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Related Concept Videos

Quartile01:15

Quartile

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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:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
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Percentile01:18

Percentile

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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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Modified Boxplots00:57

Modified Boxplots

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A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
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Microsoft Excel: Median, Quartile range, and Box Plots01:29

Microsoft Excel: Median, Quartile range, and Box Plots

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In Microsoft Excel, calculating the median, interquartile range, and creating box plots can help understand the distribution of your data.
Median and Quartile Range: The median is calculated using the formula `=MEDIAN(range)', which provides the middle value of your data set. Quartiles divide your data into four equal parts. To find the first and third quartiles, use ‘=QUARTILE(range, 1)' and ‘=QUARTILE(range, 3)', respectively. The interquartile range (IQR), which...
867
Review and Preview01:10

Review and Preview

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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.
Percentiles are a type of fractile that partition data into...
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5-Number Summary01:04

5-Number Summary

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In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
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A Quantile-Quantile Toolbox for Reference Intervals.

Douglas M Hawkins1, Rianne N Esquivel2

  • 1School of Statistics, University of Minnesota, Minneapolis, MN, United States.

The Journal of Applied Laboratory Medicine
|January 11, 2024
PubMed
Summary
This summary is machine-generated.

Parametric statistical methods offer superior estimates for reference and detection limits when data are normally distributed. A quantile-quantile (QQ) toolbox simplifies these analyses, making them accessible for clinical laboratorians.

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Area of Science:

  • Statistical methodology
  • Biostatistics
  • Clinical laboratory science

Background:

  • Parametric statistical methods generally outperform nonparametric methods but require data to follow a known distribution, often normal.
  • Applications include determining reference and detection limits, where parametric analyses provide more accurate estimates and uncertainty measures.
  • Data may be normally distributed, transformable to normal, or exhibit deviations due to extreme values or data censoring from detection/quantitation limits.

Purpose of the Study:

  • To present a quantile-quantile (QQ) toolbox as a versatile methodology for various statistical settings.
  • To demonstrate the utility of QQ methodology in addressing challenges with data distribution and censoring.
  • To provide accessible parametric methods for clinical laboratorians.

Main Methods:

  • Utilized a quantile-quantile (QQ) toolbox for statistical analysis.
  • Employed QQ methodology to develop methods for power transformations and normality testing.
  • Applied QQ methods for estimating reference limits and constructing confidence intervals.

Main Results:

  • QQ methodology facilitates the identification of optimal power transformations for data normalization.
  • Normality testing before and after transformation is streamlined.
  • Accurate estimation of reference limits and confidence intervals is achieved.

Conclusions:

  • Parametric methods, enhanced by QQ methodology, offer statistically rigorous yet accessible solutions for clinical laboratories.
  • These methods do not necessitate specialized software or advanced statistical expertise, being implementable in spreadsheets.
  • Explored reference values for amyloid beta proteins in Alzheimer disease as a practical application.