In data analysis, the identification of outliers and thus, observations that fall well outside the overall pattern of the data is very important. An outlier requires special attention. It may be the result of a measurement or recording error, an observation from a different population, or an unusual extreme observation. Note that an extreme observation need not be an outlier; it may instead be an indication of skewness (Weiss 2010).

If we observe an outlier, we should try to determine its cause. If an outlier is caused by a measurement or recording error, or if for some other reason it clearly does not belong to the data set, the outlier can simply be removed. However, if no explanation for an outlier is apparent, the decision whether to retain it in the data set is a difficult judgment call.

As a diagnostic tool for spotting observations that may be outliers we may use quartiles and the \(IQR\). Therefore, we define the **lower limit** and the **upper limit** of a data set. The lower limit is the number that lies \(1.5 \times IQRs\) below the first quartile; the upper limit is the number that lies \(1.5 \times IQRs\) above the third quartile. Observations that lie below the lower limit or above the upper limit are potential outliers (Weiss 2010).

\[ \text{Lower limit} = Q1 - 1.5 \times IQR \] \[ \text{Upper limit} = Q3 + 1.5 \times IQR \]

A boxplot, also called a **box-and-whisker diagram**, is based on the five-number summary and can be used to provide a graphical display of the center and variation of a data set. These diagrams were invented by the mathematician John Wilder Tukey. Several types of boxplots are in common use.

Box-and-whisker plots give a graphic presentation of data using five measures: the median, the first quartile, the third quartile, and the smallest and the largest values in the data set between the lower and the upper limits. The spacing between the different parts of the box indicates the degree of dispersion (spread) and skewness in the data. We can compare different distributions by making box-and-whisker plots for each of them. It also helps to detect outliers (Mann 2012). Box plots can be drawn either horizontally or vertically.

The edges of the box are always the first and third quartiles, and the band inside the box is always the second quartile (the median). The lines extending from the boxes (whiskers) indicating variability outside the upper and lower quartiles. To construct a boxplot, we also need the concept of adjacent values. The **adjacent values** of a data set are the most extreme observations that still lie within the lower and upper limits; they are the most extreme observations that are not potential outliers. Outliers may be plotted as individual points. Note that, if a data set has no potential outliers, the adjacent values are just the minimum and maximum observations (Weiss 2010).

Let us now construct a series of boxplots for the analysis the `students`

data set in more depth. We start by constructing a boxplot for the `nc.score`

variable.

```
students <- read.csv("https://userpage.fu-berlin.de/soga/200/2010_data_sets/students.csv")
nc.score <- students$nc.score
```

`boxplot(students$nc.score)`

We immediately get an impression of the spread and skewness in the data. By adding the argument `horizontal=TRUE`

to the boxplot we rotate the boxplot by 90\(^{\circ}\) and for the sake of a better better visual impression we colorize to box.

```
boxplot(students$nc.score,
col = 'blue',
horizontal = TRUE,
xlab = 'Scores',
main = 'Numerus clausus')
```

Boxplots are a very powerful technique for exploratory data analysis, as it is very easy to condition the variable of interest, in our case the `nc.score`

variable, on other variables. In R we condition one variable on another by using the `~`

(this is the so called *formula notation*).

Let us plot a boxplot of the `nc.score`

variable conditioned on the `semester`

variable. The `semester`

variable corresponds to the semester the particular student is studying. For your information: The minimum period of study for the study programs under investigation is set to 4 semesters.

```
par(mar=c(2.5, 5, 5.8, 3))
boxplot(students$nc.score ~ students$semester,
col = "blue",
horizontal = TRUE,
xlab = "Scores",
main = "Numerus clausus for students of\n different semesters")
```

Interesting, isn’t it? The plot suggests that students of higher semester (> 5^{th}), tend do score lower on the *numerus clausus*. Or in other words, those students who finish their studies within the minimum period of study, tend to have a higher *numerus clausus* score.

However, we are not yet finished. We want to know if gender has any effect on that observation. We can easily incorporate an interaction variable, by simply adding the variable by a `+`

sign. In addition, we introduce the `notch`

argument. If the notches of two plots do not overlap this is “strong evidence” that the two medians differ (Chambers, et al. (1983): Graphical Methods for Data Analysis. Wadsworth & Brooks/Cole, p. 62). For further information type `help(boxplot)`

or `help(boxplot.stats)`

in your console. Please be aware, that in order to get a nicer looking y-label we must write one additional line of code.

```
par(mar=c(2.5, 5, 5.8, 3), xpd=TRUE)
boxplot(students$nc.score ~ students$gender + students$semester,
col = c("blue" , "red"),
horizontal = TRUE,
notch = T,
xlab = 'Numerus clausus scores',
yaxt = "n",
main = "Numerus clausus for students of\n different semesters and gender")
# Add a legend
legend(x = 2,
y = 16.6,
legend = c("Female", "Male"),
col = c("blue" , "red"),
pch = 15,
bty = "n",
pt.cex = 3,
cex = 1,
horiz = T)
# Add the label of y axis
axis(2, at = seq(1.5 , 14 , 2), labels = levels(students$semester) , tick = T)
```

This plot is not as easy to interpret. Though, is seems that the above made observation is confirmed: Students of higher semester (>5^{th}), tend do score lower on the *numerus clausus*. However, the impact of gender on the *numerus clausus* scores is not as clear. We will have to apply methods of **inferential statistics** to assess, whether these differences are *statistical significant*, or whether these fluctuations around the median may be also caused solely by chance.

To wrap this section up, and in order to see a boxplot with outliers too, we plot the `height`

variable against the `gender`

variable. This time we use the extreme powerful `ggplot2`

package for advanced plotting. You may install the `ggplot2`

by calling `install.packages("ggplot2")`

, and attach it to the work space by calling `library(ggplot2)`

.

```
library(ggplot2)
ggplot(students,aes(gender, height, fill = gender)) +
geom_boxplot (outlier.color = "red", outlier.shape = 6, outlier.size = 1.5, width = .9) +
geom_line(color = "#3366FF", alpha = 0.5) +
labs(title = "The height of students based on the gender", x = "", y = "Height in cm") +
scale_fill_manual(name = "Legend", values = c("#fc8d59", "#91bfdb")) +
theme_minimal()
```

Obviously, and certainly not that unexpected, there is a difference in the height of the students among the different groups (male or female). Female students tend to be smaller than male student, however if we look at the extremes, there are large and small individuals in both groups. However, as mentioned above we will first have to test the data for *statistical significance* to be more confident, that this observed difference in height in not just observed by chance.