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Related Concept Videos

Percentile01:18

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A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile.
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Quartile01:15

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Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
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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...
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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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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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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
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Looking beyond the mean: quantile regression for comparative physiologists.

Coen Hird1, Kaitlin E Barham1, Craig E Franklin1

  • 1School of the Environment, The University of Queensland, Brisbane (Magandjin), QLD 4072, Australia.

The Journal of Experimental Biology
|February 7, 2024
PubMed
Summary

Quantile regression (QR) offers a powerful alternative to traditional least squares regression (LSR) for physiologists. QR reveals effects across the entire data distribution, uncovering insights missed by LSR, especially in data tails.

Keywords:
BiostatisticsMedian regressionOrdinary least squaresPercentilesStatistical toolbox

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Area of Science:

  • Physiology
  • Statistical modeling
  • Data analysis

Background:

  • Physiological research predominantly uses statistical analyses focused on means.
  • The tails of response distributions can exhibit unique phenomena often overlooked by mean-centric analyses.
  • Limitations exist in traditional least squares regression (LSR) for capturing the full spectrum of biological responses.

Purpose of the Study:

  • To demonstrate the utility of quantile regression (QR) as a method to analyze physiological data.
  • To compare the effectiveness of QR against LSR in identifying effects across the entire dependent variable distribution.
  • To highlight how QR can reveal biologically significant patterns in data tails.

Main Methods:

  • Simulated datasets were generated to compare LSR and QR under controlled conditions.
  • Real-world physiological datasets were analyzed using both LSR and QR.
  • The study focused on comparing the ability of each method to detect effects in different parts of the response distribution.

Main Results:

  • For simulated data, LSR failed to detect significant effects in the distribution tails, while QR identified them.
  • With real data, LSR indicated a significant change in the mean response.
  • QR revealed a lack of response in upper quantiles, providing biologically relevant information missed by LSR.

Conclusions:

  • Quantile regression (QR) provides a more comprehensive analysis of physiological data by examining the entire response distribution.
  • QR can uncover important scientific phenomena occurring in the tails of distributions, which are often missed by traditional LSR.
  • This approach enables researchers to ask and answer more nuanced questions about biological variation.