The chi square test is a statistical method used to determine if there is a significant difference between observed and expected frequencies in a given dataset. In this case, the test was conducted using two different methods: the likelihood ratio and Pearson methods. The results of the test are presented in the table above, which shows the chi square value, degrees of freedom (DF), and probability value (Prob>Chisq) for each method.

The chi square value represents the difference between the observed and expected frequencies, with higher values indicating a greater difference. The degrees of freedom (DF) represent the number of categories in the dataset minus one. The probability value (Prob>Chisq) represents the likelihood of obtaining the observed chi square value by chance alone, with lower values indicating a greater likelihood of a significant difference.

In this case, both the likelihood ratio and Pearson methods produced similar results, with chi square values of 5.9033 and 5.6000, respectively. However, the probability values were relatively high (0.3157 and 0.3471), indicating that there is not a significant difference between the observed and expected frequencies in the dataset.

To interpret the results of a chi square test, it is important to consider both the chi square value and the probability value. If the chi square value is high and the probability value is low, it suggests that there is a significant difference between the observed and expected frequencies in the dataset. Conversely, if the chi square value is low and the probability value is high, it suggests that there is not a significant difference.