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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 Statistics

Smith College
statistics undergraduate R R Markdown
Statistical and Data Sciences 220, Spring 2017
Instructor: Amelia McNamara

An application-oriented introduction to modern statistical modeling and inference: study design, ...

Introduction to Probability and Statistics

Smith College

Statistical and Data Sciences 220, Spring 2017
Instructor: Amelia McNamara

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.

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Business Analytics

Union College
economics undergraduate R Markdown
Economics 364, Winter 2016
Instructor: Tomas Dvorak

This is the first year Tomas has taught a new course entitled Business Analytics. The key learnin...

Business Analytics

Union College

Economics 364, Winter 2016
Instructor: Tomas Dvorak

This is the first year Tomas has taught a new course entitled Business Analytics. The key learning objective is for students to be able to manipulate and analyze business data. The course is very hands-on with students programming in R Markdown from the first day. The emphasis is on data manipulation: students need to load in data, summarize, reshape, merge and append the data in multiple ways to get insights. In this course students realize that data preparation and manipulation is 90% of the work of an empirical researcher or an analyst. Running a regression or a sophisticated algorithm is the last and fairly straightforward step. The value of any analysis lies mostly in the quality and organization of the data. Using R Markdown forces students to integrate data manipulation and analysis with text. Their final project is always reproducible because it is a knitted R Markdown document. Even if they don't end up using R Markdown in the future (e.g. their thesis), the course gives them programming skills that are essential to reproducible research.

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Multiple Regression

Smith College
statistics undergraduate R R Markdown
Statistical and Data Sciences 291, Spring 2016
Instructor: Amelia McNamara

Theory and applications of regression techniques; linear and nonlinear multiple regression models...

Multiple Regression

Smith College

Statistical and Data Sciences 291, Spring 2016
Instructor: Amelia McNamara

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.

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