SAS for Forecasting Time Series, Third Edition

SAS for Forecasting Time Series, Third Edition

Author: John C. Brocklebank, Ph.D.

Publisher: SAS Institute

Published: 2018-03-14

Total Pages: 616

ISBN-13: 1629605441

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To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications. Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures. Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods: ARIMA models Vector autoregressive models Exponential smoothing models Unobserved component and state-space models Seasonal adjustment Spectral analysis Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition: The ARIMA procedure The AUTOREG procedure The VARMAX procedure The ESM procedure The UCM and SSM procedures The X13 procedure The SPECTRA procedure SAS Forecast Studio Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs. This book is part of the SAS Press program.


Introduction to Statistical Quality Control

Introduction to Statistical Quality Control

Author: Douglas C. Montgomery

Publisher: John Wiley & Sons

Published: 2019-12-30

Total Pages: 773

ISBN-13: 1119657113

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"Once solely the domain of engineers, quality control has become a vital business operation used to increase productivity and secure competitive advantage. Introduction to Statistical Quality Control offers a detailed presentation of the modern statistical methods for quality control and improvement. Thorough coverage of statistical process control (SPC) demonstrates the efficacy of statistically-oriented experiments in the context of process characterization, optimization, and acceptance sampling, while examination of the implementation process provides context to real-world applications. Emphasis on Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) provides a strategic problem-solving framework that can be applied across a variety of disciplines.Adopting a balanced approach to traditional and modern methods, this text includes coverage of SQC techniques in both industrial and non-manufacturing settings, providing fundamental knowledge to students of engineering, statistics, business, and management sciences.A strong pedagogical toolset, including multiple practice problems, real-world data sets and examples, provides students with a solid base of conceptual and practical knowledge."--


Mathematical Statistics with Applications in R

Mathematical Statistics with Applications in R

Author: Kandethody M. Ramachandran

Publisher: Elsevier

Published: 2014-09-14

Total Pages: 825

ISBN-13: 012417132X

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Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner.This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students.Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. - Step-by-step procedure to solve real problems, making the topic more accessible - Exercises blend theory and modern applications - Practical, real-world chapter projects - Provides an optional section in each chapter on using Minitab, SPSS and SAS commands - Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods


Applied Linear Statistical Models

Applied Linear Statistical Models

Author: Michael H. Kutner

Publisher: McGraw-Hill/Irwin

Published: 2005

Total Pages: 1396

ISBN-13: 9780072386882

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Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.


The Practice of Statistics in the Life Sciences

The Practice of Statistics in the Life Sciences

Author: Brigitte Baldi

Publisher: Macmillan Higher Education

Published: 2013-12-15

Total Pages: 756

ISBN-13: 1464133212

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This remarkably engaging textbook gives biology students an introduction to statistical practice all their own. It covers essential statistical topics with examples and exercises drawn from across the life sciences, including the fields of nursing, public health, and allied health. Based on David Moore’s The Basic Practice of Statistics, PSLS mirrors that #1 bestseller’s signature emphasis on statistical thinking, real data, and what statisticians actually do. The new edition includes new and updated exercises, examples, and samples of real data, as well as an expanded range of media tools for students and instructors.


Fundamentals of Biostatistics

Fundamentals of Biostatistics

Author: Bernard Rosner

Publisher: Cengage Learning

Published: 2015-07-29

Total Pages: 0

ISBN-13: 9781305268920

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Bernard Rosner's FUNDAMENTALS OF BIOSTATISTICS is a practical introduction to the methods, techniques, and computation of statistics with human subjects. It prepares students for their future courses and careers by introducing the statistical methods most often used in medical literature. Rosner minimizes the amount of mathematical formulation (algebra-based) while still giving complete explanations of all the important concepts. As in previous editions, a major strength of this book is that every new concept is developed systematically through completely worked out examples from current medical research problems. Most methods are illustrated with specific instructions as to implementation using software either from SAS, Stata, R, Excel or Minitab. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.


Statistics for Research

Statistics for Research

Author: Shirley Dowdy

Publisher: John Wiley & Sons

Published: 2011-09-26

Total Pages: 646

ISBN-13: 0471477427

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Praise for the Second Edition "Statistics for Research has other fine qualities besides superior organization. The examples and the statistical methods are laid out with unusual clarity by the simple device of using special formats for each. The book was written with great care and is extremely user-friendly."—The UMAP Journal Although the goals and procedures of statistical research have changed little since the Second Edition of Statistics for Research was published, the almost universal availability of personal computers and statistical computing application packages have made it possible for today's statisticians to do more in less time than ever before. The Third Edition of this bestselling text reflects how the changes in the computing environment have transformed the way statistical analyses are performed today. Based on extensive input from university statistics departments throughout the country, the authors have made several important and timely revisions, including: Additional material on probability appears early in the text New sections on odds ratios, ratio and difference estimations, repeated measure analysis, and logistic regression New examples and exercises, many from the field of the health sciences Printouts of computer analyses on all complex procedures An accompanying Web site illustrating how to use SAS® and JMP® for all procedures The text features the most commonly used statistical techniques for the analysis of research data. As in the earlier editions, emphasis is placed on how to select the proper statistical procedure and how to interpret results. Whenever possible, to avoid using the computer as a "black box" that performs a mysterious process on the data, actual computational procedures are also given. A must for scientists who analyze data, professionals and researchers who need a self-teaching text, and graduate students in statistical methods, Statistics for Research, Third Edition brings the methodology up to date in a very practical and accessible way.