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Related Concept Videos

Equivalent Resistance01:16

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In circuit analysis, situations often arise where resistors are neither in series nor parallel configurations. To tackle such scenarios, three-terminal equivalent networks like the wye (Y) (Figure 1 (a)) or tee (T) and delta (Δ) (Figure 1 (b)) or pi (π) networks come into play. These networks offer versatile solutions and are frequently encountered in various applications, including three-phase electrical systems, electrical filters, and matching networks.
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In a balanced four-wire wye-to-wye system, the arrangement involves wye-connected sinusoidal voltage sources and loads, connected through a neutral wire that links the neutral nodes of the source and load. The load impedance is connected across each phase of the load. The wye-connected source can be connected to the wye-connected load in four-wire and three-wire arrangements. A three-phase system is considered balanced when the load on each phase is equal, leading to uniform current flow and...
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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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The Delta-to-Y Circuit01:16

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In the delta-wye circuit, the source is delta-connected, while the load is in a wye configuration. This means that the phase voltage of the delta-connected source is equal to the line voltage of the wye-connected load. The connection between two-line currents originates from the delta-connected source. The phase difference in the balanced system allows for calculating one line current given the other, utilizing the positive sequence of phases. In the delta-wye system, the phase currents in the...
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Updated: Jun 28, 2025

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The "Why" behind including "Y" in your imputation model.

Lucy D'Agostino McGowan1, Sarah C Lotspeich1, Staci A Hepler1

  • 1Department of Statistical Sciences, Wake Forest University, Winston-Salem, NC, USA.

Statistical Methods in Medical Research
|April 16, 2024
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Including the outcome in imputation models is crucial for accurate epidemiological analysis. Stochastic imputation requires the outcome for unbiased results, while deterministic imputation does not.

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Bayesian statisticsimputationmissing dataregressionstatistical applications

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Modeling

Background:

  • Missing data is prevalent in epidemiological studies.
  • Imputation methods are commonly used to handle missing covariate data.
  • The necessity of including the outcome in imputation models is debated.

Purpose of the Study:

  • To investigate the impact of including the outcome variable in imputation models for missing covariates.
  • To differentiate the requirements for deterministic versus stochastic imputation methods.
  • To provide mathematical explanations for statistical recommendations in imputation.

Main Methods:

  • Mathematical derivations of imputation models.
  • Analysis of deterministic (single imputation) and stochastic (single/multiple imputation) methods.
  • Evaluation of bias in estimating covariate-outcome relationships.

Main Results:

  • Including the outcome in imputation models is a requirement for unbiased results with stochastic imputation.
  • Deterministic imputation models do not require the outcome variable.
  • Common misconceptions regarding deterministic imputation are addressed.

Conclusions:

  • The inclusion of the outcome variable in imputation models is method-dependent.
  • Stochastic imputation necessitates outcome inclusion for unbiased covariate-outcome association estimates.
  • This study clarifies theoretical underpinnings for practical imputation strategies in epidemiology.