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Predicting Stock Returns
by David G McMillanThis book provides a comprehensive analysis of asset price movement. It examines different aspects of stock return predictability, the interaction between stock return and dividend growth predictability, the relationship between stocks and bonds, and the resulting implications for asset price movement. By contributing to our understanding of the factors that cause price movement, this book will be of benefit to researchers, practitioners and policy makers alike.
Predicting Success
by David LaheyMake the right hires every time, with an analytical approach to talent Predicting Success is a practical guide to finding the perfect member for your team. <P><P>By applying the principles and tools of human analytics to the workplace, you'll avoid bad culture fits, mismatched skillsets, entitled workers, and other hiring missteps that drain the team of productivity and morale. This book provides guidance toward implementing tools like the Predictive Index®, behavior analytics, hiring assessments, and other practical resources to build your best team and achieve the best outcomes. Written by a human analytics specialist who applies these principles daily, this book is the manager's guide to aligning people with business strategy to find the exact person your team is missing. An avalanche of research describes an evolving business landscape that will soon be populated by workers in jobs that don't fit. This is bad news for both the workers and the companies, as bad hires affect outcomes on the individual and organizational level, and can potentially hinder progress long after the situation has been rectified. Predicting Success is a guide to avoiding that by integrating analytical tools into the hiring process from the start. Hire without the worry of mismatched expectations Apply practical analytics tools to the hiring process Build the right team and avoid disconnected or dissatisfied workers Stop seeing candidates as "chances," and start seeing them as opportunities Analytics has proved to be integral in the finance, tech, marketing, and banking industries, but when applied to talent acquisition, it can build the team that takes the company to the next level. If the future will be full of unhappy workers in underperforming companies, getting out from under that weight ahead of time would confer a major advantage. Predicting Success provides evidence-based strategies that help you find precisely the talent you need.
Predicting Turning Points in the Interest Rate Cycle (Routledge Library Editions: Business Cycles)
by James W. CoonsOriginally published in 1994 and the recipient of the Stonier Library Award, this volume evaluates an alternative approach – the sequential filter- to managing the uncertainty inherent in the future course of the interest rate cycle. The specific hypothesis is that the sequential filter can produce valuable signals of cyclical peaks and troughs in interest rates. The analysis focusses on US interest rates from April 1953 to December 1988.
Predicting the Markets of Tomorrow
by James P. O'ShaughnessyA unique and timely new wealth-building strategy from a legendary investment guru In his national bestsellers How to Retire Rich and What Works on Wall Street, portfolio manager extraordinaire James P. O'Shaughnessy offered investors practical advice based on rigorous quantitative analysis--advice that has consistently beaten the market. But in a recent analysis of market data, O'Shaughnessy uncovered some astonishing trends not discussed in his previous books. The Markets of Tomorrow explains O'Shaughnessy's new research and tells ordinary investors what they must do now to revamp their portfolios. According to O'Shaughnessy, the year 2000 marked the end of a twenty-year cycle that was dominated by the stocks of larger, fastergrowing companies like those in the S&P 500. In the new cycle, the stocks of small and midsize companies are the ones that will outperform the market, along with large company value stocks and intermediate term bonds. O'Shaughnessy describes the number crunching behind his analysis and then shows individual investors exactly how to select the right mix of investments and pick top-performing small and midcap stocks. The Markets of Tomorrow is a loud and clear call to action for every investor who doesn't want to be left behind.
Predicting the Unpredictable
by Johanna Rothman"If you have trouble estimating cost or schedule for your projects, you are not alone. The question is this: who wants the estimate and why?The definition of estimate is to guess. But too often, the people who want estimates want commitments. Instead of a commitment, you can apply practical and pragmatic approaches to developing estimates and then meet your commitments. You can provide your managers with the information they want and that you can live with.Learn how to use different words for your estimates and how to report an estimate that includes uncertainty. Learn who should and should not estimate. Learn how to update your estimate when you know more about your project.Regain estimation sanity. Learn practical and pragmatic ways to estimate schedule or cost for your projects."
Prediction Markets at Google
by Andrew Mcafee Karim R. Lakhani Peter A. ColesIn its eight quarters of operation, Google's internally developed prediction market has delivered accurate and decisive predictions about future events of interest to the company. Google must now determine how to increase participation in the market, and how to best use its predictions.
Prediction Markets: Theory and Applications (Routledge International Studies In Money And Banking Ser. #66)
by Leighton Vaughan WilliamsHow does one effectively aggregate disparate pieces of information that are spread among many different individuals? In other words, how does one best access the ‘wisdom of the crowd’? Prediction markets, which are essentially speculative markets created for the purpose of aggregating information and making predictions, offer the answer to this question. The effective use of these markets has the potential not only to help forecast future events on a national and international level, but also to assist companies, for example, in providing improved estimates of the potential market size for a new product idea or the launch date of new products and services. The markets have already been used to forecast uncertain outcomes ranging from influenza to the spread of infectious diseases, to the demand for hospital services, to the box office success of movies, climate change, vote shares and election outcomes, to the probability of meeting project deadlines. The insights gained also have many potentially valuable applications for public policy more generally. These markets offer substantial promise as a tool of information aggregation as well as forecasting, whether alone or as a supplement to other mechanisms like opinion surveys, group deliberations, panels of experts and focus groups. Moreover, they can be applied at a macroeconomic and microeconomic level to yield information that is valuable for government and commercial policy-makers and which can be used for a number of social purposes. This volume of original readings, contributed by many of the leading experts in the field, marks a significant addition to the base of knowledge about this fascinating subject area. The book should be of interest to anyone looking at monetary economics, economic forecasting and microeconomics.
Prediction Revisited: The Importance of Observation
by Mark P. Kritzman David Turkington Megan CzasonisA thought-provoking and startlingly insightful reworking of the science of prediction In Prediction Revisited: The Importance of Observation, a team of renowned experts in the field of data-driven investing delivers a ground-breaking reassessment of the delicate science of prediction for anyone who relies on data to contemplate the future. The book reveals why standard approaches to prediction based on classical statistics fail to address the complexities of social dynamics, and it provides an alternative method based on the intuitive notion of relevance. The authors describe, both conceptually and with mathematical precision, how relevance plays a central role in forming predictions from observed experience. Moreover, they propose a new and more nuanced measure of a prediction’s reliability. Prediction Revisited also offers: Clarifications of commonly accepted but less commonly understood notions of statistics Insight into the efficacy of traditional prediction models in a variety of fields Colorful biographical sketches of some of the key prediction scientists throughout history Mutually supporting conceptual and mathematical descriptions of the key insights and methods discussed within With its strikingly fresh perspective grounded in scientific rigor, Prediction Revisited is sure to earn its place as an indispensable resource for data scientists, researchers, investors, and anyone else who aspires to predict the future from the data-driven lessons of the past.
Predictive Analytics
by Thomas H. Davenport Eric Siegel"The Freakonomics of big data."--Stein Kretsinger, founding executive of Advertising.com; former lead analyst at Capital OneThis book is easily understood by all readers. Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.You have been predicted -- by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future -- lifting a bit of the fog off our hazy view of tomorrow -- means pay dirt.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction:What type of mortgage risk Chase Bank predicted before the recession.Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves.Why early retirement decreases life expectancy and vegetarians miss fewer flights.Five reasons why organizations predict death, including one health insurance company.How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual.How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy!How companies ascertain untold, private truths -- how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job.How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free.What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward -- but that can be predicted in advance?Whether you are a consumer of it -- or consumed by it -- get a handle on the power of Predictive Analytics.
Predictive Analytics
by Eric Siegel"Mesmerizing & fascinating..." --The Seattle Post-Intelligencer "The Freakonomics of big data." --Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating -- surprisingly accessible -- introduction, leading expert Eric Siegel reveals how predictive analytics works, and how it affects everyone every day. Rather than a "how to" for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive Analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction -- now in its Revised and Updated edition -- former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death -- including one health insurance company. How U.S. Bank and Obama for America calculated -- and Hillary for America 2016 plans to calculate -- the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths -- how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you
Predictive Analytics For Dummies
by Mohamed Chaouchi Tommy Jung Anasse BariCombine business sense, statistics, and computers in a new and intuitive way, thanks to Big DataPredictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions.Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more.Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businessesHelps readers see how to shepherd predictive analytics projects through their companiesExplains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and moreCovers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing dataAlso covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewherePropose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies.
Predictive Analytics For Dummies
by Mohamed Chaouchi Tommy Jung Dr Anasse BariPredictive Analytics For Dummies, 2e will help the you understand the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. You will learn how to incorporate algorithms through discovering data models, identifying similarities and relationships in your data, and how to predict the future through data classification. You will develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get stakeholder buy-in. The author will also address "soft" issues, including handling people, setting realistic goals, protecting budgets, making useful presentations, and more, to help the reader prepare for shepherding predictive analysis projects through their companies. Coverage will include: Real-world tips for creating business value Common use cases to help you get started Details on modeling, k-means clustering, and more How you can predict the future with classification Information on structuring your data Methods for testing models Hands-on guides to software installation Tips on outlining business goals and approaches
Predictive Analytics Using Rattle and Qlik Sense
by Ferran Garcia PagansIf you are a business analyst who wants to understand how to improve your data analysis and how to apply predictive analytics, then this book is ideal for you. This book assumes you have some basic knowledge of statistics and a spreadsheet editor such as Excel, but knowledge of QlikView is not required.
Predictive Analytics and Generative AI for Data-Driven Marketing Strategies (Artificial Intelligence, Machine Learning, Data Analytics and Automation for Business Management)
by Hemachandran K Raul Villamarin Rodriguez Debdutta Choudhury Jorge A. Wise Revathi TIn providing an in-depth exploration of cutting-edge technologies and how they are used to support data-driven marketing strategies and empower organizations to make the right decisions, Predictive Analytics and Generative AI for Data-Driven Marketing Strategies includes real-world case studies and examples from diverse marketing domains. This book demonstrates how predictive analytics and generative AI have been successfully applied to solve marketing challenges and drive tangible results. This book showcases emerging trends in predictive analytics and generative AI for marketing, and their potential impact on the future of data-driven marketing. This book is meant for professionals and scholars to gather the skills and resources to use predictive analytics and generative AI effectively for marketing strategies.This book: • Examines the different predictive analytics models and algorithms, such as regression analysis, decision trees, and neural networks, and demonstrates how they may be utilized to get insightful conclusions from marketing data.• Includes generative AI techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), showcasing how these techniques can generate synthetic data for marketing insights and decision-making.• Highlights the importance of data-driven marketing choices and illustrates how generative AI and predictive analytics may be quite useful in this context.• Integrates the principles of data science with marketing concepts, offering a cohesive understanding of how predictive analytics and generative AI can power data-driven marketing decisions.• Presents the recent advances in predictive analytics and generative AI and discusses how they can affect the area of data-driven marketing.
Predictive Analytics for Human Resources
by Jac Fitz-Enz John Mattox IICreate and run a human resource analytics project withconfidence For any human resource professional that wants to harness thepower of analytics, this essential resource answers the questions:"Where do I start?" and "What tools are available?" PredictiveAnalytics for Human Resources is designed to answer these andother vital questions. The book explains the basics of everybusiness--the vision, the brand, and the culture, and showshow predictive analytics supports them. The authors put the focuson the fundamentals of predictability and include a framework oflogical questions to help set up an analytic program or project,then follow up by offering a clear explanation of statisticalapplications.Predictive Analytics for Human Resources is a how-toguide filled with practical and targeted advice. The book startswith the basic idea of engaging in predictive analytics and walksthrough case simulations showing statistical examples. In addition,this important resource addresses the topics of internal coaching,mentoring, and sponsoring and includes information on how torecruit a sponsor. In the book, you'll find:A comprehensive guide to developing and implementing a humanresource analytics projectIllustrative examples that show how to go to market, develop aleadership model, and link it to financial targets through causalmodelingExplanations of the ten steps required in building an analyticsfunctionHow to add value through analysis of systems such as staffing,training, and retentionFor anyone who wants to launch an analytics project or programfor HR, this complete guide provides the information andinstruction to get started the right way.
Predictive Analytics for Marketers: Using Data Mining for Business Advantage
by Barry LeventhalPredictive Analytics has revolutionised marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current data and make predictions about unknown future events. In business terms, this enables companies to forecast consumer behaviour and much more. Predictive Analytics for Marketers will guide marketing professionals on how to apply predictive analytical tools to streamline business practices. Including comprehensive coverage of an array of predictive analytic tools and techniques, this book enables readers to harness patterns from past data, to make accurate and useful predictions that can be converted to business success. Truly global in its approach, the insights these techniques offer can be used to manage resources more effectively across all industries and sectors. Written in clear, non-technical language, Predictive Analytics for Marketers contains case studies from the author's more than 25 years of experience and articles from guest contributors, demonstrating how predictive analytics has been used to successfully achieve a range of business purposes.
Predictive Analytics for the Modern Enterprise: A Practitioner's Guide To Designing And Implementing Solutions
by Nooruddin Abbas AliThe surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies.If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Explore ways that predictive analytics can provide direct input back to your businessUnderstand mathematical tools commonly used in predictive analyticsLearn the development frameworks used in predictive analytics applicationsAppreciate the role of predictive analytics in the machine learning processExamine industry implementations of predictive analyticsBuild, train, and retrain predictive models using Python and TensorFlow
Predictive Analytics im Controlling: Eine Untersuchung zur Anwendung in der Unternehmenspraxis (BestMasters)
by Zita KlingerDas vorliegende Buch zeigt, inwieweit Predictive Analytics (PA) im Controlling zur Forecast-Erstellung genutzt wird. Außerdem wird eine Handlungsempfehlung für die Implementierung eines PA Forecasts abgeleitet. Die Ergebnisse einer empirischen Untersuchung deuten darauf hin, dass PA bisher bei eher wenigen Unternehmen eingesetzt wird. Als Gründe hierfür werden insbesondere ein hoher Aufwand für den Aufbau von Know-how in Bezug auf PA und für die Implementierung in Bezug auf Zeit und Kosten von den Unternehmen angegeben. Die Ergebnisse zeigen, dass bei der Einführung eines PA Forecasts ein individuelles Vorgehen je Unternehmen erforderlich ist. Es können zwar die erforderlichen Prozessschritte und Best-Practice als Handlungsempfehlung definiert werden, allerdings ist bspw. die Auswahl der Position, für die der PA Forecast erstellt werden soll, von verschiedenen Faktoren abhängig, die von Unternehmen zu Unternehmen individuell geprüft werden müssen. Insgesamt hat sich gezeigt, dass durch den Einsatz von PA der Vorhersageprozess beschleunigt sowie die Vorhersagegenauigkeit erhöht werden kann. Die dadurch generierten Wettbewerbsvorteile für das jeweilige Unternehmen überwiegen in der Regel den erforderlichen Aufwand.
Predictive Analytics in Human Resource Management: A Hands-on Approach
by Shivinder Nijjer Sahil RajThis volume is a step-by-step guide to implementing predictive data analytics in human resource management (HRM). It demonstrates how to apply and predict various HR outcomes which have an organisational impact, to aid in strategising and better decision-making. The book: Presents key concepts and expands on the need and role of HR analytics in business management. Utilises popular analytical tools like artificial neural networks (ANNs) and K-nearest neighbour (KNN) to provide practical demonstrations through R scripts for predicting turnover and applicant screening. Discusses real-world corporate examples and employee data collected first-hand by the authors. Includes individual chapter exercises and case studies for students and teachers. Comprehensive and accessible, this guide will be useful for students, teachers, and researchers of data analytics, Big Data, human resource management, statistics, and economics. It will also be of interest to readers interested in learning more about statistics or programming.
Predictive Analytics in System Reliability (Springer Series in Reliability Engineering)
by Hoang Pham Vijay KumarThis book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques. It will also be useful to students learning reliability engineering.The book brings readers up to date with how system reliability relates to the latest techniques of AI, big data, genetic algorithm, information theory, and multi-criteria decision making and points toward future developments in the subject.
Predictive Analytics with KNIME: Analytics for Citizen Data Scientists
by Frank AcitoThis book is about data analytics, including problem definition, data preparation, and data analysis. A variety of techniques (e.g., regression, logistic regression, cluster analysis, neural nets, decision trees, and others) are covered with conceptual background as well as demonstrations of KNIME using each tool. The book uses KNIME, which is a comprehensive, open-source software tool for analytics that does not require coding but instead uses an intuitive drag-and-drop workflow to create a network of connected nodes on an interactive canvas. KNIME workflows provide graphic representations of each step taken in analyses, making the analyses self-documenting. The graphical documentation makes it easy to reproduce analyses, as well as to communicate methods and results to others. Integration with R is also available in KNIME, and several examples using R nodes in a KNIME workflow are demonstrated for special functions and tools not explicitly included in KNIME.
Predictive Analytics: Modeling and Optimization (Advanced Research in Reliability and System Assurance Engineering)
by Edited by Vijay Kumar and Mangey RamPredictive analytics refers to making predictions about the future based on different parameters which are historical data, machine learning, and artificial intelligence. This book provides the most recent advances in the field along with case studies and real-world examples. It discusses predictive modeling and analytics in reliability engineering and introduces current achievements and applications of artificial intelligence, data mining, and other techniques in supply chain management. It covers applications to reliability engineering practice, presents numerous examples to illustrate the theoretical results, and considers and analyses case studies and real-word examples. The book is written for researchers and practitioners in the field of system reliability, quality, supply chain management, and logistics management. Students taking courses in these areas will also find this book of interest.
Predictive Analytics: Parametric Models for Regression and Classification Using R (Wiley Series in Probability and Statistics)
by Ajit C. TamhaneProvides a foundation in classical parametric methods of regression and classification essential for pursuing advanced topics in predictive analytics and statistical learning This book covers a broad range of topics in parametric regression and classification including multiple regression, logistic regression (binary and multinomial), discriminant analysis, Bayesian classification, generalized linear models and Cox regression for survival data. The book also gives brief introductions to some modern computer-intensive methods such as classification and regression trees (CART), neural networks and support vector machines. The book is organized so that it can be used by both advanced undergraduate or masters students with applied interests and by doctoral students who also want to learn the underlying theory. This is done by devoting the main body of the text of each chapter with basic statistical methodology illustrated by real data examples. Derivations, proofs and extensions are relegated to the Technical Notes section of each chapter, Exercises are also divided into theoretical and applied. Answers to selected exercises are provided. A solution manual is available to instructors who adopt the text. Data sets of moderate to large sizes are used in examples and exercises. They come from a variety of disciplines including business (finance, marketing and sales), economics, education, engineering and sciences (biological, health, physical and social). All data sets are available at the book’s web site. Open source software R is used for all data analyses. R codes and outputs are provided for most examples. R codes are also available at the book’s web site. Predictive Analytics: Parametric Models for Regression and Classification Using R is ideal for a one-semester upper-level undergraduate and/or beginning level graduate course in regression for students in business, economics, finance, marketing, engineering, and computer science. It is also an excellent resource for practitioners in these fields.