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.
General
Imprint: |
Springer London
|
Country of origin: |
United Kingdom |
Series: |
Springer Undergraduate Mathematics Series |
Release date: |
September 2010 |
First published: |
2010 |
Authors: |
N. H. Bingham
• John M Fry
|
Dimensions: |
235 x 155 x 16mm (L x W x T) |
Format: |
Paperback
|
Pages: |
284 |
Edition: |
Edition. |
ISBN-13: |
978-1-84882-968-8 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Applied mathematics >
General
|
LSN: |
1-84882-968-X |
Barcode: |
9781848829688 |
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