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What is Variation?01:14

What is Variation?

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Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
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Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

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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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Variation01:19

Variation

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Graphing Antiderivatives01:30

Graphing Antiderivatives

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The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
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Graphs of Functions01:30

Graphs of Functions

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Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
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Related Experiment Video

Updated: Feb 10, 2026

Easy and Accurate Mechano-profiling on Micropost Arrays
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Easy and Accurate Mechano-profiling on Micropost Arrays

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Indexed variation graphs for efficient and accurate resistome profiling.

Will P M Rowe1,2, Martyn D Winn2

  • 1Institute of Integrative Biology, The University of Liverpool, Liverpool, UK.

Bioinformatics (Oxford, England)
|May 16, 2018
PubMed
Summary
This summary is machine-generated.

We developed GROOT, an efficient tool for profiling antimicrobial resistance (AMR) genes in metagenomes. This method accurately identifies AMR genes, aiding surveillance and treatment strategies against the global AMR threat.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Microbiology

Background:

  • Antimicrobial resistance (AMR) is a significant global health threat.
  • Profiling the metagenomic 'resistome' aids understanding of AMR gene diversity and dynamics.
  • Current resistome profiling methods face challenges with data volume, complexity, and gene similarity.

Purpose of the Study:

  • To develop an efficient and accurate method for resistome profiling.
  • To address limitations of existing tools in handling complex metagenomic data.
  • To improve the surveillance and understanding of antimicrobial resistance genes.

Main Methods:

  • Utilized a variation graph representation of gene sets.
  • Implemented a locality-sensitive hashing Forest indexing scheme for fast read classification.
  • Employed hierarchical local alignment for accurate reconstruction of full-length gene sequences.

Main Results:

  • Developed GROOT (graphing Resistance Out Of meTagenomes), an efficient resistome profiling tool.
  • GROOT demonstrates superior speed and accuracy compared to existing reference-dependent tools.
  • The method processes large metagenomes rapidly on standard hardware (e.g., 2 GB in 2 minutes on a laptop).

Conclusions:

  • GROOT offers an efficient and accurate solution for resistome profiling.
  • The method has broad applicability beyond AMR, potentially enhancing various metagenomic workflows.
  • This tool can support improved gene surveillance, personalized treatments, and sustainable antimicrobial use.