Bøger af Man van Minh Nguyen

Statistical Inference (SI), as an essential chapter of Statistical Science, provides advanced methods for problem solving and decision making based on observed data. It is also an advanced part of our DATA ANALYTICS series with textbooks that present the background of concepts, statistical methods, probabilistic models and practical data-driven research problems in management, science, engineering, technology and sustainable development. SI discusses a basic coverage of key principles to state-of-the art concepts and applications. Although statistical techniques and computational methods are emphasized throughout, the book briefly has engineering and management orientations. In this write-up we try to convey honestly methodology and mathematical methods being useful in scientific domains as actuarial and financial sciences, in practical sectors as quality control, urban traffic management, environment an manufacturing. Readers, graduate students, researchers and professionals in diverse scientific exploration and engineering sectors will find that mathematical treatments and logical elucidation of many statistical results in the text are soundly accepted in their research works.

Statistical Science (Statistics) can be described as the science of problem solving in the presence of variability or uncertainty. Data Analytics briefly refers to the techniques used to analyze data to enhance productivity and business gain. Data Analytics practically is the synergy of many building components including mostly tools from Statistics, Probability Theory, powerful algorithmic ideas from Computing, solid fundamentals of Mathematics and, last but not least, specific knowledge of application domains. We can define Data Analytics as the science and the art of making ¿good or meaningful¿ decision based on data sets, within a limited time range, a finite budget and/or computational resource.This book just presents an essential knowledge body for data analytics consisting of statistical inference, linear regression and stochastic analysis. It honestly conveys basic knowledge, theoretical methods and practical approaches to students and professionals, especially ones working in sciences (such as actuarial, computer, environmental, economic, medical), engineering, industry and services. The author hope readers find it joyful to employ the methods in this book.