The Graduate Program in Statistics and Agricultural Experimentation focuses its activities on the study and development of modern statistical methods for data analysis in the various areas of knowledge, but with particular attention to Statistics and Agricultural Experimentation. The main objective of the program is to train people and ensure their qualification, improving their knowledge in Statistics and Experimentation for teaching and research activities in teaching, research institutions and in public or private companies. The research lines are: Experimental and Applied Statistics, Mathematical Theory and Statistical Methods, Multivariate Analysis and Spatial Statistics, which involve:

- Experimental Statistics: design of experiments, analysis of data from agricultural studies and interpretation of the results;

- Regression and Time Series Analysis: estimation and prediction of linear and nonlinear models and chronological data analysis;

- Theory and Statistical Methods: population dynamics studies, Bayesian inference, statistical modeling and multiple comparison methods;

- Statistical Genetics and Statistical Genomics: emphasis on genetic and genomic evaluation, methods for predicting genetic values and inferences on genetic parameters;

- Multivariate Methods: proposition and evaluation of tests and stability analysis of cultivars;

- Computational Methods: use of computational intensive methods and data science techniques in the application and development of statistical methods;

- Spatial Statistics: theory and application of spatial and spatio-temporal statistics by using Geostatistics, Point Processes and Area Data analysis.

The selection process takes place annually, with the beginning of the course in the first semester of each year. Occasionally, selection processes are also carried out for the second semester. The program is funded from the Graduate Support Program (PROAP) of the Higher Education Personnel Improvement Coordination (CAPES) program, in addition to the funding of most students through scholarships granted by CAPES, National Council of Scientific and Technological Development (CNPq) and Minas Gerais State Research Support Foundation (FAPEMIG).