English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The mixed models are a proposal of advanced statistical modeling, that allow to improve the quality of the analysis of the fixed factors and random factors, modeling the random variability and the correlation of the errors, being very useful in the analysis of unbalanced data, data with pseudo replica, or data with some kind of hierarchical structure or grouping. In this research, with the InfoStat software, an application of the general and mixed models was carried out, on the variable "qualification of the Approved Students", of Agroindustrial Engineering, with data of 11 years. Statistical questions about academic performance behavior were answered, based on Year Academic of the students' and the Gender. In modeling the Academic Year, Gender and interaction as fixed factors, in relation to the grade of the approved students, it was determined that the inclusion of random factors: Student and Teacher, had improved: AIC, BIC model, normality and homoscedasticity of the residues, thus to get better the quality of the statistical analysis. The model with mixed factors, found significant differences in the qualifications of the students approved by the factors: "Academic Year" and "Gender". However, no interaction effect between the two factors was determined.

MODELOS LINEALES GENERALES Y MIXTOS EN LA CARACTERIZACIÓN DE LA VARIABLE CALIFICACIÓN, INGENIERÍA AGROINDUSTRIAL, UNI-NORTE