STA 5166 Statistics in Applications I FALL SEMESTER 2006
Instructor: Oce: Class Hours: Oce Hours: Text:
Xu-Feng Niu 210B OSB 644-4008 12:30 pm - 1:45 pm MW 2:00 pm - 3:00 pm MW Box, G. E., Hunter, W. G. and Hunter, J. S (2005), Statistics for Experimenter
http:/lib.stat.cmu.edu/S/cheatsheet
S Cheatsheet
Compiled by:
Barry W. Brown Department of Biomathematics, Box 237 University of Texas M. D. Anderson Cancer Center 1515 Holcombe Blvd Houston, TX 77030 [email protected] I. S EXPRESSIONS A. Liter
SCIPM
http:/www.vni.com/products/imsl/documentation/fort06/stat/NetHelp/s.
Chapter 4: Analysis of Variance
SCIPM
Computes simultaneous confidence intervals on all pairwise differences of means.
Required Arguments
NI Vector of length NGROUP containing the
An Introduction to R
Notes on R: A Programming Environment for Data Analysis and Graphics Version 2.5.1 (2007-06-27)
W. N. Venables, D. M. Smith and the R Development Core Team
Copyright Copyright Copyright Copyright Copyright
c c c c c
1990 W. N. Venable
Solutions from Montgomery, D. C. (2001) Design and Analysis of Experiments, Wiley, NY
4
R( , ) = y. +
y
i =1
i i.
= 59.83
with 4 degrees of freedom.
R ( , ) = R( , , ) R( , ) = 138.78 59.83 = 78.95 = SS Blocks
with 7-4=3 degrees of freedom. Source Tips
fit1$residuals
-10 60
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0
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fit1$fitted.values
* Analysis of Variance Model * Short Output: Call: aov(formula = y ~ site + supplier, data = prb0401, na.action = na.exclude) Terms: site supplier Residuals Sum of Squares 568.7083 665.1
Problem 4.1 a) What kind of an experimental design is this? Randomized Block Design b) Make a graphical analysis and an ANOVA.
Analyzing each of the paint suppliers, the mean is different for each of them as illustrated above.
ANOVA output > summary(fit1)
a) This is a randomized Bloack experimental design b)
Analyzing each of the paint suppliers, the mean is different for each of them as illustrated above. > fit1= aov(y~supplier + site, data=Prob4.1) Warning message: variable 'site' converted to a factor i
Jaime Frade Problem 4.1 (J )
a) What kind of an experimental design is this? Randomized Block Design b) Make a graphical analysis and an ANOVA.
Analyzing each of the paint suppliers, the mean is different for each of them as illustrated above.
Jaime Frade
Problem 4.1 a) What kind of an experimental design is this? Randomized Block Design b) Make a graphical analysis and an ANOVA.
Analyzing each of the paint suppliers, the mean is different for each of them as illustrated above.
ANOVA output > summary(fit1)
Problem 4 a) Plot the data. What kind of experimental design is this? Divided the run into 8 blocks which includes 4 treatments each.
CODE
#renumber runs 8 blocks of (1-4) Prob4.4=read.table(file="E:/FSU-FALL07-School/Fall07-school/STA5166/BHH2-Data/prb04
4.2 a) What is this design called? What characteristics does it have? The problem is a Latin-Square experimental design The study consists of 6 treatments (A-F) of Volunteers, 6 blocks (I-VI) of Positions on arm, and 6 blocks (#1-6) of Subjects. b) How ca
4.2 a) What is this design called? What characteristics does it have? The problem is a Latin-Square experimental design The study consists of 6 treatments (A-F) of Volunteers, 6 blocks (I-VI) of Positions on arm, and 6 blocks (#1-6) of Subjects. b) How ca
a) This is a randomized Bloack experimental design b)
Analyzing each of the paint suppliers, the mean is different for each of them as illustrated above. > fit1= aov(y~supplier + site, data=Prob4.1) Warning message: variable 'site' converted to a factor i
Jaime Frade STA5166 Chapter 3.2 (a )
Summary: Will try to test the hypothesis to see if there exists a significant difference between the mean values of levels of asbestos fiber in the air of the industrial plant with and without S-142 chemical. From the
Jaime Frade STA5166 HW1: Reproduce graphs from the lecture using R.
Summary I stored x1 as a random standard normal variable to generate 1000. Each of the four plots listed below exhibit the characteristics of this random normal variable.
Summary: I obtai
Jaime Frade STA5166 HW1: Reproduce graphs from the lecture using R.
Summary I stored x1 as a random standard normal variable to generate 1000. Each of the four plots listed below exhibit the characteristics of this random normal variable.
Summary: I obtai
STA5166 HW5 Problem chapter 9.2 #=parta #each pair of observations occurs in the same block one time, lambda=1 #=partb #Efficiency factor #E = [(lambda)(t)]/[rk] lambda=1 #how many times each pair is repeated t = 7 #number of treatments, total of columns
Stat 231
2.
Chapter 4
a) A Poisson distribution is a likely probability distribution, since the data involves counts of eggs. b) For data with a Poisson distribution the square root transformation is often a good variance stabilizing transformation. c) Th