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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 20 Nov 2011 11:45:50 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/20/t1321807650sz6v522vhwbxjyw.htm/, Retrieved Thu, 31 Oct 2024 23:26:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145644, Retrieved Thu, 31 Oct 2024 23:26:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-20 16:45:50] [f04206f511735117c791d4a2bb2fa643] [Current]
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Dataseries X:
119830	64507	21673	206010
116068	61865	20179	198112
114976	60844	18699	194519
110296	57604	17805	185705
107832	55672	16669	180173
105624	53636	16882	176142
114858	63487	25056	203401
119598	71468	30836	221902
106675	63200	27503	197378
103315	58166	23520	185001
100826	54664	20866	176356
103574	55860	21015	180449
104708	56190	19246	180144
101817	54300	17549	173666
97898	51362	16428	165688
95559	49802	16209	161570
92822	48088	15235	156145
90848	46696	16186	153730
101141	56586	24971	182698
105841	64148	30776	200765
93647	56449	26416	176512
90923	52538	23157	166618
89130	49359	20155	158644
90212	49583	19790	159585
93196	51050	18849	163095
91861	49610	17573	159044
90593	48321	16597	155511
89895	47692	16158	153745
88819	46243	15507	150569
87924	46248	16433	150605
96906	56381	26325	179612
101217	62329	31144	194690
98709	60673	30535	189917
98139	58393	27596	184128
95529	55742	24064	175335
98577	57135	23854	179566
100772	57961	22407	181140
100180	56571	21125	177876
99200	55615	20226	175041
96251	53494	19547	169292
94514	52623	18933	166070
93780	52820	20372	166972
105192	66825	34331	206348
107682	70695	37329	215706
99687	65660	36761	202108
99436	63238	32737	195411
102049	61741	29321	193111
102673	63642	28883	195198
105813	65521	27436	198770
105056	64006	25101	194163
103916	62728	23776	190420
103513	62438	23782	189733
101893	61109	23027	186029
102503	63422	25606	191531
113149	78094	41328	232571
116696	82030	44751	243477
108500	75892	42855	227247
107800	72431	37628	217859
105941	69194	33544	208679
108742	71171	33275	213188
111680	72545	32009	216234
111270	71503	30813	213586
110698	69624	29143	209465
108517	67407	28121	204045
107127	66103	27007	200237
107088	67466	29112	203666
116321	81088	44067	241476
125045	86781	48481	260307
116779	79964	46581	243324
122887	80407	41166	244460
120162	76589	36824	233575
123198	78083	35936	237217
123610	78000	33633	235243
122293	76431	31630	230354
121289	75461	30434	227184
119393	73739	28546	221678
117494	71988	27660	217142
116693	72929	29830	219452
125062	85785	45599	256446
127281	89261	49303	265845
120195	84012	44417	248624
119804	80924	40386	241114
117113	76588	35544	229245
119240	77546	35019	231805
115823	73054	30400	219277
116281	73430	29602	219313
113816	71093	27701	212610
114632	72202	27937	214771
112987	70872	27283	211142
111633	70452	29372	211457
116721	80506	42821	240048
114850	80400	45386	240636
112797	77613	40170	230580
105368	69056	34371	208795
102524	65321	30077	197922
101327	64018	29251	194596
102612	64767	27202	194581
98873	61099	25714	185686
95993	58329	23784	178106
93244	56396	22968	172608
90403	54656	22243	167302
88539	55259	24255	168053
98106	66912	37282	202300
96963	66631	38794	202388
90781	59907	31828	182516
89253	56274	27949	173476
87794	54045	24605	166444
89810	55792	25695	171297
90864	55499	23338	169701
89025	53216	21941	164182
87621	52259	22034	161914
87718	51257	20637	159612
83433	48150	19418	151001
84535	51125	22454	158114
92223	61046	33261	186530
91052	61022	34995	187069
88456	56742	29132	174330
88706	54485	26171	169362
89137	53862	23828	166827
94066	58228	25743	178037
99258	61951	25204	186413
100673	62874	25679	189226
102269	64013	25281	191563
100833	62937	25136	188906
99314	61897	24794	186005
101764	65267	28278	195309
108242	75228	40062	223532
108148	76161	42590	226899
104761	71480	37885	214126
103772	69070	34061	206903
103737	68293	32412	204442
111043	74685	34647	220375
109906	72664	31750	214320
109335	71965	31288	212588
107247	69238	29331	205816
105690	67738	28768	202196
102755	65187	27780	195722
102280	66170	30113	198563
110590	77309	41240	229139
109122	77134	43271	229527
102803	70957	38108	211868
101424	67749	34382	203555
99138	65081	31551	195770
101284	66600	31950	199834
104260	68384	30445	203089
102526	66677	29277	198480
100001	64507	28176	192684
97562	62526	27739	187827
95539	60570	26305	182414
93831	60663	28016	182510
101031	72923	37570	211524
98744	72952	39755	211451
95847	68503	35790	200140
94278	65289	32001	191568




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145644&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145644&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145644&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
totaal[t] = + 1.68931540618223e-11 + 0.999999999999999x1[t] + 1x2[t] + 1x3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
totaal[t] =  +  1.68931540618223e-11 +  0.999999999999999x1[t] +  1x2[t] +  1x3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145644&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]totaal[t] =  +  1.68931540618223e-11 +  0.999999999999999x1[t] +  1x2[t] +  1x3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145644&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145644&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
totaal[t] = + 1.68931540618223e-11 + 0.999999999999999x1[t] + 1x2[t] + 1x3[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.68931540618223e-1101.93580.054770.027385
x10.9999999999999990434172153111881600
x210228277173351218400
x310288307799849799800

\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) & 1.68931540618223e-11 & 0 & 1.9358 & 0.05477 & 0.027385 \tabularnewline
x1 & 0.999999999999999 & 0 & 4341721531118816 & 0 & 0 \tabularnewline
x2 & 1 & 0 & 2282771733512184 & 0 & 0 \tabularnewline
x3 & 1 & 0 & 2883077998497998 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145644&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]1.68931540618223e-11[/C][C]0[/C][C]1.9358[/C][C]0.05477[/C][C]0.027385[/C][/ROW]
[ROW][C]x1[/C][C]0.999999999999999[/C][C]0[/C][C]4341721531118816[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x2[/C][C]1[/C][C]0[/C][C]2282771733512184[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x3[/C][C]1[/C][C]0[/C][C]2883077998497998[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145644&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145644&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.68931540618223e-1101.93580.054770.027385
x10.9999999999999990434172153111881600
x210228277173351218400
x310288307799849799800







Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)3.60728437180232e+32
F-TEST (DF numerator)3
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.58211935820182e-12
Sum Squared Residuals1.37725517092239e-20

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 1 \tabularnewline
R-squared & 1 \tabularnewline
Adjusted R-squared & 1 \tabularnewline
F-TEST (value) & 3.60728437180232e+32 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.58211935820182e-12 \tabularnewline
Sum Squared Residuals & 1.37725517092239e-20 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145644&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]1[/C][/ROW]
[ROW][C]R-squared[/C][C]1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.60728437180232e+32[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/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]9.58211935820182e-12[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.37725517092239e-20[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145644&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145644&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)3.60728437180232e+32
F-TEST (DF numerator)3
F-TEST (DF denominator)150
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.58211935820182e-12
Sum Squared Residuals1.37725517092239e-20







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1206010206010-1.11456320563574e-10
21981121981128.57882821563757e-12
31945191945197.14846076751727e-12
41857051857057.13319668374274e-13
51801731801736.52300829335719e-12
61761421761427.49473466607195e-12
72034012034016.26687366434033e-12
82219022219028.88486398473331e-12
91973781973782.96794951547849e-12
101850011850013.60576294148636e-12
111763561763566.9453423866477e-12
121804491804496.1902400790307e-12
131801441801446.22697041295822e-12
141736661736666.05284625044003e-12
151656881656883.77615089673272e-12
161615701615702.6192643934589e-12
171561451561451.80801755714721e-13
18153730153730-1.20246707075586e-13
191826981826982.34364952417146e-12
202007652007652.32933153425697e-12
211765121765122.04914660978206e-12
221666181666181.51090348293275e-12
23158644158644-2.46912500443893e-12
24159585159585-7.84361617006576e-13
251630951630951.86790933834932e-14
26159044159044-5.58227335670622e-13
27155511155511-2.74285385424474e-13
28153745153745-2.71516545316654e-13
29150569150569-2.92032714055475e-13
30150605150605-1.82587212001295e-14
311796121796123.87578375400747e-12
321946901946901.79838938393835e-12
331899171899175.37762332253401e-13
341841281841282.48148580772682e-13
351753351753353.93728524582155e-12
361795661795662.48181003557758e-12
371811401811403.05493172531666e-12
381778761778762.41042518957691e-12
391750411750413.9759021862274e-12
401692921692922.64845885619719e-12
411660701660702.20384664022161e-12
421669721669724.18307426509113e-12
432063482063482.0458205499998e-12
442157062157061.96731602454777e-12
45202108202108-2.0938664572149e-12
46195411195411-2.54641401440373e-13
471931111931111.68058127755427e-12
481951981951981.57338440009371e-13
491987701987705.48603959774011e-13
501941631941633.19754527265306e-15
511904201904203.27257994446749e-13
521897331897331.27237027848107e-12
531860291860291.31218406745211e-12
54191531191531-2.91707090241746e-13
55232571232571-3.89620281354361e-13
56243477243477-1.5590629921315e-13
57227247227247-7.65528796915575e-13
58217859217859-1.88295415195935e-13
592086792086798.75260771528721e-13
602131882131885.23282335752739e-13
612162342162341.56760674881135e-12
622135862135863.71384662629272e-13
632094652094657.38235406634236e-13
642040452040459.92284099703928e-13
652002372002379.78480578626489e-13
662036662036663.9935086420544e-13
67241476241476-3.32926065237231e-13
682603072603074.30594196788541e-12
69243324243324-8.05630406330435e-13
702444602444609.0054975908016e-13
712335752335752.34131439811544e-12
722372172372174.81649338313562e-12
732352432352433.53718306392512e-12
742303542303545.26687728703994e-12
752271842271847.04029632156611e-12
762216782216784.48344728559046e-12
772171422171425.45714490180979e-12
782194522194524.18698336345293e-12
79256446256446-3.63996763418387e-15
802658452658457.69938420963754e-13
81248624248624-8.20915422577982e-13
82241114241114-1.41555918434759e-12
832292452292454.46932678056686e-12
842318052318051.916649159365e-12
852192772192774.69714470995271e-12
862193132193134.40580920989084e-12
872126102126101.39817413182222e-12
882147712147714.19319113366751e-12
892111422111427.18667149496467e-14
902114572114574.97672310517524e-14
91240048240048-1.08861227549445e-12
922406362406361.62409591846485e-12
93230580230580-3.93777190165507e-12
942087952087959.6480709826297e-13
95197922197922-5.81734122949822e-13
96194596194596-5.69337172787592e-13
97194581194581-6.51432458126833e-13
98185686185686-1.99430304105396e-13
991781061781061.72905361000239e-12
1001726081726081.78341193231155e-12
1011673021673025.48704848340123e-13
1021680531680531.94055391686616e-14
103202300202300-1.73831498904938e-12
104202388202388-4.36140685580225e-12
105182516182516-1.0216423564727e-12
1061734761734763.19136093455213e-14
107166444166444-1.26237677920536e-12
1081712971712971.41008634819307e-12
109169701169701-3.85635269120178e-13
110164182164182-4.10445972990069e-12
111161914161914-3.27959567281705e-12
112159612159612-3.41698427575876e-12
113151001151001-3.97679696272103e-12
114158114158114-5.66533104776535e-12
115186530186530-1.41206950473043e-12
116187069187069-1.13372551277866e-12
117174330174330-2.35316538897355e-14
1181693621693624.03975221563314e-14
1191668271668272.50198168134773e-13
1201780371780371.12021971873441e-12
121186413186413-1.77281045709533e-12
122189226189226-1.33921226822232e-12
123191563191563-1.03685713544055e-12
124188906188906-1.10212505979563e-12
125186005186005-6.28871774598698e-13
126195309195309-1.30188418526054e-12
1272235322235321.2392012355127e-12
128226899226899-1.57824844235397e-12
1292141262141262.11157778642299e-12
130206903206903-5.46217005620218e-13
131204442204442-2.7697757386235e-13
1322203752203757.56619059678355e-15
1332143202143201.79993900512059e-13
134212588212588-9.99299678460295e-13
135205816205816-1.46250714240333e-12
136202196202196-5.88091459633661e-13
137195722195722-1.21725134678181e-12
138198563198563-1.3121008653839e-12
1392291392291392.66135861273379e-12
140229527229527-1.98417462801873e-12
141211868211868-2.18438146757607e-12
142203555203555-6.17834467249515e-13
143195770195770-2.89410323136201e-12
144199834199834-2.18602619053094e-12
145203089203089-1.44056144010062e-12
146198480198480-1.5411954699832e-12
147192684192684-2.60764440613821e-12
148187827187827-1.88248814171977e-12
149182414182414-1.24030196438626e-12
150182510182510-1.33027300028451e-12
151211524211524-4.16942597675209e-12
152211451211451-9.15465196759124e-12
153200140200140-4.78766247886746e-12
154191568191568-3.81761374759325e-12

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 206010 & 206010 & -1.11456320563574e-10 \tabularnewline
2 & 198112 & 198112 & 8.57882821563757e-12 \tabularnewline
3 & 194519 & 194519 & 7.14846076751727e-12 \tabularnewline
4 & 185705 & 185705 & 7.13319668374274e-13 \tabularnewline
5 & 180173 & 180173 & 6.52300829335719e-12 \tabularnewline
6 & 176142 & 176142 & 7.49473466607195e-12 \tabularnewline
7 & 203401 & 203401 & 6.26687366434033e-12 \tabularnewline
8 & 221902 & 221902 & 8.88486398473331e-12 \tabularnewline
9 & 197378 & 197378 & 2.96794951547849e-12 \tabularnewline
10 & 185001 & 185001 & 3.60576294148636e-12 \tabularnewline
11 & 176356 & 176356 & 6.9453423866477e-12 \tabularnewline
12 & 180449 & 180449 & 6.1902400790307e-12 \tabularnewline
13 & 180144 & 180144 & 6.22697041295822e-12 \tabularnewline
14 & 173666 & 173666 & 6.05284625044003e-12 \tabularnewline
15 & 165688 & 165688 & 3.77615089673272e-12 \tabularnewline
16 & 161570 & 161570 & 2.6192643934589e-12 \tabularnewline
17 & 156145 & 156145 & 1.80801755714721e-13 \tabularnewline
18 & 153730 & 153730 & -1.20246707075586e-13 \tabularnewline
19 & 182698 & 182698 & 2.34364952417146e-12 \tabularnewline
20 & 200765 & 200765 & 2.32933153425697e-12 \tabularnewline
21 & 176512 & 176512 & 2.04914660978206e-12 \tabularnewline
22 & 166618 & 166618 & 1.51090348293275e-12 \tabularnewline
23 & 158644 & 158644 & -2.46912500443893e-12 \tabularnewline
24 & 159585 & 159585 & -7.84361617006576e-13 \tabularnewline
25 & 163095 & 163095 & 1.86790933834932e-14 \tabularnewline
26 & 159044 & 159044 & -5.58227335670622e-13 \tabularnewline
27 & 155511 & 155511 & -2.74285385424474e-13 \tabularnewline
28 & 153745 & 153745 & -2.71516545316654e-13 \tabularnewline
29 & 150569 & 150569 & -2.92032714055475e-13 \tabularnewline
30 & 150605 & 150605 & -1.82587212001295e-14 \tabularnewline
31 & 179612 & 179612 & 3.87578375400747e-12 \tabularnewline
32 & 194690 & 194690 & 1.79838938393835e-12 \tabularnewline
33 & 189917 & 189917 & 5.37762332253401e-13 \tabularnewline
34 & 184128 & 184128 & 2.48148580772682e-13 \tabularnewline
35 & 175335 & 175335 & 3.93728524582155e-12 \tabularnewline
36 & 179566 & 179566 & 2.48181003557758e-12 \tabularnewline
37 & 181140 & 181140 & 3.05493172531666e-12 \tabularnewline
38 & 177876 & 177876 & 2.41042518957691e-12 \tabularnewline
39 & 175041 & 175041 & 3.9759021862274e-12 \tabularnewline
40 & 169292 & 169292 & 2.64845885619719e-12 \tabularnewline
41 & 166070 & 166070 & 2.20384664022161e-12 \tabularnewline
42 & 166972 & 166972 & 4.18307426509113e-12 \tabularnewline
43 & 206348 & 206348 & 2.0458205499998e-12 \tabularnewline
44 & 215706 & 215706 & 1.96731602454777e-12 \tabularnewline
45 & 202108 & 202108 & -2.0938664572149e-12 \tabularnewline
46 & 195411 & 195411 & -2.54641401440373e-13 \tabularnewline
47 & 193111 & 193111 & 1.68058127755427e-12 \tabularnewline
48 & 195198 & 195198 & 1.57338440009371e-13 \tabularnewline
49 & 198770 & 198770 & 5.48603959774011e-13 \tabularnewline
50 & 194163 & 194163 & 3.19754527265306e-15 \tabularnewline
51 & 190420 & 190420 & 3.27257994446749e-13 \tabularnewline
52 & 189733 & 189733 & 1.27237027848107e-12 \tabularnewline
53 & 186029 & 186029 & 1.31218406745211e-12 \tabularnewline
54 & 191531 & 191531 & -2.91707090241746e-13 \tabularnewline
55 & 232571 & 232571 & -3.89620281354361e-13 \tabularnewline
56 & 243477 & 243477 & -1.5590629921315e-13 \tabularnewline
57 & 227247 & 227247 & -7.65528796915575e-13 \tabularnewline
58 & 217859 & 217859 & -1.88295415195935e-13 \tabularnewline
59 & 208679 & 208679 & 8.75260771528721e-13 \tabularnewline
60 & 213188 & 213188 & 5.23282335752739e-13 \tabularnewline
61 & 216234 & 216234 & 1.56760674881135e-12 \tabularnewline
62 & 213586 & 213586 & 3.71384662629272e-13 \tabularnewline
63 & 209465 & 209465 & 7.38235406634236e-13 \tabularnewline
64 & 204045 & 204045 & 9.92284099703928e-13 \tabularnewline
65 & 200237 & 200237 & 9.78480578626489e-13 \tabularnewline
66 & 203666 & 203666 & 3.9935086420544e-13 \tabularnewline
67 & 241476 & 241476 & -3.32926065237231e-13 \tabularnewline
68 & 260307 & 260307 & 4.30594196788541e-12 \tabularnewline
69 & 243324 & 243324 & -8.05630406330435e-13 \tabularnewline
70 & 244460 & 244460 & 9.0054975908016e-13 \tabularnewline
71 & 233575 & 233575 & 2.34131439811544e-12 \tabularnewline
72 & 237217 & 237217 & 4.81649338313562e-12 \tabularnewline
73 & 235243 & 235243 & 3.53718306392512e-12 \tabularnewline
74 & 230354 & 230354 & 5.26687728703994e-12 \tabularnewline
75 & 227184 & 227184 & 7.04029632156611e-12 \tabularnewline
76 & 221678 & 221678 & 4.48344728559046e-12 \tabularnewline
77 & 217142 & 217142 & 5.45714490180979e-12 \tabularnewline
78 & 219452 & 219452 & 4.18698336345293e-12 \tabularnewline
79 & 256446 & 256446 & -3.63996763418387e-15 \tabularnewline
80 & 265845 & 265845 & 7.69938420963754e-13 \tabularnewline
81 & 248624 & 248624 & -8.20915422577982e-13 \tabularnewline
82 & 241114 & 241114 & -1.41555918434759e-12 \tabularnewline
83 & 229245 & 229245 & 4.46932678056686e-12 \tabularnewline
84 & 231805 & 231805 & 1.916649159365e-12 \tabularnewline
85 & 219277 & 219277 & 4.69714470995271e-12 \tabularnewline
86 & 219313 & 219313 & 4.40580920989084e-12 \tabularnewline
87 & 212610 & 212610 & 1.39817413182222e-12 \tabularnewline
88 & 214771 & 214771 & 4.19319113366751e-12 \tabularnewline
89 & 211142 & 211142 & 7.18667149496467e-14 \tabularnewline
90 & 211457 & 211457 & 4.97672310517524e-14 \tabularnewline
91 & 240048 & 240048 & -1.08861227549445e-12 \tabularnewline
92 & 240636 & 240636 & 1.62409591846485e-12 \tabularnewline
93 & 230580 & 230580 & -3.93777190165507e-12 \tabularnewline
94 & 208795 & 208795 & 9.6480709826297e-13 \tabularnewline
95 & 197922 & 197922 & -5.81734122949822e-13 \tabularnewline
96 & 194596 & 194596 & -5.69337172787592e-13 \tabularnewline
97 & 194581 & 194581 & -6.51432458126833e-13 \tabularnewline
98 & 185686 & 185686 & -1.99430304105396e-13 \tabularnewline
99 & 178106 & 178106 & 1.72905361000239e-12 \tabularnewline
100 & 172608 & 172608 & 1.78341193231155e-12 \tabularnewline
101 & 167302 & 167302 & 5.48704848340123e-13 \tabularnewline
102 & 168053 & 168053 & 1.94055391686616e-14 \tabularnewline
103 & 202300 & 202300 & -1.73831498904938e-12 \tabularnewline
104 & 202388 & 202388 & -4.36140685580225e-12 \tabularnewline
105 & 182516 & 182516 & -1.0216423564727e-12 \tabularnewline
106 & 173476 & 173476 & 3.19136093455213e-14 \tabularnewline
107 & 166444 & 166444 & -1.26237677920536e-12 \tabularnewline
108 & 171297 & 171297 & 1.41008634819307e-12 \tabularnewline
109 & 169701 & 169701 & -3.85635269120178e-13 \tabularnewline
110 & 164182 & 164182 & -4.10445972990069e-12 \tabularnewline
111 & 161914 & 161914 & -3.27959567281705e-12 \tabularnewline
112 & 159612 & 159612 & -3.41698427575876e-12 \tabularnewline
113 & 151001 & 151001 & -3.97679696272103e-12 \tabularnewline
114 & 158114 & 158114 & -5.66533104776535e-12 \tabularnewline
115 & 186530 & 186530 & -1.41206950473043e-12 \tabularnewline
116 & 187069 & 187069 & -1.13372551277866e-12 \tabularnewline
117 & 174330 & 174330 & -2.35316538897355e-14 \tabularnewline
118 & 169362 & 169362 & 4.03975221563314e-14 \tabularnewline
119 & 166827 & 166827 & 2.50198168134773e-13 \tabularnewline
120 & 178037 & 178037 & 1.12021971873441e-12 \tabularnewline
121 & 186413 & 186413 & -1.77281045709533e-12 \tabularnewline
122 & 189226 & 189226 & -1.33921226822232e-12 \tabularnewline
123 & 191563 & 191563 & -1.03685713544055e-12 \tabularnewline
124 & 188906 & 188906 & -1.10212505979563e-12 \tabularnewline
125 & 186005 & 186005 & -6.28871774598698e-13 \tabularnewline
126 & 195309 & 195309 & -1.30188418526054e-12 \tabularnewline
127 & 223532 & 223532 & 1.2392012355127e-12 \tabularnewline
128 & 226899 & 226899 & -1.57824844235397e-12 \tabularnewline
129 & 214126 & 214126 & 2.11157778642299e-12 \tabularnewline
130 & 206903 & 206903 & -5.46217005620218e-13 \tabularnewline
131 & 204442 & 204442 & -2.7697757386235e-13 \tabularnewline
132 & 220375 & 220375 & 7.56619059678355e-15 \tabularnewline
133 & 214320 & 214320 & 1.79993900512059e-13 \tabularnewline
134 & 212588 & 212588 & -9.99299678460295e-13 \tabularnewline
135 & 205816 & 205816 & -1.46250714240333e-12 \tabularnewline
136 & 202196 & 202196 & -5.88091459633661e-13 \tabularnewline
137 & 195722 & 195722 & -1.21725134678181e-12 \tabularnewline
138 & 198563 & 198563 & -1.3121008653839e-12 \tabularnewline
139 & 229139 & 229139 & 2.66135861273379e-12 \tabularnewline
140 & 229527 & 229527 & -1.98417462801873e-12 \tabularnewline
141 & 211868 & 211868 & -2.18438146757607e-12 \tabularnewline
142 & 203555 & 203555 & -6.17834467249515e-13 \tabularnewline
143 & 195770 & 195770 & -2.89410323136201e-12 \tabularnewline
144 & 199834 & 199834 & -2.18602619053094e-12 \tabularnewline
145 & 203089 & 203089 & -1.44056144010062e-12 \tabularnewline
146 & 198480 & 198480 & -1.5411954699832e-12 \tabularnewline
147 & 192684 & 192684 & -2.60764440613821e-12 \tabularnewline
148 & 187827 & 187827 & -1.88248814171977e-12 \tabularnewline
149 & 182414 & 182414 & -1.24030196438626e-12 \tabularnewline
150 & 182510 & 182510 & -1.33027300028451e-12 \tabularnewline
151 & 211524 & 211524 & -4.16942597675209e-12 \tabularnewline
152 & 211451 & 211451 & -9.15465196759124e-12 \tabularnewline
153 & 200140 & 200140 & -4.78766247886746e-12 \tabularnewline
154 & 191568 & 191568 & -3.81761374759325e-12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145644&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]206010[/C][C]206010[/C][C]-1.11456320563574e-10[/C][/ROW]
[ROW][C]2[/C][C]198112[/C][C]198112[/C][C]8.57882821563757e-12[/C][/ROW]
[ROW][C]3[/C][C]194519[/C][C]194519[/C][C]7.14846076751727e-12[/C][/ROW]
[ROW][C]4[/C][C]185705[/C][C]185705[/C][C]7.13319668374274e-13[/C][/ROW]
[ROW][C]5[/C][C]180173[/C][C]180173[/C][C]6.52300829335719e-12[/C][/ROW]
[ROW][C]6[/C][C]176142[/C][C]176142[/C][C]7.49473466607195e-12[/C][/ROW]
[ROW][C]7[/C][C]203401[/C][C]203401[/C][C]6.26687366434033e-12[/C][/ROW]
[ROW][C]8[/C][C]221902[/C][C]221902[/C][C]8.88486398473331e-12[/C][/ROW]
[ROW][C]9[/C][C]197378[/C][C]197378[/C][C]2.96794951547849e-12[/C][/ROW]
[ROW][C]10[/C][C]185001[/C][C]185001[/C][C]3.60576294148636e-12[/C][/ROW]
[ROW][C]11[/C][C]176356[/C][C]176356[/C][C]6.9453423866477e-12[/C][/ROW]
[ROW][C]12[/C][C]180449[/C][C]180449[/C][C]6.1902400790307e-12[/C][/ROW]
[ROW][C]13[/C][C]180144[/C][C]180144[/C][C]6.22697041295822e-12[/C][/ROW]
[ROW][C]14[/C][C]173666[/C][C]173666[/C][C]6.05284625044003e-12[/C][/ROW]
[ROW][C]15[/C][C]165688[/C][C]165688[/C][C]3.77615089673272e-12[/C][/ROW]
[ROW][C]16[/C][C]161570[/C][C]161570[/C][C]2.6192643934589e-12[/C][/ROW]
[ROW][C]17[/C][C]156145[/C][C]156145[/C][C]1.80801755714721e-13[/C][/ROW]
[ROW][C]18[/C][C]153730[/C][C]153730[/C][C]-1.20246707075586e-13[/C][/ROW]
[ROW][C]19[/C][C]182698[/C][C]182698[/C][C]2.34364952417146e-12[/C][/ROW]
[ROW][C]20[/C][C]200765[/C][C]200765[/C][C]2.32933153425697e-12[/C][/ROW]
[ROW][C]21[/C][C]176512[/C][C]176512[/C][C]2.04914660978206e-12[/C][/ROW]
[ROW][C]22[/C][C]166618[/C][C]166618[/C][C]1.51090348293275e-12[/C][/ROW]
[ROW][C]23[/C][C]158644[/C][C]158644[/C][C]-2.46912500443893e-12[/C][/ROW]
[ROW][C]24[/C][C]159585[/C][C]159585[/C][C]-7.84361617006576e-13[/C][/ROW]
[ROW][C]25[/C][C]163095[/C][C]163095[/C][C]1.86790933834932e-14[/C][/ROW]
[ROW][C]26[/C][C]159044[/C][C]159044[/C][C]-5.58227335670622e-13[/C][/ROW]
[ROW][C]27[/C][C]155511[/C][C]155511[/C][C]-2.74285385424474e-13[/C][/ROW]
[ROW][C]28[/C][C]153745[/C][C]153745[/C][C]-2.71516545316654e-13[/C][/ROW]
[ROW][C]29[/C][C]150569[/C][C]150569[/C][C]-2.92032714055475e-13[/C][/ROW]
[ROW][C]30[/C][C]150605[/C][C]150605[/C][C]-1.82587212001295e-14[/C][/ROW]
[ROW][C]31[/C][C]179612[/C][C]179612[/C][C]3.87578375400747e-12[/C][/ROW]
[ROW][C]32[/C][C]194690[/C][C]194690[/C][C]1.79838938393835e-12[/C][/ROW]
[ROW][C]33[/C][C]189917[/C][C]189917[/C][C]5.37762332253401e-13[/C][/ROW]
[ROW][C]34[/C][C]184128[/C][C]184128[/C][C]2.48148580772682e-13[/C][/ROW]
[ROW][C]35[/C][C]175335[/C][C]175335[/C][C]3.93728524582155e-12[/C][/ROW]
[ROW][C]36[/C][C]179566[/C][C]179566[/C][C]2.48181003557758e-12[/C][/ROW]
[ROW][C]37[/C][C]181140[/C][C]181140[/C][C]3.05493172531666e-12[/C][/ROW]
[ROW][C]38[/C][C]177876[/C][C]177876[/C][C]2.41042518957691e-12[/C][/ROW]
[ROW][C]39[/C][C]175041[/C][C]175041[/C][C]3.9759021862274e-12[/C][/ROW]
[ROW][C]40[/C][C]169292[/C][C]169292[/C][C]2.64845885619719e-12[/C][/ROW]
[ROW][C]41[/C][C]166070[/C][C]166070[/C][C]2.20384664022161e-12[/C][/ROW]
[ROW][C]42[/C][C]166972[/C][C]166972[/C][C]4.18307426509113e-12[/C][/ROW]
[ROW][C]43[/C][C]206348[/C][C]206348[/C][C]2.0458205499998e-12[/C][/ROW]
[ROW][C]44[/C][C]215706[/C][C]215706[/C][C]1.96731602454777e-12[/C][/ROW]
[ROW][C]45[/C][C]202108[/C][C]202108[/C][C]-2.0938664572149e-12[/C][/ROW]
[ROW][C]46[/C][C]195411[/C][C]195411[/C][C]-2.54641401440373e-13[/C][/ROW]
[ROW][C]47[/C][C]193111[/C][C]193111[/C][C]1.68058127755427e-12[/C][/ROW]
[ROW][C]48[/C][C]195198[/C][C]195198[/C][C]1.57338440009371e-13[/C][/ROW]
[ROW][C]49[/C][C]198770[/C][C]198770[/C][C]5.48603959774011e-13[/C][/ROW]
[ROW][C]50[/C][C]194163[/C][C]194163[/C][C]3.19754527265306e-15[/C][/ROW]
[ROW][C]51[/C][C]190420[/C][C]190420[/C][C]3.27257994446749e-13[/C][/ROW]
[ROW][C]52[/C][C]189733[/C][C]189733[/C][C]1.27237027848107e-12[/C][/ROW]
[ROW][C]53[/C][C]186029[/C][C]186029[/C][C]1.31218406745211e-12[/C][/ROW]
[ROW][C]54[/C][C]191531[/C][C]191531[/C][C]-2.91707090241746e-13[/C][/ROW]
[ROW][C]55[/C][C]232571[/C][C]232571[/C][C]-3.89620281354361e-13[/C][/ROW]
[ROW][C]56[/C][C]243477[/C][C]243477[/C][C]-1.5590629921315e-13[/C][/ROW]
[ROW][C]57[/C][C]227247[/C][C]227247[/C][C]-7.65528796915575e-13[/C][/ROW]
[ROW][C]58[/C][C]217859[/C][C]217859[/C][C]-1.88295415195935e-13[/C][/ROW]
[ROW][C]59[/C][C]208679[/C][C]208679[/C][C]8.75260771528721e-13[/C][/ROW]
[ROW][C]60[/C][C]213188[/C][C]213188[/C][C]5.23282335752739e-13[/C][/ROW]
[ROW][C]61[/C][C]216234[/C][C]216234[/C][C]1.56760674881135e-12[/C][/ROW]
[ROW][C]62[/C][C]213586[/C][C]213586[/C][C]3.71384662629272e-13[/C][/ROW]
[ROW][C]63[/C][C]209465[/C][C]209465[/C][C]7.38235406634236e-13[/C][/ROW]
[ROW][C]64[/C][C]204045[/C][C]204045[/C][C]9.92284099703928e-13[/C][/ROW]
[ROW][C]65[/C][C]200237[/C][C]200237[/C][C]9.78480578626489e-13[/C][/ROW]
[ROW][C]66[/C][C]203666[/C][C]203666[/C][C]3.9935086420544e-13[/C][/ROW]
[ROW][C]67[/C][C]241476[/C][C]241476[/C][C]-3.32926065237231e-13[/C][/ROW]
[ROW][C]68[/C][C]260307[/C][C]260307[/C][C]4.30594196788541e-12[/C][/ROW]
[ROW][C]69[/C][C]243324[/C][C]243324[/C][C]-8.05630406330435e-13[/C][/ROW]
[ROW][C]70[/C][C]244460[/C][C]244460[/C][C]9.0054975908016e-13[/C][/ROW]
[ROW][C]71[/C][C]233575[/C][C]233575[/C][C]2.34131439811544e-12[/C][/ROW]
[ROW][C]72[/C][C]237217[/C][C]237217[/C][C]4.81649338313562e-12[/C][/ROW]
[ROW][C]73[/C][C]235243[/C][C]235243[/C][C]3.53718306392512e-12[/C][/ROW]
[ROW][C]74[/C][C]230354[/C][C]230354[/C][C]5.26687728703994e-12[/C][/ROW]
[ROW][C]75[/C][C]227184[/C][C]227184[/C][C]7.04029632156611e-12[/C][/ROW]
[ROW][C]76[/C][C]221678[/C][C]221678[/C][C]4.48344728559046e-12[/C][/ROW]
[ROW][C]77[/C][C]217142[/C][C]217142[/C][C]5.45714490180979e-12[/C][/ROW]
[ROW][C]78[/C][C]219452[/C][C]219452[/C][C]4.18698336345293e-12[/C][/ROW]
[ROW][C]79[/C][C]256446[/C][C]256446[/C][C]-3.63996763418387e-15[/C][/ROW]
[ROW][C]80[/C][C]265845[/C][C]265845[/C][C]7.69938420963754e-13[/C][/ROW]
[ROW][C]81[/C][C]248624[/C][C]248624[/C][C]-8.20915422577982e-13[/C][/ROW]
[ROW][C]82[/C][C]241114[/C][C]241114[/C][C]-1.41555918434759e-12[/C][/ROW]
[ROW][C]83[/C][C]229245[/C][C]229245[/C][C]4.46932678056686e-12[/C][/ROW]
[ROW][C]84[/C][C]231805[/C][C]231805[/C][C]1.916649159365e-12[/C][/ROW]
[ROW][C]85[/C][C]219277[/C][C]219277[/C][C]4.69714470995271e-12[/C][/ROW]
[ROW][C]86[/C][C]219313[/C][C]219313[/C][C]4.40580920989084e-12[/C][/ROW]
[ROW][C]87[/C][C]212610[/C][C]212610[/C][C]1.39817413182222e-12[/C][/ROW]
[ROW][C]88[/C][C]214771[/C][C]214771[/C][C]4.19319113366751e-12[/C][/ROW]
[ROW][C]89[/C][C]211142[/C][C]211142[/C][C]7.18667149496467e-14[/C][/ROW]
[ROW][C]90[/C][C]211457[/C][C]211457[/C][C]4.97672310517524e-14[/C][/ROW]
[ROW][C]91[/C][C]240048[/C][C]240048[/C][C]-1.08861227549445e-12[/C][/ROW]
[ROW][C]92[/C][C]240636[/C][C]240636[/C][C]1.62409591846485e-12[/C][/ROW]
[ROW][C]93[/C][C]230580[/C][C]230580[/C][C]-3.93777190165507e-12[/C][/ROW]
[ROW][C]94[/C][C]208795[/C][C]208795[/C][C]9.6480709826297e-13[/C][/ROW]
[ROW][C]95[/C][C]197922[/C][C]197922[/C][C]-5.81734122949822e-13[/C][/ROW]
[ROW][C]96[/C][C]194596[/C][C]194596[/C][C]-5.69337172787592e-13[/C][/ROW]
[ROW][C]97[/C][C]194581[/C][C]194581[/C][C]-6.51432458126833e-13[/C][/ROW]
[ROW][C]98[/C][C]185686[/C][C]185686[/C][C]-1.99430304105396e-13[/C][/ROW]
[ROW][C]99[/C][C]178106[/C][C]178106[/C][C]1.72905361000239e-12[/C][/ROW]
[ROW][C]100[/C][C]172608[/C][C]172608[/C][C]1.78341193231155e-12[/C][/ROW]
[ROW][C]101[/C][C]167302[/C][C]167302[/C][C]5.48704848340123e-13[/C][/ROW]
[ROW][C]102[/C][C]168053[/C][C]168053[/C][C]1.94055391686616e-14[/C][/ROW]
[ROW][C]103[/C][C]202300[/C][C]202300[/C][C]-1.73831498904938e-12[/C][/ROW]
[ROW][C]104[/C][C]202388[/C][C]202388[/C][C]-4.36140685580225e-12[/C][/ROW]
[ROW][C]105[/C][C]182516[/C][C]182516[/C][C]-1.0216423564727e-12[/C][/ROW]
[ROW][C]106[/C][C]173476[/C][C]173476[/C][C]3.19136093455213e-14[/C][/ROW]
[ROW][C]107[/C][C]166444[/C][C]166444[/C][C]-1.26237677920536e-12[/C][/ROW]
[ROW][C]108[/C][C]171297[/C][C]171297[/C][C]1.41008634819307e-12[/C][/ROW]
[ROW][C]109[/C][C]169701[/C][C]169701[/C][C]-3.85635269120178e-13[/C][/ROW]
[ROW][C]110[/C][C]164182[/C][C]164182[/C][C]-4.10445972990069e-12[/C][/ROW]
[ROW][C]111[/C][C]161914[/C][C]161914[/C][C]-3.27959567281705e-12[/C][/ROW]
[ROW][C]112[/C][C]159612[/C][C]159612[/C][C]-3.41698427575876e-12[/C][/ROW]
[ROW][C]113[/C][C]151001[/C][C]151001[/C][C]-3.97679696272103e-12[/C][/ROW]
[ROW][C]114[/C][C]158114[/C][C]158114[/C][C]-5.66533104776535e-12[/C][/ROW]
[ROW][C]115[/C][C]186530[/C][C]186530[/C][C]-1.41206950473043e-12[/C][/ROW]
[ROW][C]116[/C][C]187069[/C][C]187069[/C][C]-1.13372551277866e-12[/C][/ROW]
[ROW][C]117[/C][C]174330[/C][C]174330[/C][C]-2.35316538897355e-14[/C][/ROW]
[ROW][C]118[/C][C]169362[/C][C]169362[/C][C]4.03975221563314e-14[/C][/ROW]
[ROW][C]119[/C][C]166827[/C][C]166827[/C][C]2.50198168134773e-13[/C][/ROW]
[ROW][C]120[/C][C]178037[/C][C]178037[/C][C]1.12021971873441e-12[/C][/ROW]
[ROW][C]121[/C][C]186413[/C][C]186413[/C][C]-1.77281045709533e-12[/C][/ROW]
[ROW][C]122[/C][C]189226[/C][C]189226[/C][C]-1.33921226822232e-12[/C][/ROW]
[ROW][C]123[/C][C]191563[/C][C]191563[/C][C]-1.03685713544055e-12[/C][/ROW]
[ROW][C]124[/C][C]188906[/C][C]188906[/C][C]-1.10212505979563e-12[/C][/ROW]
[ROW][C]125[/C][C]186005[/C][C]186005[/C][C]-6.28871774598698e-13[/C][/ROW]
[ROW][C]126[/C][C]195309[/C][C]195309[/C][C]-1.30188418526054e-12[/C][/ROW]
[ROW][C]127[/C][C]223532[/C][C]223532[/C][C]1.2392012355127e-12[/C][/ROW]
[ROW][C]128[/C][C]226899[/C][C]226899[/C][C]-1.57824844235397e-12[/C][/ROW]
[ROW][C]129[/C][C]214126[/C][C]214126[/C][C]2.11157778642299e-12[/C][/ROW]
[ROW][C]130[/C][C]206903[/C][C]206903[/C][C]-5.46217005620218e-13[/C][/ROW]
[ROW][C]131[/C][C]204442[/C][C]204442[/C][C]-2.7697757386235e-13[/C][/ROW]
[ROW][C]132[/C][C]220375[/C][C]220375[/C][C]7.56619059678355e-15[/C][/ROW]
[ROW][C]133[/C][C]214320[/C][C]214320[/C][C]1.79993900512059e-13[/C][/ROW]
[ROW][C]134[/C][C]212588[/C][C]212588[/C][C]-9.99299678460295e-13[/C][/ROW]
[ROW][C]135[/C][C]205816[/C][C]205816[/C][C]-1.46250714240333e-12[/C][/ROW]
[ROW][C]136[/C][C]202196[/C][C]202196[/C][C]-5.88091459633661e-13[/C][/ROW]
[ROW][C]137[/C][C]195722[/C][C]195722[/C][C]-1.21725134678181e-12[/C][/ROW]
[ROW][C]138[/C][C]198563[/C][C]198563[/C][C]-1.3121008653839e-12[/C][/ROW]
[ROW][C]139[/C][C]229139[/C][C]229139[/C][C]2.66135861273379e-12[/C][/ROW]
[ROW][C]140[/C][C]229527[/C][C]229527[/C][C]-1.98417462801873e-12[/C][/ROW]
[ROW][C]141[/C][C]211868[/C][C]211868[/C][C]-2.18438146757607e-12[/C][/ROW]
[ROW][C]142[/C][C]203555[/C][C]203555[/C][C]-6.17834467249515e-13[/C][/ROW]
[ROW][C]143[/C][C]195770[/C][C]195770[/C][C]-2.89410323136201e-12[/C][/ROW]
[ROW][C]144[/C][C]199834[/C][C]199834[/C][C]-2.18602619053094e-12[/C][/ROW]
[ROW][C]145[/C][C]203089[/C][C]203089[/C][C]-1.44056144010062e-12[/C][/ROW]
[ROW][C]146[/C][C]198480[/C][C]198480[/C][C]-1.5411954699832e-12[/C][/ROW]
[ROW][C]147[/C][C]192684[/C][C]192684[/C][C]-2.60764440613821e-12[/C][/ROW]
[ROW][C]148[/C][C]187827[/C][C]187827[/C][C]-1.88248814171977e-12[/C][/ROW]
[ROW][C]149[/C][C]182414[/C][C]182414[/C][C]-1.24030196438626e-12[/C][/ROW]
[ROW][C]150[/C][C]182510[/C][C]182510[/C][C]-1.33027300028451e-12[/C][/ROW]
[ROW][C]151[/C][C]211524[/C][C]211524[/C][C]-4.16942597675209e-12[/C][/ROW]
[ROW][C]152[/C][C]211451[/C][C]211451[/C][C]-9.15465196759124e-12[/C][/ROW]
[ROW][C]153[/C][C]200140[/C][C]200140[/C][C]-4.78766247886746e-12[/C][/ROW]
[ROW][C]154[/C][C]191568[/C][C]191568[/C][C]-3.81761374759325e-12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145644&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145644&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1206010206010-1.11456320563574e-10
21981121981128.57882821563757e-12
31945191945197.14846076751727e-12
41857051857057.13319668374274e-13
51801731801736.52300829335719e-12
61761421761427.49473466607195e-12
72034012034016.26687366434033e-12
82219022219028.88486398473331e-12
91973781973782.96794951547849e-12
101850011850013.60576294148636e-12
111763561763566.9453423866477e-12
121804491804496.1902400790307e-12
131801441801446.22697041295822e-12
141736661736666.05284625044003e-12
151656881656883.77615089673272e-12
161615701615702.6192643934589e-12
171561451561451.80801755714721e-13
18153730153730-1.20246707075586e-13
191826981826982.34364952417146e-12
202007652007652.32933153425697e-12
211765121765122.04914660978206e-12
221666181666181.51090348293275e-12
23158644158644-2.46912500443893e-12
24159585159585-7.84361617006576e-13
251630951630951.86790933834932e-14
26159044159044-5.58227335670622e-13
27155511155511-2.74285385424474e-13
28153745153745-2.71516545316654e-13
29150569150569-2.92032714055475e-13
30150605150605-1.82587212001295e-14
311796121796123.87578375400747e-12
321946901946901.79838938393835e-12
331899171899175.37762332253401e-13
341841281841282.48148580772682e-13
351753351753353.93728524582155e-12
361795661795662.48181003557758e-12
371811401811403.05493172531666e-12
381778761778762.41042518957691e-12
391750411750413.9759021862274e-12
401692921692922.64845885619719e-12
411660701660702.20384664022161e-12
421669721669724.18307426509113e-12
432063482063482.0458205499998e-12
442157062157061.96731602454777e-12
45202108202108-2.0938664572149e-12
46195411195411-2.54641401440373e-13
471931111931111.68058127755427e-12
481951981951981.57338440009371e-13
491987701987705.48603959774011e-13
501941631941633.19754527265306e-15
511904201904203.27257994446749e-13
521897331897331.27237027848107e-12
531860291860291.31218406745211e-12
54191531191531-2.91707090241746e-13
55232571232571-3.89620281354361e-13
56243477243477-1.5590629921315e-13
57227247227247-7.65528796915575e-13
58217859217859-1.88295415195935e-13
592086792086798.75260771528721e-13
602131882131885.23282335752739e-13
612162342162341.56760674881135e-12
622135862135863.71384662629272e-13
632094652094657.38235406634236e-13
642040452040459.92284099703928e-13
652002372002379.78480578626489e-13
662036662036663.9935086420544e-13
67241476241476-3.32926065237231e-13
682603072603074.30594196788541e-12
69243324243324-8.05630406330435e-13
702444602444609.0054975908016e-13
712335752335752.34131439811544e-12
722372172372174.81649338313562e-12
732352432352433.53718306392512e-12
742303542303545.26687728703994e-12
752271842271847.04029632156611e-12
762216782216784.48344728559046e-12
772171422171425.45714490180979e-12
782194522194524.18698336345293e-12
79256446256446-3.63996763418387e-15
802658452658457.69938420963754e-13
81248624248624-8.20915422577982e-13
82241114241114-1.41555918434759e-12
832292452292454.46932678056686e-12
842318052318051.916649159365e-12
852192772192774.69714470995271e-12
862193132193134.40580920989084e-12
872126102126101.39817413182222e-12
882147712147714.19319113366751e-12
892111422111427.18667149496467e-14
902114572114574.97672310517524e-14
91240048240048-1.08861227549445e-12
922406362406361.62409591846485e-12
93230580230580-3.93777190165507e-12
942087952087959.6480709826297e-13
95197922197922-5.81734122949822e-13
96194596194596-5.69337172787592e-13
97194581194581-6.51432458126833e-13
98185686185686-1.99430304105396e-13
991781061781061.72905361000239e-12
1001726081726081.78341193231155e-12
1011673021673025.48704848340123e-13
1021680531680531.94055391686616e-14
103202300202300-1.73831498904938e-12
104202388202388-4.36140685580225e-12
105182516182516-1.0216423564727e-12
1061734761734763.19136093455213e-14
107166444166444-1.26237677920536e-12
1081712971712971.41008634819307e-12
109169701169701-3.85635269120178e-13
110164182164182-4.10445972990069e-12
111161914161914-3.27959567281705e-12
112159612159612-3.41698427575876e-12
113151001151001-3.97679696272103e-12
114158114158114-5.66533104776535e-12
115186530186530-1.41206950473043e-12
116187069187069-1.13372551277866e-12
117174330174330-2.35316538897355e-14
1181693621693624.03975221563314e-14
1191668271668272.50198168134773e-13
1201780371780371.12021971873441e-12
121186413186413-1.77281045709533e-12
122189226189226-1.33921226822232e-12
123191563191563-1.03685713544055e-12
124188906188906-1.10212505979563e-12
125186005186005-6.28871774598698e-13
126195309195309-1.30188418526054e-12
1272235322235321.2392012355127e-12
128226899226899-1.57824844235397e-12
1292141262141262.11157778642299e-12
130206903206903-5.46217005620218e-13
131204442204442-2.7697757386235e-13
1322203752203757.56619059678355e-15
1332143202143201.79993900512059e-13
134212588212588-9.99299678460295e-13
135205816205816-1.46250714240333e-12
136202196202196-5.88091459633661e-13
137195722195722-1.21725134678181e-12
138198563198563-1.3121008653839e-12
1392291392291392.66135861273379e-12
140229527229527-1.98417462801873e-12
141211868211868-2.18438146757607e-12
142203555203555-6.17834467249515e-13
143195770195770-2.89410323136201e-12
144199834199834-2.18602619053094e-12
145203089203089-1.44056144010062e-12
146198480198480-1.5411954699832e-12
147192684192684-2.60764440613821e-12
148187827187827-1.88248814171977e-12
149182414182414-1.24030196438626e-12
150182510182510-1.33027300028451e-12
151211524211524-4.16942597675209e-12
152211451211451-9.15465196759124e-12
153200140200140-4.78766247886746e-12
154191568191568-3.81761374759325e-12







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.03430051847588680.06860103695177370.965699481524113
80.01510819635994680.03021639271989360.984891803640053
90.00672321730352980.01344643460705960.99327678269647
104.25471209849485e-058.5094241969897e-050.999957452879015
110.01466198480623250.0293239696124650.985338015193768
120.9998129561746280.0003740876507444390.000187043825372219
130.002258231305368110.004516462610736210.997741768694632
140.9999999999669456.61100567169423e-113.30550283584712e-11
155.64130278731289e-050.0001128260557462580.999943586972127
160.0003259769201621310.0006519538403242620.999674023079838
170.147963447503760.295926895007520.85203655249624
180.001022756264418270.002045512528836540.998977243735582
190.2671022580888290.5342045161776590.732897741911171
202.09244955370625e-124.18489910741251e-120.999999999997908
211.81270759620073e-063.62541519240146e-060.999998187292404
220.06963263046422070.1392652609284410.930367369535779
239.44869735311956e-061.88973947062391e-050.999990551302647
240.9998447298801750.0003105402396506940.000155270119825347
250.5823921829868370.8352156340263260.417607817013163
261.37668546713045e-082.75337093426089e-080.999999986233145
270.0001868035407440730.0003736070814881460.999813196459256
280.9967827036903450.006434592619310240.00321729630965512
293.60262252218539e-057.20524504437079e-050.999963973774778
300.9710615231375710.05787695372485720.0289384768624286
310.9999998528112722.94377456048462e-071.47188728024231e-07
321.03696900278872e-092.07393800557745e-090.999999998963031
338.41968309622226e-131.68393661924445e-120.999999999999158
341.08233117930911e-112.16466235861823e-110.999999999989177
350.9999998379401313.24119737549687e-071.62059868774843e-07
360.9999999999949351.01301454648128e-115.06507273240639e-12
370.6950047746524630.6099904506950740.304995225347537
380.003664874637807130.007329749275614250.996335125362193
393.54175361571049e-147.08350723142097e-140.999999999999965
407.97735314833562e-461.59547062966712e-451
412.6889235096411e-145.37784701928221e-140.999999999999973
421.37613974200991e-072.75227948401982e-070.999999862386026
430.7415401532255530.5169196935488940.258459846774447
440.0002646143814771090.0005292287629542180.999735385618523
450.8814316669776720.2371366660446560.118568333022328
463.92189171252384e-097.84378342504767e-090.999999996078108
479.12615399485094e-231.82523079897019e-221
486.29097176139447e-141.25819435227889e-130.999999999999937
492.71796794260044e-345.43593588520087e-341
500.9916936228547680.01661275429046450.00830637714523226
510.9999999999999967.14666571684403e-153.57333285842201e-15
520.009753210293155420.01950642058631080.990246789706845
530.9385556270391970.1228887459216050.0614443729608025
5411.15461021769865e-325.77305108849327e-33
550.9998514067858470.0002971864283067280.000148593214153364
560.9999998940888382.11822324086186e-071.05911162043093e-07
571.30205879003065e-122.6041175800613e-120.999999999998698
584.03388053218835e-208.06776106437671e-201
591.3022768879085e-282.60455377581699e-281
600.002478165160025080.004956330320050160.997521834839975
610.9762812810817050.04743743783658950.0237187189182948
628.32881313332212e-061.66576262666442e-050.999991671186867
638.04720438936853e-331.60944087787371e-321
649.84824198949816e-071.96964839789963e-060.999999015175801
650.9999999960953697.80926166779591e-093.90463083389795e-09
665.53297319013526e-091.10659463802705e-080.999999994467027
670.9999999999952419.51878747255433e-124.75939373627717e-12
6819.14913078666275e-504.57456539333138e-50
692.63765176816385e-085.2753035363277e-080.999999973623482
702.07736009658027e-354.15472019316053e-351
711.19691639649106e-052.39383279298211e-050.999988030836035
720.9640125260655790.07197494786884160.0359874739344208
730.5436511486692180.9126977026615650.456348851330782
743.55096525877705e-277.1019305175541e-271
750.9999998358240853.28351829389157e-071.64175914694579e-07
7612.17093259694221e-321.08546629847111e-32
770.003029967564601940.006059935129203890.996970032435398
782.98755746476645e-225.97511492953291e-221
790.9998871950789050.0002256098421909830.000112804921095492
800.9995166696897810.0009666606204382190.000483330310219109
810.9999976622503514.67549929870389e-062.33774964935195e-06
8211.29690631670495e-466.48453158352475e-47
830.9999990705210951.85895781058695e-069.29478905293474e-07
840.09810569038276930.1962113807655390.901894309617231
8516.50518585105976e-253.25259292552988e-25
860.9999629367956677.41264086651587e-053.70632043325793e-05
871.0563664575624e-262.1127329151248e-261
880.5636073277820580.8727853444358840.436392672217942
893.93934810116584e-307.87869620233168e-301
901.15745071978218e-242.31490143956436e-241
919.01318175134392e-221.80263635026878e-211
920.0002757460667890610.0005514921335781220.999724253933211
937.81654116009375e-441.56330823201875e-431
940.999257784715140.001484430569720120.000742215284860058
950.9986729269095790.002654146180842850.00132707309042142
960.9999999999999959.02743210353878e-154.51371605176939e-15
971.42142534283555e-052.8428506856711e-050.999985785746572
9814.21340654818343e-182.10670327409172e-18
9911.03989876479619e-285.19949382398096e-29
1003.37590415449691e-096.75180830899382e-090.999999996624096
1010.9999999997555864.88827875791764e-102.44413937895882e-10
1020.0002111429810108440.0004222859620216880.999788857018989
1036.87504237968675e-071.37500847593735e-060.999999312495762
1042.69282100309121e-685.38564200618243e-681
10511.02164009711014e-215.10820048555072e-22
1060.9999999999948011.0398014370757e-115.1990071853785e-12
1070.9752523454469750.04949530910604910.0247476545530246
1086.25277386938348e-571.2505547738767e-561
1093.23702551981693e-156.47405103963387e-150.999999999999997
1100.999999979055634.18887393848476e-082.09443696924238e-08
1110.9999999952004669.59906720766217e-094.79953360383108e-09
1123.41780957381129e-136.83561914762259e-130.999999999999658
1133.63866034594275e-427.27732069188551e-421
1142.22845694440546e-124.45691388881092e-120.999999999997772
1150.9999998821713662.35657268611551e-071.17828634305775e-07
1160.446950015211910.893900030423820.55304998478809
1170.9890916711957770.02181665760844630.0109083288042232
1180.0008391433038890150.001678286607778030.999160856696111
1190.9984438686755870.003112262648825420.00155613132441271
1206.65608165409165e-121.33121633081833e-110.999999999993344
1210.9853235559627390.02935288807452150.0146764440372608
1220.1872277068774690.3744554137549380.812772293122531
1230.9999997163589195.67282161737184e-072.83641080868592e-07
1242.88711186475342e-155.77422372950683e-150.999999999999997
1253.36449347272573e-196.72898694545146e-191
1260.1974740780807410.3949481561614820.802525921919259
1270.9999999999998752.50534904409831e-131.25267452204915e-13
1280.5052023279057980.9895953441884050.494797672094202
12911.84484532680813e-249.22422663404063e-25
1304.49800862220305e-208.9960172444061e-201
1310.9999991939076781.61218464352204e-068.06092321761019e-07
1320.9999999791359914.1728017063218e-082.0864008531609e-08
1330.1083525605658230.2167051211316460.891647439434177
1340.04918148624241290.09836297248482580.950818513757587
1350.9999783889609854.32220780305328e-052.16110390152664e-05
1361.30657545555738e-142.61315091111477e-140.999999999999987
1375.07184943102701e-441.0143698862054e-431
1380.6857755433964580.6284489132070830.314224456603542
1390.9458444443661670.1083111112676650.0541555556338326
1404.25608825297027e-058.51217650594055e-050.99995743911747
1410.9886940057645080.02261198847098390.0113059942354919
1421.97683698398632e-093.95367396797263e-090.999999998023163
1431.61740620484304e-323.23481240968608e-321
1440.3408851167416310.6817702334832610.659114883258369
1450.9993780524743380.001243895051324460.000621947525662228
1460.9998230783849770.0003538432300462980.000176921615023149
1470.8827367137938110.2345265724123780.117263286206189

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0343005184758868 & 0.0686010369517737 & 0.965699481524113 \tabularnewline
8 & 0.0151081963599468 & 0.0302163927198936 & 0.984891803640053 \tabularnewline
9 & 0.0067232173035298 & 0.0134464346070596 & 0.99327678269647 \tabularnewline
10 & 4.25471209849485e-05 & 8.5094241969897e-05 & 0.999957452879015 \tabularnewline
11 & 0.0146619848062325 & 0.029323969612465 & 0.985338015193768 \tabularnewline
12 & 0.999812956174628 & 0.000374087650744439 & 0.000187043825372219 \tabularnewline
13 & 0.00225823130536811 & 0.00451646261073621 & 0.997741768694632 \tabularnewline
14 & 0.999999999966945 & 6.61100567169423e-11 & 3.30550283584712e-11 \tabularnewline
15 & 5.64130278731289e-05 & 0.000112826055746258 & 0.999943586972127 \tabularnewline
16 & 0.000325976920162131 & 0.000651953840324262 & 0.999674023079838 \tabularnewline
17 & 0.14796344750376 & 0.29592689500752 & 0.85203655249624 \tabularnewline
18 & 0.00102275626441827 & 0.00204551252883654 & 0.998977243735582 \tabularnewline
19 & 0.267102258088829 & 0.534204516177659 & 0.732897741911171 \tabularnewline
20 & 2.09244955370625e-12 & 4.18489910741251e-12 & 0.999999999997908 \tabularnewline
21 & 1.81270759620073e-06 & 3.62541519240146e-06 & 0.999998187292404 \tabularnewline
22 & 0.0696326304642207 & 0.139265260928441 & 0.930367369535779 \tabularnewline
23 & 9.44869735311956e-06 & 1.88973947062391e-05 & 0.999990551302647 \tabularnewline
24 & 0.999844729880175 & 0.000310540239650694 & 0.000155270119825347 \tabularnewline
25 & 0.582392182986837 & 0.835215634026326 & 0.417607817013163 \tabularnewline
26 & 1.37668546713045e-08 & 2.75337093426089e-08 & 0.999999986233145 \tabularnewline
27 & 0.000186803540744073 & 0.000373607081488146 & 0.999813196459256 \tabularnewline
28 & 0.996782703690345 & 0.00643459261931024 & 0.00321729630965512 \tabularnewline
29 & 3.60262252218539e-05 & 7.20524504437079e-05 & 0.999963973774778 \tabularnewline
30 & 0.971061523137571 & 0.0578769537248572 & 0.0289384768624286 \tabularnewline
31 & 0.999999852811272 & 2.94377456048462e-07 & 1.47188728024231e-07 \tabularnewline
32 & 1.03696900278872e-09 & 2.07393800557745e-09 & 0.999999998963031 \tabularnewline
33 & 8.41968309622226e-13 & 1.68393661924445e-12 & 0.999999999999158 \tabularnewline
34 & 1.08233117930911e-11 & 2.16466235861823e-11 & 0.999999999989177 \tabularnewline
35 & 0.999999837940131 & 3.24119737549687e-07 & 1.62059868774843e-07 \tabularnewline
36 & 0.999999999994935 & 1.01301454648128e-11 & 5.06507273240639e-12 \tabularnewline
37 & 0.695004774652463 & 0.609990450695074 & 0.304995225347537 \tabularnewline
38 & 0.00366487463780713 & 0.00732974927561425 & 0.996335125362193 \tabularnewline
39 & 3.54175361571049e-14 & 7.08350723142097e-14 & 0.999999999999965 \tabularnewline
40 & 7.97735314833562e-46 & 1.59547062966712e-45 & 1 \tabularnewline
41 & 2.6889235096411e-14 & 5.37784701928221e-14 & 0.999999999999973 \tabularnewline
42 & 1.37613974200991e-07 & 2.75227948401982e-07 & 0.999999862386026 \tabularnewline
43 & 0.741540153225553 & 0.516919693548894 & 0.258459846774447 \tabularnewline
44 & 0.000264614381477109 & 0.000529228762954218 & 0.999735385618523 \tabularnewline
45 & 0.881431666977672 & 0.237136666044656 & 0.118568333022328 \tabularnewline
46 & 3.92189171252384e-09 & 7.84378342504767e-09 & 0.999999996078108 \tabularnewline
47 & 9.12615399485094e-23 & 1.82523079897019e-22 & 1 \tabularnewline
48 & 6.29097176139447e-14 & 1.25819435227889e-13 & 0.999999999999937 \tabularnewline
49 & 2.71796794260044e-34 & 5.43593588520087e-34 & 1 \tabularnewline
50 & 0.991693622854768 & 0.0166127542904645 & 0.00830637714523226 \tabularnewline
51 & 0.999999999999996 & 7.14666571684403e-15 & 3.57333285842201e-15 \tabularnewline
52 & 0.00975321029315542 & 0.0195064205863108 & 0.990246789706845 \tabularnewline
53 & 0.938555627039197 & 0.122888745921605 & 0.0614443729608025 \tabularnewline
54 & 1 & 1.15461021769865e-32 & 5.77305108849327e-33 \tabularnewline
55 & 0.999851406785847 & 0.000297186428306728 & 0.000148593214153364 \tabularnewline
56 & 0.999999894088838 & 2.11822324086186e-07 & 1.05911162043093e-07 \tabularnewline
57 & 1.30205879003065e-12 & 2.6041175800613e-12 & 0.999999999998698 \tabularnewline
58 & 4.03388053218835e-20 & 8.06776106437671e-20 & 1 \tabularnewline
59 & 1.3022768879085e-28 & 2.60455377581699e-28 & 1 \tabularnewline
60 & 0.00247816516002508 & 0.00495633032005016 & 0.997521834839975 \tabularnewline
61 & 0.976281281081705 & 0.0474374378365895 & 0.0237187189182948 \tabularnewline
62 & 8.32881313332212e-06 & 1.66576262666442e-05 & 0.999991671186867 \tabularnewline
63 & 8.04720438936853e-33 & 1.60944087787371e-32 & 1 \tabularnewline
64 & 9.84824198949816e-07 & 1.96964839789963e-06 & 0.999999015175801 \tabularnewline
65 & 0.999999996095369 & 7.80926166779591e-09 & 3.90463083389795e-09 \tabularnewline
66 & 5.53297319013526e-09 & 1.10659463802705e-08 & 0.999999994467027 \tabularnewline
67 & 0.999999999995241 & 9.51878747255433e-12 & 4.75939373627717e-12 \tabularnewline
68 & 1 & 9.14913078666275e-50 & 4.57456539333138e-50 \tabularnewline
69 & 2.63765176816385e-08 & 5.2753035363277e-08 & 0.999999973623482 \tabularnewline
70 & 2.07736009658027e-35 & 4.15472019316053e-35 & 1 \tabularnewline
71 & 1.19691639649106e-05 & 2.39383279298211e-05 & 0.999988030836035 \tabularnewline
72 & 0.964012526065579 & 0.0719749478688416 & 0.0359874739344208 \tabularnewline
73 & 0.543651148669218 & 0.912697702661565 & 0.456348851330782 \tabularnewline
74 & 3.55096525877705e-27 & 7.1019305175541e-27 & 1 \tabularnewline
75 & 0.999999835824085 & 3.28351829389157e-07 & 1.64175914694579e-07 \tabularnewline
76 & 1 & 2.17093259694221e-32 & 1.08546629847111e-32 \tabularnewline
77 & 0.00302996756460194 & 0.00605993512920389 & 0.996970032435398 \tabularnewline
78 & 2.98755746476645e-22 & 5.97511492953291e-22 & 1 \tabularnewline
79 & 0.999887195078905 & 0.000225609842190983 & 0.000112804921095492 \tabularnewline
80 & 0.999516669689781 & 0.000966660620438219 & 0.000483330310219109 \tabularnewline
81 & 0.999997662250351 & 4.67549929870389e-06 & 2.33774964935195e-06 \tabularnewline
82 & 1 & 1.29690631670495e-46 & 6.48453158352475e-47 \tabularnewline
83 & 0.999999070521095 & 1.85895781058695e-06 & 9.29478905293474e-07 \tabularnewline
84 & 0.0981056903827693 & 0.196211380765539 & 0.901894309617231 \tabularnewline
85 & 1 & 6.50518585105976e-25 & 3.25259292552988e-25 \tabularnewline
86 & 0.999962936795667 & 7.41264086651587e-05 & 3.70632043325793e-05 \tabularnewline
87 & 1.0563664575624e-26 & 2.1127329151248e-26 & 1 \tabularnewline
88 & 0.563607327782058 & 0.872785344435884 & 0.436392672217942 \tabularnewline
89 & 3.93934810116584e-30 & 7.87869620233168e-30 & 1 \tabularnewline
90 & 1.15745071978218e-24 & 2.31490143956436e-24 & 1 \tabularnewline
91 & 9.01318175134392e-22 & 1.80263635026878e-21 & 1 \tabularnewline
92 & 0.000275746066789061 & 0.000551492133578122 & 0.999724253933211 \tabularnewline
93 & 7.81654116009375e-44 & 1.56330823201875e-43 & 1 \tabularnewline
94 & 0.99925778471514 & 0.00148443056972012 & 0.000742215284860058 \tabularnewline
95 & 0.998672926909579 & 0.00265414618084285 & 0.00132707309042142 \tabularnewline
96 & 0.999999999999995 & 9.02743210353878e-15 & 4.51371605176939e-15 \tabularnewline
97 & 1.42142534283555e-05 & 2.8428506856711e-05 & 0.999985785746572 \tabularnewline
98 & 1 & 4.21340654818343e-18 & 2.10670327409172e-18 \tabularnewline
99 & 1 & 1.03989876479619e-28 & 5.19949382398096e-29 \tabularnewline
100 & 3.37590415449691e-09 & 6.75180830899382e-09 & 0.999999996624096 \tabularnewline
101 & 0.999999999755586 & 4.88827875791764e-10 & 2.44413937895882e-10 \tabularnewline
102 & 0.000211142981010844 & 0.000422285962021688 & 0.999788857018989 \tabularnewline
103 & 6.87504237968675e-07 & 1.37500847593735e-06 & 0.999999312495762 \tabularnewline
104 & 2.69282100309121e-68 & 5.38564200618243e-68 & 1 \tabularnewline
105 & 1 & 1.02164009711014e-21 & 5.10820048555072e-22 \tabularnewline
106 & 0.999999999994801 & 1.0398014370757e-11 & 5.1990071853785e-12 \tabularnewline
107 & 0.975252345446975 & 0.0494953091060491 & 0.0247476545530246 \tabularnewline
108 & 6.25277386938348e-57 & 1.2505547738767e-56 & 1 \tabularnewline
109 & 3.23702551981693e-15 & 6.47405103963387e-15 & 0.999999999999997 \tabularnewline
110 & 0.99999997905563 & 4.18887393848476e-08 & 2.09443696924238e-08 \tabularnewline
111 & 0.999999995200466 & 9.59906720766217e-09 & 4.79953360383108e-09 \tabularnewline
112 & 3.41780957381129e-13 & 6.83561914762259e-13 & 0.999999999999658 \tabularnewline
113 & 3.63866034594275e-42 & 7.27732069188551e-42 & 1 \tabularnewline
114 & 2.22845694440546e-12 & 4.45691388881092e-12 & 0.999999999997772 \tabularnewline
115 & 0.999999882171366 & 2.35657268611551e-07 & 1.17828634305775e-07 \tabularnewline
116 & 0.44695001521191 & 0.89390003042382 & 0.55304998478809 \tabularnewline
117 & 0.989091671195777 & 0.0218166576084463 & 0.0109083288042232 \tabularnewline
118 & 0.000839143303889015 & 0.00167828660777803 & 0.999160856696111 \tabularnewline
119 & 0.998443868675587 & 0.00311226264882542 & 0.00155613132441271 \tabularnewline
120 & 6.65608165409165e-12 & 1.33121633081833e-11 & 0.999999999993344 \tabularnewline
121 & 0.985323555962739 & 0.0293528880745215 & 0.0146764440372608 \tabularnewline
122 & 0.187227706877469 & 0.374455413754938 & 0.812772293122531 \tabularnewline
123 & 0.999999716358919 & 5.67282161737184e-07 & 2.83641080868592e-07 \tabularnewline
124 & 2.88711186475342e-15 & 5.77422372950683e-15 & 0.999999999999997 \tabularnewline
125 & 3.36449347272573e-19 & 6.72898694545146e-19 & 1 \tabularnewline
126 & 0.197474078080741 & 0.394948156161482 & 0.802525921919259 \tabularnewline
127 & 0.999999999999875 & 2.50534904409831e-13 & 1.25267452204915e-13 \tabularnewline
128 & 0.505202327905798 & 0.989595344188405 & 0.494797672094202 \tabularnewline
129 & 1 & 1.84484532680813e-24 & 9.22422663404063e-25 \tabularnewline
130 & 4.49800862220305e-20 & 8.9960172444061e-20 & 1 \tabularnewline
131 & 0.999999193907678 & 1.61218464352204e-06 & 8.06092321761019e-07 \tabularnewline
132 & 0.999999979135991 & 4.1728017063218e-08 & 2.0864008531609e-08 \tabularnewline
133 & 0.108352560565823 & 0.216705121131646 & 0.891647439434177 \tabularnewline
134 & 0.0491814862424129 & 0.0983629724848258 & 0.950818513757587 \tabularnewline
135 & 0.999978388960985 & 4.32220780305328e-05 & 2.16110390152664e-05 \tabularnewline
136 & 1.30657545555738e-14 & 2.61315091111477e-14 & 0.999999999999987 \tabularnewline
137 & 5.07184943102701e-44 & 1.0143698862054e-43 & 1 \tabularnewline
138 & 0.685775543396458 & 0.628448913207083 & 0.314224456603542 \tabularnewline
139 & 0.945844444366167 & 0.108311111267665 & 0.0541555556338326 \tabularnewline
140 & 4.25608825297027e-05 & 8.51217650594055e-05 & 0.99995743911747 \tabularnewline
141 & 0.988694005764508 & 0.0226119884709839 & 0.0113059942354919 \tabularnewline
142 & 1.97683698398632e-09 & 3.95367396797263e-09 & 0.999999998023163 \tabularnewline
143 & 1.61740620484304e-32 & 3.23481240968608e-32 & 1 \tabularnewline
144 & 0.340885116741631 & 0.681770233483261 & 0.659114883258369 \tabularnewline
145 & 0.999378052474338 & 0.00124389505132446 & 0.000621947525662228 \tabularnewline
146 & 0.999823078384977 & 0.000353843230046298 & 0.000176921615023149 \tabularnewline
147 & 0.882736713793811 & 0.234526572412378 & 0.117263286206189 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145644&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]7[/C][C]0.0343005184758868[/C][C]0.0686010369517737[/C][C]0.965699481524113[/C][/ROW]
[ROW][C]8[/C][C]0.0151081963599468[/C][C]0.0302163927198936[/C][C]0.984891803640053[/C][/ROW]
[ROW][C]9[/C][C]0.0067232173035298[/C][C]0.0134464346070596[/C][C]0.99327678269647[/C][/ROW]
[ROW][C]10[/C][C]4.25471209849485e-05[/C][C]8.5094241969897e-05[/C][C]0.999957452879015[/C][/ROW]
[ROW][C]11[/C][C]0.0146619848062325[/C][C]0.029323969612465[/C][C]0.985338015193768[/C][/ROW]
[ROW][C]12[/C][C]0.999812956174628[/C][C]0.000374087650744439[/C][C]0.000187043825372219[/C][/ROW]
[ROW][C]13[/C][C]0.00225823130536811[/C][C]0.00451646261073621[/C][C]0.997741768694632[/C][/ROW]
[ROW][C]14[/C][C]0.999999999966945[/C][C]6.61100567169423e-11[/C][C]3.30550283584712e-11[/C][/ROW]
[ROW][C]15[/C][C]5.64130278731289e-05[/C][C]0.000112826055746258[/C][C]0.999943586972127[/C][/ROW]
[ROW][C]16[/C][C]0.000325976920162131[/C][C]0.000651953840324262[/C][C]0.999674023079838[/C][/ROW]
[ROW][C]17[/C][C]0.14796344750376[/C][C]0.29592689500752[/C][C]0.85203655249624[/C][/ROW]
[ROW][C]18[/C][C]0.00102275626441827[/C][C]0.00204551252883654[/C][C]0.998977243735582[/C][/ROW]
[ROW][C]19[/C][C]0.267102258088829[/C][C]0.534204516177659[/C][C]0.732897741911171[/C][/ROW]
[ROW][C]20[/C][C]2.09244955370625e-12[/C][C]4.18489910741251e-12[/C][C]0.999999999997908[/C][/ROW]
[ROW][C]21[/C][C]1.81270759620073e-06[/C][C]3.62541519240146e-06[/C][C]0.999998187292404[/C][/ROW]
[ROW][C]22[/C][C]0.0696326304642207[/C][C]0.139265260928441[/C][C]0.930367369535779[/C][/ROW]
[ROW][C]23[/C][C]9.44869735311956e-06[/C][C]1.88973947062391e-05[/C][C]0.999990551302647[/C][/ROW]
[ROW][C]24[/C][C]0.999844729880175[/C][C]0.000310540239650694[/C][C]0.000155270119825347[/C][/ROW]
[ROW][C]25[/C][C]0.582392182986837[/C][C]0.835215634026326[/C][C]0.417607817013163[/C][/ROW]
[ROW][C]26[/C][C]1.37668546713045e-08[/C][C]2.75337093426089e-08[/C][C]0.999999986233145[/C][/ROW]
[ROW][C]27[/C][C]0.000186803540744073[/C][C]0.000373607081488146[/C][C]0.999813196459256[/C][/ROW]
[ROW][C]28[/C][C]0.996782703690345[/C][C]0.00643459261931024[/C][C]0.00321729630965512[/C][/ROW]
[ROW][C]29[/C][C]3.60262252218539e-05[/C][C]7.20524504437079e-05[/C][C]0.999963973774778[/C][/ROW]
[ROW][C]30[/C][C]0.971061523137571[/C][C]0.0578769537248572[/C][C]0.0289384768624286[/C][/ROW]
[ROW][C]31[/C][C]0.999999852811272[/C][C]2.94377456048462e-07[/C][C]1.47188728024231e-07[/C][/ROW]
[ROW][C]32[/C][C]1.03696900278872e-09[/C][C]2.07393800557745e-09[/C][C]0.999999998963031[/C][/ROW]
[ROW][C]33[/C][C]8.41968309622226e-13[/C][C]1.68393661924445e-12[/C][C]0.999999999999158[/C][/ROW]
[ROW][C]34[/C][C]1.08233117930911e-11[/C][C]2.16466235861823e-11[/C][C]0.999999999989177[/C][/ROW]
[ROW][C]35[/C][C]0.999999837940131[/C][C]3.24119737549687e-07[/C][C]1.62059868774843e-07[/C][/ROW]
[ROW][C]36[/C][C]0.999999999994935[/C][C]1.01301454648128e-11[/C][C]5.06507273240639e-12[/C][/ROW]
[ROW][C]37[/C][C]0.695004774652463[/C][C]0.609990450695074[/C][C]0.304995225347537[/C][/ROW]
[ROW][C]38[/C][C]0.00366487463780713[/C][C]0.00732974927561425[/C][C]0.996335125362193[/C][/ROW]
[ROW][C]39[/C][C]3.54175361571049e-14[/C][C]7.08350723142097e-14[/C][C]0.999999999999965[/C][/ROW]
[ROW][C]40[/C][C]7.97735314833562e-46[/C][C]1.59547062966712e-45[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]2.6889235096411e-14[/C][C]5.37784701928221e-14[/C][C]0.999999999999973[/C][/ROW]
[ROW][C]42[/C][C]1.37613974200991e-07[/C][C]2.75227948401982e-07[/C][C]0.999999862386026[/C][/ROW]
[ROW][C]43[/C][C]0.741540153225553[/C][C]0.516919693548894[/C][C]0.258459846774447[/C][/ROW]
[ROW][C]44[/C][C]0.000264614381477109[/C][C]0.000529228762954218[/C][C]0.999735385618523[/C][/ROW]
[ROW][C]45[/C][C]0.881431666977672[/C][C]0.237136666044656[/C][C]0.118568333022328[/C][/ROW]
[ROW][C]46[/C][C]3.92189171252384e-09[/C][C]7.84378342504767e-09[/C][C]0.999999996078108[/C][/ROW]
[ROW][C]47[/C][C]9.12615399485094e-23[/C][C]1.82523079897019e-22[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]6.29097176139447e-14[/C][C]1.25819435227889e-13[/C][C]0.999999999999937[/C][/ROW]
[ROW][C]49[/C][C]2.71796794260044e-34[/C][C]5.43593588520087e-34[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0.991693622854768[/C][C]0.0166127542904645[/C][C]0.00830637714523226[/C][/ROW]
[ROW][C]51[/C][C]0.999999999999996[/C][C]7.14666571684403e-15[/C][C]3.57333285842201e-15[/C][/ROW]
[ROW][C]52[/C][C]0.00975321029315542[/C][C]0.0195064205863108[/C][C]0.990246789706845[/C][/ROW]
[ROW][C]53[/C][C]0.938555627039197[/C][C]0.122888745921605[/C][C]0.0614443729608025[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]1.15461021769865e-32[/C][C]5.77305108849327e-33[/C][/ROW]
[ROW][C]55[/C][C]0.999851406785847[/C][C]0.000297186428306728[/C][C]0.000148593214153364[/C][/ROW]
[ROW][C]56[/C][C]0.999999894088838[/C][C]2.11822324086186e-07[/C][C]1.05911162043093e-07[/C][/ROW]
[ROW][C]57[/C][C]1.30205879003065e-12[/C][C]2.6041175800613e-12[/C][C]0.999999999998698[/C][/ROW]
[ROW][C]58[/C][C]4.03388053218835e-20[/C][C]8.06776106437671e-20[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]1.3022768879085e-28[/C][C]2.60455377581699e-28[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]0.00247816516002508[/C][C]0.00495633032005016[/C][C]0.997521834839975[/C][/ROW]
[ROW][C]61[/C][C]0.976281281081705[/C][C]0.0474374378365895[/C][C]0.0237187189182948[/C][/ROW]
[ROW][C]62[/C][C]8.32881313332212e-06[/C][C]1.66576262666442e-05[/C][C]0.999991671186867[/C][/ROW]
[ROW][C]63[/C][C]8.04720438936853e-33[/C][C]1.60944087787371e-32[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]9.84824198949816e-07[/C][C]1.96964839789963e-06[/C][C]0.999999015175801[/C][/ROW]
[ROW][C]65[/C][C]0.999999996095369[/C][C]7.80926166779591e-09[/C][C]3.90463083389795e-09[/C][/ROW]
[ROW][C]66[/C][C]5.53297319013526e-09[/C][C]1.10659463802705e-08[/C][C]0.999999994467027[/C][/ROW]
[ROW][C]67[/C][C]0.999999999995241[/C][C]9.51878747255433e-12[/C][C]4.75939373627717e-12[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]9.14913078666275e-50[/C][C]4.57456539333138e-50[/C][/ROW]
[ROW][C]69[/C][C]2.63765176816385e-08[/C][C]5.2753035363277e-08[/C][C]0.999999973623482[/C][/ROW]
[ROW][C]70[/C][C]2.07736009658027e-35[/C][C]4.15472019316053e-35[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]1.19691639649106e-05[/C][C]2.39383279298211e-05[/C][C]0.999988030836035[/C][/ROW]
[ROW][C]72[/C][C]0.964012526065579[/C][C]0.0719749478688416[/C][C]0.0359874739344208[/C][/ROW]
[ROW][C]73[/C][C]0.543651148669218[/C][C]0.912697702661565[/C][C]0.456348851330782[/C][/ROW]
[ROW][C]74[/C][C]3.55096525877705e-27[/C][C]7.1019305175541e-27[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]0.999999835824085[/C][C]3.28351829389157e-07[/C][C]1.64175914694579e-07[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]2.17093259694221e-32[/C][C]1.08546629847111e-32[/C][/ROW]
[ROW][C]77[/C][C]0.00302996756460194[/C][C]0.00605993512920389[/C][C]0.996970032435398[/C][/ROW]
[ROW][C]78[/C][C]2.98755746476645e-22[/C][C]5.97511492953291e-22[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]0.999887195078905[/C][C]0.000225609842190983[/C][C]0.000112804921095492[/C][/ROW]
[ROW][C]80[/C][C]0.999516669689781[/C][C]0.000966660620438219[/C][C]0.000483330310219109[/C][/ROW]
[ROW][C]81[/C][C]0.999997662250351[/C][C]4.67549929870389e-06[/C][C]2.33774964935195e-06[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]1.29690631670495e-46[/C][C]6.48453158352475e-47[/C][/ROW]
[ROW][C]83[/C][C]0.999999070521095[/C][C]1.85895781058695e-06[/C][C]9.29478905293474e-07[/C][/ROW]
[ROW][C]84[/C][C]0.0981056903827693[/C][C]0.196211380765539[/C][C]0.901894309617231[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]6.50518585105976e-25[/C][C]3.25259292552988e-25[/C][/ROW]
[ROW][C]86[/C][C]0.999962936795667[/C][C]7.41264086651587e-05[/C][C]3.70632043325793e-05[/C][/ROW]
[ROW][C]87[/C][C]1.0563664575624e-26[/C][C]2.1127329151248e-26[/C][C]1[/C][/ROW]
[ROW][C]88[/C][C]0.563607327782058[/C][C]0.872785344435884[/C][C]0.436392672217942[/C][/ROW]
[ROW][C]89[/C][C]3.93934810116584e-30[/C][C]7.87869620233168e-30[/C][C]1[/C][/ROW]
[ROW][C]90[/C][C]1.15745071978218e-24[/C][C]2.31490143956436e-24[/C][C]1[/C][/ROW]
[ROW][C]91[/C][C]9.01318175134392e-22[/C][C]1.80263635026878e-21[/C][C]1[/C][/ROW]
[ROW][C]92[/C][C]0.000275746066789061[/C][C]0.000551492133578122[/C][C]0.999724253933211[/C][/ROW]
[ROW][C]93[/C][C]7.81654116009375e-44[/C][C]1.56330823201875e-43[/C][C]1[/C][/ROW]
[ROW][C]94[/C][C]0.99925778471514[/C][C]0.00148443056972012[/C][C]0.000742215284860058[/C][/ROW]
[ROW][C]95[/C][C]0.998672926909579[/C][C]0.00265414618084285[/C][C]0.00132707309042142[/C][/ROW]
[ROW][C]96[/C][C]0.999999999999995[/C][C]9.02743210353878e-15[/C][C]4.51371605176939e-15[/C][/ROW]
[ROW][C]97[/C][C]1.42142534283555e-05[/C][C]2.8428506856711e-05[/C][C]0.999985785746572[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]4.21340654818343e-18[/C][C]2.10670327409172e-18[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.03989876479619e-28[/C][C]5.19949382398096e-29[/C][/ROW]
[ROW][C]100[/C][C]3.37590415449691e-09[/C][C]6.75180830899382e-09[/C][C]0.999999996624096[/C][/ROW]
[ROW][C]101[/C][C]0.999999999755586[/C][C]4.88827875791764e-10[/C][C]2.44413937895882e-10[/C][/ROW]
[ROW][C]102[/C][C]0.000211142981010844[/C][C]0.000422285962021688[/C][C]0.999788857018989[/C][/ROW]
[ROW][C]103[/C][C]6.87504237968675e-07[/C][C]1.37500847593735e-06[/C][C]0.999999312495762[/C][/ROW]
[ROW][C]104[/C][C]2.69282100309121e-68[/C][C]5.38564200618243e-68[/C][C]1[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]1.02164009711014e-21[/C][C]5.10820048555072e-22[/C][/ROW]
[ROW][C]106[/C][C]0.999999999994801[/C][C]1.0398014370757e-11[/C][C]5.1990071853785e-12[/C][/ROW]
[ROW][C]107[/C][C]0.975252345446975[/C][C]0.0494953091060491[/C][C]0.0247476545530246[/C][/ROW]
[ROW][C]108[/C][C]6.25277386938348e-57[/C][C]1.2505547738767e-56[/C][C]1[/C][/ROW]
[ROW][C]109[/C][C]3.23702551981693e-15[/C][C]6.47405103963387e-15[/C][C]0.999999999999997[/C][/ROW]
[ROW][C]110[/C][C]0.99999997905563[/C][C]4.18887393848476e-08[/C][C]2.09443696924238e-08[/C][/ROW]
[ROW][C]111[/C][C]0.999999995200466[/C][C]9.59906720766217e-09[/C][C]4.79953360383108e-09[/C][/ROW]
[ROW][C]112[/C][C]3.41780957381129e-13[/C][C]6.83561914762259e-13[/C][C]0.999999999999658[/C][/ROW]
[ROW][C]113[/C][C]3.63866034594275e-42[/C][C]7.27732069188551e-42[/C][C]1[/C][/ROW]
[ROW][C]114[/C][C]2.22845694440546e-12[/C][C]4.45691388881092e-12[/C][C]0.999999999997772[/C][/ROW]
[ROW][C]115[/C][C]0.999999882171366[/C][C]2.35657268611551e-07[/C][C]1.17828634305775e-07[/C][/ROW]
[ROW][C]116[/C][C]0.44695001521191[/C][C]0.89390003042382[/C][C]0.55304998478809[/C][/ROW]
[ROW][C]117[/C][C]0.989091671195777[/C][C]0.0218166576084463[/C][C]0.0109083288042232[/C][/ROW]
[ROW][C]118[/C][C]0.000839143303889015[/C][C]0.00167828660777803[/C][C]0.999160856696111[/C][/ROW]
[ROW][C]119[/C][C]0.998443868675587[/C][C]0.00311226264882542[/C][C]0.00155613132441271[/C][/ROW]
[ROW][C]120[/C][C]6.65608165409165e-12[/C][C]1.33121633081833e-11[/C][C]0.999999999993344[/C][/ROW]
[ROW][C]121[/C][C]0.985323555962739[/C][C]0.0293528880745215[/C][C]0.0146764440372608[/C][/ROW]
[ROW][C]122[/C][C]0.187227706877469[/C][C]0.374455413754938[/C][C]0.812772293122531[/C][/ROW]
[ROW][C]123[/C][C]0.999999716358919[/C][C]5.67282161737184e-07[/C][C]2.83641080868592e-07[/C][/ROW]
[ROW][C]124[/C][C]2.88711186475342e-15[/C][C]5.77422372950683e-15[/C][C]0.999999999999997[/C][/ROW]
[ROW][C]125[/C][C]3.36449347272573e-19[/C][C]6.72898694545146e-19[/C][C]1[/C][/ROW]
[ROW][C]126[/C][C]0.197474078080741[/C][C]0.394948156161482[/C][C]0.802525921919259[/C][/ROW]
[ROW][C]127[/C][C]0.999999999999875[/C][C]2.50534904409831e-13[/C][C]1.25267452204915e-13[/C][/ROW]
[ROW][C]128[/C][C]0.505202327905798[/C][C]0.989595344188405[/C][C]0.494797672094202[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]1.84484532680813e-24[/C][C]9.22422663404063e-25[/C][/ROW]
[ROW][C]130[/C][C]4.49800862220305e-20[/C][C]8.9960172444061e-20[/C][C]1[/C][/ROW]
[ROW][C]131[/C][C]0.999999193907678[/C][C]1.61218464352204e-06[/C][C]8.06092321761019e-07[/C][/ROW]
[ROW][C]132[/C][C]0.999999979135991[/C][C]4.1728017063218e-08[/C][C]2.0864008531609e-08[/C][/ROW]
[ROW][C]133[/C][C]0.108352560565823[/C][C]0.216705121131646[/C][C]0.891647439434177[/C][/ROW]
[ROW][C]134[/C][C]0.0491814862424129[/C][C]0.0983629724848258[/C][C]0.950818513757587[/C][/ROW]
[ROW][C]135[/C][C]0.999978388960985[/C][C]4.32220780305328e-05[/C][C]2.16110390152664e-05[/C][/ROW]
[ROW][C]136[/C][C]1.30657545555738e-14[/C][C]2.61315091111477e-14[/C][C]0.999999999999987[/C][/ROW]
[ROW][C]137[/C][C]5.07184943102701e-44[/C][C]1.0143698862054e-43[/C][C]1[/C][/ROW]
[ROW][C]138[/C][C]0.685775543396458[/C][C]0.628448913207083[/C][C]0.314224456603542[/C][/ROW]
[ROW][C]139[/C][C]0.945844444366167[/C][C]0.108311111267665[/C][C]0.0541555556338326[/C][/ROW]
[ROW][C]140[/C][C]4.25608825297027e-05[/C][C]8.51217650594055e-05[/C][C]0.99995743911747[/C][/ROW]
[ROW][C]141[/C][C]0.988694005764508[/C][C]0.0226119884709839[/C][C]0.0113059942354919[/C][/ROW]
[ROW][C]142[/C][C]1.97683698398632e-09[/C][C]3.95367396797263e-09[/C][C]0.999999998023163[/C][/ROW]
[ROW][C]143[/C][C]1.61740620484304e-32[/C][C]3.23481240968608e-32[/C][C]1[/C][/ROW]
[ROW][C]144[/C][C]0.340885116741631[/C][C]0.681770233483261[/C][C]0.659114883258369[/C][/ROW]
[ROW][C]145[/C][C]0.999378052474338[/C][C]0.00124389505132446[/C][C]0.000621947525662228[/C][/ROW]
[ROW][C]146[/C][C]0.999823078384977[/C][C]0.000353843230046298[/C][C]0.000176921615023149[/C][/ROW]
[ROW][C]147[/C][C]0.882736713793811[/C][C]0.234526572412378[/C][C]0.117263286206189[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145644&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145644&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.03430051847588680.06860103695177370.965699481524113
80.01510819635994680.03021639271989360.984891803640053
90.00672321730352980.01344643460705960.99327678269647
104.25471209849485e-058.5094241969897e-050.999957452879015
110.01466198480623250.0293239696124650.985338015193768
120.9998129561746280.0003740876507444390.000187043825372219
130.002258231305368110.004516462610736210.997741768694632
140.9999999999669456.61100567169423e-113.30550283584712e-11
155.64130278731289e-050.0001128260557462580.999943586972127
160.0003259769201621310.0006519538403242620.999674023079838
170.147963447503760.295926895007520.85203655249624
180.001022756264418270.002045512528836540.998977243735582
190.2671022580888290.5342045161776590.732897741911171
202.09244955370625e-124.18489910741251e-120.999999999997908
211.81270759620073e-063.62541519240146e-060.999998187292404
220.06963263046422070.1392652609284410.930367369535779
239.44869735311956e-061.88973947062391e-050.999990551302647
240.9998447298801750.0003105402396506940.000155270119825347
250.5823921829868370.8352156340263260.417607817013163
261.37668546713045e-082.75337093426089e-080.999999986233145
270.0001868035407440730.0003736070814881460.999813196459256
280.9967827036903450.006434592619310240.00321729630965512
293.60262252218539e-057.20524504437079e-050.999963973774778
300.9710615231375710.05787695372485720.0289384768624286
310.9999998528112722.94377456048462e-071.47188728024231e-07
321.03696900278872e-092.07393800557745e-090.999999998963031
338.41968309622226e-131.68393661924445e-120.999999999999158
341.08233117930911e-112.16466235861823e-110.999999999989177
350.9999998379401313.24119737549687e-071.62059868774843e-07
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370.6950047746524630.6099904506950740.304995225347537
380.003664874637807130.007329749275614250.996335125362193
393.54175361571049e-147.08350723142097e-140.999999999999965
407.97735314833562e-461.59547062966712e-451
412.6889235096411e-145.37784701928221e-140.999999999999973
421.37613974200991e-072.75227948401982e-070.999999862386026
430.7415401532255530.5169196935488940.258459846774447
440.0002646143814771090.0005292287629542180.999735385618523
450.8814316669776720.2371366660446560.118568333022328
463.92189171252384e-097.84378342504767e-090.999999996078108
479.12615399485094e-231.82523079897019e-221
486.29097176139447e-141.25819435227889e-130.999999999999937
492.71796794260044e-345.43593588520087e-341
500.9916936228547680.01661275429046450.00830637714523226
510.9999999999999967.14666571684403e-153.57333285842201e-15
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530.9385556270391970.1228887459216050.0614443729608025
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550.9998514067858470.0002971864283067280.000148593214153364
560.9999998940888382.11822324086186e-071.05911162043093e-07
571.30205879003065e-122.6041175800613e-120.999999999998698
584.03388053218835e-208.06776106437671e-201
591.3022768879085e-282.60455377581699e-281
600.002478165160025080.004956330320050160.997521834839975
610.9762812810817050.04743743783658950.0237187189182948
628.32881313332212e-061.66576262666442e-050.999991671186867
638.04720438936853e-331.60944087787371e-321
649.84824198949816e-071.96964839789963e-060.999999015175801
650.9999999960953697.80926166779591e-093.90463083389795e-09
665.53297319013526e-091.10659463802705e-080.999999994467027
670.9999999999952419.51878747255433e-124.75939373627717e-12
6819.14913078666275e-504.57456539333138e-50
692.63765176816385e-085.2753035363277e-080.999999973623482
702.07736009658027e-354.15472019316053e-351
711.19691639649106e-052.39383279298211e-050.999988030836035
720.9640125260655790.07197494786884160.0359874739344208
730.5436511486692180.9126977026615650.456348851330782
743.55096525877705e-277.1019305175541e-271
750.9999998358240853.28351829389157e-071.64175914694579e-07
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770.003029967564601940.006059935129203890.996970032435398
782.98755746476645e-225.97511492953291e-221
790.9998871950789050.0002256098421909830.000112804921095492
800.9995166696897810.0009666606204382190.000483330310219109
810.9999976622503514.67549929870389e-062.33774964935195e-06
8211.29690631670495e-466.48453158352475e-47
830.9999990705210951.85895781058695e-069.29478905293474e-07
840.09810569038276930.1962113807655390.901894309617231
8516.50518585105976e-253.25259292552988e-25
860.9999629367956677.41264086651587e-053.70632043325793e-05
871.0563664575624e-262.1127329151248e-261
880.5636073277820580.8727853444358840.436392672217942
893.93934810116584e-307.87869620233168e-301
901.15745071978218e-242.31490143956436e-241
919.01318175134392e-221.80263635026878e-211
920.0002757460667890610.0005514921335781220.999724253933211
937.81654116009375e-441.56330823201875e-431
940.999257784715140.001484430569720120.000742215284860058
950.9986729269095790.002654146180842850.00132707309042142
960.9999999999999959.02743210353878e-154.51371605176939e-15
971.42142534283555e-052.8428506856711e-050.999985785746572
9814.21340654818343e-182.10670327409172e-18
9911.03989876479619e-285.19949382398096e-29
1003.37590415449691e-096.75180830899382e-090.999999996624096
1010.9999999997555864.88827875791764e-102.44413937895882e-10
1020.0002111429810108440.0004222859620216880.999788857018989
1036.87504237968675e-071.37500847593735e-060.999999312495762
1042.69282100309121e-685.38564200618243e-681
10511.02164009711014e-215.10820048555072e-22
1060.9999999999948011.0398014370757e-115.1990071853785e-12
1070.9752523454469750.04949530910604910.0247476545530246
1086.25277386938348e-571.2505547738767e-561
1093.23702551981693e-156.47405103963387e-150.999999999999997
1100.999999979055634.18887393848476e-082.09443696924238e-08
1110.9999999952004669.59906720766217e-094.79953360383108e-09
1123.41780957381129e-136.83561914762259e-130.999999999999658
1133.63866034594275e-427.27732069188551e-421
1142.22845694440546e-124.45691388881092e-120.999999999997772
1150.9999998821713662.35657268611551e-071.17828634305775e-07
1160.446950015211910.893900030423820.55304998478809
1170.9890916711957770.02181665760844630.0109083288042232
1180.0008391433038890150.001678286607778030.999160856696111
1190.9984438686755870.003112262648825420.00155613132441271
1206.65608165409165e-121.33121633081833e-110.999999999993344
1210.9853235559627390.02935288807452150.0146764440372608
1220.1872277068774690.3744554137549380.812772293122531
1230.9999997163589195.67282161737184e-072.83641080868592e-07
1242.88711186475342e-155.77422372950683e-150.999999999999997
1253.36449347272573e-196.72898694545146e-191
1260.1974740780807410.3949481561614820.802525921919259
1270.9999999999998752.50534904409831e-131.25267452204915e-13
1280.5052023279057980.9895953441884050.494797672094202
12911.84484532680813e-249.22422663404063e-25
1304.49800862220305e-208.9960172444061e-201
1310.9999991939076781.61218464352204e-068.06092321761019e-07
1320.9999999791359914.1728017063218e-082.0864008531609e-08
1330.1083525605658230.2167051211316460.891647439434177
1340.04918148624241290.09836297248482580.950818513757587
1350.9999783889609854.32220780305328e-052.16110390152664e-05
1361.30657545555738e-142.61315091111477e-140.999999999999987
1375.07184943102701e-441.0143698862054e-431
1380.6857755433964580.6284489132070830.314224456603542
1390.9458444443661670.1083111112676650.0541555556338326
1404.25608825297027e-058.51217650594055e-050.99995743911747
1410.9886940057645080.02261198847098390.0113059942354919
1421.97683698398632e-093.95367396797263e-090.999999998023163
1431.61740620484304e-323.23481240968608e-321
1440.3408851167416310.6817702334832610.659114883258369
1450.9993780524743380.001243895051324460.000621947525662228
1460.9998230783849770.0003538432300462980.000176921615023149
1470.8827367137938110.2345265724123780.117263286206189







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1070.75886524822695NOK
5% type I error level1170.829787234042553NOK
10% type I error level1210.858156028368794NOK

\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 & 0.75886524822695 & NOK \tabularnewline
5% type I error level & 117 & 0.829787234042553 & NOK \tabularnewline
10% type I error level & 121 & 0.858156028368794 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145644&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]0.75886524822695[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]117[/C][C]0.829787234042553[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]121[/C][C]0.858156028368794[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145644&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145644&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1070.75886524822695NOK
5% type I error level1170.829787234042553NOK
10% type I error level1210.858156028368794NOK



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