statstcs statstcum (EBM) 2 () : ( )GDP () : () : POS STEP 1: STEP 2: STEP 3: STEP 4: 3 2

Similar documents
2 () : ( )GDP () : () : POS STEP 1: STEP 2: STEP 3: STEP 4: 3 2

countif Excel E4:E10E4:E10 OK 2/25

(Nov/2009) 2 / = (,,, ) /8

countif Excel E4:E10E4:E10 OK 2/27

1 1 ( ) ( % mm % A B A B A 1

untitled

stat-excel-12.tex ( ) 2 -countif Excel E4:E10 E4:E10 OK 4. E4:E10 E4:E /1

151021slide.dvi

2変量データの共分散・相関係数・回帰分析

Excel97関数編


I L01( Wed) : Time-stamp: Wed 07:38 JST hig e, ( ) L01 I(2017) 1 / 19

0 (1 ) 0 (1 ) 01 Excel Excel ( ) = Excel Excel = =5-5 3 =5* 5 10 =5/ 5 5 =5^ 5 5 ( ), 0, Excel, Excel 13E E

untitled

2

ii : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 27 (1) Excel : : : : : : : : : : : : : : : : : : : : : :

日本の不動産市場における価格情報とボラティリティの非対称について:大阪市の実証研究から

<4D F736F F D B B83578B6594BB2D834A836F815B82D082C88C60202E646F63>

2 2.1 Excel 2013 Excel

untitled

2 Excel =sum( ) =average( ) B15:D20 : $E$26 E26 $ =A26*$E$26 $ $E26 E$26 E$26 $G34 $ E26 F4

応用数学III-4.ppt

橡Taro13-EXCEL統計学.PDF

( 28 ) ( ) ( ) 0 This note is c 2016, 2017 by Setsuo Taniguchi. It may be used for personal or classroom purposes, but not for commercial purp

情報科学概論 第1回資料

saihata.doc

関 数

1 Excel 1. [Standard] (call [Call Standard]) Excel [ ] [E ] Excel m 1 ( ) (

Microsoft Word - 研究デザインと統計学.doc

橡マニュアル1999.PDF

untitled

Excel 2007 Excel 2007 Excel 2007

() Statistik19 Statistik () 19 ( ) (18 ) ()

Fgure : (a) precse but naccurate data. (b) accurate but mprecse data. [] Fg..(p.) Fgure : Accuracy vs Precson []p.0-0 () 05. m 0.35 m 05. ± 0.35m 05.

統計学のポイント整理

¥¤¥ó¥¿¡¼¥Í¥Ã¥È·×¬¤È¥Ç¡¼¥¿²òÀÏ Âè2²ó

C:/texfile/2010/comp_rad_phys_2010.dvi


( )/2 hara/lectures/lectures-j.html 2, {H} {T } S = {H, T } {(H, H), (H, T )} {(H, T ), (T, T )} {(H, H), (T, T )} {1

untitled

Excel IT-Excel2007_dl.zip IT-Excel2007_dl IT-Excel2007_koushi.zip IT- Excel 2007_koushi _ _ Windows XP IT-Excel2007_dl Windows XP IT- Excel 200

目次

populatio sample II, B II? [1] I. [2] 1 [3] David J. Had [4] 2 [5] 3 2

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

II Time-stamp: <05/09/30 17:14:06 waki> ii

応用数学特論.dvi

Excel97関数編

II (No.2) 2 4,.. (1) (cm) (2) (cm) , (

untitled

¥¤¥ó¥¿¡¼¥Í¥Ã¥È·×¬¤È¥Ç¡¼¥¿²òÀÏ Âè2²ó

untitled


ユニセフ表紙_CS6_三.indd

食リ_表紙-表4.ai

Keynote 3 ユーザーズガイド

橡統計担当者のためのエクセル表紙.PDF

untitled

koji07-02.dvi

PDF用 レポート:中国保険市場の現状と展望.PDF

Excel ではじめる数値解析 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. このサンプルページの内容は, 初版 1 刷発行時のものです.

68 A mm 1/10 A. (a) (b) A.: (a) A.3 A.4 1 1

ii 3.,. 4. F. (), ,,. 8.,. 1. (75% ) (25% ) =9 7, =9 8 (. ). 1.,, (). 3.,. 1. ( ).,.,.,.,.,. ( ) (1 2 )., ( ), 0. 2., 1., 0,.

F8302D_1目次_ doc

A A = a 41 a 42 a 43 a 44 A (7) 1 (3) A = M 12 = = a 41 (8) a 41 a 43 a 44 (3) n n A, B a i AB = A B ii aa

/02/18

a n a n ( ) (1) a m a n = a m+n (2) (a m ) n = a mn (3) (ab) n = a n b n (4) a m a n = a m n ( m > n ) m n 4 ( ) 552

記号と準備


yamadaiR(cEFA).pdf


MICROLINK マリオネット 操作説明書

Excel 2007 Excel 2007 "Excel " " " " Excel 2003 Excel 2007 " "" Excel Web ""



( ) a C n ( R n ) R a R C n. a C n (or R n ) a 0 2. α C( R ) a C n αa = α a 3. a, b C n a + b a + b ( ) p 8..2 (p ) a = [a a n ] T C n p n a

Chapter9 9 LDPC sum-product LDPC 9.1 ( ) 9.2 c 1, c 2, {0, 1, } SUM, PROD : {0, 1, } {0, 1, } SUM(c 1, c 2,, c n ) := { c1 + + c n (c n0 (1 n

(


y = x x R = 0. 9, R = σ $ = y x w = x y x x w = x y α ε = + β + x x x y α ε = + β + γ x + x x x x' = / x y' = y/ x y' =

R/S.5.72 (LongTerm Strage 1965) NASA (?. 2? (-:2)> 2?.2 NB. -: is half

Microsoft PowerPoint - Econometrics pptx

& IT/ IT


秋植え花壇の楽しみ方

<82D282A982C1746F95F18D908F57967B95B E696E6464>


総合薬学講座 生物統計の基礎

相関係数と偏差ベクトル

PowerPoint プレゼンテーション

renshumondai-kaito.dvi

i

Microsoft PowerPoint - Lecture 10.ppt [互換モード]

TOPIX30 2 / 37

C: PC H19 A5 2.BUN Ohm s law

xy n n n- n n n n n xn n n nn n O n n n n n n n n

ii

Python Speed Learning

統計的仮説検定とExcelによるt検定

6.1 (P (P (P (P (P (P (, P (, P.

. p.1/34

Transcription:

(descrptve statstcs) 2010 9 3 1 1 2 2 3 2 3.1............................................. 3 3.2............................................. 3 4 4 4.1........................................ 5 5 6 6 -pvot table 7 7 10 8 13 9 15 10 18 11 22 12 25 13 26................................................................................ 1 1

statstcs statstcum (EBM) 2 () : ( )GDP () : () : POS STEP 1: STEP 2: STEP 3: STEP 4: 3 2

3.1 2 1 0 ( ) () ( ) 3.2 3

IQ Stanley Smth Stevens 1946 On the theory of scales of measurement 4 Wkpeda, Excel help 4 Frequency Dstrbuton Hstogram QC Wndows MacOS LnuX (McroSoft) OpenOffce.org () 4

4.1 sun open-offce calc 2 1 2 3 3 1 3 1 2 3 1 2 3 5

5 5.1 [] Mcrosoft Offce Excel ( : Offce ) Mcrosoft Offce Excel Excel (Mcrosoft Offce ) [Excel ] [ ] [] [Excel ] CALC No. (cm) (kg) (cm) 1 139.4 36.4 76.1 2 139.9 32.1 73.7 3 145.5 36.4 78.5 4 153.9 47.5 83.6 5 142.0 33.5 75.6 6 156.1 59.4 88.6 7 151.5 35.1 81.1 8 146.7 48.8 78.5 9 141.6 35.3 75.9 10 134.0 27.5 72.8 11 144.7 34.4 77.5 12 145.3 36.7 78.0 13 152.8 69.9 81.0 14 155.6 51.2 84.5 15 146.3 40.9 75.0 16 142.7 35.5 79.6 17 159.5 49.3 81.4 18 144.5 38.1 80.4 19 149.5 40.6 78.3 20 138.7 46.2 77.0 146.51 1.49 145.4 #N/A 6.70 44.84-0.560 0.246 25.5 134 159.5 2930.2 20 6

(cm) 3 10 7 BMI ( ) (m) 2 BMI (body mass ndex) 18.5 18.5 25 25 30 30 100 BMI BMI 3: 5.1 #N/A (Non Avalable) 6 -pvot table A1:A10 7

A 1 2 150 3 200 4 250 5 320 6 330 7 360 8 380 9 420 10 480 (1) (4) (1) 200? (2) 200 300? (3) 300 400? (4) 400 500?? (1) A1:A10 (2) []-[] (3) [Excel ] [] [ ] (4) [] $A$1:$A$10 [] [ ] (5) [ ] [] (6) [] (7) [], [OK] (8) [] D1 (9) [] (10) D2 -[] (11) [ ] 200, [ ] 500 [] 100 (12) [OK], 6.1 100 5 1 5 : 8

4 (tally mark) 1 12 2 26 3 15 4 7 5 5 2 (Hstogram) x skewness L L L L kurtoss, excess ; leptokurtc ; platykurtc Hstogram 9

2 4 10 100 2 7 : Q 1, Q 2, Q 3 Q 1, Q 3 360 10

(1) (max) (mn) range (2) (class) 10 20 Starjes k = 1 + log 2 n = 1 + 3.3 log 1 0n (3) (class wdth) R/k (4) A B C D E F G 1 () () 2 a 0 a 1 x 1 f 1 F 1 = f 1 p 1 = f 1/n P 1 = p 1 3 a 1 a 2 x 2 f 2 F 2 = f 1 + f 2 p 2 = f 2/n P 2 = p 1 + p 2 4 a 2 a 3 x 3 f 3 F 3 = f 1 + f 2 + f 3 p 3 = f 3/n P 3 = p 1 + p 2 + p 3 5 6 a k 1 a k x k f k F k = n p k = f k /n P k = 1 7 n 1 x = (a 1 + a )/2,( = 1, 2,, k) F k = f 1 + f 2 + + f k = n, P k = p 1 + p 2 + + p k = 1 =max() =mn() D2 D6 =frequency, ( A2 A6) CTRL+SHIFT+ENTER E2 =D2 E3 =D2+D3E3 E4 E6 F2 =D2/D$7 frequency (1) 2 (2) 1 (3) (4) 11

5 (Boxchart) 6 7 12

8 (average,mean) (SD,standard devaton),medan ( (varance) (kurtoss, excess)(bulge ) (skewness) (range) (mn) (max) (sum) (sze) (1) Excel -VBA (2)OK (3) OK n {X, = 1,, n}, sort (Order Statstcs) {X (), = 1,, n} X 1, X 2,, X n X (1) X (2) X (n) X 1 + X 2 + + X n = X (1) + X (2) + + X (n) X k x 1, x 2,, x k (k ) () x 1 x 2 x k f 1 f 2 f k n p 1 p 2 p k 1 2 f = 1, = 1, 2,, n X = X 1 + X 2 + + X n = x 1 f 1 + x 2 f 2 + + x k f k = j x f j, f = f 1 + f 2 + + f k = n X2 = X2 1 + X 2 2 + + X 2 n = x 2 1 f 1 + x 2 2 f 2 + + x 2 k f k = j x2 j f j, 1 n 1 n X = 1 n (X 1 + X 2 + + X n ) = x 1 p 1 + x 2 p 2 + + x k p k = j x j p j, X2 = 1 n (X2 1 + X 2 2 + + X 2 n) = x 2 1 p 1 + x 2 2 p 2 + + x 2 k p k = j x j p 2 j, X = 1 n n =1 X = 1 n k j=1 x jf j = k j=1 x jp j AVERAGE 13

SE SE = s = (X X) 2 = X2 nx2 j = x2 j f j nx 2 j = x2 j p j X 2 n n(n 1) n(n 1) n(n 1) n 1 Me Me = X ((n/2)+1) () = X ((n+1)/2) () = medan() 2 = quartle(, 2) Mo ( ) MODE 2 #N/A (Not Avalable) max f MODE # N/A (Not Avalable) SD SD = s = (X X) 2 STDEV VAR (n 1) s 2 s 2 = 1 n 1 VAR n (X X) 2 = ( 1 n ) (X X j ) 2 = n n 1 2 <j =1 VARP (n 1) n v 2 = 1 n n (X X) 2 = 1 n =1 k x 2 jf j X 2 = j=1 k x 2 jp j X 2 j=1 k x 2 jp j X 2 KW KURT 1 ( ) 4 X X 3 n s 3 SK SKEW 1 ( ) 3 X X n s L L L R L R = max() mn() () MAX x 1, x 2,, x n max x MIN x 1, x 2,, x n mn x SUM x 1, x 2,, x n x n, N COUNTA 95.0% 95% 1/2 95% j=1 14

TM(trmmed mean) TRIMMEAN TRIMMEAN(, ) (1) (2) 20 0.2 20 0.2 = 4 2 2 4 MD (Mean Devaton) = 1 n X X Z () Z = X X s x ( 0 1 50 10( 100) 10Z + 50 X 100 15 Q 1, Q 2, Q 3 25% 4 25% Q 1 2 50% 3 75% 3 =QUARTILE(, ) (, QUARTILE ) = (0, ), (1, 1 (25%)) (2, 2 = (50%)) (3, 3 (75%)) (4, ) Q 3 Q 1 (dectle) (percentle) ( 4 CV(Coeffcent of Varaton)= s x X ( ) 10% 10% 0.9 1.1 68.3% 0.31100 68.3 0.31 ± 0.031 X N(µ, σ 2 ) µ X 0.6826 68.3% ±2 σ 0.9544 9 1 2 15

(sample covarance): =COVAR( A, B) Cov(x, y) = 1 n 1 = COV AR(x, y ) (X X)(Y Y ) n n 1 2 X, Y 0 1 N 1 +1 2 COVAR N=2 2 COVAR VARP 2 () () (0 ) (sample correlaton coeffcent): =CORREL( A, B) = correl(x, y ) x s x, y s y ρ = Cov(x, y) s x s y ( x = Cov, y ) s x s y X X, Y Y, = 1, 2,, n s x s y N CORREL PEARSON 2 N 3 16

8 CORREL ( PEARSON) 2 2 2 1 +1 2 ( ) ( ) (0 ) 1 0.7 0.7 0.4 0.4 0.2 0.2 0 0 0.2 0.2 0.4 0.4 0.7 0.7 1 (Speaman s rank correlaton coeffcent) r s = 1 6 (x y ) 2 n(n 2 = 1 1) (x y ) 2 / ( ) n 1 3 x, y 1, 2,, n =[COUNT() + 1 - RANK(,, 0) - RANK(,, 1)]/2 RANK(,, ) 0...3 2 1 0 1 2 3... 2 2 17

2 0 : 2 9: 11 10 10 Corrado Gn 0 1 0 18

12 1 Wkpeda (Gn coeffcent) 100 Gn Index http://www.sustanablemddleclass.com/gn-coeffcent.html Japan 24.9 Unted Kngdom 36.0 Sweden 25.0 Iran 43.0 Germany 28.3 Unted States 46.6 France 32.7 Argentna 52.2 Pakstan 33.0 Mexco 54.6 Canada 33.1 South Afrca 57.8 Swtzerland 33.1 Namba 70.7 ( 0.24 0.36 0.4 0.56 0.66 Bob Sutclffe (2007), Postscrpt to the artcle World nequalty and globalzaton (Oxford Revew of Economc Polcy, Sprng 2004), http://steresources.worldbank.org/intdecineq/resources/psbsutclffe.pdf. Retreved on 2007-12-13 G = 1 2 1 0 L(x)dx = 1/2 1 0 L(x)dx 1/2 or = 1 2 L(x )(.e.l(x) ) L(x) 19

y G L (1, 1) y = x 45 1 0 L(x)dx (0.8, 0.6) y = L(x) (0.4, 0.2) 0 1 x (0, 0) F (1) = 1, 2,, n x x 1 x 2 x n p = f(x ) p 1 p 2 p n F F 1 = p 1 F 2 = p 2 + F 1 F n = p n + F n 1 = 1 L = j=1 x jf(x j ) / L = L = n j=1 x jf(x j ) L +1 + L (F +1 F ) 2 ( = 1, 2,, n) L 0 = 0 L 1 L 2 L n 1 L n = 1 0 1 (2) < x < x f(x) F (x) = x f(t)dt L(x) = x / tf(t)dt L (0 x 1) L = tf(t)dt () L(0) = 0 L(x) L(y) L(1) = 1, 0 < x < y < 1 () 0 < L(x) x, 0 < x < 1 x = (x, = 1, 2,, n) F = (/n, = 1, 2,, n) s xy G CV(x) R XF G = CV (x) R XF n X = {x 1, x 2,, x n } x = 1 n x ( ) x x j,, j = 1, 2,, n ()D D = 1 n 2 x x j = 2 n 2 x x j,j <j 20

,j n2 {(, j);, j = 1, 2,, n} <j n(n 1)/2 { < j;, j = 1, 2,, n} G G = D 2x {x 1, x 2,, x n } (order statstcs) x (1) x (2) x (n) n ( ) <j x x j = x 1 x 2 + x 1 x 3 + + x 1 x n + x 2 x 3 + + x 2 x n + + + x n 1 x n = ( ) x (2) x (1) + ( ) x (3) x (1) + + ( ) x (n) x (1) + ( ) x (3) x (2) + + ( ) x (n) x (2) + + + ( ) x (n) x (n 1) <j x x j = (n 1)x (n) + ((n 2) 1) x (n 1) + + (1 (n 2)) x (2) + (0 (n 1)) x (1) = x () (n + 1)x () = 2 x () n(n + 1)x = 2 ( x () x ) x = x () = nx, = n(n + 1)/2 x = (x (1), x (2),, x (n) ) F = (F ) = F = 1 n s 2 F = s F F = ( 1 n 2) (F F j ) 2 <j 2 s XF = = = n = n + 1 2n 1 n 1 (x () x)(f F ) ( 1 n 1 (x () x) n n + 1 ) ( ) 2n 1 n 1 (x () x) n X F ρ ρ = s XF s X s F = 1 n 1 D = 4 n 2 ( x () x ), (x () x) nd 4 = 21 ( 1 n, 2 n,, n ) n = 1 n 1 (F F ) 2 = n + 1 12n ( ) ( / ) n + 1 s X n 12n ( ) (x () x) = S xy n

G = D 2X D 2x = 2 (n 1)x n D = ( 1 n ) 4 x () x (j) = ( x () x ) n(n 1) 2 <j ( ) (x () x) = 2 n x s XF = 2 x ρs Xs F = 2 n + 1 n + 1 x ρs X 12n = ρcv (X) 3n G = n + 1 3n,j x x j /n 2 2x 0.577 G = 1 n 2 x x x j 0.577ρCV <j 11 R-2 1 / LINEST X Y (1 Y a X + b Y = a X b Y = a e X Y = a logx R2 OK 9 R2 0.5 0.8 0.8 R2 2 (1) (2) (3) R-2 R Y 2 (X 1, X 2,, X n ) b 22

13 Y a 1 X 1 + a 2 X 2 + + a n X n + b A (Y ) B (X 1 ) C (X 2 ) b Y a 1 X 1 + a 2 X 2 + b a 1, a 2, b 10 A B C 1 Y X1 X2 2 10 18 10 3 12 17 11 4 3 3 2 5 14 26 15 6 4 7 5 7 10 18 9 8 6 10 6 9 11 15 13 10 8 15 7 11 11 14 14 R 0.98 R2 0.96 R2 0.95 0.78 10.00 F 2.00 110.61 55.31 90.29 0.00 7.00 4.29 0.61 9.00 114.90 23

t P - 95% 95% 95.0% 95.0% 0.82 0.65 1.26 0.25 0.72 2.36 0.72 2.36 X 1 0.25 0.07 3.53 0.01 0.08 0.42 0.08 0.42 X 2 0.49 0.11 4.47 0.00 0.23 0.74 0.23 0.74 Y = 0.25 X 1 + 0.49 X 2 + 0.82 2 Y 2 X P P 0.05 X 0 t p 0 0 0 Y X (1) P (T t) < (2) t < t t tnv t = tnv 5%(0.05) 1%(0.01) n k 1 n k tnv 2 5% 0.1 2 t P NG 3 24

14 0.7 R2 0.5 R2 R2 R2 R2 AIC 12 http://www.stat.go.jp/data/ndex.htm 25

http://www.pref.chba.jp/outlne/statstcs/ndex-j.html 13 / = 2+3 A2:B10 A2;B10 A2 B10 = (,,, ) 26

1 (true) false 1 2 =rand(): 1 27

=f =and( 2) =or( 2) =sum( 2) =sumf( ) =count( ) =countf( ), 1 2 {=f(and( 2),, )} {=f(or( 2) )} 2 Ctrl, Shft, Home actve( shft+ ctrl+ arrow key ), nonactve () [] [ ] [ ] /VBA VBA Vsual Basc 3 28

, abs() round() nt() sum( 1 2) average( 1 2 ) count( 1 2) counta( 1 2 ) max( 1 2) mn( 1 2) mode( 1 2) medan( 1 2 ) rank() quartle var( ) varp( ) stdev( ) stdevp( ) normdst( ) normnv( ) frequency(, ) = (), 1 2 1 2 1 2 50% 2 ( 1 2 3 ) 25% 50%75% /(n-1) /n populaton varance var standard devaton varp standard devaton of populaton,true true false x nverse (0) 4 29