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The book is designed to be very accessible, with a focus on methods, examples, and computing, and theoretical details kept to an absolute minimum. It could be used as... Læs mere
For advanced undergraduate or non-major graduate students in Advanced Statistical Modeling or Regression II... Læs mere
This edited volume analyses leadership in the public relations (PR) industry with a specific focus on women and their leadership styles and preferences.
This book unpacks the complex dynamics of Hong Kong students’ choice in pursuing undergraduate education at the universities of... Læs mere
Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis.
This book is designed to make spatio-temporal modeling and analysis understandable to students and researchers, mathematicians and statisticians and... Læs mere
This is the first numerical analysis text to use Sage for the implementation of algorithms and can be used in a one-semester course for undergraduates in mathematics, math education, computer science/information technology, engineering, and physical sciences.
Introduction to Python for Science and Engineering offers an incisive introduction to the Python programming language for use in any science or... Læs mere
Introduction to Python for Science and Engineering offers an incisive introduction to the Python programming language for use in any science or... Læs mere
This textbook is designed for students in statistics, data science, biostatistics, engineering, and physical science programs who need... Læs mere
Maximum Likelihood Estimation with Stata, Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write... Læs mere
Thoroughly revised and updated, this is the first book of the second edition of Introduction to Data Science: Data Wrangling and Visualization with R. It introduces skills that can help you tackle real-world data analysis challenges. No previous knowledge of R is necessary.