Lower Quartile
Lower Quartile - Related Videos
01:15
Quartile
0 Views •
2023
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...
01:29
Microsoft Excel: Median, Quartile range, and Box Plots
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2025
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 measures data spread, is...
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We demonstrate a method to collect magnetotactic bacteria (MTB) that can be applied to natural waters. MTB can be isolated and enriched from sediment samples using a relatively simple setup that takes advantage of the bacteria's natural magnetism. Isolated MTB can then be examined in detail using both light and electron microscopy.
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Occlusion of the Great and Small Saphenous Vein Using Copolymeric Glue Based on N-Butyl Cyanoacrylate and Methacryloxy Sulfolane
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Using Computer Vision Libraries to Streamline Nuclei Quantification
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Cited by 1 •
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This article describes step-by-step methods to automate image-based nuclei quantification using an open-source executable program validated across a range of cell densities. This program provides an alternative that addresses barriers related to cost, accessibility for users with limited technological skillsets, and application-specific validation that may limit utility of existing technologies.
FAQs
The lower quartile can vary because calculation methods handle the ordered dataset differently. A common classroom procedure takes the median of the lower half, whereas statistical software may interpolate between nearby observations, especially when the dataset size does not place Q1 directly on one observation. Therefore, analysts should identify the convention used before comparing reported quartiles.
Dataset size determines whether the quartile position coincides with an observed value or falls between two values. When the position is between observations, software may interpolate rather than select one value directly. This detail matters when reproducing results, checking textbook calculations, or comparing summaries generated by different statistical programs.
The lower quartile helps establish the lower boundary of the central half of a distribution. Combined with the upper quartile, it forms the interquartile range, which supports outlier detection. Values lying unusually far below this central portion can then be examined as potential low-side outliers rather than judged only against the overall minimum.
First, arrange all observations from smallest to largest. Next, identify the lower half of the ordered data and find its median, using the selected convention for handling an odd or even number of observations. That median provides the conventional Q1 value. Recording the method alongside the result makes the calculation easier to verify.
In a five-number summary, Q1 appears with the minimum, median, upper quartile, and maximum. A box plot uses Q1 as one edge of the box, so its location helps show where the lower portion of the central data lies. Comparing box positions and lengths can reveal differences in distribution location and spread across groups.
Comparing lower quartiles helps describe how the lower portions of different groups are positioned, even when their averages are similar. Alongside the median, upper quartile, and interquartile range, Q1 can indicate differences in distribution shape and spread. This makes it useful in applied research when summaries must be compared without displaying every observation.