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When using Chebyshev's Theorem the minimum percentage of sample observations that will fall within two standard deviations of the mean will be __________ the percentage within two standard deviations if a normal distribution is assumed Empirical Rule smaller than greater than the same as
A standard distribution regards 95% of all data being within 2-standard deviations of either side. Similarly, within one standard deviation either way is 68% of all data. This creates a bell curve distribution. An abnormal distribution would be erratic and not follow such a statistical structure of representation.
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.
Nearly all the values in a sample from a normal population will lie within three standard deviations of the mean. Please see the link.
The area within the normal curve between -1 standard deviation (SD) and +1 SD is approximately 68%. This means that about 68% of the data falls within one standard deviation of the mean in a normal distribution.
95% is within 2 standard deviations of the mean.
99.7% of scores fall within -3 and plus 3 standard deviations around the mean in a normal distribution.
I believe the standard deviations are measured from the median, not the mean.1 Standard Deviation is 34% each side of median, so that is 68% total.2 Standard Deviations is 48% each side of median, so that is 96% total.
When using Chebyshev's Theorem the minimum percentage of sample observations that will fall within two standard deviations of the mean will be __________ the percentage within two standard deviations if a normal distribution is assumed Empirical Rule smaller than greater than the same as
A normal distribution is symmetric and when looked at on a graph, the graph looks like a bell shaped curve. Approximately 95 percent of its values should lie within two standard deviations of the mean. Frequency of the data lies mostly in the middle of the curve.
A standard distribution regards 95% of all data being within 2-standard deviations of either side. Similarly, within one standard deviation either way is 68% of all data. This creates a bell curve distribution. An abnormal distribution would be erratic and not follow such a statistical structure of representation.
95%
95
The empirical rule can only be used for a normal distribution, so I will assume you are referring to a normal distribution. Chebyshev's theorem can be used for any distribution. The empirical rule is more accurate than Chebyshev's theorem for a normal distribution. For 2 standard deviations (sd) from the mean, the empirical rule says 95% of the data are within that, and Chebyshev's theorem says 1 - 1/2^2 = 1 - 1/4 = 3/4 or 75% of the data are within that. From the standard normal distribution chart, the answer for 2 sd from the mean is 95.44% So, as you can see the empirical rule is more accurate.
It is 68.3%
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.
Chebyshev's rule, also known as Chebyshev's inequality, is a statistical theorem that describes the proportion of values that fall within a certain number of standard deviations from the mean in any distribution. It states that for any set of data, regardless of the shape of the distribution, at least (1 - 1/k^2) where k is greater than 1, of the data values will fall within k standard deviations of the mean.