If this is regarding probability, there are two cases:
E[Y] = (Summation) yp(y) in the discrete case. For example a fair dice has 1/6 probabilty for each event (of getting one value). so the sum is from 1 to 6 of yp(y). = 1(1/6) + 2(1/6) + 3(1/6) + 4(1/6) + 5(1/6) + 6(1/6) = (1/6) ( 1+2+3+4+5+6) = 21/6 = 3.5. So E[Y] for a fiar dice is 3.5
E[Y]= (intergral from -infinity to infinity) yf(y)dy , where f(y) is the density function for the continuous case. The integral must exits to be evaluated.
The difference between the two is basically the domain. If you dealing with integers you can use the discrete case, if you dealing with real numbers you have to use calculus.
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The expected value of a Martingale system is the last observed value.
For a population the mean and the expected value are just two names for the same thing. For a sample the mean is the same as the average and no expected value exists.
The expected value is 7.
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.
0. The expected value of the sample mean is the population mean, so the expected value of the difference is 0.
No. The expected value is the mean!
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.
The expected value of a Martingale system is the last observed value.
It is the expected value of the distribution. It also happens to be the mode and median.It is the expected value of the distribution. It also happens to be the mode and median.It is the expected value of the distribution. It also happens to be the mode and median.It is the expected value of the distribution. It also happens to be the mode and median.
For a population the mean and the expected value are just two names for the same thing. For a sample the mean is the same as the average and no expected value exists.
The expected value is 7.
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.
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.
The expected value of the standard normal distribution is equal to the total amount of the value. It is usually equal to it when the value works out to be the same.
0. The expected value of the sample mean is the population mean, so the expected value of the difference is 0.
3
yes