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Scatter Plot

Table of Contents​

Introduction​

Scatter plots are a powerful tool for visualizing the relationship between two numerical variables. They are particularly useful for identifying correlations, patterns, or clusters within datasets. Scatter plots provide a clear visual representation of how one variable might affect another, making them essential for data analysis in various fields.

This document provides an in-depth look at scatter plots, their features, customization options, and how you can generate them using Cubode-Agent.

What is a Scatter Plot?​

A Scatter Plot is a type of chart that displays values for typically two variables as a collection of points. Each point represents an observation from the dataset, with its position determined by the values of the variables on the x-axis and y-axis. Scatter plots are ideal for identifying relationships, trends, or outliers within data.

Key Characteristics:​

  • Bivariate Data: Scatter plots are used to visualize the relationship between two numerical variables.
  • Correlation: They help in identifying positive, negative, or no correlation between the variables.
  • Distribution: Scatter plots can show the distribution of data points and help identify clusters or outliers.

Features of Scatter Plots​

Scatter plots come with a range of features that make them effective for data visualization:

  • Relationship Visualization: Easily identify the relationship between two variables.
  • Outlier Detection: Spot outliers or anomalies in the dataset.
  • Trend Identification: Observe patterns or trends in the data, such as linear or non-linear relationships.
  • Customizable: Tailor the appearance of the plot with various customization options, including marker size, color, and more.

Use Cases​

Scatter plots are used across various domains to visualize data:

  • Finance: Analyzing the relationship between different financial metrics, such as revenue and profit.
  • Healthcare: Studying the correlation between variables like age and blood pressure.
  • Education: Comparing student scores across different subjects or tests.
  • Marketing: Examining the relationship between advertising spend and sales.

How to Generate a Scatter Plot with Cubode-Agent​

Generating a scatter plot with Cubode-Agent is a straightforward process:

  1. Sign In / Login: Access the Cubode-Agent platform with your credentials.
  2. Upload Your CSV File: Import the dataset you want to visualize as a scatter plot.
  3. Click Generate: Let the AI analyze your data and generate the scatter plot automatically.
  4. Customize: Use the sidebar to customize your scatter plot's appearance and settings, such as axis labels, marker size, and colors.
  5. Save or Export: Once satisfied with your plot, you can save or export it for further use.

Important Features for Proper Operation​

When using Cubode-Agent to generate scatter plots, it's important to pay attention to several key features to ensure the agent functions correctly and produces meaningful visualizations.

1. Choosing the X and Y Values​

In the Series section, you should carefully select the X Values and Y Values from your dataset. Both of these should be numerical columns because scatter plots are designed to show the relationship between two numerical variables.

Example:​

If your dataset contains columns called "Advertising Spend" and "Sales", you could use "Advertising Spend" as the X value and "Sales" as the Y value. This would help visualize how changes in advertising spend might affect sales.

2. Customizing the Color Range​

Cubode-Agent allows you to customize the color range of your scatter plots, which is especially useful for enhancing the visual appeal or distinguishing different data points.

To customize the color range:

  • Scroll to the Color Space setting in the customization sidebar.
  • At the end of the Color Space options, you'll find the ability to choose Custom Colors.
  • Choose the Marker Color to set a specific color for your data points, or use the default color palette provided by the tool.

This customization helps in creating a scatter plot that is not only informative but also visually distinct.

Customization Options​

Cubode-Agent provides a robust set of customization options to tailor your scatter plots to your specific needs. These options are divided into two main settings menus: the Main Settings Menu and the Series Menu.

Main Settings Menu​

The Main Settings Menu allows you to customize several critical aspects of your scatter plot:

  1. Chart Title: The title of the chart, which gives a clear indication of what the plot represents. This is automatically defined by the AI based on the data and plot type.
  2. Chart Subtitle: A secondary title that provides additional context or details about the plot. This is also defined by the AI.
  3. X Axis Label: The label for the x-axis, representing the variable plotted along the horizontal axis. Defined by the AI based on the selected column category.
  4. Y Axis Label: The label for the y-axis, representing the variable plotted along the vertical axis. This label is also defined by the AI according to the selected numerical data.
  5. Legend: Choose to display or hide the legend, which explains the data represented in the plot, especially useful when comparing multiple series or categories.
  6. Show Zoom Slider: Enable or disable the zoom slider feature, which allows users to zoom in and out on specific sections of the scatter plot. This is particularly useful when dealing with large datasets, enabling a closer look at clusters or outliers.

Series Menu​

The Series Menu offers additional options to fine-tune the data representation within your scatter plot:

  1. Series Name: You can add or change the name of the series. The series name is used to identify the dataset being represented by the scatter plot, which is useful when comparing multiple series within the same plot.
  2. X Values and Y Values: Although these are initially defined by the AI, you can change the selected columns for the x-axis and y-axis to represent different numerical variables from your dataset.
  3. Marker Size: Adjust the size of the data markers on the scatter plot. Larger markers can help emphasize data points, while smaller markers are useful for less cluttered visuals.
  4. Color Space: Customize the color of the data markers by using the default color palette or selecting custom colors. This allows for greater differentiation between multiple series or categories in the same plot.

Conclusion​

Scatter plots are an essential tool for visualizing relationships between two numerical variables. With Cubode-Agent, generating and customizing scatter plots becomes a seamless process, allowing you to create clear and insightful visualizations tailored to your specific data analysis needs. Whether you need to explore correlations in financial metrics, analyze scientific data, or compare marketing performance, Cubode-Agent provides the flexibility and power to create the perfect scatter plot for your data.