```
library(xlsx)
data <- read.xlsx("Experiment2.xlsx", sheetIndex = "Sheet1")
```

`head(data)`

```
## NA. Pre Post Group
## 1 1 10.221375 19.27253 system
## 2 2 11.915686 18.81053 system
## 3 3 18.337294 22.71338 system
## 4 4 14.803235 16.46261 system
## 5 5 7.123360 18.42337 system
## 6 6 6.090328 20.80002 system
```

`tail(data)`

```
## NA. Pre Post Group
## 79 79 12.55211 13.88570 traditional
## 80 80 15.78812 14.64949 traditional
## 81 81 15.17438 23.67276 traditional
## 82 82 12.83547 22.57924 traditional
## 83 83 10.67936 11.44996 traditional
## 84 84 13.41753 21.68929 traditional
```

`str(data)`

```
## 'data.frame': 84 obs. of 4 variables:
## $ NA. : Factor w/ 84 levels "1","10","11",..: 1 12 23 34 45 56 67 78 84 2 ...
## $ Pre : num 10.22 11.92 18.34 14.8 7.12 ...
## $ Post : num 19.3 18.8 22.7 16.5 18.4 ...
## $ Group: Factor w/ 2 levels "system","traditional": 1 1 1 1 1 1 1 1 1 1 ...
```

`summary(data)`

```
## NA. Pre Post Group
## 1 : 1 Min. : 3.940 Min. : 4.30 system :42
## 10 : 1 1st Qu.: 7.916 1st Qu.:12.91 traditional:42
## 11 : 1 Median :10.728 Median :16.83
## 12 : 1 Mean :10.647 Mean :16.37
## 13 : 1 3rd Qu.:12.623 3rd Qu.:20.07
## 14 : 1 Max. :18.354 Max. :24.62
## (Other):78
```

`data <- data[,-1]`

```
SysPre <- data[data$Group == "system",]$Pre
SysPost <- data[data$Group == "system",]$Post
TradPre <- data[data$Group == "traditional",]$Pre
TradPost <- data[data$Group == "traditional",]$Post
```

```
library(nortest)
nordata <- data.frame(cbind(SysPre, SysPost, TradPre, TradPost))
nortest <- apply(nordata, 2, function(x) ad.test(x))
nortest #Data sets tend to assume Normal Distribution
```

```
## $SysPre
##
## Anderson-Darling normality test
##
## data: x
## A = 0.24322, p-value = 0.7516
##
##
## $SysPost
##
## Anderson-Darling normality test
##
## data: x
## A = 0.63055, p-value = 0.09373
##
##
## $TradPre
##
## Anderson-Darling normality test
##
## data: x
## A = 0.4832, p-value = 0.2178
##
##
## $TradPost
##
## Anderson-Darling normality test
##
## data: x
## A = 0.32375, p-value = 0.5146
```

```
par(mfrow=c(2,2))
apply(nordata, 2, function(x) plot(density(x), col = "darkorchid"))
```

`## NULL`

```
library(psych)
describeBy(data$Pre, data$Group) #Mean is the measure for central tendency given the result of the normality test
```

```
##
## Descriptive statistics by group
## group: system
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 42 10.61 3.61 10.56 10.51 3.58 3.94 18.35 14.41 0.22 -0.46
## se
## X1 0.56
## --------------------------------------------------------
## group: traditional
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 42 10.69 2.84 11.23 10.66 3.06 5.19 15.84 10.65 -0.08 -1.07
## se
## X1 0.44
```

`describeBy(data$Post, data$Group)`

```
##
## Descriptive statistics by group
## group: system
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 42 16.23 4.76 16.35 16.37 6.19 7.43 23.9 16.47 -0.14 -1.23
## se
## X1 0.73
## --------------------------------------------------------
## group: traditional
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 42 16.5 4.48 17.01 16.65 5.34 4.3 24.62 20.32 -0.32 -0.35
## se
## X1 0.69
```

```
library(lawstat)
levene.test(data$Pre, data$Group, location = "mean") #Variances are equal; Student's t-test
```

```
##
## classical Levene's test based on the absolute deviations from the
## mean ( none not applied because the location is not set to median
## )
##
## data: data$Pre
## Test Statistic = 0.84335, p-value = 0.3611
```

`t.test(SysPre, TradPre, var.equal = 1) # No significant difference in the pretest ratings between System and Traditional`

```
##
## Two Sample t-test
##
## data: SysPre and TradPre
## t = -0.10981, df = 82, p-value = 0.9128
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.486689 1.331146
## sample estimates:
## mean of x mean of y
## 10.60762 10.68539
```

`t.test(SysPost, SysPre, paired = 1) # There exists a significant difference; it seems that post test ratings tend to be higher than pre test ratings by about 3.88 to 7.37 at 95% confidence interval.`

```
##
## Paired t-test
##
## data: SysPost and SysPre
## t = 6.5136, df = 41, p-value = 8.048e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 3.880497 7.368109
## sample estimates:
## mean of the differences
## 5.624303
```

`t.test(TradPost, TradPre, paired = 1) # There exists a significant difference; it seems that post test ratings tend to be higher than pre test ratings by about 4.68 to 6.96 at 95% confidence interval.`

```
##
## Paired t-test
##
## data: TradPost and TradPre
## t = 10.302, df = 41, p-value = 6.083e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.675165 6.955091
## sample estimates:
## mean of the differences
## 5.815128
```

`levene.test(data$Post, data$Group, location = "mean") #Variances are equal; Student's t-test`

```
##
## classical Levene's test based on the absolute deviations from the
## mean ( none not applied because the location is not set to median
## )
##
## data: data$Post
## Test Statistic = 0.64932, p-value = 0.4227
```

`t.test(SysPost, TradPost, var.equal = 1) # No significant difference in the post test ratings between System and Traditional`

```
##
## Two Sample t-test
##
## data: SysPost and TradPost
## t = -0.26642, df = 82, p-value = 0.7906
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.274195 1.737001
## sample estimates:
## mean of x mean of y
## 16.23193 16.50052
```