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Statistics for Engineering and the Sciences
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Summary
Topics progress from basic sampling and probability concepts to advanced methods such as confidence intervals, hypothesis testing, regression analysis, analysis of variance, nonparametric statistics, and quality control. This edition emphasises practical applications, including experimental design and reliability, preparing students for statistical challenges in real-world engineering and scientific contexts.
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Format: Hardback
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This textbook is ideal for undergraduate students majoring in engineering and physical sciences who require a thorough grounding in statistics. It is well suited for two-semester introductory courses and beneficial for learners aiming to apply statistical techniques practically in professional engineering and scientific environments.
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Book Hero Magic formatted this description to make it easier to read. While it's new and
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Description
Prepare Your Students for Statistical Work in the Real World
Statistics for Engineering and the Sciences, Sixth Edition is designed for a two-semester introductory course on statistics for students majoring in engineering or any of the physical sciences. This popular text continues to teach students the basic concepts of data description and statistical inference as well as the statistical methods necessary for real-world applications. Students will understand how to collect and analyse data and think critically about the results.
New to the Sixth Edition
- Many new and updated exercises based on contemporary engineering and scientific-related studies and real data
- More statistical software printouts and corresponding instructions for use that reflect the latest versions of the SAS, SPSS, and MINITAB software
- Introduction of the case studies at the beginning of each chapter
- Streamlined material on all basic sampling concepts, such as random sampling and sample survey designs, which gives students an earlier introduction to key sampling issues
- New examples on comparing matched pairs versus independent samples, selecting the sample size for a designed experiment, and analysing a two-factor experiment with quantitative factors
- New section on using regression residuals to check the assumptions required in a simple linear regression analysis
The first several chapters of the book identify the objectives of statistics, explain how to describe data, and present the basic concepts of probability. The text then introduces the two methods for making inferences about population parameters: estimation with confidence intervals and hypothesis testing. The remaining chapters extend these concepts to cover other topics useful in analysing engineering and scientific data, including the analysis of categorical data, regression analysis, model building, analysis of variance for designed experiments, nonparametric statistics, statistical quality control, and product and system reliability.
Book Details
INFORMATION
ISBN: 9781498728850
Publisher: Taylor & Francis Inc
Format: Hardback
Date Published: 16 December 2015
Country: United States
Imprint: Chapman & Hall/CRC
Edition: 6th edition
Illustration: 127 Tables, black and white; 454 Illustrations, black and white
Audience: Tertiary education, Professional and scholarly
DIMENSIONS
Width: 203.0mm
Height: 254.0mm
Weight: 2271g
Pages: 1166
About the Author
William Mendenhall was a professor emeritus in the Statistics Department and the first chairman of the department at the University of Florida. Dr. Mendenhall published articles in top statistics journals and was a prolific author of statistics textbooks.
Terry L. Sincich is an associate professor in the Information Systems Decision Sciences Department at the University of South Florida, where he teaches introductory statistics at the undergraduate level and advanced statistics courses at the doctoral level. He has won numerous teaching awards, including the Kahn Teaching Award and Outstanding Teacher Award. Dr. Sincich is a member of the American Statistical Association and the Decision Sciences Institute. His research interests include applied statistical analysis and statistical modeling.
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