Tag: statistics

Correlation and Linear Regression


From datascience+: “Before going into complex model building, looking at data relation is a sensible step to understand how your different variables interact together. Correlation looks at trends shared between two variables, and regression looks at relation between a predictor (independent variable) and a response (dependent) variable.”

Principal Component Analysis using R


From R-bloggers: “Curse of Dimensionality: One of the most commonly faced problems while dealing with data analytics problems such as recommendation engines, text analytics is high-dimensional and sparse data. At many times, we face a situation where we have a large set of features and fewer data points, or we have data with very high feature vectors. In such scenarios, fitting a model to the dataset, results in lower predictive … Continue Reading

Elements of Information Theory


From Amazon: “The Second Edition of this fundamental textbook maintains the book’s tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide reads with a solid understanding of the underlying theory … Continue Reading

Probability: Theory and Examples


From Amazon: “This book is an introduction to probability theory covering laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action, so there are 200 examples and 450 problems.”

Bayesian Data Analysis


From Amazon: “Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors–all leaders in the statistics community–introduce … Continue Reading

Introduction to Nonparametric Estimation


From Amazon: “This is a concise text developed from lecture notes and ready to be used for a course on the graduate level. The main idea is to introduce the fundamental concepts of the theory while maintaining the exposition suitable for a first approach in the field. Therefore the results are not always given in the most general form but rather under assumptions that lead to shorter or more elegant … Continue Reading

Asymptotic Statistics (Cambridge Series in Statistical and Probabilistic Mathematics)


From Amazon: “Here is a practical and mathematically rigorous introduction to the field of asymptotic statistics. In addition to most of the standard topics of an asymptotics course–likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures–the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, … Continue Reading

Statistical Inference


From Amazon: “This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be … Continue Reading


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From Quick-R: “R is an elegant and comprehensive statistical and graphical programming language. Unfortunately, it can also have a steep learning curve. I created this website for both current R users, and experienced users of other statistical packages…who would like to transition into R. My goal is to help you quickly access this language in your work. I assume that you are already familiar with the statistical methods covered and … Continue Reading

Data Science Essentials


from edX: “Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles with experts from Duke University and Microsoft. In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization taught alongside practical application oriented examples such as how to build a cloud data science solution using Microsoft Azure Machine Learning platform, or … Continue Reading