The Gaussian "Bell" Curve has probability density function: f(x)= exp{-((x-mu)2/(2*sigma2)) } / (sigma*sqrt(2*pi)) where mu=mean & sigma=standard deviation
It is the value that is one standard deviation greater than the mean of a Normal (Gaussian) distribution.
If X and Y are independent Gaussian random variables with mean 0 and standard deviation sigma, then sqrt(X^2 + Y^2) has a Rayleigh distribution with parameter sigma.
Thanks to the Central Limit Theorem, the sampling distribution of the mean is Gaussian (normal) whose mean is the population mean and whose standard deviation is the sample standard error.
The standard deviation is the standard deviation! Its calculation requires no assumption.
Standard error of the mean (SEM) and standard deviation of the mean is the same thing. However, standard deviation is not the same as the SEM. To obtain SEM from the standard deviation, divide the standard deviation by the square root of the sample size.
It depends on what the distribution is. In a Normal or Gaussian distribution, the standard deviation is the square root of the mean, so it could be 3.1 but, again, it depends on the distribution.
It is the value that is one standard deviation greater than the mean of a Normal (Gaussian) distribution.
yes, h=1/sigma(standard deviation)
If the Z-Score corresponds to the standard deviation, then the distribution is "normal", or Gaussian.
There are many different bases for standardisation - even if you only consider the Gaussian (Normal) distribution. If a variable X has a Gaussian distribution, then the corresponding Standard Normal deviate, Z, is obtained from X by subtracting the mean of X and then dividing the result by the standard deviation of X. The variable, Z, more commonly known as the Z-score, has a Gaussian distribution with mean 0 and standard deviation 1. But, if X is an IQ score, for example, different measures of the X variable are used so that the resulting variable has mean 100 and standard deviation 15.
It depends on the underlying distribution. If Gaussian (standrad normal) then the percentile is 77.
If X and Y are independent Gaussian random variables with mean 0 and standard deviation sigma, then sqrt(X^2 + Y^2) has a Rayleigh distribution with parameter sigma.
All Gaussian (Normal) distributions are determined by just two parameters: their mean and standard deviation. Thus, given any variable, X, that has a Gaussian distribution with mean (m) and standard deviation (s), the Z transform, which is Z = (X - m)/s has a standard normal [or N(0,1)] distribution. This is tabulated and the tables can be used for testing statistical hypothesis about Normally distributed variables.
It is the normalised Gaussian distribution. To speak of a 'standard z' distribution is somewhat redundant because a z-score is already standardised. A z-score follows a normal or Gaussian distribution with a mean of zero and a standard deviation of one. It's these specific parameters (this mean and standard deviation) that are considered 'standard'. Speaking of a z-score implies a standard normal distribution. This is important because the shape of the normal distribution remains the same no matter what the mean or standard deviation are. As a consequence, tables of probabilities and other kinds of data can be calculated for the standard normal and then used for other variations of the distribution.
In general, you cannot. If the distribution can be assumed to be Gaussian [Normal] then you could use z-scores.
Thanks to the Central Limit Theorem, the sampling distribution of the mean is Gaussian (normal) whose mean is the population mean and whose standard deviation is the sample standard error.
The interval of 1.5 sd either side of the mean contains 87 of the values of a Gaussian distribution. For other distribution the answers will be different.