Linear Models in Statistics
Bingham, N. H., Fry, John M.
2010, XIII, 284p. 50 illus..
Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.
You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.
After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.
(net)
price for USA
ISBN 978-1-84882-969-5
digitally watermarked, no DRM
Included Format: PDF and EPUB
download immediately after purchase
Softcover (also known as softback) version.
You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.
Standard shipping is free of charge for individual customers.
(net)
price for USA
ISBN 978-1-84882-968-8
free shipping for individuals worldwide
usually dispatched within 3 to 5 business days
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.
Content Level » Lower undergraduate
Keywords » ANOVA - Generalized linear model - STATISTICA - Time series - analysis of covariance - analysis of variance - general linear model - linear regression - regression
Related subjects » Applications - Statistical Theory and Methods
Get alerted on new Springer publications in the subject area of Applications of Mathematics.