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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 computationTue, 20 Nov 2012 11:45:15 -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/2012/Nov/20/t1353429939kekma9pl4vkdhwp.htm/, Retrieved Wed, 01 May 2024 23:42:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191172, Retrieved Wed, 01 May 2024 23:42:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [WS7.8] [2012-11-20 16:45:15] [6144fd9dab7e8876ce9100c6a2ac91c2] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100	100	100	100
102,815	101,542	100,254	102
104,301	102,179	102,839	103,65
104,964	105,494	104,726	104,974
104,83	106,14	103,387	104,641
105,878	106,371	101,746	104,902
107,542	107,249	100,371	105,695
107,954	109,481	101,337	106,489
108,09	111,951	102,307	107,146
109,19	111,972	101,794	107,695
110,115	110,661	100,294	107,711
110,439	113,149	100,578	108,313
111,054	113,853	97,9592	108,124
112,319	115,143	100,107	109,615
113,607	116,923	102,865	111,34
112,716	116,638	102,719	110,717
113,126	116,227	103,921	111,217
112,818	115,942	105,751	111,452
112,565	116,42	106,746	111,611
112,698	113,365	108,454	111,717
113,701	112,709	107,724	112,062
113,844	115,609	108,936	112,842
114,151	115,626	109,764	113,241
114,069	116,697	108,502	113,015
114,798	119,368	109,211	113,998
114,537	120,264	113,097	114,936
114,118	118,74	112,18	114,245
113,814	116,522	114,855	114,437
115,232	116,967	114,53	115,286
115,945	118,061	115,328	116,071
117,543	118,711	117,973	117,807
118,205	119,223	117,863	118,255
119,899	119,196	116,582	118,969
121,35	120,729	117,645	120,333
122,563	121,828	120,711	121,998
124,143	122,603	121,37	123,239
126,574	123,803	120,473	124,666
128,069	127,692	122,204	126,54
128,101	128,336	124,943	127,336
128,752	128,718	125,276	127,871
129,991	130,539	130,192	130,115
133,236	132,864	131,595	132,773
134,689	134,529	133,091	134,265
135,058	135,166	133,167	134,596
135,615	133,458	131,858	134,38
136,088	135,621	132,5	135,121
136,114	137,409	131,551	135,136
136,177	138,866	131,422	135,336
136,883	135,802	131,112	135,284
139,095	139,408	131,193	137,144
141,551	142,191	136,448	140,349
144,647	146,027	138,433	143,264
147,403	145,695	136,323	144,381
148,778	148,469	137,453	145,881
149,123	152,221	137,072	146,497
150,925	157,061	139,485	148,857
152,195	160,782	142,049	150,78
155,762	164,581	141,315	153,293
159,863	171,274	145,023	157,641
164,488	177,848	148,287	162,182
172,288	185,538	147,732	167,86
181,098	193,704	151,23	175,245
186,026	203,366	150,278	179,32
191,144	213,692	154,789	184,979
196,021	220,819	153,029	188,482
200,338	225,005	157,658	192,86
202,319	229,096	161,039	195,475
204,148	233,982	165,599	198,4
205,288	234,529	171,248	200,598
206,439	238,753	172,249	202,121
210,638	238,258	177,164	205,875
212,831	241,42	174,947	207,085
214,227	242,44	179,407	209,204
216,573	248,809	181,625	212,246
217,504	254,991	188,871	215,466
219,151	255,458	189,866	216,693
220,494	261,125	192,114	219,019
220,484	258,58	189,665	217,924
220,269	257,981	191,006	217,978
222,524	257,756	186,398	218,186
221,905	257,984	189,577	218,54
222,286	252,604	190,244	217,886
219,929	251,688	190,269	216,347
222,144	255,734	196,606	219,825
224,73	257,646	197,796	221,956
228,912	263,016	205,874	227,184
231,613	265,367	206,229	229,247
235,936	271,406	208,473	233,33
239,005	278,478	211,102	236,987
242,293	284,415	211,503	240,027
248,077	287,685	218,055	245,433
248,956	287,97	221,076	246,641
252,358	290,44	226,743	250,328
254,122	292,298	223,179	250,849
255,015	296,637	219,996	251,435
253,493	299,882	223,847	252,091
255,976	292,588	227,227	252,946
255,878	292,523	226,757	252,773
254,149	290,063	223,928	250,677
252,408	296,831	220,682	250,105
252,503	296,742	227,654	251,788
253,733	296,479	218,398	250,212
252,299	295,557	213,639	248,073
248,838	288,037	212,71	244,468
247,559	287,377	217,355	244,727
245,331	290,101	217,786	244,034
242,351	296,679	218,186	243,588
238,172	285,712	206,917	236,447
226,723	270,085	197,833	224,906
225,84	261,006	194,438	221,934
225,751	266,44	202,508	224,903
226,192	267,075	196,651	223,798
220,037	263,672	191,446	218,529
220,406	259,121	190,056	217,521
223,551	262,711	190,322	219,971
223,373	265,838	203,701	223,841
224,678	265,766	200,524	223,764
223,629	269,162	200,524	223,664
220,855	256,573	191,582	217,678
220,127	257,917	195,727	218,478
215,471	253,316	194,766	214,815
214,691	257,496	194,576	215,143
216,2	264,861	198,563	218,381
219,85	257,795	201,679	219,962
220,182	251,318	201,506	218,933
220,283	243,526	204,453	218,36
216,675	247,503	206,552	217,72
217,808	256,9	205,642	219,934
217,66	261,806	205,679	220,842
217,951	260,758	204,583	220,584
215,9	244,361	204,484	216,346
217,141	255,116	208,73	220,221
219,459	256,46	210,264	222,182
222,898	258,249	214,211	225,455
225,478	256,327	214,169	226,42
228,098	259,192	213,656	228,287
230,729	260,776	219,028	231,349
230,535	261,166	217,602	231,015
229,735	265,351	220,635	232,241
233,148	261,627	222,011	233,688
235,221	266,932	224,948	236,667
237,46	268,695	225,566	238,439
239,951	274,37	219,318	239,488
240,436	275,671	213,558	238,741
241,588	270,831	214,026	238,5
241,512	275,141	225,59	242,116
243,05	277,59	227,637	243,923
246,469	276,357	229	245,813
248,64	279,389	226,841	247,143
251,147	274,787	221,488	246,381

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 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 & 17 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191172&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]17 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=191172&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191172&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 time17 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 0.745313423069235 + 0.60534531523025SMF[t] + 0.140579094641995SSF[t] + 0.247134222645753NS[t] + 0.00229224875322625t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  0.745313423069235 +  0.60534531523025SMF[t] +  0.140579094641995SSF[t] +  0.247134222645753NS[t] +  0.00229224875322625t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191172&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  0.745313423069235 +  0.60534531523025SMF[t] +  0.140579094641995SSF[t] +  0.247134222645753NS[t] +  0.00229224875322625t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191172&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191172&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
TOT[t] = + 0.745313423069235 + 0.60534531523025SMF[t] + 0.140579094641995SSF[t] + 0.247134222645753NS[t] + 0.00229224875322625t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.7453134230692350.1558054.78364e-062e-06
SMF0.605345315230250.004484134.989400
SSF0.1405790946419950.00290148.456100
NS0.2471342226457530.00291484.812100
t0.002292248753226250.0012041.90440.0588430.029422

\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) & 0.745313423069235 & 0.155805 & 4.7836 & 4e-06 & 2e-06 \tabularnewline
SMF & 0.60534531523025 & 0.004484 & 134.9894 & 0 & 0 \tabularnewline
SSF & 0.140579094641995 & 0.002901 & 48.4561 & 0 & 0 \tabularnewline
NS & 0.247134222645753 & 0.002914 & 84.8121 & 0 & 0 \tabularnewline
t & 0.00229224875322625 & 0.001204 & 1.9044 & 0.058843 & 0.029422 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191172&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]0.745313423069235[/C][C]0.155805[/C][C]4.7836[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]SMF[/C][C]0.60534531523025[/C][C]0.004484[/C][C]134.9894[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]SSF[/C][C]0.140579094641995[/C][C]0.002901[/C][C]48.4561[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]NS[/C][C]0.247134222645753[/C][C]0.002914[/C][C]84.8121[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.00229224875322625[/C][C]0.001204[/C][C]1.9044[/C][C]0.058843[/C][C]0.029422[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191172&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191172&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)0.7453134230692350.1558054.78364e-062e-06
SMF0.605345315230250.004484134.989400
SSF0.1405790946419950.00290148.456100
NS0.2471342226457530.00291484.812100
t0.002292248753226250.0012041.90440.0588430.029422






Multiple Linear Regression - Regression Statistics
Multiple R0.999992983326871
R-squared0.999985966702975
Adjusted R-squared0.99998557957754
F-TEST (value)2583105.82522995
F-TEST (DF numerator)4
F-TEST (DF denominator)145
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.202936009187988
Sum Squared Residuals5.97153845464634

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999992983326871 \tabularnewline
R-squared & 0.999985966702975 \tabularnewline
Adjusted R-squared & 0.99998557957754 \tabularnewline
F-TEST (value) & 2583105.82522995 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 145 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.202936009187988 \tabularnewline
Sum Squared Residuals & 5.97153845464634 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191172&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999992983326871[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999985966702975[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.99998557957754[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2583105.82522995[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]145[/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]0.202936009187988[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5.97153845464634[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191172&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191172&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 R0.999992983326871
R-squared0.999985966702975
Adjusted R-squared0.99998557957754
F-TEST (value)2583105.82522995
F-TEST (DF numerator)4
F-TEST (DF denominator)145
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.202936009187988
Sum Squared Residuals5.97153845464634






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1100100.053468923622-0.053468923622119
2102102.039353291239-0.0393532912385971
3103.65103.66957952725-0.0195795272501676
4104.974105.005577696872-0.0315776968717988
5104.641104.6866550444-0.0456550444002395
6104.902104.950275695015-0.0482756950153905
7105.695105.74348143727-0.0484814372695179
8106.489106.547680154214-0.0586801542143236
9107.146107.219249925571-0.0732499255709799
10107.695107.763594325848-0.0685943258476967
11107.711107.770830464145-0.0598304641446105
12108.313108.389201501733-0.0762015017331089
13108.124108.215553699716-0.0915536997162164
14109.615109.695749687722-0.0807496877224333
15111.34111.409553677012-0.0695536770119466
16110.717110.796336611416-0.0793366114157735
17111.217111.286097767136-0.0690977671357486
18111.452111.514134244267-0.0621342442668106
19111.611111.676369487038-0.0653694870381744
20111.717111.751808780865-0.0348087808646776
21112.062112.088634512177-0.0266345121773037
22112.842112.884697193317-0.0426971933168909
23113.241113.279847434805-0.0388474348054006
24113.015113.071178189092-0.0561781890923824
25113.998114.065472098293-0.0674720982930629
26114.936114.996091677772-0.0600916777718165
27114.245114.303879617043-0.0588796170430116
28114.437114.471426503628-0.0344265036276967
29115.286115.314337484133-0.0283374841332313
30116.071116.099247581855-0.0282475818552755
31117.807117.813928074762-0.00692807476175484
32118.255118.261750654163-0.0067506541630806
33118.969118.969123292152-0.000123292151803544
34120.333120.3279830240630.00501697593727331
35121.998121.9767690918340.0212309081663191
36123.239123.2073171897220.0316828102782056
37124.666124.6282194156570.0377805843430814
38126.54126.5100043491420.0299956508581331
39127.336127.2991012207590.0368987792413602
40127.871127.8314701800210.0395298199789552
41130.115130.0546916442140.0603083557858642
42132.773132.6949051503040.0780948496958481
43134.265134.1805411317440.0844588682560918
44134.596134.5145368860250.0814631139748931
45134.38134.290398684270.0896013157302171
46135.121135.0417520197760.0792479802238968
47135.136135.0766082906540.0593917093456037
48135.336135.2899807204390.046019279560806
49135.284135.2123008067420.0716991932582532
50137.144137.0805629800980.0634370199023919
51140.349140.2595052834480.089494716551558
52143.264143.1657694671530.09823053284699
53144.381144.2682679354770.112732064522862
54145.881145.7721380727990.108861927201454
55146.497146.4165690795750.0804309204250713
56148.857148.7864312836850.0705687163154483
57150.78150.7142590408070.0657409591932464
58153.293153.2284814901090.0645185098907708
59157.641157.5705644546310.0704355453689521
60162.182162.1033918572160.0786081427836005
61167.86167.7712713089940.0887286910059043
62175.245175.1191001825870.1258998174128
63179.32179.2275375772670.0924624227326956
64184.979184.8944293589970.0845706410028477
65188.482188.4159416857850.0660583142147218
66192.86192.7639580671860.09604193281394
67195.475195.3761092683560.0988907316438793
68198.4198.2993796103510.100620389649113
69200.598200.4647235069620.133276493038371
70202.121202.0049556661810.11604433381895
71205.875205.6941709460420.1808290539578
72207.085206.9205999967480.164400003252303
73209.204209.0135636150970.190436384902739
74212.246211.8794879329840.366512067016203
75215.466215.1051512105840.360848789415679
76216.693216.4159961822520.277003817747888
77219.019218.5834866512030.435513348796601
78217.924217.6167199396810.307280060319006
79217.978217.7360630605370.241936939462883
80218.186217.9329842008880.253015799111533
81218.54218.3782594268830.161740573116589
82217.886218.01971123807-0.133711238070151
83216.347216.4726124837-0.125612483699743
84219.825219.950617191516-0.125617191515638
85221.956222.081209379358-0.125209379358221
86227.184227.366315725164-0.182315725164259
87229.247229.421879770897-0.174879770896946
88233.33233.444606165551-0.114606165550622
89236.987236.9485944153890.0384055846106389
90240.027239.874980968790.152019031209869
91245.433245.457507587089-0.0245075870894174
92246.641246.778555896516-0.137555896515816
93250.328250.587972911182-0.259972911181552
94250.849251.038503884336-0.189503884336324
95251.435251.404713960560.0302860394396804
96252.091251.8935636930550.197436306944828
97252.946253.208858115749-0.262858115749061
98252.773253.026535797814-0.253535797814479
99250.677250.93721870785-0.260218707850469
100250.105250.0348463886170.0701536113832652
101251.788251.80515470318-0.0171547031798678
102250.212250.227575022966-0.0155750229663913
103248.073248.0560763988480.0169236011516281
104244.468244.676526027044-0.208526027044003
105244.727244.959737879344-0.232737879343539
106244.034244.102773069529-0.0687730695288894
107243.588243.3247192525090.263280747490686
108236.447236.470586942982-0.0235869429815683
109224.906225.100483887179-0.1944838871792
110221.934222.452917936447-0.518917936447114
111224.903225.159614429181-0.256614429180686
112223.798224.070666545012-0.272666545011934
113218.529218.582334090585-0.0533340905851306
114217.521217.824706731465-0.30370673146501
115219.971220.301226649606-0.330226649605875
116223.841223.941767025971-0.100767025971165
117223.764223.93876779094-0.174767790940088
118223.664223.783459409421-0.119459409421007
119217.678218.126899312379-0.448899312379116
120218.478218.90180982771-0.423809827710211
121214.815215.201313886341-0.386313886341016
122215.143215.272101902515-0.129101902515489
123218.381218.2085494096780.17245059032194
124219.962220.197090414046-0.23509041404555
125218.933219.447072290941-0.514072290941298
126218.36219.143416665219-0.783416665219369
127217.72218.039440809347-0.319440809346528
128219.934219.8237189099990.110281090001194
129220.842220.4352450566490.406754943350537
130220.584220.195506792930.388493207069858
131216.346216.626694097259-0.280694097259421
132220.221219.9414799544420.279520045558104
133222.182221.9150048446360.266995155363707
134225.455225.2260144095640.228985590436361
135226.42226.509524914358-0.0895249143579058
136228.287228.373801138946-0.0868011389464091
137231.349231.519039242036-0.170039242036341
138231.015231.106306945052-0.091306945052428
139232.241231.9622045499830.27879545001725
140233.688233.847080501531-0.159080501530606
141236.667236.5758588977430.0911411022575046
142238.439238.3340892007450.104910799254831
143239.488239.097988368740.390011631260415
144238.741238.1532733750690.587726624930835
145238.5238.2881794250990.211820574901384
146242.116241.7082214784770.407778521523183
147243.923243.4916967785880.43130322141172
148245.813245.7271745818860.0858254181136716
149247.143246.9363445382670.206655461733249
150246.381246.486383004937-0.105383004937036

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100 & 100.053468923622 & -0.053468923622119 \tabularnewline
2 & 102 & 102.039353291239 & -0.0393532912385971 \tabularnewline
3 & 103.65 & 103.66957952725 & -0.0195795272501676 \tabularnewline
4 & 104.974 & 105.005577696872 & -0.0315776968717988 \tabularnewline
5 & 104.641 & 104.6866550444 & -0.0456550444002395 \tabularnewline
6 & 104.902 & 104.950275695015 & -0.0482756950153905 \tabularnewline
7 & 105.695 & 105.74348143727 & -0.0484814372695179 \tabularnewline
8 & 106.489 & 106.547680154214 & -0.0586801542143236 \tabularnewline
9 & 107.146 & 107.219249925571 & -0.0732499255709799 \tabularnewline
10 & 107.695 & 107.763594325848 & -0.0685943258476967 \tabularnewline
11 & 107.711 & 107.770830464145 & -0.0598304641446105 \tabularnewline
12 & 108.313 & 108.389201501733 & -0.0762015017331089 \tabularnewline
13 & 108.124 & 108.215553699716 & -0.0915536997162164 \tabularnewline
14 & 109.615 & 109.695749687722 & -0.0807496877224333 \tabularnewline
15 & 111.34 & 111.409553677012 & -0.0695536770119466 \tabularnewline
16 & 110.717 & 110.796336611416 & -0.0793366114157735 \tabularnewline
17 & 111.217 & 111.286097767136 & -0.0690977671357486 \tabularnewline
18 & 111.452 & 111.514134244267 & -0.0621342442668106 \tabularnewline
19 & 111.611 & 111.676369487038 & -0.0653694870381744 \tabularnewline
20 & 111.717 & 111.751808780865 & -0.0348087808646776 \tabularnewline
21 & 112.062 & 112.088634512177 & -0.0266345121773037 \tabularnewline
22 & 112.842 & 112.884697193317 & -0.0426971933168909 \tabularnewline
23 & 113.241 & 113.279847434805 & -0.0388474348054006 \tabularnewline
24 & 113.015 & 113.071178189092 & -0.0561781890923824 \tabularnewline
25 & 113.998 & 114.065472098293 & -0.0674720982930629 \tabularnewline
26 & 114.936 & 114.996091677772 & -0.0600916777718165 \tabularnewline
27 & 114.245 & 114.303879617043 & -0.0588796170430116 \tabularnewline
28 & 114.437 & 114.471426503628 & -0.0344265036276967 \tabularnewline
29 & 115.286 & 115.314337484133 & -0.0283374841332313 \tabularnewline
30 & 116.071 & 116.099247581855 & -0.0282475818552755 \tabularnewline
31 & 117.807 & 117.813928074762 & -0.00692807476175484 \tabularnewline
32 & 118.255 & 118.261750654163 & -0.0067506541630806 \tabularnewline
33 & 118.969 & 118.969123292152 & -0.000123292151803544 \tabularnewline
34 & 120.333 & 120.327983024063 & 0.00501697593727331 \tabularnewline
35 & 121.998 & 121.976769091834 & 0.0212309081663191 \tabularnewline
36 & 123.239 & 123.207317189722 & 0.0316828102782056 \tabularnewline
37 & 124.666 & 124.628219415657 & 0.0377805843430814 \tabularnewline
38 & 126.54 & 126.510004349142 & 0.0299956508581331 \tabularnewline
39 & 127.336 & 127.299101220759 & 0.0368987792413602 \tabularnewline
40 & 127.871 & 127.831470180021 & 0.0395298199789552 \tabularnewline
41 & 130.115 & 130.054691644214 & 0.0603083557858642 \tabularnewline
42 & 132.773 & 132.694905150304 & 0.0780948496958481 \tabularnewline
43 & 134.265 & 134.180541131744 & 0.0844588682560918 \tabularnewline
44 & 134.596 & 134.514536886025 & 0.0814631139748931 \tabularnewline
45 & 134.38 & 134.29039868427 & 0.0896013157302171 \tabularnewline
46 & 135.121 & 135.041752019776 & 0.0792479802238968 \tabularnewline
47 & 135.136 & 135.076608290654 & 0.0593917093456037 \tabularnewline
48 & 135.336 & 135.289980720439 & 0.046019279560806 \tabularnewline
49 & 135.284 & 135.212300806742 & 0.0716991932582532 \tabularnewline
50 & 137.144 & 137.080562980098 & 0.0634370199023919 \tabularnewline
51 & 140.349 & 140.259505283448 & 0.089494716551558 \tabularnewline
52 & 143.264 & 143.165769467153 & 0.09823053284699 \tabularnewline
53 & 144.381 & 144.268267935477 & 0.112732064522862 \tabularnewline
54 & 145.881 & 145.772138072799 & 0.108861927201454 \tabularnewline
55 & 146.497 & 146.416569079575 & 0.0804309204250713 \tabularnewline
56 & 148.857 & 148.786431283685 & 0.0705687163154483 \tabularnewline
57 & 150.78 & 150.714259040807 & 0.0657409591932464 \tabularnewline
58 & 153.293 & 153.228481490109 & 0.0645185098907708 \tabularnewline
59 & 157.641 & 157.570564454631 & 0.0704355453689521 \tabularnewline
60 & 162.182 & 162.103391857216 & 0.0786081427836005 \tabularnewline
61 & 167.86 & 167.771271308994 & 0.0887286910059043 \tabularnewline
62 & 175.245 & 175.119100182587 & 0.1258998174128 \tabularnewline
63 & 179.32 & 179.227537577267 & 0.0924624227326956 \tabularnewline
64 & 184.979 & 184.894429358997 & 0.0845706410028477 \tabularnewline
65 & 188.482 & 188.415941685785 & 0.0660583142147218 \tabularnewline
66 & 192.86 & 192.763958067186 & 0.09604193281394 \tabularnewline
67 & 195.475 & 195.376109268356 & 0.0988907316438793 \tabularnewline
68 & 198.4 & 198.299379610351 & 0.100620389649113 \tabularnewline
69 & 200.598 & 200.464723506962 & 0.133276493038371 \tabularnewline
70 & 202.121 & 202.004955666181 & 0.11604433381895 \tabularnewline
71 & 205.875 & 205.694170946042 & 0.1808290539578 \tabularnewline
72 & 207.085 & 206.920599996748 & 0.164400003252303 \tabularnewline
73 & 209.204 & 209.013563615097 & 0.190436384902739 \tabularnewline
74 & 212.246 & 211.879487932984 & 0.366512067016203 \tabularnewline
75 & 215.466 & 215.105151210584 & 0.360848789415679 \tabularnewline
76 & 216.693 & 216.415996182252 & 0.277003817747888 \tabularnewline
77 & 219.019 & 218.583486651203 & 0.435513348796601 \tabularnewline
78 & 217.924 & 217.616719939681 & 0.307280060319006 \tabularnewline
79 & 217.978 & 217.736063060537 & 0.241936939462883 \tabularnewline
80 & 218.186 & 217.932984200888 & 0.253015799111533 \tabularnewline
81 & 218.54 & 218.378259426883 & 0.161740573116589 \tabularnewline
82 & 217.886 & 218.01971123807 & -0.133711238070151 \tabularnewline
83 & 216.347 & 216.4726124837 & -0.125612483699743 \tabularnewline
84 & 219.825 & 219.950617191516 & -0.125617191515638 \tabularnewline
85 & 221.956 & 222.081209379358 & -0.125209379358221 \tabularnewline
86 & 227.184 & 227.366315725164 & -0.182315725164259 \tabularnewline
87 & 229.247 & 229.421879770897 & -0.174879770896946 \tabularnewline
88 & 233.33 & 233.444606165551 & -0.114606165550622 \tabularnewline
89 & 236.987 & 236.948594415389 & 0.0384055846106389 \tabularnewline
90 & 240.027 & 239.87498096879 & 0.152019031209869 \tabularnewline
91 & 245.433 & 245.457507587089 & -0.0245075870894174 \tabularnewline
92 & 246.641 & 246.778555896516 & -0.137555896515816 \tabularnewline
93 & 250.328 & 250.587972911182 & -0.259972911181552 \tabularnewline
94 & 250.849 & 251.038503884336 & -0.189503884336324 \tabularnewline
95 & 251.435 & 251.40471396056 & 0.0302860394396804 \tabularnewline
96 & 252.091 & 251.893563693055 & 0.197436306944828 \tabularnewline
97 & 252.946 & 253.208858115749 & -0.262858115749061 \tabularnewline
98 & 252.773 & 253.026535797814 & -0.253535797814479 \tabularnewline
99 & 250.677 & 250.93721870785 & -0.260218707850469 \tabularnewline
100 & 250.105 & 250.034846388617 & 0.0701536113832652 \tabularnewline
101 & 251.788 & 251.80515470318 & -0.0171547031798678 \tabularnewline
102 & 250.212 & 250.227575022966 & -0.0155750229663913 \tabularnewline
103 & 248.073 & 248.056076398848 & 0.0169236011516281 \tabularnewline
104 & 244.468 & 244.676526027044 & -0.208526027044003 \tabularnewline
105 & 244.727 & 244.959737879344 & -0.232737879343539 \tabularnewline
106 & 244.034 & 244.102773069529 & -0.0687730695288894 \tabularnewline
107 & 243.588 & 243.324719252509 & 0.263280747490686 \tabularnewline
108 & 236.447 & 236.470586942982 & -0.0235869429815683 \tabularnewline
109 & 224.906 & 225.100483887179 & -0.1944838871792 \tabularnewline
110 & 221.934 & 222.452917936447 & -0.518917936447114 \tabularnewline
111 & 224.903 & 225.159614429181 & -0.256614429180686 \tabularnewline
112 & 223.798 & 224.070666545012 & -0.272666545011934 \tabularnewline
113 & 218.529 & 218.582334090585 & -0.0533340905851306 \tabularnewline
114 & 217.521 & 217.824706731465 & -0.30370673146501 \tabularnewline
115 & 219.971 & 220.301226649606 & -0.330226649605875 \tabularnewline
116 & 223.841 & 223.941767025971 & -0.100767025971165 \tabularnewline
117 & 223.764 & 223.93876779094 & -0.174767790940088 \tabularnewline
118 & 223.664 & 223.783459409421 & -0.119459409421007 \tabularnewline
119 & 217.678 & 218.126899312379 & -0.448899312379116 \tabularnewline
120 & 218.478 & 218.90180982771 & -0.423809827710211 \tabularnewline
121 & 214.815 & 215.201313886341 & -0.386313886341016 \tabularnewline
122 & 215.143 & 215.272101902515 & -0.129101902515489 \tabularnewline
123 & 218.381 & 218.208549409678 & 0.17245059032194 \tabularnewline
124 & 219.962 & 220.197090414046 & -0.23509041404555 \tabularnewline
125 & 218.933 & 219.447072290941 & -0.514072290941298 \tabularnewline
126 & 218.36 & 219.143416665219 & -0.783416665219369 \tabularnewline
127 & 217.72 & 218.039440809347 & -0.319440809346528 \tabularnewline
128 & 219.934 & 219.823718909999 & 0.110281090001194 \tabularnewline
129 & 220.842 & 220.435245056649 & 0.406754943350537 \tabularnewline
130 & 220.584 & 220.19550679293 & 0.388493207069858 \tabularnewline
131 & 216.346 & 216.626694097259 & -0.280694097259421 \tabularnewline
132 & 220.221 & 219.941479954442 & 0.279520045558104 \tabularnewline
133 & 222.182 & 221.915004844636 & 0.266995155363707 \tabularnewline
134 & 225.455 & 225.226014409564 & 0.228985590436361 \tabularnewline
135 & 226.42 & 226.509524914358 & -0.0895249143579058 \tabularnewline
136 & 228.287 & 228.373801138946 & -0.0868011389464091 \tabularnewline
137 & 231.349 & 231.519039242036 & -0.170039242036341 \tabularnewline
138 & 231.015 & 231.106306945052 & -0.091306945052428 \tabularnewline
139 & 232.241 & 231.962204549983 & 0.27879545001725 \tabularnewline
140 & 233.688 & 233.847080501531 & -0.159080501530606 \tabularnewline
141 & 236.667 & 236.575858897743 & 0.0911411022575046 \tabularnewline
142 & 238.439 & 238.334089200745 & 0.104910799254831 \tabularnewline
143 & 239.488 & 239.09798836874 & 0.390011631260415 \tabularnewline
144 & 238.741 & 238.153273375069 & 0.587726624930835 \tabularnewline
145 & 238.5 & 238.288179425099 & 0.211820574901384 \tabularnewline
146 & 242.116 & 241.708221478477 & 0.407778521523183 \tabularnewline
147 & 243.923 & 243.491696778588 & 0.43130322141172 \tabularnewline
148 & 245.813 & 245.727174581886 & 0.0858254181136716 \tabularnewline
149 & 247.143 & 246.936344538267 & 0.206655461733249 \tabularnewline
150 & 246.381 & 246.486383004937 & -0.105383004937036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191172&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]100[/C][C]100.053468923622[/C][C]-0.053468923622119[/C][/ROW]
[ROW][C]2[/C][C]102[/C][C]102.039353291239[/C][C]-0.0393532912385971[/C][/ROW]
[ROW][C]3[/C][C]103.65[/C][C]103.66957952725[/C][C]-0.0195795272501676[/C][/ROW]
[ROW][C]4[/C][C]104.974[/C][C]105.005577696872[/C][C]-0.0315776968717988[/C][/ROW]
[ROW][C]5[/C][C]104.641[/C][C]104.6866550444[/C][C]-0.0456550444002395[/C][/ROW]
[ROW][C]6[/C][C]104.902[/C][C]104.950275695015[/C][C]-0.0482756950153905[/C][/ROW]
[ROW][C]7[/C][C]105.695[/C][C]105.74348143727[/C][C]-0.0484814372695179[/C][/ROW]
[ROW][C]8[/C][C]106.489[/C][C]106.547680154214[/C][C]-0.0586801542143236[/C][/ROW]
[ROW][C]9[/C][C]107.146[/C][C]107.219249925571[/C][C]-0.0732499255709799[/C][/ROW]
[ROW][C]10[/C][C]107.695[/C][C]107.763594325848[/C][C]-0.0685943258476967[/C][/ROW]
[ROW][C]11[/C][C]107.711[/C][C]107.770830464145[/C][C]-0.0598304641446105[/C][/ROW]
[ROW][C]12[/C][C]108.313[/C][C]108.389201501733[/C][C]-0.0762015017331089[/C][/ROW]
[ROW][C]13[/C][C]108.124[/C][C]108.215553699716[/C][C]-0.0915536997162164[/C][/ROW]
[ROW][C]14[/C][C]109.615[/C][C]109.695749687722[/C][C]-0.0807496877224333[/C][/ROW]
[ROW][C]15[/C][C]111.34[/C][C]111.409553677012[/C][C]-0.0695536770119466[/C][/ROW]
[ROW][C]16[/C][C]110.717[/C][C]110.796336611416[/C][C]-0.0793366114157735[/C][/ROW]
[ROW][C]17[/C][C]111.217[/C][C]111.286097767136[/C][C]-0.0690977671357486[/C][/ROW]
[ROW][C]18[/C][C]111.452[/C][C]111.514134244267[/C][C]-0.0621342442668106[/C][/ROW]
[ROW][C]19[/C][C]111.611[/C][C]111.676369487038[/C][C]-0.0653694870381744[/C][/ROW]
[ROW][C]20[/C][C]111.717[/C][C]111.751808780865[/C][C]-0.0348087808646776[/C][/ROW]
[ROW][C]21[/C][C]112.062[/C][C]112.088634512177[/C][C]-0.0266345121773037[/C][/ROW]
[ROW][C]22[/C][C]112.842[/C][C]112.884697193317[/C][C]-0.0426971933168909[/C][/ROW]
[ROW][C]23[/C][C]113.241[/C][C]113.279847434805[/C][C]-0.0388474348054006[/C][/ROW]
[ROW][C]24[/C][C]113.015[/C][C]113.071178189092[/C][C]-0.0561781890923824[/C][/ROW]
[ROW][C]25[/C][C]113.998[/C][C]114.065472098293[/C][C]-0.0674720982930629[/C][/ROW]
[ROW][C]26[/C][C]114.936[/C][C]114.996091677772[/C][C]-0.0600916777718165[/C][/ROW]
[ROW][C]27[/C][C]114.245[/C][C]114.303879617043[/C][C]-0.0588796170430116[/C][/ROW]
[ROW][C]28[/C][C]114.437[/C][C]114.471426503628[/C][C]-0.0344265036276967[/C][/ROW]
[ROW][C]29[/C][C]115.286[/C][C]115.314337484133[/C][C]-0.0283374841332313[/C][/ROW]
[ROW][C]30[/C][C]116.071[/C][C]116.099247581855[/C][C]-0.0282475818552755[/C][/ROW]
[ROW][C]31[/C][C]117.807[/C][C]117.813928074762[/C][C]-0.00692807476175484[/C][/ROW]
[ROW][C]32[/C][C]118.255[/C][C]118.261750654163[/C][C]-0.0067506541630806[/C][/ROW]
[ROW][C]33[/C][C]118.969[/C][C]118.969123292152[/C][C]-0.000123292151803544[/C][/ROW]
[ROW][C]34[/C][C]120.333[/C][C]120.327983024063[/C][C]0.00501697593727331[/C][/ROW]
[ROW][C]35[/C][C]121.998[/C][C]121.976769091834[/C][C]0.0212309081663191[/C][/ROW]
[ROW][C]36[/C][C]123.239[/C][C]123.207317189722[/C][C]0.0316828102782056[/C][/ROW]
[ROW][C]37[/C][C]124.666[/C][C]124.628219415657[/C][C]0.0377805843430814[/C][/ROW]
[ROW][C]38[/C][C]126.54[/C][C]126.510004349142[/C][C]0.0299956508581331[/C][/ROW]
[ROW][C]39[/C][C]127.336[/C][C]127.299101220759[/C][C]0.0368987792413602[/C][/ROW]
[ROW][C]40[/C][C]127.871[/C][C]127.831470180021[/C][C]0.0395298199789552[/C][/ROW]
[ROW][C]41[/C][C]130.115[/C][C]130.054691644214[/C][C]0.0603083557858642[/C][/ROW]
[ROW][C]42[/C][C]132.773[/C][C]132.694905150304[/C][C]0.0780948496958481[/C][/ROW]
[ROW][C]43[/C][C]134.265[/C][C]134.180541131744[/C][C]0.0844588682560918[/C][/ROW]
[ROW][C]44[/C][C]134.596[/C][C]134.514536886025[/C][C]0.0814631139748931[/C][/ROW]
[ROW][C]45[/C][C]134.38[/C][C]134.29039868427[/C][C]0.0896013157302171[/C][/ROW]
[ROW][C]46[/C][C]135.121[/C][C]135.041752019776[/C][C]0.0792479802238968[/C][/ROW]
[ROW][C]47[/C][C]135.136[/C][C]135.076608290654[/C][C]0.0593917093456037[/C][/ROW]
[ROW][C]48[/C][C]135.336[/C][C]135.289980720439[/C][C]0.046019279560806[/C][/ROW]
[ROW][C]49[/C][C]135.284[/C][C]135.212300806742[/C][C]0.0716991932582532[/C][/ROW]
[ROW][C]50[/C][C]137.144[/C][C]137.080562980098[/C][C]0.0634370199023919[/C][/ROW]
[ROW][C]51[/C][C]140.349[/C][C]140.259505283448[/C][C]0.089494716551558[/C][/ROW]
[ROW][C]52[/C][C]143.264[/C][C]143.165769467153[/C][C]0.09823053284699[/C][/ROW]
[ROW][C]53[/C][C]144.381[/C][C]144.268267935477[/C][C]0.112732064522862[/C][/ROW]
[ROW][C]54[/C][C]145.881[/C][C]145.772138072799[/C][C]0.108861927201454[/C][/ROW]
[ROW][C]55[/C][C]146.497[/C][C]146.416569079575[/C][C]0.0804309204250713[/C][/ROW]
[ROW][C]56[/C][C]148.857[/C][C]148.786431283685[/C][C]0.0705687163154483[/C][/ROW]
[ROW][C]57[/C][C]150.78[/C][C]150.714259040807[/C][C]0.0657409591932464[/C][/ROW]
[ROW][C]58[/C][C]153.293[/C][C]153.228481490109[/C][C]0.0645185098907708[/C][/ROW]
[ROW][C]59[/C][C]157.641[/C][C]157.570564454631[/C][C]0.0704355453689521[/C][/ROW]
[ROW][C]60[/C][C]162.182[/C][C]162.103391857216[/C][C]0.0786081427836005[/C][/ROW]
[ROW][C]61[/C][C]167.86[/C][C]167.771271308994[/C][C]0.0887286910059043[/C][/ROW]
[ROW][C]62[/C][C]175.245[/C][C]175.119100182587[/C][C]0.1258998174128[/C][/ROW]
[ROW][C]63[/C][C]179.32[/C][C]179.227537577267[/C][C]0.0924624227326956[/C][/ROW]
[ROW][C]64[/C][C]184.979[/C][C]184.894429358997[/C][C]0.0845706410028477[/C][/ROW]
[ROW][C]65[/C][C]188.482[/C][C]188.415941685785[/C][C]0.0660583142147218[/C][/ROW]
[ROW][C]66[/C][C]192.86[/C][C]192.763958067186[/C][C]0.09604193281394[/C][/ROW]
[ROW][C]67[/C][C]195.475[/C][C]195.376109268356[/C][C]0.0988907316438793[/C][/ROW]
[ROW][C]68[/C][C]198.4[/C][C]198.299379610351[/C][C]0.100620389649113[/C][/ROW]
[ROW][C]69[/C][C]200.598[/C][C]200.464723506962[/C][C]0.133276493038371[/C][/ROW]
[ROW][C]70[/C][C]202.121[/C][C]202.004955666181[/C][C]0.11604433381895[/C][/ROW]
[ROW][C]71[/C][C]205.875[/C][C]205.694170946042[/C][C]0.1808290539578[/C][/ROW]
[ROW][C]72[/C][C]207.085[/C][C]206.920599996748[/C][C]0.164400003252303[/C][/ROW]
[ROW][C]73[/C][C]209.204[/C][C]209.013563615097[/C][C]0.190436384902739[/C][/ROW]
[ROW][C]74[/C][C]212.246[/C][C]211.879487932984[/C][C]0.366512067016203[/C][/ROW]
[ROW][C]75[/C][C]215.466[/C][C]215.105151210584[/C][C]0.360848789415679[/C][/ROW]
[ROW][C]76[/C][C]216.693[/C][C]216.415996182252[/C][C]0.277003817747888[/C][/ROW]
[ROW][C]77[/C][C]219.019[/C][C]218.583486651203[/C][C]0.435513348796601[/C][/ROW]
[ROW][C]78[/C][C]217.924[/C][C]217.616719939681[/C][C]0.307280060319006[/C][/ROW]
[ROW][C]79[/C][C]217.978[/C][C]217.736063060537[/C][C]0.241936939462883[/C][/ROW]
[ROW][C]80[/C][C]218.186[/C][C]217.932984200888[/C][C]0.253015799111533[/C][/ROW]
[ROW][C]81[/C][C]218.54[/C][C]218.378259426883[/C][C]0.161740573116589[/C][/ROW]
[ROW][C]82[/C][C]217.886[/C][C]218.01971123807[/C][C]-0.133711238070151[/C][/ROW]
[ROW][C]83[/C][C]216.347[/C][C]216.4726124837[/C][C]-0.125612483699743[/C][/ROW]
[ROW][C]84[/C][C]219.825[/C][C]219.950617191516[/C][C]-0.125617191515638[/C][/ROW]
[ROW][C]85[/C][C]221.956[/C][C]222.081209379358[/C][C]-0.125209379358221[/C][/ROW]
[ROW][C]86[/C][C]227.184[/C][C]227.366315725164[/C][C]-0.182315725164259[/C][/ROW]
[ROW][C]87[/C][C]229.247[/C][C]229.421879770897[/C][C]-0.174879770896946[/C][/ROW]
[ROW][C]88[/C][C]233.33[/C][C]233.444606165551[/C][C]-0.114606165550622[/C][/ROW]
[ROW][C]89[/C][C]236.987[/C][C]236.948594415389[/C][C]0.0384055846106389[/C][/ROW]
[ROW][C]90[/C][C]240.027[/C][C]239.87498096879[/C][C]0.152019031209869[/C][/ROW]
[ROW][C]91[/C][C]245.433[/C][C]245.457507587089[/C][C]-0.0245075870894174[/C][/ROW]
[ROW][C]92[/C][C]246.641[/C][C]246.778555896516[/C][C]-0.137555896515816[/C][/ROW]
[ROW][C]93[/C][C]250.328[/C][C]250.587972911182[/C][C]-0.259972911181552[/C][/ROW]
[ROW][C]94[/C][C]250.849[/C][C]251.038503884336[/C][C]-0.189503884336324[/C][/ROW]
[ROW][C]95[/C][C]251.435[/C][C]251.40471396056[/C][C]0.0302860394396804[/C][/ROW]
[ROW][C]96[/C][C]252.091[/C][C]251.893563693055[/C][C]0.197436306944828[/C][/ROW]
[ROW][C]97[/C][C]252.946[/C][C]253.208858115749[/C][C]-0.262858115749061[/C][/ROW]
[ROW][C]98[/C][C]252.773[/C][C]253.026535797814[/C][C]-0.253535797814479[/C][/ROW]
[ROW][C]99[/C][C]250.677[/C][C]250.93721870785[/C][C]-0.260218707850469[/C][/ROW]
[ROW][C]100[/C][C]250.105[/C][C]250.034846388617[/C][C]0.0701536113832652[/C][/ROW]
[ROW][C]101[/C][C]251.788[/C][C]251.80515470318[/C][C]-0.0171547031798678[/C][/ROW]
[ROW][C]102[/C][C]250.212[/C][C]250.227575022966[/C][C]-0.0155750229663913[/C][/ROW]
[ROW][C]103[/C][C]248.073[/C][C]248.056076398848[/C][C]0.0169236011516281[/C][/ROW]
[ROW][C]104[/C][C]244.468[/C][C]244.676526027044[/C][C]-0.208526027044003[/C][/ROW]
[ROW][C]105[/C][C]244.727[/C][C]244.959737879344[/C][C]-0.232737879343539[/C][/ROW]
[ROW][C]106[/C][C]244.034[/C][C]244.102773069529[/C][C]-0.0687730695288894[/C][/ROW]
[ROW][C]107[/C][C]243.588[/C][C]243.324719252509[/C][C]0.263280747490686[/C][/ROW]
[ROW][C]108[/C][C]236.447[/C][C]236.470586942982[/C][C]-0.0235869429815683[/C][/ROW]
[ROW][C]109[/C][C]224.906[/C][C]225.100483887179[/C][C]-0.1944838871792[/C][/ROW]
[ROW][C]110[/C][C]221.934[/C][C]222.452917936447[/C][C]-0.518917936447114[/C][/ROW]
[ROW][C]111[/C][C]224.903[/C][C]225.159614429181[/C][C]-0.256614429180686[/C][/ROW]
[ROW][C]112[/C][C]223.798[/C][C]224.070666545012[/C][C]-0.272666545011934[/C][/ROW]
[ROW][C]113[/C][C]218.529[/C][C]218.582334090585[/C][C]-0.0533340905851306[/C][/ROW]
[ROW][C]114[/C][C]217.521[/C][C]217.824706731465[/C][C]-0.30370673146501[/C][/ROW]
[ROW][C]115[/C][C]219.971[/C][C]220.301226649606[/C][C]-0.330226649605875[/C][/ROW]
[ROW][C]116[/C][C]223.841[/C][C]223.941767025971[/C][C]-0.100767025971165[/C][/ROW]
[ROW][C]117[/C][C]223.764[/C][C]223.93876779094[/C][C]-0.174767790940088[/C][/ROW]
[ROW][C]118[/C][C]223.664[/C][C]223.783459409421[/C][C]-0.119459409421007[/C][/ROW]
[ROW][C]119[/C][C]217.678[/C][C]218.126899312379[/C][C]-0.448899312379116[/C][/ROW]
[ROW][C]120[/C][C]218.478[/C][C]218.90180982771[/C][C]-0.423809827710211[/C][/ROW]
[ROW][C]121[/C][C]214.815[/C][C]215.201313886341[/C][C]-0.386313886341016[/C][/ROW]
[ROW][C]122[/C][C]215.143[/C][C]215.272101902515[/C][C]-0.129101902515489[/C][/ROW]
[ROW][C]123[/C][C]218.381[/C][C]218.208549409678[/C][C]0.17245059032194[/C][/ROW]
[ROW][C]124[/C][C]219.962[/C][C]220.197090414046[/C][C]-0.23509041404555[/C][/ROW]
[ROW][C]125[/C][C]218.933[/C][C]219.447072290941[/C][C]-0.514072290941298[/C][/ROW]
[ROW][C]126[/C][C]218.36[/C][C]219.143416665219[/C][C]-0.783416665219369[/C][/ROW]
[ROW][C]127[/C][C]217.72[/C][C]218.039440809347[/C][C]-0.319440809346528[/C][/ROW]
[ROW][C]128[/C][C]219.934[/C][C]219.823718909999[/C][C]0.110281090001194[/C][/ROW]
[ROW][C]129[/C][C]220.842[/C][C]220.435245056649[/C][C]0.406754943350537[/C][/ROW]
[ROW][C]130[/C][C]220.584[/C][C]220.19550679293[/C][C]0.388493207069858[/C][/ROW]
[ROW][C]131[/C][C]216.346[/C][C]216.626694097259[/C][C]-0.280694097259421[/C][/ROW]
[ROW][C]132[/C][C]220.221[/C][C]219.941479954442[/C][C]0.279520045558104[/C][/ROW]
[ROW][C]133[/C][C]222.182[/C][C]221.915004844636[/C][C]0.266995155363707[/C][/ROW]
[ROW][C]134[/C][C]225.455[/C][C]225.226014409564[/C][C]0.228985590436361[/C][/ROW]
[ROW][C]135[/C][C]226.42[/C][C]226.509524914358[/C][C]-0.0895249143579058[/C][/ROW]
[ROW][C]136[/C][C]228.287[/C][C]228.373801138946[/C][C]-0.0868011389464091[/C][/ROW]
[ROW][C]137[/C][C]231.349[/C][C]231.519039242036[/C][C]-0.170039242036341[/C][/ROW]
[ROW][C]138[/C][C]231.015[/C][C]231.106306945052[/C][C]-0.091306945052428[/C][/ROW]
[ROW][C]139[/C][C]232.241[/C][C]231.962204549983[/C][C]0.27879545001725[/C][/ROW]
[ROW][C]140[/C][C]233.688[/C][C]233.847080501531[/C][C]-0.159080501530606[/C][/ROW]
[ROW][C]141[/C][C]236.667[/C][C]236.575858897743[/C][C]0.0911411022575046[/C][/ROW]
[ROW][C]142[/C][C]238.439[/C][C]238.334089200745[/C][C]0.104910799254831[/C][/ROW]
[ROW][C]143[/C][C]239.488[/C][C]239.09798836874[/C][C]0.390011631260415[/C][/ROW]
[ROW][C]144[/C][C]238.741[/C][C]238.153273375069[/C][C]0.587726624930835[/C][/ROW]
[ROW][C]145[/C][C]238.5[/C][C]238.288179425099[/C][C]0.211820574901384[/C][/ROW]
[ROW][C]146[/C][C]242.116[/C][C]241.708221478477[/C][C]0.407778521523183[/C][/ROW]
[ROW][C]147[/C][C]243.923[/C][C]243.491696778588[/C][C]0.43130322141172[/C][/ROW]
[ROW][C]148[/C][C]245.813[/C][C]245.727174581886[/C][C]0.0858254181136716[/C][/ROW]
[ROW][C]149[/C][C]247.143[/C][C]246.936344538267[/C][C]0.206655461733249[/C][/ROW]
[ROW][C]150[/C][C]246.381[/C][C]246.486383004937[/C][C]-0.105383004937036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191172&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191172&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
1100100.053468923622-0.053468923622119
2102102.039353291239-0.0393532912385971
3103.65103.66957952725-0.0195795272501676
4104.974105.005577696872-0.0315776968717988
5104.641104.6866550444-0.0456550444002395
6104.902104.950275695015-0.0482756950153905
7105.695105.74348143727-0.0484814372695179
8106.489106.547680154214-0.0586801542143236
9107.146107.219249925571-0.0732499255709799
10107.695107.763594325848-0.0685943258476967
11107.711107.770830464145-0.0598304641446105
12108.313108.389201501733-0.0762015017331089
13108.124108.215553699716-0.0915536997162164
14109.615109.695749687722-0.0807496877224333
15111.34111.409553677012-0.0695536770119466
16110.717110.796336611416-0.0793366114157735
17111.217111.286097767136-0.0690977671357486
18111.452111.514134244267-0.0621342442668106
19111.611111.676369487038-0.0653694870381744
20111.717111.751808780865-0.0348087808646776
21112.062112.088634512177-0.0266345121773037
22112.842112.884697193317-0.0426971933168909
23113.241113.279847434805-0.0388474348054006
24113.015113.071178189092-0.0561781890923824
25113.998114.065472098293-0.0674720982930629
26114.936114.996091677772-0.0600916777718165
27114.245114.303879617043-0.0588796170430116
28114.437114.471426503628-0.0344265036276967
29115.286115.314337484133-0.0283374841332313
30116.071116.099247581855-0.0282475818552755
31117.807117.813928074762-0.00692807476175484
32118.255118.261750654163-0.0067506541630806
33118.969118.969123292152-0.000123292151803544
34120.333120.3279830240630.00501697593727331
35121.998121.9767690918340.0212309081663191
36123.239123.2073171897220.0316828102782056
37124.666124.6282194156570.0377805843430814
38126.54126.5100043491420.0299956508581331
39127.336127.2991012207590.0368987792413602
40127.871127.8314701800210.0395298199789552
41130.115130.0546916442140.0603083557858642
42132.773132.6949051503040.0780948496958481
43134.265134.1805411317440.0844588682560918
44134.596134.5145368860250.0814631139748931
45134.38134.290398684270.0896013157302171
46135.121135.0417520197760.0792479802238968
47135.136135.0766082906540.0593917093456037
48135.336135.2899807204390.046019279560806
49135.284135.2123008067420.0716991932582532
50137.144137.0805629800980.0634370199023919
51140.349140.2595052834480.089494716551558
52143.264143.1657694671530.09823053284699
53144.381144.2682679354770.112732064522862
54145.881145.7721380727990.108861927201454
55146.497146.4165690795750.0804309204250713
56148.857148.7864312836850.0705687163154483
57150.78150.7142590408070.0657409591932464
58153.293153.2284814901090.0645185098907708
59157.641157.5705644546310.0704355453689521
60162.182162.1033918572160.0786081427836005
61167.86167.7712713089940.0887286910059043
62175.245175.1191001825870.1258998174128
63179.32179.2275375772670.0924624227326956
64184.979184.8944293589970.0845706410028477
65188.482188.4159416857850.0660583142147218
66192.86192.7639580671860.09604193281394
67195.475195.3761092683560.0988907316438793
68198.4198.2993796103510.100620389649113
69200.598200.4647235069620.133276493038371
70202.121202.0049556661810.11604433381895
71205.875205.6941709460420.1808290539578
72207.085206.9205999967480.164400003252303
73209.204209.0135636150970.190436384902739
74212.246211.8794879329840.366512067016203
75215.466215.1051512105840.360848789415679
76216.693216.4159961822520.277003817747888
77219.019218.5834866512030.435513348796601
78217.924217.6167199396810.307280060319006
79217.978217.7360630605370.241936939462883
80218.186217.9329842008880.253015799111533
81218.54218.3782594268830.161740573116589
82217.886218.01971123807-0.133711238070151
83216.347216.4726124837-0.125612483699743
84219.825219.950617191516-0.125617191515638
85221.956222.081209379358-0.125209379358221
86227.184227.366315725164-0.182315725164259
87229.247229.421879770897-0.174879770896946
88233.33233.444606165551-0.114606165550622
89236.987236.9485944153890.0384055846106389
90240.027239.874980968790.152019031209869
91245.433245.457507587089-0.0245075870894174
92246.641246.778555896516-0.137555896515816
93250.328250.587972911182-0.259972911181552
94250.849251.038503884336-0.189503884336324
95251.435251.404713960560.0302860394396804
96252.091251.8935636930550.197436306944828
97252.946253.208858115749-0.262858115749061
98252.773253.026535797814-0.253535797814479
99250.677250.93721870785-0.260218707850469
100250.105250.0348463886170.0701536113832652
101251.788251.80515470318-0.0171547031798678
102250.212250.227575022966-0.0155750229663913
103248.073248.0560763988480.0169236011516281
104244.468244.676526027044-0.208526027044003
105244.727244.959737879344-0.232737879343539
106244.034244.102773069529-0.0687730695288894
107243.588243.3247192525090.263280747490686
108236.447236.470586942982-0.0235869429815683
109224.906225.100483887179-0.1944838871792
110221.934222.452917936447-0.518917936447114
111224.903225.159614429181-0.256614429180686
112223.798224.070666545012-0.272666545011934
113218.529218.582334090585-0.0533340905851306
114217.521217.824706731465-0.30370673146501
115219.971220.301226649606-0.330226649605875
116223.841223.941767025971-0.100767025971165
117223.764223.93876779094-0.174767790940088
118223.664223.783459409421-0.119459409421007
119217.678218.126899312379-0.448899312379116
120218.478218.90180982771-0.423809827710211
121214.815215.201313886341-0.386313886341016
122215.143215.272101902515-0.129101902515489
123218.381218.2085494096780.17245059032194
124219.962220.197090414046-0.23509041404555
125218.933219.447072290941-0.514072290941298
126218.36219.143416665219-0.783416665219369
127217.72218.039440809347-0.319440809346528
128219.934219.8237189099990.110281090001194
129220.842220.4352450566490.406754943350537
130220.584220.195506792930.388493207069858
131216.346216.626694097259-0.280694097259421
132220.221219.9414799544420.279520045558104
133222.182221.9150048446360.266995155363707
134225.455225.2260144095640.228985590436361
135226.42226.509524914358-0.0895249143579058
136228.287228.373801138946-0.0868011389464091
137231.349231.519039242036-0.170039242036341
138231.015231.106306945052-0.091306945052428
139232.241231.9622045499830.27879545001725
140233.688233.847080501531-0.159080501530606
141236.667236.5758588977430.0911411022575046
142238.439238.3340892007450.104910799254831
143239.488239.097988368740.390011631260415
144238.741238.1532733750690.587726624930835
145238.5238.2881794250990.211820574901384
146242.116241.7082214784770.407778521523183
147243.923243.4916967785880.43130322141172
148245.813245.7271745818860.0858254181136716
149247.143246.9363445382670.206655461733249
150246.381246.486383004937-0.105383004937036






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
87.13783364187702e-091.4275667283754e-080.999999992862166
91.01174481844702e-112.02348963689404e-110.999999999989883
107.80044224220733e-141.56008844844147e-130.999999999999922
111.62166449586085e-163.24332899172171e-161
122.76753142107478e-195.53506284214955e-191
131.18267254802029e-212.36534509604058e-211
142.18104347689081e-244.36208695378163e-241
151.23134186486247e-262.46268372972494e-261
162.11484107122799e-294.22968214245597e-291
174.15601605225482e-328.31203210450964e-321
181.96562831610367e-343.93125663220735e-341
193.99740356152367e-377.99480712304733e-371
207.57434889711018e-401.51486977942204e-391
211.19991617274403e-422.39983234548806e-421
223.28654409575515e-456.57308819151031e-451
231.17384452619707e-472.34768905239415e-471
242.16682201249319e-504.33364402498638e-501
255.44703669791572e-531.08940733958314e-521
262.73176669098787e-555.46353338197575e-551
275.31278474045217e-581.06255694809043e-571
281.45116388701939e-602.90232777403877e-601
292.91265857363334e-635.82531714726667e-631
307.29125170084228e-661.45825034016846e-651
311.27175366860954e-682.54350733721908e-681
324.55315506701589e-719.10631013403178e-711
339.50727897858636e-741.90145579571727e-731
341.92076994388605e-763.8415398877721e-761
354.04836205980869e-798.09672411961738e-791
366.87678113457234e-821.37535622691447e-811
373.92646340550067e-847.85292681100133e-841
389.11372682538289e-871.82274536507658e-861
391.3929304086514e-892.78586081730279e-891
402.07265770251126e-924.14531540502253e-921
413.67426221775496e-957.34852443550991e-951
425.84130689018066e-981.16826137803613e-971
438.12181914986384e-1011.62436382997277e-1001
441.41005295928842e-1032.82010591857683e-1031
458.14665516172048e-1061.6293310323441e-1051
461.21027614510002e-1082.42055229020005e-1081
471.86911975863438e-1113.73823951726876e-1111
482.52739126028995e-1145.0547825205799e-1141
493.912437252766e-1177.82487450553201e-1171
505.06178264474573e-1201.01235652894915e-1191
511.76875392423468e-1223.53750784846936e-1221
522.88905466535945e-1255.7781093307189e-1251
537.58813823036036e-1281.51762764607207e-1271
541.09279772930505e-1302.18559545861009e-1301
555.75198774536366e-1331.15039754907273e-1321
568.12657949806779e-1361.62531589961356e-1351
571.08954524549774e-1382.17909049099547e-1381
581.3814424035608e-1412.76288480712161e-1411
591.96640843981138e-1433.93281687962276e-1431
602.77005966377742e-1465.54011932755485e-1461
613.67311364164096e-1497.34622728328192e-1491
625.73499212665023e-1521.14699842533005e-1511
631.21777746844671e-1542.43555493689341e-1541
641.62589041468762e-1573.25178082937525e-1571
652.81466092711487e-1605.62932185422973e-1601
663.92673755456564e-1637.85347510913127e-1631
676.46800968969753e-1661.29360193793951e-1651
688.97844270158552e-1691.7956885403171e-1681
691.98209325900186e-1713.96418651800371e-1711
704.07550109345179e-1748.15100218690357e-1741
711.05627757365008e-1762.11255514730016e-1761
722.40003332098209e-1794.80006664196418e-1791
739.61081516208388e-1821.92216303241678e-1811
741.04209933378133e-562.08419866756265e-561
751.76323270327656e-513.52646540655312e-511
768.59336685903316e-521.71867337180663e-511
771.18279955005642e-452.36559910011283e-451
782.40768655061254e-454.81537310122507e-451
792.17865478701816e-444.35730957403633e-441
803.77501917193752e-437.55003834387504e-431
811.56013069515418e-393.12026139030837e-391
822.26358664245906e-224.52717328491811e-221
839.57287956068603e-161.91457591213721e-150.999999999999999
841.30308734812693e-112.60617469625387e-110.999999999986969
851.3002513024519e-082.60050260490379e-080.999999986997487
861.45616580106319e-062.91233160212638e-060.999998543834199
874.64190210665052e-059.28380421330105e-050.999953580978933
880.000510174637550590.001020349275101180.999489825362449
890.002772357174164830.005544714348329670.997227642825835
900.01913512809450210.03827025618900420.980864871905498
910.04315554395164590.08631108790329190.956844456048354
920.06585367695915590.1317073539183120.934146323040844
930.08363543184877560.1672708636975510.916364568151224
940.09632294482685580.1926458896537120.903677055173144
950.1284086235286030.2568172470572060.871591376471397
960.1562710518207170.3125421036414330.843728948179283
970.1768817855567170.3537635711134340.823118214443283
980.1936045699824250.387209139964850.806395430017575
990.248161377049510.496322754099020.75183862295049
1000.256381135978990.512762271957980.74361886402101
1010.2206234475657380.4412468951314750.779376552434262
1020.2379404999695090.4758809999390170.762059500030491
1030.3100529680874490.6201059361748990.689947031912551
1040.5011671552266910.9976656895466170.498832844773309
1050.6310636211595150.7378727576809690.368936378840485
1060.649514000321610.7009719993567790.35048599967839
1070.6050576117280840.7898847765438330.394942388271916
1080.6126144656810220.7747710686379570.387385534318978
1090.7086441040621950.582711791875610.291355895937805
1100.9119908981028190.1760182037943630.0880091018971814
1110.9326683087055310.1346633825889380.0673316912944692
1120.9473206423509370.1053587152981260.052679357649063
1130.962338475844630.07532304831073960.0376615241553698
1140.9781465022226590.04370699555468230.0218534977773411
1150.9847888425900370.03042231481992570.0152111574099629
1160.9933999633614230.01320007327715480.00660003663857742
1170.9992670116648420.001465976670316220.000732988335158112
1180.9994808000944350.001038399811129650.000519199905564827
1190.999865163330090.0002696733398193590.00013483666990968
1200.9998769436151830.0002461127696349540.000123056384817477
1210.9998003934550710.000399213089857250.000199606544928625
1220.9998842578501140.0002314842997725010.00011574214988625
1230.9999906501467911.86997064179764e-059.34985320898821e-06
1240.9999851766590812.96466818385188e-051.48233409192594e-05
1250.9999832017739793.35964520413588e-051.67982260206794e-05
1260.9999895235960242.09528079514139e-051.04764039757069e-05
1270.9999880369706412.39260587171157e-051.19630293585579e-05
1280.9999767068824184.65862351635365e-052.32931175817683e-05
1290.9999854513888262.9097222348217e-051.45486111741085e-05
1300.9999997669571864.66085627226499e-072.33042813613249e-07
1310.9999991785065781.64298684326175e-068.21493421630875e-07
1320.99999895175632.09648739940251e-061.04824369970126e-06
1330.9999982333826293.53323474194911e-061.76661737097456e-06
1340.9999959375793818.12484123878753e-064.06242061939377e-06
1350.9999827819096183.44361807640121e-051.72180903820061e-05
1360.9999574414509968.51170980086357e-054.25585490043179e-05
1370.9998518236127370.0002963527745251640.000148176387262582
1380.9998497756966090.0003004486067827270.000150224303391364
1390.9998156028803130.000368794239374520.00018439711968726
1400.999819824640250.0003603507195009410.00018017535975047
1410.9990542011636040.001891597672792810.000945798836396406
1420.9944845766921130.0110308466157740.00551542330788702

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 7.13783364187702e-09 & 1.4275667283754e-08 & 0.999999992862166 \tabularnewline
9 & 1.01174481844702e-11 & 2.02348963689404e-11 & 0.999999999989883 \tabularnewline
10 & 7.80044224220733e-14 & 1.56008844844147e-13 & 0.999999999999922 \tabularnewline
11 & 1.62166449586085e-16 & 3.24332899172171e-16 & 1 \tabularnewline
12 & 2.76753142107478e-19 & 5.53506284214955e-19 & 1 \tabularnewline
13 & 1.18267254802029e-21 & 2.36534509604058e-21 & 1 \tabularnewline
14 & 2.18104347689081e-24 & 4.36208695378163e-24 & 1 \tabularnewline
15 & 1.23134186486247e-26 & 2.46268372972494e-26 & 1 \tabularnewline
16 & 2.11484107122799e-29 & 4.22968214245597e-29 & 1 \tabularnewline
17 & 4.15601605225482e-32 & 8.31203210450964e-32 & 1 \tabularnewline
18 & 1.96562831610367e-34 & 3.93125663220735e-34 & 1 \tabularnewline
19 & 3.99740356152367e-37 & 7.99480712304733e-37 & 1 \tabularnewline
20 & 7.57434889711018e-40 & 1.51486977942204e-39 & 1 \tabularnewline
21 & 1.19991617274403e-42 & 2.39983234548806e-42 & 1 \tabularnewline
22 & 3.28654409575515e-45 & 6.57308819151031e-45 & 1 \tabularnewline
23 & 1.17384452619707e-47 & 2.34768905239415e-47 & 1 \tabularnewline
24 & 2.16682201249319e-50 & 4.33364402498638e-50 & 1 \tabularnewline
25 & 5.44703669791572e-53 & 1.08940733958314e-52 & 1 \tabularnewline
26 & 2.73176669098787e-55 & 5.46353338197575e-55 & 1 \tabularnewline
27 & 5.31278474045217e-58 & 1.06255694809043e-57 & 1 \tabularnewline
28 & 1.45116388701939e-60 & 2.90232777403877e-60 & 1 \tabularnewline
29 & 2.91265857363334e-63 & 5.82531714726667e-63 & 1 \tabularnewline
30 & 7.29125170084228e-66 & 1.45825034016846e-65 & 1 \tabularnewline
31 & 1.27175366860954e-68 & 2.54350733721908e-68 & 1 \tabularnewline
32 & 4.55315506701589e-71 & 9.10631013403178e-71 & 1 \tabularnewline
33 & 9.50727897858636e-74 & 1.90145579571727e-73 & 1 \tabularnewline
34 & 1.92076994388605e-76 & 3.8415398877721e-76 & 1 \tabularnewline
35 & 4.04836205980869e-79 & 8.09672411961738e-79 & 1 \tabularnewline
36 & 6.87678113457234e-82 & 1.37535622691447e-81 & 1 \tabularnewline
37 & 3.92646340550067e-84 & 7.85292681100133e-84 & 1 \tabularnewline
38 & 9.11372682538289e-87 & 1.82274536507658e-86 & 1 \tabularnewline
39 & 1.3929304086514e-89 & 2.78586081730279e-89 & 1 \tabularnewline
40 & 2.07265770251126e-92 & 4.14531540502253e-92 & 1 \tabularnewline
41 & 3.67426221775496e-95 & 7.34852443550991e-95 & 1 \tabularnewline
42 & 5.84130689018066e-98 & 1.16826137803613e-97 & 1 \tabularnewline
43 & 8.12181914986384e-101 & 1.62436382997277e-100 & 1 \tabularnewline
44 & 1.41005295928842e-103 & 2.82010591857683e-103 & 1 \tabularnewline
45 & 8.14665516172048e-106 & 1.6293310323441e-105 & 1 \tabularnewline
46 & 1.21027614510002e-108 & 2.42055229020005e-108 & 1 \tabularnewline
47 & 1.86911975863438e-111 & 3.73823951726876e-111 & 1 \tabularnewline
48 & 2.52739126028995e-114 & 5.0547825205799e-114 & 1 \tabularnewline
49 & 3.912437252766e-117 & 7.82487450553201e-117 & 1 \tabularnewline
50 & 5.06178264474573e-120 & 1.01235652894915e-119 & 1 \tabularnewline
51 & 1.76875392423468e-122 & 3.53750784846936e-122 & 1 \tabularnewline
52 & 2.88905466535945e-125 & 5.7781093307189e-125 & 1 \tabularnewline
53 & 7.58813823036036e-128 & 1.51762764607207e-127 & 1 \tabularnewline
54 & 1.09279772930505e-130 & 2.18559545861009e-130 & 1 \tabularnewline
55 & 5.75198774536366e-133 & 1.15039754907273e-132 & 1 \tabularnewline
56 & 8.12657949806779e-136 & 1.62531589961356e-135 & 1 \tabularnewline
57 & 1.08954524549774e-138 & 2.17909049099547e-138 & 1 \tabularnewline
58 & 1.3814424035608e-141 & 2.76288480712161e-141 & 1 \tabularnewline
59 & 1.96640843981138e-143 & 3.93281687962276e-143 & 1 \tabularnewline
60 & 2.77005966377742e-146 & 5.54011932755485e-146 & 1 \tabularnewline
61 & 3.67311364164096e-149 & 7.34622728328192e-149 & 1 \tabularnewline
62 & 5.73499212665023e-152 & 1.14699842533005e-151 & 1 \tabularnewline
63 & 1.21777746844671e-154 & 2.43555493689341e-154 & 1 \tabularnewline
64 & 1.62589041468762e-157 & 3.25178082937525e-157 & 1 \tabularnewline
65 & 2.81466092711487e-160 & 5.62932185422973e-160 & 1 \tabularnewline
66 & 3.92673755456564e-163 & 7.85347510913127e-163 & 1 \tabularnewline
67 & 6.46800968969753e-166 & 1.29360193793951e-165 & 1 \tabularnewline
68 & 8.97844270158552e-169 & 1.7956885403171e-168 & 1 \tabularnewline
69 & 1.98209325900186e-171 & 3.96418651800371e-171 & 1 \tabularnewline
70 & 4.07550109345179e-174 & 8.15100218690357e-174 & 1 \tabularnewline
71 & 1.05627757365008e-176 & 2.11255514730016e-176 & 1 \tabularnewline
72 & 2.40003332098209e-179 & 4.80006664196418e-179 & 1 \tabularnewline
73 & 9.61081516208388e-182 & 1.92216303241678e-181 & 1 \tabularnewline
74 & 1.04209933378133e-56 & 2.08419866756265e-56 & 1 \tabularnewline
75 & 1.76323270327656e-51 & 3.52646540655312e-51 & 1 \tabularnewline
76 & 8.59336685903316e-52 & 1.71867337180663e-51 & 1 \tabularnewline
77 & 1.18279955005642e-45 & 2.36559910011283e-45 & 1 \tabularnewline
78 & 2.40768655061254e-45 & 4.81537310122507e-45 & 1 \tabularnewline
79 & 2.17865478701816e-44 & 4.35730957403633e-44 & 1 \tabularnewline
80 & 3.77501917193752e-43 & 7.55003834387504e-43 & 1 \tabularnewline
81 & 1.56013069515418e-39 & 3.12026139030837e-39 & 1 \tabularnewline
82 & 2.26358664245906e-22 & 4.52717328491811e-22 & 1 \tabularnewline
83 & 9.57287956068603e-16 & 1.91457591213721e-15 & 0.999999999999999 \tabularnewline
84 & 1.30308734812693e-11 & 2.60617469625387e-11 & 0.999999999986969 \tabularnewline
85 & 1.3002513024519e-08 & 2.60050260490379e-08 & 0.999999986997487 \tabularnewline
86 & 1.45616580106319e-06 & 2.91233160212638e-06 & 0.999998543834199 \tabularnewline
87 & 4.64190210665052e-05 & 9.28380421330105e-05 & 0.999953580978933 \tabularnewline
88 & 0.00051017463755059 & 0.00102034927510118 & 0.999489825362449 \tabularnewline
89 & 0.00277235717416483 & 0.00554471434832967 & 0.997227642825835 \tabularnewline
90 & 0.0191351280945021 & 0.0382702561890042 & 0.980864871905498 \tabularnewline
91 & 0.0431555439516459 & 0.0863110879032919 & 0.956844456048354 \tabularnewline
92 & 0.0658536769591559 & 0.131707353918312 & 0.934146323040844 \tabularnewline
93 & 0.0836354318487756 & 0.167270863697551 & 0.916364568151224 \tabularnewline
94 & 0.0963229448268558 & 0.192645889653712 & 0.903677055173144 \tabularnewline
95 & 0.128408623528603 & 0.256817247057206 & 0.871591376471397 \tabularnewline
96 & 0.156271051820717 & 0.312542103641433 & 0.843728948179283 \tabularnewline
97 & 0.176881785556717 & 0.353763571113434 & 0.823118214443283 \tabularnewline
98 & 0.193604569982425 & 0.38720913996485 & 0.806395430017575 \tabularnewline
99 & 0.24816137704951 & 0.49632275409902 & 0.75183862295049 \tabularnewline
100 & 0.25638113597899 & 0.51276227195798 & 0.74361886402101 \tabularnewline
101 & 0.220623447565738 & 0.441246895131475 & 0.779376552434262 \tabularnewline
102 & 0.237940499969509 & 0.475880999939017 & 0.762059500030491 \tabularnewline
103 & 0.310052968087449 & 0.620105936174899 & 0.689947031912551 \tabularnewline
104 & 0.501167155226691 & 0.997665689546617 & 0.498832844773309 \tabularnewline
105 & 0.631063621159515 & 0.737872757680969 & 0.368936378840485 \tabularnewline
106 & 0.64951400032161 & 0.700971999356779 & 0.35048599967839 \tabularnewline
107 & 0.605057611728084 & 0.789884776543833 & 0.394942388271916 \tabularnewline
108 & 0.612614465681022 & 0.774771068637957 & 0.387385534318978 \tabularnewline
109 & 0.708644104062195 & 0.58271179187561 & 0.291355895937805 \tabularnewline
110 & 0.911990898102819 & 0.176018203794363 & 0.0880091018971814 \tabularnewline
111 & 0.932668308705531 & 0.134663382588938 & 0.0673316912944692 \tabularnewline
112 & 0.947320642350937 & 0.105358715298126 & 0.052679357649063 \tabularnewline
113 & 0.96233847584463 & 0.0753230483107396 & 0.0376615241553698 \tabularnewline
114 & 0.978146502222659 & 0.0437069955546823 & 0.0218534977773411 \tabularnewline
115 & 0.984788842590037 & 0.0304223148199257 & 0.0152111574099629 \tabularnewline
116 & 0.993399963361423 & 0.0132000732771548 & 0.00660003663857742 \tabularnewline
117 & 0.999267011664842 & 0.00146597667031622 & 0.000732988335158112 \tabularnewline
118 & 0.999480800094435 & 0.00103839981112965 & 0.000519199905564827 \tabularnewline
119 & 0.99986516333009 & 0.000269673339819359 & 0.00013483666990968 \tabularnewline
120 & 0.999876943615183 & 0.000246112769634954 & 0.000123056384817477 \tabularnewline
121 & 0.999800393455071 & 0.00039921308985725 & 0.000199606544928625 \tabularnewline
122 & 0.999884257850114 & 0.000231484299772501 & 0.00011574214988625 \tabularnewline
123 & 0.999990650146791 & 1.86997064179764e-05 & 9.34985320898821e-06 \tabularnewline
124 & 0.999985176659081 & 2.96466818385188e-05 & 1.48233409192594e-05 \tabularnewline
125 & 0.999983201773979 & 3.35964520413588e-05 & 1.67982260206794e-05 \tabularnewline
126 & 0.999989523596024 & 2.09528079514139e-05 & 1.04764039757069e-05 \tabularnewline
127 & 0.999988036970641 & 2.39260587171157e-05 & 1.19630293585579e-05 \tabularnewline
128 & 0.999976706882418 & 4.65862351635365e-05 & 2.32931175817683e-05 \tabularnewline
129 & 0.999985451388826 & 2.9097222348217e-05 & 1.45486111741085e-05 \tabularnewline
130 & 0.999999766957186 & 4.66085627226499e-07 & 2.33042813613249e-07 \tabularnewline
131 & 0.999999178506578 & 1.64298684326175e-06 & 8.21493421630875e-07 \tabularnewline
132 & 0.9999989517563 & 2.09648739940251e-06 & 1.04824369970126e-06 \tabularnewline
133 & 0.999998233382629 & 3.53323474194911e-06 & 1.76661737097456e-06 \tabularnewline
134 & 0.999995937579381 & 8.12484123878753e-06 & 4.06242061939377e-06 \tabularnewline
135 & 0.999982781909618 & 3.44361807640121e-05 & 1.72180903820061e-05 \tabularnewline
136 & 0.999957441450996 & 8.51170980086357e-05 & 4.25585490043179e-05 \tabularnewline
137 & 0.999851823612737 & 0.000296352774525164 & 0.000148176387262582 \tabularnewline
138 & 0.999849775696609 & 0.000300448606782727 & 0.000150224303391364 \tabularnewline
139 & 0.999815602880313 & 0.00036879423937452 & 0.00018439711968726 \tabularnewline
140 & 0.99981982464025 & 0.000360350719500941 & 0.00018017535975047 \tabularnewline
141 & 0.999054201163604 & 0.00189159767279281 & 0.000945798836396406 \tabularnewline
142 & 0.994484576692113 & 0.011030846615774 & 0.00551542330788702 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191172&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]8[/C][C]7.13783364187702e-09[/C][C]1.4275667283754e-08[/C][C]0.999999992862166[/C][/ROW]
[ROW][C]9[/C][C]1.01174481844702e-11[/C][C]2.02348963689404e-11[/C][C]0.999999999989883[/C][/ROW]
[ROW][C]10[/C][C]7.80044224220733e-14[/C][C]1.56008844844147e-13[/C][C]0.999999999999922[/C][/ROW]
[ROW][C]11[/C][C]1.62166449586085e-16[/C][C]3.24332899172171e-16[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]2.76753142107478e-19[/C][C]5.53506284214955e-19[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]1.18267254802029e-21[/C][C]2.36534509604058e-21[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]2.18104347689081e-24[/C][C]4.36208695378163e-24[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]1.23134186486247e-26[/C][C]2.46268372972494e-26[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]2.11484107122799e-29[/C][C]4.22968214245597e-29[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]4.15601605225482e-32[/C][C]8.31203210450964e-32[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]1.96562831610367e-34[/C][C]3.93125663220735e-34[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]3.99740356152367e-37[/C][C]7.99480712304733e-37[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]7.57434889711018e-40[/C][C]1.51486977942204e-39[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]1.19991617274403e-42[/C][C]2.39983234548806e-42[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]3.28654409575515e-45[/C][C]6.57308819151031e-45[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]1.17384452619707e-47[/C][C]2.34768905239415e-47[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]2.16682201249319e-50[/C][C]4.33364402498638e-50[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]5.44703669791572e-53[/C][C]1.08940733958314e-52[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]2.73176669098787e-55[/C][C]5.46353338197575e-55[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]5.31278474045217e-58[/C][C]1.06255694809043e-57[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]1.45116388701939e-60[/C][C]2.90232777403877e-60[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]2.91265857363334e-63[/C][C]5.82531714726667e-63[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]7.29125170084228e-66[/C][C]1.45825034016846e-65[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]1.27175366860954e-68[/C][C]2.54350733721908e-68[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]4.55315506701589e-71[/C][C]9.10631013403178e-71[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]9.50727897858636e-74[/C][C]1.90145579571727e-73[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]1.92076994388605e-76[/C][C]3.8415398877721e-76[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]4.04836205980869e-79[/C][C]8.09672411961738e-79[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]6.87678113457234e-82[/C][C]1.37535622691447e-81[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]3.92646340550067e-84[/C][C]7.85292681100133e-84[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]9.11372682538289e-87[/C][C]1.82274536507658e-86[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]1.3929304086514e-89[/C][C]2.78586081730279e-89[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]2.07265770251126e-92[/C][C]4.14531540502253e-92[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]3.67426221775496e-95[/C][C]7.34852443550991e-95[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]5.84130689018066e-98[/C][C]1.16826137803613e-97[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]8.12181914986384e-101[/C][C]1.62436382997277e-100[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]1.41005295928842e-103[/C][C]2.82010591857683e-103[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]8.14665516172048e-106[/C][C]1.6293310323441e-105[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]1.21027614510002e-108[/C][C]2.42055229020005e-108[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]1.86911975863438e-111[/C][C]3.73823951726876e-111[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]2.52739126028995e-114[/C][C]5.0547825205799e-114[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]3.912437252766e-117[/C][C]7.82487450553201e-117[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]5.06178264474573e-120[/C][C]1.01235652894915e-119[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]1.76875392423468e-122[/C][C]3.53750784846936e-122[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]2.88905466535945e-125[/C][C]5.7781093307189e-125[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]7.58813823036036e-128[/C][C]1.51762764607207e-127[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]1.09279772930505e-130[/C][C]2.18559545861009e-130[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]5.75198774536366e-133[/C][C]1.15039754907273e-132[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]8.12657949806779e-136[/C][C]1.62531589961356e-135[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]1.08954524549774e-138[/C][C]2.17909049099547e-138[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]1.3814424035608e-141[/C][C]2.76288480712161e-141[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]1.96640843981138e-143[/C][C]3.93281687962276e-143[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]2.77005966377742e-146[/C][C]5.54011932755485e-146[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]3.67311364164096e-149[/C][C]7.34622728328192e-149[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]5.73499212665023e-152[/C][C]1.14699842533005e-151[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]1.21777746844671e-154[/C][C]2.43555493689341e-154[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]1.62589041468762e-157[/C][C]3.25178082937525e-157[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]2.81466092711487e-160[/C][C]5.62932185422973e-160[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]3.92673755456564e-163[/C][C]7.85347510913127e-163[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]6.46800968969753e-166[/C][C]1.29360193793951e-165[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]8.97844270158552e-169[/C][C]1.7956885403171e-168[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]1.98209325900186e-171[/C][C]3.96418651800371e-171[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]4.07550109345179e-174[/C][C]8.15100218690357e-174[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]1.05627757365008e-176[/C][C]2.11255514730016e-176[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]2.40003332098209e-179[/C][C]4.80006664196418e-179[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]9.61081516208388e-182[/C][C]1.92216303241678e-181[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]1.04209933378133e-56[/C][C]2.08419866756265e-56[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]1.76323270327656e-51[/C][C]3.52646540655312e-51[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]8.59336685903316e-52[/C][C]1.71867337180663e-51[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]1.18279955005642e-45[/C][C]2.36559910011283e-45[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]2.40768655061254e-45[/C][C]4.81537310122507e-45[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]2.17865478701816e-44[/C][C]4.35730957403633e-44[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]3.77501917193752e-43[/C][C]7.55003834387504e-43[/C][C]1[/C][/ROW]
[ROW][C]81[/C][C]1.56013069515418e-39[/C][C]3.12026139030837e-39[/C][C]1[/C][/ROW]
[ROW][C]82[/C][C]2.26358664245906e-22[/C][C]4.52717328491811e-22[/C][C]1[/C][/ROW]
[ROW][C]83[/C][C]9.57287956068603e-16[/C][C]1.91457591213721e-15[/C][C]0.999999999999999[/C][/ROW]
[ROW][C]84[/C][C]1.30308734812693e-11[/C][C]2.60617469625387e-11[/C][C]0.999999999986969[/C][/ROW]
[ROW][C]85[/C][C]1.3002513024519e-08[/C][C]2.60050260490379e-08[/C][C]0.999999986997487[/C][/ROW]
[ROW][C]86[/C][C]1.45616580106319e-06[/C][C]2.91233160212638e-06[/C][C]0.999998543834199[/C][/ROW]
[ROW][C]87[/C][C]4.64190210665052e-05[/C][C]9.28380421330105e-05[/C][C]0.999953580978933[/C][/ROW]
[ROW][C]88[/C][C]0.00051017463755059[/C][C]0.00102034927510118[/C][C]0.999489825362449[/C][/ROW]
[ROW][C]89[/C][C]0.00277235717416483[/C][C]0.00554471434832967[/C][C]0.997227642825835[/C][/ROW]
[ROW][C]90[/C][C]0.0191351280945021[/C][C]0.0382702561890042[/C][C]0.980864871905498[/C][/ROW]
[ROW][C]91[/C][C]0.0431555439516459[/C][C]0.0863110879032919[/C][C]0.956844456048354[/C][/ROW]
[ROW][C]92[/C][C]0.0658536769591559[/C][C]0.131707353918312[/C][C]0.934146323040844[/C][/ROW]
[ROW][C]93[/C][C]0.0836354318487756[/C][C]0.167270863697551[/C][C]0.916364568151224[/C][/ROW]
[ROW][C]94[/C][C]0.0963229448268558[/C][C]0.192645889653712[/C][C]0.903677055173144[/C][/ROW]
[ROW][C]95[/C][C]0.128408623528603[/C][C]0.256817247057206[/C][C]0.871591376471397[/C][/ROW]
[ROW][C]96[/C][C]0.156271051820717[/C][C]0.312542103641433[/C][C]0.843728948179283[/C][/ROW]
[ROW][C]97[/C][C]0.176881785556717[/C][C]0.353763571113434[/C][C]0.823118214443283[/C][/ROW]
[ROW][C]98[/C][C]0.193604569982425[/C][C]0.38720913996485[/C][C]0.806395430017575[/C][/ROW]
[ROW][C]99[/C][C]0.24816137704951[/C][C]0.49632275409902[/C][C]0.75183862295049[/C][/ROW]
[ROW][C]100[/C][C]0.25638113597899[/C][C]0.51276227195798[/C][C]0.74361886402101[/C][/ROW]
[ROW][C]101[/C][C]0.220623447565738[/C][C]0.441246895131475[/C][C]0.779376552434262[/C][/ROW]
[ROW][C]102[/C][C]0.237940499969509[/C][C]0.475880999939017[/C][C]0.762059500030491[/C][/ROW]
[ROW][C]103[/C][C]0.310052968087449[/C][C]0.620105936174899[/C][C]0.689947031912551[/C][/ROW]
[ROW][C]104[/C][C]0.501167155226691[/C][C]0.997665689546617[/C][C]0.498832844773309[/C][/ROW]
[ROW][C]105[/C][C]0.631063621159515[/C][C]0.737872757680969[/C][C]0.368936378840485[/C][/ROW]
[ROW][C]106[/C][C]0.64951400032161[/C][C]0.700971999356779[/C][C]0.35048599967839[/C][/ROW]
[ROW][C]107[/C][C]0.605057611728084[/C][C]0.789884776543833[/C][C]0.394942388271916[/C][/ROW]
[ROW][C]108[/C][C]0.612614465681022[/C][C]0.774771068637957[/C][C]0.387385534318978[/C][/ROW]
[ROW][C]109[/C][C]0.708644104062195[/C][C]0.58271179187561[/C][C]0.291355895937805[/C][/ROW]
[ROW][C]110[/C][C]0.911990898102819[/C][C]0.176018203794363[/C][C]0.0880091018971814[/C][/ROW]
[ROW][C]111[/C][C]0.932668308705531[/C][C]0.134663382588938[/C][C]0.0673316912944692[/C][/ROW]
[ROW][C]112[/C][C]0.947320642350937[/C][C]0.105358715298126[/C][C]0.052679357649063[/C][/ROW]
[ROW][C]113[/C][C]0.96233847584463[/C][C]0.0753230483107396[/C][C]0.0376615241553698[/C][/ROW]
[ROW][C]114[/C][C]0.978146502222659[/C][C]0.0437069955546823[/C][C]0.0218534977773411[/C][/ROW]
[ROW][C]115[/C][C]0.984788842590037[/C][C]0.0304223148199257[/C][C]0.0152111574099629[/C][/ROW]
[ROW][C]116[/C][C]0.993399963361423[/C][C]0.0132000732771548[/C][C]0.00660003663857742[/C][/ROW]
[ROW][C]117[/C][C]0.999267011664842[/C][C]0.00146597667031622[/C][C]0.000732988335158112[/C][/ROW]
[ROW][C]118[/C][C]0.999480800094435[/C][C]0.00103839981112965[/C][C]0.000519199905564827[/C][/ROW]
[ROW][C]119[/C][C]0.99986516333009[/C][C]0.000269673339819359[/C][C]0.00013483666990968[/C][/ROW]
[ROW][C]120[/C][C]0.999876943615183[/C][C]0.000246112769634954[/C][C]0.000123056384817477[/C][/ROW]
[ROW][C]121[/C][C]0.999800393455071[/C][C]0.00039921308985725[/C][C]0.000199606544928625[/C][/ROW]
[ROW][C]122[/C][C]0.999884257850114[/C][C]0.000231484299772501[/C][C]0.00011574214988625[/C][/ROW]
[ROW][C]123[/C][C]0.999990650146791[/C][C]1.86997064179764e-05[/C][C]9.34985320898821e-06[/C][/ROW]
[ROW][C]124[/C][C]0.999985176659081[/C][C]2.96466818385188e-05[/C][C]1.48233409192594e-05[/C][/ROW]
[ROW][C]125[/C][C]0.999983201773979[/C][C]3.35964520413588e-05[/C][C]1.67982260206794e-05[/C][/ROW]
[ROW][C]126[/C][C]0.999989523596024[/C][C]2.09528079514139e-05[/C][C]1.04764039757069e-05[/C][/ROW]
[ROW][C]127[/C][C]0.999988036970641[/C][C]2.39260587171157e-05[/C][C]1.19630293585579e-05[/C][/ROW]
[ROW][C]128[/C][C]0.999976706882418[/C][C]4.65862351635365e-05[/C][C]2.32931175817683e-05[/C][/ROW]
[ROW][C]129[/C][C]0.999985451388826[/C][C]2.9097222348217e-05[/C][C]1.45486111741085e-05[/C][/ROW]
[ROW][C]130[/C][C]0.999999766957186[/C][C]4.66085627226499e-07[/C][C]2.33042813613249e-07[/C][/ROW]
[ROW][C]131[/C][C]0.999999178506578[/C][C]1.64298684326175e-06[/C][C]8.21493421630875e-07[/C][/ROW]
[ROW][C]132[/C][C]0.9999989517563[/C][C]2.09648739940251e-06[/C][C]1.04824369970126e-06[/C][/ROW]
[ROW][C]133[/C][C]0.999998233382629[/C][C]3.53323474194911e-06[/C][C]1.76661737097456e-06[/C][/ROW]
[ROW][C]134[/C][C]0.999995937579381[/C][C]8.12484123878753e-06[/C][C]4.06242061939377e-06[/C][/ROW]
[ROW][C]135[/C][C]0.999982781909618[/C][C]3.44361807640121e-05[/C][C]1.72180903820061e-05[/C][/ROW]
[ROW][C]136[/C][C]0.999957441450996[/C][C]8.51170980086357e-05[/C][C]4.25585490043179e-05[/C][/ROW]
[ROW][C]137[/C][C]0.999851823612737[/C][C]0.000296352774525164[/C][C]0.000148176387262582[/C][/ROW]
[ROW][C]138[/C][C]0.999849775696609[/C][C]0.000300448606782727[/C][C]0.000150224303391364[/C][/ROW]
[ROW][C]139[/C][C]0.999815602880313[/C][C]0.00036879423937452[/C][C]0.00018439711968726[/C][/ROW]
[ROW][C]140[/C][C]0.99981982464025[/C][C]0.000360350719500941[/C][C]0.00018017535975047[/C][/ROW]
[ROW][C]141[/C][C]0.999054201163604[/C][C]0.00189159767279281[/C][C]0.000945798836396406[/C][/ROW]
[ROW][C]142[/C][C]0.994484576692113[/C][C]0.011030846615774[/C][C]0.00551542330788702[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191172&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191172&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
87.13783364187702e-091.4275667283754e-080.999999992862166
91.01174481844702e-112.02348963689404e-110.999999999989883
107.80044224220733e-141.56008844844147e-130.999999999999922
111.62166449586085e-163.24332899172171e-161
122.76753142107478e-195.53506284214955e-191
131.18267254802029e-212.36534509604058e-211
142.18104347689081e-244.36208695378163e-241
151.23134186486247e-262.46268372972494e-261
162.11484107122799e-294.22968214245597e-291
174.15601605225482e-328.31203210450964e-321
181.96562831610367e-343.93125663220735e-341
193.99740356152367e-377.99480712304733e-371
207.57434889711018e-401.51486977942204e-391
211.19991617274403e-422.39983234548806e-421
223.28654409575515e-456.57308819151031e-451
231.17384452619707e-472.34768905239415e-471
242.16682201249319e-504.33364402498638e-501
255.44703669791572e-531.08940733958314e-521
262.73176669098787e-555.46353338197575e-551
275.31278474045217e-581.06255694809043e-571
281.45116388701939e-602.90232777403877e-601
292.91265857363334e-635.82531714726667e-631
307.29125170084228e-661.45825034016846e-651
311.27175366860954e-682.54350733721908e-681
324.55315506701589e-719.10631013403178e-711
339.50727897858636e-741.90145579571727e-731
341.92076994388605e-763.8415398877721e-761
354.04836205980869e-798.09672411961738e-791
366.87678113457234e-821.37535622691447e-811
373.92646340550067e-847.85292681100133e-841
389.11372682538289e-871.82274536507658e-861
391.3929304086514e-892.78586081730279e-891
402.07265770251126e-924.14531540502253e-921
413.67426221775496e-957.34852443550991e-951
425.84130689018066e-981.16826137803613e-971
438.12181914986384e-1011.62436382997277e-1001
441.41005295928842e-1032.82010591857683e-1031
458.14665516172048e-1061.6293310323441e-1051
461.21027614510002e-1082.42055229020005e-1081
471.86911975863438e-1113.73823951726876e-1111
482.52739126028995e-1145.0547825205799e-1141
493.912437252766e-1177.82487450553201e-1171
505.06178264474573e-1201.01235652894915e-1191
511.76875392423468e-1223.53750784846936e-1221
522.88905466535945e-1255.7781093307189e-1251
537.58813823036036e-1281.51762764607207e-1271
541.09279772930505e-1302.18559545861009e-1301
555.75198774536366e-1331.15039754907273e-1321
568.12657949806779e-1361.62531589961356e-1351
571.08954524549774e-1382.17909049099547e-1381
581.3814424035608e-1412.76288480712161e-1411
591.96640843981138e-1433.93281687962276e-1431
602.77005966377742e-1465.54011932755485e-1461
613.67311364164096e-1497.34622728328192e-1491
625.73499212665023e-1521.14699842533005e-1511
631.21777746844671e-1542.43555493689341e-1541
641.62589041468762e-1573.25178082937525e-1571
652.81466092711487e-1605.62932185422973e-1601
663.92673755456564e-1637.85347510913127e-1631
676.46800968969753e-1661.29360193793951e-1651
688.97844270158552e-1691.7956885403171e-1681
691.98209325900186e-1713.96418651800371e-1711
704.07550109345179e-1748.15100218690357e-1741
711.05627757365008e-1762.11255514730016e-1761
722.40003332098209e-1794.80006664196418e-1791
739.61081516208388e-1821.92216303241678e-1811
741.04209933378133e-562.08419866756265e-561
751.76323270327656e-513.52646540655312e-511
768.59336685903316e-521.71867337180663e-511
771.18279955005642e-452.36559910011283e-451
782.40768655061254e-454.81537310122507e-451
792.17865478701816e-444.35730957403633e-441
803.77501917193752e-437.55003834387504e-431
811.56013069515418e-393.12026139030837e-391
822.26358664245906e-224.52717328491811e-221
839.57287956068603e-161.91457591213721e-150.999999999999999
841.30308734812693e-112.60617469625387e-110.999999999986969
851.3002513024519e-082.60050260490379e-080.999999986997487
861.45616580106319e-062.91233160212638e-060.999998543834199
874.64190210665052e-059.28380421330105e-050.999953580978933
880.000510174637550590.001020349275101180.999489825362449
890.002772357174164830.005544714348329670.997227642825835
900.01913512809450210.03827025618900420.980864871905498
910.04315554395164590.08631108790329190.956844456048354
920.06585367695915590.1317073539183120.934146323040844
930.08363543184877560.1672708636975510.916364568151224
940.09632294482685580.1926458896537120.903677055173144
950.1284086235286030.2568172470572060.871591376471397
960.1562710518207170.3125421036414330.843728948179283
970.1768817855567170.3537635711134340.823118214443283
980.1936045699824250.387209139964850.806395430017575
990.248161377049510.496322754099020.75183862295049
1000.256381135978990.512762271957980.74361886402101
1010.2206234475657380.4412468951314750.779376552434262
1020.2379404999695090.4758809999390170.762059500030491
1030.3100529680874490.6201059361748990.689947031912551
1040.5011671552266910.9976656895466170.498832844773309
1050.6310636211595150.7378727576809690.368936378840485
1060.649514000321610.7009719993567790.35048599967839
1070.6050576117280840.7898847765438330.394942388271916
1080.6126144656810220.7747710686379570.387385534318978
1090.7086441040621950.582711791875610.291355895937805
1100.9119908981028190.1760182037943630.0880091018971814
1110.9326683087055310.1346633825889380.0673316912944692
1120.9473206423509370.1053587152981260.052679357649063
1130.962338475844630.07532304831073960.0376615241553698
1140.9781465022226590.04370699555468230.0218534977773411
1150.9847888425900370.03042231481992570.0152111574099629
1160.9933999633614230.01320007327715480.00660003663857742
1170.9992670116648420.001465976670316220.000732988335158112
1180.9994808000944350.001038399811129650.000519199905564827
1190.999865163330090.0002696733398193590.00013483666990968
1200.9998769436151830.0002461127696349540.000123056384817477
1210.9998003934550710.000399213089857250.000199606544928625
1220.9998842578501140.0002314842997725010.00011574214988625
1230.9999906501467911.86997064179764e-059.34985320898821e-06
1240.9999851766590812.96466818385188e-051.48233409192594e-05
1250.9999832017739793.35964520413588e-051.67982260206794e-05
1260.9999895235960242.09528079514139e-051.04764039757069e-05
1270.9999880369706412.39260587171157e-051.19630293585579e-05
1280.9999767068824184.65862351635365e-052.32931175817683e-05
1290.9999854513888262.9097222348217e-051.45486111741085e-05
1300.9999997669571864.66085627226499e-072.33042813613249e-07
1310.9999991785065781.64298684326175e-068.21493421630875e-07
1320.99999895175632.09648739940251e-061.04824369970126e-06
1330.9999982333826293.53323474194911e-061.76661737097456e-06
1340.9999959375793818.12484123878753e-064.06242061939377e-06
1350.9999827819096183.44361807640121e-051.72180903820061e-05
1360.9999574414509968.51170980086357e-054.25585490043179e-05
1370.9998518236127370.0002963527745251640.000148176387262582
1380.9998497756966090.0003004486067827270.000150224303391364
1390.9998156028803130.000368794239374520.00018439711968726
1400.999819824640250.0003603507195009410.00018017535975047
1410.9990542011636040.001891597672792810.000945798836396406
1420.9944845766921130.0110308466157740.00551542330788702






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1070.792592592592593NOK
5% type I error level1120.82962962962963NOK
10% type I error level1140.844444444444444NOK

\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 & 107 & 0.792592592592593 & NOK \tabularnewline
5% type I error level & 112 & 0.82962962962963 & NOK \tabularnewline
10% type I error level & 114 & 0.844444444444444 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191172&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]107[/C][C]0.792592592592593[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]112[/C][C]0.82962962962963[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]114[/C][C]0.844444444444444[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191172&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191172&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 level1070.792592592592593NOK
5% type I error level1120.82962962962963NOK
10% type I error level1140.844444444444444NOK


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 7
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}