no
If you have a variable X that is normally distributed with mean m and variance s2 then the z-score, Z = (X - m)/s.Z has a standard Normal distribution.
The mean and standard deviation. If the data really are normally distributed, all other statistics are redundant.
A term is a number or the product of a number and variable(s).
It means that the data are distributed according to a probability distribution function known as the normal distribution. This site is useless for showing most mathematical functions but you can Google "normal distribution" to get more details.
The expected number is 500.
It means that the probability distribution function of the variable is the Gaussian or normal distribution.
According to the Central Limit Theorem, even if a variable has an underlying distribution which is not Normal, the means of random samples from the population will be normally distributed with the population mean as its mean.
Not really. The distribution is not only discrete but also heavily skewed. However, the average number of children in sets of families can be approximated by the normal distribution.
It is 0.37, approx.
Exactly "what it says on the tin"! The distribution is nearly, but not quite, the standard normal, or Gaussiam distribution.
If a normally distributed random variable X has mean m and standard deviation s, then z = (X - m)/s
It means that the random variable of interest is Normally distributed and so the t-distribution is an appropriate distribution for the test rather than just an approximation.
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.
Also normally distributed.
If you have a variable X that is normally distributed with mean m and variance s2 then the z-score, Z = (X - m)/s.Z has a standard Normal distribution.
If a random variable (RV) X is distributed Normally with mean m and standard deviation sthenZ = (X - m)/s is the corresponding Normal variable which is distributed with mean 0 and variance 1. The distribution of X is difficult to compute but that for Z is readily available. It can be used to find the probabilities of the RV lying in different domains and thereby for testing hypotheses.
Suppose m is the mean and s the standard deviation of a random variable, X, which is normally distributed. Then, given Z,X = m + sZ