Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Confidence Intervals

Confidence Intervals

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...

Confidence Interval for Estimating Population Mean

Confidence Interval for Estimating Population Mean

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...

Interpretation of Confidence Intervals

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...

Confidence Coefficient

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Prediction Intervals

Prediction Intervals

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.

You might also read

Related Articles

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

Sort by

Same author

To interact or not to interact: The pros and cons of including interactions in linear regression models.

Behavior research methods·2025

Same author

Penalized optimal scaling for ordinal variables with an application to international classification of functioning core sets.

The British journal of mathematical and statistical psychology·2023

Same author

With Bayesian estimation one can get all that Bayes factors offer, and more.

Psychonomic bulletin & review·2022

Same author

On the white, the black, and the many shades of gray in between: Our reply to Van Ravenzwaaij and Wagenmakers (2021).

Psychological methods·2022

Same author

A review of applications of the Bayes factor in psychological research.

Psychological methods·2022

Same author

A New Opinion Polarization Index Developed by Integrating Expert Judgments.

Frontiers in psychology·2021

Search research articles

Related Experiment Video

Updated: Nov 16, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Bootstrap confidence intervals for principal covariates regression.

Paolo Giordani¹, Henk A L Kiers²

¹Department of Statistical Sciences, Sapienza University of Rome, Italy.

The British Journal of Mathematical and Statistical Psychology

|February 25, 2021

Summary

This summary is machine-generated.

This study introduces bootstrap methods for estimating statistical uncertainties in Principal Covariate Regression (PCOVR). The findings show these methods offer appropriate statistical behavior for PCOVR parameter estimates.

Keywords:

bootstrap confidence intervals principal covariate regression

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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

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

Published on: July 3, 2020

Related Experiment Videos

Last Updated: Nov 16, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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

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

Published on: July 3, 2020

Area of Science:

Statistics
Multivariate Analysis

Background:

Principal Covariate Regression (PCOVR) addresses challenges with numerous or collinear predictor variables.
Estimating statistical uncertainties for PCOVR parameter estimates has been an unmet need.

Purpose of the Study:

To develop and evaluate methods for estimating statistical uncertainties in PCOVR parameter estimates.
To assess the performance of bootstrap-based confidence interval strategies.

Main Methods:

The bootstrap approach was employed to estimate statistical uncertainties.
Four distinct strategies for bootstrap confidence intervals were derived and tested.
Simulation experiments were conducted to assess coverage properties.
Varimax and quartimin procedures, along with Procrustes rotations, were utilized.

Main Results:

The proposed bootstrap strategies generally demonstrated appropriate statistical behavior.
Confidence interval coverage approached desired levels as sample sizes increased.
Strategies using the quartimin procedure showed limitations with complex component structures.
Accurate component extraction improved the statistical behavior of the strategies.

Conclusions:

Bootstrap methods provide a viable approach for estimating PCOVR parameter uncertainties.
The choice of procedure (varimax vs. quartimin) and component number impacts performance.
Further refinement may be needed for complex data structures.