This course aims to give participants a solid grounding in R for Data Analysis.  R is an open source computing package. It is an integrated suite of software facilities for data manipulation, calculation and graphical display. It provides an environment in which user can perform statistical analysis and produce graphics.

Statistical analysis has two main parts: descriptive and inferential. Probability and distribution theories are used for descriptive statistics and inferential theory for generalizations from samples into populations. This course of data analysis covers both descriptive and explanatory statistics using R. It provides opportunity to learn econometric model building for a particular problem while applying the theory learned in various courses to specific economic cases. Estimate, test, and forecast economic models.

Explanatory statistical models to data show how to fit data to linear such as Multiple Linear Regression Models, Time Series Models, and nonlinear models such as Generalized Linear Models, and how to interpret estimation results.

The module covers both the completion of mathematical calculations by the use of software and the interpretation of quantitative results. The module will introduce participants to the use and application of statistical techniques for data analysis using free and open source software R.