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The Data Preparation Journey: Finding Your Way with R introduces the principles of data preparation within in a systematic approach that follows a... Læs mere
This textbook is a comprehensive guide to machine learning and artificial intelligence tailored for students in business and economics. It takes a hands-on approach to teach machine learning, emphasizing practical applications over complex mathematical concepts.
Using diverse real-world examples, this book explores the use of R for data analysis, with extensive use of graphical presentation.... Læs mere
Discusses psychometrics, the assessment of various psychological domains, various measurement methods and emerging analytical frameworks... Læs mere
The intention is to guide the reader through the R landscape of 2023 and beyond. The second edition contains 24 new diagrams and flowcharts, seven new tables, and revised text and code examples for clarity.
Today, information technology plays a pivotal role in financial control and audit: most financial data is now digitally recorded and dispersed among servers, clouds and networks over which the audited firm has no control.
Functional Data Analysis with R presents many ideas for handling functional data including dimension reduction techniques, smoothing, functional regression, structured decompositions of curves and clustering.
Ways that raw and summary data can be turned into visualizations that convey meaningful insights: basic graphs, bar charts, scatter plots, and line charts, and... Læs mere
Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment.
IBM SPSS Statistics 29 Step by Step: A Simple Guide and Reference, eighteenth edition, takes a straightforward, step-by-step approach that makes SPSS software clear to beginners and experienced researchers alike.
This text makes use of symbolic algebra and vector-matrix algebra to demonstrate a new approach to learning statics. Symbolic solutions are obtained, together with the types of solutions covered in other texts, so that students can see the advantages of this new approach.
This book presents the theory of matrix algebra for statistical applications, explores various types of matrices encountered in statistics, and covers numerical linear algebra.