Multiple Linear Regression - Estimated Regression Equation |
V1[t] = + 130.707 + 1.06171V2[t] -1.38299V3[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) | 130.707 | 27.0943 | 4.824 | 0.000270043 | 0.000135021 |
V2 | 1.06171 | 0.266674 | 3.981 | 0.00136522 | 0.000682608 |
V3 | -1.38299 | 0.0838143 | -16.5 | 1.43251e-10 | 7.16255e-11 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.975337 |
R-squared | 0.951282 |
Adjusted R-squared | 0.944322 |
F-TEST (value) | 136.683 |
F-TEST (DF numerator) | 2 |
F-TEST (DF denominator) | 14 |
p-value | 6.51398e-10 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 5.56336 |
Sum Squared Residuals | 433.313 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 99.2 | 93.6924 | 5.50762 |
2 | 99 | 96.4235 | 2.57654 |
3 | 100 | 98.579 | 1.421 |
4 | 111.6 | 116.781 | -5.18145 |
5 | 122.2 | 122.452 | -0.251685 |
6 | 117.6 | 122.91 | -5.31 |
7 | 121.1 | 123.046 | -1.94553 |
8 | 136 | 135.425 | 0.574617 |
9 | 154.2 | 149.804 | 4.39583 |
10 | 153.6 | 152.057 | 1.54264 |
11 | 158.5 | 153.905 | 4.59455 |
12 | 140.6 | 145.557 | -4.95709 |
13 | 136.2 | 145.098 | -8.89752 |
14 | 168 | 161.584 | 6.41559 |
15 | 154.3 | 156.861 | -2.56142 |
16 | 149 | 156.289 | -7.28865 |
17 | 165.5 | 156.135 | 9.36496 |