Frequency distribution is a way of organizing and summarizing data by showing the number of times a particular value or range of values occurs in a dataset. There are different types of frequency distributions, including:

1. Ungrouped frequency
distribution: In an ungrouped frequency distribution, each value in the dataset
is listed along with the frequency or number of times it occurs. This type of
frequency distribution is commonly used for small datasets or when the values
in the dataset are not too many.

2. Grouped frequency
distribution: In a grouped frequency distribution, the data is grouped into intervals
or classes. Each interval represents a range of values, and the frequency
represents the number of values that fall within each interval. This type of frequency
distribution is commonly used for large datasets or when the values in the dataset
are too many to be listed individually.

3. Relative frequency distribution: In a relative frequency distribution, the frequency is expressed as a proportion or percentage of the total number of observations in the dataset. This type of frequency distribution is useful for comparing the frequency of different values or groups in the dataset.

4. Cumulative frequency distribution:
In a cumulative frequency distribution, the frequency of each value or interval
is added to the frequency of the preceding values or intervals. This type of
frequency distribution is useful for calculating percentiles or cumulative
frequencies.

5. Cumulative relative frequency
distribution: In a cumulative relative frequency distribution, the relative
frequency of each value or interval is added to the relative frequency of the preceding
values or intervals. This type of frequency distribution is useful for
calculating cumulative proportions or cumulative percentages.

6. Bivariate frequency
distribution: In a bivariate frequency distribution, the frequency of two
variables is recorded simultaneously. This type of frequency distribution is
useful for analysing the relationship between two variables or for
cross-tabulating data.

7. Joint frequency distribution:
In a joint frequency distribution, the frequency of each possible combination
of two or more variables is recorded. This type of frequency distribution is
useful for analysing the relationship between multiple variables and for identifying
patterns or trends in the data.

8. Marginal frequency distribution: ln a marginal frequency distribution, the frequency of each variable is calculated separately. This type of frequency distribution is useful for examining the frequency distribution of individual variables.

9. Conditional frequency
distribution: In a conditional frequency distribution, the frequency of one
variable is analysed with respect to a specific condition or subset of the
data/ This type of frequency distribution is useful for analysing the
relationship between variables under specific conditions.

10. Discrete frequency distribution:
In a discrete frequency distribution, the data can only take on specific values
or categories. This type of frequency distribution is useful for analysing
categorical data or discrete numerical data.

11. Continuous frequency
distribution: In a continuous frequency distribution, the data can take on any
value within a range. This type of frequency distribution is useful for analysing
continuous numerical data.