Statistic: A Concise Mathematical Introduction for Students and Scientists offers a one academic term text that prepares the student to broaden their skills in statistics, probability and inference, prior to selecting their follow-on courses in their chosen fields, whether it be engineering, computer science, programming, data sciences, business or economics.
The book places focus early on continuous measurements, as well as discrete random variables. By invoking simple and intuitive models and geometric probability, discrete and continuous experiments and probabilities are discussed throughout the book in a natural way. Classical probability, random variables, and inference are discussed, as well as material on understanding data and topics of special interest.
Topics discussed include:
* Classical equally likely outcomes
* Variety of models of discrete and continuous probability laws
* Likelihood function and ratio
* Inference
* Bayesian statistics
With the growth in the volume of data generated in many disciplines that is enabling the growth in 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, Matematica and JMP is useful, however there is an accompanying website including sample R and Mathematica code to help instructors and students.
The book places focus early on continuous measurements, as well as discrete random variables. By invoking simple and intuitive models and geometric probability, discrete and continuous experiments and probabilities are discussed throughout the book in a natural way. Classical probability, random variables, and inference are discussed, as well as material on understanding data and topics of special interest.
Topics discussed include:
* Classical equally likely outcomes
* Variety of models of discrete and continuous probability laws
* Likelihood function and ratio
* Inference
* Bayesian statistics
With the growth in the volume of data generated in many disciplines that is enabling the growth in 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, Matematica and JMP is useful, however there is an accompanying website including sample R and Mathematica code to help instructors and students.
€40.99
Giới thiệu về tác giả
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.Mua cuốn sách điện tử này và nhận thêm 1 cuốn MIỄN PHÍ!
Ngôn ngữ Anh ● định dạng PDF ● Trang 184 ● ISBN 9781119675815 ● Kích thước tập tin 4.1 MB ● Nhà xuất bản John Wiley & Sons ● Được phát hành 2020 ● Phiên bản 1 ● Có thể tải xuống 24 tháng ● Tiền tệ EUR ● TÔI 7584412 ● Sao chép bảo vệ Adobe DRM
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