1. DATA INPUT
Select CSV File
Simple Linear Regression with 1 independent variable
2. ANALYSIS OPTIONS
3. REGRESSION PLOT
4. REGRESSION EQUATION
Ŷ = ...
Where:
Ŷ = Predicted Y
X = Independent Variable
5. MODEL SUMMARY
| Multiple R | - |
| R Square (R²) | - |
| Adjusted R² | - |
| Standard Error | - |
| Observations | - |
6. ANOVA TABLE
| Source | df | SS | MS | F | Significance F |
|---|---|---|---|---|---|
| Regression | - | - | - | - | - |
| Residual | - | - | - | – | – |
| Total | - | - | – | – | – |
Significance F < 0.05 indicates that the regression model is statistically significant.
7. COEFFICIENTS TABLE
| Predictor | Coefficient | Std. Error | t Stat | P-value | Lower 95% | Upper 95% |
|---|---|---|---|---|---|---|
| Intercept | - | - | - | - | - | - |
| X Variable | - | - | - | - | - | - |
8. INTERPRETATION
Model Significance
Run analysis to generate interpretation.
Goodness of Fit
-
Coefficients Interpretation
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Practical Interpretation
-
9. PREDICTION
Predicted Price:
-
95% Prediction Interval
(- / -)
Tip:
Ensure your data is clean and contains numeric values.
Ensure your data is clean and contains numeric values.