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## Labs

Labs (and homeworks) will assume familiarity with R and RStudio. If your knowledge of R and/or RStudio is limited, I encourage you to go learn from Rebecca Ferrell's Introduction to R for Social Scientists.

The first lab and homeworks will require use of R Markdown, which is easy to use with RStudio. Some useful R Markdown resources:
Rebecca's course notes (linked above) also include some useful notes on R Markdown.

The following labs will be given as .Rpres slides. The slides (.html) and their source code (.Rpres) will be posted here.
• Lab 1: Introduction to R Markdown and R's functions for computing densities of and simulating from common distributions. (.Rmd, .pdf)
• Lab 2: Posterior Inference for Binomial Random Variables. (.Rpres, .html)
• Lab 3: Posterior Inference for Poisson Random Variables. (.Rpres, .html)
• Lab 4: Introduction to Monte Carlo Approximation. (.Rpres, .html)
• Lab 5: Posterior Inference for Normal Random Variables. (.Rpres, .html)
• Lab 6: Posterior Inference for Normal Random Variables. (.Rpres, .html)
• Lab 7: Posterior Inference for Normal Random Variables, Introduction to Markov Chain Monte Carlo Approximation. (.Rpres, .html)
• Lab 8: Posterior Inference for Multivariate Normal Random Variables, Introduction to Multiple Imputation. (.Rpres, .html)
• Lab 9: Posterior Inference for the Hierarchical Normal Model. (.Rpres, .html)
• Lab 10: Posterior Inference for Linear Regression. (.Rpres, .html)