Solution for Hw#1
1. (2.12 or 2-2) The viscosity of a liquid detergent is supposed to average 800 centistokes
at 25C. A random sample of 16 batches of detergent is collected, and the average
viscosity is 812. Suppose we know that the standard deviation of
Assignment 6 (Due Wednesday 2/27/2013)
1. A chemist wishes to test the eects of four chemical agents on the strength of a particular
type of cloth. Because there might be variability from one bolt to another, the chemist
decides to use a randomized comple
STAT 514
Final Exam
Name_
Instruction: This is a take-home exam and should be due by 5pm, Wednesday, December 16. If you run SAS or
other statistical package, please attach the programs and output. Put all the programs and output together as an
attachment
Stat 514
READING
Due 3/29
Assignment #7
- Montgomery - Sections 4.1-4.4, 5.1-5.3
1. Japanese beetles ate the Roma beans in the Oehlerts garden last year, so they ran an experiment
this year to nd the best deterrent. They have six garden beds with beans, a
Solutions from Montgomery, D. C. (2008) Design and Analysis of Experiments, Wiley, NY
Chapter 2
Simple Comparative Experiments
Solutions
2.1. The Minitab output for a random sample of data is shown below. Some of the quantities are missing.
Compute the va
Stat 514
READING
Due 4/7
Assignment #8
- Montgomery - Chapter 5 and Chapter 13
1. Montgomery Problem 5-18 (paper.dat in data directory). This is a xed eects model.
The ANOVA table is
Source
CONC
PRES
CONC*PRES
TIME
CONC*TIME
PRES*TIME
CONC*PRES*TIME
ERROR
Stat 514
READING
Due 3/10
Assignment #6
- Montgomery - Sections 4.1-4.2
1. For the vascular graft example (Table 4-3, page 127), change the data in the following manner. To
the data for resin batch 1, add .5 (e.g. 90.3 90.8), to the data for resin batch 3
Stat 514
READING
Assignment #5
- Montgomery - Sections 3-7, 13-1, 13-4, 15-3
Due 3/3
1. The grand sample mean is 100. This means the treatment sum of squares is
SSTrt = 5 32 + 12 + 66 + 92 + 22 + 32 = 700
The F test is equal to (700/5)/40=3.5. The critica
Randomization
Because it is generally extremely difficult for experimenters to eliminate bias
using only their expert judgment, the use of randomization in experiments is
common practice. In a randomized experimental design, objects or individuals
are ran
Homogeneity of Variance Tests For Two or More Groups
We covered this topic for two-group designs earlier. Basically, one transforms the scores so
that between groups variance in the scores reflects differences in variance rather than differences in
means.
Collaborative Institutional Training Initiative (CITI) User Account Set-up
https:/www.citiprogram.org
1. Click New Users Register Here.
1. Select your Institution or Organization.
Participating Institutions: scroll down until you find Purdue University, c
Users Manual for the LAP-Animal Use Qualification (AUQ) Web-based
Application
The AUQ web-based application has been designed to streamline data collection,
update, and management of Animal User Qualifications and minimize administrative
burden for both A
Design of Engineering Experiments
The 2k-p Fractional Factorial Design
Text reference, Chapter 8
Motivation for fractional factorials is obvious; as the
number of factors becomes large enough to be
interesting, the size of the designs grows very quickly
Random and Mixed Effects ANOVA
A classification variable in ANOVA may be either fixed or random. The
meaning of fixed and random are the same as they were when we discussed the
distinction between regression and correlation analysis. With a fixed variable
Design of Engineering Experiments
Part 3 The Blocking Principle
Text Reference, Chapter 4
Blocking and nuisance factors
The randomized complete block design or
the RCBD
Extension of the ANOVA to the RCBD
Other blocking scenariosLatin square
designs
D
Design of Engineering Experiments
Part 2 Basic Statistical Concepts
Simple comparative experiments
The hypothesis testing framework
The two-sample t-test
Checking assumptions, validity
Comparing more that two factor levelsthe
analysis of variance
ANO
Chapter 2 Supplemental Text Material S2-1. Models for the Data and the t-Test The model presented in the text, equation (2-23) is more properly called a means model. Since the mean is a location parameter, this type of model is also sometimes called a loc
Solutions from Montgomery, D. C. (2004) Design and Analysis of Experiments, Wiley, NY
Chapter 6
k
The 2 Factorial Design
Solutions
6-5 A router is used to cut locating notches on a printed circuit board. The vibration level at the surface
of the board as
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A
Designs Illustrated
In this appendix we give a small catalog of designs, to be used as a quick
reference. We consider the case of two treatments A and B, each at two levels,
with 16 observations available. We use the notation of Chapter 6, and denote
1. Robustness
Strictly speaking, a robust statistic is resistant to errors in the results, produced by deviations
from assumptions[1] (e.g., of normality). This means that if the assumptions are only approximately
met, the robust estimator will still have
Design of Engineering Experiments
Part 10 Nested and Split-Plot Designs
Text reference, Chapter 14, Pg. 525
These are multifactor experiments that have some
important industrial applications
Nested and split-plot designs frequently involve one
or more
Design of Engineering Experiments
Part 9 Experiments with Random
Factors
Text reference, Chapter 13, Pg. 484
Previous chapters have considered fixed factors
A specific set of factor levels is chosen for the experiment
Inference confined to those level
Design of Engineering Experiments
Part 8 Overview of
Response Surface Methods
Text reference, Chapter 11, Sections 11-1 through
11-4
Primary focus of previous chapters is factor
screening
Two-level factorials, fractional factorials are widely used
Obj
Design of Engineering Experiments Part 7
The 2k-p Fractional Factorial Design
Text reference, Chapter 8
Motivation for fractional factorials is obvious; as
the number of factors becomes large enough to be
interesting, the size of the designs grows very
Design of Engineering Experiments
Part 6 Blocking & Confounding in the 2k
Text reference, Chapter 7
Blocking is a technique for dealing with
controllable nuisance variables
Two cases are considered
Replicated designs
Unreplicated designs
DOX 6E Montg
Design of Engineering Experiments
Part 5 The 2k Factorial Design
Text reference, Chapter 6
Special case of the general factorial design; k factors,
all at two levels
The two levels are usually called low and high (they
could be either quantitative or q
Design of Engineering Experiments
Part 4 Introduction to Factorials
Text reference, Chapter 5
General principles of factorial experiments
The two-factor factorial with fixed effects
The ANOVA for factorials
Extensions to more than two factors
Quantitative
Design and Analysis of Engineering Experiments
Douglas C. Montgomery
ASU Foundation Professor of Engineering Arizona State University
DOX 6E Montgomery 1
Design of Engineering Experiments Part 1 Introduction Chapter 1, Text
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