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What are the measurements that fall beyond three standard deviations from the mean?
Wiki User
∙ 14y ago
They are observations with a low likelihood of occurrence. They may be called outliers but there is no agreed definition for outliers.
Wiki User
∙ 8y ago
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What are the names of the measurements that fall beyond three standard deviations from the mean?
Outliers.
In statistics what does the empirical rule states?
Nearly all the values in a sample from a normal population will lie within three standard deviations of the mean. Please see the link.
Importance of measures of central tendency?
Measures of the general value are a common need. Average, Median, and Mode are the three commonest.Average is the arithmetic average of all the values.Median is the actual measurement which is midwaybetween the extreme values, and is often closest to the average.Mode is the commonest value.Other indicators of central tendency, may ignore all value beyond say, three standard deviations, and thus ignore the contribution by the extreme, and uncommon, values.
What are the differences between the Emperical Rule and Chebyshev's Theorem?
The Empirical Rule applies solely to the NORMAL distribution, while Chebyshev's Theorem (Chebyshev's Inequality, Tchebysheff's Inequality, Bienaymé-Chebyshev Inequality) deals with ALL (well, rather, REAL-WORLD) distributions. The Empirical Rule is stronger than Chebyshev's Inequality, but applies to fewer cases. The Empirical Rule: - Applies to normal distributions. - About 68% of the values lie within one standard deviation of the mean. - About 95% of the values lie within two standard deviations of the mean. - About 99.7% of the values lie within three standard deviations of the mean. - For more precise values or values for another interval, use a normalcdf function on a calculator or integrate e^(-(x - mu)^2/(2*(sigma^2))) / (sigma*sqrt(2*pi)) along the desired interval (where mu is the population mean and sigma is the population standard deviation). Chebyshev's Theorem/Inequality: - Applies to all (real-world) distributions. - No more than 1/(k^2) of the values are more than k standard deviations away from the mean. This yields the following in comparison to the Empirical Rule: - No more than [all] of the values are more than 1 standard deviation away from the mean. - No more than 1/4 of the values are more than 2 standard deviations away from the mean. - No more than 1/9 of the values are more than 3 standard deviations away from the mean. - This is weaker than the Empirical Rule for the case of the normal distribution, but can be applied to all (real-world) distributions. For example, for a normal distribution, Chebyshev's Inequality states that at most 1/4 of the values are beyond 2 standard deviations from the mean, which means that at least 75% are within 2 standard deviations of the mean. The Empirical Rule makes the much stronger statement that about 95% of the values are within 2 standard deviations of the mean. However, for a distribution that has significant skew or other attributes that do not match the normal distribution, one can use Chebyshev's Inequality, but not the Empirical Rule. - Chebyshev's Inequality is a "fall-back" for distributions that cannot be modeled by approximations with more specific rules and provisions, such as the Empirical Rule.
What are the three measurements in the three-dimensional objects?
length, breadth, depth.
What are the names of the measurements that fall beyond three standard deviations from the mean?
Outliers.
Measurements that fall beyond three standard deviations?
It is one of the informal definitions for an outlier.
An eight letter word meaning measurements that fall beyond three standard deviations from the mean?
outliers
Measurements that fall beyond three standard deviations from the mean?
I believe outliers is the best answer to this question. The previous answer was average, which is the mean.
What measurements fall beyond three standard deviations from the mean?
Usually they would be observations with very low probabilities of occurrence.
What do you call measurements that fall three standard deviations from the mean?
variances
Measurements that fall beyond three standard deviations from the mean are called?
Extreme values. They might also be called outliers but there is no agreed definition for the term "outlier".
What 8 letter word with the 3rd letter t and the 6th letter e mean measurements that fall beyond three standard deviations from the mean?
Outliers
What is the measures that fall beyond three standard deviations of the mean called?
You may be referring to the statistical term 'outlier(s)'. Also, there is a rule in statistics called the '68-95-99 Rule'. It states that in a normally distributed dataset approximately 68% of the observations will be within plus/minus one standard deviation of the mean, 95% within plus/minus two standard deviations, and 99% within plus/minus three standard deviations. So if your data follow the classic bell-shaped curve, roughly 1% of the measures should fall beyond three standard deviations of the mean.
What is measurement that fall beyond three standard deviations from the mean?
It is a measurement which may, sometimes, be called an extreme observation or an outlier. However, there is no agreed definition for outliers.
What are measurements called that fall beyond three standard deviations from the mean?
Measurements. Just because a particular result lies far from the mean doesn't make it any different. If it's noticeably far from the "crowd" of all the other measurements, it can be called an outlier. An outlier isn't necessarily bad, just different. It should be examined in detail to see if there's something odd about it, but not discarded out of hand.
What if a standard score is 57 and the average is 100 Is that three standard deviations below the mean or almost three?
The answer depends on what the standard deviation is.
