This course will present statistical methods and inference procedures with an emphasis on applications, computer implementation, and interpretation of results. Familiarity with the R programming language (learned in DS 710) is highly recommended. Topics include simple and multiple regression, model selection, correlation, moderation/interaction analysis, logistic regression, the chi-square test, the Kruskal-Wallis test, analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), factor analysis, and canonical correlation analysis.
- Examine and summarize data numerically and graphically.
- Given a data set and a question, choose the appropriate statistical procedure.
- Verify conditions for statistical procedures.
- Perform hypothesis tests and compute confidence intervals.
- Explore and model relationships among variables and use models to make predictions.
- Use software package R to implement statistical analyses.
- Interpret and critically evaluate statistical information and data-based arguments.
- Effectively communicate results of a statistical analysis.
- Use R Markdown to produce statistical reports and support reproducible research.
Prerequisites: None. However, it is highly recommended that students with no prior experience in the R programming language complete DS 710: Programming for Data Science before taking this course.
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