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Modeling and Data Analysis : An Introduction with Environmental Applications.
・ISBN 978-1-4704-4869-1 hard US$ 85.00
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| 著者・編者 | Little, John B., |
|---|---|
| 出版社 | (American Mathematical Society, US) |
| 出版年月 | 2019 |
| ページ数 | 323 pp. |
| 言語 | ENG |
| ニュース番号 | <M25-13836> |
解説
Can we coexist with the other life forms that have evolved on this planet? Are there realistic alternatives to fossil fuels that would sustainably provide for human society's energy needs and have fewer harmful effects? How do we deal with threats such as emergent diseases? Mathematical models--equations of various sorts capturing relationships between variables involved in a complex situation--are fundamental for understanding the potential consequences of choices we make. Extracting insights from the vast amounts of data we are able to collect requires analysis methods and statistical reasoning.
This book on elementary topics in mathematical modeling and data analysis is intended for an undergraduate ``liberal arts mathematics''-type course but with a specific focus on environmental applications. It is suitable for introductory courses with no prerequisites beyond high school mathematics. A great variety of exercises extends the discussions of the main text to new situations and/or introduces new real-world examples. Every chapter ends with a section of problems, as well as with an extended chapter project which often involves substantial computing work either in spreadsheet software or in the ${\tt R}$ statistical package.
This book on elementary topics in mathematical modeling and data analysis is intended for an undergraduate ``liberal arts mathematics''-type course but with a specific focus on environmental applications. It is suitable for introductory courses with no prerequisites beyond high school mathematics. A great variety of exercises extends the discussions of the main text to new situations and/or introduces new real-world examples. Every chapter ends with a section of problems, as well as with an extended chapter project which often involves substantial computing work either in spreadsheet software or in the ${\tt R}$ statistical package.