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Since the end of World War II, the primary political regions of Illinois, Chicago and "downstate, " have lost population, wealth, and political power to a third region, the suburban collar, which has relentlessly expanded outward from Chicago. At the same time, legislative service has changed from a largely part-time "citizen" activity into a "professional, " career-oriented pursuit. Parochial perspectives of elected officials have intensified as reflected in candidates' promises to deliver their districts' "fair share" of government spending. The state legislature has become an arena in which each region battles for its own fair share, rather than an instrument for comprehensively addressing the state's problems. The authors foresee the emergence of political coalitions linking downstate and Chicago-historically at odds-in efforts to protect their "shares" and contend with the suburban collar. Illinois's political leaders face the challenge of looking beyond district interests to the broader concerns of work-force quality and statewide economic prosperity. Samuel K. Gove is Director Emeritus at the Institute of Government and Public Affairs, and Professor Emeritus of Political Science at the University of Illinois. He is coeditor with Louis H. Masotti of After Daley: Chicago Politics in Transition. James D. Nowlan is an adjunct professor of public policy at Knox College and a Senior Fellow with the University of Illinois Institute of Government and Public Affairs. He is the author of A New Game Plan for Illinois.
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Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.