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Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationSun, 18 Dec 2011 08:03:45 -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/18/t1324213598jt5ndkobzf60qjw.htm/, Retrieved Thu, 31 Oct 2024 23:41:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156816, Retrieved Thu, 31 Oct 2024 23:41:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact181
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2011-12-18 13:02:22] [6a3e51c0c7ab195427042dfaef1df5a0]
- RMP     [Multiple Regression] [paper] [2011-12-18 13:03:45] [8a7469f165590e0a048f07fe1c69d604] [Current]
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Dataseries X:
272545	1747	69	483	32033	186099	3	116	38	144
179444	1209	64	429	20654	113854	4	127	34	133
222373	1844	69	673	16346	99776	16	106	42	162
218443	2683	104	1137	35926	106194	2	133	38	148
167843	1228	51	374	10621	100792	1	64	27	88
70849	631	28	179	10024	47552	3	89	35	129
506574	4627	123	2251	43068	250931	0	122	33	128
33186	381	19	111	1271	6853	0	22	18	67
216660	2063	59	740	34416	115466	7	117	34	132
213274	1758	44	595	20318	110896	0	82	33	120
307153	2132	109	800	24409	169351	0	147	46	169
237633	2128	114	660	20648	94853	7	184	55	210
164292	1667	68	635	12347	72591	8	113	37	122
364402	2965	79	1172	21857	101345	4	171	55	191
244103	2098	84	674	11034	113713	10	87	44	162
384448	4904	178	1692	33433	165354	0	199	59	223
325587	2242	68	811	35902	164263	6	139	36	140
323652	2977	157	1168	22355	135213	4	92	39	144
176082	1438	55	507	31219	111669	3	85	29	111
266736	2347	87	689	21983	134163	8	193	51	199
278265	2522	70	837	40085	140303	0	160	49	187
442703	2889	103	1270	18507	150773	1	144	39	144
180393	1447	41	462	16278	111848	5	84	25	89
189897	1717	54	601	24662	102509	9	208	52	208
234247	3362	121	1242	31452	96785	1	154	45	165
237452	2898	125	1025	32580	116136	0	139	38	146
267268	2828	127	1062	22883	158376	5	127	41	158
270787	1972	86	618	27652	153990	0	148	43	154
155915	1495	51	559	9845	64057	0	99	32	117
342564	2840	69	1062	20190	230054	0	135	41	158
282172	2299	76	913	46201	184531	3	171	47	183
216584	1909	76	643	10971	114198	6	149	50	186
318563	2091	84	779	34811	198299	1	178	48	185
98672	971	37	322	3029	33750	4	137	37	141
386258	3293	95	1243	38941	189723	4	151	41	156
273950	2764	56	1186	4958	100826	0	127	42	159
425120	3682	120	1324	32344	188355	0	151	44	161
227636	1918	83	640	19433	104470	2	89	36	139
115658	947	33	284	12558	58391	1	46	17	55
349863	3433	194	1210	36524	164808	2	153	42	163
324178	3246	79	1490	26041	134097	10	122	39	145
178083	1692	67	667	16637	80238	9	111	41	148
195153	1735	73	635	28395	133252	5	108	36	115
177694	1771	61	479	16747	54518	6	142	47	174
153778	2496	82	1022	9105	121850	1	45	45	73
455168	5501	151	2068	11941	79367	2	131	41	147
78800	918	42	330	7935	56968	2	66	26	82
208051	2228	76	648	19499	106314	0	180	52	201
348077	4051	118	1367	22938	191889	10	165	47	181
175523	2081	54	868	25314	104864	3	146	45	164
224591	1875	74	588	28524	160791	0	137	40	158
24188	496	24	218	2694	15049	0	7	4	12
372238	2537	314	833	20867	191179	8	157	44	163
65029	744	17	255	3597	25109	5	61	18	67
101097	1161	64	454	5296	45824	3	41	14	52
279012	3027	58	1108	32982	129711	1	120	37	134
317644	2526	84	662	38975	210012	5	228	61	230
340471	3705	185	1119	42721	194679	5	137	39	145
358958	2667	141	1058	41455	197680	0	150	42	153
252529	2175	83	822	23923	81180	12	127	36	139
370628	3949	140	1302	26719	197765	10	161	46	178
304468	3165	117	1145	53405	214738	12	73	28	101
265870	2939	113	1185	12526	96252	11	97	43	169
264889	2610	88	931	26584	124527	8	142	42	163
228595	1426	66	557	37062	153242	2	125	37	139
216027	1646	65	436	25696	145707	0	87	30	116
198798	1971	132	596	24634	113963	6	128	35	137
238146	2746	145	837	27269	134904	9	148	44	167
234891	2308	81	848	25270	114268	2	116	36	135
175816	1684	69	625	24634	94333	5	89	28	102
239314	2537	68	865	17828	102204	13	154	45	173
73566	893	32	385	3007	23824	6	67	23	88
242622	2195	84	718	20065	111563	7	171	45	175
187167	1695	53	705	24648	91313	2	90	38	133
209049	2061	63	732	21588	89770	2	133	38	148
360592	2329	86	988	25217	100125	4	144	46	169
342846	2695	92	1077	30927	165278	3	133	36	140
207650	1809	107	524	18487	181712	6	125	41	154
206500	2290	62	697	18050	80906	2	134	38	148
182357	1791	64	644	17696	75881	0	110	37	134
153613	1678	46	622	17326	83963	1	89	28	109
456979	4023	124	1227	39361	175721	0	138	45	175
145943	1369	69	653	9648	68580	5	99	26	99
280366	2308	104	656	26759	136323	2	92	44	122
80953	870	25	437	7905	55792	0	27	8	28
150216	1966	54	822	4527	25157	0	77	27	101
167878	1459	59	423	41517	100922	6	137	38	139
369718	3795	205	1489	21261	118845	1	137	37	143
322454	2673	116	929	36099	170492	0	122	57	206
179797	3085	104	1044	39039	81716	1	159	45	171
262883	2367	91	792	13841	115750	1	85	37	138
262793	2209	77	678	23841	105590	3	138	40	148
189142	1829	63	597	8589	92795	9	90	31	114
275997	3087	74	1099	15049	82390	1	135	36	140
328875	2559	82	966	39038	135599	4	147	40	156
189252	1624	36	555	30391	111542	3	139	36	140
222504	1607	51	552	39932	162519	5	127	35	127
287386	2109	79	778	43840	211381	0	104	39	141
389104	4015	151	1322	43146	189944	12	248	65	251
397681	3705	108	1415	50099	226168	13	106	30	114
287748	2714	136	853	40312	117495	8	176	51	198
294320	2325	65	848	32616	195894	0	130	41	155
186856	1999	179	640	11338	80684	0	59	36	138
43287	602	14	214	7409	19630	4	64	19	71
185468	2146	80	716	18213	88634	4	36	23	84
235352	2325	146	795	45873	139292	0	98	44	167
268077	2617	48	1170	39844	128602	0	125	40	155
305195	2688	90	1048	28317	135848	0	124	40	150
143356	1207	72	399	24797	178377	0	83	30	112
154165	3102	88	906	7471	106330	0	127	41	161
307000	1869	68	609	27259	178303	4	143	40	149
298039	2304	88	688	23201	116938	0	115	45	164
23623	398	11	156	238	5841	0	0	1	0
195817	2205	73	779	28830	106020	0	103	40	155
61857	530	25	192	3913	24610	4	30	11	32
163766	1596	48	457	9935	74151	0	119	45	169
414506	3083	117	1195	27738	232241	1	102	38	140
21054	387	16	146	338	6622	0	0	0	0
252805	2137	52	866	13326	127097	5	77	30	111
31961	492	22	200	3988	13155	0	9	8	25
317367	3450	115	1230	24347	160501	3	137	39	146
240153	2089	65	696	27111	91502	7	163	48	183
175083	1658	88	491	3938	24469	13	146	48	181
152043	1685	53	670	17416	88229	3	84	29	107
38214	568	34	276	1888	13983	0	21	8	27
216299	2059	42	716	18700	80716	2	151	43	163
357602	2792	82	1021	36809	157384	0	187	52	198
198104	1395	61	481	24959	122975	0	171	53	205
410803	3590	80	1582	37343	191469	4	167	48	187
316105	2387	97	820	21849	231257	0	145	48	187
397297	3334	124	1153	49809	258287	3	175	50	186
187992	1250	35	473	21654	122531	0	137	40	151
102424	1121	42	401	8728	61394	0	100	36	131
286327	2880	335	954	20920	86480	4	150	40	155
407378	4104	170	1447	27195	195791	4	163	46	172
143860	1759	54	546	1037	18284	15	149	42	160
391854	4138	132	1728	42570	147581	2	161	46	172
157429	1831	77	689	17672	72558	4	112	39	143
258751	1787	48	590	34245	147341	2	135	41	151
282399	2535	94	897	16786	114651	1	124	46	158
217665	1816	113	613	20954	100187	0	45	32	125
366774	3873	116	1548	16378	130332	9	120	39	145
236660	2181	88	759	31852	134218	1	126	39	145
173260	2035	63	716	2805	10901	3	78	21	79
323545	2960	99	955	38086	145758	11	136	45	174
168994	1915	57	720	21166	75767	5	179	50	192
253330	2648	86	1023	34672	134969	2	118	36	132
301703	2633	105	818	36171	169216	1	147	44	159
1	2	0	0	0	0	9	0	0	0
14688	207	10	85	2065	7953	0	0	0	0
98	5	1	0	0	0	0	0	0	0
455	8	2	0	0	0	0	0	0	0
0	0	0	0	0	0	1	0	0	0
0	0	0	0	0	0	0	0	0	0
246435	2116	84	737	19354	105406	2	88	37	133
382374	3286	154	1080	22124	174586	3	129	52	204
0	0	0	0	0	0	0	0	0	0
203	4	4	0	0	0	0	0	0	0
7199	151	5	74	556	4245	0	0	0	0
46660	474	20	259	2089	21509	0	13	5	15
17547	141	5	69	2658	7670	0	4	1	4
116678	1047	42	285	1813	15673	0	76	43	152
969	29	2	0	0	0	0	0	0	0
206501	1822	68	591	17372	75882	2	71	34	125




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156816&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]7 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=156816&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156816&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 time7 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
X1[t] = -6621.20686091456 + 18.6792902874244X2[t] + 129.718229755785X3[t] + 96.5728322044372X4[t] -0.307030148761099X5[t] + 0.737535481670461X6[t] -145.873125893583X7[t] + 56.4219266694894X8[t] -1008.86288892542X9[t] + 487.392762192666X10[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X1[t] =  -6621.20686091456 +  18.6792902874244X2[t] +  129.718229755785X3[t] +  96.5728322044372X4[t] -0.307030148761099X5[t] +  0.737535481670461X6[t] -145.873125893583X7[t] +  56.4219266694894X8[t] -1008.86288892542X9[t] +  487.392762192666X10[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156816&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X1[t] =  -6621.20686091456 +  18.6792902874244X2[t] +  129.718229755785X3[t] +  96.5728322044372X4[t] -0.307030148761099X5[t] +  0.737535481670461X6[t] -145.873125893583X7[t] +  56.4219266694894X8[t] -1008.86288892542X9[t] +  487.392762192666X10[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156816&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156816&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
X1[t] = -6621.20686091456 + 18.6792902874244X2[t] + 129.718229755785X3[t] + 96.5728322044372X4[t] -0.307030148761099X5[t] + 0.737535481670461X6[t] -145.873125893583X7[t] + 56.4219266694894X8[t] -1008.86288892542X9[t] + 487.392762192666X10[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-6621.206860914566902.866313-0.95920.3389630.169482
X218.679290287424412.6003411.48240.1402660.070133
X3129.71822975578579.1490261.63890.1032730.051637
X496.572832204437227.3286293.53380.0005410.000271
X5-0.3070301487610990.35517-0.86450.388680.19434
X60.7375354816704610.0825788.931400
X7-145.873125893583727.693613-0.20050.8413860.420693
X856.4219266694894150.3897520.37520.7080490.354025
X9-1008.862888925421036.329162-0.97350.3318330.165917
X10487.392762192666310.842371.5680.118940.05947

\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) & -6621.20686091456 & 6902.866313 & -0.9592 & 0.338963 & 0.169482 \tabularnewline
X2 & 18.6792902874244 & 12.600341 & 1.4824 & 0.140266 & 0.070133 \tabularnewline
X3 & 129.718229755785 & 79.149026 & 1.6389 & 0.103273 & 0.051637 \tabularnewline
X4 & 96.5728322044372 & 27.328629 & 3.5338 & 0.000541 & 0.000271 \tabularnewline
X5 & -0.307030148761099 & 0.35517 & -0.8645 & 0.38868 & 0.19434 \tabularnewline
X6 & 0.737535481670461 & 0.082578 & 8.9314 & 0 & 0 \tabularnewline
X7 & -145.873125893583 & 727.693613 & -0.2005 & 0.841386 & 0.420693 \tabularnewline
X8 & 56.4219266694894 & 150.389752 & 0.3752 & 0.708049 & 0.354025 \tabularnewline
X9 & -1008.86288892542 & 1036.329162 & -0.9735 & 0.331833 & 0.165917 \tabularnewline
X10 & 487.392762192666 & 310.84237 & 1.568 & 0.11894 & 0.05947 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156816&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]-6621.20686091456[/C][C]6902.866313[/C][C]-0.9592[/C][C]0.338963[/C][C]0.169482[/C][/ROW]
[ROW][C]X2[/C][C]18.6792902874244[/C][C]12.600341[/C][C]1.4824[/C][C]0.140266[/C][C]0.070133[/C][/ROW]
[ROW][C]X3[/C][C]129.718229755785[/C][C]79.149026[/C][C]1.6389[/C][C]0.103273[/C][C]0.051637[/C][/ROW]
[ROW][C]X4[/C][C]96.5728322044372[/C][C]27.328629[/C][C]3.5338[/C][C]0.000541[/C][C]0.000271[/C][/ROW]
[ROW][C]X5[/C][C]-0.307030148761099[/C][C]0.35517[/C][C]-0.8645[/C][C]0.38868[/C][C]0.19434[/C][/ROW]
[ROW][C]X6[/C][C]0.737535481670461[/C][C]0.082578[/C][C]8.9314[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X7[/C][C]-145.873125893583[/C][C]727.693613[/C][C]-0.2005[/C][C]0.841386[/C][C]0.420693[/C][/ROW]
[ROW][C]X8[/C][C]56.4219266694894[/C][C]150.389752[/C][C]0.3752[/C][C]0.708049[/C][C]0.354025[/C][/ROW]
[ROW][C]X9[/C][C]-1008.86288892542[/C][C]1036.329162[/C][C]-0.9735[/C][C]0.331833[/C][C]0.165917[/C][/ROW]
[ROW][C]X10[/C][C]487.392762192666[/C][C]310.84237[/C][C]1.568[/C][C]0.11894[/C][C]0.05947[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156816&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156816&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)-6621.206860914566902.866313-0.95920.3389630.169482
X218.679290287424412.6003411.48240.1402660.070133
X3129.71822975578579.1490261.63890.1032730.051637
X496.572832204437227.3286293.53380.0005410.000271
X5-0.3070301487610990.35517-0.86450.388680.19434
X60.7375354816704610.0825788.931400
X7-145.873125893583727.693613-0.20050.8413860.420693
X856.4219266694894150.3897520.37520.7080490.354025
X9-1008.862888925421036.329162-0.97350.3318330.165917
X10487.392762192666310.842371.5680.118940.05947







Multiple Linear Regression - Regression Statistics
Multiple R0.961291904346723
R-squared0.924082125362549
Adjusted R-squared0.919645366455165
F-TEST (value)208.27864318359
F-TEST (DF numerator)9
F-TEST (DF denominator)154
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation32512.3635923793
Sum Squared Residuals162786283099.913

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.961291904346723 \tabularnewline
R-squared & 0.924082125362549 \tabularnewline
Adjusted R-squared & 0.919645366455165 \tabularnewline
F-TEST (value) & 208.27864318359 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 32512.3635923793 \tabularnewline
Sum Squared Residuals & 162786283099.913 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156816&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.961291904346723[/C][/ROW]
[ROW][C]R-squared[/C][C]0.924082125362549[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.919645366455165[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]208.27864318359[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/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]32512.3635923793[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]162786283099.913[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156816&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156816&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 R0.961291904346723
R-squared0.924082125362549
Adjusted R-squared0.919645366455165
F-TEST (value)208.27864318359
F-TEST (DF numerator)9
F-TEST (DF denominator)154
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation32512.3635923793
Sum Squared Residuals162786283099.913







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1272545246981.36001979325563.6399802069
2179444180427.722185863-983.722185863332
3222373210569.24411634711803.7558836529
4218443275090.521928017-56647.5219280169
5167843149243.93488550718599.0651144934
67084990225.0870277869-19376.0870277869
7506574520973.271635155-14399.2716351552
83318634080.9942684387-894.994268438681
9216660221239.722640759-4579.72264075899
10213274194758.17306355118515.8269364493
11307153286264.37243447520888.6275655248
12237633231497.7086445746135.29135542582
13164292171751.977828388-7459.97782838828
14364402286897.97940883777504.0205911631
15244103227051.60265694517051.3973430554
16384448443556.251697253-59108.2516972526
17325587271409.29468685254177.7053131476
18323652310456.82635641413195.1736435865
19176082178313.03383888-2231.03383888004
20266736262505.3830696274230.61693037346
21278265272306.5102935125958.48970648825
22442703327687.75696088115015.24303912
23180393170003.31369855110389.6863014488
24189897217867.965212247-27970.9652122474
25234247297107.669179762-62860.6691797623
26237452279029.887881729-41577.8878817288
27267268317101.434641015-49833.4346410155
28270787246163.0490970824623.9509029198
29155915156452.88504463-537.885044629872
30342564364674.570287242-22110.5702872417
31282172307251.97262375-25079.9726237502
32216584229592.661680374-13008.6616803738
33318563305767.75084657912795.2491534211
3498672109915.002693819-11243.002693819
35386258357830.4523581828427.54764182
36273950281937.240892453-7987.24089245255
37425120377172.44843265547947.5515673447
38227636209021.04764543718614.9523545633
3911565894090.684981122821567.3150188772
40349863355274.626176781-5411.62617678116
41324178335809.994573646-11631.9945736464
42178083187880.390258584-9797.39025858405
43195153211235.804040916-16082.8040409159
44177694160224.60004128517469.3999587146
45153778228983.775199136-75205.7751991358
46455168407686.25400778547481.7459922147
4778800104591.091285669-25791.0912856685
48208051235518.620734115-27467.6207341146
49348077399505.120136979-51428.1201369785
50175523234982.72346565-59459.7234656501
51224591249001.121259301-24410.1212593012
522418839290.0833765472-15102.0833765472
53372238339285.6543086232952.3456913798
546502968730.0131587009-3701.01315870092
55101097112478.298737029-11381.2987370294
56279012284594.818062011-5582.81806201133
57317644321009.560883594-3365.56088359386
58340471363441.206620576-22970.2066205763
59358958337391.00668426521566.9933157345
60252529213527.41592897539001.5840710252
61370628396670.273882499-26042.2738824994
62304468343578.453050353-39110.4530503532
63265870287374.19404573-21504.1940457296
64264889271054.950723871-6165.95072387126
65228595221190.8543359067404.14566409417
66216027205417.36181947110609.6381805285
67198798219173.639196576-20375.6391965756
68238146279479.02081826-41333.0208182598
69234891241141.742138938-6250.74213893781
70175816181911.936569726-6095.93656972596
71239314248742.57888322-9428.57888321955
727356690630.4757338055-17064.4757338055
73242622239258.5121102643363.48788973588
74187167191050.676129491-3883.67612949122
75209049211330.474428436-2281.47442843567
76360592253058.895893248107533.104106752
77342846311047.78538922131798.2146107794
78207650259869.23402324-52219.2340232395
79206500206703.444630804-203.444630804052
80182357182049.112237271307.887762729431
81153613177117.398268151-23504.3982681515
82456979368302.09152164788676.908478353
83145943165459.168738754-19516.1687387537
84280366225631.3195368354734.6804631701
8580953100896.051623848-19943.0516238477
86150216159986.044285987-9770.04428598738
87167878167087.515805511790.484194489356
88369718355733.68928965713984.3107103428
89322454312513.6292444159940.37075558485
90179797260369.98138238-80572.9813823796
91262883241585.10791082321297.8920891766
92262793219790.71479298743002.2852070132
93189142187225.1080498371916.89195016292
94275997262306.51515723813690.4848427619
95328875276517.86462764252357.1353723582
96189252194237.919291058-4985.91929105784
97222504223948.34551428-1444.34551428028
98287386295840.213065021-8454.2130650211
99389104411477.853075969-22373.8530759685
100397681394051.9732451093629.0267548907
101287748272187.45782397315560.5421760269
102294320303115.620176152-8795.62017615172
103186856206041.105991991-19185.1059919908
1044328757773.4043628349-14486.4043628349
105185468191951.778553158-6483.77855315828
106235352263704.77749001-28352.7774900099
107268077286338.516711824-18261.5167118241
108305195287720.95933035317474.0406696466
109143356216748.03173448-73392.0317344801
110154165270632.908285133-116467.908285133
111307000258811.3843052448188.6156947604
112298039234417.81297559263621.1870244083
1132362320531.42170879283091.57829120717
114195817229610.948890009-33793.9488900086
1156185747621.337658870814235.6623411292
116163766168874.604839511-5108.60483951079
117414506379825.5483154434680.4516845602
1182105421562.9874275997-508.987427599667
119252805240770.75313084112034.246869159
1203196137836.9277675673-5875.92776756732
121317367341530.319163854-24163.3191638538
122240153216151.40295738724001.5970426131
123175083146153.11418727728929.8858127228
124152043183352.866207239-31309.8662072392
1253821451059.9991696589-12845.9991696589
126216299204515.0961946111783.9038053903
127357602314137.73662097643464.263379024
128198104212929.940367908-14825.9403679081
129410803404898.8756054365904.12439456418
130316105344488.798142186-28383.7981421859
131397297407940.172364413-10643.1723644132
132187992191641.118423603-3649.1184236033
133102424134264.223651173-31840.2236511728
134286327283191.3948805633135.6051194365
135407378413921.856083284-6543.85608328389
136143860140965.2948290062894.70517099356
137391854396666.353361697-4812.35336169667
138157429188283.09703442-30854.0970344196
139258751227676.24136583631074.7586341639
140282399256406.31985102125992.6801489787
141217665199795.11749834717869.882501653
142366774358145.7488642278628.25113577327
143236660246332.90958245-9672.90958244952
144173260137169.39837372736090.6016262734
145323545295023.08664463128521.9133553693
146168994207964.688172917-38970.6881729174
147253330276073.227735387-22743.2277353869
148301703290129.20405940911573.7959405914
1491-7896.706413381897897.70641338189
1501468811982.88169205092705.11830794912
15198-6398.09217972166496.0921797216
152455-6212.336079103546667.33607910354
1530-6767.079986808096767.07998680809
1540-6621.206860914516621.20686091451
155246435218941.77727977827493.2227202223
156382374354812.90420433927561.0957956612
1570-6621.206860914516621.20686091451
158203-6027.616780741666230.61678074166
15971996954.4760613738244.523938626199
1604666048061.8316008438-1401.83160084383
161175479331.896516026078215.10348397393
16211667891900.842898916824777.1571010832
163969-5820.070983067626789.07098306762
164206501174276.75076719432224.249232806

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 272545 & 246981.360019793 & 25563.6399802069 \tabularnewline
2 & 179444 & 180427.722185863 & -983.722185863332 \tabularnewline
3 & 222373 & 210569.244116347 & 11803.7558836529 \tabularnewline
4 & 218443 & 275090.521928017 & -56647.5219280169 \tabularnewline
5 & 167843 & 149243.934885507 & 18599.0651144934 \tabularnewline
6 & 70849 & 90225.0870277869 & -19376.0870277869 \tabularnewline
7 & 506574 & 520973.271635155 & -14399.2716351552 \tabularnewline
8 & 33186 & 34080.9942684387 & -894.994268438681 \tabularnewline
9 & 216660 & 221239.722640759 & -4579.72264075899 \tabularnewline
10 & 213274 & 194758.173063551 & 18515.8269364493 \tabularnewline
11 & 307153 & 286264.372434475 & 20888.6275655248 \tabularnewline
12 & 237633 & 231497.708644574 & 6135.29135542582 \tabularnewline
13 & 164292 & 171751.977828388 & -7459.97782838828 \tabularnewline
14 & 364402 & 286897.979408837 & 77504.0205911631 \tabularnewline
15 & 244103 & 227051.602656945 & 17051.3973430554 \tabularnewline
16 & 384448 & 443556.251697253 & -59108.2516972526 \tabularnewline
17 & 325587 & 271409.294686852 & 54177.7053131476 \tabularnewline
18 & 323652 & 310456.826356414 & 13195.1736435865 \tabularnewline
19 & 176082 & 178313.03383888 & -2231.03383888004 \tabularnewline
20 & 266736 & 262505.383069627 & 4230.61693037346 \tabularnewline
21 & 278265 & 272306.510293512 & 5958.48970648825 \tabularnewline
22 & 442703 & 327687.75696088 & 115015.24303912 \tabularnewline
23 & 180393 & 170003.313698551 & 10389.6863014488 \tabularnewline
24 & 189897 & 217867.965212247 & -27970.9652122474 \tabularnewline
25 & 234247 & 297107.669179762 & -62860.6691797623 \tabularnewline
26 & 237452 & 279029.887881729 & -41577.8878817288 \tabularnewline
27 & 267268 & 317101.434641015 & -49833.4346410155 \tabularnewline
28 & 270787 & 246163.04909708 & 24623.9509029198 \tabularnewline
29 & 155915 & 156452.88504463 & -537.885044629872 \tabularnewline
30 & 342564 & 364674.570287242 & -22110.5702872417 \tabularnewline
31 & 282172 & 307251.97262375 & -25079.9726237502 \tabularnewline
32 & 216584 & 229592.661680374 & -13008.6616803738 \tabularnewline
33 & 318563 & 305767.750846579 & 12795.2491534211 \tabularnewline
34 & 98672 & 109915.002693819 & -11243.002693819 \tabularnewline
35 & 386258 & 357830.45235818 & 28427.54764182 \tabularnewline
36 & 273950 & 281937.240892453 & -7987.24089245255 \tabularnewline
37 & 425120 & 377172.448432655 & 47947.5515673447 \tabularnewline
38 & 227636 & 209021.047645437 & 18614.9523545633 \tabularnewline
39 & 115658 & 94090.6849811228 & 21567.3150188772 \tabularnewline
40 & 349863 & 355274.626176781 & -5411.62617678116 \tabularnewline
41 & 324178 & 335809.994573646 & -11631.9945736464 \tabularnewline
42 & 178083 & 187880.390258584 & -9797.39025858405 \tabularnewline
43 & 195153 & 211235.804040916 & -16082.8040409159 \tabularnewline
44 & 177694 & 160224.600041285 & 17469.3999587146 \tabularnewline
45 & 153778 & 228983.775199136 & -75205.7751991358 \tabularnewline
46 & 455168 & 407686.254007785 & 47481.7459922147 \tabularnewline
47 & 78800 & 104591.091285669 & -25791.0912856685 \tabularnewline
48 & 208051 & 235518.620734115 & -27467.6207341146 \tabularnewline
49 & 348077 & 399505.120136979 & -51428.1201369785 \tabularnewline
50 & 175523 & 234982.72346565 & -59459.7234656501 \tabularnewline
51 & 224591 & 249001.121259301 & -24410.1212593012 \tabularnewline
52 & 24188 & 39290.0833765472 & -15102.0833765472 \tabularnewline
53 & 372238 & 339285.65430862 & 32952.3456913798 \tabularnewline
54 & 65029 & 68730.0131587009 & -3701.01315870092 \tabularnewline
55 & 101097 & 112478.298737029 & -11381.2987370294 \tabularnewline
56 & 279012 & 284594.818062011 & -5582.81806201133 \tabularnewline
57 & 317644 & 321009.560883594 & -3365.56088359386 \tabularnewline
58 & 340471 & 363441.206620576 & -22970.2066205763 \tabularnewline
59 & 358958 & 337391.006684265 & 21566.9933157345 \tabularnewline
60 & 252529 & 213527.415928975 & 39001.5840710252 \tabularnewline
61 & 370628 & 396670.273882499 & -26042.2738824994 \tabularnewline
62 & 304468 & 343578.453050353 & -39110.4530503532 \tabularnewline
63 & 265870 & 287374.19404573 & -21504.1940457296 \tabularnewline
64 & 264889 & 271054.950723871 & -6165.95072387126 \tabularnewline
65 & 228595 & 221190.854335906 & 7404.14566409417 \tabularnewline
66 & 216027 & 205417.361819471 & 10609.6381805285 \tabularnewline
67 & 198798 & 219173.639196576 & -20375.6391965756 \tabularnewline
68 & 238146 & 279479.02081826 & -41333.0208182598 \tabularnewline
69 & 234891 & 241141.742138938 & -6250.74213893781 \tabularnewline
70 & 175816 & 181911.936569726 & -6095.93656972596 \tabularnewline
71 & 239314 & 248742.57888322 & -9428.57888321955 \tabularnewline
72 & 73566 & 90630.4757338055 & -17064.4757338055 \tabularnewline
73 & 242622 & 239258.512110264 & 3363.48788973588 \tabularnewline
74 & 187167 & 191050.676129491 & -3883.67612949122 \tabularnewline
75 & 209049 & 211330.474428436 & -2281.47442843567 \tabularnewline
76 & 360592 & 253058.895893248 & 107533.104106752 \tabularnewline
77 & 342846 & 311047.785389221 & 31798.2146107794 \tabularnewline
78 & 207650 & 259869.23402324 & -52219.2340232395 \tabularnewline
79 & 206500 & 206703.444630804 & -203.444630804052 \tabularnewline
80 & 182357 & 182049.112237271 & 307.887762729431 \tabularnewline
81 & 153613 & 177117.398268151 & -23504.3982681515 \tabularnewline
82 & 456979 & 368302.091521647 & 88676.908478353 \tabularnewline
83 & 145943 & 165459.168738754 & -19516.1687387537 \tabularnewline
84 & 280366 & 225631.31953683 & 54734.6804631701 \tabularnewline
85 & 80953 & 100896.051623848 & -19943.0516238477 \tabularnewline
86 & 150216 & 159986.044285987 & -9770.04428598738 \tabularnewline
87 & 167878 & 167087.515805511 & 790.484194489356 \tabularnewline
88 & 369718 & 355733.689289657 & 13984.3107103428 \tabularnewline
89 & 322454 & 312513.629244415 & 9940.37075558485 \tabularnewline
90 & 179797 & 260369.98138238 & -80572.9813823796 \tabularnewline
91 & 262883 & 241585.107910823 & 21297.8920891766 \tabularnewline
92 & 262793 & 219790.714792987 & 43002.2852070132 \tabularnewline
93 & 189142 & 187225.108049837 & 1916.89195016292 \tabularnewline
94 & 275997 & 262306.515157238 & 13690.4848427619 \tabularnewline
95 & 328875 & 276517.864627642 & 52357.1353723582 \tabularnewline
96 & 189252 & 194237.919291058 & -4985.91929105784 \tabularnewline
97 & 222504 & 223948.34551428 & -1444.34551428028 \tabularnewline
98 & 287386 & 295840.213065021 & -8454.2130650211 \tabularnewline
99 & 389104 & 411477.853075969 & -22373.8530759685 \tabularnewline
100 & 397681 & 394051.973245109 & 3629.0267548907 \tabularnewline
101 & 287748 & 272187.457823973 & 15560.5421760269 \tabularnewline
102 & 294320 & 303115.620176152 & -8795.62017615172 \tabularnewline
103 & 186856 & 206041.105991991 & -19185.1059919908 \tabularnewline
104 & 43287 & 57773.4043628349 & -14486.4043628349 \tabularnewline
105 & 185468 & 191951.778553158 & -6483.77855315828 \tabularnewline
106 & 235352 & 263704.77749001 & -28352.7774900099 \tabularnewline
107 & 268077 & 286338.516711824 & -18261.5167118241 \tabularnewline
108 & 305195 & 287720.959330353 & 17474.0406696466 \tabularnewline
109 & 143356 & 216748.03173448 & -73392.0317344801 \tabularnewline
110 & 154165 & 270632.908285133 & -116467.908285133 \tabularnewline
111 & 307000 & 258811.38430524 & 48188.6156947604 \tabularnewline
112 & 298039 & 234417.812975592 & 63621.1870244083 \tabularnewline
113 & 23623 & 20531.4217087928 & 3091.57829120717 \tabularnewline
114 & 195817 & 229610.948890009 & -33793.9488900086 \tabularnewline
115 & 61857 & 47621.3376588708 & 14235.6623411292 \tabularnewline
116 & 163766 & 168874.604839511 & -5108.60483951079 \tabularnewline
117 & 414506 & 379825.54831544 & 34680.4516845602 \tabularnewline
118 & 21054 & 21562.9874275997 & -508.987427599667 \tabularnewline
119 & 252805 & 240770.753130841 & 12034.246869159 \tabularnewline
120 & 31961 & 37836.9277675673 & -5875.92776756732 \tabularnewline
121 & 317367 & 341530.319163854 & -24163.3191638538 \tabularnewline
122 & 240153 & 216151.402957387 & 24001.5970426131 \tabularnewline
123 & 175083 & 146153.114187277 & 28929.8858127228 \tabularnewline
124 & 152043 & 183352.866207239 & -31309.8662072392 \tabularnewline
125 & 38214 & 51059.9991696589 & -12845.9991696589 \tabularnewline
126 & 216299 & 204515.09619461 & 11783.9038053903 \tabularnewline
127 & 357602 & 314137.736620976 & 43464.263379024 \tabularnewline
128 & 198104 & 212929.940367908 & -14825.9403679081 \tabularnewline
129 & 410803 & 404898.875605436 & 5904.12439456418 \tabularnewline
130 & 316105 & 344488.798142186 & -28383.7981421859 \tabularnewline
131 & 397297 & 407940.172364413 & -10643.1723644132 \tabularnewline
132 & 187992 & 191641.118423603 & -3649.1184236033 \tabularnewline
133 & 102424 & 134264.223651173 & -31840.2236511728 \tabularnewline
134 & 286327 & 283191.394880563 & 3135.6051194365 \tabularnewline
135 & 407378 & 413921.856083284 & -6543.85608328389 \tabularnewline
136 & 143860 & 140965.294829006 & 2894.70517099356 \tabularnewline
137 & 391854 & 396666.353361697 & -4812.35336169667 \tabularnewline
138 & 157429 & 188283.09703442 & -30854.0970344196 \tabularnewline
139 & 258751 & 227676.241365836 & 31074.7586341639 \tabularnewline
140 & 282399 & 256406.319851021 & 25992.6801489787 \tabularnewline
141 & 217665 & 199795.117498347 & 17869.882501653 \tabularnewline
142 & 366774 & 358145.748864227 & 8628.25113577327 \tabularnewline
143 & 236660 & 246332.90958245 & -9672.90958244952 \tabularnewline
144 & 173260 & 137169.398373727 & 36090.6016262734 \tabularnewline
145 & 323545 & 295023.086644631 & 28521.9133553693 \tabularnewline
146 & 168994 & 207964.688172917 & -38970.6881729174 \tabularnewline
147 & 253330 & 276073.227735387 & -22743.2277353869 \tabularnewline
148 & 301703 & 290129.204059409 & 11573.7959405914 \tabularnewline
149 & 1 & -7896.70641338189 & 7897.70641338189 \tabularnewline
150 & 14688 & 11982.8816920509 & 2705.11830794912 \tabularnewline
151 & 98 & -6398.0921797216 & 6496.0921797216 \tabularnewline
152 & 455 & -6212.33607910354 & 6667.33607910354 \tabularnewline
153 & 0 & -6767.07998680809 & 6767.07998680809 \tabularnewline
154 & 0 & -6621.20686091451 & 6621.20686091451 \tabularnewline
155 & 246435 & 218941.777279778 & 27493.2227202223 \tabularnewline
156 & 382374 & 354812.904204339 & 27561.0957956612 \tabularnewline
157 & 0 & -6621.20686091451 & 6621.20686091451 \tabularnewline
158 & 203 & -6027.61678074166 & 6230.61678074166 \tabularnewline
159 & 7199 & 6954.4760613738 & 244.523938626199 \tabularnewline
160 & 46660 & 48061.8316008438 & -1401.83160084383 \tabularnewline
161 & 17547 & 9331.89651602607 & 8215.10348397393 \tabularnewline
162 & 116678 & 91900.8428989168 & 24777.1571010832 \tabularnewline
163 & 969 & -5820.07098306762 & 6789.07098306762 \tabularnewline
164 & 206501 & 174276.750767194 & 32224.249232806 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156816&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]272545[/C][C]246981.360019793[/C][C]25563.6399802069[/C][/ROW]
[ROW][C]2[/C][C]179444[/C][C]180427.722185863[/C][C]-983.722185863332[/C][/ROW]
[ROW][C]3[/C][C]222373[/C][C]210569.244116347[/C][C]11803.7558836529[/C][/ROW]
[ROW][C]4[/C][C]218443[/C][C]275090.521928017[/C][C]-56647.5219280169[/C][/ROW]
[ROW][C]5[/C][C]167843[/C][C]149243.934885507[/C][C]18599.0651144934[/C][/ROW]
[ROW][C]6[/C][C]70849[/C][C]90225.0870277869[/C][C]-19376.0870277869[/C][/ROW]
[ROW][C]7[/C][C]506574[/C][C]520973.271635155[/C][C]-14399.2716351552[/C][/ROW]
[ROW][C]8[/C][C]33186[/C][C]34080.9942684387[/C][C]-894.994268438681[/C][/ROW]
[ROW][C]9[/C][C]216660[/C][C]221239.722640759[/C][C]-4579.72264075899[/C][/ROW]
[ROW][C]10[/C][C]213274[/C][C]194758.173063551[/C][C]18515.8269364493[/C][/ROW]
[ROW][C]11[/C][C]307153[/C][C]286264.372434475[/C][C]20888.6275655248[/C][/ROW]
[ROW][C]12[/C][C]237633[/C][C]231497.708644574[/C][C]6135.29135542582[/C][/ROW]
[ROW][C]13[/C][C]164292[/C][C]171751.977828388[/C][C]-7459.97782838828[/C][/ROW]
[ROW][C]14[/C][C]364402[/C][C]286897.979408837[/C][C]77504.0205911631[/C][/ROW]
[ROW][C]15[/C][C]244103[/C][C]227051.602656945[/C][C]17051.3973430554[/C][/ROW]
[ROW][C]16[/C][C]384448[/C][C]443556.251697253[/C][C]-59108.2516972526[/C][/ROW]
[ROW][C]17[/C][C]325587[/C][C]271409.294686852[/C][C]54177.7053131476[/C][/ROW]
[ROW][C]18[/C][C]323652[/C][C]310456.826356414[/C][C]13195.1736435865[/C][/ROW]
[ROW][C]19[/C][C]176082[/C][C]178313.03383888[/C][C]-2231.03383888004[/C][/ROW]
[ROW][C]20[/C][C]266736[/C][C]262505.383069627[/C][C]4230.61693037346[/C][/ROW]
[ROW][C]21[/C][C]278265[/C][C]272306.510293512[/C][C]5958.48970648825[/C][/ROW]
[ROW][C]22[/C][C]442703[/C][C]327687.75696088[/C][C]115015.24303912[/C][/ROW]
[ROW][C]23[/C][C]180393[/C][C]170003.313698551[/C][C]10389.6863014488[/C][/ROW]
[ROW][C]24[/C][C]189897[/C][C]217867.965212247[/C][C]-27970.9652122474[/C][/ROW]
[ROW][C]25[/C][C]234247[/C][C]297107.669179762[/C][C]-62860.6691797623[/C][/ROW]
[ROW][C]26[/C][C]237452[/C][C]279029.887881729[/C][C]-41577.8878817288[/C][/ROW]
[ROW][C]27[/C][C]267268[/C][C]317101.434641015[/C][C]-49833.4346410155[/C][/ROW]
[ROW][C]28[/C][C]270787[/C][C]246163.04909708[/C][C]24623.9509029198[/C][/ROW]
[ROW][C]29[/C][C]155915[/C][C]156452.88504463[/C][C]-537.885044629872[/C][/ROW]
[ROW][C]30[/C][C]342564[/C][C]364674.570287242[/C][C]-22110.5702872417[/C][/ROW]
[ROW][C]31[/C][C]282172[/C][C]307251.97262375[/C][C]-25079.9726237502[/C][/ROW]
[ROW][C]32[/C][C]216584[/C][C]229592.661680374[/C][C]-13008.6616803738[/C][/ROW]
[ROW][C]33[/C][C]318563[/C][C]305767.750846579[/C][C]12795.2491534211[/C][/ROW]
[ROW][C]34[/C][C]98672[/C][C]109915.002693819[/C][C]-11243.002693819[/C][/ROW]
[ROW][C]35[/C][C]386258[/C][C]357830.45235818[/C][C]28427.54764182[/C][/ROW]
[ROW][C]36[/C][C]273950[/C][C]281937.240892453[/C][C]-7987.24089245255[/C][/ROW]
[ROW][C]37[/C][C]425120[/C][C]377172.448432655[/C][C]47947.5515673447[/C][/ROW]
[ROW][C]38[/C][C]227636[/C][C]209021.047645437[/C][C]18614.9523545633[/C][/ROW]
[ROW][C]39[/C][C]115658[/C][C]94090.6849811228[/C][C]21567.3150188772[/C][/ROW]
[ROW][C]40[/C][C]349863[/C][C]355274.626176781[/C][C]-5411.62617678116[/C][/ROW]
[ROW][C]41[/C][C]324178[/C][C]335809.994573646[/C][C]-11631.9945736464[/C][/ROW]
[ROW][C]42[/C][C]178083[/C][C]187880.390258584[/C][C]-9797.39025858405[/C][/ROW]
[ROW][C]43[/C][C]195153[/C][C]211235.804040916[/C][C]-16082.8040409159[/C][/ROW]
[ROW][C]44[/C][C]177694[/C][C]160224.600041285[/C][C]17469.3999587146[/C][/ROW]
[ROW][C]45[/C][C]153778[/C][C]228983.775199136[/C][C]-75205.7751991358[/C][/ROW]
[ROW][C]46[/C][C]455168[/C][C]407686.254007785[/C][C]47481.7459922147[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]104591.091285669[/C][C]-25791.0912856685[/C][/ROW]
[ROW][C]48[/C][C]208051[/C][C]235518.620734115[/C][C]-27467.6207341146[/C][/ROW]
[ROW][C]49[/C][C]348077[/C][C]399505.120136979[/C][C]-51428.1201369785[/C][/ROW]
[ROW][C]50[/C][C]175523[/C][C]234982.72346565[/C][C]-59459.7234656501[/C][/ROW]
[ROW][C]51[/C][C]224591[/C][C]249001.121259301[/C][C]-24410.1212593012[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]39290.0833765472[/C][C]-15102.0833765472[/C][/ROW]
[ROW][C]53[/C][C]372238[/C][C]339285.65430862[/C][C]32952.3456913798[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]68730.0131587009[/C][C]-3701.01315870092[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]112478.298737029[/C][C]-11381.2987370294[/C][/ROW]
[ROW][C]56[/C][C]279012[/C][C]284594.818062011[/C][C]-5582.81806201133[/C][/ROW]
[ROW][C]57[/C][C]317644[/C][C]321009.560883594[/C][C]-3365.56088359386[/C][/ROW]
[ROW][C]58[/C][C]340471[/C][C]363441.206620576[/C][C]-22970.2066205763[/C][/ROW]
[ROW][C]59[/C][C]358958[/C][C]337391.006684265[/C][C]21566.9933157345[/C][/ROW]
[ROW][C]60[/C][C]252529[/C][C]213527.415928975[/C][C]39001.5840710252[/C][/ROW]
[ROW][C]61[/C][C]370628[/C][C]396670.273882499[/C][C]-26042.2738824994[/C][/ROW]
[ROW][C]62[/C][C]304468[/C][C]343578.453050353[/C][C]-39110.4530503532[/C][/ROW]
[ROW][C]63[/C][C]265870[/C][C]287374.19404573[/C][C]-21504.1940457296[/C][/ROW]
[ROW][C]64[/C][C]264889[/C][C]271054.950723871[/C][C]-6165.95072387126[/C][/ROW]
[ROW][C]65[/C][C]228595[/C][C]221190.854335906[/C][C]7404.14566409417[/C][/ROW]
[ROW][C]66[/C][C]216027[/C][C]205417.361819471[/C][C]10609.6381805285[/C][/ROW]
[ROW][C]67[/C][C]198798[/C][C]219173.639196576[/C][C]-20375.6391965756[/C][/ROW]
[ROW][C]68[/C][C]238146[/C][C]279479.02081826[/C][C]-41333.0208182598[/C][/ROW]
[ROW][C]69[/C][C]234891[/C][C]241141.742138938[/C][C]-6250.74213893781[/C][/ROW]
[ROW][C]70[/C][C]175816[/C][C]181911.936569726[/C][C]-6095.93656972596[/C][/ROW]
[ROW][C]71[/C][C]239314[/C][C]248742.57888322[/C][C]-9428.57888321955[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]90630.4757338055[/C][C]-17064.4757338055[/C][/ROW]
[ROW][C]73[/C][C]242622[/C][C]239258.512110264[/C][C]3363.48788973588[/C][/ROW]
[ROW][C]74[/C][C]187167[/C][C]191050.676129491[/C][C]-3883.67612949122[/C][/ROW]
[ROW][C]75[/C][C]209049[/C][C]211330.474428436[/C][C]-2281.47442843567[/C][/ROW]
[ROW][C]76[/C][C]360592[/C][C]253058.895893248[/C][C]107533.104106752[/C][/ROW]
[ROW][C]77[/C][C]342846[/C][C]311047.785389221[/C][C]31798.2146107794[/C][/ROW]
[ROW][C]78[/C][C]207650[/C][C]259869.23402324[/C][C]-52219.2340232395[/C][/ROW]
[ROW][C]79[/C][C]206500[/C][C]206703.444630804[/C][C]-203.444630804052[/C][/ROW]
[ROW][C]80[/C][C]182357[/C][C]182049.112237271[/C][C]307.887762729431[/C][/ROW]
[ROW][C]81[/C][C]153613[/C][C]177117.398268151[/C][C]-23504.3982681515[/C][/ROW]
[ROW][C]82[/C][C]456979[/C][C]368302.091521647[/C][C]88676.908478353[/C][/ROW]
[ROW][C]83[/C][C]145943[/C][C]165459.168738754[/C][C]-19516.1687387537[/C][/ROW]
[ROW][C]84[/C][C]280366[/C][C]225631.31953683[/C][C]54734.6804631701[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]100896.051623848[/C][C]-19943.0516238477[/C][/ROW]
[ROW][C]86[/C][C]150216[/C][C]159986.044285987[/C][C]-9770.04428598738[/C][/ROW]
[ROW][C]87[/C][C]167878[/C][C]167087.515805511[/C][C]790.484194489356[/C][/ROW]
[ROW][C]88[/C][C]369718[/C][C]355733.689289657[/C][C]13984.3107103428[/C][/ROW]
[ROW][C]89[/C][C]322454[/C][C]312513.629244415[/C][C]9940.37075558485[/C][/ROW]
[ROW][C]90[/C][C]179797[/C][C]260369.98138238[/C][C]-80572.9813823796[/C][/ROW]
[ROW][C]91[/C][C]262883[/C][C]241585.107910823[/C][C]21297.8920891766[/C][/ROW]
[ROW][C]92[/C][C]262793[/C][C]219790.714792987[/C][C]43002.2852070132[/C][/ROW]
[ROW][C]93[/C][C]189142[/C][C]187225.108049837[/C][C]1916.89195016292[/C][/ROW]
[ROW][C]94[/C][C]275997[/C][C]262306.515157238[/C][C]13690.4848427619[/C][/ROW]
[ROW][C]95[/C][C]328875[/C][C]276517.864627642[/C][C]52357.1353723582[/C][/ROW]
[ROW][C]96[/C][C]189252[/C][C]194237.919291058[/C][C]-4985.91929105784[/C][/ROW]
[ROW][C]97[/C][C]222504[/C][C]223948.34551428[/C][C]-1444.34551428028[/C][/ROW]
[ROW][C]98[/C][C]287386[/C][C]295840.213065021[/C][C]-8454.2130650211[/C][/ROW]
[ROW][C]99[/C][C]389104[/C][C]411477.853075969[/C][C]-22373.8530759685[/C][/ROW]
[ROW][C]100[/C][C]397681[/C][C]394051.973245109[/C][C]3629.0267548907[/C][/ROW]
[ROW][C]101[/C][C]287748[/C][C]272187.457823973[/C][C]15560.5421760269[/C][/ROW]
[ROW][C]102[/C][C]294320[/C][C]303115.620176152[/C][C]-8795.62017615172[/C][/ROW]
[ROW][C]103[/C][C]186856[/C][C]206041.105991991[/C][C]-19185.1059919908[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]57773.4043628349[/C][C]-14486.4043628349[/C][/ROW]
[ROW][C]105[/C][C]185468[/C][C]191951.778553158[/C][C]-6483.77855315828[/C][/ROW]
[ROW][C]106[/C][C]235352[/C][C]263704.77749001[/C][C]-28352.7774900099[/C][/ROW]
[ROW][C]107[/C][C]268077[/C][C]286338.516711824[/C][C]-18261.5167118241[/C][/ROW]
[ROW][C]108[/C][C]305195[/C][C]287720.959330353[/C][C]17474.0406696466[/C][/ROW]
[ROW][C]109[/C][C]143356[/C][C]216748.03173448[/C][C]-73392.0317344801[/C][/ROW]
[ROW][C]110[/C][C]154165[/C][C]270632.908285133[/C][C]-116467.908285133[/C][/ROW]
[ROW][C]111[/C][C]307000[/C][C]258811.38430524[/C][C]48188.6156947604[/C][/ROW]
[ROW][C]112[/C][C]298039[/C][C]234417.812975592[/C][C]63621.1870244083[/C][/ROW]
[ROW][C]113[/C][C]23623[/C][C]20531.4217087928[/C][C]3091.57829120717[/C][/ROW]
[ROW][C]114[/C][C]195817[/C][C]229610.948890009[/C][C]-33793.9488900086[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]47621.3376588708[/C][C]14235.6623411292[/C][/ROW]
[ROW][C]116[/C][C]163766[/C][C]168874.604839511[/C][C]-5108.60483951079[/C][/ROW]
[ROW][C]117[/C][C]414506[/C][C]379825.54831544[/C][C]34680.4516845602[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]21562.9874275997[/C][C]-508.987427599667[/C][/ROW]
[ROW][C]119[/C][C]252805[/C][C]240770.753130841[/C][C]12034.246869159[/C][/ROW]
[ROW][C]120[/C][C]31961[/C][C]37836.9277675673[/C][C]-5875.92776756732[/C][/ROW]
[ROW][C]121[/C][C]317367[/C][C]341530.319163854[/C][C]-24163.3191638538[/C][/ROW]
[ROW][C]122[/C][C]240153[/C][C]216151.402957387[/C][C]24001.5970426131[/C][/ROW]
[ROW][C]123[/C][C]175083[/C][C]146153.114187277[/C][C]28929.8858127228[/C][/ROW]
[ROW][C]124[/C][C]152043[/C][C]183352.866207239[/C][C]-31309.8662072392[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]51059.9991696589[/C][C]-12845.9991696589[/C][/ROW]
[ROW][C]126[/C][C]216299[/C][C]204515.09619461[/C][C]11783.9038053903[/C][/ROW]
[ROW][C]127[/C][C]357602[/C][C]314137.736620976[/C][C]43464.263379024[/C][/ROW]
[ROW][C]128[/C][C]198104[/C][C]212929.940367908[/C][C]-14825.9403679081[/C][/ROW]
[ROW][C]129[/C][C]410803[/C][C]404898.875605436[/C][C]5904.12439456418[/C][/ROW]
[ROW][C]130[/C][C]316105[/C][C]344488.798142186[/C][C]-28383.7981421859[/C][/ROW]
[ROW][C]131[/C][C]397297[/C][C]407940.172364413[/C][C]-10643.1723644132[/C][/ROW]
[ROW][C]132[/C][C]187992[/C][C]191641.118423603[/C][C]-3649.1184236033[/C][/ROW]
[ROW][C]133[/C][C]102424[/C][C]134264.223651173[/C][C]-31840.2236511728[/C][/ROW]
[ROW][C]134[/C][C]286327[/C][C]283191.394880563[/C][C]3135.6051194365[/C][/ROW]
[ROW][C]135[/C][C]407378[/C][C]413921.856083284[/C][C]-6543.85608328389[/C][/ROW]
[ROW][C]136[/C][C]143860[/C][C]140965.294829006[/C][C]2894.70517099356[/C][/ROW]
[ROW][C]137[/C][C]391854[/C][C]396666.353361697[/C][C]-4812.35336169667[/C][/ROW]
[ROW][C]138[/C][C]157429[/C][C]188283.09703442[/C][C]-30854.0970344196[/C][/ROW]
[ROW][C]139[/C][C]258751[/C][C]227676.241365836[/C][C]31074.7586341639[/C][/ROW]
[ROW][C]140[/C][C]282399[/C][C]256406.319851021[/C][C]25992.6801489787[/C][/ROW]
[ROW][C]141[/C][C]217665[/C][C]199795.117498347[/C][C]17869.882501653[/C][/ROW]
[ROW][C]142[/C][C]366774[/C][C]358145.748864227[/C][C]8628.25113577327[/C][/ROW]
[ROW][C]143[/C][C]236660[/C][C]246332.90958245[/C][C]-9672.90958244952[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]137169.398373727[/C][C]36090.6016262734[/C][/ROW]
[ROW][C]145[/C][C]323545[/C][C]295023.086644631[/C][C]28521.9133553693[/C][/ROW]
[ROW][C]146[/C][C]168994[/C][C]207964.688172917[/C][C]-38970.6881729174[/C][/ROW]
[ROW][C]147[/C][C]253330[/C][C]276073.227735387[/C][C]-22743.2277353869[/C][/ROW]
[ROW][C]148[/C][C]301703[/C][C]290129.204059409[/C][C]11573.7959405914[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]-7896.70641338189[/C][C]7897.70641338189[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]11982.8816920509[/C][C]2705.11830794912[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]-6398.0921797216[/C][C]6496.0921797216[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]-6212.33607910354[/C][C]6667.33607910354[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]-6767.07998680809[/C][C]6767.07998680809[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]-6621.20686091451[/C][C]6621.20686091451[/C][/ROW]
[ROW][C]155[/C][C]246435[/C][C]218941.777279778[/C][C]27493.2227202223[/C][/ROW]
[ROW][C]156[/C][C]382374[/C][C]354812.904204339[/C][C]27561.0957956612[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]-6621.20686091451[/C][C]6621.20686091451[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]-6027.61678074166[/C][C]6230.61678074166[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]6954.4760613738[/C][C]244.523938626199[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]48061.8316008438[/C][C]-1401.83160084383[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]9331.89651602607[/C][C]8215.10348397393[/C][/ROW]
[ROW][C]162[/C][C]116678[/C][C]91900.8428989168[/C][C]24777.1571010832[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]-5820.07098306762[/C][C]6789.07098306762[/C][/ROW]
[ROW][C]164[/C][C]206501[/C][C]174276.750767194[/C][C]32224.249232806[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156816&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156816&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
1272545246981.36001979325563.6399802069
2179444180427.722185863-983.722185863332
3222373210569.24411634711803.7558836529
4218443275090.521928017-56647.5219280169
5167843149243.93488550718599.0651144934
67084990225.0870277869-19376.0870277869
7506574520973.271635155-14399.2716351552
83318634080.9942684387-894.994268438681
9216660221239.722640759-4579.72264075899
10213274194758.17306355118515.8269364493
11307153286264.37243447520888.6275655248
12237633231497.7086445746135.29135542582
13164292171751.977828388-7459.97782838828
14364402286897.97940883777504.0205911631
15244103227051.60265694517051.3973430554
16384448443556.251697253-59108.2516972526
17325587271409.29468685254177.7053131476
18323652310456.82635641413195.1736435865
19176082178313.03383888-2231.03383888004
20266736262505.3830696274230.61693037346
21278265272306.5102935125958.48970648825
22442703327687.75696088115015.24303912
23180393170003.31369855110389.6863014488
24189897217867.965212247-27970.9652122474
25234247297107.669179762-62860.6691797623
26237452279029.887881729-41577.8878817288
27267268317101.434641015-49833.4346410155
28270787246163.0490970824623.9509029198
29155915156452.88504463-537.885044629872
30342564364674.570287242-22110.5702872417
31282172307251.97262375-25079.9726237502
32216584229592.661680374-13008.6616803738
33318563305767.75084657912795.2491534211
3498672109915.002693819-11243.002693819
35386258357830.4523581828427.54764182
36273950281937.240892453-7987.24089245255
37425120377172.44843265547947.5515673447
38227636209021.04764543718614.9523545633
3911565894090.684981122821567.3150188772
40349863355274.626176781-5411.62617678116
41324178335809.994573646-11631.9945736464
42178083187880.390258584-9797.39025858405
43195153211235.804040916-16082.8040409159
44177694160224.60004128517469.3999587146
45153778228983.775199136-75205.7751991358
46455168407686.25400778547481.7459922147
4778800104591.091285669-25791.0912856685
48208051235518.620734115-27467.6207341146
49348077399505.120136979-51428.1201369785
50175523234982.72346565-59459.7234656501
51224591249001.121259301-24410.1212593012
522418839290.0833765472-15102.0833765472
53372238339285.6543086232952.3456913798
546502968730.0131587009-3701.01315870092
55101097112478.298737029-11381.2987370294
56279012284594.818062011-5582.81806201133
57317644321009.560883594-3365.56088359386
58340471363441.206620576-22970.2066205763
59358958337391.00668426521566.9933157345
60252529213527.41592897539001.5840710252
61370628396670.273882499-26042.2738824994
62304468343578.453050353-39110.4530503532
63265870287374.19404573-21504.1940457296
64264889271054.950723871-6165.95072387126
65228595221190.8543359067404.14566409417
66216027205417.36181947110609.6381805285
67198798219173.639196576-20375.6391965756
68238146279479.02081826-41333.0208182598
69234891241141.742138938-6250.74213893781
70175816181911.936569726-6095.93656972596
71239314248742.57888322-9428.57888321955
727356690630.4757338055-17064.4757338055
73242622239258.5121102643363.48788973588
74187167191050.676129491-3883.67612949122
75209049211330.474428436-2281.47442843567
76360592253058.895893248107533.104106752
77342846311047.78538922131798.2146107794
78207650259869.23402324-52219.2340232395
79206500206703.444630804-203.444630804052
80182357182049.112237271307.887762729431
81153613177117.398268151-23504.3982681515
82456979368302.09152164788676.908478353
83145943165459.168738754-19516.1687387537
84280366225631.3195368354734.6804631701
8580953100896.051623848-19943.0516238477
86150216159986.044285987-9770.04428598738
87167878167087.515805511790.484194489356
88369718355733.68928965713984.3107103428
89322454312513.6292444159940.37075558485
90179797260369.98138238-80572.9813823796
91262883241585.10791082321297.8920891766
92262793219790.71479298743002.2852070132
93189142187225.1080498371916.89195016292
94275997262306.51515723813690.4848427619
95328875276517.86462764252357.1353723582
96189252194237.919291058-4985.91929105784
97222504223948.34551428-1444.34551428028
98287386295840.213065021-8454.2130650211
99389104411477.853075969-22373.8530759685
100397681394051.9732451093629.0267548907
101287748272187.45782397315560.5421760269
102294320303115.620176152-8795.62017615172
103186856206041.105991991-19185.1059919908
1044328757773.4043628349-14486.4043628349
105185468191951.778553158-6483.77855315828
106235352263704.77749001-28352.7774900099
107268077286338.516711824-18261.5167118241
108305195287720.95933035317474.0406696466
109143356216748.03173448-73392.0317344801
110154165270632.908285133-116467.908285133
111307000258811.3843052448188.6156947604
112298039234417.81297559263621.1870244083
1132362320531.42170879283091.57829120717
114195817229610.948890009-33793.9488900086
1156185747621.337658870814235.6623411292
116163766168874.604839511-5108.60483951079
117414506379825.5483154434680.4516845602
1182105421562.9874275997-508.987427599667
119252805240770.75313084112034.246869159
1203196137836.9277675673-5875.92776756732
121317367341530.319163854-24163.3191638538
122240153216151.40295738724001.5970426131
123175083146153.11418727728929.8858127228
124152043183352.866207239-31309.8662072392
1253821451059.9991696589-12845.9991696589
126216299204515.0961946111783.9038053903
127357602314137.73662097643464.263379024
128198104212929.940367908-14825.9403679081
129410803404898.8756054365904.12439456418
130316105344488.798142186-28383.7981421859
131397297407940.172364413-10643.1723644132
132187992191641.118423603-3649.1184236033
133102424134264.223651173-31840.2236511728
134286327283191.3948805633135.6051194365
135407378413921.856083284-6543.85608328389
136143860140965.2948290062894.70517099356
137391854396666.353361697-4812.35336169667
138157429188283.09703442-30854.0970344196
139258751227676.24136583631074.7586341639
140282399256406.31985102125992.6801489787
141217665199795.11749834717869.882501653
142366774358145.7488642278628.25113577327
143236660246332.90958245-9672.90958244952
144173260137169.39837372736090.6016262734
145323545295023.08664463128521.9133553693
146168994207964.688172917-38970.6881729174
147253330276073.227735387-22743.2277353869
148301703290129.20405940911573.7959405914
1491-7896.706413381897897.70641338189
1501468811982.88169205092705.11830794912
15198-6398.09217972166496.0921797216
152455-6212.336079103546667.33607910354
1530-6767.079986808096767.07998680809
1540-6621.206860914516621.20686091451
155246435218941.77727977827493.2227202223
156382374354812.90420433927561.0957956612
1570-6621.206860914516621.20686091451
158203-6027.616780741666230.61678074166
15971996954.4760613738244.523938626199
1604666048061.8316008438-1401.83160084383
161175479331.896516026078215.10348397393
16211667891900.842898916824777.1571010832
163969-5820.070983067626789.07098306762
164206501174276.75076719432224.249232806







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.06277624072583350.1255524814516670.937223759274166
140.2181873239908260.4363746479816520.781812676009174
150.1419034206370580.2838068412741160.858096579362942
160.2940377708493190.5880755416986370.705962229150681
170.3495573717516390.6991147435032780.650442628248361
180.6255020535755960.7489958928488070.374497946424404
190.5270490001360220.9459019997279550.472950999863978
200.4295713254008110.8591426508016220.570428674599189
210.3418033929804880.6836067859609750.658196607019512
220.8800509889869310.2398980220261380.119949011013069
230.840924210713720.318151578572560.15907578928628
240.854239156904690.2915216861906210.14576084309531
250.8585552031753370.2828895936493260.141444796824663
260.8293646923093530.3412706153812940.170635307690647
270.8733040075654270.2533919848691470.126695992434573
280.8396077469575540.3207845060848920.160392253042446
290.7952868825072790.4094262349854420.204713117492721
300.8693762806377770.2612474387244460.130623719362223
310.8677421129216920.2645157741566150.132257887078308
320.8594464107865450.2811071784269090.140553589213455
330.8235380489870990.3529239020258020.176461951012901
340.7827482647841480.4345034704317040.217251735215852
350.7695768149409040.4608463701181920.230423185059096
360.7207144839571510.5585710320856970.279285516042849
370.7557943568209760.4884112863580480.244205643179024
380.7671909696534030.4656180606931930.232809030346597
390.7270769577696330.5458460844607340.272923042230367
400.696501063338550.6069978733228990.30349893666145
410.6608991593492880.6782016813014230.339100840650712
420.6269709385039070.7460581229921850.373029061496093
430.7107838904259730.5784322191480540.289216109574027
440.6857449874910070.6285100250179860.314255012508993
450.9103677636937330.1792644726125340.0896322363062668
460.9205130034760880.1589739930478240.0794869965239118
470.9184626509594490.1630746980811030.0815373490405515
480.9122713280938510.1754573438122970.0877286719061486
490.9573219888128010.08535602237439730.0426780111871987
500.9763906675006840.04721866499863230.0236093324993161
510.972578400104940.05484319979011980.0274215998950599
520.9685776894920450.06284462101591020.0314223105079551
530.965524143331310.06895171333737910.0344758566686895
540.9555940086514450.0888119826971110.0444059913485555
550.9459146474068960.1081707051862070.0540853525931036
560.9330725877741830.1338548244516340.066927412225817
570.9154436277061450.1691127445877110.0845563722938553
580.9035741379447850.1928517241104310.0964258620552154
590.8917279839050360.2165440321899280.108272016094964
600.9029708119671660.1940583760656680.0970291880328341
610.8975288801986020.2049422396027960.102471119801398
620.9028503780625850.1942992438748290.0971496219374146
630.8878146425638220.2243707148723570.112185357436178
640.8633326925712610.2733346148574780.136667307428739
650.8383319303315190.3233361393369610.161668069668481
660.8138938400800650.3722123198398690.186106159919935
670.7908716092996610.4182567814006780.209128390700339
680.8059655332058280.3880689335883450.194034466794172
690.7727020579377440.4545958841245110.227297942062256
700.7383647792876470.5232704414247050.261635220712353
710.7031498247827420.5937003504345160.296850175217258
720.6755062826992890.6489874346014210.324493717300711
730.6327794102406470.7344411795187050.367220589759353
740.5972059140570480.8055881718859040.402794085942952
750.5506219729203730.8987560541592550.449378027079627
760.9003647759649330.1992704480701340.0996352240350669
770.9031778703208880.1936442593582240.0968221296791118
780.9316902217624070.1366195564751860.0683097782375928
790.9147989538473780.1704020923052430.0852010461526216
800.8948139986250.210372002750.105186001375
810.8821328670742560.2357342658514870.117867132925744
820.9809132708444030.03817345831119440.0190867291555972
830.978014153858350.04397169228329920.0219858461416496
840.9817227170351030.03655456592979360.0182772829648968
850.9781726456692810.04365470866143880.0218273543307194
860.971807611270670.056384777458660.02819238872933
870.9637306253368230.07253874932635390.036269374663177
880.957226361410550.08554727717889940.0427736385894497
890.9452039984212720.1095920031574560.0547960015787281
900.9869652853769480.02606942924610450.0130347146230523
910.984532938777170.03093412244566090.0154670612228305
920.9870123820622640.02597523587547210.0129876179377361
930.982610621993570.03477875601285980.0173893780064299
940.9798420709866170.04031585802676550.0201579290133828
950.9899453894606180.02010922107876350.0100546105393818
960.9861867926352060.02762641472958790.013813207364794
970.9821281850911450.03574362981770960.0178718149088548
980.9780812394431940.04383752111361140.0219187605568057
990.9745228496680080.05095430066398350.0254771503319917
1000.9691258723659780.06174825526804480.0308741276340224
1010.9612699500310450.077460099937910.038730049968955
1020.9503131962869140.09937360742617180.0496868037130859
1030.9398300253833570.1203399492332850.0601699746166426
1040.9288425822711530.1423148354576950.0711574177288473
1050.9164402204415190.1671195591169620.0835597795584812
1060.9300676356190150.1398647287619690.0699323643809846
1070.9173812447932490.1652375104135020.082618755206751
1080.9036476007911970.1927047984176070.0963523992088035
1090.9817879245706690.03642415085866230.0182120754293311
1100.9998958954302330.000208209139534170.000104104569767085
1110.9999590046986428.19906027161464e-054.09953013580732e-05
1120.9999878370743592.43258512818242e-051.21629256409121e-05
1130.9999775087017614.49825964778065e-052.24912982389032e-05
1140.9999929696414621.40607170760881e-057.03035853804407e-06
1150.9999880015792022.39968415951582e-051.19984207975791e-05
1160.9999827274738743.45450522511714e-051.72725261255857e-05
1170.999994726733221.05465335605906e-055.27326678029528e-06
1180.9999898349678162.03300643684355e-051.01650321842178e-05
1190.9999867298579222.65402841569086e-051.32701420784543e-05
1200.9999767343573114.6531285378864e-052.3265642689432e-05
1210.9999879161916052.41676167899087e-051.20838083949544e-05
1220.9999799225953654.0154809270809e-052.00774046354045e-05
1230.9999807268938353.85462123299292e-051.92731061649646e-05
1240.9999851701911052.96596177895425e-051.48298088947712e-05
1250.9999724766250785.50467498435296e-052.75233749217648e-05
1260.9999439848287410.0001120303425183855.60151712591923e-05
1270.9999933074872051.33850255906132e-056.69251279530658e-06
1280.9999871172963882.57654072233543e-051.28827036116771e-05
1290.9999978452692914.30946141891017e-062.15473070945508e-06
1300.9999952663434829.46731303689551e-064.73365651844775e-06
1310.9999903129237561.93741524874791e-059.68707624373954e-06
1320.9999966832620216.63347595810964e-063.31673797905482e-06
1330.9999962746680757.45066385002322e-063.72533192501161e-06
1340.999999741410445.17179119486438e-072.58589559743219e-07
1350.9999995232999789.53400044809929e-074.76700022404964e-07
1360.9999989200650032.15986999417394e-061.07993499708697e-06
1370.9999997572366084.85526784817392e-072.42763392408696e-07
1380.9999994521435411.09571291828221e-065.47856459141104e-07
1390.9999999997086855.82629926352331e-102.91314963176165e-10
1400.9999999990119091.97618209953892e-099.88091049769462e-10
1410.999999995259439.48114004977495e-094.74057002488748e-09
1420.9999999730154975.39690057493964e-082.69845028746982e-08
1430.9999998577927522.84414495896922e-071.42207247948461e-07
1440.9999993863540551.22729188915719e-066.13645944578594e-07
1450.9999986422767752.7154464490024e-061.3577232245012e-06
1460.999999999987532.49400752434153e-111.24700376217077e-11
1470.9999999996903136.19374800899537e-103.09687400449769e-10
1480.9999999927219781.45560433564566e-087.27802167822831e-09
1490.9999997782405464.43518908773532e-072.21759454386766e-07
1500.9999975813870994.83722580276344e-062.41861290138172e-06
1510.9999221223658040.000155755268391517.7877634195755e-05

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.0627762407258335 & 0.125552481451667 & 0.937223759274166 \tabularnewline
14 & 0.218187323990826 & 0.436374647981652 & 0.781812676009174 \tabularnewline
15 & 0.141903420637058 & 0.283806841274116 & 0.858096579362942 \tabularnewline
16 & 0.294037770849319 & 0.588075541698637 & 0.705962229150681 \tabularnewline
17 & 0.349557371751639 & 0.699114743503278 & 0.650442628248361 \tabularnewline
18 & 0.625502053575596 & 0.748995892848807 & 0.374497946424404 \tabularnewline
19 & 0.527049000136022 & 0.945901999727955 & 0.472950999863978 \tabularnewline
20 & 0.429571325400811 & 0.859142650801622 & 0.570428674599189 \tabularnewline
21 & 0.341803392980488 & 0.683606785960975 & 0.658196607019512 \tabularnewline
22 & 0.880050988986931 & 0.239898022026138 & 0.119949011013069 \tabularnewline
23 & 0.84092421071372 & 0.31815157857256 & 0.15907578928628 \tabularnewline
24 & 0.85423915690469 & 0.291521686190621 & 0.14576084309531 \tabularnewline
25 & 0.858555203175337 & 0.282889593649326 & 0.141444796824663 \tabularnewline
26 & 0.829364692309353 & 0.341270615381294 & 0.170635307690647 \tabularnewline
27 & 0.873304007565427 & 0.253391984869147 & 0.126695992434573 \tabularnewline
28 & 0.839607746957554 & 0.320784506084892 & 0.160392253042446 \tabularnewline
29 & 0.795286882507279 & 0.409426234985442 & 0.204713117492721 \tabularnewline
30 & 0.869376280637777 & 0.261247438724446 & 0.130623719362223 \tabularnewline
31 & 0.867742112921692 & 0.264515774156615 & 0.132257887078308 \tabularnewline
32 & 0.859446410786545 & 0.281107178426909 & 0.140553589213455 \tabularnewline
33 & 0.823538048987099 & 0.352923902025802 & 0.176461951012901 \tabularnewline
34 & 0.782748264784148 & 0.434503470431704 & 0.217251735215852 \tabularnewline
35 & 0.769576814940904 & 0.460846370118192 & 0.230423185059096 \tabularnewline
36 & 0.720714483957151 & 0.558571032085697 & 0.279285516042849 \tabularnewline
37 & 0.755794356820976 & 0.488411286358048 & 0.244205643179024 \tabularnewline
38 & 0.767190969653403 & 0.465618060693193 & 0.232809030346597 \tabularnewline
39 & 0.727076957769633 & 0.545846084460734 & 0.272923042230367 \tabularnewline
40 & 0.69650106333855 & 0.606997873322899 & 0.30349893666145 \tabularnewline
41 & 0.660899159349288 & 0.678201681301423 & 0.339100840650712 \tabularnewline
42 & 0.626970938503907 & 0.746058122992185 & 0.373029061496093 \tabularnewline
43 & 0.710783890425973 & 0.578432219148054 & 0.289216109574027 \tabularnewline
44 & 0.685744987491007 & 0.628510025017986 & 0.314255012508993 \tabularnewline
45 & 0.910367763693733 & 0.179264472612534 & 0.0896322363062668 \tabularnewline
46 & 0.920513003476088 & 0.158973993047824 & 0.0794869965239118 \tabularnewline
47 & 0.918462650959449 & 0.163074698081103 & 0.0815373490405515 \tabularnewline
48 & 0.912271328093851 & 0.175457343812297 & 0.0877286719061486 \tabularnewline
49 & 0.957321988812801 & 0.0853560223743973 & 0.0426780111871987 \tabularnewline
50 & 0.976390667500684 & 0.0472186649986323 & 0.0236093324993161 \tabularnewline
51 & 0.97257840010494 & 0.0548431997901198 & 0.0274215998950599 \tabularnewline
52 & 0.968577689492045 & 0.0628446210159102 & 0.0314223105079551 \tabularnewline
53 & 0.96552414333131 & 0.0689517133373791 & 0.0344758566686895 \tabularnewline
54 & 0.955594008651445 & 0.088811982697111 & 0.0444059913485555 \tabularnewline
55 & 0.945914647406896 & 0.108170705186207 & 0.0540853525931036 \tabularnewline
56 & 0.933072587774183 & 0.133854824451634 & 0.066927412225817 \tabularnewline
57 & 0.915443627706145 & 0.169112744587711 & 0.0845563722938553 \tabularnewline
58 & 0.903574137944785 & 0.192851724110431 & 0.0964258620552154 \tabularnewline
59 & 0.891727983905036 & 0.216544032189928 & 0.108272016094964 \tabularnewline
60 & 0.902970811967166 & 0.194058376065668 & 0.0970291880328341 \tabularnewline
61 & 0.897528880198602 & 0.204942239602796 & 0.102471119801398 \tabularnewline
62 & 0.902850378062585 & 0.194299243874829 & 0.0971496219374146 \tabularnewline
63 & 0.887814642563822 & 0.224370714872357 & 0.112185357436178 \tabularnewline
64 & 0.863332692571261 & 0.273334614857478 & 0.136667307428739 \tabularnewline
65 & 0.838331930331519 & 0.323336139336961 & 0.161668069668481 \tabularnewline
66 & 0.813893840080065 & 0.372212319839869 & 0.186106159919935 \tabularnewline
67 & 0.790871609299661 & 0.418256781400678 & 0.209128390700339 \tabularnewline
68 & 0.805965533205828 & 0.388068933588345 & 0.194034466794172 \tabularnewline
69 & 0.772702057937744 & 0.454595884124511 & 0.227297942062256 \tabularnewline
70 & 0.738364779287647 & 0.523270441424705 & 0.261635220712353 \tabularnewline
71 & 0.703149824782742 & 0.593700350434516 & 0.296850175217258 \tabularnewline
72 & 0.675506282699289 & 0.648987434601421 & 0.324493717300711 \tabularnewline
73 & 0.632779410240647 & 0.734441179518705 & 0.367220589759353 \tabularnewline
74 & 0.597205914057048 & 0.805588171885904 & 0.402794085942952 \tabularnewline
75 & 0.550621972920373 & 0.898756054159255 & 0.449378027079627 \tabularnewline
76 & 0.900364775964933 & 0.199270448070134 & 0.0996352240350669 \tabularnewline
77 & 0.903177870320888 & 0.193644259358224 & 0.0968221296791118 \tabularnewline
78 & 0.931690221762407 & 0.136619556475186 & 0.0683097782375928 \tabularnewline
79 & 0.914798953847378 & 0.170402092305243 & 0.0852010461526216 \tabularnewline
80 & 0.894813998625 & 0.21037200275 & 0.105186001375 \tabularnewline
81 & 0.882132867074256 & 0.235734265851487 & 0.117867132925744 \tabularnewline
82 & 0.980913270844403 & 0.0381734583111944 & 0.0190867291555972 \tabularnewline
83 & 0.97801415385835 & 0.0439716922832992 & 0.0219858461416496 \tabularnewline
84 & 0.981722717035103 & 0.0365545659297936 & 0.0182772829648968 \tabularnewline
85 & 0.978172645669281 & 0.0436547086614388 & 0.0218273543307194 \tabularnewline
86 & 0.97180761127067 & 0.05638477745866 & 0.02819238872933 \tabularnewline
87 & 0.963730625336823 & 0.0725387493263539 & 0.036269374663177 \tabularnewline
88 & 0.95722636141055 & 0.0855472771788994 & 0.0427736385894497 \tabularnewline
89 & 0.945203998421272 & 0.109592003157456 & 0.0547960015787281 \tabularnewline
90 & 0.986965285376948 & 0.0260694292461045 & 0.0130347146230523 \tabularnewline
91 & 0.98453293877717 & 0.0309341224456609 & 0.0154670612228305 \tabularnewline
92 & 0.987012382062264 & 0.0259752358754721 & 0.0129876179377361 \tabularnewline
93 & 0.98261062199357 & 0.0347787560128598 & 0.0173893780064299 \tabularnewline
94 & 0.979842070986617 & 0.0403158580267655 & 0.0201579290133828 \tabularnewline
95 & 0.989945389460618 & 0.0201092210787635 & 0.0100546105393818 \tabularnewline
96 & 0.986186792635206 & 0.0276264147295879 & 0.013813207364794 \tabularnewline
97 & 0.982128185091145 & 0.0357436298177096 & 0.0178718149088548 \tabularnewline
98 & 0.978081239443194 & 0.0438375211136114 & 0.0219187605568057 \tabularnewline
99 & 0.974522849668008 & 0.0509543006639835 & 0.0254771503319917 \tabularnewline
100 & 0.969125872365978 & 0.0617482552680448 & 0.0308741276340224 \tabularnewline
101 & 0.961269950031045 & 0.07746009993791 & 0.038730049968955 \tabularnewline
102 & 0.950313196286914 & 0.0993736074261718 & 0.0496868037130859 \tabularnewline
103 & 0.939830025383357 & 0.120339949233285 & 0.0601699746166426 \tabularnewline
104 & 0.928842582271153 & 0.142314835457695 & 0.0711574177288473 \tabularnewline
105 & 0.916440220441519 & 0.167119559116962 & 0.0835597795584812 \tabularnewline
106 & 0.930067635619015 & 0.139864728761969 & 0.0699323643809846 \tabularnewline
107 & 0.917381244793249 & 0.165237510413502 & 0.082618755206751 \tabularnewline
108 & 0.903647600791197 & 0.192704798417607 & 0.0963523992088035 \tabularnewline
109 & 0.981787924570669 & 0.0364241508586623 & 0.0182120754293311 \tabularnewline
110 & 0.999895895430233 & 0.00020820913953417 & 0.000104104569767085 \tabularnewline
111 & 0.999959004698642 & 8.19906027161464e-05 & 4.09953013580732e-05 \tabularnewline
112 & 0.999987837074359 & 2.43258512818242e-05 & 1.21629256409121e-05 \tabularnewline
113 & 0.999977508701761 & 4.49825964778065e-05 & 2.24912982389032e-05 \tabularnewline
114 & 0.999992969641462 & 1.40607170760881e-05 & 7.03035853804407e-06 \tabularnewline
115 & 0.999988001579202 & 2.39968415951582e-05 & 1.19984207975791e-05 \tabularnewline
116 & 0.999982727473874 & 3.45450522511714e-05 & 1.72725261255857e-05 \tabularnewline
117 & 0.99999472673322 & 1.05465335605906e-05 & 5.27326678029528e-06 \tabularnewline
118 & 0.999989834967816 & 2.03300643684355e-05 & 1.01650321842178e-05 \tabularnewline
119 & 0.999986729857922 & 2.65402841569086e-05 & 1.32701420784543e-05 \tabularnewline
120 & 0.999976734357311 & 4.6531285378864e-05 & 2.3265642689432e-05 \tabularnewline
121 & 0.999987916191605 & 2.41676167899087e-05 & 1.20838083949544e-05 \tabularnewline
122 & 0.999979922595365 & 4.0154809270809e-05 & 2.00774046354045e-05 \tabularnewline
123 & 0.999980726893835 & 3.85462123299292e-05 & 1.92731061649646e-05 \tabularnewline
124 & 0.999985170191105 & 2.96596177895425e-05 & 1.48298088947712e-05 \tabularnewline
125 & 0.999972476625078 & 5.50467498435296e-05 & 2.75233749217648e-05 \tabularnewline
126 & 0.999943984828741 & 0.000112030342518385 & 5.60151712591923e-05 \tabularnewline
127 & 0.999993307487205 & 1.33850255906132e-05 & 6.69251279530658e-06 \tabularnewline
128 & 0.999987117296388 & 2.57654072233543e-05 & 1.28827036116771e-05 \tabularnewline
129 & 0.999997845269291 & 4.30946141891017e-06 & 2.15473070945508e-06 \tabularnewline
130 & 0.999995266343482 & 9.46731303689551e-06 & 4.73365651844775e-06 \tabularnewline
131 & 0.999990312923756 & 1.93741524874791e-05 & 9.68707624373954e-06 \tabularnewline
132 & 0.999996683262021 & 6.63347595810964e-06 & 3.31673797905482e-06 \tabularnewline
133 & 0.999996274668075 & 7.45066385002322e-06 & 3.72533192501161e-06 \tabularnewline
134 & 0.99999974141044 & 5.17179119486438e-07 & 2.58589559743219e-07 \tabularnewline
135 & 0.999999523299978 & 9.53400044809929e-07 & 4.76700022404964e-07 \tabularnewline
136 & 0.999998920065003 & 2.15986999417394e-06 & 1.07993499708697e-06 \tabularnewline
137 & 0.999999757236608 & 4.85526784817392e-07 & 2.42763392408696e-07 \tabularnewline
138 & 0.999999452143541 & 1.09571291828221e-06 & 5.47856459141104e-07 \tabularnewline
139 & 0.999999999708685 & 5.82629926352331e-10 & 2.91314963176165e-10 \tabularnewline
140 & 0.999999999011909 & 1.97618209953892e-09 & 9.88091049769462e-10 \tabularnewline
141 & 0.99999999525943 & 9.48114004977495e-09 & 4.74057002488748e-09 \tabularnewline
142 & 0.999999973015497 & 5.39690057493964e-08 & 2.69845028746982e-08 \tabularnewline
143 & 0.999999857792752 & 2.84414495896922e-07 & 1.42207247948461e-07 \tabularnewline
144 & 0.999999386354055 & 1.22729188915719e-06 & 6.13645944578594e-07 \tabularnewline
145 & 0.999998642276775 & 2.7154464490024e-06 & 1.3577232245012e-06 \tabularnewline
146 & 0.99999999998753 & 2.49400752434153e-11 & 1.24700376217077e-11 \tabularnewline
147 & 0.999999999690313 & 6.19374800899537e-10 & 3.09687400449769e-10 \tabularnewline
148 & 0.999999992721978 & 1.45560433564566e-08 & 7.27802167822831e-09 \tabularnewline
149 & 0.999999778240546 & 4.43518908773532e-07 & 2.21759454386766e-07 \tabularnewline
150 & 0.999997581387099 & 4.83722580276344e-06 & 2.41861290138172e-06 \tabularnewline
151 & 0.999922122365804 & 0.00015575526839151 & 7.7877634195755e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156816&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]13[/C][C]0.0627762407258335[/C][C]0.125552481451667[/C][C]0.937223759274166[/C][/ROW]
[ROW][C]14[/C][C]0.218187323990826[/C][C]0.436374647981652[/C][C]0.781812676009174[/C][/ROW]
[ROW][C]15[/C][C]0.141903420637058[/C][C]0.283806841274116[/C][C]0.858096579362942[/C][/ROW]
[ROW][C]16[/C][C]0.294037770849319[/C][C]0.588075541698637[/C][C]0.705962229150681[/C][/ROW]
[ROW][C]17[/C][C]0.349557371751639[/C][C]0.699114743503278[/C][C]0.650442628248361[/C][/ROW]
[ROW][C]18[/C][C]0.625502053575596[/C][C]0.748995892848807[/C][C]0.374497946424404[/C][/ROW]
[ROW][C]19[/C][C]0.527049000136022[/C][C]0.945901999727955[/C][C]0.472950999863978[/C][/ROW]
[ROW][C]20[/C][C]0.429571325400811[/C][C]0.859142650801622[/C][C]0.570428674599189[/C][/ROW]
[ROW][C]21[/C][C]0.341803392980488[/C][C]0.683606785960975[/C][C]0.658196607019512[/C][/ROW]
[ROW][C]22[/C][C]0.880050988986931[/C][C]0.239898022026138[/C][C]0.119949011013069[/C][/ROW]
[ROW][C]23[/C][C]0.84092421071372[/C][C]0.31815157857256[/C][C]0.15907578928628[/C][/ROW]
[ROW][C]24[/C][C]0.85423915690469[/C][C]0.291521686190621[/C][C]0.14576084309531[/C][/ROW]
[ROW][C]25[/C][C]0.858555203175337[/C][C]0.282889593649326[/C][C]0.141444796824663[/C][/ROW]
[ROW][C]26[/C][C]0.829364692309353[/C][C]0.341270615381294[/C][C]0.170635307690647[/C][/ROW]
[ROW][C]27[/C][C]0.873304007565427[/C][C]0.253391984869147[/C][C]0.126695992434573[/C][/ROW]
[ROW][C]28[/C][C]0.839607746957554[/C][C]0.320784506084892[/C][C]0.160392253042446[/C][/ROW]
[ROW][C]29[/C][C]0.795286882507279[/C][C]0.409426234985442[/C][C]0.204713117492721[/C][/ROW]
[ROW][C]30[/C][C]0.869376280637777[/C][C]0.261247438724446[/C][C]0.130623719362223[/C][/ROW]
[ROW][C]31[/C][C]0.867742112921692[/C][C]0.264515774156615[/C][C]0.132257887078308[/C][/ROW]
[ROW][C]32[/C][C]0.859446410786545[/C][C]0.281107178426909[/C][C]0.140553589213455[/C][/ROW]
[ROW][C]33[/C][C]0.823538048987099[/C][C]0.352923902025802[/C][C]0.176461951012901[/C][/ROW]
[ROW][C]34[/C][C]0.782748264784148[/C][C]0.434503470431704[/C][C]0.217251735215852[/C][/ROW]
[ROW][C]35[/C][C]0.769576814940904[/C][C]0.460846370118192[/C][C]0.230423185059096[/C][/ROW]
[ROW][C]36[/C][C]0.720714483957151[/C][C]0.558571032085697[/C][C]0.279285516042849[/C][/ROW]
[ROW][C]37[/C][C]0.755794356820976[/C][C]0.488411286358048[/C][C]0.244205643179024[/C][/ROW]
[ROW][C]38[/C][C]0.767190969653403[/C][C]0.465618060693193[/C][C]0.232809030346597[/C][/ROW]
[ROW][C]39[/C][C]0.727076957769633[/C][C]0.545846084460734[/C][C]0.272923042230367[/C][/ROW]
[ROW][C]40[/C][C]0.69650106333855[/C][C]0.606997873322899[/C][C]0.30349893666145[/C][/ROW]
[ROW][C]41[/C][C]0.660899159349288[/C][C]0.678201681301423[/C][C]0.339100840650712[/C][/ROW]
[ROW][C]42[/C][C]0.626970938503907[/C][C]0.746058122992185[/C][C]0.373029061496093[/C][/ROW]
[ROW][C]43[/C][C]0.710783890425973[/C][C]0.578432219148054[/C][C]0.289216109574027[/C][/ROW]
[ROW][C]44[/C][C]0.685744987491007[/C][C]0.628510025017986[/C][C]0.314255012508993[/C][/ROW]
[ROW][C]45[/C][C]0.910367763693733[/C][C]0.179264472612534[/C][C]0.0896322363062668[/C][/ROW]
[ROW][C]46[/C][C]0.920513003476088[/C][C]0.158973993047824[/C][C]0.0794869965239118[/C][/ROW]
[ROW][C]47[/C][C]0.918462650959449[/C][C]0.163074698081103[/C][C]0.0815373490405515[/C][/ROW]
[ROW][C]48[/C][C]0.912271328093851[/C][C]0.175457343812297[/C][C]0.0877286719061486[/C][/ROW]
[ROW][C]49[/C][C]0.957321988812801[/C][C]0.0853560223743973[/C][C]0.0426780111871987[/C][/ROW]
[ROW][C]50[/C][C]0.976390667500684[/C][C]0.0472186649986323[/C][C]0.0236093324993161[/C][/ROW]
[ROW][C]51[/C][C]0.97257840010494[/C][C]0.0548431997901198[/C][C]0.0274215998950599[/C][/ROW]
[ROW][C]52[/C][C]0.968577689492045[/C][C]0.0628446210159102[/C][C]0.0314223105079551[/C][/ROW]
[ROW][C]53[/C][C]0.96552414333131[/C][C]0.0689517133373791[/C][C]0.0344758566686895[/C][/ROW]
[ROW][C]54[/C][C]0.955594008651445[/C][C]0.088811982697111[/C][C]0.0444059913485555[/C][/ROW]
[ROW][C]55[/C][C]0.945914647406896[/C][C]0.108170705186207[/C][C]0.0540853525931036[/C][/ROW]
[ROW][C]56[/C][C]0.933072587774183[/C][C]0.133854824451634[/C][C]0.066927412225817[/C][/ROW]
[ROW][C]57[/C][C]0.915443627706145[/C][C]0.169112744587711[/C][C]0.0845563722938553[/C][/ROW]
[ROW][C]58[/C][C]0.903574137944785[/C][C]0.192851724110431[/C][C]0.0964258620552154[/C][/ROW]
[ROW][C]59[/C][C]0.891727983905036[/C][C]0.216544032189928[/C][C]0.108272016094964[/C][/ROW]
[ROW][C]60[/C][C]0.902970811967166[/C][C]0.194058376065668[/C][C]0.0970291880328341[/C][/ROW]
[ROW][C]61[/C][C]0.897528880198602[/C][C]0.204942239602796[/C][C]0.102471119801398[/C][/ROW]
[ROW][C]62[/C][C]0.902850378062585[/C][C]0.194299243874829[/C][C]0.0971496219374146[/C][/ROW]
[ROW][C]63[/C][C]0.887814642563822[/C][C]0.224370714872357[/C][C]0.112185357436178[/C][/ROW]
[ROW][C]64[/C][C]0.863332692571261[/C][C]0.273334614857478[/C][C]0.136667307428739[/C][/ROW]
[ROW][C]65[/C][C]0.838331930331519[/C][C]0.323336139336961[/C][C]0.161668069668481[/C][/ROW]
[ROW][C]66[/C][C]0.813893840080065[/C][C]0.372212319839869[/C][C]0.186106159919935[/C][/ROW]
[ROW][C]67[/C][C]0.790871609299661[/C][C]0.418256781400678[/C][C]0.209128390700339[/C][/ROW]
[ROW][C]68[/C][C]0.805965533205828[/C][C]0.388068933588345[/C][C]0.194034466794172[/C][/ROW]
[ROW][C]69[/C][C]0.772702057937744[/C][C]0.454595884124511[/C][C]0.227297942062256[/C][/ROW]
[ROW][C]70[/C][C]0.738364779287647[/C][C]0.523270441424705[/C][C]0.261635220712353[/C][/ROW]
[ROW][C]71[/C][C]0.703149824782742[/C][C]0.593700350434516[/C][C]0.296850175217258[/C][/ROW]
[ROW][C]72[/C][C]0.675506282699289[/C][C]0.648987434601421[/C][C]0.324493717300711[/C][/ROW]
[ROW][C]73[/C][C]0.632779410240647[/C][C]0.734441179518705[/C][C]0.367220589759353[/C][/ROW]
[ROW][C]74[/C][C]0.597205914057048[/C][C]0.805588171885904[/C][C]0.402794085942952[/C][/ROW]
[ROW][C]75[/C][C]0.550621972920373[/C][C]0.898756054159255[/C][C]0.449378027079627[/C][/ROW]
[ROW][C]76[/C][C]0.900364775964933[/C][C]0.199270448070134[/C][C]0.0996352240350669[/C][/ROW]
[ROW][C]77[/C][C]0.903177870320888[/C][C]0.193644259358224[/C][C]0.0968221296791118[/C][/ROW]
[ROW][C]78[/C][C]0.931690221762407[/C][C]0.136619556475186[/C][C]0.0683097782375928[/C][/ROW]
[ROW][C]79[/C][C]0.914798953847378[/C][C]0.170402092305243[/C][C]0.0852010461526216[/C][/ROW]
[ROW][C]80[/C][C]0.894813998625[/C][C]0.21037200275[/C][C]0.105186001375[/C][/ROW]
[ROW][C]81[/C][C]0.882132867074256[/C][C]0.235734265851487[/C][C]0.117867132925744[/C][/ROW]
[ROW][C]82[/C][C]0.980913270844403[/C][C]0.0381734583111944[/C][C]0.0190867291555972[/C][/ROW]
[ROW][C]83[/C][C]0.97801415385835[/C][C]0.0439716922832992[/C][C]0.0219858461416496[/C][/ROW]
[ROW][C]84[/C][C]0.981722717035103[/C][C]0.0365545659297936[/C][C]0.0182772829648968[/C][/ROW]
[ROW][C]85[/C][C]0.978172645669281[/C][C]0.0436547086614388[/C][C]0.0218273543307194[/C][/ROW]
[ROW][C]86[/C][C]0.97180761127067[/C][C]0.05638477745866[/C][C]0.02819238872933[/C][/ROW]
[ROW][C]87[/C][C]0.963730625336823[/C][C]0.0725387493263539[/C][C]0.036269374663177[/C][/ROW]
[ROW][C]88[/C][C]0.95722636141055[/C][C]0.0855472771788994[/C][C]0.0427736385894497[/C][/ROW]
[ROW][C]89[/C][C]0.945203998421272[/C][C]0.109592003157456[/C][C]0.0547960015787281[/C][/ROW]
[ROW][C]90[/C][C]0.986965285376948[/C][C]0.0260694292461045[/C][C]0.0130347146230523[/C][/ROW]
[ROW][C]91[/C][C]0.98453293877717[/C][C]0.0309341224456609[/C][C]0.0154670612228305[/C][/ROW]
[ROW][C]92[/C][C]0.987012382062264[/C][C]0.0259752358754721[/C][C]0.0129876179377361[/C][/ROW]
[ROW][C]93[/C][C]0.98261062199357[/C][C]0.0347787560128598[/C][C]0.0173893780064299[/C][/ROW]
[ROW][C]94[/C][C]0.979842070986617[/C][C]0.0403158580267655[/C][C]0.0201579290133828[/C][/ROW]
[ROW][C]95[/C][C]0.989945389460618[/C][C]0.0201092210787635[/C][C]0.0100546105393818[/C][/ROW]
[ROW][C]96[/C][C]0.986186792635206[/C][C]0.0276264147295879[/C][C]0.013813207364794[/C][/ROW]
[ROW][C]97[/C][C]0.982128185091145[/C][C]0.0357436298177096[/C][C]0.0178718149088548[/C][/ROW]
[ROW][C]98[/C][C]0.978081239443194[/C][C]0.0438375211136114[/C][C]0.0219187605568057[/C][/ROW]
[ROW][C]99[/C][C]0.974522849668008[/C][C]0.0509543006639835[/C][C]0.0254771503319917[/C][/ROW]
[ROW][C]100[/C][C]0.969125872365978[/C][C]0.0617482552680448[/C][C]0.0308741276340224[/C][/ROW]
[ROW][C]101[/C][C]0.961269950031045[/C][C]0.07746009993791[/C][C]0.038730049968955[/C][/ROW]
[ROW][C]102[/C][C]0.950313196286914[/C][C]0.0993736074261718[/C][C]0.0496868037130859[/C][/ROW]
[ROW][C]103[/C][C]0.939830025383357[/C][C]0.120339949233285[/C][C]0.0601699746166426[/C][/ROW]
[ROW][C]104[/C][C]0.928842582271153[/C][C]0.142314835457695[/C][C]0.0711574177288473[/C][/ROW]
[ROW][C]105[/C][C]0.916440220441519[/C][C]0.167119559116962[/C][C]0.0835597795584812[/C][/ROW]
[ROW][C]106[/C][C]0.930067635619015[/C][C]0.139864728761969[/C][C]0.0699323643809846[/C][/ROW]
[ROW][C]107[/C][C]0.917381244793249[/C][C]0.165237510413502[/C][C]0.082618755206751[/C][/ROW]
[ROW][C]108[/C][C]0.903647600791197[/C][C]0.192704798417607[/C][C]0.0963523992088035[/C][/ROW]
[ROW][C]109[/C][C]0.981787924570669[/C][C]0.0364241508586623[/C][C]0.0182120754293311[/C][/ROW]
[ROW][C]110[/C][C]0.999895895430233[/C][C]0.00020820913953417[/C][C]0.000104104569767085[/C][/ROW]
[ROW][C]111[/C][C]0.999959004698642[/C][C]8.19906027161464e-05[/C][C]4.09953013580732e-05[/C][/ROW]
[ROW][C]112[/C][C]0.999987837074359[/C][C]2.43258512818242e-05[/C][C]1.21629256409121e-05[/C][/ROW]
[ROW][C]113[/C][C]0.999977508701761[/C][C]4.49825964778065e-05[/C][C]2.24912982389032e-05[/C][/ROW]
[ROW][C]114[/C][C]0.999992969641462[/C][C]1.40607170760881e-05[/C][C]7.03035853804407e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999988001579202[/C][C]2.39968415951582e-05[/C][C]1.19984207975791e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999982727473874[/C][C]3.45450522511714e-05[/C][C]1.72725261255857e-05[/C][/ROW]
[ROW][C]117[/C][C]0.99999472673322[/C][C]1.05465335605906e-05[/C][C]5.27326678029528e-06[/C][/ROW]
[ROW][C]118[/C][C]0.999989834967816[/C][C]2.03300643684355e-05[/C][C]1.01650321842178e-05[/C][/ROW]
[ROW][C]119[/C][C]0.999986729857922[/C][C]2.65402841569086e-05[/C][C]1.32701420784543e-05[/C][/ROW]
[ROW][C]120[/C][C]0.999976734357311[/C][C]4.6531285378864e-05[/C][C]2.3265642689432e-05[/C][/ROW]
[ROW][C]121[/C][C]0.999987916191605[/C][C]2.41676167899087e-05[/C][C]1.20838083949544e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999979922595365[/C][C]4.0154809270809e-05[/C][C]2.00774046354045e-05[/C][/ROW]
[ROW][C]123[/C][C]0.999980726893835[/C][C]3.85462123299292e-05[/C][C]1.92731061649646e-05[/C][/ROW]
[ROW][C]124[/C][C]0.999985170191105[/C][C]2.96596177895425e-05[/C][C]1.48298088947712e-05[/C][/ROW]
[ROW][C]125[/C][C]0.999972476625078[/C][C]5.50467498435296e-05[/C][C]2.75233749217648e-05[/C][/ROW]
[ROW][C]126[/C][C]0.999943984828741[/C][C]0.000112030342518385[/C][C]5.60151712591923e-05[/C][/ROW]
[ROW][C]127[/C][C]0.999993307487205[/C][C]1.33850255906132e-05[/C][C]6.69251279530658e-06[/C][/ROW]
[ROW][C]128[/C][C]0.999987117296388[/C][C]2.57654072233543e-05[/C][C]1.28827036116771e-05[/C][/ROW]
[ROW][C]129[/C][C]0.999997845269291[/C][C]4.30946141891017e-06[/C][C]2.15473070945508e-06[/C][/ROW]
[ROW][C]130[/C][C]0.999995266343482[/C][C]9.46731303689551e-06[/C][C]4.73365651844775e-06[/C][/ROW]
[ROW][C]131[/C][C]0.999990312923756[/C][C]1.93741524874791e-05[/C][C]9.68707624373954e-06[/C][/ROW]
[ROW][C]132[/C][C]0.999996683262021[/C][C]6.63347595810964e-06[/C][C]3.31673797905482e-06[/C][/ROW]
[ROW][C]133[/C][C]0.999996274668075[/C][C]7.45066385002322e-06[/C][C]3.72533192501161e-06[/C][/ROW]
[ROW][C]134[/C][C]0.99999974141044[/C][C]5.17179119486438e-07[/C][C]2.58589559743219e-07[/C][/ROW]
[ROW][C]135[/C][C]0.999999523299978[/C][C]9.53400044809929e-07[/C][C]4.76700022404964e-07[/C][/ROW]
[ROW][C]136[/C][C]0.999998920065003[/C][C]2.15986999417394e-06[/C][C]1.07993499708697e-06[/C][/ROW]
[ROW][C]137[/C][C]0.999999757236608[/C][C]4.85526784817392e-07[/C][C]2.42763392408696e-07[/C][/ROW]
[ROW][C]138[/C][C]0.999999452143541[/C][C]1.09571291828221e-06[/C][C]5.47856459141104e-07[/C][/ROW]
[ROW][C]139[/C][C]0.999999999708685[/C][C]5.82629926352331e-10[/C][C]2.91314963176165e-10[/C][/ROW]
[ROW][C]140[/C][C]0.999999999011909[/C][C]1.97618209953892e-09[/C][C]9.88091049769462e-10[/C][/ROW]
[ROW][C]141[/C][C]0.99999999525943[/C][C]9.48114004977495e-09[/C][C]4.74057002488748e-09[/C][/ROW]
[ROW][C]142[/C][C]0.999999973015497[/C][C]5.39690057493964e-08[/C][C]2.69845028746982e-08[/C][/ROW]
[ROW][C]143[/C][C]0.999999857792752[/C][C]2.84414495896922e-07[/C][C]1.42207247948461e-07[/C][/ROW]
[ROW][C]144[/C][C]0.999999386354055[/C][C]1.22729188915719e-06[/C][C]6.13645944578594e-07[/C][/ROW]
[ROW][C]145[/C][C]0.999998642276775[/C][C]2.7154464490024e-06[/C][C]1.3577232245012e-06[/C][/ROW]
[ROW][C]146[/C][C]0.99999999998753[/C][C]2.49400752434153e-11[/C][C]1.24700376217077e-11[/C][/ROW]
[ROW][C]147[/C][C]0.999999999690313[/C][C]6.19374800899537e-10[/C][C]3.09687400449769e-10[/C][/ROW]
[ROW][C]148[/C][C]0.999999992721978[/C][C]1.45560433564566e-08[/C][C]7.27802167822831e-09[/C][/ROW]
[ROW][C]149[/C][C]0.999999778240546[/C][C]4.43518908773532e-07[/C][C]2.21759454386766e-07[/C][/ROW]
[ROW][C]150[/C][C]0.999997581387099[/C][C]4.83722580276344e-06[/C][C]2.41861290138172e-06[/C][/ROW]
[ROW][C]151[/C][C]0.999922122365804[/C][C]0.00015575526839151[/C][C]7.7877634195755e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156816&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156816&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
130.06277624072583350.1255524814516670.937223759274166
140.2181873239908260.4363746479816520.781812676009174
150.1419034206370580.2838068412741160.858096579362942
160.2940377708493190.5880755416986370.705962229150681
170.3495573717516390.6991147435032780.650442628248361
180.6255020535755960.7489958928488070.374497946424404
190.5270490001360220.9459019997279550.472950999863978
200.4295713254008110.8591426508016220.570428674599189
210.3418033929804880.6836067859609750.658196607019512
220.8800509889869310.2398980220261380.119949011013069
230.840924210713720.318151578572560.15907578928628
240.854239156904690.2915216861906210.14576084309531
250.8585552031753370.2828895936493260.141444796824663
260.8293646923093530.3412706153812940.170635307690647
270.8733040075654270.2533919848691470.126695992434573
280.8396077469575540.3207845060848920.160392253042446
290.7952868825072790.4094262349854420.204713117492721
300.8693762806377770.2612474387244460.130623719362223
310.8677421129216920.2645157741566150.132257887078308
320.8594464107865450.2811071784269090.140553589213455
330.8235380489870990.3529239020258020.176461951012901
340.7827482647841480.4345034704317040.217251735215852
350.7695768149409040.4608463701181920.230423185059096
360.7207144839571510.5585710320856970.279285516042849
370.7557943568209760.4884112863580480.244205643179024
380.7671909696534030.4656180606931930.232809030346597
390.7270769577696330.5458460844607340.272923042230367
400.696501063338550.6069978733228990.30349893666145
410.6608991593492880.6782016813014230.339100840650712
420.6269709385039070.7460581229921850.373029061496093
430.7107838904259730.5784322191480540.289216109574027
440.6857449874910070.6285100250179860.314255012508993
450.9103677636937330.1792644726125340.0896322363062668
460.9205130034760880.1589739930478240.0794869965239118
470.9184626509594490.1630746980811030.0815373490405515
480.9122713280938510.1754573438122970.0877286719061486
490.9573219888128010.08535602237439730.0426780111871987
500.9763906675006840.04721866499863230.0236093324993161
510.972578400104940.05484319979011980.0274215998950599
520.9685776894920450.06284462101591020.0314223105079551
530.965524143331310.06895171333737910.0344758566686895
540.9555940086514450.0888119826971110.0444059913485555
550.9459146474068960.1081707051862070.0540853525931036
560.9330725877741830.1338548244516340.066927412225817
570.9154436277061450.1691127445877110.0845563722938553
580.9035741379447850.1928517241104310.0964258620552154
590.8917279839050360.2165440321899280.108272016094964
600.9029708119671660.1940583760656680.0970291880328341
610.8975288801986020.2049422396027960.102471119801398
620.9028503780625850.1942992438748290.0971496219374146
630.8878146425638220.2243707148723570.112185357436178
640.8633326925712610.2733346148574780.136667307428739
650.8383319303315190.3233361393369610.161668069668481
660.8138938400800650.3722123198398690.186106159919935
670.7908716092996610.4182567814006780.209128390700339
680.8059655332058280.3880689335883450.194034466794172
690.7727020579377440.4545958841245110.227297942062256
700.7383647792876470.5232704414247050.261635220712353
710.7031498247827420.5937003504345160.296850175217258
720.6755062826992890.6489874346014210.324493717300711
730.6327794102406470.7344411795187050.367220589759353
740.5972059140570480.8055881718859040.402794085942952
750.5506219729203730.8987560541592550.449378027079627
760.9003647759649330.1992704480701340.0996352240350669
770.9031778703208880.1936442593582240.0968221296791118
780.9316902217624070.1366195564751860.0683097782375928
790.9147989538473780.1704020923052430.0852010461526216
800.8948139986250.210372002750.105186001375
810.8821328670742560.2357342658514870.117867132925744
820.9809132708444030.03817345831119440.0190867291555972
830.978014153858350.04397169228329920.0219858461416496
840.9817227170351030.03655456592979360.0182772829648968
850.9781726456692810.04365470866143880.0218273543307194
860.971807611270670.056384777458660.02819238872933
870.9637306253368230.07253874932635390.036269374663177
880.957226361410550.08554727717889940.0427736385894497
890.9452039984212720.1095920031574560.0547960015787281
900.9869652853769480.02606942924610450.0130347146230523
910.984532938777170.03093412244566090.0154670612228305
920.9870123820622640.02597523587547210.0129876179377361
930.982610621993570.03477875601285980.0173893780064299
940.9798420709866170.04031585802676550.0201579290133828
950.9899453894606180.02010922107876350.0100546105393818
960.9861867926352060.02762641472958790.013813207364794
970.9821281850911450.03574362981770960.0178718149088548
980.9780812394431940.04383752111361140.0219187605568057
990.9745228496680080.05095430066398350.0254771503319917
1000.9691258723659780.06174825526804480.0308741276340224
1010.9612699500310450.077460099937910.038730049968955
1020.9503131962869140.09937360742617180.0496868037130859
1030.9398300253833570.1203399492332850.0601699746166426
1040.9288425822711530.1423148354576950.0711574177288473
1050.9164402204415190.1671195591169620.0835597795584812
1060.9300676356190150.1398647287619690.0699323643809846
1070.9173812447932490.1652375104135020.082618755206751
1080.9036476007911970.1927047984176070.0963523992088035
1090.9817879245706690.03642415085866230.0182120754293311
1100.9998958954302330.000208209139534170.000104104569767085
1110.9999590046986428.19906027161464e-054.09953013580732e-05
1120.9999878370743592.43258512818242e-051.21629256409121e-05
1130.9999775087017614.49825964778065e-052.24912982389032e-05
1140.9999929696414621.40607170760881e-057.03035853804407e-06
1150.9999880015792022.39968415951582e-051.19984207975791e-05
1160.9999827274738743.45450522511714e-051.72725261255857e-05
1170.999994726733221.05465335605906e-055.27326678029528e-06
1180.9999898349678162.03300643684355e-051.01650321842178e-05
1190.9999867298579222.65402841569086e-051.32701420784543e-05
1200.9999767343573114.6531285378864e-052.3265642689432e-05
1210.9999879161916052.41676167899087e-051.20838083949544e-05
1220.9999799225953654.0154809270809e-052.00774046354045e-05
1230.9999807268938353.85462123299292e-051.92731061649646e-05
1240.9999851701911052.96596177895425e-051.48298088947712e-05
1250.9999724766250785.50467498435296e-052.75233749217648e-05
1260.9999439848287410.0001120303425183855.60151712591923e-05
1270.9999933074872051.33850255906132e-056.69251279530658e-06
1280.9999871172963882.57654072233543e-051.28827036116771e-05
1290.9999978452692914.30946141891017e-062.15473070945508e-06
1300.9999952663434829.46731303689551e-064.73365651844775e-06
1310.9999903129237561.93741524874791e-059.68707624373954e-06
1320.9999966832620216.63347595810964e-063.31673797905482e-06
1330.9999962746680757.45066385002322e-063.72533192501161e-06
1340.999999741410445.17179119486438e-072.58589559743219e-07
1350.9999995232999789.53400044809929e-074.76700022404964e-07
1360.9999989200650032.15986999417394e-061.07993499708697e-06
1370.9999997572366084.85526784817392e-072.42763392408696e-07
1380.9999994521435411.09571291828221e-065.47856459141104e-07
1390.9999999997086855.82629926352331e-102.91314963176165e-10
1400.9999999990119091.97618209953892e-099.88091049769462e-10
1410.999999995259439.48114004977495e-094.74057002488748e-09
1420.9999999730154975.39690057493964e-082.69845028746982e-08
1430.9999998577927522.84414495896922e-071.42207247948461e-07
1440.9999993863540551.22729188915719e-066.13645944578594e-07
1450.9999986422767752.7154464490024e-061.3577232245012e-06
1460.999999999987532.49400752434153e-111.24700376217077e-11
1470.9999999996903136.19374800899537e-103.09687400449769e-10
1480.9999999927219781.45560433564566e-087.27802167822831e-09
1490.9999997782405464.43518908773532e-072.21759454386766e-07
1500.9999975813870994.83722580276344e-062.41861290138172e-06
1510.9999221223658040.000155755268391517.7877634195755e-05







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level420.302158273381295NOK
5% type I error level570.410071942446043NOK
10% type I error level690.496402877697842NOK

\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 & 42 & 0.302158273381295 & NOK \tabularnewline
5% type I error level & 57 & 0.410071942446043 & NOK \tabularnewline
10% type I error level & 69 & 0.496402877697842 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156816&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]42[/C][C]0.302158273381295[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]57[/C][C]0.410071942446043[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]69[/C][C]0.496402877697842[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156816&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156816&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 level420.302158273381295NOK
5% type I error level570.410071942446043NOK
10% type I error level690.496402877697842NOK



Parameters (Session):
par1 = 1 ; par2 = none ; par3 = 0 ; par4 = no ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}