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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 30 Nov 2010 16:26:03 +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/Nov/30/t12911342673li956fm190xkok.htm/, Retrieved Thu, 31 Oct 2024 23:53:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103660, Retrieved Thu, 31 Oct 2024 23:53:23 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact186
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
37	0
30	0
47	0
35	0
30	0
43	0
82	0
40	0
47	0
19	0
52	0
136	0
80	0
42	0
54	0
66	0
81	0
63	0
137	0
72	0
107	0
58	0
36	0
52	0
79	0
77	0
54	0
84	0
48	0
96	0
83	0
66	0
61	0
53	0
30	0
74	0
69	0
59	0
42	0
65	0
70	0
100	0
63	0
105	0
82	0
81	0
75	0
102	0
121	1
98	1
76	1
77	1
63	1
37	1
35	1
23	1
40	1
29	1
37	1
51	1
20	1
28	1
13	1
22	1
25	1
13	1
16	1
13	1
16	1
17	1
9	1
17	1
25	1
14	1
8	1
7	1
10	1
7	1
10	1
3	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103660&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103660&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103660&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Multiple Linear Regression - Estimated Regression Equation
y[t] = + 84.7854609929078 -33.9867021276596x[t] -7.365142688281M1[t] -19.1876055386694M2[t] -26.8672112462006M3[t] -17.9753883823033M4[t] -22.0835655184059M5[t] -17.4774569402229M6[t] -7.87134836203985M7[t] -22.6938112124282M8[t] -13.2707066869301M9[t] -29.2360266801756M10[t] -32.2013466734211M11[t] -0.0346800067544744t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  84.7854609929078 -33.9867021276596x[t] -7.365142688281M1[t] -19.1876055386694M2[t] -26.8672112462006M3[t] -17.9753883823033M4[t] -22.0835655184059M5[t] -17.4774569402229M6[t] -7.87134836203985M7[t] -22.6938112124282M8[t] -13.2707066869301M9[t] -29.2360266801756M10[t] -32.2013466734211M11[t] -0.0346800067544744t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103660&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  84.7854609929078 -33.9867021276596x[t] -7.365142688281M1[t] -19.1876055386694M2[t] -26.8672112462006M3[t] -17.9753883823033M4[t] -22.0835655184059M5[t] -17.4774569402229M6[t] -7.87134836203985M7[t] -22.6938112124282M8[t] -13.2707066869301M9[t] -29.2360266801756M10[t] -32.2013466734211M11[t] -0.0346800067544744t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103660&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103660&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
y[t] = + 84.7854609929078 -33.9867021276596x[t] -7.365142688281M1[t] -19.1876055386694M2[t] -26.8672112462006M3[t] -17.9753883823033M4[t] -22.0835655184059M5[t] -17.4774569402229M6[t] -7.87134836203985M7[t] -22.6938112124282M8[t] -13.2707066869301M9[t] -29.2360266801756M10[t] -32.2013466734211M11[t] -0.0346800067544744t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)84.785460992907813.6252216.222700
x-33.986702127659612.189533-2.78820.0069180.003459
M1-7.36514268828115.519574-0.47460.6366590.31833
M2-19.187605538669415.483608-1.23920.2196530.109826
M3-26.867211246200615.451885-1.73880.0867390.043369
M4-17.975388382303315.424432-1.16540.2480580.124029
M5-22.083565518405915.40127-1.43390.1563280.078164
M6-17.477456940222915.382421-1.13620.2599830.129992
M7-7.8713483620398515.367898-0.51220.6102250.305112
M8-22.693811212428215.357716-1.47770.1442490.072125
M9-13.270706686930115.936455-0.83270.4080020.204001
M10-29.236026680175615.925964-1.83570.0709010.03545
M11-32.201346673421115.919667-2.02270.0471550.023577
t-0.03468000675447440.258555-0.13410.8937080.446854

\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) & 84.7854609929078 & 13.625221 & 6.2227 & 0 & 0 \tabularnewline
x & -33.9867021276596 & 12.189533 & -2.7882 & 0.006918 & 0.003459 \tabularnewline
M1 & -7.365142688281 & 15.519574 & -0.4746 & 0.636659 & 0.31833 \tabularnewline
M2 & -19.1876055386694 & 15.483608 & -1.2392 & 0.219653 & 0.109826 \tabularnewline
M3 & -26.8672112462006 & 15.451885 & -1.7388 & 0.086739 & 0.043369 \tabularnewline
M4 & -17.9753883823033 & 15.424432 & -1.1654 & 0.248058 & 0.124029 \tabularnewline
M5 & -22.0835655184059 & 15.40127 & -1.4339 & 0.156328 & 0.078164 \tabularnewline
M6 & -17.4774569402229 & 15.382421 & -1.1362 & 0.259983 & 0.129992 \tabularnewline
M7 & -7.87134836203985 & 15.367898 & -0.5122 & 0.610225 & 0.305112 \tabularnewline
M8 & -22.6938112124282 & 15.357716 & -1.4777 & 0.144249 & 0.072125 \tabularnewline
M9 & -13.2707066869301 & 15.936455 & -0.8327 & 0.408002 & 0.204001 \tabularnewline
M10 & -29.2360266801756 & 15.925964 & -1.8357 & 0.070901 & 0.03545 \tabularnewline
M11 & -32.2013466734211 & 15.919667 & -2.0227 & 0.047155 & 0.023577 \tabularnewline
t & -0.0346800067544744 & 0.258555 & -0.1341 & 0.893708 & 0.446854 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103660&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]84.7854609929078[/C][C]13.625221[/C][C]6.2227[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x[/C][C]-33.9867021276596[/C][C]12.189533[/C][C]-2.7882[/C][C]0.006918[/C][C]0.003459[/C][/ROW]
[ROW][C]M1[/C][C]-7.365142688281[/C][C]15.519574[/C][C]-0.4746[/C][C]0.636659[/C][C]0.31833[/C][/ROW]
[ROW][C]M2[/C][C]-19.1876055386694[/C][C]15.483608[/C][C]-1.2392[/C][C]0.219653[/C][C]0.109826[/C][/ROW]
[ROW][C]M3[/C][C]-26.8672112462006[/C][C]15.451885[/C][C]-1.7388[/C][C]0.086739[/C][C]0.043369[/C][/ROW]
[ROW][C]M4[/C][C]-17.9753883823033[/C][C]15.424432[/C][C]-1.1654[/C][C]0.248058[/C][C]0.124029[/C][/ROW]
[ROW][C]M5[/C][C]-22.0835655184059[/C][C]15.40127[/C][C]-1.4339[/C][C]0.156328[/C][C]0.078164[/C][/ROW]
[ROW][C]M6[/C][C]-17.4774569402229[/C][C]15.382421[/C][C]-1.1362[/C][C]0.259983[/C][C]0.129992[/C][/ROW]
[ROW][C]M7[/C][C]-7.87134836203985[/C][C]15.367898[/C][C]-0.5122[/C][C]0.610225[/C][C]0.305112[/C][/ROW]
[ROW][C]M8[/C][C]-22.6938112124282[/C][C]15.357716[/C][C]-1.4777[/C][C]0.144249[/C][C]0.072125[/C][/ROW]
[ROW][C]M9[/C][C]-13.2707066869301[/C][C]15.936455[/C][C]-0.8327[/C][C]0.408002[/C][C]0.204001[/C][/ROW]
[ROW][C]M10[/C][C]-29.2360266801756[/C][C]15.925964[/C][C]-1.8357[/C][C]0.070901[/C][C]0.03545[/C][/ROW]
[ROW][C]M11[/C][C]-32.2013466734211[/C][C]15.919667[/C][C]-2.0227[/C][C]0.047155[/C][C]0.023577[/C][/ROW]
[ROW][C]t[/C][C]-0.0346800067544744[/C][C]0.258555[/C][C]-0.1341[/C][C]0.893708[/C][C]0.446854[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103660&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103660&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)84.785460992907813.6252216.222700
x-33.986702127659612.189533-2.78820.0069180.003459
M1-7.36514268828115.519574-0.47460.6366590.31833
M2-19.187605538669415.483608-1.23920.2196530.109826
M3-26.867211246200615.451885-1.73880.0867390.043369
M4-17.975388382303315.424432-1.16540.2480580.124029
M5-22.083565518405915.40127-1.43390.1563280.078164
M6-17.477456940222915.382421-1.13620.2599830.129992
M7-7.8713483620398515.367898-0.51220.6102250.305112
M8-22.693811212428215.357716-1.47770.1442490.072125
M9-13.270706686930115.936455-0.83270.4080020.204001
M10-29.236026680175615.925964-1.83570.0709010.03545
M11-32.201346673421115.919667-2.02270.0471550.023577
t-0.03468000675447440.258555-0.13410.8937080.446854







Multiple Linear Regression - Regression Statistics
Multiple R0.613762687419943
R-squared0.376704636468951
Adjusted R-squared0.253934337591623
F-TEST (value)3.06836946650553
F-TEST (DF numerator)13
F-TEST (DF denominator)66
p-value0.00135859852503972
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27.5700345856201
Sum Squared Residuals50167.0492654509

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.613762687419943 \tabularnewline
R-squared & 0.376704636468951 \tabularnewline
Adjusted R-squared & 0.253934337591623 \tabularnewline
F-TEST (value) & 3.06836946650553 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 66 \tabularnewline
p-value & 0.00135859852503972 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 27.5700345856201 \tabularnewline
Sum Squared Residuals & 50167.0492654509 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103660&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.613762687419943[/C][/ROW]
[ROW][C]R-squared[/C][C]0.376704636468951[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.253934337591623[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.06836946650553[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]66[/C][/ROW]
[ROW][C]p-value[/C][C]0.00135859852503972[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]27.5700345856201[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]50167.0492654509[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103660&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103660&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 R0.613762687419943
R-squared0.376704636468951
Adjusted R-squared0.253934337591623
F-TEST (value)3.06836946650553
F-TEST (DF numerator)13
F-TEST (DF denominator)66
p-value0.00135859852503972
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation27.5700345856201
Sum Squared Residuals50167.0492654509







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13777.3856382978724-40.3856382978724
23065.5284954407294-35.5284954407295
34757.8142097264437-10.8142097264438
43566.6713525835866-31.6713525835866
53062.5284954407295-32.5284954407295
64367.099924012158-24.0999240121581
78276.67135258358665.32864741641338
84061.8142097264438-21.8142097264438
94771.2026342451874-24.2026342451874
101955.2026342451874-36.2026342451874
115252.2026342451874-0.202634245187440
1213684.36930091185451.6306990881459
138076.96947821681863.03052178318137
144265.1123353596758-23.1123353596758
155457.3980496453901-3.39804964539007
166666.2551925025329-0.255192502532925
178162.112335359675818.8876646403242
186366.6837639311044-3.68376393110436
1913776.25519250253360.744807497467
207261.398049645390110.6019503546099
2110770.786474164133736.2135258358663
225854.78647416413373.21352583586626
233651.7864741641337-15.7864741641337
245283.9531408308004-31.9531408308004
257976.5533181357652.44668186423506
267764.696175278622112.3038247213779
275456.9818895643364-2.98188956433638
288465.839032421479218.1609675785208
294861.6961752786221-13.6961752786221
309666.267603850050729.7323961499493
318375.83903242147927.16096757852078
326660.98188956433645.01811043566362
336170.37031408308-9.37031408308004
345354.37031408308-1.37031408308004
353051.37031408308-21.3703140830800
367483.5369807497467-9.53698074974671
376976.1371580547112-7.13715805471124
385964.2800151975684-5.2800151975684
394256.5657294832827-14.5657294832827
406565.4228723404255-0.42287234042554
417061.28001519756848.7199848024316
4210065.85144376899734.1485562310030
436375.4228723404255-12.4228723404255
4410560.565729483282744.4342705167173
458269.954154002026312.0458459979737
468153.954154002026427.0458459979736
477550.954154002026324.0458459979737
4810283.12082066869318.879179331307
4912141.734295845997979.265704154002
509829.877152988855168.1228470111449
517622.162867274569453.8371327254306
527731.020010131712245.9799898682878
536326.877152988855136.1228470111449
543731.44858156028375.55141843971632
553541.0200101317122-6.02001013171225
562326.1628672745694-3.16286727456939
574035.55129179331314.44870820668694
582919.55129179331319.44870820668693
593716.551291793313120.4487082066869
605148.71795845997972.28204154002026
612041.3181357649443-21.3181357649443
622829.4609929078014-1.46099290780141
631321.7467071935157-8.74670719351572
642230.6038500506586-8.60385005065856
652526.4609929078014-1.46099290780142
661331.03242147923-18.03242147923
671640.6038500506586-24.6038500506586
681325.7467071935157-12.7467071935157
691635.1351317122594-19.1351317122594
701719.1351317122594-2.13513171225938
71916.1351317122594-7.13513171225938
721748.301798378926-31.3017983789260
732540.9019756838906-15.9019756838906
741429.0448328267477-15.0448328267477
75821.330547112462-13.3305471124620
76730.1876899696049-23.1876899696049
771026.0448328267477-16.0448328267477
78730.6162613981763-23.6162613981763
791040.1876899696049-30.1876899696049
80325.330547112462-22.330547112462

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 37 & 77.3856382978724 & -40.3856382978724 \tabularnewline
2 & 30 & 65.5284954407294 & -35.5284954407295 \tabularnewline
3 & 47 & 57.8142097264437 & -10.8142097264438 \tabularnewline
4 & 35 & 66.6713525835866 & -31.6713525835866 \tabularnewline
5 & 30 & 62.5284954407295 & -32.5284954407295 \tabularnewline
6 & 43 & 67.099924012158 & -24.0999240121581 \tabularnewline
7 & 82 & 76.6713525835866 & 5.32864741641338 \tabularnewline
8 & 40 & 61.8142097264438 & -21.8142097264438 \tabularnewline
9 & 47 & 71.2026342451874 & -24.2026342451874 \tabularnewline
10 & 19 & 55.2026342451874 & -36.2026342451874 \tabularnewline
11 & 52 & 52.2026342451874 & -0.202634245187440 \tabularnewline
12 & 136 & 84.369300911854 & 51.6306990881459 \tabularnewline
13 & 80 & 76.9694782168186 & 3.03052178318137 \tabularnewline
14 & 42 & 65.1123353596758 & -23.1123353596758 \tabularnewline
15 & 54 & 57.3980496453901 & -3.39804964539007 \tabularnewline
16 & 66 & 66.2551925025329 & -0.255192502532925 \tabularnewline
17 & 81 & 62.1123353596758 & 18.8876646403242 \tabularnewline
18 & 63 & 66.6837639311044 & -3.68376393110436 \tabularnewline
19 & 137 & 76.255192502533 & 60.744807497467 \tabularnewline
20 & 72 & 61.3980496453901 & 10.6019503546099 \tabularnewline
21 & 107 & 70.7864741641337 & 36.2135258358663 \tabularnewline
22 & 58 & 54.7864741641337 & 3.21352583586626 \tabularnewline
23 & 36 & 51.7864741641337 & -15.7864741641337 \tabularnewline
24 & 52 & 83.9531408308004 & -31.9531408308004 \tabularnewline
25 & 79 & 76.553318135765 & 2.44668186423506 \tabularnewline
26 & 77 & 64.6961752786221 & 12.3038247213779 \tabularnewline
27 & 54 & 56.9818895643364 & -2.98188956433638 \tabularnewline
28 & 84 & 65.8390324214792 & 18.1609675785208 \tabularnewline
29 & 48 & 61.6961752786221 & -13.6961752786221 \tabularnewline
30 & 96 & 66.2676038500507 & 29.7323961499493 \tabularnewline
31 & 83 & 75.8390324214792 & 7.16096757852078 \tabularnewline
32 & 66 & 60.9818895643364 & 5.01811043566362 \tabularnewline
33 & 61 & 70.37031408308 & -9.37031408308004 \tabularnewline
34 & 53 & 54.37031408308 & -1.37031408308004 \tabularnewline
35 & 30 & 51.37031408308 & -21.3703140830800 \tabularnewline
36 & 74 & 83.5369807497467 & -9.53698074974671 \tabularnewline
37 & 69 & 76.1371580547112 & -7.13715805471124 \tabularnewline
38 & 59 & 64.2800151975684 & -5.2800151975684 \tabularnewline
39 & 42 & 56.5657294832827 & -14.5657294832827 \tabularnewline
40 & 65 & 65.4228723404255 & -0.42287234042554 \tabularnewline
41 & 70 & 61.2800151975684 & 8.7199848024316 \tabularnewline
42 & 100 & 65.851443768997 & 34.1485562310030 \tabularnewline
43 & 63 & 75.4228723404255 & -12.4228723404255 \tabularnewline
44 & 105 & 60.5657294832827 & 44.4342705167173 \tabularnewline
45 & 82 & 69.9541540020263 & 12.0458459979737 \tabularnewline
46 & 81 & 53.9541540020264 & 27.0458459979736 \tabularnewline
47 & 75 & 50.9541540020263 & 24.0458459979737 \tabularnewline
48 & 102 & 83.120820668693 & 18.879179331307 \tabularnewline
49 & 121 & 41.7342958459979 & 79.265704154002 \tabularnewline
50 & 98 & 29.8771529888551 & 68.1228470111449 \tabularnewline
51 & 76 & 22.1628672745694 & 53.8371327254306 \tabularnewline
52 & 77 & 31.0200101317122 & 45.9799898682878 \tabularnewline
53 & 63 & 26.8771529888551 & 36.1228470111449 \tabularnewline
54 & 37 & 31.4485815602837 & 5.55141843971632 \tabularnewline
55 & 35 & 41.0200101317122 & -6.02001013171225 \tabularnewline
56 & 23 & 26.1628672745694 & -3.16286727456939 \tabularnewline
57 & 40 & 35.5512917933131 & 4.44870820668694 \tabularnewline
58 & 29 & 19.5512917933131 & 9.44870820668693 \tabularnewline
59 & 37 & 16.5512917933131 & 20.4487082066869 \tabularnewline
60 & 51 & 48.7179584599797 & 2.28204154002026 \tabularnewline
61 & 20 & 41.3181357649443 & -21.3181357649443 \tabularnewline
62 & 28 & 29.4609929078014 & -1.46099290780141 \tabularnewline
63 & 13 & 21.7467071935157 & -8.74670719351572 \tabularnewline
64 & 22 & 30.6038500506586 & -8.60385005065856 \tabularnewline
65 & 25 & 26.4609929078014 & -1.46099290780142 \tabularnewline
66 & 13 & 31.03242147923 & -18.03242147923 \tabularnewline
67 & 16 & 40.6038500506586 & -24.6038500506586 \tabularnewline
68 & 13 & 25.7467071935157 & -12.7467071935157 \tabularnewline
69 & 16 & 35.1351317122594 & -19.1351317122594 \tabularnewline
70 & 17 & 19.1351317122594 & -2.13513171225938 \tabularnewline
71 & 9 & 16.1351317122594 & -7.13513171225938 \tabularnewline
72 & 17 & 48.301798378926 & -31.3017983789260 \tabularnewline
73 & 25 & 40.9019756838906 & -15.9019756838906 \tabularnewline
74 & 14 & 29.0448328267477 & -15.0448328267477 \tabularnewline
75 & 8 & 21.330547112462 & -13.3305471124620 \tabularnewline
76 & 7 & 30.1876899696049 & -23.1876899696049 \tabularnewline
77 & 10 & 26.0448328267477 & -16.0448328267477 \tabularnewline
78 & 7 & 30.6162613981763 & -23.6162613981763 \tabularnewline
79 & 10 & 40.1876899696049 & -30.1876899696049 \tabularnewline
80 & 3 & 25.330547112462 & -22.330547112462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103660&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]37[/C][C]77.3856382978724[/C][C]-40.3856382978724[/C][/ROW]
[ROW][C]2[/C][C]30[/C][C]65.5284954407294[/C][C]-35.5284954407295[/C][/ROW]
[ROW][C]3[/C][C]47[/C][C]57.8142097264437[/C][C]-10.8142097264438[/C][/ROW]
[ROW][C]4[/C][C]35[/C][C]66.6713525835866[/C][C]-31.6713525835866[/C][/ROW]
[ROW][C]5[/C][C]30[/C][C]62.5284954407295[/C][C]-32.5284954407295[/C][/ROW]
[ROW][C]6[/C][C]43[/C][C]67.099924012158[/C][C]-24.0999240121581[/C][/ROW]
[ROW][C]7[/C][C]82[/C][C]76.6713525835866[/C][C]5.32864741641338[/C][/ROW]
[ROW][C]8[/C][C]40[/C][C]61.8142097264438[/C][C]-21.8142097264438[/C][/ROW]
[ROW][C]9[/C][C]47[/C][C]71.2026342451874[/C][C]-24.2026342451874[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]55.2026342451874[/C][C]-36.2026342451874[/C][/ROW]
[ROW][C]11[/C][C]52[/C][C]52.2026342451874[/C][C]-0.202634245187440[/C][/ROW]
[ROW][C]12[/C][C]136[/C][C]84.369300911854[/C][C]51.6306990881459[/C][/ROW]
[ROW][C]13[/C][C]80[/C][C]76.9694782168186[/C][C]3.03052178318137[/C][/ROW]
[ROW][C]14[/C][C]42[/C][C]65.1123353596758[/C][C]-23.1123353596758[/C][/ROW]
[ROW][C]15[/C][C]54[/C][C]57.3980496453901[/C][C]-3.39804964539007[/C][/ROW]
[ROW][C]16[/C][C]66[/C][C]66.2551925025329[/C][C]-0.255192502532925[/C][/ROW]
[ROW][C]17[/C][C]81[/C][C]62.1123353596758[/C][C]18.8876646403242[/C][/ROW]
[ROW][C]18[/C][C]63[/C][C]66.6837639311044[/C][C]-3.68376393110436[/C][/ROW]
[ROW][C]19[/C][C]137[/C][C]76.255192502533[/C][C]60.744807497467[/C][/ROW]
[ROW][C]20[/C][C]72[/C][C]61.3980496453901[/C][C]10.6019503546099[/C][/ROW]
[ROW][C]21[/C][C]107[/C][C]70.7864741641337[/C][C]36.2135258358663[/C][/ROW]
[ROW][C]22[/C][C]58[/C][C]54.7864741641337[/C][C]3.21352583586626[/C][/ROW]
[ROW][C]23[/C][C]36[/C][C]51.7864741641337[/C][C]-15.7864741641337[/C][/ROW]
[ROW][C]24[/C][C]52[/C][C]83.9531408308004[/C][C]-31.9531408308004[/C][/ROW]
[ROW][C]25[/C][C]79[/C][C]76.553318135765[/C][C]2.44668186423506[/C][/ROW]
[ROW][C]26[/C][C]77[/C][C]64.6961752786221[/C][C]12.3038247213779[/C][/ROW]
[ROW][C]27[/C][C]54[/C][C]56.9818895643364[/C][C]-2.98188956433638[/C][/ROW]
[ROW][C]28[/C][C]84[/C][C]65.8390324214792[/C][C]18.1609675785208[/C][/ROW]
[ROW][C]29[/C][C]48[/C][C]61.6961752786221[/C][C]-13.6961752786221[/C][/ROW]
[ROW][C]30[/C][C]96[/C][C]66.2676038500507[/C][C]29.7323961499493[/C][/ROW]
[ROW][C]31[/C][C]83[/C][C]75.8390324214792[/C][C]7.16096757852078[/C][/ROW]
[ROW][C]32[/C][C]66[/C][C]60.9818895643364[/C][C]5.01811043566362[/C][/ROW]
[ROW][C]33[/C][C]61[/C][C]70.37031408308[/C][C]-9.37031408308004[/C][/ROW]
[ROW][C]34[/C][C]53[/C][C]54.37031408308[/C][C]-1.37031408308004[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]51.37031408308[/C][C]-21.3703140830800[/C][/ROW]
[ROW][C]36[/C][C]74[/C][C]83.5369807497467[/C][C]-9.53698074974671[/C][/ROW]
[ROW][C]37[/C][C]69[/C][C]76.1371580547112[/C][C]-7.13715805471124[/C][/ROW]
[ROW][C]38[/C][C]59[/C][C]64.2800151975684[/C][C]-5.2800151975684[/C][/ROW]
[ROW][C]39[/C][C]42[/C][C]56.5657294832827[/C][C]-14.5657294832827[/C][/ROW]
[ROW][C]40[/C][C]65[/C][C]65.4228723404255[/C][C]-0.42287234042554[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]61.2800151975684[/C][C]8.7199848024316[/C][/ROW]
[ROW][C]42[/C][C]100[/C][C]65.851443768997[/C][C]34.1485562310030[/C][/ROW]
[ROW][C]43[/C][C]63[/C][C]75.4228723404255[/C][C]-12.4228723404255[/C][/ROW]
[ROW][C]44[/C][C]105[/C][C]60.5657294832827[/C][C]44.4342705167173[/C][/ROW]
[ROW][C]45[/C][C]82[/C][C]69.9541540020263[/C][C]12.0458459979737[/C][/ROW]
[ROW][C]46[/C][C]81[/C][C]53.9541540020264[/C][C]27.0458459979736[/C][/ROW]
[ROW][C]47[/C][C]75[/C][C]50.9541540020263[/C][C]24.0458459979737[/C][/ROW]
[ROW][C]48[/C][C]102[/C][C]83.120820668693[/C][C]18.879179331307[/C][/ROW]
[ROW][C]49[/C][C]121[/C][C]41.7342958459979[/C][C]79.265704154002[/C][/ROW]
[ROW][C]50[/C][C]98[/C][C]29.8771529888551[/C][C]68.1228470111449[/C][/ROW]
[ROW][C]51[/C][C]76[/C][C]22.1628672745694[/C][C]53.8371327254306[/C][/ROW]
[ROW][C]52[/C][C]77[/C][C]31.0200101317122[/C][C]45.9799898682878[/C][/ROW]
[ROW][C]53[/C][C]63[/C][C]26.8771529888551[/C][C]36.1228470111449[/C][/ROW]
[ROW][C]54[/C][C]37[/C][C]31.4485815602837[/C][C]5.55141843971632[/C][/ROW]
[ROW][C]55[/C][C]35[/C][C]41.0200101317122[/C][C]-6.02001013171225[/C][/ROW]
[ROW][C]56[/C][C]23[/C][C]26.1628672745694[/C][C]-3.16286727456939[/C][/ROW]
[ROW][C]57[/C][C]40[/C][C]35.5512917933131[/C][C]4.44870820668694[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]19.5512917933131[/C][C]9.44870820668693[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]16.5512917933131[/C][C]20.4487082066869[/C][/ROW]
[ROW][C]60[/C][C]51[/C][C]48.7179584599797[/C][C]2.28204154002026[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]41.3181357649443[/C][C]-21.3181357649443[/C][/ROW]
[ROW][C]62[/C][C]28[/C][C]29.4609929078014[/C][C]-1.46099290780141[/C][/ROW]
[ROW][C]63[/C][C]13[/C][C]21.7467071935157[/C][C]-8.74670719351572[/C][/ROW]
[ROW][C]64[/C][C]22[/C][C]30.6038500506586[/C][C]-8.60385005065856[/C][/ROW]
[ROW][C]65[/C][C]25[/C][C]26.4609929078014[/C][C]-1.46099290780142[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]31.03242147923[/C][C]-18.03242147923[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]40.6038500506586[/C][C]-24.6038500506586[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]25.7467071935157[/C][C]-12.7467071935157[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]35.1351317122594[/C][C]-19.1351317122594[/C][/ROW]
[ROW][C]70[/C][C]17[/C][C]19.1351317122594[/C][C]-2.13513171225938[/C][/ROW]
[ROW][C]71[/C][C]9[/C][C]16.1351317122594[/C][C]-7.13513171225938[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]48.301798378926[/C][C]-31.3017983789260[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]40.9019756838906[/C][C]-15.9019756838906[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]29.0448328267477[/C][C]-15.0448328267477[/C][/ROW]
[ROW][C]75[/C][C]8[/C][C]21.330547112462[/C][C]-13.3305471124620[/C][/ROW]
[ROW][C]76[/C][C]7[/C][C]30.1876899696049[/C][C]-23.1876899696049[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]26.0448328267477[/C][C]-16.0448328267477[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]30.6162613981763[/C][C]-23.6162613981763[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]40.1876899696049[/C][C]-30.1876899696049[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]25.330547112462[/C][C]-22.330547112462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103660&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103660&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
13777.3856382978724-40.3856382978724
23065.5284954407294-35.5284954407295
34757.8142097264437-10.8142097264438
43566.6713525835866-31.6713525835866
53062.5284954407295-32.5284954407295
64367.099924012158-24.0999240121581
78276.67135258358665.32864741641338
84061.8142097264438-21.8142097264438
94771.2026342451874-24.2026342451874
101955.2026342451874-36.2026342451874
115252.2026342451874-0.202634245187440
1213684.36930091185451.6306990881459
138076.96947821681863.03052178318137
144265.1123353596758-23.1123353596758
155457.3980496453901-3.39804964539007
166666.2551925025329-0.255192502532925
178162.112335359675818.8876646403242
186366.6837639311044-3.68376393110436
1913776.25519250253360.744807497467
207261.398049645390110.6019503546099
2110770.786474164133736.2135258358663
225854.78647416413373.21352583586626
233651.7864741641337-15.7864741641337
245283.9531408308004-31.9531408308004
257976.5533181357652.44668186423506
267764.696175278622112.3038247213779
275456.9818895643364-2.98188956433638
288465.839032421479218.1609675785208
294861.6961752786221-13.6961752786221
309666.267603850050729.7323961499493
318375.83903242147927.16096757852078
326660.98188956433645.01811043566362
336170.37031408308-9.37031408308004
345354.37031408308-1.37031408308004
353051.37031408308-21.3703140830800
367483.5369807497467-9.53698074974671
376976.1371580547112-7.13715805471124
385964.2800151975684-5.2800151975684
394256.5657294832827-14.5657294832827
406565.4228723404255-0.42287234042554
417061.28001519756848.7199848024316
4210065.85144376899734.1485562310030
436375.4228723404255-12.4228723404255
4410560.565729483282744.4342705167173
458269.954154002026312.0458459979737
468153.954154002026427.0458459979736
477550.954154002026324.0458459979737
4810283.12082066869318.879179331307
4912141.734295845997979.265704154002
509829.877152988855168.1228470111449
517622.162867274569453.8371327254306
527731.020010131712245.9799898682878
536326.877152988855136.1228470111449
543731.44858156028375.55141843971632
553541.0200101317122-6.02001013171225
562326.1628672745694-3.16286727456939
574035.55129179331314.44870820668694
582919.55129179331319.44870820668693
593716.551291793313120.4487082066869
605148.71795845997972.28204154002026
612041.3181357649443-21.3181357649443
622829.4609929078014-1.46099290780141
631321.7467071935157-8.74670719351572
642230.6038500506586-8.60385005065856
652526.4609929078014-1.46099290780142
661331.03242147923-18.03242147923
671640.6038500506586-24.6038500506586
681325.7467071935157-12.7467071935157
691635.1351317122594-19.1351317122594
701719.1351317122594-2.13513171225938
71916.1351317122594-7.13513171225938
721748.301798378926-31.3017983789260
732540.9019756838906-15.9019756838906
741429.0448328267477-15.0448328267477
75821.330547112462-13.3305471124620
76730.1876899696049-23.1876899696049
771026.0448328267477-16.0448328267477
78730.6162613981763-23.6162613981763
791040.1876899696049-30.1876899696049
80325.330547112462-22.330547112462







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.2550755756348820.5101511512697650.744924424365118
180.1416990770804180.2833981541608370.858300922919582
190.1510504113136590.3021008226273190.84894958868634
200.079751748565080.159503497130160.92024825143492
210.08438419029443650.1687683805888730.915615809705563
220.04730001247989950.0946000249597990.9526999875201
230.1608741209961430.3217482419922860.839125879003857
240.8859264905206330.2281470189587340.114073509479367
250.8485858812252920.3028282375494170.151414118774708
260.801182190028060.3976356199438810.198817809971940
270.7870197091168760.4259605817662470.212980290883124
280.7198158020699430.5603683958601150.280184197930057
290.767450599200790.4650988015984190.232549400799209
300.7155653196378540.5688693607242910.284434680362146
310.7650956895494640.4698086209010710.234904310450536
320.7187680806329050.562463838734190.281231919367095
330.729780549930530.540438900138940.27021945006947
340.7020781541738090.5958436916523820.297921845826191
350.7991519548902560.4016960902194870.200848045109743
360.8205487388538490.3589025222923030.179451261146151
370.827265392176510.3454692156469810.172734607823491
380.8509343718373490.2981312563253020.149065628162651
390.9164887120659180.1670225758681640.0835112879340822
400.9274823733667240.1450352532665520.072517626633276
410.9332770518983390.1334458962033220.0667229481016611
420.914629117903660.1707417641926790.0853708820963394
430.9538101841092670.09237963178146690.0461898158907334
440.953576757670420.09284648465915950.0464232423295797
450.9303140104679170.1393719790641660.0696859895320828
460.9077028016292860.1845943967414290.0922971983707143
470.8824720758130980.2350558483738030.117527924186902
480.834782134511450.3304357309770980.165217865488549
490.9622698503597730.07546029928045480.0377301496402274
500.989098708646490.02180258270702160.0109012913535108
510.9970153420964840.005969315807031520.00298465790351576
520.9998035509787050.0003928980425905880.000196449021295294
530.999954589211539.08215769417236e-054.54107884708618e-05
540.9999462284423930.0001075431152132125.37715576066058e-05
550.9999177084983550.0001645830032893468.22915016446731e-05
560.999821494046880.0003570119062392810.000178505953119641
570.9996332261236440.0007335477527114850.000366773876355743
580.9987957148369370.002408570326126860.00120428516306343
590.9982377312903120.003524537419374990.00176226870968749
600.9997055750391370.0005888499217261480.000294424960863074
610.9998800160189940.0002399679620114290.000119983981005715
620.9993106052809090.001378789438182540.00068939471909127
630.9964651291425930.007069741714814990.00353487085740750

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.255075575634882 & 0.510151151269765 & 0.744924424365118 \tabularnewline
18 & 0.141699077080418 & 0.283398154160837 & 0.858300922919582 \tabularnewline
19 & 0.151050411313659 & 0.302100822627319 & 0.84894958868634 \tabularnewline
20 & 0.07975174856508 & 0.15950349713016 & 0.92024825143492 \tabularnewline
21 & 0.0843841902944365 & 0.168768380588873 & 0.915615809705563 \tabularnewline
22 & 0.0473000124798995 & 0.094600024959799 & 0.9526999875201 \tabularnewline
23 & 0.160874120996143 & 0.321748241992286 & 0.839125879003857 \tabularnewline
24 & 0.885926490520633 & 0.228147018958734 & 0.114073509479367 \tabularnewline
25 & 0.848585881225292 & 0.302828237549417 & 0.151414118774708 \tabularnewline
26 & 0.80118219002806 & 0.397635619943881 & 0.198817809971940 \tabularnewline
27 & 0.787019709116876 & 0.425960581766247 & 0.212980290883124 \tabularnewline
28 & 0.719815802069943 & 0.560368395860115 & 0.280184197930057 \tabularnewline
29 & 0.76745059920079 & 0.465098801598419 & 0.232549400799209 \tabularnewline
30 & 0.715565319637854 & 0.568869360724291 & 0.284434680362146 \tabularnewline
31 & 0.765095689549464 & 0.469808620901071 & 0.234904310450536 \tabularnewline
32 & 0.718768080632905 & 0.56246383873419 & 0.281231919367095 \tabularnewline
33 & 0.72978054993053 & 0.54043890013894 & 0.27021945006947 \tabularnewline
34 & 0.702078154173809 & 0.595843691652382 & 0.297921845826191 \tabularnewline
35 & 0.799151954890256 & 0.401696090219487 & 0.200848045109743 \tabularnewline
36 & 0.820548738853849 & 0.358902522292303 & 0.179451261146151 \tabularnewline
37 & 0.82726539217651 & 0.345469215646981 & 0.172734607823491 \tabularnewline
38 & 0.850934371837349 & 0.298131256325302 & 0.149065628162651 \tabularnewline
39 & 0.916488712065918 & 0.167022575868164 & 0.0835112879340822 \tabularnewline
40 & 0.927482373366724 & 0.145035253266552 & 0.072517626633276 \tabularnewline
41 & 0.933277051898339 & 0.133445896203322 & 0.0667229481016611 \tabularnewline
42 & 0.91462911790366 & 0.170741764192679 & 0.0853708820963394 \tabularnewline
43 & 0.953810184109267 & 0.0923796317814669 & 0.0461898158907334 \tabularnewline
44 & 0.95357675767042 & 0.0928464846591595 & 0.0464232423295797 \tabularnewline
45 & 0.930314010467917 & 0.139371979064166 & 0.0696859895320828 \tabularnewline
46 & 0.907702801629286 & 0.184594396741429 & 0.0922971983707143 \tabularnewline
47 & 0.882472075813098 & 0.235055848373803 & 0.117527924186902 \tabularnewline
48 & 0.83478213451145 & 0.330435730977098 & 0.165217865488549 \tabularnewline
49 & 0.962269850359773 & 0.0754602992804548 & 0.0377301496402274 \tabularnewline
50 & 0.98909870864649 & 0.0218025827070216 & 0.0109012913535108 \tabularnewline
51 & 0.997015342096484 & 0.00596931580703152 & 0.00298465790351576 \tabularnewline
52 & 0.999803550978705 & 0.000392898042590588 & 0.000196449021295294 \tabularnewline
53 & 0.99995458921153 & 9.08215769417236e-05 & 4.54107884708618e-05 \tabularnewline
54 & 0.999946228442393 & 0.000107543115213212 & 5.37715576066058e-05 \tabularnewline
55 & 0.999917708498355 & 0.000164583003289346 & 8.22915016446731e-05 \tabularnewline
56 & 0.99982149404688 & 0.000357011906239281 & 0.000178505953119641 \tabularnewline
57 & 0.999633226123644 & 0.000733547752711485 & 0.000366773876355743 \tabularnewline
58 & 0.998795714836937 & 0.00240857032612686 & 0.00120428516306343 \tabularnewline
59 & 0.998237731290312 & 0.00352453741937499 & 0.00176226870968749 \tabularnewline
60 & 0.999705575039137 & 0.000588849921726148 & 0.000294424960863074 \tabularnewline
61 & 0.999880016018994 & 0.000239967962011429 & 0.000119983981005715 \tabularnewline
62 & 0.999310605280909 & 0.00137878943818254 & 0.00068939471909127 \tabularnewline
63 & 0.996465129142593 & 0.00706974171481499 & 0.00353487085740750 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103660&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]17[/C][C]0.255075575634882[/C][C]0.510151151269765[/C][C]0.744924424365118[/C][/ROW]
[ROW][C]18[/C][C]0.141699077080418[/C][C]0.283398154160837[/C][C]0.858300922919582[/C][/ROW]
[ROW][C]19[/C][C]0.151050411313659[/C][C]0.302100822627319[/C][C]0.84894958868634[/C][/ROW]
[ROW][C]20[/C][C]0.07975174856508[/C][C]0.15950349713016[/C][C]0.92024825143492[/C][/ROW]
[ROW][C]21[/C][C]0.0843841902944365[/C][C]0.168768380588873[/C][C]0.915615809705563[/C][/ROW]
[ROW][C]22[/C][C]0.0473000124798995[/C][C]0.094600024959799[/C][C]0.9526999875201[/C][/ROW]
[ROW][C]23[/C][C]0.160874120996143[/C][C]0.321748241992286[/C][C]0.839125879003857[/C][/ROW]
[ROW][C]24[/C][C]0.885926490520633[/C][C]0.228147018958734[/C][C]0.114073509479367[/C][/ROW]
[ROW][C]25[/C][C]0.848585881225292[/C][C]0.302828237549417[/C][C]0.151414118774708[/C][/ROW]
[ROW][C]26[/C][C]0.80118219002806[/C][C]0.397635619943881[/C][C]0.198817809971940[/C][/ROW]
[ROW][C]27[/C][C]0.787019709116876[/C][C]0.425960581766247[/C][C]0.212980290883124[/C][/ROW]
[ROW][C]28[/C][C]0.719815802069943[/C][C]0.560368395860115[/C][C]0.280184197930057[/C][/ROW]
[ROW][C]29[/C][C]0.76745059920079[/C][C]0.465098801598419[/C][C]0.232549400799209[/C][/ROW]
[ROW][C]30[/C][C]0.715565319637854[/C][C]0.568869360724291[/C][C]0.284434680362146[/C][/ROW]
[ROW][C]31[/C][C]0.765095689549464[/C][C]0.469808620901071[/C][C]0.234904310450536[/C][/ROW]
[ROW][C]32[/C][C]0.718768080632905[/C][C]0.56246383873419[/C][C]0.281231919367095[/C][/ROW]
[ROW][C]33[/C][C]0.72978054993053[/C][C]0.54043890013894[/C][C]0.27021945006947[/C][/ROW]
[ROW][C]34[/C][C]0.702078154173809[/C][C]0.595843691652382[/C][C]0.297921845826191[/C][/ROW]
[ROW][C]35[/C][C]0.799151954890256[/C][C]0.401696090219487[/C][C]0.200848045109743[/C][/ROW]
[ROW][C]36[/C][C]0.820548738853849[/C][C]0.358902522292303[/C][C]0.179451261146151[/C][/ROW]
[ROW][C]37[/C][C]0.82726539217651[/C][C]0.345469215646981[/C][C]0.172734607823491[/C][/ROW]
[ROW][C]38[/C][C]0.850934371837349[/C][C]0.298131256325302[/C][C]0.149065628162651[/C][/ROW]
[ROW][C]39[/C][C]0.916488712065918[/C][C]0.167022575868164[/C][C]0.0835112879340822[/C][/ROW]
[ROW][C]40[/C][C]0.927482373366724[/C][C]0.145035253266552[/C][C]0.072517626633276[/C][/ROW]
[ROW][C]41[/C][C]0.933277051898339[/C][C]0.133445896203322[/C][C]0.0667229481016611[/C][/ROW]
[ROW][C]42[/C][C]0.91462911790366[/C][C]0.170741764192679[/C][C]0.0853708820963394[/C][/ROW]
[ROW][C]43[/C][C]0.953810184109267[/C][C]0.0923796317814669[/C][C]0.0461898158907334[/C][/ROW]
[ROW][C]44[/C][C]0.95357675767042[/C][C]0.0928464846591595[/C][C]0.0464232423295797[/C][/ROW]
[ROW][C]45[/C][C]0.930314010467917[/C][C]0.139371979064166[/C][C]0.0696859895320828[/C][/ROW]
[ROW][C]46[/C][C]0.907702801629286[/C][C]0.184594396741429[/C][C]0.0922971983707143[/C][/ROW]
[ROW][C]47[/C][C]0.882472075813098[/C][C]0.235055848373803[/C][C]0.117527924186902[/C][/ROW]
[ROW][C]48[/C][C]0.83478213451145[/C][C]0.330435730977098[/C][C]0.165217865488549[/C][/ROW]
[ROW][C]49[/C][C]0.962269850359773[/C][C]0.0754602992804548[/C][C]0.0377301496402274[/C][/ROW]
[ROW][C]50[/C][C]0.98909870864649[/C][C]0.0218025827070216[/C][C]0.0109012913535108[/C][/ROW]
[ROW][C]51[/C][C]0.997015342096484[/C][C]0.00596931580703152[/C][C]0.00298465790351576[/C][/ROW]
[ROW][C]52[/C][C]0.999803550978705[/C][C]0.000392898042590588[/C][C]0.000196449021295294[/C][/ROW]
[ROW][C]53[/C][C]0.99995458921153[/C][C]9.08215769417236e-05[/C][C]4.54107884708618e-05[/C][/ROW]
[ROW][C]54[/C][C]0.999946228442393[/C][C]0.000107543115213212[/C][C]5.37715576066058e-05[/C][/ROW]
[ROW][C]55[/C][C]0.999917708498355[/C][C]0.000164583003289346[/C][C]8.22915016446731e-05[/C][/ROW]
[ROW][C]56[/C][C]0.99982149404688[/C][C]0.000357011906239281[/C][C]0.000178505953119641[/C][/ROW]
[ROW][C]57[/C][C]0.999633226123644[/C][C]0.000733547752711485[/C][C]0.000366773876355743[/C][/ROW]
[ROW][C]58[/C][C]0.998795714836937[/C][C]0.00240857032612686[/C][C]0.00120428516306343[/C][/ROW]
[ROW][C]59[/C][C]0.998237731290312[/C][C]0.00352453741937499[/C][C]0.00176226870968749[/C][/ROW]
[ROW][C]60[/C][C]0.999705575039137[/C][C]0.000588849921726148[/C][C]0.000294424960863074[/C][/ROW]
[ROW][C]61[/C][C]0.999880016018994[/C][C]0.000239967962011429[/C][C]0.000119983981005715[/C][/ROW]
[ROW][C]62[/C][C]0.999310605280909[/C][C]0.00137878943818254[/C][C]0.00068939471909127[/C][/ROW]
[ROW][C]63[/C][C]0.996465129142593[/C][C]0.00706974171481499[/C][C]0.00353487085740750[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103660&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103660&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
170.2550755756348820.5101511512697650.744924424365118
180.1416990770804180.2833981541608370.858300922919582
190.1510504113136590.3021008226273190.84894958868634
200.079751748565080.159503497130160.92024825143492
210.08438419029443650.1687683805888730.915615809705563
220.04730001247989950.0946000249597990.9526999875201
230.1608741209961430.3217482419922860.839125879003857
240.8859264905206330.2281470189587340.114073509479367
250.8485858812252920.3028282375494170.151414118774708
260.801182190028060.3976356199438810.198817809971940
270.7870197091168760.4259605817662470.212980290883124
280.7198158020699430.5603683958601150.280184197930057
290.767450599200790.4650988015984190.232549400799209
300.7155653196378540.5688693607242910.284434680362146
310.7650956895494640.4698086209010710.234904310450536
320.7187680806329050.562463838734190.281231919367095
330.729780549930530.540438900138940.27021945006947
340.7020781541738090.5958436916523820.297921845826191
350.7991519548902560.4016960902194870.200848045109743
360.8205487388538490.3589025222923030.179451261146151
370.827265392176510.3454692156469810.172734607823491
380.8509343718373490.2981312563253020.149065628162651
390.9164887120659180.1670225758681640.0835112879340822
400.9274823733667240.1450352532665520.072517626633276
410.9332770518983390.1334458962033220.0667229481016611
420.914629117903660.1707417641926790.0853708820963394
430.9538101841092670.09237963178146690.0461898158907334
440.953576757670420.09284648465915950.0464232423295797
450.9303140104679170.1393719790641660.0696859895320828
460.9077028016292860.1845943967414290.0922971983707143
470.8824720758130980.2350558483738030.117527924186902
480.834782134511450.3304357309770980.165217865488549
490.9622698503597730.07546029928045480.0377301496402274
500.989098708646490.02180258270702160.0109012913535108
510.9970153420964840.005969315807031520.00298465790351576
520.9998035509787050.0003928980425905880.000196449021295294
530.999954589211539.08215769417236e-054.54107884708618e-05
540.9999462284423930.0001075431152132125.37715576066058e-05
550.9999177084983550.0001645830032893468.22915016446731e-05
560.999821494046880.0003570119062392810.000178505953119641
570.9996332261236440.0007335477527114850.000366773876355743
580.9987957148369370.002408570326126860.00120428516306343
590.9982377312903120.003524537419374990.00176226870968749
600.9997055750391370.0005888499217261480.000294424960863074
610.9998800160189940.0002399679620114290.000119983981005715
620.9993106052809090.001378789438182540.00068939471909127
630.9964651291425930.007069741714814990.00353487085740750







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level130.276595744680851NOK
5% type I error level140.297872340425532NOK
10% type I error level180.382978723404255NOK

\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 & 13 & 0.276595744680851 & NOK \tabularnewline
5% type I error level & 14 & 0.297872340425532 & NOK \tabularnewline
10% type I error level & 18 & 0.382978723404255 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103660&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]13[/C][C]0.276595744680851[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.297872340425532[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]18[/C][C]0.382978723404255[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103660&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103660&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 level130.276595744680851NOK
5% type I error level140.297872340425532NOK
10% type I error level180.382978723404255NOK



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')
}