A Modern introduction to probability and statistics : understanding why and how
Probability and Statistics are studied by most science students, usually as a second- or third-year course. Many current texts in the area are just cookbooks and, as a result, students do not know why they perform the methods they are taught, or why the methods work. The strength of this book is tha...
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
London :
Springer London
[20..].
Cham : Springer Nature |
| Édition: | 1st ed. 2005. |
| Collection: | Springer Texts in Statistics
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| Sujets: | |
| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • A modern introduction to probability and statistics, understanding why and how, F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester, 2005, New York, Springer, 1 vol. (XV-487 p.), Springer texts in statistics, 1-85233-896-2 • A Modern Introduction to Probability and Statistics, Texte imprimé, 9781848008069 |
Table des matières:
- Why probability and statistics? Outcomes, events, and probability Conditional probability and independence Discrete random variables Continuous random variables Simulation Expectation and variance Computations with random variables Joint distributions and independence Covariance and correlation More computations with more random variables The Poisson process The law of large numbers The central limit theorem Exploratory data analysis: graphical summaries Exploratory data analysis: numerical summaries Basic statistical models The bootstrap Unbiased estimators Efficiency and mean squared error Maximum likelihood The method of least squares Confidence intervals for the mean More on confidence intervals Testing hypotheses: essentials Testing hypotheses: elaboration The t-test Comparing two samples