The linear statistical model for ANOVA is given as([6, 43]: {y}_{ij}=mu +{alpha}_i+{varepsilon}_{ij},kern0.5em i=1,2,3,dots, akern0.5em andkern0.5em j=1 whereots, n (1)where: μ the grand mean of y ij, α i the i th level effect on y ij and ε ij ∼ iidN 0, σ 2).
This study establishes an inverse linear statistical relationship between high BT consumption and diabetes prevalence in the world, and confirms the findings of the European ecological study establishing a similar relationship.
Linear statistical analyses were carried out using the application PASW Statistics (Version 18, SPSS, Chicago, IL, USA).
Linear statistical analyses were performed using SigmaPlot 12 (Aspire Software).
The analysis of survey data using generalized linear statistical models.
Linear statistical equations are modeled.
Statistical Language Learning.
The derivation of the FBSL-SPKS is obtained by making use of the relationship between the SPKF and weighted statistical linear regression (WSLR).
The relationship between grain yield under stress and secondary traits was analyzed using linear regression (SPSS statistical package v.21, IBM Corp. Released 2012) considering yield under stress as the fixed effect.
For carrying out multivariate linear regression (MLR), statistical software package SPSS [ 18] was used.
All data were analyzed using generalized linear models (JMP statistical software; SAS Institute, Cary, NC), and checked for normality of distribution and homogeneity of variance.
