Publications by Eddie J. Liu
Homework5_Jiachen,Liu
Problem 3.7 (c): r1 <- c(3129,3000,2865,2890) r2 <- c(3200,3300,2975,3150) r3 <- c(2800,2900,2985,3050) r4 <- c(2600,2700,2600,2765) I1=4 J1=4 clm <- c(r1,r2,r3,r4) type <- c(rep(1,4),rep(2,4),rep(3,4),rep(4,4)) dat1 <- cbind(clm,type) dat1 <- as.data.frame(dat1) dat1$type <- as.factor(dat1$type) aov.model<-aov(clm~type,data=dat1) s...
2798 sym R (4549 sym/59 pcs) 9 img
Project-Part 1_Group8
Project Part 1 Q1: Determine how many samples should be collected to detect a mean difference with a medium effect (i.e. 50% of the standard deviation) with a probability of 75%. library(pwr) pwr.anova.test(k=3,n=NULL,f=0.5,sig.level=0.05, power=0.75) ## ## Balanced one-way analysis of variance power calculation ## ## ...
3777 sym R (3138 sym/18 pcs) 5 img
Project-Part1_Group8
Project Part 1 Q1: Determine how many samples should be collected to detect a mean difference with a medium effect (i.e. 50% of the standard deviation) with a probability of 75%. library(pwr) pwr.anova.test(k=3,n=NULL,f=0.5,sig.level=0.05, power=0.75) ## ## Balanced one-way analysis of variance power calculation ## ## ...
3777 sym R (3138 sym/18 pcs) 5 img
Assignment11_Group6
Problem 1: \(\overline{y_{ij}}=\mu_{i}+\beta{j}+\epsilon_{ij}\) \(\overline{y_{ij}}=\mu+\tau_{i}+\beta{j}+\epsilon_{ij}\) Corresponding to: \(\tau_{i}=0\) null hypothesis: \(H_0: \mu_{1}=\mu_{2}=\cdots =\mu_{i}=\mu\) \(\tau_{i}\ne 0\) alternative hypothesis: \(H_1:\) at least one \(\mu_{i}\) differs lm: linear model \(\tau_{i}\):fixed effec...
1460 sym R (2241 sym/21 pcs) 5 img
Assignment12_Group2
Q1. Is this a valid Latin Square? (explain) Ans: Yes.This is a valid Latin Square. Because none of the factors have been replicated in either indentical column or row. ———————————————————————————————— Q2. Write the model equation \(\overline{y_{ijk}}=\mu+\tau_{i}+\beta_{j}+\alpha...
1251 sym R (2096 sym/9 pcs) 5 img
HW12
Problem 7.12 A <- rep(rep(c(-1,1),8),7) # Factor A B <- rep(rep(c(1,1,-1,-1),4),7) # Factor B C <- rep(rep(c(rep(1,4),rep(-1,4)),2),7) # Factor C D <- rep(c(rep(1,8),rep(-1,8)),7) # Factor D I <- c(10.0,0.0,4.0,0.0,0.0,5.0,6.5,16.5,4.5,19.5,15.0,41.5,8.0,21.5,0.0,18.0) II <- c(18.0,16.5,6.0,10.0,0.0,20.5,18.5,4.5,18.0,18.0,16.0,39.0,4.5,10....
3762 sym R (7688 sym/60 pcs) 10 img
Homework13
Problem 8.2 In normal case (16 runs): A <- rep(c(-1,1),8) # Factor A B <- rep(c(-1,-1,1,1),4) # Factor B C <- rep(c(rep(-1,4),rep(1,4)),2) # Factor C D <- c(rep(-1,8),rep(1,8)) # Factor D I <-c(7.037,14.707,11.635,17.273,10.403,4.368,9.360,13.440,8.561,16.867,13.876,19.824,11.846,6.125,11.190,15.653) II <- c(6.376,15.219,12.089,17.815,10.15...
3538 sym R (23286 sym/83 pcs) 11 img