Mathematics for Agricultural and Applied Economics
Covers fundamental mathematics and statistics for quantitative methods in agricultural and applied economics, including optimization, linear algebra, and statistical inference.
Summary
What you’ll be able to do
Solve a constrained optimization problem by applying calculus rules.
Create and interpret basic data visualizations.
Articulate the law of large numbers as it pertains to empirical economic analysis.
Determine statistical significance of parameter estimates & interpret the results.
Use R for basic calculations and simulations.
Explain statistical concepts to a general audience.
Source: UW Guide
Grade history
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Prerequisites
Course map(ECON 101, 111,A A E 101, or 215 prior to Fall 2024) and (MATH 211, 217, or 221) or graduate/professional standing
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Professors
Spring 2027No instructors recorded for this selection.
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Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
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Student experience
the class
The fundamental mathematics and statistics necessary for the study of quantitative methods in agricultural and applied economics (AAE). Topics include the mathematics of optimization and its role in basic welfare theory and consumer demand; linear and matrix algebra and their application in both modeling consumer behavior and the statistical analysis of models; and the fundamentals of statistical analysis relevant to econometric analysis, including probability theory, sampling distributions and statistical inference.
difficulty & workload
No workload feedback recorded.
Topics
- Optimization, welfare theory, and consumer demand
- Linear and matrix algebra applications
- Probability theory, sampling distributions, and statistical inference
Skills
- Solving constrained optimization problems using calculus
- Determining statistical significance of parameter estimates
- Using R for calculations and simulations
Grades
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Sources & history
Catalog & offerings
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Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
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Full model traces
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Model & dataset provenance
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"observed_at": "2026-10-14 17:50:09.728220+00:00"
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