In SAS (Statistical Analysis System), Analysis of Variance (ANOVA) is a statistical tool for examining mean differences across groups. It is useful in determining if there are statistically significant differences between the means of three or more independent (unrelated) groups.
To prepare data, format it using a variable for the dependent variable (response) and another for the grouping component (categorical).
PROC ANOVA DATA=your_dataset;
CLASS grouping_variable;
MODEL dependent_variable = grouping_variable;
RUN;
Output Interpretation:
The output will include many tables, including the ANOVA table, which contains information on the sources of variation, degrees of freedom, sums of squares, mean squares, F-statistic, and p-values.
The p-value indicates if there are significant variations between the group means. A low p-value (<0.05) indicates substantial differences.
Post-hoc Tests:
If ANOVA shows significant differences, you may wish to do post-hoc testing to determine which groups vary from one another.
Tukey’s HSD (Honestly Significant Difference) and Dunnett’s test are two often used post-hoc tests.
Additional options:
Additional options can be added to your ANOVA study, such as the LSMEANS statement for computing least squares means, the CONTRAST statement for particular linear combinations of group means, and so on.