The Truthful Art is an introduction to quantitative thinking and statistical and cartographical representation written specifically for journalists and designers. A follow-up to The Functional Art, it goes into the specifics of how to create functional charts, maps, and graphs.
Discusses data-driven organizations and explains the analytics value chain to adopt when building predictive business models, covering such topics as data quality, privacy and ethics, statistical and visualization tools, and data-driven culture.
If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you?ll learn the entire process of exploratory data analysis?from collecting data and generating statistics to identifying patterns and testing hypotheses. You?ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries. * Develop an understanding of probability and statistics by writing and testing code * Run experiments to test statistical behavior, such as generating samples from several distributions * Use simulations to understand concepts that are hard to grasp mathematically * Import data from most sources with Python, rather than rely on data that?s cleaned and formatted for statistics tools * Use statistical inference to answer questions about real-world data
If you know how to program with Python and also know a little about probability, you?re ready to tackle Bayesian statistics. With this book, you´ll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you?ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book?s computational approach helps you get a solid start. * Use your existing programming skills to learn and understand Bayesian statistics * Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing * Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey * Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.
This tutorial manual provides a comprehensive introduction to R, a software package for statistical computing and graphics. R supports a wide range of statistical techniques and is easily extensible via user-defined functions. One of R´s strengths is the ease with which publication-quality plots can be produced in a wide variety of formats. This is a printed edition of the tutorial documentation from the R distribution, with additional examples, notes and corrections. It is based on R version 2.9.0, released April 2009. R is free software, distributed under the terms of the GNU General Public License (GPL). It can be used with GNU/Linux, Unix and Microsoft Windows. All the money raised from the sale of this book supports the development of free software and documentation.
With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you?re a beginner, R Cookbook will help get you started. If you?re an experienced data programmer, it will jog your memory and expand your horizons. You?ll get the job done faster and learn more about R in the process. * Create vectors, handle variables, and perform other basic functions * Input and output data * Tackle data structures such as matrices, lists, factors, and data frames * Work with probability, probability distributions, and random variables * Calculate statistics and confidence intervals, and perform statistical tests * Create a variety of graphic displays * Build statistical models with linear regressions and analysis of variance (ANOVA) * Explore advanced statistical techniques, such as finding clusters in your data ´´Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language?one practical example at a time.´´?Jeffrey Ryan, software consultant and R package author
This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. It develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples. Experimentation is necessary - a purely theoretical approach is not reasonable. The new experimentalism, a development in the modern philosophy of science, considers that an experiment can have a life of its own. It provides a statistical methodology to learn from experiments, where the experimenter should distinguish between statistical significance and scientific meaning. This book introduces the new experimentalism in evolutionary computation, providing tools to understand algorithms and programs and their interaction with optimization problems. The book develops and applies statistical techniques to analyze and compare modern search heuristics such as evolutionary algorithms and particle swarm optimization. Treating optimization runs as experiments, the author offers methods for solving complex real-world problems that involve optimization via simulation, and he describes successful applications in engineering and industrial control projects. The book bridges the gap between theory and experiment by providing a self-contained experimental methodology and many examples, so it is suitable for practitioners and researchers and also for lecturers and students. It summarizes results from the author´s consulting to industry and his experience teaching university courses and conducting tutorials at international conferences. The book will be supported online with downloads and exercises.
If you?re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You?ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports. Updated for R 2.14 and 2.15, this second edition includes new and expanded chapters on R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop. * Get started quickly with an R tutorial and hundreds of examples * Explore R syntax, objects, and other language details * Find thousands of user-contributed R packages online, including Bioconductor * Learn how to use R to prepare data for analysis * Visualize your data with R?s graphics, lattice, and ggplot2 packages * Use R to calculate statistical fests, fit models, and compute probability distributions * Speed up intensive computations by writing parallel R programs for Hadoop * Get a complete desktop reference to R