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Related Concept Videos

z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of zero.
Normal Distribution01:11

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The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is extremely...
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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...

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Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
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Published on: January 29, 2018

Thinking outside the curve, part I: modeling birthweight distribution.

Richard Charnigo1, Lorie W Chesnut, Tony Lobianco

  • 1Department of Statistics and Biostatistics University of Kentucky Lexington, KY 40506-0027, USA. RJCharn2@aol.com

BMC Pregnancy and Childbirth
|July 30, 2010
PubMed
Summary

This study introduces a flexible framework for analyzing birthweight distributions to better understand fetal-infant mortality. The normal mixture model approach reveals hidden patterns in birthweight data, crucial for public health insights.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Understanding fetal-infant mortality requires analyzing birthweight distributions and prognostic factors.
  • Existing frameworks for birthweight analysis may not capture complex population heterogeneity.
  • A realistic and tractable analytical framework is needed for public health implications.

Purpose of the Study:

  • To introduce a novel framework for analyzing birthweight distributions.
  • To improve the understanding of relationships between birthweight and fetal-infant mortality.
  • To develop a method that can reveal previously undetectable heterogeneity in birthweight.

Main Methods:

  • Describing birthweight distributions using a normal mixture model.
  • Determining the number of model components from data using model selection criteria.
  • Addressing methodological issues such as sample size dependency and criterion influence.

Main Results:

  • A 4-component normal mixture model effectively describes birthweight for white singleton infants of smoking mothers.
  • A 6-component normal mixture model may be more suitable for general black singleton populations.
  • The proposed model was compared to a contaminated normal model and a 2-component normal mixture model.

Conclusions:

  • The developed framework does not assume specific birthweight intervals for compromised pregnancies.
  • It avoids constraining birthweights within compromised pregnancies to be normally distributed.
  • This approach can detect birthweight heterogeneity missed by other models like the contaminated normal model.