Multivariate statistics exercises and solutions / [electronic resource] :

• 作者： Hardle, Wolfgang Karl.
• 其他作者：
  • Hlavka, Zdenek.
  • SpringerLink (Online service)
• 出版： Berlin, Heidelberg : Springer Berlin Heidelberg :Imprint: Springer
• 版本：2nd ed.
• 主題： Multivariate analysis , Statistics , Statistical Theory and Methods. , Computational Mathematics and Numerical Analysis. , Visualization , Data Mining and Knowledge Discovery. , Computational intelligence
• ISBN： 9783642360053 (electronic bk.) 、 9783642360046 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Part I Descriptive Techniques: Comparison of Batches -- Part II Multivariate Random Variables: A Short Excursion into Matrix Algebra -- Moving to Higher -- Multivariate -- Theory of the Multinormal -- Theory of Estimation -- Part III Multivariate Techniques: Regression Models -- Variable Selection -- Decomposition of Data Matrices by Factors -- Principal Component Analysis -- Factor Analysis -- Cluster Analysis -- Discriminant Analysis -- Correspondence Analysis -- Canonical Correlation Analysis -- Multidimensional Scaling -- Conjoint Measurement Analysis -- Applications in Finance -- Highly Interactive, Computationally Intensive Techniques -- Data Sets -- References -- Index.
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• 摘要註: The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether more than 250 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R language. All R codes and data sets may be downloaded via the quantlet download center www.quantlet.org or via the Springer webpage. For interactive display of low-dimensional projections of a multivariate data set, we recommend GGobi.
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