ANOVA
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                      Df Sum Sq Mean Sq   F value    Pr(>F)    
comm.strat             3 0.0556  0.0185    2.0376 0.1070259    
migr.freq              1 0.2534  0.2534   27.8759 1.607e-07 ***
max.cpu                1 0.4017  0.4017   44.1950 5.029e-11 ***
instance               1 9.1943  9.1943 1011.5850 < 2.2e-16 ***
comm.strat:migr.freq   3 0.1533  0.0511    5.6229 0.0008038 ***
comm.strat:max.cpu     3 0.0100  0.0033    0.3652 0.7781648    
comm.strat:instance    3 0.0693  0.0231    2.5413 0.0551106 .  
migr.freq:max.cpu      1 0.0373  0.0373    4.0989 0.0431923 *  
migr.freq:instance     1 0.1530  0.1530   16.8286 4.445e-05 ***
max.cpu:instance       1 0.0914  0.0914   10.0604 0.0015640 ** 
Residuals            941 8.5528  0.0091                        
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
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	 Simultaneous Confidence Intervals for General Linear Hypotheses

Multiple Comparisons of Means: Mean Contrasts


Fit: aov(formula = best ~ (comm.strat + migr.freq + max.cpu + instance)^2, 
    data = BestDF)

Estimated Quantile = 2.24

Linear Hypotheses:
       Estimate lwr    upr   
4 == 0 0.2996   0.2899 0.3094
8 == 0 0.2587   0.2490 0.2685

95% family-wise confidence level
 

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	 Simultaneous Confidence Intervals for General Linear Hypotheses

Multiple Comparisons of Means: Mean Contrasts


Fit: aov(formula = best ~ (comm.strat + migr.freq + max.cpu + instance)^2, 
    data = BestDF)

Estimated Quantile = 2.24

Linear Hypotheses:
             Estimate lwr    upr   
fnl4461 == 0 0.1813   0.1716 0.1911
rl5915 == 0  0.3770   0.3673 0.3868

95% family-wise confidence level
 

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	 Simultaneous Confidence Intervals for General Linear Hypotheses

Multiple Comparisons of Means: Mean Contrasts


Fit: aov(formula = best ~ (comm.strat + migr.freq + max.cpu + instance)^2, 
    data = BestDF)

Estimated Quantile = 2.24

Linear Hypotheses:
       Estimate lwr    upr   
d == 0 0.2629   0.2532 0.2727
s == 0 0.2954   0.2857 0.3052

95% family-wise confidence level
 

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	 Simultaneous Confidence Intervals for General Linear Hypotheses

Multiple Comparisons of Means: Mean Contrasts


Fit: aov(formula = best ~ (comm.strat + migr.freq + max.cpu + instance)^2, 
    data = BestDF)

Estimated Quantile = 2.4955

Linear Hypotheses:
        Estimate lwr    upr   
FC == 0 0.2856   0.2702 0.3009
HC == 0 0.2853   0.2700 0.3007
R == 0  0.2668   0.2514 0.2822
RW == 0 0.2791   0.2637 0.2944

95% family-wise confidence level
 

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