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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 10:06:44 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290506730fixmk3j74232bop.htm/, Retrieved Thu, 28 Mar 2024 12:02:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98887, Retrieved Thu, 28 Mar 2024 12:02:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Workshop 7 mini-t...] [2010-11-20 16:10:06] [87d60b8864dc39f7ed759c345edfb471]
-   PD    [Multiple Regression] [Workshop 7 mini-t...] [2010-11-21 12:07:24] [87d60b8864dc39f7ed759c345edfb471]
-   P       [Multiple Regression] [Workshop 7 mini-t...] [2010-11-21 12:21:55] [87d60b8864dc39f7ed759c345edfb471]
-   PD          [Multiple Regression] [WS 7 (4)] [2010-11-23 10:06:44] [c1f1b5e209adb4577289f490325e36f2] [Current]
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Dataseries X:
0	1.3954	1.0685 
0	1.4790 	1.1010
0	1.4619 	1.0996 
0	1.4670 	1.0978 
0	1.4799 	1.0893 
0	1.4508 	1.1018 
0	1.4678 	1.0931 
0	1.4824 	1.0842 
0	1.5189 	1.0409 
0	1.5348 	1.0245 
0	1.5666 	0.9994 
0	1.5446 	1.0090 
0	1.5803 	0.9947 
0	1.5718 	1.0080 
0	1.5832 	0.9986 
0	1.5801 	1.0184 
0	1.5605 	1.0357 
0	1.5416 	1.0556 
0	1.5479 	1.0409 
0	1.5580 	1.0474 
0	1.5790 	1.0219 
0	1.5554 	1.0427 
0	1.5761 	1.0205 
0	1.5360 	1.0490 
0	1.5621 	1.0344 
0	1.5773 	1.0193 
0	1.5710 	1.0238 
0	1.5925 	1.0165 
0	1.5844 	1.0218 
0	1.5696 	1.0370 
0	1.5540 	1.0508 
0	1.5012 	1.0813 
0	1.4676 	1.0970 
0	1.4770 	1.0989 
0	1.4660 	1.1018 
0	1.4241 	1.1166 
0	1.4214 	1.1319 
1	1.4469 	1.1020
1	1.4618 	1.0884 
1	1.3834 	1.1263 
1	1.3412 	1.1345 
1	1.3437 	1.1337 
1	1.2630 	1.1660 
1	1.2759 	1.1550 
1	1.2743 	1.1782 
1	1.2797 	1.1856 
1	1.2573 	1.2219 
1	1.2705 	1.2130 
1	1.2680 	1.2230 
1	1.3371 	1.1767 
1	1.3885 	1.1077 
1	1.4060 	1.0672 
1	1.3855	1.0840
1	1.3431	1.1154
1	1.3257	1.1184
1	1.2978	1.1570
1	1.2793	1.1625
1	1.2945	1.1627
1	1.2890	1.1578
1	1.2848	1.1533
1	1.2694	1.1684
1	1.2636	1.1597
1	1.2900	1.1888
1	1.3559	1.1296
1	1.3305	1.1424
1	1.3482	1.1317
1	1.3146	1.1581
1	1.3027	1.1672
1	1.3247	1.1391
1	1.3267	1.1357
1	1.3621	1.1065
1	1.3479	1.1232
1	1.4011	1.0845
1	1.4135	1.0676
1	1.3964	1.0863
1	1.4010	1.0792
1	1.3955	1.0799
1	1.4077	1.0817
1	1.3975	1.0869
1	1.3949	1.0843
1	1.4138	1.0747
1	1.4210	1.0711
1	1.4253	1.0688
1	1.4169	1.0828
1	1.4174	1.0746
1	1.4346	1.0568
1	1.4296	1.0600
1	1.4311	1.0593
1	1.4594	1.0370
1	1.4722	1.0288
1	1.4669	1.0295
1	1.4571	1.0352
1	1.4709	1.0324
1	1.4893	1.0186
1	1.4997	1.0094
1	1.4713	1.0258
1	1.4846	1.0170
1	1.4914	1.0117
1	1.4859	1.0175
1	1.4957	1.0064
1	1.4843	1.0168
1	1.4619	1.0340
1	1.4340	1.0423
1	1.4426	1.0356
1	1.4318	1.0348




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98887&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98887&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98887&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Multiple Linear Regression - Estimated Regression Equation
eu/us[t] = + 2.8265990970667 -0.0835405630382506Crisis[t] -1.23598378810765`us/ch`[t] -3.73098538767963e-05t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
eu/us[t] =  +  2.8265990970667 -0.0835405630382506Crisis[t] -1.23598378810765`us/ch`[t] -3.73098538767963e-05t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98887&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]eu/us[t] =  +  2.8265990970667 -0.0835405630382506Crisis[t] -1.23598378810765`us/ch`[t] -3.73098538767963e-05t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98887&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98887&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
eu/us[t] = + 2.8265990970667 -0.0835405630382506Crisis[t] -1.23598378810765`us/ch`[t] -3.73098538767963e-05t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.82659909706670.06246445.25200
Crisis-0.08354056303825060.012012-6.954700
`us/ch`-1.235983788107650.057024-21.674900
t-3.73098538767963e-050.000174-0.2140.8309610.415481

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 2.8265990970667 & 0.062464 & 45.252 & 0 & 0 \tabularnewline
Crisis & -0.0835405630382506 & 0.012012 & -6.9547 & 0 & 0 \tabularnewline
`us/ch` & -1.23598378810765 & 0.057024 & -21.6749 & 0 & 0 \tabularnewline
t & -3.73098538767963e-05 & 0.000174 & -0.214 & 0.830961 & 0.415481 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98887&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]2.8265990970667[/C][C]0.062464[/C][C]45.252[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Crisis[/C][C]-0.0835405630382506[/C][C]0.012012[/C][C]-6.9547[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`us/ch`[/C][C]-1.23598378810765[/C][C]0.057024[/C][C]-21.6749[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-3.73098538767963e-05[/C][C]0.000174[/C][C]-0.214[/C][C]0.830961[/C][C]0.415481[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98887&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98887&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.82659909706670.06246445.25200
Crisis-0.08354056303825060.012012-6.954700
`us/ch`-1.235983788107650.057024-21.674900
t-3.73098538767963e-050.000174-0.2140.8309610.415481







Multiple Linear Regression - Regression Statistics
Multiple R0.9734892777905
R-squared0.947681373973069
Adjusted R-squared0.94612735537821
F-TEST (value)609.826276923566
F-TEST (DF numerator)3
F-TEST (DF denominator)101
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0223779855757989
Sum Squared Residuals0.050578198081497

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.9734892777905 \tabularnewline
R-squared & 0.947681373973069 \tabularnewline
Adjusted R-squared & 0.94612735537821 \tabularnewline
F-TEST (value) & 609.826276923566 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 101 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0223779855757989 \tabularnewline
Sum Squared Residuals & 0.050578198081497 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98887&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.9734892777905[/C][/ROW]
[ROW][C]R-squared[/C][C]0.947681373973069[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.94612735537821[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]609.826276923566[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]101[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0223779855757989[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.050578198081497[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98887&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98887&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.9734892777905
R-squared0.947681373973069
Adjusted R-squared0.94612735537821
F-TEST (value)609.826276923566
F-TEST (DF numerator)3
F-TEST (DF denominator)101
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0223779855757989
Sum Squared Residuals0.050578198081497







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.39541.50591310961979-0.110513109619787
21.4791.465706326652420.0132936733475817
31.46191.46739939410189-0.00549939410189227
41.4671.46958685506661-0.00258685506660896
51.47991.48005540741165-0.000155407411647522
61.45081.46456830020643-0.013768300206425
71.46781.47528404930908-0.00748404930908482
81.48241.48624699516937-0.00384699516936607
91.51891.53972778334055-0.0208277833405508
101.53481.55996060761164-0.0251606076116395
111.56661.59094649083926-0.0243464908392647
121.54461.57904373661955-0.0344437366195546
131.58031.59668099493562-0.016380994935617
141.57181.58020510069991-0.00840510069990837
151.58321.59178603845424-0.00858603845424362
161.58011.567276249595840.0128237504041648
171.56051.54585642020770.014643579792304
181.54161.521223032970480.0203769670295232
191.54791.539354684801780.00854531519821726
201.5581.531283480325210.026716519674794
211.5791.562763757068070.0162362429319255
221.55541.537017984421560.0183820155784413
231.57611.564419514663670.0116804853363284
241.5361.529156666848730.00684333315127321
251.56211.547164720301220.0149352796987784
261.57731.565790765647770.0115092343522296
271.5711.560191528747410.0108084712525908
281.59251.569176900546720.0233230994532817
291.58441.562588876615870.0218111233841292
301.56961.543764613182760.0258353868172422
311.5541.5266707270530.0273292729470046
321.50121.488935911661840.0122640883381648
331.46761.46949365633467-0.00189365633466822
341.4771.467107977283390.0098920227166132
351.4661.4634863144440.00251368555600195
361.42411.44515644452613-0.0210564445261279
371.42141.4262085827142-0.00480858271420407
381.44691.37958662508650.0673133749135048
391.46181.396358694750880.0654413052491173
401.38341.349477599327730.0339224006722742
411.34121.339305222411370.00189477758863372
421.34371.340256699587980.00344330041202418
431.2631.30029711337822-0.0372971133782218
441.27591.31385562519353-0.0379556251935289
451.27431.28514349145555-0.0108434914555548
461.27971.275959901569680.00374009843031882
471.25731.23105638020750.0262436197925034
481.27051.242019326067780.0284806739322221
491.2681.229622178332820.0383778216671755
501.33711.286810917868330.0502890821316679
511.38851.372056489393880.0164435106061165
521.4061.42207652295837-0.0160765229583668
531.38551.40127468546428-0.0157746854642812
541.34311.36242748466382-0.0193274846638242
551.32571.35868222344562-0.0329822234456242
561.29781.31093593937079-0.013135939370792
571.27931.30410071868232-0.024800718682323
581.29451.30381621207082-0.00931621207082485
591.2891.30983522277868-0.0208352227786758
601.28481.31535983997128-0.0305598399712833
611.26941.29665917491698-0.0272591749169807
621.26361.30737492401964-0.0437749240196407
631.291.271370485931830.0186295140681689
641.35591.344503416333930.0113965836660725
651.33051.328645513992270.00185448600772739
661.34821.341833230671150.00636676932885215
671.31461.309165948811230.00543405118877091
681.30271.297881186485570.00481881351442748
691.32471.33257502107752-0.0078750210775208
701.32671.33674005610321-0.0100400561032101
711.36211.37279347286208-0.0106934728620766
721.34791.3521152337468-0.00421523374680203
731.40111.399910496492690.00118950350730856
741.41351.42076131265783-0.00726131265783391
751.39641.39761110596634-0.00121110596634396
761.4011.40634928100803-0.00534928100803171
771.39551.40544678250248-0.00994678250247943
781.40771.403184701830010.00451529816999115
791.39751.396720276277970.000779723722027607
801.39491.39989652427318-0.00499652427317535
811.41381.411724658785130.00207534121486782
821.4211.416136890568440.0048631094315571
831.42531.418942343427210.00635765657278631
841.41691.401601260539830.0152987394601703
851.41741.411699017748440.0057009822515643
861.43461.433662219322880.000937780677124924
871.42961.42966976134705-6.97613470537839e-05
881.43111.430497640144850.000602359855147532
891.45941.458022768765780.00137723123422367
901.47221.468120525974380.00407947402561763
911.46691.46721802746883-0.000318027468829886
921.45711.46013561002274-0.00303561002273971
931.47091.463559054775560.00734094522443578
941.48931.480578321197570.0087216788024269
951.49971.491912062194290.0077879378057134
961.47131.47160461821544-0.000304618215444314
971.48461.482443965696920.00215603430308485
981.49141.488957369920010.0024426300799914
991.48591.481751354095110.00414864590489256
1001.49571.495433464289230.000266535710774298
1011.48431.482541923039030.00175807696097056
1021.46191.46124569202970.000654307970299132
1031.4341.45094971673453-0.0169497167345306
1041.44261.45919349826097-0.0165934982609748
1051.43181.46014497543758-0.0283449754375845

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.3954 & 1.50591310961979 & -0.110513109619787 \tabularnewline
2 & 1.479 & 1.46570632665242 & 0.0132936733475817 \tabularnewline
3 & 1.4619 & 1.46739939410189 & -0.00549939410189227 \tabularnewline
4 & 1.467 & 1.46958685506661 & -0.00258685506660896 \tabularnewline
5 & 1.4799 & 1.48005540741165 & -0.000155407411647522 \tabularnewline
6 & 1.4508 & 1.46456830020643 & -0.013768300206425 \tabularnewline
7 & 1.4678 & 1.47528404930908 & -0.00748404930908482 \tabularnewline
8 & 1.4824 & 1.48624699516937 & -0.00384699516936607 \tabularnewline
9 & 1.5189 & 1.53972778334055 & -0.0208277833405508 \tabularnewline
10 & 1.5348 & 1.55996060761164 & -0.0251606076116395 \tabularnewline
11 & 1.5666 & 1.59094649083926 & -0.0243464908392647 \tabularnewline
12 & 1.5446 & 1.57904373661955 & -0.0344437366195546 \tabularnewline
13 & 1.5803 & 1.59668099493562 & -0.016380994935617 \tabularnewline
14 & 1.5718 & 1.58020510069991 & -0.00840510069990837 \tabularnewline
15 & 1.5832 & 1.59178603845424 & -0.00858603845424362 \tabularnewline
16 & 1.5801 & 1.56727624959584 & 0.0128237504041648 \tabularnewline
17 & 1.5605 & 1.5458564202077 & 0.014643579792304 \tabularnewline
18 & 1.5416 & 1.52122303297048 & 0.0203769670295232 \tabularnewline
19 & 1.5479 & 1.53935468480178 & 0.00854531519821726 \tabularnewline
20 & 1.558 & 1.53128348032521 & 0.026716519674794 \tabularnewline
21 & 1.579 & 1.56276375706807 & 0.0162362429319255 \tabularnewline
22 & 1.5554 & 1.53701798442156 & 0.0183820155784413 \tabularnewline
23 & 1.5761 & 1.56441951466367 & 0.0116804853363284 \tabularnewline
24 & 1.536 & 1.52915666684873 & 0.00684333315127321 \tabularnewline
25 & 1.5621 & 1.54716472030122 & 0.0149352796987784 \tabularnewline
26 & 1.5773 & 1.56579076564777 & 0.0115092343522296 \tabularnewline
27 & 1.571 & 1.56019152874741 & 0.0108084712525908 \tabularnewline
28 & 1.5925 & 1.56917690054672 & 0.0233230994532817 \tabularnewline
29 & 1.5844 & 1.56258887661587 & 0.0218111233841292 \tabularnewline
30 & 1.5696 & 1.54376461318276 & 0.0258353868172422 \tabularnewline
31 & 1.554 & 1.526670727053 & 0.0273292729470046 \tabularnewline
32 & 1.5012 & 1.48893591166184 & 0.0122640883381648 \tabularnewline
33 & 1.4676 & 1.46949365633467 & -0.00189365633466822 \tabularnewline
34 & 1.477 & 1.46710797728339 & 0.0098920227166132 \tabularnewline
35 & 1.466 & 1.463486314444 & 0.00251368555600195 \tabularnewline
36 & 1.4241 & 1.44515644452613 & -0.0210564445261279 \tabularnewline
37 & 1.4214 & 1.4262085827142 & -0.00480858271420407 \tabularnewline
38 & 1.4469 & 1.3795866250865 & 0.0673133749135048 \tabularnewline
39 & 1.4618 & 1.39635869475088 & 0.0654413052491173 \tabularnewline
40 & 1.3834 & 1.34947759932773 & 0.0339224006722742 \tabularnewline
41 & 1.3412 & 1.33930522241137 & 0.00189477758863372 \tabularnewline
42 & 1.3437 & 1.34025669958798 & 0.00344330041202418 \tabularnewline
43 & 1.263 & 1.30029711337822 & -0.0372971133782218 \tabularnewline
44 & 1.2759 & 1.31385562519353 & -0.0379556251935289 \tabularnewline
45 & 1.2743 & 1.28514349145555 & -0.0108434914555548 \tabularnewline
46 & 1.2797 & 1.27595990156968 & 0.00374009843031882 \tabularnewline
47 & 1.2573 & 1.2310563802075 & 0.0262436197925034 \tabularnewline
48 & 1.2705 & 1.24201932606778 & 0.0284806739322221 \tabularnewline
49 & 1.268 & 1.22962217833282 & 0.0383778216671755 \tabularnewline
50 & 1.3371 & 1.28681091786833 & 0.0502890821316679 \tabularnewline
51 & 1.3885 & 1.37205648939388 & 0.0164435106061165 \tabularnewline
52 & 1.406 & 1.42207652295837 & -0.0160765229583668 \tabularnewline
53 & 1.3855 & 1.40127468546428 & -0.0157746854642812 \tabularnewline
54 & 1.3431 & 1.36242748466382 & -0.0193274846638242 \tabularnewline
55 & 1.3257 & 1.35868222344562 & -0.0329822234456242 \tabularnewline
56 & 1.2978 & 1.31093593937079 & -0.013135939370792 \tabularnewline
57 & 1.2793 & 1.30410071868232 & -0.024800718682323 \tabularnewline
58 & 1.2945 & 1.30381621207082 & -0.00931621207082485 \tabularnewline
59 & 1.289 & 1.30983522277868 & -0.0208352227786758 \tabularnewline
60 & 1.2848 & 1.31535983997128 & -0.0305598399712833 \tabularnewline
61 & 1.2694 & 1.29665917491698 & -0.0272591749169807 \tabularnewline
62 & 1.2636 & 1.30737492401964 & -0.0437749240196407 \tabularnewline
63 & 1.29 & 1.27137048593183 & 0.0186295140681689 \tabularnewline
64 & 1.3559 & 1.34450341633393 & 0.0113965836660725 \tabularnewline
65 & 1.3305 & 1.32864551399227 & 0.00185448600772739 \tabularnewline
66 & 1.3482 & 1.34183323067115 & 0.00636676932885215 \tabularnewline
67 & 1.3146 & 1.30916594881123 & 0.00543405118877091 \tabularnewline
68 & 1.3027 & 1.29788118648557 & 0.00481881351442748 \tabularnewline
69 & 1.3247 & 1.33257502107752 & -0.0078750210775208 \tabularnewline
70 & 1.3267 & 1.33674005610321 & -0.0100400561032101 \tabularnewline
71 & 1.3621 & 1.37279347286208 & -0.0106934728620766 \tabularnewline
72 & 1.3479 & 1.3521152337468 & -0.00421523374680203 \tabularnewline
73 & 1.4011 & 1.39991049649269 & 0.00118950350730856 \tabularnewline
74 & 1.4135 & 1.42076131265783 & -0.00726131265783391 \tabularnewline
75 & 1.3964 & 1.39761110596634 & -0.00121110596634396 \tabularnewline
76 & 1.401 & 1.40634928100803 & -0.00534928100803171 \tabularnewline
77 & 1.3955 & 1.40544678250248 & -0.00994678250247943 \tabularnewline
78 & 1.4077 & 1.40318470183001 & 0.00451529816999115 \tabularnewline
79 & 1.3975 & 1.39672027627797 & 0.000779723722027607 \tabularnewline
80 & 1.3949 & 1.39989652427318 & -0.00499652427317535 \tabularnewline
81 & 1.4138 & 1.41172465878513 & 0.00207534121486782 \tabularnewline
82 & 1.421 & 1.41613689056844 & 0.0048631094315571 \tabularnewline
83 & 1.4253 & 1.41894234342721 & 0.00635765657278631 \tabularnewline
84 & 1.4169 & 1.40160126053983 & 0.0152987394601703 \tabularnewline
85 & 1.4174 & 1.41169901774844 & 0.0057009822515643 \tabularnewline
86 & 1.4346 & 1.43366221932288 & 0.000937780677124924 \tabularnewline
87 & 1.4296 & 1.42966976134705 & -6.97613470537839e-05 \tabularnewline
88 & 1.4311 & 1.43049764014485 & 0.000602359855147532 \tabularnewline
89 & 1.4594 & 1.45802276876578 & 0.00137723123422367 \tabularnewline
90 & 1.4722 & 1.46812052597438 & 0.00407947402561763 \tabularnewline
91 & 1.4669 & 1.46721802746883 & -0.000318027468829886 \tabularnewline
92 & 1.4571 & 1.46013561002274 & -0.00303561002273971 \tabularnewline
93 & 1.4709 & 1.46355905477556 & 0.00734094522443578 \tabularnewline
94 & 1.4893 & 1.48057832119757 & 0.0087216788024269 \tabularnewline
95 & 1.4997 & 1.49191206219429 & 0.0077879378057134 \tabularnewline
96 & 1.4713 & 1.47160461821544 & -0.000304618215444314 \tabularnewline
97 & 1.4846 & 1.48244396569692 & 0.00215603430308485 \tabularnewline
98 & 1.4914 & 1.48895736992001 & 0.0024426300799914 \tabularnewline
99 & 1.4859 & 1.48175135409511 & 0.00414864590489256 \tabularnewline
100 & 1.4957 & 1.49543346428923 & 0.000266535710774298 \tabularnewline
101 & 1.4843 & 1.48254192303903 & 0.00175807696097056 \tabularnewline
102 & 1.4619 & 1.4612456920297 & 0.000654307970299132 \tabularnewline
103 & 1.434 & 1.45094971673453 & -0.0169497167345306 \tabularnewline
104 & 1.4426 & 1.45919349826097 & -0.0165934982609748 \tabularnewline
105 & 1.4318 & 1.46014497543758 & -0.0283449754375845 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98887&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]1.3954[/C][C]1.50591310961979[/C][C]-0.110513109619787[/C][/ROW]
[ROW][C]2[/C][C]1.479[/C][C]1.46570632665242[/C][C]0.0132936733475817[/C][/ROW]
[ROW][C]3[/C][C]1.4619[/C][C]1.46739939410189[/C][C]-0.00549939410189227[/C][/ROW]
[ROW][C]4[/C][C]1.467[/C][C]1.46958685506661[/C][C]-0.00258685506660896[/C][/ROW]
[ROW][C]5[/C][C]1.4799[/C][C]1.48005540741165[/C][C]-0.000155407411647522[/C][/ROW]
[ROW][C]6[/C][C]1.4508[/C][C]1.46456830020643[/C][C]-0.013768300206425[/C][/ROW]
[ROW][C]7[/C][C]1.4678[/C][C]1.47528404930908[/C][C]-0.00748404930908482[/C][/ROW]
[ROW][C]8[/C][C]1.4824[/C][C]1.48624699516937[/C][C]-0.00384699516936607[/C][/ROW]
[ROW][C]9[/C][C]1.5189[/C][C]1.53972778334055[/C][C]-0.0208277833405508[/C][/ROW]
[ROW][C]10[/C][C]1.5348[/C][C]1.55996060761164[/C][C]-0.0251606076116395[/C][/ROW]
[ROW][C]11[/C][C]1.5666[/C][C]1.59094649083926[/C][C]-0.0243464908392647[/C][/ROW]
[ROW][C]12[/C][C]1.5446[/C][C]1.57904373661955[/C][C]-0.0344437366195546[/C][/ROW]
[ROW][C]13[/C][C]1.5803[/C][C]1.59668099493562[/C][C]-0.016380994935617[/C][/ROW]
[ROW][C]14[/C][C]1.5718[/C][C]1.58020510069991[/C][C]-0.00840510069990837[/C][/ROW]
[ROW][C]15[/C][C]1.5832[/C][C]1.59178603845424[/C][C]-0.00858603845424362[/C][/ROW]
[ROW][C]16[/C][C]1.5801[/C][C]1.56727624959584[/C][C]0.0128237504041648[/C][/ROW]
[ROW][C]17[/C][C]1.5605[/C][C]1.5458564202077[/C][C]0.014643579792304[/C][/ROW]
[ROW][C]18[/C][C]1.5416[/C][C]1.52122303297048[/C][C]0.0203769670295232[/C][/ROW]
[ROW][C]19[/C][C]1.5479[/C][C]1.53935468480178[/C][C]0.00854531519821726[/C][/ROW]
[ROW][C]20[/C][C]1.558[/C][C]1.53128348032521[/C][C]0.026716519674794[/C][/ROW]
[ROW][C]21[/C][C]1.579[/C][C]1.56276375706807[/C][C]0.0162362429319255[/C][/ROW]
[ROW][C]22[/C][C]1.5554[/C][C]1.53701798442156[/C][C]0.0183820155784413[/C][/ROW]
[ROW][C]23[/C][C]1.5761[/C][C]1.56441951466367[/C][C]0.0116804853363284[/C][/ROW]
[ROW][C]24[/C][C]1.536[/C][C]1.52915666684873[/C][C]0.00684333315127321[/C][/ROW]
[ROW][C]25[/C][C]1.5621[/C][C]1.54716472030122[/C][C]0.0149352796987784[/C][/ROW]
[ROW][C]26[/C][C]1.5773[/C][C]1.56579076564777[/C][C]0.0115092343522296[/C][/ROW]
[ROW][C]27[/C][C]1.571[/C][C]1.56019152874741[/C][C]0.0108084712525908[/C][/ROW]
[ROW][C]28[/C][C]1.5925[/C][C]1.56917690054672[/C][C]0.0233230994532817[/C][/ROW]
[ROW][C]29[/C][C]1.5844[/C][C]1.56258887661587[/C][C]0.0218111233841292[/C][/ROW]
[ROW][C]30[/C][C]1.5696[/C][C]1.54376461318276[/C][C]0.0258353868172422[/C][/ROW]
[ROW][C]31[/C][C]1.554[/C][C]1.526670727053[/C][C]0.0273292729470046[/C][/ROW]
[ROW][C]32[/C][C]1.5012[/C][C]1.48893591166184[/C][C]0.0122640883381648[/C][/ROW]
[ROW][C]33[/C][C]1.4676[/C][C]1.46949365633467[/C][C]-0.00189365633466822[/C][/ROW]
[ROW][C]34[/C][C]1.477[/C][C]1.46710797728339[/C][C]0.0098920227166132[/C][/ROW]
[ROW][C]35[/C][C]1.466[/C][C]1.463486314444[/C][C]0.00251368555600195[/C][/ROW]
[ROW][C]36[/C][C]1.4241[/C][C]1.44515644452613[/C][C]-0.0210564445261279[/C][/ROW]
[ROW][C]37[/C][C]1.4214[/C][C]1.4262085827142[/C][C]-0.00480858271420407[/C][/ROW]
[ROW][C]38[/C][C]1.4469[/C][C]1.3795866250865[/C][C]0.0673133749135048[/C][/ROW]
[ROW][C]39[/C][C]1.4618[/C][C]1.39635869475088[/C][C]0.0654413052491173[/C][/ROW]
[ROW][C]40[/C][C]1.3834[/C][C]1.34947759932773[/C][C]0.0339224006722742[/C][/ROW]
[ROW][C]41[/C][C]1.3412[/C][C]1.33930522241137[/C][C]0.00189477758863372[/C][/ROW]
[ROW][C]42[/C][C]1.3437[/C][C]1.34025669958798[/C][C]0.00344330041202418[/C][/ROW]
[ROW][C]43[/C][C]1.263[/C][C]1.30029711337822[/C][C]-0.0372971133782218[/C][/ROW]
[ROW][C]44[/C][C]1.2759[/C][C]1.31385562519353[/C][C]-0.0379556251935289[/C][/ROW]
[ROW][C]45[/C][C]1.2743[/C][C]1.28514349145555[/C][C]-0.0108434914555548[/C][/ROW]
[ROW][C]46[/C][C]1.2797[/C][C]1.27595990156968[/C][C]0.00374009843031882[/C][/ROW]
[ROW][C]47[/C][C]1.2573[/C][C]1.2310563802075[/C][C]0.0262436197925034[/C][/ROW]
[ROW][C]48[/C][C]1.2705[/C][C]1.24201932606778[/C][C]0.0284806739322221[/C][/ROW]
[ROW][C]49[/C][C]1.268[/C][C]1.22962217833282[/C][C]0.0383778216671755[/C][/ROW]
[ROW][C]50[/C][C]1.3371[/C][C]1.28681091786833[/C][C]0.0502890821316679[/C][/ROW]
[ROW][C]51[/C][C]1.3885[/C][C]1.37205648939388[/C][C]0.0164435106061165[/C][/ROW]
[ROW][C]52[/C][C]1.406[/C][C]1.42207652295837[/C][C]-0.0160765229583668[/C][/ROW]
[ROW][C]53[/C][C]1.3855[/C][C]1.40127468546428[/C][C]-0.0157746854642812[/C][/ROW]
[ROW][C]54[/C][C]1.3431[/C][C]1.36242748466382[/C][C]-0.0193274846638242[/C][/ROW]
[ROW][C]55[/C][C]1.3257[/C][C]1.35868222344562[/C][C]-0.0329822234456242[/C][/ROW]
[ROW][C]56[/C][C]1.2978[/C][C]1.31093593937079[/C][C]-0.013135939370792[/C][/ROW]
[ROW][C]57[/C][C]1.2793[/C][C]1.30410071868232[/C][C]-0.024800718682323[/C][/ROW]
[ROW][C]58[/C][C]1.2945[/C][C]1.30381621207082[/C][C]-0.00931621207082485[/C][/ROW]
[ROW][C]59[/C][C]1.289[/C][C]1.30983522277868[/C][C]-0.0208352227786758[/C][/ROW]
[ROW][C]60[/C][C]1.2848[/C][C]1.31535983997128[/C][C]-0.0305598399712833[/C][/ROW]
[ROW][C]61[/C][C]1.2694[/C][C]1.29665917491698[/C][C]-0.0272591749169807[/C][/ROW]
[ROW][C]62[/C][C]1.2636[/C][C]1.30737492401964[/C][C]-0.0437749240196407[/C][/ROW]
[ROW][C]63[/C][C]1.29[/C][C]1.27137048593183[/C][C]0.0186295140681689[/C][/ROW]
[ROW][C]64[/C][C]1.3559[/C][C]1.34450341633393[/C][C]0.0113965836660725[/C][/ROW]
[ROW][C]65[/C][C]1.3305[/C][C]1.32864551399227[/C][C]0.00185448600772739[/C][/ROW]
[ROW][C]66[/C][C]1.3482[/C][C]1.34183323067115[/C][C]0.00636676932885215[/C][/ROW]
[ROW][C]67[/C][C]1.3146[/C][C]1.30916594881123[/C][C]0.00543405118877091[/C][/ROW]
[ROW][C]68[/C][C]1.3027[/C][C]1.29788118648557[/C][C]0.00481881351442748[/C][/ROW]
[ROW][C]69[/C][C]1.3247[/C][C]1.33257502107752[/C][C]-0.0078750210775208[/C][/ROW]
[ROW][C]70[/C][C]1.3267[/C][C]1.33674005610321[/C][C]-0.0100400561032101[/C][/ROW]
[ROW][C]71[/C][C]1.3621[/C][C]1.37279347286208[/C][C]-0.0106934728620766[/C][/ROW]
[ROW][C]72[/C][C]1.3479[/C][C]1.3521152337468[/C][C]-0.00421523374680203[/C][/ROW]
[ROW][C]73[/C][C]1.4011[/C][C]1.39991049649269[/C][C]0.00118950350730856[/C][/ROW]
[ROW][C]74[/C][C]1.4135[/C][C]1.42076131265783[/C][C]-0.00726131265783391[/C][/ROW]
[ROW][C]75[/C][C]1.3964[/C][C]1.39761110596634[/C][C]-0.00121110596634396[/C][/ROW]
[ROW][C]76[/C][C]1.401[/C][C]1.40634928100803[/C][C]-0.00534928100803171[/C][/ROW]
[ROW][C]77[/C][C]1.3955[/C][C]1.40544678250248[/C][C]-0.00994678250247943[/C][/ROW]
[ROW][C]78[/C][C]1.4077[/C][C]1.40318470183001[/C][C]0.00451529816999115[/C][/ROW]
[ROW][C]79[/C][C]1.3975[/C][C]1.39672027627797[/C][C]0.000779723722027607[/C][/ROW]
[ROW][C]80[/C][C]1.3949[/C][C]1.39989652427318[/C][C]-0.00499652427317535[/C][/ROW]
[ROW][C]81[/C][C]1.4138[/C][C]1.41172465878513[/C][C]0.00207534121486782[/C][/ROW]
[ROW][C]82[/C][C]1.421[/C][C]1.41613689056844[/C][C]0.0048631094315571[/C][/ROW]
[ROW][C]83[/C][C]1.4253[/C][C]1.41894234342721[/C][C]0.00635765657278631[/C][/ROW]
[ROW][C]84[/C][C]1.4169[/C][C]1.40160126053983[/C][C]0.0152987394601703[/C][/ROW]
[ROW][C]85[/C][C]1.4174[/C][C]1.41169901774844[/C][C]0.0057009822515643[/C][/ROW]
[ROW][C]86[/C][C]1.4346[/C][C]1.43366221932288[/C][C]0.000937780677124924[/C][/ROW]
[ROW][C]87[/C][C]1.4296[/C][C]1.42966976134705[/C][C]-6.97613470537839e-05[/C][/ROW]
[ROW][C]88[/C][C]1.4311[/C][C]1.43049764014485[/C][C]0.000602359855147532[/C][/ROW]
[ROW][C]89[/C][C]1.4594[/C][C]1.45802276876578[/C][C]0.00137723123422367[/C][/ROW]
[ROW][C]90[/C][C]1.4722[/C][C]1.46812052597438[/C][C]0.00407947402561763[/C][/ROW]
[ROW][C]91[/C][C]1.4669[/C][C]1.46721802746883[/C][C]-0.000318027468829886[/C][/ROW]
[ROW][C]92[/C][C]1.4571[/C][C]1.46013561002274[/C][C]-0.00303561002273971[/C][/ROW]
[ROW][C]93[/C][C]1.4709[/C][C]1.46355905477556[/C][C]0.00734094522443578[/C][/ROW]
[ROW][C]94[/C][C]1.4893[/C][C]1.48057832119757[/C][C]0.0087216788024269[/C][/ROW]
[ROW][C]95[/C][C]1.4997[/C][C]1.49191206219429[/C][C]0.0077879378057134[/C][/ROW]
[ROW][C]96[/C][C]1.4713[/C][C]1.47160461821544[/C][C]-0.000304618215444314[/C][/ROW]
[ROW][C]97[/C][C]1.4846[/C][C]1.48244396569692[/C][C]0.00215603430308485[/C][/ROW]
[ROW][C]98[/C][C]1.4914[/C][C]1.48895736992001[/C][C]0.0024426300799914[/C][/ROW]
[ROW][C]99[/C][C]1.4859[/C][C]1.48175135409511[/C][C]0.00414864590489256[/C][/ROW]
[ROW][C]100[/C][C]1.4957[/C][C]1.49543346428923[/C][C]0.000266535710774298[/C][/ROW]
[ROW][C]101[/C][C]1.4843[/C][C]1.48254192303903[/C][C]0.00175807696097056[/C][/ROW]
[ROW][C]102[/C][C]1.4619[/C][C]1.4612456920297[/C][C]0.000654307970299132[/C][/ROW]
[ROW][C]103[/C][C]1.434[/C][C]1.45094971673453[/C][C]-0.0169497167345306[/C][/ROW]
[ROW][C]104[/C][C]1.4426[/C][C]1.45919349826097[/C][C]-0.0165934982609748[/C][/ROW]
[ROW][C]105[/C][C]1.4318[/C][C]1.46014497543758[/C][C]-0.0283449754375845[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98887&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98887&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.39541.50591310961979-0.110513109619787
21.4791.465706326652420.0132936733475817
31.46191.46739939410189-0.00549939410189227
41.4671.46958685506661-0.00258685506660896
51.47991.48005540741165-0.000155407411647522
61.45081.46456830020643-0.013768300206425
71.46781.47528404930908-0.00748404930908482
81.48241.48624699516937-0.00384699516936607
91.51891.53972778334055-0.0208277833405508
101.53481.55996060761164-0.0251606076116395
111.56661.59094649083926-0.0243464908392647
121.54461.57904373661955-0.0344437366195546
131.58031.59668099493562-0.016380994935617
141.57181.58020510069991-0.00840510069990837
151.58321.59178603845424-0.00858603845424362
161.58011.567276249595840.0128237504041648
171.56051.54585642020770.014643579792304
181.54161.521223032970480.0203769670295232
191.54791.539354684801780.00854531519821726
201.5581.531283480325210.026716519674794
211.5791.562763757068070.0162362429319255
221.55541.537017984421560.0183820155784413
231.57611.564419514663670.0116804853363284
241.5361.529156666848730.00684333315127321
251.56211.547164720301220.0149352796987784
261.57731.565790765647770.0115092343522296
271.5711.560191528747410.0108084712525908
281.59251.569176900546720.0233230994532817
291.58441.562588876615870.0218111233841292
301.56961.543764613182760.0258353868172422
311.5541.5266707270530.0273292729470046
321.50121.488935911661840.0122640883381648
331.46761.46949365633467-0.00189365633466822
341.4771.467107977283390.0098920227166132
351.4661.4634863144440.00251368555600195
361.42411.44515644452613-0.0210564445261279
371.42141.4262085827142-0.00480858271420407
381.44691.37958662508650.0673133749135048
391.46181.396358694750880.0654413052491173
401.38341.349477599327730.0339224006722742
411.34121.339305222411370.00189477758863372
421.34371.340256699587980.00344330041202418
431.2631.30029711337822-0.0372971133782218
441.27591.31385562519353-0.0379556251935289
451.27431.28514349145555-0.0108434914555548
461.27971.275959901569680.00374009843031882
471.25731.23105638020750.0262436197925034
481.27051.242019326067780.0284806739322221
491.2681.229622178332820.0383778216671755
501.33711.286810917868330.0502890821316679
511.38851.372056489393880.0164435106061165
521.4061.42207652295837-0.0160765229583668
531.38551.40127468546428-0.0157746854642812
541.34311.36242748466382-0.0193274846638242
551.32571.35868222344562-0.0329822234456242
561.29781.31093593937079-0.013135939370792
571.27931.30410071868232-0.024800718682323
581.29451.30381621207082-0.00931621207082485
591.2891.30983522277868-0.0208352227786758
601.28481.31535983997128-0.0305598399712833
611.26941.29665917491698-0.0272591749169807
621.26361.30737492401964-0.0437749240196407
631.291.271370485931830.0186295140681689
641.35591.344503416333930.0113965836660725
651.33051.328645513992270.00185448600772739
661.34821.341833230671150.00636676932885215
671.31461.309165948811230.00543405118877091
681.30271.297881186485570.00481881351442748
691.32471.33257502107752-0.0078750210775208
701.32671.33674005610321-0.0100400561032101
711.36211.37279347286208-0.0106934728620766
721.34791.3521152337468-0.00421523374680203
731.40111.399910496492690.00118950350730856
741.41351.42076131265783-0.00726131265783391
751.39641.39761110596634-0.00121110596634396
761.4011.40634928100803-0.00534928100803171
771.39551.40544678250248-0.00994678250247943
781.40771.403184701830010.00451529816999115
791.39751.396720276277970.000779723722027607
801.39491.39989652427318-0.00499652427317535
811.41381.411724658785130.00207534121486782
821.4211.416136890568440.0048631094315571
831.42531.418942343427210.00635765657278631
841.41691.401601260539830.0152987394601703
851.41741.411699017748440.0057009822515643
861.43461.433662219322880.000937780677124924
871.42961.42966976134705-6.97613470537839e-05
881.43111.430497640144850.000602359855147532
891.45941.458022768765780.00137723123422367
901.47221.468120525974380.00407947402561763
911.46691.46721802746883-0.000318027468829886
921.45711.46013561002274-0.00303561002273971
931.47091.463559054775560.00734094522443578
941.48931.480578321197570.0087216788024269
951.49971.491912062194290.0077879378057134
961.47131.47160461821544-0.000304618215444314
971.48461.482443965696920.00215603430308485
981.49141.488957369920010.0024426300799914
991.48591.481751354095110.00414864590489256
1001.49571.495433464289230.000266535710774298
1011.48431.482541923039030.00175807696097056
1021.46191.46124569202970.000654307970299132
1031.4341.45094971673453-0.0169497167345306
1041.44261.45919349826097-0.0165934982609748
1051.43181.46014497543758-0.0283449754375845







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.774383394983940.4512332100321190.225616605016059
80.7579992395746250.484001520850750.242000760425375
90.9463346118888130.1073307762223750.0536653881111874
100.9387089017381340.1225821965237320.0612910982618662
110.9400036540246960.1199926919506080.0599963459753042
120.9339672583029750.132065483394050.0660327416970248
130.9209802604206630.1580394791586730.0790197395793365
140.8932883804647660.2134232390704690.106711619535234
150.8651372662032290.2697254675935430.134862733796771
160.8183422892293580.3633154215412850.181657710770642
170.8125167910475740.3749664179048530.187483208952426
180.8256482429450540.3487035141098920.174351757054946
190.8299120304677750.3401759390644510.170087969532225
200.787524138790750.4249517224184990.21247586120925
210.7367594339129570.5264811321740860.263240566087043
220.7150720725676440.5698558548647110.284927927432356
230.6796746740677790.6406506518644410.320325325932221
240.7362778596053420.5274442807893160.263722140394658
250.7034149037625430.5931701924749150.296585096237457
260.6653108883506620.6693782232986770.334689111649338
270.6374503531980720.7250992936038560.362549646801928
280.5739932459725960.8520135080548080.426006754027404
290.5170343448397490.9659313103205020.482965655160251
300.472226761189660.944453522379320.52777323881034
310.4481023768154080.8962047536308160.551897623184592
320.5320366481385220.9359267037229560.467963351861478
330.6580660225958380.6838679548083240.341933977404162
340.6583200529004620.6833598941990760.341679947099538
350.6682807625840990.6634384748318020.331719237415901
360.7656705564333550.468658887133290.234329443566645
370.7464379537250190.5071240925499630.253562046274982
380.8278317788471250.3443364423057490.172168221152875
390.93139401945170.1372119610966010.0686059805483005
400.9640677677021910.0718644645956180.035932232297809
410.981077237513290.037845524973420.01892276248671
420.9855377934038550.02892441319229080.0144622065961454
430.9975566982862510.004886603427497170.00244330171374859
440.9994971165495670.001005766900866930.000502883450433463
450.9993225481697080.001354903660583660.000677451830291829
460.9988987001914960.002202599617008750.00110129980850438
470.9991032132203060.001793573559388050.000896786779694024
480.9993880063553660.001223987289268850.000611993644634427
490.9998742945010660.000251410997867730.000125705498933865
500.9999990840593171.83188136571279e-069.15940682856395e-07
510.9999996395364437.20927113265874e-073.60463556632937e-07
520.9999997544105214.91178957147809e-072.45589478573904e-07
530.9999997592674624.81465075259227e-072.40732537629614e-07
540.9999997797335634.40532873125863e-072.20266436562932e-07
550.9999999681946166.36107680791894e-083.18053840395947e-08
560.9999999499122051.0017559024552e-075.00877951227601e-08
570.9999999687456646.250867205792e-083.125433602896e-08
580.9999999378437271.24312545035866e-076.21562725179329e-08
590.9999999425427821.14914435378617e-075.74572176893087e-08
600.999999989136612.17267800605479e-081.0863390030274e-08
610.9999999967462066.5075882414319e-093.25379412071595e-09
620.9999999999993831.23290746591488e-126.1645373295744e-13
630.9999999999998962.08206348486043e-131.04103174243022e-13
640.999999999999784.39477377520254e-132.19738688760127e-13
650.9999999999992181.56399654240196e-127.8199827120098e-13
660.9999999999978184.36477102832621e-122.18238551416311e-12
670.999999999996686.6415370923481e-123.32076854617405e-12
680.9999999999984653.06931001054293e-121.53465500527146e-12
690.9999999999945371.09265163430817e-115.46325817154085e-12
700.9999999999817323.6536406329531e-111.82682031647655e-11
710.9999999999722285.55440681595651e-112.77720340797825e-11
720.999999999900681.98637474560796e-109.93187372803981e-11
730.9999999996675236.64954652279563e-103.32477326139781e-10
740.9999999998538472.92305850523509e-101.46152925261754e-10
750.9999999995844648.3107224252552e-104.1553612126276e-10
760.9999999995550198.89962929685978e-104.44981464842989e-10
770.9999999999145961.70807535200674e-108.54037676003369e-11
780.9999999996643786.71244236106391e-103.35622118053196e-10
790.9999999988043182.39136310554524e-091.19568155277262e-09
800.9999999983861673.22766664360217e-091.61383332180108e-09
810.9999999953469829.30603576160273e-094.65301788080137e-09
820.9999999831613163.36773679416142e-081.68386839708071e-08
830.999999935098821.29802361625884e-076.4901180812942e-08
840.9999999840123123.19753762582353e-081.59876881291176e-08
850.9999999763509284.72981442113844e-082.36490721056922e-08
860.9999998930030772.13993845681972e-071.06996922840986e-07
870.9999995550029978.89994006200225e-074.44997003100112e-07
880.9999987267999072.54640018587547e-061.27320009293774e-06
890.9999949012164261.01975671473276e-055.09878357366379e-06
900.9999808836391323.82327217368198e-051.91163608684099e-05
910.9999535350642059.29298715898294e-054.64649357949147e-05
920.9999184455009260.0001631089981485468.15544990742728e-05
930.9996773217682940.0006453564634125370.000322678231706268
940.9987890844453090.002421831109382950.00121091555469147
950.996048859467250.007902281065499280.00395114053274964
960.9902767544789450.01944649104210950.00972324552105474
970.9801830563491630.03963388730167440.0198169436508372
980.9626863625615750.07462727487684990.0373136374384249

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.77438339498394 & 0.451233210032119 & 0.225616605016059 \tabularnewline
8 & 0.757999239574625 & 0.48400152085075 & 0.242000760425375 \tabularnewline
9 & 0.946334611888813 & 0.107330776222375 & 0.0536653881111874 \tabularnewline
10 & 0.938708901738134 & 0.122582196523732 & 0.0612910982618662 \tabularnewline
11 & 0.940003654024696 & 0.119992691950608 & 0.0599963459753042 \tabularnewline
12 & 0.933967258302975 & 0.13206548339405 & 0.0660327416970248 \tabularnewline
13 & 0.920980260420663 & 0.158039479158673 & 0.0790197395793365 \tabularnewline
14 & 0.893288380464766 & 0.213423239070469 & 0.106711619535234 \tabularnewline
15 & 0.865137266203229 & 0.269725467593543 & 0.134862733796771 \tabularnewline
16 & 0.818342289229358 & 0.363315421541285 & 0.181657710770642 \tabularnewline
17 & 0.812516791047574 & 0.374966417904853 & 0.187483208952426 \tabularnewline
18 & 0.825648242945054 & 0.348703514109892 & 0.174351757054946 \tabularnewline
19 & 0.829912030467775 & 0.340175939064451 & 0.170087969532225 \tabularnewline
20 & 0.78752413879075 & 0.424951722418499 & 0.21247586120925 \tabularnewline
21 & 0.736759433912957 & 0.526481132174086 & 0.263240566087043 \tabularnewline
22 & 0.715072072567644 & 0.569855854864711 & 0.284927927432356 \tabularnewline
23 & 0.679674674067779 & 0.640650651864441 & 0.320325325932221 \tabularnewline
24 & 0.736277859605342 & 0.527444280789316 & 0.263722140394658 \tabularnewline
25 & 0.703414903762543 & 0.593170192474915 & 0.296585096237457 \tabularnewline
26 & 0.665310888350662 & 0.669378223298677 & 0.334689111649338 \tabularnewline
27 & 0.637450353198072 & 0.725099293603856 & 0.362549646801928 \tabularnewline
28 & 0.573993245972596 & 0.852013508054808 & 0.426006754027404 \tabularnewline
29 & 0.517034344839749 & 0.965931310320502 & 0.482965655160251 \tabularnewline
30 & 0.47222676118966 & 0.94445352237932 & 0.52777323881034 \tabularnewline
31 & 0.448102376815408 & 0.896204753630816 & 0.551897623184592 \tabularnewline
32 & 0.532036648138522 & 0.935926703722956 & 0.467963351861478 \tabularnewline
33 & 0.658066022595838 & 0.683867954808324 & 0.341933977404162 \tabularnewline
34 & 0.658320052900462 & 0.683359894199076 & 0.341679947099538 \tabularnewline
35 & 0.668280762584099 & 0.663438474831802 & 0.331719237415901 \tabularnewline
36 & 0.765670556433355 & 0.46865888713329 & 0.234329443566645 \tabularnewline
37 & 0.746437953725019 & 0.507124092549963 & 0.253562046274982 \tabularnewline
38 & 0.827831778847125 & 0.344336442305749 & 0.172168221152875 \tabularnewline
39 & 0.9313940194517 & 0.137211961096601 & 0.0686059805483005 \tabularnewline
40 & 0.964067767702191 & 0.071864464595618 & 0.035932232297809 \tabularnewline
41 & 0.98107723751329 & 0.03784552497342 & 0.01892276248671 \tabularnewline
42 & 0.985537793403855 & 0.0289244131922908 & 0.0144622065961454 \tabularnewline
43 & 0.997556698286251 & 0.00488660342749717 & 0.00244330171374859 \tabularnewline
44 & 0.999497116549567 & 0.00100576690086693 & 0.000502883450433463 \tabularnewline
45 & 0.999322548169708 & 0.00135490366058366 & 0.000677451830291829 \tabularnewline
46 & 0.998898700191496 & 0.00220259961700875 & 0.00110129980850438 \tabularnewline
47 & 0.999103213220306 & 0.00179357355938805 & 0.000896786779694024 \tabularnewline
48 & 0.999388006355366 & 0.00122398728926885 & 0.000611993644634427 \tabularnewline
49 & 0.999874294501066 & 0.00025141099786773 & 0.000125705498933865 \tabularnewline
50 & 0.999999084059317 & 1.83188136571279e-06 & 9.15940682856395e-07 \tabularnewline
51 & 0.999999639536443 & 7.20927113265874e-07 & 3.60463556632937e-07 \tabularnewline
52 & 0.999999754410521 & 4.91178957147809e-07 & 2.45589478573904e-07 \tabularnewline
53 & 0.999999759267462 & 4.81465075259227e-07 & 2.40732537629614e-07 \tabularnewline
54 & 0.999999779733563 & 4.40532873125863e-07 & 2.20266436562932e-07 \tabularnewline
55 & 0.999999968194616 & 6.36107680791894e-08 & 3.18053840395947e-08 \tabularnewline
56 & 0.999999949912205 & 1.0017559024552e-07 & 5.00877951227601e-08 \tabularnewline
57 & 0.999999968745664 & 6.250867205792e-08 & 3.125433602896e-08 \tabularnewline
58 & 0.999999937843727 & 1.24312545035866e-07 & 6.21562725179329e-08 \tabularnewline
59 & 0.999999942542782 & 1.14914435378617e-07 & 5.74572176893087e-08 \tabularnewline
60 & 0.99999998913661 & 2.17267800605479e-08 & 1.0863390030274e-08 \tabularnewline
61 & 0.999999996746206 & 6.5075882414319e-09 & 3.25379412071595e-09 \tabularnewline
62 & 0.999999999999383 & 1.23290746591488e-12 & 6.1645373295744e-13 \tabularnewline
63 & 0.999999999999896 & 2.08206348486043e-13 & 1.04103174243022e-13 \tabularnewline
64 & 0.99999999999978 & 4.39477377520254e-13 & 2.19738688760127e-13 \tabularnewline
65 & 0.999999999999218 & 1.56399654240196e-12 & 7.8199827120098e-13 \tabularnewline
66 & 0.999999999997818 & 4.36477102832621e-12 & 2.18238551416311e-12 \tabularnewline
67 & 0.99999999999668 & 6.6415370923481e-12 & 3.32076854617405e-12 \tabularnewline
68 & 0.999999999998465 & 3.06931001054293e-12 & 1.53465500527146e-12 \tabularnewline
69 & 0.999999999994537 & 1.09265163430817e-11 & 5.46325817154085e-12 \tabularnewline
70 & 0.999999999981732 & 3.6536406329531e-11 & 1.82682031647655e-11 \tabularnewline
71 & 0.999999999972228 & 5.55440681595651e-11 & 2.77720340797825e-11 \tabularnewline
72 & 0.99999999990068 & 1.98637474560796e-10 & 9.93187372803981e-11 \tabularnewline
73 & 0.999999999667523 & 6.64954652279563e-10 & 3.32477326139781e-10 \tabularnewline
74 & 0.999999999853847 & 2.92305850523509e-10 & 1.46152925261754e-10 \tabularnewline
75 & 0.999999999584464 & 8.3107224252552e-10 & 4.1553612126276e-10 \tabularnewline
76 & 0.999999999555019 & 8.89962929685978e-10 & 4.44981464842989e-10 \tabularnewline
77 & 0.999999999914596 & 1.70807535200674e-10 & 8.54037676003369e-11 \tabularnewline
78 & 0.999999999664378 & 6.71244236106391e-10 & 3.35622118053196e-10 \tabularnewline
79 & 0.999999998804318 & 2.39136310554524e-09 & 1.19568155277262e-09 \tabularnewline
80 & 0.999999998386167 & 3.22766664360217e-09 & 1.61383332180108e-09 \tabularnewline
81 & 0.999999995346982 & 9.30603576160273e-09 & 4.65301788080137e-09 \tabularnewline
82 & 0.999999983161316 & 3.36773679416142e-08 & 1.68386839708071e-08 \tabularnewline
83 & 0.99999993509882 & 1.29802361625884e-07 & 6.4901180812942e-08 \tabularnewline
84 & 0.999999984012312 & 3.19753762582353e-08 & 1.59876881291176e-08 \tabularnewline
85 & 0.999999976350928 & 4.72981442113844e-08 & 2.36490721056922e-08 \tabularnewline
86 & 0.999999893003077 & 2.13993845681972e-07 & 1.06996922840986e-07 \tabularnewline
87 & 0.999999555002997 & 8.89994006200225e-07 & 4.44997003100112e-07 \tabularnewline
88 & 0.999998726799907 & 2.54640018587547e-06 & 1.27320009293774e-06 \tabularnewline
89 & 0.999994901216426 & 1.01975671473276e-05 & 5.09878357366379e-06 \tabularnewline
90 & 0.999980883639132 & 3.82327217368198e-05 & 1.91163608684099e-05 \tabularnewline
91 & 0.999953535064205 & 9.29298715898294e-05 & 4.64649357949147e-05 \tabularnewline
92 & 0.999918445500926 & 0.000163108998148546 & 8.15544990742728e-05 \tabularnewline
93 & 0.999677321768294 & 0.000645356463412537 & 0.000322678231706268 \tabularnewline
94 & 0.998789084445309 & 0.00242183110938295 & 0.00121091555469147 \tabularnewline
95 & 0.99604885946725 & 0.00790228106549928 & 0.00395114053274964 \tabularnewline
96 & 0.990276754478945 & 0.0194464910421095 & 0.00972324552105474 \tabularnewline
97 & 0.980183056349163 & 0.0396338873016744 & 0.0198169436508372 \tabularnewline
98 & 0.962686362561575 & 0.0746272748768499 & 0.0373136374384249 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98887&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.77438339498394[/C][C]0.451233210032119[/C][C]0.225616605016059[/C][/ROW]
[ROW][C]8[/C][C]0.757999239574625[/C][C]0.48400152085075[/C][C]0.242000760425375[/C][/ROW]
[ROW][C]9[/C][C]0.946334611888813[/C][C]0.107330776222375[/C][C]0.0536653881111874[/C][/ROW]
[ROW][C]10[/C][C]0.938708901738134[/C][C]0.122582196523732[/C][C]0.0612910982618662[/C][/ROW]
[ROW][C]11[/C][C]0.940003654024696[/C][C]0.119992691950608[/C][C]0.0599963459753042[/C][/ROW]
[ROW][C]12[/C][C]0.933967258302975[/C][C]0.13206548339405[/C][C]0.0660327416970248[/C][/ROW]
[ROW][C]13[/C][C]0.920980260420663[/C][C]0.158039479158673[/C][C]0.0790197395793365[/C][/ROW]
[ROW][C]14[/C][C]0.893288380464766[/C][C]0.213423239070469[/C][C]0.106711619535234[/C][/ROW]
[ROW][C]15[/C][C]0.865137266203229[/C][C]0.269725467593543[/C][C]0.134862733796771[/C][/ROW]
[ROW][C]16[/C][C]0.818342289229358[/C][C]0.363315421541285[/C][C]0.181657710770642[/C][/ROW]
[ROW][C]17[/C][C]0.812516791047574[/C][C]0.374966417904853[/C][C]0.187483208952426[/C][/ROW]
[ROW][C]18[/C][C]0.825648242945054[/C][C]0.348703514109892[/C][C]0.174351757054946[/C][/ROW]
[ROW][C]19[/C][C]0.829912030467775[/C][C]0.340175939064451[/C][C]0.170087969532225[/C][/ROW]
[ROW][C]20[/C][C]0.78752413879075[/C][C]0.424951722418499[/C][C]0.21247586120925[/C][/ROW]
[ROW][C]21[/C][C]0.736759433912957[/C][C]0.526481132174086[/C][C]0.263240566087043[/C][/ROW]
[ROW][C]22[/C][C]0.715072072567644[/C][C]0.569855854864711[/C][C]0.284927927432356[/C][/ROW]
[ROW][C]23[/C][C]0.679674674067779[/C][C]0.640650651864441[/C][C]0.320325325932221[/C][/ROW]
[ROW][C]24[/C][C]0.736277859605342[/C][C]0.527444280789316[/C][C]0.263722140394658[/C][/ROW]
[ROW][C]25[/C][C]0.703414903762543[/C][C]0.593170192474915[/C][C]0.296585096237457[/C][/ROW]
[ROW][C]26[/C][C]0.665310888350662[/C][C]0.669378223298677[/C][C]0.334689111649338[/C][/ROW]
[ROW][C]27[/C][C]0.637450353198072[/C][C]0.725099293603856[/C][C]0.362549646801928[/C][/ROW]
[ROW][C]28[/C][C]0.573993245972596[/C][C]0.852013508054808[/C][C]0.426006754027404[/C][/ROW]
[ROW][C]29[/C][C]0.517034344839749[/C][C]0.965931310320502[/C][C]0.482965655160251[/C][/ROW]
[ROW][C]30[/C][C]0.47222676118966[/C][C]0.94445352237932[/C][C]0.52777323881034[/C][/ROW]
[ROW][C]31[/C][C]0.448102376815408[/C][C]0.896204753630816[/C][C]0.551897623184592[/C][/ROW]
[ROW][C]32[/C][C]0.532036648138522[/C][C]0.935926703722956[/C][C]0.467963351861478[/C][/ROW]
[ROW][C]33[/C][C]0.658066022595838[/C][C]0.683867954808324[/C][C]0.341933977404162[/C][/ROW]
[ROW][C]34[/C][C]0.658320052900462[/C][C]0.683359894199076[/C][C]0.341679947099538[/C][/ROW]
[ROW][C]35[/C][C]0.668280762584099[/C][C]0.663438474831802[/C][C]0.331719237415901[/C][/ROW]
[ROW][C]36[/C][C]0.765670556433355[/C][C]0.46865888713329[/C][C]0.234329443566645[/C][/ROW]
[ROW][C]37[/C][C]0.746437953725019[/C][C]0.507124092549963[/C][C]0.253562046274982[/C][/ROW]
[ROW][C]38[/C][C]0.827831778847125[/C][C]0.344336442305749[/C][C]0.172168221152875[/C][/ROW]
[ROW][C]39[/C][C]0.9313940194517[/C][C]0.137211961096601[/C][C]0.0686059805483005[/C][/ROW]
[ROW][C]40[/C][C]0.964067767702191[/C][C]0.071864464595618[/C][C]0.035932232297809[/C][/ROW]
[ROW][C]41[/C][C]0.98107723751329[/C][C]0.03784552497342[/C][C]0.01892276248671[/C][/ROW]
[ROW][C]42[/C][C]0.985537793403855[/C][C]0.0289244131922908[/C][C]0.0144622065961454[/C][/ROW]
[ROW][C]43[/C][C]0.997556698286251[/C][C]0.00488660342749717[/C][C]0.00244330171374859[/C][/ROW]
[ROW][C]44[/C][C]0.999497116549567[/C][C]0.00100576690086693[/C][C]0.000502883450433463[/C][/ROW]
[ROW][C]45[/C][C]0.999322548169708[/C][C]0.00135490366058366[/C][C]0.000677451830291829[/C][/ROW]
[ROW][C]46[/C][C]0.998898700191496[/C][C]0.00220259961700875[/C][C]0.00110129980850438[/C][/ROW]
[ROW][C]47[/C][C]0.999103213220306[/C][C]0.00179357355938805[/C][C]0.000896786779694024[/C][/ROW]
[ROW][C]48[/C][C]0.999388006355366[/C][C]0.00122398728926885[/C][C]0.000611993644634427[/C][/ROW]
[ROW][C]49[/C][C]0.999874294501066[/C][C]0.00025141099786773[/C][C]0.000125705498933865[/C][/ROW]
[ROW][C]50[/C][C]0.999999084059317[/C][C]1.83188136571279e-06[/C][C]9.15940682856395e-07[/C][/ROW]
[ROW][C]51[/C][C]0.999999639536443[/C][C]7.20927113265874e-07[/C][C]3.60463556632937e-07[/C][/ROW]
[ROW][C]52[/C][C]0.999999754410521[/C][C]4.91178957147809e-07[/C][C]2.45589478573904e-07[/C][/ROW]
[ROW][C]53[/C][C]0.999999759267462[/C][C]4.81465075259227e-07[/C][C]2.40732537629614e-07[/C][/ROW]
[ROW][C]54[/C][C]0.999999779733563[/C][C]4.40532873125863e-07[/C][C]2.20266436562932e-07[/C][/ROW]
[ROW][C]55[/C][C]0.999999968194616[/C][C]6.36107680791894e-08[/C][C]3.18053840395947e-08[/C][/ROW]
[ROW][C]56[/C][C]0.999999949912205[/C][C]1.0017559024552e-07[/C][C]5.00877951227601e-08[/C][/ROW]
[ROW][C]57[/C][C]0.999999968745664[/C][C]6.250867205792e-08[/C][C]3.125433602896e-08[/C][/ROW]
[ROW][C]58[/C][C]0.999999937843727[/C][C]1.24312545035866e-07[/C][C]6.21562725179329e-08[/C][/ROW]
[ROW][C]59[/C][C]0.999999942542782[/C][C]1.14914435378617e-07[/C][C]5.74572176893087e-08[/C][/ROW]
[ROW][C]60[/C][C]0.99999998913661[/C][C]2.17267800605479e-08[/C][C]1.0863390030274e-08[/C][/ROW]
[ROW][C]61[/C][C]0.999999996746206[/C][C]6.5075882414319e-09[/C][C]3.25379412071595e-09[/C][/ROW]
[ROW][C]62[/C][C]0.999999999999383[/C][C]1.23290746591488e-12[/C][C]6.1645373295744e-13[/C][/ROW]
[ROW][C]63[/C][C]0.999999999999896[/C][C]2.08206348486043e-13[/C][C]1.04103174243022e-13[/C][/ROW]
[ROW][C]64[/C][C]0.99999999999978[/C][C]4.39477377520254e-13[/C][C]2.19738688760127e-13[/C][/ROW]
[ROW][C]65[/C][C]0.999999999999218[/C][C]1.56399654240196e-12[/C][C]7.8199827120098e-13[/C][/ROW]
[ROW][C]66[/C][C]0.999999999997818[/C][C]4.36477102832621e-12[/C][C]2.18238551416311e-12[/C][/ROW]
[ROW][C]67[/C][C]0.99999999999668[/C][C]6.6415370923481e-12[/C][C]3.32076854617405e-12[/C][/ROW]
[ROW][C]68[/C][C]0.999999999998465[/C][C]3.06931001054293e-12[/C][C]1.53465500527146e-12[/C][/ROW]
[ROW][C]69[/C][C]0.999999999994537[/C][C]1.09265163430817e-11[/C][C]5.46325817154085e-12[/C][/ROW]
[ROW][C]70[/C][C]0.999999999981732[/C][C]3.6536406329531e-11[/C][C]1.82682031647655e-11[/C][/ROW]
[ROW][C]71[/C][C]0.999999999972228[/C][C]5.55440681595651e-11[/C][C]2.77720340797825e-11[/C][/ROW]
[ROW][C]72[/C][C]0.99999999990068[/C][C]1.98637474560796e-10[/C][C]9.93187372803981e-11[/C][/ROW]
[ROW][C]73[/C][C]0.999999999667523[/C][C]6.64954652279563e-10[/C][C]3.32477326139781e-10[/C][/ROW]
[ROW][C]74[/C][C]0.999999999853847[/C][C]2.92305850523509e-10[/C][C]1.46152925261754e-10[/C][/ROW]
[ROW][C]75[/C][C]0.999999999584464[/C][C]8.3107224252552e-10[/C][C]4.1553612126276e-10[/C][/ROW]
[ROW][C]76[/C][C]0.999999999555019[/C][C]8.89962929685978e-10[/C][C]4.44981464842989e-10[/C][/ROW]
[ROW][C]77[/C][C]0.999999999914596[/C][C]1.70807535200674e-10[/C][C]8.54037676003369e-11[/C][/ROW]
[ROW][C]78[/C][C]0.999999999664378[/C][C]6.71244236106391e-10[/C][C]3.35622118053196e-10[/C][/ROW]
[ROW][C]79[/C][C]0.999999998804318[/C][C]2.39136310554524e-09[/C][C]1.19568155277262e-09[/C][/ROW]
[ROW][C]80[/C][C]0.999999998386167[/C][C]3.22766664360217e-09[/C][C]1.61383332180108e-09[/C][/ROW]
[ROW][C]81[/C][C]0.999999995346982[/C][C]9.30603576160273e-09[/C][C]4.65301788080137e-09[/C][/ROW]
[ROW][C]82[/C][C]0.999999983161316[/C][C]3.36773679416142e-08[/C][C]1.68386839708071e-08[/C][/ROW]
[ROW][C]83[/C][C]0.99999993509882[/C][C]1.29802361625884e-07[/C][C]6.4901180812942e-08[/C][/ROW]
[ROW][C]84[/C][C]0.999999984012312[/C][C]3.19753762582353e-08[/C][C]1.59876881291176e-08[/C][/ROW]
[ROW][C]85[/C][C]0.999999976350928[/C][C]4.72981442113844e-08[/C][C]2.36490721056922e-08[/C][/ROW]
[ROW][C]86[/C][C]0.999999893003077[/C][C]2.13993845681972e-07[/C][C]1.06996922840986e-07[/C][/ROW]
[ROW][C]87[/C][C]0.999999555002997[/C][C]8.89994006200225e-07[/C][C]4.44997003100112e-07[/C][/ROW]
[ROW][C]88[/C][C]0.999998726799907[/C][C]2.54640018587547e-06[/C][C]1.27320009293774e-06[/C][/ROW]
[ROW][C]89[/C][C]0.999994901216426[/C][C]1.01975671473276e-05[/C][C]5.09878357366379e-06[/C][/ROW]
[ROW][C]90[/C][C]0.999980883639132[/C][C]3.82327217368198e-05[/C][C]1.91163608684099e-05[/C][/ROW]
[ROW][C]91[/C][C]0.999953535064205[/C][C]9.29298715898294e-05[/C][C]4.64649357949147e-05[/C][/ROW]
[ROW][C]92[/C][C]0.999918445500926[/C][C]0.000163108998148546[/C][C]8.15544990742728e-05[/C][/ROW]
[ROW][C]93[/C][C]0.999677321768294[/C][C]0.000645356463412537[/C][C]0.000322678231706268[/C][/ROW]
[ROW][C]94[/C][C]0.998789084445309[/C][C]0.00242183110938295[/C][C]0.00121091555469147[/C][/ROW]
[ROW][C]95[/C][C]0.99604885946725[/C][C]0.00790228106549928[/C][C]0.00395114053274964[/C][/ROW]
[ROW][C]96[/C][C]0.990276754478945[/C][C]0.0194464910421095[/C][C]0.00972324552105474[/C][/ROW]
[ROW][C]97[/C][C]0.980183056349163[/C][C]0.0396338873016744[/C][C]0.0198169436508372[/C][/ROW]
[ROW][C]98[/C][C]0.962686362561575[/C][C]0.0746272748768499[/C][C]0.0373136374384249[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98887&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98887&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.774383394983940.4512332100321190.225616605016059
80.7579992395746250.484001520850750.242000760425375
90.9463346118888130.1073307762223750.0536653881111874
100.9387089017381340.1225821965237320.0612910982618662
110.9400036540246960.1199926919506080.0599963459753042
120.9339672583029750.132065483394050.0660327416970248
130.9209802604206630.1580394791586730.0790197395793365
140.8932883804647660.2134232390704690.106711619535234
150.8651372662032290.2697254675935430.134862733796771
160.8183422892293580.3633154215412850.181657710770642
170.8125167910475740.3749664179048530.187483208952426
180.8256482429450540.3487035141098920.174351757054946
190.8299120304677750.3401759390644510.170087969532225
200.787524138790750.4249517224184990.21247586120925
210.7367594339129570.5264811321740860.263240566087043
220.7150720725676440.5698558548647110.284927927432356
230.6796746740677790.6406506518644410.320325325932221
240.7362778596053420.5274442807893160.263722140394658
250.7034149037625430.5931701924749150.296585096237457
260.6653108883506620.6693782232986770.334689111649338
270.6374503531980720.7250992936038560.362549646801928
280.5739932459725960.8520135080548080.426006754027404
290.5170343448397490.9659313103205020.482965655160251
300.472226761189660.944453522379320.52777323881034
310.4481023768154080.8962047536308160.551897623184592
320.5320366481385220.9359267037229560.467963351861478
330.6580660225958380.6838679548083240.341933977404162
340.6583200529004620.6833598941990760.341679947099538
350.6682807625840990.6634384748318020.331719237415901
360.7656705564333550.468658887133290.234329443566645
370.7464379537250190.5071240925499630.253562046274982
380.8278317788471250.3443364423057490.172168221152875
390.93139401945170.1372119610966010.0686059805483005
400.9640677677021910.0718644645956180.035932232297809
410.981077237513290.037845524973420.01892276248671
420.9855377934038550.02892441319229080.0144622065961454
430.9975566982862510.004886603427497170.00244330171374859
440.9994971165495670.001005766900866930.000502883450433463
450.9993225481697080.001354903660583660.000677451830291829
460.9988987001914960.002202599617008750.00110129980850438
470.9991032132203060.001793573559388050.000896786779694024
480.9993880063553660.001223987289268850.000611993644634427
490.9998742945010660.000251410997867730.000125705498933865
500.9999990840593171.83188136571279e-069.15940682856395e-07
510.9999996395364437.20927113265874e-073.60463556632937e-07
520.9999997544105214.91178957147809e-072.45589478573904e-07
530.9999997592674624.81465075259227e-072.40732537629614e-07
540.9999997797335634.40532873125863e-072.20266436562932e-07
550.9999999681946166.36107680791894e-083.18053840395947e-08
560.9999999499122051.0017559024552e-075.00877951227601e-08
570.9999999687456646.250867205792e-083.125433602896e-08
580.9999999378437271.24312545035866e-076.21562725179329e-08
590.9999999425427821.14914435378617e-075.74572176893087e-08
600.999999989136612.17267800605479e-081.0863390030274e-08
610.9999999967462066.5075882414319e-093.25379412071595e-09
620.9999999999993831.23290746591488e-126.1645373295744e-13
630.9999999999998962.08206348486043e-131.04103174243022e-13
640.999999999999784.39477377520254e-132.19738688760127e-13
650.9999999999992181.56399654240196e-127.8199827120098e-13
660.9999999999978184.36477102832621e-122.18238551416311e-12
670.999999999996686.6415370923481e-123.32076854617405e-12
680.9999999999984653.06931001054293e-121.53465500527146e-12
690.9999999999945371.09265163430817e-115.46325817154085e-12
700.9999999999817323.6536406329531e-111.82682031647655e-11
710.9999999999722285.55440681595651e-112.77720340797825e-11
720.999999999900681.98637474560796e-109.93187372803981e-11
730.9999999996675236.64954652279563e-103.32477326139781e-10
740.9999999998538472.92305850523509e-101.46152925261754e-10
750.9999999995844648.3107224252552e-104.1553612126276e-10
760.9999999995550198.89962929685978e-104.44981464842989e-10
770.9999999999145961.70807535200674e-108.54037676003369e-11
780.9999999996643786.71244236106391e-103.35622118053196e-10
790.9999999988043182.39136310554524e-091.19568155277262e-09
800.9999999983861673.22766664360217e-091.61383332180108e-09
810.9999999953469829.30603576160273e-094.65301788080137e-09
820.9999999831613163.36773679416142e-081.68386839708071e-08
830.999999935098821.29802361625884e-076.4901180812942e-08
840.9999999840123123.19753762582353e-081.59876881291176e-08
850.9999999763509284.72981442113844e-082.36490721056922e-08
860.9999998930030772.13993845681972e-071.06996922840986e-07
870.9999995550029978.89994006200225e-074.44997003100112e-07
880.9999987267999072.54640018587547e-061.27320009293774e-06
890.9999949012164261.01975671473276e-055.09878357366379e-06
900.9999808836391323.82327217368198e-051.91163608684099e-05
910.9999535350642059.29298715898294e-054.64649357949147e-05
920.9999184455009260.0001631089981485468.15544990742728e-05
930.9996773217682940.0006453564634125370.000322678231706268
940.9987890844453090.002421831109382950.00121091555469147
950.996048859467250.007902281065499280.00395114053274964
960.9902767544789450.01944649104210950.00972324552105474
970.9801830563491630.03963388730167440.0198169436508372
980.9626863625615750.07462727487684990.0373136374384249







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level530.576086956521739NOK
5% type I error level570.619565217391304NOK
10% type I error level590.641304347826087NOK

\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 & 53 & 0.576086956521739 & NOK \tabularnewline
5% type I error level & 57 & 0.619565217391304 & NOK \tabularnewline
10% type I error level & 59 & 0.641304347826087 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98887&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]53[/C][C]0.576086956521739[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]57[/C][C]0.619565217391304[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]59[/C][C]0.641304347826087[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98887&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98887&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level530.576086956521739NOK
5% type I error level570.619565217391304NOK
10% type I error level590.641304347826087NOK



Parameters (Session):
par1 = 0 ; par2 = 36 ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}