ANOVA
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                      Df Sum Sq Mean Sq  F value    Pr(>F)    
comm.strat             3 0.2941  0.0980  27.7031 < 2.2e-16 ***
migr.freq              1 0.2318  0.2318  65.4995 1.787e-15 ***
max.cpu                1 0.1759  0.1759  49.7126 3.443e-12 ***
instance               1 0.5794  0.5794 163.7102 < 2.2e-16 ***
comm.strat:migr.freq   3 0.1015  0.0338   9.5633 3.178e-06 ***
comm.strat:max.cpu     3 0.0488  0.0163   4.5944  0.003358 ** 
comm.strat:instance    3 0.0878  0.0293   8.2671 1.971e-05 ***
migr.freq:max.cpu      1 0.0170  0.0170   4.8036  0.028645 *  
migr.freq:instance     1 0.0687  0.0687  19.4010 1.181e-05 ***
max.cpu:instance       1 0.0250  0.0250   7.0763  0.007944 ** 
Residuals            941 3.3303  0.0035                       
---
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 0.05452  0.04844 0.06060
8 == 0 0.02745  0.02136 0.03353

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  0.01642  0.01034 0.02250
pr2392 == 0 0.06555  0.05947 0.07163

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 0.02545  0.01936 0.03153
s == 0 0.05652  0.05044 0.06261

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 0.056072 0.046489 0.065655
HC == 0 0.052258 0.042675 0.061841
R == 0  0.043981 0.034398 0.053564
RW == 0 0.011627 0.002044 0.021210

95% family-wise confidence level
 

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