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 statistical 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 introduces graphics. The graphics are interactive and cover a wide range of displays of statistical data. The regression chapter guides the user first to linear scatter-plot regression and then to multiple regression and nonlinear models. The teachware quantlets comprise a basic set of interactive, illustrative examples in introductory statistics. For the student, they provide an opportunity to understand some important basic concepts in statistics through trial and error. For the teacher, they can aid the instruction by allowing the students to independently work on exploratory examples at their own pace. Additionally, with a modicum of understanding of the XploRe programming language, the teacher can modify these examples to fit his/her own preferences.
In the second part, nonparametric smoothing methods are introduced. They provide a flexible analysis tool, often based on interactive graphical data representation. The focus there is on function estimation by kernel smoothing. More refined tools are the neural networks. They consist of many simple processing units (neurons) that are connected by communication channels. This guide shows how to adapt such networks to real data. The time series quantlets cover the standard analysis techniques and offer nonlinear methods for modern time series data analysis. The Kalman filtering module is a set of recursive quantlets based on the construction of an estimate from the previous periods and observations available at the time. The finance quantlib considers the statistical analysis of financial markets. This quantlib offers functions to predict, to simulate and to estimate finance data series such as, for example, stock returns, to determine option prices and to evaluate different scenarios (e.g. for portfolio strategies).
The microeconometrics section introduces the tools available in XploRe for analyzing micro data, i.e. data sets consisting of observations on individual units, such as persons, households or firms. The quantlib metrics provides the statistical tools such as panel analysis for analyzing observed individual behavior. The analysis of risk is one of the primary objectives of insurance firms and the profit and loss analysis on financial markets. It is also applied to flood discharges and high concentration of air pollutants. The `value at risk' and the extreme (upper or lower) parts of a sample are estimated and analyzed in this module. Wavelets comprise a powerful tool which can be used for a wide range of applications, namely describing a signal for parsimonious representation, for data compression, for smoothing and image denoising and for jump detection. This guide introduces the wavelet quantlib twave .
XploRe offers a large variety of commands and tools for creating and manipulating multidimensional objects called matrices and lists. The last part of this guide presents the basic instructions for matrix handling and illustrates further topics in matrix algebra and list handling with XploRe . The quantlet construction technique and the generation of a user defined APSS are the topics of the final chapter on quantlets and quantlibs.
XploRe and this learning guide have benefited at several stages from cooperation with many colleagues and students. We want to mention in particular: Gökhan Aydinli, Silke Baars, Alexander Benkwitz, Marco Bianchi, Rong Chen, Vila Co, Stefan Daske, Sven Denkert, Ulrich Dorazelski, Michaela Dranganska, Dorit Feldmann, Jörg Feuerhake, Frank Geppert, Birgit Grund, Janet Grassmann, Christian Hafner, Susanne Hannappel, Nicholas Hengartner, Holger Jakob, Christopher Kath, Jussi Klemelä, Petra Korndörfer, Gunnar Krause, Katharina Kröl, Stephan Lauer, Pascal Lavergne, Michel Lejeune, Torsten Kleinow, Thomas Kötter, Thomas Kühn, Hua Liang, Steve Marron, Danilo Mercurio, Hans-Joachim Mucha, Fabian Nötzel, Isabel Proenca, Andreas Ruppin, Jerome Saracco, Swetlana Schmelzer, Levon Shahbaghyan, Hizir Sofyan, Hans Gerhard Strohe, Wolfgang Stockmeyer, Elke Tscharf, Berwin Turlach, Rodrigo Witzel, Lijian Yang, Kerstin Zanter.
We owe special thanks to M. Feith, Springer Verlag, for professional editorship and continuous encouragement at several critical junctions. We gratefully acknowledge the support of the Deutsche Forschungsgemeinschaft, Sonderforschungsbereich 373 ``Quantifikation und Simulation Ökonomischer Prozesse''.
Berlin, May 1999
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