For the construction of a grouped frequency distribution we take?

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Construction of a Grouped Frequency Distribution

A grouped frequency distribution is a way of summarizing a large set of data by dividing it into smaller groups or intervals and then recording the number of observations that fall within each interval. This method is particularly useful for data that spans a wide range of values, as it allows us to identify patterns and trends more easily. The following steps are involved in constructing a grouped frequency distribution:

Determine the range of the data: Calculate the difference between the highest and lowest values in the data set. This will give you an idea of the overall spread of the data.

Choose the number of intervals: Decide how many intervals you want to use to group the data. This will depend on the range of the data and the level of detail you want to show.

Determine the interval size: Divide the range of the data by the number of intervals to find the size of each interval. Round this number up to make the intervals more manageable.

Create the intervals: Use the interval size to create a series of non-overlapping intervals that cover the entire range of the data. Make sure that each interval is the same size.

Tally the data: Count the number of observations that fall within each interval and record this information in a frequency table. You can use tally marks or numbers to keep track of the counts.

Calculate the cumulative frequency: Add up the frequencies for each interval to find the cumulative frequency. This tells you how many observations fall within each interval and all the intervals before it.

Draw a histogram: Use the frequency table to create a histogram, which is a graphical representation of the grouped frequency distribution. The x-axis shows the intervals and the y-axis shows the frequency counts.

Overall, constructing a grouped frequency distribution is a straightforward process that requires a bit of planning and organization. By grouping the data into intervals, we can gain a better understanding of its underlying patterns and characteristics, which can help us make more informed decisions and predictions.

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