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Related Concept Videos

Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
Unusual Results01:16

Unusual Results

Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value = μ + 2σ
Minimum unusual value...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
Specialized Care Centers and Settings-I01:30

Specialized Care Centers and Settings-I

Specialized care settings or centers are situated in convenient locations within the community and offer care to a specific group or population. They consist of daycare facilities, mental health facilities, rural health facilities, educational institutions, industries, shelters for the homeless, and rehabilitation facilities.
Daycare centers
They provide several functions. Some facilities care for healthy newborns and children whose parents work, while others are medically focused and care for...

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Related Experiment Video

Updated: Jun 23, 2026

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

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Published on: August 8, 2019

Expected values for steps/day in special populations.

Catrine Tudor-Locke1, Tracy L Washington, Teresa L Hart

  • 1Walking Behavior Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA. Tudor-Locke@pbrc.edu

Preventive Medicine
|May 5, 2009
PubMed
Summary

This study compiled daily step counts for individuals with chronic illnesses and disabilities. Results show significant variation, with Type 1 diabetes patients achieving the highest steps, while disabled older adults and those with COPD had the lowest.

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Area of Science:

  • Physical activity research
  • Chronic illness management
  • Disability studies

Background:

  • Understanding physical activity levels is crucial for managing chronic illnesses and disabilities.
  • Existing data on free-living daily step counts in special populations is fragmented.
  • Establishing expected values aids in health monitoring and intervention planning.

Purpose of the Study:

  • To aggregate and present expected free-living daily step counts for diverse populations with chronic illnesses and disabilities.
  • To provide a reference for researchers and clinicians assessing physical activity in these groups.

Main Methods:

  • Systematic review of studies published since 2000.
  • Categorization of studies by illness/disability type.
  • Analysis of pedometer and accelerometer data, differentiating between waist-mounted and ankle-mounted devices.

Main Results:

  • Sixty studies were analyzed, covering conditions like heart disease, COPD, diabetes, cancer, and various disabilities.
  • Lowest median steps/day (waist-mounted): disabled older adults (1214) and COPD patients (2237).
  • Highest median steps/day (waist-mounted): Type 1 diabetes (8008), intellectual disability (7787), and HIV (7545).

Conclusions:

  • This compilation offers valuable data for researchers and practitioners.
  • The findings support surveillance, screening, intervention, and program evaluation for individuals with chronic conditions.
  • Expected step counts vary significantly across different chronic illness and disability groups.