10-804-135Quantitative Reasoning
Course Information
Description
This course is intended to develop analytic reasoning and the ability to solve quantitative problems. Topics include logic, probability, descriptive and inferential statistics, linear and non-linear modeling, graphical representation, and functions.  The course emphasizes appropriate use of units, dimensions, estimates, mathematical notation, and technology.
Total Credits
3
Course Competencies
  1. Analyze logical arguments
    Assessment Strategies
    Oral, Written or Graphic Assessment
    Criteria
    identify logical fallacies in popular arguments
    recognize arguments as inductive or deductive
    identify inconsistencies in statistical arguments
    test conditions and/or reasonableness of assumptions

  2. Employ counting principles
    Assessment Strategies
    Oral, Written or Graphic Assessment
    Criteria
    apply the multiplication principle to determine the number of outcomes
    determine the size of intersections, unions, and complements of sets
    apply rules of counting in solving applied contexts

  3. Utilize probability models and rules
    Assessment Strategies
    Oral, Written or Graphic Assessment
    Criteria
    distinguish between theoretical and empirical probability
    compute probability using the basic definition
    compute the probability of joint and disjoint events
    compute conditional probabilities
    determine if two events are independent

  4. Employ descriptive statistics
    Assessment Strategies
    Oral, Written or Graphic Assessment
    Criteria
    generate frequency distributions from a given data set
    calculate the mean, median, and mode of a distribution
    interpret the mean, median, and mode as measures of central tendency
    calculate quartile and percentile ranks as measures of position
    calculate range, standard deviation, and interquartile range as measures of spread for a distribution
    interpret outliers
    use measures of central tendency and spread to compare and contrast two distributions
    construct a modified box-and-whisker plot to summarize comparisons
    use the language of probability to describe and evaluate statements involving risk

  5. Apply inferential statistics
    Assessment Strategies
    Oral, Written or Graphic Assessment
    Criteria
    evaluate sampling strategies
    determine sources of bias
    describe the difference between correlation and causation
    identify confounding variables
    interpret a confidence interval in applied contexts
    interpret a confidence interval to estimate a population parameter
    interpret the error term for a confidence interval

  6. Apply non-linear mathematical models
    Assessment Strategies
    Oral, Written or Graphic Assessment
    Criteria
    identify appropriate models for given data sets and applications
    construct a non-linear model to fit source data
    identify reasonable domain and range for a non-linear model
    employ solution techniques to solve for an unknown value in the non-linear function model
    utilize solutions to interpret results in an applied context
    identify important characteristics of models

  7. Develop graphical representations
    Assessment Strategies
    Oral, Written or Graphic Assessment
    Criteria
    plot points to construct the graph of a given equation
    evaluate graphs in an applied context
    construct pie charts, bar graphs, and line graphs
    construct appropriate charts or graphs for specific scenarios
    utilize function tables
    employ calculators, spreadsheets, or other technological tools for construction of various graphs
    construct scatterplots of bivariate data

  8. Apply principles of measurement
    Assessment Strategies
    Oral, Written or Graphic Assessment
    Criteria
    use appropriate units
    convert units as needed
    round values appropriately in an applied context

  9. Apply linear mathematical models
    Assessment Strategies
    Oral, Written or Graphic Assessment
    Criteria
    construct a linear model to fit source data
    identify reasonable domain and range for a linear model
    compute the slope and intercept
    interpret the slope and intercept in an applied context
    employ solution techniques to solve for an unknown value in the linear functional model
    utilize solutions to interpret results in an applied context