Data Science students and practitioners want to find a forecast that “works” and don’t want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject. 
This book is an accessible guide that doesn’t require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed. 
Features: Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models. Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy. Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank. There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.

Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)

Business & Money

Woodward, Wayne A.

Robertson, Stephen

Sadler, Bivin Philip

Education & Reference

Chapman and Hall/CRC

SAS Essentials provides an introduction to SAS statistical software, the premiere statistical data analysis tool for scientific research. Through its straightforward approach, the text presents SAS with step-by-step examples. With over fifteen years of teaching SAS courses and over fifty combined years of teaching and consulting by the authors, this valuable reference presents data manipulation and statistical techniques, including a website with examples. This textbook is essential for teachers because the chapters are self-contained and may be used accordingly to the teacher?s preference, whether for a one-semester or two-semesters course.

SAS Essentials: A Guide to Mastering SAS for Research

Elliott, Alan C.

Computers & Technology

Jossey-Bass

Applied Time Series Analysis with R

Virtually any random process developing chronologically can be viewed as a time series. In economics closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Analysis with R, Second Edition includes examples across a variety of fields, develops theory, and provides an R-based software package to aid in addressing time series problems in a broad spectrum of fields. The material is organized in an optimal format for graduate students in statistics as well as in the natural and social sciences to learn to use and understand the tools of applied time series analysis.\nFeatures\nGives readers the ability to actually solve significant real-world problems\nAddresses many types of nonstationary time series and cutting-edge methodologies\nPromotes understanding of the data and associated models rather than viewing it as the output of a "black box"\nProvides the R package tswge available on CRAN which contains functions and over 100 real and simulated data sets to accompany the book. Extensive help regarding the use of tswge functions is provided in appendices and on an associated website.\nOver 150 exercises and extensive support for instructors\nThe second edition includes additional real-data examples, uses R-based code that helps students easily analyze data, generate realizations from models, and explore the associated characteristics. It also adds discussion of new advances in the analysis of long memory data and data with time-varying frequencies (TVF).

Science & Math

Gray, Henry L.

Mathematics

CRC-Press

A step-by-step introduction to using SAS® statistical software as a foundational approach to data analysis and interpretation Presenting a straightforward introduction from the ground up, SAS® Essentials: Mastering SAS for Data Analytics, Second Edition illustrates SAS using hands-on learning techniques and numerous real-world examples. Keeping different experience levels in mind, the highly-qualified author team has developed the book over 20 years of teaching introductory SAS courses. Divided into two sections, the first part of the book provides an introduction to data manipulation, statistical techniques, and the SAS programming language. The second section is designed to introduce users to statistical analysis using SAS Procedures. Featuring self-contained chapters to enhance the learning process, the Second Edition also includes: <ul>
<li>Programming approaches for the most up-to-date version of the SAS platform including information on how to use the SAS University Edition</li> <li>Discussions to illustrate the concepts and highlight key fundamental computational skills that are utilized by business, government, and organizations alike</li> <li>New chapters on reporting results in tables and factor analysis</li> <li>Additional information on the DATA step for data management with an emphasis on importing data from other sources, combining data sets, and data cleaning</li> <li>Updated ANOVA and regression examples as well as other data analysis techniques</li> <li>A companion website with the discussed data sets, additional code, and related PowerPoint® slides</li> </ul>
SAS Essentials: Mastering SAS for Data Analytics, Second Edition is an ideal textbook for upper-undergraduate and graduate-level courses in statistics, data analytics, applied SAS programming, and statistical computer applications as well as an excellent supplement for statistical methodology courses. The book is an appropriate reference for researchers and academicians who require a basic introduction to SAS for statistical analysis and for preparation for the Basic SAS Certification Exam.

SAS Essentials: Mastering SAS for Data Analytics

Software

wiley

SAS ESSENTIALS 
Valuable step-by-step introduction to using SAS® statistical software as a foundational approach to data analysis and interpretation 
Presenting a straightforward introduction from the ground up, SAS® Essentials illustrates SAS using hands-on learning techniques and numerous real-world examples; keeping different experience levels in mind, the highly qualified author team has developed the book over 25 years of teaching introductory SAS courses. 
This book introduces data manipulation, statistical techniques, and the SAS programming language, including SAS macros, reporting results in tables, and factor analysis, as well as sections on character functions, variable manipulation, and merging, appending, and updating files. It features self-contained chapters to enhance the learning process and includes programming approaches for the latest version of the SAS platform. 
The Third Edition has been updated with expanded examples, a new chapter introducing PROC SQL as well as new end-of-chapter exercises. The Third Edition also includes a companion website with data sets, additional code, notes on SAS programming, functions, ODS, PROC SQL, and SAS Arrays, along with solutions for instructors. 
Specific sample topics covered in SAS® Essentials include: Getting data into SAS, reading, writing, and importing data, preparing data for analysis, preparing to use SAS procedures, and controlling output using ODS Techniques for creating, editing, and debugging SAS programs, cleaning up messy data sets, and manipulating data using character, time, and numeric functions Other essential computational skills that are utilized by business, government, and organizations alike, and DATA step for data management Using PROC to analyze data, including evaluating quantitative data, analyzing counts and tables, comparing means using T-Tests, correlation and regression, and analysis of variance, nonparametric analysis, logistic regression, factor analysis, and creating custom graphs and reports. 
SAS® Essentials is a fundamental study resource for professionals preparing for the SAS Base Certification Exam, as well as an ideal textbook for courses in statistics, data analytics, applied SAS programming, and statistical computer applications.

Statistical Analysis Quick Reference Guidebook: With SPSS Examples is a practical "cut to the chase" handbook that quickly explains the when, where, and how of statistical data analysis as it is used for real-world decision-making in a wide variety of disciplines. In this one-stop reference, authors Alan C. Elliott and Wayne A. Woodward provide succinct guidelines for performing an analysis, avoiding pitfalls, interpreting results, and reporting outcomes.

Statistical Analysis Quick Reference Guidebook: With SPSS Examples

Politics & Social Sciences

Social Sciences

Sage Publications, Inc.

The updated Second Edition of Alan C. Elliott and Wayne A. Woodward’s "cut to the chase" IBM SPSS guide quickly explains the when, where, and how of statistical data analysis as it is used for real-world decision making in a wide variety of disciplines. This one-stop reference provides succinct guidelines for performing an analysis using SPSS software, avoiding pitfalls, interpreting results, and reporting outcomes. Written from a practical perspective, IBM SPSS by Example, Second Edition provides a wealth of information―from assumptions and design to computation, interpretation, and presentation of results―to help users save time, money, and frustration.

IBM SPSS by Example: A Practical Guide to Statistical Data Analysis

Virtually any random process developing chronologically can be viewed as a time series. In economics closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Analysis with R, Second Edition includes examples across a variety of fields, develops theory, and provides an R-based software package to aid in addressing time series problems in a broad spectrum of fields. The material is organized in an optimal format for graduate students in statistics as well as in the natural and social sciences to learn to use and understand the tools of applied time series analysis. Features <ul>
<li>Gives readers the ability to actually solve significant real-world problems</li> <li>Addresses many types of nonstationary time series and cutting-edge methodologies </li> <li>Promotes understanding of the data and associated models rather than viewing it as the output of a "black box"</li> <li>Provides the R package tswge available on CRAN which contains functions and over 100 real and simulated data sets to accompany the book. Extensive help regarding the use of tswge functions is provided in appendices and on an associated website.</li> <li>Over 150 exercises and extensive support for instructors</li> </ul> The second edition includes additional real-data examples, uses R-based code that helps students easily analyze data, generate realizations from models, and explore the associated characteristics. It also adds discussion of new advances in the analysis of long memory data and data with time-varying frequencies (TVF).

Alan C. Elliott and Wayne A. Woodward’s Quick Guide to IBM® SPSS®: Statistical Analysis With Step-by-Step Examples gives students the extra guidance with SPSS they need without taking up valuable in-class time. A practical, accessible guide for using software while doing data analysis in the social sciences, students can learn SPSS on their own, allowing instructors to focus on the concepts and calculations in their lectures, rather than SPSS tutorials. Designed to work across disciplines, the authors have provided a number of SPSS "step-by-step" examples in chapters showing the user how to plan a study, prepare data for analysis, perform the analysis and interpret the output from SPSS. The new Third Edition covers IBM® SPSS® version 25, includes a new section on Syntax, and all chapters have been updated to reflect current menu options along with many SPSS screenshots, making the process much simpler for the user. In addition, helpful hints and insights are provided through the features "Tips and Caveats" and "Sidebars."

Quick Guide to IBM® SPSS®: Statistical Analysis With Step-by-Step Examples

See the best price to sell, buy, or rent books by Woodward, Wayne A.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Woodward, Wayne A.

See the best price to sell, buy, or rent books by Woodward, Wayne A.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)

Time Series for Data Science (Chapman & Hall/CRC Texts in Statistical Science)

SAS Essentials: A Guide to Mastering SAS for Research

Applied Time Series Analysis with R

SAS Essentials: Mastering SAS for Data Analytics

SAS Essentials: Mastering SAS for Data Analytics

Statistical Analysis Quick Reference Guidebook: With SPSS Examples

IBM SPSS by Example: A Practical Guide to Statistical Data Analysis

Applied Time Series Analysis with R

Quick Guide to IBM® SPSS®: Statistical Analysis With Step-by-Step Examples