日本労働研究機構『IT活用企業についての実態調査』及び
|
|
- きみかず にかどり
- 5 years ago
- Views:
Transcription
1 March 2007
2
3 JGSS NFRJ
4
5 B SSJDA 2 NFRJ 98SSM95 NFRJ98 NFRJ03 NFRJ (( ( B
6 2006 COE
7
8
9
10
11
12
13
14
15
16
17
18 1
19
20 1
21
22
23
24
25
26
27 - -
28
29
30
31 [ P( t + t > T t T t) t] h( t) = lim / t 0 h( t) = h h ln h0 0 () t exp( bi X i ) () t = bi X i = a + b X + + bk X k () t
32 15
33
34 Z Z ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** * * * ** ** ** ** (4) (6) (14) (5) (7) (15)
35
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
61
62
63
64
65 1
66
67
68
69
70
71
72
73
74
75
76
77 モデル 1 モデル 2 きょうだい女性のみ ** (-2.72) 出生年 ** ** (-3.17) (-2.82) 14 大都市圏 ref ref 10 万人以上都市 0.974** (-0.30) 10 万人未満都市 1.165* 町村 1.194** (-0.10) 切片 ** ** N カイ二乗値 *** 9.202
78 妻 夫 継承 非継承 無配偶 継承 非継承 無配偶 近接ポイント ( 父 ) 近接ポイント ( 母 ) 近接ポイント ( 義父 ) 近接ポイント ( 義母 ) 金銭的サポート ( 両方向 ) 金銭的サポート ( 親へ ) 金銭的サポート ( 親から ) 非金銭的サポート ( 両方向 ) 非金銭的サポート ( 親へ ) 非金銭的サポート ( 親から )
79
80 N 第 1 子性別 第 2 子性別 第 3 子性別 第 4 子性別 長子出生年中央値 402 F M M M M F F F F F M M M F F M M F M F M F F F F F M F M M M M F F F M F F F F M M M F M F M F M M F M M F F F M M F F F M F M M F F M F M F F F F M F F M M M F F M M M M M M F M M F M F M M F M
81
82
83 性別 継承率 第 1 子 第 2 子 第 3 子 N 推計 1 累積 % 推計 3 累積 % F % % F F % % F F F % % F F M % % F M % % M F % % F M F % % M F F % % M M % % M % % F M M % % M F M % % M M F % % M M M % %
84 対象 継承成功率 全体 89 3 人以下 無配偶継承率が 人以下 無配偶継承率が 人以下 無配偶継承率が 人以下 無配偶継承率が
85
86
87
88
89 1
90 (2000-SSM SSM85,95 Raymo&Xie(2000) (N=231) (N=1,055) (1) (2) (3) (1) (2) (3) (1) (2) (3) (N=807) (N=1,745) (1) (2) (3) (1) (2) (3) (1) (2) (3) (N=778) (N=1,361) (N=1,840) (1) (2) (3) (1) (2) (3) (1) (2) (3) (1) (2) (3) (N=356) (N=1,343) (1) (2) (3) (1) (2) (3) (1) (2) (3)
91 Raymo&Xie(2000) log e Fijk = H i W j C k HC ik WC jk HW ij C k
92 G 2 df p -value BIC N=2,172 1 WC+HC WC+HC+WH WC+HC+δ WC+HC+δ*C WC+HC+δ*φ c WC+HC+δ*φ(1+βX) N=4,161 1 WC+HC WC+HC+WH WC+HC+δ WC+HC+δ*C WC+HC+δ*φ c WC+HC+δ*φ(1+βX) Raymo&Xie(2000) N=3,183 1 WC+HC WC+HC+WH WC+HC+δ WC+HC+δ*C WC+HC+δ*φ c
93 3 BICRaftery1995 4
94 SSM JGSS NFRJ A B A B F A B P log = (1) e Fijklm H i W j C k S l D m HCSD iklm WCSD jklm HW ij log = (2) e Fijklm H i W j C k S l D m HCSD iklm WCSD jklm HW ij C k log e F ijklm H W C S D HCSD WCSD HW = + i + j + k + l + m + iklm + jklm + ij (1 + 1X ) (3)
95 log e F ijklm H W C S D HCSD WCSD HW 2 = (1 + X + ) (4) i j k l m iklm jklm ij 1 2 X G 2 df BIC M1: M2: M3: M4: M1 vs M M3 vs M M4 vs M M3 vs M SSM JGSS 2NFRJ98 3 NFRJ HCSHCD 3 CS 2 4 HCSD
96 % AIC 1 8 Cox1972Yamaguchi S01
97 N Model χ df 7 7 5% 16
98 2006 General Social SurveysJGSS SSJ
99
100
1 1.1 Excel Excel Excel log 1, log 2, log 3,, log 10 e = ln 10 log cm 1mm 1 10 =0.1mm = f(x) f(x) = n
1 1.1 Excel Excel Excel log 1, log, log,, log e.7188188 ln log 1. 5cm 1mm 1 0.1mm 0.1 4 4 1 4.1 fx) fx) n0 f n) 0) x n n! n + 1 R n+1 x) fx) f0) + f 0) 1! x + f 0)! x + + f n) 0) x n + R n+1 x) n! 1 .
More information1 0/1, a/b/c/ {0, 1} S = {s 1, s 2,..., s q } S x = X 1 X 2 X 3 X n S (n = 1, 2, 3,...) n n s i P (X n = s i ) X m (m < n) P (X n = s i X n 1 = s j )
(Communication and Network) 1 1 0/1, a/b/c/ {0, 1} S = {s 1, s 2,..., s q } S x = X 1 X 2 X 3 X n S (n = 1, 2, 3,...) n n s i P (X n = s i ) X m (m < n) P (X n = s i X n 1 = s j ) p i = P (X n = s i )
More informationJA2008
A1 1 10 vs 3 2 1 3 2 0 3 2 10 2 0 0 2 1 0 3 A2 3 11 vs 0 4 4 0 0 0 0 0 3 6 0 1 4 x 11 A3 5 4 vs 5 6 5 1 0 0 3 0 4 6 0 0 1 0 4 5 A4 7 11 vs 2 8 8 2 0 0 0 0 2 7 2 7 0 2 x 11 A5 9 5 vs 3 10 9 4 0 1 0 0 5
More informationMicrosoft Word - 坂本様本文確定
,., 2000-2005.,,,,.,,,.,.,.,,. I.,, 1986. 7 1, 25% 2007a: 2006.,, 90 , 2005: 2004: 1999.,,. 2000,, 2008: 2006. 30,, 2005 4 2011 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% 4.7 5.7 6.1 8.2 34.6 32.3 32.0 25.2 35.7
More information‚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 informationSFGÇÃÉXÉyÉNÉgÉãå`.pdf
SFG 1 SFG SFG I SFG (ω) χ SFG (ω). SFG χ χ SFG (ω) = χ NR e iϕ +. ω ω + iγ SFG φ = ±π/, χ φ = ±π 3 χ SFG χ SFG = χ NR + χ (ω ω ) + Γ + χ NR χ (ω ω ) (ω ω ) + Γ cosϕ χ NR χ Γ (ω ω ) + Γ sinϕ. 3 (θ) 180
More informationII 1 II 2012 II Gauss-Bonnet II
II 1 II 212 II Gauss-Bonnet II 1 1 1.1......................................... 1 1.2............................................ 2 1.3.................................. 3 1.4.............................................
More informationスライド 1
61 SAS SAS LOHAS 18 18 12 01 LOHAS ( ) ( ) LOHAS 29% 35% LOHAS LOHAS 18 5 20 60 GMO 500 Yes No Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Q23 Q24 LOHAS Q25 LOHAS / 2
More information厚生年金保険標準報酬月額保険料額表
1 1,000 円 1,000 円以上 68.87 円 137.74 円 18.5 円 18.5 円 37 円 87.37 円 87.37 円 174.74 円 2 2,000 円 2,000 円以上 3,000 円未満 137.74 275.48 37. 37. 74 174.74 174.74 349.48 3 3,000 円 3,000 円以上 4,000 円未満 206.61 413.22
More information厚生年金保険標準報酬月額保険料額表
1 1,000 円 1,000 円以上 70.64 円 141.28 円 18.5 円 18.5 円 37 円 89.14 円 89.14 円 178.28 円 2 2,000 円 2,000 円以上 3,000 円未満 141.28 282.56 37. 37. 74 178.28 178.28 356.56 3 3,000 円 3,000 円以上 4,000 円未満 211.92 423.84
More information2/24
Dec. 18 20, 2006 in DEX-SMI 2006 DC http://www.smapip.is.tohou.ac.jp/ jun/ in collaboration with M. Yasuda and K. Tanaa 1/24 2/24 scientific papers scientists Glucose 2 Lactate 2 ATP 2-Triose-P 2 P 2 NAD
More informationD = [a, b] [c, d] D ij P ij (ξ ij, η ij ) f S(f,, {P ij }) S(f,, {P ij }) = = k m i=1 j=1 m n f(ξ ij, η ij )(x i x i 1 )(y j y j 1 ) = i=1 j
6 6.. [, b] [, d] ij P ij ξ ij, η ij f Sf,, {P ij } Sf,, {P ij } k m i j m fξ ij, η ij i i j j i j i m i j k i i j j m i i j j k i i j j kb d {P ij } lim Sf,, {P ij} kb d f, k [, b] [, d] f, d kb d 6..
More informationi Armitage Q. Bonferroni 1 SAS ver9.1.3 version up 2 *1 *2 FWE *3 2.1 vs vs vs 2.2 5µg 10µg 20µg 5µg 10µg 20µg vs 5µg vs 10µg vs 20µg *1 *2 *3 FWE 1
i Armitage Q Boferroi SAS ver93 versio up * * FWE *3 vs vs vs 5µg 0µg 0µg 5µg 0µg 0µg vs 5µg vs 0µg vs 0µg * * *3 FWE 3 A B C D E (i A B C D E (ii A B C D E (iii A B C D E (iv A B C D A < B C D A < B
More information1 s 1 H(s 1 ) N s 1, s,, s N H({s 1,, s N }) = N H(s k ) k=1 Z N =Tr {s1,,s N }e βh({s 1,,s N }) =Tr s1 Tr s Tr sn e β P k H(s k) N = Tr sk e βh(s k)
19 1 14 007 3 1 1 Ising 4.1................................. 4................................... 5 3 9 3.1........................ 9 3................... 9 3.3........................ 11 4 14 4.1 Legendre..............................
More information1 1.1 H = µc i c i + c i t ijc j + 1 c i c j V ijklc k c l (1) V ijkl = V jikl = V ijlk = V jilk () t ij = t ji, V ijkl = V lkji (3) (1) V 0 H mf = µc
013 6 30 BCS 1 1.1........................ 1................................ 3 1.3............................ 3 1.4............................... 5 1.5.................................... 5 6 3 7 4 8
More informationchap9.dvi
9 AR (i) (ii) MA (iii) (iv) (v) 9.1 2 1 AR 1 9.1.1 S S y j = (α i + β i j) D ij + η j, η j = ρ S η j S + ε j (j =1,,T) (1) i=1 {ε j } i.i.d(,σ 2 ) η j (j ) D ij j i S 1 S =1 D ij =1 S>1 S =4 (1) y j =
More information1966 1994 2009 2007 2006 56 司会 50 3 宮原浩二郎 50 3 4 3 11 4 1960 G 650 57 社会学部紀要社会学部創設 50 周年記念 2010 15 1995 司会 50 10 20 50 58 1989 1994 2000 吉川徹 50 私と関西学院大学社会学部 50 59 社会学部紀要社会学部創設 50 周年記念 2010 1985 50 25 1985
More informationPart () () Γ Part ,
Contents a 6 6 6 6 6 6 6 7 7. 8.. 8.. 8.3. 8 Part. 9. 9.. 9.. 3. 3.. 3.. 3 4. 5 4.. 5 4.. 9 4.3. 3 Part. 6 5. () 6 5.. () 7 5.. 9 5.3. Γ 3 6. 3 6.. 3 6.. 3 6.3. 33 Part 3. 34 7. 34 7.. 34 7.. 34 8. 35
More informationmeiji_resume_1.PDF
β β β (q 1,q,..., q n ; p 1, p,..., p n ) H(q 1,q,..., q n ; p 1, p,..., p n ) Hψ = εψ ε k = k +1/ ε k = k(k 1) (x, y, z; p x, p y, p z ) (r; p r ), (θ; p θ ), (ϕ; p ϕ ) ε k = 1/ k p i dq i E total = E
More informationAHPを用いた大相撲の新しい番付編成
5304050 2008/2/15 1 2008/2/15 2 42 2008/2/15 3 2008/2/15 4 195 2008/2/15 5 2008/2/15 6 i j ij >1 ij ij1/>1 i j i 1 ji 1/ j ij 2008/2/15 7 1 =2.01/=0.5 =1.51/=0.67 2008/2/15 8 1 2008/2/15 9 () u ) i i i
More information³ÎΨÏÀ
2017 12 12 Makoto Nakashima 2017 12 12 1 / 22 2.1. C, D π- C, D. A 1, A 2 C A 1 A 2 C A 3, A 4 D A 1 A 2 D Makoto Nakashima 2017 12 12 2 / 22 . (,, L p - ). Makoto Nakashima 2017 12 12 3 / 22 . (,, L p
More information日本労働研究機構『IT活用企業についての実態調査』及び
March 200 1 1 15 29 49 5 59 2000 2007 2007 2007 5 2008 3 B 1 3 2005 20 44 2 1 2 3 1 2 3 2 4 2003 General Social Surveys 2003 5 5 20 49 2005 5 2 2008 3 31 2007 B 2007 2008 3 200418 49 50 2005 30 1990
More information1 8, : 8.1 1, 2 z = ax + by + c ax by + z c = a b +1 x y z c = 0, (0, 0, c), n = ( a, b, 1). f = n i=1 a ii x 2 i + i<j 2a ij x i x j = ( x, A x), f =
1 8, : 8.1 1, z = ax + by + c ax by + z c = a b +1 x y z c = 0, (0, 0, c), n = ( a, b, 1). f = a ii x i + i
More informationN cos s s cos ψ e e e e 3 3 e e 3 e 3 e
3 3 5 5 5 3 3 7 5 33 5 33 9 5 8 > e > f U f U u u > u ue u e u ue u ue u e u e u u e u u e u N cos s s cos ψ e e e e 3 3 e e 3 e 3 e 3 > A A > A E A f A A f A [ ] f A A e > > A e[ ] > f A E A < < f ; >
More informationX G P G (X) G BG [X, BG] S 2 2 2 S 2 2 S 2 = { (x 1, x 2, x 3 ) R 3 x 2 1 + x 2 2 + x 2 3 = 1 } R 3 S 2 S 2 v x S 2 x x v(x) T x S 2 T x S 2 S 2 x T x S 2 = { ξ R 3 x ξ } R 3 T x S 2 S 2 x x T x S 2
More information5 1
5 1 40.6 46.0 2 44.5 53.8 2 NFRJ03 57 90 57 90 28-57 1945 48-57 57 11.7% 2 15.3% 2.7 9.4% 1.6 57 20% 3.8 28.2% 5.4 11%2.1 28-37 12%28-37 2003 2.2% SSM, 2000159-176 28-37 38-47 92 84% 55% 48-57 38-47 91%
More information弾性定数の対称性について
() by T. oyama () ij C ij = () () C, C, C () ij ji ij ijlk ij ij () C C C C C C * C C C C C * * C C C C = * * * C C C * * * * C C * * * * * C () * P (,, ) P (,, ) lij = () P (,, ) P(,, ) (,, ) P (, 00,
More informationstat2_slides-13.key
!2 !3 !4 !5 !6 !7 !8 !9 !10 !11 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
More information10:30 12:00 P.G. vs vs vs 2
1 10:30 12:00 P.G. vs vs vs 2 LOGIT PROBIT TOBIT mean median mode CV 3 4 5 0.5 1000 6 45 7 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
More informationq quark L left-handed lepton. λ Gell-Mann SU(3), a = 8 σ Pauli, i =, 2, 3 U() T a T i 2 Ỹ = 60 traceless tr Ỹ 2 = 2 notation. 2 off-diagonal matrices
Grand Unification M.Dine, Supersymmetry And String Theory: Beyond the Standard Model 6 2009 2 24 by Standard Model Coupling constant θ-parameter 8 Charge quantization. hypercharge charge Gauge group. simple
More information実践交流会 「スポーツ方法Ⅰ サッカーの授業」 高津
1 2008 10 21 1 HP 346 1 2008 2007 2 2007 3 4 2007 5 2007 2 6 2008.4 4 4 7 1 2008 7 8 1 2008 10 17 8 1 50 3 2 11-11 2 3 2 0 3 0 1920 1960 3 2 4 2 2 3 4 2 3 4 2 3 3 4 3 7 6 1 11 27 11 28 2006 5 50 37 2008
More information第2章図式解法
φ φr ( ct φl ( ct φr ( > φl ( < v t v v,v N, NdN, d A, ρ,e d. v m E E (. ρ. : > : < φ t φ E (. ρ φ v ε E,ρ, N ε N A, E, v t A (. R ( ct ( ct R L L R E E E R L R L ( L R EvR, L EvL φ d φ( ξ dξ (. A,E,ρ
More informationMUFFIN3
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 information2 G(k) e ikx = (ik) n x n n! n=0 (k ) ( ) X n = ( i) n n k n G(k) k=0 F (k) ln G(k) = ln e ikx n κ n F (k) = F (k) (ik) n n= n! κ n κ n = ( i) n n k n
. X {x, x 2, x 3,... x n } X X {, 2, 3, 4, 5, 6} X x i P i. 0 P i 2. n P i = 3. P (i ω) = i ω P i P 3 {x, x 2, x 3,... x n } ω P i = 6 X f(x) f(x) X n n f(x i )P i n x n i P i X n 2 G(k) e ikx = (ik) n
More informationall.dvi
38 5 Cauchy.,,,,., σ.,, 3,,. 5.1 Cauchy (a) (b) (a) (b) 5.1: 5.1. Cauchy 39 F Q Newton F F F Q F Q 5.2: n n ds df n ( 5.1). df n n df(n) df n, t n. t n = df n (5.1) ds 40 5 Cauchy t l n mds df n 5.3: t
More information第10章 アイソパラメトリック要素
June 5, 2019 1 / 26 10.1 ( ) 2 / 26 10.2 8 2 3 4 3 4 6 10.1 4 2 3 4 3 (a) 4 (b) 2 3 (c) 2 4 10.1: 3 / 26 8.3 3 5.1 4 10.4 Gauss 10.1 Ω i 2 3 4 Ξ 3 4 6 Ξ ( ) Ξ 5.1 Gauss ˆx : Ξ Ω i ˆx h u 4 / 26 10.2.1
More information2 H23 BioS (i) data d1; input group patno t sex censor; cards;
H BioS (i) data d1; input group patno t sex censor; cards; 0 1 0 0 0 0 1 0 1 1 0 4 4 0 1 0 5 5 1 1 0 6 5 1 1 0 7 10 1 0 0 8 15 0 1 0 9 15 0 1 0 10 4 1 0 0 11 4 1 0 1 1 5 1 0 1 1 7 0 1 1 14 8 1 0 1 15 8
More information, 3, 6 = 3, 3,,,, 3,, 9, 3, 9, 3, 3, 4, 43, 4, 3, 9, 6, 6,, 0 p, p, p 3,..., p n N = p p p 3 p n + N p n N p p p, p 3,..., p n p, p,..., p n N, 3,,,,
6,,3,4,, 3 4 8 6 6................................. 6.................................. , 3, 6 = 3, 3,,,, 3,, 9, 3, 9, 3, 3, 4, 43, 4, 3, 9, 6, 6,, 0 p, p, p 3,..., p n N = p p p 3 p n + N p n N p p p,
More informationA
A 2563 15 4 21 1 3 1.1................................................ 3 1.2............................................. 3 2 3 2.1......................................... 3 2.2............................................
More informationuntitled
. x2.0 0.5 0 0.5.0 x 2 t= 0: : x α ij β j O x2 u I = α x j ij i i= 0 y j = + exp( u ) j v J = β y j= 0 j j o = + exp( v ) 0 0 e x p e x p J j I j ij i i o x β α = = = + +.. 2 3 8 x 75 58 28 36 x2 3 3 4
More information20 4 20 i 1 1 1.1............................ 1 1.2............................ 4 2 11 2.1................... 11 2.2......................... 11 2.3....................... 19 3 25 3.1.............................
More informationp = mv p x > h/4π λ = h p m v Ψ 2 Ψ
II p = mv p x > h/4π λ = h p m v Ψ 2 Ψ Ψ Ψ 2 0 x P'(x) m d 2 x = mω 2 x = kx = F(x) dt 2 x = cos(ωt + φ) mω 2 = k ω = m k v = dx = -ωsin(ωt + φ) dt = d 2 x dt 2 0 y v θ P(x,y) θ = ωt + φ ν = ω [Hz] 2π
More informationNesstar Webview Version Nesstar Webview Nesstar Webview....
Nesstar Webview 2016 1 Version 1.0 1 2 1.1............................... 2 1.2 Nesstar Webview............................. 3 1.3 Nesstar Webview............................... 3 2 4 2.1...............................................
More information* n x 11,, x 1n N(µ 1, σ 2 ) x 21,, x 2n N(µ 2, σ 2 ) H 0 µ 1 = µ 2 (= µ ) H 1 µ 1 µ 2 H 0, H 1 *2 σ 2 σ 2 0, σ 2 1 *1 *2 H 0 H
1 1 1.1 *1 1. 1.3.1 n x 11,, x 1n Nµ 1, σ x 1,, x n Nµ, σ H 0 µ 1 = µ = µ H 1 µ 1 µ H 0, H 1 * σ σ 0, σ 1 *1 * H 0 H 0, H 1 H 1 1 H 0 µ, σ 0 H 1 µ 1, µ, σ 1 L 0 µ, σ x L 1 µ 1, µ, σ x x H 0 L 0 µ, σ 0
More informationall.dvi
5,, Euclid.,..,... Euclid,.,.,, e i (i =,, ). 6 x a x e e e x.:,,. a,,. a a = a e + a e + a e = {e, e, e } a (.) = a i e i = a i e i (.) i= {a,a,a } T ( T ),.,,,,. (.),.,...,,. a 0 0 a = a 0 + a + a 0
More information( ) ( 40 )+( 60 ) Schrödinger 3. (a) (b) (c) yoshioka/education-09.html pdf 1
2009 1 ( ) ( 40 )+( 60 ) 1 1. 2. Schrödinger 3. (a) (b) (c) http://goofy.phys.nara-wu.ac.jp/ yoshioka/education-09.html pdf 1 1. ( photon) ν λ = c ν (c = 3.0 108 /m : ) ɛ = hν (1) p = hν/c = h/λ (2) h
More information,,,,., = (),, (1) (4) :,,,, (1),. (2),, =. (3),,. (4),,,,.. (1) (3), (4).,,., () : = , ( ) : = F 1 + F 2 + F 3 + ( ) : = i Fj j=1 2
6 2 6.1 2 2, 2 5.2 R 2, 2 (R 2, B, µ)., R 2,,., 1, 2, 3,., 1, 2, 3,,. () : = 1 + 2 + 3 + (6.1.1).,,, 1 ,,,,., = (),, (1) (4) :,,,, (1),. (2),, =. (3),,. (4),,,,.. (1) (3), (4).,,., () : = 1 + 2 + 3 +,
More information24 I ( ) 1. R 3 (i) C : x 2 + y 2 1 = 0 (ii) C : y = ± 1 x 2 ( 1 x 1) (iii) C : x = cos t, y = sin t (0 t 2π) 1.1. γ : [a, b] R n ; t γ(t) = (x
24 I 1.1.. ( ) 1. R 3 (i) C : x 2 + y 2 1 = 0 (ii) C : y = ± 1 x 2 ( 1 x 1) (iii) C : x = cos t, y = sin t (0 t 2π) 1.1. γ : [a, b] R n ; t γ(t) = (x 1 (t), x 2 (t),, x n (t)) ( ) ( ), γ : (i) x 1 (t),
More informationNo δs δs = r + δr r = δr (3) δs δs = r r = δr + u(r + δr, t) u(r, t) (4) δr = (δx, δy, δz) u i (r + δr, t) u i (r, t) = u i x j δx j (5) δs 2
No.2 1 2 2 δs δs = r + δr r = δr (3) δs δs = r r = δr + u(r + δr, t) u(r, t) (4) δr = (δx, δy, δz) u i (r + δr, t) u i (r, t) = u i δx j (5) δs 2 = δx i δx i + 2 u i δx i δx j = δs 2 + 2s ij δx i δx j
More information教育収益率の地域差と地域移動効果―JGSS データを用いた所得関数の分析―
JGSS A JGSS Data-based Analysis of Rate of Return to Education: Focusing on the Regional Difference and Migration Kouhei HIRAGI Graduate School of Education The University of Tokyo This paper aims to clarify
More information行列代数2010A
a ij i j 1) i +j i, j) ij ij 1 j a i1 a ij a i a 1 a j a ij 1) i +j 1,j 1,j +1 a i1,1 a i1,j 1 a i1,j +1 a i1, a i +1,1 a i +1.j 1 a i +1,j +1 a i +1, a 1 a,j 1 a,j +1 a, ij i j 1,j 1,j +1 ij 1) i +j a
More information1 1.1 ( ). z = a + bi, a, b R 0 a, b 0 a 2 + b 2 0 z = a + bi = ( ) a 2 + b 2 a a 2 + b + b 2 a 2 + b i 2 r = a 2 + b 2 θ cos θ = a a 2 + b 2, sin θ =
1 1.1 ( ). z = + bi,, b R 0, b 0 2 + b 2 0 z = + bi = ( ) 2 + b 2 2 + b + b 2 2 + b i 2 r = 2 + b 2 θ cos θ = 2 + b 2, sin θ = b 2 + b 2 2π z = r(cos θ + i sin θ) 1.2 (, ). 1. < 2. > 3. ±,, 1.3 ( ). A
More information) ] [ h m x + y + + V x) φ = Eφ 1) z E = i h t 13) x << 1) N n n= = N N + 1) 14) N n n= = N N + 1)N + 1) 6 15) N n 3 n= = 1 4 N N + 1) 16) N n 4
1. k λ ν ω T v p v g k = π λ ω = πν = π T v p = λν = ω k v g = dω dk 1) ) 3) 4). p = hk = h λ 5) E = hν = hω 6) h = h π 7) h =6.6618 1 34 J sec) hc=197.3 MeV fm = 197.3 kev pm= 197.3 ev nm = 1.97 1 3 ev
More information: , 2.0, 3.0, 2.0, (%) ( 2.
2017 1 2 1.1...................................... 2 1.2......................................... 4 1.3........................................... 10 1.4................................. 14 1.5..........................................
More information親の教育意識が家計の教育費負担に及ぼす影響-JGSS-2006データによる分析-
JGSS-6 JGSS Effects of Parental Educational Attitudes on Educational Costs: An Analysis Using JGSS-6 data Mondo TSUMURA JGSS Post-doctoral Fellow, Institute of Regional Studies Osaka University of Commerce
More information( )
7..-8..8.......................................................................... 4.................................... 3...................................... 3..3.................................. 4.3....................................
More information1 Ricci V, V i, W f : V W f f(v ) = Imf W ( ) f : V 1 V k W 1
1 Ricci V, V i, W f : V W f f(v = Imf W ( f : V 1 V k W 1 {f(v 1,, v k v i V i } W < Imf > < > f W V, V i, W f : U V L(U; V f : V 1 V r W L(V 1,, V r ; W L(V 1,, V r ; W (f + g(v 1,, v r = f(v 1,, v r
More information00 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応力とひずみ.ppt
in yukawa@numse.nagoya-u.ac.jp 2 3 4 5 x 2 6 Continuum) 7 8 9 F F 10 F L L F L 1 L F L F L F 11 F L F F L F L L L 1 L 2 12 F L F! A A! S! = F S 13 F L L F F n = F " cos# F t = F " sin# S $ = S cos# S S
More information14 10 15 14 10 15 46.7 14 8 14 3 1 14 8 31 1 14 8 80,956 454,528 2.9 3.4 14,550 2.7 14,227 2.4 13 8 78,658 439,432 14,956 14,571 14 2 153,889 860,441 24,545 23,378 1 ( ) - 1-1 14 8 7,262 6.1 84 28 13 8
More informationII (Percolation) ( 3-4 ) 1. [ ],,,,,,,. 2. [ ],.. 3. [ ],. 4. [ ] [ ] G. Grimmett Percolation Springer-Verlag New-York [ ] 3
II (Percolation) 12 9 27 ( 3-4 ) 1 [ ] 2 [ ] 3 [ ] 4 [ ] 1992 5 [ ] G Grimmett Percolation Springer-Verlag New-York 1989 6 [ ] 3 1 3 p H 2 3 2 FKG BK Russo 2 p H = p T (=: p c ) 3 2 Kesten p c =1/2 ( )
More information,, Poisson 3 3. t t y,, y n Nµ, σ 2 y i µ + ɛ i ɛ i N0, σ 2 E[y i ] µ * i y i x i y i α + βx i + ɛ i ɛ i N0, σ 2, α, β *3 y i E[y i ] α + βx i
Armitage.? SAS.2 µ, µ 2, µ 3 a, a 2, a 3 a µ + a 2 µ 2 + a 3 µ 3 µ, µ 2, µ 3 µ, µ 2, µ 3 log a, a 2, a 3 a µ + a 2 µ 2 + a 3 µ 3 µ, µ 2, µ 3 * 2 2. y t y y y Poisson y * ,, Poisson 3 3. t t y,, y n Nµ,
More information1 No.1 5 C 1 I III F 1 F 2 F 1 F 2 2 Φ 2 (t) = Φ 1 (t) Φ 1 (t t). = Φ 1(t) t = ( 1.5e 0.5t 2.4e 4t 2e 10t ) τ < 0 t > τ Φ 2 (t) < 0 lim t Φ 2 (t) = 0
1 No.1 5 C 1 I III F 1 F 2 F 1 F 2 2 Φ 2 (t) = Φ 1 (t) Φ 1 (t t). = Φ 1(t) t = ( 1.5e 0.5t 2.4e 4t 2e 10t ) τ < 0 t > τ Φ 2 (t) < 0 lim t Φ 2 (t) = 0 0 < t < τ I II 0 No.2 2 C x y x y > 0 x 0 x > b a dx
More information201711grade1ouyou.pdf
2017 11 26 1 2 52 3 12 13 22 23 32 33 42 3 5 3 4 90 5 6 A 1 2 Web Web 3 4 1 2... 5 6 7 7 44 8 9 1 2 3 1 p p >2 2 A 1 2 0.6 0.4 0.52... (a) 0.6 0.4...... B 1 2 0.8-0.2 0.52..... (b) 0.6 0.52.... 1 A B 2
More information日本統計学会誌, 第44巻, 第2号, 251頁-270頁
44, 2, 205 3 25 270 Multiple Comparison Procedures for Checking Differences among Sequence of Normal Means with Ordered Restriction Tsunehisa Imada Lee and Spurrier (995) Lee and Spurrier (995) (204) (2006)
More information( ) 1.1 Polychoric Correlation Polyserial Correlation Graded Response Model Partial Credit Model Tetrachoric Correlation ( ) 2 x y x y s r 1 x 2
1 (,2007) SPSSver8 1997 (2002) 1. 2. polychoric correlation coefficient (polyserial correlation coefficient) 3. (1999) M-plus R 1 ( ) 1.1 Polychoric Correlation Polyserial Correlation Graded Response Model
More information( )/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
( )/2 http://www2.math.kyushu-u.ac.jp/ hara/lectures/lectures-j.html 1 2011 ( )/2 2 2011 4 1 2 1.1 1 2 1 2 3 4 5 1.1.1 sample space S S = {H, T } H T T H S = {(H, H), (H, T ), (T, H), (T, T )} (T, H) S
More informationMicrosoft Word - 表紙.docx
黒住英司 [ 著 ] サピエンティア 計量経済学 訂正および練習問題解答 (206/2/2 版 ) 訂正 練習問題解答 3 .69, 3.8 4 (X i X)U i i i (X i μ x )U i ( X μx ) U i. i E [ ] (X i μ x )U i i E[(X i μ x )]E[U i ]0. i V [ ] (X i μ x )U i i 2 i j E [(X i
More information2009 IA 5 I 22, 23, 24, 25, 26, (1) Arcsin 1 ( 2 (4) Arccos 1 ) 2 3 (2) Arcsin( 1) (3) Arccos 2 (5) Arctan 1 (6) Arctan ( 3 ) 3 2. n (1) ta
009 IA 5 I, 3, 4, 5, 6, 7 6 3. () Arcsin ( (4) Arccos ) 3 () Arcsin( ) (3) Arccos (5) Arctan (6) Arctan ( 3 ) 3. n () tan x (nπ π/, nπ + π/) f n (x) f n (x) fn (x) Arctan x () sin x [nπ π/, nπ +π/] g n
More informationMicrosoft Word - 倫理 第40,43,45,46講 テキスト.docx
6 538 ( 552 ) (1) () (2) () ( )( ) 1 vs () (1) (2) () () () ) ()() (3) () ( () 2 () () () ()( ) () (7) (8) () 3 4 5 abc b c 6 a (a) b b ()() 7 c (c) ()() 8 9 10 () 1 ()()() 2 () 3 1 1052 1051 () 1053 11
More information沖縄観光の推移
(1) 28 28 2 28 876 9,200 83 2,900 10.5 200 28 3 (2) 28 664 100 37 4,100 6.0 212 9,100 45 8,800 27.5 28 4 28 - - 28 5 28 6,602 9,400 580 8,000 9.6 75,297 584 0.8 1.9 4.2 4.6 28 6 (2) 28 74,763 680 0.9 3
More informationLCR e ix LC AM m k x m x x > 0 x < 0 F x > 0 x < 0 F = k x (k > 0) k x = x(t)
338 7 7.3 LCR 2.4.3 e ix LC AM 7.3.1 7.3.1.1 m k x m x x > 0 x < 0 F x > 0 x < 0 F = k x k > 0 k 5.3.1.1 x = xt 7.3 339 m 2 x t 2 = k x 2 x t 2 = ω 2 0 x ω0 = k m ω 0 1.4.4.3 2 +α 14.9.3.1 5.3.2.1 2 x
More information学歴内婚のシミュレーション分析
18 i 1 3 2 5 2.1... 5 2.2... 8 3 1 11 3.1 1... 11 3.2 1... 14 4 2 15 4.1 2... 15 4.2 2... 15 5 3 17 5.1 3... 17 5.2 3... 17 6 19 6.1... 19 6.2... 19 21 1 2.1 1:-1955... 6 2.2 2:1956-70... 6 2.3 3:1971-85...
More informationMorse ( ) 2014
Morse ( ) 2014 1 1 Morse 1 1.1 Morse................................ 1 1.2 Morse.............................. 7 2 12 2.1....................... 12 2.2.................. 13 2.3 Smale..............................
More information211 kotaro@math.titech.ac.jp 1 R *1 n n R n *2 R n = {(x 1,..., x n ) x 1,..., x n R}. R R 2 R 3 R n R n R n D D R n *3 ) (x 1,..., x n ) f(x 1,..., x n ) f D *4 n 2 n = 1 ( ) 1 f D R n f : D R 1.1. (x,
More information物理化学I-第12回(13).ppt
I- 12-1 11 11.1 2Mg(s) + O 2 (g) 2MgO(s) [Mg 2+ O 2 ] Zn(s) + Cu 2+ (aq) Zn 2+ (aq) + Cu(s) - 2Mg(s) 2Mg 2+ (s) + 4e +) O 2 (g) + 4e 2O 2 (s) 2Mg(s) + O 2 (g) 2MgO(s) Zn(s) Zn 2+ (aq) + 2e +) Cu 2+ (aq)
More information親からの住宅援助と親子の居住関係-JGSS-2006 データによる検討-
JGSS-2006 Parental housing assistance as a determinant of parent-child proximity in Japan: Results from the JGSS-2006 Rokuro TABUCHI Faculty of Human Sciences Sophia University Exchange theory and some
More informationSO(3) 49 u = Ru (6.9), i u iv i = i u iv i (C ) π π : G Hom(V, V ) : g D(g). π : R 3 V : i 1. : u u = u 1 u 2 u 3 (6.10) 6.2 i R α (1) = 0 cos α
SO(3) 48 6 SO(3) t 6.1 u, v u = u 1 1 + u 2 2 + u 3 3 = u 1 e 1 + u 2 e 2 + u 3 e 3, v = v 1 1 + v 2 2 + v 3 3 = v 1 e 1 + v 2 e 2 + v 3 e 3 (6.1) i (e i ) e i e j = i j = δ ij (6.2) ( u, v ) = u v = ij
More informationHi-Stat Discussion Paper Series No.248 東京圏における 1990 年代以降の住み替え行動 住宅需要実態調査 を用いた Mixed Logit 分析 小林庸平行武憲史 March 2008 Hitotsubashi University Research Unit
Hi-Stat Discussion Paper Series No.248 東京圏における 1990 年代以降の住み替え行動 住宅需要実態調査 を用いた Logit 分析 小林庸平行武憲史 March 2008 Hitotsubashi University Research Unit for Statistical Analysis in Social Sciences A 21st-Century
More informationt χ 2 F Q t χ 2 F 1 2 µ, σ 2 N(µ, σ 2 ) f(x µ, σ 2 ) = 1 ( exp (x ) µ)2 2πσ 2 2σ 2 0, N(0, 1) (100 α) z(α) t χ 2 *1 2.1 t (i)x N(µ, σ 2 ) x µ σ N(0, 1
t χ F Q t χ F µ, σ N(µ, σ ) f(x µ, σ ) = ( exp (x ) µ) πσ σ 0, N(0, ) (00 α) z(α) t χ *. t (i)x N(µ, σ ) x µ σ N(0, ) (ii)x,, x N(µ, σ ) x = x+ +x N(µ, σ ) (iii) (i),(ii) z = x µ N(0, ) σ N(0, ) ( 9 97.
More information高齢期における幸福感規定要因の男女差について―JGSS-2000/2001統合データに基づく検討―
日本版 General Social Surveys 研究論文集 [6] JGSS で見た日本人の意識と行動 JGSS Research Series No.3 JGSS-2000/2001 Difference between Men's Happiness and Women's Happiness in Later Life An Analysis Based on JGSS Integrated
More information(I) (II) ˆk AIC T ( 47, 1999) C1 C ( : 3 ) Y N ( µ(x a,x b,x c ),σ 2) µ(x a,x b,x c )=β 0 + β a x a + β b x b + β c x c x a,x b,x c
000 7 6 (I) (II) ˆk IC T ( 7, 999) C C.. ( : ) Y N ( µ(x a,x b,x c ),σ ) µ(x a,x b,x c )=β 0 + β a x a + β b x b + β c x c x a,x b,x c. α α {a, b, c} Θ α = {(σ, β) σ >0,β i =0,i α c }, α + C C α M α M
More informationchap10.dvi
. q {y j } I( ( L y j =Δy j = u j = C l ε j l = C(L ε j, {ε j } i.i.d.(,i q ( l= y O p ( {u j } q {C l } A l C l
More informationλ n numbering Num(λ) Young numbering T i j T ij Young T (content) cont T (row word) word T µ n S n µ C(µ) 0.2. Young λ, µ n Kostka K µλ K µλ def = #{T
0 2 8 8 6 3 0 0 Young Young [F] 0.. Young λ n λ n λ = (λ,, λ l ) λ λ 2 λ l λ = ( m, 2 m 2, ) λ = n, l(λ) = l {λ n n 0} P λ = (λ, ), µ = (µ, ) n λ µ k k k λ i µ i λ µ λ = µ k i= i= i < k λ i = µ i λ k >
More informationω 0 m(ẍ + γẋ + ω0x) 2 = ee (2.118) e iωt x = e 1 m ω0 2 E(ω). (2.119) ω2 iωγ Z N P(ω) = χ(ω)e = exzn (2.120) ϵ = ϵ 0 (1 + χ) ϵ(ω) ϵ 0 = 1 +
2.6 2.6.1 ω 0 m(ẍ + γẋ + ω0x) 2 = ee (2.118) e iωt x = e 1 m ω0 2 E(ω). (2.119) ω2 iωγ Z N P(ω) = χ(ω)e = exzn (2.120) ϵ = ϵ 0 (1 + χ) ϵ(ω) ϵ 0 = 1 + Ne2 m j f j ω 2 j ω2 iωγ j (2.121) Z ω ω j γ j f j
More information