Regression : linear models in statistics 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 requires a basic knowledge of (one dimensional) Statistics as well as Probability and Standard Linear Algebra. Possible companions include John Haighs Probability Models and T. S. Blyth & E. F. Robertsons Basic Linear Algebra and Further Linear Algebra. BOOK JACKET