Multiple Linear Regression - Estimated Regression Equation |
S[t] = -0.44848360563522 + 1.01946778957491D[t] -0.0701399164268452SWS[t] + 0.236237232406613PS[t] + 0.0731354251388876L[t] + 0.00206321954317301WB[t] -0.00542342168080034Wbr[t] + 0.00257997006234144Tg[t] -0.272489817386976`P `[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) | -0.44848360563522 | 1.034829 | -0.4334 | 0.669151 | 0.334576 |
D | 1.01946778957491 | 0.43024 | 2.3695 | 0.027469 | 0.013735 |
SWS | -0.0701399164268452 | 0.056213 | -1.2478 | 0.225853 | 0.112927 |
PS | 0.236237232406613 | 0.165002 | 1.4317 | 0.16694 | 0.08347 |
L | 0.0731354251388876 | 0.029431 | 2.485 | 0.021461 | 0.010731 |
WB | 0.00206321954317301 | 0.002053 | 1.0052 | 0.326253 | 0.163126 |
Wbr | -0.00542342168080034 | 0.002222 | -2.4413 | 0.023577 | 0.011788 |
Tg | 0.00257997006234144 | 0.002667 | 0.9675 | 0.344304 | 0.172152 |
`P ` | -0.272489817386976 | 0.348129 | -0.7827 | 0.442526 | 0.221263 |
Multiple Linear Regression - Regression Statistics | |
Multiple R | 0.900009031117382 |
R-squared | 0.810016256092849 |
Adjusted R-squared | 0.737641496509172 |
F-TEST (value) | 11.1919716314407 |
F-TEST (DF numerator) | 8 |
F-TEST (DF denominator) | 21 |
p-value | 4.88135444098869e-06 |
Multiple Linear Regression - Residual Statistics | |
Residual Standard Deviation | 0.7416523261178 |
Sum Squared Residuals | 11.5510116295548 |
Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
1 | 1 | 0.710690447170703 | 0.289309552829297 |
2 | 2 | 2.08093048810084 | -0.0809304881008395 |
3 | 2 | 1.70612817600359 | 0.293871823996411 |
4 | 5 | 4.45098237857557 | 0.549017621424427 |
5 | 1 | 1.12598187454436 | -0.125981874544365 |
6 | 3 | 2.53429737288629 | 0.465702627113708 |
7 | 1 | 2.03019482194413 | -1.03019482194413 |
8 | 3 | 2.64047699447143 | 0.359523005528566 |
9 | 5 | 5.08179789513783 | -0.0817978951378251 |
10 | 1 | 1.39837781055102 | -0.398377810551017 |
11 | 1 | 1.29088913147572 | -0.290889131475716 |
12 | 1 | 1.27333476719684 | -0.273334767196844 |
13 | 1 | 0.746978887029197 | 0.253021112970803 |
14 | 1 | 0.488964149854557 | 0.511035850145443 |
15 | 2 | 1.56032084710727 | 0.439679152892726 |
16 | 4 | 3.36351182855728 | 0.636488171442723 |
17 | 1 | 1.57216977795725 | -0.572169777957252 |
18 | 4 | 4.22364485357897 | -0.22364485357897 |
19 | 5 | 4.30892176222187 | 0.691078237778132 |
20 | 1 | 2.05137307080173 | -1.05137307080173 |
21 | 1 | 0.932711234425785 | 0.067288765574215 |
22 | 3 | 2.32706155092513 | 0.672938449074866 |
23 | 2 | 1.88556889225615 | 0.114431107743847 |
24 | 2 | 2.47972488655911 | -0.47972488655911 |
25 | 5 | 4.22139473487986 | 0.778605265120141 |
26 | 1 | 1.28894251066482 | -0.288942510664816 |
27 | 1 | 2.56881139006279 | -1.56881139006279 |
28 | 2 | 0.734898550094146 | 1.26510144990585 |
29 | 3 | 3.99194378204456 | -0.99194378204456 |
30 | 1 | 0.928975132921195 | 0.0710248670788049 |
Goldfeld-Quandt test for Heteroskedasticity | |||
p-values | Alternative Hypothesis | ||
breakpoint index | greater | 2-sided | less |
12 | 0.0780588152283114 | 0.156117630456623 | 0.921941184771689 |
13 | 0.118727145662097 | 0.237454291324194 | 0.881272854337903 |
14 | 0.0483935325123255 | 0.096787065024651 | 0.951606467487675 |
15 | 0.0452520604065756 | 0.0905041208131513 | 0.954747939593424 |
16 | 0.0251135983029102 | 0.0502271966058204 | 0.97488640169709 |
17 | 0.0141974231908183 | 0.0283948463816366 | 0.985802576809182 |
18 | 0.0061956797615397 | 0.0123913595230794 | 0.99380432023846 |
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity | |||
Description | # significant tests | % significant tests | OK/NOK |
1% type I error level | 0 | 0 | OK |
5% type I error level | 2 | 0.285714285714286 | NOK |
10% type I error level | 5 | 0.714285714285714 | NOK |