Selected article for: "confidence interval and model fit"

Author: Ross D. Overacker; Somdev Banerjee; George F. Neuhaus; Selena Milicevic Sephton; Alexander Herrmann; James A. Strother; Ruth Brack-Werner; Paul R. Blakemore; Sandra Loesgen
Title: Biological Evaluation of Molecules of the azaBINOL Class as Antiviral Agents: Specific Inhibition of HIV-1 RNase H Activity by 7-Isopropoxy-8-(naphth-1-yl)quinoline
  • Document date: 2019_1_23
  • ID: m2zw8eq4_46
    Snippet: values, but all constraints were inactive at the converged optimum. A parametric bootstrap 542 analysis was used to compute a 95% confidence interval for the computed KD values, using a 543 normally-distributed error with variance estimated from the sum of squared residuals of the 544 model fit (MATLAB R2018a, 100 iterations). Random noise in response curves was filtered 545 prior to plotting using a smoothing spline (MATLAB R2018a, spaps functio.....
    Document: values, but all constraints were inactive at the converged optimum. A parametric bootstrap 542 analysis was used to compute a 95% confidence interval for the computed KD values, using a 543 normally-distributed error with variance estimated from the sum of squared residuals of the 544 model fit (MATLAB R2018a, 100 iterations). Random noise in response curves was filtered 545 prior to plotting using a smoothing spline (MATLAB R2018a, spaps function). 546

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