Introduction to Linear Models and Statistical Inference

Steven J. Janke/ Frederick Tinsley

ISBN 10: 0471662593 / ISBN 13: 9780471662594

Edité par Wiley-Interscience, 2005

A propos de cet article

1st edition. 600 pages. 9.25x6.25x1.25 inches. In Stock. N° de réf. du vendeur x-0471662593

Détails bibliographiques

Titre : Introduction to Linear Models and ...

Éditeur : Wiley-Interscience

Date d'édition : 2005

Reliure : Hardcover

Etat : Brand New

A propos de ce titre

Synopsis :

Book by Janke Steven J Tinsley Frederick

Présentation de l'éditeur:

A multidisciplinary approach that emphasizes learning by analyzingreal–world data sets

This book is the result of the authors′ hands–on classroomexperience and is tailored to reflect how students best learn toanalyze linear relationships. The text begins with the introductionof four simple examples of actual data sets. These examples aredeveloped and analyzed throughout the text, and more complicatedexamples of data sets are introduced along the way. Taking amultidisciplinary approach, the book traces the conclusion of theanalyses of data sets taken from geology, biology, economics,psychology, education, sociology, and environmental science.

As students learn to analyze the data sets, they masterincreasingly sophisticated linear modeling techniques,including:
∗ Simple linear models
∗ Multivariate models
∗ Model building
∗ Analysis of variance (ANOVA)
∗ Analysis of covariance (ANCOVA)
∗ Logistic regression
∗ Total least squares

The basics of statistical analysis are developed and emphasized,particularly in testing the assumptions and drawing inferences fromlinear models. Exercises are included at the end of each chapter totest students′ skills before moving on to more advanced techniquesand models. These exercises are marked to indicate whethercalculus, linear algebra, or computer skills are needed.

Unlike other texts in the field, the mathematics underlying themodels is carefully explained and accessible to students who maynot have any background in calculus or linear algebra. Mostchapters include an optional final section on linear algebra forstudents interested in developing a deeper understanding.

The many data sets that appear in the text are available on thebook′s Web site. The MINITAB(r) software program is used toillustrate many of the examples. For students unfamiliar withMINITAB(r), an appendix introduces the key features needed to studylinear models.

With its multidisciplinary approach and use of real–world data setsthat bring the subject alive, this is an excellent introduction tolinear models for students in any of the natural or socialsciences.