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The text is designed to be quite flexible. A typical one-semester course would cover Chapters 1-10, the core of classical statistical inference, and perhaps two or three additional chapters that may be selected to satisfy specific course objectives. The accompanying chart indicates the interdependence of the chapters. In a two-semester sequence, we suggest Chapters 1-10, the linear models chapters (Chapters 13-17), including analysis of variance (Chapter 12), and a selection of two or three additional chapters. We have included what we feel arc the most important special statistical methods in this text. Nonparametric statistical methods (Chapters 19 and 20) are becoming increasingly important in business and social science applications, where, all too often, the data do not support the appropriate classical statistics assumptions. Forecasting (Chapter 17) also has become an increasingly used statistical tool in recent years. Most financial institutions depend heavily on the accuracy of forecasts for their success. Statistical decision theory (Chapters 21-23) is finding an important place in business applications of statistics, where prior information relevant to a current problem is available and may be combined with current information for decision-making purposes