Null = (the statistic U sign) less than or equal to 42
Alternative = (the statistic U sign) Greater than 42
Test Statistic= Z
Value= 2.652
Critical Value = 1.645
YES
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.
mean
If I have understood the question correctly, despite your challenging spelling, the standard deviation is the square root of the average of the squared deviations while the mean absolute deviation is the average of the deviation. One consequence of this difference is that a large deviation affects the standard deviation more than it affects the mean absolute deviation.
The mean is the average value and the standard deviation is the variation from the mean value.
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 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.
Deviation, actually called "standard deviation" is, in a set of numbers, the average distance a number in that set is away from the mean, or average, number.
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.
beta
Standard deviation shows how much variation there is from the "average" (mean). A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data are spread out over a large range of values.
It's used in determining how far from the standard (average) a certain item or data point happen to be. (Ie, one standard deviation; two standard deviations, etc.)