Juaci Vitoria Malaquiasa holds a bachelor's degree in statistics from the State University of Rio de Janeiro-UERJ (1999). He has a specialization in population studies and social research from the national school of statistical sciences-ENCE/IBGE (2001). He holds a master's degree in materials science in computational modeling and simulation from the University of Brasília-UNB (2014). He is currently an employee of EMBRAPA-Brazilian Agricultural Research Corporation and holds the position of Statistics Analyst. He has proven experience in teaching in higher education teaching in the applied statistics subject. He is interested in the areas of mathematical modeling, experimental analysis, biostatistics and multivariate analysis. Regarding data analysis, he has mastered SPSS and SAS statistical software, being an expert in using the R program, including teaching face-to-face courses. He works mainly on the following topics: computational modeling and simulation studies; population projections; design of agricultural experimental studies; and application of quantitative methods.
Juaci Vitoria Malaquiasa holds a bachelor's degree in statistics from the State University of Rio de Janeiro-UERJ (1999). He has a specialization in population studies and social research from the national school of statistical sciences-ENCE/IBGE (2001). He holds a master's degree in materials science in computational modeling and simulation from the University of Brasília-UNB (2014). He is currently an employee of EMBRAPA-Brazilian Agricultural Research Corporation and holds the position of Statistics Analyst. He has proven experience in teaching in higher education teaching in the applied statistics subject. He is interested in the areas of mathematical modeling, experimental analysis, biostatistics and multivariate analysis. Regarding data analysis, he has mastered SPSS and SAS statistical software, being an expert in using the R program, including teaching face-to-face courses. He works mainly on the following topics: computational modeling and simulation studies; population projections; design of agricultural experimental studies; and application of quantitative methods.