ISYE6413
Fall 2010
Exam II
Dr. Kobi Abayomi
October 20, 2010
You must show all work to receive full credit.
1
1
Consider an ANCOVA model with two treatments on response Y with covariate X.
You get these outputs:
Call:
lm(formula = y ~ x + factor(t), data
Problem 1
To repeat the analysis, simply note that the new estimate for is 235.21. Use equation (3.41)
in the textbook, we can get the following table for multiple comparison statistics.
A vs. B
A vs. C
A vs. D
B vs. C
B vs. D
C vs. D
-4.22
-2.21
-3.25
2.
HW 1 solution (45 pts in total)
Problem 3 in Chapter 1 (5pts)
Hard to change factors include oven temperature in a smelting plant, humidity in manufacturing
plant, and dust levels in the air. One could randomize other important factors within a certain
ba
HW 2 Solution
Problem 10 in Chapter 2
The variances appear to be equal, however it seems that the residuals for machine two are mostly
negative. It may be the case that the data are not normally distributed. In that case, we will
learn ways to deal with t
Notes for ISyE 6413
Design and Analysis of Experiments
Instructor : C. F. Jeff Wu
School of Industrial and Systems Engineering
Georgia Institute of Technology
Text book : Experiments : Planning, Analysis, and Optimization
(by Wu and Hamada; Second Edition
Unit 3: Experiments with More Than One Factor
Sources : Chapter 3.
Paired comparison design (Section 3.1).
Randomized block design (Section 3.2).
Two-way and multi-way layout with fixed effects (Sections 3.3 and 3.5).
Latin and Graeco-Latin square des
Unit 6: Fractional Factorial Experiments at Three
Levels
Source : Chapter 6 (Sections 6.1 - 6.6)
Larger-the-better and smaller-the-better problems.
Basic concepts for 3k full factorial designs.
Analysis of 3k designs using orthogonal components system.
Unit 4: Full Factorial Experiments at Two Levels
Source : Chapter 4 (sections 4.1-4.4, 4.6-4.12, 4.15).
An Epitaxial Layer Growth Experiment (Section 4.1).
Basic concepts for 2k designs (Section 4.2).
Factorial effects and plots (Section 4.3).
Using R
Unit 5: Fractional Factorial Experiments at Two
Levels
Source : Chapter 5 (sections 5.1 - 5.5, part of section 5.6).
Leaf Spring Experiment (Section 5.1)
Effect aliasing, resolution, minimum aberration criteria (Section 5.2).
Analysis of Fractional Fac
ISyE 6413(Fall 2009): ASSESS YOUR BACKGROUND Quiz Solution
Problem 1.
1. We have 26 letters and 10 digits, so the total number is 265 10 = 118813760.
2. 265 5 = 59406880. 3. 255 10 = 97656250. Problem 2. The event that the sample will contain no more than
ISyE 6413(Fall 2009): ASSESS YOUR BACKGROUND Quiz Solution
Problem 1.
1. We have 26 letters and 10 digits, so the total number is 265 10 = 118813760.
2. 265 5 = 59406880. 3. 255 10 = 97656250. Problem 2. The event that the sample will contain no more than
Homework 5 (Due Tuesday, November 10th)
1. Problem 6 in Chapter 5. 2. Problem 16 in Chapter 5. 3. Problem 21 in Chapter 5. 4. Problem 30 in Chapter 5. Ignore part (c). Also for part (d), you do not need to compare the results of the two approaches.
1
Question1 WefirstfitthemodelgiveninSection3.10:
(1) Thismodelassumestherelationshipof and isalinearrelationshipthatdoesnotdependon thetypeoffilm.TheMINITABoutputisshownbelow. Regression Analysis: y versus x, tau2, tau3
The regression equation is y = 158
3.9 In this study, the response of interest is the blood pressure two hours after the application of a treatment minus the blood pressure just before the treatment is applied, i.e. the reduction in blood pressure after two hours. We want to assess the eff
Homework 2 (Due Thursday, September 17th)
1. Problem 1 in Chapter 2. For this problem, please explain why one method gives shorter intervals than the other. Also explain why we can not always use the method with shorter intervals. 2. Problem 16 in Chapter
1.6 (a)
Consider the following statistical model: yi = + li + i , i=1,2,6, where yi=the ith observed difference between A and B (A-B) =the intrinsic difference between A and B (A-B) li=learning effect of the ith transcript i=errors with mean 0 When the te