Mathematics And Statistics For Science


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Essential Mathematics and Statistics for Science


Essential Mathematics and Statistics for Science

Author: Dr. Graham Currell

language: en

Publisher: Wiley

Release Date: 2005-10-21


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Basic Mathematics and Statistics for Science is a low-level introduction to the essential techniques students need to understand. It assumes little prior knowledge, and adopts a gentle approach that leads through examples in the book and website. No other text provides this range of educational support for science students. The integration between book and website provides study options that would be impossible through a book alone, and allows students to study in ways that suit their own circumstances and preferences. The combination of book and website also provides ready-prepared material for lectures, tutorials and computer practicals. Tutors can use the material to develop a variety of coherent programme using different learning styles. The book develops the mathematics and statistics through examples and questions that reflect the scientific context, and has succeeded in being relevant to a range of undergraduate science programmes.

Mathematics and Statistics for the Bio-sciences


Mathematics and Statistics for the Bio-sciences

Author: G. Eason

language: en

Publisher: Prentice Hall

Release Date: 1986


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All of Statistics


All of Statistics

Author: Larry Wasserman

language: en

Publisher: Springer Science & Business Media

Release Date: 2013-12-11


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Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.