Statistical Methods for Psychology

590 Chapter 16 Analyses of Variance and Covariance as General Linear Models

Exhibit 16.2 Regression solutions using all predictors for data in Table 16.2

DataAnova;

infile ‘Ex162.dat’;

input A1 B1 B2 B3 dv;

AB11 = A1 *B1;

AB12 = A1 *B2;

AB13 = A1 *B3;

Run;

Proc CorrData = Anova;

Var A1 B1 B2 B3 AB11 AB12 AB13;

Run;

Proc RegData = Anova;

Model dv = A1 B1 B2 B3 AB11 AB12 AB13;

Run;

Pearson Correlation Coefficients, N = 32

Prob > |r | under H0: Rho = 0

A1 B1 B2 B3 AB11 AB12 AB13

A1 1.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

B1 0.00000 1.00000 0.50000 0.50000 0.00000 0.00000 0.00000

1.0000 0.0036 0.0036 1.0000 1.0000 1.0000

B2 0.00000 0.50000 1.00000 0.50000 0.00000 0.00000 0.00000

1.0000 0.0036 0.0036 1.0000 1.0000 1.0000

B3 0.00000 0.50000 0.50000 1.00000 0.00000 0.00000 0.00000

1.0000 0.0036 0.0036 1.0000 1.0000 1.0000

AB11 0.00000 0.00000 0.00000 0.00000 1.00000 0.50000 0.50000

1.0000 1.0000 1.0000 1.0000 0.0036 0.0036

AB12 0.00000 0.00000 0.00000 0.00000 0.50000 1.00000 0.50000

1.0000 1.0000 1.0000 1.0000 0.0036 0.0036

AB13 0.00000 0.00000 0.00000 0.00000 0.50000 0.50000 1.00000

1.0000 1.0000 1.0000 1.0000 0.0036 0.0036

The REG Procedure

Dependent Variable: dv

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr > F

Model 7 231.96875 33.13839 5.71 0.0006

Error 24 139.25000 5.80208

Corrected Total 31 371.21875

Root MSE 2.40875 R-Square 0.6249

Dependent Mean 9.34375 Adj R-Sq 0.5155

Coeff Var 25.77928

(continues)