Sas statistical software version 6

This investigation was carried out using a subsampling dataset (n = 36, termed the two‐thirds dataset) from the total dataset (n = 53). The models were developed using a multiple linear regression analysis, which relates the independent variable(s) to Y_m. This investigation was conducted in a sequential manner to increase model complexity at each level and thus increase the model's predictive power, which is based on complex information (IPCC, 2006, Moraes, Strathe, Fadel, Casper, & Kebreab, 2014). According to the dataset availability and extent models (Table 2), five complexity levels were performed, namely dietary, intake, digestibility, integrated dietary, intake and digestibility, and energy levels (Table 3). All variables were computed under the selected most probable model at these levels of complexity. Specifically, the regression analysis for model complexity at each level was analyzed using the REG procedure (stepwise and collinearity diagnostics) of the SAS statistical software version 6.12 (SAS Institute Inc. Cary, NC, USA). The statistical model was $Y_{\mathrm{m}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1{X}_1+{\mathrm{\beta }}_2{X}_2+,\dots ,+{\mathrm{\beta }}_n{X}_n+\mathrm{\epsilon },$ where Y_m = methane conversion factor (% of GE intake); β₀ = intercept, β₁, β₂, …, β_n = slopes, X₁, X₂, …, X_n = independent variables, and ɛ = error.

All data are presented as the mean ± standard deviation (SD), and significant differences (P < 0.05) between groups were analyzed by Student's t-test using SAS statistical software version 6.12 (SAS Institute).