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RD Sharma Solutions for Class 7 Maths Chapter 23: Data Handling 2 (Central values)

The RD Sharma Solutions for Class 7 Maths Chapter 23 data handling 2 deals with topics related to central values. A central value is also called a central tendency, which can be stated as the statistical measure representing the single value of the entire distribution or a dataset. It also aims at providing an accurate description of the whole data in the distribution. So, while diving deep into this topic, we can clearly understand that the measure of central value consists of three divisions. They are the mean, median, and mode. These entities are how one can measure the central values.

Arithmetic mean can be defined as a number obtained by dividing the sum of the elements of a set by the number of values contained in the set. There are two types of arithmetic mean. They are simple arithmetic mean, and weighted arithmetic mean. The formula is,

A = sum of all observations / total number of observations

The range is another important concept in statistics. It can be described as the difference between the largest and the smallest value of data. For example, in 3, 5, 20, 45, 58, the range can be easily calculated by subtracting the largest to the smallest value. So,

Range = 58 – 3 = 55

The range of the example mentioned above is said to be 55. Similarly, the median can be identified as the middle number in a sorted (either ascending or descending order) list of numbers and can also be descriptive of that data set similar to the arithmetic mean. On the other hand, the mode is the value that appears most frequently in a data set where it may have one mode, or more than one mode, or no mode at all. In statistics, data can be easily distributed in many different ways. The chapter teaches the students that the most common one is the normal distribution.

Lastly, students also get to know about how frequency distribution is being constructed. This can be easily done in six steps.
1. Find the range of data 2. Decide the approximate number of classes 3. Determine the class interval sizes 4. Decide the starting point 5. Determine boundary 6. Distribute the data into respective classes.

 

 

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