Fundamental principles of inferential statistics are presented in lecture augmented by computer labs using Excel. Essential topics include sampling methods; descriptive statistics; counting and probability; poisson, binomal, normal and other probability distributions; confidence intervals; hypothesis testing; inferences from two samples; correlation and regression. Optional topics include goodness-of-fit and contingency tables; ANOVA; nonparemetrics; and statistical process control. Meets MnTC Goal 4.
Prerequisite: Math placement at the introductory college level.
Course Effective Dates: 8/21/06 – Present
Outline of Major Content Areas
As noted on course syllabus
Learning Outcomes
Generate and interpret a wide variety of descriptive statistics and charts.
Work with basic concepts of probability, including conditional probability, complements, compound events and Bayes? Theorem.
Understand and work with fundamental probability distributions such as Poisson, binomial, uniform, and normal.
Construct confidence interval estimates of proportion, mean, and standard deviation, interpreting results
Perform one- and two-population hypothesis tests for proportion, mean (including matched pairs), and standard deviation, interpreting results.
Use Excel to calculate experimental probabilities, work with probability distributions, generate confidence intervals, conduct hypothesis tests, and perform correlation and regression analyses
Minnesota Transfer Curriculum Goal Area(s) and Competencies Goal 04 — Mathematical/Logical Reasoning
Clearly express mathematical/logical ideas in writing.
Explain what constitutes a valid mathematical/logical argument(proof).
