こんにちは由美子です
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- みちしげ さかわ
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1 1
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4 λ λ. correlate father mother first second (obs=20) father mother first second father mother first second regress father mother Source SS df MS Number of obs = F( 1, 18) = 0.96 Model Prob > F = Residual R-squared =
5 Adj R-squared = Total Root MSE = father Coef. Std. Err. t P> t [95% Conf. Interval] mother _cons regress father first Source SS df MS Number of obs = F( 1, 18) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = father Coef. Std. Err. t P> t [95% Conf. Interval] first _cons regress father second Source SS df MS Number of obs = F( 1, 18) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = father Coef. Std. Err. t P> t [95% Conf. Interval] second _cons regress mother first Source SS df MS Number of obs = F( 1, 18) = 9.32 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = mother Coef. Std. Err. t P> t [95% Conf. Interval] first _cons regress mother second Source SS df MS Number of obs = F( 1, 18) = 5.25 Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = mother Coef. Std. Err. t P> t [95% Conf. Interval] second _cons
6 . regress first second Source SS df MS Number of obs = F( 1, 18) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = first Coef. Std. Err. t P> t [95% Conf. Interval] second _cons
7 . sort twin. by twin: sum sbp -> twin= 0 sbp > twin= 1 sbp sort id. by id: sum sbp -> id= 1 7
8 sbp > id= 2 sbp > id= 3 sbp > id= 4 sbp > id= 5 sbp > id= 6 sbp > id= 7 sbp > id= 8 sbp > id= 9 sbp > id= 10 sbp MSAMZ = sum of squares among monozygous twin, MZ/(nMZ-1) = /4 = MSWMZ = sum of squares within monozygous twin, MZ/nMZ = 44/5 = 8.8 MSADZ = sum of squares among dizygous twin, DZ/(nDZ-1) = 1312/4 = MSWDZ = sum of squares within dizygous twin, DZ/nDZ = 522/5 =
9 rmz = (MSAMZ-MSWMZ) / (MSAMZ+MSWMZ)=( ) / ( ) = 0.95 rdz = (MSADZ-MSWDZ) / (MSADZ+MSWDZ)=( ) / ( ) = h2 = w)rmz rdz) = 2 ( ) = χ 9
10 10
11 (sbp) sib pair linkage study Angiotensin I converting enzyme (ACEI) microsatellite marker 4 A1 = 0.20, A2 = 0.25, A3 = 0.10, A4 = 0.05, A5 = 0.27, A6 = ID 12 (sbp) IBD allele Haseman-Elston method 11
12 identical by descent (IBD) AB CD AB AC AC AC AB AC IBD = 2 IBD = 0 2 A B C A C A A IBD 0 identical by state (IBS) 1 IBS IBD=<IBS IBD I II III IV V AA AA AA CC AA AC AA BC AC AC IBD = 2 or 1 or 0 =0 =1 or 0 =0 =2 or 1 or 0 IBS = 2 =0 =1 =0 =2 VI VII AC AB AB CD IBD = 0 or 1 = 0 IBS = 1 =0 IBD 0 IBD 1 IBD 2 I 4 P A 3 P A 2 P A II 2P 2 2 A P C 0 0 III 4P 3 A P C 2P 2 A P C 0 IV 4P 3 A P C P B 0 0 V 4P 2 2 A P C P A P C (P A + P C ) 2P A P C VI 8P 3 A P B P C 2P A P B P C 0 VII P A P B P C P D 0 0 π = [f2*(ibd 2 ) + f1*(ibd 1 /2)] / [f2*(ibd 2 ) + f1*(ibd 1 /2) + f0*(ibd 0 )] fi:i IBD ; f2= 1/4, f1 = 1/2, f0 = 1/4 12
13 ID Y 2 π (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (y 1j y 2j ) 2 = α + βπ H 0 : β=0 versus H A : β,< 0. regress Y X Source SS df MS Number of obs = F( 1, 10) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Y Coef. Std. Err. t P> t [95% Conf. Interval] X _cons E(Y) = π, p=
卒業論文
Y = ax 1 b1 X 2 b2...x k bk e u InY = Ina + b 1 InX 1 + b 2 InX 2 +...+ b k InX k + u X 1 Y b = ab 1 X 1 1 b 1 X 2 2...X bk k e u = b 1 (ax b1 1 X b2 2...X bk k e u ) / X 1 = b 1 Y / X 1 X 1 X 1 q YX1
こんにちは由美子です
Analysis of Variance 2 two sample t test analysis of variance (ANOVA) CO 3 3 1 EFV1 µ 1 µ 2 µ 3 H 0 H 0 : µ 1 = µ 2 = µ 3 H A : Group 1 Group 2.. Group k population mean µ 1 µ µ κ SD σ 1 σ σ κ sample mean
AR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t
87 6.1 AR(1) y t = φy t 1 + ɛ t, ɛ t N(0, σ 2 ) 1. Mean of y t given y t 1, y t 2, E(y t y t 1, y t 2, ) = φy t 1 2. Variance of y t given y t 1, y t 2, V(y t y t 1, y t 2, ) = σ 2 3. Thus, y t y t 1,
Stata11 whitepapers mwp-037 regress - regress regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F(
mwp-037 regress - regress 1. 1.1 1.2 1.3 2. 3. 4. 5. 1. regress. regress mpg weight foreign Source SS df MS Number of obs = 74 F( 2, 71) = 69.75 Model 1619.2877 2 809.643849 Prob > F = 0.0000 Residual
% 10%, 35%( 1029 ) p (a) 1 p 95% (b) 1 Std. Err. (c) p 40% 5% (d) p 1: STATA (1). prtesti One-sample test of pr
1 1. 2014 6 2014 6 10 10% 10%, 35%( 1029 ) p (a) 1 p 95% (b) 1 Std. Err. (c) p 40% 5% (d) p 1: STATA (1). prtesti 1029 0.35 0.40 One-sample test of proportion x: Number of obs = 1029 Variable Mean Std.
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Stata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestim
TS001 Stata 11 Stata ts (ARMA) ARCH/GARCH whitepaper mwp 3 mwp-083 arch ARCH 11 mwp-051 arch postestimation 27 mwp-056 arima ARMA 35 mwp-003 arima postestimation 49 mwp-055 corrgram/ac/pac 56 mwp-009 dfgls
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Stata 11 Stata ROC whitepaper mwp anova/oneway 3 mwp-042 kwallis Kruskal Wallis 28 mwp-045 ranksum/median / 31 mwp-047 roctab/roccomp ROC 34 mwp-050 s
BR003 Stata 11 Stata ROC whitepaper mwp anova/oneway 3 mwp-042 kwallis Kruskal Wallis 28 mwp-045 ranksum/median / 31 mwp-047 roctab/roccomp ROC 34 mwp-050 sampsi 47 mwp-044 sdtest 54 mwp-043 signrank/signtest
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s = 1.15 (s = 1.07), R = 0.786, R = 0.679, DW =.03 5 Y = 0.3 (0.095) (.708) X, R = 0.786, R = 0.679, s = 1.07, DW =.03, t û Y = 0.3 (3.163) + 0
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BR001
BR001 Stata 11 Stata Stata11 whitepaper mwp 3 mwp-027 22 mwp-028 / 40 mwp-001 logistic/logit 50 mwp-039 logistic/logit postestimation 60 mwp-040 margins 74 mwp-029 regress 90 mwp-037 regress postestimation
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1 Stata SEM LightStone 3 2 SEM. 2., 2,. Alan C. Acock, Discovering Structural Equation Modeling Using Stata, Revised Edition, Stata Press.
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