The probability of Z from minus infinity to -1.96 is 0.025. Therefore the probability of Z greater than -1.96 is 1 - 0.025 or 0.975 or 97.5%.
From the related link, you can read directly the probability that Z is less than 1.51 is 0.9345.
The mean deviation (also called the mean absolute deviation) is the mean of the absolute deviations of a set of data about the data's mean. The standard deviation sigma of a probability distribution is defined as the square root of the variance sigma^2,
with mean of and standard deviation of 1.
The probability is 0.5
with mean and standard deviation . Once standardized, , the test statistic follows Standard Normal Probability Distribution.
The mean and standard deviation do not, by themselves, provide enough information to calculate probability. You also need to know the distribution of the variable in question.
Standard deviation can be greater than the mean.
It does not indicate anything if the mean is greater than the standard deviation.
30 percent.
0.8413
The Poisson distribution is a discrete distribution, with random variable k, related to the number events. The discrete probability function (probability mass function) is given as: f(k; L) where L (lambda) is the mean and square root of lambda is the standard deviation, as given in the link below: http://en.wikipedia.org/wiki/Poisson_distribution
From the related link, you can read directly the probability that Z is less than 1.51 is 0.9345.
In general, a mean can be greater or less than the standard deviation.
with mean of and standard deviation of 1.
The mean deviation (also called the mean absolute deviation) is the mean of the absolute deviations of a set of data about the data's mean. The standard deviation sigma of a probability distribution is defined as the square root of the variance sigma^2,
The probability is 0.5
z = (x-mean)/sd = (16.1-15.2)/0.9 = 0.9/0.9 = 1 Pr(Z > 1) = 15.8655 %