Publications by Eddie J. Liu
Assignment9_Group7
Question 1 Here we have 4 polulations, diff mean =1, power= 80%, error probability=0.05, type 1, variance= 4.5, f is actually the effect size. library(pwr) pwr.anova.test(k=4,n=NULL,f=sqrt((1)^2/4.5),sig.level=0.05,power=0.80) ## ## Balanced one-way analysis of variance power calculation ## ## k = 4 ## n...
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Homework3_Jiachen,Liu
2.32 Reading data: C1 <- c(0.265, 0.265, 0.266, 0.267, 0.267, 0.265, 0.267, 0.267, 0.265, 0.268, 0.268, 0.265) C2 <- c(0.264, 0.265, 0.264, 0.266, 0.267, 0.268, 0.264, 0.265, 0.265, 0.267, 0.268, 0.269) Since same inspector is using caplier 1 and capiler 2 to measure the ball bearing, this is a paired t-test. ———————————...
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Homework2_Jiachen,LIU
2.24 Reading data datM1 <- c(16.03, 16.04, 16.05, 16.05, 16.02, 16.01, 15.96, 15.98, 16.02, 15.99) datM2 <- c(16.02, 15.97, 15.96, 16.01, 15.99, 16.03, 16.04, 16.02, 16.01, 16.00) Assume the filling process have close variance, since standard deviations are 0.015 and 0.018. Also, we do not have large number of data-set (n<40), we can use two ...
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Assignment2_group1_TTU_IE5342
# Draft Kings Salaries of Players on Houston Rockets and LA Lakers dat <- as.data.frame(read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/main/normtemp.csv")) male <- dat[1:65,3] female <- dat[66:130,3] summary(male) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 58.00 70.00 73.00 73.37 78.00 86.00 sd(male) ##...
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Test 2 Document
You can put chunks of R code by putting three backticks at the beginning and end of the chunk. You need to give each chunk a unique name in the {} Echo Here is an example with echo=FALSE ## [1] 0.8810438 Here is an example with echo=TRUE x <- rnorm(100) y <- 2*x+rnorm(100) cor(x,y) ## [1] 0.8915812 and here is an example with echo being defaul...
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Assignment3_group1_TTU_IE5342
Reading data dat <- as.data.frame(read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/main/normtemp.csv")) male <- dat[1:65,3] female <- dat[66:130,3] Descriptive statistics Sample min, sample max, sample mean, sample standard deviation, sample median, quartiles, histogram and normal probability plot for Male heart beats: summary(m...
2809 sym R (1463 sym/15 pcs) 5 img
Assignment4_group4_TTU_IE5342
Reading data dat <- as.data.frame(read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/main/US_Japanese_Cars.csv")) datUS <- dat[1:35,1] str(datUS) ## int [1:35] 18 15 18 16 17 15 14 14 14 15 ... datJAP <- dat[1:28,2] Q1 Ans: According to textbook, both sample sizes should be more than 40 to use CLT, it is not large enough to assu...
1564 sym R (1248 sym/11 pcs) 6 img
Assignment5_Group6
Q1: Paired test since correlated: library(pwr) power.t.test(n=NULL,delta =.015,sd= 0.03, sig.level=0.05,power=.75,type="paired", alternative = "one.sided") ## ## Paired t test power calculation ## ## n = 22.92961 ## delta = 0.015 ## sd = 0.03 ## sig.level = 0.05 ## power = 0.75...
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Assignment6_Group6
Q1 1. One method for assessing the bioavailability of a drug is to note its concentration in blood and/or urine samples at certain periods of time after the drug is given. Suppose we want to compare the concentrations of two types of aspirin (types A and B) in urine specimens taken from the same person 1 hour after he or she has taken the drug. ...
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Homework4_Jiachen,Liu
3.7 (a): null hypothesis: \(H_0: \mu_{1}=\mu_{2}=\cdots =\mu_{i}=\mu\) alternative hypothesis: \(H_1:\) at least one \(\mu_{i}\) differs Linear effects equation: \(\overline{y_{ij}}=\mu_{i}+\epsilon_{ij}\) \(\overline{y_{ij}}=\mu+\tau_{i}+\epsilon_{ij}\) where, \(\mu_{i}\): mean of a certain population, \(\mu\): ground mean, \(\epsilon_{ij}...
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