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 structure to generate a Scatter Plot with Mean and SD.
Today, we are going to identify outliers and generate a scatter plot with Mean & SD using the data from the “Level of Index X.csv” file.
This analysis will help identify anomalies or unusual data points, which can be crucial for understanding data integrity and variability.
Outlier detection is useful for identifying errors in data collection or areas that require further investigation.
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, “Index”, which is a column table containing grouped variables in columns, while the rows lack grouping variables. plot aims to visually distinguish the outliers along with Mean and Standard Deviation (SD) metrics.
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 effectively identify and visualize outliers. Let’s start it right now.
Using different prompt inputs, we’ll demonstrate how AI generates the scatter plot with Mean & SD.
Our steps will include:
Step 1 — Incorrect Method
Step 2 — Correct Method (Initial)
Step 3 — Correct Method Optimized)
Step 1 — Incorrect Method
In step, we won’t achieve an effective visual distinction of outliers.
The Prompt is:
Read Level of index X.csv , identify outliers, and create a Scatter Plot with Bar, Plot with Mean & SD.
Once the above prompt is written, click ‘Run’ to see a scatter plot with bars, but it may not correctly distinguish outliers.
Step 2 — Correct Method (Initial)
Here, we aim for a more precise approach to identify and display outliers.
The Prompt is:
Read Level of index X.csv , identify outliers, and create a scatter plot with Mean & SD.
X-axis displays only one category (show: Index), no data ticks needed.
Y-axis displays data ticks.
Plot specific data points as scatter and use different colors to indicate outliers.
This is a Column table, meaning columns have grouped variables while rows do not.ial)
Once the above prompt is written, click ‘Run’ to visualize the chart with Mean & SD, correctly showing ‘Index’ on the X-axis and data ticks on the Y-axis.
Step 3 — Correct Method (Optimized)
We’ll optimize the chart by fine-tuning the visual elements for better clarity.
The Prompt is:
Draw a bar from the Mean value line down to the X-axis so that the bar is in the middle of the chart area. Adjust the scatter points horizontally within the bar width (bar width should occupy 10% of the chart area, with transparency to not obscure the points below). Both Mean & SD should adhere to the bar width.
Once the above prompt is written, click ‘Run’ to generate an enhanced scatter plot with a mean value line and controlled bar width that improves data point visibility.
Thank you for reading this installment of the AI and Statistics series!
We demonstrated how to identify outliers and a scatter plot with Mean & SD using AI.
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.
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