Regression: Linear Models in Statistics
商品資訊
系列名：Springer Undergraduate Mathematics Series
ISBN13：9781848829688
出版社：Springer Verlag
作者：N. H. Bingham; John M. Fry
出版日：2010/09/30
裝訂：平裝
規格：23.5cm*15.2cm*1.9cm (高/寬/厚)
定價
：NT$ 2498 元無庫存，下單後進貨(到貨天數約30-45天)
下單可得紅利積點：74 點
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The Springer Undergraduate Mathematics Series (SUMS) is designed for undergraduates in the mathematical sciences. From core foundational material to final year topics, SUMS books take a fresh and modern approach and are ideal for self-study or for a one-or two-semester course. Each book includes numerous examples, problems and fully-worked solutions. N. H. Bingham. John M. Fry Regression
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 requries 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.
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 requries 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.
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