(untitled) (Nexo Revista Científica, 2018)
Univariate and multivariate statistics were used to look for patterns of academic performance that would allow early prediction of dropout and success at the end of the career in 7 generations of university students of agro-industrial engineering at the UNI North Campus, Nicaragua. By means of the discriminant analysis, the score academic of the first semester of first year of the race, allowed to classify the students that arrive to fifth year of successful form or not, with a high level of success, 79%. Mathematics first and then chemistry were the most important subjects to predict success or failure in the final race.