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Stata Textbook Examples
Experimental Design by Roger Kirk
Chapter 7: Randomized Block Designs
Table 7.2-1, page 260.
use http://www.ats.ucla.edu/stat/stata/examples/kirk/rb4, clear
tabdisp s a, cellvar(y)
----------+-----------------------
| a
s | 1 2 3 4
----------+-----------------------
1 | 3 4 4 3
2 | 2 4 4 5
3 | 2 3 3 6
4 | 3 3 3 5
5 | 1 2 4 7
6 | 3 3 6 6
7 | 4 4 5 10
8 | 6 5 5 8
----------+-----------------------
Table 7.2-2, page 261.
anova y a s
Number of obs = 32 R-squared = 0.7318
Root MSE = 1.18523 Adj R-squared = 0.6041
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 80.50 10 8.05 5.73 0.0004
|
a | 49.00 3 16.3333333 11.63 0.0001
s | 31.50 7 4.50 3.20 0.0180
|
Residual | 29.50 21 1.4047619
-----------+----------------------------------------------------
Total | 110.00 31 3.5483871
Table 7.3-2, page 271.
Note: The nonadd command can be downloaded by typing findit noadd (see How can I use the findit command to search for programs and get additional help? for more information about using findit).
nonadd y a s Tukey's test of nonadditivity for randomized block designs F (1,20) = 1.2795813 Pr > F: .27135918
Figure 7.3-1, page 272.
anova y a s, noanova /* rerun anova without redisplaying anova table */ predict yhat /* yhat is the fitted value */ predict e, rstandard /* e is the standardized residual */ graph twoway scatter e yhat, ylabel(-2.5(.5)2.5) xlabel(1(1)9)
Various computations on compound symmetry, pages 274-282.
anova y a s, repeated(a) /* anova with conservative p-values for repeated measures */
Number of obs = 32 R-squared = 0.7318
Root MSE = 1.18523 Adj R-squared = 0.6041
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 80.50 10 8.05 5.73 0.0004
|
a | 49.00 3 16.3333333 11.63 0.0001
s | 31.50 7 4.50 3.20 0.0180
|
Residual | 29.50 21 1.4047619
-----------+----------------------------------------------------
Total | 110.00 31 3.5483871
Between-subjects error term: s
Levels: 8 (7 df)
Lowest b.s.e. variable: s
Repeated variable: a
Huynh-Feldt epsilon = 0.8343
Greenhouse-Geisser epsilon = 0.6195
Box's conservative epsilon = 0.3333
------------ Prob > F ------------
Source | df F Regular H-F G-G Box
-----------+----------------------------------------------------
a | 3 11.63 0.0001 0.0003 0.0015 0.0113
Residual | 21
-----------+----------------------------------------------------
matrix list e(Srep) /* display variance-covariance matrix */
symmetric e(Srep)[4,4]
c1 c2 c3 c4
r1 2.2857143
r2 1.1428571 .85714286
r3 .71428571 .28571429 1.0714286
r4 1.2857143 .28571429 .92857143 4.5
Table 7.8-2, page 299.
use http://www.ats.ucla.edu/stat/stata/examples/kirk/rb3, clear
tabdisp s a, cellvar(y)
----------+-----------------
| a
s | 1 2 3
----------+-----------------
1 | 15 12 11
2 | 11 9
3 | 13 13
----------+-----------------
Table 7.8-3, page 301.
anova y a s
Number of obs = 7 R-squared = 0.8970
Root MSE = 1.06458 Adj R-squared = 0.6909
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 19.7333333 4 4.93333333 4.35 0.1954
|
a | 8.40 2 4.20 3.71 0.2125
s | 3.73333333 2 1.86666667 1.65 0.3778
|
Residual | 2.26666667 2 1.13333333
-----------+----------------------------------------------------
Total | 22.00 6 3.66666667
Tables 7.9-1, page 305.
use http://www.ats.ucla.edu/stat/stata/examples/kirk/grb4, clear
tabdisp id a, by(g) cellvar(y)
----------+-----------------------
| a
g and id | 1 2 3 4
----------+-----------------------
1 |
1 | 3 4 4 3
2 | 3 3 3 5
----------+-----------------------
2 |
1 | 2 4 4 5
2 | 2 3 3 6
----------+-----------------------
3 |
1 | 6 5 5 8
2 | 3 3 6 6
----------+-----------------------
4 |
1 | 1 2 4 7
2 | 4 4 5 10
----------+-----------------------
table g a, cont(sum y)
----------+-----------------------
| a
g | 1 2 3 4
----------+-----------------------
1 | 6 7 7 8
2 | 4 7 7 11
3 | 9 8 11 14
4 | 5 6 9 17
----------+-----------------------
Table 7.9-2, page 306.
Note: To obtain the correct F-ratio of a, a*g is used as the error term.
anova y a g a*g
Number of obs = 32 R-squared = 0.7727
Root MSE = 1.25 Adj R-squared = 0.5597
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 85.00 15 5.66666667 3.63 0.0074
|
a | 49.00 3 16.3333333 10.45 0.0005
g | 16.75 3 5.58333333 3.57 0.0377
a*g | 19.25 9 2.13888889 1.37 0.2795
|
Residual | 25.00 16 1.5625
-----------+----------------------------------------------------
Total | 110.00 31 3.5483871
test a /a*g
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
a | 49.00 3 16.3333333 7.64 0.0076
a*g | 19.25 9 2.13888889
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