As one of the BFP's aims is to improve student performance in these critical courses, we performed a linear regression analysis to examine the relationship between multiple potentially explanatory variables and performance in the introductory biology courses.
Since objective performance may vary even within a given difficulty level, we performed a linear regression to further quantify the influence of fluctuations in objective performance on task ratings, entering objective performance and feedback presence as taskwise regressors (two per block).
To test the performance of the method directly in our material we performed a linear regression analysis of normalized read count levels (see Methods) of all miRNAs in the two liver samples.
To develop a quantitative boundary between synthesizable and non-synthesizable NCs we performed a linear regression on all the experimentally synthesized NCs with 95% confidence and superimposed the prediction bands (Supplementary Fig. 4).
We performed a linear regression of each individual behavioral parameter (i.e. error rate, switch latencies, etc.) on Predictor 1, defined as the Circadian activity (nose-poking activity) or on Predictor 2, defined as sleep parameters (i.e. sleep duration, NREM, REM, etc).
I performed a linear regression one each grouping ("the actual grouped data") and computed the "R Squared" statistic for each regression.
We then performed a linear regression on each participant's efficiency values against their block order, calculating a regression slope.
To highlight the effect of the different climate variables and phylogeny on leaf out variability, we performed a linear regression analyses.
We performed a linear regression of the log10 variance as a function of the log10 mean in the data of Table 2 of Luria and Delbrück (1943) and found that b = 1.47 ± 0.57 (standard error) with R2 = 0.53.
To this end, we performed a Linear Regression Analysis (LRA) of the results of plotting the rate of condensation of monosaccharides with pyridoxamine (k3) [54] against the global electrophilicity (omega).
Therefore, we calculated the annual mean of total ecosystem carbon with an error bar of standard deviation and performed a linear regression (Figure 6a), which shows the total ecosystem carbon increased steadily from 7,942 gC/m2 in 2006 to approximately 10,234 gC/m2 in 2050 (i.e., 28.9% increase), with a mean value of 9,056 gC/m2.
