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                      Df Sum Sq Mean Sq  F value    Pr(>F)    
comm.strat             3 0.0371  0.0124   2.0117  0.110686    
migr.freq              1 0.0395  0.0395   6.4249  0.011414 *  
max.cpu                1 0.6371  0.6371 103.5619 < 2.2e-16 ***
instance               1 2.9292  2.9292 476.1156 < 2.2e-16 ***
comm.strat:migr.freq   3 0.0366  0.0122   1.9803  0.115295    
comm.strat:max.cpu     3 0.0582  0.0194   3.1553  0.024158 *  
comm.strat:instance    3 0.0330  0.0110   1.7860  0.148170    
migr.freq:max.cpu      1 0.0067  0.0067   1.0841  0.298050    
migr.freq:instance     1 0.0577  0.0577   9.3835  0.002252 ** 
max.cpu:instance       1 0.0355  0.0355   5.7717  0.016479 *  
Residuals            941 5.7894  0.0062                       
---
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 0.13953  0.13151 0.14755
8 == 0 0.08800  0.07998 0.09602

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  0.05853  0.05051 0.06654
pr2392 == 0 0.16900  0.16098 0.17702

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 0.10735  0.09933 0.11537
s == 0 0.12018  0.11216 0.12820

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 0.11961  0.10698 0.13225
HC == 0 0.11365  0.10101 0.12628
R == 0  0.11811  0.10548 0.13075
RW == 0 0.10368  0.09105 0.11632

95% family-wise confidence level
 

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