Listing 1 - 10 of 249 | << page >> |
Sort by
|
Choose an application
Probability, Statistics and Econometrics provides a concise, yet rigorous, treatment of the field that is suitable for graduate students studying econometrics, very advanced undergraduate students, and researchers seeking to extend their knowledge of the trinity of fields that use quantitative data in economic decision-making. The book covers much of the groundwork for probability and inference before proceeding to core topics in econometrics. Authored by one of the leading econometricians in the field, it is a unique and valuable addition to the current repertoire of econometrics textbooks and reference books. --
Choose an application
Choose an application
Choose an application
Lernen Sie, wie Sie mit R Ihre Rohdaten in Erkenntnisse und Wissen umwandeln. Dieses Buch führt Sie ein in R, RStudio und tidyverse – eine Sammlung von R-Paketen, die ineinandergreifen, um Data Science schnell, flüssig und komfortabel zu machen. R für Data Science ist geeignet für Leser ohne vorherige Programmierkenntnisse und zielt darauf ab, dass Sie Techniken der Data Science so schnell wie möglich in der Praxis umsetzen können.Die Autoren Hadley Wickham und Garrett Grolemund zeigen, wie Sie Daten importieren, aufbereiten, untersuchen und modellieren und wie Sie die Ergebnisse kommunizieren können. So bekommen Sie einen vollständigen Überblick über den Data-Science-Zyklus und die Tools, die für die Detailarbeit erforderlich sind.
Choose an application
Choose an application
Pattern recognition systems --- Mathematical statistics --- Data processing
Choose an application
Artificial intelligence --- Computer science --- Mathematical statistics --- Mathematics
Choose an application
Artificial intelligence --- Computer science --- Mathematical statistics --- Mathematics
Choose an application
Long description: SPSS ist ein umfangreiches Programm zur statistischen Datenanalyse, das inzwischen in der Version 25 vorliegt. In diesem Standardwerk von Felix Brosius wird die Anwendung umfassend beschrieben - von der Bedienung der Oberfläche über die Dateneingabe bis hin zur Durchführung und Interpretation statistischer Analysen sowie dem Erstellen von Grafiken. Biographical note: Felix Brosius ist promovierter Volkswirt, seit vielen Jahren aber in verschiedenen Internet-Businesses im Marketing und der Produktentwicklung tätig. Tägliches Arbeitswerkzeug bleibt dabei auch im Marketing und der Produktoptimierung die statistische Datenanalyse, über die Brosius bereits zahlreiche Bücher verfasst hat.
Mathematical statistics. --- Social sciences --- Statistical methods.
Choose an application
A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.
Machine learning --- Mathematical statistics. --- Estimation theory. --- Mathematics.
Listing 1 - 10 of 249 | << page >> |
Sort by
|