Regression: Linear Models in Statistics
β N. H. Bingham, John M. Fry (auth.)
π Library
π
2010
π Springer
π English
β¦ LIBER β¦
π
Regression: Linear Models in Statistics
β Scribed by N. H. Bingham, John M. Fry
- Publisher
- Springer
- Year
- 2010
- Tongue
- English
- Leaves
- 293
- Series
- Springer Undergraduate Mathematics Series
- Edition
- 1
- Category
- Library
β¬ Acquire This Volume
No coin nor oath required. For personal study only.
β¦ Synopsis
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 Haighβs Probability Models, and T. S. Blyth & E.F. Robertsonsβ Basic Linear Algebra and Further Linear Algebra.
β¦ Table of Contents
Front Matter....Pages i-xiii
Linear Regression....Pages 1-32
The Analysis of Variance (ANOVA)....Pages 33-59
Multiple Regression....Pages 61-97
Further Multilinear Regression....Pages 99-127
Adding additional covariates and the Analysis of Covariance....Pages 129-148
Linear Hypotheses....Pages 149-162
Model Checking and Transformation of Data....Pages 163-180
Generalised Linear Models....Pages 181-201
Other topics....Pages 203-225
Back Matter....Pages 227-284
β¦ Subjects
Applications of Mathematics; Statistical Theory and Methods
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