ANOVA
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                      Df  Sum Sq Mean Sq  F value    Pr(>F)    
comm.strat             3  0.2103  0.0701   3.6760 0.0118911 *  
migr.freq              1  1.0974  1.0974  57.5573 7.877e-14 ***
max.cpu                1  0.7709  0.7709  40.4291 3.175e-10 ***
instance               1 14.7256 14.7256 772.3163 < 2.2e-16 ***
comm.strat:migr.freq   3  0.1873  0.0624   3.2738 0.0205730 *  
comm.strat:max.cpu     3  0.0418  0.0139   0.7311 0.5335675    
comm.strat:instance    3  0.0830  0.0277   1.4507 0.2265929    
migr.freq:max.cpu      1  0.0017  0.0017   0.0906 0.7635067    
migr.freq:instance     1  0.1589  0.1589   8.3347 0.0039785 ** 
max.cpu:instance       1  0.2279  0.2279  11.9527 0.0005701 ***
Residuals            941 17.9418  0.0191                       
---
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 0.6459   0.6318 0.6600
8 == 0 0.5892   0.5751 0.6033

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 0.4937   0.4796 0.5078
rl5915 == 0  0.7414   0.7273 0.7555

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 0.6514   0.6373 0.6655
s == 0 0.5838   0.5696 0.5979

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 0.6290   0.6068 0.6513
HC == 0 0.6302   0.6080 0.6524
R == 0  0.6177   0.5954 0.6399
RW == 0 0.5934   0.5711 0.6156

95% family-wise confidence level
 

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