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What do you mean by expected frequencies in chi-square test?
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.
Distinguish between mean deviation and standard deviation?
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.
How much error is expected between the sample mean and population mean?
0. The expected value of the sample mean is the population mean, so the expected value of the difference is 0.
Difference between mean and expected 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.
What will produce a large value for the chi-square statistic?
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.
What do you mean by expected frequencies in chi-square test?
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.
Distinguish between mean deviation and standard deviation?
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.
Are the offspring in a Punnett square real or expected?
Expected based on probability.
How much error is expected between the sample mean and population mean?
0. The expected value of the sample mean is the population mean, so the expected value of the difference is 0.
What does income expected mean on an applicatiom?
Income Expected:
What is expected outcome mean for a science fair?
what does expected outcome mean for a science fair
If the chi-square is very large what does it mean?
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!
Difference between mean and expected 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.
What does chi-square equals 0 mean?
It means that the observed values in the experiment all exactly match the expected values. That is unlikely, unless the experiment was "fixed".
Is the expected value the same as the standard deviation?
No. The expected value is the mean!
What does expected messiah mean?
Expected means anticipated. The expected messiah is the one promised in the Old Testament.
What will produce a large value for the chi-square statistic?
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.
