Outlier of a data set is the data value that is far apart from the remaining values.

The height of each bar in the graph indicate the percentage loss of the company during the particular year.

From the graph, the loss percentages in the last 5 years are 5%, 4%, 6%, 1% and 5%.

Among the values 1% is far apart from the remaining ones.

So, the outlier loss percentage is 1%, which occured in the year 1998.

Outlier is the data value which is far apart from the remaining values.

In the data, there is no value which is far apart from the rest of the values.

So, there is no outlier for the data.

An outlier is the data item which is far apart from the remaining values.

No data value among the values is far apart from the others.

So, there is no outlier for the data set.

Mean is a better measure of central tendency if there is no outlier for the data.

36 is the outlier of the data as it is far apart from other data values.

So, it may skew the central tendency.

As the outlier influences the mean, the median is the better measure of the central tendency.

The outlier in a box-and-whisker plot is the data item, which is much higher or much lower than the other set of data items.

In the plot, the number 10 is much farther from the remaining set of data items.

So, the outlier in the plot is 10.

Range is the difference between the maximum and minimum values of the data set.

Mode is used to find the occurrence of maximum times of a data value.

Mean is a better measure of central tendency if there is no outlier in the data.

27 is the outlier in the data as it is far apart from other values. So, it may skew the central tendency.

As the outlier influences the mean, the median is the better measure of the central tendency.

Mean is a better measure of central tendency if there is no outlier for the data.

45 is the outlier of the data as it is far apart from other data values.

So, it may skew the central tendency.

As the outlier influences the mean, the median is the better measure of the central tendency.

Arrange the weights of students in increasing order.
29, 36, 40, 45, 76

Q2 = 40

Q1 = 29 + 362 = 32.5

Q3 = 45 + 762 = 60.5

Interquartile Range (IQR) = Q3 - Q1

= 60.5 - 32.5 = 28

To find the outlier, compute the cut-off points for outliers.

Lower fence = Q1 - 1.5(IQR)
= 32.5 - 1.5(28) = - 9.5

Upper fence = Q3 +1.5(IQR)
= 60.5 + 1.5(28) = 102.5

If a data value is less than the lower fence or greater than the upper fence, then it is considered as an outlier.

There are no values that are less than - 9.5 or greater than 102.5.

So, there is no outlier.

Arrange the data values in increasing order.
3.5, 4.2, 4.7, 5.3, 5.8, 6.8, 12.5

Q2 = 5.3

Q1 = 4.2

Q3 = 6.8

Interquartile Range (IQR) = Q3 - Q1
= 6.8 - 4.2 = 2.6

To find the outlier, compute the cut-off points for outliers.

Lower fence = Q1 - 1.5(IQR)
= 4.2 - 1.5(2.6) = 0.3

Upper fence = Q3 + 1.5(IQR)
= 6.8 + 1.5(2.6) = 10.7

If a data value is less than the lower fence or greater than the upper fence, then it is considered as an outlier.

The value 12.5 is greater than 10.7.

So, 12.5 is the outlier.

Arrange the number of days taken in increasing order.
7, 8, 12, 15, 17, 19, 23, 45

Q2 = 15 + 172 = 16

Q1 = 8 + 122 = 10

Q3 = 19 + 232 = 21

Interquartile Range(IQR) = Q3 - Q1
= 21 - 10 = 11

To find the outlier, compute the cut-off points for outliers.

Lower fence = Q1 - 1.5(IQR)
= 10 - 1.5(11) = - 6.5

Upper fence = Q3 + 1.5(IQR)
= 21 + 1.5(11) = 37.5

If a data value is less than the lower fence or greater than the upper fence, then it is considered as an outlier.

The value 45 is greater than 37.5.

So, 45 is the outlier.