Quantitative Reasoning 2
Courses designated as satisfying the second stage of the QR requirement build upon the skills that students have mastered in QR1 (i.e., arithmetic, consumer issues, practical geometry, linear equations and linear growth, compound interest and exponential growth, data presentation and description, and basic probability and statistics). This can be accomplished in two ways (or a combination). First, a QR2 course might expand upon the ideas from QR1 in an applied setting, permitting students to see, in more depth, how these tools are used to solve problems in a specific discipline (or disciplines). Second, a QR2 course might build upon the skills covered in QR1 by increasing the breadth of quantitative skills that a student has mastered. In either case, QR2 courses will include the study of quantitative skills as a central and indispensable aspect of the course. The breadth and/or depth and the level of sophistication in a QR2 course should be above that of QR1, requiring students to master quantitative skills that are truly at the college level. Such skills might include, for example, one or more of the following:
- Study of rates of change in various systems with the aid of numerical methods, the calculus, and/or differential equations.
- The study of forms and shapes with the aid of geometry.
- The study of system behavior, competition, game strategies, and/or decision making, with the aid of probability theory.
- The study of measurement, data collection, cause and effect relationships, and/or patterns with the aid of statistical methods.
- The study of system properties that are expressed and evaluated with the aid of algebra.
- The study of resource allocation, planning and scheduling with the aid of linear programming.
Courses that satisfy the QR2 requirement need not necessarily exhibit a computing component, but its inclusion can enrich the content of the course. For example, the use of computers is encouraged to automate computation, test algorithms, and build and assess the validity of models of complex quantitative systems.