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03242nam a22004815i 4500 |
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978-1-84882-969-5 |
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DE-He213 |
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20151103123723.0 |
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cr nn 008mamaa |
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100917s2010 xxk| s |||| 0|eng d |
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|a 9781848829695
|9 978-1-84882-969-5
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|a 10.1007/978-1-84882-969-5
|2 doi
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|a T57-57.97
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|a MAT003000
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|a 519
|2 23
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|a Bingham, N. H.
|e author.
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|a Regression
|h [electronic resource] :
|b Linear Models in Statistics /
|c by N. H. Bingham, John M. Fry.
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|a London :
|b Springer London :
|b Imprint: Springer,
|c 2010.
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|a XIII, 284 p. 50 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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| 490 |
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|a Springer Undergraduate Mathematics Series,
|x 1615-2085
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| 505 |
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|a Linear Regression -- The Analysis of Variance (ANOVA) -- Multiple Regression -- Further Multilinear Regression -- Adding additional covariates and the Analysis of Covariance -- Linear Hypotheses -- Model Checking and Transformation of Data -- Generalised Linear Models -- Other topics.
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| 520 |
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|a 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.
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|a Mathematics.
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| 650 |
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|a Applied mathematics.
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| 650 |
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|a Engineering mathematics.
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| 650 |
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|a Statistics.
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| 650 |
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|a Mathematics.
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| 650 |
2 |
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|a Applications of Mathematics.
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| 650 |
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4 |
|a Statistical Theory and Methods.
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| 700 |
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|a Fry, John M.
|e author.
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|a SpringerLink (Online service)
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9781848829688
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| 830 |
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|a Springer Undergraduate Mathematics Series,
|x 1615-2085
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/978-1-84882-969-5
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-SMA
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| 950 |
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|a Mathematics and Statistics (Springer-11649)
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