Regression
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Supported formats: .csv,.xlsx,.xls,.xlsm,.xlsb,.tsv
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About Regression
Model the relationship between two variables.
This tool fits a least-squares regression line between an independent (X) and dependent (Y) numeric column, calculating the slope, intercept, and R² fit quality.
Unlike Correlation, which only tells you the strength and direction of a relationship, Regression gives you an actual equation you can use to estimate Y for a new X value.
It's the same underlying math that powers the CSV Forecast tool, but applied to any two variables rather than a value over time. Once the line is fitted, a built-in what-if panel lets you pick an X value, add its uncertainty, and run a Monte Carlo simulation to see the full distribution of predicted Y — with best, median and worst-case estimates.
Category: AI & Machine LearningCommon Use Cases
- Estimating how much a marketing spend increase moves sales
- Quantifying the relationship between employee tenure and performance score
- Testing whether square footage reliably predicts property price
Key Features
- Least-Squares Line Fitting
- Slope and Intercept Calculation
- R² Fit Score
- Scatter Plot with Trend Line
- What-If Monte Carlo Prediction Bands
Check Correlation first to confirm two columns actually move together, then use Regression to quantify that relationship with a fitted equation, or move to the Monte Carlo Simulator to model several drivers at once.
Frequently Asked Questions
The relationship between two variables, letting you see how one tends to change as the other does.
It models the relationship using standard regression techniques suited to two-variable analysis.
Handy for analysts who need a quick regression equation without opening a stats package or writing a numpy script.
Read the related guide →