Analyzing Linguistic Data: A Practical Introduction to Statistics using R de R. H. BaayenAnalyzing Linguistic Data: A Practical Introduction to Statistics using R de R. H. Baayen

Analyzing Linguistic Data: A Practical Introduction to Statistics using R

deR. H. Baayen

Couverture souple | 17 mars 2008 | Anglais

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description

Statistical analysis is a useful skill for linguists and psycholinguists, allowing them to understand the quantitative structure of their data. This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models. The visualization of data plays a key role, both in the initial stages of data exploration and later on when the reader is encouraged to criticize various models. Containing over 40 exercises with model answers, this book will be welcomed by all linguists wishing to learn more about working with and presenting quantitative data.
R. H. Baayen is Professor of Quantitative Linguistics at Radboud University of Nijmegen and the Max Planck Institute for Psycholinguistics, Nijmegen.
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Titre :Analyzing Linguistic Data: A Practical Introduction to Statistics using RFormat :Couverture soupleDimensions de l'article :368 pages, 9,72 × 6,85 × 0,83 poDimensions à l'expédition :9,72 × 6,85 × 0,83 poPublié le :17 mars 2008Publié par :Cambridge University PressLangue :Anglais

Les ISBN ci-dessous sont associés à ce titre :

ISBN - 10 :0521709180

ISBN - 13 :9780521709187

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Table des matières

1. An introduction to 'R'; 2. Graphic data exploration; 3. Probability distributions; 4. Basic statistical methods; 5. Clustering and classification; 6. Regression modeling; 7. Mixed models; Appendix A. Solutions to the exercises; Appendix B. Overview of 'R' functions.