Multiple Linear Regression - Estimated Regression Equation |
a[t] = + 130.707 + 1.06171b[t] -1.38299c[t] + e[t] |
Multiple Linear Regression - Ordinary Least Squares | |||||
Variable | Parameter | S.D. | T-STAT H0: parameter = 0 | 2-tail p-value | 1-tail p-value |
(Intercept) | +130.7 | 27.09 | +4.8240e+00 | 0.00027 | 0.000135 |
b | +1.062 | 0.2667 | +3.9810e+00 | 0.001365 | 0.0006826 |
c | -1.383 | 0.08381 | -1.6500e+01 | 1.433e-10 | 7.163e-11 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.9753 |
R-squared | 0.9513 |
Adjusted R-squared | 0.9443 |
F-TEST (value) | 136.7 |
F-TEST (DF numerator) | 2 |
F-TEST (DF denominator) | 14 |
p-value | 6.514e-10 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 5.563 |
Sum Squared Residuals | 433.3 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 99.2 | 93.69 | 5.508 |
2 | 99 | 96.42 | 2.577 |
3 | 100 | 98.58 | 1.421 |
4 | 111.6 | 116.8 | -5.181 |
5 | 122.2 | 122.5 | -0.2517 |
6 | 117.6 | 122.9 | -5.31 |
7 | 121.1 | 123 | -1.946 |
8 | 136 | 135.4 | 0.5746 |
9 | 154.2 | 149.8 | 4.396 |
10 | 153.6 | 152.1 | 1.543 |
11 | 158.5 | 153.9 | 4.595 |
12 | 140.6 | 145.6 | -4.957 |
13 | 136.2 | 145.1 | -8.898 |
14 | 168 | 161.6 | 6.416 |
15 | 154.3 | 156.9 | -2.561 |
16 | 149 | 156.3 | -7.289 |
17 | 165.5 | 156.1 | 9.365 |