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Read about courses in a range of fields at a variety of institutions where students have learned TIER-like methods of reproducible research. Course syllabi, exercises, project instructions and other course documents are available for download.
Introduction to Probability and StatisticsSmith College
An application-oriented introduction to modern statistical modeling and inference: study design, de…
Introduction to Probability and Statistics
An application-oriented introduction to modern statistical modeling and inference: study design, descriptive statistics, data visualization, random variables, probability and sampling distributions, point and interval estimates, hypothesis tests, resampling procedures, and multiple regression. A wide variety of applications from the natural and social sciences will be used. Classes meet for lecture/discussion with activities and exercises that emphasize analysis of real data.
Students complete weekly lab assignments in R and RMarkdown, and a final data analysis project. The final project is worth 25% of the course grade, and must be reproducible. Students work in groups to complete the project on a topic of their choice. Students have a number of milestone assignments along the way, including an initial proposal, revised proposal, data file submission, data appendix, and a final technical report. The technical report includes all the code needed to complete the analysis.
Students worked through labs introducing R and RStudio (http://www.science.smith.edu/~amcnamara/sds220/labs/intro_to_r.html) and introducing data analysis (http://www.science.smith.edu/~amcnamara/sds220/labs/intro_to_data.html). Both labs were developed by the OpenIntro Statistics group, and include expository videos explaining some of the topics. The OpenIntro team has created an R package called oilabs, which includes a lab report template that can be accessed through RStudio.
Multiple RegressionSmith College
Theory and applications of regression techniques; linear and nonlinear multiple regression models, …
Theory and applications of regression techniques; linear and nonlinear multiple regression models, residual and influence analysis, correlation, covariance analysis, indicator variables and time series analysis. This course includes methods for choosing, fitting, evaluating and comparing statistical models and analyzes data sets taken from the natural, physical and social sciences.
Students worked in small groups to produce a data analysis on a topic of their choice. The project is 25% of the final course grade. Students had to work in R and RMarkdown, turn in a data appendix, and document all their data cleaning and analysis in their final report.