Variations of One Factor ANOVA Designs - Agricultural Statistical Data Analysis Using Stata - page 79

e(rss) = 391207.0176783416
e(r2_a) = .6711715130479328
e(ll) = -348.6157398005913
e(ll_0) = -395.1808477381046
e(ss_1) = 1433977.529230501
e(df_1) = 19
e(F_1) = 7.331041951857553
e(ss_2) = 21980.14123374692
e(df_2) = 2
e(F_2) = 1.067523496688828
macros:
e(cmdline) : “anova yieldacre entry rep”
e(depvar) : “yieldacre”
e(cmd) : “anova”
e(properties) : “b_nonames V_nonames”
e(varnames) : “entry rep”
e(term_2) : “rep”
e(term_1) : “entry”
e(sstype) : “partial”
e(predict) : “regres_p”
e(model) : “ols”
e(estat_cmd) : “anova_estat”
matrices:
e(b) : 1 x 24
e(V) : 24 x 24
functions:
e(sample)
This information can be used to calculate the relative efficiency of
the RCBD compared to the CRD. We can use the display com-
mand to calculate the relative efficiency.
Enter the following commands:
local Eb = e(ss_2)/e(df_2)
local r = e(df_2)+1
local t = e(df_1)+1
local Ee = e(rss)/e(df_r)
local RE = ((`r'-1)*`Eb' + `r'*(`t'-1)*`Ee')/
((`r'*`t'-1)*`Ee')
display `RE'