The triangular, uniform, binomial, Poisson, geometric, exponential and Gaussian distributions are some that can be so defined. In fact, the Poisson and exponential need only the mean.
Because the average deviation will always be zero.
To calculate plus or minus one standard deviation from a mean, first determine the mean (average) of your data set. Then calculate the standard deviation, which measures the dispersion of the data points around the mean. Once you have both values, you can find the range by adding and subtracting the standard deviation from the mean: the lower limit is the mean minus one standard deviation, and the upper limit is the mean plus one standard deviation. This range contains approximately 68% of the data in a normal distribution.
Mean is the average, sum total divided by total number of data entries. Standard deviation is the square root of the sum total of the data values divided by the total number of data values. The standard normal distribution is a distribution that closely resembles a bell curve.
The mean is the average value and the standard deviation is the variation from the mean value.
Standard deviation in statistics refers to how much deviation there is from the average or mean value. Sample deviation refers to the data that was collected from a smaller pool than the population.
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
Standard deviation
No, you have it backwards, the standard deviation is the square root of the variance, so the variance is the standard deviation squared. Usually you find the variance first, as it is the average sum of squares of the distribution, and then find the standard deviation by squaring it.
No. You must have the mean ("average") as well.
The mean of a distribution is a measure of central tendency, representing the average value of the data points. In this case, the mean is 2.89. The standard deviation, which measures the dispersion of data points around the mean, is missing from the question. The standard deviation provides information about the spread of data points and how closely they cluster around the mean.
Because the average deviation will always be zero.
No. The average of the deviations, or mean deviation, will always be zero. The standard deviation is the average squared deviation which is usually non-zero.
Yes. Standard deviation depends entirely upon the distribution; it is a measure of how spread out it is (ie how far from the mean "on average" the data is): the larger it is the more spread out it is, the smaller the less spread out. If every data point was the mean, the standard deviation would be zero!
You cannot. There is no information about the distribution, or even what the graph is meant to display!
mean
Mean is the average, sum total divided by total number of data entries. Standard deviation is the square root of the sum total of the data values divided by the total number of data values. The standard normal distribution is a distribution that closely resembles a bell curve.
The mean is the average value and the standard deviation is the variation from the mean value.