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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 computationThu, 24 Nov 2011 09:43:07 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t13221459508scv28di0bs63rm.htm/, Retrieved Fri, 29 Mar 2024 12:06:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146889, Retrieved Fri, 29 Mar 2024 12:06:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple regressi...] [2011-11-24 14:43:07] [0652e0694c2cbf138ee0a1c8d686a8e4] [Current]
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Dataseries X:
10539,51	2981,85	11394,84	10407	44,23
10723,78	3080,58	11545,71	10463	45,85
10682,06	3106,22	11809,38	10556	53,38
10283,19	3119,31	11395,64	10646	53,26
10377,18	3061,26	11082,38	10702	51,8
10486,64	3097,31	11402,75	11353	55,3
10545,38	3161,69	11716,87	11346	57,81
10554,27	3257,16	12204,98	11451	63,96
10532,54	3277,01	12986,62	11964	63,77
10324,31	3295,32	13392,79	12574	59,15
10695,25	3363,99	14368,05	13031	56,12
10827,81	3494,17	15650,83	13812	57,42
10872,48	3667,03	16102,64	14544	63,52
10971,19	3813,06	16187,64	14931	61,71
11145,65	3917,96	16311,54	14886	63,01
11234,68	3895,51	17232,97	16005	68,18
11333,88	3801,06	16397,83	17064	72,03
10997,97	3570,12	14990,31	15168	69,75
11036,89	3701,61	15147,55	16050	74,41
11257,35	3862,27	15786,78	15839	74,33
11533,59	3970,1	15934,09	15137	64,24
11963,12	4138,52	16519,44	14954	60,03
12185,15	4199,75	16101,07	15648	59,44
12377,62	4290,89	16775,08	15305	62,5
12512,89	4443,91	17286,32	15579	55,04
12631,48	4502,64	17741,23	16348	58,34
12268,53	4356,98	17128,37	15928	61,92
12754,8	4591,27	17460,53	16171	67,65
13407,75	4696,96	17611,14	15937	67,68
13480,21	4621,4	18001,37	15713	70,3
13673,28	4562,84	17974,77	15594	75,26
13239,71	4202,52	16460,95	15683	71,44
13557,69	4296,49	16235,39	16438	76,36
13901,28	4435,23	16903,36	17032	81,71
13200,58	4105,18	15543,76	17696	92,6
13406,97	4116,68	15532,18	17745	90,6
12538,12	3844,49	13731,31	19394	92,23
12419,57	3720,98	13547,84	20148	94,09
12193,88	3674,4	12602,93	20108	102,79
12656,63	3857,62	13357,7	18584	109,65
12812,48	3801,06	13995,33	18441	124,05
12056,67	3504,37	14084,6	18391	132,69
11322,38	3032,6	13168,91	19178	135,81
11530,75	3047,03	12989,35	18079	116,07
11114,08	2962,34	12123,53	18483	101,42
9181,73	2197,82	9117,03	19644	75,73
8614,55	2014,45	8531,45	19195	55,48
8595,56	1862,83	8460,94	19650	43,8
8396,2	1905,41	8331,49	20830	45,29
7690,5	1810,99	7694,78	23595	44,01
7235,47	1670,07	7764,58	22937	47,48
7992,12	1864,44	8767,96	21814	51,07
8398,37	2052,02	9304,43	21928	57,84
8593	2029,6	9810,31	21777	69,04
8679,75	2070,83	9691,12	21383	65,61
9374,63	2293,41	10430,35	21467	72,87
9634,97	2443,27	10302,87	22052	68,41
9857,34	2513,17	10066,24	22680	73,25
10238,83	2466,92	9633,83	24320	77,43
10433,44	2502,66	10169,02	24977	75,28
10471,24	2539,91	10661,62	25204	77,33




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146889&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146889&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146889&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 time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
DowJones[t] = + 4069.84036496726 + 2.69563034221495Bel20[t] -0.274651783925739Nikkei[t] + 0.0282335738045733Gold[t] + 16.2381732131674Brent[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
DowJones[t] =  +  4069.84036496726 +  2.69563034221495Bel20[t] -0.274651783925739Nikkei[t] +  0.0282335738045733Gold[t] +  16.2381732131674Brent[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146889&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]DowJones[t] =  +  4069.84036496726 +  2.69563034221495Bel20[t] -0.274651783925739Nikkei[t] +  0.0282335738045733Gold[t] +  16.2381732131674Brent[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146889&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146889&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
DowJones[t] = + 4069.84036496726 + 2.69563034221495Bel20[t] -0.274651783925739Nikkei[t] + 0.0282335738045733Gold[t] + 16.2381732131674Brent[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4069.84036496726393.52068110.342100
Bel202.695630342214950.21758912.388700
Nikkei-0.2746517839257390.057291-4.7941.2e-056e-06
Gold0.02823357380457330.0156531.80370.0766590.03833
Brent16.23817321316742.6303076.173500

\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) & 4069.84036496726 & 393.520681 & 10.3421 & 0 & 0 \tabularnewline
Bel20 & 2.69563034221495 & 0.217589 & 12.3887 & 0 & 0 \tabularnewline
Nikkei & -0.274651783925739 & 0.057291 & -4.794 & 1.2e-05 & 6e-06 \tabularnewline
Gold & 0.0282335738045733 & 0.015653 & 1.8037 & 0.076659 & 0.03833 \tabularnewline
Brent & 16.2381732131674 & 2.630307 & 6.1735 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146889&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]4069.84036496726[/C][C]393.520681[/C][C]10.3421[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Bel20[/C][C]2.69563034221495[/C][C]0.217589[/C][C]12.3887[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Nikkei[/C][C]-0.274651783925739[/C][C]0.057291[/C][C]-4.794[/C][C]1.2e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]Gold[/C][C]0.0282335738045733[/C][C]0.015653[/C][C]1.8037[/C][C]0.076659[/C][C]0.03833[/C][/ROW]
[ROW][C]Brent[/C][C]16.2381732131674[/C][C]2.630307[/C][C]6.1735[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146889&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146889&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)4069.84036496726393.52068110.342100
Bel202.695630342214950.21758912.388700
Nikkei-0.2746517839257390.057291-4.7941.2e-056e-06
Gold0.02823357380457330.0156531.80370.0766590.03833
Brent16.23817321316742.6303076.173500







Multiple Linear Regression - Regression Statistics
Multiple R0.976763607583037
R-squared0.954067145098628
Adjusted R-squared0.950786226891387
F-TEST (value)290.792724729634
F-TEST (DF numerator)4
F-TEST (DF denominator)56
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation363.171721297362
Sum Squared Residuals7386047.15240499

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.976763607583037 \tabularnewline
R-squared & 0.954067145098628 \tabularnewline
Adjusted R-squared & 0.950786226891387 \tabularnewline
F-TEST (value) & 290.792724729634 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 363.171721297362 \tabularnewline
Sum Squared Residuals & 7386047.15240499 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146889&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.976763607583037[/C][/ROW]
[ROW][C]R-squared[/C][C]0.954067145098628[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.950786226891387[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]290.792724729634[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/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]363.171721297362[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7386047.15240499[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146889&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146889&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 R0.976763607583037
R-squared0.954067145098628
Adjusted R-squared0.950786226891387
F-TEST (value)290.792724729634
F-TEST (DF numerator)4
F-TEST (DF denominator)56
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation363.171721297362
Sum Squared Residuals7386047.15240499







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110539.519990.23377115513549.276228844869
210723.7810242.8235609395480.95643906047
310682.0610364.4212537052317.638746294804
410283.1910513.9339248231-230.743924823056
510377.1810421.3633485319-44.183348531887
610486.6410505.7642931453-19.1242931453103
710545.3810633.5955359588-88.2155359587744
810554.2710859.7163729885-305.446372988502
910532.5410709.944385345-177.404385344999
1010324.3110589.9481816098-265.638181609795
1110695.2510470.9012968111224.348703188928
1210827.8110512.6606856948315.149314305154
1310872.4810974.2567567799-101.776756779903
1410971.1911326.0895535664-354.899553566401
1511145.6511594.6709347923-449.020934792262
1611234.6811396.6263649462-161.946364946236
1711333.8811463.8130914815-129.933091481513
1810997.9711137.3082083021-139.338208302055
1911036.8911549.1422947644-512.25229476441
2011257.3511799.400267776-542.050267775995
2111533.5911865.9479967553-332.357996755263
2211963.1212085.6491840365-122.529184036504
2312185.1512375.6222747559-190.472274755941
2412377.6212476.1886694789-98.5686694789479
2512512.8912634.8602734827-121.97027348271
2612631.4812743.5303903145-112.050390314507
2712268.5312565.4825260694-296.95252606942
2812754.813205.7189133442-450.918913344152
2913407.7513443.1352679619-35.3852679619183
3013480.2113168.4957669491311.714233050911
3113673.2813095.126935416578.153064584028
3213239.7112480.0937404459759.616259554148
3313557.6912896.5607405173661.129259482686
3413901.2813190.7383116177710.541688382298
3513200.5812870.0428818927330.537118107277
3613406.9712873.1301971761533.839802823855
3712538.1212707.0441179882-168.924117988204
3812419.5712475.9882940432-56.4182940432316
3912193.8812750.0898138545-556.209813854503
4012656.6313105.0501799657-448.420179965655
4112812.4813007.251404041-194.771404040964
4212056.6712321.8528109297-265.182810929696
4311322.3811374.5140994152-52.1340994151898
4411530.7511111.1582827359419.591717264093
4511114.0810894.1814828565219.898517143549
469181.739274.67927133985-92.9492713398492
478614.558599.7122449142314.8377550857654
488595.568033.55088266349562.009117336507
498396.28241.39499124121154.805008758792
507690.58219.02808152942-528.52808152942
517235.477857.75792867275-622.287928672753
527992.128132.7162297864-140.596229786402
538398.378604.17118692331-205.801186923305
5485938582.3985805414810.6014194585216
558679.758659.4542034769420.295796523057
569374.639176.6775245429197.952475457096
579634.979559.7516851870475.2183148129612
589857.349909.49054043921-52.1505404392132
5910238.8310017.7584400696221.07155993036
6010433.449950.74676584248482.69323415752
6110471.249955.5628036688515.6771963312

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10539.51 & 9990.23377115513 & 549.276228844869 \tabularnewline
2 & 10723.78 & 10242.8235609395 & 480.95643906047 \tabularnewline
3 & 10682.06 & 10364.4212537052 & 317.638746294804 \tabularnewline
4 & 10283.19 & 10513.9339248231 & -230.743924823056 \tabularnewline
5 & 10377.18 & 10421.3633485319 & -44.183348531887 \tabularnewline
6 & 10486.64 & 10505.7642931453 & -19.1242931453103 \tabularnewline
7 & 10545.38 & 10633.5955359588 & -88.2155359587744 \tabularnewline
8 & 10554.27 & 10859.7163729885 & -305.446372988502 \tabularnewline
9 & 10532.54 & 10709.944385345 & -177.404385344999 \tabularnewline
10 & 10324.31 & 10589.9481816098 & -265.638181609795 \tabularnewline
11 & 10695.25 & 10470.9012968111 & 224.348703188928 \tabularnewline
12 & 10827.81 & 10512.6606856948 & 315.149314305154 \tabularnewline
13 & 10872.48 & 10974.2567567799 & -101.776756779903 \tabularnewline
14 & 10971.19 & 11326.0895535664 & -354.899553566401 \tabularnewline
15 & 11145.65 & 11594.6709347923 & -449.020934792262 \tabularnewline
16 & 11234.68 & 11396.6263649462 & -161.946364946236 \tabularnewline
17 & 11333.88 & 11463.8130914815 & -129.933091481513 \tabularnewline
18 & 10997.97 & 11137.3082083021 & -139.338208302055 \tabularnewline
19 & 11036.89 & 11549.1422947644 & -512.25229476441 \tabularnewline
20 & 11257.35 & 11799.400267776 & -542.050267775995 \tabularnewline
21 & 11533.59 & 11865.9479967553 & -332.357996755263 \tabularnewline
22 & 11963.12 & 12085.6491840365 & -122.529184036504 \tabularnewline
23 & 12185.15 & 12375.6222747559 & -190.472274755941 \tabularnewline
24 & 12377.62 & 12476.1886694789 & -98.5686694789479 \tabularnewline
25 & 12512.89 & 12634.8602734827 & -121.97027348271 \tabularnewline
26 & 12631.48 & 12743.5303903145 & -112.050390314507 \tabularnewline
27 & 12268.53 & 12565.4825260694 & -296.95252606942 \tabularnewline
28 & 12754.8 & 13205.7189133442 & -450.918913344152 \tabularnewline
29 & 13407.75 & 13443.1352679619 & -35.3852679619183 \tabularnewline
30 & 13480.21 & 13168.4957669491 & 311.714233050911 \tabularnewline
31 & 13673.28 & 13095.126935416 & 578.153064584028 \tabularnewline
32 & 13239.71 & 12480.0937404459 & 759.616259554148 \tabularnewline
33 & 13557.69 & 12896.5607405173 & 661.129259482686 \tabularnewline
34 & 13901.28 & 13190.7383116177 & 710.541688382298 \tabularnewline
35 & 13200.58 & 12870.0428818927 & 330.537118107277 \tabularnewline
36 & 13406.97 & 12873.1301971761 & 533.839802823855 \tabularnewline
37 & 12538.12 & 12707.0441179882 & -168.924117988204 \tabularnewline
38 & 12419.57 & 12475.9882940432 & -56.4182940432316 \tabularnewline
39 & 12193.88 & 12750.0898138545 & -556.209813854503 \tabularnewline
40 & 12656.63 & 13105.0501799657 & -448.420179965655 \tabularnewline
41 & 12812.48 & 13007.251404041 & -194.771404040964 \tabularnewline
42 & 12056.67 & 12321.8528109297 & -265.182810929696 \tabularnewline
43 & 11322.38 & 11374.5140994152 & -52.1340994151898 \tabularnewline
44 & 11530.75 & 11111.1582827359 & 419.591717264093 \tabularnewline
45 & 11114.08 & 10894.1814828565 & 219.898517143549 \tabularnewline
46 & 9181.73 & 9274.67927133985 & -92.9492713398492 \tabularnewline
47 & 8614.55 & 8599.71224491423 & 14.8377550857654 \tabularnewline
48 & 8595.56 & 8033.55088266349 & 562.009117336507 \tabularnewline
49 & 8396.2 & 8241.39499124121 & 154.805008758792 \tabularnewline
50 & 7690.5 & 8219.02808152942 & -528.52808152942 \tabularnewline
51 & 7235.47 & 7857.75792867275 & -622.287928672753 \tabularnewline
52 & 7992.12 & 8132.7162297864 & -140.596229786402 \tabularnewline
53 & 8398.37 & 8604.17118692331 & -205.801186923305 \tabularnewline
54 & 8593 & 8582.39858054148 & 10.6014194585216 \tabularnewline
55 & 8679.75 & 8659.45420347694 & 20.295796523057 \tabularnewline
56 & 9374.63 & 9176.6775245429 & 197.952475457096 \tabularnewline
57 & 9634.97 & 9559.75168518704 & 75.2183148129612 \tabularnewline
58 & 9857.34 & 9909.49054043921 & -52.1505404392132 \tabularnewline
59 & 10238.83 & 10017.7584400696 & 221.07155993036 \tabularnewline
60 & 10433.44 & 9950.74676584248 & 482.69323415752 \tabularnewline
61 & 10471.24 & 9955.5628036688 & 515.6771963312 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146889&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]10539.51[/C][C]9990.23377115513[/C][C]549.276228844869[/C][/ROW]
[ROW][C]2[/C][C]10723.78[/C][C]10242.8235609395[/C][C]480.95643906047[/C][/ROW]
[ROW][C]3[/C][C]10682.06[/C][C]10364.4212537052[/C][C]317.638746294804[/C][/ROW]
[ROW][C]4[/C][C]10283.19[/C][C]10513.9339248231[/C][C]-230.743924823056[/C][/ROW]
[ROW][C]5[/C][C]10377.18[/C][C]10421.3633485319[/C][C]-44.183348531887[/C][/ROW]
[ROW][C]6[/C][C]10486.64[/C][C]10505.7642931453[/C][C]-19.1242931453103[/C][/ROW]
[ROW][C]7[/C][C]10545.38[/C][C]10633.5955359588[/C][C]-88.2155359587744[/C][/ROW]
[ROW][C]8[/C][C]10554.27[/C][C]10859.7163729885[/C][C]-305.446372988502[/C][/ROW]
[ROW][C]9[/C][C]10532.54[/C][C]10709.944385345[/C][C]-177.404385344999[/C][/ROW]
[ROW][C]10[/C][C]10324.31[/C][C]10589.9481816098[/C][C]-265.638181609795[/C][/ROW]
[ROW][C]11[/C][C]10695.25[/C][C]10470.9012968111[/C][C]224.348703188928[/C][/ROW]
[ROW][C]12[/C][C]10827.81[/C][C]10512.6606856948[/C][C]315.149314305154[/C][/ROW]
[ROW][C]13[/C][C]10872.48[/C][C]10974.2567567799[/C][C]-101.776756779903[/C][/ROW]
[ROW][C]14[/C][C]10971.19[/C][C]11326.0895535664[/C][C]-354.899553566401[/C][/ROW]
[ROW][C]15[/C][C]11145.65[/C][C]11594.6709347923[/C][C]-449.020934792262[/C][/ROW]
[ROW][C]16[/C][C]11234.68[/C][C]11396.6263649462[/C][C]-161.946364946236[/C][/ROW]
[ROW][C]17[/C][C]11333.88[/C][C]11463.8130914815[/C][C]-129.933091481513[/C][/ROW]
[ROW][C]18[/C][C]10997.97[/C][C]11137.3082083021[/C][C]-139.338208302055[/C][/ROW]
[ROW][C]19[/C][C]11036.89[/C][C]11549.1422947644[/C][C]-512.25229476441[/C][/ROW]
[ROW][C]20[/C][C]11257.35[/C][C]11799.400267776[/C][C]-542.050267775995[/C][/ROW]
[ROW][C]21[/C][C]11533.59[/C][C]11865.9479967553[/C][C]-332.357996755263[/C][/ROW]
[ROW][C]22[/C][C]11963.12[/C][C]12085.6491840365[/C][C]-122.529184036504[/C][/ROW]
[ROW][C]23[/C][C]12185.15[/C][C]12375.6222747559[/C][C]-190.472274755941[/C][/ROW]
[ROW][C]24[/C][C]12377.62[/C][C]12476.1886694789[/C][C]-98.5686694789479[/C][/ROW]
[ROW][C]25[/C][C]12512.89[/C][C]12634.8602734827[/C][C]-121.97027348271[/C][/ROW]
[ROW][C]26[/C][C]12631.48[/C][C]12743.5303903145[/C][C]-112.050390314507[/C][/ROW]
[ROW][C]27[/C][C]12268.53[/C][C]12565.4825260694[/C][C]-296.95252606942[/C][/ROW]
[ROW][C]28[/C][C]12754.8[/C][C]13205.7189133442[/C][C]-450.918913344152[/C][/ROW]
[ROW][C]29[/C][C]13407.75[/C][C]13443.1352679619[/C][C]-35.3852679619183[/C][/ROW]
[ROW][C]30[/C][C]13480.21[/C][C]13168.4957669491[/C][C]311.714233050911[/C][/ROW]
[ROW][C]31[/C][C]13673.28[/C][C]13095.126935416[/C][C]578.153064584028[/C][/ROW]
[ROW][C]32[/C][C]13239.71[/C][C]12480.0937404459[/C][C]759.616259554148[/C][/ROW]
[ROW][C]33[/C][C]13557.69[/C][C]12896.5607405173[/C][C]661.129259482686[/C][/ROW]
[ROW][C]34[/C][C]13901.28[/C][C]13190.7383116177[/C][C]710.541688382298[/C][/ROW]
[ROW][C]35[/C][C]13200.58[/C][C]12870.0428818927[/C][C]330.537118107277[/C][/ROW]
[ROW][C]36[/C][C]13406.97[/C][C]12873.1301971761[/C][C]533.839802823855[/C][/ROW]
[ROW][C]37[/C][C]12538.12[/C][C]12707.0441179882[/C][C]-168.924117988204[/C][/ROW]
[ROW][C]38[/C][C]12419.57[/C][C]12475.9882940432[/C][C]-56.4182940432316[/C][/ROW]
[ROW][C]39[/C][C]12193.88[/C][C]12750.0898138545[/C][C]-556.209813854503[/C][/ROW]
[ROW][C]40[/C][C]12656.63[/C][C]13105.0501799657[/C][C]-448.420179965655[/C][/ROW]
[ROW][C]41[/C][C]12812.48[/C][C]13007.251404041[/C][C]-194.771404040964[/C][/ROW]
[ROW][C]42[/C][C]12056.67[/C][C]12321.8528109297[/C][C]-265.182810929696[/C][/ROW]
[ROW][C]43[/C][C]11322.38[/C][C]11374.5140994152[/C][C]-52.1340994151898[/C][/ROW]
[ROW][C]44[/C][C]11530.75[/C][C]11111.1582827359[/C][C]419.591717264093[/C][/ROW]
[ROW][C]45[/C][C]11114.08[/C][C]10894.1814828565[/C][C]219.898517143549[/C][/ROW]
[ROW][C]46[/C][C]9181.73[/C][C]9274.67927133985[/C][C]-92.9492713398492[/C][/ROW]
[ROW][C]47[/C][C]8614.55[/C][C]8599.71224491423[/C][C]14.8377550857654[/C][/ROW]
[ROW][C]48[/C][C]8595.56[/C][C]8033.55088266349[/C][C]562.009117336507[/C][/ROW]
[ROW][C]49[/C][C]8396.2[/C][C]8241.39499124121[/C][C]154.805008758792[/C][/ROW]
[ROW][C]50[/C][C]7690.5[/C][C]8219.02808152942[/C][C]-528.52808152942[/C][/ROW]
[ROW][C]51[/C][C]7235.47[/C][C]7857.75792867275[/C][C]-622.287928672753[/C][/ROW]
[ROW][C]52[/C][C]7992.12[/C][C]8132.7162297864[/C][C]-140.596229786402[/C][/ROW]
[ROW][C]53[/C][C]8398.37[/C][C]8604.17118692331[/C][C]-205.801186923305[/C][/ROW]
[ROW][C]54[/C][C]8593[/C][C]8582.39858054148[/C][C]10.6014194585216[/C][/ROW]
[ROW][C]55[/C][C]8679.75[/C][C]8659.45420347694[/C][C]20.295796523057[/C][/ROW]
[ROW][C]56[/C][C]9374.63[/C][C]9176.6775245429[/C][C]197.952475457096[/C][/ROW]
[ROW][C]57[/C][C]9634.97[/C][C]9559.75168518704[/C][C]75.2183148129612[/C][/ROW]
[ROW][C]58[/C][C]9857.34[/C][C]9909.49054043921[/C][C]-52.1505404392132[/C][/ROW]
[ROW][C]59[/C][C]10238.83[/C][C]10017.7584400696[/C][C]221.07155993036[/C][/ROW]
[ROW][C]60[/C][C]10433.44[/C][C]9950.74676584248[/C][C]482.69323415752[/C][/ROW]
[ROW][C]61[/C][C]10471.24[/C][C]9955.5628036688[/C][C]515.6771963312[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146889&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146889&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
110539.519990.23377115513549.276228844869
210723.7810242.8235609395480.95643906047
310682.0610364.4212537052317.638746294804
410283.1910513.9339248231-230.743924823056
510377.1810421.3633485319-44.183348531887
610486.6410505.7642931453-19.1242931453103
710545.3810633.5955359588-88.2155359587744
810554.2710859.7163729885-305.446372988502
910532.5410709.944385345-177.404385344999
1010324.3110589.9481816098-265.638181609795
1110695.2510470.9012968111224.348703188928
1210827.8110512.6606856948315.149314305154
1310872.4810974.2567567799-101.776756779903
1410971.1911326.0895535664-354.899553566401
1511145.6511594.6709347923-449.020934792262
1611234.6811396.6263649462-161.946364946236
1711333.8811463.8130914815-129.933091481513
1810997.9711137.3082083021-139.338208302055
1911036.8911549.1422947644-512.25229476441
2011257.3511799.400267776-542.050267775995
2111533.5911865.9479967553-332.357996755263
2211963.1212085.6491840365-122.529184036504
2312185.1512375.6222747559-190.472274755941
2412377.6212476.1886694789-98.5686694789479
2512512.8912634.8602734827-121.97027348271
2612631.4812743.5303903145-112.050390314507
2712268.5312565.4825260694-296.95252606942
2812754.813205.7189133442-450.918913344152
2913407.7513443.1352679619-35.3852679619183
3013480.2113168.4957669491311.714233050911
3113673.2813095.126935416578.153064584028
3213239.7112480.0937404459759.616259554148
3313557.6912896.5607405173661.129259482686
3413901.2813190.7383116177710.541688382298
3513200.5812870.0428818927330.537118107277
3613406.9712873.1301971761533.839802823855
3712538.1212707.0441179882-168.924117988204
3812419.5712475.9882940432-56.4182940432316
3912193.8812750.0898138545-556.209813854503
4012656.6313105.0501799657-448.420179965655
4112812.4813007.251404041-194.771404040964
4212056.6712321.8528109297-265.182810929696
4311322.3811374.5140994152-52.1340994151898
4411530.7511111.1582827359419.591717264093
4511114.0810894.1814828565219.898517143549
469181.739274.67927133985-92.9492713398492
478614.558599.7122449142314.8377550857654
488595.568033.55088266349562.009117336507
498396.28241.39499124121154.805008758792
507690.58219.02808152942-528.52808152942
517235.477857.75792867275-622.287928672753
527992.128132.7162297864-140.596229786402
538398.378604.17118692331-205.801186923305
5485938582.3985805414810.6014194585216
558679.758659.4542034769420.295796523057
569374.639176.6775245429197.952475457096
579634.979559.7516851870475.2183148129612
589857.349909.49054043921-52.1505404392132
5910238.8310017.7584400696221.07155993036
6010433.449950.74676584248482.69323415752
6110471.249955.5628036688515.6771963312







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.04744077338263180.09488154676526360.952559226617368
90.05028824960303770.1005764992060750.949711750396962
100.03487556086558030.06975112173116060.96512443913442
110.02074799769672850.0414959953934570.979252002303271
120.009423114303635430.01884622860727090.990576885696365
130.003741589996361560.007483179992723110.996258410003638
140.001352563736396640.002705127472793270.998647436263603
150.0005105140156239860.001021028031247970.999489485984376
160.0008818648354592650.001763729670918530.999118135164541
170.001579662461999860.003159324923999720.998420337538
180.000678686662961730.001357373325923460.999321313337038
190.0003592137462267350.000718427492453470.999640786253773
200.0003155093561294050.000631018712258810.999684490643871
210.000323120192628560.000646240385257120.999676879807371
220.0004841320064156130.0009682640128312250.999515867993584
230.0002622008872203680.0005244017744407360.99973779911278
240.000375315428629570.0007506308572591410.99962468457137
250.0002002966117424650.000400593223484930.999799703388258
260.0001339745321248770.0002679490642497540.999866025467875
270.0001555477085198260.0003110954170396530.99984445229148
280.0006685108324376620.001337021664875320.999331489167562
290.009702510451931810.01940502090386360.990297489548068
300.08936047976706310.1787209595341260.910639520232937
310.2756313964196260.5512627928392520.724368603580374
320.6209432975543850.758113404891230.379056702445615
330.7835915274524360.4328169450951290.216408472547564
340.8235941956960040.3528116086079920.176405804303996
350.7800507459329460.4398985081341080.219949254067054
360.7559702816881910.4880594366236190.244029718311809
370.692911267899770.6141774642004610.30708873210023
380.6206338749883530.7587322500232950.379366125011647
390.6264587644933610.7470824710132790.373541235506639
400.684471706477040.631056587045920.31552829352296
410.737458368747070.5250832625058590.26254163125293
420.8941786445800870.2116427108398250.105821355419913
430.8497445000807360.3005109998385290.150255499919264
440.8242362015216540.3515275969566910.175763798478346
450.8477495118090720.3045009763818550.152250488190928
460.7841462007268290.4317075985463410.215853799273171
470.702421740143910.5951565197121810.29757825985609
480.9185719067026060.1628561865947880.081428093297394
490.9978785013056460.004242997388707890.00212149869435394
500.9938853284882850.01222934302343040.00611467151171522
510.9955555787618590.008888842476281610.00444442123814081
520.9895878655569530.02082426888609440.0104121344430472
530.9627544880589640.07449102388207190.037245511941036

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0474407733826318 & 0.0948815467652636 & 0.952559226617368 \tabularnewline
9 & 0.0502882496030377 & 0.100576499206075 & 0.949711750396962 \tabularnewline
10 & 0.0348755608655803 & 0.0697511217311606 & 0.96512443913442 \tabularnewline
11 & 0.0207479976967285 & 0.041495995393457 & 0.979252002303271 \tabularnewline
12 & 0.00942311430363543 & 0.0188462286072709 & 0.990576885696365 \tabularnewline
13 & 0.00374158999636156 & 0.00748317999272311 & 0.996258410003638 \tabularnewline
14 & 0.00135256373639664 & 0.00270512747279327 & 0.998647436263603 \tabularnewline
15 & 0.000510514015623986 & 0.00102102803124797 & 0.999489485984376 \tabularnewline
16 & 0.000881864835459265 & 0.00176372967091853 & 0.999118135164541 \tabularnewline
17 & 0.00157966246199986 & 0.00315932492399972 & 0.998420337538 \tabularnewline
18 & 0.00067868666296173 & 0.00135737332592346 & 0.999321313337038 \tabularnewline
19 & 0.000359213746226735 & 0.00071842749245347 & 0.999640786253773 \tabularnewline
20 & 0.000315509356129405 & 0.00063101871225881 & 0.999684490643871 \tabularnewline
21 & 0.00032312019262856 & 0.00064624038525712 & 0.999676879807371 \tabularnewline
22 & 0.000484132006415613 & 0.000968264012831225 & 0.999515867993584 \tabularnewline
23 & 0.000262200887220368 & 0.000524401774440736 & 0.99973779911278 \tabularnewline
24 & 0.00037531542862957 & 0.000750630857259141 & 0.99962468457137 \tabularnewline
25 & 0.000200296611742465 & 0.00040059322348493 & 0.999799703388258 \tabularnewline
26 & 0.000133974532124877 & 0.000267949064249754 & 0.999866025467875 \tabularnewline
27 & 0.000155547708519826 & 0.000311095417039653 & 0.99984445229148 \tabularnewline
28 & 0.000668510832437662 & 0.00133702166487532 & 0.999331489167562 \tabularnewline
29 & 0.00970251045193181 & 0.0194050209038636 & 0.990297489548068 \tabularnewline
30 & 0.0893604797670631 & 0.178720959534126 & 0.910639520232937 \tabularnewline
31 & 0.275631396419626 & 0.551262792839252 & 0.724368603580374 \tabularnewline
32 & 0.620943297554385 & 0.75811340489123 & 0.379056702445615 \tabularnewline
33 & 0.783591527452436 & 0.432816945095129 & 0.216408472547564 \tabularnewline
34 & 0.823594195696004 & 0.352811608607992 & 0.176405804303996 \tabularnewline
35 & 0.780050745932946 & 0.439898508134108 & 0.219949254067054 \tabularnewline
36 & 0.755970281688191 & 0.488059436623619 & 0.244029718311809 \tabularnewline
37 & 0.69291126789977 & 0.614177464200461 & 0.30708873210023 \tabularnewline
38 & 0.620633874988353 & 0.758732250023295 & 0.379366125011647 \tabularnewline
39 & 0.626458764493361 & 0.747082471013279 & 0.373541235506639 \tabularnewline
40 & 0.68447170647704 & 0.63105658704592 & 0.31552829352296 \tabularnewline
41 & 0.73745836874707 & 0.525083262505859 & 0.26254163125293 \tabularnewline
42 & 0.894178644580087 & 0.211642710839825 & 0.105821355419913 \tabularnewline
43 & 0.849744500080736 & 0.300510999838529 & 0.150255499919264 \tabularnewline
44 & 0.824236201521654 & 0.351527596956691 & 0.175763798478346 \tabularnewline
45 & 0.847749511809072 & 0.304500976381855 & 0.152250488190928 \tabularnewline
46 & 0.784146200726829 & 0.431707598546341 & 0.215853799273171 \tabularnewline
47 & 0.70242174014391 & 0.595156519712181 & 0.29757825985609 \tabularnewline
48 & 0.918571906702606 & 0.162856186594788 & 0.081428093297394 \tabularnewline
49 & 0.997878501305646 & 0.00424299738870789 & 0.00212149869435394 \tabularnewline
50 & 0.993885328488285 & 0.0122293430234304 & 0.00611467151171522 \tabularnewline
51 & 0.995555578761859 & 0.00888884247628161 & 0.00444442123814081 \tabularnewline
52 & 0.989587865556953 & 0.0208242688860944 & 0.0104121344430472 \tabularnewline
53 & 0.962754488058964 & 0.0744910238820719 & 0.037245511941036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146889&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.0474407733826318[/C][C]0.0948815467652636[/C][C]0.952559226617368[/C][/ROW]
[ROW][C]9[/C][C]0.0502882496030377[/C][C]0.100576499206075[/C][C]0.949711750396962[/C][/ROW]
[ROW][C]10[/C][C]0.0348755608655803[/C][C]0.0697511217311606[/C][C]0.96512443913442[/C][/ROW]
[ROW][C]11[/C][C]0.0207479976967285[/C][C]0.041495995393457[/C][C]0.979252002303271[/C][/ROW]
[ROW][C]12[/C][C]0.00942311430363543[/C][C]0.0188462286072709[/C][C]0.990576885696365[/C][/ROW]
[ROW][C]13[/C][C]0.00374158999636156[/C][C]0.00748317999272311[/C][C]0.996258410003638[/C][/ROW]
[ROW][C]14[/C][C]0.00135256373639664[/C][C]0.00270512747279327[/C][C]0.998647436263603[/C][/ROW]
[ROW][C]15[/C][C]0.000510514015623986[/C][C]0.00102102803124797[/C][C]0.999489485984376[/C][/ROW]
[ROW][C]16[/C][C]0.000881864835459265[/C][C]0.00176372967091853[/C][C]0.999118135164541[/C][/ROW]
[ROW][C]17[/C][C]0.00157966246199986[/C][C]0.00315932492399972[/C][C]0.998420337538[/C][/ROW]
[ROW][C]18[/C][C]0.00067868666296173[/C][C]0.00135737332592346[/C][C]0.999321313337038[/C][/ROW]
[ROW][C]19[/C][C]0.000359213746226735[/C][C]0.00071842749245347[/C][C]0.999640786253773[/C][/ROW]
[ROW][C]20[/C][C]0.000315509356129405[/C][C]0.00063101871225881[/C][C]0.999684490643871[/C][/ROW]
[ROW][C]21[/C][C]0.00032312019262856[/C][C]0.00064624038525712[/C][C]0.999676879807371[/C][/ROW]
[ROW][C]22[/C][C]0.000484132006415613[/C][C]0.000968264012831225[/C][C]0.999515867993584[/C][/ROW]
[ROW][C]23[/C][C]0.000262200887220368[/C][C]0.000524401774440736[/C][C]0.99973779911278[/C][/ROW]
[ROW][C]24[/C][C]0.00037531542862957[/C][C]0.000750630857259141[/C][C]0.99962468457137[/C][/ROW]
[ROW][C]25[/C][C]0.000200296611742465[/C][C]0.00040059322348493[/C][C]0.999799703388258[/C][/ROW]
[ROW][C]26[/C][C]0.000133974532124877[/C][C]0.000267949064249754[/C][C]0.999866025467875[/C][/ROW]
[ROW][C]27[/C][C]0.000155547708519826[/C][C]0.000311095417039653[/C][C]0.99984445229148[/C][/ROW]
[ROW][C]28[/C][C]0.000668510832437662[/C][C]0.00133702166487532[/C][C]0.999331489167562[/C][/ROW]
[ROW][C]29[/C][C]0.00970251045193181[/C][C]0.0194050209038636[/C][C]0.990297489548068[/C][/ROW]
[ROW][C]30[/C][C]0.0893604797670631[/C][C]0.178720959534126[/C][C]0.910639520232937[/C][/ROW]
[ROW][C]31[/C][C]0.275631396419626[/C][C]0.551262792839252[/C][C]0.724368603580374[/C][/ROW]
[ROW][C]32[/C][C]0.620943297554385[/C][C]0.75811340489123[/C][C]0.379056702445615[/C][/ROW]
[ROW][C]33[/C][C]0.783591527452436[/C][C]0.432816945095129[/C][C]0.216408472547564[/C][/ROW]
[ROW][C]34[/C][C]0.823594195696004[/C][C]0.352811608607992[/C][C]0.176405804303996[/C][/ROW]
[ROW][C]35[/C][C]0.780050745932946[/C][C]0.439898508134108[/C][C]0.219949254067054[/C][/ROW]
[ROW][C]36[/C][C]0.755970281688191[/C][C]0.488059436623619[/C][C]0.244029718311809[/C][/ROW]
[ROW][C]37[/C][C]0.69291126789977[/C][C]0.614177464200461[/C][C]0.30708873210023[/C][/ROW]
[ROW][C]38[/C][C]0.620633874988353[/C][C]0.758732250023295[/C][C]0.379366125011647[/C][/ROW]
[ROW][C]39[/C][C]0.626458764493361[/C][C]0.747082471013279[/C][C]0.373541235506639[/C][/ROW]
[ROW][C]40[/C][C]0.68447170647704[/C][C]0.63105658704592[/C][C]0.31552829352296[/C][/ROW]
[ROW][C]41[/C][C]0.73745836874707[/C][C]0.525083262505859[/C][C]0.26254163125293[/C][/ROW]
[ROW][C]42[/C][C]0.894178644580087[/C][C]0.211642710839825[/C][C]0.105821355419913[/C][/ROW]
[ROW][C]43[/C][C]0.849744500080736[/C][C]0.300510999838529[/C][C]0.150255499919264[/C][/ROW]
[ROW][C]44[/C][C]0.824236201521654[/C][C]0.351527596956691[/C][C]0.175763798478346[/C][/ROW]
[ROW][C]45[/C][C]0.847749511809072[/C][C]0.304500976381855[/C][C]0.152250488190928[/C][/ROW]
[ROW][C]46[/C][C]0.784146200726829[/C][C]0.431707598546341[/C][C]0.215853799273171[/C][/ROW]
[ROW][C]47[/C][C]0.70242174014391[/C][C]0.595156519712181[/C][C]0.29757825985609[/C][/ROW]
[ROW][C]48[/C][C]0.918571906702606[/C][C]0.162856186594788[/C][C]0.081428093297394[/C][/ROW]
[ROW][C]49[/C][C]0.997878501305646[/C][C]0.00424299738870789[/C][C]0.00212149869435394[/C][/ROW]
[ROW][C]50[/C][C]0.993885328488285[/C][C]0.0122293430234304[/C][C]0.00611467151171522[/C][/ROW]
[ROW][C]51[/C][C]0.995555578761859[/C][C]0.00888884247628161[/C][C]0.00444442123814081[/C][/ROW]
[ROW][C]52[/C][C]0.989587865556953[/C][C]0.0208242688860944[/C][C]0.0104121344430472[/C][/ROW]
[ROW][C]53[/C][C]0.962754488058964[/C][C]0.0744910238820719[/C][C]0.037245511941036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146889&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146889&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
80.04744077338263180.09488154676526360.952559226617368
90.05028824960303770.1005764992060750.949711750396962
100.03487556086558030.06975112173116060.96512443913442
110.02074799769672850.0414959953934570.979252002303271
120.009423114303635430.01884622860727090.990576885696365
130.003741589996361560.007483179992723110.996258410003638
140.001352563736396640.002705127472793270.998647436263603
150.0005105140156239860.001021028031247970.999489485984376
160.0008818648354592650.001763729670918530.999118135164541
170.001579662461999860.003159324923999720.998420337538
180.000678686662961730.001357373325923460.999321313337038
190.0003592137462267350.000718427492453470.999640786253773
200.0003155093561294050.000631018712258810.999684490643871
210.000323120192628560.000646240385257120.999676879807371
220.0004841320064156130.0009682640128312250.999515867993584
230.0002622008872203680.0005244017744407360.99973779911278
240.000375315428629570.0007506308572591410.99962468457137
250.0002002966117424650.000400593223484930.999799703388258
260.0001339745321248770.0002679490642497540.999866025467875
270.0001555477085198260.0003110954170396530.99984445229148
280.0006685108324376620.001337021664875320.999331489167562
290.009702510451931810.01940502090386360.990297489548068
300.08936047976706310.1787209595341260.910639520232937
310.2756313964196260.5512627928392520.724368603580374
320.6209432975543850.758113404891230.379056702445615
330.7835915274524360.4328169450951290.216408472547564
340.8235941956960040.3528116086079920.176405804303996
350.7800507459329460.4398985081341080.219949254067054
360.7559702816881910.4880594366236190.244029718311809
370.692911267899770.6141774642004610.30708873210023
380.6206338749883530.7587322500232950.379366125011647
390.6264587644933610.7470824710132790.373541235506639
400.684471706477040.631056587045920.31552829352296
410.737458368747070.5250832625058590.26254163125293
420.8941786445800870.2116427108398250.105821355419913
430.8497445000807360.3005109998385290.150255499919264
440.8242362015216540.3515275969566910.175763798478346
450.8477495118090720.3045009763818550.152250488190928
460.7841462007268290.4317075985463410.215853799273171
470.702421740143910.5951565197121810.29757825985609
480.9185719067026060.1628561865947880.081428093297394
490.9978785013056460.004242997388707890.00212149869435394
500.9938853284882850.01222934302343040.00611467151171522
510.9955555787618590.008888842476281610.00444442123814081
520.9895878655569530.02082426888609440.0104121344430472
530.9627544880589640.07449102388207190.037245511941036







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.391304347826087NOK
5% type I error level230.5NOK
10% type I error level260.565217391304348NOK

\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 & 18 & 0.391304347826087 & NOK \tabularnewline
5% type I error level & 23 & 0.5 & NOK \tabularnewline
10% type I error level & 26 & 0.565217391304348 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146889&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]18[/C][C]0.391304347826087[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.5[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.565217391304348[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146889&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146889&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 level180.391304347826087NOK
5% type I error level230.5NOK
10% type I error level260.565217391304348NOK



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