This is the answer.
If a random variable X is distributed normally with probability distribution function p(x), then the expected value of X is E(X) = integral of x*p(x)dx evaluated over the whole of the real line.
Depending on whether you subtract actual value from expected value or other way around, a positive or negative percent error, will tell you on which side of the expected value that your actual value is. For example, suppose your expected value is 24, and your actual value is 24.3 then if you do the following calculation to figure percent error:[percent error] = (actual value - expected value)/(actual value) - 1 --> then convert to percent.So you have (24.3 - 24)/24 -1 = .0125 --> 1.25%, which tells me the actual is higher than the expected. If instead, you subtracted the actual from the expected, then you would get a negative 1.25%, but your actual is still greater than the expected. My preference is to subtract the expected from the actual. That way a positive error tells you the actual is greater than expected, and a negative percent error tells you that the actual is less than the expected.
It is a variable that can take a number of different values. The probability that it takes a value in any given range is determined by a random process and the value of that probability is given by the probability distribution function.It is a variable that can take a number of different values. The probability that it takes a value in any given range is determined by a random process and the value of that probability is given by the probability distribution function.It is a variable that can take a number of different values. The probability that it takes a value in any given range is determined by a random process and the value of that probability is given by the probability distribution function.It is a variable that can take a number of different values. The probability that it takes a value in any given range is determined by a random process and the value of that probability is given by the probability distribution function.
Zero is the smallest probability.
The expected value is the average of a probability distribution. It is the value that can be expected to occur on the average, in the long run.
Expected value is the outcome of confidence of how probability distribution is characterized. If the expected value is greater than the confidence interval then the results are significant.
This is the answer.
Yes it can be negative.
The combination (product, actually) of a probability and the associated benefit (or cost) of a certain situation is called the "mathematical expectation", "expectation value", or "expected value".
The mean of a discrete probability distribution is also called the Expected Value.
Consider a binomial distribution with 10 trials What is the expected value of this distribution if the probability of success on a single trial is 0.5?
The combination (product, actually) of a probability and the associated benefit (or cost) of a certain situation is called the "mathematical expectation", "expectation value", or "expected value".
The third moment. That is, the expected value of the cubes of the deviations from the mean.
Yes, it is true.
The mean deviation for any distribution is always 0 and so conveys no information whatsoever. The standard deviation is the square root of the variance. The variance of a set of values is the sum of the probability of each value multiplied by the square of its difference from the mean for the set. A simpler way to calculate the variance is Expected value of squares - Square of Expected value.
You cannot; there is insufficient information.