Preface to First Edition Before writing the graphics for SYSTAT in the 1980's, I began by teaching a seminar in statistical graphics and collecting as many different quantitative graphics as I could find.
Over two hundred novel and innovative computer algebra worksheets or "e;recipes"e; will enable readers in engineering, physics, and mathematics to easily and rapidly solve and explore most problems they encounter in their mathematical physics studies.
Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more.
The book focuses on several skew-normal mixed effects models, and systematically explores statistical inference theories, methods, and applications of parameters of interest.
Written by experts that include originators of some key ideas, chapters in the Handbook of Multiple Testing cover multiple comparison problems big and small, with guidance toward error rate control and insights on how principles developed earlier can be applied to current and emerging problems.
This volume features a collection of peer-reviewed contributions from the biannual conference organized by the Mexican Statistical Society, held in Cuernavaca, Mexico, from September 27-29, 2023.
Biological and biomedical studies have entered a new era over the past two decades thanks to the wide use of mathematical models and computational approaches.
SPSS (Statistical Package for the Social Sciences) is a data management and analysis software that allows users to generate solid, decision-making results by performing statistical analysis This book provides just the information needed: installing the software, entering data, setting up calculations, and analyzing data Covers computing cross tabulation, frequencies, descriptive ratios, means, bivariate and partial correlations, linear regression, and much more Explains how to output information into striking charts and graphs For ambitious users, also covers how to program SPSS to take their statistical analysis to the next level
This book presents the theory of matrix algebra for statistical applications, explores various types of matrices encountered in statistics, and covers numerical linear algebra.
This textbook gives an overview of statistical methods that have been developed during the last years due to increasing computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment.
Get to grips with building robust XGBoost models using Python and scikit-learn for deploymentKey FeaturesGet up and running with machine learning and understand how to boost models with XGBoost in no timeBuild real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal resultsDiscover tips and tricks and gain innovative insights from XGBoost Kaggle winnersBook DescriptionXGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently.
Praise for previous editions:"e;Gandrud has written a great outline of how a fully reproducible research project should look from start to finish, with brief explanations of each tool that he uses along the way.
This volume presents the latest advances in statistics and data science, including theoretical, methodological and computational developments and practical applications related to classification and clustering, data gathering, exploratory and multivariate data analysis, statistical modeling, and knowledge discovery and seeking.
Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians.
Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R.
Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R.
A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering.
Master the programming language of choice among statisticians and data analysts worldwide Coming to grips with R can be tough, even for seasoned statisticians and data analysts.
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architecturesKey FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook DescriptionMost programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts.
This book is intended for use as the textbook in a second course in applied statistics that covers topics in multiple regression and analysis of variance at an intermediate level.
An introduction to a popular programming language for neuroscience research, taking the reader from beginning to intermediate and advanced levels of MATLAB programming.
Using Stata for Quantitative Analysis offers a brief but thorough introduction to analyzing data in undergraduate and graduate level research methods, statistics, and data analysis courses using Stata software.
This graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability.
This book describes the uses of different mathematical modeling and soft computing techniques used in epidemiology for experiential research in projects such as how infectious diseases progress to show the likely outcome of an epidemic, and to contribute to public health interventions.
R is rapidly becoming the standard software for statistical analyses, graphical presentation of data, and programming in the natural, physical, social, and engineering sciences.
Newly revised to specifically address Microsoft Excel 2019, this book is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical biological and life science problems.
