z-scores are distributed according to the standard normal distribution. That is, with the parameters: mean 0 and variance 1.
Don't know what "this" is, but all symmetric distributions are not normal. There are many distributions, discrete and continuous that are not normal. The uniform or binomial distributions are examples of discrete symmetric distibutions that are not normal. The uniform and the beta distribution with equal parameters are examples of a continuous distribution that is not normal. The uniform distribution can be discrete or continuous.
Actually the normal distribution is the sub form of Gaussian distribution.Gaussian distribution have 2 parameters, mean and variance.When there is zero mean and unit variance the Gaussian distribution becomes normal other wise it is pronounced as Gaussian.Wrong! The standard normal distribution has mean 0 and variance 1, but a normal distribution is the same as the Gaussiand, and can have any mean and variance. Google stackexcange "what-is-the-difference-between-a-normal-and-a-gaussian-distribution"
The Normal distribution is frequntly encountered in real life. Often a matter of interest is how likely it is that the random variable (RV) being studied takes a value that it did or one that is more extreme. This requires a comparison of the observed value of the RV with its Normal distribution. Unfortunately, the general Normal distribution is extremely difficult to calculate. The Normal distribution is defined by two parameters: the mean and the variance (or standard deviation). It is impossible to tabulate the distribution of every possible combination of two parameters - both of which are continuous real numbers. However, using Z score reduces the problem to that of tabulating only one the Normal distribution: the N(0, 1) or standard Normal distribution. This allows the analysis of an RV with any Normal distribution.
A Gaussian distribution is the "official" term for the Normal distribution. This is a probability density function, of the exponential family, defined by the two parameters, its mean and variance. A population is said to be normally distributed if the values that a variable of interest can take have a normal or Gaussian distribution within that population.
The normal distribution has two parameters, the mean and the standard deviation Once we know these parameters, we know everything we need to know about a particular normal distribution. This is a very nice feature for a distribution to have. Also, the mean, median and mode are all the same in the normal distribution. Also, the normal distribution is important in the central limit theorem. These and many other facts make the normal distribution a nice distribution to have in statistics.
z-scores are distributed according to the standard normal distribution. That is, with the parameters: mean 0 and variance 1.
A normal distribution is defined by two parameters: the mean, m, and the variance s2, (or standard deviation, s).The standard normal distribution is the special case of the normal distribution in which m = 0 and s = 1.
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.
to estimate the parameter values of a known distribution like normal distribution in this we estimate the parameters pop.mean and s.d
mode and skew
Mean and Standard Deviation
Don't know what "this" is, but all symmetric distributions are not normal. There are many distributions, discrete and continuous that are not normal. The uniform or binomial distributions are examples of discrete symmetric distibutions that are not normal. The uniform and the beta distribution with equal parameters are examples of a continuous distribution that is not normal. The uniform distribution can be discrete or continuous.
Actually the normal distribution is the sub form of Gaussian distribution.Gaussian distribution have 2 parameters, mean and variance.When there is zero mean and unit variance the Gaussian distribution becomes normal other wise it is pronounced as Gaussian.Wrong! The standard normal distribution has mean 0 and variance 1, but a normal distribution is the same as the Gaussiand, and can have any mean and variance. Google stackexcange "what-is-the-difference-between-a-normal-and-a-gaussian-distribution"
The Normal distribution is frequntly encountered in real life. Often a matter of interest is how likely it is that the random variable (RV) being studied takes a value that it did or one that is more extreme. This requires a comparison of the observed value of the RV with its Normal distribution. Unfortunately, the general Normal distribution is extremely difficult to calculate. The Normal distribution is defined by two parameters: the mean and the variance (or standard deviation). It is impossible to tabulate the distribution of every possible combination of two parameters - both of which are continuous real numbers. However, using Z score reduces the problem to that of tabulating only one the Normal distribution: the N(0, 1) or standard Normal distribution. This allows the analysis of an RV with any Normal distribution.
In science, "normal" typically means something that is within expected parameters or conforms to a standard. For example, a "normal distribution" refers to a bell-shaped curve that represents the expected distribution of a set of data points.
The answer depends on which parameters are to be calculated.