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Table 1 Summary of the homoscedastic linear model calibration curves fitted by ordinary least squares

From: Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study

Curve Intercept a , β 0 (SE b ) Slope, β 1 (SE) Variance, σ 2 R 2 g c
j = 1 30.87 (0.96) −4.42 (0.56) 5.58 0.94 0.13
j = 2 32.46 (1.90) −4.79 (1.11) 21.75 0.82 0.42
j = 3 30.88 (0.64) −3.80 (0.38) 2.47 0.96 0.08
j = 4 30.82 (2.35) −4.59 (1.37) 33.00 0.74 0.69
j = 5 27.00 (0.51) −2.48 (0.30) 1.54 0.95 0.11
j = 6 33.89 (1.16) −2.69 (0.68) 8.14 0.79 0.50
j = 7 31.21 (0.53) −3.49 (0.31) 1.66 0.97 0.06
j = 8 24.09 (0.46) −2.40 (0.27) 1.25 0.95 0.09
j = 9 24.28 (0.91) −2.98 (0.53) 4.93 0.89 0.24
j = 10 30.25 (1.62) −4.73 (0.95) 15.78 0.86 0.31
j = 11 27.71 (0.58) −3.15 (0.34) 2.00 0.96 0.09
j = 12 20.85 (0.58) −2.51 (0.34) 2.00 0.93 0.14
  1. aCalibrators, x ij , were centered about their mean, \( \overline{x}, \) ensuring that ‘intercept’ terms correspond to the respective estimates at \( {x}_{ij}=\overline{x} \).
  2. bStandard error.
  3. cCalculated using a Student’s critical t value at a significance level of 5% and n + m −3 = 4 + 1–3 degrees of freedom (8).