Temporal variation in water quality was evaluated through DA. Both standard and stepwise modes were applied on the raw data after dividing the whole data set into two seasonal groups (wet and dry seasons). Season was the dependent variable, while all observed water quality parameters were independent variables. The values of Wilks' lambda and the chi-square statistic for each DF were obtained from each standard mode and stepwise mode. As shown in Table 1, the values varied from 0.222 to 0.244 and from 277.3 to 267.5 for Wilks' lambda and the chi-square, respectively. Smaller values of Wilks’ lambda indicate a greater discriminatory ability of the function. The associated chi-square statistic tests the hypothesis that the means of the functions listed are equal across groups. The small significance values (*p*-level <0.01) indicate that the DF does better than chance at separating the groups. Thus, the temporal DA was credible and effective. The first function in standard DA explained almost all (*R* = 88.2%) of the total variance in the dependent variables. The stepwise DA had similar results, which indicated that 87% of the total group differences in the data set were explained by its first DF. Therefore, the first DF alone was sufficient to explain the difference of water quality among the two wet and dry seasons.

Table 1

Mode . | DF . | Canonical correlation (R) . | Eigenvalue . | Wilks' lambda . | Chi-square . | p-level (Sig.)
. |
---|---|---|---|---|---|---|

Standard mode | 1 | 0.882 | 3.513 | 0.222 | 277.271 | 0.000 |

Stepwise mode | 1 | 0.870 | 3.102 | 0.244 | 267.490 | 0.000 |

Mode . | DF . | Canonical correlation (R) . | Eigenvalue . | Wilks' lambda . | Chi-square . | p-level (Sig.)
. |
---|---|---|---|---|---|---|

Standard mode | 1 | 0.882 | 3.513 | 0.222 | 277.271 | 0.000 |

Stepwise mode | 1 | 0.870 | 3.102 | 0.244 | 267.490 | 0.000 |

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