Linear Regression Calculator
Find the best fit line, calculate R-squared, and make predictions with linear regression.
Enter Your Data
Data Points:10 pairs
Regression Equation
y = 1.9964x + 0.0600
0.9995
R-Squared
0.9998
Correlation (r)
Prediction for X = 15
30.0055
95% Interval: [29.58, 30.43]
Regression Coefficients
Slope (b1)
Change in Y per unit X
1.996364
SE: 0.0157
Intercept (b0)
Y value when X = 0
0.060000
SE: 0.0975
Model Fit Statistics
R-Squared
0.999505
100.0% variance explained
Adj R-Squared
0.999443
Std Error
0.142701
F-Statistic
16146.4821
ANOVA Table
| Source | SS | df | MS |
|---|---|---|---|
| Regression | 328.80 | 1 | 328.80 |
| Residual | 0.16 | 8 | 0.02 |
| Total | 328.96 | 9 | - |
Data with Predictions
| X | Y | Predicted | Residual |
|---|---|---|---|
| 1 | 2.1 | 2.06 | 0.04 |
| 2 | 4.2 | 4.05 | 0.15 |
| 3 | 5.9 | 6.05 | -0.15 |
| 4 | 8.1 | 8.05 | 0.05 |
| 5 | 9.8 | 10.04 | -0.24 |
| 6 | 12.2 | 12.04 | 0.16 |
| 7 | 13.9 | 14.03 | -0.13 |
| 8 | 16.1 | 16.03 | 0.07 |
| 9 | 18 | 18.03 | -0.03 |
| 10 | 20.1 | 20.02 | 0.08 |