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

Cluster Sampling Method01:20

Cluster Sampling Method

15.5K
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...
15.5K
Prediction Intervals01:03

Prediction Intervals

3.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.5K
Correlation of Experimental Data01:23

Correlation of Experimental Data

521
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
521
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

9.1K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
9.1K
Confidence Intervals01:21

Confidence Intervals

11.2K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
11.2K
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

9.2K
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...
9.2K

You might also read

Related Articles

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

Sort by
Same author

Harmonization of late-life participation in cognitively stimulating activities across four cohort studies of cognitive aging.

Experimental gerontology·2026
Same author

The effect of missing data and imputation on the detection of bias in cognitive testing using differential item functioning methods.

BMC medical research methodology·2022
Same author

Assessing Bias in Cognitive Testing for Older Adults with Sensory Impairment: An Analysis of Differential Item Functioning in the Baltimore Longitudinal Study on Aging (BLSA) and the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS).

Journal of the International Neuropsychological Society : JINS·2021
Same author

'Alzheimer's Progression Score': Development of a Biomarker Summary Outcome for AD Prevention Trials.

The journal of prevention of Alzheimer's disease·2017
Same author

A COMPARISON OF THE PREDICTIVE ACCURACY OF A POOLING AND A SUBGROUPING PREDICTION STRATEGY.

Multivariate behavioral research·2016
Same author

A MONTE CARLO STUDY OF THE ACCURACY OF A HIERARCHICAL GROUPING PROCEDURE.

Multivariate behavioral research·2016
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Mar 27, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.5K

Bayesian Interval Estimation of Multiple Correlations with Missing Data: A Gibbs Sampling Approach.

A L Gross

    Multivariate Behavioral Research
    |January 13, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a Bayesian method using Gibbs Sampling to estimate squared multiple correlation from incomplete data. The approach accurately provides interval estimates even with complex missing data patterns.

    More Related Videos

    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

    11.2K
    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

    3.8K

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
    07:11

    Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

    Published on: November 10, 2023

    3.5K
    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

    11.2K
    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

    3.8K

    Area of Science:

    • Statistics
    • Multivariate Analysis
    • Computational Statistics

    Background:

    • Estimating population squared multiple correlation (SMC) is crucial in multivariate analysis.
    • Incomplete data sets, with missing values on any variable, pose significant challenges.
    • Existing methods often struggle with non-random missing data patterns.

    Purpose of the Study:

    • To develop a robust Bayesian method for interval estimation of population SMC.
    • To address data sets with missing values across dependent and independent variables.
    • To accommodate data missing not at random (MNAR).

    Main Methods:

    • A Bayesian approach utilizing Markov Chain Monte Carlo (MCMC) via Gibbs Sampling.
    • The method handles incomplete multivariate normal data, regardless of missingness pattern.
    • Detailed examination of Gibbs sampler convergence and prior sensitivity.

    Main Results:

    • The proposed Gibbs Sampling procedure effectively generates interval estimates for population SMC.
    • Empirical coverage probabilities indicate accurate estimation performance.
    • The method demonstrates robustness even with complex missing data scenarios.

    Conclusions:

    • The Bayesian method with Gibbs Sampling offers a reliable solution for SMC interval estimation with incomplete data.
    • Accurate estimates are achievable despite missing values in any combination of variables.
    • This approach enhances statistical inference in the presence of complex missing data.