In Infinite Regress, David Joselit considers the plurality of identities and practices within Duchamp's life and art between 1910 and 1941, conducting a synthetic reading of his early and middle career. There is not one Marcel Duchamp, but several. Within his oeuvre Duchamp practiced a variety of modernist idioms and invented an array of contradictory personas: artist and art dealer, conceptualist and craftsman, chess champion and dreamer, dandy and recluse. In Infinite Regress, David Joselit considers the plurality of identities and practices within Duchamp's life and art between 1910 and 1941, conducting a synthetic reading of his early and middle career. Taking into account underacknowledged works and focusing on the conjunction of the machine and the commodity in Duchamp's art, Joselit notes a consistent opposition between the material world and various forms of measurement, inscription, and quantification. Challenging conventional accounts, he describes the readymade strategy not merely as a rejection of painting, but as a means of producing new models of the modern self.

Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers. Key Features: 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) Clear step-by-step instructions on executing the analyses Clear guidance on how to interpret results Primary instruction in R but added sections for Python coders Discussion exercises and data exercises for each of the main chapters Final chapter of practice material and datasets ideal for class homework or project work.

We are living through a period of dramatic political change – Brexit, the election of Trump, the rise of extreme right movements in Europe and elsewhere, the resurgence of nationalism and xenophobia and a concerted assault on the liberal values and ideals associated with cosmopolitanism and globalization. Suddenly we find ourselves in a world that few would have imagined possible just a few years ago, a world that seems to many to be a move backwards. How can we make sense of these dramatic developments and how should we respond to them? Are we witnessing a worldwide rejection of liberal democracy and its replacement by some kind of populist authoritarianism? This timely volume brings together some of the world's greatest minds to analyse and seek to understand the forces behind this 'great regression'. Writers from across disciplines and countries, including Paul Mason, Pankaj Mishra, Slavoj Zizek, Zygmunt Bauman, Arjun Appadurai, Wolfgang Streeck and Eva Illouz, grapple with our current predicament, framing it in a broader historical context, discussing possible future trajectories and considering ways that we might combat this reactionary turn. The Great Regression is a key intervention that will be of great value to all those concerned about recent developments and wondering how best to respond to this unprecedented challenge to the very core of liberal democracy and internationalism across the world today. For more information, see: www.thegreatregression.eu

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

This successful book, now available in paperback, provides academics and researchers with a clear set of prescriptions for estimating, testing and probing interactions in regression models. Including the latest research in the area, such as Fuller's work on the corrected/constrained estimator, the book is appropriate for anyone who uses multiple regression to estimate models, or for those enrolled in courses on multivariate statistics.