Econometrics and economic statistics Books
Taylor & Francis Ltd Statistical Programming in SAS
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Taylor & Francis Ltd Statistical Programming in SAS
Book SynopsisStatistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming.The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining dataTrade Review"This book is useful for people who want to learn SAS programing, and assumes the students have knowledge of multiple linear regression and one-way ANOVA models.…The second edition has added a chapter on text processing, and reorganized the chapter order…Some topics that are relevant for the SAS Base and Certifications exams are covered, and a nice feature is the highlighting of programing tips in gray." ~Technometrics"This is a very complete book for programming SAS in statistical analyses. This second edition offers the possibility to debug some programs and provides new examples and applications, which are very useful. This book is a very useful companion tool for students or beginners in SAS, or for more experienced statisticians who already use SAS for statistical analyses."~ISCB NewsTable of ContentsContentsPreface ..............................................................................................................................................ixAcknowledgments ...................................................................................................................... xiiiAuthor .............................................................................................................................................xv1. Structuring, Implementing, and Debugging Programs to Learn about Data ...........11.1 Statistical Programming ................................................................................................11.2 Learning from Constructed, Artificial Data ...............................................................2Processing a Particular Data Set—Extracting Variable Names from aColumn of an Input Data Set.........................................................................................2Learning More about Unfamiliar Statistical Methods—Linear MixedEffects Models .................................................................................................................5Improving Your Intuition about Statistical Theory— Sampling Distributionof Means ...........................................................................................................................81.3 Good Programming Practice ...................................................................................... 11Document Your Programs! .......................................................................................... 11Use Meaningful Variable Names ................................................................................ 13Use a Variety of CaSeS in Program Statements ........................................................ 14Indent Program Statements That Naturally Go Together ....................................... 141.4 SAS Program Structure ................................................................................................ 151.5 What Is a SAS Data Set? ............................................................................................... 211.6 Internally Documenting SAS Programs ....................................................................221.7 Basic Debugging ...........................................................................................................231.8 Getting Help ..................................................................................................................27Using Help in SAS ........................................................................................................27Getting Help from a Web Browser Search .................................................................291.9 Exercises .........................................................................................................................292. Reading, Creating, and Formatting Data Sets ................................................................ 312.1 What Does a SAS DATA Step Do? .............................................................................. 312.2 Reading Data from External Files ..............................................................................33Reading Data Directly as Part of a Program—Anyone for Datalines? .................34Reading Data Sets Saved as Text—INFILE Can Be Your Friend (PROCIMPORT Too!) ................................................................................................................38Sometimes, Variables Are in Particular Columns or in Particular Formats .........402.3 Reading CSV, Excel, and TEXT Files .......................................................................... 412.4 Temporary versus Permanent Status of Data Sets ...................................................432.5 Formatting and Labeling Variables ............................................................................46Using Formats to Read and Display Variable Values ..............................................46Internal Representations and Output Displays ........................................................49Character, Numeric, Time, and Date Formats ..........................................................532.6 User-Defined Formatting .............................................................................................58Saving Formats for Later Use ......................................................................................632.7 Recoding and Transforming Variables in a DATA Step ........................................66Indicator Variables ......................................................................................................682.8 Writing Out a File or Making a Simple Report ......................................................73Simple Report Generation .........................................................................................73Exporting a File ...........................................................................................................772.9 Exercises .......................................................................................................................803. Programming a DATA Step ................................................................................................833.1 Writing Programs by Subdividing Tasks ................................................................83Estimate the Probability That a Randomly Selected 30- to 39-Year-OldMale Is Taller than a Randomly Selected Female of the Same Age .....................83Conditional Execution ...........................................................................................84Looping to Repeat a Task ......................................................................................86Returning to the Height Probability Simulation ............................................... 873.2 Ordering How Tasks Are Done ................................................................................90Missing Data in Functions .........................................................................................923.3 Indexable Lists of Variables (Also Known as Arrays) ...........................................93Defining Values in the Variable List .........................................................................93Inputting Values in the Variable List ........................................................................94Reassign Missing Value Codes for Numeric Variables “.” ...................................95Recoding Missing Values for All Numeric and Character Variables ..................953.4 Functions Associated with Statistical Distributions .............................................963.5 Generating Variables Using Random Number Generators ................................ 1023.6 Remembering Variable Values across Observations ........................................... 105Processing Multiple Observations for a Single Observation .............................. 1063.7 Case Study 1: Is the Two-Sample t-Test Robust to Violations of theHeterogeneous Variance Assumption? ................................................................. 109Case Study 1 (Revisited with DATA Step Programming) .................................. 1183.8 Efficiency Considerations—How Long Does It Take? .........................................1223.9 Case Study 2: Monte Carlo Integration to Estimate an Integral ........................ 1233.10 Case Study 3: Simple Percentile-Based Bootstrap ................................................ 1283.11 Case Study 4: Randomization Test for the Equality of Two Populations ......... 1303.12 Exercises ..................................................................................................................... 1344. Combining, Extracting, and Reshaping Data ............................................................... 1374.1 Adding Observations by SET-ing Data Sets.......................................................... 1374.2 Adding Variables by MERGE-ing Data Sets ......................................................... 1404.3 Working with Tables in PROC SQL ....................................................................... 1484.4 Converting Wide to Long Formats ......................................................................... 1614.5 Converting Long to Wide Formats ......................................................................... 1644.6 Case Study: Reshaping a World Bank Data Set .................................................... 1664.7 Building Training and Validation Data Sets ......................................................... 1754.8 Exercises ..................................................................................................................... 1794.9 Self-study Lab ............................................................................................................ 1805. Macro Programming .......................................................................................................... 1915.1 What Is a Macro and Why Would You Use It? ..................................................... 1915.2 Motivation for Macros: Numerical Integration to DetermineP(0 < Z < 1.645) ......................................................................................................... 1915.3 Processing Macros .................................................................................................... 1955.4 Macro Variables, Parameters, and Functions........................................................ 1955.5 Conditional Execution, Looping, and Macros ...................................................... 198More Complicated Macro Variable Construction ................................................203Changing Locations in a Macro during Execution ..............................................2045.6 Debugging Macro Code and Programs.................................................................206Write Out Values of Macro Variables .....................................................................206Useful SAS Options for Debugging Macros ......................................................... 2075.7 Saving Macros ........................................................................................................... 2115.8 Functions and Routines for Macros ....................................................................... 2115.9 Case Study: Macro for Constructing Training and Test Data Set for ModelComparison ............................................................................................................... 2165.10 Case Study: Processing Multiple Data Sets ...........................................................2235.11 Exercises .....................................................................................................................2276. Customizing Output and Generating Data Visualizations .......................................2296.1 Using the Output Delivery System ........................................................................229Basic Ideas ..................................................................................................................229Destinations—RTF, HTML, PDF, and More! .........................................................230What’s Produced and How to Select It ..................................................................235Another Destination That Stat Programmers Should Visit—OUTPUT ............ 2436.2 Graphics in SAS ......................................................................................................... 2496.3 ODS Statistical Graphics ..........................................................................................2506.4 Modifying Graphics Using the ODS Graphics Editor ......................................... 2576.5 Graphing with Styles and Templates .....................................................................2606.6 Statistical Graphics—Entering the Land of SG Procedures ............................... 266SGPLOT ...................................................................................................................... 266SGPANEL ................................................................................................................... 269SGSCATTER .............................................................................................................. 2716.7 Case Study: Using the SG Procedures ................................................................... 2736.8 Enhancing SG Displays—Options with SG Procedure Statements .................. 2796.9 Using Annotate Data Sets to Enhance SG Displays ............................................2846.10 Using Attribute Maps to Enhance SG Displays ................................................... 2876.11 Exercises .....................................................................................................................2907. Processing Text .................................................................................................................... 2937.1 Cleaning and Processing Text Data ....................................................................... 2937.2 Starting with Character Functions ......................................................................... 2937.3 Processing Text .......................................................................................................... 2987.4 Case Study: Sentiment in State of the Union Addresses .....................................3027.5 Case Study: Reading Text from a Web Page .........................................................3097.6 Regular Expressions ................................................................................................. 3157.7 Case Study (Revisited)—Applying Regular Expressions ................................... 3197.8 Exercises ..................................................................................................................... 3218. Programming with Matrices and Vectors ..................................................................... 3238.1 Defining a Matrix and Subscripting ...................................................................... 3238.2 Using Diagonal Matrices and Stacking Matrices ................................................. 3298.3 Using Elementwise Operations, Repeating, and Multiplying Matrices ........... 3328.4 Importing a Data Set into SAS/IML and Exporting Matrices fromSAS/IML to a Data Set .............................................................................................333Creating Matrices from SAS Data Sets and Vice Versa ........................................3338.5 Case Study 1: Monte Carlo Integration to Estimate π ..........................................3368.6 Case Study 2: Bisection Root Finder ...................................................................... 3378.7 Case Study 3: Randomization Test Using Matrices Imported from PROCPLAN ..........................................................................................................................3408.8 Case Study 4: SAS/IML Module to Implement Monte Carlo Integrationto Estimate π ..............................................................................................................3428.9 Storing and Loading SAS/IML Modules ..............................................................3448.10 SAS/IML and R .........................................................................................................3458.11 Exercises .....................................................................................................................350References ...................................................................................................................................355Index ............................................................................................................................................. 357
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Taylor & Francis Ltd Random Dynamical Systems in Finance
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Taylor & Francis Ltd Risk Analysis in Finance and Insurance 21 Chapman HallCRC Financial Mathematics
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Taylor & Francis Ltd Bayesian Model Selection and Statistical Modeling
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Taylor & Francis Ltd Applied Statistics for Business and Economics
Book SynopsisDesigned for a one-semester course, Applied Statistics for Business and Economics offers students in business and the social sciences an effective introduction to some of the most basic and powerful techniques available for understanding their world. Numerous interesting and important examples reflect real-life situations, stimulating students to think realistically in tackling these problems. Calculations can be performed using any standard spreadsheet package. To help with the examples, the author offers both actual and hypothetical databases on his website http://iwu.edu/~bleekley The text explores ways to describe data and the relationships found in data. It covers basic probability tools, Bayes' theorem, sampling, estimation, and confidence intervals. The text also discusses hypothesis testing for one and two samples, contingency tables, goodness-of-fit, analysis of variance, and population variances. In addition, the author deTable of ContentsIntroduction to Statistics. Describing Data: Tables and Graphs. Describing Data: Summary Statistics. Basic Probability. Probability Distributions. Sampling and Sampling Distributions. Estimation and Confidence Intervals. Tests of Hypotheses: One-Sample Tests. Tests of Hypotheses: Two-Sample Tests. Tests of Hypotheses: Contingency and Goodness-of-Fit. Tests of Hypotheses: ANOVA and Tests of Variances. Simple Regression and Correlation. Multiple Regression. Time-Series Analysis. Appendices. Index.
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Taylor & Francis Ltd Regression Modeling
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Taylor & Francis Ltd Nonlinear Time Series Semiparametric and Nonparametric Methods Chapman HallCRC Monographs on Statistics and Applied Probability
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Taylor & Francis Ltd Structured Credit Portfolio Analysis Baskets and CDOs
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Taylor & Francis Ltd Solvency
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Taylor & Francis Ltd Risk Assessment and Decision Making in Business and Industry
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Taylor & Francis Ltd Stochastic Processes and Related Topics Proceedings of the 12th Winter School Siegmundsburg Germany February 27March 4 2000
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Taylor & Francis Ltd Applied Structural Equation Modeling using AMOS
Book SynopsisThis is an essential how-to guide on the application of structural equation modeling (SEM) techniques with the AMOS software, focusing on the practical applications of both simple and advanced topics. Written in an easy-to-understand conversational style, the book covers everything from data collection and screening to confirmatory factor analysis, structural model analysis, mediation, moderation, and more advanced topics such as mixture modeling, censored date, and non-recursive models. Through step-by-step instructions, screen shots, and suggested guidelines for reporting, Collier cuts through abstract definitional perspectives to give insight on how to actually run analysis. Unlike other SEM books, the examples used will often start in SPSS and then transition to AMOS so that the reader can have full confidence in running the analysis from beginning to end. Best practices are also included on topics like how to determine if your SEM model is formative or reflective,Trade Review“This is one of the best book on Statistics and Research Methods I have ever reviewed so far. This book covers all you need to know on SEM i.e., the concept, introduction to the software, how to prepare data in SPSS and AMOS for analysis, measurement and structural elements of latent variable modelling, full SEM, mediation & moderation with latent variables, and latent growth analysis. To my knowledge, this is the most comprehensive book on the market. I highly recommend this book and I will definitely recommend it to all my students (undergraduate and postgraduate).” -- Daniel Boduszek, University of Huddersfield, UK & SWPS University, PolandTable of ContentsPreface 1. Introduction to Structural Equation Modeling 2. Data Screening, Assessing Reliability, Validity, and Identification 3. Understanding the AMOS Program 4. Measurement Model Analysis 5. Path and Full Structural Models 6. Mediation 7. Moderation 8. Using Categorical Independent Variables in SEM 9. Latent Growth Curve Models 10. Advanced Topics in SEM Appendix
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Taylor & Francis Empirical Macroeconomics and Statistical Uncertainty Spatial and Temporal Disaggregation of Regional Economic Indicators Routledge Studies in the European Economy
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Taylor & Francis Ltd Applied Spatial Statistics and Econometrics
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Taylor & Francis Ltd Machine Learning for Factor Investing R Version Chapman HallCRC Financial Mathematics Series
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Taylor & Francis Computational Finance MATLAB Oriented Modeling RoutledgeGiappichelli Studies in Business and Management
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Taylor & Francis Ltd Computational Finance MATLAB Oriented Modeling RoutledgeGiappichelli Studies in Business and Management
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Taylor & Francis Ltd Understanding Regression Analysis
Book SynopsisUnderstanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model. It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong. Unlike other regression books, this one from the outset takes a realistic approach that all models are just approximations. Hence, the emphasis is to model Nature's processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways.Key features of the book include: Numerous worked examples using the R software Key points and self-study questions displayed just-in-time within chapters <Trade Review"...The authors suggest their book is suitable for those who are “research-oriented”, regardless of any prior advanced training in statistics...I particularly like the emphasis on assumptions. Rather than discuss regression in idealized terms, Westfall and Arias are upfront about why assumptions are often wrong in practice, and what an analyst can do about violations. These discussions are woven into many of the chapters, and in some cases, they are featured in stand-alone chapters...I am a fan of learning statistics by doing, so the large amount of R code woven into the book’s chapters and the hands-on exercises at the end of each chapter are valuable and a welcomed feature of the book...To me, this textbook would be most suitable for a one-semester survey course in statistical methods for students outside of biostatistics or statistics. A motivated student could even use this book for self-study...Overall, I believe this is a worthwhile addition to the literature."- Ryan Andrews, ISCB News, June 2021 Table of Contents1. Introduction to Regression Models 2. Estimating Regression Model Parameters3. The Classical Model and Its Consequences4. Evaluating Assumptions5. Transformations6. The Multiple Regression Model7. Multiple Regression from the Matrix Point of View8. R-squared, Adjusted R-Squared, the F Test, and Multicollinearity9. Polynomial Models and Interaction (Moderator) Analysis10. ANOVA, ANCOVA, and Other Applications of Indicator Variables11. Variable Selection12. Heteroscedasticity and Non-independence13. Models for Binary, Nominal, and Ordinal Response Variables14. Models for Poisson and Negative Binomial Response15. Censored Data Models16. Outliers, Identification, Problems, and Remedies (Good and Bad)17. Neural Network Regression 18. Regression Trees19. Bookend
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Taylor & Francis Ltd Introduction to Time Series Modeling with Applications in R
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Taylor & Francis Ltd Oil and Gas Processing Equipment
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Taylor & Francis Ltd Models for Dependent Time Series
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Taylor & Francis Ltd StateSpace Methods for Time Series Analysis
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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
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Taylor & Francis A StepbyStep Guide to Exploratory Factor Analysis
Book SynopsisThis is a concise, easy to use, step-by-step guide for applied researchers conducting exploratory factor analysis (EFA) using the open source software R. In this book, Dr. Watkins systematically reviews each decision step in EFA with screen shots of R and RStudio code, and recommends evidence-based best practice procedures. This is an eminently applied, practical approach with few or no formulas and is aimed at readers with little to no mathematical background. Dr. Watkins maintains an accessible tone throughout and uses minimal jargon and formula to help facilitate grasp of the key issues users will face while applying EFA, along with how to implement, interpret, and report results. Copious scholarly references and quotations are included to support the reader in responding to editorial reviews. This is a valuable resource for upper-level undergraduate and postgraduate students, as well as for more experienced researchers undertaking multivariate or structure equation modeling courses across the behavioral, medical, and social sciences.Trade Review"This book is an important contribution to the field. I have been publishing articles using EFA for over 30 years, yet it provided me with new insights and information on EFA. More importantly, the material is easy to follow and accessible to researchers and graduate students new to EFA. I highly recommend it to anyone seeking to become competent in EFA." – Joseph J. Glutting, Ph.D. University of Delaware, USA"A Step-by-Step Guide to Exploratory Factor Analysis with R and RStudio is an important contribution to the applied exploratory factor analytic literature. It is extremely well-written and portends to be a useful resource to researchers and students alike. It does a commendable job of describing how to implement exploratory factor analysis using R. I highly recommend this book." – Stefan C. Dombrowski, Ph.D. Rider University, USA"This book is an amazing resource for those new to factor analysis as well as those who have used it for some time. It is a terrific guide to best practices in exploratory factor analysis with rich explanations and descriptions for why various procedures are used and equally terrific in providing resources and guidance for using R for conducting factor analysis. I highly recommend this book." – Gary L. Canivez, Ph.D. Eastern Illinois University, USATable of ContentsPreface 1. Introduction 2. Data 3. R and RStudio Software4. Importing and Saving Data and Results 5. Decision Steps in Exploratory Factor Analysis 6. Step 1: Variables to Include 7. Step 2: Participants 8. Step 3: Data Screening 9. Step 4: Is Exploratory Factor Analysis Appropriate10. Step 5: Factor Analysis Model 11. Step 6: Factor Extraction Method 12. Step 7: How Many Factors to Retain 13. Step 8: Rotate Factors 14. Step 9: Interpret Exploratory Factor Analysis Results 15. Step 10: Report Exploratory Factor Analysis Results 16. Exploratory Factor Analysis with Categorical Variables17. Higher-order and Bifactor Models 18. Exploratory Versus Confirmatory Factor AnalysisPractice ExercisesReferences and resources
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Taylor & Francis Ltd Machine Learning for Factor Investing
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Taylor & Francis Ltd Protecting Your Privacy in a DataDriven World
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Taylor & Francis Ltd Protecting Your Privacy in a DataDriven World
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Taylor & Francis Ltd Introduction to Econophysics
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Taylor & Francis Ltd Introduction to Statistical Methods for Financial Models
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Taylor & Francis The History of Money and Monetary Arrangements
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Taylor & Francis A New Measure of Competition in the Financial Industry
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Taylor & Francis Ltd Gini Inequality Index
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Taylor & Francis Categorical and Nonparametric Data Analysis
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Taylor & Francis Ltd ANOVA and Mixed Models
ANOVA and Mixed Models: A Short Introduction Using R provides both the practitioner and researcher a compact introduction to the analysis of data from the most popular experimental designs. Based on knowledge from an introductory course on probability and statistics, the theoretical foundations of the most important models are introduced. The focus is on an intuitive understanding of the theory, common pitfalls in practice, and the application of the methods in R. From data visualization and model fitting, up to the interpretation of the corresponding output, the whole workflow is presented using R. The book does not only cover standard ANOVA models, but also models for more advanced designs and mixed models, which are common in many practical applications.Features Accessible to readers with a basic background in probability and statistics Covers fundamental concepts of experimen
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Taylor & Francis Ltd ANOVA and Mixed Models
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Taylor & Francis A StepbyStep Guide to Exploratory Factor Analysis with SPSS
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Taylor & Francis Ltd An Introduction to Computational Risk Management of EquityLinked Insurance Chapman and HallCRC Financial Mathematics
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Taylor & Francis Ltd Design Analysis of Clinical Trials for Economic Evaluation Reimbursement
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Taylor & Francis Ltd Risk Measures and Insurance Solvency Benchmarks
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Taylor & Francis Ltd Linear Regression Models
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Taylor & Francis Ltd Linear Regression Models
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Taylor & Francis Applied Structural Equation Modeling using AMOS Basic to Advanced Techniques
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Taylor & Francis Qualitative Methods in Economics
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Taylor & Francis Ltd Probability Foundations of Economic Theory
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Taylor & Francis Trade Theory and Econometrics
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Taylor & Francis Ltd The Development of Economics in Western Europe Since 1945
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Taylor & Francis Ltd Financial Econometrics
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