Econometrics and economic statistics Books
Penguin Books Ltd The Art of Statistics
Book Synopsis''A statistical national treasure'' Jeremy Vine, BBC Radio 2''Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force'' Popular ScienceDo busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science.Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way statistical claims can be sensationalised, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of statistical literacy is more important than ever. In The Art of Statistics, David Spiegelhalter guides the reader through the essential principles we need in order to derive knowledge from data. Drawing on real world problems to introduce conceptual issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether serial killer Harold Shipman could have been caught earlier, and if screening for ovarian cancer is beneficial. ''Shines a light on how we can use the ever-growing deluge of data to improve our understanding of the world'' NatureTrade ReviewDavid Spiegelhalter is probably the greatest living statistical communicator; more than that, he's one of the great communicators in any field. This marvellous book will transform your relationship with the numbers that swirl all around us. Read it and learn. -- Tim HarfordThere is something in here for everyone ... A call to arms for greater societal data literacy ... Spiegelhalter's work serves as a reminder that there are passionate, self-aware statisticians who can argue eloquently that their discipline is needed now more than ever. * Financial Times *Shines a light on how we can use the ever-growing deluge of data to improve our understanding of the world . . . The Art of Statistics will serve students well. And it will be a boon for journalists eager to use statistics responsibly - along with anyone who wants to approach research and its reportage with healthy scepticism. * Nature *What David Spiegelhalter does here is provide a very thorough introductory grounding in statistics without making use of mathematical formulae. And it's remarkable. Spiegelhalter is warm and encouraging - it's a genuinely enjoyable read ... This book should be required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force. * Popular Science *The Art of Statistics is in the great educational tradition of its publishing imprint, Pelican Books: an attempt to get everyone up to speed with the practical uses of statistics, without pages of terrifying equations or Greek letters. In a series of spry, airy chapters, he succeeds fabulously ... Lucid and readable. In an age of scientific clickbait, 'big data' and personalised medicine, this is a book that nearly everyone would benefit from reading. * Spectator *Important and comprehensive -- Hannah Fry * New Yorker *This is an excellent book. Spiegelhalter is great at explaining difficult ideas . . . Yes, statistics can be difficult. But much less difficult if you read this book. * Evening Standard *Like the fictional investigator Sherlock Holmes, Spiegelhalter takes readers on a trail to challenge methodology and stats thrown at us by the media and others. But where other authors have attempted this and failed, he is inventive and clever in picking the right examples that spark the reader's interest to become active on their own. * Engineering and Technology *Do you trust headlines telling you . . . that bacon, ham and sausages carry the same cancer risk as cigarettes? No, nor do I. That is why we need a book like this that explains how such implausible nonsense arises in the first place. Written by a master of the subject . . . this book tells us to examine our assumptions. Bravo. * Standpoint *
£10.44
O'Reilly Lean Analytics
Book Synopsis
£25.59
Duckworth Books Standard Deviations
Book SynopsisA timely new edition, fully updated and revised,Standard Deviationsdemystifies the science behind the statistics
£10.44
John Wiley & Sons Inc A Guide to Modern Econometrics
Book SynopsisTable of ContentsPreface 1 Introduction 1.1 About Econometrics 1.2 The Structure of This Book 1.3 Illustrations and Exercises 2 An Introduction to Linear Regression 2.1 Ordinary Least Squares as an Algebraic Tool 2.2 The Linear Regression Model 2.3 Small Sample Properties of the OLS Estimator 2.4 Goodness-of-fit 2.5 Hypothesis Testing 2.6 Asymptotic Properties of the OLS Estimator 2.7 Illustration: The Capital Asset Pricing Model 2.8 Multicollinearity 2.9 Missing Data, Outliers and Influential Observations 2.10 Prediction Wrap-up Exercises 3 Interpreting and Comparing Regression Models 3.1 Interpreting the Linear Model 3.2 Selecting the Set of Regressors 3.3 Misspecifying the Functional Form 3.4 Illustration: Explaining House Prices 3.5 Illustration: Predicting Stock Index Returns 3.6 Illustration: Explaining Individual Wages Wrap-up Exercises 4 Heteroskedasticity and Autocorrelation 4.1 Consequences for the OLS Estimator 4.2 Deriving an Alternative Estimator 4.3 Heteroskedasticity 4.4 Testing for Heteroskedasticity 4.5 Illustration: Explaining Labour Demand 4.6 Autocorrelation 4.7 Testing for First-order Autocorrelation 4.8 Illustration: The Demand for Ice Cream 4.9 Alternative Autocorrelation Patterns 4.10 What to do When you Find Autocorrelation? 4.11 Illustration: Risk Premia in Foreign Exchange Markets Wrap-up Exercises 5 Endogenous Regressors, Instrumental Variables and GMM 5.1 A Review of the Properties of the OLS Estimator 5.2 Cases Where the OLS Estimator Cannot be Saved 5.3 The Instrumental Variables Estimator 5.4 Illustration: Estimating the Returns to Schooling 5.5 Alternative Approaches to Estimate Causal Effects 5.6 The Generalized Instrumental Variables Estimator 5.7 Institutions and Economic Development 5.8 The Generalized Method of Moments 5.9 Illustration: Estimating Intertemporal Asset Pricing Models Wrap-up Exercises 6 Maximum Likelihood Estimation and Specification Tests 6.1 An Introduction to Maximum Likelihood 6.2 Specification Tests 6.3 Tests in the Normal Linear Regression Model 6.4 Quasi-maximum Likelihood and Moment Conditions Tests Wrap-up Exercises 7 Models with Limited Dependent Variables 7.1 Binary Choice Models 7.2 Multiresponse Models 7.3 Models for Count Data 7.4 Tobit Models 7.5 Extensions of Tobit Models 7.6 Sample Selection Bias 7.7 Estimating Treatment Effects 7.7.1 Regression-based Estimators 7.8 Duration Models Wrap-up Exercises 8 Univariate Time Series Models 8.1 Introduction 8.2 General ARMA Processes 8.3 Stationarity and Unit Roots 8.4 Testing for Unit Roots 8.5 Illustration: Long-run Purchasing Power Parity (Part 1) 8.6 Estimation of ARMA Models 8.7 Choosing a Model 8.8 Illustration: The Persistence of Inflation 8.9 Forecasting with ARMA Models 8.10 Illustration: The Expectations Theory of the Term Structure 8.11 Autoregressive Conditional Heteroskedasticity 8.12 What about Multivariate Models? Wrap-up Exercises 9 Multivariate Time Series Models 9.1 Dynamic Models with Stationary Variables 9.2 Models with Nonstationary Variables 9.3 Illustration: Long-run Purchasing Power Parity (Part 2) 9.4 Vector Autoregressive Models 9.5 Cointegration: the Multivariate Case 9.6 Illustration: Money Demand and Inflation Wrap-up Exercises 10 Models Based on Panel Data 10.1 Introduction to Panel Data Modelling 10.2 The Static Linear Model 10.3 Illustration: Explaining Individual Wages 10.4 Dynamic Linear Models 10.5 Illustration: Explaining Capital Structure 10.6 Panel Time Series 10.7 Models with Limited Dependent Variables 10.8 Incomplete Panels and Selection Bias 10.9 Pseudo Panels and Repeated Cross-sections Wrap-up A Vectors and Matrices A.1 Terminology A.2 Matrix Manipulations A.3 Properties of Matrices and Vectors A.4 Inverse Matrices A.5 Idempotent Matrices A.6 Eigenvalues and Eigenvectors A.7 Differentiation A.8 Some Least Squares Manipulations B Statistical and Distribution Theory B.1 Discrete Random Variables B.2 Continuous Random Variables B.3 Expectations and Moments B.4 Multivariate Distributions B.5 Conditional Distributions B.6 The Normal Distribution B.7 Related Distributions Bibliograph Index
£45.59
Purchasing and Supply Chain Management
Book Synopsis
£77.99
Little, Brown Book Group How to Make the World Add Up
Book SynopsisThe Sunday Times Bestseller''Tim Harford is one of my favourite writers in the world. His storytelling is gripping but never overdone, his intellectual honesty is rare and inspiring, and his ability to make complex things simple - but not simplistic - is exceptional. How to Make the World Add Up is another one of his gems. If you''re looking for an addictive pageturner that will make you smarter, this is your book'' Rutger Bregman, author of Humankind''Tim Harford could well be Britain''s Malcolm Gladwell''Alex Bellos, author of Alex''s Adventures in Numberland''If you aren''t in love with stats before reading this book, you will be by the time you''re done. Powerful, persuasive, and in these truth-defying times, indispensable''Caroline Criado Perez, author of Invisible Women In How to Make the World Add Up, Tim Harford draws on his experience as both an economTrade ReviewNobody makes the statistics of everyday life more fascinating and enjoyable than Tim Harford -- Bill BrysonHow often do you read a blurb that says 'this book is so timely' or 'now more than ever we need a book like this'? But I promise you, by all that I hold sacred, this has never been truer of any book than it is of HOW TO MAKE THE WORLD ADD UP. We are supremely lucky to have the fabulously readable, lucid, witty and authoritative Tim Harford to remind us why facts, reason, numbers, clarity and truth matter, how beautiful they are and how crucial to our understanding of the natural world and human society. Without the kind of purity and honesty of approach that he stands for the world is doomed. Every politician and journalist should be made to read this book, but everyone else will get so much pleasure and draw so much strength from the joyful way it dispels the clouds of deceit and delusion -- Stephen FryHe's a genius at telling stories that illuminate our world -- Malcolm GladwellAn immensely enjoyable guide to using statistics wisely. I loved it -- Matt Parker, author of HUMBLE PIIf you aren't in love with stats before reading this book, you will be by the time you're done. Powerful, persuasive, and in these truth-defying times, indispensable -- Caroline Criado Perez, author of INVISIBLE WOMENWe live in a world that is awash with statistics, but what should we do when someone makes a claim that they say is based on data? This wise book, distilled from years of experience, gives us the ten commandments, from first examining our feelings, to finally having the humility to admit we may be wrong. Priceless -- Professor Sir David SpiegelhalterTim Harford is one of the finest writers of nonfiction. This is another brilliant read: wise, humane and, above all, illuminating. Nobody is better on statistics and numbers - and how to make sense of them -- Matthew Syed, author of REBEL IDEASFew people write about social science with the clarity and wit of Tim Harford. If you're staggered by statistics or daunted by data, this entertaining romp of a book is essential reading -- Daniel H. Pink * author of WHEN and DRIVE *Thanks to Tim Harford's characteristic wit and magnetic storytelling, you may not realise you're getting an advanced course in how to understand the kind of statistics we're all faced with everyday. HOW TO MAKE THE WORLD ADD UP is certainly a fun book to read, but it's also a genuinely important one -- David Epstein * author of RANGE *In a world where we are worried about misinformation, Harford gives us a brilliant guide which teaches us how to be sceptical without being cynical, and to see that statistics are not scary, but a rare treasure that help us understand our society -- Professor Hetan Shah, chief executive of the British AcademyWise and useful ... such a delight * Financial Times *Tim Harford is one of my favourite writers in the world. His storytelling is gripping but never overdone, his intellectual honesty is rare and inspiring, and his ability to make complex things simple - but not simplistic - is exceptional. How to Make the World Add Up is another one of his gems. If you're looking for an addictive pageturner that will make you smarter, this is your book -- Rutger Bregman, author of Humankind
£999.99
Pearson Education Adobe XD CC Classroom in a Book 2018 release
Book SynopsisTable of ContentsGetting Started Lesson 1 An Introduction to Adobe Xd CC Lesson 2 Setting Up a Project Lesson 3 Creating Graphics Lesson 4 Adding Images and Text Lesson 5 Organizing Content Lesson 6 Working with Assets And Cc Libraries Lesson 7 Using Effects and Repeat Grids Lesson 8 Prototyping Lesson 9 Sharing Your Prototype Lesson 10 Sharing Design Specs and Exporting
£42.27
Pearson Education Statistics for Business Economics Global Edition
Book SynopsisTable of Contents1. Statistics, Data, and Statistical Thinking 2. Methods for Describing Sets of Data 3. Probability 4. Random Variables and Probability Distributions 5. Sampling Distributions 6. Inferences Based on a Single Sample: Estimation with ConfidenceIntervals 7. Inferences Based on a Single Sample: Tests of Hypotheses 8. Inferences Based on Two Samples: Confidence Intervals and Testsof Hypotheses 9. Design of Experiments and Analysis of Variance 10. Categorical Data Analysis 11. Simple Linear Regression 12. Multiple Regression and Model Building 13. Methods for Quality Improvement: Statistical Process Control(Available Online) 14. Time Series: Descriptive Analyses, Models, and Forecasting(Available Online) 15. Nonparametric Statistics (Available Online) Appendix A: Summation Notation Appendix B: Basic Counting Rules Appendix C: Calculation Formulas for Analysis of Variance C.1 Formulas for the Calculationsin the Completely Randomized Design C.2 Formulas for the Calculationsin the Randomized Block Design C.3 Formulas for the Calculationsfor a Two-Factor Factorial Experiment C.4 Tukey's Multiple ComparisonsProcedure (Equal Sample Sizes) C.5 Bonferroni MultipleComparisons Procedure (Pairwise Comparisons) C.6 Scheffé's MultipleComparisons Procedure (Pairwise Comparisons) Appendix D: Tables Answers to Selected Exercises Index Credits
£69.34
Princeton University Press Econometrics
Book SynopsisIntroducing first year PhD students to standard graduate econometrics material, this work covers the standard material necessary for understanding the principal techniques of econometrics from ordinary least squares through cointegration. It is useful for those who intend to write a thesis on applied topics and also for the theoretically inclined.Trade Review"Students of econometrics and their teachers will find this book to be the best introduction to the subject at the graduate and advanced undergraduate level. Starting with least squares regression, Hayashi provides an elegant exposition of all the standard topics of econometrics, including a detailed discussion of stationary and non-stationary time series. The particular strength of the book is the excellent balance between econometric theory and its applications, using GMM as an organizing principle throughout. Each chapter includes a detailed empirical example taken from classic and current applications of econometrics."—Dale Jorgensen, Harvard University"Econometrics will be a very useful book for intermediate and advanced graduate courses. It covers the topics with an easy to understand approach while at the same time offering a rigorous analysis. The computer programming tips and problems should also be useful to students. I highly recommend this book for an up-to-date coverage and thoughtful discussion of topics in the methodology and application of econometrics."—Jerry A. Hausman, Massachusetts Institute of Technology"Econometrics covers both modern and classic topics without shifting gears. The coverage is quite advanced yet the presentation is simple. Hayashi brings students to the frontier of applied econometric practice through a careful and efficient discussion of modern economic theory. The empirical exercises are very useful. . . . The projects are carefully crafted and have been thoroughly debugged."—Mark W. Watson, Princeton University"Econometrics strikes a good balance between technical rigor and clear exposition. . . . The use of empirical examples is well done throughout. I very much like the use of old 'classic' examples. It gives students a sense of history—and shows that great empirical econometrics is a matter of having important ideas and good data, not just fancy new methods. . . . The style is just great, informal and engaging."—James H. Stock, John F. Kennedy School of Government, Harvard UniversityTable of ContentsList of Figures xvii Preface xix 1 Finite-Sample Properties of OLS 3 1.1 The Classical Linear Regression Model 3 The Linearity Assumption 4 Matrix Notation 6 The Strict Exogeneity Assumption 7 Implications of Strict Exogeneity 8 Strict Exogeneity in Time-Series Models 9 Other Assumptions of the Model 10 The Classical Regression Model for Random Samples 12 "Fixed" Regressors 13 1.2 The Algebra of Least Squares 15 OLS Minimizes the Sum of Squared Residuals 15 Normal Equations 16 Two Expressions for the OLS Estimator 18 More Concepts and Algebra 18 Influential Analysis (optional) 21 A Note on the Computation of OLS Estimates 23 1.3 Finite-Sample Properties of OLS 27 Finite-Sample Distribution of b 27 Finite-Sample Properties of s2 30 Estimate of Var(b | X) 31 1.4 Hypothesis Testing under Normality 33 Normally Distributed Error Terms 33 Testing Hypotheses about Individual Regression Coefficients 35 Decision Rule for the t-Test 37 Confidence Interval 38 p-Value 38 Linear Hypotheses 39 The F-Test 40 A More Convenient Expression for F 42 t versus F 43 An Example of a Test Statistic Whose Distribution Depends on X 45 1.5 Relation to Maximum Likelihood 47 The Maximum Likelihood Principle 47 Conditional versus Unconditional Likelihood 47 The Log Likelihood for the Regression Model 48 ML via Concentrated Likelihood 48 Cramer-Rao Bound for the Classical Regression Model 49 The F-Test as a Likelihood Ratio Test 52 Quasi-Maximum Likelihood 53 1.6 Generalized Least Squares (GLS) 54 Consequence of Relaxing Assumption 1.4 55 Efficient Estimation with Known V 55 A Special Case: Weighted Least Squares (WLS) 58 Limiting Nature of GLS 58 1.7 Application: Returns to Scale in Electricity Supply 60 The Electricity Supply Industry 60 The Data 60 Why Do We Need Econometrics? 61 The Cobb-Douglas Technology 62 How Do We Know Things Are Cobb-Douglas? 63 Are the OLS Assumptions Satisfied? 64 Restricted Least Squares 65 Testing the Homogeneity of the Cost Function 65 Detour: A Cautionary Note on R2 67 Testing Constant Returns to Scale 67 Importance of Plotting Residuals 68 Subsequent Developments 68 Problem Set 71 Answers to Selected Questions 84 2 Large-Sample Theory 88 2.1 Review of Limit Theorems for Sequences of Random Variables 88 Various Modes of Convergence 89 Three Useful Results 92 Viewing Estimators as Sequences of Random Variables 94 Laws of Large Numbers and Central Limit Theorems 95 2.2 Fundamental Concepts in Time-Series Analysis 97 Need for Ergodic Stationarity 97 Various Classes of Stochastic Processes 98 Different Formulation of Lack of Serial Dependence 106 The CLT for Ergodic Stationary Martingale Differences Sequences 106 2.3 Large-Sample Distribution of the OLS Estimator 109 The Model 109 Asymptotic Distribution of the OLS Estimator 113 s2 Is Consistent 115 2.4 Hypothesis Testing 117 Testing Linear Hypotheses 117 The Test Is Consistent 119 Asymptotic Power 120 Testing Nonlinear Hypotheses 121 2.5 Estimating E([not displayable]) Consistently 123 Using Residuals for the Errors 123 Data Matrix Representation of S 125 Finite-Sample Considerations 125 2.6 Implications of Conditional Homoskedasticity 126 Conditional versus Unconditional Homoskedasticity 126 Reduction to Finite-Sample Formulas 127 Large-Sample Distribution of t and F Statistics 128 Variations of Asymptotic Tests under Conditional Homoskedasticity 129 2.7 Testing Conditional Homoskedasticity 131 2.8 Estimation with Parameterized Conditional Heteroskedasticity (optional) 133 The Functional Form 133 WLS with Known [alpha] 134 Regression of e2i on zi Provides a Consistent Estimate of [alpha] 135 WLS with Estimated [alpha] 136 OLS versus WLS 137 2.9 Least Squares Projection 137 Optimally Predicting the Value of the Dependent Variable 138 Best Linear Predictor 139 OLS Consistently Estimates the Projection Coefficients 140 2.10 Testing for Serial Correlation 141 Box-Pierce and Ljung-Box 142 Sample Autocorrelations Calculated from Residuals 144 Testing with Predetermined, but Not Strictly Exogenous, Regressors 146 An Auxiliary Regression-Based Test 147 2.11 Application: Rational Expectations Econometrics 150 The Efficient Market Hypotheses 150 Testable Implications 152 Testing for Serial Correlation 153 Is the Nominal Interest Rate the Optimal Predictor? 156 Rt Is Not Strictly Exogenous 158 Subsequent Developments 159 2.12 Time Regressions 160 The Asymptotic Distribution of the OLS Estimates 161 Hypothesis Testing for Time Regressions 163 2.A Asymptotics with Fixed Regressors 164 2.B Proof of Proposition 2.10 165 Problem Set 168 Answers to Selected Questions 183 3 Single-Equation GMM 186 3.1 Endogeneity Bias: Working's Example 187 A Simultaneous Equations Model of Market Equilibrium 187 Endogeneity Bias 188 Observable Supply Shifters 189 3.2 More Examples 193 A Simple Macroeconometric Model 193 Errors-in-Variables 194 Production Function 196 3.3 The General Formulation 198 Regressors and Instruments 198 Identification 200 Order Condition for Identification 202 The Assumption for Asymptotic Normality 202 3.4 Generalized Method of Moments Defined 204 Method of Moments 205 Generalized Method of Moments 206 Sampling Error 207 3.5 Large-Sample Properties of GMM 208 Asymptotic Distribution of the GMM Estimator 209 Estimation of Error Variance 210 Hypothesis Testing 211 Estimation of S 212 Efficient GMM Estimator 212 Asymptotic Power 214 Small-Sample Properties 215 3.6 Testing Overidentifying Restrictions 217 Testing Subsets of Orthogonality Conditions 218 3.7 Hypothesis Testing by the Likelihood-Ratio Principle 222 The LR Statistic for the Regression Model 223 Variable Addition Test (optional) 224 3.8 Implications of Conditional Homoskedasticity 225 Efficient GMM Becomes 2SLS 226 J Becomes Sargan's Statistic 227 Small-Sample Properties of 2SLS 229 Alternative Derivations of 2SLS 229 When Regressors Are Predetermined 231 Testing a Subset of Orthogonality Conditions 232 Testing Conditional Homoskedasticity 234 Testing for Serial Correlation 234 3.9 Application: Returns from Schooling 236 The NLS-Y Data 236 The Semi-Log Wage Equation 237 Omitted Variable Bias 238 IQ as the Measure of Ability 239 Errors-in-Variables 239 2SLS to Correct for the Bias 242 Subsequent Developments 243 Problem Set 244 Answers to Selected Questions 254 4 Multiple-Equation GMM 258 4.1 The Multiple-Equation Model 259 Linearity 259 Stationarity and Ergodicity 260 Orthogonality Conditions 261 Identification 262 The Assumption for Asymptotic Normality 264 Connection to the "Complete" System of Simultaneous Equations 265 4.2 Multiple-Equation GMM Defined 265 4.3 Large-Sample Theory 268 4.4 Single-Equation versus Multiple-Equation Estimation 271 When Are They "Equivalent"? 272 Joint Estimation Can Be Hazardous 273 4.5 Special Cases of Multiple-Equation GMM: FIVE, 3SLS, and SUR 274 Conditional Homoskedasticity 274 Full-Information Instrumental Variables Efficient (FIVE) 275 Three-Stage Least Squares (3SLS) 276 Seemingly Unrelated Regressions (SUR) 279 SUR versus OLS 281 4.6 Common Coefficients 286 The Model with Common Coefficients 286 The GMM Estimator 287 Imposing Conditional Homoskedasticity 288 Pooled OLS 290 Beautifying the Formulas 292 The Restriction That Isn't 293 4.7 Application: Interrelated Factor Demands 296 The Translog Cost Function 296 Factor Shares 297 Substitution Elasticities 298 Properties of Cost Functions 299 Stochastic Specifications 300 The Nature of Restrictions 301 Multivariate Regression Subject to Cross-Equation Restrictions 302 Which Equation to Delete? 304 Results 305 Problem Set 308 Answers to Selected Questions 320 5 Panel Data 323 5.1 The Error-Components Model 324 Error Components 324 Group Means 327 A Reparameterization 327 5.2 The Fixed-Effects Estimator 330 The Formula 330 Large-Sample Properties 331 Digression: When [eta]i Is Spherical 333 Random Effects versus Fixed Effects 334 Relaxing Conditional Homoskedasticity 335 5.3 Unbalanced Panels (optional) 337 "Zeroing Out" Missing Observations 338 Zeroing Out versus Compression 339 No Selectivity Bias 340 5.4 Application: International Differences in Growth Rates 342 Derivation of the Estimation Equation 342 Appending the Error Term 343 Treatment of [alpha]i 344 Consistent Estimation of Speed of Convergence 345 Appendix 5.A: Distribution of Hausman Statistic 346 Problem Set 349 Answers to Selected Questions 363 6 Serial Correlation 365 6.1 Modeling Serial Correlation: Linear Processes 365 MA(q) 366 MA([infinity]) as a Mean Square Limit 366 Filters 369 Inverting Lag Polynomials 372 6.2 ARMA Processes 375 AR(1) and Its MA([infinity]) Representation 376 Autocovariances of AR(1) 378 AR(p) and Its MA([infinity]) Representation 378 ARMA(p,q) 380 ARMA(p) with Common Roots 382 Invertibility 383 Autocovariance-Generating Function and the Spectrum 383 6.3 Vector Processes 387 6.4 Estimating Autoregressions 392 Estimation of AR(1) 392 Estimation of AR(p) 393 Choice of Lag Length 394 Estimation of VARs 397 Estimation of ARMA(p,q) 398 6.5 Asymptotics for Sample Means of Serially Correlated Processes 400 LLN for Covariance-Stationary Processes 401 Two Central Limit Theorems 402 Multivariate Extension 404 6.6 Incorporating Serial Correlation in GMM 406 The Model and Asymptotic Results 406 Estimating S When Autocovariances Vanish after Finite Lags 407 Using Kernels to Estimate S 408 VARHAC 410 6.7 Estimation under Conditional Homoskedasticity (Optional) 413 Kernel-Based Estimation of S under Conditional Homoskedasticity 413 Data Matrix Representation of Estimated Long-Run Variance 414 Relation to GLS 415 6.8 Application: Forward Exchange Rates as Optimal Predictors 418 The Market Efficiency Hypothesis 419 Testing Whether the Unconditional Mean Is Zero 420 Regression Tests 423 Problem Set 428 Answers to Selected Questions 441 7 Extremum Estimators 445 7.1 Extremum Estimators 446 "Measurability" of [theta] 446 Two Classes of Extremum Estimators 447 Maximum Likelihood (ML) 448 Conditional Maximum Likelihood 450 Invariance of ML 452 Nonlinear Least Squares (NLS) 453 Linear and Nonlinear GMM 454 7.2 Consistency 456 Two Consistency Theorems for Extremum Estimators 456 Consistency of M-Estimators 458 Concavity after Reparameterization 461 Identification in NLS and ML 462 Consistency of GMM 467 7.3 Asymptotic Normality 469 Asymptotic Normality of M-Estimators 470 Consistent Asymptotic Variance Estimation 473 Asymptotic Normality of Conditional ML 474 Two Examples 476 Asymptotic Normality of GMM 478 GMM versus ML 481 Expressing the Sampling Error in a Common Format 483 7.4 Hypothesis Testing 487 The Null Hypothesis 487 The Working Assumptions 489 The Wald Statistic 489 The Lagrange Multiplier (LM) Statistic 491 The Likelihood Ratio (LR) Statistic 493 Summary of the Trinity 494 7.5 Numerical Optimization 497 Newton-Raphson 497 Gauss-Newton 498 Writing Newton-Raphson and Gauss-Newton in a Common Format 498 Equations Nonlinear in Parameters Only 499 Problem Set 501 Answers to Selected Questions 505 8 Examples of Maximum Likelihood 507 8.1 Qualitative Response (QR) Models 507 Score and Hessian for Observation t 508 Consistency 509 Asymptotic Normality 510 8.2 Truncated Regression Models 511 The Model 511 Truncated Distributions 512 The Likelihood Function 513 Reparameterizing the Likelihood Function 514 Verifying Consistency and Asymptotic Normality 515 Recovering Original Parameters 517 8.3 Censored Regression (Tobit) Models 518 Tobit Likelihood Function 518 Reparameterization 519 8.4 Multivariate Regressions 521 The Multivariate Regression Model Restated 522 The Likelihood Function 523 Maximizing the Likelihood Function 524 Consistency and Asymptotic Normality 525 8.5 FIML 526 The Multiple-Equation Model with Common Instruments Restated 526 The Complete System of Simultaneous Equations 529 Relationship between ([Gamma]0, [Beta]0) and [delta]0 530 The FIML Likelihood Function 531 The FIML Concentrated Likelihood Function 532 Testing Overidentifying Restrictions 533 Properties of the FIML Estimator 533 ML Estimation of the SUR Model 535 8.6 LIML 538 LIML Defined 538 Computation of LIML 540 LIML versus 2SLS 542 8.7 Serially Correlated Observations 543 Two Questions 543 Unconditional ML for Dependent Observations 545 ML Estimation of AR.1/ Processes 546 Conditional ML Estimation of AR(1) Processes 547 Conditional ML Estimation of AR(p) and VAR(p) Processes 549 Problem Set 551 9 Unit-Root Econometrics 557 9.1 Modeling Trends 557 Integrated Processes 558 Why Is It Important to Know if the Process Is I(1)? 560 Which Should Be Taken as the Null, I(0) or I(1)? 562 Other Approaches to Modeling Trends 563 9.2 Tools for Unit-Root Econometrics 563 Linear I(0) Processes 563 Approximating I(1) by a Random Walk 564 Relation to ARMA Models 566 The Wiener Process 567 A Useful Lemma 570 9.3 Dickey-Fuller Tests 573 The AR(1) Model 573 Deriving the Limiting Distribution under the I(1) Null 574 Incorporating the Intercept 577 Incorporating Time Trend 581 9.4 Augmented Dickey-Fuller Tests 585 The Augmented Autoregression 585 Limiting Distribution of the OLS Estimator 586 Deriving Test Statistics 590 Testing Hypotheses about [zeta] 591 What to Do When p Is Unknown? 592 A Suggestion for the Choice of pmax(T) 594 Including the Intercept in the Regression 595 Incorporating Time Trend 597 Summary of the DF and ADF Tests and Other Unit-Root Tests 599 9.5 Which Unit-Root Test to Use? 601 Local-to-Unity Asymptotics 602 Small-Sample Properties 602 9.6 Application: Purchasing Power Parity 603 The Embarrassing Resiliency of the Random Walk Model? 604 Problem Set 605 Answers to Selected Questions 619 10 Cointegration 623 10.1 Cointegrated Systems 624 Linear Vector I(0) and I(1) Processes 624 The Beveridge-Nelson Decomposition 627 Cointegration Defined 629 10.2 Alternative Representations of Cointegrated Systems 633 Phillips's Triangular Representation 633 VAR and Cointegration 636 The Vector Error-Correction Model (VECM) 638 Johansen's ML Procedure 640 10.3 Testing the Null of No Cointegration 643 Spurious Regressions 643 The Residual-Based Test for Cointegration 644 Testing the Null of Cointegration 649 10.4 Inference on Cointegrating Vectors 650 The SOLS Estimator 650 The Bivariate Example 652 Continuing with the Bivariate Example 653 Allowing for Serial Correlation 654 General Case 657 Other Estimators and Finite-Sample Properties 658 10.5 Application: the Demand for Money in the United States 659 The Data 660 (m - p, y, R) as a Cointegrated System 660 DOLS 662 Unstable Money Demand? 663 Problem Set 665 Appendix. Partitioned Matrices and Kronecker Products 670 Addition and Multiplication of Partitioned Matrices 671 Inverting Partitioned Matrices 672
£49.50
Cengage Learning, Inc Contemporary Project Management
Book SynopsisMaster the proven, traditional methods in project management as well as the latest agile practices with Kloppenborg/Anantatmula/Wells' CONTEMPORARY PROJECT MANAGEMENT, 5E. This edition presents project management techniques and expert examples drawn from successful practice and the latest research. All content reflects the knowledge areas and processes of the 6th edition of the PMBOK Guide as well as the domains and principles of the 7th edition of the PMBOK Guide. The book's focused approach helps you build a strong portfolio to showcase project management skills. New features, glossary and an integrated case highlight agile practices, mindset and techniques, while PMP-style questions prepare you for the new 2021 PMP certification exam. You also learn to use Microsoft Project to automate processes. Gain the expertise you need to become a Certified Associate in Project Management (CAPM) or Certified Project Management Professional (PMP) with this edition and MindTap digital resources.Table of ContentsPart I: ORGANIZING PROJECTS. 1. Introduction to Project Management. 2. Project Selection and Prioritization. 3. Chartering Projects. Part II: LEADING PROJECTS. 4. Organizational Capability: Structure, Culture, and Roles. 5. Leading and Managing Project Teams. 6. Stakeholder Analysis and Communication Planning. Part III: PLANNING PROJECTS. 7. Holistic Scope Planning. 8. Scheduling Projects. 9. Resourcing and Accelerating Projects. 10. Budgeting Projects. 11. Project Uncertainty Planning. 12. Project Quality Planning and Project Kick-off. Part IV: PERFORMING PROJECTS. 13. Project Procurement and Partnering. 14. Determining Project Progress and Results. 15. Finishing the Project and Realizing the Benefits. Appendix A: PMP and CAPM Exam Prep Suggestions Appendix B: PMP Exam Content 2021 Outline with CPM 5e Chapter Coverage Appendix C: Answers to Selected Exercises Appendix D: Project Deliverables Appendix E: Strengths Themes as Used in Project Management Glossary Terms consistent the PMBOK�� 6e and 7e Guides; multiple other PMI Guides and Standards; and current agile practice. Index.
£83.99
Little, Brown Book Group Statistical
Book Synopsis''Refreshingly clear and engaging'' Tim Harford''Delightful . . . full of unique insights'' Prof Sir David SpiegelhalterThere''s no getting away from statistics. We encounter them every day. We are all users of statistics whether we like it or not.Do missed appointments really cost the NHS 1bn per year?What''s the difference between the mean gender pay gap and the median gender pay gap?How can we work out if a claim that we use 42 billion single-use plastic straws per year in the UK is accurate?What did the Vote Leave campaign''s 350m bus really mean?How can we tell if the headline ''Public pensions cost you 4,000 a year'' is correct?Does snow really cost the UK economy 1bn per day?But how do we distinguish statistical fact from fiction? What can we do to decide whether a number, claim or news story is accurate? Without an understanding of data, we cannot truly understand what is going on in the woTrade ReviewFascinating . . . timely . . . a lovely humorous undercurrent to it all -- Marcus Berkmann * Daily Mail *A refreshingly clear and engaging guide to the statistical claims all around us * Tim Harford, author of Fifty Things That Made The Modern Economy & Presenter of BBC More or Less *Having spent his journalistic career working in a newsroom, being inundated with press releases full with dodgy statistics, Reuben has learned all the ways in which numbers can tell a misleading story. In this delightful book, full of unique insights from personal experience, he warns us of the phrases to look out for, and all the questions to ask about shabby surveys and dubious economic forecasts - there's also a great chapter on how to interpret big numbers. And he advises that we all ask the big question - is this number reasonably likely to be true? * Prof Sir David Spiegelhalter *Statistics can clarify or confuse. That's why you need to read this book * John Humphrys *
£8.09
Cengage Learning, Inc Transportation
Book SynopsisTRANSPORTATION: A SUPPLY CHAIN PERSPECTIVE, 9E equips you with a solid understanding of what is arguably the most critical and complex component of global supply chains. You learn the fundamental role and importance of transportation in companies and society as you study the complex environment of transportation service. The authors provide an overview of the operating and service characteristics, cost structure, and challenges providers of transportation face. This edition highlights critical transportation management issues with insights into the strategic activities and challenges in the movement of goods through the supply chain. Completely up to date, TRANSPORTATION emphasizes global topics with the latest coverage of hard and soft technology and in-depth discussions of fuel, energy, managerial, economic, and environmental issues.Table of ContentsPart I. 1. Global Supply Chains: Role and Importance of Transportation 2. Transportation and the Economy. 3. Transportation Technology and Systems. 4. Costing and Pricing for Transportation. Suggested Readings for Part I. Part II. 5. Motor Carriers. 6. Railroads. 7. Airlines. 8. Water Carriers and Pipelines. 9. Logistics Services Suggested Readings for Part II. Part III. 10. Transportation Risk Management. 11. Global Transportation Management. 12. Transportation Regulation and Public Policy. 13. Issues and Challenges for Global Supply Chains. Suggested Readings for Part III. Glossary. Name Index. Subject Index.
£71.99
Taylor & Francis Inc The Russian Far East: An Economic Handbook: An Economic Handbook
Book SynopsisAn analysis of, and factual details on, the economy, natural resources, populations, foreign economic activity, and radical economic reform in the Russian Far East. Features of the public and private sectors are discussed providing a comprehensive discussion of this area.Table of Contents1. Natural Resources and Population, 2 Economic Development, Economic Reform and the System of Economic Regulation.
£104.50
Introductory Econometrics
Book Synopsis
£68.39
Princeton University Press Mostly Harmless Econometrics
Book SynopsisShows how the basic tools of applied econometrics allow the data to speak. This book covers regression-discontinuity designs and quantile regression - as well as how to get standard errors right. It is suitable for various areas in contemporary social science.Trade Review"A quirky and thought-provoking read for any budding econometrician... Insightful and refreshing."--James Davidson, Times Higher Education "I'd recommend it to the entire range of empirical economists, from those still in training to those who, like me, have only a hazy memory of statistical theory and stick to our tried and tested methods of estimation ... an excellent guide to how to do basic regression/IV/panel data estimation really well. In particular, it demonstrates through many examples how to bring about a happy marriage between one's underlying model and the data which might or might not confirm the researcher's hypotheses."--Diane Coyle, The Enlightened Economist Blog "The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social sciences."--Pavel Stoynov, Zentralblatt MATH "[T]he matter covered in the book is surely of interest to most agricultural economists. Even if it is not a complete overview of existing econometric research methods, it certainly contains a good deal of hands on advice driven by years of experience."--European Review of Agricultural Economics "This book is an extremely thought-provoking contribution to the literature. It champions a different paradigm to that characterising most econometrics texts and does so with considerable (idiosyncratic) style and grace. Highly recommended!"--David Harris and Christopher L. Skeels, Economic RecordTable of ContentsList of Figures vii List of Tables ix Preface xi Acknowledgments xv Organization of This Book xvii PART I: PRELIMINARIES 1 Chapter 1: Questions about Questions 3 Chapter 2: The Experimental Ideal 11 2.1 The Selection Problem 12 2.2 Random Assignment Solves the Selection Problem 15 2.3 Regression Analysis of Experiments 22 PART II: THE CORE 25 Chapter 3: Making Regression Make Sense 27 3.1 Regression Fundamentals 28 3.2 Regression and Causality 51 3.3 Heterogeneity and Nonlinearity 68 3.4 Regression Details 91 3.5 Appendix: Derivation of the Average Derivative Weighting Function 110 Chapter 4: Instrumental Variables in Action: Sometimes You Get What You Need 113 4.1 IV and Causality 115 4.2 Asymptotic 2SLS Inference 138 4.3 Two-Sample IV and Split-Sample IV 147 4.4 IV with Heterogeneous Potential Outcomes 150 4.5 Generalizing LATE 173 4.6 IV Details 188 4.7 Appendix 216 Chapter 5: Parallel Worlds: Fixed Effects, Differences-in-Differences, and Panel Data 221 5.1 Individual Fixed Effects 221 5.2 Differences-in-Differences 227 5.3 Fixed Effects versus Lagged Dependent Variables 243 5.4 Appendix: More on Fixed Effects and Lagged Dependent Variables 246 PART III: EXTENSIONS 249 Chapter 6: Getting a Little Jumpy: Regression Discontinuity Designs 251 6.1 Sharp RD 251 6.2 Fuzzy RD Is IV 259 Chapter 7: Quantile Regression 269 7.1 The Quantile Regression Model 270 7.2 IV Estimation of Quantile Treatment Effects 283 Chapter 8: Nonstandard Standard Error Issues 293 8.1 The Bias of Robust Standard Error Estimates 294 8.2 Clustering and Serial Correlation in Panels 308 8.3 Appendix: Derivation of the Simple Moulton Factor 323 Last Words 327 Acronyms and Abbreviations 329 Empirical Studies Index 335 References 339 Index 361
£38.25
Oxford University Press Introduction to Econometrics
Book SynopsisIntroduction to Econometrics provides students with clear and simple mathematics notation and step-by-step explanations of mathematical proofs, to give them a thorough understanding of the subject. Extensive exercises throughout build confidence by encouraging students to apply econometric techniques. Retaining its student-friendly approach, Introduction to Econometrics has a comprehensive revision guide to all the essential statistical concepts needed to study econometrics, additional Monte Carlo simulations, new summaries, and non-technical introductions to more advanced topics at the end of chapters.This book is supported by online resources, which include:For lecturers: Instructor''s manual for the text and data sets, detailing the exercises and their solutions. Customizable PowerPoint slides.For students: Data sets referred to in the book. A comprehensive study guide offers students the opportunity to gain experience with econometrics through practice with exercises. Software manual. PowerPoint slides with explanations.Trade ReviewReview from previous edition What sets this book apart is abundance of available online material... * Sunčica Vujić, University of Antwerp *This is an excellent text for introductory econometrics courses and this edition is even better, especially with the increase in figures and charts. * Dr Bruce Morley, University of Bath *Students of finance need to be comfortable with the econometric tools necessary to both grasp empirical work and undertake it. This text provides an excellent point of reference and constant companion in developing precisely that understanding. * Paul Stewart, University of Ulster *Excellent textbook, which I have adopted as required reading for my class. The explanations are very clear, and yet it is very concise and does not overwhelm students. * Thomas Chadefaux, Trinity College Dublin *Table of ContentsINTRODUCTION; REVIEW: RANDOM VARIABLES, SAMPLING, ESTIMATION AND INFERENCE
£68.39
MIT Press Ltd Econometric Analysis of Cross Section and Panel
Book SynopsisThe second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated.The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis.Econometric Analysis of Cross Section and Panel Dat
£94.50
Taylor & Francis Ltd Stochastic Modelling of Big Data in Finance
Book SynopsisStochastic Modelling of Big Data in Finance provides a rigorous overview and exploration of stochastic modelling of big data in finance (BDF). The book describes various stochastic models, including multivariate models, to deal with big data in finance. This includes data in high-frequency and algorithmic trading, specifically in limit order books (LOB), and shows how those models can be applied to different datasets to describe the dynamics of LOB, and to figure out which model is the best with respect to a specific data set. The results of the book may be used to also solve acquisition, liquidation and market making problems, and other optimization problems in finance.Features Self-contained book suitable for graduate students and post-doctoral fellows in financial mathematics and data science, as well as for practitioners working in the financial industry who deal with big data All results are presented visually to aid in understanding oTable of Contents1. A Brief Introduction: Stochastic Modelling of Big Data in Finance. 1.1. Introduction. 1.2. Big Data in Finance: Limit Order Books. 1.3. Stochastic Modelling of Big Data in Finance: Limit Order Books (LOB). 1.4 Illustration and Justification of Our Method to Study Big Data in Finance. 1.5. Methodological Aspects of Using the Models. 1.6. Conclusion. I. Semi-Markovian Modelling of Big Data in Finance. 2. A Semi-Markovian Modelling of Big Data in Finance. 2.1. Introduction. 2.2. A Semi-Markovian Modeling of Limit Order Markets. 2.3. Main Probabilistic Results. 2.4. Diffusion Limit of the Price Process. 2.5. Numerical Results. 2.6. More Big Data. 2.7. Conclusion. 3. General Semi-Markovian Modelling of Big Data in Finance. 3.1. Introduction. 3.2. Reviewing the Assumptions with Our New Data Sets. 3.3. General Semi-Markov Model for the Limit Order Book with Two States. 3.4. General Semi-Markov Model for the Limit Order Book with arbitrary number of states. 3.5. Discussion on Price Spreads. 3.6. Conclusion. II. Modelling of Big Data in Finance with Hawkes Processes. 4. A Brief Introduction to Hawkes Processes. 4.1. Introduction. 4.2. Definition of Hawkes Processes (HPs). 4.3. Compound Hawkes Processes. 4.4. Limit Theorems for Hawkes Processes: LLN and FCLT. 4.5. Limit Theorems for Poisson Processes: LLN and FCLT. 4.6. Stylized Properties of Hawkes Process. 4.7. Conclusion. 5. Stochastic Modelling of Big Data in Finance with CHP. 5.1. Introduction. 5.2. Definitions of HP, CHP and RSCHP. 5.3. Diffusion Limits and LLNs for CHP and RSCHP in Limit Order Books. 5.4. Numerical Examples and Parameters Estimations. 5.5. Conclusion. 6. Stochastic Modelling of Big Data in Finance with GCHP. 6.1. A Brief Introduction and Literature Review. 6.2. Diffusion Limits and LLNs. 6.3. Empirical Results. 6.4. Conclusion. 7. Quantitative and Comparative Analyses of Big Data with GCHP. 7.1. Introduction. 7.2. Theoretical Analysis. 7.3. Application. 7.4. Hawkes Process and Models Calibrations. 7.5. Error Measurement. 7.6. Conclusion. III. Multivariate Modelling of Big Data in Finance. 8. Multivariate General Compound Hawkes Processes in BDF. 8.1. Introduction. 8.2. Hawkes Processes and Limit Theorems. 8.3. Multivariate General Compound Hawkes Processes (MGCHP) and Limit Theorems. 8.4. FCLT II for MGCHP: Deterministic Centralization. 8.5. Numerical Example. 8.6. Conclusion. 9. Multivariate General Compound Point Processes in BDF. 9.1. Introduction. 9.2. Definition of Multivariate General Compound Point Process (MGCPP). 9.3. LLNs and Diffusion Limits for MGCPP. 9.4. Diffusion Limit for the MGCPP: Deterministic Centralization. 9.5. Conclusion. IV. Appendix: Basics in Stochastic Processes
£73.14
Pearson Education Basic Business Statistics Global Edition
Book SynopsisAbout our authors Mark L. Berenson is Professor of Information Management and Business Analytics at Montclair State University and Professor Emeritus of Information Systems and Statistics at Baruch College. He currently teaches graduate and undergraduate courses in statistics and operations management in the School of Business, and an undergraduate course in international justice and human rights that he co-developed in the College of Humanities and Social Sciences. Berenson received a BA in economic statistics and an MBA in business statistics from City College of New York and a PhD in business from the City University of New York. Berenson's research has been published in Decision Sciences Journal of Innovative Education, Review of Business Research, The American Statistician, Communications in Statistics, Psychometrika, Educational and Psychological Measurement, Journal of Management Sciences and Applied Cybernetics, Research Quarterly, Stats Magazine, The New York Statistician, Journal of Health Administration Education, Journal of Behavioral Medicine, and Journal of Surgical Oncology. His invited articles have appeared in The Encyclopedia of Measurement & Statistics and the Encyclopedia of Statistical Sciences. He has coauthored numerous statistics texts published by Pearson. Over the years, Berenson has received several awards for teaching and for innovative contributions to statistics education. In 2005, he was the first recipient of the Catherine A. Becker Service for Educational Excellence Award at Montclair State University and in 2012, he was the recipient of the Khubani/Telebrands Faculty Research Fellowship in the School of Business. David Levine, Professor Emeritus of Statistics and CIS at Baruch College, CUNY, has been a nationally recognized innovator in statistics education for more than 3 decades. Levine has coauthored 14 books, including several business statistics textbooks; textbooks and professional titles that explain and explore quality management and the Six Sigma approach; and, with David Stephan, a trade paperback that explains statistical concepts to a general audience. Levine has presented or chaired numerous sessions about business education at leading conferences conducted by the Decision Sciences Institute (DSI) and the American Statistical Association, and he and his coauthors have been active participants in the annual DSI Data, Analytics, and Statistics Instruction (DASI) mini-conference. During his many years teaching at Baruch College, Levine was recognized for his contributions to teaching and curriculum development with the College's highest distinguished teaching honor. He earned BBA and MBA degrees from CCNY, and a PhD in industrial engineering and operations research from New York University. Kathryn Szabat, Associate Professor of Business Systems and Analytics at La Salle University, has transformed several business school majors into 1 interdisciplinary major that better supports careers in new and emerging disciplines of data analysis, including analytics. Szabat strives to inspire, stimulate, challenge and motivate students through innovation and curricular enhancements, and shares her coauthors' commitment to teaching excellence and the continual improvement of statistics presentations. Beyond the classroom, she has provided statistical advice to numerous business, non-business and academic communities, with particular interest in the areas of education, medicine, and nonprofit capacity building. Her research activities have led to journal publications, chapters in scholarly books, and conference presentations. Szabat is a member of the American Statistical Association (ASA), DSI, Institute for Operation Research and Management Sciences (INFORMS), and DSI DASI. She received a BS from SUNY-Albany, an MS in statistics from the Wharton School of the University of Pennsylvania, and a PhD degree in statistics, with a cognate in operations research, from the Wharton School of the University of Pennsylvania. David Stephan's professional life has always been shaped by advances in computing. As an undergraduate, he helped professors use statistics software that was considered advanced, even though it could compute only several things discussed in Chapter 3, thereby gaining an early appreciation for the benefits of using software to solve problems (and perhaps positively influencing his grades). An early advocate of using computers to support instruction, he developed a prototype of a mainframe-based system that anticipated features found today in Pearson's MathXL, and served as special assistant for computing to the Dean and Provost at Baruch College. In his many years teaching at Baruch, Stephan implemented the first computer-based classroom; helped redevelop the CIS curriculum; and as part of a FIPSE project team, designed and implemented a multimedia learning environment. He was also nominated for teaching honors. Stephan has presented at SEDSI and DSI DASI (formerly MSMESB) mini-conferences, sometimes with his coauthors. Stephan earned a BA from Franklin & Marshall College and an MS from Baruch College, CUNY, and completed the instructional technology graduate program at Teachers College, Columbia University.
£71.24
WW Norton & Co Naked Statistics Stripping the Dread from the
Book SynopsisA New York Times bestseller "Brilliant, funny…the best math teacher you never had." —San Francisco ChronicleTrade Review"Sparkling and intensely readable…A riff on basic statistics that is neither textbook nor essay but a happy amalgam of the two." -- New York Times"Naked Statistics is an apt title. Charles Wheelan strips away the superfluous outer garments and exposes the underlying beauty of the subject in a way that everyone can appreciate." -- Hal Varian, chief economist at Google"[Wheelan] does something unique here: he makes statistics interesting and fun. His book strips the subject of its complexity to expose the sexy stuff underneath." -- The Economist"Almost anyone interested in sports, politics, business, and the myriad of other areas in which statistics rule the roost today will benefit from this highly readable, on target, and important book." -- Frank Newport, Gallup editor-in-chief"A fun, engaging book that shows why statistics is a vital tool for anyone who wants to understand the modern world." -- Jacob J. Goldstein, NPR’s Planet Money"Two phrases you don’t often see together: ‘statistics primer’ and ‘rollicking good time.’ Until Charlie Wheelan got to it, that is. This book explains the way statistical ideas can help you understand much of everyday life." -- Austan Goolsbee, professor of economics at the University of Chicago and former chairman of the Council of Economic Advisers"A well written, surprisingly funny, and enthusiastic primer on statistics…It is hard to imagine a more accessible introduction to a field with an undeserved reputation for inaccessibility." -- New Republic"With humor and an engaging conversational style, [Wheelan] walks the reader through the basics of statistical concepts and their applications, using real-world examples to illustrate how statistics work and why they matter. All in all, it’s an excellent book." -- Science News"Naked Statistics is the book that I wish I had in 1991, the year that I took stats during my first semester at grad school…Wheelan is a master of explaining the core concepts and methods of statistics in a way that is both accessible and relevant. He is clearly a master teacher, and his gifts are in abundant display in Naked Statistics." -- Inside Higher Ed
£13.29
Princeton University Press The Tyranny of Metrics
Book SynopsisTrade Review“Mercilessly exposes the downside of the cult of measurement and managerialism.”—The Economist“Muller delivers a riposte to bean counters everywhere with this trenchant study of our fixation with performance metrics.”—Barbara Kiser, Nature “Highly readable.”—Luke Johnson, Sunday Times“Many of us have the vague sense that metrics are leading us astray, stripping away context, devaluing subtle human judgment, and rewarding those who know how to play the system. Muller’s book crisply explains where this fashion came from, why it can be so counterproductive and why we don’t learn. It should be required reading for any manager on the verge of making the Vietnam body count mistake all over again.”—Tim Harford, Financial Times
£15.29
Cengage Learning, Inc Principles of Supply Chain Management
Book SynopsisTable of ContentsPart 1: SUPPLY CHAIN MANAGEMENT: AN OVERVIEW. 1. Introduction to Supply Chain Management. Part 2: SUPPLY ISSUES IN SUPPLY CHAIN MANAGEMENT. 2. Purchasing Management. 3. Creating and Managing Supplier Relationships. 4. Ethical and Sustainable Sourcing. Part 3: OPERATIONS ISSUES IN SUPPLY CHAIN MANAGEMENT. 5. Demand Forecasting. 6. Resource Planning Systems. 7. Inventory Management. 8. Process Management--Lean and Six Sigma in the Supply Chain. Part 4: DISTRIBUTION ISSUES IN SUPPLY CHAIN MANAGEMENT. 9. Domestic U.S. and Global Logistics. 10. Customer Relationship Management. 11. Global Location Decisions. 12. Service Response Logistics. Part 5: INTEGRATION ISSUES IN SUPPLY CHAIN MANAGEMENT. 13. Supply Chain Process Integration. 14. Performance Measurement Along the Supply Chain.
£67.99
CRC Press Exploratory Data Analysis
£42.74
G&D Media Full Voice for Leaders
a huge range and FREE tracked UK delivery on ALL orders.
£16.08
Double 9 Books The Categories
Book SynopsisThe Categories is a foundational work in philosophy by the ancient Greek philosopher Aristotle. This collection of stories authored by Aristotle seeks to gather many of his Nonfiction, history, Classics concepts into a single draft and provide them at an inexpensive price so that everyone can read them. Some stories are fascinating and fantastic, while others sneak up on you and draw you in. This 4th century BCE work is a foundational examination of ontologythe study of the nature of being and existence. Aristotle's Categories is a brief treatise divided into short chapters, and categories, each of which addresses a distinct aspect of how language and mind categorize and describe reality. The book opens with a consideration of substance, highlighting the central importance of particular beings or substances in our conceptual framework. Aristotle divides substances into two categories: substances in and of themselves (particulars) and characteristics or qualities (universals). The story has so many twists and turns that can engage a reader. Some stories are gruesome and bizarre, while others softly creep up on you and pull you in. This book additionally dives into other categories, like quantity, relation, place and time, and other one action, to explain how these ideas impact our view of the world. Aristotle also investigates the concepts of potentiality and actuality, which serve as the foundation for his metaphysical theories.
£9.49
John Wiley & Sons Inc Essential Mathematics for Economics and Business
Book Synopsis* Now in 4 colour and accompanied by an outstanding suite of resources. * Combines a non-rigorous approach to mathematics with applications in economics and business.Table of ContentsIntroduction xiii Chapter 1 Mathematical Preliminaries 1 1.1 Some Mathematical Preliminaries 2 1.2 Arithmetic Operations 3 1.3 Fractions 6 1.4 Solving Equations 11 1.5 Currency Conversions 14 1.6 Simple Inequalities 18 1.7 Calculating Percentages 21 1.8 The Calculator. Evaluation and Transposition of Formulae 24 1.9 Introducing Excel 28 Chapter 2 The Straight Line and Applications 37 2.1 The Straight Line 38 2.2 Mathematical Modelling 54 2.3 Applications: Demand, Supply, Cost, Revenue 59 2.4 More Mathematics on the Straight Line 76 2.5 Translations of Linear Functions 82 2.6 Elasticity of Demand, Supply and Income 83 2.7 Budget and Cost Constraints 91 2.8 Excel for Linear Functions 92 2.9 Summary 97 Chapter 3 Simultaneous Equations 101 3.1 Solving Simultaneous Linear Equations 102 3.2 Equilibrium and Break-even 111 3.3 Consumer and Producer Surplus 128 3.4 The National Income Model and the IS-LM Model 133 3.5 Excel for Simultaneous Linear Equations 137 3.6 Summary 142 Appendix 143 Chapter 4 Non-linear Functions and Applications 147 4.1 Quadratic, Cubic and Other Polynomial Functions 148 4.2 Exponential Functions 170 4.3 Logarithmic Functions 184 4.4 Hyperbolic (Rational) Functions of the Form a/(bx + c) 197 4.5 Excel for Non-linear Functions 202 4.6 Summary 205 Chapter 5 Financial Mathematics 209 5.1 Arithmetic and Geometric Sequences and Series 210 5.2 Simple Interest, Compound Interest and Annual Percentage Rates 218 5.3 Depreciation 228 5.4 Net Present Value and Internal Rate of Return 230 5.5 Annuities, Debt Repayments, Sinking Funds 236 5.6 The Relationship between Interest Rates and the Price of Bonds 248 5.7 Excel for Financial Mathematics 251 5.8 Summary 254 Appendix 256 Chapter 6 Differentiation and Applications 259 6.1 Slope of a Curve and Differentiation 260 6.2 Applications of Differentiation, Marginal Functions, Average Functions 270 6.3 Optimisation for Functions of One Variable 286 6.4 Economic Applications of Maximum and Minimum Points 304 6.5 Curvature and Other Applications 320 6.6 Further Differentiation and Applications 334 6.7 Elasticity and the Derivative 347 6.8 Summary 357 Chapter 7 Functions of Several Variables 361 7.1 Partial Differentiation 362 7.2 Applications of Partial Differentiation 380 7.3 Unconstrained Optimisation 400 7.4 Constrained Optimisation and Lagrange Multipliers 410 7.5 Summary 422 Chapter 8 Integration and Applications 427 8.1 Integration as the Reverse of Differentiation 428 8.2 The Power Rule for Integration 429 8.3 Integration of the Natural Exponential Function 435 8.4 Integration by Algebraic Substitution 436 8.5 The Definite Integral and the Area under a Curve 441 8.6 Consumer and Producer Surplus 448 8.7 First-order Differential Equations and Applications 456 8.8 Differential Equations for Limited and Unlimited Growth 468 8.9 Integration by Substitution and Integration by Parts website only 8.10 Summary 474 Chapter 9 Linear Algebra and Applications 477 9.1 Linear Programming 478 9.2 Matrices 488 9.3 Solution of Equations: Elimination Methods 498 9.4 Determinants 504 9.5 The Inverse Matrix and Input/Output Analysis 518 9.6 Excel for Linear Algebra 531 9.7 Summary 534 Chapter 10 Difference Equations 539 10.1 Introduction to Difference Equations 540 10.2 Solution of Difference Equations (First-order) 542 10.3 Applications of Difference Equations (First-order) 554 10.4 Summary 564 Solutions to Progress Exercises 567 Worked Examples 653 Index 659
£54.10
Cengage Learning, Inc Introductory Econometrics
Book SynopsisTable of Contents1. The Nature of Econometrics and Economic Data. Part I: REGRESSION ANALYSIS WITH CROSS-SECTIONAL DATA. 2. The Simple Regression Model. 3. Multiple Regression Analysis: Estimation. 4. Multiple Regression Analysis: Inference. 5. Multiple Regression Analysis: OLS Asymptotics. 6. Multiple Regression Analysis: Further Issues. 7. Multiple Regression Analysis with Qualitative Information. 8. Heteroskedasticity. 9. More on Specification and Data Problems. Part II: REGRESSION ANALYSIS WITH TIME SERIES DATA. 10. Basic Regression Analysis with Time Series Data. 11. Further Issues in Using OLS with Time Series Data. 12. Serial Correlation and Heteroskedasticity in Time Series Regressions. Part III: ADVANCED TOPICS. 13. Pooling Cross Sections Across Time: Simple Panel Data Methods. 14. Advanced Panel Data Methods. 15. Instrumental Variables Estimation and Two Stage Least Squares. 16. Simultaneous Equations Models. 17. Limited Dependent Variable Models and Sample Selection Corrections. 18. Advanced Time Series Topics. 19. Carrying Out an Empirical Project. Math Refresher A: Basic Mathematical Tools. Math Refresher B: Fundamentals of Probability. Math Refresher C: Fundamentals of Mathematical Statistics. Math Refresher D: Summary of Matrix Algebra. Math Refresher E: The Linear Regression Model in Matrix Form. Answers to Exploring Further Chapter Exercises. Statistical Tables. References. Glossary. Index.
£71.99
Random House Publishing Group Fooled by Randomness
Book Synopsis
£15.29
The University of Chicago Press Big Data for TwentyFirstCentury Economic
Book SynopsisThe papers in this volume analyze the deployment of Big Data to solve both existing and novel challenges in economic measurement. The existing infrastructure for the production of key economic statistics relies heavily on data collected through sample surveys and periodic censuses, together with administrative records generated in connection with tax administration. The increasing difficulty of obtaining survey and census responses threatens the viability of existing data collection approaches. The growing availability of new sources of Big Datasuch as scanner data on purchases, credit card transaction records, payroll information, and prices of various goods scraped from the websites of online sellershas changed the data landscape. These new sources of data hold the promise of allowing the statistical agencies to produce more accurate, more disaggregated, and more timely economic data to meet the needs of policymakers and other data users. This volume documents progress made toward thTable of ContentsPrefatory NoteIntroduction: Big Data for Twenty- First- Century Economic Statistics: The Future Is Now Katherine G. Abraham, Ron S. Jarmin, Brian C. Moyer, and Matthew D. Shapiro I. TOWARD COMPREHENSIVE USE OF BIG DATA IN ECONOMIC STATISTICS1. Reengineering Key National Economic Indicators Gabriel Ehrlich, John C. Haltiwanger, Ron S. Jarmin, David Johnson, and Matthew D. Shapiro2. Big Data in the US Consumer Price Index: Experiences and Plans Crystal G. Konny, Brendan K. Williams, and David M. Friedman3. Improving Retail Trade Data Products Using Alternative Data Sources Rebecca J. Hutchinson4. From Transaction Data to Economic Statistics: Constructing Real-Time, High-Frequency, Geographic Measures of Consumer Spending Aditya Aladangady, Shifrah Aron-Dine, Wendy Dunn, Laura Feiveson, Paul Lengermann, and Claudia Sahm5. Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data Tomaz Cajner, Leland D. Crane, Ryan A. Decker, Adrian Hamins-Puertolas, and Christopher Kurz II. USES OF BIG DATA FOR CLASSIFICATION6. Transforming Naturally Occurring Text Data into Economic Statistics: The Case of Online Job Vacancy Postings Arthur Turrell, Bradley Speigner, Jyldyz Djumalieva, David Copple, and James Thurgood7. Automating Response Evaluation for Franchising Questions on the 2017 Economic Census Joseph Staudt, Yifang Wei, Lisa Singh, Shawn Klimek, J. Bradford Jensen, and Andrew Baer8. Using Public Data to Generate Industrial Classification Codes John Cuffe, Sudip Bhattacharjee, Ugochukwu Etudo, Justin C. Smith, Nevada Basdeo, Nathaniel Burbank, and Shawn R. Roberts III. USES OF BIG DATA FOR SECTORAL MEASUREMENT9. Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity Edward L. Glaeser, Hyunjin Kim, and Michael Luca10. Unit Values for Import and Export Price Indexes: A Proof of Concept Don A. Fast and Susan E. Fleck11. Quantifying Productivity Growth in the Delivery of Important Episodes of Care within the Medicare Program Using Insurance Claims and Administrative Data John A. Romley, Abe Dunn, Dana Goldman, and Neeraj Sood12. Valuing Housing Services in the Era of Big Data: A User Cost Approach Leveraging Zillow Microdata Marina Gindelsky, Jeremy G. Moulton, and Scott A. Wentland IV. METHODOLOGICAL CHALLENGES AND ADVANCES13. Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators Jeffrey C. Chen, Abe Dunn, Kyle Hood, Alexander Driessen, and Andrea Batch14. A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data Rishab Guha and Serena Ng15. Estimating the Benefits of New Products W. Erwin Diewert and Robert C. Feenstra Contributors Author Index Subject Index
£106.40
MIT Press Ltd Impact Evaluation in Firms and Organizations
£34.20
Taylor & Francis Ltd Modern Data Science with R
Book SynopsisFrom a review of the first edition: Modern Data Science with R is rich with examples and is guided by a strong narrative voice. What's more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician).Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions.The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to undTrade Review"This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data." (Hunter Glanz Cal Poly San Luis Obispo)"Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills." (Lynn Collen, St. Cloud Stat Univ.)"I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much…I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R…and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course." (Matthew Beckman, Penn State University)"[...] To answer a wide range of modern research questions, this book by Baumer, Kaplan, and Horton features an excellent introduction to data wrangling, visualization, statistical modeling, machine learning, and other advanced statistical applications through the RStudio environment following the tidyverse syntax. [...] Overall, Modern Data Science with R, 2nd edition serves as an excellent introductory resource to help develop techniques to extract, transform, visualize, and learn from datasets through the R environment. It focuses on implementing those techniques in R and does not provide a theoretical background for the discussed methods. The book will be a perfect reference for a broad audience ranging from undergraduates in data science courses to advanced graduate students and professionals from a variety of research fields."-Kohma Arai and Vyacheslav Lyubchich, in Technometrics, July 2022"Overall, I enjoyed reading this book. The authors were very good at creating a complete tool for studying data science. Therefore, I recommend this book, for its content, writing, and organization, to graduate students in data science and statistics. I also recommend the book to professionals who should prepare themselves for the challenges they are going to face in the future with the voluminous and heterogenous amount of data that should be timely analyzed to extract meaningful information to guide action."-Georgios Nikolopoulos, in ISCB News, June 2022"The authors have successfully completed the job of choosing the content with relevant topics and, deciding the extent of knowledge to be delivered, and finally, putting them in an understandable sequence. This is a well-written book and does not cover much theory. .. The book’s second edition contents are updated, expanded, revised, split, rewritten and rearranged compared to the first edition. The key changes are the use of recently developed R packages, .... (and) updated exercises in the chapters ..."-Shalabh,in Journal of the Royal Statistical Society Series A, August 2021"[This book] provides an excellent basis for statisticians who want to dig deeper into, for example, data handling, for computer scientists who aim to strengthen their knowledge of statistical methods as well as for all other researchers who are interested in data science in general. ... Each section is structured as an interplay between R-code and explanatory text for understanding. The division into several stand-alone segments is an advantage, because the reader may easily choose the section she or he is interested in without missing relevant information. A key feature of the book is its focus on different example data sets that are available via R-packages or from URLs that are embedded in the text. These data sets are used to illustrate the methodology presented using R-code. Their availability allows the reader to reproduce the code while working with the book. ... It can be warmly recommended to practical researchers who seek a comprehensive overview of different topics in data science with focus on implementations in R."-Annika Hoyer, in Biometrical Journal, August 2021"This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data." -Hunter Glanz, Cal Poly San Luis Obispo"Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills." -Lynn Collen, St. Cloud Stat University"I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much…I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R…and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course." -Matthew Beckman, Penn State University"The authors have covered almost all aspects of data science, a revolutionary field that marries elements of computational thinking and traditional statistical theory. The book can thus equip the readers with the necessary knowledge and skills to extract data from a variety of sources, restructure observations in a form that allows analysis, store data in efficient databases, and work effectively on massive and complex data sets in order to produce actionable information."- Georgios Nikolopoulos, University of Cyprus, ISCB Book Reviews, June 2022.Table of ContentsI Part I: Introduction to Data Science. 1. Prologue: Why data science? 2. Data visualization. 3. A grammar for graphics. 4. Data wrangling on one table. 5. Data wrangling on multiple tables. 6. Tidy data. 7. Iteration. 8. Data science ethics. II. Part II: Statistics and Modeling. 9. Statistical foundations. 10. Predictive modeling. 11. Supervised learning. 12. Unsupervised learning. 13. Simulation. III Part III: Topics in Data Science. 14. Dynamic and customized data graphics. 15. Database querying using SQL. 16. Database administration. 17. Working with spatial data. 18.Geospatial computations. 19. Text as data. 20. Network science. IV Part IV: Appendices.
£80.74
Taylor & Francis Ltd Optimal Decision Making in Operations Research
Book SynopsisThe book provides insights in the decision-making for implementing strategies in various spheres of real-world issues. It integrates optimal policies in various decisionmaking problems and serves as a reference for researchers and industrial practitioners. Furthermore, the book provides sound knowledge of modelling of real-world problems and solution procedure using the various optimisation and statistical techniques for making optimal decisions. The book is meant for teachers, students, researchers and industrialists who are working in the field of materials science, especially operations research and applied statistics. Table of Contents1. A New Version of the Generalized Rayleigh Distribution with Copula, Properties, Applications and Different Methods of Estimation 2. Expanding the Burr X Model: Properties, Copula, Real Data Modeling and Different Methods of Estimation 3. Transmuted Burr Type X Model with Applications to Life Time Data 4. Monitoring Patients Blood Level through Enhanced Control Chart 5. Goodness of Fit in Parametric and Non-parametric Econometric Models 6. Stochastic Models for Cancer Progression and its Optimal Programming for Control with Chemotherapy 7. A New Unrelated Question Model with Two Questions Per Card 8. Hybrid of Simple Model and a New Unrelated Question Model for Two Sensitive Characteristics 9. Hybrid of Crossed Model and a New Unrelated Question Model for Two Sensitive Characteristics 10. Modified Regression Type Estimator by Ingeniously Utilizing Probabilities for more Efficient Results in Randomized Response Sampling 11. Ratio and Regression Type Estimators for a New Measure of Coefficient of Dispersion Relative to the Empirical Mode 12. Class of Exponential Ratio Type Estimator for Population Mean in Adaptive Cluster Sampling 13. An Inventory Model for Substitutable Deteriorating Products under Fuzzy and Cloud Fuzzy Demand Rate 14. Co-ordinated Selling Price and Replenishment Policies for Duopoly Retailers under Quadratic Demand and Deteriorating Nature of Items15. Quadratic Programming Approach for the Optimal Multi-objective Transportation Problem 16. Analyzing Multi-Objective Fixed-Charge Solid Transportation Problem under Rough and Fuzzy-Rough Environments 17. Overall Shale Gas Water Management: A Neutrosophic Optimization Approach 18. Memory Effect on an EOQ Model with Price Dependant Demand and Deterioration 19. Optimality Conditions of an Unconstrained Imprecise Optimization Problem via Interval Order Relation 20. Power Comparison of Different Goodness of Fit Tests for Beta Generalized Weibull Distribution 21. On the Transmuted Modified Lindley Distribution: Theory and Applications to Lifetime Data 22. Adjusted Bias and Risk for Estimating Treatment Effect after Selection with an Application in Idiopathic Osteoporosis 23. Validity Judgement of an EOQ Model using Phi-coefficient 24. Uncertain Chance-Constrained Multi-Objective Geometric Programming Problem 25. Optimal Decision Making for the Prediction of Diabetic Retinopathy in Type 2 Diabetes Mellitus Patients
£199.50
Cambridge University Press One Hundred Years of Game Theory
a huge range and FREE tracked UK delivery on ALL orders.
£36.10
Taylor & Francis Ltd Introduction to Stochastic Finance with Market
Book SynopsisIntroduction to Stochastic Finance with Market Examples, Second Edition presents an introduction to pricing and hedging in discrete and continuous-time financial models, emphasizing both analytical and probabilistic methods. It demonstrates both the power and limitations of mathematical models in finance, covering the basics of stochastic calculus for finance, and details the techniques required to model the time evolution of risky assets. The book discusses a wide range of classical topics including BlackScholes pricing, American options, derivatives, term structure modeling, and change of numéraire. It also builds up to special topics, such as exotic options, stochastic volatility, and jump processes.New to this Edition New chapters on Barrier Options, Lookback Options, Asian Options, Optimal Stopping Theorem, and Stochastic Volatility Contains over 235 exercises and 16 problems with complete solutions available online from the iTable of ContentsIntroduction. 1. Assets, Portfolios, and Arbitrage. 1.1. Portfolio Allocation and Short Selling. 1.2. Arbitrage. 1.3. Risk-Neutral Probability Measures. 1.4. Hedging of Contingent Claims. 1.5. Market Completeness. 1.6. Example: Binary Market. Exercises. 2. Discrete-Time Market Model. 2.1. Discrete-Time Compounding. 2.2. Arbitrage and Self-Financing Portfolios. 2.3. Contingent Claims. 2.4. Martingales and Conditional Expectations. 2.5. Market Completeness and Risk-Neutral Measures. 2.6. The Cox-Ross-Rubinstein (CRR) Market Model. Exercises. 3. Pricing and Hedging in Discrete Time. 3.1. Pricing Contingent Claims. 3.2. Pricing Vanilla Options in the CRR Model. 3.3. Hedging Contingent Claims. 3.4. Hedging Vanilla Options. 3.5. Hedging Exotic Options. 3.6. Convergence of the CRR Model. Exercises. 4. Brownian Motion and Stochastic Calculus. 4.1. Brownian Motion. 4.2. Three Constructions of Brownian Motion. 4.3. Wiener Stochastic Integral. 4.4. Itô Stochastic Integral. 4.5. Stochastic Calculus. Exercises. 5. Continuous-Time Market Model. 5.1. Asset Price Modeling. 5.2. Arbitrage and Risk-Neutral Measures. 5.3. Self-Financing Portfolio Strategies. 5.4. Two-Asset Portfolio Model. 5.5. Geometric Brownian Motion. Exercises. 6. Black-Scholes Pricing and Hedging. 6.1. The Black-Scholes PDE. 6.2. European Call Options. 6.3. European Put Options. 6.4. Market Terms and Data. 6.5. The Heat Equation. 6.6. Solution of the Black-Scholes PDE. Exercises. 7. Martingale Approach to Pricing and Hedging. 7.1. Martingale Property of the Itô Integral. 7.2. Risk-neutral Probability Measures. 7.3. Change of Measure and the Girsanov Theorem. 7.4. Pricing by the Martingale Method. 7.5. Hedging by the Martingale Method. Exercises. 8. Stochastic Volatility. 8.1. Stochastic Volatility Models. 8.2. Realized Variance Swaps. 8.3. Realized Variance Options. 8.4. European Options - PDE Method. 8.5. Perturbation Analysis. Exercises. 9. Volatility Estimation. 9.1. Historical Volatility. 9.2. Implied Volatility. 9.3. Local Volatility. 9.4. The VIX® Index. Exercises. 10. Maximum of Brownian motion. 10.1. Running Maximum of Brownian Motion. 10.2. The Reflection Principle. 10.3. Density of the Maximum of Brownian Motion. 10.4. Average of Geometric Brownian Extrema. Exercises. 11. Barrier Options. 11.1. Options on Extrema. 11.2. Knock-Out Barrier. 11.3. Knock-In Barrier. 11.4. PDE Method. 11.5. Hedging Barrier Options. Exercises. 12. Lookback Options. 12.1. The Lookback Put Option. 12.2. PDE Method. 12.3. The Lookback Call Option. 12.4. Delta Hedging for Lookback Options. Exercises. 13. Asian Options. 13.1. Bounds on Asian Option Prices. 13.2. Hartman-Watson Distribution. 13.3. Laplace Transform Method. 13.4. Moment Matching Approximations. 13.5. PDE Method. Exercises. 14. Optimal Stopping Theorem. 14.1. Filtrations and Information Flow. 14.2. Submartingales and Supermartingales. 14.3. Optimal Stopping Theorem. 14.4. Drifted Brownian Motion. Exercises. 15. American Options. 15.1. Perpetual American Put Options. 15.2. PDE Method for Perpetual Put Options. 15.3. Perpetual American Call Options. 15.4. Finite Expiration American Options. 15.5. PDE Method with Finite Expiration. Exercises. 16. Change of Numéraire and Forward Measures. 16.1. Notion of Numéraire. 16.2. Change of Numéraire. 16.3. Foreign Exchange. 16.4. Pricing Exchange Options. 16.5. Hedging by Change of Numéraire. Exercises. 17. Short Rates and Bond Pricing. 17.1. Vasicek model. 17.2. Affine Short Rate Models. 17.3. Zero-Coupon and Coupon Bonds. 17.4. Bond Pricing PDE. Exercises. 18. Forward Rates. 18.1. Construction of Forward Rates. 18.2. LIBOR/SOFR Swap Rates. 18.3. The HJM Model. 18.4. Yield Curve Modeling. 18.5. Two-Factor Model. 18.6. The BGM Model. Exercises. 19. Pricing of Interest Rate Derivatives. 19.1. Forward Measures and Tenor Structure. 19.2. Bond Options. 19.3. Caplet Pricing. 19.4. Forward Swap Measures. 19.5. Swaption Pricing. Exercises. 20. Stochastic Calculus for Jump Processes. 20.1. The Poisson Process. 20.2. Compound Poisson Process. 20.3. Stochastic Integrals and Itô Formula with Jumps. 20.4. Stochastic Differential Equations with Jumps. 20.5. Girsanov Theorem for Jump Processes. Exercises. 21. Pricing and Hedging in Jump Models. 21.1. Fitting the Distribution of Market Returns. 21.2. Risk-Neutral Probability Measures. 21.3. Pricing in Jump Models. 21.4. Exponential Lévy Models. 21.5. Black-Scholes PDE with Jumps. 21.6. Mean-Variance Hedging with Jumps. Exercises. 22. Basic Numerical Methods. 22.1. Discretized Heat Equation. 22.2. Discretized Black-Scholes PDE. 22.3. Euler Discretization. 22.4. Milshtein Discretization. Exercises. Bibliography. Index
£87.39
CRC Press Essential Mathematics for Economics
Book SynopsisEssential Mathematics for Economics covers mathematical topics that are essential for economic analysis in a concise but rigorous fashion. The book covers selected topics such as linear algebra, real analysis, convex analysis, constrained optimization, dynamic programming, and numerical analysis in a single volume. The book is entirely self-contained, and almost all propositions are proved. Features Replete with exercises and illuminating examples Suitable as a primary text for an advanced undergraduate or postgraduate course on mathematics for economics Basic linear algebra and real analysis are the only prerequisites. Supplementary materials such as Matlab codes, teaching slides etc. are posted on the book website https://github.com/alexisakira/EME.
£47.49
Cambridge University Press Introductory Econometrics for Finance
Book SynopsisA complete resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner that shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of previous editions, the new edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support materials, including EViews, Stata, R, and Python soTrade Review'Introductory Econometrics for Finance covers a variety of financial applications and illustrates how econometrics methods can be used for each topic. Researchers and practitioners in finance will find this book invaluable. The new fourth edition is expanded with important topics of state space models and extreme value theory. Moreover, a free companion website with various software programs is essential for performing actual empirical analysis. I constantly recommend this text to Masters and undergraduate finance students.' Elena Goldman, Pace University, New York'This is a good book introducing the general field of financial econometrics to students, assuming they have no prior knowledge of econometrics. Undergraduate, as well as beginning graduate, students should find the wide range of topics covered useful for not only getting a good toehold into the literature, but also to be able to apply the methods to data right away.' Prasad V. Bidarkota, Florida International University'Professor Brooks' book provides extraordinarily comprehensive treatment of econometric techniques with application to Finance. The unique feature of this book is the presentation of rich real-world case study examples. This is an ideal text book for MS in Finance, MBA with concentration in Finance and Seniors majoring in Finance. It is also an ideal text book for financial professional training and self-study.' George H. K. Wang, George Mason University, Virginia'Chris Brooks' book is a rather unique offering in the space of financial econometrics because it is specifically targeted to finance students who do not necessarily have prior knowledge of econometric techniques. It's a first yet comprehensive resource to enable students to familiarize with concepts and tackle a broad range of empirical applications.' Walter Distaso, Imperial College London'This new edition of Introductory Econometrics for Finance manages to give even further strength to its exhaustive, fine blend of contents and delivery, of methods and of interesting, relevant applications. This classical but always lively written textbook manages to make modern econometric approaches accessible to a wide audience of senior undergraduates and of graduate students first approaching econometrics, and at the same time leads a more experienced reader to ponder the power of statistics through a number of detailed case studies. The additional, advanced material on the Kalman filter and extreme value theory makes this textbook an invaluable classroom tool for a first approach to financial econometrics.' Massimo Guidolin, Università Commerciale Luigi Bocconi, Milan'This is one of the most readable books on financial econometrics. It will be very useful for students of finance and economics. It covers a wide variety of topics that are of interest to researchers and practitioners, in both academia and industry.' Yong Bao, Purdue University, IndianaTable of ContentsPreface to the fourth edition; 1. Introduction and mathematical foundations; 2. Statistical foundations and dealing with data; 3. A brief overview of the classical linear regression; 4. Further development of classical linear regression; 5. Classical linear regression model assumptions; 6. Univariate time-series modelling and forecasting; 7. Multivariate models; 8. Modelling volatility and correlation; 10. Switching and state space models; 11. Panel data; 12. Limited dependent variable models; 13. Simulation methods; 14. Additional econometric techniques for financial research; 15. Conducting empirical research; Appendix 1. Sources of data used in this book and the accompanying software manuals; Appendix 2. Tables of statistical distributions; Glossary; References; Index.
£49.39
Cengage Learning, Inc Contemporary Project Management
Book SynopsisLearn to master the most proven methods in project management as well as exciting new techniques emerging from current industry and today's most recent research with Kloppenborg's CONTEMPORARY PROJECT MANAGEMENT, 4E. This edition introduces time-tested manual techniques and progressive automated techniques, all consistent with the latest PMBOK Guide and standards and integrated with Microsoft Project 2016. The book's focused approach is ideal for building strong portfolios that showcase project management skills for future interviews. All content is consistent with the knowledge areas and processes of the 6th edition of the PMBOK Guide to give you an advantage as you prepare to become a Certified Associate in Project Management (CAPM) or Certified Project Management Professional (PMP), if desired.Table of ContentsPart I: ORGANIZING PROJECTS. 1. Introduction to Project Management. 2. Project Selection and Prioritization. 3. Chartering Projects. Part II: LEADING PROJECTS. 4. Organizational Capability: Structure, Culture, and Roles. 5. Leading and Managing Project Teams. 6. Stakeholder Analysis and Communication Planning. Part III: PLANNING PROJECTS. 7. Scope Planning. 8. Scheduling Projects. 9. Resourcing Projects. 10. Budgeting Projects. 11. Project Risk Planning. 12. Project Quality Planning and Project Kick-off. Part IV: PERFORMING PROJECTS. 13. Project Supply Chain Management. 14. Determining Project Progress and Results. 15. Finishing the Project and Realizing the Benefits. Appendix A PMP and CAPM Exam Prep Suggestions Appendix B Agile Differences Covered Appendix C Answers to Selected Exercises Appendix D Project Deliverables Appendix E Strengths Themes as Used in Project Management (Available Online) Glossary Terms consistent the PMBOK�� Guide and multiple other PMI Guides and Standards. Index.
£83.99
Bloomsbury Publishing PLC Applied Econometrics
Book SynopsisThis trusted textbook returns in its 4th edition with even more exercises to help consolidate understanding - and a companion website featuring additional materials, including a solutions manual for instructors. Offering a unique blend of theory and practical application, it provides ideal preparation for doing applied econometric work as it takes students from a basic level up to an advanced understanding in an intuitive, step-by-step fashion. Clear presentation of economic tests and methods of estimation is paired with practical guidance on using several types of software packages. Using real world data throughout, the authors place emphasis upon the interpretation of results, and the conclusions to be drawn from them in econometric work. This book will be essential reading for economics undergraduate and master's students taking a course in applied econometrics. Its practical nature makes it ideal for modules requiring a research project. New to this Edition:- Additional practical eTable of ContentsPART I: STATISTICAL BACKGROUND AND BASIC DATA HANDLING 1. Fundamental Concepts 2. The Structure Of Economic Data and Basic Data Handling PART II: THE CLASSICAL LINEAR REGRESSION MODEL 3. Simple Regression 4. Multiple Regression PART III: VIOLATING THE ASSUMPTIONS OF THE CLRM 5. Multicollinearity 6. Heteroskedasticity 7. Autocorrelation 8. Misspecification: Wrong Regressors, Measurement Errors And Wrong Functional Forms PART IV: TOPICS IN ECONOMETRICS 9. Dummy Variables 10. Dynamic Econometric Models 11. Simultaneous Equation Models 12. Limited Dependent Variable Regression Models PART V: TIME SERIES ECONOMETRICS 13. ARIMA Models And The Box–Jenkins Methodology 14. Modelling The Variance: ARCH–GARCH Models 15. Vector Autoregressive(VAR) Models And Causality Tests 16. Non-Stationarity and Unit Root Tests 17. Cointegration and Error-Correction Models 18. Identification In Standard and Cointegrated Systems 19. Solving Models 20. Time Varying Coefficient Models: A New Way of Estimating Bias Free Parameters PART VI: PANEL DATA ECONOMETRICS 21. Traditional Panel Data Models 22. Dynamic Heterogeneous Panels 23. Non-Stationary Panels PART VII: USING ECONOMETRIC SOFTWARE 24. Practicalities in Using Eviews and Stata.
£60.79
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Statistics for Business and Economics: Compendium
Book SynopsisThis compendium contains and explains essential statistical formulas within an economic context. A broad range of aids and supportive examples will help readers to 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.
£40.49
Cengage Learning, Inc Business Analytics Cengage International Edition
Book SynopsisDevelop the analytical skills that are in high demand in businesses today with Camm/Cochran/Fry/Ohlmann's best-selling BUSINESS ANALYTICS, CENGAGE INTERNATIONAL EDITION 5E. You master the full range of analytics as you strengthen descriptive, predictive and prescriptive analytic skills. Real examples and memorable visuals clearly illustrate data and results. Step-by-step instructions guide you through using Excel, Tableau, R or the Python-based Orange data mining software to perform advanced analytics. Practical, relevant problems at all levels of difficulty let you apply what you've learned. Updates throughout this edition address topics beyond traditional quantitative concepts, such as data wrangling, data visualization and data mining, which are increasingly important in today's business environment. MindTap and WebAssign online learning platforms are also available with an interactive eBook, algorithmic practice problems and Exploring Analytics visualizations to strengthen your understanding of key concepts.
£79.79
Taylor & Francis Ltd Statistical Rethinking
Book SynopsisWinner of the 2024 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.Features Integrates working code into the main text. Illustrates concepts through worked data analysis examples. Emphasizes understanding assumptions and how assumptions are reflected in code. Offers more detailed explanations of the mathematics in optional sections. Presents examples of using the dagitty R package to analyze causal graphs. Provides the rethinking R package on the author's website and on GitHub. Trade Review"The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreath’s engaging writing style and humor, and personally found the infusion of humor quite refreshing."- Adam Loy, Carleton College"(The chapter) ‘Generalized Linear Madness’ represents another great chapter of an even better edition of an already awesome textbook."- Benjamin K. Goodrich, Columbia University"(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory."- Josep Fortiana Gregori, University of Barcelona"I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process." - Nguyet Nguyen, Youngstown State University "As a textbook it successfully brings the statistician’s toolbox to a wider audience with an accessible style and good humour. It should be recommended to statistics students, both old and new."- Nathan Green, Journal of the Royal Statistical Society, 2021, https://doi.org/10.1111/rssa.12755"In conclusion, Statistical Rethinking frames usual methods and tools taught in graduate statistical courses into a different way to encourage the reader to understand the details and appreciate the underlying assumptions. The accompanying R package offers example codes for some interesting problems that are not available in standard library or other popular packages. This book can be used as a supplement to a graduate course or it can be used by practitioners wanting to brush up their knowledge with better understanding of statistical techniques."- Abhirup Mallik in Technometrics, August 2021"The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreath’s engaging writing style and humor, and personally found the infusion of humor quite refreshing."~Adam Loy, Carleton College"(The chapter) ‘Generalized Linear Madness’ represents another great chapter of an even better edition of an already awesome textbook."~Benjamin K. Goodrich, Columbia University"(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory."~Josep Fortiana Gregori, University of Barcelona"I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process." ~Nguyet Nguyen, Youngstown State University"In conclusion, Statistical Rethinking frames usual methods and tools taught in graduate statistical courses into a different way to encourage the reader to understand the details and appreciate the underlying assumptions. The accompanying R package offers example codes for some interesting problems that are not available in standard library or other popular packages. This book can be used as a supplement to a graduate course or it can be used by practitioners wanting to brush up their knowledge with better understanding of statistical techniques."~Abhirup Mallik in Technometrics, August 2021"As a textbook it successfully brings the statistician’s toolbox to a wider audience with an accessible style and good humour. It should be recommended to statistics students, both old and new."~ Nathan Green, Journal of the Royal Statistical Society, 2021Table of Contents1. The Golem of Prague. 2. Small Worlds and Large Worlds. Chapter 3. Sampling the Imaginary. 4. Geocentric Models. 5. The Many Variables & The Spurious Waffles. 6. The Haunted DAG & The Causal Terror. 7. Ulysses’ Compass. 8. Conditional Manatees. 8. Conditional Manatees. 9. Markov Chain Monte Carlo. 10. Big Entropy and the Generalized Linear Model. 11. God Spiked the Integers. 12. Monsters and Mixtures. 13. Models With Memory. 14. Adventures in Covariance. 15. Missing Data and Other Opportunities. 16. Generalized Linear Madness. 17. Horoscopes.
£73.14
Cengage Learning EMEA Introduction to Econometrics
Book SynopsisThis title has been adapted for use in Europe, the Middle East and Africa and has been tailored to meet the demands of today's lecturers and students.Jeffrey M. Wooldridge's Introduction to Econometrics shows how econometrics is a useful tool for answering questions in business, policy evaluation and forecasting environments. Packed with timely, relevant applications, the text incorporates close to 100 intriguing data sets, available in six formats, with appendices and questions available online.Unique organization pioneered by the author clearly presents applications for today's students. This comprehensive econometrics text pioneered the approach of explicitly covering cross-sectional applications first, followed by time series applications, and, ultimately, panel data applications in the advanced chapters.Practical application prepares students to use econometrics in business today. This unique, comprehensive text applies econometrics to actual real business problems, demonstrating Table of Contents1. The Nature of Econometrics and Economic Data 2. The Simple Regression Model 3. Multiple Regression Analysis: Estimation 4. Multiple Regression Analysis: Inference 5. Multiple Regression Analysis: OLS Asymptotics 6. Multiple Regression Analysis: Further Issues 7. Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables 8. Heteroskedasticity 9. More on Specification and Data Issues 10. Basic Regression Analysis with Time Series Data 11. Further Issues in Using OLS with Time Series Data 12. Serial Correlation and Heteroskedasticity 13. Pooling Cross Sections Across Time: Simple Panel Data Methods 14. Advanced Panel Data Methods 15. Instrumental Variables Estimation and Two Stage Least Squares 16. Simultaneous Equations Models 17. Limited Dependant Variable Models and Sample Selection Corrections 18. Advanced Time Series Topics 19. Carrying Out an Empirical Project
£999.99
Cengage Learning, Inc Spreadsheet Modeling and Decision Analysis
Book SynopsisMaster key spreadsheet and business analytics skills with SPREADSHEET MODELING AND DECISION ANALYSIS: A PRACTICAL INTRODUCTION TO BUSINESS ANALYTICS, 9E, written by respected business analytics innovator Cliff Ragsdale. This edition's clear presentation, realistic examples, fascinating topics and valuable software provide everything you need to become proficient in today's most widely used business analytics techniques using the latest version of Excel in Microsoft Office 365 or Office 2019. Become skilled in the newest Excel functions as well as Analytic Solver and Data Mining add-ins. This edition helps you develop both algebraic and spreadsheet modeling skills. Step-by-step instructions and annotated, full-color screen images make examples easy to follow and show you how to apply what you learn about descriptive, predictive and prescriptive analytics to real business situations. WebAssign online tools and author-created videos further strengthen understanding.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. 13. Queuing Theory. 14. Decision Analysis. 15. Project Management (Online).
£76.99
Princeton University Press Probability and Statistics for Economists
Book Synopsis
£49.30
Princeton University Press Time Series Analysis
Book SynopsisA graduate-level text which describes the recent dramatic changes that have taken place in the way that researchers analyze economic and financial time series. It explores such important innovations as vector regression, nonlinear time series models and the generalized methods of moments.Trade Review"A carefully prepared and well written book... Without doubt, it can be recommended as a very valuable encyclopedia and textbook for a reader who is looking for a mainly theoretical textbook which combines traditional time series analysis with a review of recent research areas."--Journal of EconomicsTable of ContentsPreface1Difference Equations12Lag Operators253Stationary ARMA Processes434Forecasting725Maximum Likelihood Estimation1176Spectral Analysis1527Asymptotic Distribution Theory1808Linear Regression Models2009Linear Systems of Simultaneous Equations23310Covariance-Stationary Vector Processes25711Vector Autoregressions29112Bayesian Analysis35113The Kalman Filter37214Generalized Method of Moments40915Models of Nonstationary Time Series43516Processes with Deterministic Time Trends45417Univariate Processes with Unit Roots47518Unit Roots in Multivariate Time Series54419Cointegration57120Full-Information Maximum Likelihood Analysis of Cointegrated Systems63021Time Series Models of Heteroskedasticity65722Modeling Time Series with Changes in Regime677A Mathematical Review704B Statistical Tables751C Answers to Selected Exercises769D Greek Letters and Mathematical Symbols Used in the Text786Author Index789Subject Index792
£58.50
Princeton University Press GDP
Book SynopsisWhy did the size of the U.S. economy increase by 3 percent on one day in mid-2013 - or Ghana's balloon by 60 percent overnight in 2010? Why did the U.K. financial industry show its fastest expansion ever at the end of 2008 - just as the world's financial system went into meltdown? This title deals with these questions.Trade ReviewWinner of the 2015 Bronze Medal in Economics, Axiom Business Book Awards One of The Wall Street Journal's Best Books of 2014 One of Choice's Outstanding Academic Titles for 2014 One of FA-mag.com's Books of the Year 2014 One of "The Books Quartz Read" in 2014 One of Minnpost.com's 'Three (plus) books for the econ buff on your list' 2014 Longlisted for the Financial Times and McKinsey Business Book of the Year 2014 "GDP is, as Diane Coyle points out in her entertaining and informative GDP: A Brief but Affectionate History, a bodge, an ongoing argument."--John Lanchester, London Review of Books "[A] little charmer of a book... GDP: A Brief but Affectionate History is just what the title promises... Cowperthwaite himself would nod in agreement over Ms. Coyle's informed discussion of what the GDP misses and how it misfires... Ms. Coyle--a graceful and witty writer, by the way--recounts familiar problems and adds some new ones... [E]xcellent."--James Grant, Wall Street Journal "Anyone who wants to know how GDP and the SNA have come to play such important roles in economic policy-making will gain from reading Coyle's book. As will anyone who wants to gain more understanding of the concept's strengths and weaknesses."--Nicholas Oulton, Science "Diane Coyle's new book, GDP: A Brief But Affectionate History, is a timely contribution to discussions of modern economic performance."--Arnold Kling, American "[E]xcellent."--Adam Creighton, The Australian "Diane Coyle's book is as good a simple guide as we are likely to see."--Samuel Brittan, Financial Times "Coyle does good work explicating a topic that few understand, even if it affects each of us daily. A pleasure for facts-and-numbers geeks, though accessibly written and full of meaningful real-world examples."--Kirkus Reviews "[S]mart and lucid... [S]hort but masterful."--Todd G. Buchholz, Finance & Development "[G]reat (and well-timed) new book."--Uri Friedman, The Atlantic "In a charming and accessible new book, Diane Coyle untangles the history, assumptions, challenges and shortcomings of this popular rhetorical device, which has become so central to policy debates around the world... Coyle's book is a good primer for the average citizen as well as the seasoned economist."--Adam Gurri, Umlaut "[I]t is interesting and important, particularly when it comes to the emphasis now given to GDP, and the inadequacies of this now time-honoured measurement of how our economies are doing... With clarity and precision, she explains its strengths and weaknesses."--Peter Day, BBC News Business "Diane Coyle has bravely attempted in a recent book to make the subject once more accessible, and even interesting."--John Kay, Financial Times "[T]his is as engaging a book about GDP as you could ever hope to read. It falls into that genre of books that are 'biographies of things'--be they histories of longitude, the number zero or the potato--and is both enlightening and entertaining."--Andrew Sawers, FS Focus "GDP: A Brief But Affectionate History is a fascinating 140-page book that I cannot recommend highly enough. This is simply the best book on GDP that I've ever seen."--John Mauldin "As a potted history of approaches to quantifying national output from the 18th century onward, GDP: A Brief but Affectionate History deserves high marks. It is particularly edifying to learn about the military motivation behind the initial attempts."--Martin S. Fridson, Financial Analysts Journal "The strongest part of the book charts the development of national accounting from the 17th century through to the creation of GDP itself and its literal and metaphorical rises and falls in the 20th and 21st centuries... This is lively and surprisingly readable stuff."--Eilis Lawlor, LSE Review of Books "Coyle has written an engaging, introductory to mid-level book on the GDP that makes sense of a statistic that hardly anyone actually understands... It does not require any training in economics, but it covers many topics that even professional economists would find beneficial, including an argument that GDP is an increasingly inappropriate measure for the 21st century."--Choice "[A] little charmer of a book."--Wall Street Journal (A Best Non-Fiction Book of 2014) "GDP is a thought-provoking account of how the gross domestic product statistic came to be so important... The book is a useful and timely contribution."--Louise Rawlings, Economic RecordTable of ContentsNote on the Paperback Edition vii Introduction 1 ONE From the Eighteenth Century to the 1930s: War and Depression 7 TWO 1945 to 1975: The Golden Age 43 THREE The Legacy of the 1970s: A Crisis of Capitalism 61 FOUR 1995 to 2005: The New Paradigm 79 FIVE Our Times: The Great Crash 95 SIX The Future: Twenty-first-Century GDP 123 Acknowledgments 147 Notes 149 Index 161
£12.34
Pearson Education Limited Statistics for Business and Economics Global
Book SynopsisDr. Bill Carlson is professor emeritus of economics at St. Olaf College, where he taught for 31 years, serving several times as department chair and in various administrative functions, including director of academic computing. He has also held leave assignments with the U.S. government and the University of Minnesota in addition to lecturing at many different universities. He was elected an honorary member of Phi Beta Kappa. In addition, he spent 10 years in private industry and contract research prior to beginning his career at St. Olaf. His education includes engineering degrees from Michigan Technological University (BS) and from the Illinois Institute of Technology (MS) and a PhD in quantitative management from the Rackham Graduate School at the University of Michigan. Numerous research projects related to management, highway safety, and statistical education have produced more than50 publications. He received the Metropolitan Insurance Award of Merit for SafetTable of Contents Describing Data: Graphical Describing Data: Numerical Probability Discrete Random Variables and Probability Distributions Continuous Random Variables and Probability Distributions Sampling and Sampling Distributions Estimation: Single Population Estimation: Additional Topics Hypothesis Testing: Single Population Hypothesis Testing: Additional Topics Simple Regression Multiple Regression Additional Topics in Regression Analysis Analysis of Categorical Data Analysis of Variance Time-Series Analysis and Forecasting Additional Topics in Sampling
£51.29
John Wiley & Sons Nutrition DiagnosisRelated Care
Book SynopsisAn authoritative go-to resource for students and clinical nutrition practitioners that covers over 270 conditions. Each section provides an overview followed by essential evidence-based information on nutrition management of each condition. Nutrition therapy and practical guidance is presented in a style that is easy-to-navigate and implement.
£100.80
