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

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
Variation01:19

Variation

8.2K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.2K
Multiple Regression01:25

Multiple Regression

4.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.2K
Improving Translational Accuracy02:07

Improving Translational Accuracy

15.3K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
15.3K
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
Regression Analysis01:11

Regression Analysis

8.8K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
8.8K

You might also read

Related Articles

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

Sort by
Same author

Machine learning workflows in climate modelling: design patterns and insights from case studies.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

The effect of praise from peers on empathy and political inclusion towards racial or ethnic outgroups.

Nature human behaviour·2026
Same author

Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score.

Artificial intelligence in medicine·2023
Same author

Toward a taxonomy of trust for probabilistic machine learning.

Science advances·2023
Same author

The polarization of politics and public opinion and their effects on racial inequality in COVID mortality.

PloS one·2022
Same author

DISCUSSION of the Perone-Ham paper, Measurement and Control of Information Content in Electrochemical Experiments.

Journal of research of the National Bureau of Standards (1977)·2021
Same journal

Tau protein as a regulator of mitochondrial function and dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A scalable, dividing cell model for the robust propagation and quantification of human sporadic Creutzfeldt-Jakob disease prions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Epigenetic regulation of mesenchymal BMP signaling directs postnatal organ innervation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Single-shot wide-field biochemical imaging at 1 kHz frame rate.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Morphogenesis and topological evolution of a frustrated nematic liquid crystal under confinement.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

B cell-intrinsic CXCR3 drives efficient generation of ectopic pulmonary germinal center responses to influenza A virus infection.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

Related Experiment Videos

Framework for making better predictions by directly estimating variables' predictivity.

Adeline Lo1, Herman Chernoff2, Tian Zheng3

  • 1Department of Politics, Princeton University, Princeton, NJ 08540.

Proceedings of the National Academy of Sciences of the United States of America
|December 3, 2016
PubMed
Summary
This summary is machine-generated.

We introduce a new method for selecting predictive variables using the [Formula: see text]-score, which provides an unbiased estimate for assessing variable set predictivity. This approach effectively identifies relevant variables, improving prediction accuracy in data analysis.

Keywords:
high-dimensional datapredictionpredictivityvariable selection

Related Experiment Videos

Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Assessing variable set predictivity is crucial for accurate modeling.
  • Existing methods like naive estimators can be biased and sensitive to irrelevant variables.

Purpose of the Study:

  • To propose a novel framework for evaluating variable set predictivity.
  • To introduce a robust measure for identifying highly predictive variables.

Main Methods:

  • Defined the prediction rate for a variable set.
  • Evaluated and rejected the naive estimator due to bias and sensitivity.
  • Utilized the [Formula: see text]-score with the Partition Retention (PR) method for variable selection.

Main Results:

  • The [Formula: see text]-score of the PR method provides a relatively unbiased estimate of predictivity.
  • This measure is insensitive to noisy variables and serves as a lower bound.
  • Simulations and real-data application demonstrated the effectiveness of the [Formula: see text]-score.

Conclusions:

  • The PR method combined with the [Formula: see text]-score is an effective approach for selecting highly predictive variables.
  • Further research into sample-based predictivity measures is warranted.