Quartile Formula Article byHarsh Katara Formula to Calculate Quartile in Statistics

Quartile Formula is a statistical tool to calculate the variance from the given data by dividing the same into 4 defined intervals and then comparing the results with the entire given set of observations and also commenting on the differences if any to the data sets.

It is often used in statistics to measure the variances which describe a division of all the given observations into 4 defined intervals that are based upon the values of the data and to observe as to where they stand when compared with the entire set of the given observations.

It is divided into 3 points –A lower quartile denoted by Q1, which falls between the smallest value and the median of the given data set, median denoted by Q2, which is the median, and the upper quartile, which is denoted by Q3 and is the middle point which lies between the and the highest number of the given dataset of the distribution.

Quartile Formula in statistics is represented as follows,

The Quartile Formula for Q1= ¼(n+1)th term
The Quartile Formula for Q3= ¾(n+1)th term
The Quartile Formula for Q2= Q3–Q1(Equivalent to Median)

For eg:
Source: Quartile Formula (wallstreetmojo.com)

Explanation

The quartiles will divide the set of measurements of the given data set or the given sample into 4 similar or say equal parts. 25% of the measurements of the given dataset (that are represented by Q1) are not greater than the lower quartile, then the 50% of the measurements are not greater than the median, i.e., Q2, and lastly, 75% of the measurements will be less than the upper quartile which is denoted by Q3.  So, one can say that 50% of the measurements of the given dataset are in between the Q1, which the lower quartile is, and Q2, which is the upper quartile.

Examples

Let’s see some simple to advanced examples of a to understand it better.

You can download this Quartile Formula Excel Template here – Quartile Formula Excel Template

Example #1

Consider a data set of the following numbers: 10, 2, 4, 7, 8, 5, 11, 3, 12. You are required to calculate all the 3 quartiles.

Solution:

Use the following data for the calculation of quartile.

Calculation of Median or Q2 can be done as follows,

Median or Q2 = Sum(2+3+4+5+7+8+10+11+12)/9

Median or Q2 will be –

Median or Q2 = 7

Now since the number of observations is odd, which is 9, the median would lie in the 5th position, which is 7, and the same will be Q2 for this example.

Calculation of Q1 can be done as follows,

Q1= ¼ (9 + 1)

= ¼ (10)

Q1 will be –

Q1 = 2.5

This means that Q1 is the average of 2nd and 3rd position of the observations, which is 3 & 4 here, and the average of the same is (3+4)/2 = 3.5

Calculation of Q3 can be done as follows,

Q3 = ¾ (9 + 1)

= ¾ (10)

Q3 will be –

Q3 = 7.5 Term

This means that Q3 is the average of the 8th and 9th position of the observations, which is 10 & 11 here, and the average of the same is (10+11)/2 = 10.5

Example #2

Simple ltd. is a clothing manufacturer and is working upon a scheme to please their employees for their efforts. The management is in discussion to start a new initiative which states they want to divide their employees as per the following:

• Top 25% lying above Q3- \$25 per cloth
• Greater than Middle one but less than Q3 – \$20 per cloth
• Greater than Q1 but less than Q2 – \$18 per cloth
• The management has collected its average daily production data for the last 10 days per (average) employee.
• 55, 69, 88, 50, 77, 45, 40, 90, 75, 56.
• Use the quartile formula to build the reward structure.
• What rewards would an employee get if he has produced 76 clothes ready?

Solution:

Use the following data for the calculation of quartile.

The number of observations here is 10, and our first step would be converting the above raw data in ascending order.

40, 45, 50, 55, 56, 69, 75, 77, 88, 90

Calculation of quartile Q1 can be done as follows,

Q1 = ¼ (n+1)th term

= ¼ (10+1)

= ¼ (11)

Q1 will be –

Q1 = 2.75 Term

Here the average needs to be taken, which is of 2nd and 3rd terms which are 45 and 50, and the of same is (45+50)/2 = 47.50

The Q1 is 47.50, which is bottom 25%

Calculation of quartile Q3 can be done as follows,

Q3 = ¾ (n+1)th term

= ¾ (11)

Q3 will be –

Q3 = 8.25 Term

Here the average needs to be taken, which is of 8th and 9th terms which are 88 and 90 and the average of same is (88+90)/2 = 89.00

The Q3 is 89, which is the top 25%

Calculation of Median or Q2 can be done as follows,

The Median Value (Q2) = 8.25 – 2.75

Median or Q2 will be –

Median or Q2= 5.5 Term

Here the average needs to be taken, which is of 5th and 6th 56 and 69, and the average of same is (56+69)/2 = 62.5

The Q2 or median is 62.5

Which is 50% of the population.

The Reward Range would be:

47.50 – 62.50 will get \$18 per cloth

>62.50 – 89 will get \$20 per cloth

>89.00 will get \$25 per cloth

If an employee produces 76, then he would lie above Q1 and hence would be eligible for a \$20 bonus.

Example #3

Teaching private coaching classes is considering rewarding students who are in the top 25% quartile advice to interquartile students lying in that range and retake sessions for the students lying in below Q1.Use the quartile formula to determine what repercussion will student face if he scores an average of 63?

Solution :

Use the following data for the calculation of quartile.

The data is for the 25 students.

The number of observations here is 25, and our first step would be converting the above raw data in ascending order.

Calculation of quartile Q1 can be done as follows,

Q1 = ¼ (n+1)th term

= ¼ (25+1)

= ¼ (26)

Q1 will be –

Q1 = 6.5 Term

The Q1 is 56.00, which is the bottom 25%

Calculation of quartile Q3 can be done as follows,

Q3 = ¾ (n+1)th term

= ¾ (26)

Q3 will be –

Q3 = 19.50 Term

Here the average needs to be taken, which is of 19th and 20th terms which are 77 and 77 and the average of same is (77+77)/2 = 77.00

The Q3 is 77, which is the top 25%.

Median or Q2 will be –

Median or Q2=19.50 – 6.5

Median or Q2 will be –

Median or Q2 = 13 Term

The Q2 or median is 68.00

Which is 50% of the population.

The Range would be:

56.00 – 68.00

>68.00 – 77.00

77.00

Relevance and Use of Quartile Formula

Quartiles let one quickly divide a given dataset or given sample into 4 major groups, making it simple as well easy for the user to evaluate which of the 4 groups a data point in. is. While the median, which measures the central point of the dataset, is a robust estimator of the location, but it does not say anything about how much the data of the observations lie on either side or how widely it is dispersed or spread. The quartile measures the spread or dispersion of values that are above and below the or arithmetic average by dividing the distribution into 4 major groups, which are already discussed above.

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