Statistics for biology
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Statistics for biology a practical guide for experimental biologist by O. N. Bishop

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Published by Longmans .
Written in English


Book details:

Edition Notes

Statementby O.N. Bishop.
SeriesThe principles of modern biology
ID Numbers
Open LibraryOL21274565M

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Introduction. The two instances of modern in the title of this book reflect the two major recent revolutions in biological data analyses. Biology, formerly a science with sparse, often only qualitative data has turned into a field whose production of quantitative data is on par with high energy physics or astronomy, and whose data are wildly more heterogeneous and complex. "Statistics for Terrified Biologists provides a valuable guide to statistics in clear language, which make it invaluable for pre-health and biology undergraduates students. Highly recommended." (CHOICE, March ) "I would like to express my utmost gratitude to you for writing THE book on statistics for by: Applied Statistics for Network Biology: Methods in Systems Biology 1st Edition by Matthias Dehmer (Editor), Frank Emmert-Streib (Editor), Armin Graber (Editor), & ISBN ISBN Why is ISBN important? ISBN. This bar-code number lets you verify that you're getting exactly the right version or edition of a book. Format: Hardcover. high school math. social sciences. literature and english. foreign languages.

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Statistics is the discipline that concerns the collection, organization, analysis, interpretation and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every. Biology books online These biology books are designed to allow students and all biology enthusiasts to gain insights into subjects such as kinetics, cancer biology or clinical biochemistry. Book supplements Introduction 1 Generative Models for Discrete Data 2 Statistical Modeling 3 High Quality Graphics in R 4 Mixture Models 5 Clustering 6 Testing 7 Multivariate Analysis 8 High-Throughput Count Data 9 Multivariate methods for heterogeneous data 10 Networks and Trees 11 Image data 12 Supervised Learning 13 Design of High Throughput. Introductory Statistics follows scope and sequence requirements of a one-semester introduction to statistics course and is geared toward students majoring in fields other than math or engineering. The text assumes some knowledge of intermediate algebra and focuses on statistics application over theory. Introductory Statistics includes innovative practical applications that make the text.