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Complete Solutions Manual — Design and Analysis of Experiments, 10th Edition — Douglas C. Montgomery — ISBN  — Latest Update 2025/2026 — (All Chapters Covered 1‑15)

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This verified Solutions Manual for Design and Analysis of Experiments (10th Edition) by Douglas C. Montgomery (ISBN ) provides complete, chapter‑by‑chapter solutions matching the official textbook structure. Designed for instructors and advanced students in statistics and experimental design, this resource supports comprehensive coverage of single‑factor and factorial designs, blocking and confounding, analysis of variance, regression modeling, robust design, split‑plot designs, and advanced topics in experimentation. The chapter sequence begins with Chapter 1: Introduction, which discusses the strategy of experimentation, typical applications of experimental design, basic principles, guidelines for designing experiments, and a brief history of statistical design. Chapter 2: Simple Comparative Experiments covers basic statistical concepts, sampling distributions, inferences about differences in means and variances, paired comparisons, and sample‑size considerations. Chapter 3: Experiments with a Single Factor: The Analysis of Variance introduces fixed‑effects and random‑effects models, model adequacy checking, contrasts, orthogonal contrasts, sample‑size determination, and regression approaches to ANOVA. Chapter 4: Randomized Blocks, Latin Squares, and Related Designs discusses the randomized complete block design, Latin square design, Graeco‑Latin square design, and balanced incomplete block designs. Chapter 5: Introduction to Factorial Designs introduces basic definitions and principles of factorial designs, the two‑factor factorial design, general factorial designs, response curves and surfaces, and blocking in factorial experiments. Chapter 6: The 2ᵏ Factorial Design covers the 2², 2³, and general 2ᵏ designs, optimality of 2ᵏ designs, center points, coded design variables, and other advanced features. Chapter 7: Blocking and Confounding in the 2ᵏ Factorial Design presents blocking, confounding in two‑block and four‑block designs, partial confounding, and applications of blocking in factorial experiments. Chapter 8: Two‑Level Fractional Factorial Designs includes the one‑half and one‑quarter fractions of the 2ᵏ design, design resolution, analysis of fractional factorials, alias structures, fold‑over designs, and advanced features such as Plackett–Burman designs. Chapter 9: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs discusses 3ᵏ factorial designs, confounding in 3ᵏ designs, fractional replication of 3ᵏ designs, factors at mixed levels, nonregular fractional factorial designs, and optimal‑design tools. Chapter 10: Fitting Regression Models covers linear regression, parameter estimation, hypothesis testing in multiple regression, confidence intervals, prediction, regression diagnostics, and tests for lack of fit. Chapter 11: Response Surface Methods and Designs introduces the method of steepest ascent, second‑order response surfaces, location of the stationary point, ridge systems, mixture experiments, evolutionary operation, and computer‑model experiments. Chapter 12: Robust Parameter Design and Process Robustness Studies discusses crossed array designs, combined array designs, response models, choice of designs, and process robustness. Chapter 13: Experiments with Random Factors covers random‑effects models, mixed models, rules for expected mean squares, approximate F‑tests, and estimation of variance components. Chapter 14: Nested and Split‑Plot Designs includes two‑stage nested designs, variance components, split‑plot designs, split‑split‑plot design, and strip‑split‑plot design. Chapter 15: Other Design and Analysis Topics (Available in e‑text form for students) concludes the text with additional advanced topics in experimental design and analysis.

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Design and Analysis of Experiments

10th Edition
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SOLUTIONS
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MANUAL
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Douglas Montgomery
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Comprehensive Solutions Manual for

Instructors and Students
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© Douglas Montgomery. All rights reserved. Reproduction or distribution without permission
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is prohibited.




©MedConnoisseur

, Solutions Manual for
Design and Analysis
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of Experiments, 10e
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Douglas Montgomery
(All Chapters)
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, Solutions from Montgomery, D. C. (2019) Design and Analysis of Experiments, Wiley, NY



Chapter 1
Introduction
Solutions

1.1S. Suppose that you want to design an experiment to study the proportion of unpopped kernels of
popcorn. Complete steps 1-3 of the guidelines for designing experiments in Section 1.4. Are there any major
sources of variation that would be difficult to control?
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Step 1 – Recognition of and statement of the problem. Possible problem statement would be – find the best
combination of inputs that maximizes yield on popcorn – minimize unpopped kernels.

Step 2 – Selection of the response variable. Possible responses are number of unpopped kernels per 100
kernals in experiment, weight of unpopped kernels versus the total weight of kernels cooked.
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Step 3 – Choice of factors, levels and range. Possible factors and levels are brand of popcorn (levels: cheap,
expensive), age of popcorn (levels: fresh, old), type of cooking method (levels: stovetop, microwave),
temperature (levels: 150C, 250C), cooking time (levels: 3 minutes, 5 minutes), amount of cooking oil (levels,
1 oz, 3 oz), etc.
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1.2. Suppose that you want to investigate the factors that potentially affect cooked rice.

(a) What would you use as a response variable in this experiment? How would you measure the
response?
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(b) List all of the potential sources of variability that could impact the response.

(c) Complete the first three steps of the guidelines for designing experiments in Section 1.4.

Step 1 – Recognition of and statement of the problem.
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Step 2 – Selection of the response variable.

Step 3 – Choice of factors, levels and range.
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1.3. Suppose that you want to compare the growth of garden flowers with different conditions of
sunlight, water, fertilizer and soil conditions. Complete steps 1-3 of the guidelines for designing
experiments in Section 1.4.

Step 1 – Recognition of and statement of the problem.
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Step 2 – Selection of the response variable.

Step 3 – Choice of factors, levels and range.


1.4. Select an experiment of interest to you. Complete steps 1-3 of the guidelines for designing
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experiments in Section 1.4.




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, Solutions from Montgomery, D. C. (2019) Design and Analysis of Experiments, Wiley, NY


1.5. Search the World Wide Web for information about Sir Ronald A. Fisher and his work on
experimental design in agricultural science at the Rothamsted Experimental Station.

Sample searches could include the following:
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1.6. Find a Web Site for a business that you are interested in. Develop a list of factors that you would
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use in an experimental design to improve the effectiveness of this Web Site.


1.7. Almost everyone is concerned about the rising price of gasoline. Construct a cause and effect
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diagram identifying the factors that potentially influence the gasoline mileage that you get in your car.
How would you go about conducting an experiment to determine any of these factors actually affect your
gasoline mileage?


1.8. What is replication? Why do we need replication in an experiment? Present an example that
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illustrates the differences between replication and repeated measures.

Repetition of the experimental runs. Replication enables the experimenter to estimate the experimental
error, and provides more precise estimate of the mean for the response variable.
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1.9S. Why is randomization important in an experiment?

To assure the observations, or errors, are independently distributed randome variables as required by
statistical methods. Also, to “average out” the effects of extraneous factors that might occur while running
the experiment.
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1.10S. What are the potential risks of a single, large, comprehensive experiment in contrast to a sequential
approach?

The important factors and levels are not always known at the beginning of the experimental process. Even
new response variables might be discovered during the experimental process. By running a large
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comprehensive experiment, valuable information learned early in the experimental process can not likely
be incorporated in the remaining experimental runs.




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