## Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 24 Nov 2009 01:40:29 -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/24/t1259052091ppd6k4lcrltk02z.htm/, Retrieved Wed, 11 Sep 2024 06:50:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58959, Retrieved Wed, 11 Sep 2024 06:50:11 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact210
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 14:03:14] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [] [2009-11-24 08:40:29] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-    D        [Multiple Regression] [] [2009-11-30 18:23:57] [74be16979710d4c4e7c6647856088456]
-   P           [Multiple Regression] [] [2009-11-30 18:43:43] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
267413	294912
267366	293488
264777	290555
258863	284736
254844	281818
254868	287854
277267	316263
285351	325412
286602	326011
283042	328282
276687	317480
277915	317539
277128	313737
277103	312276
275037	309391
270150	302950
267140	300316
264993	304035
287259	333476
291186	337698
292300	335932
288186	323931
281477	313927
282656	314485
280190	313218
280408	309664
276836	302963
275216	298989
274352	298423
271311	301631
289802	329765
290726	335083
292300	327616
278506	309119
269826	295916
265861	291413
269034	291542
264176	284678
255198	276475
253353	272566
246057	264981
235372	263290
258556	296806
260993	303598
254663	286994
250643	276427
243422	266424
247105	267153
248541	268381
245039	262522
237080	255542
237085	253158
225554	243803
226839	250741
247934	280445
248333	285257
246969	270976
245098	261076
246263	255603
255765	260376
264319	263903
268347	264291
273046	263276
273963	262572
267430	256167
271993	264221
292710	293860


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58959&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]7 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=58959&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58959&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135

 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 93124.3064063537 + 0.595245145625902X[t] + 1460.64625979357M1[t] + 2625.50165379033M2[t] + 2063.61079494683M3[t] + 2144.30079128605M4[t] -474.914587784631M5[t] -4747.33433857108M6[t] -1131.23893509993M7[t] -6743.02998141279M8[t] -2789.33139941479M9[t] -2464.15797519326M10[t] -2133.01676893371M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  93124.3064063537 +  0.595245145625902X[t] +  1460.64625979357M1[t] +  2625.50165379033M2[t] +  2063.61079494683M3[t] +  2144.30079128605M4[t] -474.914587784631M5[t] -4747.33433857108M6[t] -1131.23893509993M7[t] -6743.02998141279M8[t] -2789.33139941479M9[t] -2464.15797519326M10[t] -2133.01676893371M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58959&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  93124.3064063537 +  0.595245145625902X[t] +  1460.64625979357M1[t] +  2625.50165379033M2[t] +  2063.61079494683M3[t] +  2144.30079128605M4[t] -474.914587784631M5[t] -4747.33433857108M6[t] -1131.23893509993M7[t] -6743.02998141279M8[t] -2789.33139941479M9[t] -2464.15797519326M10[t] -2133.01676893371M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58959&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58959&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] = + 93124.3064063537 + 0.595245145625902X[t] + 1460.64625979357M1[t] + 2625.50165379033M2[t] + 2063.61079494683M3[t] + 2144.30079128605M4[t] -474.914587784631M5[t] -4747.33433857108M6[t] -1131.23893509993M7[t] -6743.02998141279M8[t] -2789.33139941479M9[t] -2464.15797519326M10[t] -2133.01676893371M11[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 93124.3064063537 16338.164757 5.6998 1e-06 0 X 0.595245145625902 0.054336 10.955 0 0 M1 1460.64625979357 5794.131653 0.2521 0.801928 0.400964 M2 2625.50165379033 5795.421135 0.453 0.652342 0.326171 M3 2063.61079494683 5807.031147 0.3554 0.723701 0.361851 M4 2144.30079128605 5824.907886 0.3681 0.714218 0.357109 M5 -474.914587784631 5858.378469 -0.0811 0.935689 0.467845 M6 -4747.33433857108 5827.960201 -0.8146 0.418891 0.209446 M7 -1131.23893509993 5878.16325 -0.1924 0.848113 0.424057 M8 -6743.02998141279 6229.689339 -1.0824 0.283884 0.141942 M9 -2789.33139941479 6141.928098 -0.4541 0.651544 0.325772 M10 -2464.15797519326 6073.938233 -0.4057 0.686571 0.343285 M11 -2133.01676893371 6051.646679 -0.3525 0.725859 0.362929

\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) & 93124.3064063537 & 16338.164757 & 5.6998 & 1e-06 & 0 \tabularnewline
X & 0.595245145625902 & 0.054336 & 10.955 & 0 & 0 \tabularnewline
M1 & 1460.64625979357 & 5794.131653 & 0.2521 & 0.801928 & 0.400964 \tabularnewline
M2 & 2625.50165379033 & 5795.421135 & 0.453 & 0.652342 & 0.326171 \tabularnewline
M3 & 2063.61079494683 & 5807.031147 & 0.3554 & 0.723701 & 0.361851 \tabularnewline
M4 & 2144.30079128605 & 5824.907886 & 0.3681 & 0.714218 & 0.357109 \tabularnewline
M5 & -474.914587784631 & 5858.378469 & -0.0811 & 0.935689 & 0.467845 \tabularnewline
M6 & -4747.33433857108 & 5827.960201 & -0.8146 & 0.418891 & 0.209446 \tabularnewline
M7 & -1131.23893509993 & 5878.16325 & -0.1924 & 0.848113 & 0.424057 \tabularnewline
M8 & -6743.02998141279 & 6229.689339 & -1.0824 & 0.283884 & 0.141942 \tabularnewline
M9 & -2789.33139941479 & 6141.928098 & -0.4541 & 0.651544 & 0.325772 \tabularnewline
M10 & -2464.15797519326 & 6073.938233 & -0.4057 & 0.686571 & 0.343285 \tabularnewline
M11 & -2133.01676893371 & 6051.646679 & -0.3525 & 0.725859 & 0.362929 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58959&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]93124.3064063537[/C][C]16338.164757[/C][C]5.6998[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]0.595245145625902[/C][C]0.054336[/C][C]10.955[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]1460.64625979357[/C][C]5794.131653[/C][C]0.2521[/C][C]0.801928[/C][C]0.400964[/C][/ROW]
[ROW][C]M2[/C][C]2625.50165379033[/C][C]5795.421135[/C][C]0.453[/C][C]0.652342[/C][C]0.326171[/C][/ROW]
[ROW][C]M3[/C][C]2063.61079494683[/C][C]5807.031147[/C][C]0.3554[/C][C]0.723701[/C][C]0.361851[/C][/ROW]
[ROW][C]M4[/C][C]2144.30079128605[/C][C]5824.907886[/C][C]0.3681[/C][C]0.714218[/C][C]0.357109[/C][/ROW]
[ROW][C]M5[/C][C]-474.914587784631[/C][C]5858.378469[/C][C]-0.0811[/C][C]0.935689[/C][C]0.467845[/C][/ROW]
[ROW][C]M6[/C][C]-4747.33433857108[/C][C]5827.960201[/C][C]-0.8146[/C][C]0.418891[/C][C]0.209446[/C][/ROW]
[ROW][C]M7[/C][C]-1131.23893509993[/C][C]5878.16325[/C][C]-0.1924[/C][C]0.848113[/C][C]0.424057[/C][/ROW]
[ROW][C]M8[/C][C]-6743.02998141279[/C][C]6229.689339[/C][C]-1.0824[/C][C]0.283884[/C][C]0.141942[/C][/ROW]
[ROW][C]M9[/C][C]-2789.33139941479[/C][C]6141.928098[/C][C]-0.4541[/C][C]0.651544[/C][C]0.325772[/C][/ROW]
[ROW][C]M10[/C][C]-2464.15797519326[/C][C]6073.938233[/C][C]-0.4057[/C][C]0.686571[/C][C]0.343285[/C][/ROW]
[ROW][C]M11[/C][C]-2133.01676893371[/C][C]6051.646679[/C][C]-0.3525[/C][C]0.725859[/C][C]0.362929[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58959&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58959&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 93124.3064063537 16338.164757 5.6998 1e-06 0 X 0.595245145625902 0.054336 10.955 0 0 M1 1460.64625979357 5794.131653 0.2521 0.801928 0.400964 M2 2625.50165379033 5795.421135 0.453 0.652342 0.326171 M3 2063.61079494683 5807.031147 0.3554 0.723701 0.361851 M4 2144.30079128605 5824.907886 0.3681 0.714218 0.357109 M5 -474.914587784631 5858.378469 -0.0811 0.935689 0.467845 M6 -4747.33433857108 5827.960201 -0.8146 0.418891 0.209446 M7 -1131.23893509993 5878.16325 -0.1924 0.848113 0.424057 M8 -6743.02998141279 6229.689339 -1.0824 0.283884 0.141942 M9 -2789.33139941479 6141.928098 -0.4541 0.651544 0.325772 M10 -2464.15797519326 6073.938233 -0.4057 0.686571 0.343285 M11 -2133.01676893371 6051.646679 -0.3525 0.725859 0.362929

 Multiple Linear Regression - Regression Statistics Multiple R 0.860025172821497 R-squared 0.739643297886646 Adjusted R-squared 0.681786252972568 F-TEST (value) 12.7839798763498 F-TEST (DF numerator) 12 F-TEST (DF denominator) 54 p-value 7.89301957127009e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9568.45326181193 Sum Squared Residuals 4943986082.46788

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.860025172821497 \tabularnewline
R-squared & 0.739643297886646 \tabularnewline
F-TEST (value) & 12.7839798763498 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 7.89301957127009e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9568.45326181193 \tabularnewline
Sum Squared Residuals & 4943986082.46788 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58959&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.860025172821497[/C][/ROW]
[ROW][C]R-squared[/C][C]0.739643297886646[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.7839798763498[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/C][/ROW]
[ROW][C]p-value[/C][C]7.89301957127009e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9568.45326181193[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4943986082.46788[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58959&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58959&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 R 0.860025172821497 R-squared 0.739643297886646 Adjusted R-squared 0.681786252972568 F-TEST (value) 12.7839798763498 F-TEST (DF numerator) 12 F-TEST (DF denominator) 54 p-value 7.89301957127009e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9568.45326181193 Sum Squared Residuals 4943986082.46788

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 267413 270129.889052973 -2716.88905297294 2 267366 270447.115359599 -3081.11535959852 3 264777 268139.370488634 -3362.37048863429 4 258863 264756.328982576 -5893.32898257637 5 254844 260400.188268569 -5556.18826856933 6 254868 259720.668216781 -4852.66821678082 7 277267 280247.082962338 -2980.08296233820 8 285351 280081.189753357 5269.81024664329 9 286602 284391.440177585 2210.55982241537 10 283042 286068.415327523 -3026.41532752258 11 276687 279969.718470731 -3282.71847073114 12 277915 282137.854703257 -4222.85470325678 13 277128 281335.378919381 -4207.37891938067 14 277103 281630.581155618 -4527.58115561799 15 275037 279351.408051644 -4314.40805164376 16 270150 275598.124065007 -5448.12406500655 17 267140 271411.032972357 -4271.03297235725 18 264993 269352.329918154 -4359.32991815353 19 287259 290493.037653997 -3234.03765399685 20 291186 287394.371612517 3791.62838748346 21 292300 290296.867267339 2003.1327326608 22 288186 283478.503698904 4707.49630109572 23 281477 277854.812468322 3622.18753167769 24 282656 280319.976028515 2336.02397148472 25 280190 281026.446688801 -836.446688800826 26 280408 280075.800835243 332.199164756861 27 276836 275525.172255560 1310.82774443953 28 275216 273240.358043182 1975.64195681765 29 274352 270284.233911687 4067.76608831259 30 271311 267921.360588069 3389.63941193114 31 289802 288284.082918579 1517.91708142087 32 290726 285837.805556705 4888.19444329519 33 292300 285346.808636314 6953.1913636858 34 278506 274661.732601893 3844.26739810657 35 269826 267133.852150454 2692.1478495458 36 265861 266586.480028634 -725.480028634476 37 269034 268123.912912214 910.087087786215 38 264176 265203.005626634 -1027.00562663437 39 255198 259758.318838222 -4560.31883822159 40 253353 257512.195560309 -4159.19556030916 41 246057 250378.045751666 -4321.04575166602 42 235372 245099.066459626 -9727.06645962618 43 258556 268665.398163895 -10109.3981638950 44 260993 267096.512146673 -6103.5121466733 45 254663 261166.760330699 -6503.76033069883 46 250643 255201.978301091 -4558.97830109146 47 243422 249578.882315655 -6156.88231565511 48 247105 252145.83279575 -5040.8327957501 49 248541 254337.440094372 -5796.44009437228 50 245039 252014.754180147 -6975.75418014688 51 237080 247298.052204835 -10218.0522048346 52 237085 245959.677774002 -8874.67777400166 53 225554 237771.944057601 -12217.9440576007 54 226839 237629.335127167 -10790.3351271667 55 247934 258926.592336310 -10992.5923363097 56 248333 256179.120930749 -7846.12093074864 57 246969 251632.123588063 -4663.12358806314 58 245098 246064.370070588 -966.370070588245 59 246263 243137.734594837 3125.26540516277 60 255765 248111.856443843 7653.14355615663 61 264319 251671.932332259 12647.0676677405 62 268347 253067.742842759 15279.2571572409 63 273046 251901.678161105 21144.3218388947 64 273963 251563.315574924 22399.6844250761 65 267430 245131.555038119 22298.4449618807 66 271993 245653.239690204 26339.7603097961 67 292710 266911.805964881 25798.1940351189

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 267413 & 270129.889052973 & -2716.88905297294 \tabularnewline
2 & 267366 & 270447.115359599 & -3081.11535959852 \tabularnewline
3 & 264777 & 268139.370488634 & -3362.37048863429 \tabularnewline
4 & 258863 & 264756.328982576 & -5893.32898257637 \tabularnewline
5 & 254844 & 260400.188268569 & -5556.18826856933 \tabularnewline
6 & 254868 & 259720.668216781 & -4852.66821678082 \tabularnewline
7 & 277267 & 280247.082962338 & -2980.08296233820 \tabularnewline
8 & 285351 & 280081.189753357 & 5269.81024664329 \tabularnewline
9 & 286602 & 284391.440177585 & 2210.55982241537 \tabularnewline
10 & 283042 & 286068.415327523 & -3026.41532752258 \tabularnewline
11 & 276687 & 279969.718470731 & -3282.71847073114 \tabularnewline
12 & 277915 & 282137.854703257 & -4222.85470325678 \tabularnewline
13 & 277128 & 281335.378919381 & -4207.37891938067 \tabularnewline
14 & 277103 & 281630.581155618 & -4527.58115561799 \tabularnewline
15 & 275037 & 279351.408051644 & -4314.40805164376 \tabularnewline
16 & 270150 & 275598.124065007 & -5448.12406500655 \tabularnewline
17 & 267140 & 271411.032972357 & -4271.03297235725 \tabularnewline
18 & 264993 & 269352.329918154 & -4359.32991815353 \tabularnewline
19 & 287259 & 290493.037653997 & -3234.03765399685 \tabularnewline
20 & 291186 & 287394.371612517 & 3791.62838748346 \tabularnewline
21 & 292300 & 290296.867267339 & 2003.1327326608 \tabularnewline
22 & 288186 & 283478.503698904 & 4707.49630109572 \tabularnewline
23 & 281477 & 277854.812468322 & 3622.18753167769 \tabularnewline
24 & 282656 & 280319.976028515 & 2336.02397148472 \tabularnewline
25 & 280190 & 281026.446688801 & -836.446688800826 \tabularnewline
26 & 280408 & 280075.800835243 & 332.199164756861 \tabularnewline
27 & 276836 & 275525.172255560 & 1310.82774443953 \tabularnewline
28 & 275216 & 273240.358043182 & 1975.64195681765 \tabularnewline
29 & 274352 & 270284.233911687 & 4067.76608831259 \tabularnewline
30 & 271311 & 267921.360588069 & 3389.63941193114 \tabularnewline
31 & 289802 & 288284.082918579 & 1517.91708142087 \tabularnewline
32 & 290726 & 285837.805556705 & 4888.19444329519 \tabularnewline
33 & 292300 & 285346.808636314 & 6953.1913636858 \tabularnewline
34 & 278506 & 274661.732601893 & 3844.26739810657 \tabularnewline
35 & 269826 & 267133.852150454 & 2692.1478495458 \tabularnewline
36 & 265861 & 266586.480028634 & -725.480028634476 \tabularnewline
37 & 269034 & 268123.912912214 & 910.087087786215 \tabularnewline
38 & 264176 & 265203.005626634 & -1027.00562663437 \tabularnewline
39 & 255198 & 259758.318838222 & -4560.31883822159 \tabularnewline
40 & 253353 & 257512.195560309 & -4159.19556030916 \tabularnewline
41 & 246057 & 250378.045751666 & -4321.04575166602 \tabularnewline
42 & 235372 & 245099.066459626 & -9727.06645962618 \tabularnewline
43 & 258556 & 268665.398163895 & -10109.3981638950 \tabularnewline
44 & 260993 & 267096.512146673 & -6103.5121466733 \tabularnewline
45 & 254663 & 261166.760330699 & -6503.76033069883 \tabularnewline
46 & 250643 & 255201.978301091 & -4558.97830109146 \tabularnewline
47 & 243422 & 249578.882315655 & -6156.88231565511 \tabularnewline
48 & 247105 & 252145.83279575 & -5040.8327957501 \tabularnewline
49 & 248541 & 254337.440094372 & -5796.44009437228 \tabularnewline
50 & 245039 & 252014.754180147 & -6975.75418014688 \tabularnewline
51 & 237080 & 247298.052204835 & -10218.0522048346 \tabularnewline
52 & 237085 & 245959.677774002 & -8874.67777400166 \tabularnewline
53 & 225554 & 237771.944057601 & -12217.9440576007 \tabularnewline
54 & 226839 & 237629.335127167 & -10790.3351271667 \tabularnewline
55 & 247934 & 258926.592336310 & -10992.5923363097 \tabularnewline
56 & 248333 & 256179.120930749 & -7846.12093074864 \tabularnewline
57 & 246969 & 251632.123588063 & -4663.12358806314 \tabularnewline
58 & 245098 & 246064.370070588 & -966.370070588245 \tabularnewline
59 & 246263 & 243137.734594837 & 3125.26540516277 \tabularnewline
60 & 255765 & 248111.856443843 & 7653.14355615663 \tabularnewline
61 & 264319 & 251671.932332259 & 12647.0676677405 \tabularnewline
62 & 268347 & 253067.742842759 & 15279.2571572409 \tabularnewline
63 & 273046 & 251901.678161105 & 21144.3218388947 \tabularnewline
64 & 273963 & 251563.315574924 & 22399.6844250761 \tabularnewline
65 & 267430 & 245131.555038119 & 22298.4449618807 \tabularnewline
66 & 271993 & 245653.239690204 & 26339.7603097961 \tabularnewline
67 & 292710 & 266911.805964881 & 25798.1940351189 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58959&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]267413[/C][C]270129.889052973[/C][C]-2716.88905297294[/C][/ROW]
[ROW][C]2[/C][C]267366[/C][C]270447.115359599[/C][C]-3081.11535959852[/C][/ROW]
[ROW][C]3[/C][C]264777[/C][C]268139.370488634[/C][C]-3362.37048863429[/C][/ROW]
[ROW][C]4[/C][C]258863[/C][C]264756.328982576[/C][C]-5893.32898257637[/C][/ROW]
[ROW][C]5[/C][C]254844[/C][C]260400.188268569[/C][C]-5556.18826856933[/C][/ROW]
[ROW][C]6[/C][C]254868[/C][C]259720.668216781[/C][C]-4852.66821678082[/C][/ROW]
[ROW][C]7[/C][C]277267[/C][C]280247.082962338[/C][C]-2980.08296233820[/C][/ROW]
[ROW][C]8[/C][C]285351[/C][C]280081.189753357[/C][C]5269.81024664329[/C][/ROW]
[ROW][C]9[/C][C]286602[/C][C]284391.440177585[/C][C]2210.55982241537[/C][/ROW]
[ROW][C]10[/C][C]283042[/C][C]286068.415327523[/C][C]-3026.41532752258[/C][/ROW]
[ROW][C]11[/C][C]276687[/C][C]279969.718470731[/C][C]-3282.71847073114[/C][/ROW]
[ROW][C]12[/C][C]277915[/C][C]282137.854703257[/C][C]-4222.85470325678[/C][/ROW]
[ROW][C]13[/C][C]277128[/C][C]281335.378919381[/C][C]-4207.37891938067[/C][/ROW]
[ROW][C]14[/C][C]277103[/C][C]281630.581155618[/C][C]-4527.58115561799[/C][/ROW]
[ROW][C]15[/C][C]275037[/C][C]279351.408051644[/C][C]-4314.40805164376[/C][/ROW]
[ROW][C]16[/C][C]270150[/C][C]275598.124065007[/C][C]-5448.12406500655[/C][/ROW]
[ROW][C]17[/C][C]267140[/C][C]271411.032972357[/C][C]-4271.03297235725[/C][/ROW]
[ROW][C]18[/C][C]264993[/C][C]269352.329918154[/C][C]-4359.32991815353[/C][/ROW]
[ROW][C]19[/C][C]287259[/C][C]290493.037653997[/C][C]-3234.03765399685[/C][/ROW]
[ROW][C]20[/C][C]291186[/C][C]287394.371612517[/C][C]3791.62838748346[/C][/ROW]
[ROW][C]21[/C][C]292300[/C][C]290296.867267339[/C][C]2003.1327326608[/C][/ROW]
[ROW][C]22[/C][C]288186[/C][C]283478.503698904[/C][C]4707.49630109572[/C][/ROW]
[ROW][C]23[/C][C]281477[/C][C]277854.812468322[/C][C]3622.18753167769[/C][/ROW]
[ROW][C]24[/C][C]282656[/C][C]280319.976028515[/C][C]2336.02397148472[/C][/ROW]
[ROW][C]25[/C][C]280190[/C][C]281026.446688801[/C][C]-836.446688800826[/C][/ROW]
[ROW][C]26[/C][C]280408[/C][C]280075.800835243[/C][C]332.199164756861[/C][/ROW]
[ROW][C]27[/C][C]276836[/C][C]275525.172255560[/C][C]1310.82774443953[/C][/ROW]
[ROW][C]28[/C][C]275216[/C][C]273240.358043182[/C][C]1975.64195681765[/C][/ROW]
[ROW][C]29[/C][C]274352[/C][C]270284.233911687[/C][C]4067.76608831259[/C][/ROW]
[ROW][C]30[/C][C]271311[/C][C]267921.360588069[/C][C]3389.63941193114[/C][/ROW]
[ROW][C]31[/C][C]289802[/C][C]288284.082918579[/C][C]1517.91708142087[/C][/ROW]
[ROW][C]32[/C][C]290726[/C][C]285837.805556705[/C][C]4888.19444329519[/C][/ROW]
[ROW][C]33[/C][C]292300[/C][C]285346.808636314[/C][C]6953.1913636858[/C][/ROW]
[ROW][C]34[/C][C]278506[/C][C]274661.732601893[/C][C]3844.26739810657[/C][/ROW]
[ROW][C]35[/C][C]269826[/C][C]267133.852150454[/C][C]2692.1478495458[/C][/ROW]
[ROW][C]36[/C][C]265861[/C][C]266586.480028634[/C][C]-725.480028634476[/C][/ROW]
[ROW][C]37[/C][C]269034[/C][C]268123.912912214[/C][C]910.087087786215[/C][/ROW]
[ROW][C]38[/C][C]264176[/C][C]265203.005626634[/C][C]-1027.00562663437[/C][/ROW]
[ROW][C]39[/C][C]255198[/C][C]259758.318838222[/C][C]-4560.31883822159[/C][/ROW]
[ROW][C]40[/C][C]253353[/C][C]257512.195560309[/C][C]-4159.19556030916[/C][/ROW]
[ROW][C]41[/C][C]246057[/C][C]250378.045751666[/C][C]-4321.04575166602[/C][/ROW]
[ROW][C]42[/C][C]235372[/C][C]245099.066459626[/C][C]-9727.06645962618[/C][/ROW]
[ROW][C]43[/C][C]258556[/C][C]268665.398163895[/C][C]-10109.3981638950[/C][/ROW]
[ROW][C]44[/C][C]260993[/C][C]267096.512146673[/C][C]-6103.5121466733[/C][/ROW]
[ROW][C]45[/C][C]254663[/C][C]261166.760330699[/C][C]-6503.76033069883[/C][/ROW]
[ROW][C]46[/C][C]250643[/C][C]255201.978301091[/C][C]-4558.97830109146[/C][/ROW]
[ROW][C]47[/C][C]243422[/C][C]249578.882315655[/C][C]-6156.88231565511[/C][/ROW]
[ROW][C]48[/C][C]247105[/C][C]252145.83279575[/C][C]-5040.8327957501[/C][/ROW]
[ROW][C]49[/C][C]248541[/C][C]254337.440094372[/C][C]-5796.44009437228[/C][/ROW]
[ROW][C]50[/C][C]245039[/C][C]252014.754180147[/C][C]-6975.75418014688[/C][/ROW]
[ROW][C]51[/C][C]237080[/C][C]247298.052204835[/C][C]-10218.0522048346[/C][/ROW]
[ROW][C]52[/C][C]237085[/C][C]245959.677774002[/C][C]-8874.67777400166[/C][/ROW]
[ROW][C]53[/C][C]225554[/C][C]237771.944057601[/C][C]-12217.9440576007[/C][/ROW]
[ROW][C]54[/C][C]226839[/C][C]237629.335127167[/C][C]-10790.3351271667[/C][/ROW]
[ROW][C]55[/C][C]247934[/C][C]258926.592336310[/C][C]-10992.5923363097[/C][/ROW]
[ROW][C]56[/C][C]248333[/C][C]256179.120930749[/C][C]-7846.12093074864[/C][/ROW]
[ROW][C]57[/C][C]246969[/C][C]251632.123588063[/C][C]-4663.12358806314[/C][/ROW]
[ROW][C]58[/C][C]245098[/C][C]246064.370070588[/C][C]-966.370070588245[/C][/ROW]
[ROW][C]59[/C][C]246263[/C][C]243137.734594837[/C][C]3125.26540516277[/C][/ROW]
[ROW][C]60[/C][C]255765[/C][C]248111.856443843[/C][C]7653.14355615663[/C][/ROW]
[ROW][C]61[/C][C]264319[/C][C]251671.932332259[/C][C]12647.0676677405[/C][/ROW]
[ROW][C]62[/C][C]268347[/C][C]253067.742842759[/C][C]15279.2571572409[/C][/ROW]
[ROW][C]63[/C][C]273046[/C][C]251901.678161105[/C][C]21144.3218388947[/C][/ROW]
[ROW][C]64[/C][C]273963[/C][C]251563.315574924[/C][C]22399.6844250761[/C][/ROW]
[ROW][C]65[/C][C]267430[/C][C]245131.555038119[/C][C]22298.4449618807[/C][/ROW]
[ROW][C]66[/C][C]271993[/C][C]245653.239690204[/C][C]26339.7603097961[/C][/ROW]
[ROW][C]67[/C][C]292710[/C][C]266911.805964881[/C][C]25798.1940351189[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58959&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58959&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 267413 270129.889052973 -2716.88905297294 2 267366 270447.115359599 -3081.11535959852 3 264777 268139.370488634 -3362.37048863429 4 258863 264756.328982576 -5893.32898257637 5 254844 260400.188268569 -5556.18826856933 6 254868 259720.668216781 -4852.66821678082 7 277267 280247.082962338 -2980.08296233820 8 285351 280081.189753357 5269.81024664329 9 286602 284391.440177585 2210.55982241537 10 283042 286068.415327523 -3026.41532752258 11 276687 279969.718470731 -3282.71847073114 12 277915 282137.854703257 -4222.85470325678 13 277128 281335.378919381 -4207.37891938067 14 277103 281630.581155618 -4527.58115561799 15 275037 279351.408051644 -4314.40805164376 16 270150 275598.124065007 -5448.12406500655 17 267140 271411.032972357 -4271.03297235725 18 264993 269352.329918154 -4359.32991815353 19 287259 290493.037653997 -3234.03765399685 20 291186 287394.371612517 3791.62838748346 21 292300 290296.867267339 2003.1327326608 22 288186 283478.503698904 4707.49630109572 23 281477 277854.812468322 3622.18753167769 24 282656 280319.976028515 2336.02397148472 25 280190 281026.446688801 -836.446688800826 26 280408 280075.800835243 332.199164756861 27 276836 275525.172255560 1310.82774443953 28 275216 273240.358043182 1975.64195681765 29 274352 270284.233911687 4067.76608831259 30 271311 267921.360588069 3389.63941193114 31 289802 288284.082918579 1517.91708142087 32 290726 285837.805556705 4888.19444329519 33 292300 285346.808636314 6953.1913636858 34 278506 274661.732601893 3844.26739810657 35 269826 267133.852150454 2692.1478495458 36 265861 266586.480028634 -725.480028634476 37 269034 268123.912912214 910.087087786215 38 264176 265203.005626634 -1027.00562663437 39 255198 259758.318838222 -4560.31883822159 40 253353 257512.195560309 -4159.19556030916 41 246057 250378.045751666 -4321.04575166602 42 235372 245099.066459626 -9727.06645962618 43 258556 268665.398163895 -10109.3981638950 44 260993 267096.512146673 -6103.5121466733 45 254663 261166.760330699 -6503.76033069883 46 250643 255201.978301091 -4558.97830109146 47 243422 249578.882315655 -6156.88231565511 48 247105 252145.83279575 -5040.8327957501 49 248541 254337.440094372 -5796.44009437228 50 245039 252014.754180147 -6975.75418014688 51 237080 247298.052204835 -10218.0522048346 52 237085 245959.677774002 -8874.67777400166 53 225554 237771.944057601 -12217.9440576007 54 226839 237629.335127167 -10790.3351271667 55 247934 258926.592336310 -10992.5923363097 56 248333 256179.120930749 -7846.12093074864 57 246969 251632.123588063 -4663.12358806314 58 245098 246064.370070588 -966.370070588245 59 246263 243137.734594837 3125.26540516277 60 255765 248111.856443843 7653.14355615663 61 264319 251671.932332259 12647.0676677405 62 268347 253067.742842759 15279.2571572409 63 273046 251901.678161105 21144.3218388947 64 273963 251563.315574924 22399.6844250761 65 267430 245131.555038119 22298.4449618807 66 271993 245653.239690204 26339.7603097961 67 292710 266911.805964881 25798.1940351189

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.000272740306682288 0.000545480613364576 0.999727259693318 17 8.40239482044959e-05 0.000168047896408992 0.999915976051795 18 7.49170597548235e-06 1.49834119509647e-05 0.999992508294025 19 4.92950080567018e-07 9.85900161134036e-07 0.99999950704992 20 5.98715748209359e-08 1.19743149641872e-07 0.999999940128425 21 3.62299972378704e-09 7.24599944757409e-09 0.999999996377 22 2.10110891749833e-06 4.20221783499666e-06 0.999997898891082 23 3.95714747858896e-06 7.91429495717792e-06 0.999996042852521 24 3.70853660413269e-06 7.41707320826538e-06 0.999996291463396 25 1.19816370468405e-06 2.39632740936809e-06 0.999998801836295 26 5.85719310664065e-07 1.17143862132813e-06 0.99999941428069 27 3.79400249429956e-07 7.58800498859913e-07 0.99999962059975 28 6.51165243118333e-07 1.30233048623667e-06 0.999999348834757 29 1.29136350023109e-06 2.58272700046217e-06 0.9999987086365 30 1.29952461265898e-06 2.59904922531795e-06 0.999998700475387 31 5.39656413898947e-07 1.07931282779789e-06 0.999999460343586 32 1.45978497106152e-07 2.91956994212303e-07 0.999999854021503 33 6.76760033735602e-08 1.35352006747120e-07 0.999999932323997 34 2.27338012034522e-08 4.54676024069044e-08 0.999999977266199 35 6.51820861675371e-09 1.30364172335074e-08 0.999999993481791 36 1.53816005183794e-09 3.07632010367588e-09 0.99999999846184 37 4.89495386889852e-10 9.78990773779704e-10 0.999999999510505 38 1.24912743062200e-10 2.49825486124401e-10 0.999999999875087 39 4.88148937542593e-11 9.76297875085186e-11 0.999999999951185 40 1.89791477030563e-11 3.79582954061126e-11 0.99999999998102 41 9.34239246869916e-12 1.86847849373983e-11 0.999999999990658 42 2.21511899107560e-11 4.43023798215119e-11 0.999999999977849 43 1.08375414951116e-10 2.16750829902233e-10 0.999999999891625 44 2.56484865842372e-10 5.12969731684744e-10 0.999999999743515 45 4.75735421326044e-10 9.51470842652088e-10 0.999999999524265 46 2.34334157908895e-09 4.68668315817789e-09 0.999999997656658 47 7.47462107226761e-08 1.49492421445352e-07 0.99999992525379 48 5.70879206197699e-06 1.14175841239540e-05 0.999994291207938 49 0.0181150133568992 0.0362300267137984 0.9818849866431 50 0.382683330595654 0.765366661191308 0.617316669404346 51 0.798556772882816 0.402886454234369 0.201443227117184

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.000272740306682288 & 0.000545480613364576 & 0.999727259693318 \tabularnewline
17 & 8.40239482044959e-05 & 0.000168047896408992 & 0.999915976051795 \tabularnewline
18 & 7.49170597548235e-06 & 1.49834119509647e-05 & 0.999992508294025 \tabularnewline
19 & 4.92950080567018e-07 & 9.85900161134036e-07 & 0.99999950704992 \tabularnewline
20 & 5.98715748209359e-08 & 1.19743149641872e-07 & 0.999999940128425 \tabularnewline
21 & 3.62299972378704e-09 & 7.24599944757409e-09 & 0.999999996377 \tabularnewline
22 & 2.10110891749833e-06 & 4.20221783499666e-06 & 0.999997898891082 \tabularnewline
23 & 3.95714747858896e-06 & 7.91429495717792e-06 & 0.999996042852521 \tabularnewline
24 & 3.70853660413269e-06 & 7.41707320826538e-06 & 0.999996291463396 \tabularnewline
25 & 1.19816370468405e-06 & 2.39632740936809e-06 & 0.999998801836295 \tabularnewline
26 & 5.85719310664065e-07 & 1.17143862132813e-06 & 0.99999941428069 \tabularnewline
27 & 3.79400249429956e-07 & 7.58800498859913e-07 & 0.99999962059975 \tabularnewline
28 & 6.51165243118333e-07 & 1.30233048623667e-06 & 0.999999348834757 \tabularnewline
29 & 1.29136350023109e-06 & 2.58272700046217e-06 & 0.9999987086365 \tabularnewline
30 & 1.29952461265898e-06 & 2.59904922531795e-06 & 0.999998700475387 \tabularnewline
31 & 5.39656413898947e-07 & 1.07931282779789e-06 & 0.999999460343586 \tabularnewline
32 & 1.45978497106152e-07 & 2.91956994212303e-07 & 0.999999854021503 \tabularnewline
33 & 6.76760033735602e-08 & 1.35352006747120e-07 & 0.999999932323997 \tabularnewline
34 & 2.27338012034522e-08 & 4.54676024069044e-08 & 0.999999977266199 \tabularnewline
35 & 6.51820861675371e-09 & 1.30364172335074e-08 & 0.999999993481791 \tabularnewline
36 & 1.53816005183794e-09 & 3.07632010367588e-09 & 0.99999999846184 \tabularnewline
37 & 4.89495386889852e-10 & 9.78990773779704e-10 & 0.999999999510505 \tabularnewline
38 & 1.24912743062200e-10 & 2.49825486124401e-10 & 0.999999999875087 \tabularnewline
39 & 4.88148937542593e-11 & 9.76297875085186e-11 & 0.999999999951185 \tabularnewline
40 & 1.89791477030563e-11 & 3.79582954061126e-11 & 0.99999999998102 \tabularnewline
41 & 9.34239246869916e-12 & 1.86847849373983e-11 & 0.999999999990658 \tabularnewline
42 & 2.21511899107560e-11 & 4.43023798215119e-11 & 0.999999999977849 \tabularnewline
43 & 1.08375414951116e-10 & 2.16750829902233e-10 & 0.999999999891625 \tabularnewline
44 & 2.56484865842372e-10 & 5.12969731684744e-10 & 0.999999999743515 \tabularnewline
45 & 4.75735421326044e-10 & 9.51470842652088e-10 & 0.999999999524265 \tabularnewline
46 & 2.34334157908895e-09 & 4.68668315817789e-09 & 0.999999997656658 \tabularnewline
47 & 7.47462107226761e-08 & 1.49492421445352e-07 & 0.99999992525379 \tabularnewline
48 & 5.70879206197699e-06 & 1.14175841239540e-05 & 0.999994291207938 \tabularnewline
49 & 0.0181150133568992 & 0.0362300267137984 & 0.9818849866431 \tabularnewline
50 & 0.382683330595654 & 0.765366661191308 & 0.617316669404346 \tabularnewline
51 & 0.798556772882816 & 0.402886454234369 & 0.201443227117184 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58959&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]16[/C][C]0.000272740306682288[/C][C]0.000545480613364576[/C][C]0.999727259693318[/C][/ROW]
[ROW][C]17[/C][C]8.40239482044959e-05[/C][C]0.000168047896408992[/C][C]0.999915976051795[/C][/ROW]
[ROW][C]18[/C][C]7.49170597548235e-06[/C][C]1.49834119509647e-05[/C][C]0.999992508294025[/C][/ROW]
[ROW][C]19[/C][C]4.92950080567018e-07[/C][C]9.85900161134036e-07[/C][C]0.99999950704992[/C][/ROW]
[ROW][C]20[/C][C]5.98715748209359e-08[/C][C]1.19743149641872e-07[/C][C]0.999999940128425[/C][/ROW]
[ROW][C]21[/C][C]3.62299972378704e-09[/C][C]7.24599944757409e-09[/C][C]0.999999996377[/C][/ROW]
[ROW][C]22[/C][C]2.10110891749833e-06[/C][C]4.20221783499666e-06[/C][C]0.999997898891082[/C][/ROW]
[ROW][C]23[/C][C]3.95714747858896e-06[/C][C]7.91429495717792e-06[/C][C]0.999996042852521[/C][/ROW]
[ROW][C]24[/C][C]3.70853660413269e-06[/C][C]7.41707320826538e-06[/C][C]0.999996291463396[/C][/ROW]
[ROW][C]25[/C][C]1.19816370468405e-06[/C][C]2.39632740936809e-06[/C][C]0.999998801836295[/C][/ROW]
[ROW][C]26[/C][C]5.85719310664065e-07[/C][C]1.17143862132813e-06[/C][C]0.99999941428069[/C][/ROW]
[ROW][C]27[/C][C]3.79400249429956e-07[/C][C]7.58800498859913e-07[/C][C]0.99999962059975[/C][/ROW]
[ROW][C]28[/C][C]6.51165243118333e-07[/C][C]1.30233048623667e-06[/C][C]0.999999348834757[/C][/ROW]
[ROW][C]29[/C][C]1.29136350023109e-06[/C][C]2.58272700046217e-06[/C][C]0.9999987086365[/C][/ROW]
[ROW][C]30[/C][C]1.29952461265898e-06[/C][C]2.59904922531795e-06[/C][C]0.999998700475387[/C][/ROW]
[ROW][C]31[/C][C]5.39656413898947e-07[/C][C]1.07931282779789e-06[/C][C]0.999999460343586[/C][/ROW]
[ROW][C]32[/C][C]1.45978497106152e-07[/C][C]2.91956994212303e-07[/C][C]0.999999854021503[/C][/ROW]
[ROW][C]33[/C][C]6.76760033735602e-08[/C][C]1.35352006747120e-07[/C][C]0.999999932323997[/C][/ROW]
[ROW][C]34[/C][C]2.27338012034522e-08[/C][C]4.54676024069044e-08[/C][C]0.999999977266199[/C][/ROW]
[ROW][C]35[/C][C]6.51820861675371e-09[/C][C]1.30364172335074e-08[/C][C]0.999999993481791[/C][/ROW]
[ROW][C]36[/C][C]1.53816005183794e-09[/C][C]3.07632010367588e-09[/C][C]0.99999999846184[/C][/ROW]
[ROW][C]37[/C][C]4.89495386889852e-10[/C][C]9.78990773779704e-10[/C][C]0.999999999510505[/C][/ROW]
[ROW][C]38[/C][C]1.24912743062200e-10[/C][C]2.49825486124401e-10[/C][C]0.999999999875087[/C][/ROW]
[ROW][C]39[/C][C]4.88148937542593e-11[/C][C]9.76297875085186e-11[/C][C]0.999999999951185[/C][/ROW]
[ROW][C]40[/C][C]1.89791477030563e-11[/C][C]3.79582954061126e-11[/C][C]0.99999999998102[/C][/ROW]
[ROW][C]41[/C][C]9.34239246869916e-12[/C][C]1.86847849373983e-11[/C][C]0.999999999990658[/C][/ROW]
[ROW][C]42[/C][C]2.21511899107560e-11[/C][C]4.43023798215119e-11[/C][C]0.999999999977849[/C][/ROW]
[ROW][C]43[/C][C]1.08375414951116e-10[/C][C]2.16750829902233e-10[/C][C]0.999999999891625[/C][/ROW]
[ROW][C]44[/C][C]2.56484865842372e-10[/C][C]5.12969731684744e-10[/C][C]0.999999999743515[/C][/ROW]
[ROW][C]45[/C][C]4.75735421326044e-10[/C][C]9.51470842652088e-10[/C][C]0.999999999524265[/C][/ROW]
[ROW][C]46[/C][C]2.34334157908895e-09[/C][C]4.68668315817789e-09[/C][C]0.999999997656658[/C][/ROW]
[ROW][C]47[/C][C]7.47462107226761e-08[/C][C]1.49492421445352e-07[/C][C]0.99999992525379[/C][/ROW]
[ROW][C]48[/C][C]5.70879206197699e-06[/C][C]1.14175841239540e-05[/C][C]0.999994291207938[/C][/ROW]
[ROW][C]49[/C][C]0.0181150133568992[/C][C]0.0362300267137984[/C][C]0.9818849866431[/C][/ROW]
[ROW][C]50[/C][C]0.382683330595654[/C][C]0.765366661191308[/C][C]0.617316669404346[/C][/ROW]
[ROW][C]51[/C][C]0.798556772882816[/C][C]0.402886454234369[/C][C]0.201443227117184[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58959&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58959&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-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.000272740306682288 0.000545480613364576 0.999727259693318 17 8.40239482044959e-05 0.000168047896408992 0.999915976051795 18 7.49170597548235e-06 1.49834119509647e-05 0.999992508294025 19 4.92950080567018e-07 9.85900161134036e-07 0.99999950704992 20 5.98715748209359e-08 1.19743149641872e-07 0.999999940128425 21 3.62299972378704e-09 7.24599944757409e-09 0.999999996377 22 2.10110891749833e-06 4.20221783499666e-06 0.999997898891082 23 3.95714747858896e-06 7.91429495717792e-06 0.999996042852521 24 3.70853660413269e-06 7.41707320826538e-06 0.999996291463396 25 1.19816370468405e-06 2.39632740936809e-06 0.999998801836295 26 5.85719310664065e-07 1.17143862132813e-06 0.99999941428069 27 3.79400249429956e-07 7.58800498859913e-07 0.99999962059975 28 6.51165243118333e-07 1.30233048623667e-06 0.999999348834757 29 1.29136350023109e-06 2.58272700046217e-06 0.9999987086365 30 1.29952461265898e-06 2.59904922531795e-06 0.999998700475387 31 5.39656413898947e-07 1.07931282779789e-06 0.999999460343586 32 1.45978497106152e-07 2.91956994212303e-07 0.999999854021503 33 6.76760033735602e-08 1.35352006747120e-07 0.999999932323997 34 2.27338012034522e-08 4.54676024069044e-08 0.999999977266199 35 6.51820861675371e-09 1.30364172335074e-08 0.999999993481791 36 1.53816005183794e-09 3.07632010367588e-09 0.99999999846184 37 4.89495386889852e-10 9.78990773779704e-10 0.999999999510505 38 1.24912743062200e-10 2.49825486124401e-10 0.999999999875087 39 4.88148937542593e-11 9.76297875085186e-11 0.999999999951185 40 1.89791477030563e-11 3.79582954061126e-11 0.99999999998102 41 9.34239246869916e-12 1.86847849373983e-11 0.999999999990658 42 2.21511899107560e-11 4.43023798215119e-11 0.999999999977849 43 1.08375414951116e-10 2.16750829902233e-10 0.999999999891625 44 2.56484865842372e-10 5.12969731684744e-10 0.999999999743515 45 4.75735421326044e-10 9.51470842652088e-10 0.999999999524265 46 2.34334157908895e-09 4.68668315817789e-09 0.999999997656658 47 7.47462107226761e-08 1.49492421445352e-07 0.99999992525379 48 5.70879206197699e-06 1.14175841239540e-05 0.999994291207938 49 0.0181150133568992 0.0362300267137984 0.9818849866431 50 0.382683330595654 0.765366661191308 0.617316669404346 51 0.798556772882816 0.402886454234369 0.201443227117184

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 33 0.916666666666667 NOK 5% type I error level 34 0.944444444444444 NOK 10% type I error level 34 0.944444444444444 NOK

\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 & 33 & 0.916666666666667 & NOK \tabularnewline
5% type I error level & 34 & 0.944444444444444 & NOK \tabularnewline
10% type I error level & 34 & 0.944444444444444 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58959&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]33[/C][C]0.916666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]34[/C][C]0.944444444444444[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.944444444444444[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58959&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58959&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 tests OK/NOK 1% type I error level 33 0.916666666666667 NOK 5% type I error level 34 0.944444444444444 NOK 10% type I error level 34 0.944444444444444 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}