This course covers introductory and intermediate ideas of the analysis of variance (ANOVA) method of statistical analysis. The course builds on the ideas of hypothesis testing learned in STAT 201 Statistics I. The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include one-factor ANOVA models, randomized block models, two-factor ANOVA models, repeated-measures designs, random and mixed effects, analysis of covariance, principle component analysis, and cluster analysis. Completion of STAT 201 Statistics I is a prerequisite.
- Understand the role of experimental design in controlling for variation among experimental outcomes.
- Analyze multivariate data using principle component analysis and cluster analysis
- Communicate understanding of analysis results through clearly written conclusions summarizing the results of the statistical models when applied to specified data sets.
- Demonstrate the ability to appropriately select among different ANOVA models for hypothesis testing based on the experimental design in the context of answering questions about representative real-world problems.
- Understand and learn to interpret a more advance set of hypothesis testing techniques (than are covered in STAT 201 Statistics I) such as one and two factor ANOVA models, multiple comparisons, nonparametric ANOVA, randomized block ANOVA and analyzing categorical data with ANOVA.
- Understand statistical principles and methods for analysis of variance (ANOVA).