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Two approaches are considered: regression and interpolation.
All statistics including linear regression and interpolation for the ELISA assay were performed using GraphPad Prism 5. To compare sensitivity and specificity of measurements in the tumour cohort compared to the benign cohort receiver-operator curves (ROC) were performed.
An arbitrary choice of threshold is required; (Bushdid et al., 2014) chose a threshold of 50% significantly discriminable, and computed d from the fraction significantly discriminable using linear regression and interpolation.
The plot is obtained by regression and interpolation of results in A combined with Equation 1, with colors corresponding to values of N as shown in A. For a value of ∼ 51%% as derived in A, one obtains the 'trillions' figure reported in (Bushdid et al., 2014 ).
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On the basis of the results, obtained by performing a big number of computer runs, by using regression analysis and interpolation functions, equations for the calculation of tooth deflections and fillet stresses are derived.
It is also the theoretical foundation of many other complex regression and auto-regression interpolation methods.
Missing values are imputed using linear regression model, Lagrange interpolation, Linear interpolation and linear spline interpolation.
In data management, missing values are treated (imputed) by using regression estimates, Lagrange interpolation, linear interpolation and linear spline interpolation based on the appropriate method which best fits for the trend of data with minimum error at each time level.
Classification and regression tree, and spatial interpolation, analyses confirm geographical heterogeneity for linkages at 6p25.3-p24.3, 8p23.1-p21.3 and Xq21.1-q26.1, with specific clusters of families contributing LOD scores of 2.13 (P = 0.0009), 1.75 (P = 0.002) and 1.84 (P = 0.001), respectively.
A standard curve was created using linear regression and sample concentrations calculated by interpolation (PRISM 4.02, GraphPad Software, La Jolla, CA, USA).
For ELISA and MTS assays linear regression with interpolation was used to determine either antigen concentration or cell number, respectively.
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