If you need to analyze a data set using a parametric nonlinear regression model, if you are not on familiar terms with statistics and software, and if you make do with S-PLUS, this book is for you. In each chapter we ...
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ISBN:
(数字)9781475725230
If you need to analyze a data set using a parametric nonlinear regression model, if you are not on familiar terms with statistics and software, and if you make do with S-PLUS, this book is for you. In each chapter we start by presenting practical examples. We then describe the problems posed by these examples in terms of statistical problems, and we demonstrate how to solve these problems. Finally, we apply the proposed methods to the example data sets. You will not find any mathematical proofs here. Rather, we try when possible to explain the solutions using intuitive arguments. This is really a cook book. Most of the methods proposed in the book are derived from classical nonlinear regression theory, but we have also made attempts to provide you with more modern methods that have proved to perform well in practice. Although the theoretical grounds are not developed here, we give, when appropriate, some technical background using a sans serif type style. You can skip these passages if you are not interested in this information. The first chapter introduces several examples, from experiments in agron omy and biochemistry, to which we will return throughout the book. Each example illustrates a different problem, and we show how to methodically handle these problems by using parametric nonlinear regression models.
It is generally accepted that training in statistics must include some exposure to the mechanics of computational statistics. This learning guide is intended for beginners in computer-aided statistical data analysis. ...
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ISBN:
(数字)9783642602320
ISBN:
(纸本)9783540662075
It is generally accepted that training in statistics must include some exposure to the mechanics of computational statistics. This learning guide is intended for beginners in computer-aided statistical data analysis. The prerequisites for XploRe - the statistical computing environment - are an introductory course in statistics or mathematics. The reader of this book should be familiar with basic elements of matrix algebra and the use of HTML browsers. This guide is designed to help students to XploRe their data, to learn (via data interaction) about statistical methods and to disseminate their findings via the HTML outlet. The XploRe APSS (Auto Pilot Support System) is a powerful tool for finding the appropriate statistical technique (quantlet) for the data under analysis. Homogeneous quantlets are combined in XploRe into quantlibs. The XploRe language is intuitive and users with prior experience of other sta tistical programs will find it easy to reproduce the examples explained in this guide. The quantlets in this guide are available on the CD-ROM as well as on the Internet. The statistical operations that the student is guided into range from basic one-dimensional data analysis to more complicated tasks such as time series analysis, multivariate graphics construction, microeconometrics, panel data analysis, etc. The guide starts with a simple data analysis of pullover sales data, then in troduces graphics. The graphics are interactive and cover a wide range of dis plays of statistical data.
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