (population) ( ) ( ) 2 (data generating process) (sample) (sampling) ( ) (statistical inference) ( ) (error) ( ) ( ) (Toyama/NIHU) R ( 3 ) October 23, / 34
18 (sample size): ( ) N (number of samples): ( ) ,500 2, ,500 ( ) 2,000 ( ) (Toyama/NIHU) R ( 3 ) October 23, / 34
19 ( ) (parameter) (parameter) (e.g., ) % ( ( ) ( standard error) 1 ( ) 2 (Toyama/NIHU) R ( 3 ) October 23, / 34
20 (Central Limit Theorem, CLT) ( ) n X 1, X 2,..., X n X n, σ 2 X X E[X] n Z n (X n E[X] ) 0, 1 ( ) N (0, 1) ( ) Z n = n(xn E[X]) n(xn E[X]) = (4) σ 2 X σ X X n E[X] N (0, σx 2 /n) ( ) n X n E[X] µ, σ ( σ 2 ) N (µ, σ 2 ) (Toyama/NIHU) R ( 3 ) October 23, / 34
21 ( ) ( ) n X 1, X 2,..., X n X n, σx 2 X E[X] ( ) n 95% X n 1.96 σx 2 /n E[X] X n /n (5) X n N (E[X], σx 2 /n) 95% ( ) = σ 2 X (Toyama/NIHU) R ( 3 ) October 23, / 34
22 (5) E[X] 95% (confidence interval, CI) (standard error, SE): σx 2 /n = σ X/ n ( ) σx n ( n ) 95% CI [X n 1.96SE, X n SE] 95% (X n E[X]) n ( ) t t 1.96 (Toyama/NIHU) R ( 3 ) October 23, / 34
23 ( ) α% α (confidence coefficient) 95%, 90%, 99% ( 94%, 96%, etc. ) 5% 10% 1% p < 0.05, p < 0.1, p < 0.01 ( (Type I/α error) ( ) 5%, 10%, 1%) (Type I/α error) H 0 (e.g., ) H 0 (interval estimation) 100 ( ) 95% 95 95% 1 (point estimation) (Toyama/NIHU) R ( 3 ) October 23, / 34
24 SD = σ X, SE = σ X / n SD > SE n (n 2) n n ( ) n SE = σx/ n SE 95% [Xn 1.96SE, X n SE] (Toyama/NIHU) R ( 3 ) October 23, / 34
25 α% 1 1 α% α% ( ) 100 ( ) 95% 95 95% % % % 0 1 (Toyama/NIHU) R ( 3 ) October 23, / 34
26 (file) path: URL PC URL education_2017/r_2017/ sample ( ) path /Users/Gaku/Desktop/sample sample.csv path /Users/Gaku/Desktop/sample.csv path OS (Win 10 ) Google!. sample.csv.csv, sample.xls.xls OS (Win 10 ) Google! (Toyama/NIHU) R ( 3 ) October 23, / 34
27 R (R path, encoding ) R /. R ( ) (1) (2) ( ) Mac Macintosh HD ( / ) Windows C (Toyama/NIHU) R ( 3 ) October 23, / 34
28 R R a ( ) ( ) a A R (Toyama/NIHU) R ( 3 ) October 23, / 34
29 R R A A A A A A R Google Error: object x not found (1) (2) (Toyama/NIHU) R ( 3 ) October 23, / 34
30 R (object) R R x 1 > x < <- ( = ) R ( ) ( ) vector, matrix, data.frame (tibble), list (e.g., ) 1 > x2 <- x/2 2 > x2 3 [1] 1 (Toyama/NIHU) R ( 3 ) October 23, / 34
31 R R double ( ), integer ( ), logical ( ), character ( ), factor ( ) 1 > x_num < > x_num 3 [1] 2 4 > x_chr <- "2" 5 > x_chr 6 [1] "2" 7 > class(x_num) 8 [1] "numeric" 9 > class(x_chr) 10 [1] "character" (Toyama/NIHU) R ( 3 ) October 23, / 34
32 R ( ) (5 8 ) x_chr 2 2 (9 10 ) 1 > num_vec <- c(1, 2, 3, 4, 5, 6) 2 > mean(num_vec) 3 [1] > chr_vec <- c("1", "2", "3", "4", "5", "6") 5 > mean(chr_vec) 6 [1] NA 7 Warning message: 8 In mean.default(chr_vec) : argument is not numeric or logical: returning NA 9 > mean(as.numeric(chr_vec)) 10 [1] 3.5 (Toyama/NIHU) R ( 3 ) October 23, / 34
33 R 1 (URL: /r_2017/rcode_fall2017/) 2 R 2. R R R (Toyama/NIHU) R ( 3 ) October 23, / 34
34 ( ) (1 ) R R 1 3, 6 ( ) Gelman & Hill. Data analysis. Chap. 1 2 ( ) Stata 5 ( ) 1 2 ( ) R R (Toyama/NIHU) R ( 3 ) October 23, / 34
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 informationI L01( Wed) : Time-stamp: Wed 07:38 JST hig e, ( ) L01 I(2017) 1 / 19
I L01(2017-09-20 Wed) : Time-stamp: 2017-09-20 Wed 07:38 JST hig e, http://hig3.net ( ) L01 I(2017) 1 / 19 ? 1? 2? ( ) L01 I(2017) 2 / 19 ?,,.,., 1..,. 1,2,.,.,. ( ) L01 I(2017) 3 / 19 ? I. M (3 ) II,
More information¥¤¥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 informationRによる計量分析:データ解析と可視化 - 第2回 セットアップ
R 2 2017 Email: gito@eco.u-toyama.ac.jp October 16, 2017 Outline 1 ( ) 2 R RStudio 3 4 R (Toyama/NIHU) R October 16, 2017 1 / 34 R RStudio, R PC ( ) ( ) (Toyama/NIHU) R October 16, 2017 2 / 34 R ( ) R
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 information第11回:線形回帰モデルのOLS推定
11 OLS 2018 7 13 1 / 45 1. 2. 3. 2 / 45 n 2 ((y 1, x 1 ), (y 2, x 2 ),, (y n, x n )) linear regression model y i = β 0 + β 1 x i + u i, E(u i x i ) = 0, E(u i u j x i ) = 0 (i j), V(u i x i ) = σ 2, i
More informationStata11 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 informationJune 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 informationkubostat2017c 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第13回:交差項を含む回帰・弾力性の推定
13 2018 7 27 1 / 31 1. 2. 2 / 31 y i = β 0 + β X x i + β Z z i + β XZ x i z i + u i, E(u i x i, z i ) = 0, E(u i u j x i, z i ) = 0 (i j), V(u i x i, z i ) = σ 2, i = 1, 2,, n x i z i 1 3 / 31 y i = β
More information講義のーと : データ解析のための統計モデリング. 第2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: (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 informationtokei01.dvi
2. :,,,. :.... Apr. - Jul., 26FY Dept. of Mechanical Engineering, Saga Univ., JAPAN 4 3. (probability),, 1. : : n, α A, A a/n. :, p, p Apr. - Jul., 26FY Dept. of Mechanical Engineering, Saga Univ., JAPAN
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 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 informationkubostat2017b p.1 agenda I 2017 (b) probability distribution and maximum likelihood estimation :
kubostat2017b p.1 agenda I 2017 (b) probabilit distribution and maimum likelihood estimation kubo@ees.hokudai.ac.jp http://goo.gl/76c4i 2017 11 14 : 2017 11 07 15:43 1 : 2 3? 4 kubostat2017b (http://goo.gl/76c4i)
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分布
(normal distribution) 30 2 Skewed graph 1 2 (variance) s 2 = 1/(n-1) (xi x) 2 x = mean, s = variance (variance) (standard deviation) SD = SQR (var) or 8 8 0.3 0.2 0.1 0.0 0 1 2 3 4 5 6 7 8 8 0 1 8 (probability
More informationstat-base_ppt [互換モード]
データ解析の基礎ーデータの分類とまとめ方ー 統計学と統計について 統計学 statistics とは何か? 髙木廣文東邦大学看護学部国際広域保健分野 統計 : 統計をとる (?) 統計学 : 統計学を使う (?) e-mail: halwin@med.toho-u.ac.jp http://homepage2.nifty.com/halwin/takagi.html 1 2 統計をとる とは? アンケート調査で学生のアルバイト実施を調べる
More information1 Stata SEM LightStone 3 2 SEM. 2., 2,. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press.
1 Stata SEM LightStone 3 2 SEM. 2., 2,. Alan C. Acock, 2013. Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press. 2 3 2 Conservative Depress. 3.1 2. SEM. 1. x SEM. Depress.
More information現代日本論演習/比較現代日本論研究演習I「統計分析の基礎」
URL: http://tsigeto.info/statg/ I ( ) 3 2017 2 ( 7F) 1 : (1) ; (2) 1998 (70 20% 6 8 ) (30%) ( 2) ( 2) 2 1. (4/13) 2. SPSS (4/20) 3. (4/27) [ ] 4. (5/11 6/1) [1, 4 ] 5. (6/8) 6. (6/15 6/29) [2, 5 ] 7. (7/6
More informationR int num factor character 1 2 (dichotomous variable) (trichotomous variable) 3 (nominal scale) M F 1 2 coding as.numeric() as.integer() 2
2006 10 16 http://phi.med.gunma-u.ac.jp/medstat/p01.xls p01.txt R p01
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 informationstat-base [互換モード]
データ解析の基礎ーデータの分類とまとめ方ー 統計学と統計について 統計学 statistics とは何か? 高木廣文東邦大学看護学部国際保健看護学研究室 統計 : 統計をとる (?) 統計学 : 統計学を使う (?) e-mail: halwin@med.toho-u.ac.jp http://homepage2.nifty.com/halwin/takagi.html 1 2 統計をとる とは?
More information1 Stata SEM LightStone 4 SEM 4.. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 3.
1 Stata SEM LightStone 4 SEM 4.. Alan C. Acock, 2013. Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press 3. 2 4, 2. 1 2 2 Depress Conservative. 3., 3,. SES66 Alien67 Alien71,
More informationy i OLS [0, 1] OLS x i = (1, x 1,i,, x k,i ) β = (β 0, β 1,, β k ) G ( x i β) 1 G i 1 π i π i P {y i = 1 x i } = G (
7 2 2008 7 10 1 2 2 1.1 2............................................. 2 1.2 2.......................................... 2 1.3 2........................................ 3 1.4................................................
More information( 30 ) 30 4 5 1 4 1.1............................................... 4 1.............................................. 4 1..1.................................. 4 1.......................................
More information!!! 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(pdf) (cdf) Matlab χ ( ) F t
(, ) (univariate) (bivariate) (multi-variate) Matlab Octave Matlab Matlab/Octave --...............3. (pdf) (cdf)...3.4....4.5....4.6....7.7. Matlab...8.7.....9.7.. χ ( )...0.7.3.....7.4. F....7.5. t-...3.8....4.8.....4.8.....5.8.3....6.8.4....8.8.5....8.8.6....8.9....9.9.....9.9.....0.9.3....0.9.4.....9.5.....0....3
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 information¥¤¥ó¥¿¡¼¥Í¥Ã¥È·×¬¤È¥Ç¡¼¥¿²òÀÏ Âè2²ó
2 212 4 13 1 (4/6) : ruby 2 / 35 ( ) : gnuplot 3 / 35 ( ) 4 / 35 (summary statistics) : (mean) (median) (mode) : (range) (variance) (standard deviation) 5 / 35 (mean): x = 1 n (median): { xr+1 m, m = 2r
More information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数理統計学Iノート
I ver. 0/Apr/208 * (inferential statistics) *2 A, B *3 5.9 *4 *5 [6] [],.., 7 2004. [2].., 973. [3]. R (Wonderful R )., 9 206. [4]. ( )., 7 99. [5]. ( )., 8 992. [6],.., 989. [7]. - 30., 0 996. [4] [5]
More information/22 R MCMC R R MCMC? 3. Gibbs sampler : kubo/
2006-12-09 1/22 R MCMC R 1. 2. R MCMC? 3. Gibbs sampler : kubo@ees.hokudai.ac.jp http://hosho.ees.hokudai.ac.jp/ kubo/ 2006-12-09 2/22 : ( ) : : ( ) : (?) community ( ) 2006-12-09 3/22 :? 1. ( ) 2. ( )
More information5.2 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現代日本論演習/比較現代日本論研究演習I「統計分析の基礎」
URL: http://tsigeto.info/statg/ I () 3 2016 2 ( 7F) 1 : (1); (2) 1998 (70 20% 6 9 ) (30%) ( 2) ( 2) 2 1. (4/14) 2. SPSS (4/21) 3. (4/28) [] 4. (5/126/2) [1, 4] 5. (6/9) 6. (6/166/30) [2, 5] 7. (7/78/4)
More informationkubostat2017e 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 information1 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 informationmain.dvi
1 F77 5 hmogi-2008f@kiban.civil.saitama-u.ac.jp 2013/5/13 1 2 f77... f77.exe f77.exe CDROM (CDROM D D: setupond E E: setupone 5 C:work\T66160\20130422>f77 menseki.f -o menseki f77(.exe) f77 f77(.exe) C:work\T66160\20130422>set
More informationuntitled
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 informationWindows Macintosh 3 3 4 = 3 4 = 4 5 6 Windows Macintosh 7 8 9 Windows Macintosh 0 Windows Macintosh 3 4 5 Windows Macintosh Windows Macintosh 3 4 5 = Windows Macintosh 3 4 3 4 5 3 4 5 3 5 4 5 6 3 4 3 5
More informationohpmain.dvi
fujisawa@ism.ac.jp 1 Contents 1. 2. 3. 4. γ- 2 1. 3 10 5.6, 5.7, 5.4, 5.5, 5.8, 5.5, 5.3, 5.6, 5.4, 5.2. 5.5 5.6 +5.7 +5.4 +5.5 +5.8 +5.5 +5.3 +5.6 +5.4 +5.2 =5.5. 10 outlier 5 5.6, 5.7, 5.4, 5.5, 5.8,
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 informationkubostat2015e 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 information2変量データの共分散・相関係数・回帰分析
2, 1, Excel 2, Excel http://hig3.net ( ) L04 2 I(2017) 1 / 24 2 I L04(2017-10-11 Wed) : Time-stamp: 2017-10-10 Tue 23:02 JST hig L03-Q1 L03-Q2 Quiz : 1.6m, 0.0025m 2, 0.05m. L03-Q3 Quiz : Sx 2 = 4, S x
More informationStata 11 Stata ROC whitepaper mwp anova/oneway 3 mwp-042 kwallis Kruskal Wallis 28 mwp-045 ranksum/median / 31 mwp-047 roctab/roccomp ROC 34 mwp-050 s
BR003 Stata 11 Stata ROC whitepaper mwp anova/oneway 3 mwp-042 kwallis Kruskal Wallis 28 mwp-045 ranksum/median / 31 mwp-047 roctab/roccomp ROC 34 mwp-050 sampsi 47 mwp-044 sdtest 54 mwp-043 signrank/signtest
More informationpopulatio sample II, B II? [1] I. [2] 1 [3] David J. Had [4] 2 [5] 3 2
(2015 ) 1 NHK 2012 5 28 2013 7 3 2014 9 17 2015 4 8!? New York Times 2009 8 5 For Today s Graduate, Just Oe Word: Statistics Google Hal Varia I keep sayig that the sexy job i the ext 10 years will be statisticias.
More information<4D F736F F F696E74202D2088E38A77939D8C7695D78BAD89EF313591E63189F18AEE967B939D8C7697CA2E >
2015/10/1 第 1 回 医学統計勉強会 東北大学病院循環器内科 東北大学臨床研究推進センター 共催 東北大学大学院医学系研究科 EBM 開発学寄附講座 宮田 敏 医学統計勉強会 10 月 2 日 ~11 月 26 日 (11 月 12 日を除く ) 木曜日 19:00~20:30 臨床大講堂 第 1 回 基本統計量 第 5 回 比率と分割表 第 2 回 回帰分析 第 6 回 継時的繰り返し測定データの解析
More informationデータ分析のまとめ方
R ではさまざまなデータを分析することができる R のデータセットを使う 外部ファイルを使う 作業ディレクトリの確認と変更 データの探し方 作業ディレクトリ (working directory) の確認と変更 Windows や mac では作業ディレクトリを変更できる 作業ディレクトリを自分の PC の デスクトップ に設定すると操作しやすい メニューでは : Windows の場合 : ファイル
More informationKLCシリーズ インストール/セットアップ・ガイド
KORG Legacy Collection J 1 / 2 Windows XP Windows XP 1 2 Windows XP 3 4 5 6 3 / 7 8 4 Mac OS X Mac OS X 1 2 3 4 5 Mac OS X 6 5 / 7 8 6 USB / USB 1 2 USB 7 / 1 2 3 Windows Mac 4 1 Windows Mac 8 2 Windows
More informationAR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t
87 6.1 AR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t 2, V(y t y t 1, y t 2, ) = σ 2 3. Thus, y t y t 1,
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 information4.9 Hausman Test Time Fixed Effects Model vs Time Random Effects Model Two-way Fixed Effects Model
1 EViews 5 2007 7 11 2010 5 17 1 ( ) 3 1.1........................................... 4 1.2................................... 9 2 11 3 14 3.1 Pooled OLS.............................................. 14
More informationNew version (2.15.1) of Specview is now available Dismiss Windows Specview.bat set spv= Specview set jhome= JAVA (C:\Program Files\Java\jre<version>\
Specview VO 2012 2012/3/26 Specview Specview STSCI(Space Telescope SCience Institute) VO Specview Web page http://www.stsci.edu/resources/software hardware/specview http://specview.stsci.edu/javahelp/main.html
More information151021slide.dvi
: Mac I 1 ( 5 Windows (Mac Excel : Excel 2007 9 10 1 4 http://asakura.co.jp/ books/isbn/978-4-254-12172-8/ (1 1 9 1/29 (,,... (,,,... (,,, (3 3/29 (, (F7, Ctrl + i, (Shift +, Shift + Ctrl (, a i (, Enter,
More informationJapan Research Review 1998年7月号
Japan Research Review 1998.7 Perspectives ****************************************************************************************** - 1 - Japan Research Review 1998.7-2 - Japan Research Review 1998.7-3
More informationuntitled
WinLD R (16) WinLD https://www.biostat.wisc.edu/content/lan-demets-method-statistical-programs-clinical-trials WinLD.zip 2 2 1 α = 5% Type I error rate 1 5.0 % 2 9.8 % 3 14.3 % 5 22.6 % 10 40.1 % 3 Type
More informationuntitled
PC Internet Explorer Windows SafariMac OS X Mozilla FireFoxWindows / Macintosh Google ChromeWindows / Macintosh IE + Google Windows 1 Internet Explorer 10.x (O)(A) Internet Explorer (O) (B) (B) (B) 2 Web
More information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 informationDAA09
> 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<4D6963726F736F667420576F7264202D204850835483938376838B8379815B83578B6594BB2D834A836F815B82D082C88C60202E646F63>
例 題 で 学 ぶ Excel 統 計 入 門 第 2 版 サンプルページ この 本 の 定 価 判 型 などは, 以 下 の URL からご 覧 いただけます. http://www.morikita.co.jp/books/mid/084302 このサンプルページの 内 容 は, 第 2 版 発 行 当 時 のものです. i 2 9 2 Web 2 Excel Excel Excel 11 Excel
More information10
z c j = N 1 N t= j1 [ ( z t z ) ( )] z t j z q 2 1 2 r j /N j=1 1/ N J Q = N(N 2) 1 N j j=1 r j 2 2 χ J B d z t = z t d (1 B) 2 z t = (z t z t 1 ) (z t 1 z t 2 ) (1 B s )z t = z t z t s _ARIMA CONSUME
More informationk3 ( :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 information4 OLS 4 OLS 4.1 nurseries dual c dual i = c + βnurseries i + ε i (1) 1. OLS Workfile Quick - Estimate Equation OK Equation specification dual c nurser
1 EViews 2 2007/5/17 2007/5/21 4 OLS 2 4.1.............................................. 2 4.2................................................ 9 4.3.............................................. 11 4.4
More information0.2 Button TextBox: menu tab 2
Specview VO 2012 2012/9/27 Specview Specview STSCI(Space Telescope SCience Institute) VO Specview Web page http://www.stsci.edu/resources/software hardware/specview http://specview.stsci.edu/javahelp/main.html
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 information1. 2 Blank and Winnick (1953) 1 Smith (1974) Shilling et al. (1987) Shilling et al. (1987) Frew and Jud (1988) James Shilling Voith (1992) (Shilling e
Estimation of the Natural Vacancy Rate and it s Instability: Evidence from the Tokyo Office Market * ** *** Sho Kuroda*, Morito Tsutsumi**, Toyokazu Imazeki*** * ** *** rent adjustment mechanismnatural
More information28
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 informationIsogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206,
H28. (TMU) 206 8 29 / 34 2 3 4 5 6 Isogai, T., Building a dynamic correlation network for fat-tailed financial asset returns, Applied Network Science (7):-24, 206, http://link.springer.com/article/0.007/s409-06-0008-x
More informationMicrosoft 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 informationkubostat2018d 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 informationR 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鉄鋼協会プレゼン
NN :~:, 8 Nov., Adaptive H Control for Linear Slider with Friction Compensation positioning mechanism moving table stand manipulator Point to Point Control [G] Continuous Path Control ground Fig. Positoining
More information10県別セミ-01.ai
2010 4 Point 1 K O 2 Point 2 Point 3 3 4 4/25 4/2 4/2 4/25 25 25 4/2 4/25 25 1 / 1 /2 25 25 2 J 1 / 1 / 25 25 J 5/2 5/2 5/29 29 29 5/2 5/29 29 30 30 30 30 30 30 J 6/14 18 J 6/14 18 J 7/26 30 7/27 30 J
More information<4D F736F F D20939D8C7689F090CD985F93C18EEA8D758B E646F63>
Gretl OLS omitted variable omitted variable AIC,BIC a) gretl gretl sample file Greene greene8_3 Add Define new variable l_g_percapita=log(g/pop) Pg,Y,Pnc,Puc,Ppt,Pd,Pn,Ps Add logs of selected variables
More informationuntitled
January 2009 Rotor-Gene Q Sample & Assay Technologies 1 1-1 1.1 1-1 1.2 Rotor-Gene Q 1-1 1.3 Rotor-Gene Q 1-1 1.4 1-1 2 2-1 2.1 Run File 2-1 2.2 2-2 2.3 2-2 2.4 PCR 2-3 2.5 2-3 2.6 2-4 2.7 2-4 2.8 2-5
More information<4D6963726F736F667420576F7264202D2095C48D9182CC92AA97AC82A982E793FA967B82CC8AD392E8955D89BF82CC95FB8CFC90AB82F08D6C82A682E981838FE38184288354834383678CF68A4A8CB38CB48D65292E646F63>
米 国 の 潮 流 から 日 本 の 鑑 定 評 価 の 方 向 性 を 考 える - 統 計 学 の 活 用 を 中 心 に- < 上 > A.I.テキストブック 最 新 刊 *1 における 統 計 学 の 位 置 づけ 不 動 産 鑑 定 士 / 大 阪 経 済 大 学 大 学 院 経 営 学 研 究 科 非 常 勤 講 師 堀 田 勝 己 本 稿 は ( 株 ) 住 宅 新 報 社 より 発
More information<4D F736F F F696E74202D F95618A7789EF B836A F838C834E B88E38A77939D8C76322E >
204 年 9 月 26 日第 62 回日本心臓病学会学術集会モーニングレクチャー 医学統計の基礎 於 : 仙台国際センター第 9 会場 医学統計の基礎 東北大学大学院医学系研究科循環器内科学分野 宮田敏 miyata@cardio.med.tohou.ac.jp 日本心臓病学会 COI 開示 東北大学大学院医学系研究科循環器内科学宮田敏 演題発表に関連し 開示すべき CO I 関係にある企業などはありません
More informationATM M.Shimura JCD02773@nifty.ne.jp 2003 12 13 JAPLA2003 1 queue ATM ATM queue 1.1 ATM No (Sec (Sec 1 13 37 60 26 28 99 1 25 40 39 143 202 14 88 190 27 1 184 2 170 37 40 130 317 15 121 72 28 48 115 3 101
More informationSpecview Specview Specview STSCI(Space Telescope SCience Institute) VO Specview Web page htt
Specview Specview Specview STSCI(Space Telescope SCience Institute) VO Specview Web page http://www.stsci.edu/resources/software_hardware/specview http://specview.stsci.edu/javahelp/main.html Specview
More information(1) ) ) (2) (3) (4) (5) (1) (2) b (3)..
... -1... -1... -2... -6.... -1 (1)... -1 1)... -1 2)... -14 (2)... -19 (3)... -52 (4)... -18 (5)... -136.... -196 (1)... -196 (2) b... -224 (3)... -233.... -251.... -286.... -289 (1)... -289 (2)... -32
More informationbron.dvi
1p 76p 12 2 4 80238 1 1 7 1.1... 8 1.1.1... 8 1.1.2... 8 1.1.3... 9 1.2... 10 1.3... 10 2 11 2.1... 12 2.2... 13 2.2.1 (SEM)... 13 2.2.2... 14 2.2.3... 17 2.2.4 SEM 3... 17 2.3... 19 2.3.1... 19 2.3.2...
More information12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? ( :51 ) 2/ 71
2010-12-02 (2010 12 02 10 :51 ) 1/ 71 GCOE 2010-12-02 WinBUGS kubo@ees.hokudai.ac.jp http://goo.gl/bukrb 12/1 ( ) GLM, R MCMC, WinBUGS 12/2 ( ) WinBUGS WinBUGS 12/2 ( ) : 12/3 ( ) :? 2010-12-02 (2010 12
More informationA Nutritional Study of Anemia in Pregnancy Hematologic Characteristics in Pregnancy (Part 1) Keizo Shiraki, Fumiko Hisaoka Department of Nutrition, Sc
A Nutritional Study of Anemia in Pregnancy Hematologic Characteristics in Pregnancy (Part 1) Keizo Shiraki, Fumiko Hisaoka Department of Nutrition, School of Medicine, Tokushima University, Tokushima Fetal
More informationFig. 1.1 Annual commercial landings of masu salmon in Hokkaido during 1970-2002. Fig. 1.2 Number of hatchery-reared masu salmon stocked in Hokkaido, 1980-2001. Fig. 2.2a Masu salmon landed
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 informationσ t σ t σt nikkei HP nikkei4csv H R nikkei4<-readcsv("h:=y=ynikkei4csv",header=t) (1) nikkei header=t nikkei4csv 4 4 nikkei nikkei4<-dataframe(n
R 1 R R R tseries fseries 1 tseries fseries R Japan(Tokyo) R library(tseries) library(fseries) 2 t r t t 1 Ω t 1 E[r t Ω t 1 ] ɛ t r t = E[r t Ω t 1 ] + ɛ t ɛ t 2 iid (independently, identically distributed)
More information研修コーナー
l l l l l l l l l l l α α β l µ l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l
More information(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総合薬学講座 生物統計の基礎
2013 10 22 ( ) 2013 10 22 1 / 40 p.682 1. 2. 3 2 t Mann Whitney U ). 4 χ 2. 5. 6 Dunnett Tukey. 7. 8 Kaplan Meier.. U. ( ) 2013 10 22 2 / 40 1 93 ( 20 ) 230. a t b c χ 2 d 1.0 +1.0 e, b ( ) e ( ) ( ) 2013
More information自由集会時系列part2web.key
spurious correlation spurious regression xt=xt-1+n(0,σ^2) yt=yt-1+n(0,σ^2) n=20 type1error(5%)=0.4703 no trend 0 1000 2000 3000 4000 p for r xt=xt-1+n(0,σ^2) random walk random walk variable -5 0 5 variable
More information