Regression : linear models in statistics
Regression is the branch of Statistics in which a dependent variable of interest is modelled as a linear combination of one or more predictor variables, together with a random error. The subject is inherently two- or higher- dimensional, thus an understanding of Statistics in one dimension is essent...
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| Auteurs principaux: | , |
|---|---|
| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
London :
Springer London
[20..].
Cham : Springer Nature |
| Édition: | 1st ed. 2010. |
| Collection: | Springer Undergraduate Mathematics Series
|
| 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: | • Regression, linear models in statistics, N. H. Bingham, John M. Fry, 2010, London, Springer, 1 vol. (XIII-284 p.), Springer Undergraduate Mathematics Series, 978-1-84882-968-8 • Regression, Texte imprimé, 9781848829701 |
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| 100 | 1 | |a Bingham, Nicholas H., |d 1945- | |
| 245 | 1 | 0 | |a Regression : |b linear models in statistics |c by N. H. Bingham, John M. Fry. |
| 250 | |a 1st ed. 2010. | ||
| 260 | |a London : |b Springer London. | ||
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| 490 | 0 | |a Springer Undergraduate Mathematics Series |x 2197-4144 | |
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| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a Linear Regression The Analysis of Variance (ANOVA) Multiple Regression Further Multilinear Regression Adding additional covariates and the Analysis of Covariance Linear Hypotheses Model Checking and Transformation of Data Generalised Linear Models Other topics | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a Regression is the branch of Statistics in which a dependent variable of interest is modelled as a linear combination of one or more predictor variables, together with a random error. The subject is inherently two- or higher- dimensional, thus an understanding of Statistics in one dimension is essential. Regression: Linear Models in Statistics fills the gap between introductory statistical theory and more specialist sources of information. In doing so, it provides the reader with a number of worked examples, and exercises with full solutions. The book begins with simple linear regression (one predictor variable), and analysis of variance (ANOVA), and then further explores the area through inclusion of topics such as multiple linear regression (several predictor variables) and analysis of covariance (ANCOVA). The book concludes with special topics such as non-parametric regression and mixed models, time series, spatial processes and design of experiments. Aimed at 2nd and 3rd year undergraduates studying Statistics, Regression: Linear Models in Statistics requires a basic knowledge of (one-dimensional) Statistics, as well as Probability and standard Linear Algebra. Possible companions include John Haigh s Probability Models, and T. S. Blyth & E.F. Robertsons Basic Linear Algebra and Further Linear Algebra | ||
| 650 | |a Statistique mathématique | ||
| 650 | |a Analyse de régression | ||
| 650 | |a Modèles linéaires (statistique) | ||
| 700 | 1 | |a Fry, John M. |4 aut | |
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