Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R

Basic Statistics with R: Reaching Decisions with Data, First Edition, provides readers with an understanding of the processes at work in using data for results. The work begins with data collection an

Several decades of psychometric research have led to the development of sophisticated models for multidimensional test data, and in recent years, multidimensional item response theory (MIRT) has becom

Drawing on examples from across the social and behavioral sciences, Statistics with R: Solving Problems Using Real-World Data introduces foundational statistics concepts with beginner-friendly R progr

An R Companion for the Third Edition of The Fundamentals of Political Science Research offers students a chance to delve into the world of R using real political data sets and statistical analysis techniques directly from Paul M. Kellstedt and Guy D. Whitten's best-selling textbook. Built in parallel with the main text, this workbook teaches students to apply the techniques they learn in each chapter by reproducing the analyses and results from each lesson using R. Students will also learn to create all of the tables and figures found in the textbook, leading to an even greater mastery of the core material. This accessible, informative, and engaging companion walks through the use of R step-by-step, using command lines and screenshots to demonstrate proper use of the software. With the help of these guides, students will become comfortable creating, editing, and using data sets in R to produce original statistical analyses for evaluating causal claims. End-of-chapter exercises encourag

Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it.' Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.The book also introduces modern topics that do not normally appear in mathematical statistics texts but ar

Data Structures using Python provides an introduction to design, analysis, and implementation of data structures using the powerful programming language, Python. This book is designed for a first cour

In the global race to reach the end of AIDS, why is the world slipping off track? The answer has to do with stigma, money, and data. Global funding for AIDS response is declining. Tough choices must be made: some people will win and some will lose. Global aid agencies and governments use health data to make these choices. While aid agencies prioritize a shrinking list of countries, many governments deny that sex workers, men who have sex with men, drug users, and transgender people exist. Since no data is gathered about their needs, life-saving services are not funded, and the lack of data reinforces the denial. The Uncounted cracks open this and other data paradoxes through interviews with global health leaders and activists, ethnographic research, analysis of gaps in mathematical models, and the author's experience as an activist and senior official. It shows what is counted, what is not, and why empowering communities to gather their own data could be key to ending AIDS.

Introduction to Time Series Modeling: with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal

Introduction to Time Series Modeling: with Applications in R, Second Edition covers numerous stationary and nonstationary time series models and tools for estimating and utilizing them. The goal

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.

In the global race to reach the end of AIDS, why is the world slipping off track? The answer has to do with stigma, money, and data. Global funding for AIDS response is declining. Tough choices must be made: some people will win and some will lose. Global aid agencies and governments use health data to make these choices. While aid agencies prioritize a shrinking list of countries, many governments deny that sex workers, men who have sex with men, drug users, and transgender people exist. Since no data is gathered about their needs, life-saving services are not funded, and the lack of data reinforces the denial. The Uncounted cracks open this and other data paradoxes through interviews with global health leaders and activists, ethnographic research, analysis of gaps in mathematical models, and the author's experience as an activist and senior official. It shows what is counted, what is not, and why empowering communities to gather their own data could be key to ending AIDS.

Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to • Use the scientific method to evaluate hypotheses using controlled experiments • Define key metrics and ideally an Overall Evaluation Criterion • Test for trustworthiness of the results and alert experimenters to violated assumptions • Build a scalable platform that lowers the marginal cost of experiments close to zero • Avoid pitfalls like carryover effects and Twyman's law • Unde

Learn how to analyse and manage evolutionary and sequential user behaviours in modern networks, and how to optimize network performance by using indirect reciprocity, evolutionary games, and sequential decision making. Understand the latest theory without the need to go through the details of traditional game theory. With practical management tools to regulate user behaviour, and simulations and experiments with real data sets, this is an ideal tool for graduate students and researchers working in networking, communications, and signal processing.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.

Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting a business analytics or data science degree program. To

How organizations--including Google, StubHub, Airbnb, and Facebook--learn from experiments in a data-driven world.Have you logged into Facebook recently? Searched for something on Google? Chosen a mov

Drug Development for Cancer and Diabetes: A Path to 2030 focuses on new developments in the discovery of drugs for the treatment of cancer and diabetes. This new book presents important recent advance

This book takes the reader through the basics of using the R statistical programming environment for data analysis using a series of case studies based around tasks that answer real research questions

This book takes the reader through the basics of using the R statistical programming environment for data analysis using a series of case studies based around tasks that answer real research questions

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

This first-of-its-kind handbook offers crucial information on the safety of drugs taken during pregnancy. It covers an exhaustive list of common and less common drugs and provides for each drug the FDA letter categorization and newly approved "Pregnancy and Lactation Labeling Rule (PLLR)" systems for rating drug risks in pregnancy, imposed by the U.S. Food and Drug Administration (FDA). Drugs in Pregnancy: A Handbook for Pharmacists and Physicians covers the pregnancy ramifications of using anti-infective, cardiovascular, hematologic, dermatologic drugs and drugs affecting the endocrine, central, autonomic, gastrointestinal, musculoskeletal systems in addition to herbs, vitamins and dietary supplements. To enable the reader to develop well-informed knowledge about a drug safety profile during pregnancy, three sections of data have been provided for each drug: FDA Category, Risk Summary, and Further Reading.

Where did the regulatory underpinnings for the global drug wars come from? This book is the first fully-focused history of the 1961 UN Single Convention on Narcotic Drugs, the bedrock of the modern multilateral drug control system and the focal point of global drug regulations and prohibitions. Although far from the propagator of the drug wars, the UN enabled the creation of a uniform global legal framework to effectively legalise, or regulate, their pursuit. This book thereby answers the question of where the international legal framework for drug control came from, what state interests informed its development and how complex diplomatic negotiations resulted in the current regulatory system, binding states into an element of global policy uniformity.

A concise introduction to geophysical data processing - many of the techniques associated with the general field of time series analysis - for advanced students, researchers, and professionals. The textbook begins with calculus before transitioning to discrete time series via the sampling theorem, aliasing, use of complex sinusoids, development of the discrete Fourier transform from the Fourier series, and an overview of linear digital filter types and descriptions. Aimed at senior undergraduate and graduate students in geophysics, environmental science, and engineering with no previous background in linear algebra, probability, or statistics, this textbook draws scenarios and datasets from across the world of geophysics, and shows how data processing techniques can be applied to real-world problems using detailed examples, illustrations, and exercises (using MATLAB or similar computing environment). Online supplementary resources include datasets for students, and a solutions manual a

Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques to the detection, mitigation, treatment, and elimination of the COVID-19 virus around the world. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes. Data Science for COVID-19, Volume 2 extends the coverage of Volume 1 into research on statistical data and applications of Data Science to a variety of perspectives for analysis and understanding of the wide variety of impacts COVID-19 is having on societies and medical practice around the world.

Gain a better understanding of human behavior by exploring thought experiments in Shakespearean plays and the historical roots of experimental psychology within early modern literature. This book combines scientific psychology with English literature to discuss thought experiments in selected Shakespeare plays and examine the central role of thought experiments in the natural sciences. Thought experiments are essential for progress in scientific research. Indeed, Albert Einstein and a number of other leading scientists relied almost exclusively on thought experiments. Thought experiments also play a pivotal role in English literature, particularly in Shakespeare plays. By focussing on thought experiments and experimental psychology's place within early modern English literature, the volume establishes a more wholistic approach to understanding human behavior.

Gain a better understanding of human behavior by exploring thought experiments in Shakespearean plays and the historical roots of experimental psychology within early modern literature. This book combines scientific psychology with English literature to discuss thought experiments in selected Shakespeare plays and examine the central role of thought experiments in the natural sciences. Thought experiments are essential for progress in scientific research. Indeed, Albert Einstein and a number of other leading scientists relied almost exclusively on thought experiments. Thought experiments also play a pivotal role in English literature, particularly in Shakespeare plays. By focussing on thought experiments and experimental psychology's place within early modern English literature, the volume establishes a more wholistic approach to understanding human behavior.

This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box–Muller and Metropolis–Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical data

Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method w

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

As one of the first texts to take a behavioral approach to macroeconomic expectations, this book introduces a new way of doing economics. Rötheli uses cognitive psychology in a bottom-up method of modeling macroeconomic expectations. His research is based on laboratory experiments and historical data, which he extends to real-world situations. Pattern extrapolation is shown to be the key to understanding expectations of inflation and income. The quantitative model of expectations is used to analyze the course of inflation and nominal interest rates in a range of countries and historical periods. The model of expected income is applied to the analysis of business cycle phenomena such as the great recession in the United States. Data and spreadsheets are provided for readers to do their own computations of macroeconomic expectations. This book offers new perspectives in many areas of macro and financial economics.

As one of the first texts to take a behavioral approach to macroeconomic expectations, this book introduces a new way of doing economics. Rötheli uses cognitive psychology in a bottom-up method of modeling macroeconomic expectations. His research is based on laboratory experiments and historical data, which he extends to real-world situations. Pattern extrapolation is shown to be the key to understanding expectations of inflation and income. The quantitative model of expectations is used to analyze the course of inflation and nominal interest rates in a range of countries and historical periods. The model of expected income is applied to the analysis of business cycle phenomena such as the great recession in the United States. Data and spreadsheets are provided for readers to do their own computations of macroeconomic expectations. This book offers new perspectives in many areas of macro and financial economics.

Multilevel Modeling is a concise, practical guide to building models for multilevel and longitudinal data. Author Douglas A. Luke begins by providing a rationale for multilevel models; outlines the ba

In this new book, Porsche 911: Legends Made of LEGO (R) , by Joachim (Joe) Klang, LEGO (R) enthusiasts can bring sports cars from the legendary Porsche 911 series to life in their own home using their

This book shows how to analyse and simulate power electronics circuits using the software PSIM(R). It helps engineers and students in understanding, simulating and designing appropriate circuit compon

Lying at the very heart of Libby Clegg's life and achievements is the relationship with her Labrador Retriever cross guide dog, Hatti. A relationship primarily based on trust, with a healthy dose of r

Lying at the very heart of Libby Clegg's life and achievements is the relationship with her Labrador Retriever cross guide dog, Hatti. A relationship primarily based on trust, with a healthy dose of r

America's racial sands are quickly shifting, with parallel growth in theories to explain how varied groups respond, politically, to demographic changes. This Element develops a unified framework to predict when, why, and how racial groups react defensively toward others. America's racial groups can be arrayed along two dimensions: how American and how superior are they considered? This Element claims that location along these axes motivates political reactions to outgroups. Using original survey data and experiments, this Element reveals the acute sensitivity that people of color have to their social station and how it animates political responses to racial diversity.

Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern