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Author: Pierre Moulin
Publisher: Cambridge University Press
Published: 2019
Total Pages: 423
ISBN-13: 1107185920
DOWNLOAD EBOOK →A mathematically accessible textbook introducing all the tools needed to address modern inference problems in engineering and data science.
Author: Ronald E. Walpole
Publisher:
Published: 2016
Total Pages: 816
ISBN-13: 9781292161365
DOWNLOAD EBOOK →MyStatLabTM is not included. Students, if MyStatLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN and course ID. MyStatLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.
Author: Alan Agresti
Publisher: CRC Press
Published: 2021-11-22
Total Pages: 486
ISBN-13: 1000462919
DOWNLOAD EBOOK →Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on "why it works" as well as "how to do it." Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.
Author: Michael Clifford
Publisher: CRC Press
Published: 2022-12-27
Total Pages: 0
ISBN-13: 9781000763515
DOWNLOAD EBOOK →Updated throughout for the second edition, Introduction to Mechanical Engineering: Part 1 continues to be the essential text for all first-year undergraduate students, alongside those studying for foundation degrees and HNDs. Written by an experienced team of lecturers at the internationally renowned University of Nottingham, this book provides a comprehensive grounding in the following core engineering topics: thermodynamics, fluid mechanics, solid mechanics, dynamics, electrical and electronic systems and material science. It includes questions and answers for instructors and for self-guided learning. As well as mechanical engineers, this book is highly relevant to civil, automotive and aerospace engineering students.
Author: Ronald E. Walpole
Publisher:
Published: 2013-07-23
Total Pages: 853
ISBN-13: 9781292023922
DOWNLOAD EBOOK →For junior/senior undergraduates taking probability and statistics as applied to engineering, science, or computer science. This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding. This latest edition is also available in as an enhanced Pearson eText. This exciting new version features an embedded version of StatCrunch, allowing students to analyze data sets while reading the book."
Author: Robert M. Bethea
Publisher: Routledge
Published: 2018-04-20
Total Pages: 672
ISBN-13: 1351414372
DOWNLOAD EBOOK →This work details the fundamentals of applied statistics and experimental design, presenting a unified approach to data handling that emphasizes the analysis of variance, regression analysis and the use of Statistical Analysis System computer programs. This edition: discusses modern nonparametric methods; contains information on statistical process control and reliability; supplies fault and event trees; furnishes numerous additional end-of-chapter problems and worked examples; and more.
Author: Ronald E. Walpole
Publisher:
Published: 2012
Total Pages: 0
ISBN-13: 9780321629111
DOWNLOAD EBOOK →This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance of theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.
Author: Chester Ismay
Publisher: CRC Press
Published: 2019-12-23
Total Pages: 461
ISBN-13: 1000763463
DOWNLOAD EBOOK →Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.
Author: Ronald Edgar Walpole
Publisher: Macmillan College
Published: 1989
Total Pages: 115
ISBN-13: 9780024242228
DOWNLOAD EBOOK →This classic book provides a rigorous introduction to basic probability theory and statistical inference that is well motivated by interesting, relevant applications. The new edition features many new, real-data based exercises and examples, an increased emphasis on the analysis of statistical output and greater use of graphical techniques and statistical methods in quality improvement.
Author: Ronald E. Walpole
Publisher: Prentice Hall
Published: 2007
Total Pages: 856
ISBN-13:
DOWNLOAD EBOOK →This classic text provides a rigorous introduction to basic probability theory and statistical inference, illustrated by relevant applications. It assumes a background in calculus and offers a balance of theory and methodology
