Maestría en Matemática Aplicada

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Subject: search.filters.subject.REGRESIÓN LINEAL

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    Modelo de predicción del resultado en exámenes de acceso a la educación superior para estudiantes que se preparan en centros de capacitación preuniversitaria usando algoritmos de Machine Learning.
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Maestría en Matemática Aplicada, 2023) Coba Gavilánez, Christian Danilo; Benalcázar Palacios, Marco Enrique
    La nota de admisión para el ingreso a la educación superior define si un estudiante ingresa o no la carrera de su interés. En Ecuador se ofertan cupos para el 56% de los postulantes a tercer nivel [1]. Esto hace que los estudiantes que optan por un cupo se preparen adicionalmente en un programa preuniversitario. Los cursos de preparación preuniversitaria tienen la misión de hacer que un estudiante obtenga una buena nota y pueda postular para tener una mayor probabilidad de ingreso a la universidad. Usualmente un programa preuniversitario consta de varios procesos académicos y evaluaciones continuas. En este trabajo se propone tener una predicción de la nota que sacará un estudiante en su examen de ingreso a la universidad antes de completar el programa preuniversitario. Adicionalmente se desea conocer cuáles son los factores de mayor relevancia que hacen que esta nota varíe. En los resultados se puede ver que la filial Ambato, un curso de 10 meses y los simulacros de exámenes son factores que tienen un impacto directo en la nota final de admisión. Los modelos de predicción implementados en este trabajo se basan en el uso de regresión lineal y redes neuronales artificiales (RNA). Los resultados de predicción de ambos modelos son similares, pero la ventaja del modelo de regresión lineal es que se puede interpretar cada una de las variables predictoras. Los datos y las variables de interés se obtuvieron del centro de estudios Quality Up, con información de procesos de admisión de 300 estudiantes pertenecientes al ciclo sierra 2022.
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    Modelo matemático para medir la probabilidad de la incurrencia en mora de créditos utilizando regresión lineal en una institución financiera de la ciudad de Ambato
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Maestría en Matemática Aplicada, 2022) Marcalla Pilamunga, Luis Alberto; Bustamante Romero, Edgar Johni
    This paper deals with the analysis of credit concessions in financial institutions in the city of Ambato, in order to estimate the probability of return on investment, reducing risk and granting agreements between the borrower and the lender. The intrinsic characteristics of the research, of the predictor variables and the independent variable that were used resulted in the application of a generalized linear model with the binomial logit option. The predictor variables that for the present study are all categorical, became fictitious or dummy variables when incorporated into the model, and the dependent variable, which can take only two possible values, 0 or 1, the same one that estimates the probability of default; grant to the present investigation a different approach that considers the socioeconomic, social, territorial and even gender environment to predict if a loan holder will diligently comply with the payments in the agreed installments. The institution that lent its collaboration for the design of the model was the "Cooperative of savings and credit Chibuleo Ltda.", located in the center of the city of Ambato, and in force in the financial market for the last 16 years. The cooperative belonging to the popular and solidarity economy sector has been promoting the local economy through the products and services it offers. The methodology applied in the cooperative to check if a member is a good payer is carried out with the help of different bureaus and with the thoroughness of a credit analyst, who decides whether or not to grant a loan; This methodology is useful, however, the history of loans granted with the same characteristics is not considered. The model made fills that gap and gives us a better overview of the possible scenarios in the future. The data was extracted from the cooperative's database, a relational database in SQL Server 2014, which was loaded into statistical software for processing and analysis. R is used, a programming language focused on a statistical analysis of large volumes of data, in addition to a graphic environment RStudio version 2020.02.1. As the variables studied were categorical, and not continuous, a version of linear regression, logit regression, was used to calculate the probability of relapse into default: 1 to indicate the occurrence of default on a loan bonus and 0 to predict loans without news in payments. For the development of the model, the maximum likelihood method is used with the help of R and the variables were tested to determine their significance with a confidence level of 95%.
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    Modelo matemático para estimar la deserción de estudiantes de la U.E. PCEI San Miguel de Salcedo
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Maestría en Matemática Aplicada, 2022) Tipanluisa Arequipa, Jaime Paúl; Meneses Freire, Manuel Antonio
    Dropout is inversely proportional to the development of the country, so it is extremely important to mitigate this problem predictively in order to take appropriate precautions. The present investigation aims to alleviate this problem since it aims to determine the mathematical model to estimate the dropout of students in the U. E. PCEI "San Miguel de Salcedo", which will be based on a correlational methodology, since it is intended establish the degrees of relationship between the descriptive variables or independent variables in relation to the dependent variable, it is of a descriptive type since it intends to describe all the aforementioned variables, once the methods, types and levels of investigation were established, the information was collected of the population studied 132 students of the institution, to whom a survey was applied virtually. When modeling the data obtained, it was concluded that the generalized linear model with the logit binomial option, which generated the same coefficients as with the significance criterion, it was established that only one variable had an adequate valuation, for which the Odd Ratio was applied. , being the variables that increase the probability of dropping out of school the following: in an extremely high way they are, if they have a criminal record, economic status; The factors that increase the probability of dropping out of school in a high to medium way are marital status, the sex of the students, having a dysfunctional family and occupations.