"Spurious Correlations ... is the most fun you'll ever have with graphs." -- Bustle Military intelligence analyst and Harvard Law student Tyler Vigen illustrates the golden rule that "correlation does not equal causation" through hilarious graphs inspired by his viral website. Is there a correlation between Nic Cage films and swimming pool accidents? What about beef consumption and people getting struck by lightning? Absolutely not. But that hasn't stopped millions of people from going to tylervigen.com and asking, "Wait, what?" Vigen has designed software that scours enormous data sets to find unlikely statistical correlations. He began pulling the funniest ones for his website and has since gained millions of views, hundreds of thousands of likes, and tons of media coverage. Subversive and clever, Spurious Correlations is geek humor at its finest, nailing our obsession with data and conspiracy theory.
This book Correlation and Regression is an outcome of authors long teaching experience of the subject. This book present a thorough treatment of what is required for the students of B.A/B.Sc., of all Indian Universities. It includes fundamental concepts, illustrated examples and application to various problems. These illustrative examples have been selected carefully on such topic and sufficient number of unsolved questions are provided which aims at sharpening the skill of students. Contents: Correlation Analysis, Regression Analysis, Partial and Multiple Correlation.
". . . the writing makes this book interesting to all levels of students. Bobko tackles tough issues in an easy way but provides references for more complex and complete treatment of the subject. . . . there is a familiarity and love of the material that radiates through the words." --Malcolm James Ree, ORGANIZATIONAL RESEARCH METHODS, April 2002 "This book provides one of the clearest treatments of correlations and regression of any statistics book I have seen. . . . Bobko has achieved his objective of making the topics of correlation and regression accessible to students. . . . For someone looking for a very clearly written treatment of applied correlation and regression, this book would be an excellent choice." --Paul E. Spector, University of South Florida "As a quantitative methods instructor, I have reviewed and used many statistical textbooks. This textbook and approach is one of the very best when it comes to user-friendliness, approachability, clarity, and practical utility." --Steven G. Rogelberg, Bowling Green State University Building on the classical examples in the first edition, this updated edition provides students with an accessible textbook on statistical theories in correlation and regression. Taking an applied approach, the author uses concrete examples to help the student thoroughly understand how statistical techniques work and how to creatively apply them based on specific circumstances they face in the "real world." The author uses a layered approach in each chapter, first offering the student an intuitive understanding of the problems or examples and progressing through to the underlying statistics. This layered approach and the applied examples provide students with the foundation and reasoning behind each technique, so they will be able to use their own judgement to effectively choose from the alternative data analytic options.
The concept of dependence permeates the Earth and its inhabitants in a most profound manner. Examples of interdependent meteorological phenomena in nature and interdependence in the medical, social, and political aspects of our existence, not to mention the economic structures, are too numerous to be cited individually. Moreover, the dependence is obviously not deterministic but of a stochastic nature. However, it seems that none of the departments of statistics, engineering, economics and mathematics in the academic institutions throughout the world offer courses dealing with dependence concepts and measures.This book can thus be viewed as an attempt to remedy the situation, and it has been written for a graduate course or a seminar on correlation and dependence concepts and measures. A modest background in mathematical statistics and probability and integral calculus is required. The book is not a full-scale expedition up another statistical Alp. Rather, it is a tour over a somewhat neglected but important terrain. The chapter on correlation is written for a layman.
A blueprint for historians to understand and evaluate the variables and discusses the fundamentals of regression analysis. 2 looks at procedures for assessing the level of association among diagnostic methods for identifying and correcting shortcomings Finally, part 3 presents more advanced topics, including in regression models. quantitative analyses they're likely to encounter in journal literature and monographs on research in the social sciences. ignore the fact that most historians have little background in mathematics would be folly, to decipher equations and follow their logic. Concepts are introduced carefully, and the operation of equations is explained step by step. Annotation copyright by Book News, Inc., Portland, OR
Correlation is a robust and general technique for pattern recognition and is used in many applications, such as automatic target recognition, biometric recognition and optical character recognition. The design, analysis and use of correlation pattern recognition algorithms requires background information, including linear systems theory, random variables and processes, matrix/vector methods, detection and estimation theory, digital signal processing and optical processing. This book provides a needed review of this diverse background material and develops the signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises. It shows both digital and optical implementations. It also contains technology presented by the team that developed it and includes case studies of significant interest, such as face and fingerprint recognition. Suitable for graduate students taking courses in pattern recognition theory, whilst reaching technical levels of interest to the professional practitioner.
This book goes beyond the truism that 'correlation does not imply causation' and explores the logical and methodological relationships between correlation and causation. It presents a series of statistical methods that can test, and potentially discover, cause-effect relationships between variables in situations in which it is not possible to conduct randomised or experimentally controlled experiments. Many of these methods are quite new and most are generally unknown to biologists. In addition to describing how to conduct these statistical tests, the book also puts the methods into historical context and explains when they can and cannot justifiably be used to test or discover causal claims. Written in a conversational style that minimises technical jargon, the book is aimed at practising biologists and advanced students, and assumes only a very basic knowledge of introductory statistics.
In Volatility and Correlation 2nd edition: The Perfect Hedger and the Fox, Rebonato looks at derivatives pricing from the angle of volatility and correlation. With both practical and theoretical applications, this is a thorough update of the highly successful Volatility & Correlation – with over 80% new or fully reworked material and is a must have both for practitioners and for students. The new and updated material includes a critical examination of the ‘perfect-replication’ approach to derivatives pricing, with special attention given to exotic options; a thorough analysis of the role of quadratic variation in derivatives pricing and hedging; a discussion of the informational efficiency of markets in commonly-used calibration and hedging practices. Treatment of new models including Variance Gamma, displaced diffusion, stochastic volatility for interest-rate smiles and equity/FX options. The book is split into four parts. Part I deals with a Black world without smiles, sets out the author’s ‘philosophical’ approach and covers deterministic volatility. Part II looks at smiles in equity and FX worlds. It begins with a review of relevant empirical information about smiles, and provides coverage of local-stochastic-volatility, general-stochastic-volatility, jump-diffusion and Variance-Gamma processes. Part II concludes with an important chapter that discusses if and to what extent one can dispense with an explicit specification of a model, and can directly prescribe the dynamics of the smile surface. Part III focusses on interest rates when the volatility is deterministic. Part IV extends this setting in order to account for smiles in a financially motivated and computationally tractable manner. In this final part the author deals with CEV processes, with diffusive stochastic volatility and with Markov-chain processes. Praise for the First Edition: “In this book, Dr Rebonato brings his penetrating eye to bear on option pricing and hedging.... The book is a must-read for those who already know the basics of options and are looking for an edge in applying the more sophisticated approaches that have recently been developed.” —Professor Ian Cooper, London Business School “Volatility and correlation are at the very core of all option pricing and hedging. In this book, Riccardo Rebonato presents the subject in his characteristically elegant and simple fashion...A rare combination of intellectual insight and practical common sense.” —Anthony Neuberger, London Business School
This book presents a distinctive way of understanding quantum correlations beyond entanglement, introducing readers to this less explored yet very fundamental aspect of quantum theory. It takes into account most of the new ideas involving quantum phenomena, resources, and applications without entanglement, both from a theoretical and an experimental point of view. This book serves as a reference for both beginner students and experienced researchers in physics and applied mathematics, with an interest in joining this novel venture towards understanding the quantum nature of the world.
**Correlation Is Not Causation: Learn How to Avoid the 5 Traps That Even Pros Fall Into** Ever heard someone confidently declare that because two things are correlated, one must cause the other? We've all been there. "Correlation Is Not Causation: Learn How to Avoid the 5 Traps That Even Pros Fall Into" is your friendly, chatty guide to understanding the nuances of correlation and causation, and how to avoid the common mistakes that even experts can make. **Benefits of this book:** - **Master the basics:** Learn why correlation doesn’t imply causation with simple, clear explanations. - **Identify common pitfalls:** Understand the five traps that can mislead you into thinking correlation equals causation. - **Develop critical thinking:** Enhance your ability to critically analyze data and avoid false conclusions. - **Easy to understand:** Written in plain English, perfect for beginners and those without a technical background. - **Visual examples:** Packed with intuitive, visual examples to make complex concepts easy to grasp. - **Practical strategies:** Get actionable strategies to correctly interpret data and identify true causal relationships. We often look for patterns and explanations in the world around us. When two things seem related, it's tempting to conclude that one causes the other. This book dives into the reasons why this assumption can be misleading and how to avoid falling into that trap. In "Correlation Is Not Causation," you'll discover the five alternatives to one variable being the direct cause of another when a correlation is found. We break down each alternative and show you how to systematically test for them, ensuring you understand the real relationship between variables. From formulating a plan to analyze data to interpreting results without falling into common pitfalls, this book provides a comprehensive yet accessible guide. With no statistical jargon, it's perfect for anyone looking to improve their data literacy. Ready to navigate the world of data with confidence? Equip yourself with the knowledge to discern true causal relationships and avoid misleading correlations. Get your copy of "Correlation Is Not Causation" today and start making smarter, data-driven decisions!
