We have to give a gap on x axis while sketching a histogram or frequency polygon when class intervals begin with this like situation such as 0 to directly 90 and then 90-100,100-110 nd so on........ So for covering the big gap just like this we have to jump I. E. To make a kink on x-axis..... Okkkkk bhai understood or not reply me then I will give u a big explanation..... Nope it's not just like that...... Suddenly getting jumped nd then the gap between the class intervals is common then u have to put a kink

Introduction:

Histograms and frequency polygons are both graphical representations of data distributions. They are used to display the frequency or relative frequency of data values in different intervals or bins. While both are effective ways to visualize data, there are certain situations where one may be preferred over the other.

When to Use Histograms:

Histograms are particularly useful when dealing with continuous data, such as measurements or time intervals. They provide a visual representation of the distribution of data and help identify patterns, outliers, and the shape of the distribution. Here are some situations where histograms are preferred:

1. Large Data Sets: Histograms are ideal for large data sets as they can efficiently summarize and display a large amount of data.

2. Identifying Skewness: Histograms allow us to easily identify the skewness of the distribution. A positively skewed distribution will have a longer tail on the right side, while a negatively skewed distribution will have a longer tail on the left side.

3. Comparing Distributions: When comparing the distributions of two or more data sets, histograms provide an easy visual comparison.

4. Continuous Data: As histograms are constructed using continuous intervals, they are more suitable for continuous data rather than discrete data.

When to Use Frequency Polygons:

Frequency polygons are another graphical representation of data distributions, but they are better suited for certain situations. Here are some scenarios where frequency polygons are preferred:

1. Smoothing Data: Frequency polygons can be used to smooth out the data and provide a clearer representation of the overall trend.

2. Connecting Data Points: Frequency polygons connect the midpoints of each class interval, making it easier to understand the shape of the distribution and identify any trends.

3. Comparing Distributions: Similar to histograms, frequency polygons can also be used to compare the distributions of different data sets.

4. Discrete Data: Frequency polygons can be used for both continuous and discrete data, making them more versatile in certain situations.

Conclusion:

In summary, histograms and frequency polygons are both useful tools for visualizing data distributions. Histograms are especially effective for large data sets, identifying skewness, and working with continuous data. On the other hand, frequency polygons are better suited for smoothing data, connecting data points, and working with both continuous and discrete data. Ultimately, the choice between using a histogram or frequency polygon depends on the specific characteristics of the data and the purpose of the analysis.