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

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


Regression is the department of facts within which a established variable of curiosity is modelled as a linear blend of 1 or extra predictor variables, including a random blunders. the topic is inherently - or larger- dimensional, hence an figuring out of records in a single measurement is essential.

Regression: Linear versions in Statistics fills the distance among introductory statistical idea and extra professional resources of knowledge. In doing so, it presents the reader with a couple of labored examples, and workouts with complete solutions.

The e-book starts off with uncomplicated linear regression (one predictor variable), and research of variance (ANOVA), after which extra explores the realm via inclusion of themes comparable to a number of linear regression (several predictor variables) and research of covariance (ANCOVA). The e-book concludes with distinctive issues equivalent to non-parametric regression and combined versions, time sequence, spatial approaches and layout of experiments.

Aimed at 2d and third 12 months undergraduates learning Statistics, Regression: Linear versions in Statistics calls for a easy wisdom of (one-dimensional) data, in addition to chance and conventional Linear Algebra. attainable partners contain John Haigh’s chance versions, and T. S. Blyth & E.F. Robertsons’ simple Linear Algebra and extra Linear Algebra.

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