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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 01:54:58 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258622313rtnaskvoz02b12n.htm/, Retrieved Thu, 28 Mar 2024 08:53:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57662, Retrieved Thu, 28 Mar 2024 08:53:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact188
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-19 08:54:58] [b4088cbf8335906ce53a9289ed6fac01] [Current]
-    D        [Multiple Regression] [multiple regression] [2009-11-20 17:47:16] [34d27ebe78dc2d31581e8710befe8733]
-    D          [Multiple Regression] [multiple regression] [2009-12-14 18:53:52] [34d27ebe78dc2d31581e8710befe8733]
-    D          [Multiple Regression] [multiple regression] [2009-12-14 19:25:40] [34d27ebe78dc2d31581e8710befe8733]
-    D        [Multiple Regression] [mutiple regression] [2009-11-23 13:24:59] [25d480487237d24b5bee738546d96a8b]
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Dataseries X:
8.4	420
8.4	418
8.4	410
8.6	418
8.9	426
8.8	428
8.3	430
7.5	424
7.2	423
7.4	427
8.8	441
9.3	449
9.3	452
8.7	462
8.2	455
8.3	461
8.5	461
8.6	463
8.5	462
8.2	456
8.1	455
7.9	456
8.6	472
8.7	472
8.7	471
8.5	465
8.4	459
8.5	465
8.7	468
8.7	467
8.6	463
8.5	460
8.3	462
8,00	461
8.2	476
8.1	476
8.1	471
8,00	453
7.9	443
7.9	442
8,00	444
8,00	438
7.9	427
8,00	424
7.7	416
7.2	406
7.5	431
7.3	434
7,00	418
7,00	412
7,00	404
7.2	409
7.3	412
7.1	406
6.8	398
6.4	397
6.1	385
6.5	390
7.7	413
7.9	413
7.5	401
6.9	397
6.6	397
6.9	409
7.7	419
8,00	424
8,00	428
7.7	430
7.3	424
7.4	433
8.1	456
8.3	459
8.2	446




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57662&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57662&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57662&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
wgb[t] = -1.18542050343351 + 0.0208973933596284nwwz[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
wgb[t] =  -1.18542050343351 +  0.0208973933596284nwwz[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57662&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]wgb[t] =  -1.18542050343351 +  0.0208973933596284nwwz[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57662&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57662&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
wgb[t] = -1.18542050343351 + 0.0208973933596284nwwz[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.185420503433510.972694-1.21870.2269930.113496
nwwz0.02089739335962840.0022269.386300

\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) & -1.18542050343351 & 0.972694 & -1.2187 & 0.226993 & 0.113496 \tabularnewline
nwwz & 0.0208973933596284 & 0.002226 & 9.3863 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57662&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]-1.18542050343351[/C][C]0.972694[/C][C]-1.2187[/C][C]0.226993[/C][C]0.113496[/C][/ROW]
[ROW][C]nwwz[/C][C]0.0208973933596284[/C][C]0.002226[/C][C]9.3863[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57662&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57662&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.185420503433510.972694-1.21870.2269930.113496
nwwz0.02089739335962840.0022269.386300







Multiple Linear Regression - Regression Statistics
Multiple R0.744142345521879
R-squared0.553747830398803
Adjusted R-squared0.547462588573434
F-TEST (value)88.1028679220781
F-TEST (DF numerator)1
F-TEST (DF denominator)71
p-value4.55191440096314e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.467505192541123
Sum Squared Residuals15.5178384587568

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.744142345521879 \tabularnewline
R-squared & 0.553747830398803 \tabularnewline
Adjusted R-squared & 0.547462588573434 \tabularnewline
F-TEST (value) & 88.1028679220781 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 71 \tabularnewline
p-value & 4.55191440096314e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.467505192541123 \tabularnewline
Sum Squared Residuals & 15.5178384587568 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57662&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.744142345521879[/C][/ROW]
[ROW][C]R-squared[/C][C]0.553747830398803[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.547462588573434[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]88.1028679220781[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]71[/C][/ROW]
[ROW][C]p-value[/C][C]4.55191440096314e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.467505192541123[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]15.5178384587568[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57662&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57662&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.744142345521879
R-squared0.553747830398803
Adjusted R-squared0.547462588573434
F-TEST (value)88.1028679220781
F-TEST (DF numerator)1
F-TEST (DF denominator)71
p-value4.55191440096314e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.467505192541123
Sum Squared Residuals15.5178384587568







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.47.59148470761040.808515292389594
28.47.549689920891150.850310079108852
38.47.382510774014121.01748922598588
48.67.549689920891151.05031007910885
58.97.716869067768181.18313093223182
68.87.758663854487431.04133614551257
78.37.800458641206690.499541358793311
87.57.67507428104892-0.175074281048919
97.27.65417688768929-0.454176887689291
107.47.7377664611278-0.337766461127804
118.88.03032996816260.769670031837399
129.38.197509115039631.10249088496037
139.38.260201295118511.03979870488149
148.78.46917522871480.230824771285202
158.28.3228934751974-0.122893475197400
168.38.44827783535517-0.148277835355169
178.58.448277835355170.0517221646448307
188.68.490072622074430.109927377925574
198.58.46917522871480.0308247712852023
208.28.34379086855703-0.143790868557028
218.18.3228934751974-0.222893475197399
227.98.34379086855703-0.443790868557027
238.68.67814916231108-0.0781491623110818
248.78.678149162311080.0218508376889179
258.78.657251768951450.0427482310485462
268.58.53186740879368-0.0318674087936828
278.48.40648304863591-0.00648304863591216
288.58.53186740879368-0.0318674087936828
298.78.594559588872570.105440411127431
308.78.573662195512940.126337804487060
318.68.490072622074430.109927377925574
328.58.427380441995540.0726195580044591
338.38.4691752287148-0.169175228714797
3488.44827783535517-0.448277835355169
358.28.7617387357496-0.561738735749596
368.18.7617387357496-0.661738735749595
378.18.65725176895145-0.557251768951453
3888.28109868847814-0.281098688478142
397.98.07212475488186-0.172124754881858
407.98.05122736152223-0.151227361522230
4188.09302214824149-0.0930221482414869
4287.967637788083720.0323622119162833
437.97.73776646112780.162233538872196
4487.675074281048920.324925718951081
457.77.507895134171890.192104865828108
467.27.29892120057561-0.0989212005756086
477.57.82135603456632-0.321356034566318
487.37.8840482146452-0.584048214645203
4977.54968992089115-0.549689920891149
5077.42430556073338-0.424305560733379
5177.25712641385635-0.257126413856352
527.27.36161338065449-0.161613380654494
537.37.42430556073338-0.124305560733379
547.17.29892120057561-0.198921200575609
556.87.13174205369858-0.331742053698582
566.47.11084466033895-0.710844660338953
576.16.86007594002341-0.760075940023413
586.56.96456290682155-0.464562906821555
597.77.445202954093010.254797045906993
607.97.445202954093010.454797045906993
617.57.194434233777470.305565766222533
626.97.11084466033895-0.210844660338953
636.67.11084466033895-0.510844660338954
646.97.36161338065449-0.461613380654494
657.77.570587314250780.129412685749223
6687.675074281048920.324925718951081
6787.758663854487430.241336145512567
687.77.80045864120669-0.100458641206690
697.37.67507428104892-0.375074281048920
707.47.86315082128557-0.463150821285574
718.18.34379086855703-0.243790868557028
728.38.40648304863591-0.106483048635912
738.28.134816934960740.0651830650392556

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8.4 & 7.5914847076104 & 0.808515292389594 \tabularnewline
2 & 8.4 & 7.54968992089115 & 0.850310079108852 \tabularnewline
3 & 8.4 & 7.38251077401412 & 1.01748922598588 \tabularnewline
4 & 8.6 & 7.54968992089115 & 1.05031007910885 \tabularnewline
5 & 8.9 & 7.71686906776818 & 1.18313093223182 \tabularnewline
6 & 8.8 & 7.75866385448743 & 1.04133614551257 \tabularnewline
7 & 8.3 & 7.80045864120669 & 0.499541358793311 \tabularnewline
8 & 7.5 & 7.67507428104892 & -0.175074281048919 \tabularnewline
9 & 7.2 & 7.65417688768929 & -0.454176887689291 \tabularnewline
10 & 7.4 & 7.7377664611278 & -0.337766461127804 \tabularnewline
11 & 8.8 & 8.0303299681626 & 0.769670031837399 \tabularnewline
12 & 9.3 & 8.19750911503963 & 1.10249088496037 \tabularnewline
13 & 9.3 & 8.26020129511851 & 1.03979870488149 \tabularnewline
14 & 8.7 & 8.4691752287148 & 0.230824771285202 \tabularnewline
15 & 8.2 & 8.3228934751974 & -0.122893475197400 \tabularnewline
16 & 8.3 & 8.44827783535517 & -0.148277835355169 \tabularnewline
17 & 8.5 & 8.44827783535517 & 0.0517221646448307 \tabularnewline
18 & 8.6 & 8.49007262207443 & 0.109927377925574 \tabularnewline
19 & 8.5 & 8.4691752287148 & 0.0308247712852023 \tabularnewline
20 & 8.2 & 8.34379086855703 & -0.143790868557028 \tabularnewline
21 & 8.1 & 8.3228934751974 & -0.222893475197399 \tabularnewline
22 & 7.9 & 8.34379086855703 & -0.443790868557027 \tabularnewline
23 & 8.6 & 8.67814916231108 & -0.0781491623110818 \tabularnewline
24 & 8.7 & 8.67814916231108 & 0.0218508376889179 \tabularnewline
25 & 8.7 & 8.65725176895145 & 0.0427482310485462 \tabularnewline
26 & 8.5 & 8.53186740879368 & -0.0318674087936828 \tabularnewline
27 & 8.4 & 8.40648304863591 & -0.00648304863591216 \tabularnewline
28 & 8.5 & 8.53186740879368 & -0.0318674087936828 \tabularnewline
29 & 8.7 & 8.59455958887257 & 0.105440411127431 \tabularnewline
30 & 8.7 & 8.57366219551294 & 0.126337804487060 \tabularnewline
31 & 8.6 & 8.49007262207443 & 0.109927377925574 \tabularnewline
32 & 8.5 & 8.42738044199554 & 0.0726195580044591 \tabularnewline
33 & 8.3 & 8.4691752287148 & -0.169175228714797 \tabularnewline
34 & 8 & 8.44827783535517 & -0.448277835355169 \tabularnewline
35 & 8.2 & 8.7617387357496 & -0.561738735749596 \tabularnewline
36 & 8.1 & 8.7617387357496 & -0.661738735749595 \tabularnewline
37 & 8.1 & 8.65725176895145 & -0.557251768951453 \tabularnewline
38 & 8 & 8.28109868847814 & -0.281098688478142 \tabularnewline
39 & 7.9 & 8.07212475488186 & -0.172124754881858 \tabularnewline
40 & 7.9 & 8.05122736152223 & -0.151227361522230 \tabularnewline
41 & 8 & 8.09302214824149 & -0.0930221482414869 \tabularnewline
42 & 8 & 7.96763778808372 & 0.0323622119162833 \tabularnewline
43 & 7.9 & 7.7377664611278 & 0.162233538872196 \tabularnewline
44 & 8 & 7.67507428104892 & 0.324925718951081 \tabularnewline
45 & 7.7 & 7.50789513417189 & 0.192104865828108 \tabularnewline
46 & 7.2 & 7.29892120057561 & -0.0989212005756086 \tabularnewline
47 & 7.5 & 7.82135603456632 & -0.321356034566318 \tabularnewline
48 & 7.3 & 7.8840482146452 & -0.584048214645203 \tabularnewline
49 & 7 & 7.54968992089115 & -0.549689920891149 \tabularnewline
50 & 7 & 7.42430556073338 & -0.424305560733379 \tabularnewline
51 & 7 & 7.25712641385635 & -0.257126413856352 \tabularnewline
52 & 7.2 & 7.36161338065449 & -0.161613380654494 \tabularnewline
53 & 7.3 & 7.42430556073338 & -0.124305560733379 \tabularnewline
54 & 7.1 & 7.29892120057561 & -0.198921200575609 \tabularnewline
55 & 6.8 & 7.13174205369858 & -0.331742053698582 \tabularnewline
56 & 6.4 & 7.11084466033895 & -0.710844660338953 \tabularnewline
57 & 6.1 & 6.86007594002341 & -0.760075940023413 \tabularnewline
58 & 6.5 & 6.96456290682155 & -0.464562906821555 \tabularnewline
59 & 7.7 & 7.44520295409301 & 0.254797045906993 \tabularnewline
60 & 7.9 & 7.44520295409301 & 0.454797045906993 \tabularnewline
61 & 7.5 & 7.19443423377747 & 0.305565766222533 \tabularnewline
62 & 6.9 & 7.11084466033895 & -0.210844660338953 \tabularnewline
63 & 6.6 & 7.11084466033895 & -0.510844660338954 \tabularnewline
64 & 6.9 & 7.36161338065449 & -0.461613380654494 \tabularnewline
65 & 7.7 & 7.57058731425078 & 0.129412685749223 \tabularnewline
66 & 8 & 7.67507428104892 & 0.324925718951081 \tabularnewline
67 & 8 & 7.75866385448743 & 0.241336145512567 \tabularnewline
68 & 7.7 & 7.80045864120669 & -0.100458641206690 \tabularnewline
69 & 7.3 & 7.67507428104892 & -0.375074281048920 \tabularnewline
70 & 7.4 & 7.86315082128557 & -0.463150821285574 \tabularnewline
71 & 8.1 & 8.34379086855703 & -0.243790868557028 \tabularnewline
72 & 8.3 & 8.40648304863591 & -0.106483048635912 \tabularnewline
73 & 8.2 & 8.13481693496074 & 0.0651830650392556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57662&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]8.4[/C][C]7.5914847076104[/C][C]0.808515292389594[/C][/ROW]
[ROW][C]2[/C][C]8.4[/C][C]7.54968992089115[/C][C]0.850310079108852[/C][/ROW]
[ROW][C]3[/C][C]8.4[/C][C]7.38251077401412[/C][C]1.01748922598588[/C][/ROW]
[ROW][C]4[/C][C]8.6[/C][C]7.54968992089115[/C][C]1.05031007910885[/C][/ROW]
[ROW][C]5[/C][C]8.9[/C][C]7.71686906776818[/C][C]1.18313093223182[/C][/ROW]
[ROW][C]6[/C][C]8.8[/C][C]7.75866385448743[/C][C]1.04133614551257[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]7.80045864120669[/C][C]0.499541358793311[/C][/ROW]
[ROW][C]8[/C][C]7.5[/C][C]7.67507428104892[/C][C]-0.175074281048919[/C][/ROW]
[ROW][C]9[/C][C]7.2[/C][C]7.65417688768929[/C][C]-0.454176887689291[/C][/ROW]
[ROW][C]10[/C][C]7.4[/C][C]7.7377664611278[/C][C]-0.337766461127804[/C][/ROW]
[ROW][C]11[/C][C]8.8[/C][C]8.0303299681626[/C][C]0.769670031837399[/C][/ROW]
[ROW][C]12[/C][C]9.3[/C][C]8.19750911503963[/C][C]1.10249088496037[/C][/ROW]
[ROW][C]13[/C][C]9.3[/C][C]8.26020129511851[/C][C]1.03979870488149[/C][/ROW]
[ROW][C]14[/C][C]8.7[/C][C]8.4691752287148[/C][C]0.230824771285202[/C][/ROW]
[ROW][C]15[/C][C]8.2[/C][C]8.3228934751974[/C][C]-0.122893475197400[/C][/ROW]
[ROW][C]16[/C][C]8.3[/C][C]8.44827783535517[/C][C]-0.148277835355169[/C][/ROW]
[ROW][C]17[/C][C]8.5[/C][C]8.44827783535517[/C][C]0.0517221646448307[/C][/ROW]
[ROW][C]18[/C][C]8.6[/C][C]8.49007262207443[/C][C]0.109927377925574[/C][/ROW]
[ROW][C]19[/C][C]8.5[/C][C]8.4691752287148[/C][C]0.0308247712852023[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]8.34379086855703[/C][C]-0.143790868557028[/C][/ROW]
[ROW][C]21[/C][C]8.1[/C][C]8.3228934751974[/C][C]-0.222893475197399[/C][/ROW]
[ROW][C]22[/C][C]7.9[/C][C]8.34379086855703[/C][C]-0.443790868557027[/C][/ROW]
[ROW][C]23[/C][C]8.6[/C][C]8.67814916231108[/C][C]-0.0781491623110818[/C][/ROW]
[ROW][C]24[/C][C]8.7[/C][C]8.67814916231108[/C][C]0.0218508376889179[/C][/ROW]
[ROW][C]25[/C][C]8.7[/C][C]8.65725176895145[/C][C]0.0427482310485462[/C][/ROW]
[ROW][C]26[/C][C]8.5[/C][C]8.53186740879368[/C][C]-0.0318674087936828[/C][/ROW]
[ROW][C]27[/C][C]8.4[/C][C]8.40648304863591[/C][C]-0.00648304863591216[/C][/ROW]
[ROW][C]28[/C][C]8.5[/C][C]8.53186740879368[/C][C]-0.0318674087936828[/C][/ROW]
[ROW][C]29[/C][C]8.7[/C][C]8.59455958887257[/C][C]0.105440411127431[/C][/ROW]
[ROW][C]30[/C][C]8.7[/C][C]8.57366219551294[/C][C]0.126337804487060[/C][/ROW]
[ROW][C]31[/C][C]8.6[/C][C]8.49007262207443[/C][C]0.109927377925574[/C][/ROW]
[ROW][C]32[/C][C]8.5[/C][C]8.42738044199554[/C][C]0.0726195580044591[/C][/ROW]
[ROW][C]33[/C][C]8.3[/C][C]8.4691752287148[/C][C]-0.169175228714797[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]8.44827783535517[/C][C]-0.448277835355169[/C][/ROW]
[ROW][C]35[/C][C]8.2[/C][C]8.7617387357496[/C][C]-0.561738735749596[/C][/ROW]
[ROW][C]36[/C][C]8.1[/C][C]8.7617387357496[/C][C]-0.661738735749595[/C][/ROW]
[ROW][C]37[/C][C]8.1[/C][C]8.65725176895145[/C][C]-0.557251768951453[/C][/ROW]
[ROW][C]38[/C][C]8[/C][C]8.28109868847814[/C][C]-0.281098688478142[/C][/ROW]
[ROW][C]39[/C][C]7.9[/C][C]8.07212475488186[/C][C]-0.172124754881858[/C][/ROW]
[ROW][C]40[/C][C]7.9[/C][C]8.05122736152223[/C][C]-0.151227361522230[/C][/ROW]
[ROW][C]41[/C][C]8[/C][C]8.09302214824149[/C][C]-0.0930221482414869[/C][/ROW]
[ROW][C]42[/C][C]8[/C][C]7.96763778808372[/C][C]0.0323622119162833[/C][/ROW]
[ROW][C]43[/C][C]7.9[/C][C]7.7377664611278[/C][C]0.162233538872196[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]7.67507428104892[/C][C]0.324925718951081[/C][/ROW]
[ROW][C]45[/C][C]7.7[/C][C]7.50789513417189[/C][C]0.192104865828108[/C][/ROW]
[ROW][C]46[/C][C]7.2[/C][C]7.29892120057561[/C][C]-0.0989212005756086[/C][/ROW]
[ROW][C]47[/C][C]7.5[/C][C]7.82135603456632[/C][C]-0.321356034566318[/C][/ROW]
[ROW][C]48[/C][C]7.3[/C][C]7.8840482146452[/C][C]-0.584048214645203[/C][/ROW]
[ROW][C]49[/C][C]7[/C][C]7.54968992089115[/C][C]-0.549689920891149[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]7.42430556073338[/C][C]-0.424305560733379[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]7.25712641385635[/C][C]-0.257126413856352[/C][/ROW]
[ROW][C]52[/C][C]7.2[/C][C]7.36161338065449[/C][C]-0.161613380654494[/C][/ROW]
[ROW][C]53[/C][C]7.3[/C][C]7.42430556073338[/C][C]-0.124305560733379[/C][/ROW]
[ROW][C]54[/C][C]7.1[/C][C]7.29892120057561[/C][C]-0.198921200575609[/C][/ROW]
[ROW][C]55[/C][C]6.8[/C][C]7.13174205369858[/C][C]-0.331742053698582[/C][/ROW]
[ROW][C]56[/C][C]6.4[/C][C]7.11084466033895[/C][C]-0.710844660338953[/C][/ROW]
[ROW][C]57[/C][C]6.1[/C][C]6.86007594002341[/C][C]-0.760075940023413[/C][/ROW]
[ROW][C]58[/C][C]6.5[/C][C]6.96456290682155[/C][C]-0.464562906821555[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]7.44520295409301[/C][C]0.254797045906993[/C][/ROW]
[ROW][C]60[/C][C]7.9[/C][C]7.44520295409301[/C][C]0.454797045906993[/C][/ROW]
[ROW][C]61[/C][C]7.5[/C][C]7.19443423377747[/C][C]0.305565766222533[/C][/ROW]
[ROW][C]62[/C][C]6.9[/C][C]7.11084466033895[/C][C]-0.210844660338953[/C][/ROW]
[ROW][C]63[/C][C]6.6[/C][C]7.11084466033895[/C][C]-0.510844660338954[/C][/ROW]
[ROW][C]64[/C][C]6.9[/C][C]7.36161338065449[/C][C]-0.461613380654494[/C][/ROW]
[ROW][C]65[/C][C]7.7[/C][C]7.57058731425078[/C][C]0.129412685749223[/C][/ROW]
[ROW][C]66[/C][C]8[/C][C]7.67507428104892[/C][C]0.324925718951081[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]7.75866385448743[/C][C]0.241336145512567[/C][/ROW]
[ROW][C]68[/C][C]7.7[/C][C]7.80045864120669[/C][C]-0.100458641206690[/C][/ROW]
[ROW][C]69[/C][C]7.3[/C][C]7.67507428104892[/C][C]-0.375074281048920[/C][/ROW]
[ROW][C]70[/C][C]7.4[/C][C]7.86315082128557[/C][C]-0.463150821285574[/C][/ROW]
[ROW][C]71[/C][C]8.1[/C][C]8.34379086855703[/C][C]-0.243790868557028[/C][/ROW]
[ROW][C]72[/C][C]8.3[/C][C]8.40648304863591[/C][C]-0.106483048635912[/C][/ROW]
[ROW][C]73[/C][C]8.2[/C][C]8.13481693496074[/C][C]0.0651830650392556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57662&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57662&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.47.59148470761040.808515292389594
28.47.549689920891150.850310079108852
38.47.382510774014121.01748922598588
48.67.549689920891151.05031007910885
58.97.716869067768181.18313093223182
68.87.758663854487431.04133614551257
78.37.800458641206690.499541358793311
87.57.67507428104892-0.175074281048919
97.27.65417688768929-0.454176887689291
107.47.7377664611278-0.337766461127804
118.88.03032996816260.769670031837399
129.38.197509115039631.10249088496037
139.38.260201295118511.03979870488149
148.78.46917522871480.230824771285202
158.28.3228934751974-0.122893475197400
168.38.44827783535517-0.148277835355169
178.58.448277835355170.0517221646448307
188.68.490072622074430.109927377925574
198.58.46917522871480.0308247712852023
208.28.34379086855703-0.143790868557028
218.18.3228934751974-0.222893475197399
227.98.34379086855703-0.443790868557027
238.68.67814916231108-0.0781491623110818
248.78.678149162311080.0218508376889179
258.78.657251768951450.0427482310485462
268.58.53186740879368-0.0318674087936828
278.48.40648304863591-0.00648304863591216
288.58.53186740879368-0.0318674087936828
298.78.594559588872570.105440411127431
308.78.573662195512940.126337804487060
318.68.490072622074430.109927377925574
328.58.427380441995540.0726195580044591
338.38.4691752287148-0.169175228714797
3488.44827783535517-0.448277835355169
358.28.7617387357496-0.561738735749596
368.18.7617387357496-0.661738735749595
378.18.65725176895145-0.557251768951453
3888.28109868847814-0.281098688478142
397.98.07212475488186-0.172124754881858
407.98.05122736152223-0.151227361522230
4188.09302214824149-0.0930221482414869
4287.967637788083720.0323622119162833
437.97.73776646112780.162233538872196
4487.675074281048920.324925718951081
457.77.507895134171890.192104865828108
467.27.29892120057561-0.0989212005756086
477.57.82135603456632-0.321356034566318
487.37.8840482146452-0.584048214645203
4977.54968992089115-0.549689920891149
5077.42430556073338-0.424305560733379
5177.25712641385635-0.257126413856352
527.27.36161338065449-0.161613380654494
537.37.42430556073338-0.124305560733379
547.17.29892120057561-0.198921200575609
556.87.13174205369858-0.331742053698582
566.47.11084466033895-0.710844660338953
576.16.86007594002341-0.760075940023413
586.56.96456290682155-0.464562906821555
597.77.445202954093010.254797045906993
607.97.445202954093010.454797045906993
617.57.194434233777470.305565766222533
626.97.11084466033895-0.210844660338953
636.67.11084466033895-0.510844660338954
646.97.36161338065449-0.461613380654494
657.77.570587314250780.129412685749223
6687.675074281048920.324925718951081
6787.758663854487430.241336145512567
687.77.80045864120669-0.100458641206690
697.37.67507428104892-0.375074281048920
707.47.86315082128557-0.463150821285574
718.18.34379086855703-0.243790868557028
728.38.40648304863591-0.106483048635912
738.28.134816934960740.0651830650392556







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.09681848603471280.1936369720694260.903181513965287
60.04277189701201030.08554379402402050.95722810298799
70.1713098878038640.3426197756077290.828690112196136
80.8349865401285990.3300269197428030.165013459871402
90.9855078436937060.02898431261258810.0144921563062940
100.9930951962467270.01380960750654660.0069048037532733
110.9965980526926740.006803894614652780.00340194730732639
120.9994316994392480.001136601121503790.000568300560751897
130.9999096891562650.0001806216874693259.03108437346625e-05
140.9999271124900730.0001457750198540417.28875099270207e-05
150.9999477834528970.0001044330942053615.22165471026804e-05
160.9999391774656780.0001216450686432366.08225343216179e-05
170.9998911929787690.0002176140424626600.000108807021231330
180.9998064998051390.0003870003897229280.000193500194861464
190.999658261655320.0006834766893598480.000341738344679924
200.9995079759815520.0009840480368951510.000492024018447575
210.9993641760536860.001271647892627840.00063582394631392
220.9995078041952660.0009843916094685220.000492195804734261
230.999083900133990.001832199732019060.000916099866009532
240.9984331804198510.003133639160297380.00156681958014869
250.9974367172587780.005126565482443310.00256328274122165
260.9957477882273750.008504423545250820.00425221177262541
270.9933334688055350.01333306238893070.00666653119446535
280.9895545209687850.02089095806242980.0104454790312149
290.985747045445220.02850590910955830.0142529545547792
300.9816248803577530.03675023928449480.0183751196422474
310.9764195458720630.04716090825587490.0235804541279375
320.969574173340010.06085165331998050.0304258266599903
330.9584802052124310.08303958957513710.0415197947875686
340.9575914846216360.08481703075672840.0424085153783642
350.9569578506468270.08608429870634630.0430421493531731
360.9688225677557630.06235486448847390.0311774322442369
370.9771411677999870.04571766440002560.0228588322000128
380.9744839683639970.05103206327200570.0255160316360029
390.969342687022460.06131462595508110.0306573129775405
400.9623076953506530.07538460929869340.0376923046493467
410.9501832589818950.09963348203620920.0498167410181046
420.9339939992730740.1320120014538510.0660060007269256
430.9240111499492780.1519777001014440.0759888500507219
440.9291209995139790.1417580009720420.0708790004860211
450.9321384753572710.1357230492854580.0678615246427292
460.9362485495154590.1275029009690830.0637514504845413
470.93233743395310.1353251320937980.0676625660468992
480.9590895876164180.08182082476716470.0409104123835823
490.9727656918980090.05446861620398230.0272343081019912
500.9733600117519660.05327997649606760.0266399882480338
510.9654588464003010.06908230719939770.0345411535996988
520.9513018558522580.0973962882954840.048698144147742
530.930777060580340.138445878839320.06922293941966
540.9051016068050260.1897967863899470.0948983931949737
550.8779623684221580.2440752631556840.122037631577842
560.9068090971797630.1863818056404740.0931909028202371
570.9427373318215740.1145253363568510.0572626681784256
580.941559040051110.1168819198977820.0584409599488909
590.9270737876056360.1458524247887290.0729262123943645
600.9515101593915250.09697968121695070.0484898406084754
610.9649247450654020.07015050986919680.0350752549345984
620.9377281842797020.1245436314405950.0622718157202977
630.9260137323369150.1479725353261700.0739862676630851
640.9394666160612880.1210667678774230.0605333839387117
650.8923201430086650.2153597139826690.107679856991335
660.9072542895394470.1854914209211060.0927457104605532
670.9556986761058480.08860264778830490.0443013238941524
680.9219715360940290.1560569278119430.0780284639059714

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0968184860347128 & 0.193636972069426 & 0.903181513965287 \tabularnewline
6 & 0.0427718970120103 & 0.0855437940240205 & 0.95722810298799 \tabularnewline
7 & 0.171309887803864 & 0.342619775607729 & 0.828690112196136 \tabularnewline
8 & 0.834986540128599 & 0.330026919742803 & 0.165013459871402 \tabularnewline
9 & 0.985507843693706 & 0.0289843126125881 & 0.0144921563062940 \tabularnewline
10 & 0.993095196246727 & 0.0138096075065466 & 0.0069048037532733 \tabularnewline
11 & 0.996598052692674 & 0.00680389461465278 & 0.00340194730732639 \tabularnewline
12 & 0.999431699439248 & 0.00113660112150379 & 0.000568300560751897 \tabularnewline
13 & 0.999909689156265 & 0.000180621687469325 & 9.03108437346625e-05 \tabularnewline
14 & 0.999927112490073 & 0.000145775019854041 & 7.28875099270207e-05 \tabularnewline
15 & 0.999947783452897 & 0.000104433094205361 & 5.22165471026804e-05 \tabularnewline
16 & 0.999939177465678 & 0.000121645068643236 & 6.08225343216179e-05 \tabularnewline
17 & 0.999891192978769 & 0.000217614042462660 & 0.000108807021231330 \tabularnewline
18 & 0.999806499805139 & 0.000387000389722928 & 0.000193500194861464 \tabularnewline
19 & 0.99965826165532 & 0.000683476689359848 & 0.000341738344679924 \tabularnewline
20 & 0.999507975981552 & 0.000984048036895151 & 0.000492024018447575 \tabularnewline
21 & 0.999364176053686 & 0.00127164789262784 & 0.00063582394631392 \tabularnewline
22 & 0.999507804195266 & 0.000984391609468522 & 0.000492195804734261 \tabularnewline
23 & 0.99908390013399 & 0.00183219973201906 & 0.000916099866009532 \tabularnewline
24 & 0.998433180419851 & 0.00313363916029738 & 0.00156681958014869 \tabularnewline
25 & 0.997436717258778 & 0.00512656548244331 & 0.00256328274122165 \tabularnewline
26 & 0.995747788227375 & 0.00850442354525082 & 0.00425221177262541 \tabularnewline
27 & 0.993333468805535 & 0.0133330623889307 & 0.00666653119446535 \tabularnewline
28 & 0.989554520968785 & 0.0208909580624298 & 0.0104454790312149 \tabularnewline
29 & 0.98574704544522 & 0.0285059091095583 & 0.0142529545547792 \tabularnewline
30 & 0.981624880357753 & 0.0367502392844948 & 0.0183751196422474 \tabularnewline
31 & 0.976419545872063 & 0.0471609082558749 & 0.0235804541279375 \tabularnewline
32 & 0.96957417334001 & 0.0608516533199805 & 0.0304258266599903 \tabularnewline
33 & 0.958480205212431 & 0.0830395895751371 & 0.0415197947875686 \tabularnewline
34 & 0.957591484621636 & 0.0848170307567284 & 0.0424085153783642 \tabularnewline
35 & 0.956957850646827 & 0.0860842987063463 & 0.0430421493531731 \tabularnewline
36 & 0.968822567755763 & 0.0623548644884739 & 0.0311774322442369 \tabularnewline
37 & 0.977141167799987 & 0.0457176644000256 & 0.0228588322000128 \tabularnewline
38 & 0.974483968363997 & 0.0510320632720057 & 0.0255160316360029 \tabularnewline
39 & 0.96934268702246 & 0.0613146259550811 & 0.0306573129775405 \tabularnewline
40 & 0.962307695350653 & 0.0753846092986934 & 0.0376923046493467 \tabularnewline
41 & 0.950183258981895 & 0.0996334820362092 & 0.0498167410181046 \tabularnewline
42 & 0.933993999273074 & 0.132012001453851 & 0.0660060007269256 \tabularnewline
43 & 0.924011149949278 & 0.151977700101444 & 0.0759888500507219 \tabularnewline
44 & 0.929120999513979 & 0.141758000972042 & 0.0708790004860211 \tabularnewline
45 & 0.932138475357271 & 0.135723049285458 & 0.0678615246427292 \tabularnewline
46 & 0.936248549515459 & 0.127502900969083 & 0.0637514504845413 \tabularnewline
47 & 0.9323374339531 & 0.135325132093798 & 0.0676625660468992 \tabularnewline
48 & 0.959089587616418 & 0.0818208247671647 & 0.0409104123835823 \tabularnewline
49 & 0.972765691898009 & 0.0544686162039823 & 0.0272343081019912 \tabularnewline
50 & 0.973360011751966 & 0.0532799764960676 & 0.0266399882480338 \tabularnewline
51 & 0.965458846400301 & 0.0690823071993977 & 0.0345411535996988 \tabularnewline
52 & 0.951301855852258 & 0.097396288295484 & 0.048698144147742 \tabularnewline
53 & 0.93077706058034 & 0.13844587883932 & 0.06922293941966 \tabularnewline
54 & 0.905101606805026 & 0.189796786389947 & 0.0948983931949737 \tabularnewline
55 & 0.877962368422158 & 0.244075263155684 & 0.122037631577842 \tabularnewline
56 & 0.906809097179763 & 0.186381805640474 & 0.0931909028202371 \tabularnewline
57 & 0.942737331821574 & 0.114525336356851 & 0.0572626681784256 \tabularnewline
58 & 0.94155904005111 & 0.116881919897782 & 0.0584409599488909 \tabularnewline
59 & 0.927073787605636 & 0.145852424788729 & 0.0729262123943645 \tabularnewline
60 & 0.951510159391525 & 0.0969796812169507 & 0.0484898406084754 \tabularnewline
61 & 0.964924745065402 & 0.0701505098691968 & 0.0350752549345984 \tabularnewline
62 & 0.937728184279702 & 0.124543631440595 & 0.0622718157202977 \tabularnewline
63 & 0.926013732336915 & 0.147972535326170 & 0.0739862676630851 \tabularnewline
64 & 0.939466616061288 & 0.121066767877423 & 0.0605333839387117 \tabularnewline
65 & 0.892320143008665 & 0.215359713982669 & 0.107679856991335 \tabularnewline
66 & 0.907254289539447 & 0.185491420921106 & 0.0927457104605532 \tabularnewline
67 & 0.955698676105848 & 0.0886026477883049 & 0.0443013238941524 \tabularnewline
68 & 0.921971536094029 & 0.156056927811943 & 0.0780284639059714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57662&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]5[/C][C]0.0968184860347128[/C][C]0.193636972069426[/C][C]0.903181513965287[/C][/ROW]
[ROW][C]6[/C][C]0.0427718970120103[/C][C]0.0855437940240205[/C][C]0.95722810298799[/C][/ROW]
[ROW][C]7[/C][C]0.171309887803864[/C][C]0.342619775607729[/C][C]0.828690112196136[/C][/ROW]
[ROW][C]8[/C][C]0.834986540128599[/C][C]0.330026919742803[/C][C]0.165013459871402[/C][/ROW]
[ROW][C]9[/C][C]0.985507843693706[/C][C]0.0289843126125881[/C][C]0.0144921563062940[/C][/ROW]
[ROW][C]10[/C][C]0.993095196246727[/C][C]0.0138096075065466[/C][C]0.0069048037532733[/C][/ROW]
[ROW][C]11[/C][C]0.996598052692674[/C][C]0.00680389461465278[/C][C]0.00340194730732639[/C][/ROW]
[ROW][C]12[/C][C]0.999431699439248[/C][C]0.00113660112150379[/C][C]0.000568300560751897[/C][/ROW]
[ROW][C]13[/C][C]0.999909689156265[/C][C]0.000180621687469325[/C][C]9.03108437346625e-05[/C][/ROW]
[ROW][C]14[/C][C]0.999927112490073[/C][C]0.000145775019854041[/C][C]7.28875099270207e-05[/C][/ROW]
[ROW][C]15[/C][C]0.999947783452897[/C][C]0.000104433094205361[/C][C]5.22165471026804e-05[/C][/ROW]
[ROW][C]16[/C][C]0.999939177465678[/C][C]0.000121645068643236[/C][C]6.08225343216179e-05[/C][/ROW]
[ROW][C]17[/C][C]0.999891192978769[/C][C]0.000217614042462660[/C][C]0.000108807021231330[/C][/ROW]
[ROW][C]18[/C][C]0.999806499805139[/C][C]0.000387000389722928[/C][C]0.000193500194861464[/C][/ROW]
[ROW][C]19[/C][C]0.99965826165532[/C][C]0.000683476689359848[/C][C]0.000341738344679924[/C][/ROW]
[ROW][C]20[/C][C]0.999507975981552[/C][C]0.000984048036895151[/C][C]0.000492024018447575[/C][/ROW]
[ROW][C]21[/C][C]0.999364176053686[/C][C]0.00127164789262784[/C][C]0.00063582394631392[/C][/ROW]
[ROW][C]22[/C][C]0.999507804195266[/C][C]0.000984391609468522[/C][C]0.000492195804734261[/C][/ROW]
[ROW][C]23[/C][C]0.99908390013399[/C][C]0.00183219973201906[/C][C]0.000916099866009532[/C][/ROW]
[ROW][C]24[/C][C]0.998433180419851[/C][C]0.00313363916029738[/C][C]0.00156681958014869[/C][/ROW]
[ROW][C]25[/C][C]0.997436717258778[/C][C]0.00512656548244331[/C][C]0.00256328274122165[/C][/ROW]
[ROW][C]26[/C][C]0.995747788227375[/C][C]0.00850442354525082[/C][C]0.00425221177262541[/C][/ROW]
[ROW][C]27[/C][C]0.993333468805535[/C][C]0.0133330623889307[/C][C]0.00666653119446535[/C][/ROW]
[ROW][C]28[/C][C]0.989554520968785[/C][C]0.0208909580624298[/C][C]0.0104454790312149[/C][/ROW]
[ROW][C]29[/C][C]0.98574704544522[/C][C]0.0285059091095583[/C][C]0.0142529545547792[/C][/ROW]
[ROW][C]30[/C][C]0.981624880357753[/C][C]0.0367502392844948[/C][C]0.0183751196422474[/C][/ROW]
[ROW][C]31[/C][C]0.976419545872063[/C][C]0.0471609082558749[/C][C]0.0235804541279375[/C][/ROW]
[ROW][C]32[/C][C]0.96957417334001[/C][C]0.0608516533199805[/C][C]0.0304258266599903[/C][/ROW]
[ROW][C]33[/C][C]0.958480205212431[/C][C]0.0830395895751371[/C][C]0.0415197947875686[/C][/ROW]
[ROW][C]34[/C][C]0.957591484621636[/C][C]0.0848170307567284[/C][C]0.0424085153783642[/C][/ROW]
[ROW][C]35[/C][C]0.956957850646827[/C][C]0.0860842987063463[/C][C]0.0430421493531731[/C][/ROW]
[ROW][C]36[/C][C]0.968822567755763[/C][C]0.0623548644884739[/C][C]0.0311774322442369[/C][/ROW]
[ROW][C]37[/C][C]0.977141167799987[/C][C]0.0457176644000256[/C][C]0.0228588322000128[/C][/ROW]
[ROW][C]38[/C][C]0.974483968363997[/C][C]0.0510320632720057[/C][C]0.0255160316360029[/C][/ROW]
[ROW][C]39[/C][C]0.96934268702246[/C][C]0.0613146259550811[/C][C]0.0306573129775405[/C][/ROW]
[ROW][C]40[/C][C]0.962307695350653[/C][C]0.0753846092986934[/C][C]0.0376923046493467[/C][/ROW]
[ROW][C]41[/C][C]0.950183258981895[/C][C]0.0996334820362092[/C][C]0.0498167410181046[/C][/ROW]
[ROW][C]42[/C][C]0.933993999273074[/C][C]0.132012001453851[/C][C]0.0660060007269256[/C][/ROW]
[ROW][C]43[/C][C]0.924011149949278[/C][C]0.151977700101444[/C][C]0.0759888500507219[/C][/ROW]
[ROW][C]44[/C][C]0.929120999513979[/C][C]0.141758000972042[/C][C]0.0708790004860211[/C][/ROW]
[ROW][C]45[/C][C]0.932138475357271[/C][C]0.135723049285458[/C][C]0.0678615246427292[/C][/ROW]
[ROW][C]46[/C][C]0.936248549515459[/C][C]0.127502900969083[/C][C]0.0637514504845413[/C][/ROW]
[ROW][C]47[/C][C]0.9323374339531[/C][C]0.135325132093798[/C][C]0.0676625660468992[/C][/ROW]
[ROW][C]48[/C][C]0.959089587616418[/C][C]0.0818208247671647[/C][C]0.0409104123835823[/C][/ROW]
[ROW][C]49[/C][C]0.972765691898009[/C][C]0.0544686162039823[/C][C]0.0272343081019912[/C][/ROW]
[ROW][C]50[/C][C]0.973360011751966[/C][C]0.0532799764960676[/C][C]0.0266399882480338[/C][/ROW]
[ROW][C]51[/C][C]0.965458846400301[/C][C]0.0690823071993977[/C][C]0.0345411535996988[/C][/ROW]
[ROW][C]52[/C][C]0.951301855852258[/C][C]0.097396288295484[/C][C]0.048698144147742[/C][/ROW]
[ROW][C]53[/C][C]0.93077706058034[/C][C]0.13844587883932[/C][C]0.06922293941966[/C][/ROW]
[ROW][C]54[/C][C]0.905101606805026[/C][C]0.189796786389947[/C][C]0.0948983931949737[/C][/ROW]
[ROW][C]55[/C][C]0.877962368422158[/C][C]0.244075263155684[/C][C]0.122037631577842[/C][/ROW]
[ROW][C]56[/C][C]0.906809097179763[/C][C]0.186381805640474[/C][C]0.0931909028202371[/C][/ROW]
[ROW][C]57[/C][C]0.942737331821574[/C][C]0.114525336356851[/C][C]0.0572626681784256[/C][/ROW]
[ROW][C]58[/C][C]0.94155904005111[/C][C]0.116881919897782[/C][C]0.0584409599488909[/C][/ROW]
[ROW][C]59[/C][C]0.927073787605636[/C][C]0.145852424788729[/C][C]0.0729262123943645[/C][/ROW]
[ROW][C]60[/C][C]0.951510159391525[/C][C]0.0969796812169507[/C][C]0.0484898406084754[/C][/ROW]
[ROW][C]61[/C][C]0.964924745065402[/C][C]0.0701505098691968[/C][C]0.0350752549345984[/C][/ROW]
[ROW][C]62[/C][C]0.937728184279702[/C][C]0.124543631440595[/C][C]0.0622718157202977[/C][/ROW]
[ROW][C]63[/C][C]0.926013732336915[/C][C]0.147972535326170[/C][C]0.0739862676630851[/C][/ROW]
[ROW][C]64[/C][C]0.939466616061288[/C][C]0.121066767877423[/C][C]0.0605333839387117[/C][/ROW]
[ROW][C]65[/C][C]0.892320143008665[/C][C]0.215359713982669[/C][C]0.107679856991335[/C][/ROW]
[ROW][C]66[/C][C]0.907254289539447[/C][C]0.185491420921106[/C][C]0.0927457104605532[/C][/ROW]
[ROW][C]67[/C][C]0.955698676105848[/C][C]0.0886026477883049[/C][C]0.0443013238941524[/C][/ROW]
[ROW][C]68[/C][C]0.921971536094029[/C][C]0.156056927811943[/C][C]0.0780284639059714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57662&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57662&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.09681848603471280.1936369720694260.903181513965287
60.04277189701201030.08554379402402050.95722810298799
70.1713098878038640.3426197756077290.828690112196136
80.8349865401285990.3300269197428030.165013459871402
90.9855078436937060.02898431261258810.0144921563062940
100.9930951962467270.01380960750654660.0069048037532733
110.9965980526926740.006803894614652780.00340194730732639
120.9994316994392480.001136601121503790.000568300560751897
130.9999096891562650.0001806216874693259.03108437346625e-05
140.9999271124900730.0001457750198540417.28875099270207e-05
150.9999477834528970.0001044330942053615.22165471026804e-05
160.9999391774656780.0001216450686432366.08225343216179e-05
170.9998911929787690.0002176140424626600.000108807021231330
180.9998064998051390.0003870003897229280.000193500194861464
190.999658261655320.0006834766893598480.000341738344679924
200.9995079759815520.0009840480368951510.000492024018447575
210.9993641760536860.001271647892627840.00063582394631392
220.9995078041952660.0009843916094685220.000492195804734261
230.999083900133990.001832199732019060.000916099866009532
240.9984331804198510.003133639160297380.00156681958014869
250.9974367172587780.005126565482443310.00256328274122165
260.9957477882273750.008504423545250820.00425221177262541
270.9933334688055350.01333306238893070.00666653119446535
280.9895545209687850.02089095806242980.0104454790312149
290.985747045445220.02850590910955830.0142529545547792
300.9816248803577530.03675023928449480.0183751196422474
310.9764195458720630.04716090825587490.0235804541279375
320.969574173340010.06085165331998050.0304258266599903
330.9584802052124310.08303958957513710.0415197947875686
340.9575914846216360.08481703075672840.0424085153783642
350.9569578506468270.08608429870634630.0430421493531731
360.9688225677557630.06235486448847390.0311774322442369
370.9771411677999870.04571766440002560.0228588322000128
380.9744839683639970.05103206327200570.0255160316360029
390.969342687022460.06131462595508110.0306573129775405
400.9623076953506530.07538460929869340.0376923046493467
410.9501832589818950.09963348203620920.0498167410181046
420.9339939992730740.1320120014538510.0660060007269256
430.9240111499492780.1519777001014440.0759888500507219
440.9291209995139790.1417580009720420.0708790004860211
450.9321384753572710.1357230492854580.0678615246427292
460.9362485495154590.1275029009690830.0637514504845413
470.93233743395310.1353251320937980.0676625660468992
480.9590895876164180.08182082476716470.0409104123835823
490.9727656918980090.05446861620398230.0272343081019912
500.9733600117519660.05327997649606760.0266399882480338
510.9654588464003010.06908230719939770.0345411535996988
520.9513018558522580.0973962882954840.048698144147742
530.930777060580340.138445878839320.06922293941966
540.9051016068050260.1897967863899470.0948983931949737
550.8779623684221580.2440752631556840.122037631577842
560.9068090971797630.1863818056404740.0931909028202371
570.9427373318215740.1145253363568510.0572626681784256
580.941559040051110.1168819198977820.0584409599488909
590.9270737876056360.1458524247887290.0729262123943645
600.9515101593915250.09697968121695070.0484898406084754
610.9649247450654020.07015050986919680.0350752549345984
620.9377281842797020.1245436314405950.0622718157202977
630.9260137323369150.1479725353261700.0739862676630851
640.9394666160612880.1210667678774230.0605333839387117
650.8923201430086650.2153597139826690.107679856991335
660.9072542895394470.1854914209211060.0927457104605532
670.9556986761058480.08860264778830490.0443013238941524
680.9219715360940290.1560569278119430.0780284639059714







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.25NOK
5% type I error level240.375NOK
10% type I error level420.65625NOK

\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 & 16 & 0.25 & NOK \tabularnewline
5% type I error level & 24 & 0.375 & NOK \tabularnewline
10% type I error level & 42 & 0.65625 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57662&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]16[/C][C]0.25[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]24[/C][C]0.375[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]42[/C][C]0.65625[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57662&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57662&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.25NOK
5% type I error level240.375NOK
10% type I error level420.65625NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}