An example research question that could be answered using a Chi-Square analysis would be: Is there a significant relationship between voter intent and political party membership? How does the Chi-Square statistic work?

1:29 5:11 Number. So it's the sum over all the cells of the observed minus expected squared divided by theMoreNumber. So it's the sum over all the cells of the observed minus expected squared divided by the expected. Value. In this case works out to 18 minus 15 or three squared divided by fifteen.

The Chi-square test of independence determines whether there is a statistically significant relationship between categorical variables. It is a hypothesis test that answers the question—do the values of one categorical variable depend on the value of other categorical variables?

Here's how it is calculated. We first find the difference between the observed (o) and expected (e) values. We then take the square of that number and divide it by the expected value. Finally, we add all of these calculated values from the various categories to get the chi-square.

Thus, the formula for determining the expected cell frequencies in the χ2 test of independence is as follows: Expected Cell Frequency = (Row Total * Column Total)/N. The above computes the expected frequency in one step rather than computing the expected probability first and then converting to a frequency.

Market researchers use the Chi-Square test when they find themselves in one of the following situations: They need to estimate how closely an observed distribution matches an expected distribution. This is referred to as a “goodness-of-fit” test. They need to estimate whether two random variables are independent.