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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.8K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.8K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

525
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
525
Outliers and Influential Points01:08

Outliers and Influential Points

4.2K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.2K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.3K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.3K
What Are Outliers?01:12

What Are Outliers?

4.0K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
4.0K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.2K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Isocyanate Additives Improve the Low-Temperature Performance of LiNi<sub>0.8</sub>Mn<sub>0.1</sub>Co<sub>0.1</sub>O<sub>2</sub>||SiOx@Graphite Lithium-Ion Batteries.

ACS applied materials & interfaces·2023
Same author

Recent achievements of bioluminescence imaging based on firefly luciferin-luciferase system.

European journal of medicinal chemistry·2020
Same author

Bioinspired design and assembly of a multilayer cage-shaped sensor capable of multistage load bearing and collapse prevention.

Nanotechnology·2020
Same author

Decoupled Redox Catalytic Hydrogen Production with a Robust Electrolyte-Borne Electron and Proton Carrier.

Journal of the American Chemical Society·2020
Same author

Long exposure convolutional memory network for accurate estimation of finger kinematics from surface electromyographic signals.

Journal of neural engineering·2020
Same author

Synergistically enhanced heterogeneous activation of persulfate for aqueous carbamazepine degradation using Fe<sub>3</sub>O<sub>4</sub>@SBA-15.

The Science of the total environment·2020
Same journal

High-turnover copper-catalyzed amination of aryl bromides: exploring catalyst and ligand degradation pathways.

RSC advances·2026
Same journal

Sb-based metal oxide and sulfide anode materials for alkali-ion batteries.

RSC advances·2026
Same journal

Directed evolution of a cytochrome P450 monooxygenase for improved perillyl alcohol biosynthesis <i>via</i> a tailored genetically encoded biosensor.

RSC advances·2026
Same journal

Superspin-glass dynamics and magnetic memory in ZnFe<sub>2</sub>O<sub>4</sub> nanoparticles synthesized <i>via</i> a green egg-white-assisted route.

RSC advances·2026
Same journal

Porous and luminescent Dy-doped Co-BTC MOFs for label-free detection of tetracycline and vanadium traces in water.

RSC advances·2026
Same journal

An optimized green simultaneous HPLC analysis of dissolution rate monitoring for valsartan and sacubitril in tablet medications.

RSC advances·2026
See all related articles

Related Experiment Video

Updated: Aug 16, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K

Parameter estimation of three-parameter Weibull probability model based on outlier detection.

Hang Zhang1, Zhefeng Gao1, Chenran Du1

  • 1China Automotive Battery Research Institute Co., Ltd. No. 11 Xingke East Street, Yanqi Economic Development Area, Huairou District Beijing 101407 China fangyy@glabat.com.

RSC Advances
|December 22, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a simplified Weibull parameter estimation method for used lithium-ion batteries, effectively handling outliers for more accurate capacity distribution analysis.

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

Related Experiment Videos

Last Updated: Aug 16, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

Area of Science:

  • Statistical modeling
  • Battery degradation analysis
  • Reliability engineering

Background:

  • The Weibull model is popular for Li-ion battery inconsistency evaluation due to its flexibility.
  • Conventional Weibull methods struggle with complex calculations, outlier interference, and accurate data fitting.
  • A gap exists in methods balancing estimation accuracy with outlier detection.

Purpose of the Study:

  • To propose a simplified Weibull parameter estimation method for Li-ion batteries.
  • To address the challenge of outlier interference in statistical analysis.
  • To improve the accuracy of capacity distribution fitting for used batteries.

Main Methods:

  • Developed a Weibull parameter estimation technique with simplified computation.
  • Implemented outlier identification based on estimated Weibull parameters.
  • Excluded identified outliers from sample data for refined analysis.

Main Results:

  • The proposed method demonstrated simplified computation and effective outlier elimination.
  • Statistical tests (chi-square, Anderson-Darling) validated the model's performance.
  • Achieved superior goodness-of-fit and reduced error compared to maximum likelihood estimation and normal distribution models.

Conclusions:

  • The novel Weibull estimation method offers improved accuracy for Li-ion battery capacity distribution.
  • Simplified computation and robust outlier handling enhance reliability analysis.
  • Further parameter discussion optimized the method's application.