Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
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
查看所有相关文章

相关实验视频

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

多路数据建模以提高分类性能:光数据作为研究案例.

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
概括
此摘要是机器生成的。

高级数据建模,特别是第三级化学测量,可显著提高复杂系统的分类准确性. 这种方法增强了歧视能力,即使使用有限的数据,也提供了可靠的,可解释的结果.

关键词:
分类 分类 分类 分类.光光谱学是一种光谱学.多路数据建模的多路数据建模.模拟数据是模拟数据.第三阶段歧视模式

更多相关视频

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

相关实验视频

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

科学领域:

  • 分析化学 分析化学
  • 化学测量 化学测量 化学测量
  • 多变量数据分析 多变量数据分析

背景情况:

  • 复杂系统的分类在分析化学中具有挑战性.
  • 数据结构对分类性能产生重大影响.
  • 本研究使用光光谱学研究第一到第三阶数据结构.

研究的目的:

  • 系统地评估数据维度对分类性能的影响.
  • 为了比较不同的化学测量模型,包括PLS-DA,N-PLS-DA和PARAFAC-DA.
  • 在各种条件下评估模型性能,如类不平衡,噪声和样本大小.

主要方法:

  • 使用模拟和实验光数据集.
  • 采用了部分最小平方差异分析 (PLS-DA),多途径PLS-DA (N-PLS-DA) 和PARAFAC-DA.
  • 评估了具有不同数据顺序 (第一到第三) 的模型.

主要成果:

  • 第三阶层模型的准确度达到了>93%,超过了第一和第二阶层模型的性能.
  • N-PLS-DA和PARAFAC-DA成功地对橄油样本进行了歧视.
  • 在降解和氧化过程中,PARAFAC-DA提供了更高的解释性.

结论:

  • 高级数据建模,特别是第三级,提高了分类的可靠性和可解释性.
  • 第三阶化学测量模型对于复杂的矩阵来说是强大的和可概括的.
  • 这种方法为具有复杂数据的分析应用提供了巨大的潜力.