Modeling and Data Analysis: An Introduction With Environmental Applications

by John B Little

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 R statistical package.

323 pages, Hardcover

Published March 28, 2019

Reviews

[Tom Schulte · Rating 4 out of 5]

"...This text covers a gamut from secondary education topics, such as an introduction to the metric system and functions, to undergraduate material. The undergraduate material is generally taken a bit further than first year introductory courses. This includes logarithms to The Weber–Fechner law as well as semi-log and log-log visualizations of exponential relationships. Related topics include power laws, difference equations, and logistic models. Section-length treatment is given to classic modeling scenarios: epidemiology, predator-prey, and population growth. Matrix-modeled systems are taken up to the introduction of eigenvalues and eigenvectors. There is over one chapter of probability fundamentals, including various distributions, as well as three chapters on basic statistics including hypothesis testing. This varied material presented by using environmental topics as a clothesline to attach many fundamental topics fits together elegantly and efficiently. I have encountered many less ambitious textbooks that do not come across as well on a subset of the material presented here."

[Look for my entire review at MAA Reviews]