James A. Middleton

Experimental Statistics and Data Analysis for Mechanical and Aerospace Engineers (eBook, ePUB)

• Format: ePub

Produktbeschreibung

This book develops foundational concepts in probability and statistics with primary applications in mechanical and aerospace engineering. It was designed utilizing the latest research in statistics learning and in engagement teaching practices.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 586
• Erscheinungstermin: 23. November 2021
• Englisch
• ISBN-13: 9781000469622
• Artikelnr.: 62833077

Autorenporträt

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.

Inhaltsangabe

1. Introduction
2. Dealing with Variation
3. Types of Data
4. Introduction to Probability
5. Sampling Distribution of the Mean
6. Experimental Designs
7. Sampling Distribution of the Proportion
8. Hypothesis Testing Using the Normal Distribution
9. Two-sample Statistics
10. Simple Linear Regression
11. The General Linear Model
12. The GLM with Categorical Independent Variables
13. Factorial ANOVA
14. Bootstrap
15. Principal Components Analysis