Multiple Linear Regression - Estimated Regression Equation

Y_t[t] = -0.187358145378251 + 0.000331876523507789X_1t[t] + 0.00144758264854112X_2t[t] + 0.456273556485971X_3t[t] + 0.000383387024625746X_4t[t] -0.00015911681280923X_5t[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)	-0.187358145378251	0.433319	-0.4324	0.669326	0.334663
X_1t	0.000331876523507789	0.000519	0.6396	0.528514	0.264257
X_2t	0.00144758264854112	0.001937	0.7473	0.462153	0.231076
X_3t	0.456273556485971	0.003896	117.1233	0	0
X_4t	0.000383387024625746	0.000813	0.4713	0.641678	0.320839
X_5t	-0.00015911681280923	0.000173	-0.92	0.366719	0.183359

Multiple Linear Regression - Regression Statistics
Multiple R	0.999257578304255
R-squared	0.998515707798483
Adjusted R-squared	0.9982064802565
F-TEST (value)	3229.06459559325
F-TEST (DF numerator)	5
F-TEST (DF denominator)	24
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.283271046221723
Sum Squared Residuals	1.9258196550612

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	34	33.7367117839698	0.263288216030209
2	36	36.0683992538277	-0.068399253827721
3	37	36.4928020371123	0.507197962887703
4	17	16.9189272561321	0.0810727438678629
5	25	25.1233731631316	-0.123373163131633
6	21	21.054217116964	-0.0542171169640256
7	21	21.0626316719109	-0.0626316719108542
8	29	28.7524287586606	0.247571241339359
9	36	35.7169220669053	0.283077933094736
10	24	24.2288441730085	-0.228844173008505
11	22	22.0086589365306	-0.00865893653059613
12	21	20.6737785795072	0.326221420492843
13	30	30.0498993170141	-0.0498993170141071
14	22	21.9201314362483	0.0798685637517401
15	37	37.0347860082792	-0.0347860082792011
16	31	30.9331220867945	0.0668779132054991
17	19	19.2624196935567	-0.262419693556652
18	31	31.4654031534266	-0.465403153426588
19	18	18.4036971469088	-0.403697146908821
20	30	29.9986088906636	0.00139110933643514
21	21	21.0691046884935	-0.0691046884934589
22	17	16.5917721173103	0.408227882689664
23	38	38.4348360356437	-0.434836035643678
24	30	29.7279070378323	0.272092962167685
25	35	35.2242556596721	-0.224255659672146
26	26	26.1508811633847	-0.150881163384722
27	29	29.1959251894967	-0.195925189496733
28	23	22.8968199012985	0.103180098701535
29	32	31.5648055283436	0.435194471656432
30	34	34.2379301479723	-0.237930147972263

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.318464790045139	0.636929580090278	0.681535209954861
10	0.463151657928709	0.926303315857417	0.536848342071291
11	0.303027489920949	0.606054979841898	0.696972510079051
12	0.421727386161545	0.84345477232309	0.578272613838455
13	0.308249017692138	0.616498035384275	0.691750982307862
14	0.234960941172282	0.469921882344563	0.765039058827718
15	0.188801091781526	0.377602183563052	0.811198908218474
16	0.111531156716533	0.223062313433067	0.888468843283467
17	0.0961235558840794	0.192247111768159	0.90387644411592
18	0.203130856158211	0.406261712316423	0.796869143841789
19	0.639839683486058	0.720320633027884	0.360160316513942
20	0.647896874545044	0.704206250909913	0.352103125454957
21	0.890350176107103	0.219299647785794	0.109649823892897

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	0	0	OK
10% type I error level	0	0	OK