| Multiple Linear Regression - Estimated Regression Equation |
| y[t] = + 1.11823809523810 + 0.159114404761905x[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) | 1.11823809523810 | 0.013241 | 84.4521 | 0 | 0 |
| x | 0.159114404761905 | 0.016352 | 9.7309 | 0 | 0 |
| Multiple Linear Regression - Regression Statistics | |
| Multiple R | 0.784926126611354 |
| R-squared | 0.616109024237103 |
| Adjusted R-squared | 0.609602397529258 |
| F-TEST (value) | 94.6894684298083 |
| F-TEST (DF numerator) | 1 |
| F-TEST (DF denominator) | 59 |
| p-value | 7.07212066686225e-14 |
| Multiple Linear Regression - Residual Statistics | |
| Residual Standard Deviation | 0.0606782971943308 |
| Sum Squared Residuals | 0.217229489273809 |
| Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
| Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
| 1 | 0.9808 | 1.11823809523809 | -0.137438095238092 |
| 2 | 0.9811 | 1.11823809523810 | -0.137138095238096 |
| 3 | 1.0014 | 1.11823809523810 | -0.116838095238095 |
| 4 | 1.0183 | 1.11823809523810 | -0.0999380952380954 |
| 5 | 1.0622 | 1.11823809523810 | -0.0560380952380954 |
| 6 | 1.0773 | 1.11823809523810 | -0.0409380952380955 |
| 7 | 1.0807 | 1.11823809523810 | -0.0375380952380954 |
| 8 | 1.0848 | 1.11823809523810 | -0.0334380952380954 |
| 9 | 1.1582 | 1.11823809523810 | 0.0399619047619045 |
| 10 | 1.1663 | 1.11823809523810 | 0.0480619047619045 |
| 11 | 1.1372 | 1.11823809523810 | 0.0189619047619046 |
| 12 | 1.1139 | 1.11823809523810 | -0.00433809523809552 |
| 13 | 1.1222 | 1.11823809523810 | 0.00396190476190468 |
| 14 | 1.1692 | 1.11823809523810 | 0.0509619047619046 |
| 15 | 1.1702 | 1.11823809523810 | 0.0519619047619045 |
| 16 | 1.2286 | 1.2773525 | -0.0487525000000001 |
| 17 | 1.2613 | 1.2773525 | -0.0160524999999999 |
| 18 | 1.2646 | 1.2773525 | -0.0127525000000000 |
| 19 | 1.2262 | 1.2773525 | -0.0511525 |
| 20 | 1.1985 | 1.11823809523810 | 0.0802619047619045 |
| 21 | 1.2007 | 1.11823809523810 | 0.0824619047619047 |
| 22 | 1.2138 | 1.2773525 | -0.0635525 |
| 23 | 1.2266 | 1.2773525 | -0.0507525000000001 |
| 24 | 1.2176 | 1.2773525 | -0.0597525 |
| 25 | 1.2218 | 1.2773525 | -0.0555525 |
| 26 | 1.249 | 1.2773525 | -0.0283524999999999 |
| 27 | 1.2991 | 1.2773525 | 0.0217474999999999 |
| 28 | 1.3408 | 1.2773525 | 0.0634475 |
| 29 | 1.3119 | 1.2773525 | 0.0345475000000001 |
| 30 | 1.3014 | 1.2773525 | 0.0240474999999999 |
| 31 | 1.3201 | 1.2773525 | 0.0427475000000001 |
| 32 | 1.2938 | 1.2773525 | 0.0164475000000001 |
| 33 | 1.2694 | 1.2773525 | -0.0079524999999999 |
| 34 | 1.2165 | 1.2773525 | -0.0608525 |
| 35 | 1.2037 | 1.2773525 | -0.0736525 |
| 36 | 1.2292 | 1.2773525 | -0.0481524999999999 |
| 37 | 1.2256 | 1.2773525 | -0.0517525 |
| 38 | 1.2015 | 1.2773525 | -0.0758525 |
| 39 | 1.1786 | 1.11823809523810 | 0.0603619047619047 |
| 40 | 1.1856 | 1.11823809523810 | 0.0673619047619046 |
| 41 | 1.2103 | 1.2773525 | -0.0670525 |
| 42 | 1.1938 | 1.11823809523810 | 0.0755619047619046 |
| 43 | 1.202 | 1.11823809523810 | 0.0837619047619046 |
| 44 | 1.2271 | 1.2773525 | -0.0502524999999999 |
| 45 | 1.277 | 1.2773525 | -0.000352500000000066 |
| 46 | 1.265 | 1.2773525 | -0.0123525000000001 |
| 47 | 1.2684 | 1.2773525 | -0.0089525 |
| 48 | 1.2811 | 1.2773525 | 0.00374749999999993 |
| 49 | 1.2727 | 1.2773525 | -0.00465250000000004 |
| 50 | 1.2611 | 1.2773525 | -0.0162524999999999 |
| 51 | 1.2881 | 1.2773525 | 0.0107475000000000 |
| 52 | 1.3213 | 1.2773525 | 0.0439474999999999 |
| 53 | 1.2999 | 1.2773525 | 0.0225475000000001 |
| 54 | 1.3074 | 1.2773525 | 0.0300474999999999 |
| 55 | 1.3242 | 1.2773525 | 0.0468475000000001 |
| 56 | 1.3516 | 1.2773525 | 0.0742475 |
| 57 | 1.3511 | 1.2773525 | 0.0737475 |
| 58 | 1.3419 | 1.2773525 | 0.0645475000000001 |
| 59 | 1.3716 | 1.2773525 | 0.0942475 |
| 60 | 1.3622 | 1.2773525 | 0.0848475000000001 |
| 61 | 1.3896 | 1.2773525 | 0.1122475 |
