Data from Chapter 2 from Table 2.2, page 22

clear input a score 1 16 1 18 1 10 1 12 1 19 2 4 2 7 2 8 2 10 2 1 3 2 3 10 3 9 3 13 3 11 end

Table 4.4, page 70. The analysis of variance of orthogonal contrasts in a one-way design

NOTE1: For these contrast, we are going to exploit **xi3** (**xi3**
must be installed; if it is not, it can be done by typing **search xi3** in
the Stata
command window and proceed with installation) user defined coding scheme **
u.varname**, where **u** is defined in the **char** command and the
variables are expanded with the **xi3** command. The user defined variables
must be treated as continuous to obtain the correct Sums of Squares.

NOTE2: The orthogonal contrast exclusively partitions the model sums of squares
between the two comparisons.

char a[user] (1 -.5 -.5 0 1 -1) xi3 u.a

u.a _Ia_1-3 (naturally coded; _Ia_3 omitted)

anova score _Ia_1 _Ia_2, continuous(_Ia_1 _Ia_2)

Number of obs = 15 R-squared = 0.5385 Root MSE = 3.87298 Adj R-squared = 0.4615 Source | Partial SS df MS F Prob > F -----------+---------------------------------------------------- Model | 210 2 105 7.00 0.0097 | _Ia_1 | 187.5 1 187.5 12.50 0.0041 _Ia_2 | 22.5 1 22.5 1.50 0.2442 | Residual | 180 12 15 -----------+---------------------------------------------------- Total | 390 14 27.8571429

Non orthogonal contrast in a one-way design, top of page 78

NOTE: The non-orthogonal contrast does not uniquely partition the model sums of squares

char a[user] (1 -1 01 0 -1) xi3, pre(non) u.a

u.a nona_1-3 (naturally coded; nona_3 omitted)

anova score nona_1 nona_2, continuous(nona_1 nona_2)

Number of obs = 15 R-squared = 0.5385 Root MSE = 3.87298 Adj R-squared = 0.4615 Source | Partial SS df MS F Prob > F -----------+---------------------------------------------------- Model | 210 2 105 7.00 0.0097 | nona_1 | 202.5 1 202.5 13.50 0.0032 nona_2 | 90 1 90 6.00 0.0306 | Residual | 180 12 15 -----------+---------------------------------------------------- Total | 390 14 27.8571429