# Single equation linear regression analysis, Regression

Kostenfreier Versand für Individualkunden weltweit Gewöhnlich versandfertig in Werktagen. Über dieses Lehrbuch Regression is the branch single equation linear regression analysis 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 variableand analysis of variance ANOVAand 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.

Product information Description Combining single-equation linear regression analysis with intuitive real-world examples and exercises is key to the success of Using Econometrics. Clear writing and a practical approach to econometrics that eschews the use of complex matrix algebra and calculus evidence this essential text's accessibility. As the subtitle, A Practical Guide, implies, this book is aimed not only at beginning econometrics students, but also at regression users looking for a refresher and at experienced practitioners who want a convenient reference.

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. The fully-worked examples and solutions to the exercises are detailed.

Includes a useful index and bibliography. Summing Up: Recommended.

Upper-division undergraduates, graduate students, and professionals. Chopra, Choice, Vol.