Tabular Representation of Statistical Data
FAQs on RD Sharma Class 9 Solutions Chapter 22 Tabular Representation of Statistical Data (Ex 22.1) Exercise 22.1
1. What is the first step in creating a frequency distribution table from raw data as shown in RD Sharma Solutions for Ex 22.1?
The first step is to determine the range of the data. The range is calculated by subtracting the minimum value from the maximum value in the dataset. This helps in deciding the number and size of class intervals if you are creating a grouped frequency distribution table.
2. How do you create an ungrouped frequency distribution table for a given dataset in Class 9 Maths?
To create an ungrouped frequency distribution table, you should follow these steps:
- First, draw a table with three columns: one for the variable (the data value), one for tally marks, and one for the frequency.
- Go through the raw data one value at a time and place a tally mark against the corresponding variable in the table.
- After every four tally marks, the fifth mark is drawn diagonally across the previous four to create a bundle.
- Finally, count the tally marks for each variable to find its frequency and write this total in the third column.
3. What is a class interval, and how is its class mark calculated in a grouped frequency distribution table?
A class interval is a specific range used to group data points, for example, '10-20' or '20-30'. The class mark, or mid-value, represents the midpoint of that interval. It is calculated by finding the average of its upper and lower class limits using the formula: Class Mark = (Upper Limit + Lower Limit) / 2.
4. Why is it important to organise raw statistical data into a tabular format?
Organising raw data into a tabular format, like a frequency distribution table, is crucial for several reasons:
- Simplification: It condenses large and complex datasets into a structured, easy-to-read format.
- Analysis: It helps in quickly identifying key patterns, trends, and the distribution of data.
- Comparison: It makes it easier to compare different sets of data or values within the same dataset.
- Foundation: It serves as a necessary preliminary step for creating graphical representations such as histograms and frequency polygons.
5. When is it better to use a grouped frequency distribution table over an ungrouped one?
A grouped frequency distribution table is preferred over an ungrouped one when the range of the data is large. If you have a wide variety of distinct data points (e.g., the marks of 100 students out of 100), an ungrouped table would be extremely long and impractical. Grouping the data into class intervals makes the table more compact and the data's distribution easier to interpret.
6. How does changing the class size impact the information we can get from a grouped frequency distribution table?
The choice of class size significantly affects the interpretation of a grouped frequency distribution table.
- A very small class size can create too many classes, many with zero or low frequencies, which can hide the overall pattern of distribution.
- A very large class size can oversimplify the data by grouping too many values together, which may conceal important details and variations within the data.
The key is to select a balanced class size that provides a clear summary without losing crucial information.
7. What are the essential components that every statistical table must have for clarity?
For a statistical table to be clear and self-explanatory, it should include these essential components:
- Table Number: For easy referencing in the text.
- Title: A clear and concise description of what the table contains.
- Stubs and Captions: These are the labels for the rows (stubs) and columns (captions) that describe the data within them.
- Body: The main part of the table where the numerical data is presented.
- Footnote: (If necessary) To clarify any specific item or term used in the table.
