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Table 2 Summary of linear regression analyses for variables predicting log(Vp) counts in oysters. Estimates in bold are significant at the 0.05 level, and the percent change in Vp counts is given only for significant estimates

From: Remote sensing measurements of sea surface temperature as an indicator of Vibrio parahaemolyticus in oyster meat and human illnesses

Variable(s) in linear model Coefficient [95%CI] Change in Vp count per
1-unit increase [95%CI]*
Model R2
SST 0.56 [0.51, 0.61] ↑ 75% [67, 84] increase 0.24
Salinity -0.08 [-0.10, -0.06] ↓ 8% [6, 10] decrease 0.04
Chlorophyll a 0.00 [-0.01, 0.01] - 0.00
SST 0.54 [0.50, 0.59] ↑ 72% [65, 80] increase 0.25
Salinity -0.04 [-0.08, 0.00] ↓ 4% [0, 8] decrease
SST 0.58 [0.53, 0.63] ↑ 79% [70, 88] increase 0.28
Salinity -0.04 [-0.08, 0.00] ↓ 4% [0, 8] decrease
Data source**   
  A Reference  
  B -0.18 [-0.39, 0.15] -
  C -1.64 [-2.11, -1.16] ↓ 81% [69, 88] decrease
  D 0.28 [0.00, 0.55] ↑ 32% [0, 73] increase
  1. *Because analyses were conducted with log(Vp) counts, the change per each 1-unit increase is calculated by taking the antilog of the variable coefficient and taking difference from the baseline value of 1 (i.e. a coefficient of 0)
  2. **Effect estimates reflect the change in Vp counts when compared with category A. Bolded coefficient represent statistically significant results at a p value of less than 0.05