This book develops foundational concepts in probability and
statistics with primary applications in mechanical and aerospace
engineering. It develops the mindset a data analyst must have to
interpret an ill-defined problem, operationalize it, collect or
interpret data, and use this evidence to make decisions that can
improve the quality of engineered products and systems. It was
designed utilizing the latest research in statistics learning and
in engagement teaching practices The author's focus is on
developing students' conceptual understanding of statistical theory
with the goal of effective design and conduct of experiments.
Engineering statistics is primarily a form of data modeling.
Emphasis is placed on modelling variation in observations,
characterizing its distribution, and making inferences with regards
to quality assurance and control. Fitting multivariate models,
experimental design and hypothesis testing are all critical skills
developed. All topics are developed utilizing real data from
engineering projects, simulations, and laboratory experiences. In
other words, we begin with data, we end with models. The key
features are: Realistic contexts situating the learning of the
statistics in actual engineering practice. A balance of rigorous
mathematics, conceptual scaffolding, and real, messy data, to
ensure that students learn the important concepts and can apply
them in practice. The consistency of text, lecture notes, data
sets, and simulations yield a coherent set of instructional
resources for the instructor and a coherent set of learning
experiences for the students. MatLab is used as a computational
tool. Other tools are easily substituted. Table of Contents 1.
Introduction 2. Dealing with Variation 3. Types of Data 4.
Introduction to Probability 5. Sampling Distribution of the Mean 6.
The Ten Building Blocks of Experimental Design 7. Sampling
Distribution of the Proportion 8. Hypothesis Testing Using the
1-sample Statistics 9. 2-sample Statistics 10. Simple Linear
Regression 11. The General Linear Model: Regression with Multiple
Predictors 12. The GLM with Categorical Independent Variables: The
Analysis of Variance 13. The General Linear Model: Randomized Block
Factorial ANOVA 14. Factorial Analysis of Variance 15. The
Bootstrap 16. Data Reduction: Principal Components Analysis Index
Author Biography James A. Middleton is Professor of Mechanical and
Aerospace Engineering and former Director of the Center for
Research on Education in Science, Mathematics, Engineering, and
Technology at Arizona State University. Previously, he held the
Elmhurst Energy Chair in STEM education at the University of
Birmingham in the UK. He received his Ph.D. from the University of
Wisconsin-Madison. He has been Senior co-Chair of the Special
Interest Group for Mathematics Education in the American
Educational Research Association, and as Chair of the National
Council of Teachers of Mathematics' Research Committee. He has been
a consultant for the College Board, the Rand Corporation, the
National Academies, the American Statistical Association, the IEEE,
and numerous school systems around the United States, the UK, and
Australia. He has garnered over $30 million in grants to study and
improve mathematics education in urban schools.
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