Statistics: A Concise Mathematical Introduction for Students, Scientists, and Engineers - Tapa blanda

Acerca del autor

DAVID W. SCOTT is the Noah Harding Professor of Statistics at Rice University in Houston, Texas. He is a Fellow of the ASA, IMS, AAAS, an elected member of the ISI and received the 2004 Army Wilks Award and the 2008 ASA Founder's Award. He was formerly the Editor of the Journal of Computational and Graphical Statistics and currently serves as Co-Editor of Wiley Interdisciplinary Reviews: Computational Statistics. He is also the author of Multivariate Density Estimation: Theory, Practice, and Visualization.

De la contraportada

Statistics: A Concise Mathematical Introduction for Students, Scientists, and Engineers offers a one academic term text that prepares the student in statistics, probability and inference, prior to selecting follow-on courses in his/her chosen fields, whether it be engineering, computer science, programming, data sciences, business or economics.

The book places early focus on continuous measurements, as well as discrete random variables. By invoking simple and intuitive models and geometric probability, discrete and continuous experiments, classical probability, and inference are discussed throughout the book, as well as material on understanding data and topics of special interest.

Topics covered in the book include:

• Classical, equally likely outcomes and counting methods
• Variety of models of discrete and continuous probability laws
• Likelihood function, ratio, and maximum likelihood
• Inference, testing, and confidence intervals
• Bayesian statistics, regression, and simulation

Due to the rapid growth of data science, companies now demand statistically literate scientists and this textbook is the answer, suited for undergraduates studying science or engineering, be it computer science, economics, life sciences, environmental, business, amongst many others. Basic knowledge of bivariate calculus, R language, Mathematica and JMP is useful; however, there is an accompanying online resource that includes sample R and Mathematica code as well as homework and exam problems to help instructors and students.