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Variance01:15

Variance

12.4K
The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
The standard deviation measures the spread in the same units as the data....
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Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

718
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
718
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

511
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
511
Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
581
Data Reporting and Recording01:24

Data Reporting and Recording

5.5K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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What are Estimates?01:06

What are Estimates?

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
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Video Experimental Relacionado

Updated: Feb 9, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

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Estimación de varianza basada en aprendizaje automático bajo muestreo en dos fases utilizando datos del sector de

Sanaa Al-Marzouki1, Ibrahim A Nafisah2, Mhassen E E Dalam3

  • 1Statistics Department, Faculty of Science, King Abdul Aziz University, Jeddah, Kingdom of Saudi Arabia.

Scientific reports
|February 7, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un nuevo estimador de varianza para el muestreo en dos fases, que mejora la eficiencia de la estimación utilizando datos auxiliares mínimos. El novedoso método muestra un rendimiento analítico y empírico superior en comparación con los estimadores existentes.

Palabras clave:
Distribución de probabilidad yInformación auxiliarModelado híbridoAprendizaje automáticoMuestreo en dos fasesEstimación de varianza

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Área de la Ciencia:

  • Estadística
  • Metodología de Encuestas

Sus antecedentes:

  • La estimación precisa de la varianza es crucial para una inferencia estadística fiable.
  • Los métodos tradicionales pueden carecer de eficiencia cuando la información auxiliar es limitada.
  • El muestreo en dos fases ofrece un marco para incorporar datos auxiliares.

Objetivo del estudio:

  • Proponer un novedoso estimador de varianza para el muestreo en dos fases.
  • Mejorar la eficiencia de la estimación utilizando una variable auxiliar y un atributo binario.
  • Demostrar la superioridad analítica y empírica del estimador propuesto.

Principales métodos:

  • Desarrollo de un novedoso estimador de varianza que incorpora información auxiliar.
  • Derivación de propiedades teóricas, incluido el sesgo y el error cuadrático medio (MSE).
  • Realización de estudios de simulación utilizando conjuntos de datos de salud y educación.
  • Entrenamiento y evaluación de clasificadores de aprendizaje automático (Árbol de Regresión, Random Forest, Regresión de Vectores de Soporte).

Principales resultados:

  • El estimador propuesto demostró superioridad analítica con fórmulas probadas de sesgo y MSE.
  • Los resultados de la simulación mostraron valores de MSE consistentemente más bajos en comparación con los estimadores clásicos y competitivos.
  • Los modelos de aprendizaje automático mostraron un buen poder predictivo, pero el estimador propuesto ofreció una mejor interpretabilidad.
  • Se identificó la distribución de Weibull de tres parámetros como la más adecuada para el análisis.

Conclusiones:

  • El novedoso estimador de varianza mejora la precisión de la estimación en el muestreo en dos fases.
  • La información auxiliar mínima, el muestreo estructurado y el modelado híbrido mejoran la estimación de la varianza.
  • El estimador propuesto proporciona un enfoque teóricamente sólido y empíricamente validado para estudios aplicados.