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A Thorough Guide to Elementary Matrix Algebra and Implementation in R Basics of Matrix Algebra for Statistics with R provides a guide to elementary matrix algebra sufficient for undertaking specialized courses, such as multivariate data analysis and linear models. It also covers advanced topics, such as generalized inverses of singular and rectangular matrices and manipulation of partitioned matrices, for those who want to delve deeper into the subject. The book introduces the definition of a matrix and the basic rules of addition, subtraction, multiplication, and inversion. Later topics include determinants, calculation of eigenvectors and eigenvalues, and differentiation of linear and quadratic forms with respect to vectors. The text explores how these concepts arise in statistical techniques, including principal component analysis, canonical correlation analysis, and linear modeling. In addition to the algebraic manipulation of matrices, the book presents numerical examples that illustrate how to perform calculations by hand and using R. Many theoretical and numerical exercises of varying levels of difficulty aid readers in assessing their knowledge of the material. Outline solutions at the back of the book enable readers to verify the techniques required and obtain numerical answers. Avoiding vector spaces and other advanced mathematics, this book shows how to manipulate matrices and perform numerical calculations in R. It prepares readers for higher-level and specialized studies in statistics.
A valuable guide for experienced programmers who want to develop professional level expertise in C. The book also gives readers detailed programming information on developing serious business applications for commercial use.
The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these "big data" that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps. Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book: Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation Provides a gradual entry to the methodological aspects of spatio-temporal statistics Provides broad coverage of using R as well as "R Tips" throughout. Features detailed examples and applications in end-of-chapter Labs Features "Technical Notes" throughout to provide additional technical detail where relevant Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data.
The ideal book for every programmer and software developer, The Programmer's Job Handbook covers what every programmer needs to know to optimize their career. The book covers such topics as where the best jobs are, who pays the most, and what skills are going to be in the greatest demand. It also discusses building the perfect resume and having the perfect interview.
You know him. He's the funny, sweet guy with the great eyes who asks you a million questions and seems mesmerized by every reply. He takes you on the greatest, longest date of your life. He swears he loves cats and cuddling. And his apartment is so clean. He just might be the One. Then he doesn't call, doesn't write. He sees you coming down the street and he hides behind a tree. He's a cad. And this is his story. After all the girl's guides to sex in the city, here - at last - is the view from the other side of the bed. In Cad: Confessions of a Toxic Bachelor, Rick Marin offers himself up for an in-depth look at man's superficial nature. In this rollicking, frequently insensitive and ultimately poignant memoir, Marin proves a master of the light touch even in his darkest hours. Part Hugh Hefner, part Hugh Grant, his tale is a rake's progress (in spite of himself) from incorrigible cad to reconstructed romantic. It is one man's story but many men will read it as their own. And for any woman who has ever wondered What was he thinking? This is what he was thinking. Laugh out loud funny' ElleMove over Bridget Jones' The Week'A very good, intelligent and funny book' Evening Standard
Textual Statistics with R comprehensively covers the main multidimensional methods in textual statistics supported by a specially-written package in R. Methods discussed include correspondence analysis, clustering, and multiple factor analysis for contigency tables. Each method is illuminated by applications. The book is aimed at researchers and students in statistics, social sciences, hiistory, literature and linguistics. The book will be of interest to anyone from practitioners needing to extract information from texts to students in the field of massive data, where the ability to process textual data is becoming essential.
Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields, including medical and health outcomes research, business analytics and data science, ecology, fishe