| Multiple Linear Regression - Estimated Regression Equation |
| c[t] = + 90.7546 -0.687712a[t] + 0.757979b[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) | +90.75 | 19.58 | +4.6340e+00 | 0.0003866 | 0.0001933 |
| a | -0.6877 | 0.04168 | -1.6500e+01 | 1.433e-10 | 7.163e-11 |
| b | +0.758 | 0.1854 | +4.0890e+00 | 0.001106 | 0.0005529 |
| Multiple Linear Regression - Regression Statistics | |
| Multiple R | 0.976 |
| R-squared | 0.9527 |
| Adjusted R-squared | 0.9459 |
| F-TEST (value) | 140.9 |
| F-TEST (DF numerator) | 2 |
| F-TEST (DF denominator) | 14 |
| p-value | 5.329e-10 |
| Multiple Linear Regression - Residual Statistics | |
| Residual Standard Deviation | 3.923 |
| Sum Squared Residuals | 215.5 |
| Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
| Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
| 1 | 101 | 95.83 | 5.17 |
| 2 | 100.1 | 97.03 | 3.071 |
| 3 | 100 | 97.78 | 2.219 |
| 4 | 90.6 | 93.52 | -2.918 |
| 5 | 86.5 | 86.23 | 0.2718 |
| 6 | 89.7 | 92.88 | -3.178 |
| 7 | 90.6 | 91.46 | -0.8568 |
| 8 | 82.8 | 82.35 | 0.4532 |
| 9 | 70.1 | 67.56 | 2.543 |
| 10 | 65.4 | 64.94 | 0.4628 |
| 11 | 61.3 | 58.84 | 2.461 |
| 12 | 62.5 | 66.37 | -3.874 |
| 13 | 63.6 | 70.16 | -6.557 |
| 14 | 52.6 | 49.2 | 3.402 |
| 15 | 59.7 | 62.26 | -2.558 |
| 16 | 59.5 | 65.3 | -5.796 |
| 17 | 61.3 | 55.62 | 5.683 |
| Ramsey RESET F-Test for powers (2 and 3) of fitted values |
> reset_test_fitted RESET test data: mylm RESET = 4.4505, df1 = 2, df2 = 12, p-value = 0.03582 |
| Ramsey RESET F-Test for powers (2 and 3) of regressors |
> reset_test_regressors RESET test data: mylm RESET = 8.3556, df1 = 4, df2 = 10, p-value = 0.003141 |
| Ramsey RESET F-Test for powers (2 and 3) of principal components |
> reset_test_principal_components RESET test data: mylm RESET = 16.111, df1 = 2, df2 = 12, p-value = 0.0003992 |
| Variance Inflation Factors (Multicollinearity) |
> vif
a b
1.00383 1.00383
|
