R

x <- c(23, 33, 14, 15, 42, 28, 33, 45, 23, 34, 39, 
       21, 36, 23, 34, 36, 25, 9, 11, 19, 35, 24, 
       31, 29, 16, 23, 34, 24, 38, 15, 13, 35, 28)
summary(x)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   9.00   21.00   28.00   26.91   34.00   45.00 

mean(x)

[1] 26.90909

sd(x)

[1] 9.494616

quantile(x,c(.1,.9),type = 1)

10% 90% 
 14  38 

hist(x)

library(TeachingDemos)
z.test(x,mu = mean(x),sd = sd(x),conf.level = 0.9)

    One Sample z-test

data:  x
z = 0, n = 33.0000, Std. Dev. = 9.4946, Std. Dev. of the sample
mean = 1.6528, p-value = 1
alternative hypothesis: true mean is not equal to 26.90909
90 percent confidence interval:
 24.19048 29.62771
sample estimates:
mean of x 
 26.90909 

Note we had to load the 'TeachingDemos' library because r does not have z-tests by default since normally t-tests are used

z.test(x,mu = mean(x),sd = sd(x), conf.level = 0.9, alternative = 'greater')

    One Sample z-test

data:  x
z = 0, n = 33.0000, Std. Dev. = 9.4946, Std. Dev. of the sample
mean = 1.6528, p-value = 0.5
alternative hypothesis: true mean is greater than 26.90909
90 percent confidence interval:
 24.79094      Inf
sample estimates:
mean of x 
 26.90909 

All you have to do is change 'greater' to 'less' to get the other side of the interval

z.test(x,mu = mean(x),sd = sd(x), conf.level = 0.9, alternative = 'less')

RISC_I <- c(140,120,176,288,992,144,2736,2796,752,17720,96)
t.test(RISC_I,mu = mean(RISC_I),conf.level = 0.9)

    One Sample t-test

data:  RISC_I
t = 0, df = 10, p-value = 1
alternative hypothesis: true mean is not equal to 2360
90 percent confidence interval:
 -478.3372 5198.3372
sample estimates:
mean of x 
     2360 

Z8002 <- c(126,180,141,374,1091,302,1368,1398,602,17720,240)
t.test(RISC_I,Z8002,conf.level=0.9,paired = TRUE)

    Paired t-test

data:  RISC_I and Z8002
t = 1.2531, df = 10, p-value = 0.2387
alternative hypothesis: true difference in means is not equal to 0
90 percent confidence interval:
 -98.11371 537.75007
sample estimates:
mean of the differences 
               219.8182 

Note that in this case the p-value is the alpha with which we can have confidence. So this is saying we can only be sure with a confidence of 76% (i.e. at a confidence of 0.76 the interval will no longer pass through 0)

Data Frames (along with vectors) are one of the fundamental data structures in R. Think of it as an excel worksheet where you have rows and columns.

DiskIOs <- c(14,16,27,42,39,50,83)
CPUTime <- c(2,5,7,9,10,13,20)
data <- data.frame(DiskIOs,CPUTime)
data

  DiskIOs CPUTime
1      14       2
2      16       5
3      27       7
4      42       9
5      39      10
6      50      13
7      83      20

One of the nice things about them is you can do operations on columns, rows or even the entire data frame.

log10(data$DiskIOs)

[1] 1.146128 1.204120 1.431364 1.623249 1.591065 1.698970 1.919078

log10(data)

   DiskIOs   CPUTime
1 1.146128 0.3010300
2 1.204120 0.6989700
3 1.431364 0.8450980
4 1.623249 0.9542425
5 1.591065 1.0000000
6 1.698970 1.1139434
7 1.919078 1.3010300

fit <- lm(DiskIOs ~ CPUTime, data)
fit$coefficients

(Intercept)     CPUTime 
   1.137500    3.985417 

confint(fit,level=0.9)

                  5 %     95 %
(Intercept) -5.556274 7.831274
CPUTime      3.369950 4.600884

predict(fit,data.frame(CPUTime=c(40)),
        interval='prediction',level=0.9)

       fit      lwr      upr
1 160.5542 139.5045 181.6038

predict(fit,data.frame(CPUTime=c(40)),
        interval='confidence',level=0.9)

       fit      lwr      upr
1 160.5542 141.4449 179.6634

summary(fit)

Call:
lm(formula = DiskIOs ~ CPUTime, data = data)

Residuals:
     1      2      3      4      5      6      7 
 4.892 -5.065 -2.035  4.994 -1.992 -2.948  2.154 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.1375     3.3219   0.342    0.746    
CPUTime       3.9854     0.3054  13.048 4.72e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.381 on 5 degrees of freedom
Multiple R-squared:  0.9715,    Adjusted R-squared:  0.9658 
F-statistic: 170.3 on 1 and 5 DF,  p-value: 4.716e-05

plot(DiskIOs ~ CPUTime,data=data)
abline(fit)

plot(data$CPUTime,fit$residuals, xlab="CPU Time", ylab="Residuals")

data <- read.csv("table20_1.csv")
data

   Processor Size
1          R  144
2          R  120
3          R  176
4          R  288
5          R  144
6          V  101
7          V  144
8          V  211
9          V  288
10         V   72
11         Z  130
12         Z  180
13         Z  141
14         Z  374
15         Z  302

data.anova <- aov(Size~Processor,data)
summary(data.anova)

            Df Sum Sq Mean Sq F value Pr(>F)
Processor    2  10992    5496   0.699  0.516
Residuals   12  94365    7864               

plot(TukeyHSD(data.anova,'Processor',conf.level=.9))

data <- read.csv("table18_11.csv")
data

   Processor Workload  Time
1          A        I 41.16
2          A        I 39.02
3          A        I 42.56
4          A        J 51.50
5          A        J 52.50
6          A        J 50.50
7          B        I 63.17
8          B        I 59.25
9          B        I 64.23
10         B        J 48.08
11         B        J 48.98
12         B        J 47.10

data.anova <- aov(Time~Processor*Workload,data)
summary(data.anova)

                   Df Sum Sq Mean Sq F value   Pr(>F)    
Processor           1  239.1   239.1  80.086 1.93e-05 ***
Workload            1    9.6     9.6   3.213    0.111    
Processor:Workload  1  459.4   459.4 153.853 1.67e-06 ***
Residuals           8   23.9     3.0                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

plot(TukeyHSD(data.anova,'Workload',
              conf.level=.9))

HSD.test(data.anova,"Workload",alpha=0.1,
         console=TRUE)

Study: data.anova ~ "Workload"

HSD Test for Time 

Mean Square Error:  2.9861 

Workload,  means

      Time       std r   Min   Max
I 51.56500 11.839498 6 39.02 64.23
J 49.77667  2.077861 6 47.10 52.50

alpha: 0.1 ; Df Error: 8 
Critical Value of Studentized Range: 2.629798 

Honestly Significant Difference: 1.855235 

Means with the same letter are not significantly different.

Groups, Treatments and means
a    I   51.56 
a    J   49.78 

HSD.test(data.anova,"Processor",alpha=0.1,
         console=TRUE)

Study: data.anova ~ "Processor"

HSD Test for Time 

Mean Square Error:  2.9861 

Processor,  means

      Time      std r   Min   Max
A 46.20667 5.940931 6 39.02 52.50
B 55.13500 7.955272 6 47.10 64.23

alpha: 0.1 ; Df Error: 8 
Critical Value of Studentized Range: 2.629798 

Honestly Significant Difference: 1.855235 

Means with the same letter are not significantly different.

Groups, Treatments and means
a    B   55.14 
b    A   46.21 

plot(TukeyHSD(data.anova,"Processor:Workload",
              conf.level=.9))

tx <- with(data,interaction(Processor,Workload))
HSD.test(aov(Time~tx,data),"tx",alpha=0.1,
         console=TRUE)

Study: aov(Time ~ tx, data) ~ "tx"

HSD Test for Time 

Mean Square Error:  2.9861 

tx,  means

        Time       std r   Min   Max
A.I 40.91333 1.7828442 3 39.02 42.56
A.J 51.50000 1.0000000 3 50.50 52.50
B.I 62.21667 2.6233058 3 59.25 64.23
B.J 48.05333 0.9402836 3 47.10 48.98

alpha: 0.1 ; Df Error: 8 
Critical Value of Studentized Range: 3.834156 

Honestly Significant Difference: 3.825263 

Means with the same letter are not significantly different.

Groups, Treatments and means
a    B.I     62.22 
b    A.J     51.5 
b    B.J     48.05 
c    A.I     40.91