ANOVA
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                      Df  Sum Sq Mean Sq  F value    Pr(>F)    
comm.strat             3  0.2379  0.0793   3.7311  0.011027 *  
migr.freq              1  0.1298  0.1298   6.1053  0.013654 *  
max.cpu                1  1.7846  1.7846  83.9557 < 2.2e-16 ***
instance               1  5.7621  5.7621 271.0789 < 2.2e-16 ***
comm.strat:migr.freq   3  0.3172  0.1057   4.9742  0.001983 ** 
comm.strat:max.cpu     3  0.0509  0.0170   0.7986  0.494727    
comm.strat:instance    3  0.1531  0.0510   2.4005  0.066435 .  
migr.freq:max.cpu      1  0.0136  0.0136   0.6375  0.424834    
migr.freq:instance     1  0.0043  0.0043   0.2014  0.653720    
max.cpu:instance       1  0.0421  0.0421   1.9816  0.159556    
Residuals            941 20.0022  0.0213                       
---
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 0.5223   0.5074 0.5372
8 == 0 0.4361   0.4212 0.4510

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   
d1655 == 0  0.4017   0.3868 0.4166
pr2392 == 0 0.5567   0.5418 0.5716

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 0.4676   0.4527 0.4825
s == 0 0.4908   0.4759 0.5057

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 0.4787   0.4552 0.5022
HC == 0 0.5025   0.4790 0.5260
R == 0  0.4775   0.4540 0.5010
RW == 0 0.4581   0.4346 0.4816

95% family-wise confidence level
 

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