Best visualization of joint/marginal distributions?

In summary, the conversation discusses the speaker's struggle with visualizing joint/marginal distributions and their desire to fully understand this concept on a statistical level. They mention being comfortable with the mathematical aspects of integration, but feeling lacking in their ability to identify the necessary limits. They inquire about the best visualizations for various forms of ƒ(x,y) given certain conditions, and mention having seen Σp(x,y) used to represent marginals. They also provide a link to a Google search for visualizations of joint/marginal distributions.
  • #1
nycixc
1
0
I'm trying to get a better visualization of joint/marginal distributions. It's my weakest conceptual area as I pursue the actuary exams, and I want to fully understand this on a more statistical level.

I've taken linear/diff-eq/multivariate, so I'm completely comfortable with the integration and other menial work involved, but I feel like I am lacking in the area of visualizing the distribution, and therefore am incorrectly identifying the limits needed. I'm used to looking at a shape and identifying the limits that way or being given the limits flat-out.

So for ƒ(x,y), what are the best visualizations you've got for:
ƒx(x), ƒy(y), ƒ(X|Y=y), and ƒ(Y|X=x), given 0 < x < y < 1 or x>0, y>0.(I've seen the marginals represented using Σp(x,y), but anything will help!)
 
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  • #2
https://www.google.com/search?q=Bes...ChMI4de43IydxwIVhnU-Ch2w9go6&biw=1024&bih=653

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1. What is the purpose of visualizing joint/marginal distributions?

The purpose of visualizing joint/marginal distributions is to understand the relationship between two or more variables and their individual distributions. It allows for a better understanding of the data and can reveal patterns, trends, and correlations.

2. What are the common types of visualizations used for joint/marginal distributions?

The most common types of visualizations used for joint/marginal distributions include scatter plots, line graphs, histograms, and box plots. These can be used individually or in combination to provide a comprehensive view of the data.

3. How can joint/marginal distributions be used for data analysis?

Joint/marginal distributions can be used for data analysis by providing a visual representation of the data, allowing for easy identification of patterns and relationships. They can also be used for hypothesis testing and to make data-driven decisions.

4. What are the key considerations when choosing a visualization for joint/marginal distributions?

When choosing a visualization for joint/marginal distributions, it is important to consider the type of data, the number of variables being represented, and the objective of the analysis. Other factors to consider include the audience, the level of detail needed, and the type of insights being sought.

5. How can the accuracy of joint/marginal distributions be ensured?

The accuracy of joint/marginal distributions can be ensured by carefully selecting appropriate visualizations, using accurate and reliable data, and avoiding misleading or deceptive visualizations. It is also important to clearly label and explain the visualizations to avoid misinterpretation.

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