In order to recognise possible dose-response relationships which may not be linear, effects of radon concentrations on childhood cancer risk were investigated for the following pre-defined levels 0 24, 25 49, 50 99, 100 199, and 200+ Bq m−3, as used in a UK study of residential radon exposure and lung cancer risk published earlier (Darby et al, 1998).
In addition, we sought to determine whether there was linear effect of pH on melamine migration.
There were linear effects (P < 0.01) of phytase on Ca utilization in diets that were not supplemented with antimicrobials but only tendencies (P < 0.10) in diets supplemented with tylosin or virginiamycin.
While there was evidence of cohort curvature, it should be noted that there could be underlying linear effect of period in the cohort IRRs; making its interpretation less straightforward.
We used the following equations for our mixed-effects models: E(Y) = α + β1 T + β2 AP + β3(T × AP) + β4 DOS + (1| Participant), [1] where β1 is the main effect of temperature; β2 is the main effect of ozone versus clean air; β3 is the interaction of ozone and temperature; β4 is the linear effect of DOS; and (1| Participant) is a random participant intercept.
In contrast, in the Singapore participants, there were no significant linear effects of repetition, suggesting that duration of fixation was not modulated by data-driven changes in the study.
ISO + ε.> Where, μ is the mean, Year is a 2-level fixed factor (2008, 2012), T is a linear effect of temperature in °C (mean centred across all observations), Pop is a 3-level fixed factor (Lewistown, Show Low, Chiricahua) and Year.Pop and T.Year.Pop are interaction terms.
[2] Here, β1 is the main effect of temperature; β ´2 is the stratum-specific effect of ozone when T = 0; β ´3 is the stratum-specific effect of ozone when T = 1; β4 is the linear effect of DOS; and (1| Participant) is a random participant intercept.
Even though the relationship between ICV and volumes may not be linear and the effect of ICV itself may be removed if data is normalized, we still believe this may be the best approach to take.
The model had the formula: where y it denotes the log ratio tumor volume for mouse i at time t, β0 is the intercept representing the log ratio of the tumor volume on day 26 to the initial volume for the control group, β1 is the linear effect of time for the control group, β2 is the group effect on day 26, and β3 is the group effect in terms of the linear effect of time.
The coefficients of A, B, C and D are the main linear effects of the independent process variables: seed particle size (x1), time (x2), solvent ratio (x3), and temperature (x4) respectively, in coded units.
