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The “5 number” is a statistical concept that refers to five specific values of a dataset. These values are crucial for understanding the distribution and variability of the data, allowing for more accurate analysis and predictions. The five numbers are:

Minimum value
First quartile
Median
Third quartile
Maximum value

Minimum Value

The minimum value is the smallest number in the dataset. It is useful in identifying outliers and determining the range of the data. For example, if a dataset has a minimum value of 10 and a maximum value of 100, the range of the data is 90.

First Quartile

The first quartile is the value that separates the lowest 25% of the data from the rest of the dataset. It is also known as Q1 or the 25th percentile. For example, if a dataset has 100 data points, Q1 would be the value that separates the lowest 25 values from the highest 75 values.

Median

The median is the middle value of the dataset. It is also known as the second quartile or Q2. This value is useful in determining the central tendency of the data. If the dataset has an odd number of values, the median is the middle value. If the dataset has an even number of values, the median is the average of the two middle values.

Third Quartile

The third quartile is the value that separates the highest 25% of the data from the rest of the dataset. It is also known as Q3 or the 75th percentile. For example, if a dataset has 100 data points, Q3 would be the value that separates the highest 25 values from the lowest 75 values.

Maximum Value

The maximum value is the largest number in the dataset. It is useful in identifying outliers and determining the range of the data. For example, if a dataset has a minimum value of 10 and a maximum value of 100, the range of the data is 90.

These five values are used in various statistical calculations and graphs, such as box plots and quartile range plots. It is important to understand the “5 number” when analyzing and interpreting data, as they provide valuable insight into the distribution and variability of the data.