Mathematics and Statistics for Science
Jun 2022 · Springer Nature
Ebook
964
Pages
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About this ebook
Mathematics and statistics are the bedrock of modern science. No matter which branch of science you plan to work in, you simply cannot avoid quantitative approaches. And while you won’t always need to know a great deal of theory, you will need to know how to apply mathematical and statistical methods in realistic scenarios. That is precisely what this book teaches. It covers the mathematical and statistical topics that are ubiquitous in early undergraduate courses, but does so in a way that is directly linked to science.
Beginning with the use of units and functions, this book covers key topics such as complex numbers, vectors and matrices, differentiation (both single and multivariable), integration, elementary differential equations, probability, random variables, inference and linear regression. Each topic is illustrated with widely-used scientific equations (such as the ideal gas law or the Nernst equation) and real scientific data, often taken directly from recent scientific papers. The emphasis throughout is on practical solutions, including the use of computational tools (such as Wolfram Alpha or R), not theoretical development. There is a large number of exercises, divided into mathematical drills and scientific applications, and full solutions to all the exercises are available to instructors.
Mathematics and Statistics for Science covers the core methods in mathematics and statistics necessary for a university degree in science, highlighting practical solutions and scientific applications. Its pragmatic approach is ideal for students who need to apply mathematics and statistics in a real scientific setting, whether in the physical sciences, life sciences or medicine.
Beginning with the use of units and functions, this book covers key topics such as complex numbers, vectors and matrices, differentiation (both single and multivariable), integration, elementary differential equations, probability, random variables, inference and linear regression. Each topic is illustrated with widely-used scientific equations (such as the ideal gas law or the Nernst equation) and real scientific data, often taken directly from recent scientific papers. The emphasis throughout is on practical solutions, including the use of computational tools (such as Wolfram Alpha or R), not theoretical development. There is a large number of exercises, divided into mathematical drills and scientific applications, and full solutions to all the exercises are available to instructors.
Mathematics and Statistics for Science covers the core methods in mathematics and statistics necessary for a university degree in science, highlighting practical solutions and scientific applications. Its pragmatic approach is ideal for students who need to apply mathematics and statistics in a real scientific setting, whether in the physical sciences, life sciences or medicine.
About the author
James Sneyd is a Professor of Applied Mathematics at the University of Auckland, New Zealand. His research is mostly in mathematical physiology and cell biology, and he is the co-author (with Jim Keener) of the prize-winning book Mathematical Physiology. He lives beside a beach, and it is absolutely untrue that he would ever waste his time playing computer games.
Rachel Fewster is a Professor of Statistics at the University of Auckland, New Zealand. She has over 20 years experience of teaching mathematical statistics to students with diverse interests, for which she has received a national tertiary educator award. She is a leading researcher in statistical methods for wildlife conservation, and has pioneered numerous methods for analysing novel data types generated by emerging technologies for remote sensing and animal recognition. She leads a large citizen science project for New Zealand community conservationists and enjoys the challengeof communicating technical information to diverse audiences in an engaging and accessible manner. She is a recipient of the Campbell Award, the premier award for New Zealand statisticians.
Duncan McGillivray is a Professor of Physical Chemistry at the University of Auckland, New Zealand, and a Fellow of the Royal Society of Chemistry and the New Zealand Institute of Chemistry. He uses a wide range of mathematical tools in his studies of biologically relevant colloids and interfaces using neutron scattering. He is passionate about teaching and learning for science students, and has been involved in teaching at all levels of tertiary study.
Rachel Fewster is a Professor of Statistics at the University of Auckland, New Zealand. She has over 20 years experience of teaching mathematical statistics to students with diverse interests, for which she has received a national tertiary educator award. She is a leading researcher in statistical methods for wildlife conservation, and has pioneered numerous methods for analysing novel data types generated by emerging technologies for remote sensing and animal recognition. She leads a large citizen science project for New Zealand community conservationists and enjoys the challengeof communicating technical information to diverse audiences in an engaging and accessible manner. She is a recipient of the Campbell Award, the premier award for New Zealand statisticians.
Duncan McGillivray is a Professor of Physical Chemistry at the University of Auckland, New Zealand, and a Fellow of the Royal Society of Chemistry and the New Zealand Institute of Chemistry. He uses a wide range of mathematical tools in his studies of biologically relevant colloids and interfaces using neutron scattering. He is passionate about teaching and learning for science students, and has been involved in teaching at all levels of tertiary study.
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