Open App

Class 10 Exam > Class 10 Videos > Cumulative Frequency-Less Than Type of ogive

Cumulative Frequency-Less Than Type of ogive Video Lecture - Class 10

FAQs on Cumulative Frequency-Less Than Type of ogive Video Lecture - Class 10

1. What is cumulative frequency in statistics?

Ans. Cumulative frequency in statistics refers to the running total of the frequencies of data values in a dataset. It helps in analyzing the distribution of data and understanding the frequency with which values occur up to a particular point.

2. What is an ogive in statistics?

Ans. An ogive in statistics is a graph that represents the cumulative frequency distribution of a dataset. It is used to visualize the cumulative frequency and understand how it changes as the values increase. The ogive can be of two types: less than type and more than type.

3. How do you construct a less than type ogive using cumulative frequency?

Ans. To construct a less than type ogive using cumulative frequency, follow these steps: 1. Plot the cumulative frequency on the y-axis and the corresponding data values on the x-axis. 2. Start with the first data value and the corresponding cumulative frequency. 3. Mark a point on the graph at this value. 4. Move to the next data value and add its cumulative frequency to the previous one. 5. Mark another point on the graph at this new cumulative frequency value. 6. Repeat steps 4 and 5 for all data values. 7. Connect the marked points with a smooth curve to obtain the less than type ogive.

4. What is the purpose of using an ogive in statistics?

Ans. The purpose of using an ogive in statistics is to visually represent the cumulative frequency distribution of a dataset. It helps in understanding the shape, spread, and concentration of the data values. By analyzing the ogive, one can identify the median, quartiles, and other percentiles of the dataset, as well as any outliers or unusual patterns in the data.

5. How can an ogive be helpful in comparing two datasets?

Ans. An ogive can be helpful in comparing two datasets by visually comparing their cumulative frequency distributions. By plotting the ogives of two datasets on the same graph, one can easily observe the differences in their frequencies and identify any similarities or disparities. This comparison can provide insights into the relative distribution patterns and help in making comparisons or drawing conclusions between the two datasets.

Related Exams

Class 10 SSS 1

About this Video

4.61/5 Rating

Feb 09, 2025 Last updated

Cumulative Frequency-Less Than Type of ogive for Class 102025 is part of Class 10 preparation. The notes and questions for Cumulative Frequency-Less Than Type of ogive have been prepared according to the Class 10 exam syllabus. Information about Cumulative Frequency-Less Than Type of ogive covers all important topics for Class 102025 Exam. Find important definitions, questions, notes, meanings, examples, exercises and tests below for Cumulative Frequency-Less Than Type of ogive.

Introduction of Cumulative Frequency-Less Than Type of ogive in English is available as part of our Class 10 preparation & Cumulative Frequency-Less Than Type of ogive in Hindi for Class 10 courses. Download more important topics, notes, lectures and mock test series for Class 10 Exam by signing up for free.

Description

Video Lecture & Questions for Cumulative Frequency-Less Than Type of ogive Video Lecture - Class 10 - Class 10 full syllabus preparation | Free video for Class 10 exam.

Information about Cumulative Frequency-Less Than Type of ogive

Here you can find the Cumulative Frequency-Less Than Type of ogive defined & explained in the simplest way possible. Besides explaining types of Cumulative Frequency-Less Than Type of ogive theory, EduRev gives you an ample number of questions to practice Cumulative Frequency-Less Than Type of ogive tests, examples and also practice Class 10 tests.

Top Courses for Class 10

View all courses for Class 10

pdf

Sample Paper

past year papers

video lectures

Previous Year Questions with Solutions

Semester Notes