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 table featuring measurements of “%C20–22 POLYUNSATURATED FATTY ACIDS” and “INSULIN SENSITIVITY (MG/M2/MIN)” to generate a Simple Linear Regression Chart.
We aim to explore the relationship between these two variables using linear regression.
Linear regression helps in modeling the relationship between a dependent variable and one or more independent variables.
Our analysis will provide insights into how fatty acid percentages might affect insulin sensitivity, aiding in understanding biomarker interactions.
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 two key columns: the percentage of muscle fatty acids and insulin sensitivity data. This chart will demonstrate the correlation between these variables using linear regression.
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 understand correlations through linear regression charts. Let’s start it right now.
Using different prompt inputs, we’ll demonstrate how AI generates chart results.
Our steps will include:
Step 1 — Initial Linear Fit
Step 2 — Chart Enhancement and Styling
Step 1 — Initial Linear Fit
Perform an initial linear regression fit on the given dataset.
The Prompt is:
XY: Simple linear regression.csv is an XY dataset showing the relationship between insulin sensitivity and skeletal muscle phosphatidic fatty acids.“%C20–22 POLYUNSATURATED FATTY ACIDS” refers to the percentage of muscle fatty acids.“INSULIN SENSITIVITY (MG/M2/MIN)” refers to insulin sensitivity data.
Perform a simple linear regression on this dataset, with insulin sensitivity as the dependent variable and the muscle fatty acid percentage as the independent variable.
Notes:
Insert the standard curve equation and the goodness-of-fit R² into the chart, placing them in the upper left and lower right of the fitted curve respectively.
Handle missing values before fitting the data.
Use X and Y datasets within the 95% confidence interval.
Chart Title:
XY: Simple Linear Regression
Once the above prompt is written, click ‘Run’ to generate a chart displaying the fitted linear regression curve and R² value.
Step 2 — Chart Enhancement and Styling
Enhance and style the initial chart for better visualization.
The Prompt is:
XY: Simple linear regression.csv is an XY dataset showing the relationship between insulin sensitivity and skeletal muscle phosphatidic fatty acids.
“%C20–22 POLYUNSATURATED FATTY ACIDS” refers to the percentage of muscle fatty acids.
“INSULIN SENSITIVITY (MG/M2/MIN)” refers to insulin sensitivity data.
Perform a simple linear regression on this dataset, with insulin sensitivity as the dependent variable and the muscle fatty acid percentage as the independent variable.
Notes:
Insert the standard curve equation and the goodness-of-fit R² into the chart, placing them in the upper left and lower right of the fitted curve respectively.
Handle missing values before fitting the data.
Use X and Y datasets within the 95% confidence interval.
Chart Enhancement:
1. Chart Title: XY: Simple Linear Regression
2. Display X and Y axes using 1pt black lines
3. Remove background grid
4. Direct the X-axis tick marks to the left and the Y-axis tick marks downward.
Once the above prompt is written, click ‘Run’ to enhance the chart with a title, customize the axes, and style the curves.
Thank you for reading this installment of the AI and Statistics series!
We showed how to perform and enhance a simple linear regression analysis to understand variable relationships.
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.