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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 17 Dec 2011 08:26:43 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/17/t1324128837smbigbb7ceus111.htm/, Retrieved Fri, 29 Mar 2024 06:00:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156275, Retrieved Fri, 29 Mar 2024 06:00:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-12-17 13:26:43] [7dc03dd48c8acabd98b217fada4a6bc0] [Current]
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Dataseries X:
38	1724	270018	90	476	140824	165	3
34	1209	179444	63	429	110459	135	4
42	1844	222373	59	673	105079	121	16
38	2683	218443	135	1137	112098	148	2
27	1149	162874	48	348	43929	73	1
35	631	70849	46	179	76173	49	3
33	4513	498732	109	2201	187326	185	0
18	381	33186	46	111	22807	5	0
34	1997	207822	75	735	144408	125	7
33	1758	213274	72	595	66485	93	0
44	2079	298841	80	780	79089	154	0
55	2128	237633	61	660	81625	98	7
37	1659	164107	60	633	68788	70	8
52	2934	358752	114	1163	103297	148	4
43	1944	222781	46	622	69446	100	10
59	4764	369889	127	1650	114948	150	0
36	2122	305704	58	746	167949	197	6
39	2956	322896	90	1157	125081	114	4
29	1438	176082	41	507	125818	169	3
49	2320	263411	62	683	136588	200	8
45	2471	271965	99	828	112431	148	0
39	2769	425544	101	1203	103037	140	1
25	1442	179306	62	461	82317	74	5
52	1717	189897	65	601	118906	128	9
41	3220	220665	150	1201	83515	140	1
38	2733	214779	72	990	104581	116	0
41	2824	267198	91	1061	103129	147	5
43	1968	270750	73	617	83243	132	0
32	1495	155915	53	559	37110	70	0
41	2745	330118	140	1031	113344	144	0
46	2290	281588	50	911	139165	155	3
49	1830	204039	83	615	86652	165	6
48	2090	318563	53	779	112302	161	1
37	945	97717	40	310	69652	31	4
39	3092	369331	72	1198	119442	199	4
42	2764	273950	87	1186	69867	78	0
43	3658	422946	74	1317	101629	121	0
36	1842	215710	67	611	70168	112	2
17	934	115469	36	276	31081	41	1
39	3342	343095	45	1185	103925	158	2
39	3246	324178	42	1490	92622	123	10
41	1629	170369	75	646	79011	104	9
36	1735	195153	82	635	93487	94	5
42	1714	173510	85	470	64520	73	6
45	2496	153778	82	1022	93473	52	1
41	5501	455168	848	2068	114360	71	2
26	918	78800	57	330	33032	21	2
52	2228	208051	80	648	96125	155	0
47	3942	334657	116	1342	151911	174	10
45	2081	175523	68	868	89256	136	3
40	1816	213060	48	559	95671	128	0
4	496	24188	20	218	5950	7	0
44	2533	372238	81	833	149695	165	8
18	744	65029	21	255	32551	21	5
14	1161	101097	70	454	31701	35	3
37	3027	279012	125	1108	100087	137	1
56	2433	302218	80	642	169707	174	5
39	3576	323514	220	1079	150491	257	5
42	2606	339837	63	1046	120192	207	0
36	2175	252529	77	822	95893	103	12
46	3937	370483	65	1298	151715	171	10
28	3161	303406	146	1143	176225	279	12
43	2790	250858	72	1124	59900	83	11
42	2610	264889	59	931	104767	130	8
37	1426	228595	58	557	114799	131	2
30	1646	216027	58	436	72128	126	0
35	1867	188780	54	566	143592	158	6
44	2736	237856	89	832	89626	138	9
36	2277	232765	78	834	131072	200	2
28	1675	175699	62	621	126817	104	5
45	2537	239314	64	865	81351	111	13
23	893	73566	39	385	22618	26	6
45	2190	242585	58	716	88977	115	7
38	1694	187167	94	705	92059	127	2
38	1948	191920	61	683	81897	140	1
45	2314	359644	95	982	108146	121	4
36	2645	341637	48	1056	126372	183	3
41	1804	206059	50	522	249771	68	6
38	2250	201783	58	690	71154	112	2
37	1787	182231	67	644	71571	103	0
28	1678	153613	41	622	55918	63	1
45	4009	454794	114	1226	160141	166	0
26	1369	145943	45	653	38692	38	5
44	2306	280343	57	656	102812	163	2
8	870	80953	31	437	56622	59	0
27	1966	150216	175	822	15986	27	0
36	1338	156923	68	390	123534	108	6
37	3731	365448	278	1467	108535	88	1
57	2617	318651	91	907	93879	92	0
45	3085	179797	72	1044	144551	170	1
37	2312	251466	58	786	56750	98	1
38	2136	254506	71	655	127654	205	3
31	1808	185890	86	590	65594	96	9
36	2992	263577	89	1072	59938	107	1
36	2474	314255	134	947	146975	150	4
36	1624	189252	64	555	143372	123	3
35	1606	222504	72	552	168553	176	5
39	2091	285198	61	771	183500	213	0
65	3930	376927	130	1291	165986	208	12
30	3705	397681	73	1415	184923	307	13
51	2676	287015	83	846	140358	125	8
41	2296	285330	85	838	149959	208	0
36	1997	186856	116	640	57224	73	0
19	602	43287	43	214	43750	49	4
23	2146	185468	85	716	48029	82	4
40	2157	222268	72	755	104978	206	0
40	2549	259692	110	1140	100046	112	0
40	2649	301614	55	1030	101047	139	0
30	1110	121726	44	356	197426	60	0
41	3102	154165	79	906	160902	70	0
40	1861	306952	58	606	147172	112	4
45	2295	297982	70	684	109432	142	0
1	398	23623	9	156	1168	11	0
40	2205	195817	54	779	83248	130	0
11	530	61857	25	192	25162	31	4
45	1596	163766	107	457	45724	132	0
38	2949	384053	63	1162	110529	219	1
0	387	21054	2	146	855	4	0
30	2137	252805	67	866	101382	102	5
8	492	31961	22	200	14116	39	0
39	3397	311281	153	1211	89506	125	3
48	2089	240153	79	696	135356	121	7
48	1638	174892	112	485	116066	42	13
29	1685	152043	47	670	144244	111	3
8	568	38214	52	276	8773	16	0
43	1917	199336	113	662	102153	70	2
52	2759	353021	115	1010	117440	162	0
53	1288	196269	64	445	104128	173	0
48	3554	403932	134	1564	134238	171	4
48	2387	316105	120	820	134047	172	0
50	3328	396725	111	1151	279488	254	3
40	1250	187992	49	473	79756	90	0
36	1121	102424	55	401	66089	50	0
40	2867	284271	149	949	102070	113	4
46	4024	401260	155	1429	146760	187	4
40	1721	137843	104	534	154771	16	15
46	4061	383703	146	1698	165933	175	0
39	1830	157429	76	689	64593	90	4
41	1627	236370	83	528	92280	140	1
46	2535	282399	192	897	67150	145	1
32	1808	217478	69	610	128692	141	0
39	3873	366774	117	1548	124089	125	9
39	2181	236660	67	759	125386	241	1
21	2035	173260	37	716	37238	16	3
45	2960	323545	56	955	140015	175	11
50	1915	168994	122	720	150047	132	5
36	2604	246745	52	1011	154451	154	2
44	2633	301703	64	818	156349	198	1
0	2	1	0	0	0	0	9
0	207	14688	0	85	6023	5	0
0	5	98	0	0	0	0	0
0	8	455	0	0	0	0	0
0	0	0	0	0	0	0	1
0	0	0	0	0	0	0	0
37	2030	233143	58	699	84601	125	2
47	3179	372078	118	1052	68946	174	3
0	0	0	0	0	0	0	0
0	4	203	0	0	0	0	0
0	151	7199	0	74	1644	6	0
5	474	46660	7	259	6179	13	0
1	141	17547	3	69	3926	3	0
43	1047	116678	89	285	52789	35	0
0	29	969	0	0	0	0	0
32	1767	201582	48	582	100350	80	2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156275&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156275&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156275&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
PR[t] = + 10.8604909962911 + 0.0136318261592929Pagevieuws[t] + 5.37631803596578e-05Time_RFC[t] -0.0015854205624214Compendium_Vieuws[t] -0.0299200727233781`Course_compendium-vieuws`[t] + 5.24476830987969e-05Compendium_writing_nbr[t] + 0.00654253313448936Inlcuded_hyperlinks[t] + 0.22633627113802Shared[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PR[t] =  +  10.8604909962911 +  0.0136318261592929Pagevieuws[t] +  5.37631803596578e-05Time_RFC[t] -0.0015854205624214Compendium_Vieuws[t] -0.0299200727233781`Course_compendium-vieuws`[t] +  5.24476830987969e-05Compendium_writing_nbr[t] +  0.00654253313448936Inlcuded_hyperlinks[t] +  0.22633627113802Shared[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156275&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PR[t] =  +  10.8604909962911 +  0.0136318261592929Pagevieuws[t] +  5.37631803596578e-05Time_RFC[t] -0.0015854205624214Compendium_Vieuws[t] -0.0299200727233781`Course_compendium-vieuws`[t] +  5.24476830987969e-05Compendium_writing_nbr[t] +  0.00654253313448936Inlcuded_hyperlinks[t] +  0.22633627113802Shared[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156275&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156275&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
PR[t] = + 10.8604909962911 + 0.0136318261592929Pagevieuws[t] + 5.37631803596578e-05Time_RFC[t] -0.0015854205624214Compendium_Vieuws[t] -0.0299200727233781`Course_compendium-vieuws`[t] + 5.24476830987969e-05Compendium_writing_nbr[t] + 0.00654253313448936Inlcuded_hyperlinks[t] + 0.22633627113802Shared[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.86049099629111.5554816.982100
Pagevieuws0.01363182615929290.0030334.49471.4e-057e-06
Time_RFC5.37631803596578e-051.7e-053.12740.0021040.001052
Compendium_Vieuws-0.00158542056242140.011902-0.13320.8942030.447101
`Course_compendium-vieuws`-0.02992007272337810.006757-4.42821.8e-059e-06
Compendium_writing_nbr5.24476830987969e-052.1e-052.46570.0147590.007379
Inlcuded_hyperlinks0.006542533134489360.0197120.33190.7404110.370206
Shared0.226336271138020.1877291.20570.2297770.114889

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 10.8604909962911 & 1.555481 & 6.9821 & 0 & 0 \tabularnewline
Pagevieuws & 0.0136318261592929 & 0.003033 & 4.4947 & 1.4e-05 & 7e-06 \tabularnewline
Time_RFC & 5.37631803596578e-05 & 1.7e-05 & 3.1274 & 0.002104 & 0.001052 \tabularnewline
Compendium_Vieuws & -0.0015854205624214 & 0.011902 & -0.1332 & 0.894203 & 0.447101 \tabularnewline
`Course_compendium-vieuws` & -0.0299200727233781 & 0.006757 & -4.4282 & 1.8e-05 & 9e-06 \tabularnewline
Compendium_writing_nbr & 5.24476830987969e-05 & 2.1e-05 & 2.4657 & 0.014759 & 0.007379 \tabularnewline
Inlcuded_hyperlinks & 0.00654253313448936 & 0.019712 & 0.3319 & 0.740411 & 0.370206 \tabularnewline
Shared & 0.22633627113802 & 0.187729 & 1.2057 & 0.229777 & 0.114889 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156275&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]10.8604909962911[/C][C]1.555481[/C][C]6.9821[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Pagevieuws[/C][C]0.0136318261592929[/C][C]0.003033[/C][C]4.4947[/C][C]1.4e-05[/C][C]7e-06[/C][/ROW]
[ROW][C]Time_RFC[/C][C]5.37631803596578e-05[/C][C]1.7e-05[/C][C]3.1274[/C][C]0.002104[/C][C]0.001052[/C][/ROW]
[ROW][C]Compendium_Vieuws[/C][C]-0.0015854205624214[/C][C]0.011902[/C][C]-0.1332[/C][C]0.894203[/C][C]0.447101[/C][/ROW]
[ROW][C]`Course_compendium-vieuws`[/C][C]-0.0299200727233781[/C][C]0.006757[/C][C]-4.4282[/C][C]1.8e-05[/C][C]9e-06[/C][/ROW]
[ROW][C]Compendium_writing_nbr[/C][C]5.24476830987969e-05[/C][C]2.1e-05[/C][C]2.4657[/C][C]0.014759[/C][C]0.007379[/C][/ROW]
[ROW][C]Inlcuded_hyperlinks[/C][C]0.00654253313448936[/C][C]0.019712[/C][C]0.3319[/C][C]0.740411[/C][C]0.370206[/C][/ROW]
[ROW][C]Shared[/C][C]0.22633627113802[/C][C]0.187729[/C][C]1.2057[/C][C]0.229777[/C][C]0.114889[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156275&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156275&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.86049099629111.5554816.982100
Pagevieuws0.01363182615929290.0030334.49471.4e-057e-06
Time_RFC5.37631803596578e-051.7e-053.12740.0021040.001052
Compendium_Vieuws-0.00158542056242140.011902-0.13320.8942030.447101
`Course_compendium-vieuws`-0.02992007272337810.006757-4.42821.8e-059e-06
Compendium_writing_nbr5.24476830987969e-052.1e-052.46570.0147590.007379
Inlcuded_hyperlinks0.006542533134489360.0197120.33190.7404110.370206
Shared0.226336271138020.1877291.20570.2297770.114889







Multiple Linear Regression - Regression Statistics
Multiple R0.815918637317371
R-squared0.665723222721836
Adjusted R-squared0.650723623741405
F-TEST (value)44.382734737801
F-TEST (DF numerator)7
F-TEST (DF denominator)156
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.45337755668344
Sum Squared Residuals11147.6963700709

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.815918637317371 \tabularnewline
R-squared & 0.665723222721836 \tabularnewline
Adjusted R-squared & 0.650723623741405 \tabularnewline
F-TEST (value) & 44.382734737801 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.45337755668344 \tabularnewline
Sum Squared Residuals & 11147.6963700709 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156275&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.815918637317371[/C][/ROW]
[ROW][C]R-squared[/C][C]0.665723222721836[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.650723623741405[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]44.382734737801[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]8.45337755668344[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11147.6963700709[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156275&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156275&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.815918637317371
R-squared0.665723222721836
Adjusted R-squared0.650723623741405
F-TEST (value)44.382734737801
F-TEST (DF numerator)7
F-TEST (DF denominator)156
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.45337755668344
Sum Squared Residuals11147.6963700709







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13843.6385625676301-5.63856256763008
23431.63516195069112.36483804930894
34237.64748632394914.35251367605088
43832.24596435276035.75403564723966
52727.7997134572886-0.799713457288583
63522.83730880643912.162691193561
73344.2026333288995-11.2026333288995
81815.67323122233772.3267687776623
93437.1222950719477-3.12229507194773
103332.47037615378040.529623846219565
114436.95879470540517.04120529459494
125539.307528528021115.6924714719789
133729.14051227971537.85948772028474
145242.45746321615269.54253678384737
154337.21485937915715.78514062084291
165953.12958768122855.87041231877147
173645.2659493338014-9.26594933380137
183941.9672755285711-2.96727552857112
192933.0788657288988-4.07886572889881
204946.39735597773162.60264402226837
214541.10070029416673.89929970583329
223941.8779843247323-2.87798432473225
232532.1993602724989-7.19936027249885
245235.501602017249716.4983979827503
254135.96926216210045.03073783789957
263836.1724167172231.82758328277703
274139.33502274194551.66497725805451
284338.89740224672164.10259775327838
293225.2175202672446.78247973275601
304141.8452484854944-0.845248485494356
314638.87196532077277.12803467922733
324935.226316023538713.7736839764613
334840.2558675659597.74413243404101
343724.418753680202212.5812463197978
353945.3798745861382-6.37987458613823
364231.818823780801710.1811762191983
374350.0644283639931-7.06442836399311
383634.04576808643941.95423191356057
391723.6102886581663-6.6102886581663
403946.2743205272006-7.27432052720063
413935.81664656143313.18335343856687
424129.640435338538111.3595646614619
433632.52436067411833.4756393258817
444234.57624258891017.4237574110899
454527.91379490624817.086205093752
464154.0164083816301-13.0164083816301
472619.969570658316.03042934168996
485238.958268520177913.0417314798221
494753.6218714498342-6.62187144983416
504528.836627940281916.1633720597181
514036.12441620059083.8755837994092
52412.7258777592742-8.72587775927424
534451.0921298021376-7.09212980213758
541819.8121222222043-1.8121222222043
551420.9982864313035-6.99828643130352
563740.1469776800463-3.14697768004634
575652.11022562712223.88977437287785
583955.0745085290141-16.0745085290141
594240.91756661305731.08243338694268
603637.7893797457756-1.78937974577555
614656.8472643218241-10.8472643218241
622849.6166454133479-21.6166454133479
634334.81024330810888.18975669189118
644240.88771072748661.11228927251343
653733.16272036916973.83727963083032
663036.3829749795254-6.38297497952544
673539.3629553257843-4.36295532578431
684443.55103151805170.448968481948311
693637.97294427055-1.97294427055004
702832.9246382322475-4.92463823224746
714540.26365005578424.73634994421584
722318.12217966709674.87782033290328
734539.24498763764515.75501236235491
743828.88467239440859.11532760559145
753832.6389964175535.36100358244701
764539.57701432860415.42298567139586
773642.1166768388565-6.11667683885655
784145.7359637149048-4.73596371490482
793836.56108980201231.43891019798768
803730.0707464914476.92925350855297
812826.88941664130171.11058335869833
824562.5839917338587-17.5839917338587
832621.16927279517714.83072720482286
844444.5610333505454-0.561033350545382
85817.303952844335-9.30395284433501
862721.88007980597545.11992019402462
873634.30360027792131.69639972207874
883743.5294760032414-6.52947600324137
895741.910541098666715.0894589013333
904540.15035897674954.84964102325053
913736.13166396987710.868336030122884
923842.6662958036895-4.66629580368947
933133.8170441605238-2.81704416052382
943637.6722288111403-1.67222881114027
953642.5294452120737-6.52944521207367
963635.46952842113590.530471578864083
973539.2090775837735-4.20907758377353
983942.5504136898295-3.55041368982953
996558.6479067992086.35209320079199
1003054.944075739205-24.944075739205
1015147.31608429412353.68391570587645
1024141.5174794218384-0.517479421838409
1033632.27533647394373.7246635260563
1041918.44354382914140.556456170858634
1052332.4890492244665-9.48904922446651
1064036.36398410947193.63601589052814
1074031.26654915813678.73345084186334
1084038.49114647702141.5088535229786
1093032.5619788028293-2.56197880282926
1104143.0988967561294-2.0988967561294
1114043.8654559661364-3.86545596613637
1124544.25817742160480.741822578395182
113113.0074320457559-12.0074320457559
1144033.26975703422916.73024296577093
1151118.0545506451278-7.05455064512778
1164530.840085542301414.1599144576986
1173844.2977920395338-6.29779203953378
118012.9674491620791-12.9674491620791
1193034.6825688967259-4.68256889672593
120814.2622909639571-6.26229096395709
1213943.6186913695486-4.61869136954856
1224840.77425505682467.22574494317543
1234837.207920713832410.7920792861676
1242930.8539634072712-1.85396340727124
125812.8822965421128-4.88229654211278
1264333.99173643068029.00826356931984
1275244.26798051914677.73201948085334
1285332.1475600355520.85243996445
1294843.08182037509374.91817962490635
1304843.82553034092894.17446965907108
1315059.9417310790571-9.9417310790571
1324028.549286887195311.4507131128047
1333623.356602398049112.6433976019509
1344043.5938593033611-3.59385930336115
1354654.1124698558005-8.11246985580047
1364037.20664427208082.79335572791917
1374645.66042043448020.339579565519836
1383928.417120780542910.5828792194571
1394135.80024990077195.1997500992281
1404638.15399819535387.84600180464622
1413236.510597655796-4.51059765579602
1423946.236747272973-7.23674727297303
1433938.87883168847510.12116831152493
1442129.1715693956171-8.17156939561709
1454550.9211562450738-5.92115624507383
1465034.180132558957315.8198674410427
1473638.8527466076321-2.85274660763213
1484448.1194163106216-4.11941631062165
149012.9248348520323-12.9248348520323
150012.2773514838768-12.2773514838768
151010.9339189187629-10.9339189187629
152010.9940078526291-10.9940078526291
153011.0868272674292-11.0868272674292
154010.8604909962911-10.8604909962911
1553735.76913765391421.23086234608577
1564747.9706343849294-0.970634384929408
157010.8604909962911-10.8604909962911
158010.9259322265413-10.9259322265413
159011.2173316900449-11.2173316900449
160512.4792969767016-7.47929697670156
161111.8822569341714-10.8822569341714
1624325.735319589666217.2646804103338
163011.3079104766791-11.3079104766791
1643234.5352349230194-2.53523492301942

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 38 & 43.6385625676301 & -5.63856256763008 \tabularnewline
2 & 34 & 31.6351619506911 & 2.36483804930894 \tabularnewline
3 & 42 & 37.6474863239491 & 4.35251367605088 \tabularnewline
4 & 38 & 32.2459643527603 & 5.75403564723966 \tabularnewline
5 & 27 & 27.7997134572886 & -0.799713457288583 \tabularnewline
6 & 35 & 22.837308806439 & 12.162691193561 \tabularnewline
7 & 33 & 44.2026333288995 & -11.2026333288995 \tabularnewline
8 & 18 & 15.6732312223377 & 2.3267687776623 \tabularnewline
9 & 34 & 37.1222950719477 & -3.12229507194773 \tabularnewline
10 & 33 & 32.4703761537804 & 0.529623846219565 \tabularnewline
11 & 44 & 36.9587947054051 & 7.04120529459494 \tabularnewline
12 & 55 & 39.3075285280211 & 15.6924714719789 \tabularnewline
13 & 37 & 29.1405122797153 & 7.85948772028474 \tabularnewline
14 & 52 & 42.4574632161526 & 9.54253678384737 \tabularnewline
15 & 43 & 37.2148593791571 & 5.78514062084291 \tabularnewline
16 & 59 & 53.1295876812285 & 5.87041231877147 \tabularnewline
17 & 36 & 45.2659493338014 & -9.26594933380137 \tabularnewline
18 & 39 & 41.9672755285711 & -2.96727552857112 \tabularnewline
19 & 29 & 33.0788657288988 & -4.07886572889881 \tabularnewline
20 & 49 & 46.3973559777316 & 2.60264402226837 \tabularnewline
21 & 45 & 41.1007002941667 & 3.89929970583329 \tabularnewline
22 & 39 & 41.8779843247323 & -2.87798432473225 \tabularnewline
23 & 25 & 32.1993602724989 & -7.19936027249885 \tabularnewline
24 & 52 & 35.5016020172497 & 16.4983979827503 \tabularnewline
25 & 41 & 35.9692621621004 & 5.03073783789957 \tabularnewline
26 & 38 & 36.172416717223 & 1.82758328277703 \tabularnewline
27 & 41 & 39.3350227419455 & 1.66497725805451 \tabularnewline
28 & 43 & 38.8974022467216 & 4.10259775327838 \tabularnewline
29 & 32 & 25.217520267244 & 6.78247973275601 \tabularnewline
30 & 41 & 41.8452484854944 & -0.845248485494356 \tabularnewline
31 & 46 & 38.8719653207727 & 7.12803467922733 \tabularnewline
32 & 49 & 35.2263160235387 & 13.7736839764613 \tabularnewline
33 & 48 & 40.255867565959 & 7.74413243404101 \tabularnewline
34 & 37 & 24.4187536802022 & 12.5812463197978 \tabularnewline
35 & 39 & 45.3798745861382 & -6.37987458613823 \tabularnewline
36 & 42 & 31.8188237808017 & 10.1811762191983 \tabularnewline
37 & 43 & 50.0644283639931 & -7.06442836399311 \tabularnewline
38 & 36 & 34.0457680864394 & 1.95423191356057 \tabularnewline
39 & 17 & 23.6102886581663 & -6.6102886581663 \tabularnewline
40 & 39 & 46.2743205272006 & -7.27432052720063 \tabularnewline
41 & 39 & 35.8166465614331 & 3.18335343856687 \tabularnewline
42 & 41 & 29.6404353385381 & 11.3595646614619 \tabularnewline
43 & 36 & 32.5243606741183 & 3.4756393258817 \tabularnewline
44 & 42 & 34.5762425889101 & 7.4237574110899 \tabularnewline
45 & 45 & 27.913794906248 & 17.086205093752 \tabularnewline
46 & 41 & 54.0164083816301 & -13.0164083816301 \tabularnewline
47 & 26 & 19.96957065831 & 6.03042934168996 \tabularnewline
48 & 52 & 38.9582685201779 & 13.0417314798221 \tabularnewline
49 & 47 & 53.6218714498342 & -6.62187144983416 \tabularnewline
50 & 45 & 28.8366279402819 & 16.1633720597181 \tabularnewline
51 & 40 & 36.1244162005908 & 3.8755837994092 \tabularnewline
52 & 4 & 12.7258777592742 & -8.72587775927424 \tabularnewline
53 & 44 & 51.0921298021376 & -7.09212980213758 \tabularnewline
54 & 18 & 19.8121222222043 & -1.8121222222043 \tabularnewline
55 & 14 & 20.9982864313035 & -6.99828643130352 \tabularnewline
56 & 37 & 40.1469776800463 & -3.14697768004634 \tabularnewline
57 & 56 & 52.1102256271222 & 3.88977437287785 \tabularnewline
58 & 39 & 55.0745085290141 & -16.0745085290141 \tabularnewline
59 & 42 & 40.9175666130573 & 1.08243338694268 \tabularnewline
60 & 36 & 37.7893797457756 & -1.78937974577555 \tabularnewline
61 & 46 & 56.8472643218241 & -10.8472643218241 \tabularnewline
62 & 28 & 49.6166454133479 & -21.6166454133479 \tabularnewline
63 & 43 & 34.8102433081088 & 8.18975669189118 \tabularnewline
64 & 42 & 40.8877107274866 & 1.11228927251343 \tabularnewline
65 & 37 & 33.1627203691697 & 3.83727963083032 \tabularnewline
66 & 30 & 36.3829749795254 & -6.38297497952544 \tabularnewline
67 & 35 & 39.3629553257843 & -4.36295532578431 \tabularnewline
68 & 44 & 43.5510315180517 & 0.448968481948311 \tabularnewline
69 & 36 & 37.97294427055 & -1.97294427055004 \tabularnewline
70 & 28 & 32.9246382322475 & -4.92463823224746 \tabularnewline
71 & 45 & 40.2636500557842 & 4.73634994421584 \tabularnewline
72 & 23 & 18.1221796670967 & 4.87782033290328 \tabularnewline
73 & 45 & 39.2449876376451 & 5.75501236235491 \tabularnewline
74 & 38 & 28.8846723944085 & 9.11532760559145 \tabularnewline
75 & 38 & 32.638996417553 & 5.36100358244701 \tabularnewline
76 & 45 & 39.5770143286041 & 5.42298567139586 \tabularnewline
77 & 36 & 42.1166768388565 & -6.11667683885655 \tabularnewline
78 & 41 & 45.7359637149048 & -4.73596371490482 \tabularnewline
79 & 38 & 36.5610898020123 & 1.43891019798768 \tabularnewline
80 & 37 & 30.070746491447 & 6.92925350855297 \tabularnewline
81 & 28 & 26.8894166413017 & 1.11058335869833 \tabularnewline
82 & 45 & 62.5839917338587 & -17.5839917338587 \tabularnewline
83 & 26 & 21.1692727951771 & 4.83072720482286 \tabularnewline
84 & 44 & 44.5610333505454 & -0.561033350545382 \tabularnewline
85 & 8 & 17.303952844335 & -9.30395284433501 \tabularnewline
86 & 27 & 21.8800798059754 & 5.11992019402462 \tabularnewline
87 & 36 & 34.3036002779213 & 1.69639972207874 \tabularnewline
88 & 37 & 43.5294760032414 & -6.52947600324137 \tabularnewline
89 & 57 & 41.9105410986667 & 15.0894589013333 \tabularnewline
90 & 45 & 40.1503589767495 & 4.84964102325053 \tabularnewline
91 & 37 & 36.1316639698771 & 0.868336030122884 \tabularnewline
92 & 38 & 42.6662958036895 & -4.66629580368947 \tabularnewline
93 & 31 & 33.8170441605238 & -2.81704416052382 \tabularnewline
94 & 36 & 37.6722288111403 & -1.67222881114027 \tabularnewline
95 & 36 & 42.5294452120737 & -6.52944521207367 \tabularnewline
96 & 36 & 35.4695284211359 & 0.530471578864083 \tabularnewline
97 & 35 & 39.2090775837735 & -4.20907758377353 \tabularnewline
98 & 39 & 42.5504136898295 & -3.55041368982953 \tabularnewline
99 & 65 & 58.647906799208 & 6.35209320079199 \tabularnewline
100 & 30 & 54.944075739205 & -24.944075739205 \tabularnewline
101 & 51 & 47.3160842941235 & 3.68391570587645 \tabularnewline
102 & 41 & 41.5174794218384 & -0.517479421838409 \tabularnewline
103 & 36 & 32.2753364739437 & 3.7246635260563 \tabularnewline
104 & 19 & 18.4435438291414 & 0.556456170858634 \tabularnewline
105 & 23 & 32.4890492244665 & -9.48904922446651 \tabularnewline
106 & 40 & 36.3639841094719 & 3.63601589052814 \tabularnewline
107 & 40 & 31.2665491581367 & 8.73345084186334 \tabularnewline
108 & 40 & 38.4911464770214 & 1.5088535229786 \tabularnewline
109 & 30 & 32.5619788028293 & -2.56197880282926 \tabularnewline
110 & 41 & 43.0988967561294 & -2.0988967561294 \tabularnewline
111 & 40 & 43.8654559661364 & -3.86545596613637 \tabularnewline
112 & 45 & 44.2581774216048 & 0.741822578395182 \tabularnewline
113 & 1 & 13.0074320457559 & -12.0074320457559 \tabularnewline
114 & 40 & 33.2697570342291 & 6.73024296577093 \tabularnewline
115 & 11 & 18.0545506451278 & -7.05455064512778 \tabularnewline
116 & 45 & 30.8400855423014 & 14.1599144576986 \tabularnewline
117 & 38 & 44.2977920395338 & -6.29779203953378 \tabularnewline
118 & 0 & 12.9674491620791 & -12.9674491620791 \tabularnewline
119 & 30 & 34.6825688967259 & -4.68256889672593 \tabularnewline
120 & 8 & 14.2622909639571 & -6.26229096395709 \tabularnewline
121 & 39 & 43.6186913695486 & -4.61869136954856 \tabularnewline
122 & 48 & 40.7742550568246 & 7.22574494317543 \tabularnewline
123 & 48 & 37.2079207138324 & 10.7920792861676 \tabularnewline
124 & 29 & 30.8539634072712 & -1.85396340727124 \tabularnewline
125 & 8 & 12.8822965421128 & -4.88229654211278 \tabularnewline
126 & 43 & 33.9917364306802 & 9.00826356931984 \tabularnewline
127 & 52 & 44.2679805191467 & 7.73201948085334 \tabularnewline
128 & 53 & 32.14756003555 & 20.85243996445 \tabularnewline
129 & 48 & 43.0818203750937 & 4.91817962490635 \tabularnewline
130 & 48 & 43.8255303409289 & 4.17446965907108 \tabularnewline
131 & 50 & 59.9417310790571 & -9.9417310790571 \tabularnewline
132 & 40 & 28.5492868871953 & 11.4507131128047 \tabularnewline
133 & 36 & 23.3566023980491 & 12.6433976019509 \tabularnewline
134 & 40 & 43.5938593033611 & -3.59385930336115 \tabularnewline
135 & 46 & 54.1124698558005 & -8.11246985580047 \tabularnewline
136 & 40 & 37.2066442720808 & 2.79335572791917 \tabularnewline
137 & 46 & 45.6604204344802 & 0.339579565519836 \tabularnewline
138 & 39 & 28.4171207805429 & 10.5828792194571 \tabularnewline
139 & 41 & 35.8002499007719 & 5.1997500992281 \tabularnewline
140 & 46 & 38.1539981953538 & 7.84600180464622 \tabularnewline
141 & 32 & 36.510597655796 & -4.51059765579602 \tabularnewline
142 & 39 & 46.236747272973 & -7.23674727297303 \tabularnewline
143 & 39 & 38.8788316884751 & 0.12116831152493 \tabularnewline
144 & 21 & 29.1715693956171 & -8.17156939561709 \tabularnewline
145 & 45 & 50.9211562450738 & -5.92115624507383 \tabularnewline
146 & 50 & 34.1801325589573 & 15.8198674410427 \tabularnewline
147 & 36 & 38.8527466076321 & -2.85274660763213 \tabularnewline
148 & 44 & 48.1194163106216 & -4.11941631062165 \tabularnewline
149 & 0 & 12.9248348520323 & -12.9248348520323 \tabularnewline
150 & 0 & 12.2773514838768 & -12.2773514838768 \tabularnewline
151 & 0 & 10.9339189187629 & -10.9339189187629 \tabularnewline
152 & 0 & 10.9940078526291 & -10.9940078526291 \tabularnewline
153 & 0 & 11.0868272674292 & -11.0868272674292 \tabularnewline
154 & 0 & 10.8604909962911 & -10.8604909962911 \tabularnewline
155 & 37 & 35.7691376539142 & 1.23086234608577 \tabularnewline
156 & 47 & 47.9706343849294 & -0.970634384929408 \tabularnewline
157 & 0 & 10.8604909962911 & -10.8604909962911 \tabularnewline
158 & 0 & 10.9259322265413 & -10.9259322265413 \tabularnewline
159 & 0 & 11.2173316900449 & -11.2173316900449 \tabularnewline
160 & 5 & 12.4792969767016 & -7.47929697670156 \tabularnewline
161 & 1 & 11.8822569341714 & -10.8822569341714 \tabularnewline
162 & 43 & 25.7353195896662 & 17.2646804103338 \tabularnewline
163 & 0 & 11.3079104766791 & -11.3079104766791 \tabularnewline
164 & 32 & 34.5352349230194 & -2.53523492301942 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156275&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]38[/C][C]43.6385625676301[/C][C]-5.63856256763008[/C][/ROW]
[ROW][C]2[/C][C]34[/C][C]31.6351619506911[/C][C]2.36483804930894[/C][/ROW]
[ROW][C]3[/C][C]42[/C][C]37.6474863239491[/C][C]4.35251367605088[/C][/ROW]
[ROW][C]4[/C][C]38[/C][C]32.2459643527603[/C][C]5.75403564723966[/C][/ROW]
[ROW][C]5[/C][C]27[/C][C]27.7997134572886[/C][C]-0.799713457288583[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]22.837308806439[/C][C]12.162691193561[/C][/ROW]
[ROW][C]7[/C][C]33[/C][C]44.2026333288995[/C][C]-11.2026333288995[/C][/ROW]
[ROW][C]8[/C][C]18[/C][C]15.6732312223377[/C][C]2.3267687776623[/C][/ROW]
[ROW][C]9[/C][C]34[/C][C]37.1222950719477[/C][C]-3.12229507194773[/C][/ROW]
[ROW][C]10[/C][C]33[/C][C]32.4703761537804[/C][C]0.529623846219565[/C][/ROW]
[ROW][C]11[/C][C]44[/C][C]36.9587947054051[/C][C]7.04120529459494[/C][/ROW]
[ROW][C]12[/C][C]55[/C][C]39.3075285280211[/C][C]15.6924714719789[/C][/ROW]
[ROW][C]13[/C][C]37[/C][C]29.1405122797153[/C][C]7.85948772028474[/C][/ROW]
[ROW][C]14[/C][C]52[/C][C]42.4574632161526[/C][C]9.54253678384737[/C][/ROW]
[ROW][C]15[/C][C]43[/C][C]37.2148593791571[/C][C]5.78514062084291[/C][/ROW]
[ROW][C]16[/C][C]59[/C][C]53.1295876812285[/C][C]5.87041231877147[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]45.2659493338014[/C][C]-9.26594933380137[/C][/ROW]
[ROW][C]18[/C][C]39[/C][C]41.9672755285711[/C][C]-2.96727552857112[/C][/ROW]
[ROW][C]19[/C][C]29[/C][C]33.0788657288988[/C][C]-4.07886572889881[/C][/ROW]
[ROW][C]20[/C][C]49[/C][C]46.3973559777316[/C][C]2.60264402226837[/C][/ROW]
[ROW][C]21[/C][C]45[/C][C]41.1007002941667[/C][C]3.89929970583329[/C][/ROW]
[ROW][C]22[/C][C]39[/C][C]41.8779843247323[/C][C]-2.87798432473225[/C][/ROW]
[ROW][C]23[/C][C]25[/C][C]32.1993602724989[/C][C]-7.19936027249885[/C][/ROW]
[ROW][C]24[/C][C]52[/C][C]35.5016020172497[/C][C]16.4983979827503[/C][/ROW]
[ROW][C]25[/C][C]41[/C][C]35.9692621621004[/C][C]5.03073783789957[/C][/ROW]
[ROW][C]26[/C][C]38[/C][C]36.172416717223[/C][C]1.82758328277703[/C][/ROW]
[ROW][C]27[/C][C]41[/C][C]39.3350227419455[/C][C]1.66497725805451[/C][/ROW]
[ROW][C]28[/C][C]43[/C][C]38.8974022467216[/C][C]4.10259775327838[/C][/ROW]
[ROW][C]29[/C][C]32[/C][C]25.217520267244[/C][C]6.78247973275601[/C][/ROW]
[ROW][C]30[/C][C]41[/C][C]41.8452484854944[/C][C]-0.845248485494356[/C][/ROW]
[ROW][C]31[/C][C]46[/C][C]38.8719653207727[/C][C]7.12803467922733[/C][/ROW]
[ROW][C]32[/C][C]49[/C][C]35.2263160235387[/C][C]13.7736839764613[/C][/ROW]
[ROW][C]33[/C][C]48[/C][C]40.255867565959[/C][C]7.74413243404101[/C][/ROW]
[ROW][C]34[/C][C]37[/C][C]24.4187536802022[/C][C]12.5812463197978[/C][/ROW]
[ROW][C]35[/C][C]39[/C][C]45.3798745861382[/C][C]-6.37987458613823[/C][/ROW]
[ROW][C]36[/C][C]42[/C][C]31.8188237808017[/C][C]10.1811762191983[/C][/ROW]
[ROW][C]37[/C][C]43[/C][C]50.0644283639931[/C][C]-7.06442836399311[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]34.0457680864394[/C][C]1.95423191356057[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]23.6102886581663[/C][C]-6.6102886581663[/C][/ROW]
[ROW][C]40[/C][C]39[/C][C]46.2743205272006[/C][C]-7.27432052720063[/C][/ROW]
[ROW][C]41[/C][C]39[/C][C]35.8166465614331[/C][C]3.18335343856687[/C][/ROW]
[ROW][C]42[/C][C]41[/C][C]29.6404353385381[/C][C]11.3595646614619[/C][/ROW]
[ROW][C]43[/C][C]36[/C][C]32.5243606741183[/C][C]3.4756393258817[/C][/ROW]
[ROW][C]44[/C][C]42[/C][C]34.5762425889101[/C][C]7.4237574110899[/C][/ROW]
[ROW][C]45[/C][C]45[/C][C]27.913794906248[/C][C]17.086205093752[/C][/ROW]
[ROW][C]46[/C][C]41[/C][C]54.0164083816301[/C][C]-13.0164083816301[/C][/ROW]
[ROW][C]47[/C][C]26[/C][C]19.96957065831[/C][C]6.03042934168996[/C][/ROW]
[ROW][C]48[/C][C]52[/C][C]38.9582685201779[/C][C]13.0417314798221[/C][/ROW]
[ROW][C]49[/C][C]47[/C][C]53.6218714498342[/C][C]-6.62187144983416[/C][/ROW]
[ROW][C]50[/C][C]45[/C][C]28.8366279402819[/C][C]16.1633720597181[/C][/ROW]
[ROW][C]51[/C][C]40[/C][C]36.1244162005908[/C][C]3.8755837994092[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]12.7258777592742[/C][C]-8.72587775927424[/C][/ROW]
[ROW][C]53[/C][C]44[/C][C]51.0921298021376[/C][C]-7.09212980213758[/C][/ROW]
[ROW][C]54[/C][C]18[/C][C]19.8121222222043[/C][C]-1.8121222222043[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]20.9982864313035[/C][C]-6.99828643130352[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]40.1469776800463[/C][C]-3.14697768004634[/C][/ROW]
[ROW][C]57[/C][C]56[/C][C]52.1102256271222[/C][C]3.88977437287785[/C][/ROW]
[ROW][C]58[/C][C]39[/C][C]55.0745085290141[/C][C]-16.0745085290141[/C][/ROW]
[ROW][C]59[/C][C]42[/C][C]40.9175666130573[/C][C]1.08243338694268[/C][/ROW]
[ROW][C]60[/C][C]36[/C][C]37.7893797457756[/C][C]-1.78937974577555[/C][/ROW]
[ROW][C]61[/C][C]46[/C][C]56.8472643218241[/C][C]-10.8472643218241[/C][/ROW]
[ROW][C]62[/C][C]28[/C][C]49.6166454133479[/C][C]-21.6166454133479[/C][/ROW]
[ROW][C]63[/C][C]43[/C][C]34.8102433081088[/C][C]8.18975669189118[/C][/ROW]
[ROW][C]64[/C][C]42[/C][C]40.8877107274866[/C][C]1.11228927251343[/C][/ROW]
[ROW][C]65[/C][C]37[/C][C]33.1627203691697[/C][C]3.83727963083032[/C][/ROW]
[ROW][C]66[/C][C]30[/C][C]36.3829749795254[/C][C]-6.38297497952544[/C][/ROW]
[ROW][C]67[/C][C]35[/C][C]39.3629553257843[/C][C]-4.36295532578431[/C][/ROW]
[ROW][C]68[/C][C]44[/C][C]43.5510315180517[/C][C]0.448968481948311[/C][/ROW]
[ROW][C]69[/C][C]36[/C][C]37.97294427055[/C][C]-1.97294427055004[/C][/ROW]
[ROW][C]70[/C][C]28[/C][C]32.9246382322475[/C][C]-4.92463823224746[/C][/ROW]
[ROW][C]71[/C][C]45[/C][C]40.2636500557842[/C][C]4.73634994421584[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]18.1221796670967[/C][C]4.87782033290328[/C][/ROW]
[ROW][C]73[/C][C]45[/C][C]39.2449876376451[/C][C]5.75501236235491[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]28.8846723944085[/C][C]9.11532760559145[/C][/ROW]
[ROW][C]75[/C][C]38[/C][C]32.638996417553[/C][C]5.36100358244701[/C][/ROW]
[ROW][C]76[/C][C]45[/C][C]39.5770143286041[/C][C]5.42298567139586[/C][/ROW]
[ROW][C]77[/C][C]36[/C][C]42.1166768388565[/C][C]-6.11667683885655[/C][/ROW]
[ROW][C]78[/C][C]41[/C][C]45.7359637149048[/C][C]-4.73596371490482[/C][/ROW]
[ROW][C]79[/C][C]38[/C][C]36.5610898020123[/C][C]1.43891019798768[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]30.070746491447[/C][C]6.92925350855297[/C][/ROW]
[ROW][C]81[/C][C]28[/C][C]26.8894166413017[/C][C]1.11058335869833[/C][/ROW]
[ROW][C]82[/C][C]45[/C][C]62.5839917338587[/C][C]-17.5839917338587[/C][/ROW]
[ROW][C]83[/C][C]26[/C][C]21.1692727951771[/C][C]4.83072720482286[/C][/ROW]
[ROW][C]84[/C][C]44[/C][C]44.5610333505454[/C][C]-0.561033350545382[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]17.303952844335[/C][C]-9.30395284433501[/C][/ROW]
[ROW][C]86[/C][C]27[/C][C]21.8800798059754[/C][C]5.11992019402462[/C][/ROW]
[ROW][C]87[/C][C]36[/C][C]34.3036002779213[/C][C]1.69639972207874[/C][/ROW]
[ROW][C]88[/C][C]37[/C][C]43.5294760032414[/C][C]-6.52947600324137[/C][/ROW]
[ROW][C]89[/C][C]57[/C][C]41.9105410986667[/C][C]15.0894589013333[/C][/ROW]
[ROW][C]90[/C][C]45[/C][C]40.1503589767495[/C][C]4.84964102325053[/C][/ROW]
[ROW][C]91[/C][C]37[/C][C]36.1316639698771[/C][C]0.868336030122884[/C][/ROW]
[ROW][C]92[/C][C]38[/C][C]42.6662958036895[/C][C]-4.66629580368947[/C][/ROW]
[ROW][C]93[/C][C]31[/C][C]33.8170441605238[/C][C]-2.81704416052382[/C][/ROW]
[ROW][C]94[/C][C]36[/C][C]37.6722288111403[/C][C]-1.67222881114027[/C][/ROW]
[ROW][C]95[/C][C]36[/C][C]42.5294452120737[/C][C]-6.52944521207367[/C][/ROW]
[ROW][C]96[/C][C]36[/C][C]35.4695284211359[/C][C]0.530471578864083[/C][/ROW]
[ROW][C]97[/C][C]35[/C][C]39.2090775837735[/C][C]-4.20907758377353[/C][/ROW]
[ROW][C]98[/C][C]39[/C][C]42.5504136898295[/C][C]-3.55041368982953[/C][/ROW]
[ROW][C]99[/C][C]65[/C][C]58.647906799208[/C][C]6.35209320079199[/C][/ROW]
[ROW][C]100[/C][C]30[/C][C]54.944075739205[/C][C]-24.944075739205[/C][/ROW]
[ROW][C]101[/C][C]51[/C][C]47.3160842941235[/C][C]3.68391570587645[/C][/ROW]
[ROW][C]102[/C][C]41[/C][C]41.5174794218384[/C][C]-0.517479421838409[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]32.2753364739437[/C][C]3.7246635260563[/C][/ROW]
[ROW][C]104[/C][C]19[/C][C]18.4435438291414[/C][C]0.556456170858634[/C][/ROW]
[ROW][C]105[/C][C]23[/C][C]32.4890492244665[/C][C]-9.48904922446651[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]36.3639841094719[/C][C]3.63601589052814[/C][/ROW]
[ROW][C]107[/C][C]40[/C][C]31.2665491581367[/C][C]8.73345084186334[/C][/ROW]
[ROW][C]108[/C][C]40[/C][C]38.4911464770214[/C][C]1.5088535229786[/C][/ROW]
[ROW][C]109[/C][C]30[/C][C]32.5619788028293[/C][C]-2.56197880282926[/C][/ROW]
[ROW][C]110[/C][C]41[/C][C]43.0988967561294[/C][C]-2.0988967561294[/C][/ROW]
[ROW][C]111[/C][C]40[/C][C]43.8654559661364[/C][C]-3.86545596613637[/C][/ROW]
[ROW][C]112[/C][C]45[/C][C]44.2581774216048[/C][C]0.741822578395182[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]13.0074320457559[/C][C]-12.0074320457559[/C][/ROW]
[ROW][C]114[/C][C]40[/C][C]33.2697570342291[/C][C]6.73024296577093[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]18.0545506451278[/C][C]-7.05455064512778[/C][/ROW]
[ROW][C]116[/C][C]45[/C][C]30.8400855423014[/C][C]14.1599144576986[/C][/ROW]
[ROW][C]117[/C][C]38[/C][C]44.2977920395338[/C][C]-6.29779203953378[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]12.9674491620791[/C][C]-12.9674491620791[/C][/ROW]
[ROW][C]119[/C][C]30[/C][C]34.6825688967259[/C][C]-4.68256889672593[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]14.2622909639571[/C][C]-6.26229096395709[/C][/ROW]
[ROW][C]121[/C][C]39[/C][C]43.6186913695486[/C][C]-4.61869136954856[/C][/ROW]
[ROW][C]122[/C][C]48[/C][C]40.7742550568246[/C][C]7.22574494317543[/C][/ROW]
[ROW][C]123[/C][C]48[/C][C]37.2079207138324[/C][C]10.7920792861676[/C][/ROW]
[ROW][C]124[/C][C]29[/C][C]30.8539634072712[/C][C]-1.85396340727124[/C][/ROW]
[ROW][C]125[/C][C]8[/C][C]12.8822965421128[/C][C]-4.88229654211278[/C][/ROW]
[ROW][C]126[/C][C]43[/C][C]33.9917364306802[/C][C]9.00826356931984[/C][/ROW]
[ROW][C]127[/C][C]52[/C][C]44.2679805191467[/C][C]7.73201948085334[/C][/ROW]
[ROW][C]128[/C][C]53[/C][C]32.14756003555[/C][C]20.85243996445[/C][/ROW]
[ROW][C]129[/C][C]48[/C][C]43.0818203750937[/C][C]4.91817962490635[/C][/ROW]
[ROW][C]130[/C][C]48[/C][C]43.8255303409289[/C][C]4.17446965907108[/C][/ROW]
[ROW][C]131[/C][C]50[/C][C]59.9417310790571[/C][C]-9.9417310790571[/C][/ROW]
[ROW][C]132[/C][C]40[/C][C]28.5492868871953[/C][C]11.4507131128047[/C][/ROW]
[ROW][C]133[/C][C]36[/C][C]23.3566023980491[/C][C]12.6433976019509[/C][/ROW]
[ROW][C]134[/C][C]40[/C][C]43.5938593033611[/C][C]-3.59385930336115[/C][/ROW]
[ROW][C]135[/C][C]46[/C][C]54.1124698558005[/C][C]-8.11246985580047[/C][/ROW]
[ROW][C]136[/C][C]40[/C][C]37.2066442720808[/C][C]2.79335572791917[/C][/ROW]
[ROW][C]137[/C][C]46[/C][C]45.6604204344802[/C][C]0.339579565519836[/C][/ROW]
[ROW][C]138[/C][C]39[/C][C]28.4171207805429[/C][C]10.5828792194571[/C][/ROW]
[ROW][C]139[/C][C]41[/C][C]35.8002499007719[/C][C]5.1997500992281[/C][/ROW]
[ROW][C]140[/C][C]46[/C][C]38.1539981953538[/C][C]7.84600180464622[/C][/ROW]
[ROW][C]141[/C][C]32[/C][C]36.510597655796[/C][C]-4.51059765579602[/C][/ROW]
[ROW][C]142[/C][C]39[/C][C]46.236747272973[/C][C]-7.23674727297303[/C][/ROW]
[ROW][C]143[/C][C]39[/C][C]38.8788316884751[/C][C]0.12116831152493[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]29.1715693956171[/C][C]-8.17156939561709[/C][/ROW]
[ROW][C]145[/C][C]45[/C][C]50.9211562450738[/C][C]-5.92115624507383[/C][/ROW]
[ROW][C]146[/C][C]50[/C][C]34.1801325589573[/C][C]15.8198674410427[/C][/ROW]
[ROW][C]147[/C][C]36[/C][C]38.8527466076321[/C][C]-2.85274660763213[/C][/ROW]
[ROW][C]148[/C][C]44[/C][C]48.1194163106216[/C][C]-4.11941631062165[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]12.9248348520323[/C][C]-12.9248348520323[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]12.2773514838768[/C][C]-12.2773514838768[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]10.9339189187629[/C][C]-10.9339189187629[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]10.9940078526291[/C][C]-10.9940078526291[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]11.0868272674292[/C][C]-11.0868272674292[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]10.8604909962911[/C][C]-10.8604909962911[/C][/ROW]
[ROW][C]155[/C][C]37[/C][C]35.7691376539142[/C][C]1.23086234608577[/C][/ROW]
[ROW][C]156[/C][C]47[/C][C]47.9706343849294[/C][C]-0.970634384929408[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]10.8604909962911[/C][C]-10.8604909962911[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]10.9259322265413[/C][C]-10.9259322265413[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]11.2173316900449[/C][C]-11.2173316900449[/C][/ROW]
[ROW][C]160[/C][C]5[/C][C]12.4792969767016[/C][C]-7.47929697670156[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]11.8822569341714[/C][C]-10.8822569341714[/C][/ROW]
[ROW][C]162[/C][C]43[/C][C]25.7353195896662[/C][C]17.2646804103338[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]11.3079104766791[/C][C]-11.3079104766791[/C][/ROW]
[ROW][C]164[/C][C]32[/C][C]34.5352349230194[/C][C]-2.53523492301942[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156275&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156275&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13843.6385625676301-5.63856256763008
23431.63516195069112.36483804930894
34237.64748632394914.35251367605088
43832.24596435276035.75403564723966
52727.7997134572886-0.799713457288583
63522.83730880643912.162691193561
73344.2026333288995-11.2026333288995
81815.67323122233772.3267687776623
93437.1222950719477-3.12229507194773
103332.47037615378040.529623846219565
114436.95879470540517.04120529459494
125539.307528528021115.6924714719789
133729.14051227971537.85948772028474
145242.45746321615269.54253678384737
154337.21485937915715.78514062084291
165953.12958768122855.87041231877147
173645.2659493338014-9.26594933380137
183941.9672755285711-2.96727552857112
192933.0788657288988-4.07886572889881
204946.39735597773162.60264402226837
214541.10070029416673.89929970583329
223941.8779843247323-2.87798432473225
232532.1993602724989-7.19936027249885
245235.501602017249716.4983979827503
254135.96926216210045.03073783789957
263836.1724167172231.82758328277703
274139.33502274194551.66497725805451
284338.89740224672164.10259775327838
293225.2175202672446.78247973275601
304141.8452484854944-0.845248485494356
314638.87196532077277.12803467922733
324935.226316023538713.7736839764613
334840.2558675659597.74413243404101
343724.418753680202212.5812463197978
353945.3798745861382-6.37987458613823
364231.818823780801710.1811762191983
374350.0644283639931-7.06442836399311
383634.04576808643941.95423191356057
391723.6102886581663-6.6102886581663
403946.2743205272006-7.27432052720063
413935.81664656143313.18335343856687
424129.640435338538111.3595646614619
433632.52436067411833.4756393258817
444234.57624258891017.4237574110899
454527.91379490624817.086205093752
464154.0164083816301-13.0164083816301
472619.969570658316.03042934168996
485238.958268520177913.0417314798221
494753.6218714498342-6.62187144983416
504528.836627940281916.1633720597181
514036.12441620059083.8755837994092
52412.7258777592742-8.72587775927424
534451.0921298021376-7.09212980213758
541819.8121222222043-1.8121222222043
551420.9982864313035-6.99828643130352
563740.1469776800463-3.14697768004634
575652.11022562712223.88977437287785
583955.0745085290141-16.0745085290141
594240.91756661305731.08243338694268
603637.7893797457756-1.78937974577555
614656.8472643218241-10.8472643218241
622849.6166454133479-21.6166454133479
634334.81024330810888.18975669189118
644240.88771072748661.11228927251343
653733.16272036916973.83727963083032
663036.3829749795254-6.38297497952544
673539.3629553257843-4.36295532578431
684443.55103151805170.448968481948311
693637.97294427055-1.97294427055004
702832.9246382322475-4.92463823224746
714540.26365005578424.73634994421584
722318.12217966709674.87782033290328
734539.24498763764515.75501236235491
743828.88467239440859.11532760559145
753832.6389964175535.36100358244701
764539.57701432860415.42298567139586
773642.1166768388565-6.11667683885655
784145.7359637149048-4.73596371490482
793836.56108980201231.43891019798768
803730.0707464914476.92925350855297
812826.88941664130171.11058335869833
824562.5839917338587-17.5839917338587
832621.16927279517714.83072720482286
844444.5610333505454-0.561033350545382
85817.303952844335-9.30395284433501
862721.88007980597545.11992019402462
873634.30360027792131.69639972207874
883743.5294760032414-6.52947600324137
895741.910541098666715.0894589013333
904540.15035897674954.84964102325053
913736.13166396987710.868336030122884
923842.6662958036895-4.66629580368947
933133.8170441605238-2.81704416052382
943637.6722288111403-1.67222881114027
953642.5294452120737-6.52944521207367
963635.46952842113590.530471578864083
973539.2090775837735-4.20907758377353
983942.5504136898295-3.55041368982953
996558.6479067992086.35209320079199
1003054.944075739205-24.944075739205
1015147.31608429412353.68391570587645
1024141.5174794218384-0.517479421838409
1033632.27533647394373.7246635260563
1041918.44354382914140.556456170858634
1052332.4890492244665-9.48904922446651
1064036.36398410947193.63601589052814
1074031.26654915813678.73345084186334
1084038.49114647702141.5088535229786
1093032.5619788028293-2.56197880282926
1104143.0988967561294-2.0988967561294
1114043.8654559661364-3.86545596613637
1124544.25817742160480.741822578395182
113113.0074320457559-12.0074320457559
1144033.26975703422916.73024296577093
1151118.0545506451278-7.05455064512778
1164530.840085542301414.1599144576986
1173844.2977920395338-6.29779203953378
118012.9674491620791-12.9674491620791
1193034.6825688967259-4.68256889672593
120814.2622909639571-6.26229096395709
1213943.6186913695486-4.61869136954856
1224840.77425505682467.22574494317543
1234837.207920713832410.7920792861676
1242930.8539634072712-1.85396340727124
125812.8822965421128-4.88229654211278
1264333.99173643068029.00826356931984
1275244.26798051914677.73201948085334
1285332.1475600355520.85243996445
1294843.08182037509374.91817962490635
1304843.82553034092894.17446965907108
1315059.9417310790571-9.9417310790571
1324028.549286887195311.4507131128047
1333623.356602398049112.6433976019509
1344043.5938593033611-3.59385930336115
1354654.1124698558005-8.11246985580047
1364037.20664427208082.79335572791917
1374645.66042043448020.339579565519836
1383928.417120780542910.5828792194571
1394135.80024990077195.1997500992281
1404638.15399819535387.84600180464622
1413236.510597655796-4.51059765579602
1423946.236747272973-7.23674727297303
1433938.87883168847510.12116831152493
1442129.1715693956171-8.17156939561709
1454550.9211562450738-5.92115624507383
1465034.180132558957315.8198674410427
1473638.8527466076321-2.85274660763213
1484448.1194163106216-4.11941631062165
149012.9248348520323-12.9248348520323
150012.2773514838768-12.2773514838768
151010.9339189187629-10.9339189187629
152010.9940078526291-10.9940078526291
153011.0868272674292-11.0868272674292
154010.8604909962911-10.8604909962911
1553735.76913765391421.23086234608577
1564747.9706343849294-0.970634384929408
157010.8604909962911-10.8604909962911
158010.9259322265413-10.9259322265413
159011.2173316900449-11.2173316900449
160512.4792969767016-7.47929697670156
161111.8822569341714-10.8822569341714
1624325.735319589666217.2646804103338
163011.3079104766791-11.3079104766791
1643234.5352349230194-2.53523492301942







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.3987798205255890.7975596410511780.601220179474411
120.5008549400408220.9982901199183560.499145059959178
130.3586888520378790.7173777040757580.641311147962121
140.3774576953461120.7549153906922230.622542304653888
150.296025839102750.5920516782054990.70397416089725
160.2122590066619960.4245180133239920.787740993338004
170.1479918512920690.2959837025841390.852008148707931
180.1063883348174970.2127766696349940.893611665182503
190.06675006251328240.1335001250265650.933249937486718
200.04129764633709610.08259529267419210.958702353662904
210.02534625805418470.05069251610836950.974653741945815
220.01525210457599340.03050420915198690.984747895424007
230.02871791245819460.05743582491638920.971282087541805
240.08716005216447850.1743201043289570.912839947835521
250.08820606283947770.1764121256789550.911793937160522
260.06105735120480090.1221147024096020.938942648795199
270.04521975963311490.09043951926622980.954780240366885
280.03178994827131380.06357989654262770.968210051728686
290.02150353677927810.04300707355855610.978496463220722
300.01407761350322380.02815522700644770.985922386496776
310.0215608284189950.04312165683799010.978439171581005
320.02055863167348270.04111726334696540.979441368326517
330.02237127519480270.04474255038960540.977628724805197
340.03061014359486120.06122028718972250.969389856405139
350.03546619135542810.07093238271085630.964533808644572
360.03292988488518450.0658597697703690.967070115114815
370.03005490177859240.06010980355718490.969945098221408
380.02248693954863630.04497387909727250.977513060451364
390.04143433312248350.08286866624496710.958565666877516
400.03849952130167660.07699904260335320.961500478698323
410.0278532334371010.0557064668742020.972146766562899
420.02386747522203950.04773495044407910.97613252477796
430.01731101984849220.03462203969698440.982688980151508
440.01323335113808860.02646670227617730.986766648861911
450.02722108383181330.05444216766362660.972778916168187
460.05950758132206070.1190151626441210.940492418677939
470.04815919136608950.09631838273217910.95184080863391
480.06021793775665030.1204358755133010.93978206224335
490.0661450395233740.1322900790467480.933854960476626
500.08744315069236990.174886301384740.91255684930763
510.07085178020634570.1417035604126910.929148219793654
520.1914128998399440.3828257996798880.808587100160056
530.1693303078610310.3386606157220620.830669692138969
540.1734551171626710.3469102343253420.826544882837329
550.2171526622034990.4343053244069980.782847337796501
560.1978222095669110.3956444191338230.802177790433089
570.1771448770432960.3542897540865920.822855122956704
580.3890289405322950.778057881064590.610971059467705
590.3459223033298690.6918446066597370.654077696670131
600.3124964229873560.6249928459747130.687503577012644
610.3278449926103260.6556899852206520.672155007389674
620.6529848089863330.6940303820273330.347015191013667
630.6622089631196440.6755820737607120.337791036880356
640.6257523732755320.7484952534489360.374247626724468
650.5887439303395720.8225121393208570.411256069660428
660.5841991025749250.831601794850150.415800897425075
670.5535019922142660.8929960155714670.446498007785734
680.5065953523103510.9868092953792980.493404647689649
690.4630543288592350.926108657718470.536945671140765
700.4427556165977330.8855112331954660.557244383402267
710.4268138375414060.8536276750828120.573186162458594
720.4116577205989640.8233154411979280.588342279401036
730.4044268932615240.8088537865230480.595573106738476
740.3967333568472550.793466713694510.603266643152745
750.3700586087358980.7401172174717960.629941391264102
760.3579401142620650.7158802285241310.642059885737934
770.3284968303327390.6569936606654790.671503169667261
780.2911640748788830.5823281497577660.708835925121117
790.2591253352706270.5182506705412550.740874664729373
800.245018672061750.4900373441235010.75498132793825
810.2265954542924710.4531909085849410.773404545707529
820.3470388597654920.6940777195309830.652961140234508
830.3693250895586160.7386501791172310.630674910441384
840.3265555480690610.6531110961381220.673444451930939
850.3895122080318270.7790244160636530.610487791968173
860.3454942723775850.690988544755170.654505727622415
870.3042142291173540.6084284582347080.695785770882646
880.5270859312728470.9458281374543060.472914068727153
890.6676971481537540.6646057036924930.332302851846246
900.6606526910853640.6786946178292730.339347308914636
910.6366925709357490.7266148581285010.363307429064251
920.6045620493581190.7908759012837630.395437950641881
930.5721812903965680.8556374192068640.427818709603432
940.5348898797003850.9302202405992310.465110120299615
950.5989263011401640.8021473977196730.401073698859836
960.5524585981438850.895082803712230.447541401856115
970.5347890622674960.9304218754650070.465210937732504
980.5002346247904540.9995307504190920.499765375209546
990.5310797261959490.9378405476081020.468920273804051
1000.7649971687818980.4700056624362030.235002831218101
1010.7519312792390780.4961374415218440.248068720760922
1020.7300310136059810.5399379727880370.269968986394019
1030.6880899753063770.6238200493872470.311910024693623
1040.654142015958480.691715968083040.34585798404152
1050.6865816970725580.6268366058548830.313418302927442
1060.6477954335672960.7044091328654080.352204566432704
1070.6449666727951180.7100666544097640.355033327204882
1080.6498079234226960.7003841531546080.350192076577304
1090.6166519213046680.7666961573906640.383348078695332
1100.584605319635030.830789360729940.41539468036497
1110.5435602477825330.9128795044349340.456439752217467
1120.497881112767460.9957622255349190.50211888723254
1130.5702674541419780.8594650917160430.429732545858022
1140.6562313676079140.6875372647841720.343768632392086
1150.6520108530634350.6959782938731290.347989146936565
1160.691769086421770.6164618271564590.30823091357823
1170.6528863706812830.6942272586374340.347113629318717
1180.6920994994017060.6158010011965870.307900500598294
1190.656806611584520.6863867768309610.34319338841548
1200.6312544362468450.7374911275063090.368745563753155
1210.5981399636445620.8037200727108770.401860036355438
1220.583404855155840.8331902896883210.41659514484416
1230.5642971408142020.8714057183715970.435702859185798
1240.5095609485058890.9808781029882230.490439051494111
1250.4833637843326930.9667275686653860.516636215667307
1260.4541805958564330.9083611917128660.545819404143567
1270.4224672012238260.8449344024476520.577532798776174
1280.6725480992499470.6549038015001060.327451900750053
1290.6316769048913050.736646190217390.368323095108695
1300.5769426048763780.8461147902472430.423057395123622
1310.8034047333742460.3931905332515080.196595266625754
1320.8637477211953670.2725045576092670.136252278804633
1330.9367692523713080.1264614952573840.0632307476286918
1340.9517405337933180.09651893241336490.0482594662066824
1350.9893200132996570.02135997340068530.0106799867003426
1360.9928552514442160.01428949711156710.00714474855578354
1370.9883578924710090.02328421505798160.0116421075289908
1380.9972872331111440.005425533777711830.00271276688885592
1390.9970638960525920.005872207894815160.00293610394740758
1400.9993030842486570.001393831502685670.000696915751342833
1410.9997269677452320.0005460645095365590.00027303225476828
1420.9998950727746140.0002098544507718190.00010492722538591
1430.9999491124141710.0001017751716585385.08875858292692e-05
1440.9998782967745230.0002434064509532820.000121703225476641
1450.999834131637540.0003317367249205960.000165868362460298
1460.9999963600626047.27987479212712e-063.63993739606356e-06
1470.9999969287041546.14259169160122e-063.07129584580061e-06
1480.9999999978056864.38862778199962e-092.19431389099981e-09
1490.9999999684407096.31185824206694e-083.15592912103347e-08
1500.9999995396892349.2062153253083e-074.60310766265415e-07
1510.9999933949666881.32100666237455e-056.60503331187277e-06
1520.9999136119189240.0001727761621512628.63880810756309e-05
1530.9990171541233760.00196569175324880.000982845876624402

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.398779820525589 & 0.797559641051178 & 0.601220179474411 \tabularnewline
12 & 0.500854940040822 & 0.998290119918356 & 0.499145059959178 \tabularnewline
13 & 0.358688852037879 & 0.717377704075758 & 0.641311147962121 \tabularnewline
14 & 0.377457695346112 & 0.754915390692223 & 0.622542304653888 \tabularnewline
15 & 0.29602583910275 & 0.592051678205499 & 0.70397416089725 \tabularnewline
16 & 0.212259006661996 & 0.424518013323992 & 0.787740993338004 \tabularnewline
17 & 0.147991851292069 & 0.295983702584139 & 0.852008148707931 \tabularnewline
18 & 0.106388334817497 & 0.212776669634994 & 0.893611665182503 \tabularnewline
19 & 0.0667500625132824 & 0.133500125026565 & 0.933249937486718 \tabularnewline
20 & 0.0412976463370961 & 0.0825952926741921 & 0.958702353662904 \tabularnewline
21 & 0.0253462580541847 & 0.0506925161083695 & 0.974653741945815 \tabularnewline
22 & 0.0152521045759934 & 0.0305042091519869 & 0.984747895424007 \tabularnewline
23 & 0.0287179124581946 & 0.0574358249163892 & 0.971282087541805 \tabularnewline
24 & 0.0871600521644785 & 0.174320104328957 & 0.912839947835521 \tabularnewline
25 & 0.0882060628394777 & 0.176412125678955 & 0.911793937160522 \tabularnewline
26 & 0.0610573512048009 & 0.122114702409602 & 0.938942648795199 \tabularnewline
27 & 0.0452197596331149 & 0.0904395192662298 & 0.954780240366885 \tabularnewline
28 & 0.0317899482713138 & 0.0635798965426277 & 0.968210051728686 \tabularnewline
29 & 0.0215035367792781 & 0.0430070735585561 & 0.978496463220722 \tabularnewline
30 & 0.0140776135032238 & 0.0281552270064477 & 0.985922386496776 \tabularnewline
31 & 0.021560828418995 & 0.0431216568379901 & 0.978439171581005 \tabularnewline
32 & 0.0205586316734827 & 0.0411172633469654 & 0.979441368326517 \tabularnewline
33 & 0.0223712751948027 & 0.0447425503896054 & 0.977628724805197 \tabularnewline
34 & 0.0306101435948612 & 0.0612202871897225 & 0.969389856405139 \tabularnewline
35 & 0.0354661913554281 & 0.0709323827108563 & 0.964533808644572 \tabularnewline
36 & 0.0329298848851845 & 0.065859769770369 & 0.967070115114815 \tabularnewline
37 & 0.0300549017785924 & 0.0601098035571849 & 0.969945098221408 \tabularnewline
38 & 0.0224869395486363 & 0.0449738790972725 & 0.977513060451364 \tabularnewline
39 & 0.0414343331224835 & 0.0828686662449671 & 0.958565666877516 \tabularnewline
40 & 0.0384995213016766 & 0.0769990426033532 & 0.961500478698323 \tabularnewline
41 & 0.027853233437101 & 0.055706466874202 & 0.972146766562899 \tabularnewline
42 & 0.0238674752220395 & 0.0477349504440791 & 0.97613252477796 \tabularnewline
43 & 0.0173110198484922 & 0.0346220396969844 & 0.982688980151508 \tabularnewline
44 & 0.0132333511380886 & 0.0264667022761773 & 0.986766648861911 \tabularnewline
45 & 0.0272210838318133 & 0.0544421676636266 & 0.972778916168187 \tabularnewline
46 & 0.0595075813220607 & 0.119015162644121 & 0.940492418677939 \tabularnewline
47 & 0.0481591913660895 & 0.0963183827321791 & 0.95184080863391 \tabularnewline
48 & 0.0602179377566503 & 0.120435875513301 & 0.93978206224335 \tabularnewline
49 & 0.066145039523374 & 0.132290079046748 & 0.933854960476626 \tabularnewline
50 & 0.0874431506923699 & 0.17488630138474 & 0.91255684930763 \tabularnewline
51 & 0.0708517802063457 & 0.141703560412691 & 0.929148219793654 \tabularnewline
52 & 0.191412899839944 & 0.382825799679888 & 0.808587100160056 \tabularnewline
53 & 0.169330307861031 & 0.338660615722062 & 0.830669692138969 \tabularnewline
54 & 0.173455117162671 & 0.346910234325342 & 0.826544882837329 \tabularnewline
55 & 0.217152662203499 & 0.434305324406998 & 0.782847337796501 \tabularnewline
56 & 0.197822209566911 & 0.395644419133823 & 0.802177790433089 \tabularnewline
57 & 0.177144877043296 & 0.354289754086592 & 0.822855122956704 \tabularnewline
58 & 0.389028940532295 & 0.77805788106459 & 0.610971059467705 \tabularnewline
59 & 0.345922303329869 & 0.691844606659737 & 0.654077696670131 \tabularnewline
60 & 0.312496422987356 & 0.624992845974713 & 0.687503577012644 \tabularnewline
61 & 0.327844992610326 & 0.655689985220652 & 0.672155007389674 \tabularnewline
62 & 0.652984808986333 & 0.694030382027333 & 0.347015191013667 \tabularnewline
63 & 0.662208963119644 & 0.675582073760712 & 0.337791036880356 \tabularnewline
64 & 0.625752373275532 & 0.748495253448936 & 0.374247626724468 \tabularnewline
65 & 0.588743930339572 & 0.822512139320857 & 0.411256069660428 \tabularnewline
66 & 0.584199102574925 & 0.83160179485015 & 0.415800897425075 \tabularnewline
67 & 0.553501992214266 & 0.892996015571467 & 0.446498007785734 \tabularnewline
68 & 0.506595352310351 & 0.986809295379298 & 0.493404647689649 \tabularnewline
69 & 0.463054328859235 & 0.92610865771847 & 0.536945671140765 \tabularnewline
70 & 0.442755616597733 & 0.885511233195466 & 0.557244383402267 \tabularnewline
71 & 0.426813837541406 & 0.853627675082812 & 0.573186162458594 \tabularnewline
72 & 0.411657720598964 & 0.823315441197928 & 0.588342279401036 \tabularnewline
73 & 0.404426893261524 & 0.808853786523048 & 0.595573106738476 \tabularnewline
74 & 0.396733356847255 & 0.79346671369451 & 0.603266643152745 \tabularnewline
75 & 0.370058608735898 & 0.740117217471796 & 0.629941391264102 \tabularnewline
76 & 0.357940114262065 & 0.715880228524131 & 0.642059885737934 \tabularnewline
77 & 0.328496830332739 & 0.656993660665479 & 0.671503169667261 \tabularnewline
78 & 0.291164074878883 & 0.582328149757766 & 0.708835925121117 \tabularnewline
79 & 0.259125335270627 & 0.518250670541255 & 0.740874664729373 \tabularnewline
80 & 0.24501867206175 & 0.490037344123501 & 0.75498132793825 \tabularnewline
81 & 0.226595454292471 & 0.453190908584941 & 0.773404545707529 \tabularnewline
82 & 0.347038859765492 & 0.694077719530983 & 0.652961140234508 \tabularnewline
83 & 0.369325089558616 & 0.738650179117231 & 0.630674910441384 \tabularnewline
84 & 0.326555548069061 & 0.653111096138122 & 0.673444451930939 \tabularnewline
85 & 0.389512208031827 & 0.779024416063653 & 0.610487791968173 \tabularnewline
86 & 0.345494272377585 & 0.69098854475517 & 0.654505727622415 \tabularnewline
87 & 0.304214229117354 & 0.608428458234708 & 0.695785770882646 \tabularnewline
88 & 0.527085931272847 & 0.945828137454306 & 0.472914068727153 \tabularnewline
89 & 0.667697148153754 & 0.664605703692493 & 0.332302851846246 \tabularnewline
90 & 0.660652691085364 & 0.678694617829273 & 0.339347308914636 \tabularnewline
91 & 0.636692570935749 & 0.726614858128501 & 0.363307429064251 \tabularnewline
92 & 0.604562049358119 & 0.790875901283763 & 0.395437950641881 \tabularnewline
93 & 0.572181290396568 & 0.855637419206864 & 0.427818709603432 \tabularnewline
94 & 0.534889879700385 & 0.930220240599231 & 0.465110120299615 \tabularnewline
95 & 0.598926301140164 & 0.802147397719673 & 0.401073698859836 \tabularnewline
96 & 0.552458598143885 & 0.89508280371223 & 0.447541401856115 \tabularnewline
97 & 0.534789062267496 & 0.930421875465007 & 0.465210937732504 \tabularnewline
98 & 0.500234624790454 & 0.999530750419092 & 0.499765375209546 \tabularnewline
99 & 0.531079726195949 & 0.937840547608102 & 0.468920273804051 \tabularnewline
100 & 0.764997168781898 & 0.470005662436203 & 0.235002831218101 \tabularnewline
101 & 0.751931279239078 & 0.496137441521844 & 0.248068720760922 \tabularnewline
102 & 0.730031013605981 & 0.539937972788037 & 0.269968986394019 \tabularnewline
103 & 0.688089975306377 & 0.623820049387247 & 0.311910024693623 \tabularnewline
104 & 0.65414201595848 & 0.69171596808304 & 0.34585798404152 \tabularnewline
105 & 0.686581697072558 & 0.626836605854883 & 0.313418302927442 \tabularnewline
106 & 0.647795433567296 & 0.704409132865408 & 0.352204566432704 \tabularnewline
107 & 0.644966672795118 & 0.710066654409764 & 0.355033327204882 \tabularnewline
108 & 0.649807923422696 & 0.700384153154608 & 0.350192076577304 \tabularnewline
109 & 0.616651921304668 & 0.766696157390664 & 0.383348078695332 \tabularnewline
110 & 0.58460531963503 & 0.83078936072994 & 0.41539468036497 \tabularnewline
111 & 0.543560247782533 & 0.912879504434934 & 0.456439752217467 \tabularnewline
112 & 0.49788111276746 & 0.995762225534919 & 0.50211888723254 \tabularnewline
113 & 0.570267454141978 & 0.859465091716043 & 0.429732545858022 \tabularnewline
114 & 0.656231367607914 & 0.687537264784172 & 0.343768632392086 \tabularnewline
115 & 0.652010853063435 & 0.695978293873129 & 0.347989146936565 \tabularnewline
116 & 0.69176908642177 & 0.616461827156459 & 0.30823091357823 \tabularnewline
117 & 0.652886370681283 & 0.694227258637434 & 0.347113629318717 \tabularnewline
118 & 0.692099499401706 & 0.615801001196587 & 0.307900500598294 \tabularnewline
119 & 0.65680661158452 & 0.686386776830961 & 0.34319338841548 \tabularnewline
120 & 0.631254436246845 & 0.737491127506309 & 0.368745563753155 \tabularnewline
121 & 0.598139963644562 & 0.803720072710877 & 0.401860036355438 \tabularnewline
122 & 0.58340485515584 & 0.833190289688321 & 0.41659514484416 \tabularnewline
123 & 0.564297140814202 & 0.871405718371597 & 0.435702859185798 \tabularnewline
124 & 0.509560948505889 & 0.980878102988223 & 0.490439051494111 \tabularnewline
125 & 0.483363784332693 & 0.966727568665386 & 0.516636215667307 \tabularnewline
126 & 0.454180595856433 & 0.908361191712866 & 0.545819404143567 \tabularnewline
127 & 0.422467201223826 & 0.844934402447652 & 0.577532798776174 \tabularnewline
128 & 0.672548099249947 & 0.654903801500106 & 0.327451900750053 \tabularnewline
129 & 0.631676904891305 & 0.73664619021739 & 0.368323095108695 \tabularnewline
130 & 0.576942604876378 & 0.846114790247243 & 0.423057395123622 \tabularnewline
131 & 0.803404733374246 & 0.393190533251508 & 0.196595266625754 \tabularnewline
132 & 0.863747721195367 & 0.272504557609267 & 0.136252278804633 \tabularnewline
133 & 0.936769252371308 & 0.126461495257384 & 0.0632307476286918 \tabularnewline
134 & 0.951740533793318 & 0.0965189324133649 & 0.0482594662066824 \tabularnewline
135 & 0.989320013299657 & 0.0213599734006853 & 0.0106799867003426 \tabularnewline
136 & 0.992855251444216 & 0.0142894971115671 & 0.00714474855578354 \tabularnewline
137 & 0.988357892471009 & 0.0232842150579816 & 0.0116421075289908 \tabularnewline
138 & 0.997287233111144 & 0.00542553377771183 & 0.00271276688885592 \tabularnewline
139 & 0.997063896052592 & 0.00587220789481516 & 0.00293610394740758 \tabularnewline
140 & 0.999303084248657 & 0.00139383150268567 & 0.000696915751342833 \tabularnewline
141 & 0.999726967745232 & 0.000546064509536559 & 0.00027303225476828 \tabularnewline
142 & 0.999895072774614 & 0.000209854450771819 & 0.00010492722538591 \tabularnewline
143 & 0.999949112414171 & 0.000101775171658538 & 5.08875858292692e-05 \tabularnewline
144 & 0.999878296774523 & 0.000243406450953282 & 0.000121703225476641 \tabularnewline
145 & 0.99983413163754 & 0.000331736724920596 & 0.000165868362460298 \tabularnewline
146 & 0.999996360062604 & 7.27987479212712e-06 & 3.63993739606356e-06 \tabularnewline
147 & 0.999996928704154 & 6.14259169160122e-06 & 3.07129584580061e-06 \tabularnewline
148 & 0.999999997805686 & 4.38862778199962e-09 & 2.19431389099981e-09 \tabularnewline
149 & 0.999999968440709 & 6.31185824206694e-08 & 3.15592912103347e-08 \tabularnewline
150 & 0.999999539689234 & 9.2062153253083e-07 & 4.60310766265415e-07 \tabularnewline
151 & 0.999993394966688 & 1.32100666237455e-05 & 6.60503331187277e-06 \tabularnewline
152 & 0.999913611918924 & 0.000172776162151262 & 8.63880810756309e-05 \tabularnewline
153 & 0.999017154123376 & 0.0019656917532488 & 0.000982845876624402 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156275&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.398779820525589[/C][C]0.797559641051178[/C][C]0.601220179474411[/C][/ROW]
[ROW][C]12[/C][C]0.500854940040822[/C][C]0.998290119918356[/C][C]0.499145059959178[/C][/ROW]
[ROW][C]13[/C][C]0.358688852037879[/C][C]0.717377704075758[/C][C]0.641311147962121[/C][/ROW]
[ROW][C]14[/C][C]0.377457695346112[/C][C]0.754915390692223[/C][C]0.622542304653888[/C][/ROW]
[ROW][C]15[/C][C]0.29602583910275[/C][C]0.592051678205499[/C][C]0.70397416089725[/C][/ROW]
[ROW][C]16[/C][C]0.212259006661996[/C][C]0.424518013323992[/C][C]0.787740993338004[/C][/ROW]
[ROW][C]17[/C][C]0.147991851292069[/C][C]0.295983702584139[/C][C]0.852008148707931[/C][/ROW]
[ROW][C]18[/C][C]0.106388334817497[/C][C]0.212776669634994[/C][C]0.893611665182503[/C][/ROW]
[ROW][C]19[/C][C]0.0667500625132824[/C][C]0.133500125026565[/C][C]0.933249937486718[/C][/ROW]
[ROW][C]20[/C][C]0.0412976463370961[/C][C]0.0825952926741921[/C][C]0.958702353662904[/C][/ROW]
[ROW][C]21[/C][C]0.0253462580541847[/C][C]0.0506925161083695[/C][C]0.974653741945815[/C][/ROW]
[ROW][C]22[/C][C]0.0152521045759934[/C][C]0.0305042091519869[/C][C]0.984747895424007[/C][/ROW]
[ROW][C]23[/C][C]0.0287179124581946[/C][C]0.0574358249163892[/C][C]0.971282087541805[/C][/ROW]
[ROW][C]24[/C][C]0.0871600521644785[/C][C]0.174320104328957[/C][C]0.912839947835521[/C][/ROW]
[ROW][C]25[/C][C]0.0882060628394777[/C][C]0.176412125678955[/C][C]0.911793937160522[/C][/ROW]
[ROW][C]26[/C][C]0.0610573512048009[/C][C]0.122114702409602[/C][C]0.938942648795199[/C][/ROW]
[ROW][C]27[/C][C]0.0452197596331149[/C][C]0.0904395192662298[/C][C]0.954780240366885[/C][/ROW]
[ROW][C]28[/C][C]0.0317899482713138[/C][C]0.0635798965426277[/C][C]0.968210051728686[/C][/ROW]
[ROW][C]29[/C][C]0.0215035367792781[/C][C]0.0430070735585561[/C][C]0.978496463220722[/C][/ROW]
[ROW][C]30[/C][C]0.0140776135032238[/C][C]0.0281552270064477[/C][C]0.985922386496776[/C][/ROW]
[ROW][C]31[/C][C]0.021560828418995[/C][C]0.0431216568379901[/C][C]0.978439171581005[/C][/ROW]
[ROW][C]32[/C][C]0.0205586316734827[/C][C]0.0411172633469654[/C][C]0.979441368326517[/C][/ROW]
[ROW][C]33[/C][C]0.0223712751948027[/C][C]0.0447425503896054[/C][C]0.977628724805197[/C][/ROW]
[ROW][C]34[/C][C]0.0306101435948612[/C][C]0.0612202871897225[/C][C]0.969389856405139[/C][/ROW]
[ROW][C]35[/C][C]0.0354661913554281[/C][C]0.0709323827108563[/C][C]0.964533808644572[/C][/ROW]
[ROW][C]36[/C][C]0.0329298848851845[/C][C]0.065859769770369[/C][C]0.967070115114815[/C][/ROW]
[ROW][C]37[/C][C]0.0300549017785924[/C][C]0.0601098035571849[/C][C]0.969945098221408[/C][/ROW]
[ROW][C]38[/C][C]0.0224869395486363[/C][C]0.0449738790972725[/C][C]0.977513060451364[/C][/ROW]
[ROW][C]39[/C][C]0.0414343331224835[/C][C]0.0828686662449671[/C][C]0.958565666877516[/C][/ROW]
[ROW][C]40[/C][C]0.0384995213016766[/C][C]0.0769990426033532[/C][C]0.961500478698323[/C][/ROW]
[ROW][C]41[/C][C]0.027853233437101[/C][C]0.055706466874202[/C][C]0.972146766562899[/C][/ROW]
[ROW][C]42[/C][C]0.0238674752220395[/C][C]0.0477349504440791[/C][C]0.97613252477796[/C][/ROW]
[ROW][C]43[/C][C]0.0173110198484922[/C][C]0.0346220396969844[/C][C]0.982688980151508[/C][/ROW]
[ROW][C]44[/C][C]0.0132333511380886[/C][C]0.0264667022761773[/C][C]0.986766648861911[/C][/ROW]
[ROW][C]45[/C][C]0.0272210838318133[/C][C]0.0544421676636266[/C][C]0.972778916168187[/C][/ROW]
[ROW][C]46[/C][C]0.0595075813220607[/C][C]0.119015162644121[/C][C]0.940492418677939[/C][/ROW]
[ROW][C]47[/C][C]0.0481591913660895[/C][C]0.0963183827321791[/C][C]0.95184080863391[/C][/ROW]
[ROW][C]48[/C][C]0.0602179377566503[/C][C]0.120435875513301[/C][C]0.93978206224335[/C][/ROW]
[ROW][C]49[/C][C]0.066145039523374[/C][C]0.132290079046748[/C][C]0.933854960476626[/C][/ROW]
[ROW][C]50[/C][C]0.0874431506923699[/C][C]0.17488630138474[/C][C]0.91255684930763[/C][/ROW]
[ROW][C]51[/C][C]0.0708517802063457[/C][C]0.141703560412691[/C][C]0.929148219793654[/C][/ROW]
[ROW][C]52[/C][C]0.191412899839944[/C][C]0.382825799679888[/C][C]0.808587100160056[/C][/ROW]
[ROW][C]53[/C][C]0.169330307861031[/C][C]0.338660615722062[/C][C]0.830669692138969[/C][/ROW]
[ROW][C]54[/C][C]0.173455117162671[/C][C]0.346910234325342[/C][C]0.826544882837329[/C][/ROW]
[ROW][C]55[/C][C]0.217152662203499[/C][C]0.434305324406998[/C][C]0.782847337796501[/C][/ROW]
[ROW][C]56[/C][C]0.197822209566911[/C][C]0.395644419133823[/C][C]0.802177790433089[/C][/ROW]
[ROW][C]57[/C][C]0.177144877043296[/C][C]0.354289754086592[/C][C]0.822855122956704[/C][/ROW]
[ROW][C]58[/C][C]0.389028940532295[/C][C]0.77805788106459[/C][C]0.610971059467705[/C][/ROW]
[ROW][C]59[/C][C]0.345922303329869[/C][C]0.691844606659737[/C][C]0.654077696670131[/C][/ROW]
[ROW][C]60[/C][C]0.312496422987356[/C][C]0.624992845974713[/C][C]0.687503577012644[/C][/ROW]
[ROW][C]61[/C][C]0.327844992610326[/C][C]0.655689985220652[/C][C]0.672155007389674[/C][/ROW]
[ROW][C]62[/C][C]0.652984808986333[/C][C]0.694030382027333[/C][C]0.347015191013667[/C][/ROW]
[ROW][C]63[/C][C]0.662208963119644[/C][C]0.675582073760712[/C][C]0.337791036880356[/C][/ROW]
[ROW][C]64[/C][C]0.625752373275532[/C][C]0.748495253448936[/C][C]0.374247626724468[/C][/ROW]
[ROW][C]65[/C][C]0.588743930339572[/C][C]0.822512139320857[/C][C]0.411256069660428[/C][/ROW]
[ROW][C]66[/C][C]0.584199102574925[/C][C]0.83160179485015[/C][C]0.415800897425075[/C][/ROW]
[ROW][C]67[/C][C]0.553501992214266[/C][C]0.892996015571467[/C][C]0.446498007785734[/C][/ROW]
[ROW][C]68[/C][C]0.506595352310351[/C][C]0.986809295379298[/C][C]0.493404647689649[/C][/ROW]
[ROW][C]69[/C][C]0.463054328859235[/C][C]0.92610865771847[/C][C]0.536945671140765[/C][/ROW]
[ROW][C]70[/C][C]0.442755616597733[/C][C]0.885511233195466[/C][C]0.557244383402267[/C][/ROW]
[ROW][C]71[/C][C]0.426813837541406[/C][C]0.853627675082812[/C][C]0.573186162458594[/C][/ROW]
[ROW][C]72[/C][C]0.411657720598964[/C][C]0.823315441197928[/C][C]0.588342279401036[/C][/ROW]
[ROW][C]73[/C][C]0.404426893261524[/C][C]0.808853786523048[/C][C]0.595573106738476[/C][/ROW]
[ROW][C]74[/C][C]0.396733356847255[/C][C]0.79346671369451[/C][C]0.603266643152745[/C][/ROW]
[ROW][C]75[/C][C]0.370058608735898[/C][C]0.740117217471796[/C][C]0.629941391264102[/C][/ROW]
[ROW][C]76[/C][C]0.357940114262065[/C][C]0.715880228524131[/C][C]0.642059885737934[/C][/ROW]
[ROW][C]77[/C][C]0.328496830332739[/C][C]0.656993660665479[/C][C]0.671503169667261[/C][/ROW]
[ROW][C]78[/C][C]0.291164074878883[/C][C]0.582328149757766[/C][C]0.708835925121117[/C][/ROW]
[ROW][C]79[/C][C]0.259125335270627[/C][C]0.518250670541255[/C][C]0.740874664729373[/C][/ROW]
[ROW][C]80[/C][C]0.24501867206175[/C][C]0.490037344123501[/C][C]0.75498132793825[/C][/ROW]
[ROW][C]81[/C][C]0.226595454292471[/C][C]0.453190908584941[/C][C]0.773404545707529[/C][/ROW]
[ROW][C]82[/C][C]0.347038859765492[/C][C]0.694077719530983[/C][C]0.652961140234508[/C][/ROW]
[ROW][C]83[/C][C]0.369325089558616[/C][C]0.738650179117231[/C][C]0.630674910441384[/C][/ROW]
[ROW][C]84[/C][C]0.326555548069061[/C][C]0.653111096138122[/C][C]0.673444451930939[/C][/ROW]
[ROW][C]85[/C][C]0.389512208031827[/C][C]0.779024416063653[/C][C]0.610487791968173[/C][/ROW]
[ROW][C]86[/C][C]0.345494272377585[/C][C]0.69098854475517[/C][C]0.654505727622415[/C][/ROW]
[ROW][C]87[/C][C]0.304214229117354[/C][C]0.608428458234708[/C][C]0.695785770882646[/C][/ROW]
[ROW][C]88[/C][C]0.527085931272847[/C][C]0.945828137454306[/C][C]0.472914068727153[/C][/ROW]
[ROW][C]89[/C][C]0.667697148153754[/C][C]0.664605703692493[/C][C]0.332302851846246[/C][/ROW]
[ROW][C]90[/C][C]0.660652691085364[/C][C]0.678694617829273[/C][C]0.339347308914636[/C][/ROW]
[ROW][C]91[/C][C]0.636692570935749[/C][C]0.726614858128501[/C][C]0.363307429064251[/C][/ROW]
[ROW][C]92[/C][C]0.604562049358119[/C][C]0.790875901283763[/C][C]0.395437950641881[/C][/ROW]
[ROW][C]93[/C][C]0.572181290396568[/C][C]0.855637419206864[/C][C]0.427818709603432[/C][/ROW]
[ROW][C]94[/C][C]0.534889879700385[/C][C]0.930220240599231[/C][C]0.465110120299615[/C][/ROW]
[ROW][C]95[/C][C]0.598926301140164[/C][C]0.802147397719673[/C][C]0.401073698859836[/C][/ROW]
[ROW][C]96[/C][C]0.552458598143885[/C][C]0.89508280371223[/C][C]0.447541401856115[/C][/ROW]
[ROW][C]97[/C][C]0.534789062267496[/C][C]0.930421875465007[/C][C]0.465210937732504[/C][/ROW]
[ROW][C]98[/C][C]0.500234624790454[/C][C]0.999530750419092[/C][C]0.499765375209546[/C][/ROW]
[ROW][C]99[/C][C]0.531079726195949[/C][C]0.937840547608102[/C][C]0.468920273804051[/C][/ROW]
[ROW][C]100[/C][C]0.764997168781898[/C][C]0.470005662436203[/C][C]0.235002831218101[/C][/ROW]
[ROW][C]101[/C][C]0.751931279239078[/C][C]0.496137441521844[/C][C]0.248068720760922[/C][/ROW]
[ROW][C]102[/C][C]0.730031013605981[/C][C]0.539937972788037[/C][C]0.269968986394019[/C][/ROW]
[ROW][C]103[/C][C]0.688089975306377[/C][C]0.623820049387247[/C][C]0.311910024693623[/C][/ROW]
[ROW][C]104[/C][C]0.65414201595848[/C][C]0.69171596808304[/C][C]0.34585798404152[/C][/ROW]
[ROW][C]105[/C][C]0.686581697072558[/C][C]0.626836605854883[/C][C]0.313418302927442[/C][/ROW]
[ROW][C]106[/C][C]0.647795433567296[/C][C]0.704409132865408[/C][C]0.352204566432704[/C][/ROW]
[ROW][C]107[/C][C]0.644966672795118[/C][C]0.710066654409764[/C][C]0.355033327204882[/C][/ROW]
[ROW][C]108[/C][C]0.649807923422696[/C][C]0.700384153154608[/C][C]0.350192076577304[/C][/ROW]
[ROW][C]109[/C][C]0.616651921304668[/C][C]0.766696157390664[/C][C]0.383348078695332[/C][/ROW]
[ROW][C]110[/C][C]0.58460531963503[/C][C]0.83078936072994[/C][C]0.41539468036497[/C][/ROW]
[ROW][C]111[/C][C]0.543560247782533[/C][C]0.912879504434934[/C][C]0.456439752217467[/C][/ROW]
[ROW][C]112[/C][C]0.49788111276746[/C][C]0.995762225534919[/C][C]0.50211888723254[/C][/ROW]
[ROW][C]113[/C][C]0.570267454141978[/C][C]0.859465091716043[/C][C]0.429732545858022[/C][/ROW]
[ROW][C]114[/C][C]0.656231367607914[/C][C]0.687537264784172[/C][C]0.343768632392086[/C][/ROW]
[ROW][C]115[/C][C]0.652010853063435[/C][C]0.695978293873129[/C][C]0.347989146936565[/C][/ROW]
[ROW][C]116[/C][C]0.69176908642177[/C][C]0.616461827156459[/C][C]0.30823091357823[/C][/ROW]
[ROW][C]117[/C][C]0.652886370681283[/C][C]0.694227258637434[/C][C]0.347113629318717[/C][/ROW]
[ROW][C]118[/C][C]0.692099499401706[/C][C]0.615801001196587[/C][C]0.307900500598294[/C][/ROW]
[ROW][C]119[/C][C]0.65680661158452[/C][C]0.686386776830961[/C][C]0.34319338841548[/C][/ROW]
[ROW][C]120[/C][C]0.631254436246845[/C][C]0.737491127506309[/C][C]0.368745563753155[/C][/ROW]
[ROW][C]121[/C][C]0.598139963644562[/C][C]0.803720072710877[/C][C]0.401860036355438[/C][/ROW]
[ROW][C]122[/C][C]0.58340485515584[/C][C]0.833190289688321[/C][C]0.41659514484416[/C][/ROW]
[ROW][C]123[/C][C]0.564297140814202[/C][C]0.871405718371597[/C][C]0.435702859185798[/C][/ROW]
[ROW][C]124[/C][C]0.509560948505889[/C][C]0.980878102988223[/C][C]0.490439051494111[/C][/ROW]
[ROW][C]125[/C][C]0.483363784332693[/C][C]0.966727568665386[/C][C]0.516636215667307[/C][/ROW]
[ROW][C]126[/C][C]0.454180595856433[/C][C]0.908361191712866[/C][C]0.545819404143567[/C][/ROW]
[ROW][C]127[/C][C]0.422467201223826[/C][C]0.844934402447652[/C][C]0.577532798776174[/C][/ROW]
[ROW][C]128[/C][C]0.672548099249947[/C][C]0.654903801500106[/C][C]0.327451900750053[/C][/ROW]
[ROW][C]129[/C][C]0.631676904891305[/C][C]0.73664619021739[/C][C]0.368323095108695[/C][/ROW]
[ROW][C]130[/C][C]0.576942604876378[/C][C]0.846114790247243[/C][C]0.423057395123622[/C][/ROW]
[ROW][C]131[/C][C]0.803404733374246[/C][C]0.393190533251508[/C][C]0.196595266625754[/C][/ROW]
[ROW][C]132[/C][C]0.863747721195367[/C][C]0.272504557609267[/C][C]0.136252278804633[/C][/ROW]
[ROW][C]133[/C][C]0.936769252371308[/C][C]0.126461495257384[/C][C]0.0632307476286918[/C][/ROW]
[ROW][C]134[/C][C]0.951740533793318[/C][C]0.0965189324133649[/C][C]0.0482594662066824[/C][/ROW]
[ROW][C]135[/C][C]0.989320013299657[/C][C]0.0213599734006853[/C][C]0.0106799867003426[/C][/ROW]
[ROW][C]136[/C][C]0.992855251444216[/C][C]0.0142894971115671[/C][C]0.00714474855578354[/C][/ROW]
[ROW][C]137[/C][C]0.988357892471009[/C][C]0.0232842150579816[/C][C]0.0116421075289908[/C][/ROW]
[ROW][C]138[/C][C]0.997287233111144[/C][C]0.00542553377771183[/C][C]0.00271276688885592[/C][/ROW]
[ROW][C]139[/C][C]0.997063896052592[/C][C]0.00587220789481516[/C][C]0.00293610394740758[/C][/ROW]
[ROW][C]140[/C][C]0.999303084248657[/C][C]0.00139383150268567[/C][C]0.000696915751342833[/C][/ROW]
[ROW][C]141[/C][C]0.999726967745232[/C][C]0.000546064509536559[/C][C]0.00027303225476828[/C][/ROW]
[ROW][C]142[/C][C]0.999895072774614[/C][C]0.000209854450771819[/C][C]0.00010492722538591[/C][/ROW]
[ROW][C]143[/C][C]0.999949112414171[/C][C]0.000101775171658538[/C][C]5.08875858292692e-05[/C][/ROW]
[ROW][C]144[/C][C]0.999878296774523[/C][C]0.000243406450953282[/C][C]0.000121703225476641[/C][/ROW]
[ROW][C]145[/C][C]0.99983413163754[/C][C]0.000331736724920596[/C][C]0.000165868362460298[/C][/ROW]
[ROW][C]146[/C][C]0.999996360062604[/C][C]7.27987479212712e-06[/C][C]3.63993739606356e-06[/C][/ROW]
[ROW][C]147[/C][C]0.999996928704154[/C][C]6.14259169160122e-06[/C][C]3.07129584580061e-06[/C][/ROW]
[ROW][C]148[/C][C]0.999999997805686[/C][C]4.38862778199962e-09[/C][C]2.19431389099981e-09[/C][/ROW]
[ROW][C]149[/C][C]0.999999968440709[/C][C]6.31185824206694e-08[/C][C]3.15592912103347e-08[/C][/ROW]
[ROW][C]150[/C][C]0.999999539689234[/C][C]9.2062153253083e-07[/C][C]4.60310766265415e-07[/C][/ROW]
[ROW][C]151[/C][C]0.999993394966688[/C][C]1.32100666237455e-05[/C][C]6.60503331187277e-06[/C][/ROW]
[ROW][C]152[/C][C]0.999913611918924[/C][C]0.000172776162151262[/C][C]8.63880810756309e-05[/C][/ROW]
[ROW][C]153[/C][C]0.999017154123376[/C][C]0.0019656917532488[/C][C]0.000982845876624402[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156275&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156275&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.3987798205255890.7975596410511780.601220179474411
120.5008549400408220.9982901199183560.499145059959178
130.3586888520378790.7173777040757580.641311147962121
140.3774576953461120.7549153906922230.622542304653888
150.296025839102750.5920516782054990.70397416089725
160.2122590066619960.4245180133239920.787740993338004
170.1479918512920690.2959837025841390.852008148707931
180.1063883348174970.2127766696349940.893611665182503
190.06675006251328240.1335001250265650.933249937486718
200.04129764633709610.08259529267419210.958702353662904
210.02534625805418470.05069251610836950.974653741945815
220.01525210457599340.03050420915198690.984747895424007
230.02871791245819460.05743582491638920.971282087541805
240.08716005216447850.1743201043289570.912839947835521
250.08820606283947770.1764121256789550.911793937160522
260.06105735120480090.1221147024096020.938942648795199
270.04521975963311490.09043951926622980.954780240366885
280.03178994827131380.06357989654262770.968210051728686
290.02150353677927810.04300707355855610.978496463220722
300.01407761350322380.02815522700644770.985922386496776
310.0215608284189950.04312165683799010.978439171581005
320.02055863167348270.04111726334696540.979441368326517
330.02237127519480270.04474255038960540.977628724805197
340.03061014359486120.06122028718972250.969389856405139
350.03546619135542810.07093238271085630.964533808644572
360.03292988488518450.0658597697703690.967070115114815
370.03005490177859240.06010980355718490.969945098221408
380.02248693954863630.04497387909727250.977513060451364
390.04143433312248350.08286866624496710.958565666877516
400.03849952130167660.07699904260335320.961500478698323
410.0278532334371010.0557064668742020.972146766562899
420.02386747522203950.04773495044407910.97613252477796
430.01731101984849220.03462203969698440.982688980151508
440.01323335113808860.02646670227617730.986766648861911
450.02722108383181330.05444216766362660.972778916168187
460.05950758132206070.1190151626441210.940492418677939
470.04815919136608950.09631838273217910.95184080863391
480.06021793775665030.1204358755133010.93978206224335
490.0661450395233740.1322900790467480.933854960476626
500.08744315069236990.174886301384740.91255684930763
510.07085178020634570.1417035604126910.929148219793654
520.1914128998399440.3828257996798880.808587100160056
530.1693303078610310.3386606157220620.830669692138969
540.1734551171626710.3469102343253420.826544882837329
550.2171526622034990.4343053244069980.782847337796501
560.1978222095669110.3956444191338230.802177790433089
570.1771448770432960.3542897540865920.822855122956704
580.3890289405322950.778057881064590.610971059467705
590.3459223033298690.6918446066597370.654077696670131
600.3124964229873560.6249928459747130.687503577012644
610.3278449926103260.6556899852206520.672155007389674
620.6529848089863330.6940303820273330.347015191013667
630.6622089631196440.6755820737607120.337791036880356
640.6257523732755320.7484952534489360.374247626724468
650.5887439303395720.8225121393208570.411256069660428
660.5841991025749250.831601794850150.415800897425075
670.5535019922142660.8929960155714670.446498007785734
680.5065953523103510.9868092953792980.493404647689649
690.4630543288592350.926108657718470.536945671140765
700.4427556165977330.8855112331954660.557244383402267
710.4268138375414060.8536276750828120.573186162458594
720.4116577205989640.8233154411979280.588342279401036
730.4044268932615240.8088537865230480.595573106738476
740.3967333568472550.793466713694510.603266643152745
750.3700586087358980.7401172174717960.629941391264102
760.3579401142620650.7158802285241310.642059885737934
770.3284968303327390.6569936606654790.671503169667261
780.2911640748788830.5823281497577660.708835925121117
790.2591253352706270.5182506705412550.740874664729373
800.245018672061750.4900373441235010.75498132793825
810.2265954542924710.4531909085849410.773404545707529
820.3470388597654920.6940777195309830.652961140234508
830.3693250895586160.7386501791172310.630674910441384
840.3265555480690610.6531110961381220.673444451930939
850.3895122080318270.7790244160636530.610487791968173
860.3454942723775850.690988544755170.654505727622415
870.3042142291173540.6084284582347080.695785770882646
880.5270859312728470.9458281374543060.472914068727153
890.6676971481537540.6646057036924930.332302851846246
900.6606526910853640.6786946178292730.339347308914636
910.6366925709357490.7266148581285010.363307429064251
920.6045620493581190.7908759012837630.395437950641881
930.5721812903965680.8556374192068640.427818709603432
940.5348898797003850.9302202405992310.465110120299615
950.5989263011401640.8021473977196730.401073698859836
960.5524585981438850.895082803712230.447541401856115
970.5347890622674960.9304218754650070.465210937732504
980.5002346247904540.9995307504190920.499765375209546
990.5310797261959490.9378405476081020.468920273804051
1000.7649971687818980.4700056624362030.235002831218101
1010.7519312792390780.4961374415218440.248068720760922
1020.7300310136059810.5399379727880370.269968986394019
1030.6880899753063770.6238200493872470.311910024693623
1040.654142015958480.691715968083040.34585798404152
1050.6865816970725580.6268366058548830.313418302927442
1060.6477954335672960.7044091328654080.352204566432704
1070.6449666727951180.7100666544097640.355033327204882
1080.6498079234226960.7003841531546080.350192076577304
1090.6166519213046680.7666961573906640.383348078695332
1100.584605319635030.830789360729940.41539468036497
1110.5435602477825330.9128795044349340.456439752217467
1120.497881112767460.9957622255349190.50211888723254
1130.5702674541419780.8594650917160430.429732545858022
1140.6562313676079140.6875372647841720.343768632392086
1150.6520108530634350.6959782938731290.347989146936565
1160.691769086421770.6164618271564590.30823091357823
1170.6528863706812830.6942272586374340.347113629318717
1180.6920994994017060.6158010011965870.307900500598294
1190.656806611584520.6863867768309610.34319338841548
1200.6312544362468450.7374911275063090.368745563753155
1210.5981399636445620.8037200727108770.401860036355438
1220.583404855155840.8331902896883210.41659514484416
1230.5642971408142020.8714057183715970.435702859185798
1240.5095609485058890.9808781029882230.490439051494111
1250.4833637843326930.9667275686653860.516636215667307
1260.4541805958564330.9083611917128660.545819404143567
1270.4224672012238260.8449344024476520.577532798776174
1280.6725480992499470.6549038015001060.327451900750053
1290.6316769048913050.736646190217390.368323095108695
1300.5769426048763780.8461147902472430.423057395123622
1310.8034047333742460.3931905332515080.196595266625754
1320.8637477211953670.2725045576092670.136252278804633
1330.9367692523713080.1264614952573840.0632307476286918
1340.9517405337933180.09651893241336490.0482594662066824
1350.9893200132996570.02135997340068530.0106799867003426
1360.9928552514442160.01428949711156710.00714474855578354
1370.9883578924710090.02328421505798160.0116421075289908
1380.9972872331111440.005425533777711830.00271276688885592
1390.9970638960525920.005872207894815160.00293610394740758
1400.9993030842486570.001393831502685670.000696915751342833
1410.9997269677452320.0005460645095365590.00027303225476828
1420.9998950727746140.0002098544507718190.00010492722538591
1430.9999491124141710.0001017751716585385.08875858292692e-05
1440.9998782967745230.0002434064509532820.000121703225476641
1450.999834131637540.0003317367249205960.000165868362460298
1460.9999963600626047.27987479212712e-063.63993739606356e-06
1470.9999969287041546.14259169160122e-063.07129584580061e-06
1480.9999999978056864.38862778199962e-092.19431389099981e-09
1490.9999999684407096.31185824206694e-083.15592912103347e-08
1500.9999995396892349.2062153253083e-074.60310766265415e-07
1510.9999933949666881.32100666237455e-056.60503331187277e-06
1520.9999136119189240.0001727761621512628.63880810756309e-05
1530.9990171541233760.00196569175324880.000982845876624402







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.111888111888112NOK
5% type I error level290.202797202797203NOK
10% type I error level440.307692307692308NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 16 & 0.111888111888112 & NOK \tabularnewline
5% type I error level & 29 & 0.202797202797203 & NOK \tabularnewline
10% type I error level & 44 & 0.307692307692308 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156275&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]16[/C][C]0.111888111888112[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.202797202797203[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.307692307692308[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156275&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156275&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.111888111888112NOK
5% type I error level290.202797202797203NOK
10% type I error level440.307692307692308NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}