This textbook provides students with a basic knowledge of the principles and procedures of applied statistics. Maintaining its clarity and comprehensive coverage from previous editions, the third edition of Principles and Procedures of Statistics A Biometrical Approach includes modern topics, the use of computer output and analysis from statistical software, and updated real-world data sets. This text assumes no knowledge of calculus.

Principles and Procedures of Statistics: A Biometrical Approach

Science & Math

Dickey, David A.

Steel, Robert George Douglas

Torrie, James Hiram

Mathematics

Mcgraw-Hill College

Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to statistical methods and a thoeretical linear models course. Applied Regression Analysis emphasizes the concepts and the analysis of data sets. It provides a review of the key concepts in simple linear regression, matrix operations, and multiple regression. Methods and criteria for selecting regression variables and geometric interpretations are discussed. Polynomial, trigonometric, analysis of variance, nonlinear, time series, logistic, random effects, and mixed effects models are also discussed. Detailed case studies and exercises based on real data sets are used to reinforce the concepts. The data sets used in the book are available on the Internet.

Applied Regression Analysis: A Research Tool (Springer Texts in Statistics)

Rawlings, John O.

Pantula, Sastry G.

Springer

Easy-to-read and comprehensive, this book shows how the SAS System performs multivariate time series analysis and features the advanced SAS procedures STATSPACE, ARIMA, and SPECTRA. The interrelationship of SAS/ETS procedures is demonstrated with an accompanying discussion of how the choice of a procedure depends on the data to be analysed and the reults desired. Other topics covered include detecting sinusoidal components in time series models and performing bivariate corr-spectral analysis and comparing the results with the standard transfer function methodology. The authors? unique approach to integrating students in a variety of disciplines and industries. Emphasis is on correct interpretation of output to draw meaningful conclusions. The volume, co-pubished by SAS and JWS, features both theory and practicality, and accompanies a soon-to-be extensive library of SAS hands-on manuals in a multitude of statistical areas. The book can be used with a number of hardware-specific computing machines including CMS, Mac, MVS, Opem VMS Alpha, Opmen VMS VAX, OS/390, OS/2, UNIX, and Windows.

SAS for Forecasting Time Series

Brocklebank, John C.

WA (Wiley-SAS)

This book shows how the SAS System performs multivariate time series analysis and features the advanced SAS procedures STATESPACE, ARIMA, and SPECTRA. The interrelationship of SAS/ETS procedures is demonstrated with an accompanying discussion of how the choice of a procedure depends on the data to be analyzed and results desired. Using this book you will learn to model and forecast simple autoregressive (AR) processes using PROC ARIMA and use the STATESPACE procedure and the AR model to do state space modeling. Other topics covered include detecting sinusoidal components in time series models and performing bivariate cross-spectral analysis and comparing the results with the standard transfer function methodology. 
Supports releases 6.09 and higher of SAS software.

Sas System for Forecasting Time Series

Computers & Technology

Brocklebank, John C., Ph.D.

Networking & Cloud Computing

SAS Inst

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