Multiple Linear Regression - Estimated Regression Equation

Y[t] = -0.292849249941967 + 0.0134244026370332X[t] + 1.08350167270624Y1[t] -0.133309614940154Y2[t] -0.137399955322172M1[t] -0.0238083435085413M2[t] -0.0357127578837311M3[t] -0.044701180135979M4[t] -0.0788911693856083M5[t] + 0.0109118261505348M6[t] -0.0355533832106162M7[t] -0.0810949181381833M8[t] -0.0331665972168241M9[t] + 0.103802503434295M10[t] -0.0288910987831445M11[t] + 0.00270333617091151t + 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)	-0.292849249941967	0.45343	-0.6459	0.522063	0.261031
X	0.0134244026370332	0.008012	1.6756	0.101622	0.050811
Y1	1.08350167270624	0.081233	13.3382	0	0
Y2	-0.133309614940154	0.112632	-1.1836	0.243564	0.121782
M1	-0.137399955322172	0.275986	-0.4979	0.621315	0.310658
M2	-0.0238083435085413	0.275796	-0.0863	0.931638	0.465819
M3	-0.0357127578837311	0.275814	-0.1295	0.897626	0.448813
M4	-0.044701180135979	0.276692	-0.1616	0.872469	0.436235
M5	-0.0788911693856083	0.275954	-0.2859	0.776441	0.388221
M6	0.0109118261505348	0.276434	0.0395	0.968709	0.484355
M7	-0.0355533832106162	0.276285	-0.1287	0.898253	0.449126
M8	-0.0810949181381833	0.276625	-0.2932	0.770916	0.385458
M9	-0.0331665972168241	0.290904	-0.114	0.909799	0.454899
M10	0.103802503434295	0.290052	0.3579	0.722318	0.361159
M11	-0.0288910987831445	0.290153	-0.0996	0.921181	0.460591
t	0.00270333617091151	0.004263	0.6341	0.529645	0.264823

Multiple Linear Regression - Regression Statistics
Multiple R	0.967121489718825
R-squared	0.935323975875959
Adjusted R-squared	0.911070466829444
F-TEST (value)	38.5644804668107
F-TEST (DF numerator)	15
F-TEST (DF denominator)	40
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.410025249215748
Sum Squared Residuals	6.72482819977743

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	3.2	2.52181716115134	0.678182838848662
2	2.8	3.17027898135704	-0.370278981357045
3	2.8	2.69957942967552	0.100420570324478
4	3	2.67334462192467	0.326655378075332
5	3.1	2.7825064140712	0.317493585928802
6	3.1	3.04474140060716	0.0552585993928412
7	3	3.05333469770135	-0.0533346977013491
8	2.4	2.90233321396010	-0.502333213960105
9	2.7	2.30295730857165	0.397042691428352
10	3	2.77976220957888	0.220237790421119
11	2.7	2.99352316789305	-0.293523167893054
12	2.7	2.80153191421858	-0.101531914218584
13	2	2.64160925348601	-0.641609253486014
14	2.4	1.98630895136821	0.413691048631793
15	2.6	2.45318630725588	0.146813692744122
16	2.4	2.67434107782542	-0.274341077825417
17	2.3	2.53826498435541	-0.238264984355405
18	2.4	2.45298801965133	-0.0529880196513338
19	2.5	2.47748998810665	0.0225100118933455
20	2.6	2.5884580433005	0.0115419566994995
21	2.6	2.74466154599297	-0.144661545992971
22	2.6	2.88711230448543	-0.287112304485426
23	2.7	2.73305155483526	-0.033051554835256
24	2.8	2.84221347213778	-0.0422134721377779
25	2.6	2.80110017735640	-0.201100177356405
26	2.6	2.72888603633747	-0.128886036337468
27	2	2.71977839927621	-0.719778399276208
28	2	2.07288283256007	-0.0728828325600675
29	2.1	2.05060637904124	0.0493936209587626
30	1.9	2.22192919221744	-0.321929192217443
31	2	2.03742543265894	-0.0374254326589376
32	2.5	2.07071883484403	0.429281165155973
33	2.9	2.65111280705911	0.248887192940894
34	3.3	3.21997170988649	0.0800282901135123
35	3.5	3.5342150761751	-0.0342150761751004
36	3.8	3.71317015767292	0.0868298423270787
37	4.6	3.85422751514858	0.745772484851418
38	4.4	4.73164478503763	-0.331644785037625
39	5.3	4.51130001563295	0.788699984367051
40	5.8	5.41600962575852	0.383990374241477
41	5.9	5.79277731180674	0.107222688193265
42	5.6	6.02700793509096	-0.427007935090962
43	5.8	5.46841001293014	0.331589987069861
44	5.5	5.49641336568616	0.00358663431384228
45	4.6	5.10126833837627	-0.501268338376275
46	4.2	4.21315377604920	-0.0131537760492046
47	4	3.63921020109659	0.36078979890341
48	3.5	3.44308445597072	0.0569155440292828
49	2.3	2.88124589285766	-0.581245892857662
50	2.2	1.78288124589966	0.417118754100345
51	1.4	1.71615584815944	-0.316155848159444
52	0.6	0.963421841931324	-0.363421841931324
53	0	0.235844910725424	-0.235844910725424
54	0.5	-0.246666547566898	0.746666547566898
55	0.1	0.36333986860292	-0.26333986860292
56	0.1	0.0420765422092107	0.0579234577907893

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.159726064873817	0.319452129747634	0.840273935126183
20	0.466394852519498	0.932789705038995	0.533605147480502
21	0.404551097718805	0.809102195437609	0.595448902281196
22	0.278577470349728	0.557154940699455	0.721422529650272
23	0.181884435691012	0.363768871382024	0.818115564308988
24	0.119796319490527	0.239592638981055	0.880203680509473
25	0.0678598887717862	0.135719777543572	0.932140111228214
26	0.0391944554640345	0.078388910928069	0.960805544535966
27	0.060765165905267	0.121530331810534	0.939234834094733
28	0.0726587429840925	0.145317485968185	0.927341257015908
29	0.062739085672566	0.125478171345132	0.937260914327434
30	0.103109844610781	0.206219689221562	0.896890155389219
31	0.0848180143884671	0.169636028776934	0.915181985611533
32	0.0604213967120436	0.120842793424087	0.939578603287956
33	0.0580151615352357	0.116030323070471	0.941984838464764
34	0.0358185585759848	0.0716371171519696	0.964181441424015
35	0.0297551638814811	0.0595103277629621	0.97024483611852
36	0.0239739836268635	0.047947967253727	0.976026016373136
37	0.0377367601647321	0.0754735203294642	0.962263239835268

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	1	0.0526315789473684	NOK
10% type I error level	5	0.263157894736842	NOK