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This Fourth Edition includes new sections on graphs, robust estimation, expected value and the bootstrap, in addition to new material on the use of computers. The regression model is well covered, including both nonlinear and multiple regression. The chapters contain many real-life examples and are relatively self-contained, making adaptable to a variety of courses.

Essential statistics practice problems with fully-worked solutions Student Workbook to Accompany Introductory Statistics for Business and Economics offers students an invaluable resource for clarifying complex statistical concepts and calculations. Fully-worked answers to all odd-numbered problems in the textbook provide clear explanations of the approach and methods used, while chapter-by-chapter review questions provide additional practice as needed. This new fourth edition aligns with the fourth edition of the text, with new material on graphs, robust estimation, expected value, and more. Real-world examples provide immediate context, and the workbook's self-contained nature makes it adaptable to a variety of courses.

This text aims to demonstrate the value of statistics as a research tool in the social sciences, life sciences, physical sciences and engineering. Regression is one of the most important tools of the applied statistician, as well as providing a focal point for understanding related techniques.

Econometrics uses a two-part format designed for an introductory course followed by a more advanced treatment. Part I is a simple presentation of important statistical concepts; difficult interpretations and developments are provided in footnotes, starred sections, and corresponding chapters in Part II, allowing for a good appreciation of main problems without any loss of continuity in presentation. Part II requires calculus, matrix algebra, and vector geometry; chapters correspond to Part I and cover topics in greater depth. This edition now covers Box/Jenkins time series analysis, Almon lags, cross-spectral analysis, treatment of serial correlation in both the error and dependent variable, principle components, and more recent simultaneous equation techniques such as SOIV, LIVE, and FIVE.

A unified framework for developing planning and control algorithms for active sensing, with examples of applications for specific sensor technologies. Active sensor systems, increasingly deployed in such applications as unmanned vehicles, mobile robots, and environmental monitoring, are characterized by a high degree of autonomy, reconfigurability, and redundancy. This book is the first to offer a unified framework for the development of planning and control algorithms for active sensing, with examples of applications for a range of specific sensor technologies. The methods presented can be characterized as information-driven because their goal is to optimize the value of information, rather than to optimize traditional guidance and navigation objectives.

Describes methodology for formulation of various economic models and scientific management models, and covers validation, experimental design, data analysis of variance, sequential sampling, spectral analysis, variance reduction, statistical analysis, rules governing sample sizes, simulation languages, etc.

Introduction to corporate planning models; The design of computer-based planning and modeling systems; The integration of corporate planning models into the planning process; Financial planning models; Marketing forecasting models; Econometric marketing models: methodology; Econometric marketing models: applications; Production planning models; A corporate planning model; The politics of corporate model building; Corporate planning models: case studies; Appendices.

Migratory farm workers provide the extra hands that are so badly needed during the planting and harvest season in the United States. Although these workers have been essential to the American agricultural system for more than a hundred years, our knowledge of them is limited and quite fragmentary; it can be divided roughly into two types of information. On the one hand, we have the statistical data collected by various censuses and the data gathered by agricultural econ omists to study the supply of and demand for farm labor. The economic aspects of farm labor generally predominate in such material. On the other, we have the scientific studies and journalistic descriptions that report on migratory farm by using a qualitative approach. The social scientists and journalists who workers have compiled these reports lived in the labor camps and have vividly described the dismal and oppressive conditions these workers must endure. The drawback of the first type of data is that its orientation to economic problems makes it too superficial and one-sided. It fails to interrelate the diverse economic factors affecting the lives and work of all farm workers, and conse quently presents a distorted and incomplete picture of migratory farm worker life. Also, because the migratory farm workers are quite elusive and usually keep a low profIle, they are often underrepresented in such data. The data gathered by using qualitative methods have the major disadvantage of being quite limited in scope.

An updated and revised edition of the popular introduction to statistics for students of economics or business, suitable for a one- or two-semester course. Presents an approach that is generally available only in much more advanced texts, yet uses the simplest mathematics consistent with a sound presentation. This Fifth Edition includes a wealth of new problems and examples (many of them real-life problems drawn from the literature) to support the theoretical discussion. Emphasizes the regression model, including nonlinear and multiple regression. Topics covered include randomization to eliminate bias, exploratory data analysis, graphs, expected value in bidding, the bootstrap, path analysis, robust estimation, maximum likelihood estimation and Bayesian estimation and decisions.