z = 0.84 approx.
The normal distribution and the t-distribution are both symmetric bell-shaped continuous probability distribution functions. The t-distribution has heavier tails: the probability of observations further from the mean is greater than for the normal distribution. There are other differences in terms of when it is appropriate to use them. Finally, the standard normal distribution is a special case of a normal distribution such that the mean is 0 and the standard deviation is 1.
95%
about 68%
It is 95%
use this link http://www.ltcconline.net/greenl/Courses/201/probdist/zScore.htm Say you start with 1000 observations from a standard normal distribution. Then the mean is 0 and the standard deviation is 1, ignoring sample error. If you multiply every observation by Beta and add Alpha, then the new results will have a mean of Alpha and a standard deviation of Beta. Or, do the reverse. Start with a normal distribution with mean Alpha and standard deviation Beta. Subtract Alpha from all observations and divide by Beta and you wind up with the standard normal distribution.
No. The curve in a normal distribution goes on out to plus and minus infinity. You might never see any observations out there, but if you were to make an infinite number of observations, you theoretically would.
The standard normal distribution has a mean of 0 and a standard deviation of 1.
The standard normal distribution is a normal distribution with mean 0 and variance 1.
The normal distribution would be a standard normal distribution if it had a mean of 0 and standard deviation of 1.
The standard deviation in a standard normal distribution is 1.
The standard normal distribution is a special case of the normal distribution. The standard normal has mean 0 and variance 1.
The standard deviation in a standard normal distribution is 1.