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I have two data sets with the same number of values with means x1 and x2 and standard deviations of n1 and n2. How do you average means with standard deviations?
Wiki User
∙ 14y ago
You can't average means with standard deviations. What are you trying to do with the two sets of data?
Wiki User
∙ 14y ago
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What is the mean and standard deviations and the standard normal distribution?
Mean is the average, sum total divided by total number of data entries. Standard deviation is the square root of the sum total of the data values divided by the total number of data values. The standard normal distribution is a distribution that closely resembles a bell curve.
Why are standard z values so important?
A z-score gives the distance (specifically number of standard deviations) from the mean so when you compare z-scores, it gives a direct comparison of how far from the mean the values are.
When computing Standard deviation should you eliminate the extreme values?
Generally not without further reason. Extreme values are often called outliers. Eliminating unusually high values will lower the standard deviation. You may want to calculate standard deviations with and without the extreme values to identify their impact on calculations. See related link for additional discussion.
What does it mean to have 95 percent confidence in an interval estimate?
It means that 95% of the values in the data set falls within 2 standard deviations of the mean value.
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 is the mean and standard deviations and the standard normal distribution?
Mean is the average, sum total divided by total number of data entries. Standard deviation is the square root of the sum total of the data values divided by the total number of data values. The standard normal distribution is a distribution that closely resembles a bell curve.
Why are standard z values so important?
A z-score gives the distance (specifically number of standard deviations) from the mean so when you compare z-scores, it gives a direct comparison of how far from the mean the values are.
According to the normal probability distribution 95 percent of the values of a normal random variable are contained w in plus or minus how many standard deviations from the mean Why is that important?
Approximately 2 standard deviations (1.96, actually) from the mean. That is important to know that if one has a sample of 1000 values, if one selects a threshold at +/- 2 standard deviations from the mean, then one expects to see about 25 values exceeding those thresholds (on each side of the mean)
What is chebychev's 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.
What are all the values that a standard deviation can possibly take?
Any real value >= 0.
What is the use of statistical mean in industry?
For different sets of data, the mean would be the summation of all observations, which are normally subdivided by the observation numbers. The mean value would frequently be quoted with standard deviations: mean would describe data central locations then standard deviations illustrate the spread. Substitute dispersion measures include mean variations that are always equal to average absolute deviations from the mean values. It is minimally responsive to the outliers. Hope this helps.
When computing Standard deviation should you eliminate the extreme values?
Generally not without further reason. Extreme values are often called outliers. Eliminating unusually high values will lower the standard deviation. You may want to calculate standard deviations with and without the extreme values to identify their impact on calculations. See related link for additional discussion.
What are extremely high or low values in a data set called?
Values that are either extremely high or low in a data set are called 'outliers'. They are typically 3 standard deviations or more from the mean.
What is the Excel function AVEDEV?
It gets the average of the absolute deviations of a set of values from their mean. It can use numbers or references to those numbers.
What makes the range less desirable than the standard deviation as a measure of dispersion?
Range can include outliers that are not normal values and can skew overall data. Most relevant values can be found within one or two standard deviations on a normal curve.
What is the Standard deviation of a set of data in which all the data values are the same?
ZeroDetails:The "Standard Deviation" for ungrouped data can be calculated in the following steps:all the deviations (differences) from the arithmetic mean of the set of numbers are squared;the arithmetic mean of these squares is then calculated;the square root of the mean is the standard deviationAccordingly,The arithmetic mean of set of data of equal values is the value.All the deviations will be zero and their squares will be zerosThe mean of squares is zeroThe square root of zero is zero which equals the standard deion
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".
