Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

1.1K
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
1.1K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

45.1K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
45.1K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

38.4K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
38.4K
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...
5.5K
Data Validation01:15

Data Validation

2.1K
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
2.1K
Data Validation01:03

Data Validation

6.9K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
6.9K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Chemometric electrochemical fingerprinting of thermal stress in honeybee larvae: a tool for discrimination and welfare assessment.

Analytical and bioanalytical chemistry·2026
Same author

Ciprofloxacin and glyphosate co-exposure alters soybean development and reprograms metabolic pathways.

Environmental science and pollution research international·2026
Same author

Comprehensive chemical fingerprinting by LC×LC-fluorescence and data-driven chemometric modelling for unsupervised classification.

Talanta·2025
Same author

Development of a chemometrics-assisted electrochemical sensor applied to gallic acid quantification in food samples.

Food chemistry·2025
Same author

Functional data analysis, a comprehensive framework for processing non-quadrilinear and low-selective data provided by four-way liquid chromatography analysis.

Analytica chimica acta·2025
Same author

Unambiguous Determination of Benzo[a]pyrene and Dibenzo[a,l]pyrene in HPLC Fractions via Room-Temperature Fluorescence Excitation-Emission Matrices.

Molecules (Basel, Switzerland)·2025
Same journal

Programmable DNA probe-mediated nanopore biosensor for multiplex nucleic acid detection and its application in milk authenticity identification.

Analytica chimica acta·2026
Same journal

A multifunctional fluorescent sensor for sequential off-on-off visual detection of Zn<sup>2+</sup> and glyphosate in food and biological matrices and efficient removal of Zn<sup>2+</sup> from aqueous media.

Analytica chimica acta·2026
Same journal

Automated carousel-based electrochemical sensing toward microbiological and oncological settings.

Analytica chimica acta·2026
Same journal

Label-free quantification of cumulative cytosol-enriched peptide concentrations by mass spectrometry.

Analytica chimica acta·2026
Same journal

Integrated multi-matrix bile acid metabolic metrics (BAMMs): A methodological framework for functional metabolic phenotyping in human subjects.

Analytica chimica acta·2026
Same journal

A dual-enzymatic activity/SERS dual-mode sensor array based on BSA-Cu nanoflowers for sensitive detection of various foodborne pathogens.

Analytica chimica acta·2026
Ver todos los artículos relacionados
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Video Experimental Relacionado

Updated: Feb 11, 2026

Quantifying X-Ray Fluorescence Data Using MAPS
14:58

Quantifying X-Ray Fluorescence Data Using MAPS

Published on: February 17, 2018

11.3K

Modelado de datos multidireccionales para mejorar el rendimiento de la clasificación: datos de fluorescencia como

Jorgelina Zaldarriaga-Heredia1, Antonella E Montemerlo1, José M Camiña1

  • 1Instituto de Ciencias de la Tierra y Ambientales de la Pampa-Facultad Ciencias Exactas y Naturales, Universidad Nacional de La Pampa, Santa Rosa, La Pampa, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2290, CP C1425FQB, Buenos Aires, Argentina.

Analytica chimica acta
|February 9, 2026
PubMed
Resumen
Este resumen es generado por máquina.

El modelado de datos de orden superior, en particular la quimiometría de tercer orden, mejora significativamente la precisión de la clasificación para sistemas complejos. Este enfoque mejora la capacidad de discriminación y proporciona resultados robustos e interpretables incluso con datos limitados.

Palabras clave:
ClasificaciónEspectroscopía de fluorescenciaModelado de datos multidireccionalesDatos simuladosModelo de discriminación de tercer orden

Más Videos Relacionados

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics
08:48

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

Published on: January 9, 2016

7.3K
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.4K

Videos de Experimentos Relacionados

Last Updated: Feb 11, 2026

Quantifying X-Ray Fluorescence Data Using MAPS
14:58

Quantifying X-Ray Fluorescence Data Using MAPS

Published on: February 17, 2018

11.3K
Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics
08:48

Combining Multiple Data Acquisition Systems to Study Corticospinal Output and Multi-segment Biomechanics

Published on: January 9, 2016

7.3K
Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.4K

Área de la Ciencia:

  • Química Analítica
  • Quimiometría
  • Análisis de Datos Multivariantes

Sus antecedentes:

  • La clasificación de sistemas complejos es un desafío en química analítica.
  • La estructura de los datos influye significativamente en el rendimiento de la clasificación.
  • Este estudio investiga estructuras de datos de primer a tercer orden utilizando espectroscopía de fluorescencia.

Objetivo del estudio:

  • Evaluar sistemáticamente la influencia de la dimensionalidad de los datos en el rendimiento de la clasificación.
  • Comparar diferentes modelos quimiométricos, incluidos PLS-DA, N-PLS-DA y PARAFAC-DA.
  • Evaluar el rendimiento del modelo en diversas condiciones, como desequilibrio de clases, ruido y tamaño de la muestra.

Principales métodos:

  • Se utilizaron conjuntos de datos de fluorescencia simulados y experimentales.
  • Se empleó el análisis discriminante de mínimos cuadrados parciales (PLS-DA), el PLS-DA multidireccional (N-PLS-DA) y el PARAFAC-DA.
  • Se evaluaron modelos con órdenes de datos variables (de primero a tercero).

Principales resultados:

  • Los modelos de tercer orden alcanzaron una precisión >93%, superando a los modelos de primer y segundo orden.
  • El N-PLS-DA y el PARAFAC-DA discriminaron con éxito las muestras de aceite de oliva.
  • El PARAFAC-DA ofreció una interpretabilidad superior de los procesos de degradación y oxidación.

Conclusiones:

  • El modelado de datos de orden superior, especialmente de tercer orden, mejora la fiabilidad y la interpretabilidad de la clasificación.
  • Los modelos quimiométricos de tercer orden son robustos y generalizables para matrices complejas.
  • Este enfoque ofrece un potencial significativo para aplicaciones analíticas con datos complejos.