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Table 6 Three candidate processes for the study population via time series models per year

From: Predictive determinants of scorpion stings in a tropical zone of south Iran: use of mixed seasonal autoregressive moving average model

Model
ARMA (1, 1) \( \widehat{b} \) S.E. (\( \widehat{b} \)) p value RMSE
 Constant 5.90 1.67 0.001* 8.68
 AR (1) 0.66 0.13 < 0.001*
 MA (1) −0.37 0.16 0.025*
 Modified Box-Pierce test Lag Chi-square df p value
12 24.0 9 0.004*
24 33.9 21 0.037*
ARMA(1, 2) \( \widehat{b} \) S.E. (\( \hat{b} \)) p value RMSE
 Constant 0.72 0.07 < 0.001* 10.06
 AR (1) 0.96 0.25 < 0.001*
 MA(1) 0.25 0.29 0.384
 MA (2) 0.71 0.20 0.001*
 Modified Box-Pierce test Lag Chi-square df p value
12 40.7 8 < 0.001*
24 80.7 20 < 0.001*
ARMA (1, 1) × (0, 1)12 \( \hat{b} \) S.E. (\( \widehat{b} \)) p value RMSE
 Constant 6.42 2.21 0.006* 8.09
 AR (1) 0.63 0.14 < 0.001*
 MA (1) −0.38 0.16 0.026*
 SMA (1) −0.41 0.14 0.005*
 Modified Box-Pierce test Lag Chi-square df p value
12 13.2 8 0.104
24 19.9 20 0.466
  1. ARMA auto-regressive moving average, ARMA (p, q) × (P, Q) h mixed seasonal ARMA, \( \widehat{\boldsymbol{b}} \) coefficient, df degree of freedom, S.E. ( \( \widehat{\boldsymbol{b}} \) ) standard error of coefficient, RMSE root mean square error
  2. *p value <0.05 is significant