One of the most popular introductory texts in its field, Statistics for Technology: A Course in Applied Studies presents the range of statistical methods commonly used in science, social science, and engineering.
The mathematics are simple and straightforward; statistical concepts are explained carefully; and real-life (rather than contrived) examples are used throughout the chapters.
Divided into three parts, the Introduction describes some simple methods of summarizing data. Theory examines the basic concepts and theory of statistics. Applications covers the planning and procedures of...

This book illuminates the complex process of problem solving, including formulating the problem, collecting and analyzing data, and presenting the conclusions.

From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space modelling to multivariate methods and including recent arrivals, such as GARCH models, neural networks, and cointegrated models.
The author compares the more important methods in terms of their theoretical inter-relationships and their practical...

Time-series analysis is an area of statistics which is of particular interest at the present time. Time series arise in many different areas, ranging from marketing to oceanography, and the analysis of such series raises many problems of both a theoretical and practical nature. I first became interested in the subject as a postgraduate student at Imperial College, when I attended a stimulating course of lectures on time-series given by Dr. (now Professor) G. M. Jenkins. The subject has fascinated me ever since. Several books have been written on theoretical aspects of time-series analysis....

This book provides an introduction to the analysis of multivariate data. It should be suitable for statisticians and other research workers who are familiar with basic probability theory and elementary inference, and also have a basic grounding in matrix algebra. The book should also be suitable as a text for undergraduate and postgraduate statistics courses on multivariate analysis. The book covers a wider range oftopics than some other books in this area. It deals with preliminary data analysis, principal component and factor analysis and traditional normal-theory material. It also covers...