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 2D data table for Nonlinear Regression Analysis and generate a chart.
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.
1. Main
We’ll start with a two-dimensional data table featuring:
* Dimension 1 corresponds to the X variable, which we use to plot the X-axis representing [Substrate].
Dimension 2 is the Y variable, indicating Enzyme Activity.
Nonlinear regression is a statistical method used to establish and analyze nonlinear relationships between variables.
Unlike linear regression, nonlinear regression models are not in the form of straight lines but use functions such as polynomial, exponential, or logarithmic to describe more complex curves.
We’ll explore how these steps influence the final chart and uncover the secrets behind effective data visualization using nonlinear regression models.
These techniques are valuable not only in enzyme kinetics but also in fields like market trend analysis and financial forecasting.
In just 2 minutes, you’ll learn to create a professional plot for enzyme kinetics and see how similar strategies apply to various business scenarios.
Our steps will include:
● Step 1: Initial Fitting
● Step 2 : Curve Enhancement
Step 1: Initial Fitting
● Prompt :
Read Michaelis-Menten1.xlsx for Michaelis-Menten analysis,
Noted it is nonlinear regression models, Plot a curve line to fit it
Dependent variable : X variable representing = [Substrate]
Independent variable : Y variables representing = “Enzyme Activity”,
Each [Substrate] was measured three times, and the corresponding data was obtained.
Step 2: Curve Enhancement
● Prompt :
Read Michaelis-Menten1.xlsx for Michaelis-Menten analysis and plot the nonlinear regression fitted curve
Noted it is nonlinear regression models
Check the data source: Verify if there are any infinite (inf) or non-numeric (NaN) values and clean or fill them appropriately.
Resolve array shape incompatibility issues.
Dependent variable : X variable representing = [Substrate]
Independent variable : Y variables representing = “Enzyme Activity2”,
Each [Substrate] was measured three times, and the corresponding data was obtained.
Data reorganization: Ensure that the repeated measurements of the three dependent variables are arranged in a structured format. Each dependent variable’s measurements can be averaged or processed separately.
Chart Enhancement:
1. Confidence interval 95%, light green fill for the confidence region (with error bands), red dashed line for the confidence region border, 1/2pt.
2. Display axis tick marks for both X and Y axes
3. Add axis lines, with single X and Y axes shown as 1pt lines (note: this applies to the X and Y axes, not the entire border).
Thank you for watching this episode of the AI and Statistics series!
We demonstrated how AI can enhance nonlinear regression analysis to make complex kinetic data visualization clear and accessible.
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.