2 CHAPTER 2. ) ( ) 2 () () Octave rand() octave:27> A=rand(10,1) A =
|
|
- はな うとだ
- 7 years ago
- Views:
Transcription
1 Chapter (cf. ) (= ) 76, 86, 77, 88, 78, 83, 86, 77, 74, 79, 82, 79, 80, 81, 78, 78, 73, 78, 81, 86, 71, 80, 81, 88, 82, 80, 80, 70, 77, 81 10? () ( 1
2 2 CHAPTER 2. ) ( ) 2 () () Octave rand() octave:27> A=rand(10,1) A = rand() (0,1)
3 octave:29> B=ceil(rand(10,1)*10) B = ceil() octave:30> hist(b) ( ) n hist(rand(n,1)) n (hist(rand(100,1) ) n n
4 4 CHAPTER A = (a 1, a 2,...a n ) µ µ = (1/n) n a i (2.1) Octave mean() Octave:> mean(a) i=1 10 n n 10,100,1000,,,, 1 80 : :
5 A = [10, 11, 10, 11, 10, 10, 11, 9, 11, 9, 11, 9, 9, 9, 11, 11, 12, 11, 11, 10] B = [4, 6, 5, 5, 6, 6, 6, 4, 5, 5, 18, 17, 17, 16, 16, 16, 15, 15, 13, 11] Octave A mean(a) A B A = (a 1, a 2,...a n ) σ 2 µ σ 2 = 1 n n (a i µ) 2 (2.2) i=1 σ 2 = 1 n = 1 n = 1 n = 1 n n (a i µ) 2 i=1 n (a 2 i 2a i µ + µ 2 ) i=1 n (a 2 i ) 1 n n 2µ a i + 1 n n µ2 i=1 i=1 n (a 2 i ) µ 2 (2.3) i=1 2 Octave var() A,B A B
6 6 CHAPTER 2. ( ) n n () Octave 1 sum(round(rand(100,1))*2-1) : #dist1.m n=10000; A=zeros(n,1); for(i=1:n) A(i,1)=sum(round(rand(100,1))*2-1); endfor mean(a) var(a) dist1.m Octave:> source("dist1.m"); n hist(a) round() round(rand()) [0,n-1] round(rand()*n) ceil(), floor() ceil(rand(n,1)*6) (n ) n? ( ) ( n )
7 : /4 3/4 (1 1 4 ) (1 3 ) = = 1 4 1/4 + 1/4 2 1 (1 ( )) 1 2 = = 1 4 1/4 + 1/4 + 1/4 (1 ( )) = 1 4 /and /or 1
8 8 CHAPTER ( 100 ) ( ) ()??( : )
9 Octave hist() ( histogram) Octave hist() ( () ) (=) randn() Octave ; (Octave ) A= randn(50,1); hist(a) hist(a,100) B1= randn(10000,1)+2.4; B2= randn(10000,1); B=[B1;B2]; hist(b) hist(b,100) hist(b) 10 hist(b,1000) 100
10 10 CHAPTER rand() (0,1) Octave rand() /6 f(x) ( ) f(x) x f(x)dx (2.4) 1 x [a, b] b a f(x)dx (2.5) x x
11 [0,1] 0.3 x < 0.4 f(x) = E(X) V (X) µ, σ 2 µ = E(X) = σ 2 = V (X) = xf(x)dx (2.6) (x µ) 2 f(x)dx (2.7) σ = V (X) 2.13 σ 2 = = (x µ) 2 f(x)dx x 2 f(x)dx 2µ xf(x)dx + µ 2 f(x)dx = E(X 2 ) 2µE(X) + µ 2 = E(X 2 ) E(X) 2 (2.8) µ = E(X) f(x)dx = 1 X X X = 1, 2, 3, 4, 5, 6 f(x) = 1/6 X E(X) E(X) = 6 k f(k) k=1 = ( 1 6 1) + (1 6 2) + (1 6 3) + (1 6 4) + (1 6 5) + (1 6 6) = 1 6 ( ) = (2.9)
12 12 CHAPTER 2. 1,2,...,n E(X) = n (2.10) V (X) 2.13 X 2 (E(X) 2 ) n k=1 k 2 = 1 n(n + 1)(2n + 1) (2.11) 6 E(X 2 ) = 1 n n k=1 k 2 = 1 n 1 n(n + 1)(2n + 1) 6 = 1 (n + 1)(2n + 1) (2.12) 6 V (X) = E(X 2 ) (E(X)) 2 (2.13) = 1 6 (n + 1)(2n + 1) 1 (n + 1)2 4 = 1 12 (n2 1) (2.14) n = ( ) ( ) p q = (1 p) n k b(k;n,p) ( ) n b(k; n, p) = p k q n k (2.15) k
13 ( ) n n! = n C k = k k!(n k)! 2 n k Octave n=10; p=0.5; x=1:n; pd=binomial_pdf(x,n,p); plot(pd); 2 1 : n 1 n ( ) binomial cdf(x,n,p) binomial cdf() np npq Figure 2.1: 2 n=50, p=0.1,0.2, 0.3, 0.4, 0.5 5% %
14 0 14 CHAPTER n 2 n ( ) x (Poisson) λ p(x, λ) p(x, λ) = λx x! e λ (2.16) λ λ n (λ = np )2 λ x Octave poisson pdf(x,lambda),poisson cdf(x,lambda) Figure 2.2: =1,2,
15 N(µ, σ 2 ) µ σ 2 x f(x) = 1 (x µ)2 e 2σ 2 (2.17) 2πσ 2 n 2 x dens ity % % 95.45% µ 3σ Figure 2.3: 2
16 16 CHAPTER 2. ( ) normal pdf(x,m,v), normal cdf(x,m,v) X m,v? n n n=100 ( 1/100 ) ( 100 ) Octave randn() normal cdf(x,m,v) 0, 1 x=-1, 1.5, 2.0 % normal cdf(x,m,v) 0, 1 x= [0.3, 0.5] % normal cdf(x,m,v) 0, 1 x= [1.0, 1.2] % URL
17 Z Z = X µ σ (2.18) µ σ X σ Z % 70 (µ = 0, σ 2 = 1) ( ) normal cdf(x,m,v) 0, 1 x=40, 55, 70 % dens ity % % 95.45% Figure 2.4: N(0,1)
18 18 CHAPTER (Zipf) 1/x(x 1 ) x α X X 2. 85% 1 95% (85%) ( 3 30 x6 ) ()
19 X x( ) 2σ 0.95 ( % ) Z Z = (X µ) σ (2.19) X µ, σ Z [-2,2] X x µ, σ 95% 60 ( 100) 95 () ( )
20 20 CHAPTER 2. 2 I false positive, II false negative, I ( ) () 2 YES/NO
21 t t t 2 2 ( ) 2 () t t t (Gosset) t t t X σ t = X µ σ/ n (2.20) Z t t t Octave ( ) (95% )
22 22 CHAPTER 2. 2 (χ 2 ) F ( ) Octave
23 χ 2 t χ 2 ( 2 ) χ 2 2 χ 2 2 A( :a 0, a 1,...a k, ( )n) P( :p 0, p 1,..., p k ) χ 2 (k-1) χ 2 χ 2 χ 2 = n (a i np i ) 2 i=1 np i (2.21) χ 2 B ( ) (B ) ˆp i = a i + b i n a + n b (2.22) n a n b A,B P [1/6,1/6,1/6,1/6,1/6,1/6] Octave χ 2 χ 2 Octave p (1-chisquare cdf(x,k)) X k chisq.m #chisq.m function x = chisq(a) length=size(a)(1,2); t0=round(sum(a)/length); x=0; for(i=1:length) x=x+ (A(1,i)-t0)^2/t0; endfor endfunction
24 24 CHAPTER 2. function p = dochisqtest(a) k=size(a)(1,2)-1; p = 1-chisquare_cdf(chisq(A),k); endfunction D1,D2,D3 D1=[11,20,9,16,19,25]; D2=[26,38,22,34,37,43]; D3=[44,52,39,56,53,56]; χ 2 Octave:134> source("chisq.m"); octave:135> chisq(d1) ans = octave:141> chisq(d2) ans = octave:142> chisq(d3) ans = k=5 95% octave:138> chisquare_inv(0.95,5) ans = χ (P: ) octave:143> dochisqtest(d1) ans = octave:144> dochisqtest(d2) ans = octave:145> dochisqtest(d3) ans = p 0.05 p 0.05 (p<0.05)
25 : Octave t t test(x,m,alt), t test 2(x,y,alt) p-value x,y ( ) m alt <> ( ) µ x < µ y < µ x > µ y > A1=randn(100,1)+10; A2=randn(200,1); 2 octave:173> t_test(a1,10) pval: ans = octave:174> t_test(a1,9) pval: 0 ans = 0 octave:175> t_test(a1,9) pval: 0 ans = 0 octave:176> t_test(a1,11) pval: e-19 ans = e-19 2 octave:177> t_test_2(a1,a2) pval: 0 ans = 0 octave:178> t_test_2(a1,a2,">") pval: 0 ans = 0 octave:179> t_test_2(a1,a2,"<") pval: 1 ans = 1 octave:180> mean(a1) ans =
26 26 CHAPTER 2. octave:181> mean(a2) ans = octave:183> A3=randn(10,1)+4; octave:184> t_test(a3,3) pval: ans = octave:185> t_test(a3,3.5) pval: ans = octave:186> t_test(a3,3.8) pval: ans = octave:187> t_test(a3,4.1) pval: ans = octave:188> t_test(a3,4.3) pval: ans = octave:189> t_test(a3,4.5) pval: ans = octave:190> t_test(a3,4.6) pval: ans = octave:191> t_test(a3,5) pval: ans = URL t t
27 : S.D. S.J.
3 3.3. I 3.3.2. [ ] N(µ, σ 2 ) σ 2 (X 1,..., X n ) X := 1 n (X 1 + + X n ): µ X N(µ, σ 2 /n) 1.8.4 Z = X µ σ/ n N(, 1) 1.8.2 < α < 1/2 Φ(z) =.5 α z α
2 2.1. : : 2 : ( ): : ( ): : : : ( ) ( ) ( ) : ( pp.53 6 2.3 2.4 ) : 2.2. ( ). i X i (i = 1, 2,..., n) X 1, X 2,..., X n X i (X 1, X 2,..., X n ) ( ) n (x 1, x 2,..., x n ) (X 1, X 2,..., X n ) : X 1,
More information1 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 informationhttp://banso.cocolog-nifty.com/ 100 100 250 5 1 1 http://www.banso.com/ 2009 5 2 10 http://www.banso.com/ 2009 5 2 http://www.banso.com/ 2009 5 2 http://www.banso.com/ < /> < /> / http://www.banso.com/
More informationn=360 28.6% 34.4% 36.9% n=360 2.5% 17.8% 19.2% n=64 0.8% 0.3% n=69 1.7% 3.6% 0.6% 1.4% 1.9% < > n=218 1.4% 5.6% 3.1% 60.6% 0.6% 6.9% 10.8% 6.4% 10.3% 33.1% 1.4% 3.6% 1.1% 0.0% 3.1% n=360 0% 50%
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 information3 3.1 *2 1 2 3 4 5 6 *2 2
Armitage 1 2 11 10 3.32 *1 9 5 5.757 3.3667 7.5 1 9 6 5.757 7 7.5 7.5 9 7 7 9 7.5 10 9 8 7 9 9 10 9 9 9 10 9 11 9 10 10 10 9 11 9 11 11 10 9 11 9 12 13 11 10 11 9 13 13 11 10 12.5 9 14 14.243 13 12.5 12.5
More informationuntitled
280 200 5 7,800 6 8,600 28 1 1 18 7 8 2 ( 31 ) 7 42 2 / / / / / / / / / / 1 3 (1) 4 5 3 1 1 1 A B C D 6 (1) -----) (2) -- ()) (3) ----(). ()() () ( )( )( )( ) ( ) ( )( )( )( ) () (). () ()() 7 () ( ) 1
More information4 4. A p X A 1 X X A 1 A 4.3 X p X p X S(X) = E ((X p) ) X = X E(X) = E(X) p p 4.3p < p < 1 X X p f(i) = P (X = i) = p(1 p) i 1, i = 1,,... 1 + r + r
4 1 4 4.1 X P (X = 1) =.4, P (X = ) =.3, P (X = 1) =., P (X = ) =.1 E(X) = 1.4 +.3 + 1. +.1 = 4. X Y = X P (X = ) = P (X = 1) = P (X = ) = P (X = 1) = P (X = ) =. Y P (Y = ) = P (X = ) =., P (Y = 1) =
More informationα = 2 2 α 2 = ( 2) 2 = 2 x = α, y = 2 x, y X 0, X 1.X 2,... x 0 X 0, x 1 X 1, x 2 X 2.. Zorn A, B A B A B A B A B B A A B N 2
1. 2. 3. 4. 5. 6. 7. 8. N Z 9. Z Q 10. Q R 2 1. 2. 3. 4. Zorn 5. 6. 7. 8. 9. x x x y x, y α = 2 2 α x = y = 2 1 α = 2 2 α 2 = ( 2) 2 = 2 x = α, y = 2 x, y X 0, X 1.X 2,... x 0 X 0, x 1 X 1, x 2 X 2.. Zorn
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 information1 1 ( ) ( 1.1 1.1.1 60% mm 100 100 60 60% 1.1.2 A B A B A 1
1 21 10 5 1 E-mail: qliu@res.otaru-uc.ac.jp 1 1 ( ) ( 1.1 1.1.1 60% mm 100 100 60 60% 1.1.2 A B A B A 1 B 1.1.3 boy W ID 1 2 3 DI DII DIII OL OL 1.1.4 2 1.1.5 1.1.6 1.1.7 1.1.8 1.2 1.2.1 1. 2. 3 1.2.2
More information16 8020
ペ-ジ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 8020 16 8020 681 40 59.713.3 292 3095 328 2984 620 CPI CPI CPI 0 1 2,3,4 6,9 1035 433 normal 34mild 24moderate 18severe 2466 42 CPI 23 CPI 0 1 CPI normal mild
More informationChapter n m A 1, A 2,...A n (A k = [a 1 k, a2 k,..., am k ]) n n m m m 2 3 Z 2 Z = w 1 X 1 + w 2 X 2 (5.1) 1
Chapter 5 5.1 n m A 1, A 2,...A n (A k = [a 1 k, a2 k,..., am k ]) n n m m m 2 3 Z 2 Z = w 1 X 1 + w 2 X 2 (5.1) 1 2 CHAPTER 5. w 1 w 2 () ( X 1 ) 5.2 X 1, X 2,...X n (X k = [x 1 k, x2 k,..., xm k ]) Z
More information1 1 1.1...................................... 1 1.2................................... 5 1.3................................... 7 1.4............................. 9 1.5....................................
More information卒論 提出用ファイル.doc
11 13 1LT99097W (i) (ii) 0. 0....1 1....3 1.1....3 1.2....4 2....7 2.1....7 2.2....8 2.2.1....8 2.2.2....9 2.2.3.... 10 2.3.... 12 3.... 15 Appendix... 17 1.... 17 2.... 19 3.... 20... 22 (1) a. b. c.
More information2 1 Introduction
1 24 11 26 1 E-mail: toyoizumi@waseda.jp 2 1 Introduction 5 1.1...................... 7 2 8 2.1................ 8 2.2....................... 8 2.3............................ 9 3 10 3.1.........................
More informationii 3.,. 4. F. ( ), ,,. 8.,. 1. (75% ) (25% ) =7 24, =7 25, =7 26 (. ). 1.,, ( ). 3.,...,.,.,.,.,. ( ) (1 2 )., ( ), 0., 1., 0,.
(1 C205) 4 10 (2 C206) 4 11 (2 B202) 4 12 25(2013) http://www.math.is.tohoku.ac.jp/~obata,.,,,..,,. 1. 2. 3. 4. 5. 6. 7. 8. 1., 2007 ( ).,. 2. P. G., 1995. 3. J. C., 1988. 1... 2.,,. ii 3.,. 4. F. ( ),..
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 information2 1 Octave Octave Window M m.m Octave Window 1.2 octave:1> a = 1 a = 1 octave:2> b = 1.23 b = octave:3> c = 3; ; % octave:4> x = pi x =
1 1 Octave GNU Octave Matlab John W. Eaton 1992 2.0.16 2.1.35 Octave Matlab gnuplot Matlab Octave MATLAB [1] Octave [1] 2.7 Octave Matlab Octave Octave 2.1.35 2.5 2.0.16 Octave 1.1 Octave octave Octave
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 informationaisatu.pdf
1 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 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 66 67 68 69 70 71
More information(1) (2) (1) (2) 2 3 {a n } a 2 + a 4 + a a n S n S n = n = S n
. 99 () 0 0 0 () 0 00 0 350 300 () 5 0 () 3 {a n } a + a 4 + a 6 + + a 40 30 53 47 77 95 30 83 4 n S n S n = n = S n 303 9 k d 9 45 k =, d = 99 a d n a n d n a n = a + (n )d a n a n S n S n = n(a + a n
More information36
36 37 38 P r R P 39 (1+r ) P =R+P g P r g P = R r g r g == == 40 41 42 τ R P = r g+τ 43 τ (1+r ) P τ ( P P ) = R+P τ ( P P ) n P P r P P g P 44 R τ P P = (1 τ )(r g) (1 τ )P R τ 45 R R σ u R= R +u u~ (0,σ
More informationNetcommunity SYSTEM αNXⅡ typeS/typeM 取扱説明書
2 3 4 5 6 7 1 2 3 4 5 6 8 3 3-38 1 2 3 4 5 9 1 2 3 10 4 5 11 6 12 1 1-2 1 1-3 1 1-4 1 1-5 1 micro SD 1-6 1 1-7 1 1 1-8 1 1-9 1 100 10 TEN 1 1-10 1 1-11 1 1-12 1 1-13 1 1-14 1 1 2 7 8 9 1 3 4 5 6 1-15 1
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 informationPart. 4. () 4.. () 4.. 3 5. 5 5.. 5 5.. 6 5.3. 7 Part 3. 8 6. 8 6.. 8 6.. 8 7. 8 7.. 8 7.. 3 8. 3 9., 34 9.. 34 9.. 37 9.3. 39. 4.. 4.. 43. 46.. 46..
Cotets 6 6 : 6 6 6 6 6 6 7 7 7 Part. 8. 8.. 8.. 9..... 3. 3 3.. 3 3.. 7 3.3. 8 Part. 4. () 4.. () 4.. 3 5. 5 5.. 5 5.. 6 5.3. 7 Part 3. 8 6. 8 6.. 8 6.. 8 7. 8 7.. 8 7.. 3 8. 3 9., 34 9.. 34 9.. 37 9.3.
More informationチュートリアル:ノンパラメトリックベイズ
{ x,x, L, xn} 2 p( θ, θ, θ, θ, θ, } { 2 3 4 5 θ6 p( p( { x,x, L, N} 2 x { θ, θ2, θ3, θ4, θ5, θ6} K n p( θ θ n N n θ x N + { x,x, L, N} 2 x { θ, θ2, θ3, θ4, θ5, θ6} log p( 6 n logθ F 6 log p( + λ θ F θ
More information応用数学III-4.ppt
III f x ( ) = 1 f x ( ) = P( X = x) = f ( x) = P( X = x) =! x ( ) b! a, X! U a,b f ( x) =! " e #!x, X! Ex (!) n! ( n! x)!x! " x 1! " x! e"!, X! Po! ( ) n! x, X! B( n;" ) ( ) ! xf ( x) = = n n!! ( n
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 information0 1-4. 1-5. (1) + b = b +, (2) b = b, (3) + 0 =, (4) 1 =, (5) ( + b) + c = + (b + c), (6) ( b) c = (b c), (7) (b + c) = b + c, (8) ( + b)c = c + bc (9
1-1. 1, 2, 3, 4, 5, 6, 7,, 100,, 1000, n, m m m n n 0 n, m m n 1-2. 0 m n m n 0 2 = 1.41421356 π = 3.141516 1-3. 1 0 1-4. 1-5. (1) + b = b +, (2) b = b, (3) + 0 =, (4) 1 =, (5) ( + b) + c = + (b + c),
More informationcalibT1.dvi
1 2 flux( ) flux 2.1 flux Flux( flux ) Flux [erg/sec/cm 2 ] erg/sec/cm 2 /Å erg/sec/cm 2 /Hz 1 Flux -2.5 Vega Vega ( Vega +0.03 ) AB cgs F ν [erg/cm 2 /s/hz] m(ab) = 2.5 logf ν 48.6 V-band 2.2 Flux Suprime-Cam
More information…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 informationII
II 16 16.0 2 1 15 x α 16 x n 1 17 (x α) 2 16.1 16.1.1 2 x P (x) P (x) = 3x 3 4x + 4 369 Q(x) = x 4 ax + b ( ) 1 P (x) x Q(x) x P (x) x P (x) x = a P (a) P (x) = x 3 7x + 4 P (2) = 2 3 7 2 + 4 = 8 14 +
More informationf (x) f (x) f (x) f (x) f (x) 2 f (x) f (x) f (x) f (x) 2 n f (x) n f (n) (x) dn f f (x) dx n dn dx n D n f (x) n C n C f (x) x = a 1 f (x) x = a x >
5.1 1. x = a f (x) a x h f (a + h) f (a) h (5.1) h 0 f (x) x = a f +(a) f (a + h) f (a) = lim h +0 h (5.2) x h h 0 f (a) f (a + h) f (a) f (a h) f (a) = lim = lim h 0 h h 0 h (5.3) f (x) x = a f (a) =
More informationA B P (A B) = P (A)P (B) (3) A B A B P (B A) A B A B P (A B) = P (B A)P (A) (4) P (B A) = P (A B) P (A) (5) P (A B) P (B A) P (A B) A B P
1 1.1 (population) (sample) (event) (trial) Ω () 1 1 Ω 1.2 P 1. A A P (A) 0 1 0 P (A) 1 (1) 2. P 1 P 0 1 6 1 1 6 0 3. A B P (A B) = P (A) + P (B) (2) A B A B A 1 B 2 A B 1 2 1 2 1 1 2 2 3 1.3 A B P (A
More information( 30 ) 30 4 5 1 4 1.1............................................... 4 1.............................................. 4 1..1.................................. 4 1.......................................
More informationMacOSX印刷ガイド
3 CHAPTER 3-1 3-2 3-3 1 2 3 3-4 4 5 6 3-5 1 2 3 4 3-6 5 6 3-7 7 8 3-8 1 2 3 4 3-9 5 6 3-10 7 1 2 3 4 3-11 5 6 3-12 7 8 9 3-13 10 3-14 1 2 3-15 3 4 1 2 3-16 3 4 5 3-17 1 2 3 4 3-18 1 2 3 4 3-19 5 6 7 8
More information荳也阜轣ス螳ウ蝣ア蜻・indd
1 2 3 CHAPTER 1 4 CHAPTER 1 5 6CHAPTER 1 CHAPTER 1 7 8CHAPTER 1 CHAPTER 2 9 10CHAPTER 2 CHAPTER 2 11 12 CHAPTER 2 13 14CHAPTER 3 CHAPTER 3 15 16CHAPTER 3 CHAPTER 3 17 18 CHAPTER 4 19 20CHAPTER 4 CHAPTER
More information第85 回日本感染症学会総会学術集会後抄録(III)
β β α α α µ µ µ µ α α α α γ αβ α γ α α γ α γ µ µ β β β β β β β β β µ β α µ µ µ β β µ µ µ µ µ µ γ γ γ γ γ γ µ α β γ β β µ µ µ µ µ β β µ β β µ α β β µ µµ β µ µ µ µ µ µ λ µ µ β µ µ µ µ µ µ µ µ
More informationMicro-D 小型高密度角型コネクタ
Micro- 1 2 0.64 1.27 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 11 12 13 1.09 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 3 4 J J
More informationuntitled
1 1 1. 2. 3. 2 2 1 (5/6) 4 =0.517... 5/6 (5/6) 4 1 (5/6) 4 1 (35/36) 24 =0.491... 0.5 2.7 3 1 n =rand() 0 1 = rand() () rand 6 0,1,2,3,4,5 1 1 6 6 *6 int() integer 1 6 = int(rand()*6)+1 1 4 3 500 260 52%
More informationACCESS入門編
ACCESS () / 255 65535 0 255-32768 32767 15 4 1 Yes/No OLE Yes=-1 NO=0 OK Like AND *[ ]* Like *[ ]* Like >= =40 40 OR 1 OR AND 2000 2 2 AND 2 OK CTRL SHIFT IME 1 IME [1] [2]2
More informationFX ) 2
(FX) 1 1 2009 12 12 13 2009 1 FX ) 2 1 (FX) 2 1 2 1 2 3 2010 8 FX 1998 1 FX FX 4 1 1 (FX) () () 1998 4 1 100 120 1 100 120 120 100 20 FX 100 100 100 1 100 100 100 1 100 1 100 100 1 100 101 101 100 100
More information4 4 4 a b c d a b A c d A a da ad bce O E O n A n O ad bc a d n A n O 5 {a n } S n a k n a n + k S n a a n+ S n n S n n log x x {xy } x, y x + y 7 fx
4 4 5 4 I II III A B C, 5 7 I II A B,, 8, 9 I II A B O A,, Bb, b, Cc, c, c b c b b c c c OA BC P BC OP BC P AP BC n f n x xn e x! e n! n f n x f n x f n x f k x k 4 e > f n x dx k k! fx sin x cos x tan
More information... 3... 3... 3... 3... 4... 7... 10... 10... 11... 12... 12... 13... 14... 15... 18... 19... 20... 22... 22... 23 2
1 ... 3... 3... 3... 3... 4... 7... 10... 10... 11... 12... 12... 13... 14... 15... 18... 19... 20... 22... 22... 23 2 3 4 5 6 7 8 9 Excel2007 10 Excel2007 11 12 13 - 14 15 16 17 18 19 20 21 22 Excel2007
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- - - - - - - - - - - - - - - - - - - - - - - - - -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 information1 (1) vs. (2) (2) (a)(c) (a) (b) (c) 31 2 (a) (b) (c) LENCHAR
() 601 1 () 265 OK 36.11.16 20 604 266 601 30.4.5 (1) 91621 3037 (2) 20-12.2 20-13 (3) ex. 2540-64 - LENCHAR 1 (1) vs. (2) (2) 605 50.2.13 41.4.27 10 10 40.3.17 (a)(c) 2 1 10 (a) (b) (c) 31 2 (a) (b) (c)
More informationexample2_time.eps
Google (20/08/2 ) ( ) Random Walk & Google Page Rank Agora on Aug. 20 / 67 Introduction ( ) Random Walk & Google Page Rank Agora on Aug. 20 2 / 67 Introduction Google ( ) Random Walk & Google Page Rank
More informationver 0.3 Chapter 0 0.1 () 0( ) 0.2 3 4 CHAPTER 0. http://www.jaist.ac.jp/~t-yama/k116 0.3 50% ( Wikipedia ) ( ) 0.4! 2006 0.4. 5 MIT OCW ( ) MIT Open Courseware MIT (Massachusetts Institute of Technology)
More informationPackageSoft/R-033U.tex (2018/March) R:
................................................................................ R: 2018 3 29................................................................................ R AI R https://cran.r-project.org/doc/contrib/manuals-jp/r-intro-170.jp.pdf
More informationN N 1,, N 2 N N N N N 1,, N 2 N N N N N 1,, N 2 N N N 8 1 6 3 5 7 4 9 2 1 12 13 8 15 6 3 10 4 9 16 5 14 7 2 11 7 11 23 5 19 3 20 9 12 21 14 22 1 18 10 16 8 15 24 2 25 4 17 6 13 8 1 6 3 5 7 4 9 2 1 12 13
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 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 information1
005 11 http://www.hyuki.com/girl/ http://www.hyuki.com/story/tetora.html http://www.hyuki.com/ Hiroshi Yuki c 005, All rights reserved. 1 1 3 (a + b)(a b) = a b (x + y)(x y) = x y a b x y a b x y 4 5 6
More information確率論と統計学の資料
5 June 015 ii........................ 1 1 1.1...................... 1 1........................... 3 1.3... 4 6.1........................... 6................... 7 ii ii.3.................. 8.4..........................
More information1. A0 A B A0 A : A1,...,A5 B : B1,...,B
1. A0 A B A0 A : A1,...,A5 B : B1,...,B12 2. 3. 4. 5. A0 A B f : A B 4 (i) f (ii) f (iii) C 2 g, h: C A f g = f h g = h (iv) C 2 g, h: B C g f = h f g = h 4 (1) (i) (iii) (2) (iii) (i) (3) (ii) (iv) (4)
More information, 1 ( f n (x))dx d dx ( f n (x)) 1 f n (x)dx d dx f n(x) lim f n (x) = [, 1] x f n (x) = n x x 1 f n (x) = x f n (x) = x 1 x n n f n(x) = [, 1] f n (x
1 1.1 4n 2 x, x 1 2n f n (x) = 4n 2 ( 1 x), 1 x 1 n 2n n, 1 x n n 1 1 f n (x)dx = 1, n = 1, 2,.. 1 lim 1 lim 1 f n (x)dx = 1 lim f n(x) = ( lim f n (x))dx = f n (x)dx 1 ( lim f n (x))dx d dx ( lim f d
More information