Multiple Linear Regression - Estimated Regression Equation |
Cons[t] = + 130.706587487139 + 1.06170962850255Inc[t] -1.38298545741215Price[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) | 130.706587487139 | 27.094293 | 4.8241 | 0.00027 | 0.000135 |
Inc | 1.06170962850255 | 0.266674 | 3.9813 | 0.001365 | 0.000683 |
Price | -1.38298545741215 | 0.083814 | -16.5006 | 0 | 0 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.97533669567652 |
R-squared | 0.951281669933193 |
Adjusted R-squared | 0.944321908495077 |
F-TEST (value) | 136.683085820079 |
F-TEST (DF numerator) | 2 |
F-TEST (DF denominator) | 14 |
p-value | 6.51398490703059e-10 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 5.56335573538939 |
Sum Squared Residuals | 433.31297853886 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 99.2 | 93.6923773647087 | 5.50762263529134 |
2 | 99 | 96.4234577562832 | 2.57654224371684 |
3 | 100 | 98.5790045961792 | 1.42099540382082 |
4 | 111.6 | 116.781445075516 | -5.18144507551596 |
5 | 122.2 | 122.451685450906 | -0.251685450905775 |
6 | 117.6 | 122.909996278299 | -5.30999627829861 |
7 | 121.1 | 123.045531883681 | -1.945531883681 |
8 | 136 | 135.425382894250 | 0.57461710575039 |
9 | 154.2 | 149.804169317876 | 4.39583068212368 |
10 | 153.6 | 152.057362453703 | 1.54263754629678 |
11 | 158.5 | 153.905448166484 | 4.59455183351612 |
12 | 140.6 | 145.557094958023 | -4.95709495802324 |
13 | 136.2 | 145.097520583372 | -8.89752058337242 |
14 | 168 | 161.584412169109 | 6.41558783089085 |
15 | 154.3 | 156.861421638295 | -2.56142163829510 |
16 | 149 | 156.288651026975 | -7.2886510269755 |
17 | 165.5 | 156.135038386339 | 9.36496161366077 |