Welcome to the AI and Statistics series!
Let's dive into how AI can transform tabular data into various types of charts.
Today, we will be using a column table from "Frequency analysis.csv" with a single field "Score" to generate a Frequency Distribution Chart, Gaussian Fit Curve, and Scatter Plot.
Today, we aim to perform a frequency analysis and Gaussian fitting on the data.
Frequency analysis helps identify data distribution patterns, while Gaussian fitting is used to model data that follow a normal distribution.
This can assist in understanding data variability and central tendencies similar to evaluating exam completion times or assessing psychological study hours. (Data for reference only)
Don't worry about using the AI Agent-driven Bayeslab, all you need is natural language to get the data analysis result.
All content will be explained in the most comprehensible natural language descriptions to help you get started with data analysis from scratch.
We’ll start with a data table featuring a single field "Score" without any row grouping variables.
This chart primarily seeks to analyze frequency distribution patterns based on the dataset provided.
We’ll delve into how these prompts influence the final charts and uncover techniques for effective data visualization.
In just 2 minutes, you'll learn to perform frequency analysis and Gaussian curve fitting to enhance data interpretation.
Step 1 - Frequency Distribution Chart
We'll create a frequency distribution chart using data bin "2" to analyze the distribution of scores.
The Prompt is:
Read Frequency analysis.csv and use "2" as the data bin to create a frequency distribution chart.
Once the above prompt is written, click 'Run' to see the frequency distribution chart.
Step 2 - Gaussian Fit Curve
By adding a Gaussian fit curve to the frequency distribution chart, we model the distribution with nonlinear regression.
The Prompt is:
Read Frequency analysis.csv and use "2" as the data bin to create a frequency distribution chart. Perform nonlinear regression analysis and add a Gaussian fit curve.
Once the above prompt is written, click 'Run' to overlay the Gaussian fit curve on the chart.
Step 3 - Gaussian Fit_Data Table
We'll save the Gaussian fitting results in a table including values like Best fit value, 95% confidence, and SD.
The Prompt is:
Read Frequency analysis.csv and use "2" as the data bin for frequency analysis. Perform nonlinear fitting (Gaussian fitting) and save the fitting results as a table, stored locally as "Nonlin fit". Required data (additional data can be added as needed): Best fit value, 95% confidence, SD.
Once the above prompt is written, click 'Run' to generate and save the data table "Nolin fit".
Step 4 - Chart Style (Scatter Plot)
Finally, we’ll change our bar chart into a scatter plot to visualize the data differently.
The Prompt is:
Change this bar chart to a scatter plot.
(This step is same prompt with Step 2 ,but continual with Finetuning feature)
Once the above prompt is written, click 'Run' to convert the chart into a scatter plot.
Thank you for reading this installment of the AI and Statistics series!
We demonstrated how to conduct frequency analysis and Gaussian fitting, enhancing your data analysis skills with practical applications.
Stay tuned for our upcoming demonstrations to explore more fascinating data visualization.
Using AI Agent and Bayeslab, anyone can organize, analyze, plot data charts, and make business data predictions like a professional data analyst based on previous data.
About Bayeslab
Bayeslab: Website
The AI First Data Workbench
X: @BayeslabAI
Documents:
https://bayeslab.gitbook.io/docs
Blogs:
Bayeslab is a powerful web-based AI code editor and data analysis assistant designed to cater to a diverse group of users, including :👥 data analysts ，🧑🏼🔬experimental scientists, 📊statisticians, 👨🏿💻 business analysts, 👩🎓university students, 🖍️academic writers, 👩🏽🏫scholars, and ⌨️ Python learners.
Its comprehensive suite of features:
✓ AI-Powered Python Code Generation: Use intuitive natural language prompts to generate Python code effortlessly, making it accessible for both beginners and experts in coding.
✓ Advanced Data Visualization Tools: Easily create and customize high-quality visualizations to interpret and communicate complex data insights effectively.
Seamless Web-Based Access: Bayeslab operates entirely in your web browser, eliminating the need for software installation and enabling access from anywhere with an internet connection.
✓ Extensive Statistical Analysis Templates: Access a library of over 300 + expertly crafted statistical analysis templates to execute data-driven decisions confidently.
✓ Collaborative Cloud Management: Enhance teamwork and data sharing with robust cloud-based file management, ensuring secure and reliable access to your projects.
✓ Rich Table Structure Support: Handle complex table structures with ease, making it simple to organize and analyze data.
✓ Embedded Business Analysis Logic: Utilize built-in business logic to perform in-depth analysis and drive decisions.
✓ Interactive Table Editing: Enjoy a seamless and interactive table editing experience for more efficient data manipulation.
🔮 Upcoming Features: Look forward to future enhancements such as web data scraping and business automation to further streamline your workflows.