Introductory Statistics

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Bol Partner 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. 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.

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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. 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.


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