The chi-square statistic test illustrates the extent of the association between the measured values and the envisaged values (Puncher 2007). The incorporation of the chi-square test into the IMBA software enables it to approximate the bioassay intake function. The chi-square scheme entails a cluster of distributions influenced by degrees of freedom. The degree of freedom is arrived at by subtracting one from the total number of the measured data points. Consequently, the chi-square probability (which is also referred to as the P value) can be established from the data using the degrees of freedom of the measured values. One of the most significant uses of the chi-square test is hypothesis testing. The following equation illustrates how the chi parameter is computed.

Chi-Square Statistics

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In the above equation, μ’ signifies the average of the experimental data while xi denotes the actual value in the group of data (Knoll 2000).

Small values of chi-square are obtained when P is greater than 0.95. Such small values of P show that there is a good relationship between the calculated and predicted variations in the data. In contrast, high chi-square values are obtained when P is smaller than 0.05. Such high values are signs of inadequate fits between the measured and expected variations in the data.