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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 08:40:42 -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/20/t1258731800zgprqq48jb4g75f.htm/, Retrieved Thu, 28 Mar 2024 22:30:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58279, Retrieved Thu, 28 Mar 2024 22:30:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [WS7] [2009-11-20 15:40:42] [b8ce264f75295a954feffaf60221d1b0] [Current]
-    D        [Multiple Regression] [Multiple Regression] [2009-12-18 16:12:21] [4d62210f0915d3a20cbf115865da7cd4]
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Dataseries X:
363	14.3
364	14.2
363	15.9
358	15.3
357	15.5
357	15.1
380	15
378	12.1
376	15.8
380	16.9
379	15.1
384	13.7
392	14.8
394	14.7
392	16
396	15.4
392	15
396	15.5
419	15.1
421	11.7
420	16.3
418	16.7
410	15
418	14.9
426	14.6
428	15.3
430	17.9
424	16.4
423	15.4
427	17.9
441	15.9
449	13.9
452	17.8
462	17.9
455	17.4
461	16.7
461	16
463	16.6
462	19.1
456	17.8
455	17.2
456	18.6
472	16.3
472	15.1
471	19.2
465	17.7
459	19.1
465	18
468	17.5
467	17.8
463	21.1
460	17.2
462	19.4
461	19.8
476	17.6
476	16.2
471	19.5
453	19.9
443	20
442	17.3




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58279&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58279&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58279&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
WK>25j[t] = + 225.948105079717 + 12.2360142217909ExpBe[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WK>25j[t] =  +  225.948105079717 +  12.2360142217909ExpBe[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58279&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WK>25j[t] =  +  225.948105079717 +  12.2360142217909ExpBe[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58279&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58279&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
WK>25j[t] = + 225.948105079717 + 12.2360142217909ExpBe[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)225.94810507971733.1167236.822800
ExpBe12.23601422179091.9914376.144300

\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) & 225.948105079717 & 33.116723 & 6.8228 & 0 & 0 \tabularnewline
ExpBe & 12.2360142217909 & 1.991437 & 6.1443 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58279&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]225.948105079717[/C][C]33.116723[/C][C]6.8228[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]ExpBe[/C][C]12.2360142217909[/C][C]1.991437[/C][C]6.1443[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58279&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58279&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)225.94810507971733.1167236.822800
ExpBe12.23601422179091.9914376.144300







Multiple Linear Regression - Regression Statistics
Multiple R0.627910959932989
R-squared0.394272173603967
Adjusted R-squared0.383828590390243
F-TEST (value)37.7525764419461
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value7.8704400552354e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation29.6188975514582
Sum Squared Residuals50882.1873454992

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.627910959932989 \tabularnewline
R-squared & 0.394272173603967 \tabularnewline
Adjusted R-squared & 0.383828590390243 \tabularnewline
F-TEST (value) & 37.7525764419461 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 7.8704400552354e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 29.6188975514582 \tabularnewline
Sum Squared Residuals & 50882.1873454992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58279&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.627910959932989[/C][/ROW]
[ROW][C]R-squared[/C][C]0.394272173603967[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.383828590390243[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]37.7525764419461[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]7.8704400552354e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]29.6188975514582[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]50882.1873454992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58279&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58279&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 R0.627910959932989
R-squared0.394272173603967
Adjusted R-squared0.383828590390243
F-TEST (value)37.7525764419461
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value7.8704400552354e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation29.6188975514582
Sum Squared Residuals50882.1873454992







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1363400.923108451326-37.9231084513259
2364399.699507029148-35.699507029148
3363420.500731206193-57.5007312061926
4358413.159122673118-55.1591226731181
5357415.606325517476-58.6063255174763
6357410.71191982876-53.7119198287599
7380409.488318406581-29.4883184065808
8378374.0038771633873.99612283661285
9376419.277129784014-43.2771297840136
10380432.736745427984-52.7367454279835
11379410.71191982876-31.7119198287599
12384393.581499918253-9.5814999182526
13392407.041115562223-15.0411155622226
14394405.817514140043-11.8175141400435
15392421.724332628372-29.7243326283717
16396414.382724095297-18.3827240952972
17392409.488318406581-17.4883184065808
18396415.606325517476-19.6063255174763
19419410.711919828768.2880801712401
20421369.10947147467151.8905285253292
21420425.395136894909-5.39513689490901
22418430.289542583625-12.2895425836254
23410409.4883184065810.511681593419189
24418408.2647169844029.73528301559828
25426404.59391271786421.4060872821356
26428413.15912267311814.8408773268819
27430444.972759649774-14.9727596497745
28424426.618738317088-2.61873831708808
29423414.3827240952978.61727590470282
30427444.972759649774-17.9727596497745
31441420.50073120619320.4992687938074
32449396.02870276261152.9712972373892
33452443.7491582275958.2508417724046
34462444.97275964977417.0272403502255
35455438.85475253887916.145247461121
36461430.28954258362530.7104574163746
37461421.72433262837239.2756673716283
38463429.06594116144633.9340588385537
39462459.6559767159242.34402328407641
40456443.74915822759512.2508417724046
41455436.40754969452118.5924503054792
42456453.5379696050282.46203039497187
43472425.39513689490946.604863105091
44472410.7119198287661.2880801712401
45471460.87957813810310.1204218618973
46465442.52555680541622.4744431945837
47459459.655976715924-0.655976715923595
48465446.19636107195418.8036389280464
49468440.07835396105827.9216460389419
50467443.74915822759523.2508417724046
51463484.128005159505-21.1280051595054
52460436.40754969452123.5924503054792
53462463.326780982461-1.32678098246083
54461468.221186671177-7.22118667117723
55476441.30195538323734.6980446167628
56476424.1715354727351.8284645272701
57471464.550382404646.44961759536006
58453469.444788093356-16.4447880933563
59443470.668389515535-27.6683895155354
60442437.63115111674.36884888330007

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 363 & 400.923108451326 & -37.9231084513259 \tabularnewline
2 & 364 & 399.699507029148 & -35.699507029148 \tabularnewline
3 & 363 & 420.500731206193 & -57.5007312061926 \tabularnewline
4 & 358 & 413.159122673118 & -55.1591226731181 \tabularnewline
5 & 357 & 415.606325517476 & -58.6063255174763 \tabularnewline
6 & 357 & 410.71191982876 & -53.7119198287599 \tabularnewline
7 & 380 & 409.488318406581 & -29.4883184065808 \tabularnewline
8 & 378 & 374.003877163387 & 3.99612283661285 \tabularnewline
9 & 376 & 419.277129784014 & -43.2771297840136 \tabularnewline
10 & 380 & 432.736745427984 & -52.7367454279835 \tabularnewline
11 & 379 & 410.71191982876 & -31.7119198287599 \tabularnewline
12 & 384 & 393.581499918253 & -9.5814999182526 \tabularnewline
13 & 392 & 407.041115562223 & -15.0411155622226 \tabularnewline
14 & 394 & 405.817514140043 & -11.8175141400435 \tabularnewline
15 & 392 & 421.724332628372 & -29.7243326283717 \tabularnewline
16 & 396 & 414.382724095297 & -18.3827240952972 \tabularnewline
17 & 392 & 409.488318406581 & -17.4883184065808 \tabularnewline
18 & 396 & 415.606325517476 & -19.6063255174763 \tabularnewline
19 & 419 & 410.71191982876 & 8.2880801712401 \tabularnewline
20 & 421 & 369.109471474671 & 51.8905285253292 \tabularnewline
21 & 420 & 425.395136894909 & -5.39513689490901 \tabularnewline
22 & 418 & 430.289542583625 & -12.2895425836254 \tabularnewline
23 & 410 & 409.488318406581 & 0.511681593419189 \tabularnewline
24 & 418 & 408.264716984402 & 9.73528301559828 \tabularnewline
25 & 426 & 404.593912717864 & 21.4060872821356 \tabularnewline
26 & 428 & 413.159122673118 & 14.8408773268819 \tabularnewline
27 & 430 & 444.972759649774 & -14.9727596497745 \tabularnewline
28 & 424 & 426.618738317088 & -2.61873831708808 \tabularnewline
29 & 423 & 414.382724095297 & 8.61727590470282 \tabularnewline
30 & 427 & 444.972759649774 & -17.9727596497745 \tabularnewline
31 & 441 & 420.500731206193 & 20.4992687938074 \tabularnewline
32 & 449 & 396.028702762611 & 52.9712972373892 \tabularnewline
33 & 452 & 443.749158227595 & 8.2508417724046 \tabularnewline
34 & 462 & 444.972759649774 & 17.0272403502255 \tabularnewline
35 & 455 & 438.854752538879 & 16.145247461121 \tabularnewline
36 & 461 & 430.289542583625 & 30.7104574163746 \tabularnewline
37 & 461 & 421.724332628372 & 39.2756673716283 \tabularnewline
38 & 463 & 429.065941161446 & 33.9340588385537 \tabularnewline
39 & 462 & 459.655976715924 & 2.34402328407641 \tabularnewline
40 & 456 & 443.749158227595 & 12.2508417724046 \tabularnewline
41 & 455 & 436.407549694521 & 18.5924503054792 \tabularnewline
42 & 456 & 453.537969605028 & 2.46203039497187 \tabularnewline
43 & 472 & 425.395136894909 & 46.604863105091 \tabularnewline
44 & 472 & 410.71191982876 & 61.2880801712401 \tabularnewline
45 & 471 & 460.879578138103 & 10.1204218618973 \tabularnewline
46 & 465 & 442.525556805416 & 22.4744431945837 \tabularnewline
47 & 459 & 459.655976715924 & -0.655976715923595 \tabularnewline
48 & 465 & 446.196361071954 & 18.8036389280464 \tabularnewline
49 & 468 & 440.078353961058 & 27.9216460389419 \tabularnewline
50 & 467 & 443.749158227595 & 23.2508417724046 \tabularnewline
51 & 463 & 484.128005159505 & -21.1280051595054 \tabularnewline
52 & 460 & 436.407549694521 & 23.5924503054792 \tabularnewline
53 & 462 & 463.326780982461 & -1.32678098246083 \tabularnewline
54 & 461 & 468.221186671177 & -7.22118667117723 \tabularnewline
55 & 476 & 441.301955383237 & 34.6980446167628 \tabularnewline
56 & 476 & 424.17153547273 & 51.8284645272701 \tabularnewline
57 & 471 & 464.55038240464 & 6.44961759536006 \tabularnewline
58 & 453 & 469.444788093356 & -16.4447880933563 \tabularnewline
59 & 443 & 470.668389515535 & -27.6683895155354 \tabularnewline
60 & 442 & 437.6311511167 & 4.36884888330007 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58279&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]363[/C][C]400.923108451326[/C][C]-37.9231084513259[/C][/ROW]
[ROW][C]2[/C][C]364[/C][C]399.699507029148[/C][C]-35.699507029148[/C][/ROW]
[ROW][C]3[/C][C]363[/C][C]420.500731206193[/C][C]-57.5007312061926[/C][/ROW]
[ROW][C]4[/C][C]358[/C][C]413.159122673118[/C][C]-55.1591226731181[/C][/ROW]
[ROW][C]5[/C][C]357[/C][C]415.606325517476[/C][C]-58.6063255174763[/C][/ROW]
[ROW][C]6[/C][C]357[/C][C]410.71191982876[/C][C]-53.7119198287599[/C][/ROW]
[ROW][C]7[/C][C]380[/C][C]409.488318406581[/C][C]-29.4883184065808[/C][/ROW]
[ROW][C]8[/C][C]378[/C][C]374.003877163387[/C][C]3.99612283661285[/C][/ROW]
[ROW][C]9[/C][C]376[/C][C]419.277129784014[/C][C]-43.2771297840136[/C][/ROW]
[ROW][C]10[/C][C]380[/C][C]432.736745427984[/C][C]-52.7367454279835[/C][/ROW]
[ROW][C]11[/C][C]379[/C][C]410.71191982876[/C][C]-31.7119198287599[/C][/ROW]
[ROW][C]12[/C][C]384[/C][C]393.581499918253[/C][C]-9.5814999182526[/C][/ROW]
[ROW][C]13[/C][C]392[/C][C]407.041115562223[/C][C]-15.0411155622226[/C][/ROW]
[ROW][C]14[/C][C]394[/C][C]405.817514140043[/C][C]-11.8175141400435[/C][/ROW]
[ROW][C]15[/C][C]392[/C][C]421.724332628372[/C][C]-29.7243326283717[/C][/ROW]
[ROW][C]16[/C][C]396[/C][C]414.382724095297[/C][C]-18.3827240952972[/C][/ROW]
[ROW][C]17[/C][C]392[/C][C]409.488318406581[/C][C]-17.4883184065808[/C][/ROW]
[ROW][C]18[/C][C]396[/C][C]415.606325517476[/C][C]-19.6063255174763[/C][/ROW]
[ROW][C]19[/C][C]419[/C][C]410.71191982876[/C][C]8.2880801712401[/C][/ROW]
[ROW][C]20[/C][C]421[/C][C]369.109471474671[/C][C]51.8905285253292[/C][/ROW]
[ROW][C]21[/C][C]420[/C][C]425.395136894909[/C][C]-5.39513689490901[/C][/ROW]
[ROW][C]22[/C][C]418[/C][C]430.289542583625[/C][C]-12.2895425836254[/C][/ROW]
[ROW][C]23[/C][C]410[/C][C]409.488318406581[/C][C]0.511681593419189[/C][/ROW]
[ROW][C]24[/C][C]418[/C][C]408.264716984402[/C][C]9.73528301559828[/C][/ROW]
[ROW][C]25[/C][C]426[/C][C]404.593912717864[/C][C]21.4060872821356[/C][/ROW]
[ROW][C]26[/C][C]428[/C][C]413.159122673118[/C][C]14.8408773268819[/C][/ROW]
[ROW][C]27[/C][C]430[/C][C]444.972759649774[/C][C]-14.9727596497745[/C][/ROW]
[ROW][C]28[/C][C]424[/C][C]426.618738317088[/C][C]-2.61873831708808[/C][/ROW]
[ROW][C]29[/C][C]423[/C][C]414.382724095297[/C][C]8.61727590470282[/C][/ROW]
[ROW][C]30[/C][C]427[/C][C]444.972759649774[/C][C]-17.9727596497745[/C][/ROW]
[ROW][C]31[/C][C]441[/C][C]420.500731206193[/C][C]20.4992687938074[/C][/ROW]
[ROW][C]32[/C][C]449[/C][C]396.028702762611[/C][C]52.9712972373892[/C][/ROW]
[ROW][C]33[/C][C]452[/C][C]443.749158227595[/C][C]8.2508417724046[/C][/ROW]
[ROW][C]34[/C][C]462[/C][C]444.972759649774[/C][C]17.0272403502255[/C][/ROW]
[ROW][C]35[/C][C]455[/C][C]438.854752538879[/C][C]16.145247461121[/C][/ROW]
[ROW][C]36[/C][C]461[/C][C]430.289542583625[/C][C]30.7104574163746[/C][/ROW]
[ROW][C]37[/C][C]461[/C][C]421.724332628372[/C][C]39.2756673716283[/C][/ROW]
[ROW][C]38[/C][C]463[/C][C]429.065941161446[/C][C]33.9340588385537[/C][/ROW]
[ROW][C]39[/C][C]462[/C][C]459.655976715924[/C][C]2.34402328407641[/C][/ROW]
[ROW][C]40[/C][C]456[/C][C]443.749158227595[/C][C]12.2508417724046[/C][/ROW]
[ROW][C]41[/C][C]455[/C][C]436.407549694521[/C][C]18.5924503054792[/C][/ROW]
[ROW][C]42[/C][C]456[/C][C]453.537969605028[/C][C]2.46203039497187[/C][/ROW]
[ROW][C]43[/C][C]472[/C][C]425.395136894909[/C][C]46.604863105091[/C][/ROW]
[ROW][C]44[/C][C]472[/C][C]410.71191982876[/C][C]61.2880801712401[/C][/ROW]
[ROW][C]45[/C][C]471[/C][C]460.879578138103[/C][C]10.1204218618973[/C][/ROW]
[ROW][C]46[/C][C]465[/C][C]442.525556805416[/C][C]22.4744431945837[/C][/ROW]
[ROW][C]47[/C][C]459[/C][C]459.655976715924[/C][C]-0.655976715923595[/C][/ROW]
[ROW][C]48[/C][C]465[/C][C]446.196361071954[/C][C]18.8036389280464[/C][/ROW]
[ROW][C]49[/C][C]468[/C][C]440.078353961058[/C][C]27.9216460389419[/C][/ROW]
[ROW][C]50[/C][C]467[/C][C]443.749158227595[/C][C]23.2508417724046[/C][/ROW]
[ROW][C]51[/C][C]463[/C][C]484.128005159505[/C][C]-21.1280051595054[/C][/ROW]
[ROW][C]52[/C][C]460[/C][C]436.407549694521[/C][C]23.5924503054792[/C][/ROW]
[ROW][C]53[/C][C]462[/C][C]463.326780982461[/C][C]-1.32678098246083[/C][/ROW]
[ROW][C]54[/C][C]461[/C][C]468.221186671177[/C][C]-7.22118667117723[/C][/ROW]
[ROW][C]55[/C][C]476[/C][C]441.301955383237[/C][C]34.6980446167628[/C][/ROW]
[ROW][C]56[/C][C]476[/C][C]424.17153547273[/C][C]51.8284645272701[/C][/ROW]
[ROW][C]57[/C][C]471[/C][C]464.55038240464[/C][C]6.44961759536006[/C][/ROW]
[ROW][C]58[/C][C]453[/C][C]469.444788093356[/C][C]-16.4447880933563[/C][/ROW]
[ROW][C]59[/C][C]443[/C][C]470.668389515535[/C][C]-27.6683895155354[/C][/ROW]
[ROW][C]60[/C][C]442[/C][C]437.6311511167[/C][C]4.36884888330007[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58279&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58279&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
1363400.923108451326-37.9231084513259
2364399.699507029148-35.699507029148
3363420.500731206193-57.5007312061926
4358413.159122673118-55.1591226731181
5357415.606325517476-58.6063255174763
6357410.71191982876-53.7119198287599
7380409.488318406581-29.4883184065808
8378374.0038771633873.99612283661285
9376419.277129784014-43.2771297840136
10380432.736745427984-52.7367454279835
11379410.71191982876-31.7119198287599
12384393.581499918253-9.5814999182526
13392407.041115562223-15.0411155622226
14394405.817514140043-11.8175141400435
15392421.724332628372-29.7243326283717
16396414.382724095297-18.3827240952972
17392409.488318406581-17.4883184065808
18396415.606325517476-19.6063255174763
19419410.711919828768.2880801712401
20421369.10947147467151.8905285253292
21420425.395136894909-5.39513689490901
22418430.289542583625-12.2895425836254
23410409.4883184065810.511681593419189
24418408.2647169844029.73528301559828
25426404.59391271786421.4060872821356
26428413.15912267311814.8408773268819
27430444.972759649774-14.9727596497745
28424426.618738317088-2.61873831708808
29423414.3827240952978.61727590470282
30427444.972759649774-17.9727596497745
31441420.50073120619320.4992687938074
32449396.02870276261152.9712972373892
33452443.7491582275958.2508417724046
34462444.97275964977417.0272403502255
35455438.85475253887916.145247461121
36461430.28954258362530.7104574163746
37461421.72433262837239.2756673716283
38463429.06594116144633.9340588385537
39462459.6559767159242.34402328407641
40456443.74915822759512.2508417724046
41455436.40754969452118.5924503054792
42456453.5379696050282.46203039497187
43472425.39513689490946.604863105091
44472410.7119198287661.2880801712401
45471460.87957813810310.1204218618973
46465442.52555680541622.4744431945837
47459459.655976715924-0.655976715923595
48465446.19636107195418.8036389280464
49468440.07835396105827.9216460389419
50467443.74915822759523.2508417724046
51463484.128005159505-21.1280051595054
52460436.40754969452123.5924503054792
53462463.326780982461-1.32678098246083
54461468.221186671177-7.22118667117723
55476441.30195538323734.6980446167628
56476424.1715354727351.8284645272701
57471464.550382404646.44961759536006
58453469.444788093356-16.4447880933563
59443470.668389515535-27.6683895155354
60442437.63115111674.36884888330007







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.002575130547097290.005150261094194580.997424869452903
60.000611770775582290.001223541551164580.999388229224418
70.01289250282583050.02578500565166110.98710749717417
80.004207731655390520.008415463310781040.99579226834461
90.006174267589971940.01234853517994390.993825732410028
100.01201546540087980.02403093080175960.98798453459912
110.01096052905368450.02192105810736910.989039470946316
120.01094886583080910.02189773166161830.98905113416919
130.02328499328491340.04656998656982670.976715006715087
140.04087250426680580.08174500853361160.959127495733194
150.07164614983776860.1432922996755370.928353850162231
160.1182735331401350.2365470662802710.881726466859865
170.1647166314383530.3294332628767060.835283368561647
180.2652103223633650.5304206447267290.734789677636635
190.5534557429671390.8930885140657220.446544257032861
200.6604191767135130.6791616465729740.339580823286487
210.8404874163482470.3190251673035060.159512583651753
220.9238407487269840.1523185025460330.0761592512730163
230.957635167636840.08472966472631820.0423648323631591
240.9787296057895080.04254078842098480.0212703942104924
250.9898211787566950.02035764248660930.0101788212433047
260.9957695433744320.008460913251135040.00423045662556752
270.9985199501874090.002960099625182890.00148004981259145
280.999644731248430.0007105375031410080.000355268751570504
290.9999678491058226.43017883560779e-053.21508941780389e-05
300.9999985122157182.97556856371764e-061.48778428185882e-06
310.9999997927272784.14545444467483e-072.07272722233741e-07
320.9999999764308054.71383906807145e-082.35691953403572e-08
330.9999999814188513.71622975040493e-081.85811487520247e-08
340.9999999727744665.44510680745652e-082.72255340372826e-08
350.9999999630350367.39299282989615e-083.69649641494808e-08
360.9999999431817391.13636522329712e-075.6818261164856e-08
370.9999999241288441.51742311617459e-077.58711558087295e-08
380.999999852490192.95019618884409e-071.47509809442204e-07
390.9999995192934589.61413083296411e-074.80706541648206e-07
400.9999989131079962.17378400726327e-061.08689200363164e-06
410.9999982332938263.53341234718722e-061.76670617359361e-06
420.9999953102525769.37949484697647e-064.68974742348824e-06
430.999991165717971.76685640603642e-058.83428203018211e-06
440.999985182814222.96343715613768e-051.48171857806884e-05
450.9999695773592776.0845281446512e-053.0422640723256e-05
460.9999073137293620.0001853725412764429.2686270638221e-05
470.9997098439964740.0005803120070529590.000290156003526480
480.9991679141550060.001664171689987650.000832085844993826
490.997877442561440.004245114877118440.00212255743855922
500.9946384376872940.01072312462541180.00536156231270588
510.9887395511778170.02252089764436660.0112604488221833
520.973994101642410.05201179671518010.0260058983575901
530.9401189939712960.1197620120574070.0598810060287036
540.8747200521782280.2505598956435430.125279947821772
550.8055066752490210.3889866495019570.194493324750979

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.00257513054709729 & 0.00515026109419458 & 0.997424869452903 \tabularnewline
6 & 0.00061177077558229 & 0.00122354155116458 & 0.999388229224418 \tabularnewline
7 & 0.0128925028258305 & 0.0257850056516611 & 0.98710749717417 \tabularnewline
8 & 0.00420773165539052 & 0.00841546331078104 & 0.99579226834461 \tabularnewline
9 & 0.00617426758997194 & 0.0123485351799439 & 0.993825732410028 \tabularnewline
10 & 0.0120154654008798 & 0.0240309308017596 & 0.98798453459912 \tabularnewline
11 & 0.0109605290536845 & 0.0219210581073691 & 0.989039470946316 \tabularnewline
12 & 0.0109488658308091 & 0.0218977316616183 & 0.98905113416919 \tabularnewline
13 & 0.0232849932849134 & 0.0465699865698267 & 0.976715006715087 \tabularnewline
14 & 0.0408725042668058 & 0.0817450085336116 & 0.959127495733194 \tabularnewline
15 & 0.0716461498377686 & 0.143292299675537 & 0.928353850162231 \tabularnewline
16 & 0.118273533140135 & 0.236547066280271 & 0.881726466859865 \tabularnewline
17 & 0.164716631438353 & 0.329433262876706 & 0.835283368561647 \tabularnewline
18 & 0.265210322363365 & 0.530420644726729 & 0.734789677636635 \tabularnewline
19 & 0.553455742967139 & 0.893088514065722 & 0.446544257032861 \tabularnewline
20 & 0.660419176713513 & 0.679161646572974 & 0.339580823286487 \tabularnewline
21 & 0.840487416348247 & 0.319025167303506 & 0.159512583651753 \tabularnewline
22 & 0.923840748726984 & 0.152318502546033 & 0.0761592512730163 \tabularnewline
23 & 0.95763516763684 & 0.0847296647263182 & 0.0423648323631591 \tabularnewline
24 & 0.978729605789508 & 0.0425407884209848 & 0.0212703942104924 \tabularnewline
25 & 0.989821178756695 & 0.0203576424866093 & 0.0101788212433047 \tabularnewline
26 & 0.995769543374432 & 0.00846091325113504 & 0.00423045662556752 \tabularnewline
27 & 0.998519950187409 & 0.00296009962518289 & 0.00148004981259145 \tabularnewline
28 & 0.99964473124843 & 0.000710537503141008 & 0.000355268751570504 \tabularnewline
29 & 0.999967849105822 & 6.43017883560779e-05 & 3.21508941780389e-05 \tabularnewline
30 & 0.999998512215718 & 2.97556856371764e-06 & 1.48778428185882e-06 \tabularnewline
31 & 0.999999792727278 & 4.14545444467483e-07 & 2.07272722233741e-07 \tabularnewline
32 & 0.999999976430805 & 4.71383906807145e-08 & 2.35691953403572e-08 \tabularnewline
33 & 0.999999981418851 & 3.71622975040493e-08 & 1.85811487520247e-08 \tabularnewline
34 & 0.999999972774466 & 5.44510680745652e-08 & 2.72255340372826e-08 \tabularnewline
35 & 0.999999963035036 & 7.39299282989615e-08 & 3.69649641494808e-08 \tabularnewline
36 & 0.999999943181739 & 1.13636522329712e-07 & 5.6818261164856e-08 \tabularnewline
37 & 0.999999924128844 & 1.51742311617459e-07 & 7.58711558087295e-08 \tabularnewline
38 & 0.99999985249019 & 2.95019618884409e-07 & 1.47509809442204e-07 \tabularnewline
39 & 0.999999519293458 & 9.61413083296411e-07 & 4.80706541648206e-07 \tabularnewline
40 & 0.999998913107996 & 2.17378400726327e-06 & 1.08689200363164e-06 \tabularnewline
41 & 0.999998233293826 & 3.53341234718722e-06 & 1.76670617359361e-06 \tabularnewline
42 & 0.999995310252576 & 9.37949484697647e-06 & 4.68974742348824e-06 \tabularnewline
43 & 0.99999116571797 & 1.76685640603642e-05 & 8.83428203018211e-06 \tabularnewline
44 & 0.99998518281422 & 2.96343715613768e-05 & 1.48171857806884e-05 \tabularnewline
45 & 0.999969577359277 & 6.0845281446512e-05 & 3.0422640723256e-05 \tabularnewline
46 & 0.999907313729362 & 0.000185372541276442 & 9.2686270638221e-05 \tabularnewline
47 & 0.999709843996474 & 0.000580312007052959 & 0.000290156003526480 \tabularnewline
48 & 0.999167914155006 & 0.00166417168998765 & 0.000832085844993826 \tabularnewline
49 & 0.99787744256144 & 0.00424511487711844 & 0.00212255743855922 \tabularnewline
50 & 0.994638437687294 & 0.0107231246254118 & 0.00536156231270588 \tabularnewline
51 & 0.988739551177817 & 0.0225208976443666 & 0.0112604488221833 \tabularnewline
52 & 0.97399410164241 & 0.0520117967151801 & 0.0260058983575901 \tabularnewline
53 & 0.940118993971296 & 0.119762012057407 & 0.0598810060287036 \tabularnewline
54 & 0.874720052178228 & 0.250559895643543 & 0.125279947821772 \tabularnewline
55 & 0.805506675249021 & 0.388986649501957 & 0.194493324750979 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58279&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.00257513054709729[/C][C]0.00515026109419458[/C][C]0.997424869452903[/C][/ROW]
[ROW][C]6[/C][C]0.00061177077558229[/C][C]0.00122354155116458[/C][C]0.999388229224418[/C][/ROW]
[ROW][C]7[/C][C]0.0128925028258305[/C][C]0.0257850056516611[/C][C]0.98710749717417[/C][/ROW]
[ROW][C]8[/C][C]0.00420773165539052[/C][C]0.00841546331078104[/C][C]0.99579226834461[/C][/ROW]
[ROW][C]9[/C][C]0.00617426758997194[/C][C]0.0123485351799439[/C][C]0.993825732410028[/C][/ROW]
[ROW][C]10[/C][C]0.0120154654008798[/C][C]0.0240309308017596[/C][C]0.98798453459912[/C][/ROW]
[ROW][C]11[/C][C]0.0109605290536845[/C][C]0.0219210581073691[/C][C]0.989039470946316[/C][/ROW]
[ROW][C]12[/C][C]0.0109488658308091[/C][C]0.0218977316616183[/C][C]0.98905113416919[/C][/ROW]
[ROW][C]13[/C][C]0.0232849932849134[/C][C]0.0465699865698267[/C][C]0.976715006715087[/C][/ROW]
[ROW][C]14[/C][C]0.0408725042668058[/C][C]0.0817450085336116[/C][C]0.959127495733194[/C][/ROW]
[ROW][C]15[/C][C]0.0716461498377686[/C][C]0.143292299675537[/C][C]0.928353850162231[/C][/ROW]
[ROW][C]16[/C][C]0.118273533140135[/C][C]0.236547066280271[/C][C]0.881726466859865[/C][/ROW]
[ROW][C]17[/C][C]0.164716631438353[/C][C]0.329433262876706[/C][C]0.835283368561647[/C][/ROW]
[ROW][C]18[/C][C]0.265210322363365[/C][C]0.530420644726729[/C][C]0.734789677636635[/C][/ROW]
[ROW][C]19[/C][C]0.553455742967139[/C][C]0.893088514065722[/C][C]0.446544257032861[/C][/ROW]
[ROW][C]20[/C][C]0.660419176713513[/C][C]0.679161646572974[/C][C]0.339580823286487[/C][/ROW]
[ROW][C]21[/C][C]0.840487416348247[/C][C]0.319025167303506[/C][C]0.159512583651753[/C][/ROW]
[ROW][C]22[/C][C]0.923840748726984[/C][C]0.152318502546033[/C][C]0.0761592512730163[/C][/ROW]
[ROW][C]23[/C][C]0.95763516763684[/C][C]0.0847296647263182[/C][C]0.0423648323631591[/C][/ROW]
[ROW][C]24[/C][C]0.978729605789508[/C][C]0.0425407884209848[/C][C]0.0212703942104924[/C][/ROW]
[ROW][C]25[/C][C]0.989821178756695[/C][C]0.0203576424866093[/C][C]0.0101788212433047[/C][/ROW]
[ROW][C]26[/C][C]0.995769543374432[/C][C]0.00846091325113504[/C][C]0.00423045662556752[/C][/ROW]
[ROW][C]27[/C][C]0.998519950187409[/C][C]0.00296009962518289[/C][C]0.00148004981259145[/C][/ROW]
[ROW][C]28[/C][C]0.99964473124843[/C][C]0.000710537503141008[/C][C]0.000355268751570504[/C][/ROW]
[ROW][C]29[/C][C]0.999967849105822[/C][C]6.43017883560779e-05[/C][C]3.21508941780389e-05[/C][/ROW]
[ROW][C]30[/C][C]0.999998512215718[/C][C]2.97556856371764e-06[/C][C]1.48778428185882e-06[/C][/ROW]
[ROW][C]31[/C][C]0.999999792727278[/C][C]4.14545444467483e-07[/C][C]2.07272722233741e-07[/C][/ROW]
[ROW][C]32[/C][C]0.999999976430805[/C][C]4.71383906807145e-08[/C][C]2.35691953403572e-08[/C][/ROW]
[ROW][C]33[/C][C]0.999999981418851[/C][C]3.71622975040493e-08[/C][C]1.85811487520247e-08[/C][/ROW]
[ROW][C]34[/C][C]0.999999972774466[/C][C]5.44510680745652e-08[/C][C]2.72255340372826e-08[/C][/ROW]
[ROW][C]35[/C][C]0.999999963035036[/C][C]7.39299282989615e-08[/C][C]3.69649641494808e-08[/C][/ROW]
[ROW][C]36[/C][C]0.999999943181739[/C][C]1.13636522329712e-07[/C][C]5.6818261164856e-08[/C][/ROW]
[ROW][C]37[/C][C]0.999999924128844[/C][C]1.51742311617459e-07[/C][C]7.58711558087295e-08[/C][/ROW]
[ROW][C]38[/C][C]0.99999985249019[/C][C]2.95019618884409e-07[/C][C]1.47509809442204e-07[/C][/ROW]
[ROW][C]39[/C][C]0.999999519293458[/C][C]9.61413083296411e-07[/C][C]4.80706541648206e-07[/C][/ROW]
[ROW][C]40[/C][C]0.999998913107996[/C][C]2.17378400726327e-06[/C][C]1.08689200363164e-06[/C][/ROW]
[ROW][C]41[/C][C]0.999998233293826[/C][C]3.53341234718722e-06[/C][C]1.76670617359361e-06[/C][/ROW]
[ROW][C]42[/C][C]0.999995310252576[/C][C]9.37949484697647e-06[/C][C]4.68974742348824e-06[/C][/ROW]
[ROW][C]43[/C][C]0.99999116571797[/C][C]1.76685640603642e-05[/C][C]8.83428203018211e-06[/C][/ROW]
[ROW][C]44[/C][C]0.99998518281422[/C][C]2.96343715613768e-05[/C][C]1.48171857806884e-05[/C][/ROW]
[ROW][C]45[/C][C]0.999969577359277[/C][C]6.0845281446512e-05[/C][C]3.0422640723256e-05[/C][/ROW]
[ROW][C]46[/C][C]0.999907313729362[/C][C]0.000185372541276442[/C][C]9.2686270638221e-05[/C][/ROW]
[ROW][C]47[/C][C]0.999709843996474[/C][C]0.000580312007052959[/C][C]0.000290156003526480[/C][/ROW]
[ROW][C]48[/C][C]0.999167914155006[/C][C]0.00166417168998765[/C][C]0.000832085844993826[/C][/ROW]
[ROW][C]49[/C][C]0.99787744256144[/C][C]0.00424511487711844[/C][C]0.00212255743855922[/C][/ROW]
[ROW][C]50[/C][C]0.994638437687294[/C][C]0.0107231246254118[/C][C]0.00536156231270588[/C][/ROW]
[ROW][C]51[/C][C]0.988739551177817[/C][C]0.0225208976443666[/C][C]0.0112604488221833[/C][/ROW]
[ROW][C]52[/C][C]0.97399410164241[/C][C]0.0520117967151801[/C][C]0.0260058983575901[/C][/ROW]
[ROW][C]53[/C][C]0.940118993971296[/C][C]0.119762012057407[/C][C]0.0598810060287036[/C][/ROW]
[ROW][C]54[/C][C]0.874720052178228[/C][C]0.250559895643543[/C][C]0.125279947821772[/C][/ROW]
[ROW][C]55[/C][C]0.805506675249021[/C][C]0.388986649501957[/C][C]0.194493324750979[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58279&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58279&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.002575130547097290.005150261094194580.997424869452903
60.000611770775582290.001223541551164580.999388229224418
70.01289250282583050.02578500565166110.98710749717417
80.004207731655390520.008415463310781040.99579226834461
90.006174267589971940.01234853517994390.993825732410028
100.01201546540087980.02403093080175960.98798453459912
110.01096052905368450.02192105810736910.989039470946316
120.01094886583080910.02189773166161830.98905113416919
130.02328499328491340.04656998656982670.976715006715087
140.04087250426680580.08174500853361160.959127495733194
150.07164614983776860.1432922996755370.928353850162231
160.1182735331401350.2365470662802710.881726466859865
170.1647166314383530.3294332628767060.835283368561647
180.2652103223633650.5304206447267290.734789677636635
190.5534557429671390.8930885140657220.446544257032861
200.6604191767135130.6791616465729740.339580823286487
210.8404874163482470.3190251673035060.159512583651753
220.9238407487269840.1523185025460330.0761592512730163
230.957635167636840.08472966472631820.0423648323631591
240.9787296057895080.04254078842098480.0212703942104924
250.9898211787566950.02035764248660930.0101788212433047
260.9957695433744320.008460913251135040.00423045662556752
270.9985199501874090.002960099625182890.00148004981259145
280.999644731248430.0007105375031410080.000355268751570504
290.9999678491058226.43017883560779e-053.21508941780389e-05
300.9999985122157182.97556856371764e-061.48778428185882e-06
310.9999997927272784.14545444467483e-072.07272722233741e-07
320.9999999764308054.71383906807145e-082.35691953403572e-08
330.9999999814188513.71622975040493e-081.85811487520247e-08
340.9999999727744665.44510680745652e-082.72255340372826e-08
350.9999999630350367.39299282989615e-083.69649641494808e-08
360.9999999431817391.13636522329712e-075.6818261164856e-08
370.9999999241288441.51742311617459e-077.58711558087295e-08
380.999999852490192.95019618884409e-071.47509809442204e-07
390.9999995192934589.61413083296411e-074.80706541648206e-07
400.9999989131079962.17378400726327e-061.08689200363164e-06
410.9999982332938263.53341234718722e-061.76670617359361e-06
420.9999953102525769.37949484697647e-064.68974742348824e-06
430.999991165717971.76685640603642e-058.83428203018211e-06
440.999985182814222.96343715613768e-051.48171857806884e-05
450.9999695773592776.0845281446512e-053.0422640723256e-05
460.9999073137293620.0001853725412764429.2686270638221e-05
470.9997098439964740.0005803120070529590.000290156003526480
480.9991679141550060.001664171689987650.000832085844993826
490.997877442561440.004245114877118440.00212255743855922
500.9946384376872940.01072312462541180.00536156231270588
510.9887395511778170.02252089764436660.0112604488221833
520.973994101642410.05201179671518010.0260058983575901
530.9401189939712960.1197620120574070.0598810060287036
540.8747200521782280.2505598956435430.125279947821772
550.8055066752490210.3889866495019570.194493324750979







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.529411764705882NOK
5% type I error level370.725490196078431NOK
10% type I error level400.784313725490196NOK

\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 & 27 & 0.529411764705882 & NOK \tabularnewline
5% type I error level & 37 & 0.725490196078431 & NOK \tabularnewline
10% type I error level & 40 & 0.784313725490196 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58279&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]27[/C][C]0.529411764705882[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]37[/C][C]0.725490196078431[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]40[/C][C]0.784313725490196[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58279&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58279&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 level270.529411764705882NOK
5% type I error level370.725490196078431NOK
10% type I error level400.784313725490196NOK



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