Statistical Inference

Probability models, statistical methods, and the information to be gained from them is vital for work in business, engineering, sciences (including social and behavioral), and other fields. Data must

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and mark

Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametric statistics. The fifth edition carries on this traditio

This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniqu

This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniqu

An intuitive, yet precise introduction to probability theory, stochastic processes, statistical inference, and probabilistic models used in science, engineering, economics, and related fields. This is

Now in its 14th edition, Statistics for Business and Economics by McClave, Benson, and Sincich places statistics in the context of contemporary business. The text places emphasis on inference, extensively covering data collection and analysis needed to evaluate the results of statistical studies and make good decisions. Students are encouraged to develop statistical thinking and to understand both the assessment of credibility and the value of data inferences.Based on the American Statistical Association's Guidelines for Assessment and Instruction in Statistics Education(GAISE) Project, the text emphasizes statistical literacy, fosters active learning in the classroom, discusses intuitive probability, stresses conceptual understanding over mere knowledge of procedures, and employs technology to develop this understanding and to analyze data.

本書計劃對具備微積分知識並主修數學、統計、工程、和科學（包括資訊科學、生物科學、醫藥科學和管理科學）的學生介紹機率與統計推論。它嘗試呈現機率與統計推論的內涵並藉由大量的範例來介紹機率與統計推論之各式各樣的可能應用。 本書的前四章包含了大多數統計學家都相信提供了機率以及單變量和雙變量的離散型與連續型的機率分配之優良課程內容。在第5章裡，這些概念被推廣到許多隨機變數，尤其是那些互相獨立者。這重要的