For a chi-square test there is a null hypothesis which describes some distribution for the variable that is being tested. The expected frequency for a particular cell is the number of observations that would be expected in that cell if the null hypothesis were 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.
0. The expected value of the sample mean is the population mean, so the expected value of the difference is 0.
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.
A chi-square statistic can be large if either there is a large difference between the observed and expected values for one or more categories. However, it can also be large if the expected value in a category is very small. In the first case, it is likely that the data are not distributed according to the null hypothesis. In the second case, it can often mean that that, because of low expected values, adjacent categories need to be combined before the chi-square statistic is calculated.
For a chi-square test there is a null hypothesis which describes some distribution for the variable that is being tested. The expected frequency for a particular cell is the number of observations that would be expected in that cell if the null hypothesis were 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.
Expected based on probability.
0. The expected value of the sample mean is the population mean, so the expected value of the difference is 0.
Income Expected:
what does expected outcome mean for a science fair
The null hypothesis in a chi-square goodness-of-fit test states that the sample of observed frequencies supports the claim about the expected frequencies. So the bigger the the calculated chi-square value is, the more likely the sample does not conform the expected frequencies, and therefore you would reject the null hypothesis. So the short answer is, REJECT!
It means that the observed values in the experiment all exactly match the expected values. That is unlikely, unless the experiment was "fixed".
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.
A chi-square statistic can be large if either there is a large difference between the observed and expected values for one or more categories. However, it can also be large if the expected value in a category is very small. In the first case, it is likely that the data are not distributed according to the null hypothesis. In the second case, it can often mean that that, because of low expected values, adjacent categories need to be combined before the chi-square statistic is calculated.
you do the observed-expected value and square it, then devide that by the expected you do this for each cell then you add them up also you can enter your data as a matrix on a calculator TI and go to stat, test, chi square test.
No. The expected value is the mean!