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Variance01:15

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The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
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Hydration of Cement01:24

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Hydration of cement is a chemical reaction between cement particles and water. This process occurs primarily through two mechanisms: through-solution and topochemical. In the through-solution process, anhydrous compounds dissolve into their constituents, hydrates form in the solution, and then precipitate from the supersaturated solution. The topochemical process involves solid-state reactions at the cement particle surface. The through-solution process dominates the topochemical process at the...
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Aqueous Solutions and Heats of Hydration02:42

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Water and other polar molecules are attracted to ions. The electrostatic attraction between an ion and a molecule with a dipole is called an ion-dipole attraction. These attractions play an important role in the dissolution of ionic compounds in water.
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Friedman Two-way Analysis of Variance by Ranks01:21

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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Strength and Heat of Hydration01:29

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The hydration of cement is an exothermic reaction in which heat is generated as cement hydrates. This heat of hydration is critical to cement's strength development. The rate at which this heat is generated affects the temperature rise, with a majority of the heat being released early in the hydration process, half within the first three days, and about 75% within the first week.
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Acid-Catalyzed Hydration of Alkenes02:45

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Alkenes react with water in the presence of an acid to form an alcohol. In the absence of acid, hydration of alkenes does not occur at a significant rate, and the acid is not consumed in the reaction. Therefore, alkene hydration is an acid-catalyzed reaction.
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Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
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Unexplained Variance in Hydration Study.

Colleen X Muñoz1, Michael Wininger2,3,4

  • 1Department of Health Sciences and Nursing, University of Hartford, West Hartford, CT 06117, USA.

Nutrients
|August 10, 2019
PubMed
Summary
This summary is machine-generated.

The National Health and Nutrition Examination Survey (NHANES) offers valuable human hydration data, but current models show poor predictive power (R² < 0.15). Improved data reporting and variable inclusion are needed for better hydration research insights.

Keywords:
NHANESbig datachronic diseasedatabasehydrationmodelingobesitywater intake

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Area of Science:

  • Nutrition Science
  • Public Health Research
  • Biostatistics

Background:

  • The National Health and Nutrition Examination Survey (NHANES) is a key resource for studying human hydration patterns.
  • Assessing the utility and limitations of NHANES data for hydration research is crucial for accurate scientific inquiry.

Purpose of the Study:

  • To identify opportunities and limitations in using NHANES data for hydration studies.
  • To critically evaluate existing hydration models derived from the NHANES dataset.
  • To assess the goodness of fit and sensitivity of NHANES-derived hydration models.

Main Methods:

  • Reproduced two published hydration models using NHANES data.
  • Assessed model goodness of fit using R-squared (R²) and variance explained.
  • Evaluated model sensitivity to parameter configuration and covariate adjustments.

Main Results:

  • Hydration models derived from NHANES data consistently showed a low goodness of fit (R² < 0.15).
  • Reconfiguring variables did not significantly improve model predictive power.
  • The overall goodness of fit for published NHANES hydration models appears to be limited.

Conclusions:

  • Current hydration models using NHANES data have limited explanatory power.
  • Complete reporting of model diagnostics is essential for contextualizing findings from NHANES data.
  • Future research should explore novel biomarkers, situational variables, and standardized covariate adjustments to enhance NHANES hydration models.