A common method of evaluating agreement for microarrays is via scatter plot of all expression values for sample 1 versus all expression values for sample 2. A y = x identity line is frequently overlaid on this plot along with a loess smoother and the formula for the linear regression of sample 1 on sample 2 with corresponding correlation value.
For simple linear regression, the sample correlation coefficient is the square root of the coefficient of determination, with the sign of the correlation coefficient being the same as the sign of b1, the coefficient of x1 in the estimated regression equation.
In our setting, since σ g / K M + μ g ≈ 0.1, the linear regression and sample average solutions are very close to each other, and either may be used when testing for a treatment effect.
A standard curve was created using linear regression and sample concentrations calculated by interpolation (PRISM 4.02, GraphPad Software, La Jolla, CA, USA).
Permeability was calculated by linear regression of sample fluorescence (Excel 5.0, Microsoft Office).
Paracellular flux was calculated by linear regression of sample fluorescence (Excel 5.0, Microsoft WA, Power Macintosh 7200).
Even more interestingly, linear regression of sample pairs reveals a statistically significant correlation between the decrease in conversion and the decrease in enzyme adsorption.
Through a literature review, we identified six statistical methods that were sometimes employed to analyze continuous outcomes in multicentre RCTs: A. simple linear regression (two sample t-test), B. fixed-effects regression, C. mixed-effects regression, D. generalized estimating equations (GEE), E-1.
Note the higher variability of the oligodendrocyte reference signal compared to the other 3 reference signals (see Additional file 2: Figure S1), which increased the detectability of oligodendrocyte expression (because in linear regression the sample variance of the coefficient is inversely proportional to the variance of the regressor).
