- Who Teaches QSci Courses?
- Is This Really Math/Statistics?
- Requirements for the CQS Minor
- Concerns About Instruction
Courses offered through the Center for Quantitative Science are largely taught by faculty from the School of Environmental and Forest Sciences and the School of Aquatic and Fishery Sciences. One characteristic that the professors and teaching assistants in CQS all have in common is that their research involves the application of quantitative methods to biological problems. Therefore, in addition to being biologically knowledgeable they also have degrees in biostatistics, biophysics, mathematics, the management of resources, or other professionally related areas. One consequence of their experience and training is that they bring a realistic sense of the methods that are important for students to know and they have the ability to extract from their years of experience relevant problems that are meaningful to large numbers of students in the class.
For information on individual instructors, see CQS Faculty.
Biological science and resource management have a long history, extending beyond the classical Greek and Roman civilizations. The Egyptians manipulated their crops and fertilized their land through the annual flooding of the Nile River, and the Greeks developed the methods for plotting out geometrical shapes for farm boundaries and for city planning. The Romans developed these further and built roads and laid out land and farm patterns that still exist. Throughout this entire period of time, the management of resources was recognized to rest both on biological science (how did crops and forests grow) as well as upon the need to quantify rates and amounts of growth and the land that was devoted to growth. These same very fundamental ideas are seen today, but in far more sophisticated forms. Many of the problems of today in the management of resources and the study of biological science still concern the rates of growth and the need to test hypotheses about how growth occurs and whether to use pesticides, how much fertilizer to use, age of sexual maturity, etc. In the modern world it is virtually impossible to practice biological science at either the molecular or ecological level or to practice resource management without a knowledge of statistics or without a knowledge of rates of growth, which falls into the area of calculus. The two most basic forms of statistics, as we teach them in the Center for Quantitative Science, are testing of hypotheses for whether one treatment or another works, and regression in order to be able to make predictions as to whether something will work or not. The Center for Quantitative Science teaches such basic courses two times a year, and in addition it also teaches classes concerned with how to design experiments and how to sample, in order to collect data. The point to get from this short history is that it is not really practical to separate out biological science and resource management from mathematics and statistics. They were linked centuries ago and continue to be so. The proof is in the history, but furthermore, the proof is in the future. Those people who are students today will be practicing professionals for the next two to three decades, and it is for those people that we design our classes.
The Center for Quantitative Science offers a minor for undergraduate students who are interested in applications of statistical and mathematical tools to problems in ecology, biology, renewable resource management, and the environment. Completing a minor in Quantitative Science provides an excellent way to increase the potential attractiveness of a degree.
The minor requires a minimum of 27 credit hours as follows:
Core Courses (24-25 credits)
- Q Sci 291 and 292, Analysis for Biologists I and II (May substitute Math 124, 125), (10 credits)
- Q Sci 381, Introduction to Probability and Statistics, (5 credits)
- Q Sci 482, Statistical Inference in Applied Research I, (5 credits)
- Q Sci 483, Statistical Inference in Applied Research II, (5 credits) OR
Q Sci 403/Stat 403, Resampling, (4 credits)
Electives (a minimum of 3 or more credits from an approved list)
- Partial approved list includes:
- Q Sci/Envir 210, Intro to Environmental Modeling, (4 credits)
- Q Sci/Stat 403, Intro to Resampling Inference, (4 credits) (if not taken as part of the core courses above)
- Q Sci/Fish 454, Ecological Modeling, (4 credits)
- Q Sci 480, Sampling Theory, (3 credits)
- Q Sci 483, Statistical Inference in Applied Research II, (5 credits) (if not taken as part of the core courses above)
- Q Sci 486, Experimental Design, (4 credits)
Additional courses may qualify at the discretion of the CQS Director.
A minimum grade of 2.0 is required in all courses taken as part of the minor.
To sign up for a Q Sci minor, please contact your departmental advisor. A printable description of the CQS minor can be found here.
Computer Access in the School of Aquatic and Fishery Sciences
The computer classroom in room 207 Fishery Sciences Building (FSH) is available for student access, except on rare occasions when a class is being taught in the room. This PC lab is equipped for general computational needs including email, word processing, database manipulation and compilation, and course work software.
Computer policies are posted on the SAFS website (http://fish.washington.edu). The site also includes hours when the computer room is scheduled for classroom instruction and unavailable to students. Software in room 207 is listed.
A wealth of information on computing at the University of Washington is available at http://washington.edu/computing.
If you have any concerns about a QSci course, course instructor, or TA, please see the instructor as soon as possible.
If you are not comfortable talking with the instructor, please contact:
Dr. Vincent Gallucci
Director of the Center for Quantitative Science
Q SCI 190 Quantitative Analysis for Environmental Science (5) NW, QSR Bare
Covers applications of precalculus techniques and concepts to environmental, ecological, biological, and natural resource problems stressing the formulation, solution, and interpretation of mathematical prcedures. Prerequisite: minimum grade of 2.0 in MATH 098 or MATH 103, a score of 151 on the MPT-G, or a score of 145 on the MPT-A test. Offered: AWSp. http://faculty.washington.edu/bare/qsxxx/qsxxx.html - Bare
Q SCI 210 Introduction to Environmental Modeling (5) NW, QSR
Introduction to the use of computer modeling software in environmental policy and decision making. In weekly computer lab meetings, students use established programs to analyze the outcomes of management strategies and policy decisions related to topics such as conservation of endangered species, climate change, and deforestation.
Q SCI 291 Analysis for Biologists I (5) NW, QSR Johnson, Toth
Introduction to differential calculus, emphasizing development of basic skills. Examples promote understanding of mathematics and applications to modeling and solving biological problems. Topics include optimization and curve analysis. Prerequisite: either MATH 120, a minimum score of 2 on advanced placement test, or a minimum score of 67% on MATHPC placement test. Offered: AWSp.
Q SCI 292 Analysis for Biologists II (5) NW, QSR Gallucci, Johnson
Introduction to integral calculus, emphasizing development of basic skills. Examples promote understanding of mathematics and applications to modeling and solving biological problems. Topics include areas under curves, volumes, and differential equations. Prerequisite: Q SCI 291. Offered: WSpS.
Q SCI 293 Analysis for Biologists III (5) NW, QSR Gallucci, Johnson
Additional topics in calculus and matrix algebra. Examples promote understanding of mathematics and applications to modeling and solving biological problems. Topics include infinite series, differential equations, vectors, functions of several variables, partial derivatives, and use of computer software. Prerequisite: Q SCI 292. (This course is currently being reviewed by the College of the Environment and UW curriculum committees for possible offering in the future.)
Q SCI 381 Introduction to Probability and Statistics (5) NW, QSR Bare, Gallucci, Greulich
Applications to biological and natural resource problems stressing the formulation and interpretation of statistical tests. Descriptive and inferential topics covered include common probability distributions, confidence intervals, hypothesis tests, and simple linear regression. Prerequisite: MATH 120, a minimum score of 2 on advanced placement test, or a minimum score of 67% on MATHPC placement test. Offered: AWSpS. http://faculty.washington.edu/bare/qs381/ - Bare
http://courses.washington.edu/qsci381f/ - Greulich
Q SCI 392 Techniques of Applied Mathematics in Biology I (3) NW
Ordinary differential equations-linear and nonlinear; systems of differential equations; approximation techniques, numerical solution techniques; applications to biological processes. Prerequisite: Q SCI 292. Not currently offered.
Q SCI 393 Techniques of Applied Mathematics in Biology II (3) NW
Applications of advanced ordinary differential equations, special functions, and partial differential equations to descriptions of biological phenomena. Particular emphasis on transport in biological systems, including diffusion and fluid flow. Prerequisite: Q SCI 392. Not currently offered.
Q SCI 403/STAT 403 Introduction to Resampling Inference (4) NW
Introduction to computer-intensive data analysis for experimental and observational studies in empirical sciences. Students design, program, carry out, and report applications of bootstrap resampling, rerandomization, and subsampling of cases. Prerequisite: either STAT 220, STAT 301, STAT/ECON 311, STAT 341, STAT/MATH 390 or STAT/ECON 481. Offered: Sp.
Q SCI 454 Ecological Modeling (5) NW Essington
Examines concepts in ecological modeling focusing on the rational, interpretation, and motivation for modeling in ecological sciences. Explores individual, population, and ecosystem-based models. Excel-based computer exercises, model building and interpretation, readings. Recommended: prior coursework in ecology and statistics. Offered: jointly with FISH 454. Offered: W.
Q SCI 458 Modeling and Estimation in Conservation and Resource Management (4) NW Branch
Explores the use of models in the evaluation of alternative management polices for natural resources, including modeling approaches, fitting models to data, and evaluating alternative management polices. Emphasizes calculating risk of extinction, and design of biological reserves. Recommended: either Q SCI 454 or FISH 454. Offered: jointly with FISH 458; Sp.
Q SCI 480 Sampling Theory for Biologists (3) NW Skalski
Theory and applications of sampling finite populations including: simple random sampling, stratified random sampling, ratio estimates, regression estimates, systematic sampling, cluster sampling, sample size determinations, applications in fisheries and forestry. Other topics include sampling plant and animal populations, sampling distributions, estimation of parameters and statistical treatment of data. Prerequisite: Q SCI 482; recommended: Q SCI 483. Offered: jointly with STAT 480; Winter quarters of odd years.
Q SCI 482 Statistical Inference in Applied Research I: Hypothesis Testing and Estimation for Ecologists and Resource Managers (5) NW Conquest, Turnblom
Parametric and nonparametric tests, and confidence intervals for estimated parameters. One-sample, two-sample, paired-sample, multi-sample comparisons (including analysis of variance). Statistical power and experimental design. Goodness-of-fit tests, chisquare tests for contingency table data. Application to biological problems. SPSS software used. Prerequisite: Q SCI 381 or STAT 311. Offered: AW.
Q SCI 483 Statistical Inference in Applied Research II: Regression Analysis for Ecologists and Resource Managers (5) NW Skalski
Analysis of linear regression models and introduction to nonlinear models. Model selection using generalized F-tests; residual analysis. Application to categorical, count, binomial, transformed variables. Introduction to matrix formulation of regression models and applications. Special topics of interest to instructor, e.g., Akike information theory, general linear models, etc. Prerequisite: Q SCI 381, Q SCI 482. Offered: Sp
Q SCI 486 Experimental Design (3) NW Conquest
Emphasizes data modeling using structured means resulting from choice of experimental design and treatment factor design. Various treatment structures and experimental designs will be examined in detail including Gradient, Crossed, and Nested Factor treatment structures, Completely Randomized Designs, Complete Block Designs, Incomplete Block Designs, Split-plot Designs, Repeated Measures Designs, and Covariance Analysis. Issues surrounding multiple comparisons, efficiency, power, sample size, and pseudo-replication are also covered. Prerequisite: Q SCI 482; recommended: Q SCI 483. Offered: jointly with STAT 486; Winter quarters of even years.
Q SCI 497 Special Topics in Quantitative Science (1-15, max. 15) NW
Topics not normally offered in regular curriculum. Format ranges from seminar/discussion, formal lectures, laboratory or modeling work. Offered: AWSpS.
Q SCI 498 Internship (1-15, max. 15)
Internship experience with a public agency or private company, supervised and approved by a faculty member. Preparation of professional report reflecting on the experience is required. Credit/no credit only. Offered: AWSpS.
Q SCI 499 Research Experience (1-15, max. 15)
Special studies in quantitative ecology and resource management for which there is not sufficient demand to warrant the organization of regular courses. Credit/no credit only. Offered: AWSpS