len <- rep(c(-1,1,-1,1),28)
type <- rep(c(-1,-1,1,1),28)
br <- rep(c(-1,-1,-1,-1,1,1,1,1),14)
slope <- rep(c(rep(-1,8),rep(1,8)),7)
block <- c(rep(1,16),rep(2,16),rep(3,16),rep(4,16),rep(5,16),rep(6,16),rep(7,16))
distance <- c(10.0,0.0,4.0,0.0,0.0,5.0,6.5,16.5,4.5,19.5,15.0,41.5,8.0,21.5,0.0,18.0,
18.0,16.5,6.0,10.0,0.0,20.5,18.5,4.5,18.0,18.0,16.0,39.0,4.5,10.5,0.0,5.0,
14.0,4.5,1.0,34.0,18.5,18.0,7.5,0.0,14.5,16.0,8.5,6.5,6.5,6.5,0.0,7.0,
12.5,17.5,14.5,11.0,19.5,20.0,6.0,23.5,10.0,5.5,0.0,3.5,10.0,0.0,4.5,10.0,
19.0,20.5,12.0,25.5,16.0,29.5,0.0,8.0,0.0,10.0,0.5,7.0,13.0,15.5,1.0,32.5,
16.0,17.5,14.0,21.5,15.0,19.0,10.0,8.0,17.5,7.0,9.0,8.5,41.0,24.0,4.0,18.5,
18.5,33.0,5.0,0.0,11.0,10.0,0.0,8.0,6.0,36.0,3.0,36.0,14.0,16.0,6.5,8.0)
model <- aov(distance~block+len*type*br*slope)
summary(model)
## Df Sum Sq Mean Sq F value Pr(>F)
## block 1 152 152.1 1.769 0.18663
## len 1 917 917.1 10.673 0.00151 **
## type 1 388 388.1 4.517 0.03616 *
## br 1 145 145.1 1.689 0.19687
## slope 1 1 1.4 0.016 0.89888
## len:type 1 219 218.7 2.545 0.11398
## len:br 1 12 11.9 0.138 0.71068
## type:br 1 115 115.0 1.338 0.25020
## len:slope 1 94 93.8 1.092 0.29877
## type:slope 1 56 56.4 0.657 0.41976
## br:slope 1 2 1.6 0.019 0.89084
## len:type:br 1 7 7.3 0.084 0.77206
## len:type:slope 1 113 113.0 1.315 0.25437
## len:br:slope 1 39 39.5 0.459 0.49952
## type:br:slope 1 34 33.8 0.393 0.53224
## len:type:br:slope 1 96 95.6 1.113 0.29411
## Residuals 95 8164 85.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## the p value is 0.29411 which is grater than alpha value 0.05 and its insegnificant.