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I created an algorithm and I tested it against a current algorithm.

The results are in this form:

Power   Processes   Method  Time(s)
1          3          1     19,94
1          4          1     20,04
1          5          1     20,06
1          6          1     19,95
1          7          1     20,1
1          8          1     20,03
1          3          0     30,3 
...

for each method where my method is "1" and the other message is represented by "0". Process indicates the available processing power (I only have 4 servers therefore 3,4 processes may run each on a single server, 5-8 servers have to share resources - not indicated in the example table)

I've made 10 replications each test.

I wanted to create a linear regression comparing both models in order to show that even with more processes my algorithm runs faster. But the graphs and statics I could generate with ANOVA didn't really help me?

Which methods do I have to use? And how may I generate graphs explaining the differences between the regressions?

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Creating the Model:

LinearModel.1 <- lm(Time.s. ~ Processes*Method+Power, data=Dataset)

show different slopes:

scatterplot(Time.s.~Processes | MethodF, log="y", reg.line=lm,
smooth=FALSE,    spread=FALSE, boxplots=FALSE, span=0.5,
ellipse=FALSE, levels=c(.5, .9),    by.groups=TRUE, data=Dataset)
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