The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in REvery experienced practitioner knows that preparing data for modeling is a painstaking, time-consuming process. Adding to the difficulty is that most modelers learn the steps involved in cleaning and managing data piecemeal, often on the fly, or they develop their own ad hoc methods. This book helps simplify their task by providing a unified, systematic approac...

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling met...

Some probability problems are so difficult that they stump the smartest mathematicians. But even the hardest of these problems can often be solved with a computer and a Monte Carlo simulation, in which a random-number generator simulates a physical process, such as a million rolls of a pair of dice. This is what Digital Dice is all about: how to get numerical answers to difficult probability problems without having to solve complicated mathematical equations.Popular-math w...

Load, wrangle, and analyze your data using the world\\'s most powerful statistical programming languageKey Features\[\*\]Load, manipulate and analyze data from different sources\[\*\]Gain a deeper understanding of fundamentals of applied statistics\[\*\]A practical guide to performing data analysis in practiceBook DescriptionFrequently the tool of choice for academics, R has spread deep into the private sector and can be foun...

This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is divided into three parts. The first describes likelihood-based inference from a frequentist viewpoint. Properties of the maximum likelihood estimate, the score function, the likelihood ratio and...

A pragmatic guide with actionable recipes on LaTex to apply for tuning text, custom designs, fonts, embedding images, tables, advanced mathematics, and graphics for all your complex documentsKey FeaturesWork with modern document classes, such as KOMA-Script classesExplore the latest LaTeX packages, including TikZ, pgfplots, and biblatexAn example-driven approach to creating stunning graphics directly within LaTeXBook Description

Key FeaturesBook DescriptionThis book is for the scientists, engineers, programmers, or analysts looking for a high-quality, open source mathematical library. Knowledge of Python is assumed. Also, some affinity, or at least interest, in mathematics and statistics is required. However, I have provided brief explanations and pointers to learning resources.What you will learnInstall NumPy, matplotlib, SciPy, and IPython on various operati...

Many of the commonly used methods for modeling and fitting psychophysical data are special cases of statistical procedures of great power and generality, notably the Generalized Linear Model (GLM). This book illustrates how to fit data from a variety of psychophysical paradigms using modern statistical methods and the statistical language R. The paradigms include signal detection theory, psychometric function fitting, classification images and more. In two chapters, recently developed meth...

Gain insights into Bitcoin, a cryptocurrency and a powerful technology, to optimize your Bitcoin mining techniquesKey Features\[\*\]Learn how to use the advanced features of Bitcoin wallets\[\*\]Set up your Bitcoin mining operations to mine with efficiency\[\*\]Explore what the future holds for mining and blockchains in this pragmatic guideBook DescriptionBlockchain is being billed as the technology of the future. Bitcoin is ...

This book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems.An important goal has been to make the topics accessible to a wide audience, with l...

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache SparkKey Features\[\*\] Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data\[\*\] Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images\[\*\] A hands-on guide to understanding the nature of data and how to turn i...

This is the first book to show the capabilities of Microsoft Excel to teach environmentall sciences statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical environmental science 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.Excel, a widely available computer program fo...