Regression Results
Coefficient Statistics
| Term | Coefficient | Standardized | Std. Error | t Statistic | p Value | Confidence Interval | VIF | Interpretation |
|---|
Observation Diagnostics
| # | Actual | Predicted | Residual | Squared Residual | Standardized Residual | Leverage | Cook's Distance |
|---|
Prediction
Charts
Exportable Report
Formula Used
Ordinary least squares estimates coefficients by minimizing squared residuals. The matrix equation produces all coefficients together. Regularized models add shrinkage to reduce unstable estimates.
How to Use
- Paste a table with headers into the dataset box.
- Enter the target column name exactly as written.
- Select OLS, Ridge, Lasso, or Elastic Net.
- Enable polynomial, interaction, or standardization options when needed.
- Click Calculate Coefficients and review diagnostics.
- Enter new predictor values to create a prediction.
- Copy or export the complete result report.
Example Data
| Hours | Experience | Department | Sales |
|---|---|---|---|
| 2 | 1 | North | 18 |
| 5 | 2 | South | 31 |
| 8 | 4 | North | 48 |
| 12 | 6 | South | 71 |
Frequently Asked Questions
What is a regression coefficient?
It estimates how much the predicted target changes. Other predictors remain fixed during interpretation. Units depend on each original variable.
What does a positive coefficient mean?
A positive value indicates an increasing fitted relationship. Larger predictor values produce larger predictions. Causation is not automatically established.
Why include an intercept?
The intercept estimates the target when predictors equal zero. It often improves model calibration. Remove it only with strong justification.
What are standardized coefficients?
They express effects in standard deviation units. This supports rough predictor comparisons. Correlated predictors still complicate importance claims.
What does a large VIF indicate?
A large VIF suggests multicollinearity among predictors. Coefficient uncertainty may become inflated. Consider combining or regularizing related features.
When should Ridge regression be used?
Ridge helps when predictors are strongly correlated. It shrinks coefficients without forcing zeros. Scaling predictors is usually recommended first.
How does Lasso differ from Ridge?
Lasso can shrink some coefficients exactly to zero. Ridge generally retains every predictor. Their behavior depends heavily on scaling.
What is Cook's distance?
Cook's distance summarizes an observation's model influence. Large values deserve careful investigation. They do not automatically justify deletion.
Can categorical predictors be used?
Yes, text columns are dummy encoded automatically. One level becomes the baseline. Coefficients compare remaining levels against it.