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idk about normal distribution but for

Mean "M" = (overall sum of "x") / "n"

frequency distribution: 'M' = Overall sum of (' x ' * ' f ') / overall sum of ( ' f ' )

M = Mean

x = Mid Point

f = frequiency

n = number of variables

ALL FOR STANDARD DEVIATION

* * * * *

A general Normal distribution is usually described in terms of its parameters, and given as N(mu, sigma2) where mu is the mean and sigma is the standard deviation.

The STANDARD Normal distribution is the N(0, 1) distribution, that is, it has mean = 0 and variance (or standard deviation) = 1.

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Q: In a standard normal distribution what is the value of the mean and standard deviation?

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It is the value that is one standard deviation greater than the mean of a Normal (Gaussian) distribution.

For data sets having a normal distribution, the following properties depend on the mean and the standard deviation. This is known as the Empirical rule. About 68% of all values fall within 1 standard deviation of the mean About 95% of all values fall within 2 standard deviation of the mean About 99.7% of all values fall within 3 standard deviation of the mean. So given any value and given the mean and standard deviation, one can say right away where that value is compared to 60, 95 and 99 percent of the other values. The mean of the any distribution is a measure of centrality, but in case of the normal distribution, it is equal to the mode and median of the distribtion. The standard deviation is a measure of data dispersion or variability. In the case of the normal distribution, the mean and the standard deviation are the two parameters of the distribution, therefore they completely define the distribution. See: http://en.wikipedia.org/wiki/Normal_distribution

Approx 0.0027

The expected value of the standard normal distribution is equal to the total amount of the value. It is usually equal to it when the value works out to be the same.

The standard deviation of a distribution is the average spread from the mean (average). If I told you I had a distribution of data with average 10000 and standard deviation 10, you'd know that most of the data is close to the middle. If I told you I had a distrubtion of data with average 10000 and standard deviation 3000, you'd know that the data in this distribution is much more spread out. dhaussling@gmail.com

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No. The standard deviation is not exactly a value but rather how far a score deviates from the mean.

It is the value that is one standard deviation greater than the mean of a Normal (Gaussian) distribution.

For data sets having a normal distribution, the following properties depend on the mean and the standard deviation. This is known as the Empirical rule. About 68% of all values fall within 1 standard deviation of the mean About 95% of all values fall within 2 standard deviation of the mean About 99.7% of all values fall within 3 standard deviation of the mean. So given any value and given the mean and standard deviation, one can say right away where that value is compared to 60, 95 and 99 percent of the other values. The mean of the any distribution is a measure of centrality, but in case of the normal distribution, it is equal to the mode and median of the distribtion. The standard deviation is a measure of data dispersion or variability. In the case of the normal distribution, the mean and the standard deviation are the two parameters of the distribution, therefore they completely define the distribution. See: http://en.wikipedia.org/wiki/Normal_distribution

Approx 0.0027

If a random variable X has a Normal distribution with mean m and standard deviation s, then z = (X - m)/s has a Standard Normal distribution. That is, Z has a Normal distribution with mean 0 and standard deviation 1. Probabilities for a general Normal distribution are extremely difficult to obtain but values for the Standard Normal have been calculated numerically and are widely tabulated. The z-transformation is, therefore, used to evaluate probabilities for Normally distributed random variables.

A normal distribution can have any value for its mean and any positive value for its variance. A standard normal distribution has mean 0 and variance 1.

The expected value of the standard normal distribution is equal to the total amount of the value. It is usually equal to it when the value works out to be the same.

The standard deviation of a distribution is the average spread from the mean (average). If I told you I had a distribution of data with average 10000 and standard deviation 10, you'd know that most of the data is close to the middle. If I told you I had a distrubtion of data with average 10000 and standard deviation 3000, you'd know that the data in this distribution is much more spread out. dhaussling@gmail.com

An x value that is smaller than the mean cannot have a positive z score.

If the minimum value is the minimum observed value then it indicates that the distribution goes below the minimum observed value.If the minimum value is the minimum defined for the distribution then it indicates thatthe data do not come from the proposed distribution,estimates for the mean or standard deviation are incorrect, oryou have got a sample which is atypical.

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.

Answer: 0 The z score is the value of the random variable associated with the standardized normal distribution (mean = 0, standard deviation =1). Now, the median and the mean of a normal distribution are the same. The 50 percentile z score = the median = mean = 0.

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