Find the cumulative frequency

Oct 13, 2025 by magicalceylontours.com 114 views

Introduction

In statistics, the first quartile (Q1) is a measure of the data distribution that represents the value below which 25% of the data falls. It is an essential concept in understanding the data distribution and is widely used in various fields such as business, economics, and social sciences. In this problem, we are given the heights of 40 students of Grade 10 class and are asked to find the Q1 of the height.

Data Description

The given data is in the form of a frequency distribution table, which shows the number of students in each height category. The table is as follows:

Height (in cm)	Frequency
118-126	3
127-135	5
136-144	9
145-153	12
154-162	5
163-171	4
172-180	2

Calculating the Q1

To find the Q1, we need to first arrange the data in ascending order. Since the data is already in the form of a frequency distribution table, we can use the following steps to calculate the Q1:

Find the cumulative frequency: The cumulative frequency is the total number of students up to a certain height category. We can calculate the cumulative frequency by adding the frequency of each height category to the cumulative frequency of the previous category.

Height (in cm)	Frequency	Cumulative Frequency
118-126	3	3
127-135	5	8
136-144	9	17
145-153	12	29
154-162	5	34
163-171	4	38
172-180	2	40

Find the Q1: The Q1 is the value below which 25% of the data falls. Since there are 40 students in total, 25% of the data is equal to 10 students. We can find the Q1 by finding the height category that contains the 10th student.

From the cumulative frequency table, we can see that the 10th student falls in the height category of 136-144. Therefore, the Q1 is the median of the height category 136-144.

Calculating the Median

To find the median of the height category 136-144, we need to find the midpoint of the height range. The midpoint is calculated as follows:

Midpoint = (Lower boundary + Upper boundary) / 2

Midpoint = (136 + 144) / 2 Midpoint = 140

Therefore, the Q1 is 140 cm.

Conclusion

In this problem, we found the Q1 of the height of 40 students of Grade 10 class. The Q1 is a measure of the data distribution that represents the value below which 25% of the data falls. We used the frequency distribution table to calculate the cumulative frequency and found the Q1 by finding the height category that contains the 10th student. The Q1 is 140 cm.

Discussion

The Q1 is an essential concept in understanding the data distribution. It helps us to identify the value below which 25% of the data falls and can be used to compare the data distribution of different groups. In this problem, we used the frequency distribution table to calculate the Q1, which is a common method used in statistics.

Limitations

One of the limitations of this problem is that the data is not normally distributed. The data is skewed to the right, which means that the Q1 is not a good representation of the data distribution. In such cases, other measures of central tendency such as the median or the mode may be more appropriate.

Future Work

In future work, we can use other methods to calculate the Q1, such as the interpolation method or the extrapolation method. We can also use other statistical measures such as the standard deviation or the variance to understand the data distribution.

References

[1] "Statistics for Dummies" by Deborah J. Rumsey
[2] "Data Analysis with Python" by Wes McKinney

Keywords

Q1
First quartile
Data distribution
Frequency distribution table
Cumulative frequency
Median
Interpolation method
Extrapolation method
Standard deviation
Variance

Introduction

In our previous article, we discussed the concept of the first quartile (Q1) and how to calculate it using a frequency distribution table. In this article, we will answer some frequently asked questions about the Q1 and provide additional insights into its calculation and interpretation.

Q1: What is the first quartile (Q1)?

A1: The first quartile (Q1) is a measure of the data distribution that represents the value below which 25% of the data falls. It is also known as the 25th percentile.

Q2: How is the Q1 calculated?

A2: The Q1 is calculated by finding the cumulative frequency of the data and identifying the height category that contains the 25th percentile. We can use the frequency distribution table to calculate the cumulative frequency and find the Q1.

Q3: What is the difference between the Q1 and the median?

A3: The Q1 and the median are both measures of the data distribution, but they represent different values. The Q1 represents the value below which 25% of the data falls, while the median represents the middle value of the data.

Q4: How is the Q1 used in real-world applications?

A4: The Q1 is used in various real-world applications, such as:

Business: The Q1 is used to identify the value below which 25% of the sales or revenue falls, which can help businesses to set targets and make informed decisions.
Economics: The Q1 is used to analyze the distribution of income or wealth in a population, which can help policymakers to understand the economic situation and make informed decisions.
Social sciences: The Q1 is used to analyze the distribution of data in social sciences, such as education, health, and crime.

Q5: What are some common mistakes when calculating the Q1?

A5: Some common mistakes when calculating the Q1 include:

Incorrectly calculating the cumulative frequency: This can lead to incorrect identification of the Q1.
Not considering the data distribution: The Q1 is sensitive to the data distribution, and ignoring this can lead to incorrect conclusions.
Not using the correct method: There are different methods to calculate the Q1, and using the wrong method can lead to incorrect results.

Q6: How can the Q1 be used to compare data distributions?

A6: The Q1 can be used to compare data distributions by identifying the value below which 25% of the data falls in each distribution. This can help to identify differences and similarities between the data distributions.

Q7: What are some limitations of the Q1?

A7: Some limitations of the Q1 include:

Sensitivity to outliers: The Q1 is sensitive to outliers, which can affect the calculation of the Q1.
Not suitable for skewed data: The Q1 is not suitable for skewed data, and other measures of central tendency such as the median or the mode may be more appropriate.
Not suitable for small samples: The Q1 is not suitable for small samples, and other methods such as the interpolation method or the extrapolation method may be more appropriate.

Q8: How can the Q1 be used in conjunction with other statistical measures?

A8: The Q1 can be used in conjunction with other statistical measures such as the standard deviation, variance, and correlation coefficient to gain a deeper understanding of the data distribution.

Q9: What are some real-world examples of using the Q1?

A9: Some real-world examples of using the Q1 include:

Analyzing the distribution of income: The Q1 can be used to analyze the distribution of income in a population, which can help policymakers to understand the economic situation and make informed decisions.
Analyzing the distribution of sales: The Q1 can be used to analyze the distribution of sales in a business, which can help businesses to set targets and make informed decisions.
Analyzing the distribution of exam scores: The Q1 can be used to analyze the distribution of exam scores in education, which can help educators to understand the performance of students and make informed decisions.

Q10: What are some resources for learning more about the Q1?

A10: Some resources for learning more about the Q1 include:

Textbooks: There are many textbooks available that cover the Q1 and other statistical measures.
Online courses: There are many online courses available that cover the Q1 and other statistical measures.
Research papers: There are many research papers available that cover the Q1 and other statistical measures.

Conclusion

In this article, we answered some frequently asked questions about the first quartile (Q1) and provided additional insights into its calculation and interpretation. We also discussed some real-world applications of the Q1 and some limitations of the Q1. We hope that this article has been helpful in understanding the Q1 and its uses in statistics.