This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analysis. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for d...

Assessing vendors is a tricky process. Large and regulated organizations are forced to demonstrate due diligence in vendor assessment, but often do not know how to do this. This results in a great deal of busywork being required by both the vendors and the organizations. Smaller organizations don't know what to look for and, as a result, often wind up selecting based on price instead of value. This results in service failures and vendors that just milk their customers for as long as they c...

This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field.The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Exampl...

Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution has had a substantial impact on applied analysis. Hence, no textbook has managed to cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can also be use...

This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data science...

This concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective. While many degree programs require a course in parametric statistics, these methods are often inadequate for real-world decision making in business environments. Much of the data collected today by business executives (for example, customer satisfaction opinions) requires nonparametric statistics for valid analysis, and this book provides t...

This book shows the capabilities of Microsoft Excel in teaching business statistics effectively. Similar to the previously published Excel 2010 for Business Statistics, this book is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical business problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you....

This guide is for practicing statisticians and data scientists who use IBM SPSS for statistical analysis of big data in business and finance. This is the first of a two-part guide to SPSS for Windows, introducing data entry into SPSS, along with elementary statistical and graphical methods for summarizing and presenting data. Part I also covers the rudiments of hypothesis testing and business forecasting while Part II will present multivariate statistical methods, more advanced forecasting...

Learn how to build a data science team within your organization rather than hiring from the outside. Teach your team to ask the right questions to gain actionable insights into your business.Most organizations still focus on objectives and deliverables. Instead, a data science team is exploratory. They use the scientific method to ask interesting questions and run small experiments. Your team needs to see if the data illuminate their questions. Then, they have to use critical think...

This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, r...

This book serves as a hands-on guide to the "acs" R package for demographers, planners, and other researchers who work with American Community Survey (ACS) data. It gathers the most common problems associated with using ACS data and implements functions as a package in the R statistical programming language. The package defines a new "acs" class object (containing estimates, standard errors, and metadata for tables from the ACS) with methods to deal appropriately with common tasks (e.g., c...

This is the second of a two-part guide to quantitative analysis using the IBM SPSS Statistics software package; this volume focuses on multivariate statistical methods and advanced forecasting techniques. More often than not, regression models involve more than one independent variable. For example, forecasting methods are commonly applied to aggregates such as inflation rates, unemployment, exchange rates, etc., that have complex relationships with determining variables. This book introdu...