Jove
Visualize
Contact Us

Related Concept Videos

What are Estimates?01:06

What are Estimates?

8.9K
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...
8.9K
Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

250
Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
250
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

7.9K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
7.9K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.4K
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...
3.4K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.7K
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 +...
9.7K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

8.9K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
8.9K

You might also read

Related Articles

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

Sort by
Same author

Graph metadata dataset from grouped botnet network activities.

Data in brief·2025
Same author

Forensic analysis of iOS binary cookie files.

Journal of forensic sciences·2024
Same author

GA-Sense: Sensor placement strategy for detecting leaks in water distribution networks based on time series flow and genetic algorithm.

MethodsX·2024
Same author

DroNER: Dataset for drone named entity recognition.

Data in brief·2023
Same author

Botnet dataset with simultaneous attack activity.

Data in brief·2022
Same author

Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding.

Heliyon·2021
Same journal

A harmonized fast-fashion garment-variant dataset for textile circularity and sustainability assessment.

Data in brief·2026
Same journal

Terahertz reflectivity dataset: Reading text on both sides of the page.

Data in brief·2026
Same journal

High-quality draft genome sequence data of <i>Levilactobacillus brevis</i> 3LB isolated from fermented milk koumiss.

Data in brief·2026
Same journal

Interview dataset: Encouraging the development of industrial symbiosis networks in Slovenia - transition to the circular economy.

Data in brief·2026
Same journal

Timeseries of multispectral and radar data and vegetation indices from Sentinel-1, Sentinel-2 and Landsat-8 at field scale.

Data in brief·2026
Same journal

BACI-VI-Bench: A dataset of variational inequality benchmark instances for multi-agent trade-network equilibrium.

Data in brief·2026
See all related articles
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 Experiment Video

Updated: Feb 10, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.1K

Dataset for drone problem identification and severity estimation.

Swardiantara Silalahi1, Tohari Ahmad1, Hudan Studiawan1

  • 1Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.

Data in Brief
|February 9, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces DroSev, a new dataset for identifying drone issues and estimating their severity. It enables better drone health monitoring and predictive maintenance through log message analysis.

Keywords:
Drone datasetDrone forensicsInfrastructureLog analysisProblem identificationProblem severity

More Related Videos

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

6.1K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

965

Related Experiment Videos

Last Updated: Feb 10, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

2.1K
Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

6.1K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

965

Area of Science:

  • Drone technology
  • Data science
  • Machine learning

Background:

  • Drone flight logs contain critical information for operational safety.
  • Accurate problem identification and severity estimation are essential for drone maintenance.

Purpose of the Study:

  • To introduce DroSev, a novel dataset for drone problem identification and severity estimation.
  • To facilitate research in automated drone health monitoring and predictive maintenance.

Main Methods:

  • Acquired drone flight log messages from Mendeley Data and AirData.
  • Developed two subtasks: binary problem identification and multiclass problem severity classification.
  • Utilized stratified sampling for an 80:20 train-test split.

Main Results:

  • The DroSev dataset provides a comprehensive resource for drone log analysis.
  • The dataset supports both identification and severity assessment of drone-related problems.
  • Syntactical characteristics of the log messages are summarized.

Conclusions:

  • DroSev is a valuable resource for advancing drone safety and reliability.
  • The dataset can be used to train machine learning models for automated drone diagnostics.
  • Further research can leverage DroSev for improved drone operational management.