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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 computationMon, 05 Nov 2012 14:41:43 -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/2012/Nov/05/t1352144521dvdi5huz1vzqz7t.htm/, Retrieved Wed, 01 Feb 2023 16:57:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186259, Retrieved Wed, 01 Feb 2023 16:57:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [Interaction effec...] [2011-11-22 18:35:27] [59e9c089bdd600b584669dddc48fbcc3]
-   PD      [Multiple Regression] [autocorrelation] [2012-11-05 19:41:43] [9f1ef512d1eac2da3e1af89c6a547aff] [Current]
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Dataseries X:
7645	27276	111528	27383
7240	27310	111627	27474
7237	27346	111733	27610
7170	27385	111849	27460
7067	27422	111970	27079
7149	27459	112093	26726
6979	27498	112222	25414
6766	27541	112354	25914
6850	27584	112486	26305
6731	27627	112493	25660
6927	27640	112596	26115
7116	27666	112619	26458
11299	27675	112695	26722
10544	27704	112737	26134
10083	27709	112803	26203
9501	27746	112852	26568
9450	27780	112912	26718
8950	27816	113029	26560
8578	27854	113154	25473
8395	27896	113281	25676
7631	27939	113414	25859
7816	27982	113546	25632
7491	28021	113573	25824
7678	28052	113660	26097
15124	28059	113666	26179
15227	28085	113758	25788
15421	28118	113769	26277
15012	28153	113857	26342
14861	28184	113953	26592
14646	28217	114060	26679
14727	28252	114173	25694
14505	28290	114288	26024
13796	28330	114411	26038
13389	28369	114530	25736
12860	28404	114632	25930
12049	28437	114648	26265
14393	28526	114728	26042
15104	28559	114735	24925
14636	28591	114821	25552
14574	28624	114910	26142
14735	28653	115001	26474
14609	28685	115102	26630
14517	28718	115207	25460
14876	28755	115317	25472
15221	28794	115433	25325
15128	28831	115542	25121
15039	28865	115640	25313
14953	28896	115731	25535
13097	28947	115828	25255
13323	28976	115907	24856
13759	29005	115988	25289
13897	29035	116067	25411
13920	29063	116156	25372
13908	29093	116250	25322
14024	29123	116347	24961
13892	29158	116453	24973
13792	29193	116559	25241
13628	29228	116664	24832
13751	29259	116755	24927
13919	29286	116808	25024
12258	29727	116832	25131
12088	29760	116896	24657
12544	29792	116986	24835
12794	29824	117081	25126
12749	29854	117177	25481
12720	29885	117277	25321
12500	29918	117381	24787
12673	29954	117492	24626
12806	29991	117600	24850
12758	30027	117710	24566




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=186259&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=186259&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186259&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 time8 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
unemployment[t] = -448953.956835141 -10.0941951303399black[t] + 6.16170741708651males[t] + 1.64164420199708`highschool\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
unemployment[t] =  -448953.956835141 -10.0941951303399black[t] +  6.16170741708651males[t] +  1.64164420199708`highschool\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186259&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]unemployment[t] =  -448953.956835141 -10.0941951303399black[t] +  6.16170741708651males[t] +  1.64164420199708`highschool\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186259&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186259&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
unemployment[t] = -448953.956835141 -10.0941951303399black[t] + 6.16170741708651males[t] + 1.64164420199708`highschool\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-448953.95683514162600.007137-7.171800
black-10.09419513033992.046178-4.93326e-063e-06
males6.161707417086510.9528826.466400
`highschool\r`1.641644201997080.5448433.01310.0036650.001833

\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) & -448953.956835141 & 62600.007137 & -7.1718 & 0 & 0 \tabularnewline
black & -10.0941951303399 & 2.046178 & -4.9332 & 6e-06 & 3e-06 \tabularnewline
males & 6.16170741708651 & 0.952882 & 6.4664 & 0 & 0 \tabularnewline
`highschool\r` & 1.64164420199708 & 0.544843 & 3.0131 & 0.003665 & 0.001833 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186259&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]-448953.956835141[/C][C]62600.007137[/C][C]-7.1718[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]black[/C][C]-10.0941951303399[/C][C]2.046178[/C][C]-4.9332[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]males[/C][C]6.16170741708651[/C][C]0.952882[/C][C]6.4664[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`highschool\r`[/C][C]1.64164420199708[/C][C]0.544843[/C][C]3.0131[/C][C]0.003665[/C][C]0.001833[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186259&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186259&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)-448953.95683514162600.007137-7.171800
black-10.09419513033992.046178-4.93326e-063e-06
males6.161707417086510.9528826.466400
`highschool\r`1.641644201997080.5448433.01310.0036650.001833







Multiple Linear Regression - Regression Statistics
Multiple R0.790717204877859
R-squared0.625233698089854
Adjusted R-squared0.608198866184848
F-TEST (value)36.7032502331939
F-TEST (DF numerator)3
F-TEST (DF denominator)66
p-value4.49640324973188e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1895.54904775069
Sum Squared Residuals237145008.700284

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.790717204877859 \tabularnewline
R-squared & 0.625233698089854 \tabularnewline
Adjusted R-squared & 0.608198866184848 \tabularnewline
F-TEST (value) & 36.7032502331939 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 66 \tabularnewline
p-value & 4.49640324973188e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1895.54904775069 \tabularnewline
Sum Squared Residuals & 237145008.700284 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186259&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.790717204877859[/C][/ROW]
[ROW][C]R-squared[/C][C]0.625233698089854[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.608198866184848[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]36.7032502331939[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]66[/C][/ROW]
[ROW][C]p-value[/C][C]4.49640324973188e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1895.54904775069[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]237145008.700284[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186259&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186259&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 R0.790717204877859
R-squared0.625233698089854
Adjusted R-squared0.608198866184848
F-TEST (value)36.7032502331939
F-TEST (DF numerator)3
F-TEST (DF denominator)66
p-value4.49640324973188e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1895.54904775069
Sum Squared Residuals237145008.700284







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
176457872.82478581753-227.824785817532
272408289.02080805921-1049.02080805921
372378802.03438104975-1565.03438104974
471708876.87220104896-1706.87220104896
570678623.48713773297-1556.48713773297
671498428.39152690706-1279.39152690706
769796675.7409806078303.259019392197
867667875.85807005714-1109.85807005714
968508897.0359414888-2047.0359414888
1067317447.25699251568-716.256992515679
1169278697.63643168984-1770.63643168984
1271169139.99059017899-2023.99059017899
13112999950.826667031731348.17333296827
14105448951.599928995231592.40007100477
15100839421.07509280904661.924907190964
1695019948.71367015263-447.713670152632
17945010221.4601110458-771.460111045826
18895010319.6090702372-1369.60907023717
1985788921.77583484925-343.775834849249
2083959613.61025435037-1218.61025435037
21763110299.4878391837-2668.48783918372
22781610306.1295937812-2490.12959378119
23749110394.0177707427-2903.01777074271
24767811065.3351341339-3387.3351341339
251512411166.26083728783957.7391627122
261522710828.80596329014398.19403670994
271542111366.24032035344054.75967964663
281501211661.88061662493350.11938337511
291486112350.89553012392510.10446987607
301464612819.91283002471826.08716997529
311472711545.86939962653181.13060037353
321450512412.62892429752092.37107570246
331379612789.73415021351006.26584978646
341338912633.5271737605755.472826239534
351286013227.2034759288-367.203475928826
361204913542.63316297-1493.63316297001
371439312771.09973269131621.90026730867
381510410647.4066716794456.59332832101
391463611883.61018002972752.38981997028
401457413067.46378002751506.53621997253
411473513880.4733712655854.526628734481
421460914435.8880717319173.111928268077
431451712829.03519488821687.96480511179
441487613153.03752136911722.96247863088
451522113232.80027397431988.19972602568
461512813196.04574540681931.95425459323
471503913771.88612463311267.11387536687
481495314384.1264633908568.873536609181
491309714007.3477546417-910.347754641695
501332313546.3749452148-223.374945214839
511375914463.5735266837-704.573526683723
521389714847.803151367-950.803151367003
531392015049.5335239603-1129.5335239603
541390815243.8259571564-1335.82595715638
551402414946.0521657826-922.052165782631
561389215265.5960528559-1373.59605285587
571379216005.4008556404-2213.40085564036
581362815627.6508262557-1999.65082625574
591375116031.4023513598-2280.4023513598
601391916244.6690635399-2325.66906353992
611225812116.6659186838141.334081316216
621208811399.7674023295688.232597670509
631254411923.5194936519620.48050634812
641279412663.5859168854130.414083114631
651274913535.0676667244-786.067666724438
661272013575.655287073-855.655287073021
671250013006.7264152824-506.726415282362
681267313062.9801973652-389.980197365199
691280613722.6876798353-916.68767983531
701275813570.8575176554-812.857517655423

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7645 & 7872.82478581753 & -227.824785817532 \tabularnewline
2 & 7240 & 8289.02080805921 & -1049.02080805921 \tabularnewline
3 & 7237 & 8802.03438104975 & -1565.03438104974 \tabularnewline
4 & 7170 & 8876.87220104896 & -1706.87220104896 \tabularnewline
5 & 7067 & 8623.48713773297 & -1556.48713773297 \tabularnewline
6 & 7149 & 8428.39152690706 & -1279.39152690706 \tabularnewline
7 & 6979 & 6675.7409806078 & 303.259019392197 \tabularnewline
8 & 6766 & 7875.85807005714 & -1109.85807005714 \tabularnewline
9 & 6850 & 8897.0359414888 & -2047.0359414888 \tabularnewline
10 & 6731 & 7447.25699251568 & -716.256992515679 \tabularnewline
11 & 6927 & 8697.63643168984 & -1770.63643168984 \tabularnewline
12 & 7116 & 9139.99059017899 & -2023.99059017899 \tabularnewline
13 & 11299 & 9950.82666703173 & 1348.17333296827 \tabularnewline
14 & 10544 & 8951.59992899523 & 1592.40007100477 \tabularnewline
15 & 10083 & 9421.07509280904 & 661.924907190964 \tabularnewline
16 & 9501 & 9948.71367015263 & -447.713670152632 \tabularnewline
17 & 9450 & 10221.4601110458 & -771.460111045826 \tabularnewline
18 & 8950 & 10319.6090702372 & -1369.60907023717 \tabularnewline
19 & 8578 & 8921.77583484925 & -343.775834849249 \tabularnewline
20 & 8395 & 9613.61025435037 & -1218.61025435037 \tabularnewline
21 & 7631 & 10299.4878391837 & -2668.48783918372 \tabularnewline
22 & 7816 & 10306.1295937812 & -2490.12959378119 \tabularnewline
23 & 7491 & 10394.0177707427 & -2903.01777074271 \tabularnewline
24 & 7678 & 11065.3351341339 & -3387.3351341339 \tabularnewline
25 & 15124 & 11166.2608372878 & 3957.7391627122 \tabularnewline
26 & 15227 & 10828.8059632901 & 4398.19403670994 \tabularnewline
27 & 15421 & 11366.2403203534 & 4054.75967964663 \tabularnewline
28 & 15012 & 11661.8806166249 & 3350.11938337511 \tabularnewline
29 & 14861 & 12350.8955301239 & 2510.10446987607 \tabularnewline
30 & 14646 & 12819.9128300247 & 1826.08716997529 \tabularnewline
31 & 14727 & 11545.8693996265 & 3181.13060037353 \tabularnewline
32 & 14505 & 12412.6289242975 & 2092.37107570246 \tabularnewline
33 & 13796 & 12789.7341502135 & 1006.26584978646 \tabularnewline
34 & 13389 & 12633.5271737605 & 755.472826239534 \tabularnewline
35 & 12860 & 13227.2034759288 & -367.203475928826 \tabularnewline
36 & 12049 & 13542.63316297 & -1493.63316297001 \tabularnewline
37 & 14393 & 12771.0997326913 & 1621.90026730867 \tabularnewline
38 & 15104 & 10647.406671679 & 4456.59332832101 \tabularnewline
39 & 14636 & 11883.6101800297 & 2752.38981997028 \tabularnewline
40 & 14574 & 13067.4637800275 & 1506.53621997253 \tabularnewline
41 & 14735 & 13880.4733712655 & 854.526628734481 \tabularnewline
42 & 14609 & 14435.8880717319 & 173.111928268077 \tabularnewline
43 & 14517 & 12829.0351948882 & 1687.96480511179 \tabularnewline
44 & 14876 & 13153.0375213691 & 1722.96247863088 \tabularnewline
45 & 15221 & 13232.8002739743 & 1988.19972602568 \tabularnewline
46 & 15128 & 13196.0457454068 & 1931.95425459323 \tabularnewline
47 & 15039 & 13771.8861246331 & 1267.11387536687 \tabularnewline
48 & 14953 & 14384.1264633908 & 568.873536609181 \tabularnewline
49 & 13097 & 14007.3477546417 & -910.347754641695 \tabularnewline
50 & 13323 & 13546.3749452148 & -223.374945214839 \tabularnewline
51 & 13759 & 14463.5735266837 & -704.573526683723 \tabularnewline
52 & 13897 & 14847.803151367 & -950.803151367003 \tabularnewline
53 & 13920 & 15049.5335239603 & -1129.5335239603 \tabularnewline
54 & 13908 & 15243.8259571564 & -1335.82595715638 \tabularnewline
55 & 14024 & 14946.0521657826 & -922.052165782631 \tabularnewline
56 & 13892 & 15265.5960528559 & -1373.59605285587 \tabularnewline
57 & 13792 & 16005.4008556404 & -2213.40085564036 \tabularnewline
58 & 13628 & 15627.6508262557 & -1999.65082625574 \tabularnewline
59 & 13751 & 16031.4023513598 & -2280.4023513598 \tabularnewline
60 & 13919 & 16244.6690635399 & -2325.66906353992 \tabularnewline
61 & 12258 & 12116.6659186838 & 141.334081316216 \tabularnewline
62 & 12088 & 11399.7674023295 & 688.232597670509 \tabularnewline
63 & 12544 & 11923.5194936519 & 620.48050634812 \tabularnewline
64 & 12794 & 12663.5859168854 & 130.414083114631 \tabularnewline
65 & 12749 & 13535.0676667244 & -786.067666724438 \tabularnewline
66 & 12720 & 13575.655287073 & -855.655287073021 \tabularnewline
67 & 12500 & 13006.7264152824 & -506.726415282362 \tabularnewline
68 & 12673 & 13062.9801973652 & -389.980197365199 \tabularnewline
69 & 12806 & 13722.6876798353 & -916.68767983531 \tabularnewline
70 & 12758 & 13570.8575176554 & -812.857517655423 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186259&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]7645[/C][C]7872.82478581753[/C][C]-227.824785817532[/C][/ROW]
[ROW][C]2[/C][C]7240[/C][C]8289.02080805921[/C][C]-1049.02080805921[/C][/ROW]
[ROW][C]3[/C][C]7237[/C][C]8802.03438104975[/C][C]-1565.03438104974[/C][/ROW]
[ROW][C]4[/C][C]7170[/C][C]8876.87220104896[/C][C]-1706.87220104896[/C][/ROW]
[ROW][C]5[/C][C]7067[/C][C]8623.48713773297[/C][C]-1556.48713773297[/C][/ROW]
[ROW][C]6[/C][C]7149[/C][C]8428.39152690706[/C][C]-1279.39152690706[/C][/ROW]
[ROW][C]7[/C][C]6979[/C][C]6675.7409806078[/C][C]303.259019392197[/C][/ROW]
[ROW][C]8[/C][C]6766[/C][C]7875.85807005714[/C][C]-1109.85807005714[/C][/ROW]
[ROW][C]9[/C][C]6850[/C][C]8897.0359414888[/C][C]-2047.0359414888[/C][/ROW]
[ROW][C]10[/C][C]6731[/C][C]7447.25699251568[/C][C]-716.256992515679[/C][/ROW]
[ROW][C]11[/C][C]6927[/C][C]8697.63643168984[/C][C]-1770.63643168984[/C][/ROW]
[ROW][C]12[/C][C]7116[/C][C]9139.99059017899[/C][C]-2023.99059017899[/C][/ROW]
[ROW][C]13[/C][C]11299[/C][C]9950.82666703173[/C][C]1348.17333296827[/C][/ROW]
[ROW][C]14[/C][C]10544[/C][C]8951.59992899523[/C][C]1592.40007100477[/C][/ROW]
[ROW][C]15[/C][C]10083[/C][C]9421.07509280904[/C][C]661.924907190964[/C][/ROW]
[ROW][C]16[/C][C]9501[/C][C]9948.71367015263[/C][C]-447.713670152632[/C][/ROW]
[ROW][C]17[/C][C]9450[/C][C]10221.4601110458[/C][C]-771.460111045826[/C][/ROW]
[ROW][C]18[/C][C]8950[/C][C]10319.6090702372[/C][C]-1369.60907023717[/C][/ROW]
[ROW][C]19[/C][C]8578[/C][C]8921.77583484925[/C][C]-343.775834849249[/C][/ROW]
[ROW][C]20[/C][C]8395[/C][C]9613.61025435037[/C][C]-1218.61025435037[/C][/ROW]
[ROW][C]21[/C][C]7631[/C][C]10299.4878391837[/C][C]-2668.48783918372[/C][/ROW]
[ROW][C]22[/C][C]7816[/C][C]10306.1295937812[/C][C]-2490.12959378119[/C][/ROW]
[ROW][C]23[/C][C]7491[/C][C]10394.0177707427[/C][C]-2903.01777074271[/C][/ROW]
[ROW][C]24[/C][C]7678[/C][C]11065.3351341339[/C][C]-3387.3351341339[/C][/ROW]
[ROW][C]25[/C][C]15124[/C][C]11166.2608372878[/C][C]3957.7391627122[/C][/ROW]
[ROW][C]26[/C][C]15227[/C][C]10828.8059632901[/C][C]4398.19403670994[/C][/ROW]
[ROW][C]27[/C][C]15421[/C][C]11366.2403203534[/C][C]4054.75967964663[/C][/ROW]
[ROW][C]28[/C][C]15012[/C][C]11661.8806166249[/C][C]3350.11938337511[/C][/ROW]
[ROW][C]29[/C][C]14861[/C][C]12350.8955301239[/C][C]2510.10446987607[/C][/ROW]
[ROW][C]30[/C][C]14646[/C][C]12819.9128300247[/C][C]1826.08716997529[/C][/ROW]
[ROW][C]31[/C][C]14727[/C][C]11545.8693996265[/C][C]3181.13060037353[/C][/ROW]
[ROW][C]32[/C][C]14505[/C][C]12412.6289242975[/C][C]2092.37107570246[/C][/ROW]
[ROW][C]33[/C][C]13796[/C][C]12789.7341502135[/C][C]1006.26584978646[/C][/ROW]
[ROW][C]34[/C][C]13389[/C][C]12633.5271737605[/C][C]755.472826239534[/C][/ROW]
[ROW][C]35[/C][C]12860[/C][C]13227.2034759288[/C][C]-367.203475928826[/C][/ROW]
[ROW][C]36[/C][C]12049[/C][C]13542.63316297[/C][C]-1493.63316297001[/C][/ROW]
[ROW][C]37[/C][C]14393[/C][C]12771.0997326913[/C][C]1621.90026730867[/C][/ROW]
[ROW][C]38[/C][C]15104[/C][C]10647.406671679[/C][C]4456.59332832101[/C][/ROW]
[ROW][C]39[/C][C]14636[/C][C]11883.6101800297[/C][C]2752.38981997028[/C][/ROW]
[ROW][C]40[/C][C]14574[/C][C]13067.4637800275[/C][C]1506.53621997253[/C][/ROW]
[ROW][C]41[/C][C]14735[/C][C]13880.4733712655[/C][C]854.526628734481[/C][/ROW]
[ROW][C]42[/C][C]14609[/C][C]14435.8880717319[/C][C]173.111928268077[/C][/ROW]
[ROW][C]43[/C][C]14517[/C][C]12829.0351948882[/C][C]1687.96480511179[/C][/ROW]
[ROW][C]44[/C][C]14876[/C][C]13153.0375213691[/C][C]1722.96247863088[/C][/ROW]
[ROW][C]45[/C][C]15221[/C][C]13232.8002739743[/C][C]1988.19972602568[/C][/ROW]
[ROW][C]46[/C][C]15128[/C][C]13196.0457454068[/C][C]1931.95425459323[/C][/ROW]
[ROW][C]47[/C][C]15039[/C][C]13771.8861246331[/C][C]1267.11387536687[/C][/ROW]
[ROW][C]48[/C][C]14953[/C][C]14384.1264633908[/C][C]568.873536609181[/C][/ROW]
[ROW][C]49[/C][C]13097[/C][C]14007.3477546417[/C][C]-910.347754641695[/C][/ROW]
[ROW][C]50[/C][C]13323[/C][C]13546.3749452148[/C][C]-223.374945214839[/C][/ROW]
[ROW][C]51[/C][C]13759[/C][C]14463.5735266837[/C][C]-704.573526683723[/C][/ROW]
[ROW][C]52[/C][C]13897[/C][C]14847.803151367[/C][C]-950.803151367003[/C][/ROW]
[ROW][C]53[/C][C]13920[/C][C]15049.5335239603[/C][C]-1129.5335239603[/C][/ROW]
[ROW][C]54[/C][C]13908[/C][C]15243.8259571564[/C][C]-1335.82595715638[/C][/ROW]
[ROW][C]55[/C][C]14024[/C][C]14946.0521657826[/C][C]-922.052165782631[/C][/ROW]
[ROW][C]56[/C][C]13892[/C][C]15265.5960528559[/C][C]-1373.59605285587[/C][/ROW]
[ROW][C]57[/C][C]13792[/C][C]16005.4008556404[/C][C]-2213.40085564036[/C][/ROW]
[ROW][C]58[/C][C]13628[/C][C]15627.6508262557[/C][C]-1999.65082625574[/C][/ROW]
[ROW][C]59[/C][C]13751[/C][C]16031.4023513598[/C][C]-2280.4023513598[/C][/ROW]
[ROW][C]60[/C][C]13919[/C][C]16244.6690635399[/C][C]-2325.66906353992[/C][/ROW]
[ROW][C]61[/C][C]12258[/C][C]12116.6659186838[/C][C]141.334081316216[/C][/ROW]
[ROW][C]62[/C][C]12088[/C][C]11399.7674023295[/C][C]688.232597670509[/C][/ROW]
[ROW][C]63[/C][C]12544[/C][C]11923.5194936519[/C][C]620.48050634812[/C][/ROW]
[ROW][C]64[/C][C]12794[/C][C]12663.5859168854[/C][C]130.414083114631[/C][/ROW]
[ROW][C]65[/C][C]12749[/C][C]13535.0676667244[/C][C]-786.067666724438[/C][/ROW]
[ROW][C]66[/C][C]12720[/C][C]13575.655287073[/C][C]-855.655287073021[/C][/ROW]
[ROW][C]67[/C][C]12500[/C][C]13006.7264152824[/C][C]-506.726415282362[/C][/ROW]
[ROW][C]68[/C][C]12673[/C][C]13062.9801973652[/C][C]-389.980197365199[/C][/ROW]
[ROW][C]69[/C][C]12806[/C][C]13722.6876798353[/C][C]-916.68767983531[/C][/ROW]
[ROW][C]70[/C][C]12758[/C][C]13570.8575176554[/C][C]-812.857517655423[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186259&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186259&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
176457872.82478581753-227.824785817532
272408289.02080805921-1049.02080805921
372378802.03438104975-1565.03438104974
471708876.87220104896-1706.87220104896
570678623.48713773297-1556.48713773297
671498428.39152690706-1279.39152690706
769796675.7409806078303.259019392197
867667875.85807005714-1109.85807005714
968508897.0359414888-2047.0359414888
1067317447.25699251568-716.256992515679
1169278697.63643168984-1770.63643168984
1271169139.99059017899-2023.99059017899
13112999950.826667031731348.17333296827
14105448951.599928995231592.40007100477
15100839421.07509280904661.924907190964
1695019948.71367015263-447.713670152632
17945010221.4601110458-771.460111045826
18895010319.6090702372-1369.60907023717
1985788921.77583484925-343.775834849249
2083959613.61025435037-1218.61025435037
21763110299.4878391837-2668.48783918372
22781610306.1295937812-2490.12959378119
23749110394.0177707427-2903.01777074271
24767811065.3351341339-3387.3351341339
251512411166.26083728783957.7391627122
261522710828.80596329014398.19403670994
271542111366.24032035344054.75967964663
281501211661.88061662493350.11938337511
291486112350.89553012392510.10446987607
301464612819.91283002471826.08716997529
311472711545.86939962653181.13060037353
321450512412.62892429752092.37107570246
331379612789.73415021351006.26584978646
341338912633.5271737605755.472826239534
351286013227.2034759288-367.203475928826
361204913542.63316297-1493.63316297001
371439312771.09973269131621.90026730867
381510410647.4066716794456.59332832101
391463611883.61018002972752.38981997028
401457413067.46378002751506.53621997253
411473513880.4733712655854.526628734481
421460914435.8880717319173.111928268077
431451712829.03519488821687.96480511179
441487613153.03752136911722.96247863088
451522113232.80027397431988.19972602568
461512813196.04574540681931.95425459323
471503913771.88612463311267.11387536687
481495314384.1264633908568.873536609181
491309714007.3477546417-910.347754641695
501332313546.3749452148-223.374945214839
511375914463.5735266837-704.573526683723
521389714847.803151367-950.803151367003
531392015049.5335239603-1129.5335239603
541390815243.8259571564-1335.82595715638
551402414946.0521657826-922.052165782631
561389215265.5960528559-1373.59605285587
571379216005.4008556404-2213.40085564036
581362815627.6508262557-1999.65082625574
591375116031.4023513598-2280.4023513598
601391916244.6690635399-2325.66906353992
611225812116.6659186838141.334081316216
621208811399.7674023295688.232597670509
631254411923.5194936519620.48050634812
641279412663.5859168854130.414083114631
651274913535.0676667244-786.067666724438
661272013575.655287073-855.655287073021
671250013006.7264152824-506.726415282362
681267313062.9801973652-389.980197365199
691280613722.6876798353-916.68767983531
701275813570.8575176554-812.857517655423







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.0003221243690903290.0006442487381806570.99967787563091
83.60718602138808e-057.21437204277617e-050.999963928139786
92.44564358250206e-064.89128716500412e-060.999997554356417
103.47010664197143e-076.94021328394286e-070.999999652989336
119.68931412810633e-081.93786282562127e-070.999999903106859
124.07910239767492e-088.15820479534983e-080.999999959208976
130.0497842062360780.0995684124721560.950215793763922
140.08461927610282650.1692385522056530.915380723897174
150.06406989067963760.1281397813592750.935930109320362
160.04106998337850930.08213996675701870.958930016621491
170.02913015401741580.05826030803483170.970869845982584
180.02695255865785010.05390511731570030.97304744134215
190.0168785599186830.0337571198373660.983121440081317
200.01552144468452490.03104288936904990.984478555315475
210.04756650444520990.09513300889041980.95243349555479
220.1145525121906790.2291050243813590.88544748780932
230.4752998990818480.9505997981636950.524700100918152
240.9993188588311660.001362282337668010.000681141168834007
250.9999993771482041.24570359198877e-066.22851795994385e-07
260.9999999829451523.41096952008756e-081.70548476004378e-08
270.9999999911506731.76986550016332e-088.84932750081658e-09
280.9999999823963963.52072076012504e-081.76036038006252e-08
290.9999999565381648.69236718427396e-084.34618359213698e-08
300.9999998852998632.29400275083269e-071.14700137541634e-07
310.9999997559774184.88045164343978e-072.44022582171989e-07
320.9999993699844341.26003113205016e-066.30015566025081e-07
330.999998809635552.38072890003302e-061.19036445001651e-06
340.9999988901543032.21969139381967e-061.10984569690983e-06
350.99999981416383.71672400762658e-071.85836200381329e-07
360.9999999999971775.64623155553569e-122.82311577776784e-12
370.9999999999973945.21300618166833e-122.60650309083416e-12
380.9999999999917911.64176767311664e-118.20883836558319e-12
390.9999999999723055.53898330193045e-112.76949165096522e-11
400.9999999999311571.37686302381858e-106.88431511909288e-11
410.9999999998134993.73001585554889e-101.86500792777444e-10
420.9999999996001267.99748889886591e-103.99874444943295e-10
430.999999998519842.96031967693683e-091.48015983846842e-09
440.9999999952527979.4944063463878e-094.7472031731939e-09
450.9999999957719398.45612180656202e-094.22806090328101e-09
460.9999999991021631.79567386683172e-098.97836933415859e-10
470.9999999999644197.11625499631476e-113.55812749815738e-11
480.9999999999999843.13901247608282e-141.56950623804141e-14
490.9999999999999975.75859765335511e-152.87929882667756e-15
500.9999999999999843.11401878551586e-141.55700939275793e-14
510.9999999999998672.65307686194474e-131.32653843097237e-13
520.9999999999990371.92548000637236e-129.62740003186178e-13
530.9999999999933281.33432687145611e-116.67163435728057e-12
540.9999999999507599.84822841731496e-114.92411420865748e-11
550.999999999960677.86601394472792e-113.93300697236396e-11
560.9999999999136421.72715318635215e-108.63576593176074e-11
570.9999999988883212.22335752965797e-091.11167876482899e-09
580.9999999864583622.70832766535469e-081.35416383267735e-08
590.999999847973453.04053099921848e-071.52026549960924e-07
600.9999996383030487.2339390327819e-073.61696951639095e-07
610.9999994659469391.06810612166832e-065.3405306083416e-07
620.9999965391009116.92179817708767e-063.46089908854384e-06
630.999915281455760.0001694370884808128.47185442404059e-05

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.000322124369090329 & 0.000644248738180657 & 0.99967787563091 \tabularnewline
8 & 3.60718602138808e-05 & 7.21437204277617e-05 & 0.999963928139786 \tabularnewline
9 & 2.44564358250206e-06 & 4.89128716500412e-06 & 0.999997554356417 \tabularnewline
10 & 3.47010664197143e-07 & 6.94021328394286e-07 & 0.999999652989336 \tabularnewline
11 & 9.68931412810633e-08 & 1.93786282562127e-07 & 0.999999903106859 \tabularnewline
12 & 4.07910239767492e-08 & 8.15820479534983e-08 & 0.999999959208976 \tabularnewline
13 & 0.049784206236078 & 0.099568412472156 & 0.950215793763922 \tabularnewline
14 & 0.0846192761028265 & 0.169238552205653 & 0.915380723897174 \tabularnewline
15 & 0.0640698906796376 & 0.128139781359275 & 0.935930109320362 \tabularnewline
16 & 0.0410699833785093 & 0.0821399667570187 & 0.958930016621491 \tabularnewline
17 & 0.0291301540174158 & 0.0582603080348317 & 0.970869845982584 \tabularnewline
18 & 0.0269525586578501 & 0.0539051173157003 & 0.97304744134215 \tabularnewline
19 & 0.016878559918683 & 0.033757119837366 & 0.983121440081317 \tabularnewline
20 & 0.0155214446845249 & 0.0310428893690499 & 0.984478555315475 \tabularnewline
21 & 0.0475665044452099 & 0.0951330088904198 & 0.95243349555479 \tabularnewline
22 & 0.114552512190679 & 0.229105024381359 & 0.88544748780932 \tabularnewline
23 & 0.475299899081848 & 0.950599798163695 & 0.524700100918152 \tabularnewline
24 & 0.999318858831166 & 0.00136228233766801 & 0.000681141168834007 \tabularnewline
25 & 0.999999377148204 & 1.24570359198877e-06 & 6.22851795994385e-07 \tabularnewline
26 & 0.999999982945152 & 3.41096952008756e-08 & 1.70548476004378e-08 \tabularnewline
27 & 0.999999991150673 & 1.76986550016332e-08 & 8.84932750081658e-09 \tabularnewline
28 & 0.999999982396396 & 3.52072076012504e-08 & 1.76036038006252e-08 \tabularnewline
29 & 0.999999956538164 & 8.69236718427396e-08 & 4.34618359213698e-08 \tabularnewline
30 & 0.999999885299863 & 2.29400275083269e-07 & 1.14700137541634e-07 \tabularnewline
31 & 0.999999755977418 & 4.88045164343978e-07 & 2.44022582171989e-07 \tabularnewline
32 & 0.999999369984434 & 1.26003113205016e-06 & 6.30015566025081e-07 \tabularnewline
33 & 0.99999880963555 & 2.38072890003302e-06 & 1.19036445001651e-06 \tabularnewline
34 & 0.999998890154303 & 2.21969139381967e-06 & 1.10984569690983e-06 \tabularnewline
35 & 0.9999998141638 & 3.71672400762658e-07 & 1.85836200381329e-07 \tabularnewline
36 & 0.999999999997177 & 5.64623155553569e-12 & 2.82311577776784e-12 \tabularnewline
37 & 0.999999999997394 & 5.21300618166833e-12 & 2.60650309083416e-12 \tabularnewline
38 & 0.999999999991791 & 1.64176767311664e-11 & 8.20883836558319e-12 \tabularnewline
39 & 0.999999999972305 & 5.53898330193045e-11 & 2.76949165096522e-11 \tabularnewline
40 & 0.999999999931157 & 1.37686302381858e-10 & 6.88431511909288e-11 \tabularnewline
41 & 0.999999999813499 & 3.73001585554889e-10 & 1.86500792777444e-10 \tabularnewline
42 & 0.999999999600126 & 7.99748889886591e-10 & 3.99874444943295e-10 \tabularnewline
43 & 0.99999999851984 & 2.96031967693683e-09 & 1.48015983846842e-09 \tabularnewline
44 & 0.999999995252797 & 9.4944063463878e-09 & 4.7472031731939e-09 \tabularnewline
45 & 0.999999995771939 & 8.45612180656202e-09 & 4.22806090328101e-09 \tabularnewline
46 & 0.999999999102163 & 1.79567386683172e-09 & 8.97836933415859e-10 \tabularnewline
47 & 0.999999999964419 & 7.11625499631476e-11 & 3.55812749815738e-11 \tabularnewline
48 & 0.999999999999984 & 3.13901247608282e-14 & 1.56950623804141e-14 \tabularnewline
49 & 0.999999999999997 & 5.75859765335511e-15 & 2.87929882667756e-15 \tabularnewline
50 & 0.999999999999984 & 3.11401878551586e-14 & 1.55700939275793e-14 \tabularnewline
51 & 0.999999999999867 & 2.65307686194474e-13 & 1.32653843097237e-13 \tabularnewline
52 & 0.999999999999037 & 1.92548000637236e-12 & 9.62740003186178e-13 \tabularnewline
53 & 0.999999999993328 & 1.33432687145611e-11 & 6.67163435728057e-12 \tabularnewline
54 & 0.999999999950759 & 9.84822841731496e-11 & 4.92411420865748e-11 \tabularnewline
55 & 0.99999999996067 & 7.86601394472792e-11 & 3.93300697236396e-11 \tabularnewline
56 & 0.999999999913642 & 1.72715318635215e-10 & 8.63576593176074e-11 \tabularnewline
57 & 0.999999998888321 & 2.22335752965797e-09 & 1.11167876482899e-09 \tabularnewline
58 & 0.999999986458362 & 2.70832766535469e-08 & 1.35416383267735e-08 \tabularnewline
59 & 0.99999984797345 & 3.04053099921848e-07 & 1.52026549960924e-07 \tabularnewline
60 & 0.999999638303048 & 7.2339390327819e-07 & 3.61696951639095e-07 \tabularnewline
61 & 0.999999465946939 & 1.06810612166832e-06 & 5.3405306083416e-07 \tabularnewline
62 & 0.999996539100911 & 6.92179817708767e-06 & 3.46089908854384e-06 \tabularnewline
63 & 0.99991528145576 & 0.000169437088480812 & 8.47185442404059e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186259&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.000322124369090329[/C][C]0.000644248738180657[/C][C]0.99967787563091[/C][/ROW]
[ROW][C]8[/C][C]3.60718602138808e-05[/C][C]7.21437204277617e-05[/C][C]0.999963928139786[/C][/ROW]
[ROW][C]9[/C][C]2.44564358250206e-06[/C][C]4.89128716500412e-06[/C][C]0.999997554356417[/C][/ROW]
[ROW][C]10[/C][C]3.47010664197143e-07[/C][C]6.94021328394286e-07[/C][C]0.999999652989336[/C][/ROW]
[ROW][C]11[/C][C]9.68931412810633e-08[/C][C]1.93786282562127e-07[/C][C]0.999999903106859[/C][/ROW]
[ROW][C]12[/C][C]4.07910239767492e-08[/C][C]8.15820479534983e-08[/C][C]0.999999959208976[/C][/ROW]
[ROW][C]13[/C][C]0.049784206236078[/C][C]0.099568412472156[/C][C]0.950215793763922[/C][/ROW]
[ROW][C]14[/C][C]0.0846192761028265[/C][C]0.169238552205653[/C][C]0.915380723897174[/C][/ROW]
[ROW][C]15[/C][C]0.0640698906796376[/C][C]0.128139781359275[/C][C]0.935930109320362[/C][/ROW]
[ROW][C]16[/C][C]0.0410699833785093[/C][C]0.0821399667570187[/C][C]0.958930016621491[/C][/ROW]
[ROW][C]17[/C][C]0.0291301540174158[/C][C]0.0582603080348317[/C][C]0.970869845982584[/C][/ROW]
[ROW][C]18[/C][C]0.0269525586578501[/C][C]0.0539051173157003[/C][C]0.97304744134215[/C][/ROW]
[ROW][C]19[/C][C]0.016878559918683[/C][C]0.033757119837366[/C][C]0.983121440081317[/C][/ROW]
[ROW][C]20[/C][C]0.0155214446845249[/C][C]0.0310428893690499[/C][C]0.984478555315475[/C][/ROW]
[ROW][C]21[/C][C]0.0475665044452099[/C][C]0.0951330088904198[/C][C]0.95243349555479[/C][/ROW]
[ROW][C]22[/C][C]0.114552512190679[/C][C]0.229105024381359[/C][C]0.88544748780932[/C][/ROW]
[ROW][C]23[/C][C]0.475299899081848[/C][C]0.950599798163695[/C][C]0.524700100918152[/C][/ROW]
[ROW][C]24[/C][C]0.999318858831166[/C][C]0.00136228233766801[/C][C]0.000681141168834007[/C][/ROW]
[ROW][C]25[/C][C]0.999999377148204[/C][C]1.24570359198877e-06[/C][C]6.22851795994385e-07[/C][/ROW]
[ROW][C]26[/C][C]0.999999982945152[/C][C]3.41096952008756e-08[/C][C]1.70548476004378e-08[/C][/ROW]
[ROW][C]27[/C][C]0.999999991150673[/C][C]1.76986550016332e-08[/C][C]8.84932750081658e-09[/C][/ROW]
[ROW][C]28[/C][C]0.999999982396396[/C][C]3.52072076012504e-08[/C][C]1.76036038006252e-08[/C][/ROW]
[ROW][C]29[/C][C]0.999999956538164[/C][C]8.69236718427396e-08[/C][C]4.34618359213698e-08[/C][/ROW]
[ROW][C]30[/C][C]0.999999885299863[/C][C]2.29400275083269e-07[/C][C]1.14700137541634e-07[/C][/ROW]
[ROW][C]31[/C][C]0.999999755977418[/C][C]4.88045164343978e-07[/C][C]2.44022582171989e-07[/C][/ROW]
[ROW][C]32[/C][C]0.999999369984434[/C][C]1.26003113205016e-06[/C][C]6.30015566025081e-07[/C][/ROW]
[ROW][C]33[/C][C]0.99999880963555[/C][C]2.38072890003302e-06[/C][C]1.19036445001651e-06[/C][/ROW]
[ROW][C]34[/C][C]0.999998890154303[/C][C]2.21969139381967e-06[/C][C]1.10984569690983e-06[/C][/ROW]
[ROW][C]35[/C][C]0.9999998141638[/C][C]3.71672400762658e-07[/C][C]1.85836200381329e-07[/C][/ROW]
[ROW][C]36[/C][C]0.999999999997177[/C][C]5.64623155553569e-12[/C][C]2.82311577776784e-12[/C][/ROW]
[ROW][C]37[/C][C]0.999999999997394[/C][C]5.21300618166833e-12[/C][C]2.60650309083416e-12[/C][/ROW]
[ROW][C]38[/C][C]0.999999999991791[/C][C]1.64176767311664e-11[/C][C]8.20883836558319e-12[/C][/ROW]
[ROW][C]39[/C][C]0.999999999972305[/C][C]5.53898330193045e-11[/C][C]2.76949165096522e-11[/C][/ROW]
[ROW][C]40[/C][C]0.999999999931157[/C][C]1.37686302381858e-10[/C][C]6.88431511909288e-11[/C][/ROW]
[ROW][C]41[/C][C]0.999999999813499[/C][C]3.73001585554889e-10[/C][C]1.86500792777444e-10[/C][/ROW]
[ROW][C]42[/C][C]0.999999999600126[/C][C]7.99748889886591e-10[/C][C]3.99874444943295e-10[/C][/ROW]
[ROW][C]43[/C][C]0.99999999851984[/C][C]2.96031967693683e-09[/C][C]1.48015983846842e-09[/C][/ROW]
[ROW][C]44[/C][C]0.999999995252797[/C][C]9.4944063463878e-09[/C][C]4.7472031731939e-09[/C][/ROW]
[ROW][C]45[/C][C]0.999999995771939[/C][C]8.45612180656202e-09[/C][C]4.22806090328101e-09[/C][/ROW]
[ROW][C]46[/C][C]0.999999999102163[/C][C]1.79567386683172e-09[/C][C]8.97836933415859e-10[/C][/ROW]
[ROW][C]47[/C][C]0.999999999964419[/C][C]7.11625499631476e-11[/C][C]3.55812749815738e-11[/C][/ROW]
[ROW][C]48[/C][C]0.999999999999984[/C][C]3.13901247608282e-14[/C][C]1.56950623804141e-14[/C][/ROW]
[ROW][C]49[/C][C]0.999999999999997[/C][C]5.75859765335511e-15[/C][C]2.87929882667756e-15[/C][/ROW]
[ROW][C]50[/C][C]0.999999999999984[/C][C]3.11401878551586e-14[/C][C]1.55700939275793e-14[/C][/ROW]
[ROW][C]51[/C][C]0.999999999999867[/C][C]2.65307686194474e-13[/C][C]1.32653843097237e-13[/C][/ROW]
[ROW][C]52[/C][C]0.999999999999037[/C][C]1.92548000637236e-12[/C][C]9.62740003186178e-13[/C][/ROW]
[ROW][C]53[/C][C]0.999999999993328[/C][C]1.33432687145611e-11[/C][C]6.67163435728057e-12[/C][/ROW]
[ROW][C]54[/C][C]0.999999999950759[/C][C]9.84822841731496e-11[/C][C]4.92411420865748e-11[/C][/ROW]
[ROW][C]55[/C][C]0.99999999996067[/C][C]7.86601394472792e-11[/C][C]3.93300697236396e-11[/C][/ROW]
[ROW][C]56[/C][C]0.999999999913642[/C][C]1.72715318635215e-10[/C][C]8.63576593176074e-11[/C][/ROW]
[ROW][C]57[/C][C]0.999999998888321[/C][C]2.22335752965797e-09[/C][C]1.11167876482899e-09[/C][/ROW]
[ROW][C]58[/C][C]0.999999986458362[/C][C]2.70832766535469e-08[/C][C]1.35416383267735e-08[/C][/ROW]
[ROW][C]59[/C][C]0.99999984797345[/C][C]3.04053099921848e-07[/C][C]1.52026549960924e-07[/C][/ROW]
[ROW][C]60[/C][C]0.999999638303048[/C][C]7.2339390327819e-07[/C][C]3.61696951639095e-07[/C][/ROW]
[ROW][C]61[/C][C]0.999999465946939[/C][C]1.06810612166832e-06[/C][C]5.3405306083416e-07[/C][/ROW]
[ROW][C]62[/C][C]0.999996539100911[/C][C]6.92179817708767e-06[/C][C]3.46089908854384e-06[/C][/ROW]
[ROW][C]63[/C][C]0.99991528145576[/C][C]0.000169437088480812[/C][C]8.47185442404059e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186259&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186259&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.0003221243690903290.0006442487381806570.99967787563091
83.60718602138808e-057.21437204277617e-050.999963928139786
92.44564358250206e-064.89128716500412e-060.999997554356417
103.47010664197143e-076.94021328394286e-070.999999652989336
119.68931412810633e-081.93786282562127e-070.999999903106859
124.07910239767492e-088.15820479534983e-080.999999959208976
130.0497842062360780.0995684124721560.950215793763922
140.08461927610282650.1692385522056530.915380723897174
150.06406989067963760.1281397813592750.935930109320362
160.04106998337850930.08213996675701870.958930016621491
170.02913015401741580.05826030803483170.970869845982584
180.02695255865785010.05390511731570030.97304744134215
190.0168785599186830.0337571198373660.983121440081317
200.01552144468452490.03104288936904990.984478555315475
210.04756650444520990.09513300889041980.95243349555479
220.1145525121906790.2291050243813590.88544748780932
230.4752998990818480.9505997981636950.524700100918152
240.9993188588311660.001362282337668010.000681141168834007
250.9999993771482041.24570359198877e-066.22851795994385e-07
260.9999999829451523.41096952008756e-081.70548476004378e-08
270.9999999911506731.76986550016332e-088.84932750081658e-09
280.9999999823963963.52072076012504e-081.76036038006252e-08
290.9999999565381648.69236718427396e-084.34618359213698e-08
300.9999998852998632.29400275083269e-071.14700137541634e-07
310.9999997559774184.88045164343978e-072.44022582171989e-07
320.9999993699844341.26003113205016e-066.30015566025081e-07
330.999998809635552.38072890003302e-061.19036445001651e-06
340.9999988901543032.21969139381967e-061.10984569690983e-06
350.99999981416383.71672400762658e-071.85836200381329e-07
360.9999999999971775.64623155553569e-122.82311577776784e-12
370.9999999999973945.21300618166833e-122.60650309083416e-12
380.9999999999917911.64176767311664e-118.20883836558319e-12
390.9999999999723055.53898330193045e-112.76949165096522e-11
400.9999999999311571.37686302381858e-106.88431511909288e-11
410.9999999998134993.73001585554889e-101.86500792777444e-10
420.9999999996001267.99748889886591e-103.99874444943295e-10
430.999999998519842.96031967693683e-091.48015983846842e-09
440.9999999952527979.4944063463878e-094.7472031731939e-09
450.9999999957719398.45612180656202e-094.22806090328101e-09
460.9999999991021631.79567386683172e-098.97836933415859e-10
470.9999999999644197.11625499631476e-113.55812749815738e-11
480.9999999999999843.13901247608282e-141.56950623804141e-14
490.9999999999999975.75859765335511e-152.87929882667756e-15
500.9999999999999843.11401878551586e-141.55700939275793e-14
510.9999999999998672.65307686194474e-131.32653843097237e-13
520.9999999999990371.92548000637236e-129.62740003186178e-13
530.9999999999933281.33432687145611e-116.67163435728057e-12
540.9999999999507599.84822841731496e-114.92411420865748e-11
550.999999999960677.86601394472792e-113.93300697236396e-11
560.9999999999136421.72715318635215e-108.63576593176074e-11
570.9999999988883212.22335752965797e-091.11167876482899e-09
580.9999999864583622.70832766535469e-081.35416383267735e-08
590.999999847973453.04053099921848e-071.52026549960924e-07
600.9999996383030487.2339390327819e-073.61696951639095e-07
610.9999994659469391.06810612166832e-065.3405306083416e-07
620.9999965391009116.92179817708767e-063.46089908854384e-06
630.999915281455760.0001694370884808128.47185442404059e-05







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level460.807017543859649NOK
5% type I error level480.842105263157895NOK
10% type I error level530.929824561403509NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 46 & 0.807017543859649 & NOK \tabularnewline
5% type I error level & 48 & 0.842105263157895 & NOK \tabularnewline
10% type I error level & 53 & 0.929824561403509 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186259&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]46[/C][C]0.807017543859649[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]48[/C][C]0.842105263157895[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]53[/C][C]0.929824561403509[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186259&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186259&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level460.807017543859649NOK
5% type I error level480.842105263157895NOK
10% type I error level530.929824561403509NOK



Parameters (Session):
par1 = 0 ; par2 = 36 ;
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')
}