Buku teks ini menyajikan konsep-konsep dasar pembelajaran mesin dengan cara yang mudah dipahami dengan memberikan saran praktis, menggunakan contoh langsung, dan menawarkan diskusi menarik tentang aplikasi yang relevan. Topik utama termasuk pengklasifikasi Bayesian, pengklasifikasi tetangga terdekat, pengklasifikasi linier dan polinomial, pohon keputusan, jaringan saraf, dan mesin vektor dukung…
Published annually by the federal government since 1878, The Statistical Abstract of the United States is the best known statistical reference publication in the country, and perhaps the world. You’ll find it behind nearly every reference desk in U.S. libraries as the authoritative go-to source. Librarians value the Statistical Abstract as both an answer book and a guide to statistical source…
Streamline data analysis with an intuitive, visual Six Sigma strategy Visual Six Sigma provides the statistical techniques that help you get more information from your data. A unique emphasis on the visual allows you to take a more active role in data-driven decision making, so you can leverage your contextual knowledge to pose relevant questions and make more sound decisions. You'll learn dyn…
This text has three main purposes. The first purpose is to facilitate conceptual understanding of multivariate statistical methods by limiting the technical nature of the discussion of those concepts and focusing on their practical applications. The multivariate statistical methods covered in this text are factorial analysis of variance (ANOVA), analysis of covariance (ANCOVA), multivariat…
LISREL for Windows (Jöreskog & Sörbom 2006) is a Windows application for Structural Equation Modeling, Multilevel Structural Equation Modeling, Multilevel Linear and Nonlinear Modeling, Formal Inference-based Recursive Modeling, and Generalized Linear Modeling. This application consists of a 32-bit Windows application LISWIN32 that interfaces with the 32-bit application
Multivariate analysis techniques are popular because they enable organizations to create knowledge and thereby improve their decision making. Multivariate analysis refers to all statistical techniques that simultaneously analyze multiple measurements on individuals or objects under investigation. Thus, any simultaneous analysis of more than two variables can be loosely considered multivariat…
The statistical science has seen new paradigms and more complex and richer data sets. These include data on human genomics, social networks, huge climate and weather data, and, of course, high frequency financial and economic data.
The fourth edition of this book on Applied Multivariate Statistical Analysis offers a new sub-chapter on Variable Selection by using least absolute shrinkage and selection operator (LASSO) and its general form the so-called Elastic Net.