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Marginal Distribution Formula:
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Marginal Distribution refers to the probabilities or frequencies of one variable in a contingency table, ignoring the other variables. It represents the distribution of a subset of variables within a larger multivariate distribution.
The calculator uses the Marginal Distribution formula:
Where:
Explanation: This calculation shows the proportion of observations that fall into a particular category relative to the entire dataset.
Details: Marginal distributions are fundamental in statistics for understanding the individual behavior of variables within multivariate data. They help identify patterns, trends, and characteristics of specific variables without the influence of other variables.
Tips: Enter the sum of counts for a specific row or column and the total count across all categories. Both values must be positive numbers, and the row/column sum cannot exceed the total count.
Q1: What's the difference between marginal and conditional distribution?
A: Marginal distribution shows the probability of a single variable, while conditional distribution shows the probability of one variable given specific values of other variables.
Q2: When should I use marginal distribution analysis?
A: Use it when you want to understand the overall distribution of a single variable in a multivariate dataset, particularly in contingency table analysis and probability studies.
Q3: Can marginal distribution be greater than 1?
A: No, since it represents a proportion of the total, marginal distribution values always range between 0 and 1.
Q4: How is marginal distribution used in real-world applications?
A: It's used in market research, medical studies, social sciences, and any field that analyzes categorical data to understand the distribution of individual variables.
Q5: What if my row/column sum exceeds the total count?
A: This indicates an error in your data input. The sum of any row or column cannot exceed the total count of all observations.