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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 19 Dec 2011 10:22:52 -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/19/t1324308405p4xar4te8p9qtbh.htm/, Retrieved Thu, 02 May 2024 08:48:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157445, Retrieved Thu, 02 May 2024 08:48:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact202

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [Paper, Pearson Co...] [2011-12-18 12:42:54] [75512e061a94450f738c2449abbaac12]
-   P   [Kendall tau Correlation Matrix] [Paper, Kendall's ...] [2011-12-19 10:46:53] [75512e061a94450f738c2449abbaac12]
- RMP       [Multiple Regression] [Paper, 3.3 Meervo...] [2011-12-19 15:22:52] [242bbde8f74d68805b56d9ecebfdbe63] [Current]
- RMP         [Recursive Partitioning (Regression Trees)] [paper, recursive ...] [2011-12-19 16:45:54] [75512e061a94450f738c2449abbaac12]
- RMP         [Recursive Partitioning (Regression Trees)] [paper, recursive ...] [2011-12-19 16:49:35] [75512e061a94450f738c2449abbaac12]
- RMPD        [Skewness and Kurtosis Test] [paper, skewness a...] [2011-12-20 18:39:20] [75512e061a94450f738c2449abbaac12]
- RMPD          [Recursive Partitioning (Regression Trees)] [paper, regression...] [2011-12-20 18:49:03] [75512e061a94450f738c2449abbaac12]
- RMPD          [Variance Reduction Matrix] [paper, VRM] [2011-12-20 19:23:13] [75512e061a94450f738c2449abbaac12]
- RMPD          [Standard Deviation-Mean Plot] [Paper, SMP] [2011-12-20 19:35:42] [75512e061a94450f738c2449abbaac12]
- RMPD          [Spectral Analysis] [paper, CP (d=0, D=1)] [2011-12-20 19:45:28] [75512e061a94450f738c2449abbaac12]
- R P             [Spectral Analysis] [CP] [2012-12-17 14:42:53] [c19fc719a172e69332289836fdae123a]
- RMPD          [Spectral Analysis] [Paper, CP (d=D=0)] [2011-12-20 19:46:40] [75512e061a94450f738c2449abbaac12]
- R P             [Spectral Analysis] [CP] [2012-12-17 14:34:33] [c19fc719a172e69332289836fdae123a]
- RMPD          [(Partial) Autocorrelation Function] [paper, ACF (d=D=0)] [2011-12-20 19:52:37] [75512e061a94450f738c2449abbaac12]
- R P             [(Partial) Autocorrelation Function] [ACF] [2012-12-17 14:55:19] [c19fc719a172e69332289836fdae123a]
- RMPD          [(Partial) Autocorrelation Function] [paper, ACF (d=0, ...] [2011-12-20 20:05:28] [75512e061a94450f738c2449abbaac12]
- R P             [(Partial) Autocorrelation Function] [ACF] [2012-12-17 15:00:59] [c19fc719a172e69332289836fdae123a]
- R  D          [Skewness and Kurtosis Test] [skewness] [2011-12-23 08:45:14] [74be16979710d4c4e7c6647856088456]
-    D          [Skewness and Kurtosis Test] [Skewness and kurt...] [2012-12-19 13:32:00] [c19fc719a172e69332289836fdae123a]
- RMPD          [Multiple Regression] [Seasonal Dummie] [2012-12-19 14:16:28] [c19fc719a172e69332289836fdae123a]
- RMPD          [Multiple Regression] [Dummie variabele] [2012-12-19 14:27:53] [c19fc719a172e69332289836fdae123a]
- RMP         [Recursive Partitioning (Regression Trees)] [paper, recursive ...] [2011-12-21 13:30:58] [75512e061a94450f738c2449abbaac12]
-   P           [Recursive Partitioning (Regression Trees)] [Paper, recursive ...] [2011-12-21 20:43:08] [75512e061a94450f738c2449abbaac12]
- R P             [Recursive Partitioning (Regression Trees)] [paper, RP (quanti...] [2011-12-21 20:57:26] [75512e061a94450f738c2449abbaac12]
- R P           [Recursive Partitioning (Regression Trees)] [paper, RP (endoge...] [2011-12-21 21:08:01] [75512e061a94450f738c2449abbaac12]
- RMPD          [ARIMA Backward Selection] [Paper, ARIMA back...] [2011-12-22 10:13:22] [75512e061a94450f738c2449abbaac12]
- R               [ARIMA Backward Selection] [Backward selection] [2012-12-17 16:20:17] [c19fc719a172e69332289836fdae123a]
- RMPD          [ARIMA Forecasting] [Paper, ARIMA fore...] [2011-12-22 10:35:36] [75512e061a94450f738c2449abbaac12]
- R               [ARIMA Forecasting] [ARIMA forecasting] [2012-12-17 16:39:26] [c19fc719a172e69332289836fdae123a]
- R  D        [Multiple Regression] [recursive partiti...] [2011-12-22 16:53:50] [74be16979710d4c4e7c6647856088456]
- R  D        [Multiple Regression] [recursive partiti...] [2011-12-22 16:53:50] [74be16979710d4c4e7c6647856088456]
-  MP           [Multiple Regression] [recursive partiti...] [2011-12-22 16:55:27] [f1aa04283d83c25edc8ae3bb0d0fb93e]
- RMPD        [Mean versus Median] [Median versus Mean] [2012-12-19 13:11:05] [c19fc719a172e69332289836fdae123a]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1683	150596	84	37	18	63	20465	23975	0
1323	154801	50	42	20	56	33629	85634	1
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6310	441870	237	118	34	130	151036	189748	1
1478	134379	52	42	23	71	33287	61834	1
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2756	271630	91	67	26	79	57803	101219	1
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1966	172071	63	71	30	91	52143	76183	0
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1263	134398	48	42	17	58	28735	46261	4
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1076	64149	52	0	15	45	13497	2341	5
2376	122294	82	54	34	110	46154	48149	0
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902	52197	52	16	15	37	10726	18743	0
2308	188915	89	77	27	78	83700	97057	0
1590	163147	66	34	25	67	40400	17675	0
1863	98575	48	38	34	69	33797	33106	1
1799	143546	80	50	21	58	36205	53311	1
1385	139780	25	39	21	60	30165	42754	0
1870	163784	146	54	25	88	58534	59056	0
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1514	151621	41	50	28	84	34711	42744	0
1487	164516	41	54	20	58	31076	65931	2
2051	120179	94	53	17	35	74608	38575	4
2843	214701	116	76	25	74	58092	28795	0
2216	196865	48	52	24	89	42009	94440	1
1	0	1	0	0	0	0	0	0
1830	181527	57	46	27	75	36022	38229	0
1563	93107	49	44	14	39	23333	31972	3
2046	129352	45	35	32	93	53349	40071	9
2005	229143	58	82	31	123	92596	132480	0
1934	177063	67	70	21	73	49598	62797	2
1572	126602	53	31	34	118	44093	40429	0
950	93742	29	25	23	76	84205	45545	2
1877	152153	72	48	24	65	63369	57568	1
1036	95704	42	44	22	82	60132	39019	2
1097	139793	84	40	22	67	37403	53866	2
730	76348	30	23	35	63	24460	38345	1
1918	188980	86	63	21	84	46456	50210	0
1826	172100	79	43	31	112	66616	80947	1
2444	146552	54	62	26	75	41554	43461	7
658	48188	28	12	22	39	22346	14812	0
1425	109185	60	63	21	63	30874	37819	0
2246	263652	68	60	27	93	68701	102738	0
1899	215609	75	53	26	69	35728	54509	0
1630	174876	54	53	33	117	29010	62956	1
1496	115124	49	35	11	30	23110	55411	6
1681	179712	60	49	26	65	38844	50611	0
816	70369	20	25	26	78	27084	26692	0
902	109215	58	47	21	80	35139	60056	0
2606	166096	85	30	38	85	57476	25155	10
1557	130414	51	50	29	107	33277	42840	6
1780	102057	71	36	19	60	31141	39358	0
1265	115310	56	43	19	53	61281	47241	11
1117	101181	32	44	24	62	25820	49611	3
1069	135228	31	14	26	90	23284	41833	0
1229	94982	37	38	29	89	35378	48930	0
2155	166919	67	58	34	127	74990	110600	8
2500	118169	64	68	25	71	29653	52235	2
1003	102361	36	48	24	75	64622	53986	0
340	31970	15	5	21	42	4157	4105	0
2586	200413	107	53	19	42	29245	59331	3
1119	103381	58	36	12	8	50008	47796	1
1251	94940	61	62	28	82	52338	38302	2
1516	101560	65	46	21	41	13310	14063	1
2473	144176	60	67	34	118	92901	54414	0
1288	71921	37	2	32	91	10956	9903	2
1911	126905	54	64	27	96	34241	53987	1
2279	131184	87	59	26	81	75043	88937	0
816	60138	23	16	21	46	21152	21928	0
1234	84971	71	34	31	60	42249	29487	0
907	80420	64	54	26	69	42005	35334	0
1827	233569	57	39	26	85	41152	57596	0
841	56252	25	26	23	17	14399	29750	0
1309	97181	32	37	25	61	28263	41029	0
764	50800	41	17	22	55	17215	12416	0
1439	125941	45	32	26	55	48140	51158	0
2500	211032	210	55	33	124	62897	79935	0
974	71960	92	39	22	65	22883	26552	0
1152	90379	53	39	24	73	41622	25807	6
1261	125650	47	28	21	67	40715	50620	0
1508	115572	36	45	28	66	65897	61467	5
2005	136266	67	66	22	61	76542	65292	1
1191	146715	55	39	22	74	37477	55516	0
1265	124626	57	27	15	55	53216	42006	0
761	49176	33	22	13	27	40911	26273	0
2156	212926	102	43	36	115	57021	90248	0
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223	19349	12	13	1	0	3895	9604	0
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802	58280	20	41	20	56	31747	30553	0
1131	115944	44	51	23	63	32665	24751	0
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591	59090	37	23	12	24	15292	24649	0
596	27676	22	2	16	17	5842	2342	0
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0	0	0	0	0	0	0	0	0
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1178	117769	39	49	21	55	31032	50848	0
1200	107653	56	52	21	70	32683	39443	0
849	71894	57	36	21	38	34545	27023	0
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285	19764	12	10	2	4	5752	9927	1
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1079	94127	18	17	22	56	23494	43485	1

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157445&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157445&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157445&T=0

As an alternative you can also use a QR Code:  
QR Code


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

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






Multiple Linear Regression - Estimated Regression Equation
Time_in_RFC[t] = + 9345.53773591231 + 40.7791270509608Pageviews[t] + 140.101090955033`#Logins`[t] -46.9531313503321blogs[t] -407.390389896566reviews[t] + 309.190865421513`Submits_+120`[t] -0.115225275653425Characters[t] + 0.939138848252773CW_time[t] -995.20115031685Shared[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Time_in_RFC[t] =  +  9345.53773591231 +  40.7791270509608Pageviews[t] +  140.101090955033`#Logins`[t] -46.9531313503321blogs[t] -407.390389896566reviews[t] +  309.190865421513`Submits_+120`[t] -0.115225275653425Characters[t] +  0.939138848252773CW_time[t] -995.20115031685Shared[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157445&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Time_in_RFC[t] =  +  9345.53773591231 +  40.7791270509608Pageviews[t] +  140.101090955033`#Logins`[t] -46.9531313503321blogs[t] -407.390389896566reviews[t] +  309.190865421513`Submits_+120`[t] -0.115225275653425Characters[t] +  0.939138848252773CW_time[t] -995.20115031685Shared[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157445&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157445&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Time_in_RFC[t] = + 9345.53773591231 + 40.7791270509608Pageviews[t] + 140.101090955033`#Logins`[t] -46.9531313503321blogs[t] -407.390389896566reviews[t] + 309.190865421513`Submits_+120`[t] -0.115225275653425Characters[t] + 0.939138848252773CW_time[t] -995.20115031685Shared[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9345.537735912315095.9515551.83390.0688690.034434
Pageviews40.77912705096085.97016.830600
`#Logins`140.101090955033106.9743461.30970.1925320.096266
blogs-46.9531313503321166.979791-0.28120.7789950.389497
reviews-407.390389896566484.286753-0.84120.4017140.200857
`Submits_+120`309.190865421513147.5318542.09580.0379710.018985
Characters-0.1152252756534250.129743-0.88810.3760630.188031
CW_time0.9391388482527730.1311087.163100
Shared-995.20115031685979.052514-1.01650.3112120.155606

\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) & 9345.53773591231 & 5095.951555 & 1.8339 & 0.068869 & 0.034434 \tabularnewline
Pageviews & 40.7791270509608 & 5.9701 & 6.8306 & 0 & 0 \tabularnewline
`#Logins` & 140.101090955033 & 106.974346 & 1.3097 & 0.192532 & 0.096266 \tabularnewline
blogs & -46.9531313503321 & 166.979791 & -0.2812 & 0.778995 & 0.389497 \tabularnewline
reviews & -407.390389896566 & 484.286753 & -0.8412 & 0.401714 & 0.200857 \tabularnewline
`Submits_+120` & 309.190865421513 & 147.531854 & 2.0958 & 0.037971 & 0.018985 \tabularnewline
Characters & -0.115225275653425 & 0.129743 & -0.8881 & 0.376063 & 0.188031 \tabularnewline
CW_time & 0.939138848252773 & 0.131108 & 7.1631 & 0 & 0 \tabularnewline
Shared & -995.20115031685 & 979.052514 & -1.0165 & 0.311212 & 0.155606 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157445&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]9345.53773591231[/C][C]5095.951555[/C][C]1.8339[/C][C]0.068869[/C][C]0.034434[/C][/ROW]
[ROW][C]Pageviews[/C][C]40.7791270509608[/C][C]5.9701[/C][C]6.8306[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`#Logins`[/C][C]140.101090955033[/C][C]106.974346[/C][C]1.3097[/C][C]0.192532[/C][C]0.096266[/C][/ROW]
[ROW][C]blogs[/C][C]-46.9531313503321[/C][C]166.979791[/C][C]-0.2812[/C][C]0.778995[/C][C]0.389497[/C][/ROW]
[ROW][C]reviews[/C][C]-407.390389896566[/C][C]484.286753[/C][C]-0.8412[/C][C]0.401714[/C][C]0.200857[/C][/ROW]
[ROW][C]`Submits_+120`[/C][C]309.190865421513[/C][C]147.531854[/C][C]2.0958[/C][C]0.037971[/C][C]0.018985[/C][/ROW]
[ROW][C]Characters[/C][C]-0.115225275653425[/C][C]0.129743[/C][C]-0.8881[/C][C]0.376063[/C][C]0.188031[/C][/ROW]
[ROW][C]CW_time[/C][C]0.939138848252773[/C][C]0.131108[/C][C]7.1631[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Shared[/C][C]-995.20115031685[/C][C]979.052514[/C][C]-1.0165[/C][C]0.311212[/C][C]0.155606[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157445&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157445&T=2

As an alternative you can also use a QR Code:  
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)9345.537735912315095.9515551.83390.0688690.034434
Pageviews40.77912705096085.97016.830600
`#Logins`140.101090955033106.9743461.30970.1925320.096266
blogs-46.9531313503321166.979791-0.28120.7789950.389497
reviews-407.390389896566484.286753-0.84120.4017140.200857
`Submits_+120`309.190865421513147.5318542.09580.0379710.018985
Characters-0.1152252756534250.129743-0.88810.3760630.188031
CW_time0.9391388482527730.1311087.163100
Shared-995.20115031685979.052514-1.01650.3112120.155606






Multiple Linear Regression - Regression Statistics
Multiple R0.939766824662841
R-squared0.883161684736879
Adjusted R-squared0.876237932721286
F-TEST (value)127.555360554218
F-TEST (DF numerator)8
F-TEST (DF denominator)135
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation23350.5716675438
Sum Squared Residuals73608641622.1486

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.939766824662841 \tabularnewline
R-squared & 0.883161684736879 \tabularnewline
Adjusted R-squared & 0.876237932721286 \tabularnewline
F-TEST (value) & 127.555360554218 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 135 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 23350.5716675438 \tabularnewline
Sum Squared Residuals & 73608641622.1486 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157445&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.939766824662841[/C][/ROW]
[ROW][C]R-squared[/C][C]0.883161684736879[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.876237932721286[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]127.555360554218[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]135[/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]23350.5716675438[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]73608641622.1486[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157445&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157445&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.939766824662841
R-squared0.883161684736879
Adjusted R-squared0.876237932721286
F-TEST (value)127.555360554218
F-TEST (DF numerator)8
F-TEST (DF denominator)135
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation23350.5716675438
Sum Squared Residuals73608641622.1486






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1150596120311.8004669730284.1995330302
2154801153048.3307070571752.66929294338
3721521344.5830379121-14129.5830379121
4122139147457.596088085-25318.5960880854
5221399243891.276576421-22492.2765764208
6441870480470.210027151-38600.2100271515
7134379140752.891849356-6373.89184935605
8140428149374.317764415-8946.31776441532
9103255109635.69329641-6380.69329641039
10271630232573.2069181739056.7930818296
11121593136749.713975992-15156.7139759921
12172071176462.8783069-4391.87830689984
1383707115341.806961446-31634.8069614461
14197412203329.40897376-5917.40897375979
15134398112763.52897846221634.4710215375
16139224126922.04136550112301.9586344988
17134153158345.536812348-24192.5368123481
186414963979.1910146086169.808985391398
19122294175249.774946288-52955.7749462876
202488949389.8775203974-24500.8775203974
215219774357.8962622259-22160.8962622259
22188915206940.359548901-18025.3595489005
23163147104309.82152635758837.1784736426
2498575123942.242182663-25367.242182663
25143546145844.988899951-2298.98889995109
26139780114168.40946828225611.5905317178
27163784169263.019454723-5479.01945472296
28152479164887.369202843-12408.3692028427
29304108236552.64807367267555.3519263282
30184024174788.0125682149235.98743178649
31151621125189.6924175226431.3075824796
32164516139325.2581307625190.7418692401
33120179131210.306671282-11031.3066712822
34214701171008.0852259143692.91477409
35196865204592.563555213-7727.56355521307
3609526.41795391828-9526.41795391828
37181527133738.72691116647788.2730888341
3893107108589.299735399-15482.2997353988
39129352135687.348390054-6335.34839005415
40229143234532.483329929-5389.48332992871
41177063159597.91507111417465.0849288859
42126602134941.201488659-8339.20148865928
439374296183.4707993188-2441.47079931885
44152153149808.957922412344.04207759026
459570499527.2139319935-3823.21393199353
46139793120011.28581662819781.7141833722
477634879655.4398093355-3307.43980933547
48188980155868.64063931233111.3593606876
49172100182206.924345038-10106.9243450377
50146552155322.688112808-8770.68811280777
514818853969.1520902646-5781.15209026461
52109185115787.465241038-6602.46524103765
53263652213969.50868241349682.4913175866
54215609152620.93627338562988.0637266148
55174876158410.44510029216465.5548997083
56115124123771.69703575-8647.69703575041
57179712136560.8140862443151.1859137599
587036979720.9690821716-9351.9690821716
59109215120579.469193181-11364.4691931812
60166096143965.66864731722130.3313526832
61130414129332.3887778781081.61122212154
62102057134374.679613483-32317.6796134832
63115310101762.03391960713547.966080393
64101181107336.481413944-6155.48141394448
65135228110463.3324053624764.6675946399
6694982120441.896014022-25459.8960140223
67166919216570.418651421-49651.4186514212
68118169172483.544289026-54314.5442890259
69102361109703.098785186-7342.09878518566
703197032884.1833018748-914.183301874799
71200413181912.94036823718500.0596317631
7210338197127.46735322686253.53264677315
7394940107891.851516656-12951.8515166559
7410156092913.30856329968646.69143670036
75144176178483.531408371-34307.5314083708
767192188106.2453504075-16185.2453504075
77126905156278.349775364-29373.3497753635
78131184201029.379796499-69845.3797964989
796013868916.153655284-8778.153655284
808497196764.3358945391-11793.3358945391
818042091848.7206364529-11428.7206364529
82233569155041.55690358678527.4430964136
835625268089.0471804685-11837.0471804685
8497181109422.582978687-12241.5829786871
855080063166.3861390385-12366.3861390385
86125941121739.6183418434201.38165815732
87211032230850.686357596-19818.6863575957
887196093556.6681194523-21596.6681194523
899037988180.08454653712198.91545346288
90125650121046.4817399914603.51826000927
91115572127927.917737329-12355.9177373286
92136266158797.087591681-22531.0875916805
93146715125524.33604087521190.6639591245
94124626111861.44103362412764.5589663764
954917666980.9125391155-17804.9125391155
96212926208612.7001084134313.29989158728
97173884144825.99276371229058.0072362883
981934927673.4021122356-8324.40211223558
99181141156043.13817484825097.8618251515
100145502150071.973393621-4569.97339362053
1014544838262.52859613117185.47140386885
1025828077129.674134691-18849.674134691
10311594488826.40629152327117.593708477
1049434191363.86293618062977.13706381936
1055909061468.5248139566-2378.52481395662
1062767636902.5307926563-9226.53079265632
107120586128283.439618914-7697.43961891371
1088801154710.895248538433300.1047514616
10909345.53773591229-9345.53773591229
1108561081030.50246718074579.49753281931
1118419385824.5181612526-1631.51816125261
112117769113174.5493252594594.45067474066
113107653110049.296759461-2396.29675946105
1147189474854.4127050796-2960.41270507962
115361613226.8151006624-9610.81510066239
11609345.53773591229-9345.53773591229
117154806120516.96920727134289.0307927286
118136061136665.218281922-604.218281922242
119141822134687.911825657134.08817435033
12010651596746.43571808219768.56428191791
1214341043616.8969100562-206.89691005615
122146920143991.802245552928.19775444985
1238887484240.54747635974633.45252364034
12411192497374.508190399714549.4918096003
1256037346241.812257241514131.1877427585
1261976430266.107816016-10502.107816016
127121665130834.795708589-9169.79570858865
12810868595017.957441803513667.0425581965
129124493114494.2125315379998.78746846316
1301179620493.3610338316-8697.36103383159
1311067414602.8020055017-3928.80200550174
132131263108932.54350476822330.4564952321
133683611030.3948279702-4194.39482797022
134153278125423.25745781227854.7425421878
135511811243.7986881661-6125.79868816608
1364024837301.29892638332946.70107361665
13709345.53773591229-9345.53773591229
13810072888091.031907579112636.9680924209
13984267107193.129149763-22926.1291497626
140713112213.8502405434-5082.8502405434
141881221330.3305018768-12518.3305018768
1426395264709.6828019057-757.682801905738
143120111119187.858272337923.141727663306
14494127100558.081153768-6431.08115376762

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 150596 & 120311.80046697 & 30284.1995330302 \tabularnewline
2 & 154801 & 153048.330707057 & 1752.66929294338 \tabularnewline
3 & 7215 & 21344.5830379121 & -14129.5830379121 \tabularnewline
4 & 122139 & 147457.596088085 & -25318.5960880854 \tabularnewline
5 & 221399 & 243891.276576421 & -22492.2765764208 \tabularnewline
6 & 441870 & 480470.210027151 & -38600.2100271515 \tabularnewline
7 & 134379 & 140752.891849356 & -6373.89184935605 \tabularnewline
8 & 140428 & 149374.317764415 & -8946.31776441532 \tabularnewline
9 & 103255 & 109635.69329641 & -6380.69329641039 \tabularnewline
10 & 271630 & 232573.20691817 & 39056.7930818296 \tabularnewline
11 & 121593 & 136749.713975992 & -15156.7139759921 \tabularnewline
12 & 172071 & 176462.8783069 & -4391.87830689984 \tabularnewline
13 & 83707 & 115341.806961446 & -31634.8069614461 \tabularnewline
14 & 197412 & 203329.40897376 & -5917.40897375979 \tabularnewline
15 & 134398 & 112763.528978462 & 21634.4710215375 \tabularnewline
16 & 139224 & 126922.041365501 & 12301.9586344988 \tabularnewline
17 & 134153 & 158345.536812348 & -24192.5368123481 \tabularnewline
18 & 64149 & 63979.1910146086 & 169.808985391398 \tabularnewline
19 & 122294 & 175249.774946288 & -52955.7749462876 \tabularnewline
20 & 24889 & 49389.8775203974 & -24500.8775203974 \tabularnewline
21 & 52197 & 74357.8962622259 & -22160.8962622259 \tabularnewline
22 & 188915 & 206940.359548901 & -18025.3595489005 \tabularnewline
23 & 163147 & 104309.821526357 & 58837.1784736426 \tabularnewline
24 & 98575 & 123942.242182663 & -25367.242182663 \tabularnewline
25 & 143546 & 145844.988899951 & -2298.98889995109 \tabularnewline
26 & 139780 & 114168.409468282 & 25611.5905317178 \tabularnewline
27 & 163784 & 169263.019454723 & -5479.01945472296 \tabularnewline
28 & 152479 & 164887.369202843 & -12408.3692028427 \tabularnewline
29 & 304108 & 236552.648073672 & 67555.3519263282 \tabularnewline
30 & 184024 & 174788.012568214 & 9235.98743178649 \tabularnewline
31 & 151621 & 125189.69241752 & 26431.3075824796 \tabularnewline
32 & 164516 & 139325.25813076 & 25190.7418692401 \tabularnewline
33 & 120179 & 131210.306671282 & -11031.3066712822 \tabularnewline
34 & 214701 & 171008.08522591 & 43692.91477409 \tabularnewline
35 & 196865 & 204592.563555213 & -7727.56355521307 \tabularnewline
36 & 0 & 9526.41795391828 & -9526.41795391828 \tabularnewline
37 & 181527 & 133738.726911166 & 47788.2730888341 \tabularnewline
38 & 93107 & 108589.299735399 & -15482.2997353988 \tabularnewline
39 & 129352 & 135687.348390054 & -6335.34839005415 \tabularnewline
40 & 229143 & 234532.483329929 & -5389.48332992871 \tabularnewline
41 & 177063 & 159597.915071114 & 17465.0849288859 \tabularnewline
42 & 126602 & 134941.201488659 & -8339.20148865928 \tabularnewline
43 & 93742 & 96183.4707993188 & -2441.47079931885 \tabularnewline
44 & 152153 & 149808.95792241 & 2344.04207759026 \tabularnewline
45 & 95704 & 99527.2139319935 & -3823.21393199353 \tabularnewline
46 & 139793 & 120011.285816628 & 19781.7141833722 \tabularnewline
47 & 76348 & 79655.4398093355 & -3307.43980933547 \tabularnewline
48 & 188980 & 155868.640639312 & 33111.3593606876 \tabularnewline
49 & 172100 & 182206.924345038 & -10106.9243450377 \tabularnewline
50 & 146552 & 155322.688112808 & -8770.68811280777 \tabularnewline
51 & 48188 & 53969.1520902646 & -5781.15209026461 \tabularnewline
52 & 109185 & 115787.465241038 & -6602.46524103765 \tabularnewline
53 & 263652 & 213969.508682413 & 49682.4913175866 \tabularnewline
54 & 215609 & 152620.936273385 & 62988.0637266148 \tabularnewline
55 & 174876 & 158410.445100292 & 16465.5548997083 \tabularnewline
56 & 115124 & 123771.69703575 & -8647.69703575041 \tabularnewline
57 & 179712 & 136560.81408624 & 43151.1859137599 \tabularnewline
58 & 70369 & 79720.9690821716 & -9351.9690821716 \tabularnewline
59 & 109215 & 120579.469193181 & -11364.4691931812 \tabularnewline
60 & 166096 & 143965.668647317 & 22130.3313526832 \tabularnewline
61 & 130414 & 129332.388777878 & 1081.61122212154 \tabularnewline
62 & 102057 & 134374.679613483 & -32317.6796134832 \tabularnewline
63 & 115310 & 101762.033919607 & 13547.966080393 \tabularnewline
64 & 101181 & 107336.481413944 & -6155.48141394448 \tabularnewline
65 & 135228 & 110463.33240536 & 24764.6675946399 \tabularnewline
66 & 94982 & 120441.896014022 & -25459.8960140223 \tabularnewline
67 & 166919 & 216570.418651421 & -49651.4186514212 \tabularnewline
68 & 118169 & 172483.544289026 & -54314.5442890259 \tabularnewline
69 & 102361 & 109703.098785186 & -7342.09878518566 \tabularnewline
70 & 31970 & 32884.1833018748 & -914.183301874799 \tabularnewline
71 & 200413 & 181912.940368237 & 18500.0596317631 \tabularnewline
72 & 103381 & 97127.4673532268 & 6253.53264677315 \tabularnewline
73 & 94940 & 107891.851516656 & -12951.8515166559 \tabularnewline
74 & 101560 & 92913.3085632996 & 8646.69143670036 \tabularnewline
75 & 144176 & 178483.531408371 & -34307.5314083708 \tabularnewline
76 & 71921 & 88106.2453504075 & -16185.2453504075 \tabularnewline
77 & 126905 & 156278.349775364 & -29373.3497753635 \tabularnewline
78 & 131184 & 201029.379796499 & -69845.3797964989 \tabularnewline
79 & 60138 & 68916.153655284 & -8778.153655284 \tabularnewline
80 & 84971 & 96764.3358945391 & -11793.3358945391 \tabularnewline
81 & 80420 & 91848.7206364529 & -11428.7206364529 \tabularnewline
82 & 233569 & 155041.556903586 & 78527.4430964136 \tabularnewline
83 & 56252 & 68089.0471804685 & -11837.0471804685 \tabularnewline
84 & 97181 & 109422.582978687 & -12241.5829786871 \tabularnewline
85 & 50800 & 63166.3861390385 & -12366.3861390385 \tabularnewline
86 & 125941 & 121739.618341843 & 4201.38165815732 \tabularnewline
87 & 211032 & 230850.686357596 & -19818.6863575957 \tabularnewline
88 & 71960 & 93556.6681194523 & -21596.6681194523 \tabularnewline
89 & 90379 & 88180.0845465371 & 2198.91545346288 \tabularnewline
90 & 125650 & 121046.481739991 & 4603.51826000927 \tabularnewline
91 & 115572 & 127927.917737329 & -12355.9177373286 \tabularnewline
92 & 136266 & 158797.087591681 & -22531.0875916805 \tabularnewline
93 & 146715 & 125524.336040875 & 21190.6639591245 \tabularnewline
94 & 124626 & 111861.441033624 & 12764.5589663764 \tabularnewline
95 & 49176 & 66980.9125391155 & -17804.9125391155 \tabularnewline
96 & 212926 & 208612.700108413 & 4313.29989158728 \tabularnewline
97 & 173884 & 144825.992763712 & 29058.0072362883 \tabularnewline
98 & 19349 & 27673.4021122356 & -8324.40211223558 \tabularnewline
99 & 181141 & 156043.138174848 & 25097.8618251515 \tabularnewline
100 & 145502 & 150071.973393621 & -4569.97339362053 \tabularnewline
101 & 45448 & 38262.5285961311 & 7185.47140386885 \tabularnewline
102 & 58280 & 77129.674134691 & -18849.674134691 \tabularnewline
103 & 115944 & 88826.406291523 & 27117.593708477 \tabularnewline
104 & 94341 & 91363.8629361806 & 2977.13706381936 \tabularnewline
105 & 59090 & 61468.5248139566 & -2378.52481395662 \tabularnewline
106 & 27676 & 36902.5307926563 & -9226.53079265632 \tabularnewline
107 & 120586 & 128283.439618914 & -7697.43961891371 \tabularnewline
108 & 88011 & 54710.8952485384 & 33300.1047514616 \tabularnewline
109 & 0 & 9345.53773591229 & -9345.53773591229 \tabularnewline
110 & 85610 & 81030.5024671807 & 4579.49753281931 \tabularnewline
111 & 84193 & 85824.5181612526 & -1631.51816125261 \tabularnewline
112 & 117769 & 113174.549325259 & 4594.45067474066 \tabularnewline
113 & 107653 & 110049.296759461 & -2396.29675946105 \tabularnewline
114 & 71894 & 74854.4127050796 & -2960.41270507962 \tabularnewline
115 & 3616 & 13226.8151006624 & -9610.81510066239 \tabularnewline
116 & 0 & 9345.53773591229 & -9345.53773591229 \tabularnewline
117 & 154806 & 120516.969207271 & 34289.0307927286 \tabularnewline
118 & 136061 & 136665.218281922 & -604.218281922242 \tabularnewline
119 & 141822 & 134687.91182565 & 7134.08817435033 \tabularnewline
120 & 106515 & 96746.4357180821 & 9768.56428191791 \tabularnewline
121 & 43410 & 43616.8969100562 & -206.89691005615 \tabularnewline
122 & 146920 & 143991.80224555 & 2928.19775444985 \tabularnewline
123 & 88874 & 84240.5474763597 & 4633.45252364034 \tabularnewline
124 & 111924 & 97374.5081903997 & 14549.4918096003 \tabularnewline
125 & 60373 & 46241.8122572415 & 14131.1877427585 \tabularnewline
126 & 19764 & 30266.107816016 & -10502.107816016 \tabularnewline
127 & 121665 & 130834.795708589 & -9169.79570858865 \tabularnewline
128 & 108685 & 95017.9574418035 & 13667.0425581965 \tabularnewline
129 & 124493 & 114494.212531537 & 9998.78746846316 \tabularnewline
130 & 11796 & 20493.3610338316 & -8697.36103383159 \tabularnewline
131 & 10674 & 14602.8020055017 & -3928.80200550174 \tabularnewline
132 & 131263 & 108932.543504768 & 22330.4564952321 \tabularnewline
133 & 6836 & 11030.3948279702 & -4194.39482797022 \tabularnewline
134 & 153278 & 125423.257457812 & 27854.7425421878 \tabularnewline
135 & 5118 & 11243.7986881661 & -6125.79868816608 \tabularnewline
136 & 40248 & 37301.2989263833 & 2946.70107361665 \tabularnewline
137 & 0 & 9345.53773591229 & -9345.53773591229 \tabularnewline
138 & 100728 & 88091.0319075791 & 12636.9680924209 \tabularnewline
139 & 84267 & 107193.129149763 & -22926.1291497626 \tabularnewline
140 & 7131 & 12213.8502405434 & -5082.8502405434 \tabularnewline
141 & 8812 & 21330.3305018768 & -12518.3305018768 \tabularnewline
142 & 63952 & 64709.6828019057 & -757.682801905738 \tabularnewline
143 & 120111 & 119187.858272337 & 923.141727663306 \tabularnewline
144 & 94127 & 100558.081153768 & -6431.08115376762 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157445&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]150596[/C][C]120311.80046697[/C][C]30284.1995330302[/C][/ROW]
[ROW][C]2[/C][C]154801[/C][C]153048.330707057[/C][C]1752.66929294338[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]21344.5830379121[/C][C]-14129.5830379121[/C][/ROW]
[ROW][C]4[/C][C]122139[/C][C]147457.596088085[/C][C]-25318.5960880854[/C][/ROW]
[ROW][C]5[/C][C]221399[/C][C]243891.276576421[/C][C]-22492.2765764208[/C][/ROW]
[ROW][C]6[/C][C]441870[/C][C]480470.210027151[/C][C]-38600.2100271515[/C][/ROW]
[ROW][C]7[/C][C]134379[/C][C]140752.891849356[/C][C]-6373.89184935605[/C][/ROW]
[ROW][C]8[/C][C]140428[/C][C]149374.317764415[/C][C]-8946.31776441532[/C][/ROW]
[ROW][C]9[/C][C]103255[/C][C]109635.69329641[/C][C]-6380.69329641039[/C][/ROW]
[ROW][C]10[/C][C]271630[/C][C]232573.20691817[/C][C]39056.7930818296[/C][/ROW]
[ROW][C]11[/C][C]121593[/C][C]136749.713975992[/C][C]-15156.7139759921[/C][/ROW]
[ROW][C]12[/C][C]172071[/C][C]176462.8783069[/C][C]-4391.87830689984[/C][/ROW]
[ROW][C]13[/C][C]83707[/C][C]115341.806961446[/C][C]-31634.8069614461[/C][/ROW]
[ROW][C]14[/C][C]197412[/C][C]203329.40897376[/C][C]-5917.40897375979[/C][/ROW]
[ROW][C]15[/C][C]134398[/C][C]112763.528978462[/C][C]21634.4710215375[/C][/ROW]
[ROW][C]16[/C][C]139224[/C][C]126922.041365501[/C][C]12301.9586344988[/C][/ROW]
[ROW][C]17[/C][C]134153[/C][C]158345.536812348[/C][C]-24192.5368123481[/C][/ROW]
[ROW][C]18[/C][C]64149[/C][C]63979.1910146086[/C][C]169.808985391398[/C][/ROW]
[ROW][C]19[/C][C]122294[/C][C]175249.774946288[/C][C]-52955.7749462876[/C][/ROW]
[ROW][C]20[/C][C]24889[/C][C]49389.8775203974[/C][C]-24500.8775203974[/C][/ROW]
[ROW][C]21[/C][C]52197[/C][C]74357.8962622259[/C][C]-22160.8962622259[/C][/ROW]
[ROW][C]22[/C][C]188915[/C][C]206940.359548901[/C][C]-18025.3595489005[/C][/ROW]
[ROW][C]23[/C][C]163147[/C][C]104309.821526357[/C][C]58837.1784736426[/C][/ROW]
[ROW][C]24[/C][C]98575[/C][C]123942.242182663[/C][C]-25367.242182663[/C][/ROW]
[ROW][C]25[/C][C]143546[/C][C]145844.988899951[/C][C]-2298.98889995109[/C][/ROW]
[ROW][C]26[/C][C]139780[/C][C]114168.409468282[/C][C]25611.5905317178[/C][/ROW]
[ROW][C]27[/C][C]163784[/C][C]169263.019454723[/C][C]-5479.01945472296[/C][/ROW]
[ROW][C]28[/C][C]152479[/C][C]164887.369202843[/C][C]-12408.3692028427[/C][/ROW]
[ROW][C]29[/C][C]304108[/C][C]236552.648073672[/C][C]67555.3519263282[/C][/ROW]
[ROW][C]30[/C][C]184024[/C][C]174788.012568214[/C][C]9235.98743178649[/C][/ROW]
[ROW][C]31[/C][C]151621[/C][C]125189.69241752[/C][C]26431.3075824796[/C][/ROW]
[ROW][C]32[/C][C]164516[/C][C]139325.25813076[/C][C]25190.7418692401[/C][/ROW]
[ROW][C]33[/C][C]120179[/C][C]131210.306671282[/C][C]-11031.3066712822[/C][/ROW]
[ROW][C]34[/C][C]214701[/C][C]171008.08522591[/C][C]43692.91477409[/C][/ROW]
[ROW][C]35[/C][C]196865[/C][C]204592.563555213[/C][C]-7727.56355521307[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]9526.41795391828[/C][C]-9526.41795391828[/C][/ROW]
[ROW][C]37[/C][C]181527[/C][C]133738.726911166[/C][C]47788.2730888341[/C][/ROW]
[ROW][C]38[/C][C]93107[/C][C]108589.299735399[/C][C]-15482.2997353988[/C][/ROW]
[ROW][C]39[/C][C]129352[/C][C]135687.348390054[/C][C]-6335.34839005415[/C][/ROW]
[ROW][C]40[/C][C]229143[/C][C]234532.483329929[/C][C]-5389.48332992871[/C][/ROW]
[ROW][C]41[/C][C]177063[/C][C]159597.915071114[/C][C]17465.0849288859[/C][/ROW]
[ROW][C]42[/C][C]126602[/C][C]134941.201488659[/C][C]-8339.20148865928[/C][/ROW]
[ROW][C]43[/C][C]93742[/C][C]96183.4707993188[/C][C]-2441.47079931885[/C][/ROW]
[ROW][C]44[/C][C]152153[/C][C]149808.95792241[/C][C]2344.04207759026[/C][/ROW]
[ROW][C]45[/C][C]95704[/C][C]99527.2139319935[/C][C]-3823.21393199353[/C][/ROW]
[ROW][C]46[/C][C]139793[/C][C]120011.285816628[/C][C]19781.7141833722[/C][/ROW]
[ROW][C]47[/C][C]76348[/C][C]79655.4398093355[/C][C]-3307.43980933547[/C][/ROW]
[ROW][C]48[/C][C]188980[/C][C]155868.640639312[/C][C]33111.3593606876[/C][/ROW]
[ROW][C]49[/C][C]172100[/C][C]182206.924345038[/C][C]-10106.9243450377[/C][/ROW]
[ROW][C]50[/C][C]146552[/C][C]155322.688112808[/C][C]-8770.68811280777[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]53969.1520902646[/C][C]-5781.15209026461[/C][/ROW]
[ROW][C]52[/C][C]109185[/C][C]115787.465241038[/C][C]-6602.46524103765[/C][/ROW]
[ROW][C]53[/C][C]263652[/C][C]213969.508682413[/C][C]49682.4913175866[/C][/ROW]
[ROW][C]54[/C][C]215609[/C][C]152620.936273385[/C][C]62988.0637266148[/C][/ROW]
[ROW][C]55[/C][C]174876[/C][C]158410.445100292[/C][C]16465.5548997083[/C][/ROW]
[ROW][C]56[/C][C]115124[/C][C]123771.69703575[/C][C]-8647.69703575041[/C][/ROW]
[ROW][C]57[/C][C]179712[/C][C]136560.81408624[/C][C]43151.1859137599[/C][/ROW]
[ROW][C]58[/C][C]70369[/C][C]79720.9690821716[/C][C]-9351.9690821716[/C][/ROW]
[ROW][C]59[/C][C]109215[/C][C]120579.469193181[/C][C]-11364.4691931812[/C][/ROW]
[ROW][C]60[/C][C]166096[/C][C]143965.668647317[/C][C]22130.3313526832[/C][/ROW]
[ROW][C]61[/C][C]130414[/C][C]129332.388777878[/C][C]1081.61122212154[/C][/ROW]
[ROW][C]62[/C][C]102057[/C][C]134374.679613483[/C][C]-32317.6796134832[/C][/ROW]
[ROW][C]63[/C][C]115310[/C][C]101762.033919607[/C][C]13547.966080393[/C][/ROW]
[ROW][C]64[/C][C]101181[/C][C]107336.481413944[/C][C]-6155.48141394448[/C][/ROW]
[ROW][C]65[/C][C]135228[/C][C]110463.33240536[/C][C]24764.6675946399[/C][/ROW]
[ROW][C]66[/C][C]94982[/C][C]120441.896014022[/C][C]-25459.8960140223[/C][/ROW]
[ROW][C]67[/C][C]166919[/C][C]216570.418651421[/C][C]-49651.4186514212[/C][/ROW]
[ROW][C]68[/C][C]118169[/C][C]172483.544289026[/C][C]-54314.5442890259[/C][/ROW]
[ROW][C]69[/C][C]102361[/C][C]109703.098785186[/C][C]-7342.09878518566[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]32884.1833018748[/C][C]-914.183301874799[/C][/ROW]
[ROW][C]71[/C][C]200413[/C][C]181912.940368237[/C][C]18500.0596317631[/C][/ROW]
[ROW][C]72[/C][C]103381[/C][C]97127.4673532268[/C][C]6253.53264677315[/C][/ROW]
[ROW][C]73[/C][C]94940[/C][C]107891.851516656[/C][C]-12951.8515166559[/C][/ROW]
[ROW][C]74[/C][C]101560[/C][C]92913.3085632996[/C][C]8646.69143670036[/C][/ROW]
[ROW][C]75[/C][C]144176[/C][C]178483.531408371[/C][C]-34307.5314083708[/C][/ROW]
[ROW][C]76[/C][C]71921[/C][C]88106.2453504075[/C][C]-16185.2453504075[/C][/ROW]
[ROW][C]77[/C][C]126905[/C][C]156278.349775364[/C][C]-29373.3497753635[/C][/ROW]
[ROW][C]78[/C][C]131184[/C][C]201029.379796499[/C][C]-69845.3797964989[/C][/ROW]
[ROW][C]79[/C][C]60138[/C][C]68916.153655284[/C][C]-8778.153655284[/C][/ROW]
[ROW][C]80[/C][C]84971[/C][C]96764.3358945391[/C][C]-11793.3358945391[/C][/ROW]
[ROW][C]81[/C][C]80420[/C][C]91848.7206364529[/C][C]-11428.7206364529[/C][/ROW]
[ROW][C]82[/C][C]233569[/C][C]155041.556903586[/C][C]78527.4430964136[/C][/ROW]
[ROW][C]83[/C][C]56252[/C][C]68089.0471804685[/C][C]-11837.0471804685[/C][/ROW]
[ROW][C]84[/C][C]97181[/C][C]109422.582978687[/C][C]-12241.5829786871[/C][/ROW]
[ROW][C]85[/C][C]50800[/C][C]63166.3861390385[/C][C]-12366.3861390385[/C][/ROW]
[ROW][C]86[/C][C]125941[/C][C]121739.618341843[/C][C]4201.38165815732[/C][/ROW]
[ROW][C]87[/C][C]211032[/C][C]230850.686357596[/C][C]-19818.6863575957[/C][/ROW]
[ROW][C]88[/C][C]71960[/C][C]93556.6681194523[/C][C]-21596.6681194523[/C][/ROW]
[ROW][C]89[/C][C]90379[/C][C]88180.0845465371[/C][C]2198.91545346288[/C][/ROW]
[ROW][C]90[/C][C]125650[/C][C]121046.481739991[/C][C]4603.51826000927[/C][/ROW]
[ROW][C]91[/C][C]115572[/C][C]127927.917737329[/C][C]-12355.9177373286[/C][/ROW]
[ROW][C]92[/C][C]136266[/C][C]158797.087591681[/C][C]-22531.0875916805[/C][/ROW]
[ROW][C]93[/C][C]146715[/C][C]125524.336040875[/C][C]21190.6639591245[/C][/ROW]
[ROW][C]94[/C][C]124626[/C][C]111861.441033624[/C][C]12764.5589663764[/C][/ROW]
[ROW][C]95[/C][C]49176[/C][C]66980.9125391155[/C][C]-17804.9125391155[/C][/ROW]
[ROW][C]96[/C][C]212926[/C][C]208612.700108413[/C][C]4313.29989158728[/C][/ROW]
[ROW][C]97[/C][C]173884[/C][C]144825.992763712[/C][C]29058.0072362883[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]27673.4021122356[/C][C]-8324.40211223558[/C][/ROW]
[ROW][C]99[/C][C]181141[/C][C]156043.138174848[/C][C]25097.8618251515[/C][/ROW]
[ROW][C]100[/C][C]145502[/C][C]150071.973393621[/C][C]-4569.97339362053[/C][/ROW]
[ROW][C]101[/C][C]45448[/C][C]38262.5285961311[/C][C]7185.47140386885[/C][/ROW]
[ROW][C]102[/C][C]58280[/C][C]77129.674134691[/C][C]-18849.674134691[/C][/ROW]
[ROW][C]103[/C][C]115944[/C][C]88826.406291523[/C][C]27117.593708477[/C][/ROW]
[ROW][C]104[/C][C]94341[/C][C]91363.8629361806[/C][C]2977.13706381936[/C][/ROW]
[ROW][C]105[/C][C]59090[/C][C]61468.5248139566[/C][C]-2378.52481395662[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]36902.5307926563[/C][C]-9226.53079265632[/C][/ROW]
[ROW][C]107[/C][C]120586[/C][C]128283.439618914[/C][C]-7697.43961891371[/C][/ROW]
[ROW][C]108[/C][C]88011[/C][C]54710.8952485384[/C][C]33300.1047514616[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]9345.53773591229[/C][C]-9345.53773591229[/C][/ROW]
[ROW][C]110[/C][C]85610[/C][C]81030.5024671807[/C][C]4579.49753281931[/C][/ROW]
[ROW][C]111[/C][C]84193[/C][C]85824.5181612526[/C][C]-1631.51816125261[/C][/ROW]
[ROW][C]112[/C][C]117769[/C][C]113174.549325259[/C][C]4594.45067474066[/C][/ROW]
[ROW][C]113[/C][C]107653[/C][C]110049.296759461[/C][C]-2396.29675946105[/C][/ROW]
[ROW][C]114[/C][C]71894[/C][C]74854.4127050796[/C][C]-2960.41270507962[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]13226.8151006624[/C][C]-9610.81510066239[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]9345.53773591229[/C][C]-9345.53773591229[/C][/ROW]
[ROW][C]117[/C][C]154806[/C][C]120516.969207271[/C][C]34289.0307927286[/C][/ROW]
[ROW][C]118[/C][C]136061[/C][C]136665.218281922[/C][C]-604.218281922242[/C][/ROW]
[ROW][C]119[/C][C]141822[/C][C]134687.91182565[/C][C]7134.08817435033[/C][/ROW]
[ROW][C]120[/C][C]106515[/C][C]96746.4357180821[/C][C]9768.56428191791[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]43616.8969100562[/C][C]-206.89691005615[/C][/ROW]
[ROW][C]122[/C][C]146920[/C][C]143991.80224555[/C][C]2928.19775444985[/C][/ROW]
[ROW][C]123[/C][C]88874[/C][C]84240.5474763597[/C][C]4633.45252364034[/C][/ROW]
[ROW][C]124[/C][C]111924[/C][C]97374.5081903997[/C][C]14549.4918096003[/C][/ROW]
[ROW][C]125[/C][C]60373[/C][C]46241.8122572415[/C][C]14131.1877427585[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]30266.107816016[/C][C]-10502.107816016[/C][/ROW]
[ROW][C]127[/C][C]121665[/C][C]130834.795708589[/C][C]-9169.79570858865[/C][/ROW]
[ROW][C]128[/C][C]108685[/C][C]95017.9574418035[/C][C]13667.0425581965[/C][/ROW]
[ROW][C]129[/C][C]124493[/C][C]114494.212531537[/C][C]9998.78746846316[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]20493.3610338316[/C][C]-8697.36103383159[/C][/ROW]
[ROW][C]131[/C][C]10674[/C][C]14602.8020055017[/C][C]-3928.80200550174[/C][/ROW]
[ROW][C]132[/C][C]131263[/C][C]108932.543504768[/C][C]22330.4564952321[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]11030.3948279702[/C][C]-4194.39482797022[/C][/ROW]
[ROW][C]134[/C][C]153278[/C][C]125423.257457812[/C][C]27854.7425421878[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]11243.7986881661[/C][C]-6125.79868816608[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]37301.2989263833[/C][C]2946.70107361665[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]9345.53773591229[/C][C]-9345.53773591229[/C][/ROW]
[ROW][C]138[/C][C]100728[/C][C]88091.0319075791[/C][C]12636.9680924209[/C][/ROW]
[ROW][C]139[/C][C]84267[/C][C]107193.129149763[/C][C]-22926.1291497626[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]12213.8502405434[/C][C]-5082.8502405434[/C][/ROW]
[ROW][C]141[/C][C]8812[/C][C]21330.3305018768[/C][C]-12518.3305018768[/C][/ROW]
[ROW][C]142[/C][C]63952[/C][C]64709.6828019057[/C][C]-757.682801905738[/C][/ROW]
[ROW][C]143[/C][C]120111[/C][C]119187.858272337[/C][C]923.141727663306[/C][/ROW]
[ROW][C]144[/C][C]94127[/C][C]100558.081153768[/C][C]-6431.08115376762[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157445&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157445&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1150596120311.8004669730284.1995330302
2154801153048.3307070571752.66929294338
3721521344.5830379121-14129.5830379121
4122139147457.596088085-25318.5960880854
5221399243891.276576421-22492.2765764208
6441870480470.210027151-38600.2100271515
7134379140752.891849356-6373.89184935605
8140428149374.317764415-8946.31776441532
9103255109635.69329641-6380.69329641039
10271630232573.2069181739056.7930818296
11121593136749.713975992-15156.7139759921
12172071176462.8783069-4391.87830689984
1383707115341.806961446-31634.8069614461
14197412203329.40897376-5917.40897375979
15134398112763.52897846221634.4710215375
16139224126922.04136550112301.9586344988
17134153158345.536812348-24192.5368123481
186414963979.1910146086169.808985391398
19122294175249.774946288-52955.7749462876
202488949389.8775203974-24500.8775203974
215219774357.8962622259-22160.8962622259
22188915206940.359548901-18025.3595489005
23163147104309.82152635758837.1784736426
2498575123942.242182663-25367.242182663
25143546145844.988899951-2298.98889995109
26139780114168.40946828225611.5905317178
27163784169263.019454723-5479.01945472296
28152479164887.369202843-12408.3692028427
29304108236552.64807367267555.3519263282
30184024174788.0125682149235.98743178649
31151621125189.6924175226431.3075824796
32164516139325.2581307625190.7418692401
33120179131210.306671282-11031.3066712822
34214701171008.0852259143692.91477409
35196865204592.563555213-7727.56355521307
3609526.41795391828-9526.41795391828
37181527133738.72691116647788.2730888341
3893107108589.299735399-15482.2997353988
39129352135687.348390054-6335.34839005415
40229143234532.483329929-5389.48332992871
41177063159597.91507111417465.0849288859
42126602134941.201488659-8339.20148865928
439374296183.4707993188-2441.47079931885
44152153149808.957922412344.04207759026
459570499527.2139319935-3823.21393199353
46139793120011.28581662819781.7141833722
477634879655.4398093355-3307.43980933547
48188980155868.64063931233111.3593606876
49172100182206.924345038-10106.9243450377
50146552155322.688112808-8770.68811280777
514818853969.1520902646-5781.15209026461
52109185115787.465241038-6602.46524103765
53263652213969.50868241349682.4913175866
54215609152620.93627338562988.0637266148
55174876158410.44510029216465.5548997083
56115124123771.69703575-8647.69703575041
57179712136560.8140862443151.1859137599
587036979720.9690821716-9351.9690821716
59109215120579.469193181-11364.4691931812
60166096143965.66864731722130.3313526832
61130414129332.3887778781081.61122212154
62102057134374.679613483-32317.6796134832
63115310101762.03391960713547.966080393
64101181107336.481413944-6155.48141394448
65135228110463.3324053624764.6675946399
6694982120441.896014022-25459.8960140223
67166919216570.418651421-49651.4186514212
68118169172483.544289026-54314.5442890259
69102361109703.098785186-7342.09878518566
703197032884.1833018748-914.183301874799
71200413181912.94036823718500.0596317631
7210338197127.46735322686253.53264677315
7394940107891.851516656-12951.8515166559
7410156092913.30856329968646.69143670036
75144176178483.531408371-34307.5314083708
767192188106.2453504075-16185.2453504075
77126905156278.349775364-29373.3497753635
78131184201029.379796499-69845.3797964989
796013868916.153655284-8778.153655284
808497196764.3358945391-11793.3358945391
818042091848.7206364529-11428.7206364529
82233569155041.55690358678527.4430964136
835625268089.0471804685-11837.0471804685
8497181109422.582978687-12241.5829786871
855080063166.3861390385-12366.3861390385
86125941121739.6183418434201.38165815732
87211032230850.686357596-19818.6863575957
887196093556.6681194523-21596.6681194523
899037988180.08454653712198.91545346288
90125650121046.4817399914603.51826000927
91115572127927.917737329-12355.9177373286
92136266158797.087591681-22531.0875916805
93146715125524.33604087521190.6639591245
94124626111861.44103362412764.5589663764
954917666980.9125391155-17804.9125391155
96212926208612.7001084134313.29989158728
97173884144825.99276371229058.0072362883
981934927673.4021122356-8324.40211223558
99181141156043.13817484825097.8618251515
100145502150071.973393621-4569.97339362053
1014544838262.52859613117185.47140386885
1025828077129.674134691-18849.674134691
10311594488826.40629152327117.593708477
1049434191363.86293618062977.13706381936
1055909061468.5248139566-2378.52481395662
1062767636902.5307926563-9226.53079265632
107120586128283.439618914-7697.43961891371
1088801154710.895248538433300.1047514616
10909345.53773591229-9345.53773591229
1108561081030.50246718074579.49753281931
1118419385824.5181612526-1631.51816125261
112117769113174.5493252594594.45067474066
113107653110049.296759461-2396.29675946105
1147189474854.4127050796-2960.41270507962
115361613226.8151006624-9610.81510066239
11609345.53773591229-9345.53773591229
117154806120516.96920727134289.0307927286
118136061136665.218281922-604.218281922242
119141822134687.911825657134.08817435033
12010651596746.43571808219768.56428191791
1214341043616.8969100562-206.89691005615
122146920143991.802245552928.19775444985
1238887484240.54747635974633.45252364034
12411192497374.508190399714549.4918096003
1256037346241.812257241514131.1877427585
1261976430266.107816016-10502.107816016
127121665130834.795708589-9169.79570858865
12810868595017.957441803513667.0425581965
129124493114494.2125315379998.78746846316
1301179620493.3610338316-8697.36103383159
1311067414602.8020055017-3928.80200550174
132131263108932.54350476822330.4564952321
133683611030.3948279702-4194.39482797022
134153278125423.25745781227854.7425421878
135511811243.7986881661-6125.79868816608
1364024837301.29892638332946.70107361665
13709345.53773591229-9345.53773591229
13810072888091.031907579112636.9680924209
13984267107193.129149763-22926.1291497626
140713112213.8502405434-5082.8502405434
141881221330.3305018768-12518.3305018768
1426395264709.6828019057-757.682801905738
143120111119187.858272337923.141727663306
14494127100558.081153768-6431.08115376762






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.8961562971060770.2076874057878460.103843702893923
130.8288370235993260.3423259528013480.171162976400674
140.7977258854241630.4045482291516730.202274114575837
150.7405834084064790.5188331831870420.259416591593521
160.7036426385448590.5927147229102820.296357361455141
170.6284675949189860.7430648101620280.371532405081014
180.5290380264757790.9419239470484430.470961973524221
190.5656633346436820.8686733307126370.434336665356318
200.4930766716497980.9861533432995970.506923328350202
210.4202235069304350.840447013860870.579776493069565
220.3519338891109380.7038677782218770.648066110889062
230.9525342473274120.09493150534517640.0474657526725882
240.9417158232076460.1165683535847070.0582841767923535
250.9176958842164350.1646082315671290.0823041157835647
260.9135080008042940.1729839983914110.0864919991957056
270.9025178655419980.1949642689160040.0974821344580019
280.8736571249318180.2526857501363650.126342875068182
290.9840685452455180.03186290950896360.0159314547544818
300.9768055658496880.04638886830062430.0231944341503122
310.9803054487688760.03938910246224720.0196945512311236
320.9781601805883730.04367963882325450.0218398194116272
330.9703226972650410.05935460546991890.0296773027349594
340.9919117411942670.01617651761146560.0080882588057328
350.9888258969989070.02234820600218630.0111741030010932
360.9864275117438880.0271449765122250.0135724882561125
370.9950421580938340.009915683812331850.00495784190616592
380.9938930608914520.01221387821709680.00610693910854842
390.9910669515308970.01786609693820610.00893304846910306
400.987670521501520.02465895699695980.0123294784984799
410.9844195795097040.03116084098059240.0155804204902962
420.9785586264875820.04288274702483540.0214413735124177
430.9705055064809020.05898898703819660.0294944935190983
440.9600836431402170.07983271371956530.0399163568597827
450.9475280419647580.1049439160704840.0524719580352421
460.9430105455991020.1139789088017960.056989454400898
470.9257855983590390.1484288032819230.0742144016409615
480.9349549565272350.130090086945530.0650450434727649
490.9184695535842820.1630608928314370.0815304464157184
500.8998158669323360.2003682661353280.100184133067664
510.8755621483641370.2488757032717250.124437851635863
520.8522970579713210.2954058840573590.147702942028679
530.9276799967186540.1446400065626920.072320003281346
540.9863761237433910.02724775251321790.0136238762566089
550.9841804958176250.03163900836475060.0158195041823753
560.9788944058303270.04221118833934620.0211055941696731
570.9901523369181710.01969532616365710.00984766308182856
580.9871649613528210.02567007729435860.0128350386471793
590.9838395016078560.03232099678428760.0161604983921438
600.98380436988090.03239126023820010.0161956301191001
610.9779080318039420.04418393639211680.0220919681960584
620.9819944565390650.03601108692187020.0180055434609351
630.9776519558680260.04469608826394780.0223480441319739
640.9708912332893940.05821753342121260.0291087667106063
650.9725655064784330.05486898704313380.0274344935215669
660.9731006764763860.05379864704722750.0268993235236137
670.9904329427343570.01913411453128610.00956705726564303
680.9980312223994280.003937555201144310.00196877760057216
690.9972039265341670.005592146931665210.0027960734658326
700.9959194338377630.008161132324474750.00408056616223738
710.9951251977291350.009749604541729680.00487480227086484
720.9933871854304410.01322562913911850.00661281456955927
730.9917283266868810.01654334662623850.00827167331311925
740.9894016597667750.02119668046644950.0105983402332247
750.9950487071804620.009902585639076060.00495129281953803
760.9956632162365010.008673567526997050.00433678376349852
770.9979393246471910.004121350705617970.00206067535280898
780.9999943750279121.12499441765103e-055.62497208825513e-06
790.9999907299053061.8540189388666e-059.27009469433299e-06
800.9999841421089763.1715782048449e-051.58578910242245e-05
810.9999729585827875.40828344267664e-052.70414172133832e-05
820.9999999762976084.74047834816753e-082.37023917408376e-08
830.9999999529953779.40092455166136e-084.70046227583068e-08
840.9999999345061961.30987608601745e-076.54938043008723e-08
850.999999893366992.13266020716191e-071.06633010358096e-07
860.9999997813443624.37311276651765e-072.18655638325882e-07
870.9999998432002233.1359955442502e-071.5679977721251e-07
880.9999999371725331.256549348908e-076.28274674453998e-08
890.9999999108433761.78313248162002e-078.91566240810012e-08
900.999999808051933.83896140549064e-071.91948070274532e-07
910.9999997549217964.90156408305101e-072.4507820415255e-07
920.9999999669683756.60632505043387e-083.30316252521694e-08
930.9999999647942867.04114282724436e-083.52057141362218e-08
940.9999999210984831.57803033904224e-077.89015169521121e-08
950.9999999327601211.34479757500489e-076.72398787502444e-08
960.9999998686647152.62670569387418e-071.31335284693709e-07
970.9999998863138152.27372369253747e-071.13686184626874e-07
980.9999997539650124.92069975314805e-072.46034987657403e-07
990.9999995290884569.41823088612669e-074.70911544306334e-07
1000.9999993617869531.27642609367744e-066.3821304683872e-07
1010.9999988118714382.37625712418438e-061.18812856209219e-06
1020.9999987629144492.47417110164733e-061.23708555082367e-06
1030.9999996118855637.76228874970944e-073.88114437485472e-07
1040.9999990886885181.82262296488424e-069.1131148244212e-07
1050.9999979146303064.17073938901157e-062.08536969450578e-06
1060.9999953259667529.34806649590559e-064.67403324795279e-06
1070.9999922332701481.55334597046386e-057.76672985231931e-06
1080.9999998649459692.7010806117753e-071.35054030588765e-07
1090.99999965773186.84536400559161e-073.4226820027958e-07
1100.9999991084977861.7830044272435e-068.9150221362175e-07
1110.9999993618788891.27624222108402e-066.3812111054201e-07
1120.9999990075324121.98493517571033e-069.92467587855167e-07
1130.9999983375854483.32482910445282e-061.66241455222641e-06
1140.9999957257177738.54856445493727e-064.27428222746864e-06
1150.9999895662281232.08675437544228e-051.04337718772114e-05
1160.9999742497434285.15005131448779e-052.57502565724389e-05
1170.9999996796919496.40616102488034e-073.20308051244017e-07
1180.9999989141631242.1716737517062e-061.0858368758531e-06
1190.9999964332388887.13352222342905e-063.56676111171453e-06
1200.9999919992696161.60014607674714e-058.00073038373572e-06
1210.9999754634926254.90730147500714e-052.45365073750357e-05
1220.99992508338540.0001498332292009037.49166146004514e-05
1230.9998511344765980.0002977310468046220.000148865523402311
1240.9998185390434670.0003629219130658510.000181460956532926
1250.9998878874864710.0002242250270589310.000112112513529466
1260.9997580431396910.0004839137206180020.000241956860309001
1270.9999098964376240.0001802071247527219.01035623763604e-05
1280.9996667509319860.0006664981360274010.0003332490680137
1290.9986315519660320.002736896067936570.00136844803396828
1300.9945719759289430.01085604814211370.00542802407105686
1310.9814596772821280.03708064543574360.0185403227178718
1320.9676212638264030.06475747234719310.0323787361735966

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.896156297106077 & 0.207687405787846 & 0.103843702893923 \tabularnewline
13 & 0.828837023599326 & 0.342325952801348 & 0.171162976400674 \tabularnewline
14 & 0.797725885424163 & 0.404548229151673 & 0.202274114575837 \tabularnewline
15 & 0.740583408406479 & 0.518833183187042 & 0.259416591593521 \tabularnewline
16 & 0.703642638544859 & 0.592714722910282 & 0.296357361455141 \tabularnewline
17 & 0.628467594918986 & 0.743064810162028 & 0.371532405081014 \tabularnewline
18 & 0.529038026475779 & 0.941923947048443 & 0.470961973524221 \tabularnewline
19 & 0.565663334643682 & 0.868673330712637 & 0.434336665356318 \tabularnewline
20 & 0.493076671649798 & 0.986153343299597 & 0.506923328350202 \tabularnewline
21 & 0.420223506930435 & 0.84044701386087 & 0.579776493069565 \tabularnewline
22 & 0.351933889110938 & 0.703867778221877 & 0.648066110889062 \tabularnewline
23 & 0.952534247327412 & 0.0949315053451764 & 0.0474657526725882 \tabularnewline
24 & 0.941715823207646 & 0.116568353584707 & 0.0582841767923535 \tabularnewline
25 & 0.917695884216435 & 0.164608231567129 & 0.0823041157835647 \tabularnewline
26 & 0.913508000804294 & 0.172983998391411 & 0.0864919991957056 \tabularnewline
27 & 0.902517865541998 & 0.194964268916004 & 0.0974821344580019 \tabularnewline
28 & 0.873657124931818 & 0.252685750136365 & 0.126342875068182 \tabularnewline
29 & 0.984068545245518 & 0.0318629095089636 & 0.0159314547544818 \tabularnewline
30 & 0.976805565849688 & 0.0463888683006243 & 0.0231944341503122 \tabularnewline
31 & 0.980305448768876 & 0.0393891024622472 & 0.0196945512311236 \tabularnewline
32 & 0.978160180588373 & 0.0436796388232545 & 0.0218398194116272 \tabularnewline
33 & 0.970322697265041 & 0.0593546054699189 & 0.0296773027349594 \tabularnewline
34 & 0.991911741194267 & 0.0161765176114656 & 0.0080882588057328 \tabularnewline
35 & 0.988825896998907 & 0.0223482060021863 & 0.0111741030010932 \tabularnewline
36 & 0.986427511743888 & 0.027144976512225 & 0.0135724882561125 \tabularnewline
37 & 0.995042158093834 & 0.00991568381233185 & 0.00495784190616592 \tabularnewline
38 & 0.993893060891452 & 0.0122138782170968 & 0.00610693910854842 \tabularnewline
39 & 0.991066951530897 & 0.0178660969382061 & 0.00893304846910306 \tabularnewline
40 & 0.98767052150152 & 0.0246589569969598 & 0.0123294784984799 \tabularnewline
41 & 0.984419579509704 & 0.0311608409805924 & 0.0155804204902962 \tabularnewline
42 & 0.978558626487582 & 0.0428827470248354 & 0.0214413735124177 \tabularnewline
43 & 0.970505506480902 & 0.0589889870381966 & 0.0294944935190983 \tabularnewline
44 & 0.960083643140217 & 0.0798327137195653 & 0.0399163568597827 \tabularnewline
45 & 0.947528041964758 & 0.104943916070484 & 0.0524719580352421 \tabularnewline
46 & 0.943010545599102 & 0.113978908801796 & 0.056989454400898 \tabularnewline
47 & 0.925785598359039 & 0.148428803281923 & 0.0742144016409615 \tabularnewline
48 & 0.934954956527235 & 0.13009008694553 & 0.0650450434727649 \tabularnewline
49 & 0.918469553584282 & 0.163060892831437 & 0.0815304464157184 \tabularnewline
50 & 0.899815866932336 & 0.200368266135328 & 0.100184133067664 \tabularnewline
51 & 0.875562148364137 & 0.248875703271725 & 0.124437851635863 \tabularnewline
52 & 0.852297057971321 & 0.295405884057359 & 0.147702942028679 \tabularnewline
53 & 0.927679996718654 & 0.144640006562692 & 0.072320003281346 \tabularnewline
54 & 0.986376123743391 & 0.0272477525132179 & 0.0136238762566089 \tabularnewline
55 & 0.984180495817625 & 0.0316390083647506 & 0.0158195041823753 \tabularnewline
56 & 0.978894405830327 & 0.0422111883393462 & 0.0211055941696731 \tabularnewline
57 & 0.990152336918171 & 0.0196953261636571 & 0.00984766308182856 \tabularnewline
58 & 0.987164961352821 & 0.0256700772943586 & 0.0128350386471793 \tabularnewline
59 & 0.983839501607856 & 0.0323209967842876 & 0.0161604983921438 \tabularnewline
60 & 0.9838043698809 & 0.0323912602382001 & 0.0161956301191001 \tabularnewline
61 & 0.977908031803942 & 0.0441839363921168 & 0.0220919681960584 \tabularnewline
62 & 0.981994456539065 & 0.0360110869218702 & 0.0180055434609351 \tabularnewline
63 & 0.977651955868026 & 0.0446960882639478 & 0.0223480441319739 \tabularnewline
64 & 0.970891233289394 & 0.0582175334212126 & 0.0291087667106063 \tabularnewline
65 & 0.972565506478433 & 0.0548689870431338 & 0.0274344935215669 \tabularnewline
66 & 0.973100676476386 & 0.0537986470472275 & 0.0268993235236137 \tabularnewline
67 & 0.990432942734357 & 0.0191341145312861 & 0.00956705726564303 \tabularnewline
68 & 0.998031222399428 & 0.00393755520114431 & 0.00196877760057216 \tabularnewline
69 & 0.997203926534167 & 0.00559214693166521 & 0.0027960734658326 \tabularnewline
70 & 0.995919433837763 & 0.00816113232447475 & 0.00408056616223738 \tabularnewline
71 & 0.995125197729135 & 0.00974960454172968 & 0.00487480227086484 \tabularnewline
72 & 0.993387185430441 & 0.0132256291391185 & 0.00661281456955927 \tabularnewline
73 & 0.991728326686881 & 0.0165433466262385 & 0.00827167331311925 \tabularnewline
74 & 0.989401659766775 & 0.0211966804664495 & 0.0105983402332247 \tabularnewline
75 & 0.995048707180462 & 0.00990258563907606 & 0.00495129281953803 \tabularnewline
76 & 0.995663216236501 & 0.00867356752699705 & 0.00433678376349852 \tabularnewline
77 & 0.997939324647191 & 0.00412135070561797 & 0.00206067535280898 \tabularnewline
78 & 0.999994375027912 & 1.12499441765103e-05 & 5.62497208825513e-06 \tabularnewline
79 & 0.999990729905306 & 1.8540189388666e-05 & 9.27009469433299e-06 \tabularnewline
80 & 0.999984142108976 & 3.1715782048449e-05 & 1.58578910242245e-05 \tabularnewline
81 & 0.999972958582787 & 5.40828344267664e-05 & 2.70414172133832e-05 \tabularnewline
82 & 0.999999976297608 & 4.74047834816753e-08 & 2.37023917408376e-08 \tabularnewline
83 & 0.999999952995377 & 9.40092455166136e-08 & 4.70046227583068e-08 \tabularnewline
84 & 0.999999934506196 & 1.30987608601745e-07 & 6.54938043008723e-08 \tabularnewline
85 & 0.99999989336699 & 2.13266020716191e-07 & 1.06633010358096e-07 \tabularnewline
86 & 0.999999781344362 & 4.37311276651765e-07 & 2.18655638325882e-07 \tabularnewline
87 & 0.999999843200223 & 3.1359955442502e-07 & 1.5679977721251e-07 \tabularnewline
88 & 0.999999937172533 & 1.256549348908e-07 & 6.28274674453998e-08 \tabularnewline
89 & 0.999999910843376 & 1.78313248162002e-07 & 8.91566240810012e-08 \tabularnewline
90 & 0.99999980805193 & 3.83896140549064e-07 & 1.91948070274532e-07 \tabularnewline
91 & 0.999999754921796 & 4.90156408305101e-07 & 2.4507820415255e-07 \tabularnewline
92 & 0.999999966968375 & 6.60632505043387e-08 & 3.30316252521694e-08 \tabularnewline
93 & 0.999999964794286 & 7.04114282724436e-08 & 3.52057141362218e-08 \tabularnewline
94 & 0.999999921098483 & 1.57803033904224e-07 & 7.89015169521121e-08 \tabularnewline
95 & 0.999999932760121 & 1.34479757500489e-07 & 6.72398787502444e-08 \tabularnewline
96 & 0.999999868664715 & 2.62670569387418e-07 & 1.31335284693709e-07 \tabularnewline
97 & 0.999999886313815 & 2.27372369253747e-07 & 1.13686184626874e-07 \tabularnewline
98 & 0.999999753965012 & 4.92069975314805e-07 & 2.46034987657403e-07 \tabularnewline
99 & 0.999999529088456 & 9.41823088612669e-07 & 4.70911544306334e-07 \tabularnewline
100 & 0.999999361786953 & 1.27642609367744e-06 & 6.3821304683872e-07 \tabularnewline
101 & 0.999998811871438 & 2.37625712418438e-06 & 1.18812856209219e-06 \tabularnewline
102 & 0.999998762914449 & 2.47417110164733e-06 & 1.23708555082367e-06 \tabularnewline
103 & 0.999999611885563 & 7.76228874970944e-07 & 3.88114437485472e-07 \tabularnewline
104 & 0.999999088688518 & 1.82262296488424e-06 & 9.1131148244212e-07 \tabularnewline
105 & 0.999997914630306 & 4.17073938901157e-06 & 2.08536969450578e-06 \tabularnewline
106 & 0.999995325966752 & 9.34806649590559e-06 & 4.67403324795279e-06 \tabularnewline
107 & 0.999992233270148 & 1.55334597046386e-05 & 7.76672985231931e-06 \tabularnewline
108 & 0.999999864945969 & 2.7010806117753e-07 & 1.35054030588765e-07 \tabularnewline
109 & 0.9999996577318 & 6.84536400559161e-07 & 3.4226820027958e-07 \tabularnewline
110 & 0.999999108497786 & 1.7830044272435e-06 & 8.9150221362175e-07 \tabularnewline
111 & 0.999999361878889 & 1.27624222108402e-06 & 6.3812111054201e-07 \tabularnewline
112 & 0.999999007532412 & 1.98493517571033e-06 & 9.92467587855167e-07 \tabularnewline
113 & 0.999998337585448 & 3.32482910445282e-06 & 1.66241455222641e-06 \tabularnewline
114 & 0.999995725717773 & 8.54856445493727e-06 & 4.27428222746864e-06 \tabularnewline
115 & 0.999989566228123 & 2.08675437544228e-05 & 1.04337718772114e-05 \tabularnewline
116 & 0.999974249743428 & 5.15005131448779e-05 & 2.57502565724389e-05 \tabularnewline
117 & 0.999999679691949 & 6.40616102488034e-07 & 3.20308051244017e-07 \tabularnewline
118 & 0.999998914163124 & 2.1716737517062e-06 & 1.0858368758531e-06 \tabularnewline
119 & 0.999996433238888 & 7.13352222342905e-06 & 3.56676111171453e-06 \tabularnewline
120 & 0.999991999269616 & 1.60014607674714e-05 & 8.00073038373572e-06 \tabularnewline
121 & 0.999975463492625 & 4.90730147500714e-05 & 2.45365073750357e-05 \tabularnewline
122 & 0.9999250833854 & 0.000149833229200903 & 7.49166146004514e-05 \tabularnewline
123 & 0.999851134476598 & 0.000297731046804622 & 0.000148865523402311 \tabularnewline
124 & 0.999818539043467 & 0.000362921913065851 & 0.000181460956532926 \tabularnewline
125 & 0.999887887486471 & 0.000224225027058931 & 0.000112112513529466 \tabularnewline
126 & 0.999758043139691 & 0.000483913720618002 & 0.000241956860309001 \tabularnewline
127 & 0.999909896437624 & 0.000180207124752721 & 9.01035623763604e-05 \tabularnewline
128 & 0.999666750931986 & 0.000666498136027401 & 0.0003332490680137 \tabularnewline
129 & 0.998631551966032 & 0.00273689606793657 & 0.00136844803396828 \tabularnewline
130 & 0.994571975928943 & 0.0108560481421137 & 0.00542802407105686 \tabularnewline
131 & 0.981459677282128 & 0.0370806454357436 & 0.0185403227178718 \tabularnewline
132 & 0.967621263826403 & 0.0647574723471931 & 0.0323787361735966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157445&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]12[/C][C]0.896156297106077[/C][C]0.207687405787846[/C][C]0.103843702893923[/C][/ROW]
[ROW][C]13[/C][C]0.828837023599326[/C][C]0.342325952801348[/C][C]0.171162976400674[/C][/ROW]
[ROW][C]14[/C][C]0.797725885424163[/C][C]0.404548229151673[/C][C]0.202274114575837[/C][/ROW]
[ROW][C]15[/C][C]0.740583408406479[/C][C]0.518833183187042[/C][C]0.259416591593521[/C][/ROW]
[ROW][C]16[/C][C]0.703642638544859[/C][C]0.592714722910282[/C][C]0.296357361455141[/C][/ROW]
[ROW][C]17[/C][C]0.628467594918986[/C][C]0.743064810162028[/C][C]0.371532405081014[/C][/ROW]
[ROW][C]18[/C][C]0.529038026475779[/C][C]0.941923947048443[/C][C]0.470961973524221[/C][/ROW]
[ROW][C]19[/C][C]0.565663334643682[/C][C]0.868673330712637[/C][C]0.434336665356318[/C][/ROW]
[ROW][C]20[/C][C]0.493076671649798[/C][C]0.986153343299597[/C][C]0.506923328350202[/C][/ROW]
[ROW][C]21[/C][C]0.420223506930435[/C][C]0.84044701386087[/C][C]0.579776493069565[/C][/ROW]
[ROW][C]22[/C][C]0.351933889110938[/C][C]0.703867778221877[/C][C]0.648066110889062[/C][/ROW]
[ROW][C]23[/C][C]0.952534247327412[/C][C]0.0949315053451764[/C][C]0.0474657526725882[/C][/ROW]
[ROW][C]24[/C][C]0.941715823207646[/C][C]0.116568353584707[/C][C]0.0582841767923535[/C][/ROW]
[ROW][C]25[/C][C]0.917695884216435[/C][C]0.164608231567129[/C][C]0.0823041157835647[/C][/ROW]
[ROW][C]26[/C][C]0.913508000804294[/C][C]0.172983998391411[/C][C]0.0864919991957056[/C][/ROW]
[ROW][C]27[/C][C]0.902517865541998[/C][C]0.194964268916004[/C][C]0.0974821344580019[/C][/ROW]
[ROW][C]28[/C][C]0.873657124931818[/C][C]0.252685750136365[/C][C]0.126342875068182[/C][/ROW]
[ROW][C]29[/C][C]0.984068545245518[/C][C]0.0318629095089636[/C][C]0.0159314547544818[/C][/ROW]
[ROW][C]30[/C][C]0.976805565849688[/C][C]0.0463888683006243[/C][C]0.0231944341503122[/C][/ROW]
[ROW][C]31[/C][C]0.980305448768876[/C][C]0.0393891024622472[/C][C]0.0196945512311236[/C][/ROW]
[ROW][C]32[/C][C]0.978160180588373[/C][C]0.0436796388232545[/C][C]0.0218398194116272[/C][/ROW]
[ROW][C]33[/C][C]0.970322697265041[/C][C]0.0593546054699189[/C][C]0.0296773027349594[/C][/ROW]
[ROW][C]34[/C][C]0.991911741194267[/C][C]0.0161765176114656[/C][C]0.0080882588057328[/C][/ROW]
[ROW][C]35[/C][C]0.988825896998907[/C][C]0.0223482060021863[/C][C]0.0111741030010932[/C][/ROW]
[ROW][C]36[/C][C]0.986427511743888[/C][C]0.027144976512225[/C][C]0.0135724882561125[/C][/ROW]
[ROW][C]37[/C][C]0.995042158093834[/C][C]0.00991568381233185[/C][C]0.00495784190616592[/C][/ROW]
[ROW][C]38[/C][C]0.993893060891452[/C][C]0.0122138782170968[/C][C]0.00610693910854842[/C][/ROW]
[ROW][C]39[/C][C]0.991066951530897[/C][C]0.0178660969382061[/C][C]0.00893304846910306[/C][/ROW]
[ROW][C]40[/C][C]0.98767052150152[/C][C]0.0246589569969598[/C][C]0.0123294784984799[/C][/ROW]
[ROW][C]41[/C][C]0.984419579509704[/C][C]0.0311608409805924[/C][C]0.0155804204902962[/C][/ROW]
[ROW][C]42[/C][C]0.978558626487582[/C][C]0.0428827470248354[/C][C]0.0214413735124177[/C][/ROW]
[ROW][C]43[/C][C]0.970505506480902[/C][C]0.0589889870381966[/C][C]0.0294944935190983[/C][/ROW]
[ROW][C]44[/C][C]0.960083643140217[/C][C]0.0798327137195653[/C][C]0.0399163568597827[/C][/ROW]
[ROW][C]45[/C][C]0.947528041964758[/C][C]0.104943916070484[/C][C]0.0524719580352421[/C][/ROW]
[ROW][C]46[/C][C]0.943010545599102[/C][C]0.113978908801796[/C][C]0.056989454400898[/C][/ROW]
[ROW][C]47[/C][C]0.925785598359039[/C][C]0.148428803281923[/C][C]0.0742144016409615[/C][/ROW]
[ROW][C]48[/C][C]0.934954956527235[/C][C]0.13009008694553[/C][C]0.0650450434727649[/C][/ROW]
[ROW][C]49[/C][C]0.918469553584282[/C][C]0.163060892831437[/C][C]0.0815304464157184[/C][/ROW]
[ROW][C]50[/C][C]0.899815866932336[/C][C]0.200368266135328[/C][C]0.100184133067664[/C][/ROW]
[ROW][C]51[/C][C]0.875562148364137[/C][C]0.248875703271725[/C][C]0.124437851635863[/C][/ROW]
[ROW][C]52[/C][C]0.852297057971321[/C][C]0.295405884057359[/C][C]0.147702942028679[/C][/ROW]
[ROW][C]53[/C][C]0.927679996718654[/C][C]0.144640006562692[/C][C]0.072320003281346[/C][/ROW]
[ROW][C]54[/C][C]0.986376123743391[/C][C]0.0272477525132179[/C][C]0.0136238762566089[/C][/ROW]
[ROW][C]55[/C][C]0.984180495817625[/C][C]0.0316390083647506[/C][C]0.0158195041823753[/C][/ROW]
[ROW][C]56[/C][C]0.978894405830327[/C][C]0.0422111883393462[/C][C]0.0211055941696731[/C][/ROW]
[ROW][C]57[/C][C]0.990152336918171[/C][C]0.0196953261636571[/C][C]0.00984766308182856[/C][/ROW]
[ROW][C]58[/C][C]0.987164961352821[/C][C]0.0256700772943586[/C][C]0.0128350386471793[/C][/ROW]
[ROW][C]59[/C][C]0.983839501607856[/C][C]0.0323209967842876[/C][C]0.0161604983921438[/C][/ROW]
[ROW][C]60[/C][C]0.9838043698809[/C][C]0.0323912602382001[/C][C]0.0161956301191001[/C][/ROW]
[ROW][C]61[/C][C]0.977908031803942[/C][C]0.0441839363921168[/C][C]0.0220919681960584[/C][/ROW]
[ROW][C]62[/C][C]0.981994456539065[/C][C]0.0360110869218702[/C][C]0.0180055434609351[/C][/ROW]
[ROW][C]63[/C][C]0.977651955868026[/C][C]0.0446960882639478[/C][C]0.0223480441319739[/C][/ROW]
[ROW][C]64[/C][C]0.970891233289394[/C][C]0.0582175334212126[/C][C]0.0291087667106063[/C][/ROW]
[ROW][C]65[/C][C]0.972565506478433[/C][C]0.0548689870431338[/C][C]0.0274344935215669[/C][/ROW]
[ROW][C]66[/C][C]0.973100676476386[/C][C]0.0537986470472275[/C][C]0.0268993235236137[/C][/ROW]
[ROW][C]67[/C][C]0.990432942734357[/C][C]0.0191341145312861[/C][C]0.00956705726564303[/C][/ROW]
[ROW][C]68[/C][C]0.998031222399428[/C][C]0.00393755520114431[/C][C]0.00196877760057216[/C][/ROW]
[ROW][C]69[/C][C]0.997203926534167[/C][C]0.00559214693166521[/C][C]0.0027960734658326[/C][/ROW]
[ROW][C]70[/C][C]0.995919433837763[/C][C]0.00816113232447475[/C][C]0.00408056616223738[/C][/ROW]
[ROW][C]71[/C][C]0.995125197729135[/C][C]0.00974960454172968[/C][C]0.00487480227086484[/C][/ROW]
[ROW][C]72[/C][C]0.993387185430441[/C][C]0.0132256291391185[/C][C]0.00661281456955927[/C][/ROW]
[ROW][C]73[/C][C]0.991728326686881[/C][C]0.0165433466262385[/C][C]0.00827167331311925[/C][/ROW]
[ROW][C]74[/C][C]0.989401659766775[/C][C]0.0211966804664495[/C][C]0.0105983402332247[/C][/ROW]
[ROW][C]75[/C][C]0.995048707180462[/C][C]0.00990258563907606[/C][C]0.00495129281953803[/C][/ROW]
[ROW][C]76[/C][C]0.995663216236501[/C][C]0.00867356752699705[/C][C]0.00433678376349852[/C][/ROW]
[ROW][C]77[/C][C]0.997939324647191[/C][C]0.00412135070561797[/C][C]0.00206067535280898[/C][/ROW]
[ROW][C]78[/C][C]0.999994375027912[/C][C]1.12499441765103e-05[/C][C]5.62497208825513e-06[/C][/ROW]
[ROW][C]79[/C][C]0.999990729905306[/C][C]1.8540189388666e-05[/C][C]9.27009469433299e-06[/C][/ROW]
[ROW][C]80[/C][C]0.999984142108976[/C][C]3.1715782048449e-05[/C][C]1.58578910242245e-05[/C][/ROW]
[ROW][C]81[/C][C]0.999972958582787[/C][C]5.40828344267664e-05[/C][C]2.70414172133832e-05[/C][/ROW]
[ROW][C]82[/C][C]0.999999976297608[/C][C]4.74047834816753e-08[/C][C]2.37023917408376e-08[/C][/ROW]
[ROW][C]83[/C][C]0.999999952995377[/C][C]9.40092455166136e-08[/C][C]4.70046227583068e-08[/C][/ROW]
[ROW][C]84[/C][C]0.999999934506196[/C][C]1.30987608601745e-07[/C][C]6.54938043008723e-08[/C][/ROW]
[ROW][C]85[/C][C]0.99999989336699[/C][C]2.13266020716191e-07[/C][C]1.06633010358096e-07[/C][/ROW]
[ROW][C]86[/C][C]0.999999781344362[/C][C]4.37311276651765e-07[/C][C]2.18655638325882e-07[/C][/ROW]
[ROW][C]87[/C][C]0.999999843200223[/C][C]3.1359955442502e-07[/C][C]1.5679977721251e-07[/C][/ROW]
[ROW][C]88[/C][C]0.999999937172533[/C][C]1.256549348908e-07[/C][C]6.28274674453998e-08[/C][/ROW]
[ROW][C]89[/C][C]0.999999910843376[/C][C]1.78313248162002e-07[/C][C]8.91566240810012e-08[/C][/ROW]
[ROW][C]90[/C][C]0.99999980805193[/C][C]3.83896140549064e-07[/C][C]1.91948070274532e-07[/C][/ROW]
[ROW][C]91[/C][C]0.999999754921796[/C][C]4.90156408305101e-07[/C][C]2.4507820415255e-07[/C][/ROW]
[ROW][C]92[/C][C]0.999999966968375[/C][C]6.60632505043387e-08[/C][C]3.30316252521694e-08[/C][/ROW]
[ROW][C]93[/C][C]0.999999964794286[/C][C]7.04114282724436e-08[/C][C]3.52057141362218e-08[/C][/ROW]
[ROW][C]94[/C][C]0.999999921098483[/C][C]1.57803033904224e-07[/C][C]7.89015169521121e-08[/C][/ROW]
[ROW][C]95[/C][C]0.999999932760121[/C][C]1.34479757500489e-07[/C][C]6.72398787502444e-08[/C][/ROW]
[ROW][C]96[/C][C]0.999999868664715[/C][C]2.62670569387418e-07[/C][C]1.31335284693709e-07[/C][/ROW]
[ROW][C]97[/C][C]0.999999886313815[/C][C]2.27372369253747e-07[/C][C]1.13686184626874e-07[/C][/ROW]
[ROW][C]98[/C][C]0.999999753965012[/C][C]4.92069975314805e-07[/C][C]2.46034987657403e-07[/C][/ROW]
[ROW][C]99[/C][C]0.999999529088456[/C][C]9.41823088612669e-07[/C][C]4.70911544306334e-07[/C][/ROW]
[ROW][C]100[/C][C]0.999999361786953[/C][C]1.27642609367744e-06[/C][C]6.3821304683872e-07[/C][/ROW]
[ROW][C]101[/C][C]0.999998811871438[/C][C]2.37625712418438e-06[/C][C]1.18812856209219e-06[/C][/ROW]
[ROW][C]102[/C][C]0.999998762914449[/C][C]2.47417110164733e-06[/C][C]1.23708555082367e-06[/C][/ROW]
[ROW][C]103[/C][C]0.999999611885563[/C][C]7.76228874970944e-07[/C][C]3.88114437485472e-07[/C][/ROW]
[ROW][C]104[/C][C]0.999999088688518[/C][C]1.82262296488424e-06[/C][C]9.1131148244212e-07[/C][/ROW]
[ROW][C]105[/C][C]0.999997914630306[/C][C]4.17073938901157e-06[/C][C]2.08536969450578e-06[/C][/ROW]
[ROW][C]106[/C][C]0.999995325966752[/C][C]9.34806649590559e-06[/C][C]4.67403324795279e-06[/C][/ROW]
[ROW][C]107[/C][C]0.999992233270148[/C][C]1.55334597046386e-05[/C][C]7.76672985231931e-06[/C][/ROW]
[ROW][C]108[/C][C]0.999999864945969[/C][C]2.7010806117753e-07[/C][C]1.35054030588765e-07[/C][/ROW]
[ROW][C]109[/C][C]0.9999996577318[/C][C]6.84536400559161e-07[/C][C]3.4226820027958e-07[/C][/ROW]
[ROW][C]110[/C][C]0.999999108497786[/C][C]1.7830044272435e-06[/C][C]8.9150221362175e-07[/C][/ROW]
[ROW][C]111[/C][C]0.999999361878889[/C][C]1.27624222108402e-06[/C][C]6.3812111054201e-07[/C][/ROW]
[ROW][C]112[/C][C]0.999999007532412[/C][C]1.98493517571033e-06[/C][C]9.92467587855167e-07[/C][/ROW]
[ROW][C]113[/C][C]0.999998337585448[/C][C]3.32482910445282e-06[/C][C]1.66241455222641e-06[/C][/ROW]
[ROW][C]114[/C][C]0.999995725717773[/C][C]8.54856445493727e-06[/C][C]4.27428222746864e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999989566228123[/C][C]2.08675437544228e-05[/C][C]1.04337718772114e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999974249743428[/C][C]5.15005131448779e-05[/C][C]2.57502565724389e-05[/C][/ROW]
[ROW][C]117[/C][C]0.999999679691949[/C][C]6.40616102488034e-07[/C][C]3.20308051244017e-07[/C][/ROW]
[ROW][C]118[/C][C]0.999998914163124[/C][C]2.1716737517062e-06[/C][C]1.0858368758531e-06[/C][/ROW]
[ROW][C]119[/C][C]0.999996433238888[/C][C]7.13352222342905e-06[/C][C]3.56676111171453e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999991999269616[/C][C]1.60014607674714e-05[/C][C]8.00073038373572e-06[/C][/ROW]
[ROW][C]121[/C][C]0.999975463492625[/C][C]4.90730147500714e-05[/C][C]2.45365073750357e-05[/C][/ROW]
[ROW][C]122[/C][C]0.9999250833854[/C][C]0.000149833229200903[/C][C]7.49166146004514e-05[/C][/ROW]
[ROW][C]123[/C][C]0.999851134476598[/C][C]0.000297731046804622[/C][C]0.000148865523402311[/C][/ROW]
[ROW][C]124[/C][C]0.999818539043467[/C][C]0.000362921913065851[/C][C]0.000181460956532926[/C][/ROW]
[ROW][C]125[/C][C]0.999887887486471[/C][C]0.000224225027058931[/C][C]0.000112112513529466[/C][/ROW]
[ROW][C]126[/C][C]0.999758043139691[/C][C]0.000483913720618002[/C][C]0.000241956860309001[/C][/ROW]
[ROW][C]127[/C][C]0.999909896437624[/C][C]0.000180207124752721[/C][C]9.01035623763604e-05[/C][/ROW]
[ROW][C]128[/C][C]0.999666750931986[/C][C]0.000666498136027401[/C][C]0.0003332490680137[/C][/ROW]
[ROW][C]129[/C][C]0.998631551966032[/C][C]0.00273689606793657[/C][C]0.00136844803396828[/C][/ROW]
[ROW][C]130[/C][C]0.994571975928943[/C][C]0.0108560481421137[/C][C]0.00542802407105686[/C][/ROW]
[ROW][C]131[/C][C]0.981459677282128[/C][C]0.0370806454357436[/C][C]0.0185403227178718[/C][/ROW]
[ROW][C]132[/C][C]0.967621263826403[/C][C]0.0647574723471931[/C][C]0.0323787361735966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157445&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157445&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.8961562971060770.2076874057878460.103843702893923
130.8288370235993260.3423259528013480.171162976400674
140.7977258854241630.4045482291516730.202274114575837
150.7405834084064790.5188331831870420.259416591593521
160.7036426385448590.5927147229102820.296357361455141
170.6284675949189860.7430648101620280.371532405081014
180.5290380264757790.9419239470484430.470961973524221
190.5656633346436820.8686733307126370.434336665356318
200.4930766716497980.9861533432995970.506923328350202
210.4202235069304350.840447013860870.579776493069565
220.3519338891109380.7038677782218770.648066110889062
230.9525342473274120.09493150534517640.0474657526725882
240.9417158232076460.1165683535847070.0582841767923535
250.9176958842164350.1646082315671290.0823041157835647
260.9135080008042940.1729839983914110.0864919991957056
270.9025178655419980.1949642689160040.0974821344580019
280.8736571249318180.2526857501363650.126342875068182
290.9840685452455180.03186290950896360.0159314547544818
300.9768055658496880.04638886830062430.0231944341503122
310.9803054487688760.03938910246224720.0196945512311236
320.9781601805883730.04367963882325450.0218398194116272
330.9703226972650410.05935460546991890.0296773027349594
340.9919117411942670.01617651761146560.0080882588057328
350.9888258969989070.02234820600218630.0111741030010932
360.9864275117438880.0271449765122250.0135724882561125
370.9950421580938340.009915683812331850.00495784190616592
380.9938930608914520.01221387821709680.00610693910854842
390.9910669515308970.01786609693820610.00893304846910306
400.987670521501520.02465895699695980.0123294784984799
410.9844195795097040.03116084098059240.0155804204902962
420.9785586264875820.04288274702483540.0214413735124177
430.9705055064809020.05898898703819660.0294944935190983
440.9600836431402170.07983271371956530.0399163568597827
450.9475280419647580.1049439160704840.0524719580352421
460.9430105455991020.1139789088017960.056989454400898
470.9257855983590390.1484288032819230.0742144016409615
480.9349549565272350.130090086945530.0650450434727649
490.9184695535842820.1630608928314370.0815304464157184
500.8998158669323360.2003682661353280.100184133067664
510.8755621483641370.2488757032717250.124437851635863
520.8522970579713210.2954058840573590.147702942028679
530.9276799967186540.1446400065626920.072320003281346
540.9863761237433910.02724775251321790.0136238762566089
550.9841804958176250.03163900836475060.0158195041823753
560.9788944058303270.04221118833934620.0211055941696731
570.9901523369181710.01969532616365710.00984766308182856
580.9871649613528210.02567007729435860.0128350386471793
590.9838395016078560.03232099678428760.0161604983921438
600.98380436988090.03239126023820010.0161956301191001
610.9779080318039420.04418393639211680.0220919681960584
620.9819944565390650.03601108692187020.0180055434609351
630.9776519558680260.04469608826394780.0223480441319739
640.9708912332893940.05821753342121260.0291087667106063
650.9725655064784330.05486898704313380.0274344935215669
660.9731006764763860.05379864704722750.0268993235236137
670.9904329427343570.01913411453128610.00956705726564303
680.9980312223994280.003937555201144310.00196877760057216
690.9972039265341670.005592146931665210.0027960734658326
700.9959194338377630.008161132324474750.00408056616223738
710.9951251977291350.009749604541729680.00487480227086484
720.9933871854304410.01322562913911850.00661281456955927
730.9917283266868810.01654334662623850.00827167331311925
740.9894016597667750.02119668046644950.0105983402332247
750.9950487071804620.009902585639076060.00495129281953803
760.9956632162365010.008673567526997050.00433678376349852
770.9979393246471910.004121350705617970.00206067535280898
780.9999943750279121.12499441765103e-055.62497208825513e-06
790.9999907299053061.8540189388666e-059.27009469433299e-06
800.9999841421089763.1715782048449e-051.58578910242245e-05
810.9999729585827875.40828344267664e-052.70414172133832e-05
820.9999999762976084.74047834816753e-082.37023917408376e-08
830.9999999529953779.40092455166136e-084.70046227583068e-08
840.9999999345061961.30987608601745e-076.54938043008723e-08
850.999999893366992.13266020716191e-071.06633010358096e-07
860.9999997813443624.37311276651765e-072.18655638325882e-07
870.9999998432002233.1359955442502e-071.5679977721251e-07
880.9999999371725331.256549348908e-076.28274674453998e-08
890.9999999108433761.78313248162002e-078.91566240810012e-08
900.999999808051933.83896140549064e-071.91948070274532e-07
910.9999997549217964.90156408305101e-072.4507820415255e-07
920.9999999669683756.60632505043387e-083.30316252521694e-08
930.9999999647942867.04114282724436e-083.52057141362218e-08
940.9999999210984831.57803033904224e-077.89015169521121e-08
950.9999999327601211.34479757500489e-076.72398787502444e-08
960.9999998686647152.62670569387418e-071.31335284693709e-07
970.9999998863138152.27372369253747e-071.13686184626874e-07
980.9999997539650124.92069975314805e-072.46034987657403e-07
990.9999995290884569.41823088612669e-074.70911544306334e-07
1000.9999993617869531.27642609367744e-066.3821304683872e-07
1010.9999988118714382.37625712418438e-061.18812856209219e-06
1020.9999987629144492.47417110164733e-061.23708555082367e-06
1030.9999996118855637.76228874970944e-073.88114437485472e-07
1040.9999990886885181.82262296488424e-069.1131148244212e-07
1050.9999979146303064.17073938901157e-062.08536969450578e-06
1060.9999953259667529.34806649590559e-064.67403324795279e-06
1070.9999922332701481.55334597046386e-057.76672985231931e-06
1080.9999998649459692.7010806117753e-071.35054030588765e-07
1090.99999965773186.84536400559161e-073.4226820027958e-07
1100.9999991084977861.7830044272435e-068.9150221362175e-07
1110.9999993618788891.27624222108402e-066.3812111054201e-07
1120.9999990075324121.98493517571033e-069.92467587855167e-07
1130.9999983375854483.32482910445282e-061.66241455222641e-06
1140.9999957257177738.54856445493727e-064.27428222746864e-06
1150.9999895662281232.08675437544228e-051.04337718772114e-05
1160.9999742497434285.15005131448779e-052.57502565724389e-05
1170.9999996796919496.40616102488034e-073.20308051244017e-07
1180.9999989141631242.1716737517062e-061.0858368758531e-06
1190.9999964332388887.13352222342905e-063.56676111171453e-06
1200.9999919992696161.60014607674714e-058.00073038373572e-06
1210.9999754634926254.90730147500714e-052.45365073750357e-05
1220.99992508338540.0001498332292009037.49166146004514e-05
1230.9998511344765980.0002977310468046220.000148865523402311
1240.9998185390434670.0003629219130658510.000181460956532926
1250.9998878874864710.0002242250270589310.000112112513529466
1260.9997580431396910.0004839137206180020.000241956860309001
1270.9999098964376240.0001802071247527219.01035623763604e-05
1280.9996667509319860.0006664981360274010.0003332490680137
1290.9986315519660320.002736896067936570.00136844803396828
1300.9945719759289430.01085604814211370.00542802407105686
1310.9814596772821280.03708064543574360.0185403227178718
1320.9676212638264030.06475747234719310.0323787361735966






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level600.495867768595041NOK
5% type I error level880.727272727272727NOK
10% type I error level960.793388429752066NOK

\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 & 60 & 0.495867768595041 & NOK \tabularnewline
5% type I error level & 88 & 0.727272727272727 & NOK \tabularnewline
10% type I error level & 96 & 0.793388429752066 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157445&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]60[/C][C]0.495867768595041[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]88[/C][C]0.727272727272727[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]96[/C][C]0.793388429752066[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157445&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157445&T=6

As an alternative you can also use a QR Code:  
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 level600.495867768595041NOK
5% type I error level880.727272727272727NOK
10% type I error level960.793388429752066NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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