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MATH 11 - Support Topics for Elementary Statistics

The following are the Student Learning Outcomes (SLOs) and Course Measurable Objectives (CMOs) for MATH 11. A Student Learning Outcome is a measurable outcome statement about what a student will think, know, or be able to do as a result of an educational experience. Course Measurable Objectives focus more on course content, and can be considered to be smaller pieces that build up to the SLOs.

Student Learning Outcomes (SLOs)

  1. Students feel that Math 11 has improved their overall mathematical understanding and ability in Math 110. (measured by survey provided by corequisite committee)
  2. Math 11 students will be able to interpret slope and y-intercept of a linear function in context with a word problem such as the application of a regression line.
  3. Math 11 students will be able to compute and understand area in relation to probabilities.
  4. Math 11 students will be able to use critical thinking to write conclusions regarding statistical studies.

Course Measurable Objectives (CMOs Effective through Summer 2024)

  1. Convert between fractions, decimals, and percentages.
  2. Apply rules for rounding and significant digits.
  3. Graph and appropriately interpret a data set.
  4. Interpret slope and y-intercept of a linear function in context with a word problem.
  5. Calculate the mean, median, and mode from a data set.
  6. Use calculators and technology for descriptive statistics and probability calculations.
  7. Apply correct calculations and notation for inequality statements.
  8. Compute and understand area in relation to probabilities.
  9. Evaluate statistical formulas.
  10. Use critical thinking to write conclusions regarding statistical studies.
  11. Communicate effectively in mathematical language.

Course Measurable Objectives (CMOs Effective Beginning Fall 2024)

  1. Define basic statistical terms and notation and distinguish among different scales of measurement. 
  2. Describe the proper methods of sampling. 
  3. Interpret data displayed in tables and graphically.
  4. Describe and calculate distributions of quantitative data in terms of center, shape, and spread for a data set.
  5. Infer from observational and experimental studies.
  6. Explain the basic concepts of probability theory, including sample space, and calculate probabilities.
  7. Calculate the mean and variance of a discrete distribution.
  8. Determine the appropriate statistical methods by data type and number of populations or treatments, including distinguishing between sampling and population distributions, and analyze the role played by the Central Limit Theorem. 
  9. Employ the principles of inferential statistics by constructing and interpreting confidence intervals and hypothesis tests formulated for samples from both one and two populations, including the use of the normal and t probability distributions.
  10. Determine and intepret levels of significance including p-values and describe possible type I or type II errors when performing hypothesis tests.
  11. Use linear regression and Analysis of Variance (ANOVA) for estimation and inference, intepreting the associated statistics.
  12. Utilize statistical techniques with a variety of applications that pertain to business, the social, natural and physical sciences, health science, and education.
  13. Utilize computer technology in statistical analyses.