1 sigma does not represent 68.8 percent of anything.The area under the standard normal curve, between -0.5 and +0.5, that i, the central 1 sigma, is equal to 0.68269 or 68.3%.
1.it is bell shaped.2.m.d=0.7979 of s.d 3.total area under the normal curve is equal to 1.
The total area under a normal distribution is not infinite. The total area under a normal distribution is a continuous value between any 2 given values. The function of a normal distribution is actually defined such that it must have a fixed value. For the "standard normal distribution" where μ=0 and σ=1, the area under the curve is equal to 1.
yes because 1 = 100% so the entire area under the curve is 100%
Total Volume of rainfall for that storm event
100%
1 sigma does not represent 68.8 percent of anything.The area under the standard normal curve, between -0.5 and +0.5, that i, the central 1 sigma, is equal to 0.68269 or 68.3%.
1.it is bell shaped.2.m.d=0.7979 of s.d 3.total area under the normal curve is equal to 1.
The total area under a normal distribution is not infinite. The total area under a normal distribution is a continuous value between any 2 given values. The function of a normal distribution is actually defined such that it must have a fixed value. For the "standard normal distribution" where μ=0 and σ=1, the area under the curve is equal to 1.
yes because 1 = 100% so the entire area under the curve is 100%
The area under the standard normal curve is 1.
Total Volume of rainfall for that storm event
yes
The area under the normal distribution curve represents the probability of an event occurring that is normally distributed. So, the area under the entire normal distribution curve must be 1 (equal to 100%). For example, if the mean (average) male height is 5'10" then there is a 50% chance that a randomly selected male will have a height that is below or exactly 5'10". This is because the area under the normal curve from the left hand side up to the mean consists of half of the entire area of the normal curve. This leads us to the definitions of z-scores and standard deviations to represent how far along the normal curve a particular value is. We can calculate the likelihood of the value by finding the area under the normal curve to that point, usually by using a z-score cdf (cumulative density function) utility of a calculator or statistics software.
The area under the normal curve is ALWAYS 1.
Characteristics of a Normal Distribution1) Continuous Random Variable.2) Mound or Bell-shaped curve.3) The normal curve extends indefinitely in both directions, approaching, but never touching, the horizontal axis as it does so.4) Unimodal5) Mean = Median = Mode6) Symmetrical with respect to the meanThat is, 50% of the area (data) under the curve lies to the left ofthe mean and 50% of the area (data) under the curve liesto the right of the mean.7) (a) 68% of the area (data) under the curve is within onestandard deviation of the mean(b) 95% of the area (data) under the curve is within twostandard deviations of the mean(c) 99.7% of the area (data) under the curve is within threestandard deviations of the mean8) The total area under the normal curve is equal to 1.
the standard normal curve 2