MATH 110 - Elementary Statistics

The following are the Student Learning Outcomes (SLOs) and Course Measurable Objectives (CMOs) for MATH 110. 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 will be able to determine descriptive statistics from a sample.
2. Students will be able to use sample statistics to develop a confidence interval for population parameters.
3. Using sample statistics from one or more samples, students will be able to test a claim made about a population parameter.
4. Using bivariate data, students will be able to determine whether a significant linear correlation exists between two variables and determine the equation of the regression line.

Course Measurable Objectives (CMOs)

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.