時系列解析と自己回帰モデル
|
|
- ふさこ さくもと
- 5 years ago
- Views:
Transcription
1 B L11( Mon) : Time-stamp: Mon 11:04 JST hig,,,.,. ( ) L11 B(2017) 1 / 28
2 L10-Q1 Quiz : , x[]={1,1,3,3,3,8}; (. ) 2 x = 0, 1, 2,..., 9 10, 10. u[]={0,2,0,3,0,0,0,0,1,0}; ( ) L10-Q3 Quiz : ( ) L11 B(2017) 2 / 28
3 i n t sum=0, count =0; f o r ( k=0;k<samplesize ; k++){ sum+=x [ k ] ; i f ( x [ k]<=5) count++; } ex =( double ) sum/samplesize ; px=(double ) count /SAMPLESIZE ; L10-Q4 Quiz : double ex =0.0, px =0.0; f o r ( x =0;x<XMAX; x++){ ex+=p [ x ] x ; i f ( x<=5) px+=p [ x ] ; } ( ) L11 B(2017) 3 / 28
4 L10-Q5 Quiz( ) B R,, 1 cm 2cm, cm. 1 30? cm 125cm., Φ(z) = 1 z 2π e u2 /2 du.. t X(t), X(t + 1) = X(t) + R(t + 1), X(0) = 100. ( ) L11 B(2017) 4 / 28
5 , R(t), { 1/3 ( 1 r < 2) f(r) = 0 ( ).. ( ) L11 B(2017) 5 / 28
6 ( ) L11 B(2017) 6 / 28
7 : : ( ) L11 B(2017) 7 / 28
8 : Time Series Analysis t x(0), x(1), x(2),..., x(t),....,. x(t) t = 0, 1, 2, 3, t X(t).. t T t > T. ( ) R f R (t),. ( ) L11 B(2017) 8 / 28
9 : Moving Average x(t) (smoothing) y(t) (2l + 1). y 2l+1 (t) = 1 2l + 1 t+l t =t l x(t ) x(0) x(1) x(2) x(3) x(4) x(5) x(6) x(7) x(8) x(9) x(10) x 2l y 2l (t) = 1 2l ( 1 2 x(t l+1)+x(t l+2)+ +x(t)+ +x(t+l 2)+ 1 2 x(t+l 1)) x(0) x(1) x(2) x(3) x(4) x(5) x(6) x(7) x(8) x(9) x(10) x ( ) L11 B(2017) 9 / 28
10 : 3 3 x(t) = + + ( ).,,, ( ). ( ) ( ), ( ) ( ). ( ) L11 B(2017) 10 / 28
11 :. ( ) L11 B(2017) 11 / 28
12 : L11-Q1 Quiz( ), 3,4. t x y 3 y 4 ( ) L11 B(2017) 12 / 28
13 : : (covariance) x, y C xy = 1 n Y (,+) (+,+) n (x i x) (y i y) i=1 Y の平均値 (, ) 1.8, 3.6 X の平均値 (+, ) X (+, ) = (x i x, y i y ). ( ) L11 B(2017) 13 / 28
14 Y X Y X Y : X Y X Y X r = C xy s x s y s x, s y : r = 0.99 r = 0.55 r = 0 r = 0.55 r = 0.99 : x y : x y / : / 2 1.8, 3.6 ( ) L11 B(2017) 14 / 28
15 :, k, t, k x(t), x(t k) 2,, k = 1 x y x(1) x(2) x(2 1) x(3) x(3 1).. x(t 1) x(t 1 1) x(t ) x(t 1) x(t ) k x y x(1). x(k + 1) x(1).. x(t ) x(t k). x(t ) ( ) L11 B(2017) 15 / 28
16 : kj autocovariance C(k) = 1 T k T t=k+1 x = 1 T k kj autocorrelation r(k) = (x(t) x)(x(t k) x) T t=k+1 = C(k) C(0) C(0) x(t) T. x(t) ( ) L11 B(2017) 16 / 28
17 : correlogram k, k. ( ), k ( ), r(k)., r(k) =. ( ) L11 B(2017) 17 / 28
18 : L11-Q2 Quiz( ),. ( ) L11 B(2017) 18 / 28
19 10 11 : ( ) L11 B(2017) 19 / 28
20 m =AR Autoregression m AR(m) X(t):, t = 0, 1, 2, 3,... X(t) = m a k X(t k) + R(t) k=1, R(t). E[R(t)] =0, E[R(t)X(s)] =0 (t > s), E[R(t)R(s)] =σ 2 δ t,s = σ 2 { 1 (t = s) 0 ( ) R(t),. ( ) L11 B(2017) 20 / 28
21 AR(1) E[R] = 0, AR(1). a 1 = 1. E[R(t)] = 0, V[R(t)] = σ 2. 1 f o r ( t ){ / / 2 x=x+getrandom ( g e t u n i f o r m ( ) ) ; 3 } AR(1) a 1 = ϕ ( ). E[R(t)] = 0, V[R(t)] = σ 2. 1 f o r ( t ){ / AR( 1 ) / 2 x=p h i x+getrandom ( g e t u n i f o r m ( ) ) ; 3 } ( ) L11 B(2017) 21 / 28
22 L11-Q3 Quiz(AR(1) ) AR(1) X(t + 1) = ϕ X(t) + R(t + 1), R(t). ϕ = 1. 1 X(2) X(0), R(1), R(2). 2 X(0) = a,, P (X(0) = a) = 1., R(t) N(0, σ 2 ) (AR(1) )., X(2). 3 (2), X(t) (t 1). ( ) L11 B(2017) 22 / 28
23 ( ) L11 B(2017) 23 / 28 X(t) E[X(t)] t E[X(t)X(s)] t s, t,., t.
24 ,, µ = E[X(t)]. k C(k) = E[(X(t) µ)(x(t + k) µ)] k = 0 C(0) = E[(X(t) µ) 2 ]=. k r(k) = C(k) C(0). ( ) L11 B(2017) 24 / 28
25 , 1,. :t :. Excel ( ) L11 B(2017) 25 / 28
26 AR(1), X(t) =ϕx(t 1) + R(t) =ϕ(ϕx(t 2) + R(t 1)) + R(t) = = ϕ t X(0) + ϕ t 1 R(1) + ϕr(t 1) + R(t). E[X(t)] =ϕ t E[X(0)]. E[X(t)] = 0. E[X(t)X(t + k)] =E[(ϕ t X(0) + ϕ t 1 R(1) + ϕr(t 1) + R(t)) (ϕ t+k X(0) + ϕ t+k 1 R(1) + + ϕ k+1 R(t 1) + ϕ k R(t) + + R(t + k))] =ϕ 2t+k E[X(0)X(0)] + (ϕ 2t+k 2 + ϕ 2t+k 4 + ϕ k )σ 2 E[X(t)X(t)] =ϕ 2t E[X(0)X(0)] + (ϕ 2t 2 + ϕ 2t ϕ 0 )σ 2. r(k) = E[X(t)X(t + k)] E[X(t)X(t)] = ϕ k. ( ) L11 B(2017) 26 / 28
27 AR(1), AR(1) r(k) = a k 1 = ϕ k. X(t), a 1 = ϕ, σ 2.. ϕ = 0.9, σ = 1 ϕ = 0.9, σ = 1 ϕ = 0.2, σ = 1 ϕ = 0.2, σ = 3 ( ) L11 B(2017) 27 / 28
28 (1-502) /Math Learn Math Moodle B (3-B105) ( ) (3-B105) (3-B105) ( ) ( ) ( ) L11 B(2017) 28 / 28
時系列解析
B L12(2016-07-11 Mon) : Time-stamp: 2016-07-11 Mon 17:25 JST hig,, Excel,. http://hig3.net ( ) L12 B(2016) 1 / 24 L11-Q1 Quiz : 1 E[R] = 1 2, V[R] = 9 12 = 3 4. R(t), E[X(30)] = E[X(0)] + 30 1 2 = 115,
More informationランダムウォークの確率の漸化式と初期条件
B L03(2019-04-25 Thu) : Time-stamp: 2019-04-25 Thu 09:16 JST hig X(t), t, t x p(x, t). p(x, t). ( ) L03 B(2019) 1 / 25 : L02-Q1 Quiz : 1 X(3) = 1 10 (3 + 3 + + ( 3)) = 1., E[X(3)] 1. 2 S 2 = 1 10 1 ((3
More information曲面のパラメタ表示と接線ベクトル
L11(2011-07-06 Wed) :Time-stamp: 2011-07-06 Wed 13:08 JST hig 1,,. 2. http://hig3.net () (L11) 2011-07-06 Wed 1 / 18 ( ) 1 V = (xy2 ) x + (2y) y = y 2 + 2. 2 V = 4y., D V ds = 2 2 ( ) 4 x 2 4y dy dx =
More informationランダムウォークの境界条件・偏微分方程式の数値計算
B L06(2018-05-22 Tue) : Time-stamp: 2018-05-22 Tue 21:53 JST hig,, 2, multiply transf http://hig3.net L06 B(2018) 1 / 38 L05-Q1 Quiz : 1 M λ 1 = 1 u 1 ( ). M u 1 = u 1, u 1 = ( 3 4 ) s (s 0)., u 1 = 1
More informationマルコフ連鎖の時間発展の数値計算
B L07(206-05-2 Mon : Time-stamp: 206-05-2 Mon 8:4 JST hig http://hig.net ( L07 B(206 / 20 L05-Q TA Prob and Sol:, {, 2}. M = ( 2 2 2.. 2 p(0 = ( 0 p(t. p(0 = 2 ( p(t. ( L07 B(206 2 / 20 M λ, λ 2, u, u
More information疎な転置推移確率行列
B E05(2019-05-15 Tue) : Time-stamp: 2019-05-17 Fri 16:18 JST hig http://hig3.net ( ) E05 B(2019) 1 / 11 x = 0,..., m 1 m. p(t), p(x, t) 1 double p [m] = { 1. 0, 0. 0,...., 0. 0 } ; /. m. / 2 / {p ( 0,
More information独立性の検定・ピボットテーブル
II L04(2016-05-12 Thu) : Time-stamp: 2016-05-12 Thu 12:48 JST hig 2, χ 2, V Excel http://hig3.net ( ) L04 II(2016) 1 / 20 L03-Q1 Quiz : 1 { 0.95 (y = 10) P (Y = y X = 1) = 0.05 (y = 20) { 0.125 (y = 10)
More information分散分析・2次元正規分布
2 II L10(2016-06-30 Thu) : Time-stamp: 2016-06-30 Thu 13:55 JST hig F 2.. http://hig3.net ( ) L10 2 II(2016) 1 / 24 F 2 F L09-Q1 Quiz :F 1 α = 0.05, 2 F 3 H 0, : σ 2 1 /σ2 2 = 1., H 1, σ 2 1 /σ2 2 1. 4
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 informationカテゴリ変数と独立性の検定
II L04(2015-05-01 Fri) : Time-stamp: 2015-05-01 Fri 22:28 JST hig 2, Excel 2, χ 2,. http://hig3.net () L04 II(2015) 1 / 20 : L03-S1 Quiz : 1 2 7 3 12 (x = 2) 12 (y = 3) P (X = x) = 5 12 (x = 3), P (Y =
More informationデータの分布
I L01(2016-09-22 Thu) : Time-stamp: 2016-09-27 Tue 11:12 JST hig e LINE@, 4, http://hig3.net () L01 I(2016) 1 / 20 ? 1? 2? () L01 I(2016) 2 / 20 ?,,.,., 1..,. (,, 1, 2 ),.,. () L01 I(2016) 3 / 20 ? I.
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 informationDVIOUT-ar
1 4 μ=0, σ=1 5 μ=2, σ=1 5 μ=0, σ=2 3 2 1 0-1 -2-3 0 10 20 30 40 50 60 70 80 90 4 3 2 1 0-1 0 10 20 30 40 50 60 70 80 90 4 3 2 1 0-1 -2-3 -4-5 0 10 20 30 40 50 60 70 80 90 8 μ=2, σ=2 5 μ=1, θ 1 =0.5, σ=1
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 informationGmech08.dvi
63 6 6.1 6.1.1 v = v 0 =v 0x,v 0y, 0) t =0 x 0,y 0, 0) t x x 0 + v 0x t v x v 0x = y = y 0 + v 0y t, v = v y = v 0y 6.1) z 0 0 v z yv z zv y zv x xv z xv y yv x = 0 0 x 0 v 0y y 0 v 0x 6.) 6.) 6.1) 6.)
More informationmugensho.dvi
1 1 f (t) lim t a f (t) = 0 f (t) t a 1.1 (1) lim(t 1) 2 = 0 t 1 (t 1) 2 t 1 (2) lim(t 1) 3 = 0 t 1 (t 1) 3 t 1 2 f (t), g(t) t a lim t a f (t) g(t) g(t) f (t) = o(g(t)) (t a) = 0 f (t) (t 1) 3 1.2 lim
More information2 Excel =sum( ) =average( ) B15:D20 : $E$26 E26 $ =A26*$E$26 $ $E26 E$26 E$26 $G34 $ E26 F4
1234567 0.1234567 = 2 3 =2+3 =2-3 =2*3 =2/3 =2^3 1:^, 2:*/, 3:+- () =2+3*4 =(2+3)*4 =3*2^2 =(3*2)^2 =(3+6)^0.5 A12 =A12+B12 ( ) ( )0.4 ( 100)0.9 % 1 2 Excel =sum( ) =average( ) B15:D20 : $E$26 E26 $ =A26*$E$26
More informationrenshumondai-kaito.dvi
3 1 13 14 1.1 1 44.5 39.5 49.5 2 0.10 2 0.10 54.5 49.5 59.5 5 0.25 7 0.35 64.5 59.5 69.5 8 0.40 15 0.75 74.5 69.5 79.5 3 0.15 18 0.90 84.5 79.5 89.5 2 0.10 20 1.00 20 1.00 2 1.2 1 16.5 20.5 12.5 2 0.10
More information5 n P j j (P i,, P k, j 1) 1 n n ) φ(n) = n (1 1Pj [ ] φ φ P j j P j j = = = = = n = φ(p j j ) (P j j P j 1 j ) P j j ( 1 1 P j ) P j j ) (1 1Pj (1 1P
p P 1 n n n 1 φ(n) φ φ(1) = 1 1 n φ(n), n φ(n) = φ()φ(n) [ ] n 1 n 1 1 n 1 φ(n) φ() φ(n) 1 3 4 5 6 7 8 9 1 3 4 5 6 7 8 9 1 4 5 7 8 1 4 5 7 8 10 11 1 13 14 15 16 17 18 19 0 1 3 4 5 6 7 19 0 1 3 4 5 6 7
More information1 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 informationx,, z v = (, b, c) v v 2 + b 2 + c 2 x,, z 1 i = (1, 0, 0), j = (0, 1, 0), k = (0, 0, 1) v 1 = ( 1, b 1, c 1 ), v 2 = ( 2, b 2, c 2 ) v
12 -- 1 4 2009 9 4-1 4-2 4-3 4-4 4-5 4-6 4-7 4-8 4-9 4-10 c 2011 1/(13) 4--1 2009 9 3 x,, z v = (, b, c) v v 2 + b 2 + c 2 x,, z 1 i = (1, 0, 0), j = (0, 1, 0), k = (0, 0, 1) v 1 = ( 1, b 1, c 1 ), v 2
More informationユニセフ表紙_CS6_三.indd
16 179 97 101 94 121 70 36 30,552 1,042 100 700 61 32 110 41 15 16 13 35 13 7 3,173 41 1 4,700 77 97 81 47 25 26 24 40 22 14 39,208 952 25 5,290 71 73 x 99 185 9 3 3 3 8 2 1 79 0 d 1 226 167 175 159 133
More information( 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
( 28) ( ) ( 28 9 22 ) 0 This ote is c 2016, 2017 by Setsuo Taiguchi. It may be used for persoal or classroom purposes, but ot for commercial purposes. i (http://www.stat.go.jp/teacher/c2epi1.htm ) = statistics
More information006 11 8 0 3 1 5 1.1..................... 5 1......................... 6 1.3.................... 6 1.4.................. 8 1.5................... 8 1.6................... 10 1.6.1......................
More information2.5 (Gauss) (flux) v(r)( ) S n S v n v n (1) v n S = v n S = v S, n S S. n n S v S v Minoru TANAKA (Osaka Univ.) I(2012), Sec p. 1/30
2.5 (Gauss) 2.5.1 (flux) v(r)( ) n v n v n (1) v n = v n = v, n. n n v v I(2012), ec. 2. 5 p. 1/30 i (2) lim v(r i ) i = v(r) d. i 0 i (flux) I(2012), ec. 2. 5 p. 2/30 2.5.2 ( ) ( ) q 1 r 2 E 2 q r 1 E
More information6.1 (P (P (P (P (P (P (, P (, P.
(011 30 7 0 ( ( 3 ( 010 1 (P.3 1 1.1 (P.4.................. 1 1. (P.4............... 1 (P.15.1 (P.16................. (P.0............3 (P.18 3.4 (P.3............... 4 3 (P.9 4 3.1 (P.30........... 4 3.
More informationSOWC04....
99 100 101 2004 284 265 260 257 235 225 222 211 207 205 200 197 192 190 183 183 183 183 180 176 171 169 166 165 156 152 149 143 141 141 138 138 136 126 126 125 123 123 122 118 110 109 108 107 107 105 100
More information2.2 h h l L h L = l cot h (1) (1) L l L l l = L tan h (2) (2) L l 2 l 3 h 2.3 a h a h (a, h)
1 16 10 5 1 2 2.1 a a a 1 1 1 2.2 h h l L h L = l cot h (1) (1) L l L l l = L tan h (2) (2) L l 2 l 3 h 2.3 a h a h (a, h) 4 2 3 4 2 5 2.4 x y (x,y) l a x = l cot h cos a, (3) y = l cot h sin a (4) h a
More information広報なんと10月号
2008.10 2 2008.10 3 2008.10 4 2008.10 5 2008.10 6 7 2008.10 8 2008.10 9 2008.10 2008.10 10 2008.10 11 12 2008.10 13 2008.10 2008.10 14 15 2008.10 16 2008.10 2008.10 17 18 2008.10 STAMP 2008.10 19 2008.10
More information6.1 (P (P (P (P (P (P (, P (, P.101
(008 0 3 7 ( ( ( 00 1 (P.3 1 1.1 (P.3.................. 1 1. (P.4............... 1 (P.15.1 (P.15................. (P.18............3 (P.17......... 3.4 (P................ 4 3 (P.7 4 3.1 ( P.7...........
More information画像工学特論
.? (x i, y i )? (x(t), y(t))? (x(t)) (X(ω)) Wiener-Khintchine 35/97 . : x(t) = X(ω)e jωt dω () π X(ω) = x(t)e jωt dt () X(ω) S(ω) = lim (3) ω S(ω)dω X(ω) : F of x : [X] [ = ] [x t] Power spectral density
More informationseminar0220a.dvi
1 Hi-Stat 2 16 2 20 16:30-18:00 2 2 217 1 COE 4 COE RA E-MAIL: ged0104@srv.cc.hit-u.ac.jp 2004 2 25 S-PLUS S-PLUS S-PLUS S-code 2 [8] [8] [8] 1 2 ARFIMA(p, d, q) FI(d) φ(l)(1 L) d x t = θ(l)ε t ({ε t }
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 informationuntitled
20 7 1 22 7 1 1 2 3 7 8 9 10 11 13 14 15 17 18 19 21 22 - 1 - - 2 - - 3 - - 4 - 50 200 50 200-5 - 50 200 50 200 50 200 - 6 - - 7 - () - 8 - (XY) - 9 - 112-10 - - 11 - - 12 - - 13 - - 14 - - 15 - - 16 -
More informationuntitled
19 1 19 19 3 8 1 19 1 61 2 479 1965 64 1237 148 1272 58 183 X 1 X 2 12 2 15 A B 5 18 B 29 X 1 12 10 31 A 1 58 Y B 14 1 25 3 31 1 5 5 15 Y B 1 232 Y B 1 4235 14 11 8 5350 2409 X 1 15 10 10 B Y Y 2 X 1 X
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 informationR = Ar l B r l. A, B A, B.. r 2 R r = r2 [lar r l B r l2 ]=larl l B r l.2 r 2 R = [lar l l Br ] r r r = ll Ar l ll B = ll R rl.3 sin θ Θ = ll.4 Θsinθ
.3.2 3.3.2 Spherical Coorinates.5: Laplace 2 V = r 2 r 2 x = r cos φ sin θ, y = r sin φ sin θ, z = r cos θ.93 r 2 sin θ sin θ θ θ r 2 sin 2 θ 2 V =.94 2.94 z V φ Laplace r 2 r 2 r 2 sin θ.96.95 V r 2 R
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 informationLLG-R8.Nisus.pdf
d M d t = γ M H + α M d M d t M γ [ 1/ ( Oe sec) ] α γ γ = gµ B h g g µ B h / π γ g = γ = 1.76 10 [ 7 1/ ( Oe sec) ] α α = λ γ λ λ λ α γ α α H α = γ H ω ω H α α H K K H K / M 1 1 > 0 α 1 M > 0 γ α γ =
More information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 information21 2 26 i 1 1 1.1............................ 1 1.2............................ 3 2 9 2.1................... 9 2.2.......... 9 2.3................... 11 2.4....................... 12 3 15 3.1..........
More informationTaro13-第6章(まとめ).PDF
% % % % % % % % 31 NO 1 52,422 10,431 19.9 10,431 19.9 1,380 2.6 1,039 2.0 33,859 64.6 5,713 10.9 2 8,292 1,591 19.2 1,591 19.2 1,827 22.0 1,782 21.5 1,431 17.3 1,661 20.0 3 1,948 1,541 79.1 1,541 79.1
More informationデータの分布と代表値
I L01(2015-09-18 Fri) : Time-stamp: 2015-09-26 Sat 10:37 JST hig e, http://hig3.net ( ) L01 I(2015) 1 / 26 ? 1? 2? ( ) L01 I(2015) 2 / 26 ?,,.,., 1..,. (,, 1, 2 ),.,. ( ) L01 I(2015) 3 / 26 ? I. M (3 )
More informationsyuryoku
248 24622 24 P.5 EX P.212 2 P271 5. P.534 P.690 P.690 P.690 P.690 P.691 P.691 P.691 P.702 P.702 P.702 P.702 1S 30% 3 1S 3% 1S 30% 3 1S 3% P.702 P.702 P.702 P.702 45 60 P.702 P.702 P.704 H17.12.22 H22.4.1
More information土壌環境行政の最新動向(環境省 水・大気環境局土壌環境課)
201022 1 18801970 19101970 19201960 1970-2 1975 1980 1986 1991 1994 3 1999 20022009 4 5 () () () () ( ( ) () 6 7 Ex Ex Ex 8 25 9 10 11 16619 123 12 13 14 5 18() 15 187 1811 16 17 3,000 2241 18 19 ( 50
More informationII A A441 : October 02, 2014 Version : Kawahira, Tomoki TA (Kondo, Hirotaka )
II 214-1 : October 2, 214 Version : 1.1 Kawahira, Tomoki TA (Kondo, Hirotaka ) http://www.math.nagoya-u.ac.jp/~kawahira/courses/14w-biseki.html pdf 1 2 1 9 1 16 1 23 1 3 11 6 11 13 11 2 11 27 12 4 12 11
More informationhttp://www.ns.kogakuin.ac.jp/~ft13389/lecture/physics1a2b/ pdf I 1 1 1.1 ( ) 1. 30 m µm 2. 20 cm km 3. 10 m 2 cm 2 4. 5 cm 3 km 3 5. 1 6. 1 7. 1 1.2 ( ) 1. 1 m + 10 cm 2. 1 hr + 6400 sec 3. 3.0 10 5 kg
More informationユニセフ表紙_CS6_三.indd
16 179 97 101 94 121 70 36 30,552 1,042 100 700 61 32 110 41 15 16 13 35 13 7 3,173 41 1 4,700 77 97 81 47 25 26 24 40 22 14 39,208 952 25 5,290 71 73 x 99 185 9 3 3 3 8 2 1 79 0 d 1 226 167 175 159 133
More informationW u = u(x, t) u tt = a 2 u xx, a > 0 (1) D := {(x, t) : 0 x l, t 0} u (0, t) = 0, u (l, t) = 0, t 0 (2)
3 215 4 27 1 1 u u(x, t) u tt a 2 u xx, a > (1) D : {(x, t) : x, t } u (, t), u (, t), t (2) u(x, ) f(x), u(x, ) t 2, x (3) u(x, t) X(x)T (t) u (1) 1 T (t) a 2 T (t) X (x) X(x) α (2) T (t) αa 2 T (t) (4)
More informationu!4 i!5 o!0!1!2!3
q r w t e y u!4 i!5 o!0!1!2!3 q w e r t y u i q w e r q w e r t y u i o!0!1!2!3!4!5 q w e r t y u i o!0!1!2!3!4!5 q w e r t y u i o!0!1!2!3!4!5 q w e r t y u i o!0!1!2!3!4!5!6 q w e r t y u i o!0!1!2!3!4!5!6
More information1 (1) () (3) I 0 3 I I d θ = L () dt θ L L θ I d θ = L = κθ (3) dt κ T I T = π κ (4) T I κ κ κ L l a θ L r δr δl L θ ϕ ϕ = rθ (5) l
1 1 ϕ ϕ ϕ S F F = ϕ (1) S 1: F 1 1 (1) () (3) I 0 3 I I d θ = L () dt θ L L θ I d θ = L = κθ (3) dt κ T I T = π κ (4) T I κ κ κ L l a θ L r δr δl L θ ϕ ϕ = rθ (5) l : l r δr θ πrδr δf (1) (5) δf = ϕ πrδr
More informationS I. dy fx x fx y fx + C 3 C vt dy fx 4 x, y dy yt gt + Ct + C dt v e kt xt v e kt + C k x v k + C C xt v k 3 r r + dr e kt S Sr πr dt d v } dt k e kt
S I. x yx y y, y,. F x, y, y, y,, y n http://ayapin.film.s.dendai.ac.jp/~matuda n /TeX/lecture.html PDF PS yx.................................... 3.3.................... 9.4................5..............
More informationII No.01 [n/2] [1]H n (x) H n (x) = ( 1) r n! r!(n 2r)! (2x)n 2r. r=0 [2]H n (x) n,, H n ( x) = ( 1) n H n (x). [3] H n (x) = ( 1) n dn x2 e dx n e x2
II No.1 [n/] [1]H n x) H n x) = 1) r n! r!n r)! x)n r r= []H n x) n,, H n x) = 1) n H n x) [3] H n x) = 1) n dn x e dx n e x [4] H n+1 x) = xh n x) nh n 1 x) ) d dx x H n x) = H n+1 x) d dx H nx) = nh
More informationohpr.dvi
2003/12/04 TASK PAF A. Fukuyama et al., Comp. Phys. Rep. 4(1986) 137 A. Fukuyama et al., Nucl. Fusion 26(1986) 151 TASK/WM MHD ψ θ ϕ ψ θ e 1 = ψ, e 2 = θ, e 3 = ϕ ϕ E = E 1 e 1 + E 2 e 2 + E 3 e 3 J :
More information統計的仮説検定とExcelによるt検定
I L14(016-01-15 Fri) : Time-stamp: 016-01-15 Fri 14:03 JST hig 1,,,, p, Excel p, t. http://hig3.net ( ) L14 Excel t I(015) 1 / 0 L13-Q1 Quiz : n = 9. σ 0.95, S n 1 (n 1)
More informationFR 34 316 13 303 54
FR 34 316 13 303 54 23 ( 1 14 ) ( 3 10 ) 8/4 8/ 100% 8 22 7 12 1 9 8 45 25 28 17 19 14 3/1 6/27 5000 8/4 12/2930 1 66 45 43 35 49 25 22 20 23 21 17 13 20 6 1 8 52 1 50 4 11 49 3/4/5 75 6/7/8 46 9/10/11
More informationII (No.2) 2 4,.. (1) (cm) (2) (cm) , (
II (No.1) 1 x 1, x 2,..., x µ = 1 V = 1 k=1 x k (x k µ) 2 k=1 σ = V. V = σ 2 = 1 x 2 k µ 2 k=1 1 µ, V σ. (1) 4, 7, 3, 1, 9, 6 (2) 14, 17, 13, 11, 19, 16 (3) 12, 21, 9, 3, 27, 18 (4) 27.2, 29.3, 29.1, 26.0,
More information18 2 F 12 r 2 r 1 (3) Coulomb km Coulomb M = kg F G = ( ) ( ) ( ) 2 = [N]. Coulomb
r 1 r 2 r 1 r 2 2 Coulomb Gauss Coulomb 2.1 Coulomb 1 2 r 1 r 2 1 2 F 12 2 1 F 21 F 12 = F 21 = 1 4πε 0 1 2 r 1 r 2 2 r 1 r 2 r 1 r 2 (2.1) Coulomb ε 0 = 107 4πc 2 =8.854 187 817 10 12 C 2 N 1 m 2 (2.2)
More information医系の統計入門第 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. このサンプルページの内容は, 第 2 版 1 刷発行時のものです.
医系の統計入門第 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/009192 このサンプルページの内容は, 第 2 版 1 刷発行時のものです. i 2 t 1. 2. 3 2 3. 6 4. 7 5. n 2 ν 6. 2 7. 2003 ii 2 2013 10 iii 1987
More informationxyr x y r x y r u u
xyr x y r x y r u u y a b u a b a b c d e f g u a b c d e g u u e e f yx a b a b a b c a b c a b a b c a b a b c a b c a b c a u xy a b u a b c d a b c d u ar ar a xy u a b c a b c a b p a b a b c a
More informationm dv = mg + kv2 dt m dv dt = mg k v v m dv dt = mg + kv2 α = mg k v = α 1 e rt 1 + e rt m dv dt = mg + kv2 dv mg + kv 2 = dt m dv α 2 + v 2 = k m dt d
m v = mg + kv m v = mg k v v m v = mg + kv α = mg k v = α e rt + e rt m v = mg + kv v mg + kv = m v α + v = k m v (v α (v + α = k m ˆ ( v α ˆ αk v = m v + α ln v α v + α = αk m t + C v α v + α = e αk m
More informationSample function Re random process Flutter, Galloping, etc. ensemble (mean value) N 1 µ = lim xk( t1) N k = 1 N autocorrelation function N 1 R( t1, t1
Sample function Re random process Flutter, Galloping, etc. ensemble (mean value) µ = lim xk( k = autocorrelation function R( t, t + τ) = lim ( ) ( + τ) xk t xk t k = V p o o R p o, o V S M R realization
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 informationgr09.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 information1 発病のとき
A A 1944 19 60 A 1 A 20 40 2 A 4 A A 23 6 A A 13 10 100 2 2 360 A 19 2 5 A A A A A TS TS A A A 194823 6 A A 23 A 361 A 3 2 4 2 16 9 A 7 18 A A 16 4 16 3 362 A A 6 A 6 4 A A 363 A 1 A A 1 A A 364 A 1 A
More informationUntitled
II 14 14-7-8 8/4 II (http://www.damp.tottori-u.ac.jp/~ooshida/edu/fluid/) [ (3.4)] Navier Stokes [ 6/ ] Navier Stokes 3 [ ] Reynolds [ (4.6), (45.8)] [ p.186] Navier Stokes I 1 balance law t (ρv i )+ j
More informationimpulse_response.dvi
5 Time Time Level Level Frequency Frequency Fig. 5.1: [1] 2004. [2] P. A. Nelson, S. J. Elliott, Active Noise Control, Academic Press, 1992. [3] M. R. Schroeder, Integrated-impulse method measuring sound
More informationS I. dy fx x fx y fx + C 3 C dy fx 4 x, y dy v C xt y C v e kt k > xt yt gt [ v dt dt v e kt xt v e kt + C k x v + C C k xt v k 3 r r + dr e kt S dt d
S I.. http://ayapin.film.s.dendai.ac.jp/~matuda /TeX/lecture.html PDF PS.................................... 3.3.................... 9.4................5.............. 3 5. Laplace................. 5....
More informationdy + P (x)y = Q(x) (1) dx dy dx = P (x)y + Q(x) P (x), Q(x) dy y dx Q(x) 0 homogeneous dy dx = P (x)y 1 y dy = P (x) dx log y = P (x) dx + C y = C exp
+ P (x)y = Q(x) (1) = P (x)y + Q(x) P (x), Q(x) y Q(x) 0 homogeneous = P (x)y 1 y = P (x) log y = P (x) + C y = C exp{ P (x) } = C e R P (x) 5.1 + P (x)y = 0 (2) y = C exp{ P (x) } = Ce R P (x) (3) αy
More informationm(ẍ + γẋ + ω 0 x) = ee (2.118) e iωt P(ω) = χ(ω)e = ex = e2 E(ω) m ω0 2 ω2 iωγ (2.119) Z N ϵ(ω) ϵ 0 = 1 + Ne2 m j f j ω 2 j ω2 iωγ j (2.120)
2.6 2.6.1 mẍ + γẋ + ω 0 x) = ee 2.118) e iωt Pω) = χω)e = ex = e2 Eω) m ω0 2 ω2 iωγ 2.119) Z N ϵω) ϵ 0 = 1 + Ne2 m j f j ω 2 j ω2 iωγ j 2.120) Z ω ω j γ j f j f j f j sum j f j = Z 2.120 ω ω j, γ ϵω) ϵ
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 information,. Black-Scholes u t t, x c u 0 t, x x u t t, x c u t, x x u t t, x + σ x u t, x + rx ut, x rux, t 0 x x,,.,. Step 3, 7,,, Step 6., Step 4,. Step 5,,.
9 α ν β Ξ ξ Γ γ o δ Π π ε ρ ζ Σ σ η τ Θ θ Υ υ ι Φ φ κ χ Λ λ Ψ ψ µ Ω ω Def, Prop, Th, Lem, Note, Remark, Ex,, Proof, R, N, Q, C [a, b {x R : a x b} : a, b {x R : a < x < b} : [a, b {x R : a x < b} : a,
More information( ) Loewner SLE 13 February
( ) Loewner SLE 3 February 00 G. F. Lawler, Conformally Invariant Processes in the Plane, (American Mathematical Society, 005)., Summer School 009 (009 8 7-9 ) . d- (BES d ) d B t = (Bt, B t,, Bd t ) (d
More information..3. Ω, Ω F, P Ω, F, P ). ) F a) A, A,..., A i,... F A i F. b) A F A c F c) Ω F. ) A F A P A),. a) 0 P A) b) P Ω) c) [ ] A, A,..., A i,... F i j A i A
.. Laplace ). A... i),. ω i i ). {ω,..., ω } Ω,. ii) Ω. Ω. A ) r, A P A) P A) r... ).. Ω {,, 3, 4, 5, 6}. i i 6). A {, 4, 6} P A) P A) 3 6. ).. i, j i, j) ) Ω {i, j) i 6, j 6}., 36. A. A {i, j) i j }.
More information高知工科大学電子 光システム工学科
卒業研究報告 題 目 量子力学に基づいた水素分子の分子軌道法的取り扱いと Hamiltonian 近似法 指導教員 山本哲也 報告者 山中昭徳 平成 14 年 月 5 日 高知工科大学電子 光システム工学科. 3. 4.1 4. 4.3 4.5 6.6 8.7 10.8 11.9 1.10 1 3. 13 3.113 3. 13 3.3 13 3.4 14 3.5 15 3.6 15 3.7 17
More informationThe Physics of Atmospheres CAPTER :
The Physics of Atmospheres CAPTER 4 1 4 2 41 : 2 42 14 43 17 44 25 45 27 46 3 47 31 48 32 49 34 41 35 411 36 maintex 23/11/28 The Physics of Atmospheres CAPTER 4 2 4 41 : 2 1 σ 2 (21) (22) k I = I exp(
More informationA 2 3. m S m = {x R m+1 x = 1} U + k = {x S m x k > 0}, U k = {x S m x k < 0}, ϕ ± k (x) = (x 0,..., ˆx k,... x m ) 1. {(U ± k, ϕ± k ) 0 k m} S m 1.2.
A A 1 A 5 A 6 1 2 3 4 5 6 7 1 1.1 1.1 (). Hausdorff M R m M M {U α } U α R m E α ϕ α : U α E α U α U β = ϕ α (ϕ β ϕβ (U α U β )) 1 : ϕ β (U α U β ) ϕ α (U α U β ) C M a m dim M a U α ϕ α {x i, 1 i m} {U,
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 informationExcel97関数編
Excel97 SUM Microsoft Excel 97... 1... 1... 1... 2... 3... 3... 4... 5... 6... 6... 7 SUM... 8... 11 Microsoft Excel 97 AVERAGE MIN MAX SUM IF 2 RANK TODAY ROUND COUNT INT VLOOKUP 1/15 Excel A B C A B
More information) a + b = i + 6 b c = 6i j ) a = 0 b = c = 0 ) â = i + j 0 ˆb = 4) a b = b c = j + ) cos α = cos β = 6) a ˆb = b ĉ = 0 7) a b = 6i j b c = i + 6j + 8)
4 4 ) a + b = i + 6 b c = 6i j ) a = 0 b = c = 0 ) â = i + j 0 ˆb = 4) a b = b c = j + ) cos α = cos β = 6) a ˆb = b ĉ = 0 7) a b = 6i j b c = i + 6j + 8) a b a b = 6i j 4 b c b c 9) a b = 4 a b) c = 7
More informationx E E E e i ω = t + ikx 0 k λ λ 2π k 2π/λ k ω/v v n v c/n k = nω c c ω/2π λ k 2πn/λ 2π/(λ/n) κ n n κ N n iκ k = Nω c iωt + inωx c iωt + i( n+ iκ ) ωx
x E E E e i ω t + ikx k λ λ π k π/λ k ω/v v n v c/n k nω c c ω/π λ k πn/λ π/(λ/n) κ n n κ N n iκ k Nω c iωt + inωx c iωt + i( n+ iκ ) ωx c κω x c iω ( t nx c) E E e E e E e e κ e ωκx/c e iω(t nx/c) I I
More informationII 2 II
II 2 II 2005 yugami@cc.utsunomiya-u.ac.jp 2005 4 1 1 2 5 2.1.................................... 5 2.2................................. 6 2.3............................. 6 2.4.................................
More informationn ξ n,i, i = 1,, n S n ξ n,i n 0 R 1,.. σ 1 σ i .10.14.15 0 1 0 1 1 3.14 3.18 3.19 3.14 3.14,. ii 1 1 1.1..................................... 1 1............................... 3 1.3.........................
More informationI A A441 : April 21, 2014 Version : Kawahira, Tomoki TA (Kondo, Hirotaka ) Google
I4 - : April, 4 Version :. Kwhir, Tomoki TA (Kondo, Hirotk) Google http://www.mth.ngoy-u.c.jp/~kwhir/courses/4s-biseki.html pdf 4 4 4 4 8 e 5 5 9 etc. 5 6 6 6 9 n etc. 6 6 6 3 6 3 7 7 etc 7 4 7 7 8 5 59
More information