Geo.Engineerings

Daniel T. Larose  ... 718 pages - Publisher: Freeman/Worth; 2nd edition (January, 2013) ... Language: English - ASIN: B00HQO0UZI by Amazon - ISBN-10: 1464127182 - ISBN-13: 978-1464127182.

Peter Goos, David Meintrup ... 648 pages - Publisher: Wiley; (April, 2016) ... Language: English - ISBN-10: 1119097150 - ISBN-13: 978-1119097150.

This book provides a first course on parameter estimation (point estimates and confidence interval estimates), hypothesis testing, ANOVA and simple linear regression. The authors approach combines mathematical depth with numerous examples and demonstrations using the JMP software. Key features: Provides a comprehensive and rigorous presentation of introductory statistics that has been extensively classroom tested. + Pays attention to the usual parametric hypothesis tests as well as to non-parametric tests (including the calculation of exact p-values). + Discusses the power of various statistical tests, along with examples in JMP to enable in-sight into this difficult topic. + Promotes the use of graphs and confidence intervals in addition to p-values. + Course materials and tutorials for teaching are available on the book's companion website. Masters and advanced students in applied statistics, industrial engineering, business engineering, civil engineering and bio-science engineering will find this book beneficial. It also provides a useful resource for teachers of statistics particularly in the area of engineering.

Ram S. Gupta ... 617 pages - Publisher: CRC Press; 3rd edition (June, 2019) ... Language: English - AmazonSIN: B07TBD778R.

Edward Allen, Joseph Iano ... 944 pages - Publisher: Wiley; 7th edition (September, 2019) ... Language: English - ASIN: B07YCXG7YH by Amazon.

Edward Allen and Joseph Iano’s Fundamentals of Building Construction has been the go-to reference for thousands of professionals and students of architecture, engineering, and construction technology for over thirty years. The materials and methods described in this new Seventh Edition have been thoroughly updated to reflect the latest advancements in the industry. Carefully selected and logically arranged topics—ranging from basic building methods to the principles of structure and enclosure—help readers gain a working knowledge of the field in an enjoyable, easy-to-understand manner. All major construction systems, including light wood frame, mass timber, masonry, steel frame, light gauge steel, and reinforced concrete construction, are addressed. Now in its Seventh Edition, Fundamentals of Building Construction contains substantial revisions and updates. New illustrations and photographs reflect the latest practices and developments in the industry. Revised chapters address exterior wall systems and high-performance buildings, an updated and comprehensive discussion of building enclosure science, evolving tools for assessing environmental and health impacts of building materials, and more. New and exciting developments in mass timber construction are also included.  For the nuts and bolts on building construction practices and materials, Fundamentals of Building Construction: Materials and Methods, 7th Edition lays the foundation that every architect and construction professional needs to build a successful career.

N. Balakrishnan, Markos V. Koutras, Konstadinos G. Politis ... 620 pages - Publisher: Wiley; (April , 2019) ... Language: English - ASIN: B07QGMBC9F by Amazon.

Introduction to Probability offers an authoritative text that presents the main ideas and concepts, as well as the theoretical background, models, and applications of probability. The authors—noted experts in the field—include a review of problems where probabilistic models naturally arise, and discuss the methodology to tackle these problems. A wide-range of topics are covered that include the concepts of probability and conditional probability, univariate discrete distributions, univariate continuous distributions, along with a detailed presentation of the most important probability distributions used in practice, with their main properties and applications.

Designed as a useful guide, the text contains theory of probability, de finitions, charts, examples with solutions, illustrations, self-assessment exercises, computational exercises, problems and a glossary. This important text: • Includes classroom-tested problems and solutions to probability exercises • Highlights real-world exercises designed to make clear the concepts presented • Uses Mathematica software to illustrate the text’s computer exercises • Features applications representing worldwide situations and processes • Offers two types of self-assessment exercises at the end of each chapter, so that students may review the material in that chapter and monitor their progress. Written for students majoring in statistics, engineering, operations research, computer science, physics, and mathematics, Introduction to Probability: Models and Applications is an accessible text that explores the basic concepts of probability and includes detailed information on models and applications.

Aileen Nielsen ... 505 pages - Publisher: O'Reilly Media; (September, 2019) ... Language: English - Amazon SIN: B07Y5WSCV2.

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data + Undertake exploratory time series data analysis + Store temporal data + Simulate time series data + Generate and select features for a time series + Measure error + Forecast and classify time series with machine or deep learning + Evaluate accuracy and performance