Regression: Linear Models in Statistics (Springer Undergraduate Mathematics Series)

Aug 4

Ebook Free Download | Regression: Linear Models in Statistics (Springer Undergraduate Mathematics Series) | Regression is a branch of statistics where the dependent variable of interest is modeled as a linear combination of one or more predictor variables, along with random errors. The subject is inherently two or more dimensions, so understanding the statistics in one dimension is very important.
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.

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