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How to get expected frequency value in chi square test?
For goodness of fit test using Chisquare test, Expected frequency = Total number of observations * theoretical probability specified or Expected frequency = Total number of observations / Number of categories if theoretical frequencies are not given. For contingency tables (test for independence) Expected frequency = (Row total * Column total) / Grand total for each cell
Which of the following accurately describes the expected frequencies for a chi-square test?
The maximum likelihood estimate under the null hypothesis gives the best estimate for expected frequencies.
How would you determine the expected frequencies for a chi-square goodness of fit test?
You first decide on a null hypothesis. Expected frequencies are calculated on the basis of the null hypothesis, that is, assuming that the null hypothesis is true.
Uses of chi-square test?
The chi-square test is used to analyze a contingency table consisting of rows and columns to determine if the observed cell frequencies differ significantly from the expected frequencies.
What is the difference between the goodness of fit test and the contingency test?
Goodness of fit test is used to test a single population. The null hypothesis will be that the observed frequencies are equal to expected frequencies (based on computed intrinsic values given the extrinsic values). A good example would be comparing observed phenotype frequencies against expected frequencies calculated from the parental genotypes (Simple dominance gives rise to 1:2:1 ratio with two parental heterozygotes). Contingency test is used to see whether or not different populations are associated. The null hypothesis will be that that different populations are independent of one another. A good example would be comparing the effect of different host plants on different populations of insects. (Effect of Host A on Insect population 1, 2, 3; Effect of Host B on Insect population 1, 2, 3; etc)
