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Related Concept Videos

Residual Plots01:07

Residual Plots

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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
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Residuals and Least-Squares Property01:11

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
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Residual Stresses01:26

Residual Stresses

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Residual stresses reside in a structure even after removing the original stress inducer. This phenomenon often arises from varied plastic deformations across different parts of a structure. Consider a rod stretched beyond its yield point. It will not regain its original length due to permanent deformation. Even after load removal, the rod does not entirely lose stress because of uneven plastic deformations, resulting in residual stresses. The computation of these stresses in structures is...
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Properties of the z-Transform II01:16

Properties of the z-Transform II

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The property of Accumulation in signal processing is derived by analyzing the accumulated sum of a discrete-time signal and using the time-shifting property to determine its z-transform. This principle reveals that the z-transform of the summed signal is related to the z-transform of the original signal by a multiplicative factor.
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Fundamental Attribution Error01:14

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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Interference and Decay

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Forgetting is a complex cognitive phenomenon influenced by several factors, among which interference and decay are particularly prominent. These processes explain why individuals often struggle to retrieve specific information from memory, leading to lapses in recall that can be observed in everyday situations.
Interference occurs when competing memories hinder the retrieval of particular information. It can be classified into two types: proactive and retroactive interference. Proactive...
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Related Experiment Video

Updated: Sep 8, 2025

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
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Residualization: justification, properties and application.

Catalina B García1, Román Salmerón1, Claudia García2

  • 1Department of Quantitative Methods for Economics and Business, University of Granada, Granada, Spain.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces residualization as a method to handle multicollinearity in econometric models. This technique mitigates collinearity while also allowing for the isolation of individual regressor variable effects.

Keywords:
Collinearityeconometricisolated effectresidualization

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Area of Science:

  • Econometrics
  • Statistical Modeling

Background:

  • Collinearity is a common issue in econometric models, often overlooked or addressed by variable removal.
  • Variable removal due to collinearity can compromise research objectives.

Purpose of the Study:

  • To fully develop and justify the residualization procedure.
  • To demonstrate its utility in mitigating multicollinearity.
  • To showcase its capability in separating individual regressor effects.

Main Methods:

  • The paper details the residualization procedure.
  • Application is illustrated using econometric models.

Main Results:

  • Residualization effectively mitigates multicollinearity in econometric models.
  • The method provides a means to interpret coefficients by isolating the effect of the residualized variable.

Conclusions:

  • Residualization is a valuable technique for addressing multicollinearity.
  • It offers an alternative interpretation of coefficients and can be applied across various fields beyond finance and ecology.