ANOVA
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                      Df  Sum Sq Mean Sq  F value    Pr(>F)    
comm.strat             3  0.0952  0.0317   2.3294 0.0729862 .  
migr.freq              1  0.9967  0.9967  73.1986 < 2.2e-16 ***
max.cpu                1  1.7342  1.7342 127.3584 < 2.2e-16 ***
instance               1  2.3133  2.3133 169.8825 < 2.2e-16 ***
comm.strat:migr.freq   3  0.0081  0.0027   0.1994 0.8967810    
comm.strat:max.cpu     3  0.0142  0.0047   0.3464 0.7917467    
comm.strat:instance    3  0.1300  0.0433   3.1833 0.0232579 *  
migr.freq:max.cpu      1  0.0517  0.0517   3.7972 0.0516357 .  
migr.freq:instance     1  0.0905  0.0905   6.6431 0.0101054 *  
max.cpu:instance       1  0.1919  0.1919  14.0943 0.0001845 ***
Residuals            941 12.8135  0.0136                       
---
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 0.5253   0.5134 0.5373
8 == 0 0.4403   0.4284 0.4523

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   
u1060 == 0   0.4337   0.4218 0.4457
nrw1379 == 0 0.5319   0.5200 0.5439

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 0.5151   0.5031 0.5270
s == 0 0.4506   0.4387 0.4625

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 0.4716   0.4528 0.4904
HC == 0 0.4944   0.4756 0.5132
R == 0  0.4744   0.4556 0.4932
RW == 0 0.4909   0.4721 0.5097

95% family-wise confidence level
 

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