Calculator Input
Example Data Table
| Sales | TV | Radio | Online |
|---|---|---|---|
| 120 | 20 | 15 | 30 |
| 165 | 35 | 24 | 45 |
| 210 | 50 | 34 | 60 |
| 265 | 65 | 44 | 78 |
| 305 | 75 | 50 | 92 |
This example models sales from three marketing variables.
Formula Used
Multivariate linear regression estimates a target using several predictors. The fitted model is:
y = β0 + β1x1 + β2x2 + ... + βpxp
The calculator solves coefficients with the normal equation:
β = (XTX)-1XTy
It also computes model quality metrics such as R², adjusted R², RMSE, MAE, residuals, coefficient standard errors, and t statistics. Predictions are found by multiplying the fitted coefficients by new predictor values.
How to Use This Calculator
- Paste your CSV dataset into the dataset field.
- Enter the target column name exactly as written.
- Enter predictor column names, separated by commas.
- Provide forecast values in the same predictor order.
- Choose your preferred decimal precision.
- Click Run Regression to generate results.
- Review the equation, metrics, coefficients, and prediction table.
- Use the CSV or PDF buttons to export results.
Frequently Asked Questions
1. What does this calculator do?
It fits a linear model using one target and multiple predictors. It returns coefficients, fitted values, residuals, summary metrics, a forecast output, and a comparison chart.
2. What kind of data should I enter?
Use numeric tabular data with a header row. Every data cell should contain a number. Text labels, blank cells, and mixed formats can break the regression calculation.
3. Why must predictor names match the header row?
The calculator reads predictor columns directly from the dataset header. Exact matching ensures the correct columns are selected for model fitting and forecasting.
4. What does R² mean here?
R² shows how much variation in the target is explained by the predictors. Higher values usually indicate stronger fit, but they do not guarantee a useful model.
5. Why is adjusted R² also included?
Adjusted R² accounts for the number of predictors. It helps you judge whether adding extra variables truly improves the model rather than inflating fit artificially.
6. What are residuals?
Residuals are the differences between actual and predicted values. They help reveal error patterns, possible outliers, and whether the model misses structure.
7. Why might the calculator show a singular matrix error?
This happens when predictors are too highly dependent or duplicated. Remove repeated variables, add more varied observations, or simplify the model structure.
8. Can I use this for forecasting?
Yes. Enter future predictor values in the forecast field. The calculator applies the fitted equation and returns the estimated target value immediately after running the model.