Max D. Morris is a professor in the Department of Statistics and the Department of Industrial and Manufacturing Systems Engineering at Iowa State University. A fellow of the American Statistical Association, Dr. Morris is a recipient of the National Institute of Statistical Sciences Sacks Award for Cross-Disciplinary Research and the American Society for Quality Wilcoxon Prize.

A distinctive feature of this excellent book is that it actually focuses on how to design an experiment. … In all, an original and very useful book for students and instructors.—Stat Papers (2014) 55:1225–1226the author has succeeded in striking a balance between the choice of topics and depth in discussion for teaching a course. The book is written with a refreshing style and succeeds in conveying the concepts to a reader. The treatment of the subject matter is thorough and the theory is clearly illustrated along with worked examples. Other books are available on similar topics but this book has the advantage that the chapters start with the classical non-matrix-theory approach to introduce the linear model and then converts it into a matrix theory-based linear model. This helps a reader, particularly a beginner, in clearly understanding the transition from a non-matrix approach to a matrix approach and to apply the results of matrix theory over linear models further.—Shalabh, Journal of the Royal Statistical Society, Series A, 2012Overall, this is a book that is easy to like, with good definitions of designs, few typographical errors, and consistent, straightforward explications of the models … I can picture a lot of students using a text aimed at a broad market design course but who need to understand more about what is going on behind the curtain. Morris’ text also fills that gap very well.—Gary W. Oehlert, Biometrics, May 2012It is truly my pleasure to read this book … after reading this book, I benefitted by gaining insights into the modeling aspect of experimental design, and consequentially it helps me appreciate the idea of statistical efficiency behind each design and understand the tools used in data analysis. … an excellent reference book that I would recommend to anyone who is serious about learning the nuts and bolts of experimental design and data analysis techniques.—Rong Pan, Journal of Quality Technology, Vol. 43, No. 3, July 2011

• Introduction. Linear Statistical Models. Completely Randomized Designs. Randomized Complete Blocks and Related Designs. Latin Squares and Related Designs. Some Data Analysis for CRDs and Orthogonally Blocked Designs. Balanced Incomplete Block Designs. Random Block Effects. Factorial Treatment Structure. Split-Plot Designs. Two-Level Factorial Experiments: Basics. Two-Level Factorial Experiments: Blocking. Two-Level Factorial Experiments: Fractional Factorials. Factorial Group Screening Experiments. Regression Experiments: First-Order Polynomial Models. Regression Experiments: Second-Order Polynomial Models. Introduction to Optimal Design. Appendices. References. Index.

Max D. Morris is a professor in the Department of Statistics and the Department of Industrial and Manufacturing Systems Engineering at Iowa State University. A fellow of the American Statistical Association, Dr. Morris is a recipient of the National Institute of Statistical Sciences Sacks Award for Cross-Disciplinary Research and the American Society for Quality Wilcoxon Prize.

A distinctive feature of this excellent book is that it actually focuses on how to design an experiment. … In all, an original and very useful book for students and instructors.—Stat Papers (2014) 55:1225–1226the author has succeeded in striking a balance between the choice of topics and depth in discussion for teaching a course. The book is written with a refreshing style and succeeds in conveying the concepts to a reader. The treatment of the subject matter is thorough and the theory is clearly illustrated along with worked examples. Other books are available on similar topics but this book has the advantage that the chapters start with the classical non-matrix-theory approach to introduce the linear model and then converts it into a matrix theory-based linear model. This helps a reader, particularly a beginner, in clearly understanding the transition from a non-matrix approach to a matrix approach and to apply the results of matrix theory over linear models further.—Shalabh, Journal of the Royal Statistical Society, Series A, 2012Overall, this is a book that is easy to like, with good definitions of designs, few typographical errors, and consistent, straightforward explications of the models … I can picture a lot of students using a text aimed at a broad market design course but who need to understand more about what is going on behind the curtain. Morris’ text also fills that gap very well.—Gary W. Oehlert, Biometrics, May 2012It is truly my pleasure to read this book … after reading this book, I benefitted by gaining insights into the modeling aspect of experimental design, and consequentially it helps me appreciate the idea of statistical efficiency behind each design and understand the tools used in data analysis. … an excellent reference book that I would recommend to anyone who is serious about learning the nuts and bolts of experimental design and data analysis techniques.—Rong Pan, Journal of Quality Technology, Vol. 43, No. 3, July 2011

• Introduction. Linear Statistical Models. Completely Randomized Designs. Randomized Complete Blocks and Related Designs. Latin Squares and Related Designs. Some Data Analysis for CRDs and Orthogonally Blocked Designs. Balanced Incomplete Block Designs. Random Block Effects. Factorial Treatment Structure. Split-Plot Designs. Two-Level Factorial Experiments: Basics. Two-Level Factorial Experiments: Blocking. Two-Level Factorial Experiments: Fractional Factorials. Factorial Group Screening Experiments. Regression Experiments: First-Order Polynomial Models. Regression Experiments: Second-Order Polynomial Models. Introduction to Optimal Design. Appendices. References. Index.

Max D. Morris is a professor in the Department of Statistics and the Department of Industrial and Manufacturing Systems Engineering at Iowa State University. A fellow of the American Statistical Association, Dr. Morris is a recipient of the National Institute of Statistical Sciences Sacks Award for Cross-Disciplinary Research and the American Society for Quality Wilcoxon Prize.

A distinctive feature of this excellent book is that it actually focuses on how to design an experiment. … In all, an original and very useful book for students and instructors.—Stat Papers (2014) 55:1225–1226the author has succeeded in striking a balance between the choice of topics and depth in discussion for teaching a course. The book is written with a refreshing style and succeeds in conveying the concepts to a reader. The treatment of the subject matter is thorough and the theory is clearly illustrated along with worked examples. Other books are available on similar topics but this book has the advantage that the chapters start with the classical non-matrix-theory approach to introduce the linear model and then converts it into a matrix theory-based linear model. This helps a reader, particularly a beginner, in clearly understanding the transition from a non-matrix approach to a matrix approach and to apply the results of matrix theory over linear models further.—Shalabh, Journal of the Royal Statistical Society, Series A, 2012Overall, this is a book that is easy to like, with good definitions of designs, few typographical errors, and consistent, straightforward explications of the models … I can picture a lot of students using a text aimed at a broad market design course but who need to understand more about what is going on behind the curtain. Morris’ text also fills that gap very well.—Gary W. Oehlert, Biometrics, May 2012It is truly my pleasure to read this book … after reading this book, I benefitted by gaining insights into the modeling aspect of experimental design, and consequentially it helps me appreciate the idea of statistical efficiency behind each design and understand the tools used in data analysis. … an excellent reference book that I would recommend to anyone who is serious about learning the nuts and bolts of experimental design and data analysis techniques.—Rong Pan, Journal of Quality Technology, Vol. 43, No. 3, July 2011

• Introduction. Linear Statistical Models. Completely Randomized Designs. Randomized Complete Blocks and Related Designs. Latin Squares and Related Designs. Some Data Analysis for CRDs and Orthogonally Blocked Designs. Balanced Incomplete Block Designs. Random Block Effects. Factorial Treatment Structure. Split-Plot Designs. Two-Level Factorial Experiments: Basics. Two-Level Factorial Experiments: Blocking. Two-Level Factorial Experiments: Fractional Factorials. Factorial Group Screening Experiments. Regression Experiments: First-Order Polynomial Models. Regression Experiments: Second-Order Polynomial Models. Introduction to Optimal Design. Appendices. References. Index.