Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

5.4K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
5.4K
Cluster Sampling Method01:20

Cluster Sampling Method

12.2K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.2K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.1K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.1K
Chebyshev's Theorem to Interpret Standard Deviation01:15

Chebyshev's Theorem to Interpret Standard Deviation

4.4K
Chebyshev’s theorem, also known as Chebyshev’s Inequality, states that the proportion of values of a dataset for K standard deviation is calculated using the equation:
4.4K
Radius of Gyration of an Area01:12

Radius of Gyration of an Area

1.8K
The second moment of area, also known as the moment of inertia of area, is a crucial factor in understanding an object's resistance against bending deformation, or stiffness. To accurately estimate the second moment of area along any axis, one needs to concentrate all areas associated with that object into a thin strip, which should be placed parallel to that particular axis.
1.8K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.9K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Efficient weapon for protracted warfare to malaria: A chondroitin sulfate derivates-containing injectable, ultra-long-lasting meshy-gel system.

Carbohydrate polymers·2019
Same author

Responsiveness and minimal clinically important difference of the Chinese version of the Low Vision Quality of Life Questionnaire after cataract surgery.

International journal of ophthalmology·2019
Same author

<i>Yersinia pestis</i> Interacts With SIGNR1 (CD209b) for Promoting Host Dissemination and Infection.

Frontiers in immunology·2019
Same author

Discovery of Potent, Selective, and Orally Bioavailable Inhibitors against Phosphodiesterase-9, a Novel Target for the Treatment of Vascular Dementia.

Journal of medicinal chemistry·2019
Same author

Genotypic Frequencies at Equilibrium for Polysomic Inheritance Under Double-Reduction.

G3 (Bethesda, Md.)·2019
Same author

End-of-life cost and its determinants for cancer patients in urban China: a population-based retrospective study.

BMJ open·2019

Related Experiment Video

Updated: Aug 14, 2025

Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.1K

A new method for identifying industrial clustering using the standard deviational ellipse.

Ziwei Zhao1,2, Zuoquan Zhao3,4, Pei Zhang5

  • 1School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing, 100049, China.

Scientific Reports
|January 11, 2023
PubMed
Summary

This study introduces an innovative ellipse-based method for identifying industrial clusters, improving spatial analysis. The approach effectively reveals multi-agglomeration structures and degrees of industrial clustering in continuous space.

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

9.1K

Related Experiment Videos

Last Updated: Aug 14, 2025

Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.1K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

9.1K

Area of Science:

  • Economics
  • Geography
  • Spatial Analysis

Background:

  • Identifying industrial agglomeration's spatial structure is challenging.
  • Existing clustering methods lack targeted approaches for continuous space.
  • Standard deviational ellipse models offer spatial representation advantages.

Purpose of the Study:

  • To propose a novel ellipse-based approach for identifying industrial clusters.
  • To enhance the analysis of multi-agglomeration spatial structures.
  • To provide a more targeted industrial clustering method.

Main Methods:

  • Developed an ellipse-based approach using group nearest neighbor (GNN) ordering.
  • Utilized a spatial compactness matrix derived from elliptical parameters.
  • Reformulated clustering to identify specific elliptical parameters and used area changes as a cutoff criterion.

Main Results:

  • The ellipse-based approach was successfully illustrated using firm location data in Shanghai.
  • Compared the new method with four established clustering techniques.
  • Demonstrated the effectiveness of combining elliptical parameters and spatial compactness for cluster identification.

Conclusions:

  • The proposed ellipse-based method offers a new analytical framework for industrial clustering research.
  • This approach enhances the identification of industrial clusters in continuous spatial data.
  • The method provides a more targeted and effective way to analyze industrial agglomeration patterns.