Continuous Data
Continuous Data - Related Videos
09:42
Using Continuous Data Tracking Technology to Study Exercise Adherence in Pulmonary Rehabilitation
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Cited by 7 •
2013
Pulmonary rehabilitation is widely recognized in the management of respiratory diseases. A key component to successful pulmonary rehabilitation is adherence to the recommended exercise training. The purpose of the present protocol is to describe how continuous data tracking technology can be used to precisely measure adherence to a prescribed aerobic training intensity.
09:40
Batch and Continuous Bioreactors
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2023
Bioreactors are used to grow organisms in large volumes, thereby enabling the production of mass quantities of the target product. These reactors can be batch reactors, which contain all of the components needed for cell growth, or continuous reactors, which have inlet and outlet ports allowing for the addition of fresh growth media and the removal of cell waste. This video presents batch and continuous reactors and demonstrates the use of bioreactors to grow bacteria in the laboratory.
01:12
Equation of Continuity
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2023
Fluid motion is represented by either velocity vectors or streamlines. The volume of a fluid flowing past a given location through an area during a period of time is called the flow rate Q, or more precisely, the volume flow rate. Flow rate and velocity are related—for instance, a river has a greater flow rate if the velocity of the water in it is greater. However, the flow rate also depends on the size and shape of the river. The relationship between flow rate (Q) and average speed (v)...
01:20
Continuity Equation
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2023
The total amount of current flowing per unit cross-sectional area is called the current density. Hence, the current passing through a cross-sectional area can be written as the surface integral of the current density. According to the conservation of charge, the total current flowing out of a given volume equals the rate of decrease of charge within that volume. Now, the total charge can be written in terms of the volume charge density. The charge density is a space function. Hence, applying...
11:21
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Cited by 55 •
2018
Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.
FAQs
Probability distributions provide a framework for representing how measured values are distributed across a range. They allow analysts to model variation rather than treating each observation as isolated. This modeling supports statistical summaries and inference, helping researchers evaluate patterns in measurements such as temperature, time, height, or mass while accounting for the variability present in the observations.
The mean summarizes the central level of measured values, while variance describes how widely those values differ around that level. Considering both prevents a typical value from being interpreted without its associated variability. Together, they help analysts compare datasets, characterize distributions, and judge whether observed differences may reflect meaningful variation or sampling error.
A confidence interval expresses uncertainty around a statistical estimate, rather than presenting the estimate as exact. For continuous measurements, it helps show how sampling variability affects conclusions about a population or experimental result. Analysts can therefore distinguish the estimated pattern from the range of values that remains plausible based on the available sample.
A histogram groups observations into intervals, making the frequency pattern visible, whereas a density curve provides a smoothed representation of that distribution across the measurement scale. Used together, they help analysts inspect concentration and variation in the data. These displays can guide interpretation before applying regression, correlation, analysis of variance, or other statistical models.
Reliable analysis begins with careful measurement, followed by an appropriate summary of variation and a visual examination of the distribution. Analysts then select a probability model or statistical method suited to the research question, such as regression, correlation, or analysis of variance. This workflow reduces the risk that measurement problems or sampling error will be mistaken for genuine patterns.
Regression and correlation are useful when researchers examine relationships among measured variables, while analysis of variance tests differences among groups or experimental conditions. Each approach addresses a different question about continuous measurements. Selecting among them depends on whether the goal is to assess association, model a relationship, or compare observed outcomes across conditions.
Small measurement problems can alter the observed variation and influence summaries, visual displays, and model results. Careful measurement helps ensure that differences in height, temperature, time, mass, or other experimental observations reflect the phenomenon being studied rather than avoidable error. Better-quality measurements strengthen statistical inference and improve confidence when researchers interpret patterns or differences.