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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 14 Dec 2011 07:10:58 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/14/t1323864676k5blt2z7aptin41.htm/, Retrieved Wed, 01 May 2024 14:30:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154881, Retrieved Wed, 01 May 2024 14:30:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact71

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-12-14 12:10:58] [bd7a66e2f212a6bc9afe853e3942ee45] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
56	79	30	112285	21
56	58	28	84786	23
54	60	38	83123	22
92	121	25	119182	22
44	43	26	116174	21
33	69	25	57635	22
84	78	38	66198	21
55	44	30	57793	21
154	158	47	97668	21
53	102	30	133824	21
119	77	31	101481	23
41	82	23	99645	21
58	101	36	99052	21
75	80	30	67654	22
33	50	25	65553	22
100	73	31	82753	21
112	81	31	85323	22
73	105	33	72654	23
40	47	25	30727	22
60	94	35	117478	22
62	44	42	74007	21
77	107	33	101494	21
99	84	36	79215	21
17	0	0	1423	20
30	33	14	31081	21
76	42	17	22996	25
146	96	32	83122	21
56	56	35	60578	21
107	57	20	39992	20
58	59	28	79892	24
34	39	28	49810	23
119	76	34	100708	21
66	91	39	82875	24
66	76	28	72260	21
24	8	4	5950	23
259	79	39	115762	23
41	76	29	80670	21
68	101	44	143558	22
168	94	21	117105	20
43	27	16	23789	18
105	123	35	105195	22
94	105	23	149193	21
57	41	29	95260	21
53	72	25	55183	23
103	67	27	106671	22
121	75	36	73511	21
62	114	28	92945	21
32	22	23	22618	21
45	69	28	83737	22
46	105	34	69094	21
75	88	28	95536	21
88	73	34	225920	23
53	62	33	61370	21
78	100	35	84651	22
45	24	24	15986	22
46	67	29	95364	20
41	46	20	26706	21
144	57	29	89691	21
91	135	37	126846	21
63	124	33	102860	21
53	33	25	51715	22
62	98	32	55801	21
63	58	29	111813	24
32	68	28	120293	22
62	131	31	161647	24
117	110	52	115929	21
34	37	21	24266	22
92	130	24	162901	22
93	93	41	109825	21
54	118	33	129838	24
144	39	32	37510	21
109	81	31	87771	22
75	51	18	44418	19
50	28	23	192565	22
61	40	17	35232	23
55	56	20	40909	20
77	27	12	13294	20
72	83	30	140867	23
53	28	13	44332	20
42	59	22	61056	20
71	133	42	101338	23
10	12	1	1168	25
65	106	32	65567	21
66	44	25	32334	22
41	71	36	40735	21
86	116	31	91413	22
16	4	0	855	22
42	62	24	97068	23
19	12	13	44339	21
19	18	8	14116	21
45	14	13	10288	20
65	60	19	65622	19
95	98	33	76643	22
64	32	38	92696	21
38	25	24	94785	21
65	100	43	93815	21
52	46	43	86687	21
62	45	14	34553	21
65	129	41	105547	21
95	136	45	213688	22
29	59	31	71220	22
247	63	31	91721	22
29	14	30	111194	22
118	36	16	51009	18
110	113	37	135777	21
67	47	30	51513	23
42	92	35	74163	21
64	50	20	33416	19
81	41	18	83305	19
95	91	31	98952	23
67	111	31	102372	21
63	41	21	37238	21
83	120	39	103772	21
32	25	18	21399	21
83	131	39	130115	20
31	45	14	24874	19
67	29	7	34988	21
66	58	17	45549	22
70	47	30	64466	21
103	109	37	54990	25
34	37	32	34777	23
40	15	17	27114	19
31	7	24	37636	19
42	54	22	65461	19
46	54	12	30080	19
33	14	19	24094	19
18	16	13	69008	20
35	32	15	46090	19
66	38	15	34029	19
60	22	17	46300	19
54	32	16	40662	19
53	32	18	28987	19
39	37	17	30594	20
45	32	16	27913	19
36	0	23	42744	19
28	5	22	12934	18
30	10	13	41385	19
22	27	16	18653	19
31	29	20	30976	21
55	25	22	63339	18
54	55	17	25568	18
14	5	17	4154	21
81	43	12	19474	20
43	34	17	39067	19
30	35	23	65892	21
23	0	17	4143	21
38	37	14	28579	20
53	26	21	38084	24
45	38	18	27717	22
39	23	18	32928	21
24	30	17	19499	21
35	18	15	36874	19
151	28	21	48259	18
30	21	14	28207	19
57	50	15	45833	19
40	12	15	29156	19
77	27	22	45588	20
35	41	21	45097	18
63	12	18	28394	19
44	21	17	18632	19
19	8	4	2325	20
13	26	10	25139	20
42	27	16	27975	21
42	37	18	21792	20
49	29	12	26263	21
30	32	16	23686	18
49	35	21	49303	19
12	10	2	5752	19
20	17	17	20055	19
27	10	16	20154	19
14	17	16	19540	19

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154881&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154881&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154881&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
log[t] = + 45.8494816429812 + 0.287132806589791blog[t] + 0.810720912550083PR[t] + 0.000104618328910731size[t] -1.29081107820149`age `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
log[t] =  +  45.8494816429812 +  0.287132806589791blog[t] +  0.810720912550083PR[t] +  0.000104618328910731size[t] -1.29081107820149`age
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154881&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]log[t] =  +  45.8494816429812 +  0.287132806589791blog[t] +  0.810720912550083PR[t] +  0.000104618328910731size[t] -1.29081107820149`age
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154881&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154881&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
log[t] = + 45.8494816429812 + 0.287132806589791blog[t] + 0.810720912550083PR[t] + 0.000104618328910731size[t] -1.29081107820149`age `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)45.849481642981234.7365251.31990.1886790.094339
blog0.2871328065897910.1133362.53350.0122210.00611
PR0.8107209125500830.3921392.06740.0402450.020123
size0.0001046183289107319e-051.16760.2446290.122315
`age `-1.290811078201491.745341-0.73960.4606020.230301

\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) & 45.8494816429812 & 34.736525 & 1.3199 & 0.188679 & 0.094339 \tabularnewline
blog & 0.287132806589791 & 0.113336 & 2.5335 & 0.012221 & 0.00611 \tabularnewline
PR & 0.810720912550083 & 0.392139 & 2.0674 & 0.040245 & 0.020123 \tabularnewline
size & 0.000104618328910731 & 9e-05 & 1.1676 & 0.244629 & 0.122315 \tabularnewline
`age
` & -1.29081107820149 & 1.745341 & -0.7396 & 0.460602 & 0.230301 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154881&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]45.8494816429812[/C][C]34.736525[/C][C]1.3199[/C][C]0.188679[/C][C]0.094339[/C][/ROW]
[ROW][C]blog[/C][C]0.287132806589791[/C][C]0.113336[/C][C]2.5335[/C][C]0.012221[/C][C]0.00611[/C][/ROW]
[ROW][C]PR[/C][C]0.810720912550083[/C][C]0.392139[/C][C]2.0674[/C][C]0.040245[/C][C]0.020123[/C][/ROW]
[ROW][C]size[/C][C]0.000104618328910731[/C][C]9e-05[/C][C]1.1676[/C][C]0.244629[/C][C]0.122315[/C][/ROW]
[ROW][C]`age
`[/C][C]-1.29081107820149[/C][C]1.745341[/C][C]-0.7396[/C][C]0.460602[/C][C]0.230301[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154881&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154881&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)45.849481642981234.7365251.31990.1886790.094339
blog0.2871328065897910.1133362.53350.0122210.00611
PR0.8107209125500830.3921392.06740.0402450.020123
size0.0001046183289107319e-051.16760.2446290.122315
`age `-1.290811078201491.745341-0.73960.4606020.230301






Multiple Linear Regression - Regression Statistics
Multiple R0.540434446157205
R-squared0.292069390593245
Adjusted R-squared0.275010821691877
F-TEST (value)17.1215646682899
F-TEST (DF numerator)4
F-TEST (DF denominator)166
p-value8.94018192809654e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation31.6975605241144
Sum Squared Residuals166786.066967863

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.540434446157205 \tabularnewline
R-squared & 0.292069390593245 \tabularnewline
Adjusted R-squared & 0.275010821691877 \tabularnewline
F-TEST (value) & 17.1215646682899 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 166 \tabularnewline
p-value & 8.94018192809654e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 31.6975605241144 \tabularnewline
Sum Squared Residuals & 166786.066967863 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154881&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.540434446157205[/C][/ROW]
[ROW][C]R-squared[/C][C]0.292069390593245[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.275010821691877[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.1215646682899[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]166[/C][/ROW]
[ROW][C]p-value[/C][C]8.94018192809654e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]31.6975605241144[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]166786.066967863[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154881&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154881&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 R0.540434446157205
R-squared0.292069390593245
Adjusted R-squared0.275010821691877
F-TEST (value)17.1215646682899
F-TEST (DF numerator)4
F-TEST (DF denominator)166
p-value8.94018192809654e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation31.6975605241144
Sum Squared Residuals166786.066967863






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15677.4946371595875-21.4946371595874
25664.3848848129825-8.38488481298248
35474.1831903488858-20.1831903488858
49284.9313520099047.06864799009596
54464.3218331532885-20.3218331532885
63363.5615017777661-30.5615017777661
78478.87172672888945.12827327111059
85561.7441269499412-6.74412694994117
9154112.43117827984441.5688217201559
105386.3520658975609-33.3520658975609
1111974.019173877003444.9808261229966
124171.3586135140746-30.3586135140746
135887.2914700333876-29.2914700333876
147571.82173825036093.17826174963913
153358.9343463808753-25.9343463808753
1610073.49297274320726.507027256793
1711274.768093223024537.2319067769755
187380.6645017191081-7.66450171910806
194054.4295100384608-14.4295100384608
206085.1077057250166-25.1077057250166
216273.1690594855008-11.1690594855008
227786.8375820944761-9.83758209447611
239980.33489853075918.665101469241
241720.1821319609914-3.18213196099144
253042.8195666747887-12.8195666747887
267641.826841169697834.1731588303022
2714680.946352370690465.0536476293096
285669.5346872377854-13.5346872377855
2910756.798144515369250.2018554846308
305862.8692044396817-4.86920443968166
313455.2702308157947-21.2702308157947
3211978.664955996218840.3350440037812
336681.2874607637466-15.2874607637466
346670.8244483000659-4.82444830006585
352422.32325200428451.67674799571548
3625982.5732611457578176.426738854242
374172.5150093587552-31.5150093587552
386897.1425696020878-29.1425696020878
3916876.300212469034791.6997875309653
404345.8277680405375-2.8277680405375
4110592.149530182110112.8504698178899
429483.146297026508710.8537029734913
435763.9917425469201-6.99174254692008
445362.8755649768449-9.87556497684491
4510369.738742366153333.2612576338467
4612177.153960323344143.846039676656
476283.8995250839964-21.8995250839964
483246.0722090976802-14.0722090976802
494568.7244121366443-23.7244121366443
504683.6844035371389-37.6844035371389
517576.7051382028695-1.70513820286953
528888.321405619617-0.321405619617002
535369.7188999687213-16.7188999687213
547883.3961966814028-5.3961966814028
554545.4725557878724-0.472555787872419
564672.7588869026628-26.7588869026628
574150.958913446772-9.95891344677201
5814468.003247978652975.9967520213471
5991100.772468203735-9.77246820373544
606391.8617484437946-28.8617484437946
615352.60538023338210.394619766617861
626278.6623406196998-16.6623406196999
636366.7323142228014-3.73231422280141
643272.2617069617152-40.2617069617152
656294.5280007318937-32.5280007318937
66117104.61284343052312.3871565694765
673447.6393592992703-13.6393592992703
689291.27863507831030.721364921689652
699390.1750654007752.824934599225
705489.0889116470051-35.0889116470051
7114459.80793117679684.192068823204
7210975.024198892197933.9758011078021
737555.207757652690619.7922423473094
745064.2837660024095-14.2837660024095
756145.114307585472915.8856924145271
765556.6069467163905-1.60694671639052
777738.905292872016138.0947071279839
787279.0517473064701-7.05174730647014
795343.25029028388729.74970971611277
804261.1975324338246-19.1975324338246
817199.0015806630484-28.0015806630484
821017.9577134877393-7.95771348773935
836581.9811056725604-16.9811056725604
846653.73623327325112.263766726749
854172.5764587486068-31.5764587486068
868685.45486707673350.545132923266512
871618.6896178201263-2.68961782012634
884263.5754547048229-21.5754547048229
891937.3660866285515-18.3660866285515
901931.8733991506708-12.8733991506708
914535.66880460219329.33119539780678
926560.8210008707724.17899912922801
939580.362705665205914.6372943347941
946468.4357941052356-4.4357941052356
953855.2943193725004-17.2943193725004
966592.1314974261429-27.1314974261429
975275.8806064218185-23.8806064218185
986246.628395191844215.3716048081558
9965100.064289226927-35.0642892269274
10095115.33982215179-20.3398221517901
1012966.975739185421-37.975739185421
10224770.2690507727791176.730949227221
1032957.4260550562079-28.4260550562079
10411851.259674212795766.7403257872043
10511095.389892754261814.6101072457382
1066759.36690010774827.63309989225182
1074281.2927082732702-39.2927082732702
1086455.39105581652528.60894418347485
1098156.404722543144424.5952774568556
1109577.774453415445217.2255465845548
1116786.4565263885187-19.4565263885187
1126351.43581056646111.564189433539
1138395.6729546087025-12.6729546087025
1143242.752473211757-10.752473211757
11583102.878187197887-19.8781871978871
1163148.1974165427202-17.1974165427202
1176736.404732871633230.5952671283668
1186652.652856481662713.3471435183373
1197063.30364347853186.69635652146816
12010380.626316277385422.3736837226146
1213456.3661215143004-22.3661215143004
1224042.2499401394368-2.2499401394368
1233146.7287181313678-15.7287181313678
1244261.5135232179289-19.5135232179289
1254649.7048129972374-3.70481299723745
1263343.2683018046368-10.2683018046368
1271842.386258489011-24.386258489011
1283547.4949934357731-12.4949934357731
1296647.955988610319518.0440113896805
1306046.267077044046613.7329229559534
1315447.73784605899576.26215394100425
1325348.13786889406314.86213110593688
1333947.6401225908201-8.64012259082005
1344546.4040669837128-1.40406698371283
1353644.4424579967652-8.44245799676517
1362843.2395398105366-15.2395398105366
1373039.0644006281726-9.06440062817256
1382243.9996372250505-21.9996372250505
1393146.5243759991944-15.5243759991944
1405554.25548281107780.744517188922149
1415454.8643235447339-0.864323544733919
1421434.3949530853455-20.3949530853455
1438144.14595905012136.854040949879
1444348.9559663501128-5.95596635011279
1453054.3321891486305-24.3321891486305
1462332.9581382507786-9.95813825077856
1473844.9971539204147-6.99715392041468
1485343.34489233926819.65510766073188
1494545.8553672212808-0.855367221280782
1503943.3843523125892-4.38435231258923
1512443.1786415072255-19.1786415072255
1523542.5109716242747-7.51097162427474
15315152.728515918323398.2714840816767
1543041.6549220748247-11.6549220748247
1555752.63649704385934.36350295614071
1564039.9807305222030.0192694777970285
1577750.391046311360126.6089536886399
1583556.1304392479749-21.1304392479749
1596342.333174093223220.6668259067768
1604443.08536431315470.914635686845283
1611925.8164437965876-6.81644379658758
1621338.2359223462737-25.2359223462737
1634242.3932671307534-0.39326713075337
1644247.5299929722979-5.52999297229788
1654939.54554251463749.45445748536255
1663047.2526563856087-17.2526563856087
1674953.5568560216332-4.55685602163316
1681226.4186056760456-14.4186056760456
1692042.0857049688355-22.0857049688355
1702739.2754116247191-12.2754116247191
1711441.2211056168964-27.2211056168964

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 56 & 77.4946371595875 & -21.4946371595874 \tabularnewline
2 & 56 & 64.3848848129825 & -8.38488481298248 \tabularnewline
3 & 54 & 74.1831903488858 & -20.1831903488858 \tabularnewline
4 & 92 & 84.931352009904 & 7.06864799009596 \tabularnewline
5 & 44 & 64.3218331532885 & -20.3218331532885 \tabularnewline
6 & 33 & 63.5615017777661 & -30.5615017777661 \tabularnewline
7 & 84 & 78.8717267288894 & 5.12827327111059 \tabularnewline
8 & 55 & 61.7441269499412 & -6.74412694994117 \tabularnewline
9 & 154 & 112.431178279844 & 41.5688217201559 \tabularnewline
10 & 53 & 86.3520658975609 & -33.3520658975609 \tabularnewline
11 & 119 & 74.0191738770034 & 44.9808261229966 \tabularnewline
12 & 41 & 71.3586135140746 & -30.3586135140746 \tabularnewline
13 & 58 & 87.2914700333876 & -29.2914700333876 \tabularnewline
14 & 75 & 71.8217382503609 & 3.17826174963913 \tabularnewline
15 & 33 & 58.9343463808753 & -25.9343463808753 \tabularnewline
16 & 100 & 73.492972743207 & 26.507027256793 \tabularnewline
17 & 112 & 74.7680932230245 & 37.2319067769755 \tabularnewline
18 & 73 & 80.6645017191081 & -7.66450171910806 \tabularnewline
19 & 40 & 54.4295100384608 & -14.4295100384608 \tabularnewline
20 & 60 & 85.1077057250166 & -25.1077057250166 \tabularnewline
21 & 62 & 73.1690594855008 & -11.1690594855008 \tabularnewline
22 & 77 & 86.8375820944761 & -9.83758209447611 \tabularnewline
23 & 99 & 80.334898530759 & 18.665101469241 \tabularnewline
24 & 17 & 20.1821319609914 & -3.18213196099144 \tabularnewline
25 & 30 & 42.8195666747887 & -12.8195666747887 \tabularnewline
26 & 76 & 41.8268411696978 & 34.1731588303022 \tabularnewline
27 & 146 & 80.9463523706904 & 65.0536476293096 \tabularnewline
28 & 56 & 69.5346872377854 & -13.5346872377855 \tabularnewline
29 & 107 & 56.7981445153692 & 50.2018554846308 \tabularnewline
30 & 58 & 62.8692044396817 & -4.86920443968166 \tabularnewline
31 & 34 & 55.2702308157947 & -21.2702308157947 \tabularnewline
32 & 119 & 78.6649559962188 & 40.3350440037812 \tabularnewline
33 & 66 & 81.2874607637466 & -15.2874607637466 \tabularnewline
34 & 66 & 70.8244483000659 & -4.82444830006585 \tabularnewline
35 & 24 & 22.3232520042845 & 1.67674799571548 \tabularnewline
36 & 259 & 82.5732611457578 & 176.426738854242 \tabularnewline
37 & 41 & 72.5150093587552 & -31.5150093587552 \tabularnewline
38 & 68 & 97.1425696020878 & -29.1425696020878 \tabularnewline
39 & 168 & 76.3002124690347 & 91.6997875309653 \tabularnewline
40 & 43 & 45.8277680405375 & -2.8277680405375 \tabularnewline
41 & 105 & 92.1495301821101 & 12.8504698178899 \tabularnewline
42 & 94 & 83.1462970265087 & 10.8537029734913 \tabularnewline
43 & 57 & 63.9917425469201 & -6.99174254692008 \tabularnewline
44 & 53 & 62.8755649768449 & -9.87556497684491 \tabularnewline
45 & 103 & 69.7387423661533 & 33.2612576338467 \tabularnewline
46 & 121 & 77.1539603233441 & 43.846039676656 \tabularnewline
47 & 62 & 83.8995250839964 & -21.8995250839964 \tabularnewline
48 & 32 & 46.0722090976802 & -14.0722090976802 \tabularnewline
49 & 45 & 68.7244121366443 & -23.7244121366443 \tabularnewline
50 & 46 & 83.6844035371389 & -37.6844035371389 \tabularnewline
51 & 75 & 76.7051382028695 & -1.70513820286953 \tabularnewline
52 & 88 & 88.321405619617 & -0.321405619617002 \tabularnewline
53 & 53 & 69.7188999687213 & -16.7188999687213 \tabularnewline
54 & 78 & 83.3961966814028 & -5.3961966814028 \tabularnewline
55 & 45 & 45.4725557878724 & -0.472555787872419 \tabularnewline
56 & 46 & 72.7588869026628 & -26.7588869026628 \tabularnewline
57 & 41 & 50.958913446772 & -9.95891344677201 \tabularnewline
58 & 144 & 68.0032479786529 & 75.9967520213471 \tabularnewline
59 & 91 & 100.772468203735 & -9.77246820373544 \tabularnewline
60 & 63 & 91.8617484437946 & -28.8617484437946 \tabularnewline
61 & 53 & 52.6053802333821 & 0.394619766617861 \tabularnewline
62 & 62 & 78.6623406196998 & -16.6623406196999 \tabularnewline
63 & 63 & 66.7323142228014 & -3.73231422280141 \tabularnewline
64 & 32 & 72.2617069617152 & -40.2617069617152 \tabularnewline
65 & 62 & 94.5280007318937 & -32.5280007318937 \tabularnewline
66 & 117 & 104.612843430523 & 12.3871565694765 \tabularnewline
67 & 34 & 47.6393592992703 & -13.6393592992703 \tabularnewline
68 & 92 & 91.2786350783103 & 0.721364921689652 \tabularnewline
69 & 93 & 90.175065400775 & 2.824934599225 \tabularnewline
70 & 54 & 89.0889116470051 & -35.0889116470051 \tabularnewline
71 & 144 & 59.807931176796 & 84.192068823204 \tabularnewline
72 & 109 & 75.0241988921979 & 33.9758011078021 \tabularnewline
73 & 75 & 55.2077576526906 & 19.7922423473094 \tabularnewline
74 & 50 & 64.2837660024095 & -14.2837660024095 \tabularnewline
75 & 61 & 45.1143075854729 & 15.8856924145271 \tabularnewline
76 & 55 & 56.6069467163905 & -1.60694671639052 \tabularnewline
77 & 77 & 38.9052928720161 & 38.0947071279839 \tabularnewline
78 & 72 & 79.0517473064701 & -7.05174730647014 \tabularnewline
79 & 53 & 43.2502902838872 & 9.74970971611277 \tabularnewline
80 & 42 & 61.1975324338246 & -19.1975324338246 \tabularnewline
81 & 71 & 99.0015806630484 & -28.0015806630484 \tabularnewline
82 & 10 & 17.9577134877393 & -7.95771348773935 \tabularnewline
83 & 65 & 81.9811056725604 & -16.9811056725604 \tabularnewline
84 & 66 & 53.736233273251 & 12.263766726749 \tabularnewline
85 & 41 & 72.5764587486068 & -31.5764587486068 \tabularnewline
86 & 86 & 85.4548670767335 & 0.545132923266512 \tabularnewline
87 & 16 & 18.6896178201263 & -2.68961782012634 \tabularnewline
88 & 42 & 63.5754547048229 & -21.5754547048229 \tabularnewline
89 & 19 & 37.3660866285515 & -18.3660866285515 \tabularnewline
90 & 19 & 31.8733991506708 & -12.8733991506708 \tabularnewline
91 & 45 & 35.6688046021932 & 9.33119539780678 \tabularnewline
92 & 65 & 60.821000870772 & 4.17899912922801 \tabularnewline
93 & 95 & 80.3627056652059 & 14.6372943347941 \tabularnewline
94 & 64 & 68.4357941052356 & -4.4357941052356 \tabularnewline
95 & 38 & 55.2943193725004 & -17.2943193725004 \tabularnewline
96 & 65 & 92.1314974261429 & -27.1314974261429 \tabularnewline
97 & 52 & 75.8806064218185 & -23.8806064218185 \tabularnewline
98 & 62 & 46.6283951918442 & 15.3716048081558 \tabularnewline
99 & 65 & 100.064289226927 & -35.0642892269274 \tabularnewline
100 & 95 & 115.33982215179 & -20.3398221517901 \tabularnewline
101 & 29 & 66.975739185421 & -37.975739185421 \tabularnewline
102 & 247 & 70.2690507727791 & 176.730949227221 \tabularnewline
103 & 29 & 57.4260550562079 & -28.4260550562079 \tabularnewline
104 & 118 & 51.2596742127957 & 66.7403257872043 \tabularnewline
105 & 110 & 95.3898927542618 & 14.6101072457382 \tabularnewline
106 & 67 & 59.3669001077482 & 7.63309989225182 \tabularnewline
107 & 42 & 81.2927082732702 & -39.2927082732702 \tabularnewline
108 & 64 & 55.3910558165252 & 8.60894418347485 \tabularnewline
109 & 81 & 56.4047225431444 & 24.5952774568556 \tabularnewline
110 & 95 & 77.7744534154452 & 17.2255465845548 \tabularnewline
111 & 67 & 86.4565263885187 & -19.4565263885187 \tabularnewline
112 & 63 & 51.435810566461 & 11.564189433539 \tabularnewline
113 & 83 & 95.6729546087025 & -12.6729546087025 \tabularnewline
114 & 32 & 42.752473211757 & -10.752473211757 \tabularnewline
115 & 83 & 102.878187197887 & -19.8781871978871 \tabularnewline
116 & 31 & 48.1974165427202 & -17.1974165427202 \tabularnewline
117 & 67 & 36.4047328716332 & 30.5952671283668 \tabularnewline
118 & 66 & 52.6528564816627 & 13.3471435183373 \tabularnewline
119 & 70 & 63.3036434785318 & 6.69635652146816 \tabularnewline
120 & 103 & 80.6263162773854 & 22.3736837226146 \tabularnewline
121 & 34 & 56.3661215143004 & -22.3661215143004 \tabularnewline
122 & 40 & 42.2499401394368 & -2.2499401394368 \tabularnewline
123 & 31 & 46.7287181313678 & -15.7287181313678 \tabularnewline
124 & 42 & 61.5135232179289 & -19.5135232179289 \tabularnewline
125 & 46 & 49.7048129972374 & -3.70481299723745 \tabularnewline
126 & 33 & 43.2683018046368 & -10.2683018046368 \tabularnewline
127 & 18 & 42.386258489011 & -24.386258489011 \tabularnewline
128 & 35 & 47.4949934357731 & -12.4949934357731 \tabularnewline
129 & 66 & 47.9559886103195 & 18.0440113896805 \tabularnewline
130 & 60 & 46.2670770440466 & 13.7329229559534 \tabularnewline
131 & 54 & 47.7378460589957 & 6.26215394100425 \tabularnewline
132 & 53 & 48.1378688940631 & 4.86213110593688 \tabularnewline
133 & 39 & 47.6401225908201 & -8.64012259082005 \tabularnewline
134 & 45 & 46.4040669837128 & -1.40406698371283 \tabularnewline
135 & 36 & 44.4424579967652 & -8.44245799676517 \tabularnewline
136 & 28 & 43.2395398105366 & -15.2395398105366 \tabularnewline
137 & 30 & 39.0644006281726 & -9.06440062817256 \tabularnewline
138 & 22 & 43.9996372250505 & -21.9996372250505 \tabularnewline
139 & 31 & 46.5243759991944 & -15.5243759991944 \tabularnewline
140 & 55 & 54.2554828110778 & 0.744517188922149 \tabularnewline
141 & 54 & 54.8643235447339 & -0.864323544733919 \tabularnewline
142 & 14 & 34.3949530853455 & -20.3949530853455 \tabularnewline
143 & 81 & 44.145959050121 & 36.854040949879 \tabularnewline
144 & 43 & 48.9559663501128 & -5.95596635011279 \tabularnewline
145 & 30 & 54.3321891486305 & -24.3321891486305 \tabularnewline
146 & 23 & 32.9581382507786 & -9.95813825077856 \tabularnewline
147 & 38 & 44.9971539204147 & -6.99715392041468 \tabularnewline
148 & 53 & 43.3448923392681 & 9.65510766073188 \tabularnewline
149 & 45 & 45.8553672212808 & -0.855367221280782 \tabularnewline
150 & 39 & 43.3843523125892 & -4.38435231258923 \tabularnewline
151 & 24 & 43.1786415072255 & -19.1786415072255 \tabularnewline
152 & 35 & 42.5109716242747 & -7.51097162427474 \tabularnewline
153 & 151 & 52.7285159183233 & 98.2714840816767 \tabularnewline
154 & 30 & 41.6549220748247 & -11.6549220748247 \tabularnewline
155 & 57 & 52.6364970438593 & 4.36350295614071 \tabularnewline
156 & 40 & 39.980730522203 & 0.0192694777970285 \tabularnewline
157 & 77 & 50.3910463113601 & 26.6089536886399 \tabularnewline
158 & 35 & 56.1304392479749 & -21.1304392479749 \tabularnewline
159 & 63 & 42.3331740932232 & 20.6668259067768 \tabularnewline
160 & 44 & 43.0853643131547 & 0.914635686845283 \tabularnewline
161 & 19 & 25.8164437965876 & -6.81644379658758 \tabularnewline
162 & 13 & 38.2359223462737 & -25.2359223462737 \tabularnewline
163 & 42 & 42.3932671307534 & -0.39326713075337 \tabularnewline
164 & 42 & 47.5299929722979 & -5.52999297229788 \tabularnewline
165 & 49 & 39.5455425146374 & 9.45445748536255 \tabularnewline
166 & 30 & 47.2526563856087 & -17.2526563856087 \tabularnewline
167 & 49 & 53.5568560216332 & -4.55685602163316 \tabularnewline
168 & 12 & 26.4186056760456 & -14.4186056760456 \tabularnewline
169 & 20 & 42.0857049688355 & -22.0857049688355 \tabularnewline
170 & 27 & 39.2754116247191 & -12.2754116247191 \tabularnewline
171 & 14 & 41.2211056168964 & -27.2211056168964 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154881&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]56[/C][C]77.4946371595875[/C][C]-21.4946371595874[/C][/ROW]
[ROW][C]2[/C][C]56[/C][C]64.3848848129825[/C][C]-8.38488481298248[/C][/ROW]
[ROW][C]3[/C][C]54[/C][C]74.1831903488858[/C][C]-20.1831903488858[/C][/ROW]
[ROW][C]4[/C][C]92[/C][C]84.931352009904[/C][C]7.06864799009596[/C][/ROW]
[ROW][C]5[/C][C]44[/C][C]64.3218331532885[/C][C]-20.3218331532885[/C][/ROW]
[ROW][C]6[/C][C]33[/C][C]63.5615017777661[/C][C]-30.5615017777661[/C][/ROW]
[ROW][C]7[/C][C]84[/C][C]78.8717267288894[/C][C]5.12827327111059[/C][/ROW]
[ROW][C]8[/C][C]55[/C][C]61.7441269499412[/C][C]-6.74412694994117[/C][/ROW]
[ROW][C]9[/C][C]154[/C][C]112.431178279844[/C][C]41.5688217201559[/C][/ROW]
[ROW][C]10[/C][C]53[/C][C]86.3520658975609[/C][C]-33.3520658975609[/C][/ROW]
[ROW][C]11[/C][C]119[/C][C]74.0191738770034[/C][C]44.9808261229966[/C][/ROW]
[ROW][C]12[/C][C]41[/C][C]71.3586135140746[/C][C]-30.3586135140746[/C][/ROW]
[ROW][C]13[/C][C]58[/C][C]87.2914700333876[/C][C]-29.2914700333876[/C][/ROW]
[ROW][C]14[/C][C]75[/C][C]71.8217382503609[/C][C]3.17826174963913[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]58.9343463808753[/C][C]-25.9343463808753[/C][/ROW]
[ROW][C]16[/C][C]100[/C][C]73.492972743207[/C][C]26.507027256793[/C][/ROW]
[ROW][C]17[/C][C]112[/C][C]74.7680932230245[/C][C]37.2319067769755[/C][/ROW]
[ROW][C]18[/C][C]73[/C][C]80.6645017191081[/C][C]-7.66450171910806[/C][/ROW]
[ROW][C]19[/C][C]40[/C][C]54.4295100384608[/C][C]-14.4295100384608[/C][/ROW]
[ROW][C]20[/C][C]60[/C][C]85.1077057250166[/C][C]-25.1077057250166[/C][/ROW]
[ROW][C]21[/C][C]62[/C][C]73.1690594855008[/C][C]-11.1690594855008[/C][/ROW]
[ROW][C]22[/C][C]77[/C][C]86.8375820944761[/C][C]-9.83758209447611[/C][/ROW]
[ROW][C]23[/C][C]99[/C][C]80.334898530759[/C][C]18.665101469241[/C][/ROW]
[ROW][C]24[/C][C]17[/C][C]20.1821319609914[/C][C]-3.18213196099144[/C][/ROW]
[ROW][C]25[/C][C]30[/C][C]42.8195666747887[/C][C]-12.8195666747887[/C][/ROW]
[ROW][C]26[/C][C]76[/C][C]41.8268411696978[/C][C]34.1731588303022[/C][/ROW]
[ROW][C]27[/C][C]146[/C][C]80.9463523706904[/C][C]65.0536476293096[/C][/ROW]
[ROW][C]28[/C][C]56[/C][C]69.5346872377854[/C][C]-13.5346872377855[/C][/ROW]
[ROW][C]29[/C][C]107[/C][C]56.7981445153692[/C][C]50.2018554846308[/C][/ROW]
[ROW][C]30[/C][C]58[/C][C]62.8692044396817[/C][C]-4.86920443968166[/C][/ROW]
[ROW][C]31[/C][C]34[/C][C]55.2702308157947[/C][C]-21.2702308157947[/C][/ROW]
[ROW][C]32[/C][C]119[/C][C]78.6649559962188[/C][C]40.3350440037812[/C][/ROW]
[ROW][C]33[/C][C]66[/C][C]81.2874607637466[/C][C]-15.2874607637466[/C][/ROW]
[ROW][C]34[/C][C]66[/C][C]70.8244483000659[/C][C]-4.82444830006585[/C][/ROW]
[ROW][C]35[/C][C]24[/C][C]22.3232520042845[/C][C]1.67674799571548[/C][/ROW]
[ROW][C]36[/C][C]259[/C][C]82.5732611457578[/C][C]176.426738854242[/C][/ROW]
[ROW][C]37[/C][C]41[/C][C]72.5150093587552[/C][C]-31.5150093587552[/C][/ROW]
[ROW][C]38[/C][C]68[/C][C]97.1425696020878[/C][C]-29.1425696020878[/C][/ROW]
[ROW][C]39[/C][C]168[/C][C]76.3002124690347[/C][C]91.6997875309653[/C][/ROW]
[ROW][C]40[/C][C]43[/C][C]45.8277680405375[/C][C]-2.8277680405375[/C][/ROW]
[ROW][C]41[/C][C]105[/C][C]92.1495301821101[/C][C]12.8504698178899[/C][/ROW]
[ROW][C]42[/C][C]94[/C][C]83.1462970265087[/C][C]10.8537029734913[/C][/ROW]
[ROW][C]43[/C][C]57[/C][C]63.9917425469201[/C][C]-6.99174254692008[/C][/ROW]
[ROW][C]44[/C][C]53[/C][C]62.8755649768449[/C][C]-9.87556497684491[/C][/ROW]
[ROW][C]45[/C][C]103[/C][C]69.7387423661533[/C][C]33.2612576338467[/C][/ROW]
[ROW][C]46[/C][C]121[/C][C]77.1539603233441[/C][C]43.846039676656[/C][/ROW]
[ROW][C]47[/C][C]62[/C][C]83.8995250839964[/C][C]-21.8995250839964[/C][/ROW]
[ROW][C]48[/C][C]32[/C][C]46.0722090976802[/C][C]-14.0722090976802[/C][/ROW]
[ROW][C]49[/C][C]45[/C][C]68.7244121366443[/C][C]-23.7244121366443[/C][/ROW]
[ROW][C]50[/C][C]46[/C][C]83.6844035371389[/C][C]-37.6844035371389[/C][/ROW]
[ROW][C]51[/C][C]75[/C][C]76.7051382028695[/C][C]-1.70513820286953[/C][/ROW]
[ROW][C]52[/C][C]88[/C][C]88.321405619617[/C][C]-0.321405619617002[/C][/ROW]
[ROW][C]53[/C][C]53[/C][C]69.7188999687213[/C][C]-16.7188999687213[/C][/ROW]
[ROW][C]54[/C][C]78[/C][C]83.3961966814028[/C][C]-5.3961966814028[/C][/ROW]
[ROW][C]55[/C][C]45[/C][C]45.4725557878724[/C][C]-0.472555787872419[/C][/ROW]
[ROW][C]56[/C][C]46[/C][C]72.7588869026628[/C][C]-26.7588869026628[/C][/ROW]
[ROW][C]57[/C][C]41[/C][C]50.958913446772[/C][C]-9.95891344677201[/C][/ROW]
[ROW][C]58[/C][C]144[/C][C]68.0032479786529[/C][C]75.9967520213471[/C][/ROW]
[ROW][C]59[/C][C]91[/C][C]100.772468203735[/C][C]-9.77246820373544[/C][/ROW]
[ROW][C]60[/C][C]63[/C][C]91.8617484437946[/C][C]-28.8617484437946[/C][/ROW]
[ROW][C]61[/C][C]53[/C][C]52.6053802333821[/C][C]0.394619766617861[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]78.6623406196998[/C][C]-16.6623406196999[/C][/ROW]
[ROW][C]63[/C][C]63[/C][C]66.7323142228014[/C][C]-3.73231422280141[/C][/ROW]
[ROW][C]64[/C][C]32[/C][C]72.2617069617152[/C][C]-40.2617069617152[/C][/ROW]
[ROW][C]65[/C][C]62[/C][C]94.5280007318937[/C][C]-32.5280007318937[/C][/ROW]
[ROW][C]66[/C][C]117[/C][C]104.612843430523[/C][C]12.3871565694765[/C][/ROW]
[ROW][C]67[/C][C]34[/C][C]47.6393592992703[/C][C]-13.6393592992703[/C][/ROW]
[ROW][C]68[/C][C]92[/C][C]91.2786350783103[/C][C]0.721364921689652[/C][/ROW]
[ROW][C]69[/C][C]93[/C][C]90.175065400775[/C][C]2.824934599225[/C][/ROW]
[ROW][C]70[/C][C]54[/C][C]89.0889116470051[/C][C]-35.0889116470051[/C][/ROW]
[ROW][C]71[/C][C]144[/C][C]59.807931176796[/C][C]84.192068823204[/C][/ROW]
[ROW][C]72[/C][C]109[/C][C]75.0241988921979[/C][C]33.9758011078021[/C][/ROW]
[ROW][C]73[/C][C]75[/C][C]55.2077576526906[/C][C]19.7922423473094[/C][/ROW]
[ROW][C]74[/C][C]50[/C][C]64.2837660024095[/C][C]-14.2837660024095[/C][/ROW]
[ROW][C]75[/C][C]61[/C][C]45.1143075854729[/C][C]15.8856924145271[/C][/ROW]
[ROW][C]76[/C][C]55[/C][C]56.6069467163905[/C][C]-1.60694671639052[/C][/ROW]
[ROW][C]77[/C][C]77[/C][C]38.9052928720161[/C][C]38.0947071279839[/C][/ROW]
[ROW][C]78[/C][C]72[/C][C]79.0517473064701[/C][C]-7.05174730647014[/C][/ROW]
[ROW][C]79[/C][C]53[/C][C]43.2502902838872[/C][C]9.74970971611277[/C][/ROW]
[ROW][C]80[/C][C]42[/C][C]61.1975324338246[/C][C]-19.1975324338246[/C][/ROW]
[ROW][C]81[/C][C]71[/C][C]99.0015806630484[/C][C]-28.0015806630484[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]17.9577134877393[/C][C]-7.95771348773935[/C][/ROW]
[ROW][C]83[/C][C]65[/C][C]81.9811056725604[/C][C]-16.9811056725604[/C][/ROW]
[ROW][C]84[/C][C]66[/C][C]53.736233273251[/C][C]12.263766726749[/C][/ROW]
[ROW][C]85[/C][C]41[/C][C]72.5764587486068[/C][C]-31.5764587486068[/C][/ROW]
[ROW][C]86[/C][C]86[/C][C]85.4548670767335[/C][C]0.545132923266512[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]18.6896178201263[/C][C]-2.68961782012634[/C][/ROW]
[ROW][C]88[/C][C]42[/C][C]63.5754547048229[/C][C]-21.5754547048229[/C][/ROW]
[ROW][C]89[/C][C]19[/C][C]37.3660866285515[/C][C]-18.3660866285515[/C][/ROW]
[ROW][C]90[/C][C]19[/C][C]31.8733991506708[/C][C]-12.8733991506708[/C][/ROW]
[ROW][C]91[/C][C]45[/C][C]35.6688046021932[/C][C]9.33119539780678[/C][/ROW]
[ROW][C]92[/C][C]65[/C][C]60.821000870772[/C][C]4.17899912922801[/C][/ROW]
[ROW][C]93[/C][C]95[/C][C]80.3627056652059[/C][C]14.6372943347941[/C][/ROW]
[ROW][C]94[/C][C]64[/C][C]68.4357941052356[/C][C]-4.4357941052356[/C][/ROW]
[ROW][C]95[/C][C]38[/C][C]55.2943193725004[/C][C]-17.2943193725004[/C][/ROW]
[ROW][C]96[/C][C]65[/C][C]92.1314974261429[/C][C]-27.1314974261429[/C][/ROW]
[ROW][C]97[/C][C]52[/C][C]75.8806064218185[/C][C]-23.8806064218185[/C][/ROW]
[ROW][C]98[/C][C]62[/C][C]46.6283951918442[/C][C]15.3716048081558[/C][/ROW]
[ROW][C]99[/C][C]65[/C][C]100.064289226927[/C][C]-35.0642892269274[/C][/ROW]
[ROW][C]100[/C][C]95[/C][C]115.33982215179[/C][C]-20.3398221517901[/C][/ROW]
[ROW][C]101[/C][C]29[/C][C]66.975739185421[/C][C]-37.975739185421[/C][/ROW]
[ROW][C]102[/C][C]247[/C][C]70.2690507727791[/C][C]176.730949227221[/C][/ROW]
[ROW][C]103[/C][C]29[/C][C]57.4260550562079[/C][C]-28.4260550562079[/C][/ROW]
[ROW][C]104[/C][C]118[/C][C]51.2596742127957[/C][C]66.7403257872043[/C][/ROW]
[ROW][C]105[/C][C]110[/C][C]95.3898927542618[/C][C]14.6101072457382[/C][/ROW]
[ROW][C]106[/C][C]67[/C][C]59.3669001077482[/C][C]7.63309989225182[/C][/ROW]
[ROW][C]107[/C][C]42[/C][C]81.2927082732702[/C][C]-39.2927082732702[/C][/ROW]
[ROW][C]108[/C][C]64[/C][C]55.3910558165252[/C][C]8.60894418347485[/C][/ROW]
[ROW][C]109[/C][C]81[/C][C]56.4047225431444[/C][C]24.5952774568556[/C][/ROW]
[ROW][C]110[/C][C]95[/C][C]77.7744534154452[/C][C]17.2255465845548[/C][/ROW]
[ROW][C]111[/C][C]67[/C][C]86.4565263885187[/C][C]-19.4565263885187[/C][/ROW]
[ROW][C]112[/C][C]63[/C][C]51.435810566461[/C][C]11.564189433539[/C][/ROW]
[ROW][C]113[/C][C]83[/C][C]95.6729546087025[/C][C]-12.6729546087025[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]42.752473211757[/C][C]-10.752473211757[/C][/ROW]
[ROW][C]115[/C][C]83[/C][C]102.878187197887[/C][C]-19.8781871978871[/C][/ROW]
[ROW][C]116[/C][C]31[/C][C]48.1974165427202[/C][C]-17.1974165427202[/C][/ROW]
[ROW][C]117[/C][C]67[/C][C]36.4047328716332[/C][C]30.5952671283668[/C][/ROW]
[ROW][C]118[/C][C]66[/C][C]52.6528564816627[/C][C]13.3471435183373[/C][/ROW]
[ROW][C]119[/C][C]70[/C][C]63.3036434785318[/C][C]6.69635652146816[/C][/ROW]
[ROW][C]120[/C][C]103[/C][C]80.6263162773854[/C][C]22.3736837226146[/C][/ROW]
[ROW][C]121[/C][C]34[/C][C]56.3661215143004[/C][C]-22.3661215143004[/C][/ROW]
[ROW][C]122[/C][C]40[/C][C]42.2499401394368[/C][C]-2.2499401394368[/C][/ROW]
[ROW][C]123[/C][C]31[/C][C]46.7287181313678[/C][C]-15.7287181313678[/C][/ROW]
[ROW][C]124[/C][C]42[/C][C]61.5135232179289[/C][C]-19.5135232179289[/C][/ROW]
[ROW][C]125[/C][C]46[/C][C]49.7048129972374[/C][C]-3.70481299723745[/C][/ROW]
[ROW][C]126[/C][C]33[/C][C]43.2683018046368[/C][C]-10.2683018046368[/C][/ROW]
[ROW][C]127[/C][C]18[/C][C]42.386258489011[/C][C]-24.386258489011[/C][/ROW]
[ROW][C]128[/C][C]35[/C][C]47.4949934357731[/C][C]-12.4949934357731[/C][/ROW]
[ROW][C]129[/C][C]66[/C][C]47.9559886103195[/C][C]18.0440113896805[/C][/ROW]
[ROW][C]130[/C][C]60[/C][C]46.2670770440466[/C][C]13.7329229559534[/C][/ROW]
[ROW][C]131[/C][C]54[/C][C]47.7378460589957[/C][C]6.26215394100425[/C][/ROW]
[ROW][C]132[/C][C]53[/C][C]48.1378688940631[/C][C]4.86213110593688[/C][/ROW]
[ROW][C]133[/C][C]39[/C][C]47.6401225908201[/C][C]-8.64012259082005[/C][/ROW]
[ROW][C]134[/C][C]45[/C][C]46.4040669837128[/C][C]-1.40406698371283[/C][/ROW]
[ROW][C]135[/C][C]36[/C][C]44.4424579967652[/C][C]-8.44245799676517[/C][/ROW]
[ROW][C]136[/C][C]28[/C][C]43.2395398105366[/C][C]-15.2395398105366[/C][/ROW]
[ROW][C]137[/C][C]30[/C][C]39.0644006281726[/C][C]-9.06440062817256[/C][/ROW]
[ROW][C]138[/C][C]22[/C][C]43.9996372250505[/C][C]-21.9996372250505[/C][/ROW]
[ROW][C]139[/C][C]31[/C][C]46.5243759991944[/C][C]-15.5243759991944[/C][/ROW]
[ROW][C]140[/C][C]55[/C][C]54.2554828110778[/C][C]0.744517188922149[/C][/ROW]
[ROW][C]141[/C][C]54[/C][C]54.8643235447339[/C][C]-0.864323544733919[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]34.3949530853455[/C][C]-20.3949530853455[/C][/ROW]
[ROW][C]143[/C][C]81[/C][C]44.145959050121[/C][C]36.854040949879[/C][/ROW]
[ROW][C]144[/C][C]43[/C][C]48.9559663501128[/C][C]-5.95596635011279[/C][/ROW]
[ROW][C]145[/C][C]30[/C][C]54.3321891486305[/C][C]-24.3321891486305[/C][/ROW]
[ROW][C]146[/C][C]23[/C][C]32.9581382507786[/C][C]-9.95813825077856[/C][/ROW]
[ROW][C]147[/C][C]38[/C][C]44.9971539204147[/C][C]-6.99715392041468[/C][/ROW]
[ROW][C]148[/C][C]53[/C][C]43.3448923392681[/C][C]9.65510766073188[/C][/ROW]
[ROW][C]149[/C][C]45[/C][C]45.8553672212808[/C][C]-0.855367221280782[/C][/ROW]
[ROW][C]150[/C][C]39[/C][C]43.3843523125892[/C][C]-4.38435231258923[/C][/ROW]
[ROW][C]151[/C][C]24[/C][C]43.1786415072255[/C][C]-19.1786415072255[/C][/ROW]
[ROW][C]152[/C][C]35[/C][C]42.5109716242747[/C][C]-7.51097162427474[/C][/ROW]
[ROW][C]153[/C][C]151[/C][C]52.7285159183233[/C][C]98.2714840816767[/C][/ROW]
[ROW][C]154[/C][C]30[/C][C]41.6549220748247[/C][C]-11.6549220748247[/C][/ROW]
[ROW][C]155[/C][C]57[/C][C]52.6364970438593[/C][C]4.36350295614071[/C][/ROW]
[ROW][C]156[/C][C]40[/C][C]39.980730522203[/C][C]0.0192694777970285[/C][/ROW]
[ROW][C]157[/C][C]77[/C][C]50.3910463113601[/C][C]26.6089536886399[/C][/ROW]
[ROW][C]158[/C][C]35[/C][C]56.1304392479749[/C][C]-21.1304392479749[/C][/ROW]
[ROW][C]159[/C][C]63[/C][C]42.3331740932232[/C][C]20.6668259067768[/C][/ROW]
[ROW][C]160[/C][C]44[/C][C]43.0853643131547[/C][C]0.914635686845283[/C][/ROW]
[ROW][C]161[/C][C]19[/C][C]25.8164437965876[/C][C]-6.81644379658758[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]38.2359223462737[/C][C]-25.2359223462737[/C][/ROW]
[ROW][C]163[/C][C]42[/C][C]42.3932671307534[/C][C]-0.39326713075337[/C][/ROW]
[ROW][C]164[/C][C]42[/C][C]47.5299929722979[/C][C]-5.52999297229788[/C][/ROW]
[ROW][C]165[/C][C]49[/C][C]39.5455425146374[/C][C]9.45445748536255[/C][/ROW]
[ROW][C]166[/C][C]30[/C][C]47.2526563856087[/C][C]-17.2526563856087[/C][/ROW]
[ROW][C]167[/C][C]49[/C][C]53.5568560216332[/C][C]-4.55685602163316[/C][/ROW]
[ROW][C]168[/C][C]12[/C][C]26.4186056760456[/C][C]-14.4186056760456[/C][/ROW]
[ROW][C]169[/C][C]20[/C][C]42.0857049688355[/C][C]-22.0857049688355[/C][/ROW]
[ROW][C]170[/C][C]27[/C][C]39.2754116247191[/C][C]-12.2754116247191[/C][/ROW]
[ROW][C]171[/C][C]14[/C][C]41.2211056168964[/C][C]-27.2211056168964[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154881&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154881&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
15677.4946371595875-21.4946371595874
25664.3848848129825-8.38488481298248
35474.1831903488858-20.1831903488858
49284.9313520099047.06864799009596
54464.3218331532885-20.3218331532885
63363.5615017777661-30.5615017777661
78478.87172672888945.12827327111059
85561.7441269499412-6.74412694994117
9154112.43117827984441.5688217201559
105386.3520658975609-33.3520658975609
1111974.019173877003444.9808261229966
124171.3586135140746-30.3586135140746
135887.2914700333876-29.2914700333876
147571.82173825036093.17826174963913
153358.9343463808753-25.9343463808753
1610073.49297274320726.507027256793
1711274.768093223024537.2319067769755
187380.6645017191081-7.66450171910806
194054.4295100384608-14.4295100384608
206085.1077057250166-25.1077057250166
216273.1690594855008-11.1690594855008
227786.8375820944761-9.83758209447611
239980.33489853075918.665101469241
241720.1821319609914-3.18213196099144
253042.8195666747887-12.8195666747887
267641.826841169697834.1731588303022
2714680.946352370690465.0536476293096
285669.5346872377854-13.5346872377855
2910756.798144515369250.2018554846308
305862.8692044396817-4.86920443968166
313455.2702308157947-21.2702308157947
3211978.664955996218840.3350440037812
336681.2874607637466-15.2874607637466
346670.8244483000659-4.82444830006585
352422.32325200428451.67674799571548
3625982.5732611457578176.426738854242
374172.5150093587552-31.5150093587552
386897.1425696020878-29.1425696020878
3916876.300212469034791.6997875309653
404345.8277680405375-2.8277680405375
4110592.149530182110112.8504698178899
429483.146297026508710.8537029734913
435763.9917425469201-6.99174254692008
445362.8755649768449-9.87556497684491
4510369.738742366153333.2612576338467
4612177.153960323344143.846039676656
476283.8995250839964-21.8995250839964
483246.0722090976802-14.0722090976802
494568.7244121366443-23.7244121366443
504683.6844035371389-37.6844035371389
517576.7051382028695-1.70513820286953
528888.321405619617-0.321405619617002
535369.7188999687213-16.7188999687213
547883.3961966814028-5.3961966814028
554545.4725557878724-0.472555787872419
564672.7588869026628-26.7588869026628
574150.958913446772-9.95891344677201
5814468.003247978652975.9967520213471
5991100.772468203735-9.77246820373544
606391.8617484437946-28.8617484437946
615352.60538023338210.394619766617861
626278.6623406196998-16.6623406196999
636366.7323142228014-3.73231422280141
643272.2617069617152-40.2617069617152
656294.5280007318937-32.5280007318937
66117104.61284343052312.3871565694765
673447.6393592992703-13.6393592992703
689291.27863507831030.721364921689652
699390.1750654007752.824934599225
705489.0889116470051-35.0889116470051
7114459.80793117679684.192068823204
7210975.024198892197933.9758011078021
737555.207757652690619.7922423473094
745064.2837660024095-14.2837660024095
756145.114307585472915.8856924145271
765556.6069467163905-1.60694671639052
777738.905292872016138.0947071279839
787279.0517473064701-7.05174730647014
795343.25029028388729.74970971611277
804261.1975324338246-19.1975324338246
817199.0015806630484-28.0015806630484
821017.9577134877393-7.95771348773935
836581.9811056725604-16.9811056725604
846653.73623327325112.263766726749
854172.5764587486068-31.5764587486068
868685.45486707673350.545132923266512
871618.6896178201263-2.68961782012634
884263.5754547048229-21.5754547048229
891937.3660866285515-18.3660866285515
901931.8733991506708-12.8733991506708
914535.66880460219329.33119539780678
926560.8210008707724.17899912922801
939580.362705665205914.6372943347941
946468.4357941052356-4.4357941052356
953855.2943193725004-17.2943193725004
966592.1314974261429-27.1314974261429
975275.8806064218185-23.8806064218185
986246.628395191844215.3716048081558
9965100.064289226927-35.0642892269274
10095115.33982215179-20.3398221517901
1012966.975739185421-37.975739185421
10224770.2690507727791176.730949227221
1032957.4260550562079-28.4260550562079
10411851.259674212795766.7403257872043
10511095.389892754261814.6101072457382
1066759.36690010774827.63309989225182
1074281.2927082732702-39.2927082732702
1086455.39105581652528.60894418347485
1098156.404722543144424.5952774568556
1109577.774453415445217.2255465845548
1116786.4565263885187-19.4565263885187
1126351.43581056646111.564189433539
1138395.6729546087025-12.6729546087025
1143242.752473211757-10.752473211757
11583102.878187197887-19.8781871978871
1163148.1974165427202-17.1974165427202
1176736.404732871633230.5952671283668
1186652.652856481662713.3471435183373
1197063.30364347853186.69635652146816
12010380.626316277385422.3736837226146
1213456.3661215143004-22.3661215143004
1224042.2499401394368-2.2499401394368
1233146.7287181313678-15.7287181313678
1244261.5135232179289-19.5135232179289
1254649.7048129972374-3.70481299723745
1263343.2683018046368-10.2683018046368
1271842.386258489011-24.386258489011
1283547.4949934357731-12.4949934357731
1296647.955988610319518.0440113896805
1306046.267077044046613.7329229559534
1315447.73784605899576.26215394100425
1325348.13786889406314.86213110593688
1333947.6401225908201-8.64012259082005
1344546.4040669837128-1.40406698371283
1353644.4424579967652-8.44245799676517
1362843.2395398105366-15.2395398105366
1373039.0644006281726-9.06440062817256
1382243.9996372250505-21.9996372250505
1393146.5243759991944-15.5243759991944
1405554.25548281107780.744517188922149
1415454.8643235447339-0.864323544733919
1421434.3949530853455-20.3949530853455
1438144.14595905012136.854040949879
1444348.9559663501128-5.95596635011279
1453054.3321891486305-24.3321891486305
1462332.9581382507786-9.95813825077856
1473844.9971539204147-6.99715392041468
1485343.34489233926819.65510766073188
1494545.8553672212808-0.855367221280782
1503943.3843523125892-4.38435231258923
1512443.1786415072255-19.1786415072255
1523542.5109716242747-7.51097162427474
15315152.728515918323398.2714840816767
1543041.6549220748247-11.6549220748247
1555752.63649704385934.36350295614071
1564039.9807305222030.0192694777970285
1577750.391046311360126.6089536886399
1583556.1304392479749-21.1304392479749
1596342.333174093223220.6668259067768
1604443.08536431315470.914635686845283
1611925.8164437965876-6.81644379658758
1621338.2359223462737-25.2359223462737
1634242.3932671307534-0.39326713075337
1644247.5299929722979-5.52999297229788
1654939.54554251463749.45445748536255
1663047.2526563856087-17.2526563856087
1674953.5568560216332-4.55685602163316
1681226.4186056760456-14.4186056760456
1692042.0857049688355-22.0857049688355
1702739.2754116247191-12.2754116247191
1711441.2211056168964-27.2211056168964






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1577311247891330.3154622495782660.842268875210867
90.0735740451375450.147148090275090.926425954862455
100.09579171034309060.1915834206861810.904208289656909
110.2289276541889760.4578553083779510.771072345811024
120.1447960234551960.2895920469103910.855203976544804
130.144141598052880.288283196105760.85585840194712
140.08995250842108360.1799050168421670.910047491578916
150.05678813807172510.113576276143450.943211861928275
160.1190702743762110.2381405487524210.880929725623789
170.1486661520349820.2973323040699640.851333847965018
180.1535898334223430.3071796668446860.846410166577657
190.1075692821175960.2151385642351910.892430717882404
200.1106812285140640.2213624570281270.889318771485936
210.07830095792188730.1566019158437750.921699042078113
220.05356627081459770.1071325416291950.946433729185402
230.04517249826725960.09034499653451920.95482750173274
240.04756303693152460.09512607386304910.952436963068475
250.03192790694684660.06385581389369320.968072093053153
260.02623019332080510.05246038664161020.973769806679195
270.1105875556958310.2211751113916620.889412444304169
280.08533337482397370.1706667496479470.914666625176026
290.1372616180515590.2745232361031180.862738381948441
300.1044038373950080.2088076747900160.895596162604992
310.0884508845398020.1769017690796040.911549115460198
320.13689943854520.2737988770904010.8631005614548
330.1191529888349370.2383059776698750.880847011165063
340.09359122868081730.1871824573616350.906408771319183
350.07075343004065130.1415068600813030.929246569959349
360.9950481100102220.009903779979557040.00495188998977852
370.9947629338889080.0104741322221830.00523706611109149
380.9947771554985690.01044568900286110.00522284450143055
390.9995934867810960.000813026437807480.00040651321890374
400.9993680582768750.001263883446250770.000631941723125384
410.9990801295427790.001839740914442350.000919870457221176
420.998657584191290.002684831617420260.00134241580871013
430.9980019881878760.003996023624248220.00199801181212411
440.9972539245347040.005492150930591270.00274607546529564
450.9971261892789330.005747621442133320.00287381072106666
460.9977690597965120.004461880406976330.00223094020348817
470.9975417187650670.004916562469865680.00245828123493284
480.9965826773594090.006834645281181450.00341732264059073
490.9960373396385240.00792532072295230.00396266036147615
500.9965905605628810.006818878874238210.00340943943711911
510.9951976681864680.009604663627063060.00480233181353153
520.9935484812464860.01290303750702780.00645151875351392
530.9916840507988890.01663189840222140.00831594920111071
540.9888617008100440.02227659837991290.0111382991899564
550.9849555861166950.03008882776660940.0150444138833047
560.9830681172432270.03386376551354680.0169318827567734
570.9778523696169890.04429526076602130.0221476303830107
580.9943912770707930.01121744585841390.00560872292920695
590.9926977776365110.01460444472697840.00730222236348921
600.9922545785556940.01549084288861150.00774542144430577
610.9894352004443780.0211295991112440.010564799555622
620.9866751438516310.02664971229673740.0133248561483687
630.9826504397083260.03469912058334720.0173495602916736
640.9852336012931210.02953279741375790.0147663987068789
650.9854885717232940.02902285655341160.0145114282767058
660.9816046472411050.03679070551778930.0183953527588946
670.9767957742933630.04640845141327320.0232042257066366
680.9698642134558350.06027157308832990.0301357865441649
690.9615394242896060.07692115142078850.0384605757103942
700.9626438821079220.07471223578415530.0373561178920777
710.9928619181246370.01427616375072660.00713808187536328
720.9932901817879380.01341963642412420.00670981821206209
730.9917726652880450.01645466942390970.00822733471195484
740.9898432424418970.02031351511620660.0101567575581033
750.9874120772375040.02517584552499110.0125879227624955
760.9833003179867320.0333993640265350.0166996820132675
770.9848769763348330.03024604733033420.0151230236651671
780.9804317945646180.0391364108707630.0195682054353815
790.9747670793929910.05046584121401870.0252329206070094
800.9706695458993350.05866090820132930.0293304541006647
810.9675741059551310.06485178808973710.0324258940448685
820.9598961499077580.08020770018448350.0401038500922418
830.9519866057518230.09602678849635380.0480133942481769
840.9423256362248360.1153487275503290.0576743637751645
850.9404619962912710.1190760074174570.0595380037087286
860.9260402938561620.1479194122876760.0739597061438381
870.9099528781793880.1800942436412240.0900471218206122
880.902289744917330.1954205101653390.0977102550826695
890.8921067065204710.2157865869590580.107893293479529
900.8764138251507030.2471723496985950.123586174849297
910.8536558923718840.2926882152562320.146344107628116
920.8263376974443410.3473246051113180.173662302555659
930.8039634474089150.392073105182170.196036552591085
940.7738979492763310.4522041014473380.226102050723669
950.7550102933676350.4899794132647290.244989706632365
960.7381300031336450.5237399937327110.261869996866355
970.7163100835500940.5673798328998120.283689916449906
980.6831108468106620.6337783063786750.316889153189338
990.6869430255606050.6261139488787890.313056974439395
1000.6968183665857960.6063632668284070.303181633414204
1010.7216147245485390.5567705509029220.278385275451461
1020.9999926592287351.46815425296788e-057.34077126483939e-06
1030.9999932144369471.35711261055011e-056.78556305275053e-06
1040.999999574453818.51092379462014e-074.25546189731007e-07
1050.9999992966420861.40671582776565e-067.03357913882824e-07
1060.9999988251338752.34973225079128e-061.17486612539564e-06
1070.999999261941151.47611770065552e-067.38058850327759e-07
1080.9999987890274372.42194512526747e-061.21097256263373e-06
1090.9999985495393812.90092123717156e-061.45046061858578e-06
1100.9999979070719314.18585613881011e-062.09292806940505e-06
1110.9999972758415085.44831698446356e-062.72415849223178e-06
1120.9999957277085368.54458292802811e-064.27229146401405e-06
1130.9999934836914181.3032617164748e-056.51630858237401e-06
1140.9999890163368542.19673262916022e-051.09836631458011e-05
1150.9999930024889381.39950221233106e-056.99751106165532e-06
1160.9999906539237811.86921524377542e-059.34607621887712e-06
1170.999992955206561.40895868809282e-057.04479344046412e-06
1180.9999883660665922.32678668167707e-051.16339334083854e-05
1190.9999791394591284.17210817441101e-052.0860540872055e-05
1200.9999694397102816.11205794375486e-053.05602897187743e-05
1210.9999590844717838.18310564342454e-054.09155282171227e-05
1220.9999292873651910.0001414252696177047.07126348088522e-05
1230.9998936614663420.0002126770673153970.000106338533657698
1240.999898683813760.0002026323724802840.000101316186240142
1250.9998304712617350.0003390574765302240.000169528738265112
1260.9997212801293250.0005574397413501350.000278719870675068
1270.9997151845325020.0005696309349960840.000284815467498042
1280.9996096211400480.0007807577199042680.000390378859952134
1290.9994605664699610.00107886706007740.000539433530038701
1300.9991730837067960.001653832586408330.000826916293204166
1310.9986566377336890.002686724532623020.00134336226631151
1320.9978863067776530.004227386444694610.00211369322234731
1330.996749887568880.006500224862240190.00325011243112009
1340.994907821103160.01018435779368050.00509217889684027
1350.9923838601846930.01523227963061430.00761613981530714
1360.9888776031586770.02224479368264670.0111223968413234
1370.9841053624033520.03178927519329560.0158946375966478
1380.9798094961567030.04038100768659380.0201905038432969
1390.9730455783934560.05390884321308820.0269544216065441
1400.9634203426289690.0731593147420610.0365796573710305
1410.9481461823402790.1037076353194410.0518538176597207
1420.9308825399242750.138234920151450.0691174600757248
1430.9634247906354830.07315041872903380.0365752093645169
1440.9480558352016470.1038883295967070.0519441647983534
1450.9797259816003060.04054803679938840.0202740183996942
1460.9692529446971150.06149411060576940.0307470553028847
1470.9538654632909180.09226907341816390.0461345367090819
1480.9327964964317040.1344070071365920.0672035035682961
1490.9077489815249220.1845020369501570.0922510184750783
1500.8765246984229170.2469506031541670.123475301577083
1510.8316243653800490.3367512692399020.168375634619951
1520.819810868148670.3603782637026610.18018913185133
1530.9996320305646990.0007359388706026740.000367969435301337
1540.9991257258412670.001748548317466710.000874274158733354
1550.9988135967626550.002372806474690870.00118640323734543
1560.997003033756030.005993932487939470.00299696624396973
1570.9958775564989460.008244887002107290.00412244350105364
1580.9902497585062090.01950048298758290.00975024149379146
1590.9964951262021690.007009747595661570.00350487379783078
1600.9956347839564070.008730432087185290.00436521604359265
1610.9879311906914650.02413761861707060.0120688093085353
1620.9968053404041760.006389319191648950.00319465959582448
1630.9849044333697740.03019113326045130.0150955666302256

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.157731124789133 & 0.315462249578266 & 0.842268875210867 \tabularnewline
9 & 0.073574045137545 & 0.14714809027509 & 0.926425954862455 \tabularnewline
10 & 0.0957917103430906 & 0.191583420686181 & 0.904208289656909 \tabularnewline
11 & 0.228927654188976 & 0.457855308377951 & 0.771072345811024 \tabularnewline
12 & 0.144796023455196 & 0.289592046910391 & 0.855203976544804 \tabularnewline
13 & 0.14414159805288 & 0.28828319610576 & 0.85585840194712 \tabularnewline
14 & 0.0899525084210836 & 0.179905016842167 & 0.910047491578916 \tabularnewline
15 & 0.0567881380717251 & 0.11357627614345 & 0.943211861928275 \tabularnewline
16 & 0.119070274376211 & 0.238140548752421 & 0.880929725623789 \tabularnewline
17 & 0.148666152034982 & 0.297332304069964 & 0.851333847965018 \tabularnewline
18 & 0.153589833422343 & 0.307179666844686 & 0.846410166577657 \tabularnewline
19 & 0.107569282117596 & 0.215138564235191 & 0.892430717882404 \tabularnewline
20 & 0.110681228514064 & 0.221362457028127 & 0.889318771485936 \tabularnewline
21 & 0.0783009579218873 & 0.156601915843775 & 0.921699042078113 \tabularnewline
22 & 0.0535662708145977 & 0.107132541629195 & 0.946433729185402 \tabularnewline
23 & 0.0451724982672596 & 0.0903449965345192 & 0.95482750173274 \tabularnewline
24 & 0.0475630369315246 & 0.0951260738630491 & 0.952436963068475 \tabularnewline
25 & 0.0319279069468466 & 0.0638558138936932 & 0.968072093053153 \tabularnewline
26 & 0.0262301933208051 & 0.0524603866416102 & 0.973769806679195 \tabularnewline
27 & 0.110587555695831 & 0.221175111391662 & 0.889412444304169 \tabularnewline
28 & 0.0853333748239737 & 0.170666749647947 & 0.914666625176026 \tabularnewline
29 & 0.137261618051559 & 0.274523236103118 & 0.862738381948441 \tabularnewline
30 & 0.104403837395008 & 0.208807674790016 & 0.895596162604992 \tabularnewline
31 & 0.088450884539802 & 0.176901769079604 & 0.911549115460198 \tabularnewline
32 & 0.1368994385452 & 0.273798877090401 & 0.8631005614548 \tabularnewline
33 & 0.119152988834937 & 0.238305977669875 & 0.880847011165063 \tabularnewline
34 & 0.0935912286808173 & 0.187182457361635 & 0.906408771319183 \tabularnewline
35 & 0.0707534300406513 & 0.141506860081303 & 0.929246569959349 \tabularnewline
36 & 0.995048110010222 & 0.00990377997955704 & 0.00495188998977852 \tabularnewline
37 & 0.994762933888908 & 0.010474132222183 & 0.00523706611109149 \tabularnewline
38 & 0.994777155498569 & 0.0104456890028611 & 0.00522284450143055 \tabularnewline
39 & 0.999593486781096 & 0.00081302643780748 & 0.00040651321890374 \tabularnewline
40 & 0.999368058276875 & 0.00126388344625077 & 0.000631941723125384 \tabularnewline
41 & 0.999080129542779 & 0.00183974091444235 & 0.000919870457221176 \tabularnewline
42 & 0.99865758419129 & 0.00268483161742026 & 0.00134241580871013 \tabularnewline
43 & 0.998001988187876 & 0.00399602362424822 & 0.00199801181212411 \tabularnewline
44 & 0.997253924534704 & 0.00549215093059127 & 0.00274607546529564 \tabularnewline
45 & 0.997126189278933 & 0.00574762144213332 & 0.00287381072106666 \tabularnewline
46 & 0.997769059796512 & 0.00446188040697633 & 0.00223094020348817 \tabularnewline
47 & 0.997541718765067 & 0.00491656246986568 & 0.00245828123493284 \tabularnewline
48 & 0.996582677359409 & 0.00683464528118145 & 0.00341732264059073 \tabularnewline
49 & 0.996037339638524 & 0.0079253207229523 & 0.00396266036147615 \tabularnewline
50 & 0.996590560562881 & 0.00681887887423821 & 0.00340943943711911 \tabularnewline
51 & 0.995197668186468 & 0.00960466362706306 & 0.00480233181353153 \tabularnewline
52 & 0.993548481246486 & 0.0129030375070278 & 0.00645151875351392 \tabularnewline
53 & 0.991684050798889 & 0.0166318984022214 & 0.00831594920111071 \tabularnewline
54 & 0.988861700810044 & 0.0222765983799129 & 0.0111382991899564 \tabularnewline
55 & 0.984955586116695 & 0.0300888277666094 & 0.0150444138833047 \tabularnewline
56 & 0.983068117243227 & 0.0338637655135468 & 0.0169318827567734 \tabularnewline
57 & 0.977852369616989 & 0.0442952607660213 & 0.0221476303830107 \tabularnewline
58 & 0.994391277070793 & 0.0112174458584139 & 0.00560872292920695 \tabularnewline
59 & 0.992697777636511 & 0.0146044447269784 & 0.00730222236348921 \tabularnewline
60 & 0.992254578555694 & 0.0154908428886115 & 0.00774542144430577 \tabularnewline
61 & 0.989435200444378 & 0.021129599111244 & 0.010564799555622 \tabularnewline
62 & 0.986675143851631 & 0.0266497122967374 & 0.0133248561483687 \tabularnewline
63 & 0.982650439708326 & 0.0346991205833472 & 0.0173495602916736 \tabularnewline
64 & 0.985233601293121 & 0.0295327974137579 & 0.0147663987068789 \tabularnewline
65 & 0.985488571723294 & 0.0290228565534116 & 0.0145114282767058 \tabularnewline
66 & 0.981604647241105 & 0.0367907055177893 & 0.0183953527588946 \tabularnewline
67 & 0.976795774293363 & 0.0464084514132732 & 0.0232042257066366 \tabularnewline
68 & 0.969864213455835 & 0.0602715730883299 & 0.0301357865441649 \tabularnewline
69 & 0.961539424289606 & 0.0769211514207885 & 0.0384605757103942 \tabularnewline
70 & 0.962643882107922 & 0.0747122357841553 & 0.0373561178920777 \tabularnewline
71 & 0.992861918124637 & 0.0142761637507266 & 0.00713808187536328 \tabularnewline
72 & 0.993290181787938 & 0.0134196364241242 & 0.00670981821206209 \tabularnewline
73 & 0.991772665288045 & 0.0164546694239097 & 0.00822733471195484 \tabularnewline
74 & 0.989843242441897 & 0.0203135151162066 & 0.0101567575581033 \tabularnewline
75 & 0.987412077237504 & 0.0251758455249911 & 0.0125879227624955 \tabularnewline
76 & 0.983300317986732 & 0.033399364026535 & 0.0166996820132675 \tabularnewline
77 & 0.984876976334833 & 0.0302460473303342 & 0.0151230236651671 \tabularnewline
78 & 0.980431794564618 & 0.039136410870763 & 0.0195682054353815 \tabularnewline
79 & 0.974767079392991 & 0.0504658412140187 & 0.0252329206070094 \tabularnewline
80 & 0.970669545899335 & 0.0586609082013293 & 0.0293304541006647 \tabularnewline
81 & 0.967574105955131 & 0.0648517880897371 & 0.0324258940448685 \tabularnewline
82 & 0.959896149907758 & 0.0802077001844835 & 0.0401038500922418 \tabularnewline
83 & 0.951986605751823 & 0.0960267884963538 & 0.0480133942481769 \tabularnewline
84 & 0.942325636224836 & 0.115348727550329 & 0.0576743637751645 \tabularnewline
85 & 0.940461996291271 & 0.119076007417457 & 0.0595380037087286 \tabularnewline
86 & 0.926040293856162 & 0.147919412287676 & 0.0739597061438381 \tabularnewline
87 & 0.909952878179388 & 0.180094243641224 & 0.0900471218206122 \tabularnewline
88 & 0.90228974491733 & 0.195420510165339 & 0.0977102550826695 \tabularnewline
89 & 0.892106706520471 & 0.215786586959058 & 0.107893293479529 \tabularnewline
90 & 0.876413825150703 & 0.247172349698595 & 0.123586174849297 \tabularnewline
91 & 0.853655892371884 & 0.292688215256232 & 0.146344107628116 \tabularnewline
92 & 0.826337697444341 & 0.347324605111318 & 0.173662302555659 \tabularnewline
93 & 0.803963447408915 & 0.39207310518217 & 0.196036552591085 \tabularnewline
94 & 0.773897949276331 & 0.452204101447338 & 0.226102050723669 \tabularnewline
95 & 0.755010293367635 & 0.489979413264729 & 0.244989706632365 \tabularnewline
96 & 0.738130003133645 & 0.523739993732711 & 0.261869996866355 \tabularnewline
97 & 0.716310083550094 & 0.567379832899812 & 0.283689916449906 \tabularnewline
98 & 0.683110846810662 & 0.633778306378675 & 0.316889153189338 \tabularnewline
99 & 0.686943025560605 & 0.626113948878789 & 0.313056974439395 \tabularnewline
100 & 0.696818366585796 & 0.606363266828407 & 0.303181633414204 \tabularnewline
101 & 0.721614724548539 & 0.556770550902922 & 0.278385275451461 \tabularnewline
102 & 0.999992659228735 & 1.46815425296788e-05 & 7.34077126483939e-06 \tabularnewline
103 & 0.999993214436947 & 1.35711261055011e-05 & 6.78556305275053e-06 \tabularnewline
104 & 0.99999957445381 & 8.51092379462014e-07 & 4.25546189731007e-07 \tabularnewline
105 & 0.999999296642086 & 1.40671582776565e-06 & 7.03357913882824e-07 \tabularnewline
106 & 0.999998825133875 & 2.34973225079128e-06 & 1.17486612539564e-06 \tabularnewline
107 & 0.99999926194115 & 1.47611770065552e-06 & 7.38058850327759e-07 \tabularnewline
108 & 0.999998789027437 & 2.42194512526747e-06 & 1.21097256263373e-06 \tabularnewline
109 & 0.999998549539381 & 2.90092123717156e-06 & 1.45046061858578e-06 \tabularnewline
110 & 0.999997907071931 & 4.18585613881011e-06 & 2.09292806940505e-06 \tabularnewline
111 & 0.999997275841508 & 5.44831698446356e-06 & 2.72415849223178e-06 \tabularnewline
112 & 0.999995727708536 & 8.54458292802811e-06 & 4.27229146401405e-06 \tabularnewline
113 & 0.999993483691418 & 1.3032617164748e-05 & 6.51630858237401e-06 \tabularnewline
114 & 0.999989016336854 & 2.19673262916022e-05 & 1.09836631458011e-05 \tabularnewline
115 & 0.999993002488938 & 1.39950221233106e-05 & 6.99751106165532e-06 \tabularnewline
116 & 0.999990653923781 & 1.86921524377542e-05 & 9.34607621887712e-06 \tabularnewline
117 & 0.99999295520656 & 1.40895868809282e-05 & 7.04479344046412e-06 \tabularnewline
118 & 0.999988366066592 & 2.32678668167707e-05 & 1.16339334083854e-05 \tabularnewline
119 & 0.999979139459128 & 4.17210817441101e-05 & 2.0860540872055e-05 \tabularnewline
120 & 0.999969439710281 & 6.11205794375486e-05 & 3.05602897187743e-05 \tabularnewline
121 & 0.999959084471783 & 8.18310564342454e-05 & 4.09155282171227e-05 \tabularnewline
122 & 0.999929287365191 & 0.000141425269617704 & 7.07126348088522e-05 \tabularnewline
123 & 0.999893661466342 & 0.000212677067315397 & 0.000106338533657698 \tabularnewline
124 & 0.99989868381376 & 0.000202632372480284 & 0.000101316186240142 \tabularnewline
125 & 0.999830471261735 & 0.000339057476530224 & 0.000169528738265112 \tabularnewline
126 & 0.999721280129325 & 0.000557439741350135 & 0.000278719870675068 \tabularnewline
127 & 0.999715184532502 & 0.000569630934996084 & 0.000284815467498042 \tabularnewline
128 & 0.999609621140048 & 0.000780757719904268 & 0.000390378859952134 \tabularnewline
129 & 0.999460566469961 & 0.0010788670600774 & 0.000539433530038701 \tabularnewline
130 & 0.999173083706796 & 0.00165383258640833 & 0.000826916293204166 \tabularnewline
131 & 0.998656637733689 & 0.00268672453262302 & 0.00134336226631151 \tabularnewline
132 & 0.997886306777653 & 0.00422738644469461 & 0.00211369322234731 \tabularnewline
133 & 0.99674988756888 & 0.00650022486224019 & 0.00325011243112009 \tabularnewline
134 & 0.99490782110316 & 0.0101843577936805 & 0.00509217889684027 \tabularnewline
135 & 0.992383860184693 & 0.0152322796306143 & 0.00761613981530714 \tabularnewline
136 & 0.988877603158677 & 0.0222447936826467 & 0.0111223968413234 \tabularnewline
137 & 0.984105362403352 & 0.0317892751932956 & 0.0158946375966478 \tabularnewline
138 & 0.979809496156703 & 0.0403810076865938 & 0.0201905038432969 \tabularnewline
139 & 0.973045578393456 & 0.0539088432130882 & 0.0269544216065441 \tabularnewline
140 & 0.963420342628969 & 0.073159314742061 & 0.0365796573710305 \tabularnewline
141 & 0.948146182340279 & 0.103707635319441 & 0.0518538176597207 \tabularnewline
142 & 0.930882539924275 & 0.13823492015145 & 0.0691174600757248 \tabularnewline
143 & 0.963424790635483 & 0.0731504187290338 & 0.0365752093645169 \tabularnewline
144 & 0.948055835201647 & 0.103888329596707 & 0.0519441647983534 \tabularnewline
145 & 0.979725981600306 & 0.0405480367993884 & 0.0202740183996942 \tabularnewline
146 & 0.969252944697115 & 0.0614941106057694 & 0.0307470553028847 \tabularnewline
147 & 0.953865463290918 & 0.0922690734181639 & 0.0461345367090819 \tabularnewline
148 & 0.932796496431704 & 0.134407007136592 & 0.0672035035682961 \tabularnewline
149 & 0.907748981524922 & 0.184502036950157 & 0.0922510184750783 \tabularnewline
150 & 0.876524698422917 & 0.246950603154167 & 0.123475301577083 \tabularnewline
151 & 0.831624365380049 & 0.336751269239902 & 0.168375634619951 \tabularnewline
152 & 0.81981086814867 & 0.360378263702661 & 0.18018913185133 \tabularnewline
153 & 0.999632030564699 & 0.000735938870602674 & 0.000367969435301337 \tabularnewline
154 & 0.999125725841267 & 0.00174854831746671 & 0.000874274158733354 \tabularnewline
155 & 0.998813596762655 & 0.00237280647469087 & 0.00118640323734543 \tabularnewline
156 & 0.99700303375603 & 0.00599393248793947 & 0.00299696624396973 \tabularnewline
157 & 0.995877556498946 & 0.00824488700210729 & 0.00412244350105364 \tabularnewline
158 & 0.990249758506209 & 0.0195004829875829 & 0.00975024149379146 \tabularnewline
159 & 0.996495126202169 & 0.00700974759566157 & 0.00350487379783078 \tabularnewline
160 & 0.995634783956407 & 0.00873043208718529 & 0.00436521604359265 \tabularnewline
161 & 0.987931190691465 & 0.0241376186170706 & 0.0120688093085353 \tabularnewline
162 & 0.996805340404176 & 0.00638931919164895 & 0.00319465959582448 \tabularnewline
163 & 0.984904433369774 & 0.0301911332604513 & 0.0150955666302256 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154881&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.157731124789133[/C][C]0.315462249578266[/C][C]0.842268875210867[/C][/ROW]
[ROW][C]9[/C][C]0.073574045137545[/C][C]0.14714809027509[/C][C]0.926425954862455[/C][/ROW]
[ROW][C]10[/C][C]0.0957917103430906[/C][C]0.191583420686181[/C][C]0.904208289656909[/C][/ROW]
[ROW][C]11[/C][C]0.228927654188976[/C][C]0.457855308377951[/C][C]0.771072345811024[/C][/ROW]
[ROW][C]12[/C][C]0.144796023455196[/C][C]0.289592046910391[/C][C]0.855203976544804[/C][/ROW]
[ROW][C]13[/C][C]0.14414159805288[/C][C]0.28828319610576[/C][C]0.85585840194712[/C][/ROW]
[ROW][C]14[/C][C]0.0899525084210836[/C][C]0.179905016842167[/C][C]0.910047491578916[/C][/ROW]
[ROW][C]15[/C][C]0.0567881380717251[/C][C]0.11357627614345[/C][C]0.943211861928275[/C][/ROW]
[ROW][C]16[/C][C]0.119070274376211[/C][C]0.238140548752421[/C][C]0.880929725623789[/C][/ROW]
[ROW][C]17[/C][C]0.148666152034982[/C][C]0.297332304069964[/C][C]0.851333847965018[/C][/ROW]
[ROW][C]18[/C][C]0.153589833422343[/C][C]0.307179666844686[/C][C]0.846410166577657[/C][/ROW]
[ROW][C]19[/C][C]0.107569282117596[/C][C]0.215138564235191[/C][C]0.892430717882404[/C][/ROW]
[ROW][C]20[/C][C]0.110681228514064[/C][C]0.221362457028127[/C][C]0.889318771485936[/C][/ROW]
[ROW][C]21[/C][C]0.0783009579218873[/C][C]0.156601915843775[/C][C]0.921699042078113[/C][/ROW]
[ROW][C]22[/C][C]0.0535662708145977[/C][C]0.107132541629195[/C][C]0.946433729185402[/C][/ROW]
[ROW][C]23[/C][C]0.0451724982672596[/C][C]0.0903449965345192[/C][C]0.95482750173274[/C][/ROW]
[ROW][C]24[/C][C]0.0475630369315246[/C][C]0.0951260738630491[/C][C]0.952436963068475[/C][/ROW]
[ROW][C]25[/C][C]0.0319279069468466[/C][C]0.0638558138936932[/C][C]0.968072093053153[/C][/ROW]
[ROW][C]26[/C][C]0.0262301933208051[/C][C]0.0524603866416102[/C][C]0.973769806679195[/C][/ROW]
[ROW][C]27[/C][C]0.110587555695831[/C][C]0.221175111391662[/C][C]0.889412444304169[/C][/ROW]
[ROW][C]28[/C][C]0.0853333748239737[/C][C]0.170666749647947[/C][C]0.914666625176026[/C][/ROW]
[ROW][C]29[/C][C]0.137261618051559[/C][C]0.274523236103118[/C][C]0.862738381948441[/C][/ROW]
[ROW][C]30[/C][C]0.104403837395008[/C][C]0.208807674790016[/C][C]0.895596162604992[/C][/ROW]
[ROW][C]31[/C][C]0.088450884539802[/C][C]0.176901769079604[/C][C]0.911549115460198[/C][/ROW]
[ROW][C]32[/C][C]0.1368994385452[/C][C]0.273798877090401[/C][C]0.8631005614548[/C][/ROW]
[ROW][C]33[/C][C]0.119152988834937[/C][C]0.238305977669875[/C][C]0.880847011165063[/C][/ROW]
[ROW][C]34[/C][C]0.0935912286808173[/C][C]0.187182457361635[/C][C]0.906408771319183[/C][/ROW]
[ROW][C]35[/C][C]0.0707534300406513[/C][C]0.141506860081303[/C][C]0.929246569959349[/C][/ROW]
[ROW][C]36[/C][C]0.995048110010222[/C][C]0.00990377997955704[/C][C]0.00495188998977852[/C][/ROW]
[ROW][C]37[/C][C]0.994762933888908[/C][C]0.010474132222183[/C][C]0.00523706611109149[/C][/ROW]
[ROW][C]38[/C][C]0.994777155498569[/C][C]0.0104456890028611[/C][C]0.00522284450143055[/C][/ROW]
[ROW][C]39[/C][C]0.999593486781096[/C][C]0.00081302643780748[/C][C]0.00040651321890374[/C][/ROW]
[ROW][C]40[/C][C]0.999368058276875[/C][C]0.00126388344625077[/C][C]0.000631941723125384[/C][/ROW]
[ROW][C]41[/C][C]0.999080129542779[/C][C]0.00183974091444235[/C][C]0.000919870457221176[/C][/ROW]
[ROW][C]42[/C][C]0.99865758419129[/C][C]0.00268483161742026[/C][C]0.00134241580871013[/C][/ROW]
[ROW][C]43[/C][C]0.998001988187876[/C][C]0.00399602362424822[/C][C]0.00199801181212411[/C][/ROW]
[ROW][C]44[/C][C]0.997253924534704[/C][C]0.00549215093059127[/C][C]0.00274607546529564[/C][/ROW]
[ROW][C]45[/C][C]0.997126189278933[/C][C]0.00574762144213332[/C][C]0.00287381072106666[/C][/ROW]
[ROW][C]46[/C][C]0.997769059796512[/C][C]0.00446188040697633[/C][C]0.00223094020348817[/C][/ROW]
[ROW][C]47[/C][C]0.997541718765067[/C][C]0.00491656246986568[/C][C]0.00245828123493284[/C][/ROW]
[ROW][C]48[/C][C]0.996582677359409[/C][C]0.00683464528118145[/C][C]0.00341732264059073[/C][/ROW]
[ROW][C]49[/C][C]0.996037339638524[/C][C]0.0079253207229523[/C][C]0.00396266036147615[/C][/ROW]
[ROW][C]50[/C][C]0.996590560562881[/C][C]0.00681887887423821[/C][C]0.00340943943711911[/C][/ROW]
[ROW][C]51[/C][C]0.995197668186468[/C][C]0.00960466362706306[/C][C]0.00480233181353153[/C][/ROW]
[ROW][C]52[/C][C]0.993548481246486[/C][C]0.0129030375070278[/C][C]0.00645151875351392[/C][/ROW]
[ROW][C]53[/C][C]0.991684050798889[/C][C]0.0166318984022214[/C][C]0.00831594920111071[/C][/ROW]
[ROW][C]54[/C][C]0.988861700810044[/C][C]0.0222765983799129[/C][C]0.0111382991899564[/C][/ROW]
[ROW][C]55[/C][C]0.984955586116695[/C][C]0.0300888277666094[/C][C]0.0150444138833047[/C][/ROW]
[ROW][C]56[/C][C]0.983068117243227[/C][C]0.0338637655135468[/C][C]0.0169318827567734[/C][/ROW]
[ROW][C]57[/C][C]0.977852369616989[/C][C]0.0442952607660213[/C][C]0.0221476303830107[/C][/ROW]
[ROW][C]58[/C][C]0.994391277070793[/C][C]0.0112174458584139[/C][C]0.00560872292920695[/C][/ROW]
[ROW][C]59[/C][C]0.992697777636511[/C][C]0.0146044447269784[/C][C]0.00730222236348921[/C][/ROW]
[ROW][C]60[/C][C]0.992254578555694[/C][C]0.0154908428886115[/C][C]0.00774542144430577[/C][/ROW]
[ROW][C]61[/C][C]0.989435200444378[/C][C]0.021129599111244[/C][C]0.010564799555622[/C][/ROW]
[ROW][C]62[/C][C]0.986675143851631[/C][C]0.0266497122967374[/C][C]0.0133248561483687[/C][/ROW]
[ROW][C]63[/C][C]0.982650439708326[/C][C]0.0346991205833472[/C][C]0.0173495602916736[/C][/ROW]
[ROW][C]64[/C][C]0.985233601293121[/C][C]0.0295327974137579[/C][C]0.0147663987068789[/C][/ROW]
[ROW][C]65[/C][C]0.985488571723294[/C][C]0.0290228565534116[/C][C]0.0145114282767058[/C][/ROW]
[ROW][C]66[/C][C]0.981604647241105[/C][C]0.0367907055177893[/C][C]0.0183953527588946[/C][/ROW]
[ROW][C]67[/C][C]0.976795774293363[/C][C]0.0464084514132732[/C][C]0.0232042257066366[/C][/ROW]
[ROW][C]68[/C][C]0.969864213455835[/C][C]0.0602715730883299[/C][C]0.0301357865441649[/C][/ROW]
[ROW][C]69[/C][C]0.961539424289606[/C][C]0.0769211514207885[/C][C]0.0384605757103942[/C][/ROW]
[ROW][C]70[/C][C]0.962643882107922[/C][C]0.0747122357841553[/C][C]0.0373561178920777[/C][/ROW]
[ROW][C]71[/C][C]0.992861918124637[/C][C]0.0142761637507266[/C][C]0.00713808187536328[/C][/ROW]
[ROW][C]72[/C][C]0.993290181787938[/C][C]0.0134196364241242[/C][C]0.00670981821206209[/C][/ROW]
[ROW][C]73[/C][C]0.991772665288045[/C][C]0.0164546694239097[/C][C]0.00822733471195484[/C][/ROW]
[ROW][C]74[/C][C]0.989843242441897[/C][C]0.0203135151162066[/C][C]0.0101567575581033[/C][/ROW]
[ROW][C]75[/C][C]0.987412077237504[/C][C]0.0251758455249911[/C][C]0.0125879227624955[/C][/ROW]
[ROW][C]76[/C][C]0.983300317986732[/C][C]0.033399364026535[/C][C]0.0166996820132675[/C][/ROW]
[ROW][C]77[/C][C]0.984876976334833[/C][C]0.0302460473303342[/C][C]0.0151230236651671[/C][/ROW]
[ROW][C]78[/C][C]0.980431794564618[/C][C]0.039136410870763[/C][C]0.0195682054353815[/C][/ROW]
[ROW][C]79[/C][C]0.974767079392991[/C][C]0.0504658412140187[/C][C]0.0252329206070094[/C][/ROW]
[ROW][C]80[/C][C]0.970669545899335[/C][C]0.0586609082013293[/C][C]0.0293304541006647[/C][/ROW]
[ROW][C]81[/C][C]0.967574105955131[/C][C]0.0648517880897371[/C][C]0.0324258940448685[/C][/ROW]
[ROW][C]82[/C][C]0.959896149907758[/C][C]0.0802077001844835[/C][C]0.0401038500922418[/C][/ROW]
[ROW][C]83[/C][C]0.951986605751823[/C][C]0.0960267884963538[/C][C]0.0480133942481769[/C][/ROW]
[ROW][C]84[/C][C]0.942325636224836[/C][C]0.115348727550329[/C][C]0.0576743637751645[/C][/ROW]
[ROW][C]85[/C][C]0.940461996291271[/C][C]0.119076007417457[/C][C]0.0595380037087286[/C][/ROW]
[ROW][C]86[/C][C]0.926040293856162[/C][C]0.147919412287676[/C][C]0.0739597061438381[/C][/ROW]
[ROW][C]87[/C][C]0.909952878179388[/C][C]0.180094243641224[/C][C]0.0900471218206122[/C][/ROW]
[ROW][C]88[/C][C]0.90228974491733[/C][C]0.195420510165339[/C][C]0.0977102550826695[/C][/ROW]
[ROW][C]89[/C][C]0.892106706520471[/C][C]0.215786586959058[/C][C]0.107893293479529[/C][/ROW]
[ROW][C]90[/C][C]0.876413825150703[/C][C]0.247172349698595[/C][C]0.123586174849297[/C][/ROW]
[ROW][C]91[/C][C]0.853655892371884[/C][C]0.292688215256232[/C][C]0.146344107628116[/C][/ROW]
[ROW][C]92[/C][C]0.826337697444341[/C][C]0.347324605111318[/C][C]0.173662302555659[/C][/ROW]
[ROW][C]93[/C][C]0.803963447408915[/C][C]0.39207310518217[/C][C]0.196036552591085[/C][/ROW]
[ROW][C]94[/C][C]0.773897949276331[/C][C]0.452204101447338[/C][C]0.226102050723669[/C][/ROW]
[ROW][C]95[/C][C]0.755010293367635[/C][C]0.489979413264729[/C][C]0.244989706632365[/C][/ROW]
[ROW][C]96[/C][C]0.738130003133645[/C][C]0.523739993732711[/C][C]0.261869996866355[/C][/ROW]
[ROW][C]97[/C][C]0.716310083550094[/C][C]0.567379832899812[/C][C]0.283689916449906[/C][/ROW]
[ROW][C]98[/C][C]0.683110846810662[/C][C]0.633778306378675[/C][C]0.316889153189338[/C][/ROW]
[ROW][C]99[/C][C]0.686943025560605[/C][C]0.626113948878789[/C][C]0.313056974439395[/C][/ROW]
[ROW][C]100[/C][C]0.696818366585796[/C][C]0.606363266828407[/C][C]0.303181633414204[/C][/ROW]
[ROW][C]101[/C][C]0.721614724548539[/C][C]0.556770550902922[/C][C]0.278385275451461[/C][/ROW]
[ROW][C]102[/C][C]0.999992659228735[/C][C]1.46815425296788e-05[/C][C]7.34077126483939e-06[/C][/ROW]
[ROW][C]103[/C][C]0.999993214436947[/C][C]1.35711261055011e-05[/C][C]6.78556305275053e-06[/C][/ROW]
[ROW][C]104[/C][C]0.99999957445381[/C][C]8.51092379462014e-07[/C][C]4.25546189731007e-07[/C][/ROW]
[ROW][C]105[/C][C]0.999999296642086[/C][C]1.40671582776565e-06[/C][C]7.03357913882824e-07[/C][/ROW]
[ROW][C]106[/C][C]0.999998825133875[/C][C]2.34973225079128e-06[/C][C]1.17486612539564e-06[/C][/ROW]
[ROW][C]107[/C][C]0.99999926194115[/C][C]1.47611770065552e-06[/C][C]7.38058850327759e-07[/C][/ROW]
[ROW][C]108[/C][C]0.999998789027437[/C][C]2.42194512526747e-06[/C][C]1.21097256263373e-06[/C][/ROW]
[ROW][C]109[/C][C]0.999998549539381[/C][C]2.90092123717156e-06[/C][C]1.45046061858578e-06[/C][/ROW]
[ROW][C]110[/C][C]0.999997907071931[/C][C]4.18585613881011e-06[/C][C]2.09292806940505e-06[/C][/ROW]
[ROW][C]111[/C][C]0.999997275841508[/C][C]5.44831698446356e-06[/C][C]2.72415849223178e-06[/C][/ROW]
[ROW][C]112[/C][C]0.999995727708536[/C][C]8.54458292802811e-06[/C][C]4.27229146401405e-06[/C][/ROW]
[ROW][C]113[/C][C]0.999993483691418[/C][C]1.3032617164748e-05[/C][C]6.51630858237401e-06[/C][/ROW]
[ROW][C]114[/C][C]0.999989016336854[/C][C]2.19673262916022e-05[/C][C]1.09836631458011e-05[/C][/ROW]
[ROW][C]115[/C][C]0.999993002488938[/C][C]1.39950221233106e-05[/C][C]6.99751106165532e-06[/C][/ROW]
[ROW][C]116[/C][C]0.999990653923781[/C][C]1.86921524377542e-05[/C][C]9.34607621887712e-06[/C][/ROW]
[ROW][C]117[/C][C]0.99999295520656[/C][C]1.40895868809282e-05[/C][C]7.04479344046412e-06[/C][/ROW]
[ROW][C]118[/C][C]0.999988366066592[/C][C]2.32678668167707e-05[/C][C]1.16339334083854e-05[/C][/ROW]
[ROW][C]119[/C][C]0.999979139459128[/C][C]4.17210817441101e-05[/C][C]2.0860540872055e-05[/C][/ROW]
[ROW][C]120[/C][C]0.999969439710281[/C][C]6.11205794375486e-05[/C][C]3.05602897187743e-05[/C][/ROW]
[ROW][C]121[/C][C]0.999959084471783[/C][C]8.18310564342454e-05[/C][C]4.09155282171227e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999929287365191[/C][C]0.000141425269617704[/C][C]7.07126348088522e-05[/C][/ROW]
[ROW][C]123[/C][C]0.999893661466342[/C][C]0.000212677067315397[/C][C]0.000106338533657698[/C][/ROW]
[ROW][C]124[/C][C]0.99989868381376[/C][C]0.000202632372480284[/C][C]0.000101316186240142[/C][/ROW]
[ROW][C]125[/C][C]0.999830471261735[/C][C]0.000339057476530224[/C][C]0.000169528738265112[/C][/ROW]
[ROW][C]126[/C][C]0.999721280129325[/C][C]0.000557439741350135[/C][C]0.000278719870675068[/C][/ROW]
[ROW][C]127[/C][C]0.999715184532502[/C][C]0.000569630934996084[/C][C]0.000284815467498042[/C][/ROW]
[ROW][C]128[/C][C]0.999609621140048[/C][C]0.000780757719904268[/C][C]0.000390378859952134[/C][/ROW]
[ROW][C]129[/C][C]0.999460566469961[/C][C]0.0010788670600774[/C][C]0.000539433530038701[/C][/ROW]
[ROW][C]130[/C][C]0.999173083706796[/C][C]0.00165383258640833[/C][C]0.000826916293204166[/C][/ROW]
[ROW][C]131[/C][C]0.998656637733689[/C][C]0.00268672453262302[/C][C]0.00134336226631151[/C][/ROW]
[ROW][C]132[/C][C]0.997886306777653[/C][C]0.00422738644469461[/C][C]0.00211369322234731[/C][/ROW]
[ROW][C]133[/C][C]0.99674988756888[/C][C]0.00650022486224019[/C][C]0.00325011243112009[/C][/ROW]
[ROW][C]134[/C][C]0.99490782110316[/C][C]0.0101843577936805[/C][C]0.00509217889684027[/C][/ROW]
[ROW][C]135[/C][C]0.992383860184693[/C][C]0.0152322796306143[/C][C]0.00761613981530714[/C][/ROW]
[ROW][C]136[/C][C]0.988877603158677[/C][C]0.0222447936826467[/C][C]0.0111223968413234[/C][/ROW]
[ROW][C]137[/C][C]0.984105362403352[/C][C]0.0317892751932956[/C][C]0.0158946375966478[/C][/ROW]
[ROW][C]138[/C][C]0.979809496156703[/C][C]0.0403810076865938[/C][C]0.0201905038432969[/C][/ROW]
[ROW][C]139[/C][C]0.973045578393456[/C][C]0.0539088432130882[/C][C]0.0269544216065441[/C][/ROW]
[ROW][C]140[/C][C]0.963420342628969[/C][C]0.073159314742061[/C][C]0.0365796573710305[/C][/ROW]
[ROW][C]141[/C][C]0.948146182340279[/C][C]0.103707635319441[/C][C]0.0518538176597207[/C][/ROW]
[ROW][C]142[/C][C]0.930882539924275[/C][C]0.13823492015145[/C][C]0.0691174600757248[/C][/ROW]
[ROW][C]143[/C][C]0.963424790635483[/C][C]0.0731504187290338[/C][C]0.0365752093645169[/C][/ROW]
[ROW][C]144[/C][C]0.948055835201647[/C][C]0.103888329596707[/C][C]0.0519441647983534[/C][/ROW]
[ROW][C]145[/C][C]0.979725981600306[/C][C]0.0405480367993884[/C][C]0.0202740183996942[/C][/ROW]
[ROW][C]146[/C][C]0.969252944697115[/C][C]0.0614941106057694[/C][C]0.0307470553028847[/C][/ROW]
[ROW][C]147[/C][C]0.953865463290918[/C][C]0.0922690734181639[/C][C]0.0461345367090819[/C][/ROW]
[ROW][C]148[/C][C]0.932796496431704[/C][C]0.134407007136592[/C][C]0.0672035035682961[/C][/ROW]
[ROW][C]149[/C][C]0.907748981524922[/C][C]0.184502036950157[/C][C]0.0922510184750783[/C][/ROW]
[ROW][C]150[/C][C]0.876524698422917[/C][C]0.246950603154167[/C][C]0.123475301577083[/C][/ROW]
[ROW][C]151[/C][C]0.831624365380049[/C][C]0.336751269239902[/C][C]0.168375634619951[/C][/ROW]
[ROW][C]152[/C][C]0.81981086814867[/C][C]0.360378263702661[/C][C]0.18018913185133[/C][/ROW]
[ROW][C]153[/C][C]0.999632030564699[/C][C]0.000735938870602674[/C][C]0.000367969435301337[/C][/ROW]
[ROW][C]154[/C][C]0.999125725841267[/C][C]0.00174854831746671[/C][C]0.000874274158733354[/C][/ROW]
[ROW][C]155[/C][C]0.998813596762655[/C][C]0.00237280647469087[/C][C]0.00118640323734543[/C][/ROW]
[ROW][C]156[/C][C]0.99700303375603[/C][C]0.00599393248793947[/C][C]0.00299696624396973[/C][/ROW]
[ROW][C]157[/C][C]0.995877556498946[/C][C]0.00824488700210729[/C][C]0.00412244350105364[/C][/ROW]
[ROW][C]158[/C][C]0.990249758506209[/C][C]0.0195004829875829[/C][C]0.00975024149379146[/C][/ROW]
[ROW][C]159[/C][C]0.996495126202169[/C][C]0.00700974759566157[/C][C]0.00350487379783078[/C][/ROW]
[ROW][C]160[/C][C]0.995634783956407[/C][C]0.00873043208718529[/C][C]0.00436521604359265[/C][/ROW]
[ROW][C]161[/C][C]0.987931190691465[/C][C]0.0241376186170706[/C][C]0.0120688093085353[/C][/ROW]
[ROW][C]162[/C][C]0.996805340404176[/C][C]0.00638931919164895[/C][C]0.00319465959582448[/C][/ROW]
[ROW][C]163[/C][C]0.984904433369774[/C][C]0.0301911332604513[/C][C]0.0150955666302256[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154881&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154881&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
80.1577311247891330.3154622495782660.842268875210867
90.0735740451375450.147148090275090.926425954862455
100.09579171034309060.1915834206861810.904208289656909
110.2289276541889760.4578553083779510.771072345811024
120.1447960234551960.2895920469103910.855203976544804
130.144141598052880.288283196105760.85585840194712
140.08995250842108360.1799050168421670.910047491578916
150.05678813807172510.113576276143450.943211861928275
160.1190702743762110.2381405487524210.880929725623789
170.1486661520349820.2973323040699640.851333847965018
180.1535898334223430.3071796668446860.846410166577657
190.1075692821175960.2151385642351910.892430717882404
200.1106812285140640.2213624570281270.889318771485936
210.07830095792188730.1566019158437750.921699042078113
220.05356627081459770.1071325416291950.946433729185402
230.04517249826725960.09034499653451920.95482750173274
240.04756303693152460.09512607386304910.952436963068475
250.03192790694684660.06385581389369320.968072093053153
260.02623019332080510.05246038664161020.973769806679195
270.1105875556958310.2211751113916620.889412444304169
280.08533337482397370.1706667496479470.914666625176026
290.1372616180515590.2745232361031180.862738381948441
300.1044038373950080.2088076747900160.895596162604992
310.0884508845398020.1769017690796040.911549115460198
320.13689943854520.2737988770904010.8631005614548
330.1191529888349370.2383059776698750.880847011165063
340.09359122868081730.1871824573616350.906408771319183
350.07075343004065130.1415068600813030.929246569959349
360.9950481100102220.009903779979557040.00495188998977852
370.9947629338889080.0104741322221830.00523706611109149
380.9947771554985690.01044568900286110.00522284450143055
390.9995934867810960.000813026437807480.00040651321890374
400.9993680582768750.001263883446250770.000631941723125384
410.9990801295427790.001839740914442350.000919870457221176
420.998657584191290.002684831617420260.00134241580871013
430.9980019881878760.003996023624248220.00199801181212411
440.9972539245347040.005492150930591270.00274607546529564
450.9971261892789330.005747621442133320.00287381072106666
460.9977690597965120.004461880406976330.00223094020348817
470.9975417187650670.004916562469865680.00245828123493284
480.9965826773594090.006834645281181450.00341732264059073
490.9960373396385240.00792532072295230.00396266036147615
500.9965905605628810.006818878874238210.00340943943711911
510.9951976681864680.009604663627063060.00480233181353153
520.9935484812464860.01290303750702780.00645151875351392
530.9916840507988890.01663189840222140.00831594920111071
540.9888617008100440.02227659837991290.0111382991899564
550.9849555861166950.03008882776660940.0150444138833047
560.9830681172432270.03386376551354680.0169318827567734
570.9778523696169890.04429526076602130.0221476303830107
580.9943912770707930.01121744585841390.00560872292920695
590.9926977776365110.01460444472697840.00730222236348921
600.9922545785556940.01549084288861150.00774542144430577
610.9894352004443780.0211295991112440.010564799555622
620.9866751438516310.02664971229673740.0133248561483687
630.9826504397083260.03469912058334720.0173495602916736
640.9852336012931210.02953279741375790.0147663987068789
650.9854885717232940.02902285655341160.0145114282767058
660.9816046472411050.03679070551778930.0183953527588946
670.9767957742933630.04640845141327320.0232042257066366
680.9698642134558350.06027157308832990.0301357865441649
690.9615394242896060.07692115142078850.0384605757103942
700.9626438821079220.07471223578415530.0373561178920777
710.9928619181246370.01427616375072660.00713808187536328
720.9932901817879380.01341963642412420.00670981821206209
730.9917726652880450.01645466942390970.00822733471195484
740.9898432424418970.02031351511620660.0101567575581033
750.9874120772375040.02517584552499110.0125879227624955
760.9833003179867320.0333993640265350.0166996820132675
770.9848769763348330.03024604733033420.0151230236651671
780.9804317945646180.0391364108707630.0195682054353815
790.9747670793929910.05046584121401870.0252329206070094
800.9706695458993350.05866090820132930.0293304541006647
810.9675741059551310.06485178808973710.0324258940448685
820.9598961499077580.08020770018448350.0401038500922418
830.9519866057518230.09602678849635380.0480133942481769
840.9423256362248360.1153487275503290.0576743637751645
850.9404619962912710.1190760074174570.0595380037087286
860.9260402938561620.1479194122876760.0739597061438381
870.9099528781793880.1800942436412240.0900471218206122
880.902289744917330.1954205101653390.0977102550826695
890.8921067065204710.2157865869590580.107893293479529
900.8764138251507030.2471723496985950.123586174849297
910.8536558923718840.2926882152562320.146344107628116
920.8263376974443410.3473246051113180.173662302555659
930.8039634474089150.392073105182170.196036552591085
940.7738979492763310.4522041014473380.226102050723669
950.7550102933676350.4899794132647290.244989706632365
960.7381300031336450.5237399937327110.261869996866355
970.7163100835500940.5673798328998120.283689916449906
980.6831108468106620.6337783063786750.316889153189338
990.6869430255606050.6261139488787890.313056974439395
1000.6968183665857960.6063632668284070.303181633414204
1010.7216147245485390.5567705509029220.278385275451461
1020.9999926592287351.46815425296788e-057.34077126483939e-06
1030.9999932144369471.35711261055011e-056.78556305275053e-06
1040.999999574453818.51092379462014e-074.25546189731007e-07
1050.9999992966420861.40671582776565e-067.03357913882824e-07
1060.9999988251338752.34973225079128e-061.17486612539564e-06
1070.999999261941151.47611770065552e-067.38058850327759e-07
1080.9999987890274372.42194512526747e-061.21097256263373e-06
1090.9999985495393812.90092123717156e-061.45046061858578e-06
1100.9999979070719314.18585613881011e-062.09292806940505e-06
1110.9999972758415085.44831698446356e-062.72415849223178e-06
1120.9999957277085368.54458292802811e-064.27229146401405e-06
1130.9999934836914181.3032617164748e-056.51630858237401e-06
1140.9999890163368542.19673262916022e-051.09836631458011e-05
1150.9999930024889381.39950221233106e-056.99751106165532e-06
1160.9999906539237811.86921524377542e-059.34607621887712e-06
1170.999992955206561.40895868809282e-057.04479344046412e-06
1180.9999883660665922.32678668167707e-051.16339334083854e-05
1190.9999791394591284.17210817441101e-052.0860540872055e-05
1200.9999694397102816.11205794375486e-053.05602897187743e-05
1210.9999590844717838.18310564342454e-054.09155282171227e-05
1220.9999292873651910.0001414252696177047.07126348088522e-05
1230.9998936614663420.0002126770673153970.000106338533657698
1240.999898683813760.0002026323724802840.000101316186240142
1250.9998304712617350.0003390574765302240.000169528738265112
1260.9997212801293250.0005574397413501350.000278719870675068
1270.9997151845325020.0005696309349960840.000284815467498042
1280.9996096211400480.0007807577199042680.000390378859952134
1290.9994605664699610.00107886706007740.000539433530038701
1300.9991730837067960.001653832586408330.000826916293204166
1310.9986566377336890.002686724532623020.00134336226631151
1320.9978863067776530.004227386444694610.00211369322234731
1330.996749887568880.006500224862240190.00325011243112009
1340.994907821103160.01018435779368050.00509217889684027
1350.9923838601846930.01523227963061430.00761613981530714
1360.9888776031586770.02224479368264670.0111223968413234
1370.9841053624033520.03178927519329560.0158946375966478
1380.9798094961567030.04038100768659380.0201905038432969
1390.9730455783934560.05390884321308820.0269544216065441
1400.9634203426289690.0731593147420610.0365796573710305
1410.9481461823402790.1037076353194410.0518538176597207
1420.9308825399242750.138234920151450.0691174600757248
1430.9634247906354830.07315041872903380.0365752093645169
1440.9480558352016470.1038883295967070.0519441647983534
1450.9797259816003060.04054803679938840.0202740183996942
1460.9692529446971150.06149411060576940.0307470553028847
1470.9538654632909180.09226907341816390.0461345367090819
1480.9327964964317040.1344070071365920.0672035035682961
1490.9077489815249220.1845020369501570.0922510184750783
1500.8765246984229170.2469506031541670.123475301577083
1510.8316243653800490.3367512692399020.168375634619951
1520.819810868148670.3603782637026610.18018913185133
1530.9996320305646990.0007359388706026740.000367969435301337
1540.9991257258412670.001748548317466710.000874274158733354
1550.9988135967626550.002372806474690870.00118640323734543
1560.997003033756030.005993932487939470.00299696624396973
1570.9958775564989460.008244887002107290.00412244350105364
1580.9902497585062090.01950048298758290.00975024149379146
1590.9964951262021690.007009747595661570.00350487379783078
1600.9956347839564070.008730432087185290.00436521604359265
1610.9879311906914650.02413761861707060.0120688093085353
1620.9968053404041760.006389319191648950.00319465959582448
1630.9849044333697740.03019113326045130.0150955666302256






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level540.346153846153846NOK
5% type I error level890.57051282051282NOK
10% type I error level1060.67948717948718NOK

\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 & 54 & 0.346153846153846 & NOK \tabularnewline
5% type I error level & 89 & 0.57051282051282 & NOK \tabularnewline
10% type I error level & 106 & 0.67948717948718 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154881&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]54[/C][C]0.346153846153846[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]89[/C][C]0.57051282051282[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]106[/C][C]0.67948717948718[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154881&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154881&T=6

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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 level540.346153846153846NOK
5% type I error level890.57051282051282NOK
10% type I error level1060.67948717948718NOK


Figures (Output of Computation)

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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')
}