Value-added in higher education: ordinary least squares and quantile regression for a Colombian case

Colombia applies two mandatory National State tests every year. The first, known as Saber 11, is applied to students who finish the high school cycle, whereas the second, called Saber Pro, is applied to students who finish the higher education cycle. The result obtained by a student on the Saber 11 exam along with his/her gender, and socioeconomic stratum are our independent variables while the Saber Pro outcome is our dependent variable.We compare the results of two statistical models for the Saber Pro exam. The first model, multi-lineal regression or ordinary least squares (OLS), produces an overall well fitted result but is highly inaccurate for some students. The second model, quantile regression (QR), weight the population according to their quantile groups. OLS minimizes the errors for the students whose Saber Pro result is close to the mean (a process known as estimation in the mean) while QR can estimate in the -quantile for every . We show that QR is more accurate than OLS and reveal the unknown behavior of the socioeconomic stratum, the gender, and the initial academic endowments (estimated by the Saber 11 exam) for each quantile group.En el sistema educativo de Colombia se realizan dos exámenes nacionales obligatorios al año. El primero, conocido como Saber 11, está dirigido a los estudiantes que finalizan el bachillerato, mientras que el segundo, conocido como Saber Pro, evalúa a los estudiantes que terminan un estudio superior. En este estudio, el resultado obtenido por un estudiante en el examen Saber 11, junto con su género y estrato socioeconómico, son nuestras variables independientes, mientras que el resultado del examen Saber Pro es nuestra variable dependiente.Comparamos los resultados de dos modelos estadísticos para Saber Pro. El primer modelo, regresión multi-lineal o mínimos cuadrados (OLS, por sus siglas en inglés), produce un buen ajuste general pero es impreciso para ciertos estudiantes. El segundo modelo, regresión cuantílica (QR, por sus siglas en inglés), mide la población de acuerdo con su cuantil. El OLS minimiza los errores para los estudiantes cuyo resultado en Saber Pro está cercano a la media (proceso conocido como estimación en la media) mientras que el QR puede estimar un valor en el cuantil θ para cada 0 θ 1. Mostraremos que el QR es más preciso que el OLS y revelaremos el comportamiento desconocido del estrato socio económico, el género y la preparación académica inicial (estimada con el examen Saber 11) para cada cuantil

The Averaged Hausdorff Distances in Multi-Objective Optimization: A Review

A brief but comprehensive review of the averaged Hausdorff distances that have recently been introduced as quality indicators in multi-objective optimization problems (MOPs) is presented. First, we introduce all the necessary preliminaries, definitions, and known properties of these distances in order to provide a stat-of-the-art overview of their behavior from a theoretical point of view. The presentation treats separately the definitions of the ( p , q ) -distances GD p , q , IGD p , q , and Δ p , q for finite sets and their generalization for arbitrary measurable sets that covers as an important example the case of continuous sets. Among the presented results, we highlight the rigorous consideration of metric properties of these definitions, including a proof of the triangle inequality for distances between disjoint subsets when p , q ⩾ 1 , and the study of the behavior of associated indicators with respect to the notion of compliance to Pareto optimality. Illustration of these results in particular situations are also provided. Finally, we discuss a collection of examples and numerical results obtained for the discrete and continuous incarnations of these distances that allow for an evaluation of their usefulness in concrete situations and for some interesting conclusions at the end, justifying their use and further study

Value-added in higher education: ordinary least squares and quantile regression for a Colombian case

Colombia applies two mandatory National State tests every year. The first, known as Saber 11, is applied to students who finish the high school cycle, whereas the second, called Saber Pro, is applied to students who finish the higher education cycle. The result obtained by a student on the Saber 11 exam along with his/her gender, and socioeconomic stratum are our independent variables while the Saber Pro outcome is our dependent variable. We compare the results of two statistical models for the Saber Pro exam. The first model, multi-lineal regression or ordinary least squares (OLS), produces an overall well fitted result but is highly inaccurate for some students. The second model, quantile regression (QR), weight the population according to their quantile groups. OLS minimizes the errors for the students whose Saber Pro result is close to the mean (a process known as estimation in the mean) while QR can estimate in the -quantile for every . We show that QR is more accurate than OLS and reveal the unknown behavior of the socioeconomic stratum, the gender, and the initial academic endowments (estimated by the Saber 11 exam) for each quantile group

A (p,q)-Averaged Hausdorff Distance for Arbitrary Measurable Sets

The Hausdorff distance is a widely used tool to measure the distance between different sets. For the approximation of certain objects via stochastic search algorithms this distance is, however, of limited use as it punishes single outliers. As a remedy in the context of evolutionary multi-objective optimization (EMO), the averaged Hausdorff distance Δ p has been proposed that is better suited as an indicator for the performance assessment of EMO algorithms since such methods tend to generate outliers. Later on, the two-parameter indicator Δ p , q has been proposed for finite sets as an extension to Δ p which also averages distances, but which yields some desired metric properties. In this paper, we extend Δ p , q to a continuous function between general bounded subsets of finite measure inside a metric measure space. In particular, this extension applies to bounded subsets of R k endowed with the Euclidean metric, which is the natural context for EMO applications. We show that our extension preserves the nice metric properties of the finite case, and finally provide some useful numerical examples that arise in EMO

Biodeterioration of plasma pretreated LDPE sheets by Pleurotus ostreatus.

Low-density polyethylene (LDPE) waste generates an environmental impact. To achieve the most suitable option for their degradation, it is necessary to implement chemical, physical and biological treatments, as well as combining procedures. Best treatment was prognosticated by Plackett-Burman Experimental Design (PB), evaluating five factors with two levels (0.25 mM or 1.0 gL-1 glucose, 1.0 or 2.0 mM CuSO4, 0.1 or 0.2 mM ABTS [2, 20-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid)], pH 4.5 ± 0.2 or 7.0 ± 0.2 and 30 or 90 day incubation), which was reproduced for 150 days. Therefore, PB identified a sequential treatment (plasma followed by fungus) for partial LDPE biodeterioration. Sheets were pretreated with glow discharge plasma (O2, 3.0 x 10(-2) mbar, 600 V, 6 min.), followed by Pleurotus ostreatus biodeterioration. Fungus growth, colonization percentage, and pigment generation followed. Laccase (Lac), manganese peroxidase (MnP) and lignin peroxidase (LiP) activities were appraised. Additionally, contact angle (CA), functional group presence and changes and carbonyl and vinyl indices (Fourier transformed infrared spectroscopy) were evaluated. LDPE surface changes were assessed by Young's modulus, yield strength, scanning electronic microscopy (SEM), Fourier transformed infrared spectroscopy (FTIR) and atomic force microscopy (AFM). Plasma discharge increased hydrophilicity, decreasing CA by 76.57% and increasing surface roughness by 99.81%. P. ostreatus colonization was 88.72% in 150 days in comparison with untreated LDPE (45.55%). After this treatment carbonyl groups (C = O), C = C insaturations, high hydrophilicity CA (16 ± 4) °, and low surface roughness (7 ± 2) nm were observed. However, the highest Lac and LiP activities were detected after 30 days (Lac: 2.817 U Lac g-1 and LiP: 70.755 U LiP g-1). In addition, highest MnP activity was observed at day 120 (1.097 U MnP g-1) only for P. ostreatus treated LDPE. Plasma favored P. ostreatus adsorption, adherence, growth and colonization (88.72%), as well as partial LDPE biodeterioration, supported by increased hydrophilicity and presence of specific functional chemical groups. The approximate 27% changes in LDPE physical properties support its biodeterioration

Biodeterioration of plasma pretreated LDPE sheets by Pleurotus ostreatus

supplementary material supporting the Low-density polyethylene (LDPE) partial deterioration after a combined treatment composed by glow discharge plasma (100% oxygen, 3.0 x10^-2mbar pressure, and 600 V voltage at a cathode distance of 5.6 cm), followed by <i>P. ostreatus </i>adherence, growth, and colonization