Enter Regression Data
Paste comma, space, or line-break separated values. Predictor 2 and Predictor 3 are optional.
Example Data Table
This sample uses a real-estate style dataset with three predictors.
| Observation | Sale Price | Size | Bedrooms | Age |
|---|---|---|---|---|
| 1 | 210 | 1200 | 2 | 30 |
| 2 | 225 | 1350 | 3 | 28 |
| 3 | 240 | 1500 | 3 | 24 |
| 4 | 265 | 1650 | 3 | 22 |
| 5 | 280 | 1800 | 4 | 18 |
| 6 | 300 | 1950 | 4 | 15 |
| 7 | 315 | 2100 | 4 | 12 |
| 8 | 340 | 2300 | 5 | 8 |
Formula Used
ŷ = β0 + β1X1 + β2X2 + β3X3
β = (X′X)-1X′Y
R² = 1 − (SSE / SST)
Adjusted R² = 1 − [(1 − R²)(n − 1) / (n − p)]
RMSE = √(Σ(actual − predicted)² / n)
MAE = Σ|actual − predicted| / n
Here, X is the design matrix, Y is the dependent series, n is the observation count, and p is the total parameter count including the intercept.
How to Use This Calculator
- Enter a label for the dependent variable and each predictor.
- Paste matching data lengths for Y and all populated predictors.
- Use commas, spaces, or line breaks between values.
- Optionally enter forecast values for new predictor inputs.
- Click Run Regression Analysis to compute the model.
- Review the equation, fit metrics, coefficient table, and residual chart.
- Download results as CSV or PDF if needed.
FAQs
1) What does this regression analysis tool calculate?
It estimates regression coefficients, predicted values, residuals, R², adjusted R², standard errors, t statistics, RMSE, MAE, F statistic, and Durbin-Watson. It also builds a forecast when you enter new predictor values.
2) When should I use simple regression instead of multiple regression?
Use simple regression when one predictor explains the outcome well. Use multiple regression when several drivers matter and you want each variable’s separate contribution while controlling for the others.
3) How many observations should I enter?
You need more observations than total model parameters. In practice, use a comfortably larger sample so the coefficients are more stable, the fit metrics are more reliable, and the residual patterns are easier to interpret.
4) What is the best regression analysis tool for real estateappraisal?
The best tool depends on your data and workflow. For real estate appraisal, choose one that handles multiple predictors, clear diagnostics, exportable reports, and transparent residual review. This page is useful for quick property-model testing and comparison.
5) Exploratory regression analysis: a tool for selecting models and determining predictor importance?
Exploratory regression compares candidate models and checks which predictors matter most. You can test different variable sets here, then compare adjusted R², error measures, t statistics, and standardized betas to judge importance and model usefulness.
6) Why does the tool show a singular matrix error?
That usually means two predictors are duplicates, nearly duplicates, or too strongly collinear. Remove one overlapping predictor, add more varied data, or simplify the model before running the analysis again.
7) How should I interpret R² and adjusted R²?
R² shows how much variation the model explains. Adjusted R² is stricter because it penalizes unnecessary predictors. When comparing models with different predictor counts, adjusted R² usually gives the fairer summary.
8) Can I export regression results and charts?
Yes. After running the model, use the CSV button for structured data export and the PDF button for a ready-to-share summary containing the equation, metrics, coefficient table, and observation output.