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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 11:50:45 -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/22/t1321980688w46vp4hb4fa6jhq.htm/, Retrieved Thu, 28 Mar 2024 23:56:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146306, Retrieved Thu, 28 Mar 2024 23:56:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact84
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Pearson Correlation] [WS7 - Scatterplot...] [2011-11-22 15:13:55] [ae1339cb5a7cf28362d01e7220b4a16c]
- RMPD    [Multiple Regression] [WS 7 Mini-Tutorial] [2011-11-22 16:50:45] [e598b5cd83fcb010b35e92a01f5e81e9] [Current]
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Dataseries X:
68900	5960	44967	1873
48500	9000	27860	928
55500	9500	31439	1126
62000	10000	39592	1265
116500	18000	72827	2214
45000	8500	27317	912
38000	8000	29856	899
83000	23000	47752	1803
59000	8100	39117	1204
47500	9000	29349	1725
40500	7300	40166	1080
40000	8000	31679	1529
97000	20000	58510	2455
45500	8000	23454	1151
40900	8000	20897	1173
80000	10500	56248	1960
56000	4000	20859	1344
37000	45000	22610	988
50000	3400	35947	1076
22400	1500	5779	962
241100	17800	50300	2100
82200	18500	36700	2300
234400	6700	49100	1600
233700	44200	52100	1300
177700	3400	65900	1700
65900	29400	13800	2300
117600	43200	28700	2400
22500	2900	45700	1000
326600	28900	28400	1800
377900	21000	7100	1700
290700	8700	34200	1400
108200	34100	44200	1200
488100	28100	5900	2100
496600	38400	68400	2200
493100	33900	43600	1100
236900	5100	66600	1900
420600	14600	9100	1500
328200	17400	25500	2300
313200	30900	8400	2100
40200	25500	36200	1800
318300	33900	45000	1100
374100	33700	11100	2000
144400	19900	12300	900
298300	26800	52800	1500
404200	30500	26000	1600
134600	19500	9000	1200
270600	42500	46700	1000
181800	12200	58200	2000
492300	6600	54100	2000
203000	2800	25500	1900
464300	7600	64500	2000
137200	37700	42100	1500
95100	28200	23500	1900
481300	20600	14000	1300
112300	23000	65900	1400
29500	15900	19200	1300
76200	20800	51200	1800
323800	10000	50400	2200
40600	22000	52100	1000
425700	40300	43400	1900




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146306&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146306&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146306&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Verkoopprijs[t] = -78272.1062535939 + 1.85589561516335Grondwaarde[t] -0.307394595424536Waardehuis[t] + 84.2703716995598Oppervlakte[t] + 3553.53943036813t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Verkoopprijs[t] =  -78272.1062535939 +  1.85589561516335Grondwaarde[t] -0.307394595424536Waardehuis[t] +  84.2703716995598Oppervlakte[t] +  3553.53943036813t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146306&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Verkoopprijs[t] =  -78272.1062535939 +  1.85589561516335Grondwaarde[t] -0.307394595424536Waardehuis[t] +  84.2703716995598Oppervlakte[t] +  3553.53943036813t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146306&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146306&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
Verkoopprijs[t] = -78272.1062535939 + 1.85589561516335Grondwaarde[t] -0.307394595424536Waardehuis[t] + 84.2703716995598Oppervlakte[t] + 3553.53943036813t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-78272.106253593969196.726951-1.13120.2628990.131449
Grondwaarde1.855895615163351.4739751.25910.2133090.106654
Waardehuis-0.3073945954245360.977951-0.31430.7544630.377232
Oppervlakte84.270371699559838.7479872.17480.033960.01698
t3553.539430368131055.0324583.36820.0013880.000694

\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) & -78272.1062535939 & 69196.726951 & -1.1312 & 0.262899 & 0.131449 \tabularnewline
Grondwaarde & 1.85589561516335 & 1.473975 & 1.2591 & 0.213309 & 0.106654 \tabularnewline
Waardehuis & -0.307394595424536 & 0.977951 & -0.3143 & 0.754463 & 0.377232 \tabularnewline
Oppervlakte & 84.2703716995598 & 38.747987 & 2.1748 & 0.03396 & 0.01698 \tabularnewline
t & 3553.53943036813 & 1055.032458 & 3.3682 & 0.001388 & 0.000694 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146306&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]-78272.1062535939[/C][C]69196.726951[/C][C]-1.1312[/C][C]0.262899[/C][C]0.131449[/C][/ROW]
[ROW][C]Grondwaarde[/C][C]1.85589561516335[/C][C]1.473975[/C][C]1.2591[/C][C]0.213309[/C][C]0.106654[/C][/ROW]
[ROW][C]Waardehuis[/C][C]-0.307394595424536[/C][C]0.977951[/C][C]-0.3143[/C][C]0.754463[/C][C]0.377232[/C][/ROW]
[ROW][C]Oppervlakte[/C][C]84.2703716995598[/C][C]38.747987[/C][C]2.1748[/C][C]0.03396[/C][C]0.01698[/C][/ROW]
[ROW][C]t[/C][C]3553.53943036813[/C][C]1055.032458[/C][C]3.3682[/C][C]0.001388[/C][C]0.000694[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146306&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146306&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)-78272.106253593969196.726951-1.13120.2628990.131449
Grondwaarde1.855895615163351.4739751.25910.2133090.106654
Waardehuis-0.3073945954245360.977951-0.31430.7544630.377232
Oppervlakte84.270371699559838.7479872.17480.033960.01698
t3553.539430368131055.0324583.36820.0013880.000694







Multiple Linear Regression - Regression Statistics
Multiple R0.575723902088091
R-squared0.331458011435537
Adjusted R-squared0.282836775903576
F-TEST (value)6.81714497398272
F-TEST (DF numerator)4
F-TEST (DF denominator)55
p-value0.000157039506671453
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation131062.762329434
Sum Squared Residuals944759621818.2

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.575723902088091 \tabularnewline
R-squared & 0.331458011435537 \tabularnewline
Adjusted R-squared & 0.282836775903576 \tabularnewline
F-TEST (value) & 6.81714497398272 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 0.000157039506671453 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 131062.762329434 \tabularnewline
Sum Squared Residuals & 944759621818.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146306&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.575723902088091[/C][/ROW]
[ROW][C]R-squared[/C][C]0.331458011435537[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.282836775903576[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.81714497398272[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/C][/ROW]
[ROW][C]p-value[/C][C]0.000157039506671453[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]131062.762329434[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]944759621818.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146306&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146306&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 R0.575723902088091
R-squared0.331458011435537
Adjusted R-squared0.282836775903576
F-TEST (value)6.81714497398272
F-TEST (DF numerator)4
F-TEST (DF denominator)55
p-value0.000157039506671453
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation131062.762329434
Sum Squared Residuals944759621818.2







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16890080358.3644639681-11458.3644639681
24850015176.924652276533323.0753477235
35550035243.780229714720256.2197702853
46200048932.66099740713067.339002593
5116500137089.68871303-20589.6887130297
64500027281.723884289817718.2761157102
73800028031.32579719919968.6742028009
883000130102.581791702-47102.581791702
95900058179.6762395908820.323760409205
1047500110311.015787183-62811.0157871835
114050053030.0555868506-12530.0555868506
124000098328.9767723035-58328.9767723035
1397000193939.923388588-96939.9233885884
144550076110.175677973-30610.1756779729
154090082303.6712662319-41403.6712662319
1680000145951.025919209-65951.0259192092
175600096409.0822215657-40409.0822215657
1837000145515.841611999-108515.841611999
195000075180.1944419565-25180.1944419565
202240074874.1899845318-52474.1899845318
21241100190892.99615326650207.0038467342
2282200216780.303351934-134580.303351934
23234400135633.32135041898766.6786495816
24233700182579.6510532751120.3489467295
25177700139878.75264793937821.2473520606
2665900258263.059513909-192363.059513909
27117600291274.816131661-173674.816131661
282250096831.5337693459-74331.5337693459
29326600221372.583054453105227.416945547
30377900208385.014837618169514.985162382
31290700155499.533155604135200.466844396
32108200186264.800916963-78064.8009169635
33488100266299.514190715221800.485809285
34496600278183.653413188218416.346586812
35493100188311.639672334304788.360327666
36236900198761.60705088138138.3929491187
37420600203913.195382388216686.804617612
38328200275038.26852989953161.731470101
39313200292048.7720068221151.2279931801
4040200251753.793852636-211553.793852636
41318300209202.523820948109097.476179052
42374100298648.89544277975451.1045572206
43144400183524.792999868-39124.7929998682
44298300237996.75407990660303.2459200945
45404200265082.319613712139117.680386288
46134600219738.566719676-85138.5667196761
47270600237534.85471138433065.1452886158
48181800265590.090854481-83790.0908544806
49492300260010.932681175232289.067318825
50203000256876.517033108-53876.5170331078
51464300265777.003364659198522.996635341
52137200289943.453899174-152743.453899174
5395100315291.67314021-220191.67314021
54481300257098.431532134224201.568467866
55112300257579.378106317-145279.378106317
5629500253884.349105395-224384.349105395
5776200298830.335846258-222630.335846258
58323800316294.2669890267505.73301097414
5940600240471.536949661-199871.536949661
60425700356505.63364731569194.3663526847

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 68900 & 80358.3644639681 & -11458.3644639681 \tabularnewline
2 & 48500 & 15176.9246522765 & 33323.0753477235 \tabularnewline
3 & 55500 & 35243.7802297147 & 20256.2197702853 \tabularnewline
4 & 62000 & 48932.660997407 & 13067.339002593 \tabularnewline
5 & 116500 & 137089.68871303 & -20589.6887130297 \tabularnewline
6 & 45000 & 27281.7238842898 & 17718.2761157102 \tabularnewline
7 & 38000 & 28031.3257971991 & 9968.6742028009 \tabularnewline
8 & 83000 & 130102.581791702 & -47102.581791702 \tabularnewline
9 & 59000 & 58179.6762395908 & 820.323760409205 \tabularnewline
10 & 47500 & 110311.015787183 & -62811.0157871835 \tabularnewline
11 & 40500 & 53030.0555868506 & -12530.0555868506 \tabularnewline
12 & 40000 & 98328.9767723035 & -58328.9767723035 \tabularnewline
13 & 97000 & 193939.923388588 & -96939.9233885884 \tabularnewline
14 & 45500 & 76110.175677973 & -30610.1756779729 \tabularnewline
15 & 40900 & 82303.6712662319 & -41403.6712662319 \tabularnewline
16 & 80000 & 145951.025919209 & -65951.0259192092 \tabularnewline
17 & 56000 & 96409.0822215657 & -40409.0822215657 \tabularnewline
18 & 37000 & 145515.841611999 & -108515.841611999 \tabularnewline
19 & 50000 & 75180.1944419565 & -25180.1944419565 \tabularnewline
20 & 22400 & 74874.1899845318 & -52474.1899845318 \tabularnewline
21 & 241100 & 190892.996153266 & 50207.0038467342 \tabularnewline
22 & 82200 & 216780.303351934 & -134580.303351934 \tabularnewline
23 & 234400 & 135633.321350418 & 98766.6786495816 \tabularnewline
24 & 233700 & 182579.65105327 & 51120.3489467295 \tabularnewline
25 & 177700 & 139878.752647939 & 37821.2473520606 \tabularnewline
26 & 65900 & 258263.059513909 & -192363.059513909 \tabularnewline
27 & 117600 & 291274.816131661 & -173674.816131661 \tabularnewline
28 & 22500 & 96831.5337693459 & -74331.5337693459 \tabularnewline
29 & 326600 & 221372.583054453 & 105227.416945547 \tabularnewline
30 & 377900 & 208385.014837618 & 169514.985162382 \tabularnewline
31 & 290700 & 155499.533155604 & 135200.466844396 \tabularnewline
32 & 108200 & 186264.800916963 & -78064.8009169635 \tabularnewline
33 & 488100 & 266299.514190715 & 221800.485809285 \tabularnewline
34 & 496600 & 278183.653413188 & 218416.346586812 \tabularnewline
35 & 493100 & 188311.639672334 & 304788.360327666 \tabularnewline
36 & 236900 & 198761.607050881 & 38138.3929491187 \tabularnewline
37 & 420600 & 203913.195382388 & 216686.804617612 \tabularnewline
38 & 328200 & 275038.268529899 & 53161.731470101 \tabularnewline
39 & 313200 & 292048.77200682 & 21151.2279931801 \tabularnewline
40 & 40200 & 251753.793852636 & -211553.793852636 \tabularnewline
41 & 318300 & 209202.523820948 & 109097.476179052 \tabularnewline
42 & 374100 & 298648.895442779 & 75451.1045572206 \tabularnewline
43 & 144400 & 183524.792999868 & -39124.7929998682 \tabularnewline
44 & 298300 & 237996.754079906 & 60303.2459200945 \tabularnewline
45 & 404200 & 265082.319613712 & 139117.680386288 \tabularnewline
46 & 134600 & 219738.566719676 & -85138.5667196761 \tabularnewline
47 & 270600 & 237534.854711384 & 33065.1452886158 \tabularnewline
48 & 181800 & 265590.090854481 & -83790.0908544806 \tabularnewline
49 & 492300 & 260010.932681175 & 232289.067318825 \tabularnewline
50 & 203000 & 256876.517033108 & -53876.5170331078 \tabularnewline
51 & 464300 & 265777.003364659 & 198522.996635341 \tabularnewline
52 & 137200 & 289943.453899174 & -152743.453899174 \tabularnewline
53 & 95100 & 315291.67314021 & -220191.67314021 \tabularnewline
54 & 481300 & 257098.431532134 & 224201.568467866 \tabularnewline
55 & 112300 & 257579.378106317 & -145279.378106317 \tabularnewline
56 & 29500 & 253884.349105395 & -224384.349105395 \tabularnewline
57 & 76200 & 298830.335846258 & -222630.335846258 \tabularnewline
58 & 323800 & 316294.266989026 & 7505.73301097414 \tabularnewline
59 & 40600 & 240471.536949661 & -199871.536949661 \tabularnewline
60 & 425700 & 356505.633647315 & 69194.3663526847 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146306&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]68900[/C][C]80358.3644639681[/C][C]-11458.3644639681[/C][/ROW]
[ROW][C]2[/C][C]48500[/C][C]15176.9246522765[/C][C]33323.0753477235[/C][/ROW]
[ROW][C]3[/C][C]55500[/C][C]35243.7802297147[/C][C]20256.2197702853[/C][/ROW]
[ROW][C]4[/C][C]62000[/C][C]48932.660997407[/C][C]13067.339002593[/C][/ROW]
[ROW][C]5[/C][C]116500[/C][C]137089.68871303[/C][C]-20589.6887130297[/C][/ROW]
[ROW][C]6[/C][C]45000[/C][C]27281.7238842898[/C][C]17718.2761157102[/C][/ROW]
[ROW][C]7[/C][C]38000[/C][C]28031.3257971991[/C][C]9968.6742028009[/C][/ROW]
[ROW][C]8[/C][C]83000[/C][C]130102.581791702[/C][C]-47102.581791702[/C][/ROW]
[ROW][C]9[/C][C]59000[/C][C]58179.6762395908[/C][C]820.323760409205[/C][/ROW]
[ROW][C]10[/C][C]47500[/C][C]110311.015787183[/C][C]-62811.0157871835[/C][/ROW]
[ROW][C]11[/C][C]40500[/C][C]53030.0555868506[/C][C]-12530.0555868506[/C][/ROW]
[ROW][C]12[/C][C]40000[/C][C]98328.9767723035[/C][C]-58328.9767723035[/C][/ROW]
[ROW][C]13[/C][C]97000[/C][C]193939.923388588[/C][C]-96939.9233885884[/C][/ROW]
[ROW][C]14[/C][C]45500[/C][C]76110.175677973[/C][C]-30610.1756779729[/C][/ROW]
[ROW][C]15[/C][C]40900[/C][C]82303.6712662319[/C][C]-41403.6712662319[/C][/ROW]
[ROW][C]16[/C][C]80000[/C][C]145951.025919209[/C][C]-65951.0259192092[/C][/ROW]
[ROW][C]17[/C][C]56000[/C][C]96409.0822215657[/C][C]-40409.0822215657[/C][/ROW]
[ROW][C]18[/C][C]37000[/C][C]145515.841611999[/C][C]-108515.841611999[/C][/ROW]
[ROW][C]19[/C][C]50000[/C][C]75180.1944419565[/C][C]-25180.1944419565[/C][/ROW]
[ROW][C]20[/C][C]22400[/C][C]74874.1899845318[/C][C]-52474.1899845318[/C][/ROW]
[ROW][C]21[/C][C]241100[/C][C]190892.996153266[/C][C]50207.0038467342[/C][/ROW]
[ROW][C]22[/C][C]82200[/C][C]216780.303351934[/C][C]-134580.303351934[/C][/ROW]
[ROW][C]23[/C][C]234400[/C][C]135633.321350418[/C][C]98766.6786495816[/C][/ROW]
[ROW][C]24[/C][C]233700[/C][C]182579.65105327[/C][C]51120.3489467295[/C][/ROW]
[ROW][C]25[/C][C]177700[/C][C]139878.752647939[/C][C]37821.2473520606[/C][/ROW]
[ROW][C]26[/C][C]65900[/C][C]258263.059513909[/C][C]-192363.059513909[/C][/ROW]
[ROW][C]27[/C][C]117600[/C][C]291274.816131661[/C][C]-173674.816131661[/C][/ROW]
[ROW][C]28[/C][C]22500[/C][C]96831.5337693459[/C][C]-74331.5337693459[/C][/ROW]
[ROW][C]29[/C][C]326600[/C][C]221372.583054453[/C][C]105227.416945547[/C][/ROW]
[ROW][C]30[/C][C]377900[/C][C]208385.014837618[/C][C]169514.985162382[/C][/ROW]
[ROW][C]31[/C][C]290700[/C][C]155499.533155604[/C][C]135200.466844396[/C][/ROW]
[ROW][C]32[/C][C]108200[/C][C]186264.800916963[/C][C]-78064.8009169635[/C][/ROW]
[ROW][C]33[/C][C]488100[/C][C]266299.514190715[/C][C]221800.485809285[/C][/ROW]
[ROW][C]34[/C][C]496600[/C][C]278183.653413188[/C][C]218416.346586812[/C][/ROW]
[ROW][C]35[/C][C]493100[/C][C]188311.639672334[/C][C]304788.360327666[/C][/ROW]
[ROW][C]36[/C][C]236900[/C][C]198761.607050881[/C][C]38138.3929491187[/C][/ROW]
[ROW][C]37[/C][C]420600[/C][C]203913.195382388[/C][C]216686.804617612[/C][/ROW]
[ROW][C]38[/C][C]328200[/C][C]275038.268529899[/C][C]53161.731470101[/C][/ROW]
[ROW][C]39[/C][C]313200[/C][C]292048.77200682[/C][C]21151.2279931801[/C][/ROW]
[ROW][C]40[/C][C]40200[/C][C]251753.793852636[/C][C]-211553.793852636[/C][/ROW]
[ROW][C]41[/C][C]318300[/C][C]209202.523820948[/C][C]109097.476179052[/C][/ROW]
[ROW][C]42[/C][C]374100[/C][C]298648.895442779[/C][C]75451.1045572206[/C][/ROW]
[ROW][C]43[/C][C]144400[/C][C]183524.792999868[/C][C]-39124.7929998682[/C][/ROW]
[ROW][C]44[/C][C]298300[/C][C]237996.754079906[/C][C]60303.2459200945[/C][/ROW]
[ROW][C]45[/C][C]404200[/C][C]265082.319613712[/C][C]139117.680386288[/C][/ROW]
[ROW][C]46[/C][C]134600[/C][C]219738.566719676[/C][C]-85138.5667196761[/C][/ROW]
[ROW][C]47[/C][C]270600[/C][C]237534.854711384[/C][C]33065.1452886158[/C][/ROW]
[ROW][C]48[/C][C]181800[/C][C]265590.090854481[/C][C]-83790.0908544806[/C][/ROW]
[ROW][C]49[/C][C]492300[/C][C]260010.932681175[/C][C]232289.067318825[/C][/ROW]
[ROW][C]50[/C][C]203000[/C][C]256876.517033108[/C][C]-53876.5170331078[/C][/ROW]
[ROW][C]51[/C][C]464300[/C][C]265777.003364659[/C][C]198522.996635341[/C][/ROW]
[ROW][C]52[/C][C]137200[/C][C]289943.453899174[/C][C]-152743.453899174[/C][/ROW]
[ROW][C]53[/C][C]95100[/C][C]315291.67314021[/C][C]-220191.67314021[/C][/ROW]
[ROW][C]54[/C][C]481300[/C][C]257098.431532134[/C][C]224201.568467866[/C][/ROW]
[ROW][C]55[/C][C]112300[/C][C]257579.378106317[/C][C]-145279.378106317[/C][/ROW]
[ROW][C]56[/C][C]29500[/C][C]253884.349105395[/C][C]-224384.349105395[/C][/ROW]
[ROW][C]57[/C][C]76200[/C][C]298830.335846258[/C][C]-222630.335846258[/C][/ROW]
[ROW][C]58[/C][C]323800[/C][C]316294.266989026[/C][C]7505.73301097414[/C][/ROW]
[ROW][C]59[/C][C]40600[/C][C]240471.536949661[/C][C]-199871.536949661[/C][/ROW]
[ROW][C]60[/C][C]425700[/C][C]356505.633647315[/C][C]69194.3663526847[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146306&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146306&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
16890080358.3644639681-11458.3644639681
24850015176.924652276533323.0753477235
35550035243.780229714720256.2197702853
46200048932.66099740713067.339002593
5116500137089.68871303-20589.6887130297
64500027281.723884289817718.2761157102
73800028031.32579719919968.6742028009
883000130102.581791702-47102.581791702
95900058179.6762395908820.323760409205
1047500110311.015787183-62811.0157871835
114050053030.0555868506-12530.0555868506
124000098328.9767723035-58328.9767723035
1397000193939.923388588-96939.9233885884
144550076110.175677973-30610.1756779729
154090082303.6712662319-41403.6712662319
1680000145951.025919209-65951.0259192092
175600096409.0822215657-40409.0822215657
1837000145515.841611999-108515.841611999
195000075180.1944419565-25180.1944419565
202240074874.1899845318-52474.1899845318
21241100190892.99615326650207.0038467342
2282200216780.303351934-134580.303351934
23234400135633.32135041898766.6786495816
24233700182579.6510532751120.3489467295
25177700139878.75264793937821.2473520606
2665900258263.059513909-192363.059513909
27117600291274.816131661-173674.816131661
282250096831.5337693459-74331.5337693459
29326600221372.583054453105227.416945547
30377900208385.014837618169514.985162382
31290700155499.533155604135200.466844396
32108200186264.800916963-78064.8009169635
33488100266299.514190715221800.485809285
34496600278183.653413188218416.346586812
35493100188311.639672334304788.360327666
36236900198761.60705088138138.3929491187
37420600203913.195382388216686.804617612
38328200275038.26852989953161.731470101
39313200292048.7720068221151.2279931801
4040200251753.793852636-211553.793852636
41318300209202.523820948109097.476179052
42374100298648.89544277975451.1045572206
43144400183524.792999868-39124.7929998682
44298300237996.75407990660303.2459200945
45404200265082.319613712139117.680386288
46134600219738.566719676-85138.5667196761
47270600237534.85471138433065.1452886158
48181800265590.090854481-83790.0908544806
49492300260010.932681175232289.067318825
50203000256876.517033108-53876.5170331078
51464300265777.003364659198522.996635341
52137200289943.453899174-152743.453899174
5395100315291.67314021-220191.67314021
54481300257098.431532134224201.568467866
55112300257579.378106317-145279.378106317
5629500253884.349105395-224384.349105395
5776200298830.335846258-222630.335846258
58323800316294.2669890267505.73301097414
5940600240471.536949661-199871.536949661
60425700356505.63364731569194.3663526847







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
82.36992089781145e-054.73984179562289e-050.999976300791022
91.69032309544403e-063.38064619088807e-060.999998309676905
105.04489306176052e-081.0089786123521e-070.99999994955107
111.22957126508813e-082.45914253017626e-080.999999987704287
124.21779567280684e-108.43559134561368e-100.99999999957822
131.9961876744273e-113.9923753488546e-110.999999999980038
141.95623355004274e-113.91246710008549e-110.999999999980438
151.9627997273656e-123.9255994547312e-120.999999999998037
161.057185010217e-132.114370020434e-130.999999999999894
172.26434939855125e-134.5286987971025e-130.999999999999774
183.00822308321357e-146.01644616642715e-140.99999999999997
191.82839376012464e-153.65678752024928e-150.999999999999998
201.20619533112898e-162.41239066225796e-161
213.68255586916742e-087.36511173833484e-080.999999963174441
221.59640175375234e-083.19280350750469e-080.999999984035982
239.94263614361474e-081.98852722872295e-070.999999900573639
246.18724632097257e-081.23744926419451e-070.999999938127537
252.12855073018338e-084.25710146036677e-080.999999978714493
261.48644356042479e-082.97288712084959e-080.999999985135564
271.9968547319576e-083.9937094639152e-080.999999980031453
282.85781108972928e-075.71562217945857e-070.99999971421889
292.00544372165039e-054.01088744330077e-050.999979945562783
300.0007301915520766840.001460383104153370.999269808447923
310.000655133133000260.001310266266000520.999344866867
320.001006899646032180.002013799292064370.998993100353968
330.005798354454217950.01159670890843590.994201645545782
340.01029121839715230.02058243679430450.989708781602848
350.02809617979397750.05619235958795510.971903820206023
360.02028416244932020.04056832489864050.97971583755068
370.02408909509794350.0481781901958870.975910904902056
380.01469633510494830.02939267020989660.985303664895052
390.00884898768081010.01769797536162020.99115101231919
400.0743552396205560.1487104792411120.925644760379444
410.05055780882232990.101115617644660.94944219117767
420.03232311186247340.06464622372494680.967676888137527
430.02524391275241390.05048782550482790.974756087247586
440.0147057801991620.02941156039832410.985294219800838
450.01040287678178040.02080575356356080.98959712321822
460.008466587249258560.01693317449851710.991533412750741
470.005896818304906730.01179363660981350.994103181695093
480.006034779098444260.01206955819688850.993965220901556
490.00746516856654930.01493033713309860.99253483143345
500.004780782403849410.009561564807698810.99521921759615
510.01468930203412720.02937860406825440.985310697965873
520.008959394894296260.01791878978859250.991040605105704

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 2.36992089781145e-05 & 4.73984179562289e-05 & 0.999976300791022 \tabularnewline
9 & 1.69032309544403e-06 & 3.38064619088807e-06 & 0.999998309676905 \tabularnewline
10 & 5.04489306176052e-08 & 1.0089786123521e-07 & 0.99999994955107 \tabularnewline
11 & 1.22957126508813e-08 & 2.45914253017626e-08 & 0.999999987704287 \tabularnewline
12 & 4.21779567280684e-10 & 8.43559134561368e-10 & 0.99999999957822 \tabularnewline
13 & 1.9961876744273e-11 & 3.9923753488546e-11 & 0.999999999980038 \tabularnewline
14 & 1.95623355004274e-11 & 3.91246710008549e-11 & 0.999999999980438 \tabularnewline
15 & 1.9627997273656e-12 & 3.9255994547312e-12 & 0.999999999998037 \tabularnewline
16 & 1.057185010217e-13 & 2.114370020434e-13 & 0.999999999999894 \tabularnewline
17 & 2.26434939855125e-13 & 4.5286987971025e-13 & 0.999999999999774 \tabularnewline
18 & 3.00822308321357e-14 & 6.01644616642715e-14 & 0.99999999999997 \tabularnewline
19 & 1.82839376012464e-15 & 3.65678752024928e-15 & 0.999999999999998 \tabularnewline
20 & 1.20619533112898e-16 & 2.41239066225796e-16 & 1 \tabularnewline
21 & 3.68255586916742e-08 & 7.36511173833484e-08 & 0.999999963174441 \tabularnewline
22 & 1.59640175375234e-08 & 3.19280350750469e-08 & 0.999999984035982 \tabularnewline
23 & 9.94263614361474e-08 & 1.98852722872295e-07 & 0.999999900573639 \tabularnewline
24 & 6.18724632097257e-08 & 1.23744926419451e-07 & 0.999999938127537 \tabularnewline
25 & 2.12855073018338e-08 & 4.25710146036677e-08 & 0.999999978714493 \tabularnewline
26 & 1.48644356042479e-08 & 2.97288712084959e-08 & 0.999999985135564 \tabularnewline
27 & 1.9968547319576e-08 & 3.9937094639152e-08 & 0.999999980031453 \tabularnewline
28 & 2.85781108972928e-07 & 5.71562217945857e-07 & 0.99999971421889 \tabularnewline
29 & 2.00544372165039e-05 & 4.01088744330077e-05 & 0.999979945562783 \tabularnewline
30 & 0.000730191552076684 & 0.00146038310415337 & 0.999269808447923 \tabularnewline
31 & 0.00065513313300026 & 0.00131026626600052 & 0.999344866867 \tabularnewline
32 & 0.00100689964603218 & 0.00201379929206437 & 0.998993100353968 \tabularnewline
33 & 0.00579835445421795 & 0.0115967089084359 & 0.994201645545782 \tabularnewline
34 & 0.0102912183971523 & 0.0205824367943045 & 0.989708781602848 \tabularnewline
35 & 0.0280961797939775 & 0.0561923595879551 & 0.971903820206023 \tabularnewline
36 & 0.0202841624493202 & 0.0405683248986405 & 0.97971583755068 \tabularnewline
37 & 0.0240890950979435 & 0.048178190195887 & 0.975910904902056 \tabularnewline
38 & 0.0146963351049483 & 0.0293926702098966 & 0.985303664895052 \tabularnewline
39 & 0.0088489876808101 & 0.0176979753616202 & 0.99115101231919 \tabularnewline
40 & 0.074355239620556 & 0.148710479241112 & 0.925644760379444 \tabularnewline
41 & 0.0505578088223299 & 0.10111561764466 & 0.94944219117767 \tabularnewline
42 & 0.0323231118624734 & 0.0646462237249468 & 0.967676888137527 \tabularnewline
43 & 0.0252439127524139 & 0.0504878255048279 & 0.974756087247586 \tabularnewline
44 & 0.014705780199162 & 0.0294115603983241 & 0.985294219800838 \tabularnewline
45 & 0.0104028767817804 & 0.0208057535635608 & 0.98959712321822 \tabularnewline
46 & 0.00846658724925856 & 0.0169331744985171 & 0.991533412750741 \tabularnewline
47 & 0.00589681830490673 & 0.0117936366098135 & 0.994103181695093 \tabularnewline
48 & 0.00603477909844426 & 0.0120695581968885 & 0.993965220901556 \tabularnewline
49 & 0.0074651685665493 & 0.0149303371330986 & 0.99253483143345 \tabularnewline
50 & 0.00478078240384941 & 0.00956156480769881 & 0.99521921759615 \tabularnewline
51 & 0.0146893020341272 & 0.0293786040682544 & 0.985310697965873 \tabularnewline
52 & 0.00895939489429626 & 0.0179187897885925 & 0.991040605105704 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146306&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]2.36992089781145e-05[/C][C]4.73984179562289e-05[/C][C]0.999976300791022[/C][/ROW]
[ROW][C]9[/C][C]1.69032309544403e-06[/C][C]3.38064619088807e-06[/C][C]0.999998309676905[/C][/ROW]
[ROW][C]10[/C][C]5.04489306176052e-08[/C][C]1.0089786123521e-07[/C][C]0.99999994955107[/C][/ROW]
[ROW][C]11[/C][C]1.22957126508813e-08[/C][C]2.45914253017626e-08[/C][C]0.999999987704287[/C][/ROW]
[ROW][C]12[/C][C]4.21779567280684e-10[/C][C]8.43559134561368e-10[/C][C]0.99999999957822[/C][/ROW]
[ROW][C]13[/C][C]1.9961876744273e-11[/C][C]3.9923753488546e-11[/C][C]0.999999999980038[/C][/ROW]
[ROW][C]14[/C][C]1.95623355004274e-11[/C][C]3.91246710008549e-11[/C][C]0.999999999980438[/C][/ROW]
[ROW][C]15[/C][C]1.9627997273656e-12[/C][C]3.9255994547312e-12[/C][C]0.999999999998037[/C][/ROW]
[ROW][C]16[/C][C]1.057185010217e-13[/C][C]2.114370020434e-13[/C][C]0.999999999999894[/C][/ROW]
[ROW][C]17[/C][C]2.26434939855125e-13[/C][C]4.5286987971025e-13[/C][C]0.999999999999774[/C][/ROW]
[ROW][C]18[/C][C]3.00822308321357e-14[/C][C]6.01644616642715e-14[/C][C]0.99999999999997[/C][/ROW]
[ROW][C]19[/C][C]1.82839376012464e-15[/C][C]3.65678752024928e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]20[/C][C]1.20619533112898e-16[/C][C]2.41239066225796e-16[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]3.68255586916742e-08[/C][C]7.36511173833484e-08[/C][C]0.999999963174441[/C][/ROW]
[ROW][C]22[/C][C]1.59640175375234e-08[/C][C]3.19280350750469e-08[/C][C]0.999999984035982[/C][/ROW]
[ROW][C]23[/C][C]9.94263614361474e-08[/C][C]1.98852722872295e-07[/C][C]0.999999900573639[/C][/ROW]
[ROW][C]24[/C][C]6.18724632097257e-08[/C][C]1.23744926419451e-07[/C][C]0.999999938127537[/C][/ROW]
[ROW][C]25[/C][C]2.12855073018338e-08[/C][C]4.25710146036677e-08[/C][C]0.999999978714493[/C][/ROW]
[ROW][C]26[/C][C]1.48644356042479e-08[/C][C]2.97288712084959e-08[/C][C]0.999999985135564[/C][/ROW]
[ROW][C]27[/C][C]1.9968547319576e-08[/C][C]3.9937094639152e-08[/C][C]0.999999980031453[/C][/ROW]
[ROW][C]28[/C][C]2.85781108972928e-07[/C][C]5.71562217945857e-07[/C][C]0.99999971421889[/C][/ROW]
[ROW][C]29[/C][C]2.00544372165039e-05[/C][C]4.01088744330077e-05[/C][C]0.999979945562783[/C][/ROW]
[ROW][C]30[/C][C]0.000730191552076684[/C][C]0.00146038310415337[/C][C]0.999269808447923[/C][/ROW]
[ROW][C]31[/C][C]0.00065513313300026[/C][C]0.00131026626600052[/C][C]0.999344866867[/C][/ROW]
[ROW][C]32[/C][C]0.00100689964603218[/C][C]0.00201379929206437[/C][C]0.998993100353968[/C][/ROW]
[ROW][C]33[/C][C]0.00579835445421795[/C][C]0.0115967089084359[/C][C]0.994201645545782[/C][/ROW]
[ROW][C]34[/C][C]0.0102912183971523[/C][C]0.0205824367943045[/C][C]0.989708781602848[/C][/ROW]
[ROW][C]35[/C][C]0.0280961797939775[/C][C]0.0561923595879551[/C][C]0.971903820206023[/C][/ROW]
[ROW][C]36[/C][C]0.0202841624493202[/C][C]0.0405683248986405[/C][C]0.97971583755068[/C][/ROW]
[ROW][C]37[/C][C]0.0240890950979435[/C][C]0.048178190195887[/C][C]0.975910904902056[/C][/ROW]
[ROW][C]38[/C][C]0.0146963351049483[/C][C]0.0293926702098966[/C][C]0.985303664895052[/C][/ROW]
[ROW][C]39[/C][C]0.0088489876808101[/C][C]0.0176979753616202[/C][C]0.99115101231919[/C][/ROW]
[ROW][C]40[/C][C]0.074355239620556[/C][C]0.148710479241112[/C][C]0.925644760379444[/C][/ROW]
[ROW][C]41[/C][C]0.0505578088223299[/C][C]0.10111561764466[/C][C]0.94944219117767[/C][/ROW]
[ROW][C]42[/C][C]0.0323231118624734[/C][C]0.0646462237249468[/C][C]0.967676888137527[/C][/ROW]
[ROW][C]43[/C][C]0.0252439127524139[/C][C]0.0504878255048279[/C][C]0.974756087247586[/C][/ROW]
[ROW][C]44[/C][C]0.014705780199162[/C][C]0.0294115603983241[/C][C]0.985294219800838[/C][/ROW]
[ROW][C]45[/C][C]0.0104028767817804[/C][C]0.0208057535635608[/C][C]0.98959712321822[/C][/ROW]
[ROW][C]46[/C][C]0.00846658724925856[/C][C]0.0169331744985171[/C][C]0.991533412750741[/C][/ROW]
[ROW][C]47[/C][C]0.00589681830490673[/C][C]0.0117936366098135[/C][C]0.994103181695093[/C][/ROW]
[ROW][C]48[/C][C]0.00603477909844426[/C][C]0.0120695581968885[/C][C]0.993965220901556[/C][/ROW]
[ROW][C]49[/C][C]0.0074651685665493[/C][C]0.0149303371330986[/C][C]0.99253483143345[/C][/ROW]
[ROW][C]50[/C][C]0.00478078240384941[/C][C]0.00956156480769881[/C][C]0.99521921759615[/C][/ROW]
[ROW][C]51[/C][C]0.0146893020341272[/C][C]0.0293786040682544[/C][C]0.985310697965873[/C][/ROW]
[ROW][C]52[/C][C]0.00895939489429626[/C][C]0.0179187897885925[/C][C]0.991040605105704[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146306&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146306&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
82.36992089781145e-054.73984179562289e-050.999976300791022
91.69032309544403e-063.38064619088807e-060.999998309676905
105.04489306176052e-081.0089786123521e-070.99999994955107
111.22957126508813e-082.45914253017626e-080.999999987704287
124.21779567280684e-108.43559134561368e-100.99999999957822
131.9961876744273e-113.9923753488546e-110.999999999980038
141.95623355004274e-113.91246710008549e-110.999999999980438
151.9627997273656e-123.9255994547312e-120.999999999998037
161.057185010217e-132.114370020434e-130.999999999999894
172.26434939855125e-134.5286987971025e-130.999999999999774
183.00822308321357e-146.01644616642715e-140.99999999999997
191.82839376012464e-153.65678752024928e-150.999999999999998
201.20619533112898e-162.41239066225796e-161
213.68255586916742e-087.36511173833484e-080.999999963174441
221.59640175375234e-083.19280350750469e-080.999999984035982
239.94263614361474e-081.98852722872295e-070.999999900573639
246.18724632097257e-081.23744926419451e-070.999999938127537
252.12855073018338e-084.25710146036677e-080.999999978714493
261.48644356042479e-082.97288712084959e-080.999999985135564
271.9968547319576e-083.9937094639152e-080.999999980031453
282.85781108972928e-075.71562217945857e-070.99999971421889
292.00544372165039e-054.01088744330077e-050.999979945562783
300.0007301915520766840.001460383104153370.999269808447923
310.000655133133000260.001310266266000520.999344866867
320.001006899646032180.002013799292064370.998993100353968
330.005798354454217950.01159670890843590.994201645545782
340.01029121839715230.02058243679430450.989708781602848
350.02809617979397750.05619235958795510.971903820206023
360.02028416244932020.04056832489864050.97971583755068
370.02408909509794350.0481781901958870.975910904902056
380.01469633510494830.02939267020989660.985303664895052
390.00884898768081010.01769797536162020.99115101231919
400.0743552396205560.1487104792411120.925644760379444
410.05055780882232990.101115617644660.94944219117767
420.03232311186247340.06464622372494680.967676888137527
430.02524391275241390.05048782550482790.974756087247586
440.0147057801991620.02941156039832410.985294219800838
450.01040287678178040.02080575356356080.98959712321822
460.008466587249258560.01693317449851710.991533412750741
470.005896818304906730.01179363660981350.994103181695093
480.006034779098444260.01206955819688850.993965220901556
490.00746516856654930.01493033713309860.99253483143345
500.004780782403849410.009561564807698810.99521921759615
510.01468930203412720.02937860406825440.985310697965873
520.008959394894296260.01791878978859250.991040605105704







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level260.577777777777778NOK
5% type I error level400.888888888888889NOK
10% type I error level430.955555555555556NOK

\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 & 26 & 0.577777777777778 & NOK \tabularnewline
5% type I error level & 40 & 0.888888888888889 & NOK \tabularnewline
10% type I error level & 43 & 0.955555555555556 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146306&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]26[/C][C]0.577777777777778[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]40[/C][C]0.888888888888889[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]43[/C][C]0.955555555555556[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146306&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146306&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 level260.577777777777778NOK
5% type I error level400.888888888888889NOK
10% type I error level430.955555555555556NOK



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