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Grouped Frequency Distribution Table: What are Class Intervals? Video Lecture - Class 9
FAQs on Grouped Frequency Distribution Table: What are Class Intervals? Video Lecture - Class 9
| 1. What are class intervals in a grouped frequency distribution table? |  |
Ans. Class intervals in a grouped frequency distribution table are the ranges or intervals in which the data is grouped. They represent a set of values within a specified range, and each class interval is mutually exclusive and exhaustive.
| 2. How are class intervals determined in a grouped frequency distribution table? | |
Ans. Class intervals are determined in a grouped frequency distribution table by dividing the range of the data into equal-sized intervals. The number of intervals can be determined using the square root of the total number of data points, and the width of each interval can be calculated by dividing the range by the number of intervals.
| 3. Why is it necessary to use class intervals in a grouped frequency distribution table? | |
Ans. It is necessary to use class intervals in a grouped frequency distribution table because they help to summarize large sets of data and make it easier to interpret. Grouping the data into intervals provides a more concise representation of the data distribution, especially when dealing with a large number of data points.
| 4. How can class intervals be chosen effectively in a grouped frequency distribution table? | |
Ans. Class intervals should be chosen effectively in a grouped frequency distribution table to accurately represent the data and facilitate analysis. The intervals should be chosen in a way that captures the variability of the data, avoids excessive overlap, and ensures that each data point falls within one interval. Additionally, the intervals should be easy to understand and interpret by the intended audience.
| 5. What are some common mistakes to avoid when selecting class intervals for a grouped frequency distribution table? | |
Ans. When selecting class intervals for a grouped frequency distribution table, it is important to avoid common mistakes such as uneven intervals, overlapping intervals, or intervals that do not cover all the data points. It is also important to avoid selecting too few or too many intervals, as this can lead to loss of information or excessive detail. Additionally, care should be taken to ensure that the intervals are representative of the data distribution and do not skew the interpretation of the results.
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