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AuthorHรคrdle, Wolfgang. author
TitleXploRe โ{128}{148} Learning Guide [electronic resource] / by Wolfgang Hรคrdle, Sigbert Klinke, Marlene Mรผller
ImprintBerlin, Heidelberg : Springer Berlin Heidelberg, 2000
Connect tohttp://dx.doi.org/10.1007/978-3-642-60232-0
Descript IV, 526 p. 86 illus. online resource

SUMMARY

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 staยญ 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 inยญ troduces graphics. The graphics are interactive and cover a wide range of disยญ plays of statistical data


CONTENT

I: First Steps -- 1 Getting Started -- 2. Descriptive Statistics -- 3 Graphics -- 4 Regression Methods -- 5 Teachware Quantlets -- II: Statistical Libraries -- 6 Smoothing Methods -- 7 Generalized Linear Models -- 8 Neural Networks -- 9 Time Series -- 10 Kalman Filtering -- 11 Finance -- 12 Microeconometrics and Panel Data -- 13 Extreme Value Analysis -- 14 Wavelets -- III: Programming -- 15 Reading and Writing Data -- 16 Matrix Handling -- 17 Quantlets and Quantlibs


Statistics Statistics Statistics and Computing/Statistics Programs



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