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The obtained (calculated) chi square statistic:is sensitive to sample sizemust always be modified using the Yates's correctioncan be negative or positiveis alw...

Question

The obtained (calculated) chi square statistic:is sensitive to sample sizemust always be modified using the Yates's correctioncan be negative or positiveis always negative

The obtained (calculated) chi square statistic: is sensitive to sample size must always be modified using the Yates's correction can be negative or positive is always negative



Answers

Can a critical value for the chi-square test be negative? Explain.

Right. We have the relative frequencies for the null hypothesis. H not as well as a sample size for a potential chi square goodness of fit test. We want to answer whether or not the necessary assumptions have been met to appropriately conduct this chi square goodness of fit test. So we have N equals 100 relative frequencies 1000.44 point 25.3 point 01 assuming the assumption of a simple random sample has already been satisfied. We want to determine whether or not the following two assumptions are met to determine if using chi square is appropriate. These are assumption one. All expected frequencies are one or greater an assumption to at most 20% of the expected frequencies are less than five. Starting off with assumption one, we can check this by determining the expected frequencies. Expected frequencies are derived from relative frequencies using E equals M. P, which in this case gives us 44 25 30 and one. So assumption one is in fact met. But since one out of four or 25% of our expected frequencies is less than five, we can immediately say the assumption to is not met, and so it's not appropriate to use chi squared.

All right. We have the relative frequencies listed below. For the null hypothesis. H not as well as the sample size for a chi square goodness of fit test, we want to answer the question are the necessary assumptions met. To properly conduct a chi square goodness of fit test, assuming the assumption of a simple random sample has already been met. We only have to check two assumptions to validate the use of chi square goodness of fit test. These are assumption one. All expected frequencies are one of greater an assumption to at most. 20% of the expected frequencies are less than five. So let's go ahead and look for assumption one expected frequencies are given by equals np. So we have for these affected frequencies 11, 10.5 12.5, 15 and one All expected frequencies are one or greater, so assumption one is met. There is only one expected frequencies at less than less than five. That is one out of five expect frequencies is 20% so assumption to is also met.

All right, We have the relative frequencies for the null hypothesis. H. Not as well as the sample size for a potential chi square goodness of fit test. We want to answer the question are the necessary assumptions met to appropriately conduct a chi square goodness of fit test, assuming that the assumption of a simple random sample is already satisfied. We want to make check the following two assumptions, assumption one. All expected frequencies are one or greater an assumption to at most 20% of the expected frequencies are less than five. Starting off with checking assumption one, we can calculate the expected frequencies from our relative frequencies following the formula E equals M. P. Which for this problem gives us expected frequencies 11 11, 12.5, 15 and 0.5. So right off the bat we see assumption one is not satisfied because the last expected frequency is less than one. Next resumption to are at most 20% of the expected frequencies less than five. This assumption actually is satisfied because only one in five of our expected frequencies is less than five, whoever. It doesn't matter because assumption one is not meant and therefore using high squared is not appropriate.

Right. We are given the following relative frequencies for no hypothesis for a chi square goodness of fit test as well as the sample size. And we want to determine based on this information alone, are the necessary assumptions met to conduct the chi square goodness of fit test. If we assume the assumption of a simple random sample has already met, we only need to check whether or not the following two assumptions are met. That is assumption one. All expected frequency. Either one or greater and assumption to almost 20% at most. 20% of the expected frequencies are less than five. So we'll check first for one, remember that, expect the frequency is E equals M. P. So for these relative frequencies above we obtain 65 30 and five. So all expected frequencies are indeed one or greater. This assumption is met Next At most, 20% of the expected frequencies are less than five, none are less than five in this case. So we can also say that assumption to has been met. So both assumptions are made


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