APPLICATIONS OF QUANTITATIVE REASONING
- Program: Math
- Course: Applications of Quantitative Reasoning
- Course #: MATH2050
- Total Credits: 3
Course DescriptionThe focus of this course is on using quantitative information to think, reason and communicate more effectively. Students are presented with real world problems, and then asked to translate them into mathematics, and solve them. Topics include thinking critically, numbers in the real world, financial management, statistical reasoning, probability, and mathematical modeling. Examples and applications will be drawn from a wide range of disciplines and everyday situations including problems involving geometry, proportional reasoning, and percentages. This course will be a mix of lecture, individual work, and team-based problem solving. Student participation and active learning will be stressed. This course meets Minnesota Transfer Curriculum (MnTC) goal areas 2 and 4.
Refer to Course Syllabus for detailed information regarding the requirements and evaluation standards for this course. The Course Syllabus will be distributed the first week of the course.
Hennepin Technical College offers reasonable accommodations to qualified students with disabilities. If you have a documented disability that may require accommodations, contact the college’s Disability Services Director: Sara Laviolette at Brooklyn Park (763-488-2477) or Jean Kreutter at Eden Prairie (952-995-1544).
The following goals will be addressed in the course:
Analyze real world data through descriptive statistics, correlations, and regression lines
Analyze misuses of statistics and data representation
Apply concepts of geometry to optimize solutions for real world situations
Apply concepts of personal finance to make informed decisions
Analyze uses and abuses of percentages, ratios, rates, and proportions
Apply inductive and deductive reasoning to solve problems
Apply function notation to model real world applications
Solve problems using a variety of problem solving strategies
Analyze ideas, patterns, and multi-step problems
Model the connections between various types of problems
Gather information from a variety of sources
Organize statistical information from a variety of sources
Analyze statistical information from a variety of sources to make informed decisions
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