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Description of Statistical Computation

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 05:24:54 -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/t1322130424erueotzdk2148fn.htm/, Retrieved Thu, 03 Apr 2025 18:24:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146612, Retrieved Thu, 03 Apr 2025 18:24:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact221

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [ws7/1] [2011-11-24 10:24:54] [d6b8e0ceefc1e2de0b53f6dffb5d636c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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

Tables (Output of Computation)





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=146612&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=146612&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146612&T=0

As an alternative you can also use a QR Code:  
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] = + 235.695592967887 -0.0208938269088003weekdag[t] + 0.933764370636922zaterdag[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
zondag[t] =  +  235.695592967887 -0.0208938269088003weekdag[t] +  0.933764370636922zaterdag[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146612&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]zondag[t] =  +  235.695592967887 -0.0208938269088003weekdag[t] +  0.933764370636922zaterdag[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146612&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146612&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
zondag[t] = + 235.695592967887 -0.0208938269088003weekdag[t] + 0.933764370636922zaterdag[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)235.695592967887114.2094062.06370.0436080.021804
weekdag-0.02089382690880030.022501-0.92860.3570190.17851
zaterdag0.9337643706369220.05424617.213400

\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) & 235.695592967887 & 114.209406 & 2.0637 & 0.043608 & 0.021804 \tabularnewline
weekdag & -0.0208938269088003 & 0.022501 & -0.9286 & 0.357019 & 0.17851 \tabularnewline
zaterdag & 0.933764370636922 & 0.054246 & 17.2134 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146612&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]235.695592967887[/C][C]114.209406[/C][C]2.0637[/C][C]0.043608[/C][C]0.021804[/C][/ROW]
[ROW][C]weekdag[/C][C]-0.0208938269088003[/C][C]0.022501[/C][C]-0.9286[/C][C]0.357019[/C][C]0.17851[/C][/ROW]
[ROW][C]zaterdag[/C][C]0.933764370636922[/C][C]0.054246[/C][C]17.2134[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146612&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146612&T=2

As an alternative you can also use a QR Code:  
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)235.695592967887114.2094062.06370.0436080.021804
weekdag-0.02089382690880030.022501-0.92860.3570190.17851
zaterdag0.9337643706369220.05424617.213400






Multiple Linear Regression - Regression Statistics
Multiple R0.989979745375768
R-squared0.980059896254271
Adjusted R-squared0.979360243491262
F-TEST (value)1400.78042719456
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation693.572523391552
Sum Squared Residuals27419442.1766123

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.989979745375768 \tabularnewline
R-squared & 0.980059896254271 \tabularnewline
Adjusted R-squared & 0.979360243491262 \tabularnewline
F-TEST (value) & 1400.78042719456 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 693.572523391552 \tabularnewline
Sum Squared Residuals & 27419442.1766123 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146612&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.989979745375768[/C][/ROW]
[ROW][C]R-squared[/C][C]0.980059896254271[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.979360243491262[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1400.78042719456[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/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]693.572523391552[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]27419442.1766123[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146612&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146612&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.989979745375768
R-squared0.980059896254271
Adjusted R-squared0.979360243491262
F-TEST (value)1400.78042719456
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation693.572523391552
Sum Squared Residuals27419442.1766123






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12227423061.2484567772-787.248456777152
21481916197.2368257136-1378.23682571356
31513613405.23994647111730.76005352892
41370413137.3369274064566.663072593549
51963820370.1826132271-732.182613227094
675519237.8760618518-1686.8760618518
780195585.32980822992433.6701917701
865096969.03487394704-460.03487394704
966346044.00221219293589.997787807068
10111669164.823181773052001.17681822695
1175087006.36300695308501.636993046924
1242754009.95908584927265.040914150727
1349444357.73274734599586.267252654011
1454414839.45790453182601.542095468178
1516891909.65746391835-220.657463918347
1615221718.09482529928-196.094825299277
1714161570.11520314903-154.115203149032
1815942121.96538527706-527.965385277058
1919091601.75947584761307.240524152391
2025992481.75299724633117.247002753665
2112622156.03979973732-894.039799737315
2211991579.29974176785-380.299741767847
2344043330.905319362611073.09468063739
2411661403.12712220271-237.127122202706
2511221282.97694764778-160.976947647785
268861259.76465918357-373.764659183574
27778900.667296733753-122.667296733753
2844363628.32484416592807.675155834084
2918902255.42605742008-365.426057420085
3031072951.60285921731155.397140782688
3110381155.02425381909-117.024253819092
32300556.055437556258-256.055437556258
339881251.48651942503-263.486519425027
3420082177.22309961058-169.223099610576
3515221541.85038857342-19.8503885734204
3613361437.49709085195-101.49709085195
379761026.58454413124-50.5845441312367
387981102.12144685854-304.121446858542
398691334.16113280875-465.161132808752
4012601423.06172999557-163.061729995566
41578758.676601375677-180.676601375677
4223591929.23951296389429.760487036112
43736777.642882059007-41.6428820590067
4416901381.93468664946308.065313350542
4512011424.78033795195-223.780337951948
468131565.8881645868-752.888164586795
47778987.892956696668-209.892956696668
48687986.491549987647-299.491549987647
4912701228.796186174641.2038138253968
50671877.761943989715-206.761943989715
511559825.156211524285733.843788475715
52489746.941858340389-257.941858340389
53773830.247847449772-57.2478474497723
54629886.775161533799-257.775161533799
55637807.57879848519-170.57879848519
56277463.521197565977-186.521197565977
57776772.2131372160493.78686278395093
581651929.81683542486721.18316457514
59377592.838824599734-215.838824599734
60222548.436011347338-326.436011347338

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 22274 & 23061.2484567772 & -787.248456777152 \tabularnewline
2 & 14819 & 16197.2368257136 & -1378.23682571356 \tabularnewline
3 & 15136 & 13405.2399464711 & 1730.76005352892 \tabularnewline
4 & 13704 & 13137.3369274064 & 566.663072593549 \tabularnewline
5 & 19638 & 20370.1826132271 & -732.182613227094 \tabularnewline
6 & 7551 & 9237.8760618518 & -1686.8760618518 \tabularnewline
7 & 8019 & 5585.3298082299 & 2433.6701917701 \tabularnewline
8 & 6509 & 6969.03487394704 & -460.03487394704 \tabularnewline
9 & 6634 & 6044.00221219293 & 589.997787807068 \tabularnewline
10 & 11166 & 9164.82318177305 & 2001.17681822695 \tabularnewline
11 & 7508 & 7006.36300695308 & 501.636993046924 \tabularnewline
12 & 4275 & 4009.95908584927 & 265.040914150727 \tabularnewline
13 & 4944 & 4357.73274734599 & 586.267252654011 \tabularnewline
14 & 5441 & 4839.45790453182 & 601.542095468178 \tabularnewline
15 & 1689 & 1909.65746391835 & -220.657463918347 \tabularnewline
16 & 1522 & 1718.09482529928 & -196.094825299277 \tabularnewline
17 & 1416 & 1570.11520314903 & -154.115203149032 \tabularnewline
18 & 1594 & 2121.96538527706 & -527.965385277058 \tabularnewline
19 & 1909 & 1601.75947584761 & 307.240524152391 \tabularnewline
20 & 2599 & 2481.75299724633 & 117.247002753665 \tabularnewline
21 & 1262 & 2156.03979973732 & -894.039799737315 \tabularnewline
22 & 1199 & 1579.29974176785 & -380.299741767847 \tabularnewline
23 & 4404 & 3330.90531936261 & 1073.09468063739 \tabularnewline
24 & 1166 & 1403.12712220271 & -237.127122202706 \tabularnewline
25 & 1122 & 1282.97694764778 & -160.976947647785 \tabularnewline
26 & 886 & 1259.76465918357 & -373.764659183574 \tabularnewline
27 & 778 & 900.667296733753 & -122.667296733753 \tabularnewline
28 & 4436 & 3628.32484416592 & 807.675155834084 \tabularnewline
29 & 1890 & 2255.42605742008 & -365.426057420085 \tabularnewline
30 & 3107 & 2951.60285921731 & 155.397140782688 \tabularnewline
31 & 1038 & 1155.02425381909 & -117.024253819092 \tabularnewline
32 & 300 & 556.055437556258 & -256.055437556258 \tabularnewline
33 & 988 & 1251.48651942503 & -263.486519425027 \tabularnewline
34 & 2008 & 2177.22309961058 & -169.223099610576 \tabularnewline
35 & 1522 & 1541.85038857342 & -19.8503885734204 \tabularnewline
36 & 1336 & 1437.49709085195 & -101.49709085195 \tabularnewline
37 & 976 & 1026.58454413124 & -50.5845441312367 \tabularnewline
38 & 798 & 1102.12144685854 & -304.121446858542 \tabularnewline
39 & 869 & 1334.16113280875 & -465.161132808752 \tabularnewline
40 & 1260 & 1423.06172999557 & -163.061729995566 \tabularnewline
41 & 578 & 758.676601375677 & -180.676601375677 \tabularnewline
42 & 2359 & 1929.23951296389 & 429.760487036112 \tabularnewline
43 & 736 & 777.642882059007 & -41.6428820590067 \tabularnewline
44 & 1690 & 1381.93468664946 & 308.065313350542 \tabularnewline
45 & 1201 & 1424.78033795195 & -223.780337951948 \tabularnewline
46 & 813 & 1565.8881645868 & -752.888164586795 \tabularnewline
47 & 778 & 987.892956696668 & -209.892956696668 \tabularnewline
48 & 687 & 986.491549987647 & -299.491549987647 \tabularnewline
49 & 1270 & 1228.7961861746 & 41.2038138253968 \tabularnewline
50 & 671 & 877.761943989715 & -206.761943989715 \tabularnewline
51 & 1559 & 825.156211524285 & 733.843788475715 \tabularnewline
52 & 489 & 746.941858340389 & -257.941858340389 \tabularnewline
53 & 773 & 830.247847449772 & -57.2478474497723 \tabularnewline
54 & 629 & 886.775161533799 & -257.775161533799 \tabularnewline
55 & 637 & 807.57879848519 & -170.57879848519 \tabularnewline
56 & 277 & 463.521197565977 & -186.521197565977 \tabularnewline
57 & 776 & 772.213137216049 & 3.78686278395093 \tabularnewline
58 & 1651 & 929.81683542486 & 721.18316457514 \tabularnewline
59 & 377 & 592.838824599734 & -215.838824599734 \tabularnewline
60 & 222 & 548.436011347338 & -326.436011347338 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146612&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]23061.2484567772[/C][C]-787.248456777152[/C][/ROW]
[ROW][C]2[/C][C]14819[/C][C]16197.2368257136[/C][C]-1378.23682571356[/C][/ROW]
[ROW][C]3[/C][C]15136[/C][C]13405.2399464711[/C][C]1730.76005352892[/C][/ROW]
[ROW][C]4[/C][C]13704[/C][C]13137.3369274064[/C][C]566.663072593549[/C][/ROW]
[ROW][C]5[/C][C]19638[/C][C]20370.1826132271[/C][C]-732.182613227094[/C][/ROW]
[ROW][C]6[/C][C]7551[/C][C]9237.8760618518[/C][C]-1686.8760618518[/C][/ROW]
[ROW][C]7[/C][C]8019[/C][C]5585.3298082299[/C][C]2433.6701917701[/C][/ROW]
[ROW][C]8[/C][C]6509[/C][C]6969.03487394704[/C][C]-460.03487394704[/C][/ROW]
[ROW][C]9[/C][C]6634[/C][C]6044.00221219293[/C][C]589.997787807068[/C][/ROW]
[ROW][C]10[/C][C]11166[/C][C]9164.82318177305[/C][C]2001.17681822695[/C][/ROW]
[ROW][C]11[/C][C]7508[/C][C]7006.36300695308[/C][C]501.636993046924[/C][/ROW]
[ROW][C]12[/C][C]4275[/C][C]4009.95908584927[/C][C]265.040914150727[/C][/ROW]
[ROW][C]13[/C][C]4944[/C][C]4357.73274734599[/C][C]586.267252654011[/C][/ROW]
[ROW][C]14[/C][C]5441[/C][C]4839.45790453182[/C][C]601.542095468178[/C][/ROW]
[ROW][C]15[/C][C]1689[/C][C]1909.65746391835[/C][C]-220.657463918347[/C][/ROW]
[ROW][C]16[/C][C]1522[/C][C]1718.09482529928[/C][C]-196.094825299277[/C][/ROW]
[ROW][C]17[/C][C]1416[/C][C]1570.11520314903[/C][C]-154.115203149032[/C][/ROW]
[ROW][C]18[/C][C]1594[/C][C]2121.96538527706[/C][C]-527.965385277058[/C][/ROW]
[ROW][C]19[/C][C]1909[/C][C]1601.75947584761[/C][C]307.240524152391[/C][/ROW]
[ROW][C]20[/C][C]2599[/C][C]2481.75299724633[/C][C]117.247002753665[/C][/ROW]
[ROW][C]21[/C][C]1262[/C][C]2156.03979973732[/C][C]-894.039799737315[/C][/ROW]
[ROW][C]22[/C][C]1199[/C][C]1579.29974176785[/C][C]-380.299741767847[/C][/ROW]
[ROW][C]23[/C][C]4404[/C][C]3330.90531936261[/C][C]1073.09468063739[/C][/ROW]
[ROW][C]24[/C][C]1166[/C][C]1403.12712220271[/C][C]-237.127122202706[/C][/ROW]
[ROW][C]25[/C][C]1122[/C][C]1282.97694764778[/C][C]-160.976947647785[/C][/ROW]
[ROW][C]26[/C][C]886[/C][C]1259.76465918357[/C][C]-373.764659183574[/C][/ROW]
[ROW][C]27[/C][C]778[/C][C]900.667296733753[/C][C]-122.667296733753[/C][/ROW]
[ROW][C]28[/C][C]4436[/C][C]3628.32484416592[/C][C]807.675155834084[/C][/ROW]
[ROW][C]29[/C][C]1890[/C][C]2255.42605742008[/C][C]-365.426057420085[/C][/ROW]
[ROW][C]30[/C][C]3107[/C][C]2951.60285921731[/C][C]155.397140782688[/C][/ROW]
[ROW][C]31[/C][C]1038[/C][C]1155.02425381909[/C][C]-117.024253819092[/C][/ROW]
[ROW][C]32[/C][C]300[/C][C]556.055437556258[/C][C]-256.055437556258[/C][/ROW]
[ROW][C]33[/C][C]988[/C][C]1251.48651942503[/C][C]-263.486519425027[/C][/ROW]
[ROW][C]34[/C][C]2008[/C][C]2177.22309961058[/C][C]-169.223099610576[/C][/ROW]
[ROW][C]35[/C][C]1522[/C][C]1541.85038857342[/C][C]-19.8503885734204[/C][/ROW]
[ROW][C]36[/C][C]1336[/C][C]1437.49709085195[/C][C]-101.49709085195[/C][/ROW]
[ROW][C]37[/C][C]976[/C][C]1026.58454413124[/C][C]-50.5845441312367[/C][/ROW]
[ROW][C]38[/C][C]798[/C][C]1102.12144685854[/C][C]-304.121446858542[/C][/ROW]
[ROW][C]39[/C][C]869[/C][C]1334.16113280875[/C][C]-465.161132808752[/C][/ROW]
[ROW][C]40[/C][C]1260[/C][C]1423.06172999557[/C][C]-163.061729995566[/C][/ROW]
[ROW][C]41[/C][C]578[/C][C]758.676601375677[/C][C]-180.676601375677[/C][/ROW]
[ROW][C]42[/C][C]2359[/C][C]1929.23951296389[/C][C]429.760487036112[/C][/ROW]
[ROW][C]43[/C][C]736[/C][C]777.642882059007[/C][C]-41.6428820590067[/C][/ROW]
[ROW][C]44[/C][C]1690[/C][C]1381.93468664946[/C][C]308.065313350542[/C][/ROW]
[ROW][C]45[/C][C]1201[/C][C]1424.78033795195[/C][C]-223.780337951948[/C][/ROW]
[ROW][C]46[/C][C]813[/C][C]1565.8881645868[/C][C]-752.888164586795[/C][/ROW]
[ROW][C]47[/C][C]778[/C][C]987.892956696668[/C][C]-209.892956696668[/C][/ROW]
[ROW][C]48[/C][C]687[/C][C]986.491549987647[/C][C]-299.491549987647[/C][/ROW]
[ROW][C]49[/C][C]1270[/C][C]1228.7961861746[/C][C]41.2038138253968[/C][/ROW]
[ROW][C]50[/C][C]671[/C][C]877.761943989715[/C][C]-206.761943989715[/C][/ROW]
[ROW][C]51[/C][C]1559[/C][C]825.156211524285[/C][C]733.843788475715[/C][/ROW]
[ROW][C]52[/C][C]489[/C][C]746.941858340389[/C][C]-257.941858340389[/C][/ROW]
[ROW][C]53[/C][C]773[/C][C]830.247847449772[/C][C]-57.2478474497723[/C][/ROW]
[ROW][C]54[/C][C]629[/C][C]886.775161533799[/C][C]-257.775161533799[/C][/ROW]
[ROW][C]55[/C][C]637[/C][C]807.57879848519[/C][C]-170.57879848519[/C][/ROW]
[ROW][C]56[/C][C]277[/C][C]463.521197565977[/C][C]-186.521197565977[/C][/ROW]
[ROW][C]57[/C][C]776[/C][C]772.213137216049[/C][C]3.78686278395093[/C][/ROW]
[ROW][C]58[/C][C]1651[/C][C]929.81683542486[/C][C]721.18316457514[/C][/ROW]
[ROW][C]59[/C][C]377[/C][C]592.838824599734[/C][C]-215.838824599734[/C][/ROW]
[ROW][C]60[/C][C]222[/C][C]548.436011347338[/C][C]-326.436011347338[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146612&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146612&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12227423061.2484567772-787.248456777152
21481916197.2368257136-1378.23682571356
31513613405.23994647111730.76005352892
41370413137.3369274064566.663072593549
51963820370.1826132271-732.182613227094
675519237.8760618518-1686.8760618518
780195585.32980822992433.6701917701
865096969.03487394704-460.03487394704
966346044.00221219293589.997787807068
10111669164.823181773052001.17681822695
1175087006.36300695308501.636993046924
1242754009.95908584927265.040914150727
1349444357.73274734599586.267252654011
1454414839.45790453182601.542095468178
1516891909.65746391835-220.657463918347
1615221718.09482529928-196.094825299277
1714161570.11520314903-154.115203149032
1815942121.96538527706-527.965385277058
1919091601.75947584761307.240524152391
2025992481.75299724633117.247002753665
2112622156.03979973732-894.039799737315
2211991579.29974176785-380.299741767847
2344043330.905319362611073.09468063739
2411661403.12712220271-237.127122202706
2511221282.97694764778-160.976947647785
268861259.76465918357-373.764659183574
27778900.667296733753-122.667296733753
2844363628.32484416592807.675155834084
2918902255.42605742008-365.426057420085
3031072951.60285921731155.397140782688
3110381155.02425381909-117.024253819092
32300556.055437556258-256.055437556258
339881251.48651942503-263.486519425027
3420082177.22309961058-169.223099610576
3515221541.85038857342-19.8503885734204
3613361437.49709085195-101.49709085195
379761026.58454413124-50.5845441312367
387981102.12144685854-304.121446858542
398691334.16113280875-465.161132808752
4012601423.06172999557-163.061729995566
41578758.676601375677-180.676601375677
4223591929.23951296389429.760487036112
43736777.642882059007-41.6428820590067
4416901381.93468664946308.065313350542
4512011424.78033795195-223.780337951948
468131565.8881645868-752.888164586795
47778987.892956696668-209.892956696668
48687986.491549987647-299.491549987647
4912701228.796186174641.2038138253968
50671877.761943989715-206.761943989715
511559825.156211524285733.843788475715
52489746.941858340389-257.941858340389
53773830.247847449772-57.2478474497723
54629886.775161533799-257.775161533799
55637807.57879848519-170.57879848519
56277463.521197565977-186.521197565977
57776772.2131372160493.78686278395093
581651929.81683542486721.18316457514
59377592.838824599734-215.838824599734
60222548.436011347338-326.436011347338






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.9999936724007191.26551985617821e-056.32759928089106e-06
70.9999999998366973.26607052928414e-101.63303526464207e-10
80.9999999999800963.98073444983949e-111.99036722491975e-11
90.9999999999087791.82441108753047e-109.12205543765233e-11
100.9999999999962187.56306493043859e-123.7815324652193e-12
110.9999999999868822.62349860857775e-111.31174930428887e-11
120.9999999999661566.76889465305962e-113.38444732652981e-11
130.9999999998953842.09231752323986e-101.04615876161993e-10
140.9999999996083547.83292964453781e-103.9164648222689e-10
150.9999999995959928.08016753557042e-104.04008376778521e-10
160.9999999993803631.23927341514012e-096.1963670757006e-10
170.9999999989144822.17103553306315e-091.08551776653158e-09
180.9999999986956562.60868750532237e-091.30434375266119e-09
190.9999999987064382.58712327527561e-091.29356163763781e-09
200.9999999960941217.81175809748111e-093.90587904874056e-09
210.9999999995752198.49562547583557e-104.24781273791779e-10
220.999999998984122.03175969728115e-091.01587984864058e-09
230.999999999738815.22379640151272e-102.61189820075636e-10
240.9999999991928011.61439886698002e-098.0719943349001e-10
250.9999999975300534.93989454307549e-092.46994727153775e-09
260.9999999934511221.30977556532792e-086.54887782663961e-09
270.9999999830908933.38182140691872e-081.69091070345936e-08
280.9999999871670962.56658084045715e-081.28329042022857e-08
290.9999999747958245.04083523358779e-082.52041761679389e-08
300.9999999227381731.54523653777432e-077.72618268887162e-08
310.9999997787658634.42468274997543e-072.21234137498772e-07
320.9999993917285461.21654290721005e-066.08271453605025e-07
330.9999983397610523.32047789674982e-061.66023894837491e-06
340.9999956915887748.61682245132455e-064.30841122566228e-06
350.9999888740118392.22519763213423e-051.11259881606712e-05
360.9999713555517745.7288896452525e-052.86444482262625e-05
370.999937852093980.0001242958120400436.21479060200216e-05
380.9998527090442350.0002945819115297270.000147290955764863
390.9997447325315320.0005105349369353560.000255267468467678
400.9994199930962680.001160013807464680.000580006903732338
410.9987045789739730.002590842052054890.00129542102602744
420.9983947557545390.003210488490921450.00160524424546072
430.9965020134595880.006995973080823480.00349798654041174
440.9968980070273510.006203985945298640.00310199297264932
450.9935751032717780.01284979345644310.00642489672822155
460.9973236538326960.005352692334607630.00267634616730382
470.9936842794566050.01263144108679030.00631572054339517
480.9877696094179830.02446078116403330.0122303905820166
490.978268385765190.04346322846962010.02173161423481
500.9658539691691190.06829206166176140.0341460308308807
510.9929937646754920.01401247064901590.00700623532450794
520.9797781703811640.04044365923767240.0202218296188362
530.9446923326371340.1106153347257330.0553076673628665
540.9070203933523820.1859592132952360.0929796066476181

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.999993672400719 & 1.26551985617821e-05 & 6.32759928089106e-06 \tabularnewline
7 & 0.999999999836697 & 3.26607052928414e-10 & 1.63303526464207e-10 \tabularnewline
8 & 0.999999999980096 & 3.98073444983949e-11 & 1.99036722491975e-11 \tabularnewline
9 & 0.999999999908779 & 1.82441108753047e-10 & 9.12205543765233e-11 \tabularnewline
10 & 0.999999999996218 & 7.56306493043859e-12 & 3.7815324652193e-12 \tabularnewline
11 & 0.999999999986882 & 2.62349860857775e-11 & 1.31174930428887e-11 \tabularnewline
12 & 0.999999999966156 & 6.76889465305962e-11 & 3.38444732652981e-11 \tabularnewline
13 & 0.999999999895384 & 2.09231752323986e-10 & 1.04615876161993e-10 \tabularnewline
14 & 0.999999999608354 & 7.83292964453781e-10 & 3.9164648222689e-10 \tabularnewline
15 & 0.999999999595992 & 8.08016753557042e-10 & 4.04008376778521e-10 \tabularnewline
16 & 0.999999999380363 & 1.23927341514012e-09 & 6.1963670757006e-10 \tabularnewline
17 & 0.999999998914482 & 2.17103553306315e-09 & 1.08551776653158e-09 \tabularnewline
18 & 0.999999998695656 & 2.60868750532237e-09 & 1.30434375266119e-09 \tabularnewline
19 & 0.999999998706438 & 2.58712327527561e-09 & 1.29356163763781e-09 \tabularnewline
20 & 0.999999996094121 & 7.81175809748111e-09 & 3.90587904874056e-09 \tabularnewline
21 & 0.999999999575219 & 8.49562547583557e-10 & 4.24781273791779e-10 \tabularnewline
22 & 0.99999999898412 & 2.03175969728115e-09 & 1.01587984864058e-09 \tabularnewline
23 & 0.99999999973881 & 5.22379640151272e-10 & 2.61189820075636e-10 \tabularnewline
24 & 0.999999999192801 & 1.61439886698002e-09 & 8.0719943349001e-10 \tabularnewline
25 & 0.999999997530053 & 4.93989454307549e-09 & 2.46994727153775e-09 \tabularnewline
26 & 0.999999993451122 & 1.30977556532792e-08 & 6.54887782663961e-09 \tabularnewline
27 & 0.999999983090893 & 3.38182140691872e-08 & 1.69091070345936e-08 \tabularnewline
28 & 0.999999987167096 & 2.56658084045715e-08 & 1.28329042022857e-08 \tabularnewline
29 & 0.999999974795824 & 5.04083523358779e-08 & 2.52041761679389e-08 \tabularnewline
30 & 0.999999922738173 & 1.54523653777432e-07 & 7.72618268887162e-08 \tabularnewline
31 & 0.999999778765863 & 4.42468274997543e-07 & 2.21234137498772e-07 \tabularnewline
32 & 0.999999391728546 & 1.21654290721005e-06 & 6.08271453605025e-07 \tabularnewline
33 & 0.999998339761052 & 3.32047789674982e-06 & 1.66023894837491e-06 \tabularnewline
34 & 0.999995691588774 & 8.61682245132455e-06 & 4.30841122566228e-06 \tabularnewline
35 & 0.999988874011839 & 2.22519763213423e-05 & 1.11259881606712e-05 \tabularnewline
36 & 0.999971355551774 & 5.7288896452525e-05 & 2.86444482262625e-05 \tabularnewline
37 & 0.99993785209398 & 0.000124295812040043 & 6.21479060200216e-05 \tabularnewline
38 & 0.999852709044235 & 0.000294581911529727 & 0.000147290955764863 \tabularnewline
39 & 0.999744732531532 & 0.000510534936935356 & 0.000255267468467678 \tabularnewline
40 & 0.999419993096268 & 0.00116001380746468 & 0.000580006903732338 \tabularnewline
41 & 0.998704578973973 & 0.00259084205205489 & 0.00129542102602744 \tabularnewline
42 & 0.998394755754539 & 0.00321048849092145 & 0.00160524424546072 \tabularnewline
43 & 0.996502013459588 & 0.00699597308082348 & 0.00349798654041174 \tabularnewline
44 & 0.996898007027351 & 0.00620398594529864 & 0.00310199297264932 \tabularnewline
45 & 0.993575103271778 & 0.0128497934564431 & 0.00642489672822155 \tabularnewline
46 & 0.997323653832696 & 0.00535269233460763 & 0.00267634616730382 \tabularnewline
47 & 0.993684279456605 & 0.0126314410867903 & 0.00631572054339517 \tabularnewline
48 & 0.987769609417983 & 0.0244607811640333 & 0.0122303905820166 \tabularnewline
49 & 0.97826838576519 & 0.0434632284696201 & 0.02173161423481 \tabularnewline
50 & 0.965853969169119 & 0.0682920616617614 & 0.0341460308308807 \tabularnewline
51 & 0.992993764675492 & 0.0140124706490159 & 0.00700623532450794 \tabularnewline
52 & 0.979778170381164 & 0.0404436592376724 & 0.0202218296188362 \tabularnewline
53 & 0.944692332637134 & 0.110615334725733 & 0.0553076673628665 \tabularnewline
54 & 0.907020393352382 & 0.185959213295236 & 0.0929796066476181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146612&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]0.999993672400719[/C][C]1.26551985617821e-05[/C][C]6.32759928089106e-06[/C][/ROW]
[ROW][C]7[/C][C]0.999999999836697[/C][C]3.26607052928414e-10[/C][C]1.63303526464207e-10[/C][/ROW]
[ROW][C]8[/C][C]0.999999999980096[/C][C]3.98073444983949e-11[/C][C]1.99036722491975e-11[/C][/ROW]
[ROW][C]9[/C][C]0.999999999908779[/C][C]1.82441108753047e-10[/C][C]9.12205543765233e-11[/C][/ROW]
[ROW][C]10[/C][C]0.999999999996218[/C][C]7.56306493043859e-12[/C][C]3.7815324652193e-12[/C][/ROW]
[ROW][C]11[/C][C]0.999999999986882[/C][C]2.62349860857775e-11[/C][C]1.31174930428887e-11[/C][/ROW]
[ROW][C]12[/C][C]0.999999999966156[/C][C]6.76889465305962e-11[/C][C]3.38444732652981e-11[/C][/ROW]
[ROW][C]13[/C][C]0.999999999895384[/C][C]2.09231752323986e-10[/C][C]1.04615876161993e-10[/C][/ROW]
[ROW][C]14[/C][C]0.999999999608354[/C][C]7.83292964453781e-10[/C][C]3.9164648222689e-10[/C][/ROW]
[ROW][C]15[/C][C]0.999999999595992[/C][C]8.08016753557042e-10[/C][C]4.04008376778521e-10[/C][/ROW]
[ROW][C]16[/C][C]0.999999999380363[/C][C]1.23927341514012e-09[/C][C]6.1963670757006e-10[/C][/ROW]
[ROW][C]17[/C][C]0.999999998914482[/C][C]2.17103553306315e-09[/C][C]1.08551776653158e-09[/C][/ROW]
[ROW][C]18[/C][C]0.999999998695656[/C][C]2.60868750532237e-09[/C][C]1.30434375266119e-09[/C][/ROW]
[ROW][C]19[/C][C]0.999999998706438[/C][C]2.58712327527561e-09[/C][C]1.29356163763781e-09[/C][/ROW]
[ROW][C]20[/C][C]0.999999996094121[/C][C]7.81175809748111e-09[/C][C]3.90587904874056e-09[/C][/ROW]
[ROW][C]21[/C][C]0.999999999575219[/C][C]8.49562547583557e-10[/C][C]4.24781273791779e-10[/C][/ROW]
[ROW][C]22[/C][C]0.99999999898412[/C][C]2.03175969728115e-09[/C][C]1.01587984864058e-09[/C][/ROW]
[ROW][C]23[/C][C]0.99999999973881[/C][C]5.22379640151272e-10[/C][C]2.61189820075636e-10[/C][/ROW]
[ROW][C]24[/C][C]0.999999999192801[/C][C]1.61439886698002e-09[/C][C]8.0719943349001e-10[/C][/ROW]
[ROW][C]25[/C][C]0.999999997530053[/C][C]4.93989454307549e-09[/C][C]2.46994727153775e-09[/C][/ROW]
[ROW][C]26[/C][C]0.999999993451122[/C][C]1.30977556532792e-08[/C][C]6.54887782663961e-09[/C][/ROW]
[ROW][C]27[/C][C]0.999999983090893[/C][C]3.38182140691872e-08[/C][C]1.69091070345936e-08[/C][/ROW]
[ROW][C]28[/C][C]0.999999987167096[/C][C]2.56658084045715e-08[/C][C]1.28329042022857e-08[/C][/ROW]
[ROW][C]29[/C][C]0.999999974795824[/C][C]5.04083523358779e-08[/C][C]2.52041761679389e-08[/C][/ROW]
[ROW][C]30[/C][C]0.999999922738173[/C][C]1.54523653777432e-07[/C][C]7.72618268887162e-08[/C][/ROW]
[ROW][C]31[/C][C]0.999999778765863[/C][C]4.42468274997543e-07[/C][C]2.21234137498772e-07[/C][/ROW]
[ROW][C]32[/C][C]0.999999391728546[/C][C]1.21654290721005e-06[/C][C]6.08271453605025e-07[/C][/ROW]
[ROW][C]33[/C][C]0.999998339761052[/C][C]3.32047789674982e-06[/C][C]1.66023894837491e-06[/C][/ROW]
[ROW][C]34[/C][C]0.999995691588774[/C][C]8.61682245132455e-06[/C][C]4.30841122566228e-06[/C][/ROW]
[ROW][C]35[/C][C]0.999988874011839[/C][C]2.22519763213423e-05[/C][C]1.11259881606712e-05[/C][/ROW]
[ROW][C]36[/C][C]0.999971355551774[/C][C]5.7288896452525e-05[/C][C]2.86444482262625e-05[/C][/ROW]
[ROW][C]37[/C][C]0.99993785209398[/C][C]0.000124295812040043[/C][C]6.21479060200216e-05[/C][/ROW]
[ROW][C]38[/C][C]0.999852709044235[/C][C]0.000294581911529727[/C][C]0.000147290955764863[/C][/ROW]
[ROW][C]39[/C][C]0.999744732531532[/C][C]0.000510534936935356[/C][C]0.000255267468467678[/C][/ROW]
[ROW][C]40[/C][C]0.999419993096268[/C][C]0.00116001380746468[/C][C]0.000580006903732338[/C][/ROW]
[ROW][C]41[/C][C]0.998704578973973[/C][C]0.00259084205205489[/C][C]0.00129542102602744[/C][/ROW]
[ROW][C]42[/C][C]0.998394755754539[/C][C]0.00321048849092145[/C][C]0.00160524424546072[/C][/ROW]
[ROW][C]43[/C][C]0.996502013459588[/C][C]0.00699597308082348[/C][C]0.00349798654041174[/C][/ROW]
[ROW][C]44[/C][C]0.996898007027351[/C][C]0.00620398594529864[/C][C]0.00310199297264932[/C][/ROW]
[ROW][C]45[/C][C]0.993575103271778[/C][C]0.0128497934564431[/C][C]0.00642489672822155[/C][/ROW]
[ROW][C]46[/C][C]0.997323653832696[/C][C]0.00535269233460763[/C][C]0.00267634616730382[/C][/ROW]
[ROW][C]47[/C][C]0.993684279456605[/C][C]0.0126314410867903[/C][C]0.00631572054339517[/C][/ROW]
[ROW][C]48[/C][C]0.987769609417983[/C][C]0.0244607811640333[/C][C]0.0122303905820166[/C][/ROW]
[ROW][C]49[/C][C]0.97826838576519[/C][C]0.0434632284696201[/C][C]0.02173161423481[/C][/ROW]
[ROW][C]50[/C][C]0.965853969169119[/C][C]0.0682920616617614[/C][C]0.0341460308308807[/C][/ROW]
[ROW][C]51[/C][C]0.992993764675492[/C][C]0.0140124706490159[/C][C]0.00700623532450794[/C][/ROW]
[ROW][C]52[/C][C]0.979778170381164[/C][C]0.0404436592376724[/C][C]0.0202218296188362[/C][/ROW]
[ROW][C]53[/C][C]0.944692332637134[/C][C]0.110615334725733[/C][C]0.0553076673628665[/C][/ROW]
[ROW][C]54[/C][C]0.907020393352382[/C][C]0.185959213295236[/C][C]0.0929796066476181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146612&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146612&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.9999936724007191.26551985617821e-056.32759928089106e-06
70.9999999998366973.26607052928414e-101.63303526464207e-10
80.9999999999800963.98073444983949e-111.99036722491975e-11
90.9999999999087791.82441108753047e-109.12205543765233e-11
100.9999999999962187.56306493043859e-123.7815324652193e-12
110.9999999999868822.62349860857775e-111.31174930428887e-11
120.9999999999661566.76889465305962e-113.38444732652981e-11
130.9999999998953842.09231752323986e-101.04615876161993e-10
140.9999999996083547.83292964453781e-103.9164648222689e-10
150.9999999995959928.08016753557042e-104.04008376778521e-10
160.9999999993803631.23927341514012e-096.1963670757006e-10
170.9999999989144822.17103553306315e-091.08551776653158e-09
180.9999999986956562.60868750532237e-091.30434375266119e-09
190.9999999987064382.58712327527561e-091.29356163763781e-09
200.9999999960941217.81175809748111e-093.90587904874056e-09
210.9999999995752198.49562547583557e-104.24781273791779e-10
220.999999998984122.03175969728115e-091.01587984864058e-09
230.999999999738815.22379640151272e-102.61189820075636e-10
240.9999999991928011.61439886698002e-098.0719943349001e-10
250.9999999975300534.93989454307549e-092.46994727153775e-09
260.9999999934511221.30977556532792e-086.54887782663961e-09
270.9999999830908933.38182140691872e-081.69091070345936e-08
280.9999999871670962.56658084045715e-081.28329042022857e-08
290.9999999747958245.04083523358779e-082.52041761679389e-08
300.9999999227381731.54523653777432e-077.72618268887162e-08
310.9999997787658634.42468274997543e-072.21234137498772e-07
320.9999993917285461.21654290721005e-066.08271453605025e-07
330.9999983397610523.32047789674982e-061.66023894837491e-06
340.9999956915887748.61682245132455e-064.30841122566228e-06
350.9999888740118392.22519763213423e-051.11259881606712e-05
360.9999713555517745.7288896452525e-052.86444482262625e-05
370.999937852093980.0001242958120400436.21479060200216e-05
380.9998527090442350.0002945819115297270.000147290955764863
390.9997447325315320.0005105349369353560.000255267468467678
400.9994199930962680.001160013807464680.000580006903732338
410.9987045789739730.002590842052054890.00129542102602744
420.9983947557545390.003210488490921450.00160524424546072
430.9965020134595880.006995973080823480.00349798654041174
440.9968980070273510.006203985945298640.00310199297264932
450.9935751032717780.01284979345644310.00642489672822155
460.9973236538326960.005352692334607630.00267634616730382
470.9936842794566050.01263144108679030.00631572054339517
480.9877696094179830.02446078116403330.0122303905820166
490.978268385765190.04346322846962010.02173161423481
500.9658539691691190.06829206166176140.0341460308308807
510.9929937646754920.01401247064901590.00700623532450794
520.9797781703811640.04044365923767240.0202218296188362
530.9446923326371340.1106153347257330.0553076673628665
540.9070203933523820.1859592132952360.0929796066476181






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level400.816326530612245NOK
5% type I error level460.938775510204082NOK
10% type I error level470.959183673469388NOK

\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 & 40 & 0.816326530612245 & NOK \tabularnewline
5% type I error level & 46 & 0.938775510204082 & NOK \tabularnewline
10% type I error level & 47 & 0.959183673469388 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146612&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]40[/C][C]0.816326530612245[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]46[/C][C]0.938775510204082[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]47[/C][C]0.959183673469388[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146612&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146612&T=6

As an alternative you can also use a QR Code:  
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 level400.816326530612245NOK
5% type I error level460.938775510204082NOK
10% type I error level470.959183673469388NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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')
}