After downloading the data from the website, I mangled it with a
Python script into a format more amenable to analysis:

The Observation column represents the search query, it's a string
value so that R interprets it as a categorical variable.

Day,Machine,Observation,Time
Monday,alpha,obs1,619
Monday,alpha,obs2,440
Monday,alpha,obs3,555
Monday,alpha,obs4,647
Monday,alpha,obs5,582
Monday,alpha,obs6,317
Monday,alpha,obs7,442
Monday,nu,obs1,677
...

This was loaded into R, the transcript of that session is below (with
mistakes, redundancy, etc. edited out).

The data file used is available at:

    http://www.cl.cam.ac.uk/~sd601/data.csv

This file is available at:

    http://www.cl.cam.ac.uk/~sd601/log.txt


sd601@chelmer:/auto/homes/sd601$ R

R version 2.14.1 (2011-12-22)

> data <- read.csv("~/Documents/data.csv")
> attach(data)
> str(data)
'data.frame':	105 obs. of  4 variables:
 $ Day        : Factor w/ 5 levels "Friday","Monday",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ Machine    : Factor w/ 3 levels "alpha","nu","omega": 1 1 1 1 1 1 1 2 2 2 ...
 $ Observation: Factor w/ 7 levels "obs1","obs2",..: 1 2 3 4 5 6 7 1 2 3 ...
 $ Time       : int  619 440 555 647 582 317 442 677 611 483 ...
> summary(aov(Time~(Day*Machine + Error(Observation/(Day*Machine)))))

Error: Observation
          Df Sum Sq Mean Sq F value Pr(>F)
Residuals  6  27904    4651               

Error: Observation:Day
          Df Sum Sq Mean Sq F value Pr(>F)  
Day        4  74074   18518   2.952 0.0408 *
Residuals 24 150574    6274                 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Error: Observation:Machine
          Df Sum Sq Mean Sq F value  Pr(>F)    
Machine    2 215990  107995   27.18 3.5e-05 ***
Residuals 12  47680    3973                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Error: Observation:Day:Machine
            Df Sum Sq Mean Sq F value Pr(>F)
Day:Machine  8  83544   10443   1.342  0.246
Residuals   48 373468    7781               

> t.test(Time[Machine=="alpha"],Time[Machine=="nu"],paired=T)

	Paired t-test

data:  Time[Machine == "alpha"] and Time[Machine == "nu"] 
t = -2.2371, df = 34, p-value = 0.03194
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -85.279662  -4.091767 
sample estimates:
mean of the differences 
              -44.68571 

> t.test(Time[Machine=="alpha"],Time[Machine=="omega"],paired=T)

	Paired t-test

data:  Time[Machine == "alpha"] and Time[Machine == "omega"] 
t = -4.6367, df = 34, p-value = 5.056e-05
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -158.82852  -62.02862 
sample estimates:
mean of the differences 
              -110.4286 

> t.test(Time[Machine=="nu"],Time[Machine=="omega"],paired=T)

	Paired t-test

data:  Time[Machine == "nu"] and Time[Machine == "omega"] 
t = -3.7575, df = 34, p-value = 0.0006451
alternative hypothesis: true difference in means is not equal to 0 
95 percent confidence interval:
 -101.30026  -30.18545 
sample estimates:
mean of the differences 
              -65.74286 

> png(filename="/home/sd601/Documents/byday.png")
> D = factor(Day, c("Monday","Tuesday","Wednesday","Thursday","Friday"))
> plot(Time~D,main="Performance by Day",xlab="Day",ylab="Response time (ms)")
> dev.off()
png 
  3 
> png(filename="/home/sd601/Documents/bymachine.png")
> plot(Time~Machine,main="Performance by Machine",xlab="Machine",ylab="Response time (ms)")
> dev.off()
png 
  3