10:30 12:00 P.G. vs vs vs 2

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "10:30 12:00 P.G. vs vs vs 2"

Transcription

1 1

2 10:30 12:00 P.G. vs vs vs 2

3 LOGIT PROBIT TOBIT mean median mode CV 3

4 4

5 5

6

7 45 7

8 P(A B) = P(A) + P(B) - P(A B) P(B A)=P(A B)/P(A) P(A B)=P(B A) P(A) P(A B) P(A) P(B A) P(B) P(A B) P(A) P(B) P(B A)P(A) P(A B) P(B A)P(A) P(B A c )P(A c ) 8

9 9

10 10

11 P( ) P( ) 1- X P( X=x ) C x p x q n-x B(n,p) 11

12 X n x λ p(x) C x λs n x λs n n-x p(x) λ x exp λ x! X 0,1, 12

13 x a<x<b a b 1/ ba E(x)= 1/ b-a ab xdx =(a+b)/2 V(x)=(b-a) 2 /12 13

14 normal distribution N µ 0 1 N 0, 1 µ 14

15 N 0 1 µ=0 σ 2 =1 - s + s µ 68.2% 95.4% 15

16 µ=0 σ 2 =0.5 N (0, 1) N (0, 2) N (0, 0.5) σ 2 =1 σ 2 =2 16

17 µ = -2 µ = 0 µ = 2 N (-2, 1) N (0, 1) N (2, 1) σ 2 =1 17

18 µ µ X,Y 18

19 19

20 X µ σ 2 X n {X 1 X 2 X n } µ /n 20

21 2 i 1, 2,., m i µ 21

22 3 Z 22

23 4 X n {X1 X2,, Xn} X µ c 23

24 5 X n {X1 X2,, Xn} c 24

25 6 n {X1 X2,, Xn} i 1, 2, n µ n 25

26 7 n {X1 X2,, Xn} n

27 χ 2 t χ 2 χ 2 27

28 2 Z1 Zk k k 28

29 χ 2 W W Z1 Z1 1 W k W Z Z 2 k 2 E(z 4 =3 V W 2k 2 Xi µ k 29

30 χ

31 t Z W k Z W k t k t t t

32 t 0 k/(k 2) 1 1 t 32

33 Z P c P Z c 33

34 F V m W k V W V W m k F F F F k/(k 2) k 3 k 1 34

35 F

36 estimator vs estimate vs vs 36

37 µ 12 76, 63, 83, 86, 53, 71 95% Z= = µ σ 2 /n= 144/6 24 Z 95% P( 1.96 Z 1.96 ) µ 64.2 µ

38 (unbiasness) consistency efficiency sufficiency 38

39 moment method (maximum likelyhood method) 39

40 (alternative) hypothesis (null hypothesis) p p 0 test statistics) 10%

41 critical value) α β 41

42 1%,5%,10% 42

43 25 P: P 0 : H 0 : P P 0 Ha: P P 0 H 0 : P P 0 H 0 : P P 0 43

44 3.1kg 0.2kg kg 3.1kg 0.2/

45 α 0.05 β=0.198 H a :µ=3.1 H 0 :µ=3.1 α β Z=-2.5( =3) Z=0 ( =3.1) Z=-1.65( =3.034) 45

46 46

47 cm 158.5cm cm 152.2cm µ cm 5.07cm 37 µ µ µ µ

48 C 48

49 n-1 49

50 µ=1200 µ n 100 1% z 0 =2.33 X 1200+(120/10)2.33=1228 1% P 0.01 α 0 ξ 100α µ 100α ξ α µ 100(1- α)% µ 0 µ 0 50

51 51

52 1) 2) n 1 +n 2-2 t 3) σ 12 σ S 22 52

53 3)

54 100 α/2 Z α/2 Z 0 b=1.65, p=1/6-1.65{1/6(1-1/6)/25} 0.5 =0.044,

55 55

56 2 X,Y ρ n-2 T r= T 3.0 n=12 5%

57 f(x,θ) L 0 L a La 0 λ 6.1 X X 1 X n θ θ 1 θ k H 0 θ 1 =c 1 θ c n -2log(λ) χ 2 χ2 57

58 χ χ E = n x x TSS = 58

59 59

60 60

61 61

62 62

63 63

64 64

65 65

66 correlation coefficient 2 66

67 20 ( ) = α + β ( ) + 67

68 ( ) (kg) 68

69 69

70 y y j y k µ k µ j x 70

71 y y j µ j y k µ k U i (0,σ 2 ) (i=j,k) x 71

72 72

73 73

74 74

75 75

76 76

77 (1),(2) a b n 77

78 78

79 79

80 80

81 81

82 82

83 83

84

85

86 86

87

88 88

89 DW DW 2(1 r) DW

90 DW dl du dw<dl du<dw<4-du 4-dL<dw DL<dw<dU 4-dU<dw<4-dL 90

91 DW 10 h< <h< <h 1-nv 0 e j e j-1 e j-1 91

92 1% % DW

93 93

94 94

95 95

96

97 1% P DW 97

98 98

99 99

100 100

101 FI* FI* FI* β 1 β 2 X i ei FI FI* FI

102 FI* FI FI Pi Pr FI= Pr FI* Pr ε i β 1 β 2 X i Pr ε i β 1 β 2 X i (1) ε i (1) P i Pr ε i β 1 β 2 X i Φ β 1 β 2 X i

103 β 1 β 2 Δ β 1 β 2 X i

104 2 Censored odel Truncated model FI* FI i * β 1 β 2 X i i FI FI*

105 FI* FI* 0

106

107 FDIB z SB ET APCC NAFTA EU _ AIC= NAFTA EU 1980

108 FDIB z- - C SB ET APCC AIC=

109 FDIB z- - C SB ET APCC _89 ) AIC=

110 4 FDIB z- - C SB ET APCC _89(80 ) AIC=

111 RDSA FDI [FDI] [RDSA]=17.44 [RDSA] LSALE FDI [FDI] ( ) [LSALE]=6.214 [LSALE]

112 33 4 p

113 < > < >

114 Rxy(τ τ

115 Dxy(θj Px (θj X Y X(t) Y(t) πθj τ θj X(t) Y(t) τ θj X(t) Y(t)

116 MBP kg AOP kg STN

117

118 χα22(v) α% α α χ2 P(θj V V

119

120

121 ii iii

122 50ha X 1 ha X 2 ha ha 1ha 100 ha 50 ha ha 80 ha 100 ha X 1 + X 2 50 X 1 + X X X X X 2 Z 122

123 XX2 2 X 2 =0.8X Z 123

124 (i) x 1,x 2,x 3,x 4 S y 1,y 2,y 3 y 1 50 y 2 =100 y 3 =3000 (ii) y 1,y 2,y 3 0 (iii)z j -C j S 0 (i) Z j -C j (ii)s R 2 (iii) (iv) (iii) 2 (v) Z j -C j Zj Cj Z j C j 2 Z j -C j 3 Z j -C j R Z j -C j S y2=20 20 =40 40 x2=10 10ha Z 124 j - C j =

125 y2 125

126 X1 + X X X X X2 360 Z X1 + X2 126

127 127

128 128

gr09.dvi

gr09.dvi .1, θ, ϕ d = A, t dt + B, t dtd + C, t d + D, t dθ +in θdϕ.1.1 t { = f1,t t = f,t { D, t = B, t =.1. t A, tdt e φ,t dt, C, td e λ,t d.1.3,t, t d = e φ,t dt + e λ,t d + dθ +in θdϕ.1.4 { = f1,t t = f,t {

More information

n Y 1 (x),..., Y n (x) 1 W (Y 1 (x),..., Y n (x)) 0 W (Y 1 (x),..., Y n (x)) = Y 1 (x)... Y n (x) Y 1(x)... Y n(x) (x)... Y n (n 1) (x) Y (n 1)

n Y 1 (x),..., Y n (x) 1 W (Y 1 (x),..., Y n (x)) 0 W (Y 1 (x),..., Y n (x)) = Y 1 (x)... Y n (x) Y 1(x)... Y n(x) (x)... Y n (n 1) (x) Y (n 1) D d dx 1 1.1 n d n y a 0 dx n + a d n 1 y 1 dx n 1 +... + a dy n 1 dx + a ny = f(x)...(1) dk y dx k = y (k) a 0 y (n) + a 1 y (n 1) +... + a n 1 y + a n y = f(x)...(2) (2) (2) f(x) 0 a 0 y (n) + a 1 y

More information

( ) 2002 1 1 1 1.1....................................... 1 1.1.1................................. 1 1.1.2................................. 1 1.1.3................... 3 1.1.4......................................

More information

‚åŁÎ“·„´Šš‡ðŠp‡¢‡½‹âfi`fiI…A…‰…S…−…Y…•‡ÌMarkovŸA“½fiI›ð’Í

‚åŁÎ“·„´Šš‡ðŠp‡¢‡½‹âfi`fiI…A…‰…S…−…Y…•‡ÌMarkovŸA“½fiI›ð’Í Markov 2009 10 2 Markov 2009 10 2 1 / 25 1 (GA) 2 GA 3 4 Markov 2009 10 2 2 / 25 (GA) (GA) L ( 1) I := {0, 1} L f : I (0, ) M( 2) S := I M GA (GA) f (i) i I Markov 2009 10 2 3 / 25 (GA) ρ(i, j), i, j I

More information

(2 X Poisso P (λ ϕ X (t = E[e itx ] = k= itk λk e k! e λ = (e it λ k e λ = e eitλ e λ = e λ(eit 1. k! k= 6.7 X N(, 1 ϕ X (t = e 1 2 t2 : Cauchy ϕ X (t

(2 X Poisso P (λ ϕ X (t = E[e itx ] = k= itk λk e k! e λ = (e it λ k e λ = e eitλ e λ = e λ(eit 1. k! k= 6.7 X N(, 1 ϕ X (t = e 1 2 t2 : Cauchy ϕ X (t 6 6.1 6.1 (1 Z ( X = e Z, Y = Im Z ( Z = X + iy, i = 1 (2 Z E[ e Z ] < E[ Im Z ] < Z E[Z] = E[e Z] + ie[im Z] 6.2 Z E[Z] E[ Z ] : E[ Z ] < e Z Z, Im Z Z E[Z] α = E[Z], Z = Z Z 1 {Z } E[Z] = α = α [ α ]

More information

Z: Q: R: C: 3. Green Cauchy

Z: Q: R: C: 3. Green Cauchy 7 Z: Q: R: C: 3. Green.............................. 3.............................. 5.3................................. 6.4 Cauchy..................... 6.5 Taylor..........................6...............................

More information

統計学のポイント整理

統計学のポイント整理 .. September 17, 2012 1 / 55 n! = n (n 1) (n 2) 1 0! = 1 10! = 10 9 8 1 = 3628800 n k np k np k = n! (n k)! (1) 5 3 5 P 3 = 5! = 5 4 3 = 60 (5 3)! n k n C k nc k = npk k! = n! k!(n k)! (2) 5 3 5C 3 = 5!

More information

研修コーナー

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

nsg02-13/ky045059301600033210

nsg02-13/ky045059301600033210 φ φ φ φ κ κ α α μ μ α α μ χ et al Neurosci. Res. Trpv J Physiol μ μ α α α β in vivo β β β β β β β β in vitro β γ μ δ μδ δ δ α θ α θ α In Biomechanics at Micro- and Nanoscale Levels, Volume I W W v W

More information

I-2 (100 ) (1) y(x) y dy dx y d2 y dx 2 (a) y + 2y 3y = 9e 2x (b) x 2 y 6y = 5x 4 (2) Bernoulli B n (n = 0, 1, 2,...) x e x 1 = n=0 B 0 B 1 B 2 (3) co

I-2 (100 ) (1) y(x) y dy dx y d2 y dx 2 (a) y + 2y 3y = 9e 2x (b) x 2 y 6y = 5x 4 (2) Bernoulli B n (n = 0, 1, 2,...) x e x 1 = n=0 B 0 B 1 B 2 (3) co 16 I ( ) (1) I-1 I-2 I-3 (2) I-1 ( ) (100 ) 2l x x = 0 y t y(x, t) y(±l, t) = 0 m T g y(x, t) l y(x, t) c = 2 y(x, t) c 2 2 y(x, t) = g (A) t 2 x 2 T/m (1) y 0 (x) y 0 (x) = g c 2 (l2 x 2 ) (B) (2) (1)

More information

1 variation 1.1 imension unit L m M kg T s Q C QT 1 A = C s 1 MKSA F = ma N N = kg m s 1.1 J E = 1 mv W = F x J = kg m s 1 = N m 1.

1 variation 1.1 imension unit L m M kg T s Q C QT 1 A = C s 1 MKSA F = ma N N = kg m s 1.1 J E = 1 mv W = F x J = kg m s 1 = N m 1. 1.1 1. 1.3.1..3.4 3.1 3. 3.3 4.1 4. 4.3 5.1 5. 5.3 6.1 6. 6.3 7.1 7. 7.3 1 1 variation 1.1 imension unit L m M kg T s Q C QT 1 A = C s 1 MKSA F = ma N N = kg m s 1.1 J E = 1 mv W = F x J = kg m s 1 = N

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

橡Taro11-報告書0.PDF

橡Taro11-報告書0.PDF Research Center RC 2001 5-1- RC RC NHK -2- -3- 00/12/16 RC 01/01/07 RC 01/01/21 1 13 01/02/11 2 9 01/02/10 01/02/14 01/02/19 01/02/25 3 7 01/03/10 4 8 01/03/23 5 8 01/04/29 2001/01/07-4- -5- RC 1990 RC

More information

『共形場理論』

『共形場理論』 T (z) SL(2, C) T (z) SU(2) S 1 /Z 2 SU(2) (ŜU(2) k ŜU(2) 1)/ŜU(2) k+1 ŜU(2)/Û(1) G H N =1 N =1 N =1 N =1 N =2 N =2 N =2 N =2 ĉ>1 N =2 N =2 N =4 N =4 1 2 2 z=x 1 +ix 2 z f(z) f(z) 1 1 4 4 N =4 1 = = 1.3

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

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

1 1 3 1.1 (Frequecy Tabulatios)................................ 3 1........................................ 8 1.3.....................................

1 1 3 1.1 (Frequecy Tabulatios)................................ 3 1........................................ 8 1.3..................................... 1 1 3 1.1 (Frequecy Tabulatios)................................ 3 1........................................ 8 1.3........................................... 1 17.1................................................

More information

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

populatio 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

2 2 MATHEMATICS.PDF 200-2-0 3 2 (p n ), ( ) 7 3 4 6 5 20 6 GL 2 (Z) SL 2 (Z) 27 7 29 8 SL 2 (Z) 35 9 2 40 0 2 46 48 2 2 5 3 2 2 58 4 2 6 5 2 65 6 2 67 7 2 69 2 , a 0 + a + a 2 +... b b 2 b 3 () + b n a

More information

untitled

untitled (a) (b) (c) (d) Wunderlich 2.5.1 = = =90 2 1 (hkl) {hkl} [hkl] L tan 2θ = r L nλ = 2dsinθ dhkl ( ) = 1 2 2 2 h k l + + a b c c l=2 l=1 l=0 Polanyi nλ = I sinφ I: B A a 110 B c 110 b b 110 µ a 110

More information

No2 4 y =sinx (5) y = p sin(2x +3) (6) y = 1 tan(3x 2) (7) y =cos 2 (4x +5) (8) y = cos x 1+sinx 5 (1) y =sinx cos x 6 f(x) = sin(sin x) f 0 (π) (2) y

No2 4 y =sinx (5) y = p sin(2x +3) (6) y = 1 tan(3x 2) (7) y =cos 2 (4x +5) (8) y = cos x 1+sinx 5 (1) y =sinx cos x 6 f(x) = sin(sin x) f 0 (π) (2) y No1 1 (1) 2 f(x) =1+x + x 2 + + x n, g(x) = 1 (n +1)xn + nx n+1 (1 x) 2 x 6= 1 f 0 (x) =g(x) y = f(x)g(x) y 0 = f 0 (x)g(x)+f(x)g 0 (x) 3 (1) y = x2 x +1 x (2) y = 1 g(x) y0 = g0 (x) {g(x)} 2 (2) y = µ

More information

<4D F736F F D B B83578B6594BB2D834A836F815B82D082C88C60202E646F63>

<4D F736F F D B B83578B6594BB2D834A836F815B82D082C88C60202E646F63> 単純適応制御 SAC 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/091961 このサンプルページの内容は, 初版 1 刷発行当時のものです. 1 2 3 4 5 9 10 12 14 15 A B F 6 8 11 13 E 7 C D URL http://www.morikita.co.jp/support

More information

* 1 2014 7 8 *1 iii 1. Newton 1 1.1 Newton........................... 1 1.2............................. 4 1.3................................. 5 2. 9 2.1......................... 9 2.2........................

More information

kawa (Spin-Orbit Tomography: Kawahara and Fujii 21,Kawahara and Fujii 211,Fujii & Kawahara submitted) 2 van Cittert-Zernike Appendix A V 2

kawa (Spin-Orbit Tomography: Kawahara and Fujii 21,Kawahara and Fujii 211,Fujii & Kawahara submitted) 2 van Cittert-Zernike Appendix A V 2 Hanbury-Brown Twiss (ver. 1.) 24 2 1 1 1 2 2 2.1 van Cittert - Zernike..................................... 2 2.2 mutual coherence................................. 3 3 Hanbury-Brown Twiss ( ) 4 3.1............................................

More information

16 16 16 1 16 2 16 3 24 4 24 5 25 6 33 7 33 33 1 33 2 34 3 34 34 34 34 34 34 4 34-1 - 5 34 34 34 1 34 34 35 36 36 2 38 38 41 46 47 48 1 48 48 48-2 - 49 50 51 2 52 52 53 53 1 54 2 54 54 54 56 57 57 58 59

More information

ab c d 6 12 1:25,000 28 3 2-1-3 18 2-1-10 25000 3120 10 14 15 16 7 2-1-4 1000ha 10100ha 110ha ha ha km 200ha 100m 0.3 ha 100m 1m 2-1-11 2-1-5 20cm 2-1-12 20cm 2003 1 05 12 2-1-13 1968 10 7 1968 7 1897

More information

(Basic of Proability Theory). (Probability Spacees ad Radom Variables , (Expectatios, Meas) (Weak Law

(Basic of Proability Theory). (Probability Spacees ad Radom Variables , (Expectatios, Meas) (Weak Law I (Radom Walks ad Percolatios) 3 4 7 ( -2 ) (Preface),.,,,...,,.,,,,.,.,,.,,. (,.) (Basic of Proability Theory). (Probability Spacees ad Radom Variables...............2, (Expectatios, Meas).............................

More information

Gauss Gauss ɛ 0 E ds = Q (1) xy σ (x, y, z) (2) a ρ(x, y, z) = x 2 + y 2 (r, θ, φ) (1) xy A Gauss ɛ 0 E ds = ɛ 0 EA Q = ρa ɛ 0 EA = ρea E = (ρ/ɛ 0 )e

Gauss Gauss ɛ 0 E ds = Q (1) xy σ (x, y, z) (2) a ρ(x, y, z) = x 2 + y 2 (r, θ, φ) (1) xy A Gauss ɛ 0 E ds = ɛ 0 EA Q = ρa ɛ 0 EA = ρea E = (ρ/ɛ 0 )e 7 -a 7 -a February 4, 2007 1. 2. 3. 4. 1. 2. 3. 1 Gauss Gauss ɛ 0 E ds = Q (1) xy σ (x, y, z) (2) a ρ(x, y, z) = x 2 + y 2 (r, θ, φ) (1) xy A Gauss ɛ 0 E ds = ɛ 0 EA Q = ρa ɛ 0 EA = ρea E = (ρ/ɛ 0 )e z

More information

PDF

PDF 1 1 1 1-1 1 1-9 1-3 1-1 13-17 -3 6-4 6 3 3-1 35 3-37 3-3 38 4 4-1 39 4- Fe C TEM 41 4-3 C TEM 44 4-4 Fe TEM 46 4-5 5 4-6 5 5 51 6 5 1 1-1 1991 1,1 multiwall nanotube 1993 singlewall nanotube ( 1,) sp 7.4eV

More information

a. How to start: b. How to continue: c. How to stop: b EAP 2. EAP EAP (expected a posteriori) (posteriori distribution) (θ) MAP (maximum a posteriori)

a. How to start: b. How to continue: c. How to stop: b EAP 2. EAP EAP (expected a posteriori) (posteriori distribution) (θ) MAP (maximum a posteriori) LET 2015 7 (pp. 25 39) EAP EAP (, 2013) (, 2014) PROX (, 2015) (computer-adaptive testing) EAP (expected a posteriori) Keywords: EAP 1. (, 1996, p. 273; Thissen & Mislevy, 2000, p. 101) 25 a. How to start:

More information

現代物理化学 1-1(4)16.ppt

現代物理化学 1-1(4)16.ppt (pdf) pdf pdf http://www1.doshisha.ac.jp/~bukka/lecture/index.html http://www.doshisha.ac.jp/ Duet -1-1-1 2-a. 1-1-2 EU E = K E + P E + U ΔE K E = 0P E ΔE = ΔU U U = εn ΔU ΔU = Q + W, du = d 'Q + d 'W

More information

mains.dvi

mains.dvi 8 Λ MRI.COM 8.1 Mellor and Yamada (198) level.5 8. Noh and Kim (1999) 8.3 Large et al. (1994) K-profile parameterization 8.1 8.1: (MRI.COM ) Mellor and Yamada Noh and Kim KPP (avdsl) K H K B K x (avm)

More information

MUFFIN3

MUFFIN3 MUFFIN - MUltiFarious FIeld simulator for Non-equilibrium system - ( ) MUFFIN WG3 - - JCII, - ( ) - ( ) - ( ) - (JSR) - - MUFFIN sec -3 msec -6 sec GOURMET SUSHI MUFFIN -9 nsec PASTA -1 psec -15 fsec COGNAC

More information

1

1 016 017 6 16 1 1 5 1.1............................................... 5 1................................................... 5 1.3................................................ 5 1.4...............................................

More information

86 7 I ( 13 ) II ( )

86 7 I ( 13 ) II ( ) 10 I 86 II 86 III 89 IV 92 V 2001 93 VI 95 86 7 I 2001 6 12 10 2001 ( 13 ) 10 66 2000 2001 4 100 1 3000 II 1988 1990 1991 ( ) 500 1994 2 87 1 1994 2 1000 1000 1000 2 1994 12 21 1000 700 5 800 ( 97 ) 1000

More information

00 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.... 0........ 0 0 0 0 0 0 0 0 0 0..0..........0 0 0 0 0 0 0 0 0 0 0.... 0........ 0 0 0 0 0 0 0 0 0 0... 0...... 0... 0 0 0 0 0 0..0 0... 0 0 0 0 0.0.....0.

More information

untitled

untitled V. 8 9 9 8.. SI 5 6 7 8 9. - - SI 6 6 6 6 6 6 6 SI -- l -- 6 -- -- 6 6 u 6cod5 6 h5 -oo ch 79 79 85 875 99 79 58 886 9 89 9 959 966 - - NM /6 Nucl Ml SI NM/6/685 85co /./ /h / /6/.6 / /.6 /h o NM o.85

More information

untitled

untitled 1 th 1 th Dec.2006 1 1 th 1 th Dec.2006 103 1 2 EITC 2 1 th 1 th Dec.2006 3 1 th 1 th Dec.2006 2006 4 1 th 1 th Dec.2006 5 1 th 1 th Dec.2006 2 6 1 th 1 th Dec.2006 7 1 th 1 th Dec.2006 3 8 1 th 1 th Dec.2006

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

- - - - - - - - - - - - - - - - - - - - - - - - - -1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -2...2...3...4...4...4...5...6...7...8...

- - - - - - - - - - - - - - - - - - - - - - - - - -1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -2...2...3...4...4...4...5...6...7...8... 取 扱 説 明 書 - - - - - - - - - - - - - - - - - - - - - - - - - -1 - - - - - - - - - - - - - - - - - - - - - - - - - - - - -2...2...3...4...4...4...5...6...7...8...9...11 - - - - - - - - - - - - - - - - -

More information

橡博論表紙.PDF

橡博論表紙.PDF Study on Retaining Wall Design For Circular Deep Shaft Undergoing Lateral Pressure During Construction 2003 3 Study on Retaining Wall Design For Circular Deep Shaft Undergoing Lateral Pressure During Construction

More information

A = A x x + A y y + A, B = B x x + B y y + B, C = C x x + C y y + C..6 x y A B C = A x x + A y y + A B x B y B C x C y C { B = A x x + A y y + A y B B

A = A x x + A y y + A, B = B x x + B y y + B, C = C x x + C y y + C..6 x y A B C = A x x + A y y + A B x B y B C x C y C { B = A x x + A y y + A y B B 9 7 A = A x x + A y y + A, B = B x x + B y y + B, C = C x x + C y y + C..6 x y A B C = A x x + A y y + A B x B y B C x C y C { B = A x x + A y y + A y B B x x B } B C y C y + x B y C x C C x C y B = A

More information

1 1.1 / Fik Γ= D n x / Newton Γ= µ vx y / Fouie Q = κ T x 1. fx, tdx t x x + dx f t = D f x 1 fx, t = 1 exp x 4πDt 4Dt lim fx, t =δx 3 t + dxfx, t = 1

1 1.1 / Fik Γ= D n x / Newton Γ= µ vx y / Fouie Q = κ T x 1. fx, tdx t x x + dx f t = D f x 1 fx, t = 1 exp x 4πDt 4Dt lim fx, t =δx 3 t + dxfx, t = 1 1 1.1......... 1............. 1.3... 1.4......... 1.5.............. 1.6................ Bownian Motion.1.......... Einstein.............. 3.3 Einstein........ 3.4..... 3.5 Langevin Eq.... 3.6................

More information

第1部 一般的コメント

第1部 一般的コメント (( 2000 11 24 2003 12 31 3122 94 2332 508 26 a () () i ii iii iv (i) (ii) (i) (ii) (iii) (iv) (a) (b)(c)(d) a) / (i) (ii) (iii) (iv) 1996 7 1996 12

More information

...J......1803.QX

...J......1803.QX 5 7 9 11 13 15 17 19 21 23 45-1111 48-2314 1 I II 100,000 80,000 60,000 40,000 20,000 0 272,437 80,348 82,207 81,393 82,293 83,696 84,028 82,232 248,983 80,411 4,615 4,757 248,434 248,688 76,708 6,299

More information

表1票4.qx4

表1票4.qx4 iii iv v 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 22 23 10 11 24 25 26 27 10 56 28 11 29 30 12 13 14 15 16 17 18 19 2010 2111 22 23 2412 2513 14 31 17 32 18 33 19 34 20 35 21 36 24 37 25 38 2614

More information

第1章 国民年金における無年金

第1章 国民年金における無年金 1 2 3 4 ILO ILO 5 i ii 6 7 8 9 10 ( ) 3 2 ( ) 3 2 2 2 11 20 60 12 1 2 3 4 5 6 7 8 9 10 11 12 13 13 14 15 16 17 14 15 8 16 2003 1 17 18 iii 19 iv 20 21 22 23 24 25 ,,, 26 27 28 29 30 (1) (2) (3) 31 1 20

More information

6 12 10661 93100 227213202 222208197 85kg cm 20 64.521 106856142 2 1 4 3 9767 100 35 cm 7747 208198 90kg 23 5828 10661 93100 cm 227213202 10639 61 64.521 85kg 78kg 70kg 61 100 197204.5 cm 15 61

More information

<82D282A982C1746F95F18D908F57967B95B E696E6464>

<82D282A982C1746F95F18D908F57967B95B E696E6464> 1 2 (90cm 70cm 2015) 3 (68cm 28cm 30cm 12kg 2015) (77.5 109.5cm 2015) 4 (22cm 50cm 50cm 4.6kg 2015) (45cm 62.5cm 2015) (47.4cm 62.5cm 2014) 5 (28.5cm 23.5cm) (45cm 62cm 2015) (97cm 107cm 2015) 6 7 8 9

More information

120 9 I I 1 I 2 I 1 I 2 ( a) ( b) ( c ) I I 2 I 1 I ( d) ( e) ( f ) 9.1: Ampère (c) (d) (e) S I 1 I 2 B ds = µ 0 ( I 1 I 2 ) I 1 I 2 B ds =0. I 1 I 2

120 9 I I 1 I 2 I 1 I 2 ( a) ( b) ( c ) I I 2 I 1 I ( d) ( e) ( f ) 9.1: Ampère (c) (d) (e) S I 1 I 2 B ds = µ 0 ( I 1 I 2 ) I 1 I 2 B ds =0. I 1 I 2 9 E B 9.1 9.1.1 Ampère Ampère Ampère s law B S µ 0 B ds = µ 0 j ds (9.1) S rot B = µ 0 j (9.2) S Ampère Biot-Savart oulomb Gauss Ampère rot B 0 Ampère µ 0 9.1 (a) (b) I B ds = µ 0 I. I 1 I 2 B ds = µ 0

More information

- 2 -

- 2 - - 2 - - 3 - (1) (2) (3) (1) - 4 - ~ - 5 - (2) - 6 - (1) (1) - 7 - - 8 - (i) (ii) (iii) (ii) (iii) (ii) 10 - 9 - (3) - 10 - (3) - 11 - - 12 - (1) - 13 - - 14 - (2) - 15 - - 16 - (3) - 17 - - 18 - (4) -

More information

2 1980 8 4 4 4 4 4 3 4 2 4 4 2 4 6 0 0 6 4 2 4 1 2 2 1 4 4 4 2 3 3 3 4 3 4 4 4 4 2 5 5 2 4 4 4 0 3 3 0 9 10 10 9 1 1

2 1980 8 4 4 4 4 4 3 4 2 4 4 2 4 6 0 0 6 4 2 4 1 2 2 1 4 4 4 2 3 3 3 4 3 4 4 4 4 2 5 5 2 4 4 4 0 3 3 0 9 10 10 9 1 1 1 1979 6 24 3 4 4 4 4 3 4 4 2 3 4 4 6 0 0 6 2 4 4 4 3 0 0 3 3 3 4 3 2 4 3? 4 3 4 3 4 4 4 4 3 3 4 4 4 4 2 1 1 2 15 4 4 15 0 1 2 1980 8 4 4 4 4 4 3 4 2 4 4 2 4 6 0 0 6 4 2 4 1 2 2 1 4 4 4 2 3 3 3 4 3 4 4

More information

180 140 22

180 140 22 21 180 140 22 23 25 50 1 3 350 140 500cm 600 140 24 25 26 27 28 29 30 31 1/12 8.3 1/15 6.7 10 1/8 12.5 1/20 140 90 75 150 60 150 10 30 15 35 2,000 30 32 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 % 100 50 33.3

More information

1 (1) (2)

1 (1) (2) 1 2 (1) (2) (3) 3-78 - 1 (1) (2) - 79 - i) ii) iii) (3) (4) (5) (6) - 80 - (7) (8) (9) (10) 2 (1) (2) (3) (4) i) - 81 - ii) (a) (b) 3 (1) (2) - 82 - - 83 - - 84 - - 85 - - 86 - (1) (2) (3) (4) (5) (6)

More information

新入_本文.smd

新入_本文.smd 52 28 220 28 4 1 017-777-1511 2 2 8 2 9 8 9 47.2% 12.8% 11.5% 6.0% 4 2 (49.6%)(13.0%) (14.7%) (7.4%)(8.4%) (52.3%)(9.1%) (11.4%) (10.0%) 33.0% 23.4% 15.6% 9.6% (26.0%) (18.3%) (46.5%) (30.0%) (20.0%) 2

More information

…K…E…X„^…x…C…W…A…fi…l…b…g…‘†[…N‡Ì“‚¢−w‘K‡Ì‹ê™v’«‡É‡Â‡¢‡Ä

…K…E…X„^…x…C…W…A…fi…l…b…g…‘†[…N‡Ì“‚¢−w‘K‡Ì‹ê™v’«‡É‡Â‡¢‡Ä 2009 8 26 1 2 3 ARMA 4 BN 5 BN 6 (Ω, F, µ) Ω: F Ω σ 1 Ω, ϕ F 2 A, B F = A B, A B, A\B F F µ F 1 µ(ϕ) = 0 2 A F = µ(a) 0 3 A, B F, A B = ϕ = µ(a B) = µ(a) + µ(b) µ(ω) = 1 X : µ X : X x 1,, x n X (Ω) x 1,,

More information

C p (.2 C p [[T ]] Bernoull B n,χ C p p q p 2 q = p p = 2 q = 4 ω Techmüller a Z p ω(a a ( mod q φ(q ω(a Z p a pz p ω(a = 0 Z p φ Euler Techmüller ω Q

C p (.2 C p [[T ]] Bernoull B n,χ C p p q p 2 q = p p = 2 q = 4 ω Techmüller a Z p ω(a a ( mod q φ(q ω(a Z p a pz p ω(a = 0 Z p φ Euler Techmüller ω Q p- L- [Iwa] [Iwa2] -Leopoldt [KL] p- L-. Kummer Remann ζ(s Bernoull B n (. ζ( n = B n n, ( n Z p a = Kummer [Kum] ( Kummer p m n 0 ( mod p m n a m n ( mod (p p a ( p m B m m ( pn B n n ( mod pa Z p Kummer

More information

http://know-star.com/ 3 1 7 1.1................................. 7 1.2................................ 8 1.3 x n.................................. 8 1.4 e x.................................. 10 1.5 sin

More information

Donaldson Seiberg-Witten [GNY] f U U C 1 f(z)dz = Res f(a) 2πi C a U U α = f(z)dz dα = 0 U f U U P 1 α 0 a P 1 Res a α = 0. P 1 Donaldson Seib

Donaldson Seiberg-Witten [GNY] f U U C 1 f(z)dz = Res f(a) 2πi C a U U α = f(z)dz dα = 0 U f U U P 1 α 0 a P 1 Res a α = 0. P 1 Donaldson Seib ( ) Donaldson Seiberg-Witten Witten Göttsche [GNY] L. Göttsche, H. Nakajima and K. Yoshioka, Donaldson = Seiberg-Witten from Mochizuki s formula and instanton counting, Publ. of RIMS, to appear Donaldson

More information

> > <., vs. > x 2 x y = ax 2 + bx + c y = 0 2 ax 2 + bx + c = 0 y = 0 x ( x ) y = ax 2 + bx + c D = b 2 4ac (1) D > 0 x (2) D = 0 x (3

> > <., vs. > x 2 x y = ax 2 + bx + c y = 0 2 ax 2 + bx + c = 0 y = 0 x ( x ) y = ax 2 + bx + c D = b 2 4ac (1) D > 0 x (2) D = 0 x (3 13 2 13.0 2 ( ) ( ) 2 13.1 ( ) ax 2 + bx + c > 0 ( a, b, c ) ( ) 275 > > 2 2 13.3 x 2 x y = ax 2 + bx + c y = 0 2 ax 2 + bx + c = 0 y = 0 x ( x ) y = ax 2 + bx + c D = b 2 4ac (1) D >

More information

ÿþ

ÿþ I O 01 II O III IV 02 II O 03 II O III IV III IV 04 II O III IV III IV 05 II O III IV 06 III O 07 III O 08 III 09 O III O 10 IV O 11 IV O 12 V O 13 V O 14 V O 15 O ( - ) ( - ) 16 本 校 志 望 の 理 由 入 学 後 の

More information

Japan Research Review 1998年7月号

Japan 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 information

学習内容と日常生活との関連性の研究-第2部-第6章

学習内容と日常生活との関連性の研究-第2部-第6章 378 379 10% 10%10% 10% 100% 380 381 2000 BSE CJD 5700 18 1996 2001 100 CJD 1 310-7 10-12 10-6 CJD 100 1 10 100 100 1 1 100 1 10-6 1 1 10-6 382 2002 14 5 1014 10 10.4 1014 100 110-6 1 383 384 385 2002 4

More information

Γ Ec Γ V BIAS THBV3_0401JA THBV3_0402JAa THBV3_0402JAb 1000 800 600 400 50 % 25 % 200 100 80 60 40 20 10 8 6 4 10 % 2.5 % 0.5 % 0.25 % 2 1.0 0.8 0.6 0.4 0.2 0.1 200 300 400 500 600 700 800 1000 1200 14001600

More information

d dt A B C = A B C d dt x = Ax, A 0 B 0 C 0 = mm 0 mm 0 mm AP = PΛ P AP = Λ P A = ΛP P d dt x = P Ax d dt (P x) = Λ(P x) d dt P x =

d dt A B C = A B C d dt x = Ax, A 0 B 0 C 0 = mm 0 mm 0 mm AP = PΛ P AP = Λ P A = ΛP P d dt x = P Ax d dt (P x) = Λ(P x) d dt P x = 3 MATLAB Runge-Kutta Butcher 3. Taylor Taylor y(x 0 + h) = y(x 0 ) + h y (x 0 ) + h! y (x 0 ) + Taylor 3. Euler, Runge-Kutta Adams Implicit Euler, Implicit Runge-Kutta Gear y n+ y n (n+ ) y n+ y n+ y n+

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