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Linear Models with Python offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using Python
This book is bridging the gap for organisations and individuals who need to learn and use R in a part-time professional context, providing a set of skills to understand the usefulness of graphing, mapping, and modelling in R, using relatable examples throughout.
John MacInnes takes the fear out of statistics for students, and helps to raise the standards of their quantitative methods skills, by clearly and accessibly introducing all that’s needed to know about using secondary data and working with IBM SPSS Statistics.
This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. Bayesian inference or rare-event problems),... Læs mere
This book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R.
This book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved.
Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and... Læs mere
This book covers methods for evaluation of experimental data commonly encountered in science and engineering. Measurements of quantities that vary in a... Læs mere
Deep Learning with R introduces deep learning and neural networks using the R programming language. The book starts with an introduction to machine learning and moves on to... Læs mere