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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 computationMon, 20 Dec 2010 20:40:20 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/20/t129287776175ssae7bx8i9fyc.htm/, Retrieved Fri, 03 May 2024 21:38:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113134, Retrieved Fri, 03 May 2024 21:38:28 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138

Tree of Dependent Computations

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-    D      [Multiple Regression] [Multiple regressi...] [2010-12-20 20:40:20] [be034431ba35f7eb1ce695fc7ca4deb9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
21454	-11,5	0,012095933	8,02	8,3	20780
23899	-11	0,017384968	8,03	8,2	19815
24939	-14,9	0,017547503	8,45	8	19761
23580	-16,2	0,014844804	7,74	7,9	21454
24562	-14,4	0,010364842	7,26	7,6	23899
24696	-17,3	0,016214531	7,9	7,6	24939
23785	-15,7	0,014814047	7,34	8,3	23580
23812	-12,6	0,017823834	6,91	8,4	24562
21917	-9,4	0,017980779	7,22	8,4	24696
19713	-8,1	0,015828678	7,47	8,4	23785
19282	-5,4	0,018533858	7,16	8,4	23812
18788	-4,6	0,017385905	8,09	8,6	21917
21453	-4,9	0,015866474	7,91	8,9	19713
24482	-4	0,012585695	7,74	8,8	19282
27474	-3,1	0,011326531	8,01	8,3	18788
27264	-1,3	0,019230769	7,56	7,5	21453
27349	0	0,026056627	7,56	7,2	24482
30632	-0,4	0,022604071	8,06	7,4	27474
29429	3	0,024091466	8,06	8,8	27264
30084	0,4	0,022602321	7,87	9,3	27349
26290	1,2	0,020302507	7,97	9,3	30632
24379	0,6	0,028617986	7,89	8,7	29429
23335	-1,3	0,025515909	7,83	8,2	30084
21346	-3,2	0,022785068	8,17	8,3	26290
21106	-1,8	0,022515213	8,84	8,5	24379
24514	-3,6	0,025666936	8,44	8,6	23335
28353	-4,2	0,03067299	8,38	8,5	21346
30805	-6,9	0,027599358	7,71	8,2	21106
31348	-8	0,025194961	6,58	8,1	24514
34556	-7,5	0,028705741	6,65	7,9	28353
33855	-8,2	0,031399522	6,59	8,6	30805
34787	-7,6	0,031063321	6,38	8,7	31348
32529	-3,7	0,031638643	6,78	8,7	34556
29998	-1,7	0,024653465	6,46	8,5	33855
29257	-0,7	0,025674068	6,61	8,4	34787
28155	0,2	0,028841372	6,46	8,5	32529
30466	0,6	0,026383654	6,58	8,7	29998
35704	2,2	0,023940887	6,48	8,7	29257
39327	3,3	0,017033774	6,67	8,6	28155
39351	5,3	0,019630823	6,7	8,5	30466
42234	5,5	0,021942657	6,58	8,3	35704
43630	6,3	0,018667963	6,47	8	39327
43722	7,7	0,016043298	7,25	8,2	39351
43121	6,5	0,016415604	7,24	8,1	42234
37985	5,5	0,012248047	6,97	8,1	43630
37135	6,9	0,012175089	6,83	8	43722
34646	5,7	0,014883541	7,42	7,9	43121
33026	6,9	0,016433059	7,34	7,9	37985
35087	6,1	0,016621569	7,11	8	37135
38846	4,8	0,017704224	7,16	8	34646
42013	3,7	0,018192319	7,51	7,9	33026
43908	5,8	0,017816092	7,07	8	35087
42868	6,8	0,01278748	6,85	7,7	38846
44423	8,5	0,012885368	7,05	7,2	42013
44167	7,2	0,013697327	7,62	7,5	43908
43636	5	0,011210336	7,66	7,3	42868
44382	4,7	0,015053354	7,2	7	44423
42142	2,3	0,022434368	7,38	7	44167
43452	2,4	0,029425769	7,57	7	43636
36912	0,1	0,030908226	7,31	7,2	44382
42413	1,9	0,03460076	8,33	7,3	42142
45344	1,7	0,036399735	7,38	7,1	43452
44873	2	0,043864625	7,41	6,8	36912
47510	-1,9	0,041501976	7,81	6,4	42413
49554	0,5	0,052105908	7,24	6,1	45344
47369	-1,3	0,058047493	7,88	6,5	44873
45998	-3,3	0,059116074	8,52	7,7	47510
48140	-2,8	0,053927095	7,66	7,9	49554
48441	-8	0,05462737	8,5	7,5	47369
44928	-13,9	0,047245565	8,82	6,9	45998
40454	-21,9	0,031359852	8,61	6,6	48140
38661	-28,8	0,026291513	8,2	6,9	48441
37246	-27,6	0,023153252	7,31	7,7	44928
36843	-31,4	0,019339537	7,43	8	40454
36424	-31,8	0,006158305	7,33	8	38661
37594	-29,4	0,005963676	7,53	7,7	37246
38144	-27,6	-0,003671861	7,61	7,3	36843
38737	-23,6	-0,011043819	7,17	7,4	36424
34560	-22,8	-0,016833525	6,81	8,1	37594
36080	-18,2	-0,007755393	6,9	8,3	38144
33508	-17,8	-0,011925952	7,33	8,1	38737
35462	-14,2	-0,00971826	7,36	7,9	34560
33374	-8,8	-0,001166024	6,33	7,9	36080
32110	-7,9	0,002606742	6,95	8,3	33508
35533	-7	0,006196121	7,25	8,6	35462
35532	-7	0,00698049	6,46	8,7	33374
37903	-3,6	0,016561656	6,51	8,5	32110
36763	-2,4	0,01796461	6,31	8,3	35533
40399	-4,9	0,022741573	5,93	8	35532
44164	-7,7	0,024585735	5,86	8,1	37903
44496	-6,5	0,025682617	5,85	8,9	36763
43110	-5,1	0,02317851	5,82	8,9	40399
43880	-3,4	0,029093857	6,17	8,7	44164
43930	-2,8	0,030071126	5,7	8,3	44496

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113134&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113134&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113134&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 time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
Vacatures[t] = + 20606.8812794916 + 168.158236460417Ondernemersvertrouwen[t] + 56401.8572264319Inflatie[t] -621.10278051104Rente[t] -1393.10882957294Werkloosheidsgraad[t] + 0.587840385394207Vacatures_3m[t] + 3649.21467940801M1[t] + 6828.56710343529M2[t] + 9676.46988365844M3[t] + 8533.49626620066M4[t] + 7432.16340329468M5[t] + 7701.319746343M6[t] + 7237.49835577374M7[t] + 6708.04816702846M8[t] + 3891.65143784099M9[t] + 2602.21309852869M10[t] + 460.457422895243M11[t] + 86.2936753033444t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Vacatures[t] =  +  20606.8812794916 +  168.158236460417Ondernemersvertrouwen[t] +  56401.8572264319Inflatie[t] -621.10278051104Rente[t] -1393.10882957294Werkloosheidsgraad[t] +  0.587840385394207Vacatures_3m[t] +  3649.21467940801M1[t] +  6828.56710343529M2[t] +  9676.46988365844M3[t] +  8533.49626620066M4[t] +  7432.16340329468M5[t] +  7701.319746343M6[t] +  7237.49835577374M7[t] +  6708.04816702846M8[t] +  3891.65143784099M9[t] +  2602.21309852869M10[t] +  460.457422895243M11[t] +  86.2936753033444t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113134&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Vacatures[t] =  +  20606.8812794916 +  168.158236460417Ondernemersvertrouwen[t] +  56401.8572264319Inflatie[t] -621.10278051104Rente[t] -1393.10882957294Werkloosheidsgraad[t] +  0.587840385394207Vacatures_3m[t] +  3649.21467940801M1[t] +  6828.56710343529M2[t] +  9676.46988365844M3[t] +  8533.49626620066M4[t] +  7432.16340329468M5[t] +  7701.319746343M6[t] +  7237.49835577374M7[t] +  6708.04816702846M8[t] +  3891.65143784099M9[t] +  2602.21309852869M10[t] +  460.457422895243M11[t] +  86.2936753033444t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113134&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113134&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
Vacatures[t] = + 20606.8812794916 + 168.158236460417Ondernemersvertrouwen[t] + 56401.8572264319Inflatie[t] -621.10278051104Rente[t] -1393.10882957294Werkloosheidsgraad[t] + 0.587840385394207Vacatures_3m[t] + 3649.21467940801M1[t] + 6828.56710343529M2[t] + 9676.46988365844M3[t] + 8533.49626620066M4[t] + 7432.16340329468M5[t] + 7701.319746343M6[t] + 7237.49835577374M7[t] + 6708.04816702846M8[t] + 3891.65143784099M9[t] + 2602.21309852869M10[t] + 460.457422895243M11[t] + 86.2936753033444t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)20606.88127949168471.003062.43260.0173410.008671
Ondernemersvertrouwen168.15823646041726.9875936.230900
Inflatie56401.857226431918671.879583.02070.0034340.001717
Rente-621.10278051104450.146998-1.37980.1717010.085851
Werkloosheidsgraad-1393.10882957294635.180267-2.19320.0313480.015674
Vacatures_3m0.5878403853942070.0709338.287300
M13649.214679408011122.4429693.25110.0017150.000857
M26828.567103435291124.8276436.070800
M39676.469883658441166.4897238.295400
M48533.496266200661170.9052967.287900
M57432.163403294681218.1140126.101400
M67701.3197463431174.0053756.559900
M77237.498355773741127.9868456.416300
M86708.048167028461154.5675465.8100
M93891.651437840991154.1402663.37190.0011760.000588
M102602.213098528691118.2345562.32710.0226270.011313
M11460.4574228952431157.4807040.39780.6918850.345943
t86.293675303344418.0201514.78878e-064e-06

\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) & 20606.8812794916 & 8471.00306 & 2.4326 & 0.017341 & 0.008671 \tabularnewline
Ondernemersvertrouwen & 168.158236460417 & 26.987593 & 6.2309 & 0 & 0 \tabularnewline
Inflatie & 56401.8572264319 & 18671.87958 & 3.0207 & 0.003434 & 0.001717 \tabularnewline
Rente & -621.10278051104 & 450.146998 & -1.3798 & 0.171701 & 0.085851 \tabularnewline
Werkloosheidsgraad & -1393.10882957294 & 635.180267 & -2.1932 & 0.031348 & 0.015674 \tabularnewline
Vacatures_3m & 0.587840385394207 & 0.070933 & 8.2873 & 0 & 0 \tabularnewline
M1 & 3649.21467940801 & 1122.442969 & 3.2511 & 0.001715 & 0.000857 \tabularnewline
M2 & 6828.56710343529 & 1124.827643 & 6.0708 & 0 & 0 \tabularnewline
M3 & 9676.46988365844 & 1166.489723 & 8.2954 & 0 & 0 \tabularnewline
M4 & 8533.49626620066 & 1170.905296 & 7.2879 & 0 & 0 \tabularnewline
M5 & 7432.16340329468 & 1218.114012 & 6.1014 & 0 & 0 \tabularnewline
M6 & 7701.319746343 & 1174.005375 & 6.5599 & 0 & 0 \tabularnewline
M7 & 7237.49835577374 & 1127.986845 & 6.4163 & 0 & 0 \tabularnewline
M8 & 6708.04816702846 & 1154.567546 & 5.81 & 0 & 0 \tabularnewline
M9 & 3891.65143784099 & 1154.140266 & 3.3719 & 0.001176 & 0.000588 \tabularnewline
M10 & 2602.21309852869 & 1118.234556 & 2.3271 & 0.022627 & 0.011313 \tabularnewline
M11 & 460.457422895243 & 1157.480704 & 0.3978 & 0.691885 & 0.345943 \tabularnewline
t & 86.2936753033444 & 18.020151 & 4.7887 & 8e-06 & 4e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113134&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]20606.8812794916[/C][C]8471.00306[/C][C]2.4326[/C][C]0.017341[/C][C]0.008671[/C][/ROW]
[ROW][C]Ondernemersvertrouwen[/C][C]168.158236460417[/C][C]26.987593[/C][C]6.2309[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Inflatie[/C][C]56401.8572264319[/C][C]18671.87958[/C][C]3.0207[/C][C]0.003434[/C][C]0.001717[/C][/ROW]
[ROW][C]Rente[/C][C]-621.10278051104[/C][C]450.146998[/C][C]-1.3798[/C][C]0.171701[/C][C]0.085851[/C][/ROW]
[ROW][C]Werkloosheidsgraad[/C][C]-1393.10882957294[/C][C]635.180267[/C][C]-2.1932[/C][C]0.031348[/C][C]0.015674[/C][/ROW]
[ROW][C]Vacatures_3m[/C][C]0.587840385394207[/C][C]0.070933[/C][C]8.2873[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]3649.21467940801[/C][C]1122.442969[/C][C]3.2511[/C][C]0.001715[/C][C]0.000857[/C][/ROW]
[ROW][C]M2[/C][C]6828.56710343529[/C][C]1124.827643[/C][C]6.0708[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]9676.46988365844[/C][C]1166.489723[/C][C]8.2954[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]8533.49626620066[/C][C]1170.905296[/C][C]7.2879[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]7432.16340329468[/C][C]1218.114012[/C][C]6.1014[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]7701.319746343[/C][C]1174.005375[/C][C]6.5599[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]7237.49835577374[/C][C]1127.986845[/C][C]6.4163[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]6708.04816702846[/C][C]1154.567546[/C][C]5.81[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]3891.65143784099[/C][C]1154.140266[/C][C]3.3719[/C][C]0.001176[/C][C]0.000588[/C][/ROW]
[ROW][C]M10[/C][C]2602.21309852869[/C][C]1118.234556[/C][C]2.3271[/C][C]0.022627[/C][C]0.011313[/C][/ROW]
[ROW][C]M11[/C][C]460.457422895243[/C][C]1157.480704[/C][C]0.3978[/C][C]0.691885[/C][C]0.345943[/C][/ROW]
[ROW][C]t[/C][C]86.2936753033444[/C][C]18.020151[/C][C]4.7887[/C][C]8e-06[/C][C]4e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113134&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113134&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)20606.88127949168471.003062.43260.0173410.008671
Ondernemersvertrouwen168.15823646041726.9875936.230900
Inflatie56401.857226431918671.879583.02070.0034340.001717
Rente-621.10278051104450.146998-1.37980.1717010.085851
Werkloosheidsgraad-1393.10882957294635.180267-2.19320.0313480.015674
Vacatures_3m0.5878403853942070.0709338.287300
M13649.214679408011122.4429693.25110.0017150.000857
M26828.567103435291124.8276436.070800
M39676.469883658441166.4897238.295400
M48533.496266200661170.9052967.287900
M57432.163403294681218.1140126.101400
M67701.3197463431174.0053756.559900
M77237.498355773741127.9868456.416300
M86708.048167028461154.5675465.8100
M93891.651437840991154.1402663.37190.0011760.000588
M102602.213098528691118.2345562.32710.0226270.011313
M11460.4574228952431157.4807040.39780.6918850.345943
t86.293675303344418.0201514.78878e-064e-06






Multiple Linear Regression - Regression Statistics
Multiple R0.972195984429727
R-squared0.945165032141285
Adjusted R-squared0.932899315646572
F-TEST (value)77.0574660313339
F-TEST (DF numerator)17
F-TEST (DF denominator)76
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2143.55200542283
Sum Squared Residuals349205955.19637

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.972195984429727 \tabularnewline
R-squared & 0.945165032141285 \tabularnewline
Adjusted R-squared & 0.932899315646572 \tabularnewline
F-TEST (value) & 77.0574660313339 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 76 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2143.55200542283 \tabularnewline
Sum Squared Residuals & 349205955.19637 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113134&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.972195984429727[/C][/ROW]
[ROW][C]R-squared[/C][C]0.945165032141285[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.932899315646572[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]77.0574660313339[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]76[/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]2143.55200542283[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]349205955.19637[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113134&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113134&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 R0.972195984429727
R-squared0.945165032141285
Adjusted R-squared0.932899315646572
F-TEST (value)77.0574660313339
F-TEST (DF numerator)17
F-TEST (DF denominator)76
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2143.55200542283
Sum Squared Residuals349205955.19637






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12145418762.07862433232691.92137566772
22389921975.94912207551923.05087792448
32493924249.5109485593689.489051440655
42358024397.2956854749-817.295685474873
52456225585.5948722032-1023.59487220317
62469625997.1675498996-1301.16754989956
72378524383.4694069888-598.469406988808
82381225336.3835743497-1524.3835743497
92191723039.4676163057-1122.46761630572
101971321242.7478801346-1529.74788013456
111928220002.3038467434-720.303846743446
121878817727.71512499941060.28487500065
132145319725.34292065641727.65707934356
142448222948.82855359121533.17144640877
152747426201.81174744321272.18825255682
162726428854.2082760411-1590.20827604112
172734930641.2670404319-3292.26704043186
183063231904.3684705505-1272.36847055046
192942930108.6717573444-679.671757344364
203008428615.7348316951468.26516830497
212629027758.214293302-1468.21429330197
222437927101.5646832996-2722.56468329957
232333525670.4951636856-2335.49516368561
242134622242.0540121122-896.054012112164
252110624379.639969341-3273.63996934095
262451427015.7891406017-2501.78914060166
272835329138.8039204666-785.803920466583
283080528147.72800595812657.2719940419
293134829656.61936029761691.38063970242
303455632786.026821769.97317999997
313385532946.1972016893908.8027983107
323478732895.29129954131891.70870045869
333252932490.805441296238.1945587037565
342999831295.2987834677-1297.29878346769
352925730059.5716293569-802.571629356902
362815528641.9030664793-486.903066479334
373046630465.07674102740.923258972624129
383570433488.51997559722215.48002440278
393932735591.61773991313735.38226008688
403935136496.90958777552854.09041222446
414223439078.1558169353155.84418306503
424363041999.4332720181630.56672798197
434372240960.31734173572761.68265826426
444312142176.6354362517944.364563748254
453798540031.6391197585-2046.63911975849
463713539340.1476077797-2205.14760777969
473464636655.2256175396-2009.22561753959
483302633600.7874497622-574.787449762219
493508736716.2799583862-1629.27995838617
503884638330.194244782515.805755218004
514201340076.56959014481936.43040985522
524390840697.31541778353210.68458221648
534286842331.0786798513536.921320148684
544442345411.9431244136-988.943124413582
554416745203.6019938997-1036.60199389968
564363643892.6501028236-256.65010282358
574438242946.58465775091435.41534224911
584214241493.8774848983648.122515101674
594345239419.40653628764032.59346371242
603691239063.4860574174-2151.4860574174
614241341220.34683086771192.65316913232
624534446192.5671262766-848.567126276623
634487346153.0681577107-1280.0681577107
644751047849.8256814648-339.825681464755
654955450331.3691234235-777.369123423511
664736949487.6286121382-2118.62861213822
674599848314.9530982183-2316.95309821828
684814049120.2800233683-980.280023368263
694844144306.33690457284134.66309542723
704492841525.89437336883402.10562663123
714045439036.70110308281417.29889691716
723866137230.04123840221430.95876159779
733724638363.5468651114-1117.54686511141
743684337652.6281901733-809.62819017326
753642438784.2278528224-2360.2278528224
763759437582.068188539711.9318114602829
773814436596.90727846631547.09272153374
783873737096.86733905171640.13266094827
793456036463.5101129533-1903.51011295327
803608037294.6961879822-1214.69618798219
813350834756.7660742434-1248.76607424343
823546232088.08838318363373.91161681644
833337432956.296103304417.703896695969
843211030492.01305082731617.98694917268
853553335125.6880902777407.311909722305
863553237559.5236469025-2027.52364690248
873790341110.3900429399-3207.39004293989
883676342749.6491569624-5986.64915696238
894039942237.0078283913-1838.00782839133
904416443523.5648119284640.435188071616
914449641631.27908717062864.72091282943
924311043438.3285439882-328.328543988178
934388043602.1858927705277.814107229519
944393043599.3808038678330.619196132168

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 21454 & 18762.0786243323 & 2691.92137566772 \tabularnewline
2 & 23899 & 21975.9491220755 & 1923.05087792448 \tabularnewline
3 & 24939 & 24249.5109485593 & 689.489051440655 \tabularnewline
4 & 23580 & 24397.2956854749 & -817.295685474873 \tabularnewline
5 & 24562 & 25585.5948722032 & -1023.59487220317 \tabularnewline
6 & 24696 & 25997.1675498996 & -1301.16754989956 \tabularnewline
7 & 23785 & 24383.4694069888 & -598.469406988808 \tabularnewline
8 & 23812 & 25336.3835743497 & -1524.3835743497 \tabularnewline
9 & 21917 & 23039.4676163057 & -1122.46761630572 \tabularnewline
10 & 19713 & 21242.7478801346 & -1529.74788013456 \tabularnewline
11 & 19282 & 20002.3038467434 & -720.303846743446 \tabularnewline
12 & 18788 & 17727.7151249994 & 1060.28487500065 \tabularnewline
13 & 21453 & 19725.3429206564 & 1727.65707934356 \tabularnewline
14 & 24482 & 22948.8285535912 & 1533.17144640877 \tabularnewline
15 & 27474 & 26201.8117474432 & 1272.18825255682 \tabularnewline
16 & 27264 & 28854.2082760411 & -1590.20827604112 \tabularnewline
17 & 27349 & 30641.2670404319 & -3292.26704043186 \tabularnewline
18 & 30632 & 31904.3684705505 & -1272.36847055046 \tabularnewline
19 & 29429 & 30108.6717573444 & -679.671757344364 \tabularnewline
20 & 30084 & 28615.734831695 & 1468.26516830497 \tabularnewline
21 & 26290 & 27758.214293302 & -1468.21429330197 \tabularnewline
22 & 24379 & 27101.5646832996 & -2722.56468329957 \tabularnewline
23 & 23335 & 25670.4951636856 & -2335.49516368561 \tabularnewline
24 & 21346 & 22242.0540121122 & -896.054012112164 \tabularnewline
25 & 21106 & 24379.639969341 & -3273.63996934095 \tabularnewline
26 & 24514 & 27015.7891406017 & -2501.78914060166 \tabularnewline
27 & 28353 & 29138.8039204666 & -785.803920466583 \tabularnewline
28 & 30805 & 28147.7280059581 & 2657.2719940419 \tabularnewline
29 & 31348 & 29656.6193602976 & 1691.38063970242 \tabularnewline
30 & 34556 & 32786.02682 & 1769.97317999997 \tabularnewline
31 & 33855 & 32946.1972016893 & 908.8027983107 \tabularnewline
32 & 34787 & 32895.2912995413 & 1891.70870045869 \tabularnewline
33 & 32529 & 32490.8054412962 & 38.1945587037565 \tabularnewline
34 & 29998 & 31295.2987834677 & -1297.29878346769 \tabularnewline
35 & 29257 & 30059.5716293569 & -802.571629356902 \tabularnewline
36 & 28155 & 28641.9030664793 & -486.903066479334 \tabularnewline
37 & 30466 & 30465.0767410274 & 0.923258972624129 \tabularnewline
38 & 35704 & 33488.5199755972 & 2215.48002440278 \tabularnewline
39 & 39327 & 35591.6177399131 & 3735.38226008688 \tabularnewline
40 & 39351 & 36496.9095877755 & 2854.09041222446 \tabularnewline
41 & 42234 & 39078.155816935 & 3155.84418306503 \tabularnewline
42 & 43630 & 41999.433272018 & 1630.56672798197 \tabularnewline
43 & 43722 & 40960.3173417357 & 2761.68265826426 \tabularnewline
44 & 43121 & 42176.6354362517 & 944.364563748254 \tabularnewline
45 & 37985 & 40031.6391197585 & -2046.63911975849 \tabularnewline
46 & 37135 & 39340.1476077797 & -2205.14760777969 \tabularnewline
47 & 34646 & 36655.2256175396 & -2009.22561753959 \tabularnewline
48 & 33026 & 33600.7874497622 & -574.787449762219 \tabularnewline
49 & 35087 & 36716.2799583862 & -1629.27995838617 \tabularnewline
50 & 38846 & 38330.194244782 & 515.805755218004 \tabularnewline
51 & 42013 & 40076.5695901448 & 1936.43040985522 \tabularnewline
52 & 43908 & 40697.3154177835 & 3210.68458221648 \tabularnewline
53 & 42868 & 42331.0786798513 & 536.921320148684 \tabularnewline
54 & 44423 & 45411.9431244136 & -988.943124413582 \tabularnewline
55 & 44167 & 45203.6019938997 & -1036.60199389968 \tabularnewline
56 & 43636 & 43892.6501028236 & -256.65010282358 \tabularnewline
57 & 44382 & 42946.5846577509 & 1435.41534224911 \tabularnewline
58 & 42142 & 41493.8774848983 & 648.122515101674 \tabularnewline
59 & 43452 & 39419.4065362876 & 4032.59346371242 \tabularnewline
60 & 36912 & 39063.4860574174 & -2151.4860574174 \tabularnewline
61 & 42413 & 41220.3468308677 & 1192.65316913232 \tabularnewline
62 & 45344 & 46192.5671262766 & -848.567126276623 \tabularnewline
63 & 44873 & 46153.0681577107 & -1280.0681577107 \tabularnewline
64 & 47510 & 47849.8256814648 & -339.825681464755 \tabularnewline
65 & 49554 & 50331.3691234235 & -777.369123423511 \tabularnewline
66 & 47369 & 49487.6286121382 & -2118.62861213822 \tabularnewline
67 & 45998 & 48314.9530982183 & -2316.95309821828 \tabularnewline
68 & 48140 & 49120.2800233683 & -980.280023368263 \tabularnewline
69 & 48441 & 44306.3369045728 & 4134.66309542723 \tabularnewline
70 & 44928 & 41525.8943733688 & 3402.10562663123 \tabularnewline
71 & 40454 & 39036.7011030828 & 1417.29889691716 \tabularnewline
72 & 38661 & 37230.0412384022 & 1430.95876159779 \tabularnewline
73 & 37246 & 38363.5468651114 & -1117.54686511141 \tabularnewline
74 & 36843 & 37652.6281901733 & -809.62819017326 \tabularnewline
75 & 36424 & 38784.2278528224 & -2360.2278528224 \tabularnewline
76 & 37594 & 37582.0681885397 & 11.9318114602829 \tabularnewline
77 & 38144 & 36596.9072784663 & 1547.09272153374 \tabularnewline
78 & 38737 & 37096.8673390517 & 1640.13266094827 \tabularnewline
79 & 34560 & 36463.5101129533 & -1903.51011295327 \tabularnewline
80 & 36080 & 37294.6961879822 & -1214.69618798219 \tabularnewline
81 & 33508 & 34756.7660742434 & -1248.76607424343 \tabularnewline
82 & 35462 & 32088.0883831836 & 3373.91161681644 \tabularnewline
83 & 33374 & 32956.296103304 & 417.703896695969 \tabularnewline
84 & 32110 & 30492.0130508273 & 1617.98694917268 \tabularnewline
85 & 35533 & 35125.6880902777 & 407.311909722305 \tabularnewline
86 & 35532 & 37559.5236469025 & -2027.52364690248 \tabularnewline
87 & 37903 & 41110.3900429399 & -3207.39004293989 \tabularnewline
88 & 36763 & 42749.6491569624 & -5986.64915696238 \tabularnewline
89 & 40399 & 42237.0078283913 & -1838.00782839133 \tabularnewline
90 & 44164 & 43523.5648119284 & 640.435188071616 \tabularnewline
91 & 44496 & 41631.2790871706 & 2864.72091282943 \tabularnewline
92 & 43110 & 43438.3285439882 & -328.328543988178 \tabularnewline
93 & 43880 & 43602.1858927705 & 277.814107229519 \tabularnewline
94 & 43930 & 43599.3808038678 & 330.619196132168 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113134&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]21454[/C][C]18762.0786243323[/C][C]2691.92137566772[/C][/ROW]
[ROW][C]2[/C][C]23899[/C][C]21975.9491220755[/C][C]1923.05087792448[/C][/ROW]
[ROW][C]3[/C][C]24939[/C][C]24249.5109485593[/C][C]689.489051440655[/C][/ROW]
[ROW][C]4[/C][C]23580[/C][C]24397.2956854749[/C][C]-817.295685474873[/C][/ROW]
[ROW][C]5[/C][C]24562[/C][C]25585.5948722032[/C][C]-1023.59487220317[/C][/ROW]
[ROW][C]6[/C][C]24696[/C][C]25997.1675498996[/C][C]-1301.16754989956[/C][/ROW]
[ROW][C]7[/C][C]23785[/C][C]24383.4694069888[/C][C]-598.469406988808[/C][/ROW]
[ROW][C]8[/C][C]23812[/C][C]25336.3835743497[/C][C]-1524.3835743497[/C][/ROW]
[ROW][C]9[/C][C]21917[/C][C]23039.4676163057[/C][C]-1122.46761630572[/C][/ROW]
[ROW][C]10[/C][C]19713[/C][C]21242.7478801346[/C][C]-1529.74788013456[/C][/ROW]
[ROW][C]11[/C][C]19282[/C][C]20002.3038467434[/C][C]-720.303846743446[/C][/ROW]
[ROW][C]12[/C][C]18788[/C][C]17727.7151249994[/C][C]1060.28487500065[/C][/ROW]
[ROW][C]13[/C][C]21453[/C][C]19725.3429206564[/C][C]1727.65707934356[/C][/ROW]
[ROW][C]14[/C][C]24482[/C][C]22948.8285535912[/C][C]1533.17144640877[/C][/ROW]
[ROW][C]15[/C][C]27474[/C][C]26201.8117474432[/C][C]1272.18825255682[/C][/ROW]
[ROW][C]16[/C][C]27264[/C][C]28854.2082760411[/C][C]-1590.20827604112[/C][/ROW]
[ROW][C]17[/C][C]27349[/C][C]30641.2670404319[/C][C]-3292.26704043186[/C][/ROW]
[ROW][C]18[/C][C]30632[/C][C]31904.3684705505[/C][C]-1272.36847055046[/C][/ROW]
[ROW][C]19[/C][C]29429[/C][C]30108.6717573444[/C][C]-679.671757344364[/C][/ROW]
[ROW][C]20[/C][C]30084[/C][C]28615.734831695[/C][C]1468.26516830497[/C][/ROW]
[ROW][C]21[/C][C]26290[/C][C]27758.214293302[/C][C]-1468.21429330197[/C][/ROW]
[ROW][C]22[/C][C]24379[/C][C]27101.5646832996[/C][C]-2722.56468329957[/C][/ROW]
[ROW][C]23[/C][C]23335[/C][C]25670.4951636856[/C][C]-2335.49516368561[/C][/ROW]
[ROW][C]24[/C][C]21346[/C][C]22242.0540121122[/C][C]-896.054012112164[/C][/ROW]
[ROW][C]25[/C][C]21106[/C][C]24379.639969341[/C][C]-3273.63996934095[/C][/ROW]
[ROW][C]26[/C][C]24514[/C][C]27015.7891406017[/C][C]-2501.78914060166[/C][/ROW]
[ROW][C]27[/C][C]28353[/C][C]29138.8039204666[/C][C]-785.803920466583[/C][/ROW]
[ROW][C]28[/C][C]30805[/C][C]28147.7280059581[/C][C]2657.2719940419[/C][/ROW]
[ROW][C]29[/C][C]31348[/C][C]29656.6193602976[/C][C]1691.38063970242[/C][/ROW]
[ROW][C]30[/C][C]34556[/C][C]32786.02682[/C][C]1769.97317999997[/C][/ROW]
[ROW][C]31[/C][C]33855[/C][C]32946.1972016893[/C][C]908.8027983107[/C][/ROW]
[ROW][C]32[/C][C]34787[/C][C]32895.2912995413[/C][C]1891.70870045869[/C][/ROW]
[ROW][C]33[/C][C]32529[/C][C]32490.8054412962[/C][C]38.1945587037565[/C][/ROW]
[ROW][C]34[/C][C]29998[/C][C]31295.2987834677[/C][C]-1297.29878346769[/C][/ROW]
[ROW][C]35[/C][C]29257[/C][C]30059.5716293569[/C][C]-802.571629356902[/C][/ROW]
[ROW][C]36[/C][C]28155[/C][C]28641.9030664793[/C][C]-486.903066479334[/C][/ROW]
[ROW][C]37[/C][C]30466[/C][C]30465.0767410274[/C][C]0.923258972624129[/C][/ROW]
[ROW][C]38[/C][C]35704[/C][C]33488.5199755972[/C][C]2215.48002440278[/C][/ROW]
[ROW][C]39[/C][C]39327[/C][C]35591.6177399131[/C][C]3735.38226008688[/C][/ROW]
[ROW][C]40[/C][C]39351[/C][C]36496.9095877755[/C][C]2854.09041222446[/C][/ROW]
[ROW][C]41[/C][C]42234[/C][C]39078.155816935[/C][C]3155.84418306503[/C][/ROW]
[ROW][C]42[/C][C]43630[/C][C]41999.433272018[/C][C]1630.56672798197[/C][/ROW]
[ROW][C]43[/C][C]43722[/C][C]40960.3173417357[/C][C]2761.68265826426[/C][/ROW]
[ROW][C]44[/C][C]43121[/C][C]42176.6354362517[/C][C]944.364563748254[/C][/ROW]
[ROW][C]45[/C][C]37985[/C][C]40031.6391197585[/C][C]-2046.63911975849[/C][/ROW]
[ROW][C]46[/C][C]37135[/C][C]39340.1476077797[/C][C]-2205.14760777969[/C][/ROW]
[ROW][C]47[/C][C]34646[/C][C]36655.2256175396[/C][C]-2009.22561753959[/C][/ROW]
[ROW][C]48[/C][C]33026[/C][C]33600.7874497622[/C][C]-574.787449762219[/C][/ROW]
[ROW][C]49[/C][C]35087[/C][C]36716.2799583862[/C][C]-1629.27995838617[/C][/ROW]
[ROW][C]50[/C][C]38846[/C][C]38330.194244782[/C][C]515.805755218004[/C][/ROW]
[ROW][C]51[/C][C]42013[/C][C]40076.5695901448[/C][C]1936.43040985522[/C][/ROW]
[ROW][C]52[/C][C]43908[/C][C]40697.3154177835[/C][C]3210.68458221648[/C][/ROW]
[ROW][C]53[/C][C]42868[/C][C]42331.0786798513[/C][C]536.921320148684[/C][/ROW]
[ROW][C]54[/C][C]44423[/C][C]45411.9431244136[/C][C]-988.943124413582[/C][/ROW]
[ROW][C]55[/C][C]44167[/C][C]45203.6019938997[/C][C]-1036.60199389968[/C][/ROW]
[ROW][C]56[/C][C]43636[/C][C]43892.6501028236[/C][C]-256.65010282358[/C][/ROW]
[ROW][C]57[/C][C]44382[/C][C]42946.5846577509[/C][C]1435.41534224911[/C][/ROW]
[ROW][C]58[/C][C]42142[/C][C]41493.8774848983[/C][C]648.122515101674[/C][/ROW]
[ROW][C]59[/C][C]43452[/C][C]39419.4065362876[/C][C]4032.59346371242[/C][/ROW]
[ROW][C]60[/C][C]36912[/C][C]39063.4860574174[/C][C]-2151.4860574174[/C][/ROW]
[ROW][C]61[/C][C]42413[/C][C]41220.3468308677[/C][C]1192.65316913232[/C][/ROW]
[ROW][C]62[/C][C]45344[/C][C]46192.5671262766[/C][C]-848.567126276623[/C][/ROW]
[ROW][C]63[/C][C]44873[/C][C]46153.0681577107[/C][C]-1280.0681577107[/C][/ROW]
[ROW][C]64[/C][C]47510[/C][C]47849.8256814648[/C][C]-339.825681464755[/C][/ROW]
[ROW][C]65[/C][C]49554[/C][C]50331.3691234235[/C][C]-777.369123423511[/C][/ROW]
[ROW][C]66[/C][C]47369[/C][C]49487.6286121382[/C][C]-2118.62861213822[/C][/ROW]
[ROW][C]67[/C][C]45998[/C][C]48314.9530982183[/C][C]-2316.95309821828[/C][/ROW]
[ROW][C]68[/C][C]48140[/C][C]49120.2800233683[/C][C]-980.280023368263[/C][/ROW]
[ROW][C]69[/C][C]48441[/C][C]44306.3369045728[/C][C]4134.66309542723[/C][/ROW]
[ROW][C]70[/C][C]44928[/C][C]41525.8943733688[/C][C]3402.10562663123[/C][/ROW]
[ROW][C]71[/C][C]40454[/C][C]39036.7011030828[/C][C]1417.29889691716[/C][/ROW]
[ROW][C]72[/C][C]38661[/C][C]37230.0412384022[/C][C]1430.95876159779[/C][/ROW]
[ROW][C]73[/C][C]37246[/C][C]38363.5468651114[/C][C]-1117.54686511141[/C][/ROW]
[ROW][C]74[/C][C]36843[/C][C]37652.6281901733[/C][C]-809.62819017326[/C][/ROW]
[ROW][C]75[/C][C]36424[/C][C]38784.2278528224[/C][C]-2360.2278528224[/C][/ROW]
[ROW][C]76[/C][C]37594[/C][C]37582.0681885397[/C][C]11.9318114602829[/C][/ROW]
[ROW][C]77[/C][C]38144[/C][C]36596.9072784663[/C][C]1547.09272153374[/C][/ROW]
[ROW][C]78[/C][C]38737[/C][C]37096.8673390517[/C][C]1640.13266094827[/C][/ROW]
[ROW][C]79[/C][C]34560[/C][C]36463.5101129533[/C][C]-1903.51011295327[/C][/ROW]
[ROW][C]80[/C][C]36080[/C][C]37294.6961879822[/C][C]-1214.69618798219[/C][/ROW]
[ROW][C]81[/C][C]33508[/C][C]34756.7660742434[/C][C]-1248.76607424343[/C][/ROW]
[ROW][C]82[/C][C]35462[/C][C]32088.0883831836[/C][C]3373.91161681644[/C][/ROW]
[ROW][C]83[/C][C]33374[/C][C]32956.296103304[/C][C]417.703896695969[/C][/ROW]
[ROW][C]84[/C][C]32110[/C][C]30492.0130508273[/C][C]1617.98694917268[/C][/ROW]
[ROW][C]85[/C][C]35533[/C][C]35125.6880902777[/C][C]407.311909722305[/C][/ROW]
[ROW][C]86[/C][C]35532[/C][C]37559.5236469025[/C][C]-2027.52364690248[/C][/ROW]
[ROW][C]87[/C][C]37903[/C][C]41110.3900429399[/C][C]-3207.39004293989[/C][/ROW]
[ROW][C]88[/C][C]36763[/C][C]42749.6491569624[/C][C]-5986.64915696238[/C][/ROW]
[ROW][C]89[/C][C]40399[/C][C]42237.0078283913[/C][C]-1838.00782839133[/C][/ROW]
[ROW][C]90[/C][C]44164[/C][C]43523.5648119284[/C][C]640.435188071616[/C][/ROW]
[ROW][C]91[/C][C]44496[/C][C]41631.2790871706[/C][C]2864.72091282943[/C][/ROW]
[ROW][C]92[/C][C]43110[/C][C]43438.3285439882[/C][C]-328.328543988178[/C][/ROW]
[ROW][C]93[/C][C]43880[/C][C]43602.1858927705[/C][C]277.814107229519[/C][/ROW]
[ROW][C]94[/C][C]43930[/C][C]43599.3808038678[/C][C]330.619196132168[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113134&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113134&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
12145418762.07862433232691.92137566772
22389921975.94912207551923.05087792448
32493924249.5109485593689.489051440655
42358024397.2956854749-817.295685474873
52456225585.5948722032-1023.59487220317
62469625997.1675498996-1301.16754989956
72378524383.4694069888-598.469406988808
82381225336.3835743497-1524.3835743497
92191723039.4676163057-1122.46761630572
101971321242.7478801346-1529.74788013456
111928220002.3038467434-720.303846743446
121878817727.71512499941060.28487500065
132145319725.34292065641727.65707934356
142448222948.82855359121533.17144640877
152747426201.81174744321272.18825255682
162726428854.2082760411-1590.20827604112
172734930641.2670404319-3292.26704043186
183063231904.3684705505-1272.36847055046
192942930108.6717573444-679.671757344364
203008428615.7348316951468.26516830497
212629027758.214293302-1468.21429330197
222437927101.5646832996-2722.56468329957
232333525670.4951636856-2335.49516368561
242134622242.0540121122-896.054012112164
252110624379.639969341-3273.63996934095
262451427015.7891406017-2501.78914060166
272835329138.8039204666-785.803920466583
283080528147.72800595812657.2719940419
293134829656.61936029761691.38063970242
303455632786.026821769.97317999997
313385532946.1972016893908.8027983107
323478732895.29129954131891.70870045869
333252932490.805441296238.1945587037565
342999831295.2987834677-1297.29878346769
352925730059.5716293569-802.571629356902
362815528641.9030664793-486.903066479334
373046630465.07674102740.923258972624129
383570433488.51997559722215.48002440278
393932735591.61773991313735.38226008688
403935136496.90958777552854.09041222446
414223439078.1558169353155.84418306503
424363041999.4332720181630.56672798197
434372240960.31734173572761.68265826426
444312142176.6354362517944.364563748254
453798540031.6391197585-2046.63911975849
463713539340.1476077797-2205.14760777969
473464636655.2256175396-2009.22561753959
483302633600.7874497622-574.787449762219
493508736716.2799583862-1629.27995838617
503884638330.194244782515.805755218004
514201340076.56959014481936.43040985522
524390840697.31541778353210.68458221648
534286842331.0786798513536.921320148684
544442345411.9431244136-988.943124413582
554416745203.6019938997-1036.60199389968
564363643892.6501028236-256.65010282358
574438242946.58465775091435.41534224911
584214241493.8774848983648.122515101674
594345239419.40653628764032.59346371242
603691239063.4860574174-2151.4860574174
614241341220.34683086771192.65316913232
624534446192.5671262766-848.567126276623
634487346153.0681577107-1280.0681577107
644751047849.8256814648-339.825681464755
654955450331.3691234235-777.369123423511
664736949487.6286121382-2118.62861213822
674599848314.9530982183-2316.95309821828
684814049120.2800233683-980.280023368263
694844144306.33690457284134.66309542723
704492841525.89437336883402.10562663123
714045439036.70110308281417.29889691716
723866137230.04123840221430.95876159779
733724638363.5468651114-1117.54686511141
743684337652.6281901733-809.62819017326
753642438784.2278528224-2360.2278528224
763759437582.068188539711.9318114602829
773814436596.90727846631547.09272153374
783873737096.86733905171640.13266094827
793456036463.5101129533-1903.51011295327
803608037294.6961879822-1214.69618798219
813350834756.7660742434-1248.76607424343
823546232088.08838318363373.91161681644
833337432956.296103304417.703896695969
843211030492.01305082731617.98694917268
853553335125.6880902777407.311909722305
863553237559.5236469025-2027.52364690248
873790341110.3900429399-3207.39004293989
883676342749.6491569624-5986.64915696238
894039942237.0078283913-1838.00782839133
904416443523.5648119284640.435188071616
914449641631.27908717062864.72091282943
924311043438.3285439882-328.328543988178
934388043602.1858927705277.814107229519
944393043599.3808038678330.619196132168






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.01631245981221420.03262491962442840.983687540187786
220.02710500251492120.05421000502984230.972894997485079
230.009743081036647490.0194861620732950.990256918963353
240.003180522383410780.006361044766821570.99681947761659
250.01764343081108230.03528686162216470.982356569188918
260.009363566329097050.01872713265819410.990636433670903
270.00874092100856930.01748184201713860.99125907899143
280.1930426186577720.3860852373155430.806957381342228
290.1569448819234930.3138897638469850.843055118076507
300.1041170564780950.208234112956190.895882943521905
310.06722085267011520.134441705340230.932779147329885
320.04994597539409830.09989195078819650.950054024605902
330.03488580536810710.06977161073621430.965114194631893
340.030238383097950.06047676619590010.96976161690205
350.02545001508296810.05090003016593630.974549984917032
360.05865910750529440.1173182150105890.941340892494706
370.08610350414107640.1722070082821530.913896495858924
380.06980704834422540.1396140966884510.930192951655775
390.06213953756371050.1242790751274210.93786046243629
400.04467771344487780.08935542688975560.955322286555122
410.05256480426196710.1051296085239340.947435195738033
420.03728058578444970.07456117156889940.96271941421555
430.08807260407088240.1761452081417650.911927395929118
440.06786537046648150.1357307409329630.932134629533519
450.06702465554425640.1340493110885130.932975344455744
460.06510747478357580.1302149495671520.934892525216424
470.07643551552020210.1528710310404040.923564484479798
480.07246447905317080.1449289581063420.92753552094683
490.1220188607576350.2440377215152690.877981139242365
500.1010707829903860.2021415659807720.898929217009614
510.08493990095345940.1698798019069190.91506009904654
520.101069243607660.2021384872153210.89893075639234
530.07779859466796390.1555971893359280.922201405332036
540.0593158283467480.1186316566934960.940684171653252
550.04608830645993560.09217661291987120.953911693540064
560.03966171850559440.07932343701118880.960338281494406
570.09045666799485830.1809133359897170.909543332005142
580.1239613850538450.2479227701076890.876038614946155
590.3944321553981680.7888643107963370.605567844601832
600.3703180460214930.7406360920429870.629681953978507
610.3545098050846670.7090196101693330.645490194915333
620.4111803729126910.8223607458253830.588819627087309
630.3463248661649910.6926497323299820.65367513383501
640.476195005390150.95239001078030.52380499460985
650.7286469687794480.5427060624411040.271353031220552
660.6431936203491040.7136127593017930.356806379650896
670.8244237624521370.3511524750957260.175576237547863
680.741238220816230.5175235583675410.25876177918377
690.930012571326210.1399748573475790.0699874286737897
700.9434807034498820.1130385931002360.056519296550118
710.8948977821693270.2102044356613450.105102217830673
720.8039947206306330.3920105587387340.196005279369367
730.7876639355085940.4246721289828120.212336064491406

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.0163124598122142 & 0.0326249196244284 & 0.983687540187786 \tabularnewline
22 & 0.0271050025149212 & 0.0542100050298423 & 0.972894997485079 \tabularnewline
23 & 0.00974308103664749 & 0.019486162073295 & 0.990256918963353 \tabularnewline
24 & 0.00318052238341078 & 0.00636104476682157 & 0.99681947761659 \tabularnewline
25 & 0.0176434308110823 & 0.0352868616221647 & 0.982356569188918 \tabularnewline
26 & 0.00936356632909705 & 0.0187271326581941 & 0.990636433670903 \tabularnewline
27 & 0.0087409210085693 & 0.0174818420171386 & 0.99125907899143 \tabularnewline
28 & 0.193042618657772 & 0.386085237315543 & 0.806957381342228 \tabularnewline
29 & 0.156944881923493 & 0.313889763846985 & 0.843055118076507 \tabularnewline
30 & 0.104117056478095 & 0.20823411295619 & 0.895882943521905 \tabularnewline
31 & 0.0672208526701152 & 0.13444170534023 & 0.932779147329885 \tabularnewline
32 & 0.0499459753940983 & 0.0998919507881965 & 0.950054024605902 \tabularnewline
33 & 0.0348858053681071 & 0.0697716107362143 & 0.965114194631893 \tabularnewline
34 & 0.03023838309795 & 0.0604767661959001 & 0.96976161690205 \tabularnewline
35 & 0.0254500150829681 & 0.0509000301659363 & 0.974549984917032 \tabularnewline
36 & 0.0586591075052944 & 0.117318215010589 & 0.941340892494706 \tabularnewline
37 & 0.0861035041410764 & 0.172207008282153 & 0.913896495858924 \tabularnewline
38 & 0.0698070483442254 & 0.139614096688451 & 0.930192951655775 \tabularnewline
39 & 0.0621395375637105 & 0.124279075127421 & 0.93786046243629 \tabularnewline
40 & 0.0446777134448778 & 0.0893554268897556 & 0.955322286555122 \tabularnewline
41 & 0.0525648042619671 & 0.105129608523934 & 0.947435195738033 \tabularnewline
42 & 0.0372805857844497 & 0.0745611715688994 & 0.96271941421555 \tabularnewline
43 & 0.0880726040708824 & 0.176145208141765 & 0.911927395929118 \tabularnewline
44 & 0.0678653704664815 & 0.135730740932963 & 0.932134629533519 \tabularnewline
45 & 0.0670246555442564 & 0.134049311088513 & 0.932975344455744 \tabularnewline
46 & 0.0651074747835758 & 0.130214949567152 & 0.934892525216424 \tabularnewline
47 & 0.0764355155202021 & 0.152871031040404 & 0.923564484479798 \tabularnewline
48 & 0.0724644790531708 & 0.144928958106342 & 0.92753552094683 \tabularnewline
49 & 0.122018860757635 & 0.244037721515269 & 0.877981139242365 \tabularnewline
50 & 0.101070782990386 & 0.202141565980772 & 0.898929217009614 \tabularnewline
51 & 0.0849399009534594 & 0.169879801906919 & 0.91506009904654 \tabularnewline
52 & 0.10106924360766 & 0.202138487215321 & 0.89893075639234 \tabularnewline
53 & 0.0777985946679639 & 0.155597189335928 & 0.922201405332036 \tabularnewline
54 & 0.059315828346748 & 0.118631656693496 & 0.940684171653252 \tabularnewline
55 & 0.0460883064599356 & 0.0921766129198712 & 0.953911693540064 \tabularnewline
56 & 0.0396617185055944 & 0.0793234370111888 & 0.960338281494406 \tabularnewline
57 & 0.0904566679948583 & 0.180913335989717 & 0.909543332005142 \tabularnewline
58 & 0.123961385053845 & 0.247922770107689 & 0.876038614946155 \tabularnewline
59 & 0.394432155398168 & 0.788864310796337 & 0.605567844601832 \tabularnewline
60 & 0.370318046021493 & 0.740636092042987 & 0.629681953978507 \tabularnewline
61 & 0.354509805084667 & 0.709019610169333 & 0.645490194915333 \tabularnewline
62 & 0.411180372912691 & 0.822360745825383 & 0.588819627087309 \tabularnewline
63 & 0.346324866164991 & 0.692649732329982 & 0.65367513383501 \tabularnewline
64 & 0.47619500539015 & 0.9523900107803 & 0.52380499460985 \tabularnewline
65 & 0.728646968779448 & 0.542706062441104 & 0.271353031220552 \tabularnewline
66 & 0.643193620349104 & 0.713612759301793 & 0.356806379650896 \tabularnewline
67 & 0.824423762452137 & 0.351152475095726 & 0.175576237547863 \tabularnewline
68 & 0.74123822081623 & 0.517523558367541 & 0.25876177918377 \tabularnewline
69 & 0.93001257132621 & 0.139974857347579 & 0.0699874286737897 \tabularnewline
70 & 0.943480703449882 & 0.113038593100236 & 0.056519296550118 \tabularnewline
71 & 0.894897782169327 & 0.210204435661345 & 0.105102217830673 \tabularnewline
72 & 0.803994720630633 & 0.392010558738734 & 0.196005279369367 \tabularnewline
73 & 0.787663935508594 & 0.424672128982812 & 0.212336064491406 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113134&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]21[/C][C]0.0163124598122142[/C][C]0.0326249196244284[/C][C]0.983687540187786[/C][/ROW]
[ROW][C]22[/C][C]0.0271050025149212[/C][C]0.0542100050298423[/C][C]0.972894997485079[/C][/ROW]
[ROW][C]23[/C][C]0.00974308103664749[/C][C]0.019486162073295[/C][C]0.990256918963353[/C][/ROW]
[ROW][C]24[/C][C]0.00318052238341078[/C][C]0.00636104476682157[/C][C]0.99681947761659[/C][/ROW]
[ROW][C]25[/C][C]0.0176434308110823[/C][C]0.0352868616221647[/C][C]0.982356569188918[/C][/ROW]
[ROW][C]26[/C][C]0.00936356632909705[/C][C]0.0187271326581941[/C][C]0.990636433670903[/C][/ROW]
[ROW][C]27[/C][C]0.0087409210085693[/C][C]0.0174818420171386[/C][C]0.99125907899143[/C][/ROW]
[ROW][C]28[/C][C]0.193042618657772[/C][C]0.386085237315543[/C][C]0.806957381342228[/C][/ROW]
[ROW][C]29[/C][C]0.156944881923493[/C][C]0.313889763846985[/C][C]0.843055118076507[/C][/ROW]
[ROW][C]30[/C][C]0.104117056478095[/C][C]0.20823411295619[/C][C]0.895882943521905[/C][/ROW]
[ROW][C]31[/C][C]0.0672208526701152[/C][C]0.13444170534023[/C][C]0.932779147329885[/C][/ROW]
[ROW][C]32[/C][C]0.0499459753940983[/C][C]0.0998919507881965[/C][C]0.950054024605902[/C][/ROW]
[ROW][C]33[/C][C]0.0348858053681071[/C][C]0.0697716107362143[/C][C]0.965114194631893[/C][/ROW]
[ROW][C]34[/C][C]0.03023838309795[/C][C]0.0604767661959001[/C][C]0.96976161690205[/C][/ROW]
[ROW][C]35[/C][C]0.0254500150829681[/C][C]0.0509000301659363[/C][C]0.974549984917032[/C][/ROW]
[ROW][C]36[/C][C]0.0586591075052944[/C][C]0.117318215010589[/C][C]0.941340892494706[/C][/ROW]
[ROW][C]37[/C][C]0.0861035041410764[/C][C]0.172207008282153[/C][C]0.913896495858924[/C][/ROW]
[ROW][C]38[/C][C]0.0698070483442254[/C][C]0.139614096688451[/C][C]0.930192951655775[/C][/ROW]
[ROW][C]39[/C][C]0.0621395375637105[/C][C]0.124279075127421[/C][C]0.93786046243629[/C][/ROW]
[ROW][C]40[/C][C]0.0446777134448778[/C][C]0.0893554268897556[/C][C]0.955322286555122[/C][/ROW]
[ROW][C]41[/C][C]0.0525648042619671[/C][C]0.105129608523934[/C][C]0.947435195738033[/C][/ROW]
[ROW][C]42[/C][C]0.0372805857844497[/C][C]0.0745611715688994[/C][C]0.96271941421555[/C][/ROW]
[ROW][C]43[/C][C]0.0880726040708824[/C][C]0.176145208141765[/C][C]0.911927395929118[/C][/ROW]
[ROW][C]44[/C][C]0.0678653704664815[/C][C]0.135730740932963[/C][C]0.932134629533519[/C][/ROW]
[ROW][C]45[/C][C]0.0670246555442564[/C][C]0.134049311088513[/C][C]0.932975344455744[/C][/ROW]
[ROW][C]46[/C][C]0.0651074747835758[/C][C]0.130214949567152[/C][C]0.934892525216424[/C][/ROW]
[ROW][C]47[/C][C]0.0764355155202021[/C][C]0.152871031040404[/C][C]0.923564484479798[/C][/ROW]
[ROW][C]48[/C][C]0.0724644790531708[/C][C]0.144928958106342[/C][C]0.92753552094683[/C][/ROW]
[ROW][C]49[/C][C]0.122018860757635[/C][C]0.244037721515269[/C][C]0.877981139242365[/C][/ROW]
[ROW][C]50[/C][C]0.101070782990386[/C][C]0.202141565980772[/C][C]0.898929217009614[/C][/ROW]
[ROW][C]51[/C][C]0.0849399009534594[/C][C]0.169879801906919[/C][C]0.91506009904654[/C][/ROW]
[ROW][C]52[/C][C]0.10106924360766[/C][C]0.202138487215321[/C][C]0.89893075639234[/C][/ROW]
[ROW][C]53[/C][C]0.0777985946679639[/C][C]0.155597189335928[/C][C]0.922201405332036[/C][/ROW]
[ROW][C]54[/C][C]0.059315828346748[/C][C]0.118631656693496[/C][C]0.940684171653252[/C][/ROW]
[ROW][C]55[/C][C]0.0460883064599356[/C][C]0.0921766129198712[/C][C]0.953911693540064[/C][/ROW]
[ROW][C]56[/C][C]0.0396617185055944[/C][C]0.0793234370111888[/C][C]0.960338281494406[/C][/ROW]
[ROW][C]57[/C][C]0.0904566679948583[/C][C]0.180913335989717[/C][C]0.909543332005142[/C][/ROW]
[ROW][C]58[/C][C]0.123961385053845[/C][C]0.247922770107689[/C][C]0.876038614946155[/C][/ROW]
[ROW][C]59[/C][C]0.394432155398168[/C][C]0.788864310796337[/C][C]0.605567844601832[/C][/ROW]
[ROW][C]60[/C][C]0.370318046021493[/C][C]0.740636092042987[/C][C]0.629681953978507[/C][/ROW]
[ROW][C]61[/C][C]0.354509805084667[/C][C]0.709019610169333[/C][C]0.645490194915333[/C][/ROW]
[ROW][C]62[/C][C]0.411180372912691[/C][C]0.822360745825383[/C][C]0.588819627087309[/C][/ROW]
[ROW][C]63[/C][C]0.346324866164991[/C][C]0.692649732329982[/C][C]0.65367513383501[/C][/ROW]
[ROW][C]64[/C][C]0.47619500539015[/C][C]0.9523900107803[/C][C]0.52380499460985[/C][/ROW]
[ROW][C]65[/C][C]0.728646968779448[/C][C]0.542706062441104[/C][C]0.271353031220552[/C][/ROW]
[ROW][C]66[/C][C]0.643193620349104[/C][C]0.713612759301793[/C][C]0.356806379650896[/C][/ROW]
[ROW][C]67[/C][C]0.824423762452137[/C][C]0.351152475095726[/C][C]0.175576237547863[/C][/ROW]
[ROW][C]68[/C][C]0.74123822081623[/C][C]0.517523558367541[/C][C]0.25876177918377[/C][/ROW]
[ROW][C]69[/C][C]0.93001257132621[/C][C]0.139974857347579[/C][C]0.0699874286737897[/C][/ROW]
[ROW][C]70[/C][C]0.943480703449882[/C][C]0.113038593100236[/C][C]0.056519296550118[/C][/ROW]
[ROW][C]71[/C][C]0.894897782169327[/C][C]0.210204435661345[/C][C]0.105102217830673[/C][/ROW]
[ROW][C]72[/C][C]0.803994720630633[/C][C]0.392010558738734[/C][C]0.196005279369367[/C][/ROW]
[ROW][C]73[/C][C]0.787663935508594[/C][C]0.424672128982812[/C][C]0.212336064491406[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113134&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113134&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
210.01631245981221420.03262491962442840.983687540187786
220.02710500251492120.05421000502984230.972894997485079
230.009743081036647490.0194861620732950.990256918963353
240.003180522383410780.006361044766821570.99681947761659
250.01764343081108230.03528686162216470.982356569188918
260.009363566329097050.01872713265819410.990636433670903
270.00874092100856930.01748184201713860.99125907899143
280.1930426186577720.3860852373155430.806957381342228
290.1569448819234930.3138897638469850.843055118076507
300.1041170564780950.208234112956190.895882943521905
310.06722085267011520.134441705340230.932779147329885
320.04994597539409830.09989195078819650.950054024605902
330.03488580536810710.06977161073621430.965114194631893
340.030238383097950.06047676619590010.96976161690205
350.02545001508296810.05090003016593630.974549984917032
360.05865910750529440.1173182150105890.941340892494706
370.08610350414107640.1722070082821530.913896495858924
380.06980704834422540.1396140966884510.930192951655775
390.06213953756371050.1242790751274210.93786046243629
400.04467771344487780.08935542688975560.955322286555122
410.05256480426196710.1051296085239340.947435195738033
420.03728058578444970.07456117156889940.96271941421555
430.08807260407088240.1761452081417650.911927395929118
440.06786537046648150.1357307409329630.932134629533519
450.06702465554425640.1340493110885130.932975344455744
460.06510747478357580.1302149495671520.934892525216424
470.07643551552020210.1528710310404040.923564484479798
480.07246447905317080.1449289581063420.92753552094683
490.1220188607576350.2440377215152690.877981139242365
500.1010707829903860.2021415659807720.898929217009614
510.08493990095345940.1698798019069190.91506009904654
520.101069243607660.2021384872153210.89893075639234
530.07779859466796390.1555971893359280.922201405332036
540.0593158283467480.1186316566934960.940684171653252
550.04608830645993560.09217661291987120.953911693540064
560.03966171850559440.07932343701118880.960338281494406
570.09045666799485830.1809133359897170.909543332005142
580.1239613850538450.2479227701076890.876038614946155
590.3944321553981680.7888643107963370.605567844601832
600.3703180460214930.7406360920429870.629681953978507
610.3545098050846670.7090196101693330.645490194915333
620.4111803729126910.8223607458253830.588819627087309
630.3463248661649910.6926497323299820.65367513383501
640.476195005390150.95239001078030.52380499460985
650.7286469687794480.5427060624411040.271353031220552
660.6431936203491040.7136127593017930.356806379650896
670.8244237624521370.3511524750957260.175576237547863
680.741238220816230.5175235583675410.25876177918377
690.930012571326210.1399748573475790.0699874286737897
700.9434807034498820.1130385931002360.056519296550118
710.8948977821693270.2102044356613450.105102217830673
720.8039947206306330.3920105587387340.196005279369367
730.7876639355085940.4246721289828120.212336064491406






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0188679245283019NOK
5% type I error level60.113207547169811NOK
10% type I error level150.283018867924528NOK

\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 & 1 & 0.0188679245283019 & NOK \tabularnewline
5% type I error level & 6 & 0.113207547169811 & NOK \tabularnewline
10% type I error level & 15 & 0.283018867924528 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113134&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]1[/C][C]0.0188679245283019[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.113207547169811[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.283018867924528[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113134&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113134&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 level10.0188679245283019NOK
5% type I error level60.113207547169811NOK
10% type I error level150.283018867924528NOK


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 9
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Figure 10
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Input Parameters & R Code

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