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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 computationFri, 03 Dec 2010 13:35:10 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/03/t1291383198to0hcqob3o3c8ye.htm/, Retrieved Thu, 31 Oct 2024 23:49:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104766, Retrieved Thu, 31 Oct 2024 23:49:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact173
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [] [2010-11-30 13:52:40] [82643889efeee0b265cd2ff213e5137b]
-           [Multiple Regression] [] [2010-12-03 13:35:10] [ca0a9c6c6ac3cc5623c7945c1ccf8fd2] [Current]
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Dataseries X:
162556	807	213118	6282154
29790	444	81767	4321023
87550	412	153198	4111912
84738	428	-26007	223193
54660	315	126942	1491348
42634	168	157214	1629616
40949	263	129352	1398893
45187	267	234817	1926517
37704	228	60448	983660
16275	129	47818	1443586
25830	104	245546	1073089
12679	122	48020	984885
18014	393	-1710	1405225
43556	190	32648	227132
24811	280	95350	929118
6575	63	151352	1071292
7123	102	288170	638830
21950	265	114337	856956
37597	234	37884	992426
17821	277	122844	444477
12988	73	82340	857217
22330	67	79801	711969
13326	103	165548	702380
16189	290	116384	358589
7146	83	134028	297978
15824	56	63838	585715
27664	236	74996	657954
11920	73	31080	209458
8568	34	32168	786690
14416	139	49857	439798
3369	26	87161	688779
11819	70	106113	574339
6984	40	80570	741409
4519	42	102129	597793
2220	12	301670	644190
18562	211	102313	377934
10327	74	88577	640273
5336	80	112477	697458
2365	83	191778	550608
4069	131	79804	207393
8636	203	128294	301607
13718	56	96448	345783
4525	89	93811	501749
6869	88	117520	379983
4628	39	69159	387475
3689	25	101792	377305
4891	49	210568	370837
7489	149	136996	430866
4901	58	121920	469107
2284	41	76403	194493
3160	90	108094	530670
4150	136	134759	518365
7285	97	188873	491303
1134	63	146216	527021
4658	114	156608	233773
2384	77	61348	405972
3748	6	50350	652925
5371	47	87720	446211
1285	51	99489	341340
9327	85	87419	387699




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 12 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104766&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104766&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104766&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 time12 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
Wealth[t] = -208139.364750439 + 16.9933350616200Costs[t] + 2823.29907158168Orders[t] + 2.96778676492997Dividends[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Wealth[t] =  -208139.364750439 +  16.9933350616200Costs[t] +  2823.29907158168Orders[t] +  2.96778676492997Dividends[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104766&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Wealth[t] =  -208139.364750439 +  16.9933350616200Costs[t] +  2823.29907158168Orders[t] +  2.96778676492997Dividends[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104766&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104766&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
Wealth[t] = -208139.364750439 + 16.9933350616200Costs[t] + 2823.29907158168Orders[t] + 2.96778676492997Dividends[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-208139.364750439187885.154935-1.10780.2726830.136342
Costs16.99333506162006.0821252.7940.0071150.003557
Orders2823.299071581681139.5588442.47750.0162720.008136
Dividends2.967786764929971.2649122.34620.0225290.011264

\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) & -208139.364750439 & 187885.154935 & -1.1078 & 0.272683 & 0.136342 \tabularnewline
Costs & 16.9933350616200 & 6.082125 & 2.794 & 0.007115 & 0.003557 \tabularnewline
Orders & 2823.29907158168 & 1139.558844 & 2.4775 & 0.016272 & 0.008136 \tabularnewline
Dividends & 2.96778676492997 & 1.264912 & 2.3462 & 0.022529 & 0.011264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104766&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]-208139.364750439[/C][C]187885.154935[/C][C]-1.1078[/C][C]0.272683[/C][C]0.136342[/C][/ROW]
[ROW][C]Costs[/C][C]16.9933350616200[/C][C]6.082125[/C][C]2.794[/C][C]0.007115[/C][C]0.003557[/C][/ROW]
[ROW][C]Orders[/C][C]2823.29907158168[/C][C]1139.558844[/C][C]2.4775[/C][C]0.016272[/C][C]0.008136[/C][/ROW]
[ROW][C]Dividends[/C][C]2.96778676492997[/C][C]1.264912[/C][C]2.3462[/C][C]0.022529[/C][C]0.011264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104766&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104766&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)-208139.364750439187885.154935-1.10780.2726830.136342
Costs16.99333506162006.0821252.7940.0071150.003557
Orders2823.299071581681139.5588442.47750.0162720.008136
Dividends2.967786764929971.2649122.34620.0225290.011264







Multiple Linear Regression - Regression Statistics
Multiple R0.813960082483913
R-squared0.662531015877218
Adjusted R-squared0.644452320299212
F-TEST (value)36.6470585788959
F-TEST (DF numerator)3
F-TEST (DF denominator)56
p-value3.06643599401468e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation615026.420022537
Sum Squared Residuals21182419850241.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.813960082483913 \tabularnewline
R-squared & 0.662531015877218 \tabularnewline
Adjusted R-squared & 0.644452320299212 \tabularnewline
F-TEST (value) & 36.6470585788959 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value & 3.06643599401468e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 615026.420022537 \tabularnewline
Sum Squared Residuals & 21182419850241.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104766&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.813960082483913[/C][/ROW]
[ROW][C]R-squared[/C][C]0.662531015877218[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.644452320299212[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]36.6470585788959[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/C][/ROW]
[ROW][C]p-value[/C][C]3.06643599401468e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]615026.420022537[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]21182419850241.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104766&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104766&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 R0.813960082483913
R-squared0.662531015877218
Adjusted R-squared0.644452320299212
F-TEST (value)36.6470585788959
F-TEST (DF numerator)3
F-TEST (DF denominator)56
p-value3.06643599401468e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation615026.420022537
Sum Squared Residuals21182419850241.3







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
162821545465120.34006103817033.659938973
243210231794303.894925522526719.10507448
341119122897485.334199791214426.66580021
42231932363030.63394254-2139837.63394254
514913481986792.32477968-495444.324779681
616296161457246.35475409172369.645245908
713988931614137.52213104-215244.522131043
819265172010446.10357185-83929.103571854
99836601255686.30309999-272026.303099992
101443586574546.371136885869039.628863115
1110730891253149.75231720-180060.752317196
12984885494274.737680744490610.262319256
1314052251202460.19281315202764.807186846
142271321165341.46309544-938209.463095436
159291181286984.47954236-357866.479542358
161071292530640.11723504540651.88276496
176388301056107.77824468-417277.778244681
188569561252366.42916306-395410.429163064
199924261203842.67011401-211416.670114009
204444771241327.49956187-796850.499561874
21857217463038.465479679394178.534520321
22711969597315.196599686114653.803400314
23702380800424.78601425-98044.7860142493
243585891231125.36217042-872536.362170424
25297978545395.355071211-247417.355071211
26585715408325.48877281177389.511227190
276579541150834.97351018-492880.973510182
28209458292760.834063558-83302.8340635581
29786690128919.463145567657770.536854433
30439798577240.069186843-137442.069186842
31688779181192.219151344507586.780848656
32574339505256.5543405869082.44565942
33741409262588.629833591478820.37016641
34597793290329.171914986307463.828085014
35644190758757.661321762-114567.661321762
363779341006650.19204737-628716.192047366
37640273439152.586005158201120.413994842
38697458442208.748823928255249.251176072
39550608635539.905816312-84931.9058163116
40207393467699.948980964-260306.948980964
41301607892494.023592718-590887.023592718
42345783469317.051537405-123534.051537405
43501749398440.137979007103308.862020993
44379983505812.472701587-125829.472701587
45387475185863.618582216201611.381417784
46377305227228.475457171150076.524542829
47370837638237.615061221-267400.615061221
48430866746370.19884005-315504.19884005
49469107400728.87891856068378.1210814396
50194493173176.48666609521316.5133339050
51530670420456.433054972110213.566945028
52518365646287.62614559-127922.626145590
53491303750051.880769504-258748.880769504
54527021422936.828340085104084.171659915
55233773657650.833809051-423877.833809051
56405972231834.557003177174137.442996823
5765292521919.5131042275631005.486895773
58446211276161.149249518170049.850750482
59341340252947.46091052688392.5390894736
60387699449778.843657147-62079.843657147

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6282154 & 5465120.34006103 & 817033.659938973 \tabularnewline
2 & 4321023 & 1794303.89492552 & 2526719.10507448 \tabularnewline
3 & 4111912 & 2897485.33419979 & 1214426.66580021 \tabularnewline
4 & 223193 & 2363030.63394254 & -2139837.63394254 \tabularnewline
5 & 1491348 & 1986792.32477968 & -495444.324779681 \tabularnewline
6 & 1629616 & 1457246.35475409 & 172369.645245908 \tabularnewline
7 & 1398893 & 1614137.52213104 & -215244.522131043 \tabularnewline
8 & 1926517 & 2010446.10357185 & -83929.103571854 \tabularnewline
9 & 983660 & 1255686.30309999 & -272026.303099992 \tabularnewline
10 & 1443586 & 574546.371136885 & 869039.628863115 \tabularnewline
11 & 1073089 & 1253149.75231720 & -180060.752317196 \tabularnewline
12 & 984885 & 494274.737680744 & 490610.262319256 \tabularnewline
13 & 1405225 & 1202460.19281315 & 202764.807186846 \tabularnewline
14 & 227132 & 1165341.46309544 & -938209.463095436 \tabularnewline
15 & 929118 & 1286984.47954236 & -357866.479542358 \tabularnewline
16 & 1071292 & 530640.11723504 & 540651.88276496 \tabularnewline
17 & 638830 & 1056107.77824468 & -417277.778244681 \tabularnewline
18 & 856956 & 1252366.42916306 & -395410.429163064 \tabularnewline
19 & 992426 & 1203842.67011401 & -211416.670114009 \tabularnewline
20 & 444477 & 1241327.49956187 & -796850.499561874 \tabularnewline
21 & 857217 & 463038.465479679 & 394178.534520321 \tabularnewline
22 & 711969 & 597315.196599686 & 114653.803400314 \tabularnewline
23 & 702380 & 800424.78601425 & -98044.7860142493 \tabularnewline
24 & 358589 & 1231125.36217042 & -872536.362170424 \tabularnewline
25 & 297978 & 545395.355071211 & -247417.355071211 \tabularnewline
26 & 585715 & 408325.48877281 & 177389.511227190 \tabularnewline
27 & 657954 & 1150834.97351018 & -492880.973510182 \tabularnewline
28 & 209458 & 292760.834063558 & -83302.8340635581 \tabularnewline
29 & 786690 & 128919.463145567 & 657770.536854433 \tabularnewline
30 & 439798 & 577240.069186843 & -137442.069186842 \tabularnewline
31 & 688779 & 181192.219151344 & 507586.780848656 \tabularnewline
32 & 574339 & 505256.55434058 & 69082.44565942 \tabularnewline
33 & 741409 & 262588.629833591 & 478820.37016641 \tabularnewline
34 & 597793 & 290329.171914986 & 307463.828085014 \tabularnewline
35 & 644190 & 758757.661321762 & -114567.661321762 \tabularnewline
36 & 377934 & 1006650.19204737 & -628716.192047366 \tabularnewline
37 & 640273 & 439152.586005158 & 201120.413994842 \tabularnewline
38 & 697458 & 442208.748823928 & 255249.251176072 \tabularnewline
39 & 550608 & 635539.905816312 & -84931.9058163116 \tabularnewline
40 & 207393 & 467699.948980964 & -260306.948980964 \tabularnewline
41 & 301607 & 892494.023592718 & -590887.023592718 \tabularnewline
42 & 345783 & 469317.051537405 & -123534.051537405 \tabularnewline
43 & 501749 & 398440.137979007 & 103308.862020993 \tabularnewline
44 & 379983 & 505812.472701587 & -125829.472701587 \tabularnewline
45 & 387475 & 185863.618582216 & 201611.381417784 \tabularnewline
46 & 377305 & 227228.475457171 & 150076.524542829 \tabularnewline
47 & 370837 & 638237.615061221 & -267400.615061221 \tabularnewline
48 & 430866 & 746370.19884005 & -315504.19884005 \tabularnewline
49 & 469107 & 400728.878918560 & 68378.1210814396 \tabularnewline
50 & 194493 & 173176.486666095 & 21316.5133339050 \tabularnewline
51 & 530670 & 420456.433054972 & 110213.566945028 \tabularnewline
52 & 518365 & 646287.62614559 & -127922.626145590 \tabularnewline
53 & 491303 & 750051.880769504 & -258748.880769504 \tabularnewline
54 & 527021 & 422936.828340085 & 104084.171659915 \tabularnewline
55 & 233773 & 657650.833809051 & -423877.833809051 \tabularnewline
56 & 405972 & 231834.557003177 & 174137.442996823 \tabularnewline
57 & 652925 & 21919.5131042275 & 631005.486895773 \tabularnewline
58 & 446211 & 276161.149249518 & 170049.850750482 \tabularnewline
59 & 341340 & 252947.460910526 & 88392.5390894736 \tabularnewline
60 & 387699 & 449778.843657147 & -62079.843657147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104766&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]6282154[/C][C]5465120.34006103[/C][C]817033.659938973[/C][/ROW]
[ROW][C]2[/C][C]4321023[/C][C]1794303.89492552[/C][C]2526719.10507448[/C][/ROW]
[ROW][C]3[/C][C]4111912[/C][C]2897485.33419979[/C][C]1214426.66580021[/C][/ROW]
[ROW][C]4[/C][C]223193[/C][C]2363030.63394254[/C][C]-2139837.63394254[/C][/ROW]
[ROW][C]5[/C][C]1491348[/C][C]1986792.32477968[/C][C]-495444.324779681[/C][/ROW]
[ROW][C]6[/C][C]1629616[/C][C]1457246.35475409[/C][C]172369.645245908[/C][/ROW]
[ROW][C]7[/C][C]1398893[/C][C]1614137.52213104[/C][C]-215244.522131043[/C][/ROW]
[ROW][C]8[/C][C]1926517[/C][C]2010446.10357185[/C][C]-83929.103571854[/C][/ROW]
[ROW][C]9[/C][C]983660[/C][C]1255686.30309999[/C][C]-272026.303099992[/C][/ROW]
[ROW][C]10[/C][C]1443586[/C][C]574546.371136885[/C][C]869039.628863115[/C][/ROW]
[ROW][C]11[/C][C]1073089[/C][C]1253149.75231720[/C][C]-180060.752317196[/C][/ROW]
[ROW][C]12[/C][C]984885[/C][C]494274.737680744[/C][C]490610.262319256[/C][/ROW]
[ROW][C]13[/C][C]1405225[/C][C]1202460.19281315[/C][C]202764.807186846[/C][/ROW]
[ROW][C]14[/C][C]227132[/C][C]1165341.46309544[/C][C]-938209.463095436[/C][/ROW]
[ROW][C]15[/C][C]929118[/C][C]1286984.47954236[/C][C]-357866.479542358[/C][/ROW]
[ROW][C]16[/C][C]1071292[/C][C]530640.11723504[/C][C]540651.88276496[/C][/ROW]
[ROW][C]17[/C][C]638830[/C][C]1056107.77824468[/C][C]-417277.778244681[/C][/ROW]
[ROW][C]18[/C][C]856956[/C][C]1252366.42916306[/C][C]-395410.429163064[/C][/ROW]
[ROW][C]19[/C][C]992426[/C][C]1203842.67011401[/C][C]-211416.670114009[/C][/ROW]
[ROW][C]20[/C][C]444477[/C][C]1241327.49956187[/C][C]-796850.499561874[/C][/ROW]
[ROW][C]21[/C][C]857217[/C][C]463038.465479679[/C][C]394178.534520321[/C][/ROW]
[ROW][C]22[/C][C]711969[/C][C]597315.196599686[/C][C]114653.803400314[/C][/ROW]
[ROW][C]23[/C][C]702380[/C][C]800424.78601425[/C][C]-98044.7860142493[/C][/ROW]
[ROW][C]24[/C][C]358589[/C][C]1231125.36217042[/C][C]-872536.362170424[/C][/ROW]
[ROW][C]25[/C][C]297978[/C][C]545395.355071211[/C][C]-247417.355071211[/C][/ROW]
[ROW][C]26[/C][C]585715[/C][C]408325.48877281[/C][C]177389.511227190[/C][/ROW]
[ROW][C]27[/C][C]657954[/C][C]1150834.97351018[/C][C]-492880.973510182[/C][/ROW]
[ROW][C]28[/C][C]209458[/C][C]292760.834063558[/C][C]-83302.8340635581[/C][/ROW]
[ROW][C]29[/C][C]786690[/C][C]128919.463145567[/C][C]657770.536854433[/C][/ROW]
[ROW][C]30[/C][C]439798[/C][C]577240.069186843[/C][C]-137442.069186842[/C][/ROW]
[ROW][C]31[/C][C]688779[/C][C]181192.219151344[/C][C]507586.780848656[/C][/ROW]
[ROW][C]32[/C][C]574339[/C][C]505256.55434058[/C][C]69082.44565942[/C][/ROW]
[ROW][C]33[/C][C]741409[/C][C]262588.629833591[/C][C]478820.37016641[/C][/ROW]
[ROW][C]34[/C][C]597793[/C][C]290329.171914986[/C][C]307463.828085014[/C][/ROW]
[ROW][C]35[/C][C]644190[/C][C]758757.661321762[/C][C]-114567.661321762[/C][/ROW]
[ROW][C]36[/C][C]377934[/C][C]1006650.19204737[/C][C]-628716.192047366[/C][/ROW]
[ROW][C]37[/C][C]640273[/C][C]439152.586005158[/C][C]201120.413994842[/C][/ROW]
[ROW][C]38[/C][C]697458[/C][C]442208.748823928[/C][C]255249.251176072[/C][/ROW]
[ROW][C]39[/C][C]550608[/C][C]635539.905816312[/C][C]-84931.9058163116[/C][/ROW]
[ROW][C]40[/C][C]207393[/C][C]467699.948980964[/C][C]-260306.948980964[/C][/ROW]
[ROW][C]41[/C][C]301607[/C][C]892494.023592718[/C][C]-590887.023592718[/C][/ROW]
[ROW][C]42[/C][C]345783[/C][C]469317.051537405[/C][C]-123534.051537405[/C][/ROW]
[ROW][C]43[/C][C]501749[/C][C]398440.137979007[/C][C]103308.862020993[/C][/ROW]
[ROW][C]44[/C][C]379983[/C][C]505812.472701587[/C][C]-125829.472701587[/C][/ROW]
[ROW][C]45[/C][C]387475[/C][C]185863.618582216[/C][C]201611.381417784[/C][/ROW]
[ROW][C]46[/C][C]377305[/C][C]227228.475457171[/C][C]150076.524542829[/C][/ROW]
[ROW][C]47[/C][C]370837[/C][C]638237.615061221[/C][C]-267400.615061221[/C][/ROW]
[ROW][C]48[/C][C]430866[/C][C]746370.19884005[/C][C]-315504.19884005[/C][/ROW]
[ROW][C]49[/C][C]469107[/C][C]400728.878918560[/C][C]68378.1210814396[/C][/ROW]
[ROW][C]50[/C][C]194493[/C][C]173176.486666095[/C][C]21316.5133339050[/C][/ROW]
[ROW][C]51[/C][C]530670[/C][C]420456.433054972[/C][C]110213.566945028[/C][/ROW]
[ROW][C]52[/C][C]518365[/C][C]646287.62614559[/C][C]-127922.626145590[/C][/ROW]
[ROW][C]53[/C][C]491303[/C][C]750051.880769504[/C][C]-258748.880769504[/C][/ROW]
[ROW][C]54[/C][C]527021[/C][C]422936.828340085[/C][C]104084.171659915[/C][/ROW]
[ROW][C]55[/C][C]233773[/C][C]657650.833809051[/C][C]-423877.833809051[/C][/ROW]
[ROW][C]56[/C][C]405972[/C][C]231834.557003177[/C][C]174137.442996823[/C][/ROW]
[ROW][C]57[/C][C]652925[/C][C]21919.5131042275[/C][C]631005.486895773[/C][/ROW]
[ROW][C]58[/C][C]446211[/C][C]276161.149249518[/C][C]170049.850750482[/C][/ROW]
[ROW][C]59[/C][C]341340[/C][C]252947.460910526[/C][C]88392.5390894736[/C][/ROW]
[ROW][C]60[/C][C]387699[/C][C]449778.843657147[/C][C]-62079.843657147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104766&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104766&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
162821545465120.34006103817033.659938973
243210231794303.894925522526719.10507448
341119122897485.334199791214426.66580021
42231932363030.63394254-2139837.63394254
514913481986792.32477968-495444.324779681
616296161457246.35475409172369.645245908
713988931614137.52213104-215244.522131043
819265172010446.10357185-83929.103571854
99836601255686.30309999-272026.303099992
101443586574546.371136885869039.628863115
1110730891253149.75231720-180060.752317196
12984885494274.737680744490610.262319256
1314052251202460.19281315202764.807186846
142271321165341.46309544-938209.463095436
159291181286984.47954236-357866.479542358
161071292530640.11723504540651.88276496
176388301056107.77824468-417277.778244681
188569561252366.42916306-395410.429163064
199924261203842.67011401-211416.670114009
204444771241327.49956187-796850.499561874
21857217463038.465479679394178.534520321
22711969597315.196599686114653.803400314
23702380800424.78601425-98044.7860142493
243585891231125.36217042-872536.362170424
25297978545395.355071211-247417.355071211
26585715408325.48877281177389.511227190
276579541150834.97351018-492880.973510182
28209458292760.834063558-83302.8340635581
29786690128919.463145567657770.536854433
30439798577240.069186843-137442.069186842
31688779181192.219151344507586.780848656
32574339505256.5543405869082.44565942
33741409262588.629833591478820.37016641
34597793290329.171914986307463.828085014
35644190758757.661321762-114567.661321762
363779341006650.19204737-628716.192047366
37640273439152.586005158201120.413994842
38697458442208.748823928255249.251176072
39550608635539.905816312-84931.9058163116
40207393467699.948980964-260306.948980964
41301607892494.023592718-590887.023592718
42345783469317.051537405-123534.051537405
43501749398440.137979007103308.862020993
44379983505812.472701587-125829.472701587
45387475185863.618582216201611.381417784
46377305227228.475457171150076.524542829
47370837638237.615061221-267400.615061221
48430866746370.19884005-315504.19884005
49469107400728.87891856068378.1210814396
50194493173176.48666609521316.5133339050
51530670420456.433054972110213.566945028
52518365646287.62614559-127922.626145590
53491303750051.880769504-258748.880769504
54527021422936.828340085104084.171659915
55233773657650.833809051-423877.833809051
56405972231834.557003177174137.442996823
5765292521919.5131042275631005.486895773
58446211276161.149249518170049.850750482
59341340252947.46091052688392.5390894736
60387699449778.843657147-62079.843657147







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.999986289622672.74207546592847e-051.37103773296423e-05
80.99999997498595.00281992988093e-082.50140996494046e-08
90.9999999280005451.43998910371049e-077.19994551855243e-08
100.999999999828633.42739934188767e-101.71369967094384e-10
110.9999999998535462.92907768330628e-101.46453884165314e-10
120.999999999901051.97898417300082e-109.89492086500408e-11
130.999999999999843.21049579706999e-131.60524789853499e-13
140.9999999999999941.23490097576875e-146.17450487884374e-15
150.9999999999999992.82308629336347e-151.41154314668174e-15
1611.74928813788188e-168.74644068940939e-17
1715.9473933458007e-172.97369667290035e-17
1811.84647638129523e-179.23238190647616e-18
1914.17225952419241e-172.08612976209621e-17
2012.85290061122660e-171.42645030561330e-17
2111.62802092691360e-178.14010463456801e-18
2217.38364354121732e-173.69182177060866e-17
2312.81086919613416e-161.40543459806708e-16
2413.98336256364578e-161.99168128182289e-16
2511.20285731834408e-156.01428659172041e-16
260.9999999999999975.71438076303761e-152.85719038151880e-15
270.9999999999999951.06924773859256e-145.3462386929628e-15
280.9999999999999959.42329924843335e-154.71164962421668e-15
290.9999999999999967.00685719276783e-153.50342859638392e-15
300.9999999999999784.35056319987513e-142.17528159993756e-14
310.9999999999999558.97137249080236e-144.48568624540118e-14
320.9999999999997584.84834162084438e-132.42417081042219e-13
330.9999999999997794.42664483307749e-132.21332241653874e-13
340.9999999999991951.61012538951493e-128.05062694757464e-13
350.9999999999958638.27318569482823e-124.13659284741412e-12
360.9999999999808963.82088779317985e-111.91044389658993e-11
370.9999999999681326.37362755131815e-113.18681377565907e-11
380.999999999982313.53802332311805e-111.76901166155902e-11
390.9999999999213251.57350722456759e-107.86753612283793e-11
400.9999999998369783.26044716003191e-101.63022358001595e-10
410.9999999993064631.38707481634215e-096.93537408171077e-10
420.9999999960972437.80551440642879e-093.90275720321439e-09
430.9999999799389374.01221263343035e-082.00610631671517e-08
440.999999887181952.25636100351071e-071.12818050175535e-07
450.999999404210861.19157828047061e-065.95789140235305e-07
460.99999704441915.91116180190129e-062.95558090095064e-06
470.9999868037434122.63925131760438e-051.31962565880219e-05
480.9999363509698690.0001272980602623696.36490301311846e-05
490.9997001985807820.0005996028384356570.000299801419217829
500.9997112194817540.0005775610364910580.000288780518245529
510.9988228316472140.002354336705571340.00117716835278567
520.998985582851770.002028834296461070.00101441714823054
530.9949431396723070.01011372065538520.0050568603276926

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.99998628962267 & 2.74207546592847e-05 & 1.37103773296423e-05 \tabularnewline
8 & 0.9999999749859 & 5.00281992988093e-08 & 2.50140996494046e-08 \tabularnewline
9 & 0.999999928000545 & 1.43998910371049e-07 & 7.19994551855243e-08 \tabularnewline
10 & 0.99999999982863 & 3.42739934188767e-10 & 1.71369967094384e-10 \tabularnewline
11 & 0.999999999853546 & 2.92907768330628e-10 & 1.46453884165314e-10 \tabularnewline
12 & 0.99999999990105 & 1.97898417300082e-10 & 9.89492086500408e-11 \tabularnewline
13 & 0.99999999999984 & 3.21049579706999e-13 & 1.60524789853499e-13 \tabularnewline
14 & 0.999999999999994 & 1.23490097576875e-14 & 6.17450487884374e-15 \tabularnewline
15 & 0.999999999999999 & 2.82308629336347e-15 & 1.41154314668174e-15 \tabularnewline
16 & 1 & 1.74928813788188e-16 & 8.74644068940939e-17 \tabularnewline
17 & 1 & 5.9473933458007e-17 & 2.97369667290035e-17 \tabularnewline
18 & 1 & 1.84647638129523e-17 & 9.23238190647616e-18 \tabularnewline
19 & 1 & 4.17225952419241e-17 & 2.08612976209621e-17 \tabularnewline
20 & 1 & 2.85290061122660e-17 & 1.42645030561330e-17 \tabularnewline
21 & 1 & 1.62802092691360e-17 & 8.14010463456801e-18 \tabularnewline
22 & 1 & 7.38364354121732e-17 & 3.69182177060866e-17 \tabularnewline
23 & 1 & 2.81086919613416e-16 & 1.40543459806708e-16 \tabularnewline
24 & 1 & 3.98336256364578e-16 & 1.99168128182289e-16 \tabularnewline
25 & 1 & 1.20285731834408e-15 & 6.01428659172041e-16 \tabularnewline
26 & 0.999999999999997 & 5.71438076303761e-15 & 2.85719038151880e-15 \tabularnewline
27 & 0.999999999999995 & 1.06924773859256e-14 & 5.3462386929628e-15 \tabularnewline
28 & 0.999999999999995 & 9.42329924843335e-15 & 4.71164962421668e-15 \tabularnewline
29 & 0.999999999999996 & 7.00685719276783e-15 & 3.50342859638392e-15 \tabularnewline
30 & 0.999999999999978 & 4.35056319987513e-14 & 2.17528159993756e-14 \tabularnewline
31 & 0.999999999999955 & 8.97137249080236e-14 & 4.48568624540118e-14 \tabularnewline
32 & 0.999999999999758 & 4.84834162084438e-13 & 2.42417081042219e-13 \tabularnewline
33 & 0.999999999999779 & 4.42664483307749e-13 & 2.21332241653874e-13 \tabularnewline
34 & 0.999999999999195 & 1.61012538951493e-12 & 8.05062694757464e-13 \tabularnewline
35 & 0.999999999995863 & 8.27318569482823e-12 & 4.13659284741412e-12 \tabularnewline
36 & 0.999999999980896 & 3.82088779317985e-11 & 1.91044389658993e-11 \tabularnewline
37 & 0.999999999968132 & 6.37362755131815e-11 & 3.18681377565907e-11 \tabularnewline
38 & 0.99999999998231 & 3.53802332311805e-11 & 1.76901166155902e-11 \tabularnewline
39 & 0.999999999921325 & 1.57350722456759e-10 & 7.86753612283793e-11 \tabularnewline
40 & 0.999999999836978 & 3.26044716003191e-10 & 1.63022358001595e-10 \tabularnewline
41 & 0.999999999306463 & 1.38707481634215e-09 & 6.93537408171077e-10 \tabularnewline
42 & 0.999999996097243 & 7.80551440642879e-09 & 3.90275720321439e-09 \tabularnewline
43 & 0.999999979938937 & 4.01221263343035e-08 & 2.00610631671517e-08 \tabularnewline
44 & 0.99999988718195 & 2.25636100351071e-07 & 1.12818050175535e-07 \tabularnewline
45 & 0.99999940421086 & 1.19157828047061e-06 & 5.95789140235305e-07 \tabularnewline
46 & 0.9999970444191 & 5.91116180190129e-06 & 2.95558090095064e-06 \tabularnewline
47 & 0.999986803743412 & 2.63925131760438e-05 & 1.31962565880219e-05 \tabularnewline
48 & 0.999936350969869 & 0.000127298060262369 & 6.36490301311846e-05 \tabularnewline
49 & 0.999700198580782 & 0.000599602838435657 & 0.000299801419217829 \tabularnewline
50 & 0.999711219481754 & 0.000577561036491058 & 0.000288780518245529 \tabularnewline
51 & 0.998822831647214 & 0.00235433670557134 & 0.00117716835278567 \tabularnewline
52 & 0.99898558285177 & 0.00202883429646107 & 0.00101441714823054 \tabularnewline
53 & 0.994943139672307 & 0.0101137206553852 & 0.0050568603276926 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104766&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]0.99998628962267[/C][C]2.74207546592847e-05[/C][C]1.37103773296423e-05[/C][/ROW]
[ROW][C]8[/C][C]0.9999999749859[/C][C]5.00281992988093e-08[/C][C]2.50140996494046e-08[/C][/ROW]
[ROW][C]9[/C][C]0.999999928000545[/C][C]1.43998910371049e-07[/C][C]7.19994551855243e-08[/C][/ROW]
[ROW][C]10[/C][C]0.99999999982863[/C][C]3.42739934188767e-10[/C][C]1.71369967094384e-10[/C][/ROW]
[ROW][C]11[/C][C]0.999999999853546[/C][C]2.92907768330628e-10[/C][C]1.46453884165314e-10[/C][/ROW]
[ROW][C]12[/C][C]0.99999999990105[/C][C]1.97898417300082e-10[/C][C]9.89492086500408e-11[/C][/ROW]
[ROW][C]13[/C][C]0.99999999999984[/C][C]3.21049579706999e-13[/C][C]1.60524789853499e-13[/C][/ROW]
[ROW][C]14[/C][C]0.999999999999994[/C][C]1.23490097576875e-14[/C][C]6.17450487884374e-15[/C][/ROW]
[ROW][C]15[/C][C]0.999999999999999[/C][C]2.82308629336347e-15[/C][C]1.41154314668174e-15[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]1.74928813788188e-16[/C][C]8.74644068940939e-17[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]5.9473933458007e-17[/C][C]2.97369667290035e-17[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]1.84647638129523e-17[/C][C]9.23238190647616e-18[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]4.17225952419241e-17[/C][C]2.08612976209621e-17[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]2.85290061122660e-17[/C][C]1.42645030561330e-17[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]1.62802092691360e-17[/C][C]8.14010463456801e-18[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]7.38364354121732e-17[/C][C]3.69182177060866e-17[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]2.81086919613416e-16[/C][C]1.40543459806708e-16[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]3.98336256364578e-16[/C][C]1.99168128182289e-16[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]1.20285731834408e-15[/C][C]6.01428659172041e-16[/C][/ROW]
[ROW][C]26[/C][C]0.999999999999997[/C][C]5.71438076303761e-15[/C][C]2.85719038151880e-15[/C][/ROW]
[ROW][C]27[/C][C]0.999999999999995[/C][C]1.06924773859256e-14[/C][C]5.3462386929628e-15[/C][/ROW]
[ROW][C]28[/C][C]0.999999999999995[/C][C]9.42329924843335e-15[/C][C]4.71164962421668e-15[/C][/ROW]
[ROW][C]29[/C][C]0.999999999999996[/C][C]7.00685719276783e-15[/C][C]3.50342859638392e-15[/C][/ROW]
[ROW][C]30[/C][C]0.999999999999978[/C][C]4.35056319987513e-14[/C][C]2.17528159993756e-14[/C][/ROW]
[ROW][C]31[/C][C]0.999999999999955[/C][C]8.97137249080236e-14[/C][C]4.48568624540118e-14[/C][/ROW]
[ROW][C]32[/C][C]0.999999999999758[/C][C]4.84834162084438e-13[/C][C]2.42417081042219e-13[/C][/ROW]
[ROW][C]33[/C][C]0.999999999999779[/C][C]4.42664483307749e-13[/C][C]2.21332241653874e-13[/C][/ROW]
[ROW][C]34[/C][C]0.999999999999195[/C][C]1.61012538951493e-12[/C][C]8.05062694757464e-13[/C][/ROW]
[ROW][C]35[/C][C]0.999999999995863[/C][C]8.27318569482823e-12[/C][C]4.13659284741412e-12[/C][/ROW]
[ROW][C]36[/C][C]0.999999999980896[/C][C]3.82088779317985e-11[/C][C]1.91044389658993e-11[/C][/ROW]
[ROW][C]37[/C][C]0.999999999968132[/C][C]6.37362755131815e-11[/C][C]3.18681377565907e-11[/C][/ROW]
[ROW][C]38[/C][C]0.99999999998231[/C][C]3.53802332311805e-11[/C][C]1.76901166155902e-11[/C][/ROW]
[ROW][C]39[/C][C]0.999999999921325[/C][C]1.57350722456759e-10[/C][C]7.86753612283793e-11[/C][/ROW]
[ROW][C]40[/C][C]0.999999999836978[/C][C]3.26044716003191e-10[/C][C]1.63022358001595e-10[/C][/ROW]
[ROW][C]41[/C][C]0.999999999306463[/C][C]1.38707481634215e-09[/C][C]6.93537408171077e-10[/C][/ROW]
[ROW][C]42[/C][C]0.999999996097243[/C][C]7.80551440642879e-09[/C][C]3.90275720321439e-09[/C][/ROW]
[ROW][C]43[/C][C]0.999999979938937[/C][C]4.01221263343035e-08[/C][C]2.00610631671517e-08[/C][/ROW]
[ROW][C]44[/C][C]0.99999988718195[/C][C]2.25636100351071e-07[/C][C]1.12818050175535e-07[/C][/ROW]
[ROW][C]45[/C][C]0.99999940421086[/C][C]1.19157828047061e-06[/C][C]5.95789140235305e-07[/C][/ROW]
[ROW][C]46[/C][C]0.9999970444191[/C][C]5.91116180190129e-06[/C][C]2.95558090095064e-06[/C][/ROW]
[ROW][C]47[/C][C]0.999986803743412[/C][C]2.63925131760438e-05[/C][C]1.31962565880219e-05[/C][/ROW]
[ROW][C]48[/C][C]0.999936350969869[/C][C]0.000127298060262369[/C][C]6.36490301311846e-05[/C][/ROW]
[ROW][C]49[/C][C]0.999700198580782[/C][C]0.000599602838435657[/C][C]0.000299801419217829[/C][/ROW]
[ROW][C]50[/C][C]0.999711219481754[/C][C]0.000577561036491058[/C][C]0.000288780518245529[/C][/ROW]
[ROW][C]51[/C][C]0.998822831647214[/C][C]0.00235433670557134[/C][C]0.00117716835278567[/C][/ROW]
[ROW][C]52[/C][C]0.99898558285177[/C][C]0.00202883429646107[/C][C]0.00101441714823054[/C][/ROW]
[ROW][C]53[/C][C]0.994943139672307[/C][C]0.0101137206553852[/C][C]0.0050568603276926[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104766&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104766&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
70.999986289622672.74207546592847e-051.37103773296423e-05
80.99999997498595.00281992988093e-082.50140996494046e-08
90.9999999280005451.43998910371049e-077.19994551855243e-08
100.999999999828633.42739934188767e-101.71369967094384e-10
110.9999999998535462.92907768330628e-101.46453884165314e-10
120.999999999901051.97898417300082e-109.89492086500408e-11
130.999999999999843.21049579706999e-131.60524789853499e-13
140.9999999999999941.23490097576875e-146.17450487884374e-15
150.9999999999999992.82308629336347e-151.41154314668174e-15
1611.74928813788188e-168.74644068940939e-17
1715.9473933458007e-172.97369667290035e-17
1811.84647638129523e-179.23238190647616e-18
1914.17225952419241e-172.08612976209621e-17
2012.85290061122660e-171.42645030561330e-17
2111.62802092691360e-178.14010463456801e-18
2217.38364354121732e-173.69182177060866e-17
2312.81086919613416e-161.40543459806708e-16
2413.98336256364578e-161.99168128182289e-16
2511.20285731834408e-156.01428659172041e-16
260.9999999999999975.71438076303761e-152.85719038151880e-15
270.9999999999999951.06924773859256e-145.3462386929628e-15
280.9999999999999959.42329924843335e-154.71164962421668e-15
290.9999999999999967.00685719276783e-153.50342859638392e-15
300.9999999999999784.35056319987513e-142.17528159993756e-14
310.9999999999999558.97137249080236e-144.48568624540118e-14
320.9999999999997584.84834162084438e-132.42417081042219e-13
330.9999999999997794.42664483307749e-132.21332241653874e-13
340.9999999999991951.61012538951493e-128.05062694757464e-13
350.9999999999958638.27318569482823e-124.13659284741412e-12
360.9999999999808963.82088779317985e-111.91044389658993e-11
370.9999999999681326.37362755131815e-113.18681377565907e-11
380.999999999982313.53802332311805e-111.76901166155902e-11
390.9999999999213251.57350722456759e-107.86753612283793e-11
400.9999999998369783.26044716003191e-101.63022358001595e-10
410.9999999993064631.38707481634215e-096.93537408171077e-10
420.9999999960972437.80551440642879e-093.90275720321439e-09
430.9999999799389374.01221263343035e-082.00610631671517e-08
440.999999887181952.25636100351071e-071.12818050175535e-07
450.999999404210861.19157828047061e-065.95789140235305e-07
460.99999704441915.91116180190129e-062.95558090095064e-06
470.9999868037434122.63925131760438e-051.31962565880219e-05
480.9999363509698690.0001272980602623696.36490301311846e-05
490.9997001985807820.0005996028384356570.000299801419217829
500.9997112194817540.0005775610364910580.000288780518245529
510.9988228316472140.002354336705571340.00117716835278567
520.998985582851770.002028834296461070.00101441714823054
530.9949431396723070.01011372065538520.0050568603276926







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level460.978723404255319NOK
5% type I error level471NOK
10% type I error level471NOK

\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 & 46 & 0.978723404255319 & NOK \tabularnewline
5% type I error level & 47 & 1 & NOK \tabularnewline
10% type I error level & 47 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104766&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]46[/C][C]0.978723404255319[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]47[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]47[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104766&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104766&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 level460.978723404255319NOK
5% type I error level471NOK
10% type I error level471NOK



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