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Related Concept Videos

Range00:59

Range

14.3K
The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
14.3K
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...
28.3K
¹H NMR: Long-Range Coupling01:27

¹H NMR: Long-Range Coupling

2.7K
The coupling interactions of nuclei across four or more bonds are usually weak, with J values less than 1 Hz. While these are usually not observed in spectra, the presence of multiple bonds along the coupling pathway can result in observable long-range coupling.
In alkenes, spin information is communicated via σ–π overlap, as seen in allylic (four-bond) and homoallylic (five-bond) couplings. These coupling interactions are stronger when the σ bond is parallel to the alkene...
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Angle of Twist - Elastic Range01:13

Angle of Twist - Elastic Range

814
Consider a cylindrical shaft with a length denoted by L and a consistent cross-sectional radius referred to as r. This shaft undergoes a torque at the free end. The highest shearing strain within the shaft is directly proportional to the twist angle and the radial distance from the shaft axis. When the shaft behaves elastically, this shearing strain can be articulated using variables such as the applied torque, radial distance, the polar moment of inertia, and the modulus of rigidity. By...
814
Range Rule of Thumb to Interpret Standard Deviation01:13

Range Rule of Thumb to Interpret Standard Deviation

13.6K
The range rule of thumb in statistics helps us calculate a dataset's minimum and maximum values with known standard deviation. This rule is based on the concept that 95% of all values in a dataset lie within two standard deviations from the mean.
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height...
13.6K
Circular Shaft - Stresses in Linear Range01:13

Circular Shaft - Stresses in Linear Range

739
Consider a scenario where a circular shaft is subject to torque that remains within the boundaries of Hooke's Law, avoiding any permanent deformation. So, the formula for shearing strain is revisited. This formula is multiplied by the modulus of rigidity, and then Hooke's Law for the shearing stress and strain is applied. As a result, the equation for shearing stress in a shaft can be derived.
739

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Related Experiment Video

Updated: Feb 6, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation.

Matthew Brehmer, Bongshin Lee, Petra Isenberg

    IEEE Transactions on Visualization and Computer Graphics
    |August 24, 2018
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    Summary
    This summary is machine-generated.

    Mobile phone users completed tasks faster with linear range visualizations than radial ones, with similar accuracy. This study offers guidance for effective small-screen data display.

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    Area of Science:

    • Human-Computer Interaction
    • Data Visualization
    • Mobile Computing

    Background:

    • Mobile phones are primary devices for accessing time-series range data (e.g., health, weather).
    • Limited research exists on optimal visualization of ranges on small displays for diverse tasks and data types.
    • Effective range visualization is crucial for accurate data interpretation on mobile devices.

    Purpose of the Study:

    • To compare linear and radial layouts for visualizing ranges over time on mobile devices.
    • To evaluate task completion time and error rates for different visualization approaches.
    • To provide empirical guidance for designing effective small-screen range visualizations.

    Main Methods:

    • Conducted a crowdsourced experiment with 87 participants using only mobile phones.
    • Compared linear and radial range visualization layouts for time-series data.
    • Assessed performance on value retrieval, comparison tasks, and superimposed range comparisons.

    Main Results:

    • Participants completed tasks significantly faster using linear layouts compared to radial layouts.
    • Error rates were similar across both linear and radial layout conditions.
    • Performance was comparable between layouts when comparing superimposed observed and average ranges.

    Conclusions:

    • Linear layouts offer a speed advantage for time-series range visualization on mobile devices.
    • Both linear and radial layouts are viable for certain comparison tasks, with no significant accuracy differences.
    • Findings inform the design of mobile applications requiring effective display of temporal range data.