This introductory textbook for business statistics teaches statistical analysis and research methods via business case studies and financial data using Excel, Minitab, and SAS. Every chapter in this textbook engages the reader with data of individual stock, stock indices, options, and futures. One studies and uses statistics to learn how to study, analyze, and understand a data set of particular interest. Some of the more popular statistical programs that have been developed to use statistical and computational methods to analyze data sets are SAS, SPSS, and Minitab. Of those, we look at Minitab and SAS in this textbook. One of the main reasons to use Minitab is that it is the easiest to use among the popular statistical programs. We look at SAS because it is the leading statistical package used in industry. We also utilize the much less costly and ubiquitous Microsoft Excel to do statistical analysis, as the benefits of Excel have become widely recognized in the academic world and its analytical capabilities extend to about 90 percent of statistical analysis done in the business world. We demonstrate much of our statistical analysis using Excel and double check the analysis and outcomes using Minitab and SAS-also helpful in some analytical methods not possible or practical to do in Excel.

Clinical Data Quality Checks for CDISC Compliance using SAS is the first book focused on identifying and correcting data quality and CDISC compliance issues with real-world innovative SAS programming techniques such as Proc SQL, metadata and macro programming. Learn to master Proc SQL's subqueries and summary functions for multi-tasking process. Drawing on his more than 25 years' experience in the pharmaceutical industry, the author provides a unique approach that empowers SAS programmers to take control of data quality and CDISC compliance. This book helps you create a system of SDTM and ADaM checks that can be tracked for continuous improvement. How often have you encountered issues such as missing required variables, duplicate records, invalid derived variables and invalid sequence of two dates? With the SAS programming techniques introduced in this book, you can start to monitor these and more complex data and CDISC compliance issues. With increased standardization in SDTM and ADaM specifications and data values, codelist dictionaries can be created for better organization, planning and maintenance. This book includes a SAS program to create excel files containing unique values from all SDTM and ADaM variables as columns. In addition, another SAS program compares SDTM and ADaM codelist dictionaries with codelists from define.xml specifications. Having tools to automate this process greatly saves time from doing it manually. Features SDTMs and ADaMs Vitals SDTMs and ADaMs Data CDISC Specifications Compliance CDISC Data Compliance Protocol Compliance Codelist Dictionary Compliance

Ready for SAP SuccessFactors Employee Central? First, master the Employee Central workforce admin functionality for hiring, time sheets, benefits, payments, and termination. Next, learn to configure permissions, implement mass changes, and set up workflows, company structures, and business rules. Finally, explore integration with SAP and third-party applications to get the most out of your HR setup. From implementation to operations, this Employee Central book has it all! Highlights include: 1) Time sheets 2) Benefits management 3) Payment 4) Time management 5) Workforce management 6) Mass changes 7) Implementation and integration 8) Reporting 9) Data import and migration 10) SAP SuccessFactors HCM Suite 11) Intelligent services

Praise for the First Edition:

". . . the book serves as an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation." – Tzvetan Semerdjiev, Zentralblatt Math

Computational statistics and statistical computing are two areas within statistics that may be broadly described as computational, graphical, and numerical approaches to solving statistical problems. Like its bestselling predecessor, Statistical Computing with R, Second Edition covers the traditional core material of these areas with an emphasis on using the R language via an examples-based approach. The new edition is up-to-date with the many advances that have been made in recent years.

Features

Provides an overview of computational statistics and an introduction to the R computing environment.

Focuses on implementation rather than theory.

Explores key topics in statistical computing including Monte Carlo methods in inference, bootstrap and jackknife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation.

Includes new sections, exercises and applications as well as new chapters on resampling methods and programming topics.

Includes coverage of recent advances including R Studio, the tidyverse, knitr and ggplot2

Accompanied by online supplements available on GitHub including R code for all the exercises as well as tutorials and extended examples on selected topics.

Suitable for an introductory course in computational statistics or for self-study, Statistical Computing with R, Second Edition provides a balanced, accessible introduction to computational statistics and statistical computing.

About the Author

Maria Rizzo is Professor in the Department of Mathematics and Statistics at Bowling Green State University in Bowling Green, Ohio, where she teaches statistics, actuarial science, computational statistics, statistical programming and data science. Prior to joining the faculty at BGSU in 2006, she was Assistant Professor in the Department of Mathematics at Ohio University in Athens, Ohio. Her main research area is energy statistics and distance correlation. She is the software developer and maintainer of the energy package for R. She also enjoys writing books including a forthcoming joint research monograph on energy statistics. 　　

　

　

　

　

　

　

　

　

　

　

Table of Contents

1. Introduction

Statistical Computing

The R Environment

Getting Started with R and RStudio

Basic Syntax

Using the R Online Help System

Distributions and Statistical Tests

Functions

Arrays, Data Frames, and Lists

Formula Specifications

Graphics Introduction to ggplot

Workspace and Files

Using Scripts

Using Packages

Using R Markdown and knitr

Exercises

2. Probability and Statistics Review

Random Variables and Probability

Some Discrete Distributions

Some Continuous Distributions

Multivariate Normal Distribution

Limit Theorems

Statistics

Bayes’ Theorem and Bayesian Statistics

Markov Chains

3. Methods for Generating Random Variables

Introduction

The Inverse Transform Method

The Acceptance-Rejection Method

Transformation Methods

Sums and Mixtures

Multivariate Distributions

Exercises

4. Generating Random Processes

Stochastic Processes

Brownian Motions

Exercises

5. Visualization of Multivariate Data

Introduction

Panel Displays

Surface Plots and 3D Scatter Plots

Contour Plots

The Grammar of Graphics and ggplot2

Other 2D Representations of Data

Principal Components Analysis

Exercises

6. Monte Carlo Integration and Variance Reduction

Introduction

Monte Carlo Integration

Variance Reduction

Antithetic Variables

Control Variates

Importance Sampling

Stratified Sampling

Stratified Importance Sampling

Exercises

RCode

7. Monte Carlo Methods in Inference

Introduction

Monte Carlo Methods for Estimation

Monte Carlo Methods for Hypothesis Tests

Application

Exercises

8. Bootstrap and Jackknife

The Bootstrap

The Jackknife

Bootstrap Confidence Intervals

Better Bootstrap Confidence Intervals

Application

Exercises

9. Resampling Applications

Jackknife-after-Bootstrap

Resampling for Regression Models

Influence

Exercises

10. Permutation Tests

Introduction

Tests for Equal Distributions

Multivariate Tests for Equal Distributions

Application

Exercises

11. Markov Chain Monte Carlo Methods

Introduction

The Metropolis-Hastings Algorithm

The Gibbs Sampler

Monitoring Convergence

Application

Exercises

R Code

12. Probability Density Estimation

Univariate Density Estimation

Kernel Density Estimation

Bivariate and Multivariate Density Estimation

Other Methods of Density Estimation

Exercises

R Code

13. Introduction to Numerical Methods in R

Introduction

Root-finding in One Dimension

Numerical Integration

Maximum Likelihood Problems

Application

Exercises

14. Optimization 401

Introduction

One-dimensional Optimization

Maximum likelihood estimation with mle

Two-dimensional Optimization

The EM Algorithm

Linear Programming – The Simplex Method

Application

Exercises

15. Programming Topics

Introduction

Benchmarking: Comparing the Execution Time of Code

Profiling

Object Size, Attributes, and Equality

Finding Source Code

Linking C/C++ Code using Rcpp

Application

Exercises

This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques. This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and dates The difference between different data structures and how to create, add additional components to, and subset each data structure How to acquire and parse data from locations previously inaccessible How to develop functions and use loop control structures to reduce code redundancy How to use pipe operators to simplify code and make it more readable How to reshape the layout of data and manipulate, summarize, and join data sets

Originally created for agile software development, scrum provides project managers with the flexibility needed to meet ever-changing consumer demands. Presenting a modified version of the agile software development framework, Scrum Project Management introduces Scrum basics and explains how to apply this adaptive technique to effectively manage a wide range of programs and complex projects. The book provides proven planning methods for controlling project scope and ensuring your project stays on schedule. It includes scrum tracking methods to help your team maintain a focus on improving throughput and streamlining communications. It also demonstrates how to: Combine traditional project management methods with scrum Adapt the familiar work breakdown structure to create scrum backlogs and sprints Use a scrum of scrums to manage programs Apply earned value management, critical path, and PERT in the context of scrum Having successfully deployed and implemented scrum across multiple companies and departments, the authors provide valuable insight into how they achieved their past successes and how they overcame the trials involved with the deployment of a scrum environment. Throughout the text they discuss improvisation, creative problem solving, and emergent phenomena-detailing the methods needed to ensure your team achieves project success.

Take an in-depth look at SAP SuccessFactors talent modules with this complete guide to configuration, administration, and best practices. This two-volume series follows a logical progression of SAP SuccessFactors modules that should be configured to complete a comprehensive talent management solution. The authors walk you through fully functional simple implementations in the primary chapters for each module before diving into advanced topics in subsequent chapters. In volume 1, we start with a brief introduction. The next two chapters jump into the Talent Profile and Job Profile Builder. These chapters lay the structures and data that will be utilized across the remaining chapters which detail each module. The following eight chapters walk you through building, administering, and using a goal plan in the Goal Management module as well as performance forms in the Performance Management module. The book also expands on performance topics with the 360 form and continuous performance management in two additional chapters. We then dive into configuring the calibration tool and how to set up calibration sessions in the next two chapters before providing a brief conclusion. Within each topic, the book touches on the integration points with other modules as well as internationalization. The authors also provide recommendations and insights from real world experience. Having finished the book, you will have an understanding of what comprises a complete SAP SuccessFactors talent management solution and how to configure, administer, and use each module within it. You will: * Develop custom talent profile portlets * Integrate Job Profile Builder with SAP SuccessFactors talent modules * Set up security, group goals, and team goals in goals management with sample XML * Configure and launch performance forms including rating scales and route maps * Configure and administrate the calibration module and its best practices

Contingency tables arise in diverse fields, including life sciences, education, social and political sciences, notably market research and opinion surveys. Their analysis plays an essential role in gaining insight into structures of the quantities under consideration and in supporting decision making. Combining both theory and applications, this book presents models and methods for the analysis of two- and multidimensional-contingency tables. An excellent reference for advanced undergraduates, graduate students, and practitioners in statistics as well as biosciences, social sciences, education, and economics, the work may also be used as a textbook for a course on categorical data analysis. Prerequisites include basic background on statistical inference and knowledge of statistical software packages.

This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data. An important aspect of nonparametric statistics is oriented towards the use of ranking data. Rank correlation is defined through the notion of distance functions and the notion of compatibility is introduced to deal with incomplete data. Ranking data are also modeled using a variety of modern tools such as CART, MCMC, EM algorithm and factor analysis. This book deals with statistical methods used for analyzing such data and provides a novel and unifying approach for hypotheses testing. The techniques described in the book are illustrated with examples and the statistical software is provided on the authors' website.

An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader's expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective "small multiple" plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the "tidyverse" of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions

Leverage the flexibility and power of SAP MII to integrate your business operations with your manufacturing processes. You'll explore important new features of the product and see how to apply best practices to connect all the stakeholders in your business. This book starts with an overview of SAP's manufacturing integration and intelligence application and explains why it is so important. You'll then see how it is applied in various manufacturing sectors. The biggest challenge in manufacturing industries is to reduce the manual work and human intervention so that the process becomes automatic. SAP MII explains how to bridge the gap between management and production and bring sound vital information to the shop floor in real time. With this book you'll see how to ensure existing manufacturing and information systems share a common interface for all users in your enterprise. What You'll Learn Understand the functional aspects of SAP MII Implement SAP MII in different Manufacturing sectors Explore new technical features of SAP MII 12.x Integrate scenarios with SAP MII Discover practice guidelines Who This Book is for All levels of SAP manufacturing professionals.

This textbook presents the basic concepts and methods of fluid mechanics, including Lagrangian and Eulerian descriptions, tensors of stresses and strains, continuity, momentum, energy, thermodynamics laws, and similarity theory. The models and their solutions are presented within a context of the mechanics of multiphase media. The treatment fully utilizes the computer algebra and software system Mathematica (R) to both develop concepts and help the reader to master modern methods of solving problems in fluid mechanics. Topics and features: Glossary of over thirty Mathematica (R) computer programs Extensive, self-contained appendix of Mathematica (R) functions and their use Chapter coverage of mechanics of multiphase heterogeneous media Detailed coverage of theory of shock waves in gas dynamics Thorough discussion of aerohydrodynamics of ideal and viscous fluids an d gases Complete worked examples with detailed solutions Problem-solving approach Foundations of Fluid Mechanics with Applications is a complete and accessible text or reference for graduates and professionals in mechanics, applied mathematics, physical sciences, materials science, and engineering. It is an essential resource for the study and use of modern solution methods for problems in fluid mechanics and the underlying mathematical models. The present, softcover reprint is designed to make this classic textbook available to a wider audience.

Among the various multi-level formulations of mathematical models in decision making processes, this book focuses on the bi-level model. Being the most frequently used, the bi-level model addresses conflicts which exist in multi-level decision making processes. From the perspective of bi-level structure and uncertainty, this book takes real-life problems as the background, focuses on the so-called random-like uncertainty, and develops the general framework of random-like bi-level decision making problems. The random-like uncertainty considered in this book includes random phenomenon, random-overlapped random (Ra-Ra) phenomenon and fuzzy-overlapped random (Ra-Fu) phenomenon. Basic theory, models, algorithms and practical applications for different types of random-like bi-level decision making problems are also presented in this book.

This book provides new insights on the study of global environmental changes using the ecoinformatics tools and the adaptive-evolutionary technology of geoinformation monitoring. The main advantage of this book is that it gathers and presents extensive interdisciplinary expertise in the parameterization of global biogeochemical cycles and other environmental processes in the context of globalization and sustainable development. In this regard, the crucial global problems concerning the dynamics of the nature-society system are considered and the key problems of ensuring the system's sustainable development are studied. A new approach to the numerical modeling of the nature-society system is proposed and results are provided on modeling the dynamics of the system's characteristics with regard to scenarios of anthropogenic impacts on biogeochemical cycles, land ecosystems and oceans. The main purpose of this book is to develop a universal guide to information-modeling technologies for assessing the function of environmental subsystems under various climatic and anthropogenic conditions.

Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data. The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's "classic" data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed. This monograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinking by considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization.

This volume compiles the major results of conference participants from the "Third International Conference in Network Analysis" held at the Higher School of Economics, Nizhny Novgorod in May 2013, with the aim to initiate further joint research among different groups. The contributions in this book cover a broad range of topics relevant to the theory and practice of network analysis, including the reliability of complex networks, software, theory, methodology, and applications. Network analysis has become a major research topic over the last several years. The broad range of applications that can be described and analyzed by means of a network has brought together researchers, practitioners from numerous fields such as operations research, computer science, transportation, energy, biomedicine, computational neuroscience and social sciences. In addition, new approaches and computer environments such as parallel computing, grid computing, cloud computing, and quantum computing have helped to solve large scale network optimization problems.

Economists can use computer algebra systems to manipulate symbolic models, derive numerical computations, and analyze empirical relationships among variables. Maxima is an open-source multi-platform computer algebra system that rivals proprietary software. Maxima's symbolic and computational capabilities enable economists and financial analysts to develop a deeper understanding of models by allowing them to explore the implications of differences in parameter values, providing numerical solutions to problems that would be otherwise intractable, and by providing graphical representations that can guide analysis. This book provides a step-by-step tutorial for using this program to examine the economic relationships that form the core of microeconomics in a way that complements traditional modeling techniques. Readers learn how to phrase the relevant analysis and how symbolic expressions, numerical computations, and graphical representations can be used to learn from microeconomic models. In particular, comparative statics analysis is facilitated. Little has been published on Maxima and its applications in economics and finance, and this volume will appeal to advanced undergraduates, graduate-level students studying microeconomics, academic researchers in economics and finance, economists, and financial analysts.

The book opens with a short introduction to Indian music, in particular classical Hindustani music, followed by a chapter on the role of statistics in computational musicology. The authors then show how to analyze musical structure using Rubato, the music software package for statistical analysis, in particular addressing modeling, melodic similarity and lengths, and entropy analysis; they then show how to analyze musical performance. Finally, they explain how the concept of seminatural composition can help a music composer to obtain the opening line of a raga-based song using Monte Carlo simulation. The book will be of interest to musicians and musicologists, particularly those engaged with Indian music.

This book highlights recent advances in natural computing, including biology and its theory, bio-inspired computing, computational aesthetics, computational models and theories, computing with natural media, philosophy of natural computing and educational technology. It presents extended versions of the best papers selected from the symposium "7th International Workshop on Natural Computing" (IWNC7), held in Tokyo, Japan, in 2013. The target audience is not limited to researchers working in natural computing but also those active in biological engineering, fine/media art design, aesthetics and philosophy.

This book offers a snapshot of the state-of-the-art in classification at the interface between statistics, computer science and application fields. The contributions span a broad spectrum, from theoretical developments to practical applications; they all share a strong computational component. The topics addressed are from the following fields: Statistics and Data Analysis; Machine Learning and Knowledge Discovery; Data Analysis in Marketing; Data Analysis in Finance and Economics; Data Analysis in Medicine and the Life Sciences; Data Analysis in the Social, Behavioural, and Health Care Sciences; Data Analysis in Interdisciplinary Domains; Classification and Subject Indexing in Library and Information Science. The book presents selected papers from the Second European Conference on Data Analysis, held at Jacobs University Bremen in July 2014. This conference unites diverse researchers in the pursuit of a common topic, creating truly unique synergies in the process.

This book gathers a selection of invited and contributed lectures from the European Conference on Numerical Mathematics and Advanced Applications (ENUMATH) held in Lausanne, Switzerland, August 26-30, 2013. It provides an overview of recent developments in numerical analysis, computational mathematics and applications from leading experts in the field. New results on finite element methods, multiscale methods, numerical linear algebra and discretization techniques for fluid mechanics and optics are presented. As such, the book offers a valuable resource for a wide range of readers looking for a state-of-the-art overview of advanced techniques, algorithms and results in numerical mathematics and scientific computing.

The R Companion to Elementary Applied Statistics includes traditional applications covered in elementary statistics courses as well as some additional methods that address questions that might arise during or after the application of commonly used methods. Beginning with basic tasks and computations with R, readers are then guided through ways to bring data into R, manipulate the data as needed, perform common statistical computations and elementary exploratory data analysis tasks, prepare customized graphics, and take advantage of R for a wide range of methods that find use in many elementary applications of statistics. Features: Requires no familiarity with R or programming to begin using this book. Can be used as a resource for a project-based elementary applied statistics course, or for researchers and professionals who wish to delve more deeply into R. Contains an extensive array of examples that illustrate ideas on various ways to use pre-packaged routines, as well as on developing individualized code. Presents quite a few methods that may be considered non-traditional, or advanced. Includes accompanying carefully documented script files that contain code for all examples presented, and more. R is a powerful and free product that is gaining popularity across the scientific community in both the professional and academic arenas. Statistical methods discussed in this book are used to introduce the fundamentals of using R functions and provide ideas for developing further skills in writing R code. These ideas are illustrated through an extensive collection of examples. About the Author: Christopher Hay-Jahans received his Doctor of Arts in mathematics from Idaho State University in 1999. After spending three years at University of South Dakota, he moved to Juneau, Alaska, in 2002 where he has taught a wide range of undergraduate courses at University of Alaska Southeast.

The main focus of this book is on presenting advances in fuzzy statistics, and on proposing a methodology for testing hypotheses in the fuzzy environment based on the estimation of fuzzy confidence intervals, a context in which not only the data but also the hypotheses are considered to be fuzzy. The proposed method for estimating these intervals is based on the likelihood method and employs the bootstrap technique. A new metric generalizing the signed distance measure is also developed. In turn, the book presents two conceptually diverse applications in which defended intervals play a role: one is a novel methodology for evaluating linguistic questionnaires developed at the global and individual levels; the other is an extension of the multi-ways analysis of variance to the space of fuzzy sets. To illustrate these approaches, the book presents several empirical and simulation-based studies with synthetic and real data sets. In closing, it presents a coherent R package called "FuzzySTs" which covers all the previously mentioned concepts with full documentation and selected use cases. Given its scope, the book will be of interest to all researchers whose work involves advanced fuzzy statistical methods.

"A well-written and -illustrated work, recommended for all college libraries. Lower-division undergraduates through faculty."
--CHOICE, December 2002

In this book, noted expert Andrew Okungbowa explains SAP Asset Accounting (FI-AA) in SAP-ERP, including its associated business benefits, and guides you through the considerable complexities of SAP-ERP configuration. Using FI-AA for fixed asset management enables you to manage assets in multinational companies across a broad range of industries and produce reports to meet various needs in line with legal requirements. Configuring SAP-ERP can be a daunting exercise, however, and there are few resources that address these issues. Asset Accounting Configuration in SAP ERP fills that resource gap by covering the major aspects of SAP FI-AA for anyone with SAP experience and the basic accounting knowledge and bookkeeping skills necessary to apply configuration. It provides configuration explanations in the simplest forms possible and provides step-by-step guidance with illustrations and practical examples. What You'll Learn "li>In-depth coverage of FI-AA syllabus * How to configure FI-AA accounting in SAP * How to integrate FI-AA accounting with other SAP modules * How to explain the functionalities of SAP FI-AA * Knowledge gained from real-world practical examples and case studies Who This Book Is For The key target audience for this book includes SAP consultants, developers, accountants, support organizations and beginners. It is also a resourceful learning manual for universities and institutions whose curricula covers SAP-ERP Asset Accounting.