The answer depends on the degrees of freedom (df). If the df > 1 then the mean is 0, and the standard deviation, for df > 2, is sqrt[df/(df - 2)].
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.
The statement is probably: The mean and standard deviation of a distribution are 55 and 4.33 respectively.
Yes, a normal distribution can have a standard deviation of 1. In fact, the standard normal distribution, which is a specific case of the normal distribution, has a mean of 0 and a standard deviation of 1. This allows for easy computation of z-scores, which standardize any normal distribution for comparison. Therefore, a normal distribution with a standard deviation of 1 is a valid and common scenario.
Yes, the normal distribution is uniquely defined by its mean and standard deviation. The mean determines the center of the distribution, while the standard deviation indicates the spread or dispersion of the data. Together, these two parameters specify the shape and location of the normal distribution curve.
It is any standardised distribution.
Mean 0, standard deviation 1.
Standard deviation describes the spread of a distribution around its mean.
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.
The mean and standard deviation often go together because they both describe different but complementary things about a distribution of data. The mean can tell you where the center of the distribution is and the standard deviation can tell you how much the data is spread around the mean.
The standard normal distribution has a mean of 0 and a standard deviation of 1.
The statement is probably: The mean and standard deviation of a distribution are 55 and 4.33 respectively.
No.
Yes, a normal distribution can have a standard deviation of 1. In fact, the standard normal distribution, which is a specific case of the normal distribution, has a mean of 0 and a standard deviation of 1. This allows for easy computation of z-scores, which standardize any normal distribution for comparison. Therefore, a normal distribution with a standard deviation of 1 is a valid and common scenario.
Yes, the normal distribution is uniquely defined by its mean and standard deviation. The mean determines the center of the distribution, while the standard deviation indicates the spread or dispersion of the data. Together, these two parameters specify the shape and location of the normal distribution curve.
If repeated samples are taken from a population, then they will not have the same mean each time. The mean itself will have some distribution. This will have the same mean as the population mean and the standard deviation of this statistic is the standard deviation of the mean.
It is any standardised distribution.
The mean and standard deviation.
with mean of and standard deviation of 1.
standard normal
Standard deviation and variance
NO
The mean, median, and mode of a normal distribution are equal; in this case, 22. The standard deviation has no bearing on this question.
The mean.The mean.The mean.The mean.
No. The standard deviation is not exactly a value but rather how far a score deviates from the mean.
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