This text will thoroughly update the existing literature on atomic physics. Intended to accompany an advanced undergraduate course in atomic physics, the book will lead the students up to the latest advances and the applications to Bose-Einstein Condensation of atoms, matter-wave inter-ferometry and quantum computing with trapped ions. The elementary atomic physics covered in the early chapters should be accessible to undergraduates when they are first introduced to the subject. To complement the usual quantum mechanical treatment of atomic structure the book strongly emphasizes the experimental basis of the subject, especially in the later chapters. It includes ample tutorial material (examples, illustrations, chapter summaries, graded problem sets).

Afrika se eeue oue magiesheid en hartstog vorm die agtergrond van die intiem verweefde komedie en tragedie in hierdie roman. Dit is gebaseer op die historiese figuur Kupido Kakkerlak (c. 1760 – c. 1825) se lewe. Maar die raaisel van 'n mens se lewe word ten beste met die verbeelding benader. Kupido is nie gebore nie, 'n arend het hom gebring. In daardie tyd het ook 'n bidsprinkaan verskyn - 'n teken van die gode. Wonderwerke het soos optelgoed op die aarde rondgele. Nadat Heitsi-Eibib in die veld aan Kupido verskyn het, raak hy as baasjagter bekend tot ver buite die grense van die boereplaas waar hy grootword. Hy is ook die drinker, leuenaar, minnaar en bakleier van die kontrei. Onder die sendeling dr. Van der Kemp se invloed word hy egter die eerste Khoi wat as sendeling aan die Kaap die Goeie Hoop georden is.

The essential introduction to the theory and application of linear models--now in a valuable new edition

Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed.

Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models.

This modern Second Edition features:

New chapters on Bayesian linear models as well as random and mixed linear models

Expanded discussion of two-waymodels with empty cells

Additional sections on the geometry of least squares

Updated coverage of simultaneous inference

The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS(R) code for all numerical examples.

Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.