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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 05:30:30 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322130659nl45q4rxcxemaj4.htm/, Retrieved Fri, 29 Mar 2024 08:46:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146615, Retrieved Fri, 29 Mar 2024 08:46:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [ws7/3] [2011-11-24 10:30:30] [d6b8e0ceefc1e2de0b53f6dffb5d636c] [Current]
- R PD    [Multiple Regression] [ws7.Lin] [2011-11-24 13:22:13] [8ae0a4da1b3ee81f40dbba5e42914d07]
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Dataseries X:
72772	26073	22274
45104	18103	14819
44525	15100	15136
41169	14738	13704
31118	22259	19638
28435	10277	7551
22162	6225	8019
20202	7663	6509
17773	6618	6634
17094	9945	11166
15153	7590	7508
11218	4293	4275
10796	4656	4944
9594	5145	5441
9309	2001	1689
8556	1779	1522
8041	1609	1416
7639	2191	1594
6884	1617	1909
6642	2554	2599
6321	2198	1262
6216	1578	1199
5865	3446	4404
5799	1380	1166
5695	1249	1122
5644	1223	886
5446	834	778
5395	3754	4436
5363	2283	1890
5338	3028	3107
5160	1100	1038
5091	457	300
5057	1201	988
5039	2192	2008
4880	1508	1522
4735	1393	1336
4693	952	976
4653	1032	798
4586	1279	869
4398	1370	1260
3974	649	578
3858	1900	2359
3826	666	736
3819	1313	1690
3556	1353	1201
3372	1500	813
3193	877	778
3126	874	687
3104	1133	1270
2967	754	671
2848	695	1559
2748	609	489
2649	696	773
2625	756	629
2572	670	637
2548	301	277
2477	630	776
2442	798	1651
2392	436	377
2372	388	222
2 346	864	1 068
2 251	497	399
2 230	449	547
2 225	919	668
2 220	536	451
2 205	673	724
2 193	837	853
2 116	534	434
2 102	845	730
2 099	626	612
2 096	871	558
2 064	740	859
2 036	391	311
1 920	435	318
1 813	424	312
1 776	338	343
1 752	744	710
1 738	368	273
1 729	393	259
1 685	938	1 274




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146615&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146615&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146615&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
zondag[t] = + 156.779781610427 -0.0122077233507859weekdag[t] + 0.918476587310948zaterdag[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
zondag[t] =  +  156.779781610427 -0.0122077233507859weekdag[t] +  0.918476587310948zaterdag[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146615&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]zondag[t] =  +  156.779781610427 -0.0122077233507859weekdag[t] +  0.918476587310948zaterdag[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146615&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146615&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
zondag[t] = + 156.779781610427 -0.0122077233507859weekdag[t] + 0.918476587310948zaterdag[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)156.77978161042784.6736651.85160.067920.03396
weekdag-0.01220772335078590.019994-0.61060.5432860.271643
zaterdag0.9184765873109480.04912418.69700

\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) & 156.779781610427 & 84.673665 & 1.8516 & 0.06792 & 0.03396 \tabularnewline
weekdag & -0.0122077233507859 & 0.019994 & -0.6106 & 0.543286 & 0.271643 \tabularnewline
zaterdag & 0.918476587310948 & 0.049124 & 18.697 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146615&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]156.779781610427[/C][C]84.673665[/C][C]1.8516[/C][C]0.06792[/C][C]0.03396[/C][/ROW]
[ROW][C]weekdag[/C][C]-0.0122077233507859[/C][C]0.019994[/C][C]-0.6106[/C][C]0.543286[/C][C]0.271643[/C][/ROW]
[ROW][C]zaterdag[/C][C]0.918476587310948[/C][C]0.049124[/C][C]18.697[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146615&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146615&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)156.77978161042784.6736651.85160.067920.03396
weekdag-0.01220772335078590.019994-0.61060.5432860.271643
zaterdag0.9184765873109480.04912418.69700







Multiple Linear Regression - Regression Statistics
Multiple R0.98966561624434
R-squared0.979438031976289
Adjusted R-squared0.978903954884764
F-TEST (value)1833.88886645501
F-TEST (DF numerator)2
F-TEST (DF denominator)77
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation637.846965302014
Sum Squared Residuals31327353.8381641

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.98966561624434 \tabularnewline
R-squared & 0.979438031976289 \tabularnewline
Adjusted R-squared & 0.978903954884764 \tabularnewline
F-TEST (value) & 1833.88886645501 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 77 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 637.846965302014 \tabularnewline
Sum Squared Residuals & 31327353.8381641 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146615&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.98966561624434[/C][/ROW]
[ROW][C]R-squared[/C][C]0.979438031976289[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.978903954884764[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1833.88886645501[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]77[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]637.846965302014[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]31327353.8381641[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146615&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146615&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 R0.98966561624434
R-squared0.979438031976289
Adjusted R-squared0.978903954884764
F-TEST (value)1833.88886645501
F-TEST (DF numerator)2
F-TEST (DF denominator)77
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation637.846965302014
Sum Squared Residuals31327353.8381641







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12227423215.8393988854-941.839398885364
21481916233.3442876867-1414.34428768666
31513613482.2273678121653.77263218801
41370413190.7079627707513.292037229335
51963820221.2702033351-583.270203335054
675519248.83705592544-1697.83705592544
780195603.748972720962415.25102727904
865096948.44544304164-439.44544304164
966346018.28996932076615.710030679241
10111669082.350619459472083.64938054053
1175086943.03344736606564.96655263394
1242753962.85353038721312.146469612792
1349444301.41219083511642.587809164886
1454414765.22092549781675.779074502188
1516891881.00973614717-192.009736147166
1615221686.30034944728-164.300349447278
1714161536.44630713007-120.446307130072
1815942075.90718573206-481.907185732059
1919091557.91845574542351.081544254581
2025992421.48528710667177.514712893333
2112622098.42630121957-836.426301219572
2211991530.25262803862-331.252628038617
2344043250.251804031591153.74819596841
2411661353.48488438833-187.484884388327
2511221234.43405467907-112.434054679075
268861211.17625729988-325.17625729988
27778856.305994059377-78.305994059377
2844363538.88022289823897.119777101766
2918902188.19181011106-298.191810111055
3031072872.76206074148234.237939258519
3110381104.11217516241-66.1121751624138
32300514.374062432679-214.374062432679
339881198.13570598595-210.135705985951
3420082108.56574303141-100.565743031414
3515221482.268785323539.7312146764995
3613361378.41409766861-42.4140976686055
37976973.8786470452112.12135295478938
387981047.84508296412-249.845082964118
398691275.52671749442-406.526717494425
4012601361.40313892967-101.403138929669
41578704.357594179209-126.357594179209
4223591854.7879008139504.212099186105
43736721.77843921941114.2215607805889
4416901316.11824527305373.88175472695
4512011356.06794000674-155.067940006744
468131493.330219438-680.330219437998
47778923.304488023069-145.304488023069
48687921.366975725638-234.366975725638
4912701159.52098175289110.479018247109
50671813.0908132611-142.0908132611
511559760.353413688497798.646586311503
52489682.585199514834-193.585199514834
53773763.7012272226159.29877277738534
54629819.10280782169-190.10280782169
55637740.760830650541-103.760830650541
56277402.13595529322-125.13595529322
57776705.18150087642770.8184991235727
581651859.912837861944791.087162138056
59377528.03469942292-151.03469942292
60222484.191977699011-262.191977699011
61864474.548265373314389.451734626686
622219.223981824221-217.223981824221
63399610.198506942921-211.198506942921
64449368.00498124524480.9950187547563
65225151.93911011216973.0608898878308
662759.103244174768-757.103244174768
67451646.397533271922-195.397533271922
68673345.04306656247327.95693343753
69193149.7783430790843.2216569209199
702930.022446142058-928.022446142058
71434645.830183325782-211.830183325782
72845250.439978069442594.560021930558
7399149.705096738975-50.7050967389753
742711.245418227135-709.245418227135
75558955.600947716587-397.600947716587
76740215.537867751626524.462132248374
7736148.130300426724-112.130300426724
781437.652780433975-436.652780433975
79318545.085991607966-227.085991607966
80424903.489039370877-479.489039370877

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 22274 & 23215.8393988854 & -941.839398885364 \tabularnewline
2 & 14819 & 16233.3442876867 & -1414.34428768666 \tabularnewline
3 & 15136 & 13482.227367812 & 1653.77263218801 \tabularnewline
4 & 13704 & 13190.7079627707 & 513.292037229335 \tabularnewline
5 & 19638 & 20221.2702033351 & -583.270203335054 \tabularnewline
6 & 7551 & 9248.83705592544 & -1697.83705592544 \tabularnewline
7 & 8019 & 5603.74897272096 & 2415.25102727904 \tabularnewline
8 & 6509 & 6948.44544304164 & -439.44544304164 \tabularnewline
9 & 6634 & 6018.28996932076 & 615.710030679241 \tabularnewline
10 & 11166 & 9082.35061945947 & 2083.64938054053 \tabularnewline
11 & 7508 & 6943.03344736606 & 564.96655263394 \tabularnewline
12 & 4275 & 3962.85353038721 & 312.146469612792 \tabularnewline
13 & 4944 & 4301.41219083511 & 642.587809164886 \tabularnewline
14 & 5441 & 4765.22092549781 & 675.779074502188 \tabularnewline
15 & 1689 & 1881.00973614717 & -192.009736147166 \tabularnewline
16 & 1522 & 1686.30034944728 & -164.300349447278 \tabularnewline
17 & 1416 & 1536.44630713007 & -120.446307130072 \tabularnewline
18 & 1594 & 2075.90718573206 & -481.907185732059 \tabularnewline
19 & 1909 & 1557.91845574542 & 351.081544254581 \tabularnewline
20 & 2599 & 2421.48528710667 & 177.514712893333 \tabularnewline
21 & 1262 & 2098.42630121957 & -836.426301219572 \tabularnewline
22 & 1199 & 1530.25262803862 & -331.252628038617 \tabularnewline
23 & 4404 & 3250.25180403159 & 1153.74819596841 \tabularnewline
24 & 1166 & 1353.48488438833 & -187.484884388327 \tabularnewline
25 & 1122 & 1234.43405467907 & -112.434054679075 \tabularnewline
26 & 886 & 1211.17625729988 & -325.17625729988 \tabularnewline
27 & 778 & 856.305994059377 & -78.305994059377 \tabularnewline
28 & 4436 & 3538.88022289823 & 897.119777101766 \tabularnewline
29 & 1890 & 2188.19181011106 & -298.191810111055 \tabularnewline
30 & 3107 & 2872.76206074148 & 234.237939258519 \tabularnewline
31 & 1038 & 1104.11217516241 & -66.1121751624138 \tabularnewline
32 & 300 & 514.374062432679 & -214.374062432679 \tabularnewline
33 & 988 & 1198.13570598595 & -210.135705985951 \tabularnewline
34 & 2008 & 2108.56574303141 & -100.565743031414 \tabularnewline
35 & 1522 & 1482.2687853235 & 39.7312146764995 \tabularnewline
36 & 1336 & 1378.41409766861 & -42.4140976686055 \tabularnewline
37 & 976 & 973.878647045211 & 2.12135295478938 \tabularnewline
38 & 798 & 1047.84508296412 & -249.845082964118 \tabularnewline
39 & 869 & 1275.52671749442 & -406.526717494425 \tabularnewline
40 & 1260 & 1361.40313892967 & -101.403138929669 \tabularnewline
41 & 578 & 704.357594179209 & -126.357594179209 \tabularnewline
42 & 2359 & 1854.7879008139 & 504.212099186105 \tabularnewline
43 & 736 & 721.778439219411 & 14.2215607805889 \tabularnewline
44 & 1690 & 1316.11824527305 & 373.88175472695 \tabularnewline
45 & 1201 & 1356.06794000674 & -155.067940006744 \tabularnewline
46 & 813 & 1493.330219438 & -680.330219437998 \tabularnewline
47 & 778 & 923.304488023069 & -145.304488023069 \tabularnewline
48 & 687 & 921.366975725638 & -234.366975725638 \tabularnewline
49 & 1270 & 1159.52098175289 & 110.479018247109 \tabularnewline
50 & 671 & 813.0908132611 & -142.0908132611 \tabularnewline
51 & 1559 & 760.353413688497 & 798.646586311503 \tabularnewline
52 & 489 & 682.585199514834 & -193.585199514834 \tabularnewline
53 & 773 & 763.701227222615 & 9.29877277738534 \tabularnewline
54 & 629 & 819.10280782169 & -190.10280782169 \tabularnewline
55 & 637 & 740.760830650541 & -103.760830650541 \tabularnewline
56 & 277 & 402.13595529322 & -125.13595529322 \tabularnewline
57 & 776 & 705.181500876427 & 70.8184991235727 \tabularnewline
58 & 1651 & 859.912837861944 & 791.087162138056 \tabularnewline
59 & 377 & 528.03469942292 & -151.03469942292 \tabularnewline
60 & 222 & 484.191977699011 & -262.191977699011 \tabularnewline
61 & 864 & 474.548265373314 & 389.451734626686 \tabularnewline
62 & 2 & 219.223981824221 & -217.223981824221 \tabularnewline
63 & 399 & 610.198506942921 & -211.198506942921 \tabularnewline
64 & 449 & 368.004981245244 & 80.9950187547563 \tabularnewline
65 & 225 & 151.939110112169 & 73.0608898878308 \tabularnewline
66 & 2 & 759.103244174768 & -757.103244174768 \tabularnewline
67 & 451 & 646.397533271922 & -195.397533271922 \tabularnewline
68 & 673 & 345.04306656247 & 327.95693343753 \tabularnewline
69 & 193 & 149.77834307908 & 43.2216569209199 \tabularnewline
70 & 2 & 930.022446142058 & -928.022446142058 \tabularnewline
71 & 434 & 645.830183325782 & -211.830183325782 \tabularnewline
72 & 845 & 250.439978069442 & 594.560021930558 \tabularnewline
73 & 99 & 149.705096738975 & -50.7050967389753 \tabularnewline
74 & 2 & 711.245418227135 & -709.245418227135 \tabularnewline
75 & 558 & 955.600947716587 & -397.600947716587 \tabularnewline
76 & 740 & 215.537867751626 & 524.462132248374 \tabularnewline
77 & 36 & 148.130300426724 & -112.130300426724 \tabularnewline
78 & 1 & 437.652780433975 & -436.652780433975 \tabularnewline
79 & 318 & 545.085991607966 & -227.085991607966 \tabularnewline
80 & 424 & 903.489039370877 & -479.489039370877 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146615&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]22274[/C][C]23215.8393988854[/C][C]-941.839398885364[/C][/ROW]
[ROW][C]2[/C][C]14819[/C][C]16233.3442876867[/C][C]-1414.34428768666[/C][/ROW]
[ROW][C]3[/C][C]15136[/C][C]13482.227367812[/C][C]1653.77263218801[/C][/ROW]
[ROW][C]4[/C][C]13704[/C][C]13190.7079627707[/C][C]513.292037229335[/C][/ROW]
[ROW][C]5[/C][C]19638[/C][C]20221.2702033351[/C][C]-583.270203335054[/C][/ROW]
[ROW][C]6[/C][C]7551[/C][C]9248.83705592544[/C][C]-1697.83705592544[/C][/ROW]
[ROW][C]7[/C][C]8019[/C][C]5603.74897272096[/C][C]2415.25102727904[/C][/ROW]
[ROW][C]8[/C][C]6509[/C][C]6948.44544304164[/C][C]-439.44544304164[/C][/ROW]
[ROW][C]9[/C][C]6634[/C][C]6018.28996932076[/C][C]615.710030679241[/C][/ROW]
[ROW][C]10[/C][C]11166[/C][C]9082.35061945947[/C][C]2083.64938054053[/C][/ROW]
[ROW][C]11[/C][C]7508[/C][C]6943.03344736606[/C][C]564.96655263394[/C][/ROW]
[ROW][C]12[/C][C]4275[/C][C]3962.85353038721[/C][C]312.146469612792[/C][/ROW]
[ROW][C]13[/C][C]4944[/C][C]4301.41219083511[/C][C]642.587809164886[/C][/ROW]
[ROW][C]14[/C][C]5441[/C][C]4765.22092549781[/C][C]675.779074502188[/C][/ROW]
[ROW][C]15[/C][C]1689[/C][C]1881.00973614717[/C][C]-192.009736147166[/C][/ROW]
[ROW][C]16[/C][C]1522[/C][C]1686.30034944728[/C][C]-164.300349447278[/C][/ROW]
[ROW][C]17[/C][C]1416[/C][C]1536.44630713007[/C][C]-120.446307130072[/C][/ROW]
[ROW][C]18[/C][C]1594[/C][C]2075.90718573206[/C][C]-481.907185732059[/C][/ROW]
[ROW][C]19[/C][C]1909[/C][C]1557.91845574542[/C][C]351.081544254581[/C][/ROW]
[ROW][C]20[/C][C]2599[/C][C]2421.48528710667[/C][C]177.514712893333[/C][/ROW]
[ROW][C]21[/C][C]1262[/C][C]2098.42630121957[/C][C]-836.426301219572[/C][/ROW]
[ROW][C]22[/C][C]1199[/C][C]1530.25262803862[/C][C]-331.252628038617[/C][/ROW]
[ROW][C]23[/C][C]4404[/C][C]3250.25180403159[/C][C]1153.74819596841[/C][/ROW]
[ROW][C]24[/C][C]1166[/C][C]1353.48488438833[/C][C]-187.484884388327[/C][/ROW]
[ROW][C]25[/C][C]1122[/C][C]1234.43405467907[/C][C]-112.434054679075[/C][/ROW]
[ROW][C]26[/C][C]886[/C][C]1211.17625729988[/C][C]-325.17625729988[/C][/ROW]
[ROW][C]27[/C][C]778[/C][C]856.305994059377[/C][C]-78.305994059377[/C][/ROW]
[ROW][C]28[/C][C]4436[/C][C]3538.88022289823[/C][C]897.119777101766[/C][/ROW]
[ROW][C]29[/C][C]1890[/C][C]2188.19181011106[/C][C]-298.191810111055[/C][/ROW]
[ROW][C]30[/C][C]3107[/C][C]2872.76206074148[/C][C]234.237939258519[/C][/ROW]
[ROW][C]31[/C][C]1038[/C][C]1104.11217516241[/C][C]-66.1121751624138[/C][/ROW]
[ROW][C]32[/C][C]300[/C][C]514.374062432679[/C][C]-214.374062432679[/C][/ROW]
[ROW][C]33[/C][C]988[/C][C]1198.13570598595[/C][C]-210.135705985951[/C][/ROW]
[ROW][C]34[/C][C]2008[/C][C]2108.56574303141[/C][C]-100.565743031414[/C][/ROW]
[ROW][C]35[/C][C]1522[/C][C]1482.2687853235[/C][C]39.7312146764995[/C][/ROW]
[ROW][C]36[/C][C]1336[/C][C]1378.41409766861[/C][C]-42.4140976686055[/C][/ROW]
[ROW][C]37[/C][C]976[/C][C]973.878647045211[/C][C]2.12135295478938[/C][/ROW]
[ROW][C]38[/C][C]798[/C][C]1047.84508296412[/C][C]-249.845082964118[/C][/ROW]
[ROW][C]39[/C][C]869[/C][C]1275.52671749442[/C][C]-406.526717494425[/C][/ROW]
[ROW][C]40[/C][C]1260[/C][C]1361.40313892967[/C][C]-101.403138929669[/C][/ROW]
[ROW][C]41[/C][C]578[/C][C]704.357594179209[/C][C]-126.357594179209[/C][/ROW]
[ROW][C]42[/C][C]2359[/C][C]1854.7879008139[/C][C]504.212099186105[/C][/ROW]
[ROW][C]43[/C][C]736[/C][C]721.778439219411[/C][C]14.2215607805889[/C][/ROW]
[ROW][C]44[/C][C]1690[/C][C]1316.11824527305[/C][C]373.88175472695[/C][/ROW]
[ROW][C]45[/C][C]1201[/C][C]1356.06794000674[/C][C]-155.067940006744[/C][/ROW]
[ROW][C]46[/C][C]813[/C][C]1493.330219438[/C][C]-680.330219437998[/C][/ROW]
[ROW][C]47[/C][C]778[/C][C]923.304488023069[/C][C]-145.304488023069[/C][/ROW]
[ROW][C]48[/C][C]687[/C][C]921.366975725638[/C][C]-234.366975725638[/C][/ROW]
[ROW][C]49[/C][C]1270[/C][C]1159.52098175289[/C][C]110.479018247109[/C][/ROW]
[ROW][C]50[/C][C]671[/C][C]813.0908132611[/C][C]-142.0908132611[/C][/ROW]
[ROW][C]51[/C][C]1559[/C][C]760.353413688497[/C][C]798.646586311503[/C][/ROW]
[ROW][C]52[/C][C]489[/C][C]682.585199514834[/C][C]-193.585199514834[/C][/ROW]
[ROW][C]53[/C][C]773[/C][C]763.701227222615[/C][C]9.29877277738534[/C][/ROW]
[ROW][C]54[/C][C]629[/C][C]819.10280782169[/C][C]-190.10280782169[/C][/ROW]
[ROW][C]55[/C][C]637[/C][C]740.760830650541[/C][C]-103.760830650541[/C][/ROW]
[ROW][C]56[/C][C]277[/C][C]402.13595529322[/C][C]-125.13595529322[/C][/ROW]
[ROW][C]57[/C][C]776[/C][C]705.181500876427[/C][C]70.8184991235727[/C][/ROW]
[ROW][C]58[/C][C]1651[/C][C]859.912837861944[/C][C]791.087162138056[/C][/ROW]
[ROW][C]59[/C][C]377[/C][C]528.03469942292[/C][C]-151.03469942292[/C][/ROW]
[ROW][C]60[/C][C]222[/C][C]484.191977699011[/C][C]-262.191977699011[/C][/ROW]
[ROW][C]61[/C][C]864[/C][C]474.548265373314[/C][C]389.451734626686[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]219.223981824221[/C][C]-217.223981824221[/C][/ROW]
[ROW][C]63[/C][C]399[/C][C]610.198506942921[/C][C]-211.198506942921[/C][/ROW]
[ROW][C]64[/C][C]449[/C][C]368.004981245244[/C][C]80.9950187547563[/C][/ROW]
[ROW][C]65[/C][C]225[/C][C]151.939110112169[/C][C]73.0608898878308[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]759.103244174768[/C][C]-757.103244174768[/C][/ROW]
[ROW][C]67[/C][C]451[/C][C]646.397533271922[/C][C]-195.397533271922[/C][/ROW]
[ROW][C]68[/C][C]673[/C][C]345.04306656247[/C][C]327.95693343753[/C][/ROW]
[ROW][C]69[/C][C]193[/C][C]149.77834307908[/C][C]43.2216569209199[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]930.022446142058[/C][C]-928.022446142058[/C][/ROW]
[ROW][C]71[/C][C]434[/C][C]645.830183325782[/C][C]-211.830183325782[/C][/ROW]
[ROW][C]72[/C][C]845[/C][C]250.439978069442[/C][C]594.560021930558[/C][/ROW]
[ROW][C]73[/C][C]99[/C][C]149.705096738975[/C][C]-50.7050967389753[/C][/ROW]
[ROW][C]74[/C][C]2[/C][C]711.245418227135[/C][C]-709.245418227135[/C][/ROW]
[ROW][C]75[/C][C]558[/C][C]955.600947716587[/C][C]-397.600947716587[/C][/ROW]
[ROW][C]76[/C][C]740[/C][C]215.537867751626[/C][C]524.462132248374[/C][/ROW]
[ROW][C]77[/C][C]36[/C][C]148.130300426724[/C][C]-112.130300426724[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]437.652780433975[/C][C]-436.652780433975[/C][/ROW]
[ROW][C]79[/C][C]318[/C][C]545.085991607966[/C][C]-227.085991607966[/C][/ROW]
[ROW][C]80[/C][C]424[/C][C]903.489039370877[/C][C]-479.489039370877[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146615&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146615&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
12227423215.8393988854-941.839398885364
21481916233.3442876867-1414.34428768666
31513613482.2273678121653.77263218801
41370413190.7079627707513.292037229335
51963820221.2702033351-583.270203335054
675519248.83705592544-1697.83705592544
780195603.748972720962415.25102727904
865096948.44544304164-439.44544304164
966346018.28996932076615.710030679241
10111669082.350619459472083.64938054053
1175086943.03344736606564.96655263394
1242753962.85353038721312.146469612792
1349444301.41219083511642.587809164886
1454414765.22092549781675.779074502188
1516891881.00973614717-192.009736147166
1615221686.30034944728-164.300349447278
1714161536.44630713007-120.446307130072
1815942075.90718573206-481.907185732059
1919091557.91845574542351.081544254581
2025992421.48528710667177.514712893333
2112622098.42630121957-836.426301219572
2211991530.25262803862-331.252628038617
2344043250.251804031591153.74819596841
2411661353.48488438833-187.484884388327
2511221234.43405467907-112.434054679075
268861211.17625729988-325.17625729988
27778856.305994059377-78.305994059377
2844363538.88022289823897.119777101766
2918902188.19181011106-298.191810111055
3031072872.76206074148234.237939258519
3110381104.11217516241-66.1121751624138
32300514.374062432679-214.374062432679
339881198.13570598595-210.135705985951
3420082108.56574303141-100.565743031414
3515221482.268785323539.7312146764995
3613361378.41409766861-42.4140976686055
37976973.8786470452112.12135295478938
387981047.84508296412-249.845082964118
398691275.52671749442-406.526717494425
4012601361.40313892967-101.403138929669
41578704.357594179209-126.357594179209
4223591854.7879008139504.212099186105
43736721.77843921941114.2215607805889
4416901316.11824527305373.88175472695
4512011356.06794000674-155.067940006744
468131493.330219438-680.330219437998
47778923.304488023069-145.304488023069
48687921.366975725638-234.366975725638
4912701159.52098175289110.479018247109
50671813.0908132611-142.0908132611
511559760.353413688497798.646586311503
52489682.585199514834-193.585199514834
53773763.7012272226159.29877277738534
54629819.10280782169-190.10280782169
55637740.760830650541-103.760830650541
56277402.13595529322-125.13595529322
57776705.18150087642770.8184991235727
581651859.912837861944791.087162138056
59377528.03469942292-151.03469942292
60222484.191977699011-262.191977699011
61864474.548265373314389.451734626686
622219.223981824221-217.223981824221
63399610.198506942921-211.198506942921
64449368.00498124524480.9950187547563
65225151.93911011216973.0608898878308
662759.103244174768-757.103244174768
67451646.397533271922-195.397533271922
68673345.04306656247327.95693343753
69193149.7783430790843.2216569209199
702930.022446142058-928.022446142058
71434645.830183325782-211.830183325782
72845250.439978069442594.560021930558
7399149.705096738975-50.7050967389753
742711.245418227135-709.245418227135
75558955.600947716587-397.600947716587
76740215.537867751626524.462132248374
7736148.130300426724-112.130300426724
781437.652780433975-436.652780433975
79318545.085991607966-227.085991607966
80424903.489039370877-479.489039370877







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.9999981837945643.63241087231047e-061.81620543615523e-06
70.9999999993809661.23806850679785e-096.19034253398927e-10
80.9999999999387471.22506467921573e-106.12532339607866e-11
90.9999999997308685.38263589338003e-102.69131794669002e-10
100.9999999999930791.38425704777778e-116.92128523888889e-12
110.9999999999763534.72934434077158e-112.36467217038579e-11
120.9999999999505649.88710385329168e-114.94355192664584e-11
130.9999999998523232.95354656855529e-101.47677328427764e-10
140.9999999995381669.236675323357e-104.6183376616785e-10
150.99999999965266.94800300770161e-103.47400150385081e-10
160.9999999995543258.91350878638582e-104.45675439319291e-10
170.9999999992203181.55936439119894e-097.79682195599472e-10
180.9999999994050291.18994243539545e-095.94971217697724e-10
190.9999999987746372.45072612071916e-091.22536306035958e-09
200.9999999966470856.70583040351468e-093.35291520175734e-09
210.9999999995022839.95433841957874e-104.97716920978937e-10
220.9999999991590291.68194282625724e-098.40971413128618e-10
230.9999999998025493.94902955816013e-101.97451477908006e-10
240.9999999995422359.15531072136296e-104.57765536068148e-10
250.9999999988346052.33079064993554e-091.16539532496777e-09
260.9999999978780724.2438554396494e-092.1219277198247e-09
270.9999999945167721.09664553761927e-085.48322768809635e-09
280.9999999985465652.9068706576432e-091.4534353288216e-09
290.9999999970058195.9883611617535e-092.99418058087675e-09
300.9999999953800439.23991378648084e-094.61995689324042e-09
310.9999999882160992.35678012089859e-081.1783900604493e-08
320.9999999791029654.17940699417428e-082.08970349708714e-08
330.9999999559149428.81701161455893e-084.40850580727946e-08
340.9999998983335162.03332968009143e-071.01666484004572e-07
350.9999997627352694.74529461781777e-072.37264730890889e-07
360.9999994434713051.11305739079279e-065.56528695396397e-07
370.9999987084504472.583099105799e-061.2915495528995e-06
380.9999976605869134.67882617322229e-062.33941308661114e-06
390.9999967027896066.59442078845151e-063.29721039422575e-06
400.9999928697574251.42604851505278e-057.13024257526392e-06
410.9999869648740692.60702518623783e-051.30351259311891e-05
420.9999950523174549.89536509184587e-064.94768254592293e-06
430.9999894238645332.11522709340701e-051.0576135467035e-05
440.9999909662403131.80675193747402e-059.03375968737012e-06
450.9999824414776453.51170447097292e-051.75585223548646e-05
460.9999742318064535.15363870943986e-052.57681935471993e-05
470.999946380814810.0001072383703808115.36191851904055e-05
480.9998979424586380.0002041150827245630.000102057541362282
490.999842051732670.0003158965346600540.000157948267330027
500.9996903477260940.0006193045478128090.000309652273906405
510.9999218134250050.0001563731499907977.81865749953985e-05
520.9998409540408020.0003180919183951550.000159045959197577
530.9996928599148610.0006142801702772970.000307140085138648
540.9993890202824180.001221959435164440.000610979717582222
550.9988149640051420.002370071989716170.00118503599485809
560.9979374093811790.004125181237642890.00206259061882145
570.9964871465906510.007025706818698060.00351285340934903
580.9999825066535183.49866929643988e-051.74933464821994e-05
590.9999844636738323.10726523355076e-051.55363261677538e-05
600.9999979759497914.04810041847866e-062.02405020923933e-06
610.9999978294222224.34115555527703e-062.17057777763852e-06
620.9999998911814862.17637027659807e-071.08818513829904e-07
630.999999528776329.42447359015002e-074.71223679507501e-07
640.9999986637180172.67256396518135e-061.33628198259067e-06
650.9999948987695671.02024608668107e-055.10123043340535e-06
660.9999824588047763.50823904480075e-051.75411952240038e-05
670.9999324555621520.0001350888756953636.75444378476815e-05
680.9997577444417460.0004845111165073480.000242255558253674
690.999143456743810.001713086512379170.000856543256189584
700.997480061912840.005039876174319560.00251993808715978
710.9922652898692160.01546942026156880.00773471013078439
720.9854109580184960.02917808396300890.0145890419815044
730.9572557307381170.08548853852376610.042744269261883
740.906774636847460.1864507263050810.0932253631525404

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.999998183794564 & 3.63241087231047e-06 & 1.81620543615523e-06 \tabularnewline
7 & 0.999999999380966 & 1.23806850679785e-09 & 6.19034253398927e-10 \tabularnewline
8 & 0.999999999938747 & 1.22506467921573e-10 & 6.12532339607866e-11 \tabularnewline
9 & 0.999999999730868 & 5.38263589338003e-10 & 2.69131794669002e-10 \tabularnewline
10 & 0.999999999993079 & 1.38425704777778e-11 & 6.92128523888889e-12 \tabularnewline
11 & 0.999999999976353 & 4.72934434077158e-11 & 2.36467217038579e-11 \tabularnewline
12 & 0.999999999950564 & 9.88710385329168e-11 & 4.94355192664584e-11 \tabularnewline
13 & 0.999999999852323 & 2.95354656855529e-10 & 1.47677328427764e-10 \tabularnewline
14 & 0.999999999538166 & 9.236675323357e-10 & 4.6183376616785e-10 \tabularnewline
15 & 0.9999999996526 & 6.94800300770161e-10 & 3.47400150385081e-10 \tabularnewline
16 & 0.999999999554325 & 8.91350878638582e-10 & 4.45675439319291e-10 \tabularnewline
17 & 0.999999999220318 & 1.55936439119894e-09 & 7.79682195599472e-10 \tabularnewline
18 & 0.999999999405029 & 1.18994243539545e-09 & 5.94971217697724e-10 \tabularnewline
19 & 0.999999998774637 & 2.45072612071916e-09 & 1.22536306035958e-09 \tabularnewline
20 & 0.999999996647085 & 6.70583040351468e-09 & 3.35291520175734e-09 \tabularnewline
21 & 0.999999999502283 & 9.95433841957874e-10 & 4.97716920978937e-10 \tabularnewline
22 & 0.999999999159029 & 1.68194282625724e-09 & 8.40971413128618e-10 \tabularnewline
23 & 0.999999999802549 & 3.94902955816013e-10 & 1.97451477908006e-10 \tabularnewline
24 & 0.999999999542235 & 9.15531072136296e-10 & 4.57765536068148e-10 \tabularnewline
25 & 0.999999998834605 & 2.33079064993554e-09 & 1.16539532496777e-09 \tabularnewline
26 & 0.999999997878072 & 4.2438554396494e-09 & 2.1219277198247e-09 \tabularnewline
27 & 0.999999994516772 & 1.09664553761927e-08 & 5.48322768809635e-09 \tabularnewline
28 & 0.999999998546565 & 2.9068706576432e-09 & 1.4534353288216e-09 \tabularnewline
29 & 0.999999997005819 & 5.9883611617535e-09 & 2.99418058087675e-09 \tabularnewline
30 & 0.999999995380043 & 9.23991378648084e-09 & 4.61995689324042e-09 \tabularnewline
31 & 0.999999988216099 & 2.35678012089859e-08 & 1.1783900604493e-08 \tabularnewline
32 & 0.999999979102965 & 4.17940699417428e-08 & 2.08970349708714e-08 \tabularnewline
33 & 0.999999955914942 & 8.81701161455893e-08 & 4.40850580727946e-08 \tabularnewline
34 & 0.999999898333516 & 2.03332968009143e-07 & 1.01666484004572e-07 \tabularnewline
35 & 0.999999762735269 & 4.74529461781777e-07 & 2.37264730890889e-07 \tabularnewline
36 & 0.999999443471305 & 1.11305739079279e-06 & 5.56528695396397e-07 \tabularnewline
37 & 0.999998708450447 & 2.583099105799e-06 & 1.2915495528995e-06 \tabularnewline
38 & 0.999997660586913 & 4.67882617322229e-06 & 2.33941308661114e-06 \tabularnewline
39 & 0.999996702789606 & 6.59442078845151e-06 & 3.29721039422575e-06 \tabularnewline
40 & 0.999992869757425 & 1.42604851505278e-05 & 7.13024257526392e-06 \tabularnewline
41 & 0.999986964874069 & 2.60702518623783e-05 & 1.30351259311891e-05 \tabularnewline
42 & 0.999995052317454 & 9.89536509184587e-06 & 4.94768254592293e-06 \tabularnewline
43 & 0.999989423864533 & 2.11522709340701e-05 & 1.0576135467035e-05 \tabularnewline
44 & 0.999990966240313 & 1.80675193747402e-05 & 9.03375968737012e-06 \tabularnewline
45 & 0.999982441477645 & 3.51170447097292e-05 & 1.75585223548646e-05 \tabularnewline
46 & 0.999974231806453 & 5.15363870943986e-05 & 2.57681935471993e-05 \tabularnewline
47 & 0.99994638081481 & 0.000107238370380811 & 5.36191851904055e-05 \tabularnewline
48 & 0.999897942458638 & 0.000204115082724563 & 0.000102057541362282 \tabularnewline
49 & 0.99984205173267 & 0.000315896534660054 & 0.000157948267330027 \tabularnewline
50 & 0.999690347726094 & 0.000619304547812809 & 0.000309652273906405 \tabularnewline
51 & 0.999921813425005 & 0.000156373149990797 & 7.81865749953985e-05 \tabularnewline
52 & 0.999840954040802 & 0.000318091918395155 & 0.000159045959197577 \tabularnewline
53 & 0.999692859914861 & 0.000614280170277297 & 0.000307140085138648 \tabularnewline
54 & 0.999389020282418 & 0.00122195943516444 & 0.000610979717582222 \tabularnewline
55 & 0.998814964005142 & 0.00237007198971617 & 0.00118503599485809 \tabularnewline
56 & 0.997937409381179 & 0.00412518123764289 & 0.00206259061882145 \tabularnewline
57 & 0.996487146590651 & 0.00702570681869806 & 0.00351285340934903 \tabularnewline
58 & 0.999982506653518 & 3.49866929643988e-05 & 1.74933464821994e-05 \tabularnewline
59 & 0.999984463673832 & 3.10726523355076e-05 & 1.55363261677538e-05 \tabularnewline
60 & 0.999997975949791 & 4.04810041847866e-06 & 2.02405020923933e-06 \tabularnewline
61 & 0.999997829422222 & 4.34115555527703e-06 & 2.17057777763852e-06 \tabularnewline
62 & 0.999999891181486 & 2.17637027659807e-07 & 1.08818513829904e-07 \tabularnewline
63 & 0.99999952877632 & 9.42447359015002e-07 & 4.71223679507501e-07 \tabularnewline
64 & 0.999998663718017 & 2.67256396518135e-06 & 1.33628198259067e-06 \tabularnewline
65 & 0.999994898769567 & 1.02024608668107e-05 & 5.10123043340535e-06 \tabularnewline
66 & 0.999982458804776 & 3.50823904480075e-05 & 1.75411952240038e-05 \tabularnewline
67 & 0.999932455562152 & 0.000135088875695363 & 6.75444378476815e-05 \tabularnewline
68 & 0.999757744441746 & 0.000484511116507348 & 0.000242255558253674 \tabularnewline
69 & 0.99914345674381 & 0.00171308651237917 & 0.000856543256189584 \tabularnewline
70 & 0.99748006191284 & 0.00503987617431956 & 0.00251993808715978 \tabularnewline
71 & 0.992265289869216 & 0.0154694202615688 & 0.00773471013078439 \tabularnewline
72 & 0.985410958018496 & 0.0291780839630089 & 0.0145890419815044 \tabularnewline
73 & 0.957255730738117 & 0.0854885385237661 & 0.042744269261883 \tabularnewline
74 & 0.90677463684746 & 0.186450726305081 & 0.0932253631525404 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146615&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]6[/C][C]0.999998183794564[/C][C]3.63241087231047e-06[/C][C]1.81620543615523e-06[/C][/ROW]
[ROW][C]7[/C][C]0.999999999380966[/C][C]1.23806850679785e-09[/C][C]6.19034253398927e-10[/C][/ROW]
[ROW][C]8[/C][C]0.999999999938747[/C][C]1.22506467921573e-10[/C][C]6.12532339607866e-11[/C][/ROW]
[ROW][C]9[/C][C]0.999999999730868[/C][C]5.38263589338003e-10[/C][C]2.69131794669002e-10[/C][/ROW]
[ROW][C]10[/C][C]0.999999999993079[/C][C]1.38425704777778e-11[/C][C]6.92128523888889e-12[/C][/ROW]
[ROW][C]11[/C][C]0.999999999976353[/C][C]4.72934434077158e-11[/C][C]2.36467217038579e-11[/C][/ROW]
[ROW][C]12[/C][C]0.999999999950564[/C][C]9.88710385329168e-11[/C][C]4.94355192664584e-11[/C][/ROW]
[ROW][C]13[/C][C]0.999999999852323[/C][C]2.95354656855529e-10[/C][C]1.47677328427764e-10[/C][/ROW]
[ROW][C]14[/C][C]0.999999999538166[/C][C]9.236675323357e-10[/C][C]4.6183376616785e-10[/C][/ROW]
[ROW][C]15[/C][C]0.9999999996526[/C][C]6.94800300770161e-10[/C][C]3.47400150385081e-10[/C][/ROW]
[ROW][C]16[/C][C]0.999999999554325[/C][C]8.91350878638582e-10[/C][C]4.45675439319291e-10[/C][/ROW]
[ROW][C]17[/C][C]0.999999999220318[/C][C]1.55936439119894e-09[/C][C]7.79682195599472e-10[/C][/ROW]
[ROW][C]18[/C][C]0.999999999405029[/C][C]1.18994243539545e-09[/C][C]5.94971217697724e-10[/C][/ROW]
[ROW][C]19[/C][C]0.999999998774637[/C][C]2.45072612071916e-09[/C][C]1.22536306035958e-09[/C][/ROW]
[ROW][C]20[/C][C]0.999999996647085[/C][C]6.70583040351468e-09[/C][C]3.35291520175734e-09[/C][/ROW]
[ROW][C]21[/C][C]0.999999999502283[/C][C]9.95433841957874e-10[/C][C]4.97716920978937e-10[/C][/ROW]
[ROW][C]22[/C][C]0.999999999159029[/C][C]1.68194282625724e-09[/C][C]8.40971413128618e-10[/C][/ROW]
[ROW][C]23[/C][C]0.999999999802549[/C][C]3.94902955816013e-10[/C][C]1.97451477908006e-10[/C][/ROW]
[ROW][C]24[/C][C]0.999999999542235[/C][C]9.15531072136296e-10[/C][C]4.57765536068148e-10[/C][/ROW]
[ROW][C]25[/C][C]0.999999998834605[/C][C]2.33079064993554e-09[/C][C]1.16539532496777e-09[/C][/ROW]
[ROW][C]26[/C][C]0.999999997878072[/C][C]4.2438554396494e-09[/C][C]2.1219277198247e-09[/C][/ROW]
[ROW][C]27[/C][C]0.999999994516772[/C][C]1.09664553761927e-08[/C][C]5.48322768809635e-09[/C][/ROW]
[ROW][C]28[/C][C]0.999999998546565[/C][C]2.9068706576432e-09[/C][C]1.4534353288216e-09[/C][/ROW]
[ROW][C]29[/C][C]0.999999997005819[/C][C]5.9883611617535e-09[/C][C]2.99418058087675e-09[/C][/ROW]
[ROW][C]30[/C][C]0.999999995380043[/C][C]9.23991378648084e-09[/C][C]4.61995689324042e-09[/C][/ROW]
[ROW][C]31[/C][C]0.999999988216099[/C][C]2.35678012089859e-08[/C][C]1.1783900604493e-08[/C][/ROW]
[ROW][C]32[/C][C]0.999999979102965[/C][C]4.17940699417428e-08[/C][C]2.08970349708714e-08[/C][/ROW]
[ROW][C]33[/C][C]0.999999955914942[/C][C]8.81701161455893e-08[/C][C]4.40850580727946e-08[/C][/ROW]
[ROW][C]34[/C][C]0.999999898333516[/C][C]2.03332968009143e-07[/C][C]1.01666484004572e-07[/C][/ROW]
[ROW][C]35[/C][C]0.999999762735269[/C][C]4.74529461781777e-07[/C][C]2.37264730890889e-07[/C][/ROW]
[ROW][C]36[/C][C]0.999999443471305[/C][C]1.11305739079279e-06[/C][C]5.56528695396397e-07[/C][/ROW]
[ROW][C]37[/C][C]0.999998708450447[/C][C]2.583099105799e-06[/C][C]1.2915495528995e-06[/C][/ROW]
[ROW][C]38[/C][C]0.999997660586913[/C][C]4.67882617322229e-06[/C][C]2.33941308661114e-06[/C][/ROW]
[ROW][C]39[/C][C]0.999996702789606[/C][C]6.59442078845151e-06[/C][C]3.29721039422575e-06[/C][/ROW]
[ROW][C]40[/C][C]0.999992869757425[/C][C]1.42604851505278e-05[/C][C]7.13024257526392e-06[/C][/ROW]
[ROW][C]41[/C][C]0.999986964874069[/C][C]2.60702518623783e-05[/C][C]1.30351259311891e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999995052317454[/C][C]9.89536509184587e-06[/C][C]4.94768254592293e-06[/C][/ROW]
[ROW][C]43[/C][C]0.999989423864533[/C][C]2.11522709340701e-05[/C][C]1.0576135467035e-05[/C][/ROW]
[ROW][C]44[/C][C]0.999990966240313[/C][C]1.80675193747402e-05[/C][C]9.03375968737012e-06[/C][/ROW]
[ROW][C]45[/C][C]0.999982441477645[/C][C]3.51170447097292e-05[/C][C]1.75585223548646e-05[/C][/ROW]
[ROW][C]46[/C][C]0.999974231806453[/C][C]5.15363870943986e-05[/C][C]2.57681935471993e-05[/C][/ROW]
[ROW][C]47[/C][C]0.99994638081481[/C][C]0.000107238370380811[/C][C]5.36191851904055e-05[/C][/ROW]
[ROW][C]48[/C][C]0.999897942458638[/C][C]0.000204115082724563[/C][C]0.000102057541362282[/C][/ROW]
[ROW][C]49[/C][C]0.99984205173267[/C][C]0.000315896534660054[/C][C]0.000157948267330027[/C][/ROW]
[ROW][C]50[/C][C]0.999690347726094[/C][C]0.000619304547812809[/C][C]0.000309652273906405[/C][/ROW]
[ROW][C]51[/C][C]0.999921813425005[/C][C]0.000156373149990797[/C][C]7.81865749953985e-05[/C][/ROW]
[ROW][C]52[/C][C]0.999840954040802[/C][C]0.000318091918395155[/C][C]0.000159045959197577[/C][/ROW]
[ROW][C]53[/C][C]0.999692859914861[/C][C]0.000614280170277297[/C][C]0.000307140085138648[/C][/ROW]
[ROW][C]54[/C][C]0.999389020282418[/C][C]0.00122195943516444[/C][C]0.000610979717582222[/C][/ROW]
[ROW][C]55[/C][C]0.998814964005142[/C][C]0.00237007198971617[/C][C]0.00118503599485809[/C][/ROW]
[ROW][C]56[/C][C]0.997937409381179[/C][C]0.00412518123764289[/C][C]0.00206259061882145[/C][/ROW]
[ROW][C]57[/C][C]0.996487146590651[/C][C]0.00702570681869806[/C][C]0.00351285340934903[/C][/ROW]
[ROW][C]58[/C][C]0.999982506653518[/C][C]3.49866929643988e-05[/C][C]1.74933464821994e-05[/C][/ROW]
[ROW][C]59[/C][C]0.999984463673832[/C][C]3.10726523355076e-05[/C][C]1.55363261677538e-05[/C][/ROW]
[ROW][C]60[/C][C]0.999997975949791[/C][C]4.04810041847866e-06[/C][C]2.02405020923933e-06[/C][/ROW]
[ROW][C]61[/C][C]0.999997829422222[/C][C]4.34115555527703e-06[/C][C]2.17057777763852e-06[/C][/ROW]
[ROW][C]62[/C][C]0.999999891181486[/C][C]2.17637027659807e-07[/C][C]1.08818513829904e-07[/C][/ROW]
[ROW][C]63[/C][C]0.99999952877632[/C][C]9.42447359015002e-07[/C][C]4.71223679507501e-07[/C][/ROW]
[ROW][C]64[/C][C]0.999998663718017[/C][C]2.67256396518135e-06[/C][C]1.33628198259067e-06[/C][/ROW]
[ROW][C]65[/C][C]0.999994898769567[/C][C]1.02024608668107e-05[/C][C]5.10123043340535e-06[/C][/ROW]
[ROW][C]66[/C][C]0.999982458804776[/C][C]3.50823904480075e-05[/C][C]1.75411952240038e-05[/C][/ROW]
[ROW][C]67[/C][C]0.999932455562152[/C][C]0.000135088875695363[/C][C]6.75444378476815e-05[/C][/ROW]
[ROW][C]68[/C][C]0.999757744441746[/C][C]0.000484511116507348[/C][C]0.000242255558253674[/C][/ROW]
[ROW][C]69[/C][C]0.99914345674381[/C][C]0.00171308651237917[/C][C]0.000856543256189584[/C][/ROW]
[ROW][C]70[/C][C]0.99748006191284[/C][C]0.00503987617431956[/C][C]0.00251993808715978[/C][/ROW]
[ROW][C]71[/C][C]0.992265289869216[/C][C]0.0154694202615688[/C][C]0.00773471013078439[/C][/ROW]
[ROW][C]72[/C][C]0.985410958018496[/C][C]0.0291780839630089[/C][C]0.0145890419815044[/C][/ROW]
[ROW][C]73[/C][C]0.957255730738117[/C][C]0.0854885385237661[/C][C]0.042744269261883[/C][/ROW]
[ROW][C]74[/C][C]0.90677463684746[/C][C]0.186450726305081[/C][C]0.0932253631525404[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146615&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146615&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
60.9999981837945643.63241087231047e-061.81620543615523e-06
70.9999999993809661.23806850679785e-096.19034253398927e-10
80.9999999999387471.22506467921573e-106.12532339607866e-11
90.9999999997308685.38263589338003e-102.69131794669002e-10
100.9999999999930791.38425704777778e-116.92128523888889e-12
110.9999999999763534.72934434077158e-112.36467217038579e-11
120.9999999999505649.88710385329168e-114.94355192664584e-11
130.9999999998523232.95354656855529e-101.47677328427764e-10
140.9999999995381669.236675323357e-104.6183376616785e-10
150.99999999965266.94800300770161e-103.47400150385081e-10
160.9999999995543258.91350878638582e-104.45675439319291e-10
170.9999999992203181.55936439119894e-097.79682195599472e-10
180.9999999994050291.18994243539545e-095.94971217697724e-10
190.9999999987746372.45072612071916e-091.22536306035958e-09
200.9999999966470856.70583040351468e-093.35291520175734e-09
210.9999999995022839.95433841957874e-104.97716920978937e-10
220.9999999991590291.68194282625724e-098.40971413128618e-10
230.9999999998025493.94902955816013e-101.97451477908006e-10
240.9999999995422359.15531072136296e-104.57765536068148e-10
250.9999999988346052.33079064993554e-091.16539532496777e-09
260.9999999978780724.2438554396494e-092.1219277198247e-09
270.9999999945167721.09664553761927e-085.48322768809635e-09
280.9999999985465652.9068706576432e-091.4534353288216e-09
290.9999999970058195.9883611617535e-092.99418058087675e-09
300.9999999953800439.23991378648084e-094.61995689324042e-09
310.9999999882160992.35678012089859e-081.1783900604493e-08
320.9999999791029654.17940699417428e-082.08970349708714e-08
330.9999999559149428.81701161455893e-084.40850580727946e-08
340.9999998983335162.03332968009143e-071.01666484004572e-07
350.9999997627352694.74529461781777e-072.37264730890889e-07
360.9999994434713051.11305739079279e-065.56528695396397e-07
370.9999987084504472.583099105799e-061.2915495528995e-06
380.9999976605869134.67882617322229e-062.33941308661114e-06
390.9999967027896066.59442078845151e-063.29721039422575e-06
400.9999928697574251.42604851505278e-057.13024257526392e-06
410.9999869648740692.60702518623783e-051.30351259311891e-05
420.9999950523174549.89536509184587e-064.94768254592293e-06
430.9999894238645332.11522709340701e-051.0576135467035e-05
440.9999909662403131.80675193747402e-059.03375968737012e-06
450.9999824414776453.51170447097292e-051.75585223548646e-05
460.9999742318064535.15363870943986e-052.57681935471993e-05
470.999946380814810.0001072383703808115.36191851904055e-05
480.9998979424586380.0002041150827245630.000102057541362282
490.999842051732670.0003158965346600540.000157948267330027
500.9996903477260940.0006193045478128090.000309652273906405
510.9999218134250050.0001563731499907977.81865749953985e-05
520.9998409540408020.0003180919183951550.000159045959197577
530.9996928599148610.0006142801702772970.000307140085138648
540.9993890202824180.001221959435164440.000610979717582222
550.9988149640051420.002370071989716170.00118503599485809
560.9979374093811790.004125181237642890.00206259061882145
570.9964871465906510.007025706818698060.00351285340934903
580.9999825066535183.49866929643988e-051.74933464821994e-05
590.9999844636738323.10726523355076e-051.55363261677538e-05
600.9999979759497914.04810041847866e-062.02405020923933e-06
610.9999978294222224.34115555527703e-062.17057777763852e-06
620.9999998911814862.17637027659807e-071.08818513829904e-07
630.999999528776329.42447359015002e-074.71223679507501e-07
640.9999986637180172.67256396518135e-061.33628198259067e-06
650.9999948987695671.02024608668107e-055.10123043340535e-06
660.9999824588047763.50823904480075e-051.75411952240038e-05
670.9999324555621520.0001350888756953636.75444378476815e-05
680.9997577444417460.0004845111165073480.000242255558253674
690.999143456743810.001713086512379170.000856543256189584
700.997480061912840.005039876174319560.00251993808715978
710.9922652898692160.01546942026156880.00773471013078439
720.9854109580184960.02917808396300890.0145890419815044
730.9572557307381170.08548853852376610.042744269261883
740.906774636847460.1864507263050810.0932253631525404







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level650.942028985507246NOK
5% type I error level670.971014492753623NOK
10% type I error level680.985507246376812NOK

\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 & 65 & 0.942028985507246 & NOK \tabularnewline
5% type I error level & 67 & 0.971014492753623 & NOK \tabularnewline
10% type I error level & 68 & 0.985507246376812 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146615&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]65[/C][C]0.942028985507246[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]67[/C][C]0.971014492753623[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]68[/C][C]0.985507246376812[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146615&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146615&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 level650.942028985507246NOK
5% type I error level670.971014492753623NOK
10% type I error level680.985507246376812NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}