This handbook presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. The book examines the advantages and limitations of the various modeling techniques and keeps probabilistic, technical details to a minimum. While the book focuses on time series of counts, some of the methods discussed can be applied to other types of discrete-valued time series, such as binary-valued or categorical time series.

This book presents analytical methods for modeling and measuring uncertainty in the cost of engineering systems. This includes the treatment of correlation between the cost of system elements, how to present the analysis to decision-makers, and the use of bivariate probability distributions to capture joint interactions between a system's cost and schedule. Analytical techniques from probability theory are stressed, along with the Monte Carlo simulation method. Numerous examples and case discussions illustrate the practical application of theoretical concepts.

A comprehensive collection of the field's most provocative, influential new work Business Forecasting compiles some of the field's important and influential literature into a single, comprehensive reference for forecast modeling and process improvement. It is packed with provocative ideas from forecasting researchers and practitioners, on topics including accuracy metrics, benchmarking, modeling of problem data, and overcoming dysfunctional behaviors. Its coverage includes often-overlooked issues at the forefront of research, such as uncertainty, randomness, and forecastability, as well as emerging areas like data mining for forecasting. The articles present critical analysis of current practices and consideration of new ideas. With a mix of formal, rigorous pieces and brief introductory chapters, the book provides practitioners with a comprehensive examination of the current state of the business forecasting field. Forecasting performance is ultimately limited by the 'forecastability' of the data. Yet failing to recognize this, many organizations continue to squander resources pursuing unachievable levels of accuracy. This book provides a wealth of ideas for improving all aspects of the process, including the avoidance of wasted efforts that fail to improve (or even harm) forecast accuracy. Analyzes the most prominent issues in business forecasting Investigates emerging approaches and new methods of analysis Combines forecasts to improve accuracy Utilizes Forecast Value Added to identify process inefficiency The business environment is evolving, and forecasting methods must evolve alongside it. This compilation delivers an array of new tools and research that can enable more efficient processes and more accurate results. Business Forecasting provides an expert's-eye view of the field's latest developments to help you achieve your desired business outcomes.

Look at an application of deseasonalisation techniques and an interpretation of the results they provide.

Statistics is the branch of mathematics that deals with real life problems. As such, it is an essential tool for economists. Unfortunately, the way the concept is introduced to students is not compatible with the way economists think and learn. The problem is worsened by the use of mathematical jargon and complex derivations. However, as this book demonstrates, neither is necessary. The book is written in simple English with minimal use of symbols, mostly for the sake of brevity and to make reading literature more meaningful. The second edition also incorporates Stata software for use by more technically oriented readers who have access to sophisticated software. The objective of this book is to address the fundamentals of statistical analysis in a simple and easy-to-comprehend way. Instead of covering numerous topics, the book covers interrelated subjects that are necessary for the comprehension of the presented topics. The second edition has augmented the explanations in the first to clarify the subjects even more. The examples are based on economic theory utilizing actual data. The hope is that the use of theory will prove useful in relating the subject to actual empirical applications and help with research.

Written by a leading contributor to volatility modeling and Risk's 2009 Quant of the Year, this book explains how stochastic volatility is used to tackle practical issues arising in the modeling of derivatives. With many unpublished results and insights, the book addresses the practicalities of modeling local volatility, local-stochastic volatility, and multi-asset stochastic volatility. It covers forward-start options, variance swaps, options on realized variance, timer options, VIX futures and options, and daily cliquets.

More and more organizations around the globe are expecting that professionals will make data-driven decisions. Employees, team leaders, managers, and executives that can think quantitatively should be in high demand. The goal of this book is to increase ability to identify a problem, collect data, organize, and analyze data that will help aid in making more effective decisions. This book will provide you with a solid foundation for thinking quantitatively within your company. To help facilitate this objective, this book follows two fictitious companies that encounter a series of business problems, while demonstrating how managers would use the concepts in the book to solve these problems and determine the next course of action. This book is for beginners and does not require prior statistical training. All computations will be completed using Microsoft Excel.

The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book covers essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The book provides a non-theoretical treatment that is accessible to readers with even a limited statistical background. This book describes exactly how regression models are developed and evaluated. The data used in the book are the kind of data managers are faced with in the real world. The book provides instructions and screen shots for using Microsoft Excel to build business/economic regression models. Upon completion, the reader will be able to interpret the output of the regression models and evaluate the models for accuracy and shortcomings.

This book describes state-of-the-art optimization techniques used to solve problems with adaptive, dynamic, and stochastic features. It presents modern advances in static and dynamic optimization, decision analysis, intelligent systems, evolutionary programming, heuristic optimization, stochastic and adaptive dynamic programming, and adaptive critics. It evaluates optimization methods for handling operational planning, Voltage/VAr, control coordination, vulnerability, reliability, resilience, and reconfiguration issues, providing mathematical formulations, algorithms for implementation, examples, and case studies. It also discusses the limitations of current optimization techniques in meeting the challenges of smart electric grids.

Offering a planned approach for determining cause and effect, DOE Simplified: Practical Tools for Effective Experimentation, Third Edition integrates the authors’ decades of combined experience in providing training, consulting, and computational tools to industrial experimenters. Supplying readers with the statistical means to analyze how numerous variables interact, it is ideal for those seeking breakthroughs in product quality and process efficiency via systematic experimentation. Following in the footsteps of its bestselling predecessors, this edition incorporates a lively approach to learning the fundamentals of the design of experiments (DOE). It lightens up the inherently dry complexities with interesting sidebars and amusing anecdotes. The book explains simple methods for collecting and displaying data and presents comparative experiments for testing hypotheses. Discussing how to block the sources of variation from your analysis, it looks at two-level factorial designs and covers analysis of variance. It also details a four-step planning process for designing and executing experiments that takes statistical power into consideration. This edition includes a major revision of the software that accompanies the book (via download) and sets the stage for introducing experiment designs where the randomization of one or more hard-to-change factors can be restricted. Along these lines, it includes a new chapter on split plots and adds coverage of a number of recent developments in the design and analysis of experiments. Readers have access to case studies, problems, practice experiments, a glossary of terms, and a glossary of statistical symbols, as well as a series of dynamic online lectures that cover the first several chapters of the book.

This book shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data. Making the techniques accessible to non-mathematicians, the authors draw on their 25 years of software development experiences, methodological research, and broad scientific collaborations to deliver a book that clearly and succinctly explains concepts and addresses real scientific questions. The book uses the authors' R package spatstat throughout to process and analyze spatial point pattern data.

Don’t let your fear of finance get in the way of your success. This digital collection, curated by Harvard Business Review, brings together everything a manager needs to know about financial intelligence. It includes Financial Intelligence, called a “must-read” for decision makers without expertise in finance; A Concise Guide to Macroeconomics, which covers the essentials of macroeconomics and examines the core ideas of output, money, and expectations; Essentials of Finance and Budgeting, which explains everything HR professionals need to know to make wise financial decisions; Ahead of the Curve, Joseph H. Ellis’s forecasting method to help managers and investors understand and predict the economic cycles that control their businesses and financial fates; Beyond Budgeting; which offers a coherent management model that overcomes the limitations of traditional budgeting; Preparing a Budget, packed with handy tools, self-tests, and real life examples to help you hone critical skills; and HBR Guide to Finance Basics for Managers, which will give you the tools and confidence you need to master the fundamentals of finance.

Learn or refresh core statistical methods for business with SAS® and approach real business analytics issues and techniques using a practical approach that avoids complex mathematics and instead employs easy-to-follow explanations.

Business Statistics Made Easy in SAS® is designed as a user-friendly, practice-oriented, introductory text to teach businesspeople, students, and others core statistical concepts and applications. It begins with absolute core principles and takes you through an overview of statistics, data and data collection, an introduction to SAS®, and basic statistics (descriptive statistics and basic associational statistics). The book also provides an overview of statistical modeling, effect size, statistical significance and power testing, basics of linear regression, introduction to comparison of means, basics of chi-square tests for categories, extrapolating statistics to business outcomes, and some topical issues in statistics, such as big data, simulation, machine learning, and data warehousing.

The book steers away from complex mathematical-based explanations, and it also avoids basing explanations on the traditional build-up of distributions, probability theory and the like, which tend to lose the practice-oriented reader. Instead, it teaches the core ideas of statistics through methods such as careful, intuitive written explanations, easy-to-follow diagrams, step-by-step technique implementation, and interesting metaphors.

With no previous SAS experience necessary, Business Statistics Made Easy in SAS® is an ideal introduction for beginners. It is suitable for introductory undergraduate classes, postgraduate courses such as MBA refresher classes, and for the business practitioner. It is compatible with SAS® University Edition.

Dive into the world of Bayesian Machine Learning with "Learning Bayesian Models with R." This comprehensive guide introduces the foundations of probability theory and Bayesian inference, teaches you how to implement these concepts with the R programming language, and progresses to practical techniques for supervised and unsupervised problems in data science. What this Book will help me do Understand and set up an R environment for Bayesian modeling Build Bayesian models including linear regression and classification for predictive analysis Learn to apply Bayesian inference to real-world machine learning problems Work with big data and high-performance computation frameworks like Hadoop and Spark Master advanced Bayesian techniques and apply them to deep learning and AI challenges Author(s) Hari Manassery Koduvely is a proficient data scientist with extensive experience in leveraging Bayesian frameworks for real-world applications. His passion for Bayesian Machine Learning is evident in his approachable and detailed teaching methodology, aimed at making these complex topics accessible for practitioners. Who is it for? This book is best suited for data scientists, analysts, and statisticians familiar with R and basic probability theory who aim to enhance their expertise in Bayesian approaches. It's ideal for professionals tackling machine learning challenges in applied data contexts. If you're looking to incorporate advanced probabilistic methods into your projects, this guide will show you how.

Beginning R, Second Edition is a hands-on book showing how to use the R language, write and save R scripts, read in data files, and write custom statistical functions as well as use built in functions. This book shows the use of R in specific cases such as one-way ANOVA analysis, linear and logistic regression, data visualization, parallel processing, bootstrapping, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. It has been completely re-written since the first edition to make use of the latest packages and features in R version 3. R is a powerful open-source language and programming environment for statistics and has become the de facto standard for doing, teaching, and learning computational statistics. R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets, with a constantly evolving ecosystem of packages providing new functionality for data analysis. R has also become popular in commercial use at companies such as Microsoft, Google, and Oracle. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for data analysis and research.

This book introduces the authors' recently developed approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the user to input prior information. The book covers the foundational motivations for this new approach, the basic theory behind its calibration properties, many important applications, and new directions for research. It explores a new way of thinking compared to existing schools of thought on statistical inference and encourages readers to think carefully about the correct approach to scientific inference.

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: descriptive methods for delineating trends over time linear mixed regression models with both fixed and random effects covariance pattern models on correlated errors generalized estimating equations nonlinear regression models for categorical repeated measurements techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.

A well-balanced introduction to probability theory and mathematical statistics Featuring updated material, An Introduction to Probability and Statistics, Third Edition remains a solid overview to probability theory and mathematical statistics. Divided intothree parts, the Third Edition begins by presenting the fundamentals and foundationsof probability. The second part addresses statistical inference, and the remainingchapters focus on special topics. An Introduction to Probability and Statistics, Third Edition includes: A new section on regression analysis to include multiple regression, logistic regression, and Poisson regression A reorganized chapter on large sample theory to emphasize the growing role of asymptotic statistics Additional topical coverage on bootstrapping, estimation procedures, and resampling Discussions on invariance, ancillary statistics, conjugate prior distributions, and invariant confidence intervals Over 550 problems and answers to most problems, as well as 350 worked out examples and 200 remarks Numerous figures to further illustrate examples and proofs throughout An Introduction to Probability and Statistics, Third Edition is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering. The book is also an excellent text for upper-undergraduate and graduate-level students majoring in probability and statistics.

Professionals in all areas - business; government; the physical, life, and social sciences; engineering; medicine, etc. - benefit from using statistical experimental design to better understand their worlds and then use that understanding to improve the products, processes, and programs they are responsible for. This book aims to provide the practitioners of tomorrow with a memorable, easy to read, engaging guide to statistics and experimental design. This book uses examples, drawn from a variety of established texts, and embeds them in a business or scientific context, seasoned with a dash of humor, to emphasize the issues and ideas that led to the experiment and the what-do-we-do-next? steps after the experiment. Graphical data displays are emphasized as means of discovery and communication and formulas are minimized, with a focus on interpreting the results that software produce. The role of subject-matter knowledge, and passion, is also illustrated. The examples do not require specialized knowledge, and the lessons they contain are transferrable to other contexts. Fundamentals of Statistical Experimental Design and Analysis introduces the basic elements of an experimental design, and the basic concepts underlying statistical analyses. Subsequent chapters address the following families of experimental designs: Completely Randomized designs, with single or multiple treatment factors, quantitative or qualitative Randomized Block designs Latin Square designs Split-Unit designs Repeated Measures designs Robust designs Optimal designs Written in an accessible, student-friendly style, this book is suitable for a general audience and particularly for those professionals seeking to improve and apply their understanding of experimental design.