Regression: Linear Models in Statistics
Language: English
Published by Springer Verlag GmbH, 2010
- Softcover
- Used
Seller: Libro Co. Italia Srl, San Casciano Val di Pesa, FI, ItalyLibro Co. Italia Srl
AbeBooks seller since July 29, 2024
Condition: Used - Fine
£ 31.21
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Add to basketItem description from seller
Heidelberg, 2010; pp. 300.
Seller Inventory # 1660996
- Title
- Regression: Linear Models in Statistics
- Author
- Fry, John M
- Publisher
- Springer Verlag GmbH
- Publication year
- 2010
- Condition
- fine
- Book Type
- Libro
- Binding
- Brossura
- Language
- English
- ISBN 10
- 184882968X
- ISBN 13
- 9781848829688
- Item weight
- 1.44 kilograms
- Series
- Book 17 of 90: Springer Undergraduate Mathematics
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
Libro Co. Italia Srl
San Casciano Val di Pesa, FI, Italy
AbeBooks seller since July 29, 2024
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