An Introduction to Statistical Programming
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Author: Larry Pace
Beginning R: An Introduction to Statistical Programming is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. R is a powerful open-source implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commercially-available S-Plus language, and has become the de facto standard for doing, teaching, and learning computational statistics. R is both an object-oriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets. R is also becoming adopted into commercial tools such as Oracle Database. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for statistical exploration and research. Covers the freely-available R language for statistics Shows the use of R in specific uses case such as simulations, discrete probability solutions, one-way ANOVA analysis, and more Takes a hands-on and example-based approach incorporating best practices with clear explanations of the statistics being done What you’ll learn Acquire and install R Import and export data and scripts Generate basic statistics and graphics Program in R to write custom functions Use R for interactive statistical explorations Implement simulations and other advanced techniques Who this book is for Beginning R: An Introduction to Statistical Programming is an easy-to-read book that serves as an instruction manual and reference for working professionals, professors, and students who want to learn and use R for basic statistics. It is the perfect book for anyone needing a free, capable, and powerful tool for exploring statistics and automating their use. Table of ContentsPart I. Learning the R Language 1. Getting R and Getting Started 2. Programming in R 3. Writing Reusable Functions 4. Summary Statistics Part II. Using R for Descriptive Statistics 5. Creating Tables and Graphs 6. Discrete Probability Distributions 7. Computing Standard Normal Probabilities Part III. Using R for Inferential Statistics 8. Creating Confidence Intervals 9. Performing t Tests 10. Implementing One-Way ANOVA 11. Implementing Advanced ANOVA 12. Simple Correlation and Regression in R 13. Multiple Correlation and Regression in R 14. Logistic Regression 15. Performing Chi-Square Tests 16. Working in Nonparametric Statistics Part IV. Taking R to the Next Level 17. Using R for Simulation 18. Resampling and Bootstrapping 19. Creating R Packages 20. Executing R Packages
The Statistical Programming Language
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Author: Mark Gardener
Publisher: John Wiley & Sons
Conquer the complexities of this open source statistical language R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields. This book examines this complex language using simple statistical examples, showing how R operates in a user-friendly context. Both students and workers in fields that require extensive statistical analysis will find this book helpful as they learn to use R for simple summary statistics, hypothesis testing, creating graphs, regression, and much more. It covers formula notation, complex statistics, manipulating data and extracting components, and rudimentary programming. R, the open source statistical language increasingly used to handle statistics and produces publication-quality graphs, is notoriously complex This book makes R easier to understand through the use of simple statistical examples, teaching the necessary elements in the context in which R is actually used Covers getting started with R and using it for simple summary statistics, hypothesis testing, and graphs Shows how to use R for formula notation, complex statistics, manipulating data, extracting components, and regression Provides beginning programming instruction for those who want to write their own scripts Beginning R offers anyone who needs to perform statistical analysis the information necessary to use R with confidence.
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Author: R. Andrew Lambert
Publisher: Tate Publishing
For I know the plans I have for you, declares the LORD, plans to prosper you and not to harm you, plans to give you hope and a future. Then you will call upon Me and come and pray to Me, and I will listen to you. You will seek Me and find Me when you seek Me with all your heart. I will be found by you, declares the LORD. (Jeremiah 29: 11-14 NIV) Beginning a Prayer Life is an inspirational book created to assist those new to Christ in starting a prayer life, and help seasoned Christians reclaim their relationship with God. It pulls together the scriptural directions and teachings of Christ regarding prayer so we can be fully aware of what God desires from us. It also offers a foundation for developing a language and personal dedication for prayer in order to diminish the awkwardness and inadequate feelings we may experience when we begin to seek God through prayer. Whether for you personally or your effort to reach out to a loved one or friend, this book can be a great tool to grab hold and understand the gift of prayer. Ultimately, may you come to have a closer relationship with God, and receive all the treasure that comes with maintaining a prayer life. This book comes to you through a unique personal history. R. Andrew Lamberts story and testimony begins as a Vietnamese orphan saved by the grace of God. Adopted and brought to the states, he grew up a Vietnamese/Black child in small town America. His journey in life has been a blessed one through a series of amazing events and struggles that has led to ultimate success and the writing of Beginning a Prayer Life.
A Guide for Graduate Students of Color
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Author: Dwayne A. Mack,Elwood Watson,Michelle Madsen Camacho
This practical guide prepares graduate students of color for their first job in academia and offers strategies for succeeding in the early years of a tenure-track position. Through the voices of faculty who have experienced the rigors of the job search and a career in academia, Beginning a Career in Academia offers advice for graduate students of color on how to transition from graduate school to an academic position. This inclusive volume shares perspectives that vary based on gender, racial, ethnic, generational, and disciplinary backgrounds, giving readers an opportunity to reflect on successful strategies for career readiness and for dealing with marginalization. The authors provide recommendations and tips to enhance the job search, identify campus fit, prepare for the interview and negotiation process, address dynamics of of racial and gender politics, find work-life balance, and demystify the promotion and tenure process. This must-read provides candid advice and mentorship for any graduate students of color embarking on a carreer in academe.
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Author: Manas A. Pathak
“We live in the age of data. In the last few years, the methodology of extracting insights from data or "data science" has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library. The goal of “Beginning Data Science with R” is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language.
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Author: Bertram K.C. Chan, PhD
Publisher: Springer Publishing Company
Since it first appeared in 1996, the open-source programming language R has become increasingly popular as an environment for statistical analysis and graphical output. This is the first textbook to present classical biostatistical analysis for epidemiology and related public health sciences to students using the R language. Based on the assumption that readers have minimal familiarity with statistical concepts, the author uses a step-by-step approach to building skills. The text encompasses biostatistics from basic descriptive and quantitative statistics to survival analysis and missing data analysis in epidemiology. Illustrative examples, including real-life research problems drawn from such areas as nutrition, environmental health, and behavioral health, engage students and reinforce the understanding of R. These examples illustrate the replication of R for biostatistical calculations and graphical display of results. The text covers both essential and advanced techniques and applications in biostatistics that are relevant to epidemiology. Also included are an instructor's guide, student solutions manual, and downloadable data sets. Key Features: First overview biostatistics textbook for epidemiology and public health that uses the open-source R program Covers essential and advanced techniques and applications in biostatistics as relevant to epidemiology Features abundant examples to illustrate the application of R language for biostatistical calculations and graphical displays of results Includes instructor's guide, student solutions manual, and downloadable data sets.
A Process Improvement Approach
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Author: Margaret K. Kulpa,Kent A. Johnson
Publisher: CRC Press
Written by experienced process improvement professionals who have developed and implemented systems in organizations around the world, Interpreting the CMMI®: A Process Improvement Approach provides you with specific techniques for performing process improvement using the CMMI® and the family of CMM models. Kulpa and Johnson describe the fundamental concepts of the CMMI® model - goals, practices, architecture, and definitions - in everyday language, give real-world examples, and provide a structured approach for implementing the concepts of the CMMI® into any organization. They walk you through the myriad charts and graphs involved in statistical process control and offer recommendations for which tools to use. The book covers roles and responsibilities, people issues, how to generate meaningful documentation, how to overcome resistance to change, and how to track the success of your efforts. It provides examples of plans, policies, processes, procedures, and team charters. The appendices include matrices summarizing the different assessment techniques that have now been approved by the SEI for use, "pros and cons" associated with this model, some of the myths that have arisen from the marketing of the CMMI® effort, and forms and templates. The book comes with a CD-ROM that contains forms and templates that can be downloaded and customized. The authors distill the knowledge gained in their combined 60 years of experience in project management, software engineering, systems engineering, metrics, quality assurance, configuration management, training, documentation, process improvement, and team building. Whether you are new to process improvement or an experienced professional, Interpreting the CMMI®: A Process Improvement Approach saves you time wasted on false starts, false promises by marketers, and failed deadlines.
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Author: Jonathan S. Golan
Publisher: Springer Science & Business Media
Linear algebra is a living, active branch of mathematics which is central to almost all other areas of mathematics, both pure and applied, as well as to computer science, to the physical, biological, and social sciences, and to engineering. It encompasses an extensive corpus of theoretical results as well as a large and rapidly-growing body of computational techniques. Unfortunately, in the past decade, the content of linear algebra courses required to complete an undergraduate degree in mathematics has been depleted to the extent that they fail to provide a sufficient theoretical or computational background. Students are not only less able to formulate or even follow mathematical proofs, they are also less able to understand the mathematics of the numerical algorithms they need for applications. Certainly, the material presented in the average undergraduate course is insufficient for graduate study. This book is intended to fill the gap which has developed by providing enough theoretical and computational material to allow the advanced undergraduate or beginning graduate student to overcome this deficiency and be able to work independently or in advanced courses. The book is intended to be used either as a self-study guide, a textbook for a course in advanced linear algebra, or as a reference book. It is also designed to prepare a student for the linear algebra portion of prelim exams or PhD qualifying exams. The volume is self-contained to the extent that it does not assume any previous formal knowledge of linear algebra, though the reader is assumed to have been exposed, at least informally, to some of the basic ideas and techniques, such as manipulation of small matrices and the solution of small systems of linear equations over the real numbers. More importantly, it assumes a seriousness of purpose, considerable motivation, and a modicum of mathematical sophistication on the part of the reader. In the latest edition, new major theorems have been added, as well as many new examples. There are over 130 additional exercises and many of the previous exercises have been revised or rewritten. In addition, a large number of additional biographical notes and thumbnail portraits of mathematicians have been included.