Econometrics and economic statistics Books

Book SynopsisAn introduction to the use of probability models for analyzing risk and economic decisions, using spreadsheets to represent and simulate uncertainty.This textbook offers an introduction to the use of probability models for analyzing risks and economic decisions. It takes a learn-by-doing approach, teaching the student to use spreadsheets to represent and simulate uncertainty and to analyze the effect of such uncertainty on an economic decision. Students in applied business and economics can more easily grasp difficult analytical methods with Excel spreadsheets. The book covers the basic ideas of probability, how to simulate random variables, and how to compute conditional probabilities via Monte Carlo simulation. The first four chapters use a large collection of probability distributions to simulate a range of problems involving worker efficiency, market entry, oil exploration, repeated investment, and subjective belief elicitation. The book then covers correlation and

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Book SynopsisUsing a reader-friendly style and straightforward, interesting approach, SUPPLY CHAIN MANAGEMENT: A LOGISTICS PERSPECTIVE, 11E blends logistics theory with practical applications. The latest content highlights emerging issues, technology developments, and global changes in the constantly evolving field of supply chain management today. This digital edition examines today's real companies and how public and private organizations are responding to the continual pressure to modernize and transform their supply chains. Updated features and short cases offer hands-on managerial experience as you examine the key decisions and circumstances that supply chain managers face daily. New profiles introduce each chapter with real organizations, people, or events that emphasize the relevance of what you are learning. Technology-focused features and global content examine key areas where change is occurring and provide a meaningful perspective on how today's changes impact current and future supply cTable of ContentsPart I. Supply Chain Foundations. 1. Supply Chain Management: An Overview. 2. Global Dimensions of Supply Chains. 3. Role of Logistics in Supply Chains. 4. Supply Chain and Omni Channel Network Design. Part II. Supply Chain Fundamentals. 5. Sourcing Materials and Services. 6. Operations ��� Producing Goods and Services. 7. Demand Management. 8. Order Management and Customer Service. Part III. Cross-Chain Logistics Processes. 9. Managing Inventory in the Supply Chain. 10 Distribution ��� Managing Fulfillment Operations. 11. Transportation ��� Managing the Flows of the Supply Chain. Part IV. Supply Chain Challenges and Future Directions. 12. Aligning Supply Chains. 13. Supply Chain Performance Measurement and Financial Analysis. 14. Supply Chain Technology ��� Managing Information Flows. 15. Strategic Challenges and Change for Supply Chains.

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Book Synopsis A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods. Trade ReviewFrom the reviews:This is an excellent book for its intended audience: statisticians who wish to learn Bayesian methods. Although designed for a statistics audience, it would also be a good book for econometricians who have been trained in frequentist methods, but wish to learn Bayes. In relatively few pages, it takes the reader through a vast amount of material, beginning with deep issues in statistical methodology such as de Finetti’s theorem, through the nitty-gritty of Bayesian computation to sophisticated models such as generalized linear mixed effects models and copulas. And it does so in a simple manner, always drawing parallels and contrasts between Bayesian and frequentist methods, so as to allow the reader to see the similarities and differences with clarity. (Econometrics Journal) “Generally, I think this is an excellent choice for a text for a one-semester Bayesian Course. It provides a good overview of the basic tenets of Bayesian thinking for the common one and two parameter distributions and gives introductions to Bayesian regression, multivariate-response modeling, hierarchical modeling, and mixed effects models. The book includes an ample collection of exercises for all the chapters. A strength of the book is its good discussion of Gibbs sampling and Metropolis-Hastings algorithms. The author goes beyond a description of the MCMC algorithms, but also provides insight into why the algorithms work. …I believe this text would be an excellent choice for my Bayesian class since it seems to cover a good number of introductory topics and giv the student a good introduction to the modern computational tools for Bayesian inference with illustrations using R. (Journal of the American Statistical Association, June 2010, Vol. 105, No. 490)“Statisticians and applied scientists. The book is accessible to readers having a basic familiarity with probability theory and grounding statistical methods. The author has succeeded in writing an acceptable introduction to the theory and application of Bayesian statistical methods which is modern and covers both the theory and practice. … this book can be useful as a quick introduction to Bayesian methods for self study. In addition, I highly recommend this book as a text for a course for Bayesian statistics.” (Lasse Koskinen, International Statistical Review, Vol. 78 (1), 2010)“The book under review covers a balanced choice of topics … presented with a focus on the interplay between Bayesian thinking and the underlying mathematical concepts. … the book by Peter D. Hoff appears to be an excellent choice for a main reading in an introductory course. After studying this text the student can go in a direction of his liking at the graduate level.” (Krzysztof Łatuszyński, Mathematical Reviews, Issue 2011 m)“The book is a good introductory treatment of methods of Bayes analysis. It should especially appeal to the reader who has had some statistical courses in estimation and modeling, and wants to understand the Bayesian interpretation of those methods. Also, readers who are primarily interested in modeling data and who are working in areas outside of statistics should find this to be a good reference book. … should appeal to the reader who wants to keep with modern approaches to data analysis.” (Richard P. Heydorn, Technometrics, Vol. 54 (1), February, 2012)Table of Contentsand examples.- Belief, probability and exchangeability.- One-parameter models.- Monte Carlo approximation.- The normal model.- Posterior approximation with the Gibbs sampler.- The multivariate normal model.- Group comparisons and hierarchical modeling.- Linear regression.- Nonconjugate priors and Metropolis-Hastings algorithms.- Linear and generalized linear mixed effects models.- Latent variable methods for ordinal data.

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Book SynopsisIntroduction to Econometric Modelling provides an introduction to econometrics for undergraduate students. In this book, Gary Koop provides a broader set of models than is offered in existing textbooks and places greater focus on models (e.g. the regression model) than the methods that are used to analyze the models.Trade Review“An introductory text offering econometric methodology for quantifying and managing this variety of risk, illustrated by empirical examples.” (Times Higher Education Supplement, Thursday 28th February)Table of ContentsPreface ix Chapter 1 An Overview of Econometrics 1 1.1 The importance of econometrics 1 1.2 Types of economic data 2 1.3 Working with data: graphical methods 6 1.4 Working with data: descriptive statistics and correlation 11 1.5 Chapter summary 26 Exercises 26 Chapter 2 A Non-technical Introduction to Regression 29 2.1 Introduction 29 2.2 The simple regression model 30 2.3 The multiple regression model 42 2.4 Chapter summary 55 Exercises 57 Chapter 3 The Econometrics of the Simple Regression Model 59 3.1 Introduction 59 3.2 A review of basic concepts in probability in the context of the regression model 60 3.3 The classical assumptions for the regression model 64 3.4 Properties of the ordinary least-squares estimator of β 67 3.5 Deriving a confidence interval for β 75 3.6 Hypothesis tests about β 77 3.7 Modifications to statistical procedures when σ2 is unknown 78 3.8 Chapter summary 81 Exercises 82 Appendix 1: Proof of the Gauss–Markov theorem 84 Appendix 2: Using asymptotic theory in the simple regression model 85 Chapter 4 The Econometrics of the Multiple Regression Model 91 4.1 Introduction 91 4.2 Basic results for the multiple regression model 92 4.3 Issues relating to the choice of explanatory variables 96 4.4 Hypothesis testing in the multiple regression model 102 4.5 Choice of functional form in the multiple regression model 109 4.6 Chapter summary 115 Exercises 116 Appendix: Wald and Lagrange multiplier tests 117 Chapter 5 The Multiple Regression Model: Freeing Up the Classical Assumptions 121 5.1 Introduction 121 5.2 Basic theoretical results 122 5.3 Heteroskedasticity 124 5.4 The regression model with autocorrelated errors 138 5.5 The instrumental variables estimator 149 5.6 Chapter summary 164 Exercises 165 Appendix: Asymptotic results for the OLS and instrumental variables estimators 168 Chapter 6 Univariate Time Series Analysis 173 6.1 Introduction 173 6.2 Time series notation 175 6.3 Trends in time series variables 177 6.4 The autocorrelation function 179 6.5 The autoregressive model 181 6.6 Defining stationarity 195 6.7 Modeling volatility 197 6.8 Chapter summary 205 Exercises 207 Appendix: MA and ARMA models 210 Chapter 7 Regression with Time Series Variables 213 7.1 Introduction 213 7.2 Time series regression when X and Yare stationary 214 7.3 Time series regression when Y and X have unit roots 217 7.4 Time series regression when Y and X have unit roots but are NOTcointegrated 227 7.5 Granger causality 227 7.6 Vector autoregressions 233 7.7 Chapter summary 247 Exercises 248 Appendix: The theory of forecasting 251 Chapter 8 Models for Panel Data 255 8.1 Introduction 255 8.2 The pooled model 256 8.3 Individual effects models 256 8.4 Chapter summary 271 Exercises 272 Chapter 9 Qualitative Choice and Limited Dependent Variable Models 277 9.1 Introduction 277 9.2 Qualitative choice models 278 9.3 Limited dependent variable models 296 9.4 Chapter summary 304 Exercises 306 Chapter 10 Bayesian Econometrics 309 10.1 An overview of Bayesian econometrics 309 10.2 The normal linear regression model with natural conjugate prior and a single explanatory variable 315 10.3 Chapter summary 326 Exercises 326 Appendix: Bayesian analysis of the simple regression model with unknown variance 328 Appendix A: Mathematical Basics 333 Appendix B: Probability Basics 338 Appendix C: Basic Concepts in Asymptotic Theory 348 Appendix D: Writing an Empirical Project 353 Tables 359 Table 1. Area under the standard normal distribution Pr(0 ≤ Z ≤ z) 359 Table 2. Area under the Student t distribution for different degrees of freedom (DF), Pr(Z ≥ z) = α 360 Table 3. Percentiles of the chi-square distribution 361 Table 4a. Area under the F-distribution for different degrees of freedom, ν1 and ν2, Pr(Z ≥ z) = 0.05 362 Table 4b. Area under the F-distribution for different degrees of freedom, ν1 and ν2, Pr(Z ≥ z) = 0.01 363 Bibliography 364 Index 365

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Book SynopsisAnalysis of Financial Time Series, Third Edition provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.Trade Review"Analysis of financial time series, third edition, is an ideal book for introductory courses on time series at the graduate level and a valuable supplement for statistics courses in time series at the upper-undergraduate level." (Mathematical Reviews, 2011) "Nevertheless, all in all the book can be a very useful reference for students as well as for professionals." (Zentralblatt MATH, 2011) "Factor models, an important technique used in quantitative finance, are given a full treatment with macroeconomic factor models and fundamental factor models. The coverage of the book is comprehensive. It starts from basic time series techniques and finishes with advanced concepts such as state space models and MCMC methods. There is a balance between the theoretical background necessary to appreciate the nuances and the practical aspect of implementation. More importantly it gives insights about what time series models can't address. The book has an excellent supporting website which has all the programs and data sets which helps to internalize the concepts. Finally, teaching professionals should find the solutions manual as a valuable tool to explain concepts and to ensure understanding." (BookPleasures.com, January 2011) "This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described." (Insurance News Net, 8 December 2010)Table of ContentsPreface xvii Preface to the Second Edition xix Preface to the First Edition xxi 1 Financial Time Series and Their Characteristics 1 1.1 Asset Returns, 2 1.2 Distributional Properties of Returns, 7 1.3 Processes Considered, 22 2 Linear Time Series Analysis and Its Applications 29 2.1 Stationarity, 30 2.2 Correlation and Autocorrelation Function, 30 2.3 White Noise and Linear Time Series, 36 2.4 Simple AR Models, 37 2.5 Simple MA Models, 57 2.6 Simple ARMA Models, 64 2.7 Unit-Root Nonstationarity, 71 2.8 Seasonal Models, 81 2.9 Regression Models with Time Series Errors, 90 2.10 Consistent Covariance Matrix Estimation, 97 2.11 Long-Memory Models, 101 3 Conditional Heteroscedastic Models 109 3.1 Characteristics of Volatility, 110 3.2 Structure of a Model, 111 3.3 Model Building, 113 3.4 The ARCH Model, 115 3.5 The GARCH Model, 131 3.6 The Integrated GARCH Model, 140 3.7 The GARCH-M Model, 142 3.8 The Exponential GARCH Model, 143 3.9 The Threshold GARCH Model, 149 3.10 The CHARMA Model, 150 3.11 Random Coefficient Autoregressive Models, 152 3.12 Stochastic Volatility Model, 153 3.13 Long-Memory Stochastic Volatility Model, 154 3.14 Application, 155 3.15 Alternative Approaches, 159 3.16 Kurtosis of GARCH Models, 165 4 Nonlinear Models and Their Applications 175 4.1 Nonlinear Models, 177 4.2 Nonlinearity Tests, 205 4.3 Modeling, 214 4.4 Forecasting, 215 4.5 Application, 218 5 High-Frequency Data Analysis and Market Microstructure 231 5.1 Nonsynchronous Trading, 232 5.2 Bid–Ask Spread, 235 5.3 Empirical Characteristics of Transactions Data, 237 5.4 Models for Price Changes, 244 5.5 Duration Models, 253 5.6 Nonlinear Duration Models, 264 5.7 Bivariate Models for Price Change and Duration, 265 5.8 Application, 270 6 Continuous-Time Models and Their Applications 287 6.1 Options, 288 6.2 Some Continuous-Time Stochastic Processes, 288 6.3 Ito's Lemma, 292 6.4 Distributions of Stock Prices and Log Returns, 297 6.5 Derivation of Black–Scholes Differential Equation, 298 6.6 Black–Scholes Pricing Formulas, 300 6.7 Extension of Ito's Lemma, 309 6.8 Stochastic Integral, 310 6.9 Jump Diffusion Models, 311 6.10 Estimation of Continuous-Time Models, 318 7 Extreme Values, Quantiles, and Value at Risk 325 7.1 Value at Risk, 326 7.2 RiskMetrics, 328 7.3 Econometric Approach to VaR Calculation, 333 7.4 Quantile Estimation, 338 7.5 Extreme Value Theory, 342 7.6 Extreme Value Approach to VaR, 353 7.7 New Approach Based on the Extreme Value Theory, 359 7.8 The Extremal Index, 377 8 Multivariate Time Series Analysis and Its Applications 389 8.1 Weak Stationarity and Cross-Correlation Matrices, 390 8.2 Vector Autoregressive Models, 399 8.3 Vector Moving-Average Models, 417 8.4 Vector ARMA Models, 422 8.5 Unit-Root Nonstationarity and Cointegration, 428 8.6 Cointegrated VAR Models, 432 8.7 Threshold Cointegration and Arbitrage, 442 8.8 Pairs Trading, 446 9 Principal Component Analysis and Factor Models 467 9.1 A Factor Model, 468 9.2 Macroeconometric Factor Models, 470 9.3 Fundamental Factor Models, 476 9.4 Principal Component Analysis, 483 9.5 Statistical Factor Analysis, 489 9.6 Asymptotic Principal Component Analysis, 498 10 Multivariate Volatility Models and Their Applications 505 10.1 Exponentially Weighted Estimate, 506 10.2 Some Multivariate GARCH Models, 510 10.3 Reparameterization, 516 10.4 GARCH Models for Bivariate Returns, 521 10.5 Higher Dimensional Volatility Models, 537 10.6 Factor–Volatility Models, 543 10.7 Application, 546 10.8 Multivariate t Distribution, 548 11 State-Space Models and Kalman Filter 557 11.1 Local Trend Model, 558 11.2 Linear State-Space Models, 576 11.3 Model Transformation, 577 11.4 Kalman Filter and Smoothing, 591 11.5 Missing Values, 600 11.6 Forecasting, 601 11.7 Application, 602 12 Markov Chain Monte Carlo Methods with Applications 613 12.1 Markov Chain Simulation, 614 12.2 Gibbs Sampling, 615 12.3 Bayesian Inference, 617 12.4 Alternative Algorithms, 622 12.5 Linear Regression with Time Series Errors, 624 12.6 Missing Values and Outliers, 628 12.7 Stochastic Volatility Models, 636 12.8 New Approach to SV Estimation, 649 12.9 Markov Switching Models, 660 12.10 Forecasting, 666 12.11 Other Applications, 669 Exercises, 670 References, 671 Index 673

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Book SynopsisIf you follow the headlines, you could be forgiven for thinking that things in Ireland are worse than ever. In fact, we live longer than ever before, we have never been healthier or better educated, we earn five times more than our grandparents did, our personal freedoms exceed those of any previous generation, and the lives of women and children have been transformed for the better.At a time when some good news is welcome, this uplifting book tells our national story through facts and stats, placing Ireland under the microscope to chart 100 undeniable achievements of the past 100 years.When the State was formed, Ireland was one of the most poverty-stricken nations in Europe. Now it has the second-highest quality of life in the world. While there is still more to be done, In Fact illustrates that Ireland, for all its imperfections, is in a much better state than you might think.

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Book SynopsisFocusing on the physics of the catastrophe process and addressed directly to advanced students, this innovative textbook quantifies dozens of perils, both natural and man-made, and covers the latest developments in catastrophe modelling. Combining basic statistics, applied physics, natural and environmental sciences, civil engineering, and psychology, the text remains at an introductory level, focusing on fundamental concepts for a comprehensive understanding of catastrophe phenomenology and risk quantification. A broad spectrum of perils are covered, including geophysical, hydrological, meteorological, climatological, biological, extraterrestrial, technological and socio-economic, as well as events caused by domino effects and global warming. Following industry standards, the text provides the necessary tools to develop a CAT model from hazard to loss assessment. Online resources include a CAT risk model starter-kit and a CAT risk modelling ''sandbox'' with Python Jupyter tutorial. Every process, described by equations, (pseudo)codes and illustrations, is fully reproducible, allowing students to solidify knowledge through practice.

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Book SynopsisThis Element presents the ?-generalized distribution, a statistical model tailored for the analysis of income distribution. Developed over years of collaborative, multidisciplinary research, it clarifies the statistical properties of the model, assesses its empirical validity and compares its effectiveness with other parametric models. It also presents formulas for calculating inequality indices within the ?-generalized framework, including the widely used Gini coefficient and the relatively lesser-known Zanardi index of Lorenz curve asymmetry. Through empirical illustrations, the Element criticizes the conventional application of the Gini index, pointing out its inadequacy in capturing the full spectrum of inequality characteristics. Instead, it advocates the adoption of the Zanardi index, accentuating its ability to capture the inherent heterogeneity and asymmetry in income distributions.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThis book is about research design, specifically concerning research that uses non-experimental data to figure out whether one thing causes another. It is separated into two halves, each with different approaches to that subject. Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data.

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Book SynopsisActuarial loss models are statistical models used by insurance companies to estimate the frequency and severity of future losses, set premiums, and reserve funds to cover potential claims. Actuarial loss models are a subject in actuarial mathematics that focus on the pricing and reserving for short-term coverages.This is a concise textbook written for undergraduate students majoring in actuarial science who wish to learn the basics of actuarial loss models. This book can be used as a textbook for a one-semester course on actuarial loss models. The prerequisite for this book is a first course on calculus. The reader is supposed to be familiar with differentiation and integration.This book covers part of the learning outcomes of the Fundamentals of Actuarial Mathematics (FAM) exam and the Advanced Short-Term Actuarial Mathematics (ASTAM) exam administered by the Society of Actuaries. It can be used by actuarial students and practitioners who prepare for the aforementione

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThe Second Edition of Advanced Issues in Partial Least Squares Structural Equation Modeling offers a straightforward and practical guide to PLS-SEM for users ready to go further than the basics of A Primer on Partial Least Squares Structural Equation Modeling,Third Edition. Even in this advanced guide, the authors have limited the emphasis on equations, formulas, and Greek symbols, and instead rely on detailed explanations of the fundamentals of PLS-SEM and provide general guidelines for understanding and evaluating the results of applying the method. A single study on corporate reputation features as an example throughout the book, along with a single software package (SmartPLS 4.0) to provide a seamless learning experience. The approach of this book is based on the authors' many years of conducting research and teaching methodology courses, including developing the SmartPLS software. The prTrade Review"Excellent guide on how to use smart pls. Good starter product for understanding the underlying concepts." -- Saurabh Gupta"Must have if you want to do PLS" -- Jason XiongTable of ContentsChapter 1: An Overview of Recent and Emerging Developments in PLS-SEM Chapter 2: Higher-order Constructs Chapter 3: Advanced Modeling and Model Assessment Chapter 4: Advanced Results Illustration Chapter 5: Modeling Observed Heterogeneity Chapter 6: Modeling Unobserved Heterogeneity

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Book SynopsisInterest in nonparametric methodology has grown considerably over the past few decades, stemming in part from vast improvements in computer hardware and the availability of new software that allows practitioners to take full advantage of these numerically intensive methods. This book is written for advanced undergraduate students, intermediate graduate students, and faculty, and provides a complete teaching and learning course at a more accessible level of theoretical rigor than Racine''s earlier book co-authored with Qi Li, Nonparametric Econometrics: Theory and Practice (2007). The open source R platform for statistical computing and graphics is used throughout in conjunction with the R package np. Recent developments in reproducible research is emphasized throughout with appendices devoted to helping the reader get up to speed with R, R Markdown, TeX and Git.Trade Review'This book will be valuable to economists wishing to learn nonparametric methods, and to practitioners needing the details of implementation. Applied economists will find this an excellent and practical reference guide.' Bruce E. Hansen, University of Wisconsin, Madison'This book manages to be comprehensive, careful, and accessible all at once - an impressive achievement for such a challenging subject. It covers topics not found elsewhere and incorporates them in a systematic, unified approach. Illustrations using the R programming language will have broad appeal for both teachers and users of nonparametric methods.' Jeffrey M. Woolridge, Michigan State UniversityTable of ContentsPart I. Probability Functions, Probability Density Functions, and their Cumulative Counterparts: 1. Discrete probability and cumulative probability functions; 2. Continuous density and cumulative distribution functions; 3. Mixed-data probability density and cumulative distribution functions; 4. Conditional probability density and cumulative distribution functions; Part II. Conditional Moment Functions and Related Statistical Objects: 5. Conditional moment functions; 6. Conditional mean function estimation; 7. Conditional mean function estimation with endogenous predictors; 8. Semiparametric conditional mean function estimation; 9. Conditional variance function estimation; Part III. Appendices: A. Large and small orders of magnitude and probability; B. R, RStudio, TeX and Git; C. Computational considerations; D. R Markdown for assignments; E. Practicum.

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Book SynopsisAbout our authors David F. Groebner is Professor Emeritus of Production Management in the College of Business and Economics at Boise State University. He has bachelor's and master's degrees in engineering and a Ph.D. in business administration. After working as an engineer, he has taught statistics and related subjects for 27 years. In addition to writing textbooks and academic papers, Groebner has worked extensively with both small and large organizations, including Hewlett-Packard, Boise Cascade, Albertson's, and Ore-Ida. He has worked with numerous government agencies, including Boise City and the U.S. Air Force. Patrick W. Shannon, Ph.D. is Dean and Professor of Supply Chain Operations Management in the College of Business and Economics at Boise State University. In addition to his administrative responsibilities, he has taught graduate and undergraduate courses in business statistics, quality management, and production and operaTable of Contents The Where, Why, and How of Data Graphs, Charts, and Tables: Describing Your Data Describing Data Using Numerical Measures 1 - 3 SPECIAL REVIEW SECTION Introduction to Probability Discrete Probability Distributions Introduction to Continuous Probability Distributions Introduction to Sampling Distributions Estimating Single Population Parameters Introduction to Hypothesis Testing Estimation and Hypothesis Testing for Two Population Parameters Hypothesis Tests and Estimation for Population Variances Analysis of Variance 8 - 12 SPECIAL REVIEW SECTION Goodness-of-Fit Tests and Contingency Analysis Introduction to Linear Regression and Correlation Analysis Multiple Regression Analysis and Model Building Analyzing and Forecasting Time-Series Data Introduction to Nonparametric Statistics Introducing Business Analytics Introduction to Decision Analysis (Online) Introduction to Quality and Statistical Process Control (Online) APPENDICES A to P

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Book SynopsisValuable software, realistic examples, clear writing, and fascinating topics help you master key spreadsheet and business analytics skills with SPREADSHEET MODELING AND DECISION ANALYSIS, 8E. You'll find everything you need to become proficient in today's most widely used business analytics techniques using Microsoft Office Excel 2016. Author Cliff Ragsdale -- respected innovator in business analytics -- guides you through the skills you need, using the latest Excel for Windows. You gain the confidence to apply what you learn to real business situations with step-by-step instructions and annotated screen images that make examples easy to follow. The World of Management Science sections further demonstrates how each topic applies to a real company.Each new edition includes extended trial licenses for Analytic Solver Platform and XLMiner with powerful simulation and optimization tools for descriptive and prescriptive analytics and a full suite of tools for data mining in Excel.Table of Contents1. Introduction to Modeling and Decision Analysis. 2. Introduction to Optimization and Linear Programming. 3. Modeling and Solving LP Problems in a Spreadsheet. 4. Sensitivity Analysis and the Simplex Method. 5. Network Modeling. 6. Integer Linear Programming. 7. Goal Programming and Multiple Objective Optimization. 8. Nonlinear Programming & Evolutionary Optimization. 9. Regression Analysis. 10. Data Mining. 11. Time Series Forecasting. 12. Introduction to Simulation Using Analytic Solver Platform. 13. Queuing Theory. 14. Decision Analysis. 15. Project Management (Online).

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Book SynopsisA high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of ''deep'' or ''Q'', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.Trade Review'Control Systems and Reinforcement Learning is a densely packed book with a vivid, conversational style. It speaks both to computer scientists interested in learning about the tools and techniques of control engineers and to control engineers who want to learn about the unique challenges posed by reinforcement learning and how to address these challenges. The author, a world-class researcher in control and probability theory, is not afraid of strong and perhaps controversial opinions, making the book entertaining and attractive for open-minded readers. Everyone interested in the "why" and "how" of RL will use this gem of a book for many years to come.' Csaba Szepesvári, Canada CIFAR AI Chair, University of Alberta, and Head of the Foundations Team at DeepMind'This book is a wild ride, from the elements of control through to bleeding-edge topics in reinforcement learning. Aimed at graduate students and very good undergraduates who are willing to invest some effort, the book is a lively read and an important contribution.' Shane G. Henderson, Charles W. Lake, Jr. Chair in Productivity, Cornell University'Reinforcement learning, now the de facto workhorse powering most AI-based algorithms, has deep connections with optimal control and dynamic programing. Meyn explores these connections in a marvelous manner and uses them to develop fast, reliable iterative algorithms for solving RL problems. This excellent, timely book from a leading expert on stochastic optimal control and approximation theory is a must-read for all practitioners in this active research area.' Panagiotis Tsiotras, David and Andrew Lewis Chair and Professor, Guggenheim School of Aerospace Engineering, Georgia Institute of TechnologyTable of Contents1. Introduction; Part I. Fundamentals Without Noise: 2. Control crash course; 3. Optimal control; 4. ODE methods for algorithm design; 5. Value function approximations; Part II. Reinforcement Learning and Stochastic Control: 6. Markov chains; 7. Stochastic control; 8. Stochastic approximation; 9. Temporal difference methods; 10. Setting the stage, return of the actors; A. Mathematical background; B. Markov decision processes; C. Partial observations and belief states; References; Glossary of Symbols and Acronyms; Index.

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Book SynopsisMaster everything you need to work successfully in today's project management environment with SUCCESSFUL PROJECT MANAGEMENT, 7E. This best-selling book details how to organize and manage project teams -- from planning and scheduling to cost management.Each chapter aligns with PMBOK (Project Management Body of Knowledge) to ensure best practices. The book covers the latest business challenges, including project constraints, stakeholder concerns, the project charter, and how projects support strategic plans.Learn the keys to effective communication and discover how project management applies in the workplace with new cases and real-world vignettes. End-of-chapter and Internet exercises review concepts critical to project management. New MindTap digital resources provide videos, quizzes, and case animations. You work with the latest, popular project management software: Microsoft Project 2016, available on the website. Find everything you need to manage projects in business today.Table of Contents1. Project Management Concepts. Part I: INITIATING A PROJECT. 2. Identifying and Selecting Projects. 3. Developing Project Proposals. Part II: PLANNING, PERFORMING, AND CONTROLLING THE PROJECT. 4. Defining Scope, Quality, Responsibility, and Activity Sequence. 5. Developing the Schedule. 6. Resource Utilization. 7. Determining Costs, Budget and Earned Value. 8. Managing Risk. 9. Closing the Project. Part III: PEOPLE: THE KEY TO PROJECT SUCCESS. 10. The Project Manager. 11. The Project Team. 12. Project Communication and Documentation. 13. Project Management Organizational Structures. Appendix A: Project Management Information Systems. Appendix B: Project Management Websites. Appendix C: Project Management Associations around the Globe. Appendix D: Acronyms. Reinforce Your Learning Answers. Glossary. Index.

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Book SynopsisThis is the perfect (and essential) supplement for all econometrics classes--from a rigorous first undergraduate course, to a first master''s, to a PhD course. Explains what is going on in textbooks full of proofs and formulas Offers intuition, skepticism, insights, humor, and practical advice (dos and don'ts) Contains new chapters that cover instrumental variables and computational considerations Includes additional information on GMM, nonparametrics, and an introduction to wavelets Trade Review“The exceptional success of this work is due to its clarity and economy of expression and the accessibility of the subject matter to a broad range of scholars. Now in its sixth edition, this guide brings practitioners and researchers up to date on the popular techniques in estimation. It holds a unique position among econometric texts. Highly recommended.” (Choice, November 2008)Table of ContentsPreface x Dedication xii 1. Introduction 1 2. Criteria for Estimators 11 3. The Classical Linear Regression Model 40 4. Interval Estimation and Hypothesis Testing 51 5. Specification 71 6. Violating Assumption One: Wrong Regressors, Nonlinearities, and Parameter Inconstancy 93 7. Violating Assumption Two: Nonzero Expected Disturbance 109 8. Violating Assumption Three: Nonspherical Disturbances 112 9. Violating Assumption Four: Instrumental Variable Estimation 137 10. Violating Assumption Four: Measurement Errors and Autoregression 157 11. Violating Assumption Four: Simultaneous Equations 171 12. Violating Assumption Five: Multicollinearity 192 13. Incorporating Extraneous Information 203 14. The Bayesian Approach 213 15. Dummy Variables 232 16. Qualitative Dependent Variables 241 17. Limited Dependent Variables 262 18. Panel Data 281 19. Time Series Econometrics 296 20. Forecasting 331 21. Robust Estimation 345 22. Applied Econometrics 361 23. Computational Considerations 385 Appendix A: Sampling Distributions, the Foundation of Statistics 403 Appendix B: All about Variance 407 Appendix C: A Primer on Asymptotics 412 Appendix D: Exercises 417 Appendix E: Answers to Even-numbered Questions 479 Glossary 503 Bibliography 511 Name Index 563 Subject Index 573

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Book SynopsisWith applications using SmartPLS the primary software used in partial least squares structural equation modeling (PLS-SEM)this practical guide provides concise instructions on how to use this evolving statistical technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways. Please note that all examples in this Second Edition use SmartPLS 3. To access this software, please visitTrade Review"A text that students will find easy to read and enjoyable." -- Toni M. SomersTable of ContentsChapter 1: An Introduction to Structural Equation Modeling What Is Structural Equation Modeling? Considerations in Using Structural Equation Modeling Structural Equation Modeling With Partial Least Squares Path Modeling PLS-SEM, CB-SEM, and Regressions Based on Sum Scores Organization of Remaining Chapters Chapter 2: Specifying the Path Model and Examining Data Stage 1: Specifying the Structural Model Stage 2: Specifying the Measurement Models Stage 3: Data Collection and Examination Case Study Illustration: Specifying the PLS-SEM Model Path Model Creation Using the SmartPLS Software Chapter 3: Path Model Estimation Stage 4: Model Estimation and the PLS-SEM Algorithm Case Study Illustration: PLS Path Model Estimation (Stage 4) Chapter 4: Assessing PLS-SEM Results Part I: Evaluation of Reflective Measurement Models Overview of Stage 5: Evaluation of Measurement Models Stage 5a: Assessing Results of Reflective Measurement Models Case Study Illustration—Reflective Measurement Models Running the PLS-SEM Algorithm Reflective Measurement Model Evaluation Chapter 5: Assessing PLS-SEM Results Part II: Evaluation of the Formative Measurement Models Stage 5b: Assessing Results of Formative Measurement Models Bootstrapping Procedure Bootstrap Confidence Intervals Case Study Illustration—Evaluation of Formative Measurement Models Chapter 6: Assessing PLS-SEM Results Part III: Evaluation of the Structural Model Stage 6: Assessing PLS-SEM Structural Model Results Case Study Illustration—How Are PLS-SEM Structural Model Results Reported? Chapter 7: Mediator and Moderator Analysis Mediation Moderation Chapter 8: Outlook on Advanced Methods Importance-Performance Map Analysis Hierarchical Component Models Confirmatory Tetrad Analysis Dealing With Observed and Unobserved Heterogeneity Consistent Partial Least Squares

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Book SynopsisThis second edition of Data Management Using Stata focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout to reflect new data management features that have been added over the last 10 years. Such features include the ability to read and write a wide variety of file formats, the ability to write highly customized Excel files, the ability to have multiple Stata datasets open at once, and the ability to store and manipulate string variables stored as Unicode. Further, this new edition includes a new chapter illustrating how to write Stata programs for solving data management tasks. As in the original edition, the chapters are organized by data management areas: reading and writing datasets, cleaning data, labeling datasets, creating variables, combining datasets, processing observations across subgroups, changing the shape of datasets, and programming for data management. Within each chapter, each section is a self-contained lesson illustrating a particular data management task (for instance, creating date variables or automating error checking) via examples. This modular design allows you to quickly identify and implement the most common data management tasks without having to read background information first. In addition to the “nuts and bolts” examples, author Michael Mitchell alerts users to common pitfalls (and how to avoid them) and provides strategic data management advice. This book can be used as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. New users will appreciate this book as a valuable way to learn data management, while experienced users will find this information to be handy and time saving—there is a good chance that even the experienced user will learn some new tricks.Table of ContentsIntroduction. Reading and importing data files. Saving and exporting data files. Data cleaning. Labeling datasets. Creating variables. Combining datasets. Processing observations across subgroups. Changing the shape of your data. Programming for data management: Part I. Programming for data management: Part II.

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Book SynopsisMicroeconometrics Using Stata, Second Edition is an invaluable reference for researchers and students interested in applied microeconometric methods.Like previous editions, this text covers all the classic microeconometric techniques ranging from linear models to instrumental-variables regression to panel-data estimation to nonlinear models such as probit, tobit, Poisson, and choice models. Each of these discussions has been updated to show the most modern implementation in Stata, and many include additional explanation of the underlying methods. In addition, the authors introduce readers to performing simulations in Stata and then use simulations to illustrate methods in other parts of the book. They even teach you how to code your own estimators in Stata.The second edition is greatly expanded—the new material is so extensive that the text now comprises two volumes. In addition to the classics, the book now teaches recently developed econometric methods and the methods newly added to Stata. Specifically, the book includes entirely new chapters on duration models randomized control trials and exogenous treatment effects endogenous treatment effects models for endogeneity and heterogeneity, including finite mixture models, structural equation models, and nonlinear mixed-effects models spatial autoregressive models semiparametric regression lasso for prediction and inference Bayesian analysis Anyone interested in learning classic and modern econometric methods will find this the perfect companion. And those who apply these methods to their own data will return to this reference over and over as they need to implement the various techniques described in this book.Table of ContentsStata basics. Data management and graphics. Linear regression basics. Linear regression extensions. Simulation. Linear regression with correlated errors. Linear instrumental-variables regression. Linear panel-data models: Basics. Linear panel-data models: Extensions. Introduction to nonlinear regression. Tests of hypotheses and model specification. Bootstrap methods. Nonlinear regression methods. Flexible regression: Finite mixtures and nonparametric. Quantile regression. Programming in Stata. Mata. Optimization in Mata

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Book SynopsisMicroeconometrics Using Stata, Second Edition is an invaluable reference for researchers and students interested in applied microeconometric methods.Like previous editions, this text covers all the classic microeconometric techniques ranging from linear models to instrumental-variables regression to panel-data estimation to nonlinear models such as probit, tobit, Poisson, and choice models. Each of these discussions has been updated to show the most modern implementation in Stata, and many include additional explanation of the underlying methods. In addition, the authors introduce readers to performing simulations in Stata and then use simulations to illustrate methods in other parts of the book. They even teach you how to code your own estimators in Stata.The second edition is greatly expanded—the new material is so extensive that the text now comprises two volumes. In addition to the classics, the book now teaches recently developed econometric methods and the methods newly added to Stata. Specifically, the book includes entirely new chapters on duration models randomized control trials and exogenous treatment effects endogenous treatment effects models for endogeneity and heterogeneity, including finite mixture models, structural equation models, and nonlinear mixed-effects models spatial autoregressive models semiparametric regression lasso for prediction and inference Bayesian analysis Anyone interested in learning classic and modern econometric methods will find this the perfect companion. And those who apply these methods to their own data will return to this reference over and over as they need to implement the various techniques described in this book.Table of ContentsNonlinear optimization methods. Binary outcome models. Multinomial models. Tobit and selection models. Count-data models. Survival analysis for duration data. Nonlinear panel models. Parametric models for heterogeneity and endogeneity. Randomized control trials and exogenous treatment effects. Endogenous treatment effects. Spatial regression. Semiparametric regression. Machine learning for prediction and inference. Bayesian methods: Basics. Bayesian methods: Markov chain Monte Carlo algorithms

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Book SynopsisEmployers Can Reduce Their Employees' Health Care Costs by Thinking Out of The BoxEmployee health care costs have skyrocketed, especially for small business owners. But employers have options that medical entrepreneurs have crafted to provide all businesses with plans to improve their employees' wellness and reduce their costs. Thus, the cost of employee health care benefits can be reduced markedly by choosing one of numerous alternatives to traditional indemnity policies.The Finance of Health Care provides business decision makers with the information they need to match the optimal health care plan with the culture of their workforce. This book is a must guide for corporate executives and entrepreneurs who want to attract—and keep--the best employees in our competitive economy.

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Book SynopsisThe World''s Greatest Book on Successful Living-Now in a Special Compact Edition!Here is the complete experience of Think and Grow Rich in an exquisitely brief and faithful condensation. In less than an hour of reading you will learn all thirteen of Napoleon Hill''s famous steps to wealth and achievement. This masterly summation of Hill''s original landmark explains: * Why you must write down your goals.* The immeasurable importance of a definite major aim.* How to benefit from hunches and sudden inspirations.* The magic of persistence in the face of setbacks.* How to program your mind for success. * The extraordinary power of a "Master Mind" group. Abridged and introduced by PEN Award-winning historian Mitch Horowitz, this concise rendition of Hill''s masterwork is both the perfect introduction to Think and Grow Rich and a great refresher for those who already know the book and its powers.Napoleon Hill was born in Wise County, Virginia. He began his writing career at age 13 as a "mountain reporter" for small town newspapers and went on to become America''s most beloved motivational author. His work stands as a monument to individual achievement and is the cornerstone of modern motivation. His most famous work, Think and Grow Rich, is one of the best-selling books of all time. Hill established the Foundation as a nonprofit educational institution whose mission is to perpetuate his philosophy of leadership, self-motivation, and individual achievement.

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Book SynopsisWithin You is a Master Key That Solves Every Human Problem and Leads to Incredible Heights of AchievementIn one of success master Napoleon Hill''s greatest books, The Master Key to Riches, he explores how to unlock the miraculous energies of thought-and explores why motivated people often fail to do so.In this powerful lesson-distilled down to its essentials and introduced by PEN Award-winning historian and New Thought scholar Mitch Horowitz-you will discover: How "applied faith" unlocks your highest potentials. How to cultivate the kind of accurate thinking that leads to lasting success. How the simple step of "going the extra mile" benefits you in undreamed of ways. Why it is vital to concentrate your energies. How to create a bridge between your own thoughts and Infinite Intelligence. The prospering power of a Positive Mental Attitude. The Master Key to Riches is one of Napoleon Hill''s core works-it provides his complete philosophy of success, along with methods, insights, and ideas that show you how to avoid pitfalls and get the most from his teaching. Now, in this special condensed edition you can experience the master''s full program in a single sitting.

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Book SynopsisBootstrapping is a conceptually simple statistical technique to increase the quality of estimates, conduct robustness checks and compute standard errors for virtually any statistic. This book provides an intelligible and compact introduction for students, scientists and practitioners. It not only gives a clear explanation of the underlying concepts but also demonstrates the application of bootstrapping using Python and Stata.

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Book SynopsisThis 2nd edition compendium contains and explains essential statistical formulas within an economic context. Expanded by more than 100 pages compared to the 1st edition, the compendium has been supplemented with numerous additional practical examples, which will help readers to better understand the formulas and their practical applications. This statistical formulary is presented in a practice-oriented, clear, and understandable manner, as it is needed for meaningful and relevant application in global business, as well as in the academic setting and economic practice.The topics presented include, but are not limited to: statistical signs and symbols, descriptive statistics, empirical distributions, ratios and index figures, correlation analysis, regression analysis, inferential statistics, probability calculation, probability distributions, theoretical distributions, statistical estimation methods, confidence intervals, statistical testing methods, the Peren-Clement index, and the usual statistical tables.Given its scope, the book offers an indispensable reference guide and is a must-read for undergraduate and graduate students, as well as managers, scholars, and lecturers in business, politics, and economics.Table of ContentsStatistical Signs and Symbols.- Descriptive Statistics.- Inferential Statistics.- Probability Calculation.- Statistical Tables.- Bibliography.- Index.

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Book SynopsisThe Statistical Yearbook is an annual compilation of a wide range of international economic, social and environmental statistics on over 200 countries and areas, compiled from sources including UN agencies and other international, national and specialized organizations. The 2020 edition contains data available to the Statistics Division as of 31 July 2020 and presents them in 32 tables on topics such as: communication; crime; development assistance; education; energy; environment; finance; gender; international merchandise trade; international tourism; labour force; national accounts; population and migration; price and production indices; and science and technology. Most tables covering the period up to 2020. Accompanying the tables are technical notes providing brief descriptions of major statistical concepts, definitions and classifications.

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Book SynopsisThis edition sets out recent developments in East Asian local currency bond markets and discusses the region's economic outlook, the risk of another taper tantrum, and price differences between labeled and unlabeled green bonds.Emerging East Asia's local currency (LCY) bond markets expanded to an aggregate USD21.7 trillion at the end of September 2021, posting growth of 3.4% quarter-on-quarter, up from 2.9% in the previous quarter. LCY bond issuance rose 6.8% quarter-on-quarter to USD2.4 trillion in Q3 2021. Sustainable bond markets in ASEAN+3 also continued to expand to reach a size of USD388.7 billion at the end of September.

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Book SynopsisHow to learn both applied statistics (econometrics) and free, open-source software R? This book allows students to have a sense of accomplishment by copying and pasting many hands-on templates provided here.The textbook is essential for anyone wishing to have a practical understanding of an extensive range of topics in Econometrics. No other text provides software snippets to learn so many new statistical tools with hands-on examples. The explicit knowledge of inputs and outputs of each new method allows the student to know which algorithm is worth studying. The book offers sufficient theoretical and algorithmic details about a vast range of statistical techniques.The second edition's preface lists the following topics generally absent in other textbooks. (i) Iteratively reweighted least squares, (ii) Pillar charts to represent 3D data. (iii) Stochastic frontier analysis (SFA) (iv) model selection with Mallows' Cp criterion. (v) Hodrick-Prescott (HP) filter. (vi) Automatic ARIMA models. (vi) Nonlinear Granger-causality using kernel regressions and bootstrap confidence intervals. (vii) new Keynesian Phillips curve (NKPC). (viii) Market-neutral pairs trading using two cointegrated stocks. (ix) Artificial neural network (ANN) for product-specific forecasting. (x) Vector AR and VARMA models. (xi) New tools for diagnosing the endogeneity problem. (xii) The elegant set-up of k-class estimators and identification. (xiii) Probit-logit models and Heckman selection bias correction. (xiv) Receiver operating characteristic (ROC) curves and areas under them. (xv) Confusion matrix. (xvi) Quantile regression (xvii) Elastic net estimator. (xviii) generalized Correlations (xix) maximum entropy bootstrap for time series. (xx) Convergence concepts quantified. (xxi) Generalized partial correlation coefficients (xxii) Panel data and duration (survival) models.

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Book SynopsisHow to learn both applied statistics (econometrics) and free, open-source software R? This book allows students to have a sense of accomplishment by copying and pasting many hands-on templates provided here.The textbook is essential for anyone wishing to have a practical understanding of an extensive range of topics in Econometrics. No other text provides software snippets to learn so many new statistical tools with hands-on examples. The explicit knowledge of inputs and outputs of each new method allows the student to know which algorithm is worth studying. The book offers sufficient theoretical and algorithmic details about a vast range of statistical techniques.The second edition's preface lists the following topics generally absent in other textbooks. (i) Iteratively reweighted least squares, (ii) Pillar charts to represent 3D data. (iii) Stochastic frontier analysis (SFA) (iv) model selection with Mallows' Cp criterion. (v) Hodrick-Prescott (HP) filter. (vi) Automatic ARIMA models. (vi) Nonlinear Granger-causality using kernel regressions and bootstrap confidence intervals. (vii) new Keynesian Phillips curve (NKPC). (viii) Market-neutral pairs trading using two cointegrated stocks. (ix) Artificial neural network (ANN) for product-specific forecasting. (x) Vector AR and VARMA models. (xi) New tools for diagnosing the endogeneity problem. (xii) The elegant set-up of k-class estimators and identification. (xiii) Probit-logit models and Heckman selection bias correction. (xiv) Receiver operating characteristic (ROC) curves and areas under them. (xv) Confusion matrix. (xvi) Quantile regression (xvii) Elastic net estimator. (xviii) generalized Correlations (xix) maximum entropy bootstrap for time series. (xx) Convergence concepts quantified. (xxi) Generalized partial correlation coefficients (xxii) Panel data and duration (survival) models.

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Book SynopsisEconomic Models for Industrial Organization focuses on the specification and estimation of econometric models for research in industrial organization. In recent decades, empirical work in industrial organization has moved towards dynamic and equilibrium models, involving econometric methods which have features distinct from those used in other areas of applied economics. These lecture notes, aimed for a first or second-year PhD course, motivate and explain these econometric methods, starting from simple models and building to models with the complexity observed in typical research papers. The covered topics include discrete-choice demand analysis, models of dynamic behavior and dynamic games, multiple equilibria in entry games and partial identification, and auction models.

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Book SynopsisUsing a reader-friendly and straightforward approach, Langley/Novack/Gibson/Coyle's SUPPLY CHAIN MANAGEMENT: A LOGISTICS PERSPECTIVE, Cengage International Edition, 12th Edition, blends logistics theory with practical applications. The latest content highlights emerging issues, technology developments and global changes in the constantly evolving and critically-important field of supply chain management. This edition examines today's real companies and how public and private organizations are responding to the continuing pressure to modernize and transform their supply chains. Dive into real-world businesses and see how organizations respond to the ongoing need for modernization in their supply chains. Experience hands-on learning with updated features, brief cases and global content -- providing a glimpse into the decisions supply chain managers make daily. Each chapter begins with profiles that spotlight real organizations, people or events -- underlining the significance of current supply chain matters.

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Book SynopsisDavid M. Levine and David F. Stephan are part of a writing team known for their series of business statistics textbooks that include Basic Business Statistics, Business Statistics: A First Course, and Statistics for Managers Using Microsoft Excel. In long teaching careers at Baruch College, both were known for their classroom innovations, with Levine being honored with a Presidential Excellence Award for Distinguished Teaching Award and Stephan granted the privilege to design and develop the College's first computer-based classroom. Both are active members of the Data, Analytics and Statistics Instruction SIG of the Decision Sciences Institute. Levine is Professor Emeritus of Information Systems at Baruch College. He is nationally recognized innovator in business statistics education and is also the coauthor of Applied Statistics for Engineers and Scientists Using Microsoft Excel and Minitab. Levine is also the authorTable of ContentsIntroduction The Even You Can Learn Statistics and Analytics Owner's Manual. xiii Chapter 1 Fundamentals of Statistics. 1 1.1 The First Three Words of Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 The Fourth and Fifth Words. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 The Branches of Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Sources of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Sampling Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.6 Sample Selection Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Chapter 2 Presenting Data in Tables and Charts . 15 2.1 Presenting Categorical Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 Presenting Numerical Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3 “Bad” Charts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Chapter 3 Descriptive Statistics. 45 3.1 Measures of Central Tendency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 Measures of Position. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.3 Measures of Variation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.4 Shape of Distributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Chapter 4 Probability. 75 4.1 Events. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.2 More Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.3 Some Rules of Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.4 Assigning Probabilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 Chapter 5 Probability Distributions. 87 5.1 Probability Distributions for Discrete Variables. . . . . . . . . . . . . . . . . . . . . . . . 87 5.2 The Binomial and Poisson Probability Distributions. . . . . . . . . . . . . . . . . . . . 93 5.3 Continuous Probability Distributions and the Normal Distribution . . . . . . . 100 5.4 The Normal Probability Plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 Chapter 6 Sampling Distributions and Confidence Intervals. 121 6.1 Foundational Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 6.2 Sampling Error and Confidence Intervals. . . . . . . . . . . . . . . . . . . . . . . . . . . 125 6.3 Confidence Interval Estimate for the Mean Using the t Distribution (? Unknown). . . 128 6.4 Confidence Interval Estimation for Categorical Variables . . . . . . . . . . . . . . . 131 6.5 Confidence Interval Estimation When Normality Cannot Be Assumed. . . . . 134 Chapter 7 Fundamentals of Hypothesis Testing. 145 7.1 The Null and Alternative Hypotheses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 7.2 Hypothesis Testing Issues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 7.3 Decision-Making Risks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 7.4 Performing Hypothesis Testing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 7.5 Types of Hypothesis Tests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Chapter 8 Hypothesis Testing: Z and t Tests. 157 8.1 Test for the Difference Between Two Proportions . . . . . . . . . . . . . . . . . . . . . 157 8.2 Test for the Difference Between the Means of Two Independent Groups . . . . 163 8.3 The Paired t Test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 Chapter 9 Hypothesis Testing: Chi-Square Tests and the One-Way Analysis of Variance (ANOVA). 183 9.1 Chi-Square Test for Two-Way Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 9.2 One-Way Analysis of Variance (ANOVA): Testing for the Differences Among the Means of More Than Two Groups. . . . . . . . . . . . . . . 191 Chapter 10 Simple Linear Regression. 211 10.1 Basics of Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 10.2 Developing a Simple Linear Regression Model. . . . . . . . . . . . . . . . . . . . . . 214 10.3 Measures of Variation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 10.4 Inferences About the Slope. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 10.5 Common Mistakes When Using Regression Analysis . . . . . . . . . . . . . . . . . 229 Chapter 11 Multiple Regression. 243 11.1 The Multiple Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 11.2 Coefficient of Multiple Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 11.3 The Overall F Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 11.4 Residual Analysis for the Multiple Regression Model . . . . . . . . . . . . . . . . . 247 11.5 Inferences Concerning the Population Regression Coefficients. . . . . . . . . . 248 Chapter 12 Introduction to Analytics. 259 12.1 Basic Concepts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 12.2 Descriptive Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 12.3 Typical Descriptive Analytics Visualizations. . . . . . . . . . . . . . . . . . . . . . . . 269 Chapter 13 Predictive Analytics. 279 13.1 Predictive Analytics Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 13.2 More About Predictive Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 13.3 Tree Induction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284 13.4 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 13.5 Association Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 Appendix A Microsoft Excel Operation and Configuration . 299 Appendix B Review of Arithmetic and Algebra. 301 Appendix C Statistical Tables. 311 Appendix D Spreadsheet Tips . 339 Appendix E Advanced Techniques. 343 Appendix F Documentation for Downloadable Files. 353 9780137654765, TOC, 4/25/2022

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Book SynopsisA provocative new analysis of immigration's long-term effects on a nation's economy and culture. Over the last two decades, as economists began using big datasets and modern computing power to reveal the sources of national prosperity, their statistical results kept pointing toward the power of culture to drive the wealth of nations. In The Culture Transplant, Garett Jones documents the cultural foundations of cross-country income differences, showing that immigrants import cultural attitudes from their homelands—toward saving, toward trust, and toward the role of government—that persist for decades, and likely for centuries, in their new national homes. Full assimilation in a generation or two, Jones reports, is a myth. And the cultural traits migrants bring to their new homes have enduring effects upon a nation's economic potential. Built upon mainstream, well-reviewed academic research that hasn't pierced the public consciousness, this book offers a compelling refutation of an unspoken consensus that a nation's economic and political institutions won't be changed by immigration. Jones refutes the common view that we can discuss migration policy without considering whether migration can, over a few generations, substantially transform the economic and political institutions of a nation. And since most of the world's technological innovations come from just a handful of nations, Jones concludes, the entire world has a stake in whether migration policy will help or hurt the quality of government and thus the quality of scientific breakthroughs in those rare innovation powerhouses. Trade Review"Immigrants change the countries they move to. The Culture Transplant is the very best book on this phenomenon, reflecting the continuing rise of Garett Jones as a thinker and writer of real import."—Tyler Cowen, blogger, Marginal Revolution"Synthesizing decades of new work in development economics, Garett Jones re-examines and rejects some of the core assumptions within the modern immigration debate. Defenders of open borders—utilitarians in particular—will have to seriously grapple with this novel and groundbreaking book."—Hrishikesh Joshi, Bowling Green State University"A unique and authoritative treatment of the deep persistence of cultural attributes that permeates across generations, and through migration, shapes institutions and contemporary outcomes. By focusing on people rather than places, Garett Jones provides a unique perspective on how we should think about the role of migration and diversity in understanding modern successes and failures. Jones's treatment of the literature is a master class in distilling rigorous research and presenting it in a breezy fashion that is hard to put down once you get started."—Areendam Chanda, Louisiana State University"The Culture Transplant is a good read, a brief dive into the intriguing question of why some places and some people are so much more prosperous than others."—Robert VerBruggen, Wall Street JournalMuch of the literature on immigrant assimilation looks at easily observable questions about subsequent generations, such as whether they are learning English, graduating high school, and moving up the income ladder. Jones's book proves that these external accomplishments do not necessarily indicate assimilation at the deeper level of cultural values. This is of the greatest possible importance, because every day social science discovers further evidence that these cultural values, more than anything else, determine what a country's politics and its economy will look like in the future."—Helen Andrews, The American Conservative"Jones has written an excellent synopsis of the deep roots of culture and the persistent effects of these deep roots. The book is concise and easy to read, led by Jones's ability to decompose complicated ideas into easily understood examples and descriptions. Researchers and the public will gain valuable insights from The Cultural Transplant, a better understanding of the persistence of culture and longrunning factors that have placed countries on socioeconomic trajectories that have yielded vast differences in living standards across the world."—C. Justin Cook, The Developing Economies"Most economists agree that immigration—including illegal immigration—leads to greater economic growth and innovation. However, Jones argues that immigrants transplant their culture in the countries they move to, making the economies there similar to those in their home countries. Where immigrants come from and their home nation's technological development are critical to the economic and cultural impacts they have on the countries they move to."—P. Z. McKay, CHOICETable of Contents0. Preface: The Best Immigration Policy 0. Introduction: How Economists Learned the Power of Culture 1. The Assimilation Myth 2. Prosperity Migrates 3. Places or Peoples? 4. The Migration of Good Government 5. Our Diversity Is Our ____________ 6. The I-7 7. The Chinese Diaspora: Building the Capitalist Road 8. The Deep Roots across the Fifty United States 9. Intercalary: Je ne sais quoi 10. Conclusion: The Goose and the Golden Eggs

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Book SynopsisApplied econometrics, known to aficionados as 'metrics, is the original data science. 'Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs. Through accessible discussion and with a dose of kung fu-themed humor, Mastering 'Metrics presents the essential tools of econometric research and demonstratTrade Review"I would be hard pressed to name another econometrics book that can be read for enjoyment yet provides useful quantitative insights."--M.S.R., Financial Analysts JournalTable of ContentsList of Figures vii List of Tables ix Introduction xi 1 Randomized Trials 1 1.1 In Sickness and in Health (Insurance) 1 1.2 The Oregon Trail 24 Masters of 'Metrics: From Daniel to R. A. Fisher 30 Appendix: Mastering Inference 33 2 Regression 47 2.1 A Tale of Two Colleges 47 2.2 Make Me a Match, Run Me a Regression 55 2.3 Ceteris Paribus? 68 Masters of 'Metrics: Galton and Yule 79 Appendix: Regression Theory 82 3 Instrumental Variables 98 3.1 The Charter Conundrum 99 3.2 Abuse Busters 115 3.3 The Population Bomb 123 Masters of 'Metrics: The Remarkable Wrights 139 Appendix: IV Theory 142 4 Regression Discontinuity Designs 147 4.1 Birthdays and Funerals 148 4.2 The Elite Illusion 164 Masters of 'Metrics: Donald Campbell 175 5 Differences-in-Differences 178 5.1 A Mississippi Experiment 178 5.2 Drink, Drank, ... 191 Masters of 'Metrics: John Snow 204 Appendix: Standard Errors for Regression DD 205 6 The Wages of Schooling 209 6.1 Schooling, Experience, and Earnings 209 6.2 Twins Double the Fun 217 6.3 Econometricians Are Known by Their ... Instruments 223 6.4 Rustling Sheepskin in the Lone Star State 235 Appendix: Bias from Measurement Error 240 Abbreviations and Acronyms 245 Empirical Notes 249 Acknowledgments 269 Index 271

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Book SynopsisMaster the fundamental concepts and applications of operations (OM) and supply chain management (SCM) with OPERATIONS AND SUPPLY CHAIN MANAGEMENT, 3E by award-winning authors Collier/Evans. This edition balances coverage of both manufacturing and service businesses with the latest updates, an additional new SCM chapter and new discussions that highlight the latest changes in OM and SCM. Clear explanations are supported with contemporary examples and new and updated case studies that demonstrate how concepts apply. Discussions highlight new techniques and principles as well as the most recent Excel techniques and digital tools. Solved problems further guide you through key formulas and computations. MindTap online learning platform is available for both manual calculations and the use of Excel spreadsheet templates and models. MindTap's algorithmic homework and interactive learning tools also show you how to apply qualitative and quantitative reasoning to today's OM and SCM concepts.Table of ContentsPART 1: BASIC CONCEPTS OF OM AND VALUE CHAINS. 1. Operations Management and Value Chains. 2. Analytics and Performance Measurement in Operations and Value Chains. 3. Operations Strategy. 4. Technology and Operations Management. PART 2: DESIGNING OPERATIONS AND SUPPLY CHAINS 5. Goods and Service Design. 6. Supply Chain Design. 7. Process Selection, Design, and Improvement. 8. Facility and Work Design. PART 3: MANAGING OPERATIONS AND SUPPLY CHAINS. 9. Forecasting and Demand Planning. 10. Capacity Management. 11. Process Analysis and Resource Utilization. 12. Managing Inventories in Supply Chains. 13. Supply Chain Management and Logistics. 14. Resource Management. 15. Operations Scheduling and Sequencing. 16. Quality Management. 17. Quality Control & SPC. 18. Lean Operating Systems. 19. Project Management. 20. Building Resilience and Continuity in Operations and Supply Chains Supplement A: Probability and Statistics. Supplement B: Decision Analysis. Supplement C: Break-Even Analysis. Supplement D: Linear Optimization. Supplement E: The Transportation and Assignment Problems. Supplement F: Queuing Models. Supplement G: Simulation. Appendix A: Areas for the Cumulative Standard Normal Distribution. Appendix B: Factors for Control Charts. Appendix C: Integrative Case: Diamond Global Supply Chain ��� Hudson Jewelers Endnotes. Glossary. Index.

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Book SynopsisThe focus of this book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.Trade Review'Boyd and Vandenberghe have written a beautiful book that I strongly recommend to everyone interested in optimization and computational mathematics: Convex Optimization is a very readable introduction to this modern field of research.' Mathematics of Operations Research'… a beautiful book that I strongly recommend to everyone interested in optimization and computational mathematics … a very readable and inspiring introduction to this modern field of research. I recommend it as one of the best optimization textbooks that have appeared in the last years.' Mathematical Methods of Operations Research'I highly recommend it either if you teach nonlinear optimization at the graduate level for a supplementary reading list and for your library, or if you solve optimization problems and wish to know more about solution methods and applications.' International Statistical institute'… the whole book is characterized by clarity. … a very good pedagogical book … excellent to grasp the important concepts of convex analysis [and] to develop an art in modelling optimization problems intelligently.' Matapli'The book by Boyd and Vandenberghe reviewed here is one of … the best I have ever seen … it is a gentle, but rigorous, introduction to the basic concepts and methods of the field … this book is meant to be a 'first book' for the student or practitioner of optimization. However, I think that even the experienced researcher in the field has something to gain from reading this book: I have very much enjoyed the easy to follow presentation of many meaningful examples and suggestive interpretations meant to help the student's understanding penetrate beyond the surface of the formal description of the concepts and techniques. For teachers of convex optimization this book can be a gold mine of exercises. MathSciNetTable of ContentsPreface; 1. Introduction; Part I. Theory: 2. Convex sets; 3. Convex functions; 4. Convex optimization problems; 5. Duality; Part II. Applications: 6. Approximation and fitting; 7. Statistical estimation; 8. Geometrical problems; Part III. Algorithms: 9. Unconstrained minimization; 10. Equality constrained minimization; 11. Interior-point methods; Appendices.

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Book SynopsisThis book deals with methods and models of microeconometrics, the statistical modeling of behavioral relationships based on data from sample surveys or actual or quasi-social experiments. The book is oriented to the graduate student and researcher using such data. The level of the book is post-first year PhD economics.Trade Review'This book presents an elegant and accessible treatment of the broad range of rapidly expanding topics currently being studied by microeconometricians. Thoughtful, intuitive, and careful in laying out central concepts of sophisticated econometric methodologies, it is not only an excellent textbook for students, but also an invaluable reference text for practitioners and researchers.' Cheng Hsiao, University of Southern California'I wish Microeconometrics was available when I was a student! Here, in one place - and in clear and readable prose - you can find all of the tools that are necessary to do cutting-edge applied economic analysis, and with many helpful examples.' Alan Krueger, Princeton University'Cameron and Trivedi have written a remarkably thorough and up-to-date treatment of microeconometric methods. This is not a superficial cookbook; the early chapters carefully lay the theoretical foundations on which the authors build their discussion of methods for discrete and limited dependent variables and for analysis of longitudinal data. A distinctive feature of the book is its attention to cutting-edge topics like semiparametric regression, bootstrap methods, simulation-based estimation, and empirical likelihood estimation. A highly valuable book.' Gary Solon, University of Michigan'The empirical analysis of micro data is more widespread than ever before. The book by Cameron and Trivedi contains a superb treatment of all the methods that economists like to apply to such data. What is more, it fully integrates a number of exciting new methods that have become applicable due to recent advances in computer technology. The text is in perfect balance between econometric theory and empirical intuition, and it contains many insightful examples.' Gerard J. van den Berg, Free University, Amsterdam, The Netherlands'… it is well organised and well written … the authors are to be congratulated on this sure-footed addition to the econometrics literature.' The Times Higher Education SupplementTable of Contents1. Introduction; 2. Causal and non-causal models; 3. Microeconomic data structures; 4. Linear models; 5. ML and NLS estimation; 6. GMM and systems estimation; 7. Hypothesis tests; 8. Specification tests and model selection; 9. Semiparametric methods; 10. Numerical optimization; 11. Bootstrap methods; 12. Simulation-based methods; 13. Bayesian methods; 14. Binary outcome models; 15. Multinomial models; 16. Tobit and selection models; 17. Transition data: survival analysis; 18. Mixture models and unobserved heterogeneity; 19. Models of multiple hazards; 20. Models of count data; 21. Linear panel models: basics; 22. Linear panel models: extensions; 23. Nonlinear panel models; 24. Stratified and clustered samples; 25. Treatment evaluation; 26. Measurement error models; 27. Missing data and imputation; A. Asymptotic theory; B. Making pseudo-random draw.

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Book SynopsisConrad Carlberg is a nationally recognized expert on quantitative analysis, data analysis, and management applications such as Microsoft Excel, SAS, and Oracle. He holds a Ph.D. in statistics from the University of Colorado and is a many-time recipient of Microsoft's Excel MVP designation. He is the author of many books, including Business Analysis with Microsoft Excel, Fifth Edition, Statistical Analysis: Microsoft Excel 2016, Regression Analysis Microsoft Excel, and R for Microsoft Excel Users. Carlberg is a Southern California native. After college he moved to Colorado, where he worked for a succession of startups and attended graduate school. He spent two years in the Middle East, teaching computer science and dodging surly camels. After finishing graduate school, Carlberg worked at US West (a Baby Bell) in product management and at Motorola. In 1995 he started a small consulting business (www.conradcarlberg.com)Table of ContentsPrefaceChapter 1 Bayesian Analysis and R: An Overview Bayes Comes Back About Structuring Priors Watching the Jargon Priors, Likelihoods, and Posteriors The Prior The Likelihood Contrasting a Frequentist Analysis with a Bayesian The Frequentist Approach The Bayesian Approach SummaryChapter 2 Generating Posterior Distributions with the Binomial Distribution Understanding the Binomial Distribution Understanding Some Related Functions Working with R's Binomial Functions Using R's dbinom Function Using R's pbinom Function Using R's qbinom Function Using R's rbinom Function Grappling with the Math SummaryChapter 3 Understanding the Beta Distribution Establishing the Beta Distribution in Excel Comparing the Beta Distribution with the Binomial Distribution Decoding Excel's Help Documentation for BETA.DIST Replicating the Analysis in R Understanding dbeta Understanding pbeta Understanding qbeta About Confidence Intervals Applying qbeta to Confidence Intervals Applying BETA.INV to Confidence Intervals SummaryChapter 4 Grid Approximation and the Beta Distribution More on Grid Approximation Setting the Prior Using the Results of the Beta Function Tracking the Shape and Location of the Distribution Inventorying the Necessary Functions Looking Behind the Curtains Moving from the Underlying Formulas to the Functions Comparing Built-in Functions with Underlying Formulas Understanding Conjugate Priors SummaryChapter 5 Grid Approximation with Multiple Parameters Setting the Stage Global Options Local Variables Specifying the Order of Execution Normal Curves, Mu and Sigma Visualizing the Arrays Combining Mu and Sigma Putting the Data Together Calculating the Probabilities Folding in the Prior Inventorying the Results Viewing the Results from Different Perspectives SummaryChapter 6 Regression Using Bayesian Methods Regression a la Bayes Sample Regression Analysis Matrix Algebra Methods Understanding quap Continuing the Code A Full Example Designing the Multiple Regression Arranging a Bayesian Multiple Regression SummaryChapter 7 Handling Nominal Variables Using Dummy Coding Supplying Text Labels in Place of Codes Comparing Group Means SummaryChapter 8 MCMC Sampling Methods Quick Review of Bayesian Sampling Grid Approximation Quadratic Approximation MCMC Gets Up To Speed A Sample MCMC Analysis ulam's Output Validating the Results Getting Trace Plot Charts Summary and Concluding ThoughtsAppendix Installation Instructions for RStan and the rethinking Package on the Windows PlatformGlossary Downloadable Bonus Content Excel Worksheets Book: Statistical Analysis: Microsoft Excel 2016 (PDF) 9780137580989 TOC 10/24/2022

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