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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 computationSat, 19 Nov 2011 07:22:28 -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/19/t13217053752342tm9ub2vfhgy.htm/, Retrieved Fri, 29 Mar 2024 11:59:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145502, Retrieved Fri, 29 Mar 2024 11:59:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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-   P       [Multiple Regression] [Tutorial Multiple...] [2011-11-19 12:22:28] [a0aae37dd27f4b65e222573f53b5a13b] [Current]
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Dataseries X:
12	65	22	114468	2
13	54	20	88594	4
11	58	24	74151	9
12	77	21	77921	2
8	41	15	53212	1
7	0	16	34956	2
18	111	20	149703	0
0	1	18	6853	0
9	36	19	58907	5
11	60	20	67067	0
13	63	25	110563	0
13	71	37	58126	7
9	38	23	57113	6
12	76	28	77993	3
11	61	25	68091	4
17	125	35	124676	0
14	84	20	109522	4
15	69	22	75865	3
13	77	19	79746	0
15	100	26	77844	5
13	78	27	98681	0
13	76	22	105531	1
8	40	15	51428	3
16	81	26	65703	5
14	102	24	72562	0
14	70	22	81728	0
14	75	21	95580	4
14	93	23	98278	0
12	42	21	46629	0
14	95	25	115189	0
2	8	4	15049	0
12	87	30	109011	5
13	87	20	134245	5
16	112	26	136692	0
15	96	27	149510	6
16	93	18	147888	6
15	98	20	79169	2
16	99	17	65469	5
14	94	22	56756	0
17	98	25	81399	3
18	109	30	104953	0
16	108	26	59633	1
10	42	20	63249	1
15	108	25	82928	2
10	27	21	50000	4
16	115	23	139357	0
17	92	33	110044	7
17	106	19	155118	7
13	73	31	83061	6
14	105	25	127122	0
12	30	20	45653	0
7	13	19	19630	4
14	69	15	67229	4
12	72	21	86060	0
16	80	22	88003	0
14	106	24	95815	0
8	28	19	85499	0
14	70	20	27220	0
15	51	23	109882	4
16	90	27	72579	0
0	12	1	5841	0
12	84	20	68369	0
8	23	11	24610	4
12	57	27	30995	0
15	84	22	150662	1
0	4	0	6622	0
11	56	17	93694	5
15	18	8	13155	0
17	86	23	111908	1
13	39	26	57550	7
8	16	20	16356	5
15	18	16	40174	2
12	16	8	13983	0
10	42	22	52316	1
13	77	33	99585	0
17	30	28	86271	0
17	104	26	131012	2
16	121	27	130274	0
18	109	35	159051	2
14	57	21	76506	0
9	28	20	49145	0
10	56	24	66398	4
15	81	26	127546	4
2	2	20	6802	8
11	88	22	99509	0
15	41	24	43106	4
14	83	23	108303	0
13	55	22	64167	1
4	3	12	8579	0
12	54	21	97811	9
11	89	24	84365	0
9	41	21	10901	3
15	94	25	91346	7
16	101	32	33660	5
14	70	24	93634	2
16	111	29	109348	1
0	0	0	0	9
0	4	0	7953	0
0	0	0	0	0
0	0	0	0	0
0	0	0	0	1
0	0	0	0	0
10	42	20	63538	2
12	97	27	108281	1
0	0	0	0	0
0	0	0	0	0
2	7	0	4245	0
4	12	5	21509	0
0	0	1	7670	0
5	37	23	10641	0
0	0	0	0	0
3	39	16	41243	2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145502&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145502&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145502&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'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Score_op_20[t] = + 2.18333042035118 + 0.05873548578048Blogs[t] + 0.21287615177627Reviews[t] + 2.27085778483719e-05Compendium_Writing[t] + 0.0102403804219312Gedeelde_compendia[t] -0.00707953190265559t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Score_op_20[t] =  +  2.18333042035118 +  0.05873548578048Blogs[t] +  0.21287615177627Reviews[t] +  2.27085778483719e-05Compendium_Writing[t] +  0.0102403804219312Gedeelde_compendia[t] -0.00707953190265559t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145502&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Score_op_20[t] =  +  2.18333042035118 +  0.05873548578048Blogs[t] +  0.21287615177627Reviews[t] +  2.27085778483719e-05Compendium_Writing[t] +  0.0102403804219312Gedeelde_compendia[t] -0.00707953190265559t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145502&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145502&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
Score_op_20[t] = + 2.18333042035118 + 0.05873548578048Blogs[t] + 0.21287615177627Reviews[t] + 2.27085778483719e-05Compendium_Writing[t] + 0.0102403804219312Gedeelde_compendia[t] -0.00707953190265559t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.183330420351180.8899292.45340.0157820.007891
Blogs0.058735485780480.0134194.37722.8e-051.4e-05
Reviews0.212876151776270.0425595.0022e-061e-06
Compendium_Writing2.27085778483719e-051e-052.24540.0268190.013409
Gedeelde_compendia0.01024038042193120.0972510.10530.9163380.458169
t-0.007079531902655590.007974-0.88780.3766480.188324

\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) & 2.18333042035118 & 0.889929 & 2.4534 & 0.015782 & 0.007891 \tabularnewline
Blogs & 0.05873548578048 & 0.013419 & 4.3772 & 2.8e-05 & 1.4e-05 \tabularnewline
Reviews & 0.21287615177627 & 0.042559 & 5.002 & 2e-06 & 1e-06 \tabularnewline
Compendium_Writing & 2.27085778483719e-05 & 1e-05 & 2.2454 & 0.026819 & 0.013409 \tabularnewline
Gedeelde_compendia & 0.0102403804219312 & 0.097251 & 0.1053 & 0.916338 & 0.458169 \tabularnewline
t & -0.00707953190265559 & 0.007974 & -0.8878 & 0.376648 & 0.188324 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145502&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]2.18333042035118[/C][C]0.889929[/C][C]2.4534[/C][C]0.015782[/C][C]0.007891[/C][/ROW]
[ROW][C]Blogs[/C][C]0.05873548578048[/C][C]0.013419[/C][C]4.3772[/C][C]2.8e-05[/C][C]1.4e-05[/C][/ROW]
[ROW][C]Reviews[/C][C]0.21287615177627[/C][C]0.042559[/C][C]5.002[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Compendium_Writing[/C][C]2.27085778483719e-05[/C][C]1e-05[/C][C]2.2454[/C][C]0.026819[/C][C]0.013409[/C][/ROW]
[ROW][C]Gedeelde_compendia[/C][C]0.0102403804219312[/C][C]0.097251[/C][C]0.1053[/C][C]0.916338[/C][C]0.458169[/C][/ROW]
[ROW][C]t[/C][C]-0.00707953190265559[/C][C]0.007974[/C][C]-0.8878[/C][C]0.376648[/C][C]0.188324[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145502&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145502&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)2.183330420351180.8899292.45340.0157820.007891
Blogs0.058735485780480.0134194.37722.8e-051.4e-05
Reviews0.212876151776270.0425595.0022e-061e-06
Compendium_Writing2.27085778483719e-051e-052.24540.0268190.013409
Gedeelde_compendia0.01024038042193120.0972510.10530.9163380.458169
t-0.007079531902655590.007974-0.88780.3766480.188324







Multiple Linear Regression - Regression Statistics
Multiple R0.89102546186053
R-squared0.79392637368377
Adjusted R-squared0.78420591961225
F-TEST (value)81.675852572554
F-TEST (DF numerator)5
F-TEST (DF denominator)106
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.47834946368829
Sum Squared Residuals651.07490280139

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.89102546186053 \tabularnewline
R-squared & 0.79392637368377 \tabularnewline
Adjusted R-squared & 0.78420591961225 \tabularnewline
F-TEST (value) & 81.675852572554 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.47834946368829 \tabularnewline
Sum Squared Residuals & 651.07490280139 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145502&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.89102546186053[/C][/ROW]
[ROW][C]R-squared[/C][C]0.79392637368377[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.78420591961225[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]81.675852572554[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]106[/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]2.47834946368829[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]651.07490280139[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145502&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145502&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.89102546186053
R-squared0.79392637368377
Adjusted R-squared0.78420591961225
F-TEST (value)81.675852572554
F-TEST (DF numerator)5
F-TEST (DF denominator)106
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.47834946368829
Sum Squared Residuals651.07490280139







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11213.297219053249-1.29721905324897
21311.65121589180361.34878410819642
31112.4538048223736-1.45380482237355
41212.937999740506-0.937999740506045
588.96783917937113-0.967839179371134
676.361153465467120.638846534532878
71816.31047788382611.6895221161739
806.17282226687817-6.17282226687817
999.6676351024974-0.667635102497396
101111.4171834742356-0.41718347423559
111313.6384234606485-0.638423460648512
121315.5366546026234-2.53665460262338
13910.5777937453148-1.57779374531477
141214.3104773961599-2.31047739615992
151112.5691171647886-1.5691171647886
161719.6938735964618-2.69387359646177
171413.78233260388890.217667396111125
181512.5454301037672.45456989623302
191312.42701685214310.572983147856913
201515.2689967426674-0.268996742667417
211314.6045894098873-1.60458940988734
221313.5814522862257-0.581452286225652
2388.76164077730522-0.76164077730522
241613.84899954157062.15100045842941
251414.7541691408578-0.754169140857802
261412.64994858498541.35005141501458
271413.07919107225230.920808927747732
281414.575408809298-0.575408809297976
29129.974191861747742.02580813825226
301415.4884977806-1.48849778059998
3123.62699481275792-1.62699481275792
321215.9797438975946-3.9797438975946
331314.4169311013551-1.41693110135506
341617.1598616125073-1.15986161250733
351516.7784112932853-1.77841129328529
361614.64240662478471.3575933752153
371513.7532845424871.24671545751301
381612.88592566577913.1140743342209
391413.40048772295290.599512277047124
401714.85730721468422.14269278531583
411817.06485548662290.93514451337709
421615.12862349416840.871376505831592
431010.0498592075962-0.0498592075961631
441515.4408249799866-0.440824979986581
45109.097399202212640.902600797787364
461616.673003591654-0.673003591654006
471716.84979552504720.150204474952792
481715.7083131071411.291686892859
491314.6709239913557-1.67092399135569
501416.2052434598163-2.20524345981629
51128.878576606767283.12142339323273
5277.11013386515973-0.110133865159731
531410.62164252686353.37835747313647
541212.4546900707349-0.454690070734902
551613.17449334361172.82550665638826
561415.2976881557056-1.29768815570559
5789.41059828496034-1.41059828496034
581410.75985209918883.24014790081115
591512.19352477657572.80647522342427
601614.44057419605131.55942580394867
6102.80182175864356-2.80182175864356
621212.4882660423876-0.4882660423876
6386.029693375510051.97030662448995
641211.52967153643820.47032846356183
651514.77177712753020.228222872469825
6602.10139946040975-2.10139946040975
671110.79594296181170.204057038188255
68154.7609015508247310.2390984491753
691714.1937578973212.80624210267902
701310.89178839691442.10821160308564
7187.300597864673350.699402135326653
72157.06963646315337.9303635368467
73124.626835622208957.37316437779105
741010.0078731405501-0.0078731405501307
751315.461344666396-2.46134466639601
761711.32697453845625.67302546154378
771716.27705389311440.722946106885587
781617.4441140799602-1.44411407996023
791819.1091834384884-1.10918343848844
801411.17263220179532.82736779820469
8198.628018031973160.371981968026839
821011.5497893243347-1.54978932433471
831514.82543335876880.174566641231156
8426.20003053751455-4.20003053751455
851113.6932761694993-2.69327616949928
861510.11151071477264.88848928522739
871413.79801506216640.201984937833575
881310.94144036514022.05855963485975
8944.4787892490327-0.478789249032698
901211.50160010028430.49839989971574
911113.7913880644806-2.79138806448063
9298.688834937999120.311165062000881
931514.5139938262670.486006173733012
941615.07774797465650.922252025343459
951412.87806227596131.12193772403869
961616.6901206318271-0.690120631827068
9701.58877924959096-1.58877924959096
9801.90507955664095-1.90507955664095
9901.48245676198827-1.48245676198827
10001.47537723008562-1.47537723008562
10101.47853807860489-1.47853807860489
10201.46121816628031-1.46121816628031
103109.641890452856940.358109547143062
1041215.3612052195623-3.36120521956235
10501.43997957057234-1.43997957057234
10601.43290003866968-1.43290003866968
10721.933366820196730.0666331798032738
10843.676386364052120.323613635947883
10901.798712386835-1.798712386835
11058.71558835267557-3.71558835267557
11101.39750237915641-1.39750237915641
11238.04417585815706-5.04417585815706

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 13.297219053249 & -1.29721905324897 \tabularnewline
2 & 13 & 11.6512158918036 & 1.34878410819642 \tabularnewline
3 & 11 & 12.4538048223736 & -1.45380482237355 \tabularnewline
4 & 12 & 12.937999740506 & -0.937999740506045 \tabularnewline
5 & 8 & 8.96783917937113 & -0.967839179371134 \tabularnewline
6 & 7 & 6.36115346546712 & 0.638846534532878 \tabularnewline
7 & 18 & 16.3104778838261 & 1.6895221161739 \tabularnewline
8 & 0 & 6.17282226687817 & -6.17282226687817 \tabularnewline
9 & 9 & 9.6676351024974 & -0.667635102497396 \tabularnewline
10 & 11 & 11.4171834742356 & -0.41718347423559 \tabularnewline
11 & 13 & 13.6384234606485 & -0.638423460648512 \tabularnewline
12 & 13 & 15.5366546026234 & -2.53665460262338 \tabularnewline
13 & 9 & 10.5777937453148 & -1.57779374531477 \tabularnewline
14 & 12 & 14.3104773961599 & -2.31047739615992 \tabularnewline
15 & 11 & 12.5691171647886 & -1.5691171647886 \tabularnewline
16 & 17 & 19.6938735964618 & -2.69387359646177 \tabularnewline
17 & 14 & 13.7823326038889 & 0.217667396111125 \tabularnewline
18 & 15 & 12.545430103767 & 2.45456989623302 \tabularnewline
19 & 13 & 12.4270168521431 & 0.572983147856913 \tabularnewline
20 & 15 & 15.2689967426674 & -0.268996742667417 \tabularnewline
21 & 13 & 14.6045894098873 & -1.60458940988734 \tabularnewline
22 & 13 & 13.5814522862257 & -0.581452286225652 \tabularnewline
23 & 8 & 8.76164077730522 & -0.76164077730522 \tabularnewline
24 & 16 & 13.8489995415706 & 2.15100045842941 \tabularnewline
25 & 14 & 14.7541691408578 & -0.754169140857802 \tabularnewline
26 & 14 & 12.6499485849854 & 1.35005141501458 \tabularnewline
27 & 14 & 13.0791910722523 & 0.920808927747732 \tabularnewline
28 & 14 & 14.575408809298 & -0.575408809297976 \tabularnewline
29 & 12 & 9.97419186174774 & 2.02580813825226 \tabularnewline
30 & 14 & 15.4884977806 & -1.48849778059998 \tabularnewline
31 & 2 & 3.62699481275792 & -1.62699481275792 \tabularnewline
32 & 12 & 15.9797438975946 & -3.9797438975946 \tabularnewline
33 & 13 & 14.4169311013551 & -1.41693110135506 \tabularnewline
34 & 16 & 17.1598616125073 & -1.15986161250733 \tabularnewline
35 & 15 & 16.7784112932853 & -1.77841129328529 \tabularnewline
36 & 16 & 14.6424066247847 & 1.3575933752153 \tabularnewline
37 & 15 & 13.753284542487 & 1.24671545751301 \tabularnewline
38 & 16 & 12.8859256657791 & 3.1140743342209 \tabularnewline
39 & 14 & 13.4004877229529 & 0.599512277047124 \tabularnewline
40 & 17 & 14.8573072146842 & 2.14269278531583 \tabularnewline
41 & 18 & 17.0648554866229 & 0.93514451337709 \tabularnewline
42 & 16 & 15.1286234941684 & 0.871376505831592 \tabularnewline
43 & 10 & 10.0498592075962 & -0.0498592075961631 \tabularnewline
44 & 15 & 15.4408249799866 & -0.440824979986581 \tabularnewline
45 & 10 & 9.09739920221264 & 0.902600797787364 \tabularnewline
46 & 16 & 16.673003591654 & -0.673003591654006 \tabularnewline
47 & 17 & 16.8497955250472 & 0.150204474952792 \tabularnewline
48 & 17 & 15.708313107141 & 1.291686892859 \tabularnewline
49 & 13 & 14.6709239913557 & -1.67092399135569 \tabularnewline
50 & 14 & 16.2052434598163 & -2.20524345981629 \tabularnewline
51 & 12 & 8.87857660676728 & 3.12142339323273 \tabularnewline
52 & 7 & 7.11013386515973 & -0.110133865159731 \tabularnewline
53 & 14 & 10.6216425268635 & 3.37835747313647 \tabularnewline
54 & 12 & 12.4546900707349 & -0.454690070734902 \tabularnewline
55 & 16 & 13.1744933436117 & 2.82550665638826 \tabularnewline
56 & 14 & 15.2976881557056 & -1.29768815570559 \tabularnewline
57 & 8 & 9.41059828496034 & -1.41059828496034 \tabularnewline
58 & 14 & 10.7598520991888 & 3.24014790081115 \tabularnewline
59 & 15 & 12.1935247765757 & 2.80647522342427 \tabularnewline
60 & 16 & 14.4405741960513 & 1.55942580394867 \tabularnewline
61 & 0 & 2.80182175864356 & -2.80182175864356 \tabularnewline
62 & 12 & 12.4882660423876 & -0.4882660423876 \tabularnewline
63 & 8 & 6.02969337551005 & 1.97030662448995 \tabularnewline
64 & 12 & 11.5296715364382 & 0.47032846356183 \tabularnewline
65 & 15 & 14.7717771275302 & 0.228222872469825 \tabularnewline
66 & 0 & 2.10139946040975 & -2.10139946040975 \tabularnewline
67 & 11 & 10.7959429618117 & 0.204057038188255 \tabularnewline
68 & 15 & 4.76090155082473 & 10.2390984491753 \tabularnewline
69 & 17 & 14.193757897321 & 2.80624210267902 \tabularnewline
70 & 13 & 10.8917883969144 & 2.10821160308564 \tabularnewline
71 & 8 & 7.30059786467335 & 0.699402135326653 \tabularnewline
72 & 15 & 7.0696364631533 & 7.9303635368467 \tabularnewline
73 & 12 & 4.62683562220895 & 7.37316437779105 \tabularnewline
74 & 10 & 10.0078731405501 & -0.0078731405501307 \tabularnewline
75 & 13 & 15.461344666396 & -2.46134466639601 \tabularnewline
76 & 17 & 11.3269745384562 & 5.67302546154378 \tabularnewline
77 & 17 & 16.2770538931144 & 0.722946106885587 \tabularnewline
78 & 16 & 17.4441140799602 & -1.44411407996023 \tabularnewline
79 & 18 & 19.1091834384884 & -1.10918343848844 \tabularnewline
80 & 14 & 11.1726322017953 & 2.82736779820469 \tabularnewline
81 & 9 & 8.62801803197316 & 0.371981968026839 \tabularnewline
82 & 10 & 11.5497893243347 & -1.54978932433471 \tabularnewline
83 & 15 & 14.8254333587688 & 0.174566641231156 \tabularnewline
84 & 2 & 6.20003053751455 & -4.20003053751455 \tabularnewline
85 & 11 & 13.6932761694993 & -2.69327616949928 \tabularnewline
86 & 15 & 10.1115107147726 & 4.88848928522739 \tabularnewline
87 & 14 & 13.7980150621664 & 0.201984937833575 \tabularnewline
88 & 13 & 10.9414403651402 & 2.05855963485975 \tabularnewline
89 & 4 & 4.4787892490327 & -0.478789249032698 \tabularnewline
90 & 12 & 11.5016001002843 & 0.49839989971574 \tabularnewline
91 & 11 & 13.7913880644806 & -2.79138806448063 \tabularnewline
92 & 9 & 8.68883493799912 & 0.311165062000881 \tabularnewline
93 & 15 & 14.513993826267 & 0.486006173733012 \tabularnewline
94 & 16 & 15.0777479746565 & 0.922252025343459 \tabularnewline
95 & 14 & 12.8780622759613 & 1.12193772403869 \tabularnewline
96 & 16 & 16.6901206318271 & -0.690120631827068 \tabularnewline
97 & 0 & 1.58877924959096 & -1.58877924959096 \tabularnewline
98 & 0 & 1.90507955664095 & -1.90507955664095 \tabularnewline
99 & 0 & 1.48245676198827 & -1.48245676198827 \tabularnewline
100 & 0 & 1.47537723008562 & -1.47537723008562 \tabularnewline
101 & 0 & 1.47853807860489 & -1.47853807860489 \tabularnewline
102 & 0 & 1.46121816628031 & -1.46121816628031 \tabularnewline
103 & 10 & 9.64189045285694 & 0.358109547143062 \tabularnewline
104 & 12 & 15.3612052195623 & -3.36120521956235 \tabularnewline
105 & 0 & 1.43997957057234 & -1.43997957057234 \tabularnewline
106 & 0 & 1.43290003866968 & -1.43290003866968 \tabularnewline
107 & 2 & 1.93336682019673 & 0.0666331798032738 \tabularnewline
108 & 4 & 3.67638636405212 & 0.323613635947883 \tabularnewline
109 & 0 & 1.798712386835 & -1.798712386835 \tabularnewline
110 & 5 & 8.71558835267557 & -3.71558835267557 \tabularnewline
111 & 0 & 1.39750237915641 & -1.39750237915641 \tabularnewline
112 & 3 & 8.04417585815706 & -5.04417585815706 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145502&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]12[/C][C]13.297219053249[/C][C]-1.29721905324897[/C][/ROW]
[ROW][C]2[/C][C]13[/C][C]11.6512158918036[/C][C]1.34878410819642[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]12.4538048223736[/C][C]-1.45380482237355[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.937999740506[/C][C]-0.937999740506045[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]8.96783917937113[/C][C]-0.967839179371134[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]6.36115346546712[/C][C]0.638846534532878[/C][/ROW]
[ROW][C]7[/C][C]18[/C][C]16.3104778838261[/C][C]1.6895221161739[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]6.17282226687817[/C][C]-6.17282226687817[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]9.6676351024974[/C][C]-0.667635102497396[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]11.4171834742356[/C][C]-0.41718347423559[/C][/ROW]
[ROW][C]11[/C][C]13[/C][C]13.6384234606485[/C][C]-0.638423460648512[/C][/ROW]
[ROW][C]12[/C][C]13[/C][C]15.5366546026234[/C][C]-2.53665460262338[/C][/ROW]
[ROW][C]13[/C][C]9[/C][C]10.5777937453148[/C][C]-1.57779374531477[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]14.3104773961599[/C][C]-2.31047739615992[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]12.5691171647886[/C][C]-1.5691171647886[/C][/ROW]
[ROW][C]16[/C][C]17[/C][C]19.6938735964618[/C][C]-2.69387359646177[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.7823326038889[/C][C]0.217667396111125[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]12.545430103767[/C][C]2.45456989623302[/C][/ROW]
[ROW][C]19[/C][C]13[/C][C]12.4270168521431[/C][C]0.572983147856913[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]15.2689967426674[/C][C]-0.268996742667417[/C][/ROW]
[ROW][C]21[/C][C]13[/C][C]14.6045894098873[/C][C]-1.60458940988734[/C][/ROW]
[ROW][C]22[/C][C]13[/C][C]13.5814522862257[/C][C]-0.581452286225652[/C][/ROW]
[ROW][C]23[/C][C]8[/C][C]8.76164077730522[/C][C]-0.76164077730522[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]13.8489995415706[/C][C]2.15100045842941[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]14.7541691408578[/C][C]-0.754169140857802[/C][/ROW]
[ROW][C]26[/C][C]14[/C][C]12.6499485849854[/C][C]1.35005141501458[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]13.0791910722523[/C][C]0.920808927747732[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]14.575408809298[/C][C]-0.575408809297976[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]9.97419186174774[/C][C]2.02580813825226[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]15.4884977806[/C][C]-1.48849778059998[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]3.62699481275792[/C][C]-1.62699481275792[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]15.9797438975946[/C][C]-3.9797438975946[/C][/ROW]
[ROW][C]33[/C][C]13[/C][C]14.4169311013551[/C][C]-1.41693110135506[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]17.1598616125073[/C][C]-1.15986161250733[/C][/ROW]
[ROW][C]35[/C][C]15[/C][C]16.7784112932853[/C][C]-1.77841129328529[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.6424066247847[/C][C]1.3575933752153[/C][/ROW]
[ROW][C]37[/C][C]15[/C][C]13.753284542487[/C][C]1.24671545751301[/C][/ROW]
[ROW][C]38[/C][C]16[/C][C]12.8859256657791[/C][C]3.1140743342209[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]13.4004877229529[/C][C]0.599512277047124[/C][/ROW]
[ROW][C]40[/C][C]17[/C][C]14.8573072146842[/C][C]2.14269278531583[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]17.0648554866229[/C][C]0.93514451337709[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]15.1286234941684[/C][C]0.871376505831592[/C][/ROW]
[ROW][C]43[/C][C]10[/C][C]10.0498592075962[/C][C]-0.0498592075961631[/C][/ROW]
[ROW][C]44[/C][C]15[/C][C]15.4408249799866[/C][C]-0.440824979986581[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]9.09739920221264[/C][C]0.902600797787364[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]16.673003591654[/C][C]-0.673003591654006[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]16.8497955250472[/C][C]0.150204474952792[/C][/ROW]
[ROW][C]48[/C][C]17[/C][C]15.708313107141[/C][C]1.291686892859[/C][/ROW]
[ROW][C]49[/C][C]13[/C][C]14.6709239913557[/C][C]-1.67092399135569[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.2052434598163[/C][C]-2.20524345981629[/C][/ROW]
[ROW][C]51[/C][C]12[/C][C]8.87857660676728[/C][C]3.12142339323273[/C][/ROW]
[ROW][C]52[/C][C]7[/C][C]7.11013386515973[/C][C]-0.110133865159731[/C][/ROW]
[ROW][C]53[/C][C]14[/C][C]10.6216425268635[/C][C]3.37835747313647[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]12.4546900707349[/C][C]-0.454690070734902[/C][/ROW]
[ROW][C]55[/C][C]16[/C][C]13.1744933436117[/C][C]2.82550665638826[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]15.2976881557056[/C][C]-1.29768815570559[/C][/ROW]
[ROW][C]57[/C][C]8[/C][C]9.41059828496034[/C][C]-1.41059828496034[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]10.7598520991888[/C][C]3.24014790081115[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]12.1935247765757[/C][C]2.80647522342427[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.4405741960513[/C][C]1.55942580394867[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]2.80182175864356[/C][C]-2.80182175864356[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.4882660423876[/C][C]-0.4882660423876[/C][/ROW]
[ROW][C]63[/C][C]8[/C][C]6.02969337551005[/C][C]1.97030662448995[/C][/ROW]
[ROW][C]64[/C][C]12[/C][C]11.5296715364382[/C][C]0.47032846356183[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.7717771275302[/C][C]0.228222872469825[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]2.10139946040975[/C][C]-2.10139946040975[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]10.7959429618117[/C][C]0.204057038188255[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]4.76090155082473[/C][C]10.2390984491753[/C][/ROW]
[ROW][C]69[/C][C]17[/C][C]14.193757897321[/C][C]2.80624210267902[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]10.8917883969144[/C][C]2.10821160308564[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]7.30059786467335[/C][C]0.699402135326653[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]7.0696364631533[/C][C]7.9303635368467[/C][/ROW]
[ROW][C]73[/C][C]12[/C][C]4.62683562220895[/C][C]7.37316437779105[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]10.0078731405501[/C][C]-0.0078731405501307[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]15.461344666396[/C][C]-2.46134466639601[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]11.3269745384562[/C][C]5.67302546154378[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]16.2770538931144[/C][C]0.722946106885587[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]17.4441140799602[/C][C]-1.44411407996023[/C][/ROW]
[ROW][C]79[/C][C]18[/C][C]19.1091834384884[/C][C]-1.10918343848844[/C][/ROW]
[ROW][C]80[/C][C]14[/C][C]11.1726322017953[/C][C]2.82736779820469[/C][/ROW]
[ROW][C]81[/C][C]9[/C][C]8.62801803197316[/C][C]0.371981968026839[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]11.5497893243347[/C][C]-1.54978932433471[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]14.8254333587688[/C][C]0.174566641231156[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]6.20003053751455[/C][C]-4.20003053751455[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]13.6932761694993[/C][C]-2.69327616949928[/C][/ROW]
[ROW][C]86[/C][C]15[/C][C]10.1115107147726[/C][C]4.88848928522739[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]13.7980150621664[/C][C]0.201984937833575[/C][/ROW]
[ROW][C]88[/C][C]13[/C][C]10.9414403651402[/C][C]2.05855963485975[/C][/ROW]
[ROW][C]89[/C][C]4[/C][C]4.4787892490327[/C][C]-0.478789249032698[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.5016001002843[/C][C]0.49839989971574[/C][/ROW]
[ROW][C]91[/C][C]11[/C][C]13.7913880644806[/C][C]-2.79138806448063[/C][/ROW]
[ROW][C]92[/C][C]9[/C][C]8.68883493799912[/C][C]0.311165062000881[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.513993826267[/C][C]0.486006173733012[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]15.0777479746565[/C][C]0.922252025343459[/C][/ROW]
[ROW][C]95[/C][C]14[/C][C]12.8780622759613[/C][C]1.12193772403869[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]16.6901206318271[/C][C]-0.690120631827068[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]1.58877924959096[/C][C]-1.58877924959096[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]1.90507955664095[/C][C]-1.90507955664095[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]1.48245676198827[/C][C]-1.48245676198827[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]1.47537723008562[/C][C]-1.47537723008562[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]1.47853807860489[/C][C]-1.47853807860489[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]1.46121816628031[/C][C]-1.46121816628031[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]9.64189045285694[/C][C]0.358109547143062[/C][/ROW]
[ROW][C]104[/C][C]12[/C][C]15.3612052195623[/C][C]-3.36120521956235[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]1.43997957057234[/C][C]-1.43997957057234[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]1.43290003866968[/C][C]-1.43290003866968[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]1.93336682019673[/C][C]0.0666331798032738[/C][/ROW]
[ROW][C]108[/C][C]4[/C][C]3.67638636405212[/C][C]0.323613635947883[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]1.798712386835[/C][C]-1.798712386835[/C][/ROW]
[ROW][C]110[/C][C]5[/C][C]8.71558835267557[/C][C]-3.71558835267557[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]1.39750237915641[/C][C]-1.39750237915641[/C][/ROW]
[ROW][C]112[/C][C]3[/C][C]8.04417585815706[/C][C]-5.04417585815706[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145502&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145502&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
11213.297219053249-1.29721905324897
21311.65121589180361.34878410819642
31112.4538048223736-1.45380482237355
41212.937999740506-0.937999740506045
588.96783917937113-0.967839179371134
676.361153465467120.638846534532878
71816.31047788382611.6895221161739
806.17282226687817-6.17282226687817
999.6676351024974-0.667635102497396
101111.4171834742356-0.41718347423559
111313.6384234606485-0.638423460648512
121315.5366546026234-2.53665460262338
13910.5777937453148-1.57779374531477
141214.3104773961599-2.31047739615992
151112.5691171647886-1.5691171647886
161719.6938735964618-2.69387359646177
171413.78233260388890.217667396111125
181512.5454301037672.45456989623302
191312.42701685214310.572983147856913
201515.2689967426674-0.268996742667417
211314.6045894098873-1.60458940988734
221313.5814522862257-0.581452286225652
2388.76164077730522-0.76164077730522
241613.84899954157062.15100045842941
251414.7541691408578-0.754169140857802
261412.64994858498541.35005141501458
271413.07919107225230.920808927747732
281414.575408809298-0.575408809297976
29129.974191861747742.02580813825226
301415.4884977806-1.48849778059998
3123.62699481275792-1.62699481275792
321215.9797438975946-3.9797438975946
331314.4169311013551-1.41693110135506
341617.1598616125073-1.15986161250733
351516.7784112932853-1.77841129328529
361614.64240662478471.3575933752153
371513.7532845424871.24671545751301
381612.88592566577913.1140743342209
391413.40048772295290.599512277047124
401714.85730721468422.14269278531583
411817.06485548662290.93514451337709
421615.12862349416840.871376505831592
431010.0498592075962-0.0498592075961631
441515.4408249799866-0.440824979986581
45109.097399202212640.902600797787364
461616.673003591654-0.673003591654006
471716.84979552504720.150204474952792
481715.7083131071411.291686892859
491314.6709239913557-1.67092399135569
501416.2052434598163-2.20524345981629
51128.878576606767283.12142339323273
5277.11013386515973-0.110133865159731
531410.62164252686353.37835747313647
541212.4546900707349-0.454690070734902
551613.17449334361172.82550665638826
561415.2976881557056-1.29768815570559
5789.41059828496034-1.41059828496034
581410.75985209918883.24014790081115
591512.19352477657572.80647522342427
601614.44057419605131.55942580394867
6102.80182175864356-2.80182175864356
621212.4882660423876-0.4882660423876
6386.029693375510051.97030662448995
641211.52967153643820.47032846356183
651514.77177712753020.228222872469825
6602.10139946040975-2.10139946040975
671110.79594296181170.204057038188255
68154.7609015508247310.2390984491753
691714.1937578973212.80624210267902
701310.89178839691442.10821160308564
7187.300597864673350.699402135326653
72157.06963646315337.9303635368467
73124.626835622208957.37316437779105
741010.0078731405501-0.0078731405501307
751315.461344666396-2.46134466639601
761711.32697453845625.67302546154378
771716.27705389311440.722946106885587
781617.4441140799602-1.44411407996023
791819.1091834384884-1.10918343848844
801411.17263220179532.82736779820469
8198.628018031973160.371981968026839
821011.5497893243347-1.54978932433471
831514.82543335876880.174566641231156
8426.20003053751455-4.20003053751455
851113.6932761694993-2.69327616949928
861510.11151071477264.88848928522739
871413.79801506216640.201984937833575
881310.94144036514022.05855963485975
8944.4787892490327-0.478789249032698
901211.50160010028430.49839989971574
911113.7913880644806-2.79138806448063
9298.688834937999120.311165062000881
931514.5139938262670.486006173733012
941615.07774797465650.922252025343459
951412.87806227596131.12193772403869
961616.6901206318271-0.690120631827068
9701.58877924959096-1.58877924959096
9801.90507955664095-1.90507955664095
9901.48245676198827-1.48245676198827
10001.47537723008562-1.47537723008562
10101.47853807860489-1.47853807860489
10201.46121816628031-1.46121816628031
103109.641890452856940.358109547143062
1041215.3612052195623-3.36120521956235
10501.43997957057234-1.43997957057234
10601.43290003866968-1.43290003866968
10721.933366820196730.0666331798032738
10843.676386364052120.323613635947883
10901.798712386835-1.798712386835
11058.71558835267557-3.71558835267557
11101.39750237915641-1.39750237915641
11238.04417585815706-5.04417585815706







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2784944204839090.5569888409678170.721505579516092
100.2992316192090120.5984632384180250.700768380790988
110.1937923850856450.3875847701712890.806207614914355
120.1918838690026340.3837677380052680.808116130997366
130.1375731841883670.2751463683767340.862426815811633
140.08957659437503960.1791531887500790.91042340562496
150.05339346987003880.1067869397400780.946606530129961
160.03291831743996650.0658366348799330.967081682560033
170.02011298665530430.04022597331060870.979887013344696
180.0454354093131690.0908708186263380.95456459068683
190.0270313036725860.05406260734517210.972968696327414
200.01537254125760640.03074508251521270.984627458742394
210.009749075490118270.01949815098023650.990250924509882
220.006699758431671370.01339951686334270.993300241568329
230.004682840171925690.009365680343851370.995317159828074
240.008021503690736760.01604300738147350.991978496309263
250.004645283681087710.009290567362175420.995354716318912
260.003715026989995530.007430053979991070.996284973010005
270.002176556288520580.004353112577041150.99782344371148
280.001409115390280470.002818230780560930.99859088460972
290.002577405789276320.005154811578552640.997422594210724
300.002942769248606330.005885538497212660.997057230751394
310.005169766309620990.0103395326192420.99483023369038
320.01943944168194780.03887888336389570.980560558318052
330.02043722421236420.04087444842472840.979562775787636
340.01532029927542420.03064059855084840.984679700724576
350.01263485062948020.02526970125896040.98736514937052
360.008558704723697350.01711740944739470.991441295276303
370.005652599090009620.01130519818001920.99434740090999
380.00451812772433760.00903625544867520.995481872275662
390.002818849899506550.00563769979901310.997181150100493
400.002663389260817080.005326778521634150.997336610739183
410.002160888769599910.004321777539199810.9978391112304
420.00129978952283070.002599579045661410.99870021047717
430.0009653896745564720.001930779349112940.999034610325443
440.0006893073210272760.001378614642054550.999310692678973
450.0006531242452750870.001306248490550170.999346875754725
460.0004799401393984120.0009598802787968240.999520059860602
470.0003223719193088710.0006447438386177410.999677628080691
480.0001862859407171230.0003725718814342460.999813714059283
490.0001704806637479570.0003409613274959130.999829519336252
500.0002320021545355390.0004640043090710790.999767997845464
510.000551651593996870.001103303187993740.999448348406003
520.0004540975365494820.0009081950730989640.99954590246345
530.0003672572875752070.0007345145751504140.999632742712425
540.0002761863935810120.0005523727871620250.99972381360642
550.0002718291868283410.0005436583736566820.999728170813172
560.0003056910645915080.0006113821291830150.999694308935408
570.0004054591087618320.0008109182175236630.999594540891238
580.0003450613121725560.0006901226243451130.999654938687827
590.0004225123480021310.0008450246960042630.999577487651998
600.0002717225403215930.0005434450806431850.999728277459678
610.003278313629031360.006556627258062730.996721686370969
620.003056463599240890.006112927198481790.99694353640076
630.002093961899855240.004187923799710470.997906038100145
640.001846129786016730.003692259572033470.998153870213983
650.001322174268003230.002644348536006460.998677825731997
660.00898864705073130.01797729410146260.991011352949269
670.008778804357397110.01755760871479420.991221195642603
680.2205898035957070.4411796071914130.779410196404293
690.1922300394437110.3844600788874230.807769960556289
700.1588334115004160.3176668230008330.841166588499584
710.1469015831126920.2938031662253840.853098416887308
720.3961916814161060.7923833628322130.603808318583894
730.6854001402598980.6291997194802040.314599859740102
740.648185804709250.7036283905814990.35181419529075
750.7399816577842330.5200366844315330.260018342215767
760.8643385003718480.2713229992563040.135661499628152
770.8291625591357290.3416748817285420.170837440864271
780.8304600704803230.3390798590393550.169539929519677
790.8137513737465420.3724972525069160.186248626253458
800.8097662779106970.3804674441786070.190233722089303
810.7670100879176790.4659798241646430.232989912082321
820.7701152814525850.459769437094830.229884718547415
830.7176991857752190.5646016284495630.282300814224781
840.964174482688710.07165103462257980.0358255173112899
850.9794969139201850.04100617215963090.0205030860798155
860.9931627819606630.01367443607867320.00683721803933661
870.9884741293602580.02305174127948480.0115258706397424
880.9877277894387890.02454442112242280.0122722105612114
890.9818657862737670.03626842745246650.0181342137262332
900.9701637423459070.05967251530818680.0298362576540934
910.9857488734774490.02850225304510240.0142511265225512
920.9753360805680060.0493278388639870.0246639194319935
930.9605539122555830.07889217548883460.0394460877444173
940.9859609697465830.02807806050683440.0140390302534172
950.9780456783894590.04390864322108290.0219543216105415
960.9854828791728390.02903424165432270.0145171208271614
970.9879125817400580.02417483651988360.0120874182599418
980.9845355168649260.03092896627014710.0154644831350735
990.9729748164184960.05405036716300790.027025183581504
1000.9571362923109840.0857274153780330.0428637076890165
1010.9108201087909680.1783597824180630.0891798912090316
1020.8699010805394510.2601978389210970.130098919460548
1030.873241689111340.2535166217773190.12675831088866

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.278494420483909 & 0.556988840967817 & 0.721505579516092 \tabularnewline
10 & 0.299231619209012 & 0.598463238418025 & 0.700768380790988 \tabularnewline
11 & 0.193792385085645 & 0.387584770171289 & 0.806207614914355 \tabularnewline
12 & 0.191883869002634 & 0.383767738005268 & 0.808116130997366 \tabularnewline
13 & 0.137573184188367 & 0.275146368376734 & 0.862426815811633 \tabularnewline
14 & 0.0895765943750396 & 0.179153188750079 & 0.91042340562496 \tabularnewline
15 & 0.0533934698700388 & 0.106786939740078 & 0.946606530129961 \tabularnewline
16 & 0.0329183174399665 & 0.065836634879933 & 0.967081682560033 \tabularnewline
17 & 0.0201129866553043 & 0.0402259733106087 & 0.979887013344696 \tabularnewline
18 & 0.045435409313169 & 0.090870818626338 & 0.95456459068683 \tabularnewline
19 & 0.027031303672586 & 0.0540626073451721 & 0.972968696327414 \tabularnewline
20 & 0.0153725412576064 & 0.0307450825152127 & 0.984627458742394 \tabularnewline
21 & 0.00974907549011827 & 0.0194981509802365 & 0.990250924509882 \tabularnewline
22 & 0.00669975843167137 & 0.0133995168633427 & 0.993300241568329 \tabularnewline
23 & 0.00468284017192569 & 0.00936568034385137 & 0.995317159828074 \tabularnewline
24 & 0.00802150369073676 & 0.0160430073814735 & 0.991978496309263 \tabularnewline
25 & 0.00464528368108771 & 0.00929056736217542 & 0.995354716318912 \tabularnewline
26 & 0.00371502698999553 & 0.00743005397999107 & 0.996284973010005 \tabularnewline
27 & 0.00217655628852058 & 0.00435311257704115 & 0.99782344371148 \tabularnewline
28 & 0.00140911539028047 & 0.00281823078056093 & 0.99859088460972 \tabularnewline
29 & 0.00257740578927632 & 0.00515481157855264 & 0.997422594210724 \tabularnewline
30 & 0.00294276924860633 & 0.00588553849721266 & 0.997057230751394 \tabularnewline
31 & 0.00516976630962099 & 0.010339532619242 & 0.99483023369038 \tabularnewline
32 & 0.0194394416819478 & 0.0388788833638957 & 0.980560558318052 \tabularnewline
33 & 0.0204372242123642 & 0.0408744484247284 & 0.979562775787636 \tabularnewline
34 & 0.0153202992754242 & 0.0306405985508484 & 0.984679700724576 \tabularnewline
35 & 0.0126348506294802 & 0.0252697012589604 & 0.98736514937052 \tabularnewline
36 & 0.00855870472369735 & 0.0171174094473947 & 0.991441295276303 \tabularnewline
37 & 0.00565259909000962 & 0.0113051981800192 & 0.99434740090999 \tabularnewline
38 & 0.0045181277243376 & 0.0090362554486752 & 0.995481872275662 \tabularnewline
39 & 0.00281884989950655 & 0.0056376997990131 & 0.997181150100493 \tabularnewline
40 & 0.00266338926081708 & 0.00532677852163415 & 0.997336610739183 \tabularnewline
41 & 0.00216088876959991 & 0.00432177753919981 & 0.9978391112304 \tabularnewline
42 & 0.0012997895228307 & 0.00259957904566141 & 0.99870021047717 \tabularnewline
43 & 0.000965389674556472 & 0.00193077934911294 & 0.999034610325443 \tabularnewline
44 & 0.000689307321027276 & 0.00137861464205455 & 0.999310692678973 \tabularnewline
45 & 0.000653124245275087 & 0.00130624849055017 & 0.999346875754725 \tabularnewline
46 & 0.000479940139398412 & 0.000959880278796824 & 0.999520059860602 \tabularnewline
47 & 0.000322371919308871 & 0.000644743838617741 & 0.999677628080691 \tabularnewline
48 & 0.000186285940717123 & 0.000372571881434246 & 0.999813714059283 \tabularnewline
49 & 0.000170480663747957 & 0.000340961327495913 & 0.999829519336252 \tabularnewline
50 & 0.000232002154535539 & 0.000464004309071079 & 0.999767997845464 \tabularnewline
51 & 0.00055165159399687 & 0.00110330318799374 & 0.999448348406003 \tabularnewline
52 & 0.000454097536549482 & 0.000908195073098964 & 0.99954590246345 \tabularnewline
53 & 0.000367257287575207 & 0.000734514575150414 & 0.999632742712425 \tabularnewline
54 & 0.000276186393581012 & 0.000552372787162025 & 0.99972381360642 \tabularnewline
55 & 0.000271829186828341 & 0.000543658373656682 & 0.999728170813172 \tabularnewline
56 & 0.000305691064591508 & 0.000611382129183015 & 0.999694308935408 \tabularnewline
57 & 0.000405459108761832 & 0.000810918217523663 & 0.999594540891238 \tabularnewline
58 & 0.000345061312172556 & 0.000690122624345113 & 0.999654938687827 \tabularnewline
59 & 0.000422512348002131 & 0.000845024696004263 & 0.999577487651998 \tabularnewline
60 & 0.000271722540321593 & 0.000543445080643185 & 0.999728277459678 \tabularnewline
61 & 0.00327831362903136 & 0.00655662725806273 & 0.996721686370969 \tabularnewline
62 & 0.00305646359924089 & 0.00611292719848179 & 0.99694353640076 \tabularnewline
63 & 0.00209396189985524 & 0.00418792379971047 & 0.997906038100145 \tabularnewline
64 & 0.00184612978601673 & 0.00369225957203347 & 0.998153870213983 \tabularnewline
65 & 0.00132217426800323 & 0.00264434853600646 & 0.998677825731997 \tabularnewline
66 & 0.0089886470507313 & 0.0179772941014626 & 0.991011352949269 \tabularnewline
67 & 0.00877880435739711 & 0.0175576087147942 & 0.991221195642603 \tabularnewline
68 & 0.220589803595707 & 0.441179607191413 & 0.779410196404293 \tabularnewline
69 & 0.192230039443711 & 0.384460078887423 & 0.807769960556289 \tabularnewline
70 & 0.158833411500416 & 0.317666823000833 & 0.841166588499584 \tabularnewline
71 & 0.146901583112692 & 0.293803166225384 & 0.853098416887308 \tabularnewline
72 & 0.396191681416106 & 0.792383362832213 & 0.603808318583894 \tabularnewline
73 & 0.685400140259898 & 0.629199719480204 & 0.314599859740102 \tabularnewline
74 & 0.64818580470925 & 0.703628390581499 & 0.35181419529075 \tabularnewline
75 & 0.739981657784233 & 0.520036684431533 & 0.260018342215767 \tabularnewline
76 & 0.864338500371848 & 0.271322999256304 & 0.135661499628152 \tabularnewline
77 & 0.829162559135729 & 0.341674881728542 & 0.170837440864271 \tabularnewline
78 & 0.830460070480323 & 0.339079859039355 & 0.169539929519677 \tabularnewline
79 & 0.813751373746542 & 0.372497252506916 & 0.186248626253458 \tabularnewline
80 & 0.809766277910697 & 0.380467444178607 & 0.190233722089303 \tabularnewline
81 & 0.767010087917679 & 0.465979824164643 & 0.232989912082321 \tabularnewline
82 & 0.770115281452585 & 0.45976943709483 & 0.229884718547415 \tabularnewline
83 & 0.717699185775219 & 0.564601628449563 & 0.282300814224781 \tabularnewline
84 & 0.96417448268871 & 0.0716510346225798 & 0.0358255173112899 \tabularnewline
85 & 0.979496913920185 & 0.0410061721596309 & 0.0205030860798155 \tabularnewline
86 & 0.993162781960663 & 0.0136744360786732 & 0.00683721803933661 \tabularnewline
87 & 0.988474129360258 & 0.0230517412794848 & 0.0115258706397424 \tabularnewline
88 & 0.987727789438789 & 0.0245444211224228 & 0.0122722105612114 \tabularnewline
89 & 0.981865786273767 & 0.0362684274524665 & 0.0181342137262332 \tabularnewline
90 & 0.970163742345907 & 0.0596725153081868 & 0.0298362576540934 \tabularnewline
91 & 0.985748873477449 & 0.0285022530451024 & 0.0142511265225512 \tabularnewline
92 & 0.975336080568006 & 0.049327838863987 & 0.0246639194319935 \tabularnewline
93 & 0.960553912255583 & 0.0788921754888346 & 0.0394460877444173 \tabularnewline
94 & 0.985960969746583 & 0.0280780605068344 & 0.0140390302534172 \tabularnewline
95 & 0.978045678389459 & 0.0439086432210829 & 0.0219543216105415 \tabularnewline
96 & 0.985482879172839 & 0.0290342416543227 & 0.0145171208271614 \tabularnewline
97 & 0.987912581740058 & 0.0241748365198836 & 0.0120874182599418 \tabularnewline
98 & 0.984535516864926 & 0.0309289662701471 & 0.0154644831350735 \tabularnewline
99 & 0.972974816418496 & 0.0540503671630079 & 0.027025183581504 \tabularnewline
100 & 0.957136292310984 & 0.085727415378033 & 0.0428637076890165 \tabularnewline
101 & 0.910820108790968 & 0.178359782418063 & 0.0891798912090316 \tabularnewline
102 & 0.869901080539451 & 0.260197838921097 & 0.130098919460548 \tabularnewline
103 & 0.87324168911134 & 0.253516621777319 & 0.12675831088866 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145502&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]9[/C][C]0.278494420483909[/C][C]0.556988840967817[/C][C]0.721505579516092[/C][/ROW]
[ROW][C]10[/C][C]0.299231619209012[/C][C]0.598463238418025[/C][C]0.700768380790988[/C][/ROW]
[ROW][C]11[/C][C]0.193792385085645[/C][C]0.387584770171289[/C][C]0.806207614914355[/C][/ROW]
[ROW][C]12[/C][C]0.191883869002634[/C][C]0.383767738005268[/C][C]0.808116130997366[/C][/ROW]
[ROW][C]13[/C][C]0.137573184188367[/C][C]0.275146368376734[/C][C]0.862426815811633[/C][/ROW]
[ROW][C]14[/C][C]0.0895765943750396[/C][C]0.179153188750079[/C][C]0.91042340562496[/C][/ROW]
[ROW][C]15[/C][C]0.0533934698700388[/C][C]0.106786939740078[/C][C]0.946606530129961[/C][/ROW]
[ROW][C]16[/C][C]0.0329183174399665[/C][C]0.065836634879933[/C][C]0.967081682560033[/C][/ROW]
[ROW][C]17[/C][C]0.0201129866553043[/C][C]0.0402259733106087[/C][C]0.979887013344696[/C][/ROW]
[ROW][C]18[/C][C]0.045435409313169[/C][C]0.090870818626338[/C][C]0.95456459068683[/C][/ROW]
[ROW][C]19[/C][C]0.027031303672586[/C][C]0.0540626073451721[/C][C]0.972968696327414[/C][/ROW]
[ROW][C]20[/C][C]0.0153725412576064[/C][C]0.0307450825152127[/C][C]0.984627458742394[/C][/ROW]
[ROW][C]21[/C][C]0.00974907549011827[/C][C]0.0194981509802365[/C][C]0.990250924509882[/C][/ROW]
[ROW][C]22[/C][C]0.00669975843167137[/C][C]0.0133995168633427[/C][C]0.993300241568329[/C][/ROW]
[ROW][C]23[/C][C]0.00468284017192569[/C][C]0.00936568034385137[/C][C]0.995317159828074[/C][/ROW]
[ROW][C]24[/C][C]0.00802150369073676[/C][C]0.0160430073814735[/C][C]0.991978496309263[/C][/ROW]
[ROW][C]25[/C][C]0.00464528368108771[/C][C]0.00929056736217542[/C][C]0.995354716318912[/C][/ROW]
[ROW][C]26[/C][C]0.00371502698999553[/C][C]0.00743005397999107[/C][C]0.996284973010005[/C][/ROW]
[ROW][C]27[/C][C]0.00217655628852058[/C][C]0.00435311257704115[/C][C]0.99782344371148[/C][/ROW]
[ROW][C]28[/C][C]0.00140911539028047[/C][C]0.00281823078056093[/C][C]0.99859088460972[/C][/ROW]
[ROW][C]29[/C][C]0.00257740578927632[/C][C]0.00515481157855264[/C][C]0.997422594210724[/C][/ROW]
[ROW][C]30[/C][C]0.00294276924860633[/C][C]0.00588553849721266[/C][C]0.997057230751394[/C][/ROW]
[ROW][C]31[/C][C]0.00516976630962099[/C][C]0.010339532619242[/C][C]0.99483023369038[/C][/ROW]
[ROW][C]32[/C][C]0.0194394416819478[/C][C]0.0388788833638957[/C][C]0.980560558318052[/C][/ROW]
[ROW][C]33[/C][C]0.0204372242123642[/C][C]0.0408744484247284[/C][C]0.979562775787636[/C][/ROW]
[ROW][C]34[/C][C]0.0153202992754242[/C][C]0.0306405985508484[/C][C]0.984679700724576[/C][/ROW]
[ROW][C]35[/C][C]0.0126348506294802[/C][C]0.0252697012589604[/C][C]0.98736514937052[/C][/ROW]
[ROW][C]36[/C][C]0.00855870472369735[/C][C]0.0171174094473947[/C][C]0.991441295276303[/C][/ROW]
[ROW][C]37[/C][C]0.00565259909000962[/C][C]0.0113051981800192[/C][C]0.99434740090999[/C][/ROW]
[ROW][C]38[/C][C]0.0045181277243376[/C][C]0.0090362554486752[/C][C]0.995481872275662[/C][/ROW]
[ROW][C]39[/C][C]0.00281884989950655[/C][C]0.0056376997990131[/C][C]0.997181150100493[/C][/ROW]
[ROW][C]40[/C][C]0.00266338926081708[/C][C]0.00532677852163415[/C][C]0.997336610739183[/C][/ROW]
[ROW][C]41[/C][C]0.00216088876959991[/C][C]0.00432177753919981[/C][C]0.9978391112304[/C][/ROW]
[ROW][C]42[/C][C]0.0012997895228307[/C][C]0.00259957904566141[/C][C]0.99870021047717[/C][/ROW]
[ROW][C]43[/C][C]0.000965389674556472[/C][C]0.00193077934911294[/C][C]0.999034610325443[/C][/ROW]
[ROW][C]44[/C][C]0.000689307321027276[/C][C]0.00137861464205455[/C][C]0.999310692678973[/C][/ROW]
[ROW][C]45[/C][C]0.000653124245275087[/C][C]0.00130624849055017[/C][C]0.999346875754725[/C][/ROW]
[ROW][C]46[/C][C]0.000479940139398412[/C][C]0.000959880278796824[/C][C]0.999520059860602[/C][/ROW]
[ROW][C]47[/C][C]0.000322371919308871[/C][C]0.000644743838617741[/C][C]0.999677628080691[/C][/ROW]
[ROW][C]48[/C][C]0.000186285940717123[/C][C]0.000372571881434246[/C][C]0.999813714059283[/C][/ROW]
[ROW][C]49[/C][C]0.000170480663747957[/C][C]0.000340961327495913[/C][C]0.999829519336252[/C][/ROW]
[ROW][C]50[/C][C]0.000232002154535539[/C][C]0.000464004309071079[/C][C]0.999767997845464[/C][/ROW]
[ROW][C]51[/C][C]0.00055165159399687[/C][C]0.00110330318799374[/C][C]0.999448348406003[/C][/ROW]
[ROW][C]52[/C][C]0.000454097536549482[/C][C]0.000908195073098964[/C][C]0.99954590246345[/C][/ROW]
[ROW][C]53[/C][C]0.000367257287575207[/C][C]0.000734514575150414[/C][C]0.999632742712425[/C][/ROW]
[ROW][C]54[/C][C]0.000276186393581012[/C][C]0.000552372787162025[/C][C]0.99972381360642[/C][/ROW]
[ROW][C]55[/C][C]0.000271829186828341[/C][C]0.000543658373656682[/C][C]0.999728170813172[/C][/ROW]
[ROW][C]56[/C][C]0.000305691064591508[/C][C]0.000611382129183015[/C][C]0.999694308935408[/C][/ROW]
[ROW][C]57[/C][C]0.000405459108761832[/C][C]0.000810918217523663[/C][C]0.999594540891238[/C][/ROW]
[ROW][C]58[/C][C]0.000345061312172556[/C][C]0.000690122624345113[/C][C]0.999654938687827[/C][/ROW]
[ROW][C]59[/C][C]0.000422512348002131[/C][C]0.000845024696004263[/C][C]0.999577487651998[/C][/ROW]
[ROW][C]60[/C][C]0.000271722540321593[/C][C]0.000543445080643185[/C][C]0.999728277459678[/C][/ROW]
[ROW][C]61[/C][C]0.00327831362903136[/C][C]0.00655662725806273[/C][C]0.996721686370969[/C][/ROW]
[ROW][C]62[/C][C]0.00305646359924089[/C][C]0.00611292719848179[/C][C]0.99694353640076[/C][/ROW]
[ROW][C]63[/C][C]0.00209396189985524[/C][C]0.00418792379971047[/C][C]0.997906038100145[/C][/ROW]
[ROW][C]64[/C][C]0.00184612978601673[/C][C]0.00369225957203347[/C][C]0.998153870213983[/C][/ROW]
[ROW][C]65[/C][C]0.00132217426800323[/C][C]0.00264434853600646[/C][C]0.998677825731997[/C][/ROW]
[ROW][C]66[/C][C]0.0089886470507313[/C][C]0.0179772941014626[/C][C]0.991011352949269[/C][/ROW]
[ROW][C]67[/C][C]0.00877880435739711[/C][C]0.0175576087147942[/C][C]0.991221195642603[/C][/ROW]
[ROW][C]68[/C][C]0.220589803595707[/C][C]0.441179607191413[/C][C]0.779410196404293[/C][/ROW]
[ROW][C]69[/C][C]0.192230039443711[/C][C]0.384460078887423[/C][C]0.807769960556289[/C][/ROW]
[ROW][C]70[/C][C]0.158833411500416[/C][C]0.317666823000833[/C][C]0.841166588499584[/C][/ROW]
[ROW][C]71[/C][C]0.146901583112692[/C][C]0.293803166225384[/C][C]0.853098416887308[/C][/ROW]
[ROW][C]72[/C][C]0.396191681416106[/C][C]0.792383362832213[/C][C]0.603808318583894[/C][/ROW]
[ROW][C]73[/C][C]0.685400140259898[/C][C]0.629199719480204[/C][C]0.314599859740102[/C][/ROW]
[ROW][C]74[/C][C]0.64818580470925[/C][C]0.703628390581499[/C][C]0.35181419529075[/C][/ROW]
[ROW][C]75[/C][C]0.739981657784233[/C][C]0.520036684431533[/C][C]0.260018342215767[/C][/ROW]
[ROW][C]76[/C][C]0.864338500371848[/C][C]0.271322999256304[/C][C]0.135661499628152[/C][/ROW]
[ROW][C]77[/C][C]0.829162559135729[/C][C]0.341674881728542[/C][C]0.170837440864271[/C][/ROW]
[ROW][C]78[/C][C]0.830460070480323[/C][C]0.339079859039355[/C][C]0.169539929519677[/C][/ROW]
[ROW][C]79[/C][C]0.813751373746542[/C][C]0.372497252506916[/C][C]0.186248626253458[/C][/ROW]
[ROW][C]80[/C][C]0.809766277910697[/C][C]0.380467444178607[/C][C]0.190233722089303[/C][/ROW]
[ROW][C]81[/C][C]0.767010087917679[/C][C]0.465979824164643[/C][C]0.232989912082321[/C][/ROW]
[ROW][C]82[/C][C]0.770115281452585[/C][C]0.45976943709483[/C][C]0.229884718547415[/C][/ROW]
[ROW][C]83[/C][C]0.717699185775219[/C][C]0.564601628449563[/C][C]0.282300814224781[/C][/ROW]
[ROW][C]84[/C][C]0.96417448268871[/C][C]0.0716510346225798[/C][C]0.0358255173112899[/C][/ROW]
[ROW][C]85[/C][C]0.979496913920185[/C][C]0.0410061721596309[/C][C]0.0205030860798155[/C][/ROW]
[ROW][C]86[/C][C]0.993162781960663[/C][C]0.0136744360786732[/C][C]0.00683721803933661[/C][/ROW]
[ROW][C]87[/C][C]0.988474129360258[/C][C]0.0230517412794848[/C][C]0.0115258706397424[/C][/ROW]
[ROW][C]88[/C][C]0.987727789438789[/C][C]0.0245444211224228[/C][C]0.0122722105612114[/C][/ROW]
[ROW][C]89[/C][C]0.981865786273767[/C][C]0.0362684274524665[/C][C]0.0181342137262332[/C][/ROW]
[ROW][C]90[/C][C]0.970163742345907[/C][C]0.0596725153081868[/C][C]0.0298362576540934[/C][/ROW]
[ROW][C]91[/C][C]0.985748873477449[/C][C]0.0285022530451024[/C][C]0.0142511265225512[/C][/ROW]
[ROW][C]92[/C][C]0.975336080568006[/C][C]0.049327838863987[/C][C]0.0246639194319935[/C][/ROW]
[ROW][C]93[/C][C]0.960553912255583[/C][C]0.0788921754888346[/C][C]0.0394460877444173[/C][/ROW]
[ROW][C]94[/C][C]0.985960969746583[/C][C]0.0280780605068344[/C][C]0.0140390302534172[/C][/ROW]
[ROW][C]95[/C][C]0.978045678389459[/C][C]0.0439086432210829[/C][C]0.0219543216105415[/C][/ROW]
[ROW][C]96[/C][C]0.985482879172839[/C][C]0.0290342416543227[/C][C]0.0145171208271614[/C][/ROW]
[ROW][C]97[/C][C]0.987912581740058[/C][C]0.0241748365198836[/C][C]0.0120874182599418[/C][/ROW]
[ROW][C]98[/C][C]0.984535516864926[/C][C]0.0309289662701471[/C][C]0.0154644831350735[/C][/ROW]
[ROW][C]99[/C][C]0.972974816418496[/C][C]0.0540503671630079[/C][C]0.027025183581504[/C][/ROW]
[ROW][C]100[/C][C]0.957136292310984[/C][C]0.085727415378033[/C][C]0.0428637076890165[/C][/ROW]
[ROW][C]101[/C][C]0.910820108790968[/C][C]0.178359782418063[/C][C]0.0891798912090316[/C][/ROW]
[ROW][C]102[/C][C]0.869901080539451[/C][C]0.260197838921097[/C][C]0.130098919460548[/C][/ROW]
[ROW][C]103[/C][C]0.87324168911134[/C][C]0.253516621777319[/C][C]0.12675831088866[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145502&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145502&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
90.2784944204839090.5569888409678170.721505579516092
100.2992316192090120.5984632384180250.700768380790988
110.1937923850856450.3875847701712890.806207614914355
120.1918838690026340.3837677380052680.808116130997366
130.1375731841883670.2751463683767340.862426815811633
140.08957659437503960.1791531887500790.91042340562496
150.05339346987003880.1067869397400780.946606530129961
160.03291831743996650.0658366348799330.967081682560033
170.02011298665530430.04022597331060870.979887013344696
180.0454354093131690.0908708186263380.95456459068683
190.0270313036725860.05406260734517210.972968696327414
200.01537254125760640.03074508251521270.984627458742394
210.009749075490118270.01949815098023650.990250924509882
220.006699758431671370.01339951686334270.993300241568329
230.004682840171925690.009365680343851370.995317159828074
240.008021503690736760.01604300738147350.991978496309263
250.004645283681087710.009290567362175420.995354716318912
260.003715026989995530.007430053979991070.996284973010005
270.002176556288520580.004353112577041150.99782344371148
280.001409115390280470.002818230780560930.99859088460972
290.002577405789276320.005154811578552640.997422594210724
300.002942769248606330.005885538497212660.997057230751394
310.005169766309620990.0103395326192420.99483023369038
320.01943944168194780.03887888336389570.980560558318052
330.02043722421236420.04087444842472840.979562775787636
340.01532029927542420.03064059855084840.984679700724576
350.01263485062948020.02526970125896040.98736514937052
360.008558704723697350.01711740944739470.991441295276303
370.005652599090009620.01130519818001920.99434740090999
380.00451812772433760.00903625544867520.995481872275662
390.002818849899506550.00563769979901310.997181150100493
400.002663389260817080.005326778521634150.997336610739183
410.002160888769599910.004321777539199810.9978391112304
420.00129978952283070.002599579045661410.99870021047717
430.0009653896745564720.001930779349112940.999034610325443
440.0006893073210272760.001378614642054550.999310692678973
450.0006531242452750870.001306248490550170.999346875754725
460.0004799401393984120.0009598802787968240.999520059860602
470.0003223719193088710.0006447438386177410.999677628080691
480.0001862859407171230.0003725718814342460.999813714059283
490.0001704806637479570.0003409613274959130.999829519336252
500.0002320021545355390.0004640043090710790.999767997845464
510.000551651593996870.001103303187993740.999448348406003
520.0004540975365494820.0009081950730989640.99954590246345
530.0003672572875752070.0007345145751504140.999632742712425
540.0002761863935810120.0005523727871620250.99972381360642
550.0002718291868283410.0005436583736566820.999728170813172
560.0003056910645915080.0006113821291830150.999694308935408
570.0004054591087618320.0008109182175236630.999594540891238
580.0003450613121725560.0006901226243451130.999654938687827
590.0004225123480021310.0008450246960042630.999577487651998
600.0002717225403215930.0005434450806431850.999728277459678
610.003278313629031360.006556627258062730.996721686370969
620.003056463599240890.006112927198481790.99694353640076
630.002093961899855240.004187923799710470.997906038100145
640.001846129786016730.003692259572033470.998153870213983
650.001322174268003230.002644348536006460.998677825731997
660.00898864705073130.01797729410146260.991011352949269
670.008778804357397110.01755760871479420.991221195642603
680.2205898035957070.4411796071914130.779410196404293
690.1922300394437110.3844600788874230.807769960556289
700.1588334115004160.3176668230008330.841166588499584
710.1469015831126920.2938031662253840.853098416887308
720.3961916814161060.7923833628322130.603808318583894
730.6854001402598980.6291997194802040.314599859740102
740.648185804709250.7036283905814990.35181419529075
750.7399816577842330.5200366844315330.260018342215767
760.8643385003718480.2713229992563040.135661499628152
770.8291625591357290.3416748817285420.170837440864271
780.8304600704803230.3390798590393550.169539929519677
790.8137513737465420.3724972525069160.186248626253458
800.8097662779106970.3804674441786070.190233722089303
810.7670100879176790.4659798241646430.232989912082321
820.7701152814525850.459769437094830.229884718547415
830.7176991857752190.5646016284495630.282300814224781
840.964174482688710.07165103462257980.0358255173112899
850.9794969139201850.04100617215963090.0205030860798155
860.9931627819606630.01367443607867320.00683721803933661
870.9884741293602580.02305174127948480.0115258706397424
880.9877277894387890.02454442112242280.0122722105612114
890.9818657862737670.03626842745246650.0181342137262332
900.9701637423459070.05967251530818680.0298362576540934
910.9857488734774490.02850225304510240.0142511265225512
920.9753360805680060.0493278388639870.0246639194319935
930.9605539122555830.07889217548883460.0394460877444173
940.9859609697465830.02807806050683440.0140390302534172
950.9780456783894590.04390864322108290.0219543216105415
960.9854828791728390.02903424165432270.0145171208271614
970.9879125817400580.02417483651988360.0120874182599418
980.9845355168649260.03092896627014710.0154644831350735
990.9729748164184960.05405036716300790.027025183581504
1000.9571362923109840.0857274153780330.0428637076890165
1010.9108201087909680.1783597824180630.0891798912090316
1020.8699010805394510.2601978389210970.130098919460548
1030.873241689111340.2535166217773190.12675831088866







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level350.368421052631579NOK
5% type I error level610.642105263157895NOK
10% type I error level690.726315789473684NOK

\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 & 35 & 0.368421052631579 & NOK \tabularnewline
5% type I error level & 61 & 0.642105263157895 & NOK \tabularnewline
10% type I error level & 69 & 0.726315789473684 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145502&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]35[/C][C]0.368421052631579[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]61[/C][C]0.642105263157895[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]69[/C][C]0.726315789473684[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145502&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145502&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 level350.368421052631579NOK
5% type I error level610.642105263157895NOK
10% type I error level690.726315789473684NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}