Multiple Linear Regression - Estimated Regression Equation |
y[t] = + 1.06387299075026 + 0.09483098663926x[t] -0.0120027823455514M1[t] -0.0213809638003883M2[t] + 0.00987794964028782M3[t] + 0.0195444684252598M4[t] + 0.00797098721023191M5[t] + 0.0239099006509078M6[t] + 0.022842616763732M7[t] + 0.0205553328765560M8[t] + 0.03786804898938M9[t] + 0.005514567774352M10[t] + 0.00350728388717602M11[t] + 0.00280728388717597t + 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) | 1.06387299075026 | 0.025042 | 42.4838 | 0 | 0 |
x | 0.09483098663926 | 0.017279 | 5.4883 | 2e-06 | 1e-06 |
M1 | -0.0120027823455514 | 0.028969 | -0.4143 | 0.68052 | 0.34026 |
M2 | -0.0213809638003883 | 0.030398 | -0.7034 | 0.4853 | 0.24265 |
M3 | 0.00987794964028782 | 0.030654 | 0.3222 | 0.748697 | 0.374349 |
M4 | 0.0195444684252598 | 0.03034 | 0.6442 | 0.522594 | 0.261297 |
M5 | 0.00797098721023191 | 0.030359 | 0.2626 | 0.79404 | 0.39702 |
M6 | 0.0239099006509078 | 0.03031 | 0.7888 | 0.434167 | 0.217083 |
M7 | 0.022842616763732 | 0.030306 | 0.7537 | 0.454762 | 0.227381 |
M8 | 0.0205553328765560 | 0.030308 | 0.6782 | 0.500958 | 0.250479 |
M9 | 0.03786804898938 | 0.030317 | 1.2491 | 0.217824 | 0.108912 |
M10 | 0.005514567774352 | 0.030203 | 0.1826 | 0.855909 | 0.427955 |
M11 | 0.00350728388717602 | 0.030192 | 0.1162 | 0.908017 | 0.454009 |
t | 0.00280728388717597 | 0.000459 | 6.1214 | 0 | 0 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.900419879564424 |
R-squared | 0.810755959514812 |
Adjusted R-squared | 0.758411863210399 |
F-TEST (value) | 15.4889666028383 |
F-TEST (DF numerator) | 13 |
F-TEST (DF denominator) | 47 |
p-value | 8.29780688604842e-13 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 0.0477328775107169 |
Sum Squared Residuals | 0.107086096986296 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 0.9808 | 1.05467749229188 | -0.0738774922918776 |
2 | 0.9811 | 1.04810659472422 | -0.067006594724221 |
3 | 1.0014 | 1.08217279205207 | -0.0807727920520726 |
4 | 1.0183 | 1.09464659472422 | -0.0763465947242208 |
5 | 1.0622 | 1.08588039739637 | -0.0236803973963688 |
6 | 1.0773 | 1.10462659472422 | -0.0273265947242208 |
7 | 1.0807 | 1.10636659472422 | -0.0256665947242208 |
8 | 1.0848 | 1.10688659472422 | -0.0220865947242208 |
9 | 1.1582 | 1.12700659472422 | 0.0311934052757790 |
10 | 1.1663 | 1.09746039739637 | 0.0688396026036311 |
11 | 1.1372 | 1.09826039739637 | 0.0389396026036312 |
12 | 1.1139 | 1.09756039739637 | 0.0163396026036312 |
13 | 1.1222 | 1.08836489893799 | 0.0338351010620068 |
14 | 1.1692 | 1.08179400137033 | 0.0874059986296675 |
15 | 1.1702 | 1.11586019869818 | 0.0543398013018154 |
16 | 1.2286 | 1.22316498800959 | 0.0054350119904076 |
17 | 1.2613 | 1.21439879068174 | 0.0469012093182597 |
18 | 1.2646 | 1.23314498800959 | 0.0314550119904077 |
19 | 1.2262 | 1.23488498800959 | -0.00868498800959239 |
20 | 1.1985 | 1.14057400137033 | 0.0579259986296675 |
21 | 1.2007 | 1.16069400137033 | 0.0400059986296677 |
22 | 1.2138 | 1.22597879068174 | -0.0121787906817403 |
23 | 1.2266 | 1.22677879068174 | -0.000178790681740365 |
24 | 1.2176 | 1.22607879068174 | -0.00847879068174021 |
25 | 1.2218 | 1.21688329222336 | 0.00491670777663516 |
26 | 1.249 | 1.21031239465570 | 0.0386876053442962 |
27 | 1.2991 | 1.24437859198356 | 0.054721408016444 |
28 | 1.3408 | 1.25685239465570 | 0.083947605344296 |
29 | 1.3119 | 1.24808619732785 | 0.0638138026721481 |
30 | 1.3014 | 1.26683239465570 | 0.034567605344296 |
31 | 1.3201 | 1.26857239465570 | 0.0515276053442961 |
32 | 1.2938 | 1.26909239465570 | 0.0247076053442961 |
33 | 1.2694 | 1.28921239465570 | -0.0198123946557039 |
34 | 1.2165 | 1.25966619732785 | -0.043166197327852 |
35 | 1.2037 | 1.26046619732785 | -0.056766197327852 |
36 | 1.2292 | 1.25976619732785 | -0.0305661973278518 |
37 | 1.2256 | 1.25057069886948 | -0.0249706988694765 |
38 | 1.2015 | 1.24399980130182 | -0.0424998013018156 |
39 | 1.1786 | 1.18323501199041 | -0.00463501199040760 |
40 | 1.1856 | 1.19570881466256 | -0.0101088146625556 |
41 | 1.2103 | 1.28177360397396 | -0.0714736039739637 |
42 | 1.1938 | 1.20568881466256 | -0.0118888146625556 |
43 | 1.202 | 1.20742881466256 | -0.00542881466255573 |
44 | 1.2271 | 1.30277980130182 | -0.0756798013018155 |
45 | 1.277 | 1.32289980130182 | -0.0458998013018157 |
46 | 1.265 | 1.29335360397396 | -0.0283536039739637 |
47 | 1.2684 | 1.29415360397396 | -0.0257536039739636 |
48 | 1.2811 | 1.29345360397396 | -0.0123536039739636 |
49 | 1.2727 | 1.28425810551559 | -0.0115581055155882 |
50 | 1.2611 | 1.27768720794793 | -0.0165872079479271 |
51 | 1.2881 | 1.31175340527578 | -0.0236534052757792 |
52 | 1.3213 | 1.32422720794793 | -0.00292720794792730 |
53 | 1.2999 | 1.31546101062008 | -0.0155610106200752 |
54 | 1.3074 | 1.33420720794793 | -0.0268072079479273 |
55 | 1.3242 | 1.33594720794793 | -0.0117472079479272 |
56 | 1.3516 | 1.33646720794793 | 0.0151327920520727 |
57 | 1.3511 | 1.35658720794793 | -0.00548720794792729 |
58 | 1.3419 | 1.32704101062008 | 0.0148589893799249 |
59 | 1.3716 | 1.32784101062008 | 0.0437589893799247 |
60 | 1.3622 | 1.32714101062008 | 0.0350589893799249 |
61 | 1.3896 | 1.3179455121617 | 0.0716544878383002 |