FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 19 Dec 2010 11:46:47 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/19/t1292759279ivlokg9tkbbpdql.htm/, Retrieved Sat, 04 May 2024 20:48:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112301, Retrieved Sat, 04 May 2024 20:48:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple regressi...] [2010-12-19 11:46:47] [0605ea080d54454c99180f574351b8e4] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15561600	15,73	3,56	142,86
14917500	16,17	1,33	380,71
14805920	12,00	0,00	460,00
16958000	12,86	0,69	361,43
17605000	10,30	10,05	140,00
17131200	12,97	0,51	275,00
18474600	12,06	0,91	274,29
17286700	10,49	2,67	212,86
18574400	5,97	1,39	172,86
18056000	9,26	1,24	186,43
19701600	9,74	2,79	77,14
19061700	5,46	3,37	17,86
19681900	2,71	1,60	37,14
34521200	3,90	4,73	42,86
19922700	1,51	0,79	85,00
20177900	5,01	0,67	45,00
19759900	2,96	0,00	206,43
23076700	-1,97	0,60	178,57
22532000	-4,61	0,40	285,71
22029400	4,27	2,24	58,57
22587000	4,01	5,74	88,57
23256600	0,04	0,06	309,29
22680300	3,04	0,87	58,57
21916400	2,29	4,91	132,14
19640200	4,37	1,93	3,57
18813100	6,39	0,41	102,86
18730000	5,74	1,21	185,71
18154700	7,64	2,01	177,14
17848800	7,07	0,00	530,00
18077500	6,23	6,49	162,86
17133100	10,20	0,00	553,57
16602600	14,07	0,31	258,57
15878900	12,83	4,87	326,43
15789100	12,04	1,37	580,00
15422000	11,97	0,19	286,43
14661400	12,63	0,34	310,71
15879200	13,56	3,60	148,57
14339300	15,66	0,10	627,14
13169600	16,34	2,10	477,86
14528900	14,09	0,10	385,71
13375800	15,03	7,27	327,86
12309900	16,09	0,76	402,14
11933900	19,27	1,09	567,86
10061900	22,50	0,34	678,57
12609600	16,07	4,13	253,57
11156500	19,11	1,89	459,29
12187200	18,66	3,80	331,43
11284300	18,29	2,47	421,43
10177000	20,26	0,00	595,00
10970720	19,20	1,01	425,71
10820680	20,10	1,21	603,57
11492390	17,93	0,54	420,00
14573750	16,11	2,86	308,57
13992820	16,90	0,04	325,00
14727070	16,14	1,03	319,29
15685360	15,04	0,23	452,86
16736210	13,41	0,20	83,57
17950180	14,14	13,87	99,43
17002730	9,59	0,36	312,71
17415160	10,74	0,56	128,00
17929810	11,67	1,98	152,67
17865790	8,09	3,83	135,00
19202360	10,07	1,46	57,71
19085000	11,80	2,00	190,43
18188880	12,01	4,96	12,86
18466410	6,61	2,76	32,43
18520400	6,47	2,10	38,29
20025500	-3,11	2,09	210,14
20636100	1,94	2,21	109,14
20672000	1,10	2,90	71,43
22589100	-3,40	0,57	102,29
21864800	1,64	1,79	48,43
22750100	3,11	0,80	70,43
22548746	-0,16	2,66	139,86
21325495	3,80	1,70	83,14
21556563	-2,39	0,79	27,71
21415269	1,51	0,30	96,14
20401054	7,24	8,09	40,57
19062253	2,00	0,97	364,71
19085706	2,11	0,07	207,43
19279967	10,54	1,47	156,29
18552045	11,10	2,74	229,00
17800733	7,34	3,14	160,43
17142490	9,53	0,96	357,43
17593173	9,71	0,00	542,00
17633859	10,14	0,00	578,43
17336613	13,93	2,80	427,43
17008347	8,33	0,23	130,29
17951965	8,31	2,69	174,29
14520929	13,83	0,23	679,14
16941217	14,50	3,60	389,43
15436824	16,71	0,93	532,57
14744261	16,49	2,56	253,71
14248004	14,57	0,74	414,14
11540953	19,04	0,07	719,71
12881661	22,84	0,76	639,86
15185757	22,23	2,73	619,71
13554339	19,56	4,30	507,14
13575106	19,76	0,19	463,86
12238400	18,36	1,19	254,14
13303614	16,99	1,43	226,29
14151478	16,87	9,63	299,57
14172009	18,50	10,44	274,00
14022320	16,51	4,36	253,29

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112301&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112301&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112301&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Kijkcijfers[t] = + 22305689.4422709 -434868.354365401Temperatuur[t] + 126331.126374946Neerslag[t] -2972.05562531293Zonneschijnduur[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Kijkcijfers[t] =  +  22305689.4422709 -434868.354365401Temperatuur[t] +  126331.126374946Neerslag[t] -2972.05562531293Zonneschijnduur[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112301&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Kijkcijfers[t] =  +  22305689.4422709 -434868.354365401Temperatuur[t] +  126331.126374946Neerslag[t] -2972.05562531293Zonneschijnduur[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112301&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112301&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Kijkcijfers[t] = + 22305689.4422709 -434868.354365401Temperatuur[t] + 126331.126374946Neerslag[t] -2972.05562531293Zonneschijnduur[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)22305689.4422709416070.77586153.610300
Temperatuur-434868.35436540139673.981852-10.96100
Neerslag126331.12637494684185.1881561.50060.1366010.068301
Zonneschijnduur-2972.055625312931457.840352-2.03870.0441220.022061

\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) & 22305689.4422709 & 416070.775861 & 53.6103 & 0 & 0 \tabularnewline
Temperatuur & -434868.354365401 & 39673.981852 & -10.961 & 0 & 0 \tabularnewline
Neerslag & 126331.126374946 & 84185.188156 & 1.5006 & 0.136601 & 0.068301 \tabularnewline
Zonneschijnduur & -2972.05562531293 & 1457.840352 & -2.0387 & 0.044122 & 0.022061 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112301&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]22305689.4422709[/C][C]416070.775861[/C][C]53.6103[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Temperatuur[/C][C]-434868.354365401[/C][C]39673.981852[/C][C]-10.961[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Neerslag[/C][C]126331.126374946[/C][C]84185.188156[/C][C]1.5006[/C][C]0.136601[/C][C]0.068301[/C][/ROW]
[ROW][C]Zonneschijnduur[/C][C]-2972.05562531293[/C][C]1457.840352[/C][C]-2.0387[/C][C]0.044122[/C][C]0.022061[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112301&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112301&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)22305689.4422709416070.77586153.610300
Temperatuur-434868.35436540139673.981852-10.96100
Neerslag126331.12637494684185.1881561.50060.1366010.068301
Zonneschijnduur-2972.055625312931457.840352-2.03870.0441220.022061






Multiple Linear Regression - Regression Statistics
Multiple R0.869955756825498
R-squared0.756823018833825
Adjusted R-squared0.74952770939884
F-TEST (value)103.741044239250
F-TEST (DF numerator)3
F-TEST (DF denominator)100
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1878997.01130159
Sum Squared Residuals353062976848032

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.869955756825498 \tabularnewline
R-squared & 0.756823018833825 \tabularnewline
Adjusted R-squared & 0.74952770939884 \tabularnewline
F-TEST (value) & 103.741044239250 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 100 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1878997.01130159 \tabularnewline
Sum Squared Residuals & 353062976848032 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112301&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.869955756825498[/C][/ROW]
[ROW][C]R-squared[/C][C]0.756823018833825[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.74952770939884[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]103.741044239250[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]100[/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]1878997.01130159[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]353062976848032[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112301&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112301&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.869955756825498
R-squared0.756823018833825
Adjusted R-squared0.74952770939884
F-TEST (value)103.741044239250
F-TEST (DF numerator)3
F-TEST (DF denominator)100
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1878997.01130159
Sum Squared Residuals353062976848032






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11556160015490361.171365671238.82863437
21491750014310397.2531482607102.74685184
31480592015720123.6022421-914203.602242134
41695800015726260.81767371231739.18232631
51760500018680085.4248317-1075085.42483165
61713120015912560.46364181218639.53635820
71847460016360933.27615832113666.72384173
81728670017448592.7519948-161892.751994826
91857440019371376.0969790-796976.096979023
101805600017881378.7473251174621.252674884
111970160018193271.14240131508328.85759866
121906170020303963.2098513-1242263.20985127
131968190021218943.8582164-1537043.85821644
143452120021079866.783898413441333.2161016
151992270021496215.0888637-1573515.08886374
162017790020077898.3384324100001.661567644
171975990020404957.6706159-645057.670615948
182307670022707458.8031836369241.196816442
192253200023511818.9937372-979818.9937372
202202940020557709.99423591471690.00576408
212258700021023773.03992391563226.96007615
222325660021376647.49132571879952.50867427
232268030020919524.42697171760775.57302831
242191640021537399.3109462379000.689053752
251964020020638523.5690154-998323.56901536
261881310019272970.77807-459870.778070012
271873000019410465.3009503-680465.300950303
281815470018710750.8454649-556050.845464931
291784880017655980.6954916192819.304508351
301807750019932319.6256094-1854819.62560937
311713310016224791.3952393908308.60476068
321660260015457769.92248881144830.07751123
331587890016371392.9234379-492492.923437881
341578910015519155.8361636269944.163836361
351542200016273032.2617699-851032.261769895
361466140015932807.3062624-1271407.30626237
371587920016422108.3077731-542908.307773114
381433930013644389.1606875694910.839312545
391316960014045009.3962156-875409.396215589
401452890015044675.8666604-515775.866660434
411337580015713627.2075897-2337827.20758967
421230990014209486.8274132-1899586.82741321
431193390012375765.6740081-441865.674008106
441006190010547356.2663483-485456.26634826
451260960015085478.3946368-2475878.39463682
461115650012869085.5910468-1712585.59104675
471218720013686075.8341398-1498875.83413984
481128430013411471.7208982-2127171.72089820
491017700011726883.4857667-1549883.48576668
501097072012818577.6758419-1847857.67584192
511082068011923852.5686699-1103172.56866989
521149239013328455.2941103-1836065.29411029
531457375014744180.0705738-170430.070573815
541399282013995549.4203239-2729.42032390920
551472707014468087.6223733258982.377626654
561568536014448400.44120231236959.55879772
571673621016250996.3468984485213.653101552
581795018017613352.1435398336827.856460245
591700273017251389.6148101-248659.614810066
601741516017325525.627116489634.3728836058
611792981017027167.6447325902642.355267474
621786579018870225.1600536-1004435.16005359
631920236017939491.22818191262868.77181809
641908500016860936.56078072224063.43921929
651818888017671302.2578206517577.742179371
661846641019683499.7647815-1217089.76478154
671852040019643586.5450209-1123186.54502089
682002550023297614.3093677-3272114.30936766
692063610021416866.473144-780766.473143981
702067200021981400.5856402-1309400.58564018
712258910023552239.0192337-963139.019233702
722186480021674701.4033889190098.596611128
732275010020844991.88360371905108.11639635
742254874622295637.4753704253108.524629565
752132549520620855.9058313704639.09416875
762155656323362470.7376630-1805907.73766297
772141526921401204.137274014064.8627259743
782040105420058685.0723197342368.927680251
791906225320474555.5190159-1412302.5190159
801908570620780466.8950475-1694760.89504747
811927996717443381.16935061836585.83064943
821855204517144197.25688561407847.74311437
831780073319033628.5740772-1232895.57407722
841714249017220370.0643330-77880.0643329632
851759317316472263.57246321120909.42753676
861763385916176998.19365601456860.80634404
871733661315331354.68388322005258.31611680
881700834718325063.0820513-1316716.08205131
891795196518513764.5725072-561799.572507217
901452092914302074.4030886218854.596911391
911694121715297482.73675681643734.26324323
921543682413573699.52398081863124.47601917
931474426114704077.729607140183.2703928535
941424800414832295.4360174-584291.43601736
951154095311895620.9999059-354667.999905936
961288166110567608.37219742314052.62780264
971518575711141637.30816904044119.69183104
981355433912835639.9844747718699.015525282
991357510612358075.95166421217030.04833585
1001223840013716522.2798913-1478122.27989129
1011330361414425383.1448668-1121769.14486684
1021415147815295690.3474423-1144212.34744231
1031417200914765178.6045297-593169.604529667
1041402232014924024.6533574-901704.653357371

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15561600 & 15490361.1713656 & 71238.82863437 \tabularnewline
2 & 14917500 & 14310397.2531482 & 607102.74685184 \tabularnewline
3 & 14805920 & 15720123.6022421 & -914203.602242134 \tabularnewline
4 & 16958000 & 15726260.8176737 & 1231739.18232631 \tabularnewline
5 & 17605000 & 18680085.4248317 & -1075085.42483165 \tabularnewline
6 & 17131200 & 15912560.4636418 & 1218639.53635820 \tabularnewline
7 & 18474600 & 16360933.2761583 & 2113666.72384173 \tabularnewline
8 & 17286700 & 17448592.7519948 & -161892.751994826 \tabularnewline
9 & 18574400 & 19371376.0969790 & -796976.096979023 \tabularnewline
10 & 18056000 & 17881378.7473251 & 174621.252674884 \tabularnewline
11 & 19701600 & 18193271.1424013 & 1508328.85759866 \tabularnewline
12 & 19061700 & 20303963.2098513 & -1242263.20985127 \tabularnewline
13 & 19681900 & 21218943.8582164 & -1537043.85821644 \tabularnewline
14 & 34521200 & 21079866.7838984 & 13441333.2161016 \tabularnewline
15 & 19922700 & 21496215.0888637 & -1573515.08886374 \tabularnewline
16 & 20177900 & 20077898.3384324 & 100001.661567644 \tabularnewline
17 & 19759900 & 20404957.6706159 & -645057.670615948 \tabularnewline
18 & 23076700 & 22707458.8031836 & 369241.196816442 \tabularnewline
19 & 22532000 & 23511818.9937372 & -979818.9937372 \tabularnewline
20 & 22029400 & 20557709.9942359 & 1471690.00576408 \tabularnewline
21 & 22587000 & 21023773.0399239 & 1563226.96007615 \tabularnewline
22 & 23256600 & 21376647.4913257 & 1879952.50867427 \tabularnewline
23 & 22680300 & 20919524.4269717 & 1760775.57302831 \tabularnewline
24 & 21916400 & 21537399.3109462 & 379000.689053752 \tabularnewline
25 & 19640200 & 20638523.5690154 & -998323.56901536 \tabularnewline
26 & 18813100 & 19272970.77807 & -459870.778070012 \tabularnewline
27 & 18730000 & 19410465.3009503 & -680465.300950303 \tabularnewline
28 & 18154700 & 18710750.8454649 & -556050.845464931 \tabularnewline
29 & 17848800 & 17655980.6954916 & 192819.304508351 \tabularnewline
30 & 18077500 & 19932319.6256094 & -1854819.62560937 \tabularnewline
31 & 17133100 & 16224791.3952393 & 908308.60476068 \tabularnewline
32 & 16602600 & 15457769.9224888 & 1144830.07751123 \tabularnewline
33 & 15878900 & 16371392.9234379 & -492492.923437881 \tabularnewline
34 & 15789100 & 15519155.8361636 & 269944.163836361 \tabularnewline
35 & 15422000 & 16273032.2617699 & -851032.261769895 \tabularnewline
36 & 14661400 & 15932807.3062624 & -1271407.30626237 \tabularnewline
37 & 15879200 & 16422108.3077731 & -542908.307773114 \tabularnewline
38 & 14339300 & 13644389.1606875 & 694910.839312545 \tabularnewline
39 & 13169600 & 14045009.3962156 & -875409.396215589 \tabularnewline
40 & 14528900 & 15044675.8666604 & -515775.866660434 \tabularnewline
41 & 13375800 & 15713627.2075897 & -2337827.20758967 \tabularnewline
42 & 12309900 & 14209486.8274132 & -1899586.82741321 \tabularnewline
43 & 11933900 & 12375765.6740081 & -441865.674008106 \tabularnewline
44 & 10061900 & 10547356.2663483 & -485456.26634826 \tabularnewline
45 & 12609600 & 15085478.3946368 & -2475878.39463682 \tabularnewline
46 & 11156500 & 12869085.5910468 & -1712585.59104675 \tabularnewline
47 & 12187200 & 13686075.8341398 & -1498875.83413984 \tabularnewline
48 & 11284300 & 13411471.7208982 & -2127171.72089820 \tabularnewline
49 & 10177000 & 11726883.4857667 & -1549883.48576668 \tabularnewline
50 & 10970720 & 12818577.6758419 & -1847857.67584192 \tabularnewline
51 & 10820680 & 11923852.5686699 & -1103172.56866989 \tabularnewline
52 & 11492390 & 13328455.2941103 & -1836065.29411029 \tabularnewline
53 & 14573750 & 14744180.0705738 & -170430.070573815 \tabularnewline
54 & 13992820 & 13995549.4203239 & -2729.42032390920 \tabularnewline
55 & 14727070 & 14468087.6223733 & 258982.377626654 \tabularnewline
56 & 15685360 & 14448400.4412023 & 1236959.55879772 \tabularnewline
57 & 16736210 & 16250996.3468984 & 485213.653101552 \tabularnewline
58 & 17950180 & 17613352.1435398 & 336827.856460245 \tabularnewline
59 & 17002730 & 17251389.6148101 & -248659.614810066 \tabularnewline
60 & 17415160 & 17325525.6271164 & 89634.3728836058 \tabularnewline
61 & 17929810 & 17027167.6447325 & 902642.355267474 \tabularnewline
62 & 17865790 & 18870225.1600536 & -1004435.16005359 \tabularnewline
63 & 19202360 & 17939491.2281819 & 1262868.77181809 \tabularnewline
64 & 19085000 & 16860936.5607807 & 2224063.43921929 \tabularnewline
65 & 18188880 & 17671302.2578206 & 517577.742179371 \tabularnewline
66 & 18466410 & 19683499.7647815 & -1217089.76478154 \tabularnewline
67 & 18520400 & 19643586.5450209 & -1123186.54502089 \tabularnewline
68 & 20025500 & 23297614.3093677 & -3272114.30936766 \tabularnewline
69 & 20636100 & 21416866.473144 & -780766.473143981 \tabularnewline
70 & 20672000 & 21981400.5856402 & -1309400.58564018 \tabularnewline
71 & 22589100 & 23552239.0192337 & -963139.019233702 \tabularnewline
72 & 21864800 & 21674701.4033889 & 190098.596611128 \tabularnewline
73 & 22750100 & 20844991.8836037 & 1905108.11639635 \tabularnewline
74 & 22548746 & 22295637.4753704 & 253108.524629565 \tabularnewline
75 & 21325495 & 20620855.9058313 & 704639.09416875 \tabularnewline
76 & 21556563 & 23362470.7376630 & -1805907.73766297 \tabularnewline
77 & 21415269 & 21401204.1372740 & 14064.8627259743 \tabularnewline
78 & 20401054 & 20058685.0723197 & 342368.927680251 \tabularnewline
79 & 19062253 & 20474555.5190159 & -1412302.5190159 \tabularnewline
80 & 19085706 & 20780466.8950475 & -1694760.89504747 \tabularnewline
81 & 19279967 & 17443381.1693506 & 1836585.83064943 \tabularnewline
82 & 18552045 & 17144197.2568856 & 1407847.74311437 \tabularnewline
83 & 17800733 & 19033628.5740772 & -1232895.57407722 \tabularnewline
84 & 17142490 & 17220370.0643330 & -77880.0643329632 \tabularnewline
85 & 17593173 & 16472263.5724632 & 1120909.42753676 \tabularnewline
86 & 17633859 & 16176998.1936560 & 1456860.80634404 \tabularnewline
87 & 17336613 & 15331354.6838832 & 2005258.31611680 \tabularnewline
88 & 17008347 & 18325063.0820513 & -1316716.08205131 \tabularnewline
89 & 17951965 & 18513764.5725072 & -561799.572507217 \tabularnewline
90 & 14520929 & 14302074.4030886 & 218854.596911391 \tabularnewline
91 & 16941217 & 15297482.7367568 & 1643734.26324323 \tabularnewline
92 & 15436824 & 13573699.5239808 & 1863124.47601917 \tabularnewline
93 & 14744261 & 14704077.7296071 & 40183.2703928535 \tabularnewline
94 & 14248004 & 14832295.4360174 & -584291.43601736 \tabularnewline
95 & 11540953 & 11895620.9999059 & -354667.999905936 \tabularnewline
96 & 12881661 & 10567608.3721974 & 2314052.62780264 \tabularnewline
97 & 15185757 & 11141637.3081690 & 4044119.69183104 \tabularnewline
98 & 13554339 & 12835639.9844747 & 718699.015525282 \tabularnewline
99 & 13575106 & 12358075.9516642 & 1217030.04833585 \tabularnewline
100 & 12238400 & 13716522.2798913 & -1478122.27989129 \tabularnewline
101 & 13303614 & 14425383.1448668 & -1121769.14486684 \tabularnewline
102 & 14151478 & 15295690.3474423 & -1144212.34744231 \tabularnewline
103 & 14172009 & 14765178.6045297 & -593169.604529667 \tabularnewline
104 & 14022320 & 14924024.6533574 & -901704.653357371 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112301&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]15561600[/C][C]15490361.1713656[/C][C]71238.82863437[/C][/ROW]
[ROW][C]2[/C][C]14917500[/C][C]14310397.2531482[/C][C]607102.74685184[/C][/ROW]
[ROW][C]3[/C][C]14805920[/C][C]15720123.6022421[/C][C]-914203.602242134[/C][/ROW]
[ROW][C]4[/C][C]16958000[/C][C]15726260.8176737[/C][C]1231739.18232631[/C][/ROW]
[ROW][C]5[/C][C]17605000[/C][C]18680085.4248317[/C][C]-1075085.42483165[/C][/ROW]
[ROW][C]6[/C][C]17131200[/C][C]15912560.4636418[/C][C]1218639.53635820[/C][/ROW]
[ROW][C]7[/C][C]18474600[/C][C]16360933.2761583[/C][C]2113666.72384173[/C][/ROW]
[ROW][C]8[/C][C]17286700[/C][C]17448592.7519948[/C][C]-161892.751994826[/C][/ROW]
[ROW][C]9[/C][C]18574400[/C][C]19371376.0969790[/C][C]-796976.096979023[/C][/ROW]
[ROW][C]10[/C][C]18056000[/C][C]17881378.7473251[/C][C]174621.252674884[/C][/ROW]
[ROW][C]11[/C][C]19701600[/C][C]18193271.1424013[/C][C]1508328.85759866[/C][/ROW]
[ROW][C]12[/C][C]19061700[/C][C]20303963.2098513[/C][C]-1242263.20985127[/C][/ROW]
[ROW][C]13[/C][C]19681900[/C][C]21218943.8582164[/C][C]-1537043.85821644[/C][/ROW]
[ROW][C]14[/C][C]34521200[/C][C]21079866.7838984[/C][C]13441333.2161016[/C][/ROW]
[ROW][C]15[/C][C]19922700[/C][C]21496215.0888637[/C][C]-1573515.08886374[/C][/ROW]
[ROW][C]16[/C][C]20177900[/C][C]20077898.3384324[/C][C]100001.661567644[/C][/ROW]
[ROW][C]17[/C][C]19759900[/C][C]20404957.6706159[/C][C]-645057.670615948[/C][/ROW]
[ROW][C]18[/C][C]23076700[/C][C]22707458.8031836[/C][C]369241.196816442[/C][/ROW]
[ROW][C]19[/C][C]22532000[/C][C]23511818.9937372[/C][C]-979818.9937372[/C][/ROW]
[ROW][C]20[/C][C]22029400[/C][C]20557709.9942359[/C][C]1471690.00576408[/C][/ROW]
[ROW][C]21[/C][C]22587000[/C][C]21023773.0399239[/C][C]1563226.96007615[/C][/ROW]
[ROW][C]22[/C][C]23256600[/C][C]21376647.4913257[/C][C]1879952.50867427[/C][/ROW]
[ROW][C]23[/C][C]22680300[/C][C]20919524.4269717[/C][C]1760775.57302831[/C][/ROW]
[ROW][C]24[/C][C]21916400[/C][C]21537399.3109462[/C][C]379000.689053752[/C][/ROW]
[ROW][C]25[/C][C]19640200[/C][C]20638523.5690154[/C][C]-998323.56901536[/C][/ROW]
[ROW][C]26[/C][C]18813100[/C][C]19272970.77807[/C][C]-459870.778070012[/C][/ROW]
[ROW][C]27[/C][C]18730000[/C][C]19410465.3009503[/C][C]-680465.300950303[/C][/ROW]
[ROW][C]28[/C][C]18154700[/C][C]18710750.8454649[/C][C]-556050.845464931[/C][/ROW]
[ROW][C]29[/C][C]17848800[/C][C]17655980.6954916[/C][C]192819.304508351[/C][/ROW]
[ROW][C]30[/C][C]18077500[/C][C]19932319.6256094[/C][C]-1854819.62560937[/C][/ROW]
[ROW][C]31[/C][C]17133100[/C][C]16224791.3952393[/C][C]908308.60476068[/C][/ROW]
[ROW][C]32[/C][C]16602600[/C][C]15457769.9224888[/C][C]1144830.07751123[/C][/ROW]
[ROW][C]33[/C][C]15878900[/C][C]16371392.9234379[/C][C]-492492.923437881[/C][/ROW]
[ROW][C]34[/C][C]15789100[/C][C]15519155.8361636[/C][C]269944.163836361[/C][/ROW]
[ROW][C]35[/C][C]15422000[/C][C]16273032.2617699[/C][C]-851032.261769895[/C][/ROW]
[ROW][C]36[/C][C]14661400[/C][C]15932807.3062624[/C][C]-1271407.30626237[/C][/ROW]
[ROW][C]37[/C][C]15879200[/C][C]16422108.3077731[/C][C]-542908.307773114[/C][/ROW]
[ROW][C]38[/C][C]14339300[/C][C]13644389.1606875[/C][C]694910.839312545[/C][/ROW]
[ROW][C]39[/C][C]13169600[/C][C]14045009.3962156[/C][C]-875409.396215589[/C][/ROW]
[ROW][C]40[/C][C]14528900[/C][C]15044675.8666604[/C][C]-515775.866660434[/C][/ROW]
[ROW][C]41[/C][C]13375800[/C][C]15713627.2075897[/C][C]-2337827.20758967[/C][/ROW]
[ROW][C]42[/C][C]12309900[/C][C]14209486.8274132[/C][C]-1899586.82741321[/C][/ROW]
[ROW][C]43[/C][C]11933900[/C][C]12375765.6740081[/C][C]-441865.674008106[/C][/ROW]
[ROW][C]44[/C][C]10061900[/C][C]10547356.2663483[/C][C]-485456.26634826[/C][/ROW]
[ROW][C]45[/C][C]12609600[/C][C]15085478.3946368[/C][C]-2475878.39463682[/C][/ROW]
[ROW][C]46[/C][C]11156500[/C][C]12869085.5910468[/C][C]-1712585.59104675[/C][/ROW]
[ROW][C]47[/C][C]12187200[/C][C]13686075.8341398[/C][C]-1498875.83413984[/C][/ROW]
[ROW][C]48[/C][C]11284300[/C][C]13411471.7208982[/C][C]-2127171.72089820[/C][/ROW]
[ROW][C]49[/C][C]10177000[/C][C]11726883.4857667[/C][C]-1549883.48576668[/C][/ROW]
[ROW][C]50[/C][C]10970720[/C][C]12818577.6758419[/C][C]-1847857.67584192[/C][/ROW]
[ROW][C]51[/C][C]10820680[/C][C]11923852.5686699[/C][C]-1103172.56866989[/C][/ROW]
[ROW][C]52[/C][C]11492390[/C][C]13328455.2941103[/C][C]-1836065.29411029[/C][/ROW]
[ROW][C]53[/C][C]14573750[/C][C]14744180.0705738[/C][C]-170430.070573815[/C][/ROW]
[ROW][C]54[/C][C]13992820[/C][C]13995549.4203239[/C][C]-2729.42032390920[/C][/ROW]
[ROW][C]55[/C][C]14727070[/C][C]14468087.6223733[/C][C]258982.377626654[/C][/ROW]
[ROW][C]56[/C][C]15685360[/C][C]14448400.4412023[/C][C]1236959.55879772[/C][/ROW]
[ROW][C]57[/C][C]16736210[/C][C]16250996.3468984[/C][C]485213.653101552[/C][/ROW]
[ROW][C]58[/C][C]17950180[/C][C]17613352.1435398[/C][C]336827.856460245[/C][/ROW]
[ROW][C]59[/C][C]17002730[/C][C]17251389.6148101[/C][C]-248659.614810066[/C][/ROW]
[ROW][C]60[/C][C]17415160[/C][C]17325525.6271164[/C][C]89634.3728836058[/C][/ROW]
[ROW][C]61[/C][C]17929810[/C][C]17027167.6447325[/C][C]902642.355267474[/C][/ROW]
[ROW][C]62[/C][C]17865790[/C][C]18870225.1600536[/C][C]-1004435.16005359[/C][/ROW]
[ROW][C]63[/C][C]19202360[/C][C]17939491.2281819[/C][C]1262868.77181809[/C][/ROW]
[ROW][C]64[/C][C]19085000[/C][C]16860936.5607807[/C][C]2224063.43921929[/C][/ROW]
[ROW][C]65[/C][C]18188880[/C][C]17671302.2578206[/C][C]517577.742179371[/C][/ROW]
[ROW][C]66[/C][C]18466410[/C][C]19683499.7647815[/C][C]-1217089.76478154[/C][/ROW]
[ROW][C]67[/C][C]18520400[/C][C]19643586.5450209[/C][C]-1123186.54502089[/C][/ROW]
[ROW][C]68[/C][C]20025500[/C][C]23297614.3093677[/C][C]-3272114.30936766[/C][/ROW]
[ROW][C]69[/C][C]20636100[/C][C]21416866.473144[/C][C]-780766.473143981[/C][/ROW]
[ROW][C]70[/C][C]20672000[/C][C]21981400.5856402[/C][C]-1309400.58564018[/C][/ROW]
[ROW][C]71[/C][C]22589100[/C][C]23552239.0192337[/C][C]-963139.019233702[/C][/ROW]
[ROW][C]72[/C][C]21864800[/C][C]21674701.4033889[/C][C]190098.596611128[/C][/ROW]
[ROW][C]73[/C][C]22750100[/C][C]20844991.8836037[/C][C]1905108.11639635[/C][/ROW]
[ROW][C]74[/C][C]22548746[/C][C]22295637.4753704[/C][C]253108.524629565[/C][/ROW]
[ROW][C]75[/C][C]21325495[/C][C]20620855.9058313[/C][C]704639.09416875[/C][/ROW]
[ROW][C]76[/C][C]21556563[/C][C]23362470.7376630[/C][C]-1805907.73766297[/C][/ROW]
[ROW][C]77[/C][C]21415269[/C][C]21401204.1372740[/C][C]14064.8627259743[/C][/ROW]
[ROW][C]78[/C][C]20401054[/C][C]20058685.0723197[/C][C]342368.927680251[/C][/ROW]
[ROW][C]79[/C][C]19062253[/C][C]20474555.5190159[/C][C]-1412302.5190159[/C][/ROW]
[ROW][C]80[/C][C]19085706[/C][C]20780466.8950475[/C][C]-1694760.89504747[/C][/ROW]
[ROW][C]81[/C][C]19279967[/C][C]17443381.1693506[/C][C]1836585.83064943[/C][/ROW]
[ROW][C]82[/C][C]18552045[/C][C]17144197.2568856[/C][C]1407847.74311437[/C][/ROW]
[ROW][C]83[/C][C]17800733[/C][C]19033628.5740772[/C][C]-1232895.57407722[/C][/ROW]
[ROW][C]84[/C][C]17142490[/C][C]17220370.0643330[/C][C]-77880.0643329632[/C][/ROW]
[ROW][C]85[/C][C]17593173[/C][C]16472263.5724632[/C][C]1120909.42753676[/C][/ROW]
[ROW][C]86[/C][C]17633859[/C][C]16176998.1936560[/C][C]1456860.80634404[/C][/ROW]
[ROW][C]87[/C][C]17336613[/C][C]15331354.6838832[/C][C]2005258.31611680[/C][/ROW]
[ROW][C]88[/C][C]17008347[/C][C]18325063.0820513[/C][C]-1316716.08205131[/C][/ROW]
[ROW][C]89[/C][C]17951965[/C][C]18513764.5725072[/C][C]-561799.572507217[/C][/ROW]
[ROW][C]90[/C][C]14520929[/C][C]14302074.4030886[/C][C]218854.596911391[/C][/ROW]
[ROW][C]91[/C][C]16941217[/C][C]15297482.7367568[/C][C]1643734.26324323[/C][/ROW]
[ROW][C]92[/C][C]15436824[/C][C]13573699.5239808[/C][C]1863124.47601917[/C][/ROW]
[ROW][C]93[/C][C]14744261[/C][C]14704077.7296071[/C][C]40183.2703928535[/C][/ROW]
[ROW][C]94[/C][C]14248004[/C][C]14832295.4360174[/C][C]-584291.43601736[/C][/ROW]
[ROW][C]95[/C][C]11540953[/C][C]11895620.9999059[/C][C]-354667.999905936[/C][/ROW]
[ROW][C]96[/C][C]12881661[/C][C]10567608.3721974[/C][C]2314052.62780264[/C][/ROW]
[ROW][C]97[/C][C]15185757[/C][C]11141637.3081690[/C][C]4044119.69183104[/C][/ROW]
[ROW][C]98[/C][C]13554339[/C][C]12835639.9844747[/C][C]718699.015525282[/C][/ROW]
[ROW][C]99[/C][C]13575106[/C][C]12358075.9516642[/C][C]1217030.04833585[/C][/ROW]
[ROW][C]100[/C][C]12238400[/C][C]13716522.2798913[/C][C]-1478122.27989129[/C][/ROW]
[ROW][C]101[/C][C]13303614[/C][C]14425383.1448668[/C][C]-1121769.14486684[/C][/ROW]
[ROW][C]102[/C][C]14151478[/C][C]15295690.3474423[/C][C]-1144212.34744231[/C][/ROW]
[ROW][C]103[/C][C]14172009[/C][C]14765178.6045297[/C][C]-593169.604529667[/C][/ROW]
[ROW][C]104[/C][C]14022320[/C][C]14924024.6533574[/C][C]-901704.653357371[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112301&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112301&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11556160015490361.171365671238.82863437
21491750014310397.2531482607102.74685184
31480592015720123.6022421-914203.602242134
41695800015726260.81767371231739.18232631
51760500018680085.4248317-1075085.42483165
61713120015912560.46364181218639.53635820
71847460016360933.27615832113666.72384173
81728670017448592.7519948-161892.751994826
91857440019371376.0969790-796976.096979023
101805600017881378.7473251174621.252674884
111970160018193271.14240131508328.85759866
121906170020303963.2098513-1242263.20985127
131968190021218943.8582164-1537043.85821644
143452120021079866.783898413441333.2161016
151992270021496215.0888637-1573515.08886374
162017790020077898.3384324100001.661567644
171975990020404957.6706159-645057.670615948
182307670022707458.8031836369241.196816442
192253200023511818.9937372-979818.9937372
202202940020557709.99423591471690.00576408
212258700021023773.03992391563226.96007615
222325660021376647.49132571879952.50867427
232268030020919524.42697171760775.57302831
242191640021537399.3109462379000.689053752
251964020020638523.5690154-998323.56901536
261881310019272970.77807-459870.778070012
271873000019410465.3009503-680465.300950303
281815470018710750.8454649-556050.845464931
291784880017655980.6954916192819.304508351
301807750019932319.6256094-1854819.62560937
311713310016224791.3952393908308.60476068
321660260015457769.92248881144830.07751123
331587890016371392.9234379-492492.923437881
341578910015519155.8361636269944.163836361
351542200016273032.2617699-851032.261769895
361466140015932807.3062624-1271407.30626237
371587920016422108.3077731-542908.307773114
381433930013644389.1606875694910.839312545
391316960014045009.3962156-875409.396215589
401452890015044675.8666604-515775.866660434
411337580015713627.2075897-2337827.20758967
421230990014209486.8274132-1899586.82741321
431193390012375765.6740081-441865.674008106
441006190010547356.2663483-485456.26634826
451260960015085478.3946368-2475878.39463682
461115650012869085.5910468-1712585.59104675
471218720013686075.8341398-1498875.83413984
481128430013411471.7208982-2127171.72089820
491017700011726883.4857667-1549883.48576668
501097072012818577.6758419-1847857.67584192
511082068011923852.5686699-1103172.56866989
521149239013328455.2941103-1836065.29411029
531457375014744180.0705738-170430.070573815
541399282013995549.4203239-2729.42032390920
551472707014468087.6223733258982.377626654
561568536014448400.44120231236959.55879772
571673621016250996.3468984485213.653101552
581795018017613352.1435398336827.856460245
591700273017251389.6148101-248659.614810066
601741516017325525.627116489634.3728836058
611792981017027167.6447325902642.355267474
621786579018870225.1600536-1004435.16005359
631920236017939491.22818191262868.77181809
641908500016860936.56078072224063.43921929
651818888017671302.2578206517577.742179371
661846641019683499.7647815-1217089.76478154
671852040019643586.5450209-1123186.54502089
682002550023297614.3093677-3272114.30936766
692063610021416866.473144-780766.473143981
702067200021981400.5856402-1309400.58564018
712258910023552239.0192337-963139.019233702
722186480021674701.4033889190098.596611128
732275010020844991.88360371905108.11639635
742254874622295637.4753704253108.524629565
752132549520620855.9058313704639.09416875
762155656323362470.7376630-1805907.73766297
772141526921401204.137274014064.8627259743
782040105420058685.0723197342368.927680251
791906225320474555.5190159-1412302.5190159
801908570620780466.8950475-1694760.89504747
811927996717443381.16935061836585.83064943
821855204517144197.25688561407847.74311437
831780073319033628.5740772-1232895.57407722
841714249017220370.0643330-77880.0643329632
851759317316472263.57246321120909.42753676
861763385916176998.19365601456860.80634404
871733661315331354.68388322005258.31611680
881700834718325063.0820513-1316716.08205131
891795196518513764.5725072-561799.572507217
901452092914302074.4030886218854.596911391
911694121715297482.73675681643734.26324323
921543682413573699.52398081863124.47601917
931474426114704077.729607140183.2703928535
941424800414832295.4360174-584291.43601736
951154095311895620.9999059-354667.999905936
961288166110567608.37219742314052.62780264
971518575711141637.30816904044119.69183104
981355433912835639.9844747718699.015525282
991357510612358075.95166421217030.04833585
1001223840013716522.2798913-1478122.27989129
1011330361414425383.1448668-1121769.14486684
1021415147815295690.3474423-1144212.34744231
1031417200914765178.6045297-593169.604529667
1041402232014924024.6533574-901704.653357371






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.1887793421984840.3775586843969690.811220657801516
80.1298847724086170.2597695448172340.870115227591383
90.08998903766062730.1799780753212550.910010962339373
100.04191620189364960.08383240378729920.95808379810635
110.02814624289057230.05629248578114470.971853757109428
120.02074560261339410.04149120522678820.979254397386606
130.01154750718492430.02309501436984860.988452492815076
140.9999999998764632.47073627798120e-101.23536813899060e-10
150.9999999999174221.65155558565784e-108.25777792828921e-11
160.9999999997591254.81750852227103e-102.40875426113552e-10
170.999999999392191.21561959645478e-096.07809798227388e-10
180.9999999983166343.36673272220064e-091.68336636110032e-09
190.9999999961485577.70288563352005e-093.85144281676003e-09
200.9999999933777771.32444456700882e-086.62222283504409e-09
210.9999999902011471.95977057799035e-089.79885288995176e-09
220.9999999908000041.83999917004563e-089.19999585022817e-09
230.9999999894298152.11403694849846e-081.05701847424923e-08
240.9999999797626294.0474742660253e-082.02373713301265e-08
250.999999968588696.2822620748779e-083.14113103743895e-08
260.9999999309169181.38166163183575e-076.90830815917875e-08
270.9999998599152972.80169404998429e-071.40084702499214e-07
280.9999997198684085.60263183294047e-072.80131591647023e-07
290.9999993992658061.20146838851502e-066.00734194257508e-07
300.9999995364866869.27026628746296e-074.63513314373148e-07
310.999999197921991.60415602175062e-068.02078010875312e-07
320.9999987122436262.57551274822201e-061.28775637411100e-06
330.9999975983241044.8033517927108e-062.4016758963554e-06
340.999995237222859.52555429910376e-064.76277714955188e-06
350.9999918432964131.63134071744172e-058.1567035872086e-06
360.9999882230882452.35538235104834e-051.17769117552417e-05
370.999979289908144.14201837212818e-052.07100918606409e-05
380.9999650044304856.99911390292732e-053.49955695146366e-05
390.9999435848114030.0001128303771936015.64151885968005e-05
400.9999015509405080.0001968981189848579.84490594924286e-05
410.999930113576610.0001397728467818356.98864233909177e-05
420.9999302599801630.0001394800396735316.97400198367655e-05
430.9998811436836510.0002377126326978000.000118856316348900
440.9998106470231210.0003787059537572760.000189352976878638
450.9998802804860690.0002394390278628680.000119719513931434
460.9998796703879590.0002406592240826310.000120329612041316
470.9998634797282010.0002730405435975330.000136520271798767
480.9999069091111450.0001861817777099989.3090888854999e-05
490.9999210624901430.0001578750197141987.89375098570989e-05
500.9999495097531350.0001009804937295325.04902468647660e-05
510.9999548112339419.037753211747e-054.5188766058735e-05
520.999978683067014.26338659794134e-052.13169329897067e-05
530.9999642062956057.15874087904927e-053.57937043952463e-05
540.9999433819070240.0001132361859515275.66180929757637e-05
550.999904336358210.0001913272835803899.56636417901943e-05
560.9998575534034790.0002848931930423220.000142446596521161
570.9997494438528440.0005011122943118710.000250556147155935
580.9996179969218630.0007640061562732810.000382003078136641
590.9993716718933140.001256656213371790.000628328106685895
600.9989430676782930.002113864643414570.00105693232170729
610.9984097172616020.003180565476796450.00159028273839823
620.9977316112176190.004536777564762620.00226838878238131
630.9971615095418290.005676980916342030.00283849045817102
640.9979993146689140.004001370662172870.00200068533108644
650.9971252001470170.0057495997059660.002874799852983
660.9960539667986510.007892066402697810.00394603320134891
670.9945119811482730.01097603770345450.00548801885172723
680.9979962118529060.004007576294188720.00200378814709436
690.9968243704374960.006351259125008480.00317562956250424
700.9955606019400720.008878796119856850.00443939805992843
710.9933311332270670.01333773354586610.00666886677293305
720.990241815460790.01951636907841910.00975818453920953
730.9946070941591680.01078581168166360.00539290584083181
740.9925483588608040.01490328227839280.00745164113919642
750.9921881871106040.01562362577879160.00781181288939582
760.9890325328880380.02193493422392470.0109674671119624
770.9855712845409280.02885743091814430.0144287154590722
780.9852164614423640.02956707711527190.0147835385576360
790.9806602241378280.03867955172434480.0193397758621724
800.9762385436206570.04752291275868680.0237614563793434
810.9882928633454850.02341427330902970.0117071366545149
820.9920783331416450.01584333371671080.00792166685835539
830.9865280961895820.02694380762083630.0134719038104181
840.977069784128980.04586043174204060.0229302158710203
850.9644763159111050.07104736817779050.0355236840888952
860.9487606768277340.1024786463445320.051239323172266
870.957600650149420.08479869970115830.0423993498505792
880.9335894073307340.1328211853385320.0664105926692661
890.9267293539101360.1465412921797270.0732706460898636
900.8929277470763050.2141445058473910.107072252923695
910.9355242575642160.1289514848715670.0644757424357835
920.9542263795696910.0915472408606180.045773620430309
930.9417887135987120.1164225728025760.0582112864012881
940.958764805248410.08247038950318060.0412351947515903
950.968163641078880.06367271784223840.0318363589211192
960.9634190134427740.0731619731144510.0365809865572255
970.972453713525010.05509257294998170.0275462864749909

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.188779342198484 & 0.377558684396969 & 0.811220657801516 \tabularnewline
8 & 0.129884772408617 & 0.259769544817234 & 0.870115227591383 \tabularnewline
9 & 0.0899890376606273 & 0.179978075321255 & 0.910010962339373 \tabularnewline
10 & 0.0419162018936496 & 0.0838324037872992 & 0.95808379810635 \tabularnewline
11 & 0.0281462428905723 & 0.0562924857811447 & 0.971853757109428 \tabularnewline
12 & 0.0207456026133941 & 0.0414912052267882 & 0.979254397386606 \tabularnewline
13 & 0.0115475071849243 & 0.0230950143698486 & 0.988452492815076 \tabularnewline
14 & 0.999999999876463 & 2.47073627798120e-10 & 1.23536813899060e-10 \tabularnewline
15 & 0.999999999917422 & 1.65155558565784e-10 & 8.25777792828921e-11 \tabularnewline
16 & 0.999999999759125 & 4.81750852227103e-10 & 2.40875426113552e-10 \tabularnewline
17 & 0.99999999939219 & 1.21561959645478e-09 & 6.07809798227388e-10 \tabularnewline
18 & 0.999999998316634 & 3.36673272220064e-09 & 1.68336636110032e-09 \tabularnewline
19 & 0.999999996148557 & 7.70288563352005e-09 & 3.85144281676003e-09 \tabularnewline
20 & 0.999999993377777 & 1.32444456700882e-08 & 6.62222283504409e-09 \tabularnewline
21 & 0.999999990201147 & 1.95977057799035e-08 & 9.79885288995176e-09 \tabularnewline
22 & 0.999999990800004 & 1.83999917004563e-08 & 9.19999585022817e-09 \tabularnewline
23 & 0.999999989429815 & 2.11403694849846e-08 & 1.05701847424923e-08 \tabularnewline
24 & 0.999999979762629 & 4.0474742660253e-08 & 2.02373713301265e-08 \tabularnewline
25 & 0.99999996858869 & 6.2822620748779e-08 & 3.14113103743895e-08 \tabularnewline
26 & 0.999999930916918 & 1.38166163183575e-07 & 6.90830815917875e-08 \tabularnewline
27 & 0.999999859915297 & 2.80169404998429e-07 & 1.40084702499214e-07 \tabularnewline
28 & 0.999999719868408 & 5.60263183294047e-07 & 2.80131591647023e-07 \tabularnewline
29 & 0.999999399265806 & 1.20146838851502e-06 & 6.00734194257508e-07 \tabularnewline
30 & 0.999999536486686 & 9.27026628746296e-07 & 4.63513314373148e-07 \tabularnewline
31 & 0.99999919792199 & 1.60415602175062e-06 & 8.02078010875312e-07 \tabularnewline
32 & 0.999998712243626 & 2.57551274822201e-06 & 1.28775637411100e-06 \tabularnewline
33 & 0.999997598324104 & 4.8033517927108e-06 & 2.4016758963554e-06 \tabularnewline
34 & 0.99999523722285 & 9.52555429910376e-06 & 4.76277714955188e-06 \tabularnewline
35 & 0.999991843296413 & 1.63134071744172e-05 & 8.1567035872086e-06 \tabularnewline
36 & 0.999988223088245 & 2.35538235104834e-05 & 1.17769117552417e-05 \tabularnewline
37 & 0.99997928990814 & 4.14201837212818e-05 & 2.07100918606409e-05 \tabularnewline
38 & 0.999965004430485 & 6.99911390292732e-05 & 3.49955695146366e-05 \tabularnewline
39 & 0.999943584811403 & 0.000112830377193601 & 5.64151885968005e-05 \tabularnewline
40 & 0.999901550940508 & 0.000196898118984857 & 9.84490594924286e-05 \tabularnewline
41 & 0.99993011357661 & 0.000139772846781835 & 6.98864233909177e-05 \tabularnewline
42 & 0.999930259980163 & 0.000139480039673531 & 6.97400198367655e-05 \tabularnewline
43 & 0.999881143683651 & 0.000237712632697800 & 0.000118856316348900 \tabularnewline
44 & 0.999810647023121 & 0.000378705953757276 & 0.000189352976878638 \tabularnewline
45 & 0.999880280486069 & 0.000239439027862868 & 0.000119719513931434 \tabularnewline
46 & 0.999879670387959 & 0.000240659224082631 & 0.000120329612041316 \tabularnewline
47 & 0.999863479728201 & 0.000273040543597533 & 0.000136520271798767 \tabularnewline
48 & 0.999906909111145 & 0.000186181777709998 & 9.3090888854999e-05 \tabularnewline
49 & 0.999921062490143 & 0.000157875019714198 & 7.89375098570989e-05 \tabularnewline
50 & 0.999949509753135 & 0.000100980493729532 & 5.04902468647660e-05 \tabularnewline
51 & 0.999954811233941 & 9.037753211747e-05 & 4.5188766058735e-05 \tabularnewline
52 & 0.99997868306701 & 4.26338659794134e-05 & 2.13169329897067e-05 \tabularnewline
53 & 0.999964206295605 & 7.15874087904927e-05 & 3.57937043952463e-05 \tabularnewline
54 & 0.999943381907024 & 0.000113236185951527 & 5.66180929757637e-05 \tabularnewline
55 & 0.99990433635821 & 0.000191327283580389 & 9.56636417901943e-05 \tabularnewline
56 & 0.999857553403479 & 0.000284893193042322 & 0.000142446596521161 \tabularnewline
57 & 0.999749443852844 & 0.000501112294311871 & 0.000250556147155935 \tabularnewline
58 & 0.999617996921863 & 0.000764006156273281 & 0.000382003078136641 \tabularnewline
59 & 0.999371671893314 & 0.00125665621337179 & 0.000628328106685895 \tabularnewline
60 & 0.998943067678293 & 0.00211386464341457 & 0.00105693232170729 \tabularnewline
61 & 0.998409717261602 & 0.00318056547679645 & 0.00159028273839823 \tabularnewline
62 & 0.997731611217619 & 0.00453677756476262 & 0.00226838878238131 \tabularnewline
63 & 0.997161509541829 & 0.00567698091634203 & 0.00283849045817102 \tabularnewline
64 & 0.997999314668914 & 0.00400137066217287 & 0.00200068533108644 \tabularnewline
65 & 0.997125200147017 & 0.005749599705966 & 0.002874799852983 \tabularnewline
66 & 0.996053966798651 & 0.00789206640269781 & 0.00394603320134891 \tabularnewline
67 & 0.994511981148273 & 0.0109760377034545 & 0.00548801885172723 \tabularnewline
68 & 0.997996211852906 & 0.00400757629418872 & 0.00200378814709436 \tabularnewline
69 & 0.996824370437496 & 0.00635125912500848 & 0.00317562956250424 \tabularnewline
70 & 0.995560601940072 & 0.00887879611985685 & 0.00443939805992843 \tabularnewline
71 & 0.993331133227067 & 0.0133377335458661 & 0.00666886677293305 \tabularnewline
72 & 0.99024181546079 & 0.0195163690784191 & 0.00975818453920953 \tabularnewline
73 & 0.994607094159168 & 0.0107858116816636 & 0.00539290584083181 \tabularnewline
74 & 0.992548358860804 & 0.0149032822783928 & 0.00745164113919642 \tabularnewline
75 & 0.992188187110604 & 0.0156236257787916 & 0.00781181288939582 \tabularnewline
76 & 0.989032532888038 & 0.0219349342239247 & 0.0109674671119624 \tabularnewline
77 & 0.985571284540928 & 0.0288574309181443 & 0.0144287154590722 \tabularnewline
78 & 0.985216461442364 & 0.0295670771152719 & 0.0147835385576360 \tabularnewline
79 & 0.980660224137828 & 0.0386795517243448 & 0.0193397758621724 \tabularnewline
80 & 0.976238543620657 & 0.0475229127586868 & 0.0237614563793434 \tabularnewline
81 & 0.988292863345485 & 0.0234142733090297 & 0.0117071366545149 \tabularnewline
82 & 0.992078333141645 & 0.0158433337167108 & 0.00792166685835539 \tabularnewline
83 & 0.986528096189582 & 0.0269438076208363 & 0.0134719038104181 \tabularnewline
84 & 0.97706978412898 & 0.0458604317420406 & 0.0229302158710203 \tabularnewline
85 & 0.964476315911105 & 0.0710473681777905 & 0.0355236840888952 \tabularnewline
86 & 0.948760676827734 & 0.102478646344532 & 0.051239323172266 \tabularnewline
87 & 0.95760065014942 & 0.0847986997011583 & 0.0423993498505792 \tabularnewline
88 & 0.933589407330734 & 0.132821185338532 & 0.0664105926692661 \tabularnewline
89 & 0.926729353910136 & 0.146541292179727 & 0.0732706460898636 \tabularnewline
90 & 0.892927747076305 & 0.214144505847391 & 0.107072252923695 \tabularnewline
91 & 0.935524257564216 & 0.128951484871567 & 0.0644757424357835 \tabularnewline
92 & 0.954226379569691 & 0.091547240860618 & 0.045773620430309 \tabularnewline
93 & 0.941788713598712 & 0.116422572802576 & 0.0582112864012881 \tabularnewline
94 & 0.95876480524841 & 0.0824703895031806 & 0.0412351947515903 \tabularnewline
95 & 0.96816364107888 & 0.0636727178422384 & 0.0318363589211192 \tabularnewline
96 & 0.963419013442774 & 0.073161973114451 & 0.0365809865572255 \tabularnewline
97 & 0.97245371352501 & 0.0550925729499817 & 0.0275462864749909 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112301&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]0.188779342198484[/C][C]0.377558684396969[/C][C]0.811220657801516[/C][/ROW]
[ROW][C]8[/C][C]0.129884772408617[/C][C]0.259769544817234[/C][C]0.870115227591383[/C][/ROW]
[ROW][C]9[/C][C]0.0899890376606273[/C][C]0.179978075321255[/C][C]0.910010962339373[/C][/ROW]
[ROW][C]10[/C][C]0.0419162018936496[/C][C]0.0838324037872992[/C][C]0.95808379810635[/C][/ROW]
[ROW][C]11[/C][C]0.0281462428905723[/C][C]0.0562924857811447[/C][C]0.971853757109428[/C][/ROW]
[ROW][C]12[/C][C]0.0207456026133941[/C][C]0.0414912052267882[/C][C]0.979254397386606[/C][/ROW]
[ROW][C]13[/C][C]0.0115475071849243[/C][C]0.0230950143698486[/C][C]0.988452492815076[/C][/ROW]
[ROW][C]14[/C][C]0.999999999876463[/C][C]2.47073627798120e-10[/C][C]1.23536813899060e-10[/C][/ROW]
[ROW][C]15[/C][C]0.999999999917422[/C][C]1.65155558565784e-10[/C][C]8.25777792828921e-11[/C][/ROW]
[ROW][C]16[/C][C]0.999999999759125[/C][C]4.81750852227103e-10[/C][C]2.40875426113552e-10[/C][/ROW]
[ROW][C]17[/C][C]0.99999999939219[/C][C]1.21561959645478e-09[/C][C]6.07809798227388e-10[/C][/ROW]
[ROW][C]18[/C][C]0.999999998316634[/C][C]3.36673272220064e-09[/C][C]1.68336636110032e-09[/C][/ROW]
[ROW][C]19[/C][C]0.999999996148557[/C][C]7.70288563352005e-09[/C][C]3.85144281676003e-09[/C][/ROW]
[ROW][C]20[/C][C]0.999999993377777[/C][C]1.32444456700882e-08[/C][C]6.62222283504409e-09[/C][/ROW]
[ROW][C]21[/C][C]0.999999990201147[/C][C]1.95977057799035e-08[/C][C]9.79885288995176e-09[/C][/ROW]
[ROW][C]22[/C][C]0.999999990800004[/C][C]1.83999917004563e-08[/C][C]9.19999585022817e-09[/C][/ROW]
[ROW][C]23[/C][C]0.999999989429815[/C][C]2.11403694849846e-08[/C][C]1.05701847424923e-08[/C][/ROW]
[ROW][C]24[/C][C]0.999999979762629[/C][C]4.0474742660253e-08[/C][C]2.02373713301265e-08[/C][/ROW]
[ROW][C]25[/C][C]0.99999996858869[/C][C]6.2822620748779e-08[/C][C]3.14113103743895e-08[/C][/ROW]
[ROW][C]26[/C][C]0.999999930916918[/C][C]1.38166163183575e-07[/C][C]6.90830815917875e-08[/C][/ROW]
[ROW][C]27[/C][C]0.999999859915297[/C][C]2.80169404998429e-07[/C][C]1.40084702499214e-07[/C][/ROW]
[ROW][C]28[/C][C]0.999999719868408[/C][C]5.60263183294047e-07[/C][C]2.80131591647023e-07[/C][/ROW]
[ROW][C]29[/C][C]0.999999399265806[/C][C]1.20146838851502e-06[/C][C]6.00734194257508e-07[/C][/ROW]
[ROW][C]30[/C][C]0.999999536486686[/C][C]9.27026628746296e-07[/C][C]4.63513314373148e-07[/C][/ROW]
[ROW][C]31[/C][C]0.99999919792199[/C][C]1.60415602175062e-06[/C][C]8.02078010875312e-07[/C][/ROW]
[ROW][C]32[/C][C]0.999998712243626[/C][C]2.57551274822201e-06[/C][C]1.28775637411100e-06[/C][/ROW]
[ROW][C]33[/C][C]0.999997598324104[/C][C]4.8033517927108e-06[/C][C]2.4016758963554e-06[/C][/ROW]
[ROW][C]34[/C][C]0.99999523722285[/C][C]9.52555429910376e-06[/C][C]4.76277714955188e-06[/C][/ROW]
[ROW][C]35[/C][C]0.999991843296413[/C][C]1.63134071744172e-05[/C][C]8.1567035872086e-06[/C][/ROW]
[ROW][C]36[/C][C]0.999988223088245[/C][C]2.35538235104834e-05[/C][C]1.17769117552417e-05[/C][/ROW]
[ROW][C]37[/C][C]0.99997928990814[/C][C]4.14201837212818e-05[/C][C]2.07100918606409e-05[/C][/ROW]
[ROW][C]38[/C][C]0.999965004430485[/C][C]6.99911390292732e-05[/C][C]3.49955695146366e-05[/C][/ROW]
[ROW][C]39[/C][C]0.999943584811403[/C][C]0.000112830377193601[/C][C]5.64151885968005e-05[/C][/ROW]
[ROW][C]40[/C][C]0.999901550940508[/C][C]0.000196898118984857[/C][C]9.84490594924286e-05[/C][/ROW]
[ROW][C]41[/C][C]0.99993011357661[/C][C]0.000139772846781835[/C][C]6.98864233909177e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999930259980163[/C][C]0.000139480039673531[/C][C]6.97400198367655e-05[/C][/ROW]
[ROW][C]43[/C][C]0.999881143683651[/C][C]0.000237712632697800[/C][C]0.000118856316348900[/C][/ROW]
[ROW][C]44[/C][C]0.999810647023121[/C][C]0.000378705953757276[/C][C]0.000189352976878638[/C][/ROW]
[ROW][C]45[/C][C]0.999880280486069[/C][C]0.000239439027862868[/C][C]0.000119719513931434[/C][/ROW]
[ROW][C]46[/C][C]0.999879670387959[/C][C]0.000240659224082631[/C][C]0.000120329612041316[/C][/ROW]
[ROW][C]47[/C][C]0.999863479728201[/C][C]0.000273040543597533[/C][C]0.000136520271798767[/C][/ROW]
[ROW][C]48[/C][C]0.999906909111145[/C][C]0.000186181777709998[/C][C]9.3090888854999e-05[/C][/ROW]
[ROW][C]49[/C][C]0.999921062490143[/C][C]0.000157875019714198[/C][C]7.89375098570989e-05[/C][/ROW]
[ROW][C]50[/C][C]0.999949509753135[/C][C]0.000100980493729532[/C][C]5.04902468647660e-05[/C][/ROW]
[ROW][C]51[/C][C]0.999954811233941[/C][C]9.037753211747e-05[/C][C]4.5188766058735e-05[/C][/ROW]
[ROW][C]52[/C][C]0.99997868306701[/C][C]4.26338659794134e-05[/C][C]2.13169329897067e-05[/C][/ROW]
[ROW][C]53[/C][C]0.999964206295605[/C][C]7.15874087904927e-05[/C][C]3.57937043952463e-05[/C][/ROW]
[ROW][C]54[/C][C]0.999943381907024[/C][C]0.000113236185951527[/C][C]5.66180929757637e-05[/C][/ROW]
[ROW][C]55[/C][C]0.99990433635821[/C][C]0.000191327283580389[/C][C]9.56636417901943e-05[/C][/ROW]
[ROW][C]56[/C][C]0.999857553403479[/C][C]0.000284893193042322[/C][C]0.000142446596521161[/C][/ROW]
[ROW][C]57[/C][C]0.999749443852844[/C][C]0.000501112294311871[/C][C]0.000250556147155935[/C][/ROW]
[ROW][C]58[/C][C]0.999617996921863[/C][C]0.000764006156273281[/C][C]0.000382003078136641[/C][/ROW]
[ROW][C]59[/C][C]0.999371671893314[/C][C]0.00125665621337179[/C][C]0.000628328106685895[/C][/ROW]
[ROW][C]60[/C][C]0.998943067678293[/C][C]0.00211386464341457[/C][C]0.00105693232170729[/C][/ROW]
[ROW][C]61[/C][C]0.998409717261602[/C][C]0.00318056547679645[/C][C]0.00159028273839823[/C][/ROW]
[ROW][C]62[/C][C]0.997731611217619[/C][C]0.00453677756476262[/C][C]0.00226838878238131[/C][/ROW]
[ROW][C]63[/C][C]0.997161509541829[/C][C]0.00567698091634203[/C][C]0.00283849045817102[/C][/ROW]
[ROW][C]64[/C][C]0.997999314668914[/C][C]0.00400137066217287[/C][C]0.00200068533108644[/C][/ROW]
[ROW][C]65[/C][C]0.997125200147017[/C][C]0.005749599705966[/C][C]0.002874799852983[/C][/ROW]
[ROW][C]66[/C][C]0.996053966798651[/C][C]0.00789206640269781[/C][C]0.00394603320134891[/C][/ROW]
[ROW][C]67[/C][C]0.994511981148273[/C][C]0.0109760377034545[/C][C]0.00548801885172723[/C][/ROW]
[ROW][C]68[/C][C]0.997996211852906[/C][C]0.00400757629418872[/C][C]0.00200378814709436[/C][/ROW]
[ROW][C]69[/C][C]0.996824370437496[/C][C]0.00635125912500848[/C][C]0.00317562956250424[/C][/ROW]
[ROW][C]70[/C][C]0.995560601940072[/C][C]0.00887879611985685[/C][C]0.00443939805992843[/C][/ROW]
[ROW][C]71[/C][C]0.993331133227067[/C][C]0.0133377335458661[/C][C]0.00666886677293305[/C][/ROW]
[ROW][C]72[/C][C]0.99024181546079[/C][C]0.0195163690784191[/C][C]0.00975818453920953[/C][/ROW]
[ROW][C]73[/C][C]0.994607094159168[/C][C]0.0107858116816636[/C][C]0.00539290584083181[/C][/ROW]
[ROW][C]74[/C][C]0.992548358860804[/C][C]0.0149032822783928[/C][C]0.00745164113919642[/C][/ROW]
[ROW][C]75[/C][C]0.992188187110604[/C][C]0.0156236257787916[/C][C]0.00781181288939582[/C][/ROW]
[ROW][C]76[/C][C]0.989032532888038[/C][C]0.0219349342239247[/C][C]0.0109674671119624[/C][/ROW]
[ROW][C]77[/C][C]0.985571284540928[/C][C]0.0288574309181443[/C][C]0.0144287154590722[/C][/ROW]
[ROW][C]78[/C][C]0.985216461442364[/C][C]0.0295670771152719[/C][C]0.0147835385576360[/C][/ROW]
[ROW][C]79[/C][C]0.980660224137828[/C][C]0.0386795517243448[/C][C]0.0193397758621724[/C][/ROW]
[ROW][C]80[/C][C]0.976238543620657[/C][C]0.0475229127586868[/C][C]0.0237614563793434[/C][/ROW]
[ROW][C]81[/C][C]0.988292863345485[/C][C]0.0234142733090297[/C][C]0.0117071366545149[/C][/ROW]
[ROW][C]82[/C][C]0.992078333141645[/C][C]0.0158433337167108[/C][C]0.00792166685835539[/C][/ROW]
[ROW][C]83[/C][C]0.986528096189582[/C][C]0.0269438076208363[/C][C]0.0134719038104181[/C][/ROW]
[ROW][C]84[/C][C]0.97706978412898[/C][C]0.0458604317420406[/C][C]0.0229302158710203[/C][/ROW]
[ROW][C]85[/C][C]0.964476315911105[/C][C]0.0710473681777905[/C][C]0.0355236840888952[/C][/ROW]
[ROW][C]86[/C][C]0.948760676827734[/C][C]0.102478646344532[/C][C]0.051239323172266[/C][/ROW]
[ROW][C]87[/C][C]0.95760065014942[/C][C]0.0847986997011583[/C][C]0.0423993498505792[/C][/ROW]
[ROW][C]88[/C][C]0.933589407330734[/C][C]0.132821185338532[/C][C]0.0664105926692661[/C][/ROW]
[ROW][C]89[/C][C]0.926729353910136[/C][C]0.146541292179727[/C][C]0.0732706460898636[/C][/ROW]
[ROW][C]90[/C][C]0.892927747076305[/C][C]0.214144505847391[/C][C]0.107072252923695[/C][/ROW]
[ROW][C]91[/C][C]0.935524257564216[/C][C]0.128951484871567[/C][C]0.0644757424357835[/C][/ROW]
[ROW][C]92[/C][C]0.954226379569691[/C][C]0.091547240860618[/C][C]0.045773620430309[/C][/ROW]
[ROW][C]93[/C][C]0.941788713598712[/C][C]0.116422572802576[/C][C]0.0582112864012881[/C][/ROW]
[ROW][C]94[/C][C]0.95876480524841[/C][C]0.0824703895031806[/C][C]0.0412351947515903[/C][/ROW]
[ROW][C]95[/C][C]0.96816364107888[/C][C]0.0636727178422384[/C][C]0.0318363589211192[/C][/ROW]
[ROW][C]96[/C][C]0.963419013442774[/C][C]0.073161973114451[/C][C]0.0365809865572255[/C][/ROW]
[ROW][C]97[/C][C]0.97245371352501[/C][C]0.0550925729499817[/C][C]0.0275462864749909[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112301&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112301&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.1887793421984840.3775586843969690.811220657801516
80.1298847724086170.2597695448172340.870115227591383
90.08998903766062730.1799780753212550.910010962339373
100.04191620189364960.08383240378729920.95808379810635
110.02814624289057230.05629248578114470.971853757109428
120.02074560261339410.04149120522678820.979254397386606
130.01154750718492430.02309501436984860.988452492815076
140.9999999998764632.47073627798120e-101.23536813899060e-10
150.9999999999174221.65155558565784e-108.25777792828921e-11
160.9999999997591254.81750852227103e-102.40875426113552e-10
170.999999999392191.21561959645478e-096.07809798227388e-10
180.9999999983166343.36673272220064e-091.68336636110032e-09
190.9999999961485577.70288563352005e-093.85144281676003e-09
200.9999999933777771.32444456700882e-086.62222283504409e-09
210.9999999902011471.95977057799035e-089.79885288995176e-09
220.9999999908000041.83999917004563e-089.19999585022817e-09
230.9999999894298152.11403694849846e-081.05701847424923e-08
240.9999999797626294.0474742660253e-082.02373713301265e-08
250.999999968588696.2822620748779e-083.14113103743895e-08
260.9999999309169181.38166163183575e-076.90830815917875e-08
270.9999998599152972.80169404998429e-071.40084702499214e-07
280.9999997198684085.60263183294047e-072.80131591647023e-07
290.9999993992658061.20146838851502e-066.00734194257508e-07
300.9999995364866869.27026628746296e-074.63513314373148e-07
310.999999197921991.60415602175062e-068.02078010875312e-07
320.9999987122436262.57551274822201e-061.28775637411100e-06
330.9999975983241044.8033517927108e-062.4016758963554e-06
340.999995237222859.52555429910376e-064.76277714955188e-06
350.9999918432964131.63134071744172e-058.1567035872086e-06
360.9999882230882452.35538235104834e-051.17769117552417e-05
370.999979289908144.14201837212818e-052.07100918606409e-05
380.9999650044304856.99911390292732e-053.49955695146366e-05
390.9999435848114030.0001128303771936015.64151885968005e-05
400.9999015509405080.0001968981189848579.84490594924286e-05
410.999930113576610.0001397728467818356.98864233909177e-05
420.9999302599801630.0001394800396735316.97400198367655e-05
430.9998811436836510.0002377126326978000.000118856316348900
440.9998106470231210.0003787059537572760.000189352976878638
450.9998802804860690.0002394390278628680.000119719513931434
460.9998796703879590.0002406592240826310.000120329612041316
470.9998634797282010.0002730405435975330.000136520271798767
480.9999069091111450.0001861817777099989.3090888854999e-05
490.9999210624901430.0001578750197141987.89375098570989e-05
500.9999495097531350.0001009804937295325.04902468647660e-05
510.9999548112339419.037753211747e-054.5188766058735e-05
520.999978683067014.26338659794134e-052.13169329897067e-05
530.9999642062956057.15874087904927e-053.57937043952463e-05
540.9999433819070240.0001132361859515275.66180929757637e-05
550.999904336358210.0001913272835803899.56636417901943e-05
560.9998575534034790.0002848931930423220.000142446596521161
570.9997494438528440.0005011122943118710.000250556147155935
580.9996179969218630.0007640061562732810.000382003078136641
590.9993716718933140.001256656213371790.000628328106685895
600.9989430676782930.002113864643414570.00105693232170729
610.9984097172616020.003180565476796450.00159028273839823
620.9977316112176190.004536777564762620.00226838878238131
630.9971615095418290.005676980916342030.00283849045817102
640.9979993146689140.004001370662172870.00200068533108644
650.9971252001470170.0057495997059660.002874799852983
660.9960539667986510.007892066402697810.00394603320134891
670.9945119811482730.01097603770345450.00548801885172723
680.9979962118529060.004007576294188720.00200378814709436
690.9968243704374960.006351259125008480.00317562956250424
700.9955606019400720.008878796119856850.00443939805992843
710.9933311332270670.01333773354586610.00666886677293305
720.990241815460790.01951636907841910.00975818453920953
730.9946070941591680.01078581168166360.00539290584083181
740.9925483588608040.01490328227839280.00745164113919642
750.9921881871106040.01562362577879160.00781181288939582
760.9890325328880380.02193493422392470.0109674671119624
770.9855712845409280.02885743091814430.0144287154590722
780.9852164614423640.02956707711527190.0147835385576360
790.9806602241378280.03867955172434480.0193397758621724
800.9762385436206570.04752291275868680.0237614563793434
810.9882928633454850.02341427330902970.0117071366545149
820.9920783331416450.01584333371671080.00792166685835539
830.9865280961895820.02694380762083630.0134719038104181
840.977069784128980.04586043174204060.0229302158710203
850.9644763159111050.07104736817779050.0355236840888952
860.9487606768277340.1024786463445320.051239323172266
870.957600650149420.08479869970115830.0423993498505792
880.9335894073307340.1328211853385320.0664105926692661
890.9267293539101360.1465412921797270.0732706460898636
900.8929277470763050.2141445058473910.107072252923695
910.9355242575642160.1289514848715670.0644757424357835
920.9542263795696910.0915472408606180.045773620430309
930.9417887135987120.1164225728025760.0582112864012881
940.958764805248410.08247038950318060.0412351947515903
950.968163641078880.06367271784223840.0318363589211192
960.9634190134427740.0731619731144510.0365809865572255
970.972453713525010.05509257294998170.0275462864749909






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level560.615384615384615NOK
5% type I error level730.802197802197802NOK
10% type I error level820.901098901098901NOK

\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 & 56 & 0.615384615384615 & NOK \tabularnewline
5% type I error level & 73 & 0.802197802197802 & NOK \tabularnewline
10% type I error level & 82 & 0.901098901098901 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112301&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]56[/C][C]0.615384615384615[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]73[/C][C]0.802197802197802[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]82[/C][C]0.901098901098901[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112301&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112301&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level560.615384615384615NOK
5% type I error level730.802197802197802NOK
10% type I error level820.901098901098901NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

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