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Books > Business & Economics

Showing 1 - 15 of 15 matches in Business & Economics

Statistical Rethinking - A Bayesian Course with Examples in R and Stan (Hardcover): Richard McElreath Statistical Rethinking - A Bayesian Course with Examples in R and Stan (Hardcover)
Richard McElreath
R1,727 Discovery Miles 17 270 Shipped within 7 - 12 working days

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers' knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

The Analysis of Time Series - An Introduction with R (Paperback, 7th New edition): Chris Chatfield, Haipeng Xing The Analysis of Time Series - An Introduction with R (Paperback, 7th New edition)
Chris Chatfield, Haipeng Xing
R1,350 Discovery Miles 13 500 Shipped within 7 - 12 working days

This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis. The book covers a wide range of topics, including ARIMA models, forecasting methods, spectral analysis, linear systems, state-space models, the Kalman filters, nonlinear models, volatility models, and multivariate models. It also presents many examples and implementations of time series models and methods to reflect advances in the field. Highlights of the seventh edition: A new chapter on univariate volatility models A revised chapter on linear time series models A new section on multivariate volatility models A new section on regime switching models Many new worked examples, with R code integrated into the text The book can be used as a textbook for an undergraduate or a graduate level time series course in statistics. The book does not assume many prerequisites in probability and statistics, so it is also intended for students and data analysts in engineering, economics, and finance.

Statistical Methods for SPC and TQM (Hardcover, Softcover Repri): Derek Bissell Statistical Methods for SPC and TQM (Hardcover, Softcover Repri)
Derek Bissell
R3,172 Discovery Miles 31 720 Shipped within 7 - 12 working days

Statistical Methods for SPC and TQM sets out to fill the gap for those in statistical process control (SPC) and total quality management (TQM) who need a practical guide to the logical basis of data presentation, control charting, and capability indices.
Statistical theory is introduced in a practical context, usually by way of numerical examples. Several methods familiar to statisticians have been simplified to make them more accessible. Suitable tabulations of these functions are included; in several cases, effective and simple approximations are offered.
Contents
Data Collection and Graphical Summaries
Numerical Data Summaries-Location and Dispersion
Probability and Distribution
Sampling, Estimation, and Confidence
Sample Tests of Hypothesis; "Significance Tests"
Control Charts for Process Management and Improvement
Control Charts for Average and Variation
Control Charts for "Single-Valued" Observations
Control Charts for Attributes and Events
Control Charts: Problems and Special Cases
Cusum Methods
Process Capability-Attributes, Events, and Normally Distributed Data
Capability; Non-Normal Distributions
Evaluating the Precision of a Measurement System (Gauge Capability)
Getting More from Control Chart Data
SPC in "Non-Product" Applications
Appendices

Bayesian Statistical Methods (Hardcover): Brian J. Reich, Sujit K. Ghosh Bayesian Statistical Methods (Hardcover)
Brian J. Reich, Sujit K. Ghosh
R1,347 Discovery Miles 13 470 Shipped within 7 - 12 working days

Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book's website. Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.

Introduction to Functional Data Analysis (Hardcover): Piotr Kokoszka, Matthew Reimherr Introduction to Functional Data Analysis (Hardcover)
Piotr Kokoszka, Matthew Reimherr
R1,330 Discovery Miles 13 300 Shipped within 7 - 12 working days

Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework. The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors, as well as for MS and PhD students in other disciplines, including applied mathematics, environmental science, public health, medical research, geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation. Each chapter contains plentiful examples of relevant R code and theoretical and data analytic problems. The material of the book can be roughly divided into four parts of approximately equal length: 1) basic concepts and techniques of FDA, 2) functional regression models, 3) sparse and dependent functional data, and 4) introduction to the Hilbert space framework of FDA. The book assumes advanced undergraduate background in calculus, linear algebra, distributional probability theory, foundations of statistical inference, and some familiarity with R programming. Other required statistics background is provided in scalar settings before the related functional concepts are developed. Most chapters end with references to more advanced research for those who wish to gain a more in-depth understanding of a specific topic.

Introduction to Statistical Methods for Financial Models (Hardcover): Thomas A. Severini Introduction to Statistical Methods for Financial Models (Hardcover)
Thomas A. Severini
R1,397 Discovery Miles 13 970 Shipped within 7 - 11 working days

This book provides an introduction to the use of statistical concepts and methods to model and analyze financial data. The ten chapters of the book fall naturally into three sections. Chapters 1 to 3 cover some basic concepts of finance, focusing on the properties of returns on an asset. Chapters 4 through 6 cover aspects of portfolio theory and the methods of estimation needed to implement that theory. The remainder of the book, Chapters 7 through 10, discusses several models for financial data, along with the implications of those models for portfolio theory and for understanding the properties of return data. The audience for the book is students majoring in Statistics and Economics as well as in quantitative fields such as Mathematics and Engineering. Readers are assumed to have some background in statistical methods along with courses in multivariate calculus and linear algebra.

Sampling: Design and Analysis - Design and Analysis (Paperback, 2nd New edition): Sharon Lohr Sampling: Design and Analysis - Design and Analysis (Paperback, 2nd New edition)
Sharon Lohr
R1,053 Discovery Miles 10 530 Shipped within 7 - 11 working days

What is the unemployment rate? How many adults have high blood pressure? What is the total area of land planted with soybeans? Sampling: Design and Analysis tells you how to design and analyze surveys to answer these and other questions. This authoritative text, used as a standard reference by numerous survey organizations, teaches sampling using real data sets from social sciences, public opinion research, medicine, public health, economics, agriculture, ecology, and other fields. The book is accessible to students from a wide range of statistical backgrounds. By appropriate choice of sections, it can be used for a graduate class for statistics students or for a class with students from business, sociology, psychology, or biology. Readers should be familiar with concepts from an introductory statistics class including linear regression; optional sections contain the statistical theory, for readers who have studied mathematical statistics. Distinctive features include: More than 450 exercises. In each chapter, Introductory Exercises develop skills, Working with Data Exercises give practice with data from surveys, Working with Theory Exercises allow students to investigate statistical properties of estimators, and Projects and Activities Exercises integrate concepts. A solutions manual is available. An emphasis on survey design. Coverage of simple random, stratified, and cluster sampling; ratio estimation; constructing survey weights; jackknife and bootstrap; nonresponse; chi-squared tests and regression analysis. Graphing data from surveys. Computer code using SAS (R) software. Online supplements containing data sets, computer programs, and additional material. Sharon Lohr, the author of Measuring Crime: Behind the Statistics, has published widely about survey sampling and statistical methods for education, public policy, law, and crime. She has been recognized as Fellow of the American Statistical Association, elected member of the International Statistical Institute, and recipient of the Gertrude M. Cox Statistics Award and the Deming Lecturer Award. Formerly Dean's Distinguished Professor of Statistics at Arizona State University and a Vice President at Westat, she is now a freelance statistical consultant and writer. Visit her website at www.sharonlohr.com. This edition is a reprint of the second edition published by Cengage Learning, Inc. Reprinted with permission.

Introduction to Statistical Process Control (Hardcover, New): Peihua Qiu Introduction to Statistical Process Control (Hardcover, New)
Peihua Qiu
R1,806 Discovery Miles 18 060 Special order

A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, Introduction to Statistical Process Control describes many recent SPC methods that improve upon the more established techniques. The author-a leading researcher on SPC-shows how these methods can handle new applications. After exploring the role of SPC and other statistical methods in quality control and management, the book covers basic statistical concepts and methods useful in SPC. It then systematically describes traditional SPC charts, including the Shewhart, CUSUM, and EWMA charts, as well as recent control charts based on change-point detection and fundamental multivariate SPC charts under the normality assumption. The text also introduces novel univariate and multivariate control charts for cases when the normality assumption is invalid and discusses control charts for profile monitoring. All computations in the examples are solved using R, with R functions and datasets available for download on the author's website. Offering a systematic description of both traditional and newer SPC methods, this book is ideal as a primary textbook for a one-semester course in disciplines concerned with process quality control, such as statistics, industrial and systems engineering, and management sciences. It can also be used as a supplemental textbook for courses on quality improvement and system management. In addition, the book provides researchers with many useful, recent research results on SPC and gives quality control practitioners helpful guidelines on implementing up-to-date SPC techniques.

The Analysis of Time Series - An Introduction with R (Hardcover, 7th New edition): Chris Chatfield, Haipeng Xing The Analysis of Time Series - An Introduction with R (Hardcover, 7th New edition)
Chris Chatfield, Haipeng Xing
R3,248 Discovery Miles 32 480 Special order

This is the seventh edition of a classic introductory textbook on time series analysis. In this edition, a new co-author has been added to modernize the book. It has been revised to include three new chapters - on Volatility Models, Nonlinear Models, and High-Frequency Time Series Models - plus R code throughout the book, and many new examples and exercises. The book is set apart from the competition due to its accessibility and a perfect balance of theory and examples, so the book should maintain its position as one of the leading introductory textbooks on the topic. Provides an accessible introduction to time series analysis. Written in a lucid style, with a good balance of theory and examples. New edition includes three new chapters on Volatility Models, Nonlinear Models, and High-Frequency Time Series Models. Now includes R code throughout, with data and code on a supplementary website. Includes many new examples and exercises.

Pragmatics of Uncertainty (Hardcover): Joseph B. Kadane Pragmatics of Uncertainty (Hardcover)
Joseph B. Kadane
R1,625 Discovery Miles 16 250 Special order

A fair question to ask of an advocate of subjective Bayesianism (which the author is) is "how would you model uncertainty?" In this book, the author writes about how he has done it using real problems from the past, and offers additional comments about the context in which he was working.

Statistics for Finance (Hardcover): Henrik Madsen, Erik Lindstrom, Jan Nygaard Nielsen Statistics for Finance (Hardcover)
Henrik Madsen, Erik Lindstrom, Jan Nygaard Nielsen
R1,703 Discovery Miles 17 030 Special order

Statistics for Finance develops students' professional skills in statistics with applications in finance. Developed from the authors' courses at the Technical University of Denmark and Lund University, the text bridges the gap between classical, rigorous treatments of financial mathematics that rarely connect concepts to data and books on econometrics and time series analysis that do not cover specific problems related to option valuation. The book discusses applications of financial derivatives pertaining to risk assessment and elimination. The authors cover various statistical and mathematical techniques, including linear and nonlinear time series analysis, stochastic calculus models, stochastic differential equations, Ito's formula, the Black-Scholes model, the generalized method-of-moments, and the Kalman filter. They explain how these tools are used to price financial derivatives, identify interest rate models, value bonds, estimate parameters, and much more. This textbook will help students understand and manage empirical research in financial engineering. It includes examples of how the statistical tools can be used to improve value-at-risk calculations and other issues. In addition, end-of-chapter exercises develop students' financial reasoning skills.

Elements of Simulation (Paperback): Byron J.T. Morgan Elements of Simulation (Paperback)
Byron J.T. Morgan
R2,569 Discovery Miles 25 690 Special order
Time Series - Modeling, Computation, and Inference, Second Edition (Hardcover, 2nd New edition): Raquel Prado, Marco A. R.... Time Series - Modeling, Computation, and Inference, Second Edition (Hardcover, 2nd New edition)
Raquel Prado, Marco A. R. Ferreira, Mike West
R2,105 Discovery Miles 21 050 Special order

This is the second edition of a popular graduate level textbook on time series modeling, computation and inference. The book is essentially unique in its approach, with a focus on Bayesian methods, although classical methods are also covered. The second edition has been updated with an additional author, new references throughout, and new material on multivariate time series. There are also lots of new examples and exercises, and the computing has been greatly enhanced. Code, data, and other material are now made available on a supplementary website.

Sampling: Design and Analysis - Design and Analysis (Hardcover, 2nd New edition): Sharon Lohr Sampling: Design and Analysis - Design and Analysis (Hardcover, 2nd New edition)
Sharon Lohr
R2,526 Discovery Miles 25 260 Special order

What is the unemployment rate? How many adults have high blood pressure? What is the total area of land planted with soybeans? Sampling: Design and Analysis tells you how to design and analyze surveys to answer these and other questions. This authoritative text, used as a standard reference by numerous survey organizations, teaches sampling using real data sets from social sciences, public opinion research, medicine, public health, economics, agriculture, ecology, and other fields. The book is accessible to students from a wide range of statistical backgrounds. By appropriate choice of sections, it can be used for a graduate class for statistics students or for a class with students from business, sociology, psychology, or biology. Readers should be familiar with concepts from an introductory statistics class including linear regression; optional sections contain the statistical theory, for readers who have studied mathematical statistics. Distinctive features include: More than 450 exercises. In each chapter, Introductory Exercises develop skills, Working with Data Exercises give practice with data from surveys, Working with Theory Exercises allow students to investigate statistical properties of estimators, and Projects and Activities Exercises integrate concepts. A solutions manual is available. An emphasis on survey design. Coverage of simple random, stratified, and cluster sampling; ratio estimation; constructing survey weights; jackknife and bootstrap; nonresponse; chi-squared tests and regression analysis. Graphing data from surveys. Computer code using SAS (R) software. Online supplements containing data sets, computer programs, and additional material. Sharon Lohr, the author of Measuring Crime: Behind the Statistics, has published widely about survey sampling and statistical methods for education, public policy, law, and crime. She has been recognized as Fellow of the American Statistical Association, elected member of the International Statistical Institute, and recipient of the Gertrude M. Cox Statistics Award and the Deming Lecturer Award. Formerly Dean's Distinguished Professor of Statistics at Arizona State University and a Vice President at Westat, she is now a freelance statistical consultant and writer. Visit her website at www.sharonlohr.com. This edition is a reprint of the second edition published by Cengage Learning, Inc. Reprinted with permission.

Readings in Decision Analysis (Paperback): S. French Readings in Decision Analysis (Paperback)
S. French
R2,098 Discovery Miles 20 980 Special order

Provides an introduction to decision analysis. This book is based upon a number of papers and articles taken from the Operational Research Society's journal and other publications. However, the book is not simply a 'collection of reprints': Professor French has provided extensive notes and commentary to weave the extracts into a coherent whole. Although techniques are presented, the main thrust is to convey the purpose of decision analysis and the interpretation that should be placed upon its output: vital topics, but ones seldom discussed in introductory texts. The writing is aimed at the non-technical reader.

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