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Related Concept Videos

Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
Regression Toward the Mean01:52

Regression Toward the Mean

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 researchers try to extrapolate results...
Midrange01:07

Midrange

A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to outliers and...
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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.
The process of fitting the best-fit...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...

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Related Experiment Video

Updated: Jun 24, 2026

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model
08:03

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model

Published on: November 4, 2025

Reference range for cervical length throughout pregnancy: non-parametric LMS-based model applied to a large sample.

L J Salomon1, C Diaz-Garcia, J P Bernard

  • 1Maternité, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Faculté de Médecine, Université Paris Descartes, Paris, France. laurentsalomon@orange.fr

Ultrasound in Obstetrics & Gynecology : the Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology
|March 12, 2009
PubMed
Summary

This study developed a new statistical model to accurately assess cervical length changes during pregnancy. This method allows for reliable Z-score calculations, improving preterm delivery risk assessment by accounting for gestational age.

Related Experiment Videos

Last Updated: Jun 24, 2026

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model
08:03

Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model

Published on: November 4, 2025

Area of Science:

  • Obstetrics and Gynecology
  • Maternal-Fetal Medicine
  • Biostatistics

Background:

  • Short cervical length is a significant predictor of preterm delivery.
  • Accurate risk assessment requires considering gestational age (GA) due to natural changes in cervical length throughout pregnancy.
  • Existing methods may not adequately account for the non-normal distribution of cervical length measurements at specific GAs.

Purpose of the Study:

  • To model cervical length changes throughout pregnancy.
  • To develop a method for calculating Z-scores that accounts for GA.
  • To provide a tool for more accurate monitoring of cervical length and preterm delivery risk.

Main Methods:

  • Prospective measurement of cervical length in 6614 singleton pregnancies over 3 years.
  • Measurements were taken between 16 and 36 weeks of gestation.
  • The non-parametric LMS method was used to model the non-normally distributed cervical length data.

Main Results:

  • The LMS method successfully modeled cervical length variations across different GAs.
  • New reference charts and L, M, S values were computed.
  • A formula was provided to calculate Z-scores for any cervical length measurement at any GA.

Conclusions:

  • Cervical length measurements exhibit non-normal distributions at specific GAs.
  • The developed statistical model facilitates easy Z-score calculation.
  • This approach effectively mitigates the confounding effect of GA, enabling straightforward cervical length monitoring.