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 STAT201 (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, two-factor ANOVA models, repeated-measures designs, random and mixed effects, principle component analysis, linear discriminant analysis and cluster analysis.
Note: Students whose prerequisites are not identified by the system should contact the Math and Statistics Department for an override at MATH@metrostate.edu.
- 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).
- Understand the role of experimental design in controlling for variation among experimental outcomes.
- Analyze multivariate data using principle component analysis and cluster analysis