Sas jmp pro 16

The obtained data were subjected to an analysis of variance (ANOVA), and the significance of differences was tested using Tukey’s test at a significance level of p ≤ 0.05. A multivariate ANOVA (MANOVA) was used for the data analysis of repeated measures. For percentage data analysis, values were arc-sin-transformed. In justified cases, a restricted maximum likelihood (REML) analysis of the effects of variance components was performed. The correlation coefficient was determined for the selected data using Pearson’s R method. To interpret the correlation strength, the classification of Guilford [69 ] was adopted. All statistical analyses were performed using SAS JMP^® Pro 16.

We conducted analysis of variance (ANOVA) on the visual estimates of termination efficiency 14 and 28 DAT and percentage biomass change 28 DAT for each species using SAS PROC MIX model (SAS Institute Inc., Cary, NC, USA). The effect of the experimental run and its interaction with termination treatment were found non-significant (Table 4), therefore, for final analysis, the experimental run was considered as a random effect. Means were separated using Fisher’s protected least significance difference (LSD) at 5% level of significance. Visible termination efficiency data 28 DAT was correlated with different vegetative indices (Table 3) values using SAS JMP Pro 16 (SAS Institute Inc., Cary, NC, USA), to evaluate vegetative indices that provide good estimate of termination efficiency of different herbicides. Two best performing indices were selected for each cover crops and linear regression was fitted for termination efficiency and respective indices value using SAS JMP Pro 16 to better understand the relationship between them.