This example illustrates the use of PROC ANOVA in analyzing a randomized complete block design. Researchers are interested in whether three treatments have different effects on the yield and worth of a particular crop. They believe that the experimental units are not homogeneous. So, a blocking factor is introduced that allows the experimental units to be homogeneous within each block. The three treatments are then randomly assigned within each block.

The data from this study are input into the SAS data set `RCB`

:

title1 'Randomized Complete Block'; data RCB; input Block Treatment $ Yield Worth @@; datalines; 1 A 32.6 112 1 B 36.4 130 1 C 29.5 106 2 A 42.7 139 2 B 47.1 143 2 C 32.9 112 3 A 35.3 124 3 B 40.1 134 3 C 33.6 116 ;

The variables `Yield`

and `Worth`

are continuous response variables, and the variables `Block`

and `Treatment`

are the classification variables. Because the data for the analysis are balanced, you can use PROC ANOVA to run the analysis.

The statements for the analysis are

proc anova data=RCB; class Block Treatment; model Yield Worth=Block Treatment; run;

The `Block`

and `Treatment`

effects appear in the CLASS
statement. The MODEL
statement requests an analysis for each of the two dependent variables, `Yield`

and `Worth`

.

Figure 26.5 shows the "Class Level Information" table.

The "Class Level Information" table lists the number of levels and their values for all effects specified in the CLASS statement. The number of observations in the data set are also displayed. Use this information to make sure that the data have been read correctly.

The overall ANOVA table for `Yield`

in Figure 26.6 appears first in the output because it is the first response variable listed on the left side in the MODEL
statement.

The overall F statistic is significant , indicating that the model as a whole accounts for a significant portion of the variation in `Yield`

and that you can proceed to evaluate the tests of effects.

The degrees of freedom (DF) are used to ensure correctness of the data and model. The Corrected Total degrees of freedom are one less than the total number of observations in the data set; in this case, 9 – 1 = 8. The Model degrees of freedom for a randomized complete block are , where b = number of block levels and t = number of treatment levels. In this case, this formula leads to model degrees of freedom.

Several simple statistics follow the ANOVA table. The R-Square indicates that the model accounts for nearly 90% of the variation
in the variable `Yield`

. The coefficient of variation (C.V.) is listed along with the Root MSE and the mean of the dependent variable. The Root MSE
is an estimate of the standard deviation of the dependent variable. The C.V. is a unitless measure of variability.

The tests of the effects shown in Figure 26.7 are displayed after the simple statistics.

For `Yield`

, both the `Block`

and `Treatment`

effects are significant and , respectively) at the 95% level. From this you can conclude that blocking is useful for this variable and that some contrast
between the treatment means is significantly different from zero.

Figure 26.8 shows the ANOVA table, simple statistics, and tests of effects for the variable `Worth`

.

The overall F test is significant at the 95% level for the variable `Worth`

. The `Block`

effect is not significant at the 0.05 level but is significant at the 0.10 confidence level . Generally, the usefulness of blocking should be determined before the analysis. However, since there are two dependent variables
of interest, and `Block`

is significant for one of them (`Yield`

), blocking appears to be generally useful. For `Worth`

, as with `Yield`

, the effect of `Treatment`

is significant .

Issuing the following command produces the `Treatment`

means.

means Treatment; run;

Figure 26.9 displays the treatment means and their standard deviations for both dependent variables.