**Author**: Jay L. Devore

**Publisher:**Cengage Learning

**ISBN:**1305465326

**Category:**Business & Economics

**Page:**768

**View:**7258

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# Search Results for: probability-and-statistics-for-engineering-and-the-sciences

**Author**: Jay L. Devore

**Publisher:** Cengage Learning

**ISBN:** 1305465326

**Category:** Business & Economics

**Page:** 768

**View:** 7258

Put statistical theories into practice with PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9th Edition. Always a favorite with statistics students, this calculus-based text offers a comprehensive introduction to probability and statistics while demonstrating how professionals apply concepts, models, and methodologies in today's engineering and scientific careers. Jay Devore, an award-winning professor and internationally recognized author and statistician, emphasizes authentic problem scenarios in a multitude of examples and exercises, many of which involve real data, to show how statistics makes sense of the world. Mathematical development and derivations are kept to a minimum. The book also includes output, graphics, and screen shots from various statistical software packages to give you a solid perspective of statistics in action. A Student Solutions Manual, which includes worked-out solutions to almost all the odd-numbered exercises in the book, is available. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

**Author**: Sheldon M. Ross

**Publisher:** Academic Press

**ISBN:** 0123948428

**Category:** Mathematics

**Page:** 686

**View:** 2713

Introduction to Probability and Statistics for Engineers and Scientists provides a superior introduction to applied probability and statistics for engineering or science majors. Ross emphasizes the manner in which probability yields insight into statistical problems; ultimately resulting in an intuitive understanding of the statistical procedures most often used by practicing engineers and scientists. Real data sets are incorporated in a wide variety of exercises and examples throughout the book, and this emphasis on data motivates the probability coverage. As with the previous editions, Ross' text has tremendously clear exposition, plus real-data examples and exercises throughout the text. Numerous exercises, examples, and applications connect probability theory to everyday statistical problems and situations. Clear exposition by a renowned expert author Real data examples that use significant real data from actual studies across life science, engineering, computing and business End of Chapter review material that emphasizes key ideas as well as the risks associated with practical application of the material 25% New Updated problem sets and applications, that demonstrate updated applications to engineering as well as biological, physical and computer science New additions to proofs in the estimation section New coverage of Pareto and lognormal distributions, prediction intervals, use of dummy variables in multiple regression models, and testing equality of multiple population distributions.

**Author**: Jay L. Devore,Elizabeth M. Eltinge

**Publisher:** Wadsworth Publishing Company

**ISBN:** 9780534242657

**Category:** Mathematical statistics

**Page:** 183

**View:** 9723

This text emphasizes models, methodology, and applications rather than rigorous mathematical development and theory. It uses real data in both exercise sets and examples.
*Manual*

**Author**: Jay L DeVore

**Publisher:** Thomson

**ISBN:** 9780534068295

**Category:** Mathematical statistics

**Page:** 206

**View:** 6961

**Author**: Edward R. Dougherty

**Publisher:** N.A

**ISBN:** N.A

**Category:** Mathematics

**Page:** 800

**View:** 7759

**Author**: Bhisham C. Gupta,Irwin Guttman

**Publisher:** John Wiley & Sons

**ISBN:** 1118522206

**Category:** Mathematics

**Page:** 896

**View:** 7150

Introducing the tools of statistics and probability from the ground up An understanding of statistical tools is essential for engineers and scientists who often need to deal with data analysis over the course of their work. Statistics and Probability with Applications for Engineers and Scientists walks readers through a wide range of popular statistical techniques, explaining step-by-step how to generate, analyze, and interpret data for diverse applications in engineering and the natural sciences. Unique among books of this kind, Statistics and Probability with Applications for Engineers and Scientists covers descriptive statistics first, then goes on to discuss the fundamentals of probability theory. Along with case studies, examples, and real-world data sets, the book incorporates clear instructions on how to use the statistical packages Minitab® and Microsoft® Office Excel® to analyze various data sets. The book also features: • Detailed discussions on sampling distributions, statistical estimation of population parameters, hypothesis testing, reliability theory, statistical quality control including Phase I and Phase II control charts, and process capability indices • A clear presentation of nonparametric methods and simple and multiple linear regression methods, as well as a brief discussion on logistic regression method • Comprehensive guidance on the design of experiments, including randomized block designs, one- and two-way layout designs, Latin square designs, random effects and mixed effects models, factorial and fractional factorial designs, and response surface methodology • A companion website containing data sets for Minitab and Microsoft Office Excel, as well as JMP ® routines and results Assuming no background in probability and statistics, Statistics and Probability with Applications for Engineers and Scientists features a unique, yet tried-and-true, approach that is ideal for all undergraduate students as well as statistical practitioners who analyze and illustrate real-world data in engineering and the natural sciences.

**Author**: Anthony J. Hayter

**Publisher:** Cengage Learning

**ISBN:** 1111827044

**Category:** Mathematics

**Page:** 864

**View:** 7605

PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS, Fourth Edition, continues the student-oriented approach that has made previous editions successful. As a teacher and researcher at a premier engineering school, author Tony Hayter is in touch with engineers daily--and understands their vocabulary. The result of this familiarity with the professional community is a clear and readable writing style that students understand and appreciate, as well as high-interest, relevant examples and data sets that keep students' attention. A flexible approach to the use of computer tools, including tips for using various software packages, allows instructors to choose the program that best suits their needs. At the same time, substantial computer output (using MINITAB and other programs) gives students the necessary practice in interpreting output. Extensive use of examples and data sets illustrates the importance of statistical data collection and analysis for students in the fields of aerospace, biochemical, civil, electrical, environmental, industrial, mechanical, and textile engineering, as well as for students in physics, chemistry, computing, biology, management, and mathematics. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

**Author**: Douglas C. Montgomery,George C. Runger

**Publisher:** John Wiley & Sons

**ISBN:** 047088844X

**Category:** Technology & Engineering

**Page:** 398

**View:** 2340

Montgomery and Runger's bestselling engineering statistics text provides a practical approach oriented to engineering as well as chemical and physical sciences. By providing unique problem sets that reflect realistic situations, students learn how the material will be relevant in their careers. With a focus on how statistical tools are integrated into the engineering problem-solving process, all major aspects of engineering statistics are covered. Developed with sponsorship from the National Science Foundation, this text incorporates many insights from the authors' teaching experience along with feedback from numerous adopters of previous editions.

**Author**: Michael Baron

**Publisher:** CRC Press

**ISBN:** 1498760600

**Category:** Mathematics

**Page:** 449

**View:** 8076

Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic modeling, simulation, and data analysis; make optimal decisions under uncertainty; model and evaluate computer systems and networks; and prepare for advanced probability-based courses. Written in a lively style with simple language, this classroom-tested book can now be used in both one- and two-semester courses. New to the Second Edition Axiomatic introduction of probability Expanded coverage of statistical inference, including standard errors of estimates and their estimation, inference about variances, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap More exercises at the end of each chapter Additional MATLAB® codes, particularly new commands of the Statistics Toolbox In-Depth yet Accessible Treatment of Computer Science-Related Topics Starting with the fundamentals of probability, the text takes students through topics heavily featured in modern computer science, computer engineering, software engineering, and associated fields, such as computer simulations, Monte Carlo methods, stochastic processes, Markov chains, queuing theory, statistical inference, and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). Encourages Practical Implementation of Skills Using simple MATLAB commands (easily translatable to other computer languages), the book provides short programs for implementing the methods of probability and statistics as well as for visualizing randomness, the behavior of random variables and stochastic processes, convergence results, and Monte Carlo simulations. Preliminary knowledge of MATLAB is not required. Along with numerous computer science applications and worked examples, the text presents interesting facts and paradoxical statements. Each chapter concludes with a short summary and many exercises.

**Author**: Matthew A. Carlton

**Publisher:** Brooks/Cole

**ISBN:** 9780495382195

**Category:** Mathematics

**Page:** 283

**View:** 7492

Check your work-and your understanding-with this manual, which provides worked-out solutions to the odd-numbered problems in the text.

**Author**: Allen B. Downey

**Publisher:** "O'Reilly Media, Inc."

**ISBN:** 1491907371

**Category:** Computers

**Page:** 226

**View:** 5526

If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. Develop an understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data

**Author**: Matthew A. Carlton,Jay L. Devore

**Publisher:** Springer

**ISBN:** 3319524011

**Category:** Mathematics

**Page:** 643

**View:** 9979

This updated and revised first-course textbook in applied probability provides a contemporary and lively post-calculus introduction to the subject of probability. The exposition reflects a desirable balance between fundamental theory and many applications involving a broad range of real problem scenarios. It is intended to appeal to a wide audience, including mathematics and statistics majors, prospective engineers and scientists, and those business and social science majors interested in the quantitative aspects of their disciplines. The textbook contains enough material for a year-long course, though many instructors will use it for a single term (one semester or one quarter). As such, three course syllabi with expanded course outlines are now available for download on the book’s page on the Springer website. A one-term course would cover material in the core chapters (1-4), supplemented by selections from one or more of the remaining chapters on statistical inference (Ch. 5), Markov chains (Ch. 6), stochastic processes (Ch. 7), and signal processing (Ch. 8 – available exclusively online and specifically designed for electrical and computer engineers, making the book suitable for a one-term class on random signals and noise). For a year-long course, core chapters (1-4) are accessible to those who have taken a year of univariate differential and integral calculus; matrix algebra, multivariate calculus, and engineering mathematics are needed for the latter, more advanced chapters. At the heart of the textbook’s pedagogy are 1,100 applied exercises, ranging from straightforward to reasonably challenging, roughly 700 exercises in the first four “core” chapters alone—a self-contained textbook of problems introducing basic theoretical knowledge necessary for solving problems and illustrating how to solve the problems at hand – in R and MATLAB, including code so that students can create simulations.
*principles and applications for engineering and the computing sciences*

**Author**: Janet Susan Milton,Jesse C. Arnold

**Publisher:** McGraw-Hill Science/Engineering/Math

**ISBN:** 9780072468366

**Category:** Business & Economics

**Page:** 798

**View:** 4188

This well-respected text is designed for the first course in probability and statistics taken by students majoring in Engineering and the Computing Sciences. The prerequisite is one year of calculus. The text offers a balanced presentation of applications and theory. The authors take care to develop the theoretical foundations for the statistical methods presented at a level that is accessible to students with only a calculus background. They explore the practical implications of the formal results to problem-solving so students gain an understanding of the logic behind the techniques as well as practice in using them. The examples, exercises, and applications were chosen specifically for students in engineering and computer science and include opportunities for real data analysis.

**Author**: William Mendenhall,Terry Sincich

**Publisher:** N.A

**ISBN:** N.A

**Category:** Mathematics

**Page:** 1036

**View:** 7340

**Author**: Jay L. Devore

**Publisher:** Duxbury Press

**ISBN:** 9780840065391

**Category:** Mathematics

**Page:** 222

**View:** 1913

Go beyond the answers--see what it takes to get there and improve your grade! This manual provides worked-out, step-by-step solutions to all of the odd-numbered problems in the text, giving you the information you need to truly understand how these problems are solved.

**Author**: Edward L. Robinson

**Publisher:** Princeton University Press

**ISBN:** 1400883067

**Category:** Science

**Page:** 408

**View:** 5284

Data Analysis for Scientists and Engineers is a modern, graduate-level text on data analysis techniques for physical science and engineering students as well as working scientists and engineers. Edward Robinson emphasizes the principles behind various techniques so that practitioners can adapt them to their own problems, or develop new techniques when necessary. Robinson divides the book into three sections. The first section covers basic concepts in probability and includes a chapter on Monte Carlo methods with an extended discussion of Markov chain Monte Carlo sampling. The second section introduces statistics and then develops tools for fitting models to data, comparing and contrasting techniques from both frequentist and Bayesian perspectives. The final section is devoted to methods for analyzing sequences of data, such as correlation functions, periodograms, and image reconstruction. While it goes beyond elementary statistics, the text is self-contained and accessible to readers from a wide variety of backgrounds. Specialized mathematical topics are included in an appendix. Based on a graduate course on data analysis that the author has taught for many years, and couched in the looser, workaday language of scientists and engineers who wrestle directly with data, this book is ideal for courses on data analysis and a valuable resource for students, instructors, and practitioners in the physical sciences and engineering. In-depth discussion of data analysis for scientists and engineers Coverage of both frequentist and Bayesian approaches to data analysis Extensive look at analysis techniques for time-series data and images Detailed exploration of linear and nonlinear modeling of data Emphasis on error analysis Instructor's manual (available only to professors)

**Author**: William M. Mendenhall,Terry L. Sincich

**Publisher:** CRC Press

**ISBN:** 1498728871

**Category:** Mathematics

**Page:** 1166

**View:** 6741

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 analyze 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 analyzing 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 analyzing 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.
*The Science of Uncertainty with Applications to Investments, Insurance, and Engineering*

**Author**: Michael A. Bean

**Publisher:** American Mathematical Soc.

**ISBN:** 0821847929

**Category:** Mathematics

**Page:** 448

**View:** 5831

This book covers the basic probability of distributions with an emphasis on applications from the areas of investments, insurance, and engineering. Written by a Fellow of the Casualty Actuarial Society and the Society of Actuaries with many years of experience as a university professor and industry practitioner, the book is suitable as a text for senior undergraduate and beginning graduate students in mathematics, statistics, actuarial science, finance, or engineering as well as a reference for practitioners in these fields. The book is particularly well suited for students preparing for professional exams, and for several years it has been recommended as a textbook on the syllabus of examinations for the Casualty Actuarial Society and the Society of Actuaries. In addition to covering the standard topics and probability distributions, this book includes separate sections on more specialized topics such as mixtures and compound distributions, distributions of transformations, and the application of specialized distributions such as the Pareto, beta, and Weibull. The book also has a number of unique features such as a detailed description of the celebrated Markowitz investment portfolio selection model. A separate section contains information on how graphs of the specific distributions studied in the book can be created using MathematicaTM. The book includes a large number of problems of varying difficulty. An instructor's manual with complete solutions to all the problems as well as supplementary material and a student manual with solutions to selected problems are available.

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