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In terms of probability theory, the cumulative distribution function (cdf) is the result of the summation or integration of the probability density function (pdf). The cdf F(a) is the area under the pdf from its lower limit to a. I hope I am responding to your question. If not, perhaps you can clarify it and resubmit it.
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What is the area underneath curve for chi-square distribution?
1. It is a probability distribution function and so the area under the curve must be 1.
Is The total area under the curve of any normal distribution is 1.0?
yes because 1 = 100% so the entire area under the curve is 100%
What is the area under the standard normal distribution curve between z1.50 and z2.50?
~0.0606
What is the area under normal distribution curve between z1.50 and z2.50?
Approx 0.0606
What is the area under the normal distribution curve to the right of z that equals 3.24?
0.0006 (approx).
What is the area underneath curve for chi-square distribution?
1. It is a probability distribution function and so the area under the curve must be 1.
What is the area under a curve with mu equals 15 and sigma equals 2?
If the question is to do with a probability distribution curve, the answer is ONE - whatever the values of mu and sigma. The area under the curve of any probability distribution curve is 1.
Is The total area under the curve of any normal distribution is 1.0?
yes because 1 = 100% so the entire area under the curve is 100%
What does area have to do with probability?
A normalized probability distribution curve has an area under the curve of 1.Note: I said "normalized", not "normal". Do not confuse the terms.
What is the area under the standard normal distribution curve between z1.50 and z2.50?
~0.0606
What is the area under normal distribution curve between z1.50 and z2.50?
Approx 0.0606
What is the area under the standard normal distribution curve between z0.75 and z1.89?
0.1972
If the tails of the normal distribution curve are infinitely long. Is it True or False that the total area under the curve is also infinite?
False. A normalized distribution curve (do not confuse normalized with normal), by definition, has an area under the curve of exactly 1. That is because the probability of all possible events is also always exactly 1. The shape of the curve does not matter.
From a uniform distribution why is the total area under the curve equal to 1?
Because the area under the curve is a probability and probabilities range from 0.00 to 1.00 or could also be written as 0% to 100%
What is the area under the normal distribution curve to the right of z that equals 3.24?
0.0006 (approx).
What is shapes of distribution?
It is any shape that you want, provided that the total area under the curve is 1.
Is the total shape of a bell curve greater than 1?
It is assumed that by "shape" you mean "area". The quick answer is yes, probably. The "Bell curve" is called a Gaussian function (see related link). The area under a Gaussian is not necessarily 1; it can be anything. However, if you're talking about probability, where the probability distribution is in the same of a Gaussian, then the area under the curve must be exactly 1. This isn't however, because it is a bell curve, but because it's a probability distribution. The area under any probability distribution must always be exactly 1, or it isn't a valid distribution. The proper term for the total area under any curve f(x) is the integral from negative infinity to infinity of f(x) dx
