Yes. Normal (or Gaussian) distribution are parametric distributions and they are defined by two parameters: the mean and the variance (square of standard deviation). Each pair of these parameters gives rise to a different normal distribution. However, they can all be "re-parametrised" to the standard normal distribution using z-transformations. The standard normal distribution has mean 0 and variance 1.
Yes. And that is true of most probability distributions.
Yes. Most do.
True. Two normal distributions that have the same mean are centered at the same point on the horizontal axis, regardless of their standard deviations. The standard deviation affects the spread or width of the distributions, but it does not change their center location. Therefore, even with different standard deviations, the distributions will overlap at the mean.
The probability of the mean plus or minus 1.96 standard deviations is 0. The probability that a continuous distribution takes any particular value is always zero. The probability between the mean plus or minus 1.96 standard deviations is 0.95
How many standard deviations is 16.50 from the mean?
Yes. And that is true of most probability distributions.
Yes. Most do.
The probability of the mean plus or minus 1.96 standard deviations is 0. The probability that a continuous distribution takes any particular value is always zero. The probability between the mean plus or minus 1.96 standard deviations is 0.95
The probability of an event occurring within 5 standard deviations from the mean is extremely rare, as it falls outside the normal range of outcomes.
They do happen, particularly with very skewed distributions.
Standard deviations are measures of data distributions. Therefore, a single number cannot have meaningful standard deviation.
It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.It would mean that the result was 2 standard deviations above the mean. Depending on the distribution of the variable, it may be possible to attach a probability to this, or more extreme, observations.
Finance:When the standard deviations of a variable, monitored over time, are non-constant.Math:An irregular scattering of values in multiple distributions with a comparable scatter of variances.
s= bracket n over sigma i (xi-x-)^2 all over n-1 closed bracket ^ 1/2
The normal distribution, also known as the Gaussian distribution, has a familiar "bell curve" shape and approximates many different naturally occurring distributions over real numbers.
A family that is defined by two parameters: the mean and variance (or standard deviation).
How many standard deviations is 16.50 from the mean?