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Books > Business & Economics

Showing 1 - 9 of 9 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
R1,692 Discovery Miles 16 920 Dispatched 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.

Introduction to Functional Data Analysis (Hardcover): Piotr Kokoszka, Matthew Reimherr Introduction to Functional Data Analysis (Hardcover)
Piotr Kokoszka, Matthew Reimherr
R1,319 Discovery Miles 13 190 Dispatched 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.

Statistical Methods for SPC and TQM (Hardcover, Softcover Repri): Derek Bissell Statistical Methods for SPC and TQM (Hardcover, Softcover Repri)
Derek Bissell
R3,315 R3,145 Discovery Miles 31 450 Save R170 (5%) Dispatched 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

Introduction to Statistical Methods for Financial Models (Hardcover): Thomas A. Severini Introduction to Statistical Methods for Financial Models (Hardcover)
Thomas A. Severini
R1,363 Discovery Miles 13 630 Dispatched within 7 - 12 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.

Statistics for Finance (Hardcover): Henrik Madsen, Erik Lindstrom, Jan Nygaard Nielsen Statistics for Finance (Hardcover)
Henrik Madsen, Erik Lindstrom, Jan Nygaard Nielsen
R2,016 Discovery Miles 20 160 Dispatched within 8 - 13 working days

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.

Pragmatics of Uncertainty (Hardcover): Joseph B. Kadane Pragmatics of Uncertainty (Hardcover)
Joseph B. Kadane
R2,027 Discovery Miles 20 270 Dispatched within 8 - 13 working days

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.

Elements of Simulation (Paperback): Byron J.T. Morgan Elements of Simulation (Paperback)
Byron J.T. Morgan
R2,529 Discovery Miles 25 290 Special order
Readings in Decision Analysis (Paperback): S. French Readings in Decision Analysis (Paperback)
S. French
R2,065 Discovery Miles 20 650 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.

Introduction to Statistical Process Control (Hardcover, New): Peihua Qiu Introduction to Statistical Process Control (Hardcover, New)
Peihua Qiu
R1,778 Discovery Miles 17 780 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.

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