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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 10:59:51 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258653649x1vfg0xj5bluev0.htm/, Retrieved Fri, 29 Mar 2024 11:33:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57857, Retrieved Fri, 29 Mar 2024 11:33:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2009-11-19 09:51:18] [74be16979710d4c4e7c6647856088456]
-   P         [Multiple Regression] [] [2009-11-19 17:59:51] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-               [Multiple Regression] [] [2009-12-13 12:31:55] [80b559301b076f6db87527dfd2199d75]
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Dataseries X:
519	97.4
517	97
510	105.4
509	102.7
501	98.1
507	104.5
569	87.4
580	89.9
578	109.8
565	111.7
547	98.6
555	96.9
562	95.1
561	97
555	112.7
544	102.9
537	97.4
543	111.4
594	87.4
611	96.8
613	114.1
611	110.3
594	103.9
595	101.6
591	94.6
589	95.9
584	104.7
573	102.8
567	98.1
569	113.9
621	80.9
629	95.7
628	113.2
612	105.9
595	108.8
597	102.3
593	99
590	100.7
580	115.5
574	100.7
573	109.9
573	114.6
620	85.4
626	100.5
620	114.8
588	116.5
566	112.9
557	102
561	106
549	105.3
532	118.8
526	106.1
511	109.3
499	117.2
555	92.5
565	104.2
542	112.5
527	122.4
510	113.3
514	100
517	110.7
508	112.8
493	109.8
490	117.3
469	109.1
478	115.9
528	96
534	99.8
518	116.8
506	115.7
502	99.4
516	94.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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=57857&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=57857&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57857&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 time7 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 663.747235364029 -0.849955888088473X[t] -0.514576813656158t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  663.747235364029 -0.849955888088473X[t] -0.514576813656158t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57857&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  663.747235364029 -0.849955888088473X[t] -0.514576813656158t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57857&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57857&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] = + 663.747235364029 -0.849955888088473X[t] -0.514576813656158t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)663.74723536402955.06013312.05500
X-0.8499558880884730.549176-1.54770.1262710.063135
t-0.5145768136561580.234269-2.19650.0314170.015709

\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) & 663.747235364029 & 55.060133 & 12.055 & 0 & 0 \tabularnewline
X & -0.849955888088473 & 0.549176 & -1.5477 & 0.126271 & 0.063135 \tabularnewline
t & -0.514576813656158 & 0.234269 & -2.1965 & 0.031417 & 0.015709 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57857&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]663.747235364029[/C][C]55.060133[/C][C]12.055[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-0.849955888088473[/C][C]0.549176[/C][C]-1.5477[/C][C]0.126271[/C][C]0.063135[/C][/ROW]
[ROW][C]t[/C][C]-0.514576813656158[/C][C]0.234269[/C][C]-2.1965[/C][C]0.031417[/C][C]0.015709[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57857&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57857&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)663.74723536402955.06013312.05500
X-0.8499558880884730.549176-1.54770.1262710.063135
t-0.5145768136561580.234269-2.19650.0314170.015709







Multiple Linear Regression - Regression Statistics
Multiple R0.373029972736068
R-squared0.139151360559472
Adjusted R-squared0.114199226082935
F-TEST (value)5.57673174975546
F-TEST (DF numerator)2
F-TEST (DF denominator)69
p-value0.00568819544894872
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation38.499212515376
Sum Squared Residuals102271.066136982

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.373029972736068 \tabularnewline
R-squared & 0.139151360559472 \tabularnewline
Adjusted R-squared & 0.114199226082935 \tabularnewline
F-TEST (value) & 5.57673174975546 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 69 \tabularnewline
p-value & 0.00568819544894872 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 38.499212515376 \tabularnewline
Sum Squared Residuals & 102271.066136982 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57857&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.373029972736068[/C][/ROW]
[ROW][C]R-squared[/C][C]0.139151360559472[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.114199226082935[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.57673174975546[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]69[/C][/ROW]
[ROW][C]p-value[/C][C]0.00568819544894872[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]38.499212515376[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]102271.066136982[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57857&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57857&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 R0.373029972736068
R-squared0.139151360559472
Adjusted R-squared0.114199226082935
F-TEST (value)5.57673174975546
F-TEST (DF numerator)2
F-TEST (DF denominator)69
p-value0.00568819544894872
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation38.499212515376
Sum Squared Residuals102271.066136982







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1519580.446955050553-61.4469550505531
2517580.272360592135-63.272360592135
3510572.618154318536-62.6181543185356
4509574.398458402718-65.3984584027184
5501577.793678674269-76.7936786742692
6507571.839384176847-64.8393841768468
7569585.859053049503-16.8590530495035
8580583.219586515626-3.21958651562619
9578565.79088752900912.2091124709906
10565563.6613945279851.33860547201484
11547574.281239848288-27.281239848288
12555575.211588044382-20.2115880443822
13562576.226931829285-14.2269318292854
14561574.097438828261-13.0974388282611
15555560.238554571616-5.2385545716159
16544568.053545461227-24.0535454612268
17537572.213726032057-35.2137260320572
18543559.799766785162-16.7997667851624
19594579.68413128563014.3158687143704
20611571.17996912394239.8200308760582
21613555.96115544635557.0388445536449
22611558.67641100743552.3235889925649
23594563.60155187754530.3984481224548
24595565.04187360649329.9581263935075
25591570.47698800945620.5230119905443
26589568.85746854128520.1425314587155
27584560.8632799124523.1367200875502
28573561.96361928616211.0363807138383
29567565.4438351465211.55616485347860
30569551.49995530106717.5000446989326
31621579.03392279433141.9660772056692
32629565.93999883696563.0600011630347
33628550.55119398176177.4488060182392
34612556.24129515115155.7587048488495
35595553.26184626203841.7381537379622
36597558.27198272095738.7280172790433
37593560.56226033799232.4377396620075
38590558.60275851458631.3972414854141
39580545.5088345572234.4911654427796
40574557.57360488727416.4263951127264
41573549.23943390320323.7605660967965
42573544.73006441553228.2699355844685
43620569.03419953405950.9658004659412
44626555.68528881026770.3147111897333
45620543.01634279694576.9836572030546
46588541.05684097353946.9431590264612
47566543.60210535700122.3978946429988
48557552.3520477235094.64795227649065
49561548.43764735749912.5623526425007
50549548.5180396655050.481960334494919
51532536.529058362654-4.52905836265454
52526546.808921327722-20.808921327722
53511543.574485672183-32.5744856721827
54499536.345257342628-37.3452573426276
55555556.824590964757-1.82459096475674
56565546.36553026046518.6344697395346
57542538.7963195756753.20368042432503
58527529.867179469943-2.86717946994292
59510537.087201237892-27.0872012378919
60514547.877037735812-33.8770377358124
61517538.26793291961-21.2679329196096
62508535.968448740968-27.9684487409676
63493538.003739591577-45.0037395915769
64490531.114493617257-41.1144936172572
65469537.569555085926-68.5695550859265
66478531.275278233269-53.2752782332687
67528547.674823592573-19.6748235925732
68534543.930414404181-9.93041440418084
69518528.966587493021-10.9665874930206
70506529.386962156262-23.3869621562618
71502542.726666318448-40.7266663184478
72516546.546864534043-30.5468645340428

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 519 & 580.446955050553 & -61.4469550505531 \tabularnewline
2 & 517 & 580.272360592135 & -63.272360592135 \tabularnewline
3 & 510 & 572.618154318536 & -62.6181543185356 \tabularnewline
4 & 509 & 574.398458402718 & -65.3984584027184 \tabularnewline
5 & 501 & 577.793678674269 & -76.7936786742692 \tabularnewline
6 & 507 & 571.839384176847 & -64.8393841768468 \tabularnewline
7 & 569 & 585.859053049503 & -16.8590530495035 \tabularnewline
8 & 580 & 583.219586515626 & -3.21958651562619 \tabularnewline
9 & 578 & 565.790887529009 & 12.2091124709906 \tabularnewline
10 & 565 & 563.661394527985 & 1.33860547201484 \tabularnewline
11 & 547 & 574.281239848288 & -27.281239848288 \tabularnewline
12 & 555 & 575.211588044382 & -20.2115880443822 \tabularnewline
13 & 562 & 576.226931829285 & -14.2269318292854 \tabularnewline
14 & 561 & 574.097438828261 & -13.0974388282611 \tabularnewline
15 & 555 & 560.238554571616 & -5.2385545716159 \tabularnewline
16 & 544 & 568.053545461227 & -24.0535454612268 \tabularnewline
17 & 537 & 572.213726032057 & -35.2137260320572 \tabularnewline
18 & 543 & 559.799766785162 & -16.7997667851624 \tabularnewline
19 & 594 & 579.684131285630 & 14.3158687143704 \tabularnewline
20 & 611 & 571.179969123942 & 39.8200308760582 \tabularnewline
21 & 613 & 555.961155446355 & 57.0388445536449 \tabularnewline
22 & 611 & 558.676411007435 & 52.3235889925649 \tabularnewline
23 & 594 & 563.601551877545 & 30.3984481224548 \tabularnewline
24 & 595 & 565.041873606493 & 29.9581263935075 \tabularnewline
25 & 591 & 570.476988009456 & 20.5230119905443 \tabularnewline
26 & 589 & 568.857468541285 & 20.1425314587155 \tabularnewline
27 & 584 & 560.86327991245 & 23.1367200875502 \tabularnewline
28 & 573 & 561.963619286162 & 11.0363807138383 \tabularnewline
29 & 567 & 565.443835146521 & 1.55616485347860 \tabularnewline
30 & 569 & 551.499955301067 & 17.5000446989326 \tabularnewline
31 & 621 & 579.033922794331 & 41.9660772056692 \tabularnewline
32 & 629 & 565.939998836965 & 63.0600011630347 \tabularnewline
33 & 628 & 550.551193981761 & 77.4488060182392 \tabularnewline
34 & 612 & 556.241295151151 & 55.7587048488495 \tabularnewline
35 & 595 & 553.261846262038 & 41.7381537379622 \tabularnewline
36 & 597 & 558.271982720957 & 38.7280172790433 \tabularnewline
37 & 593 & 560.562260337992 & 32.4377396620075 \tabularnewline
38 & 590 & 558.602758514586 & 31.3972414854141 \tabularnewline
39 & 580 & 545.50883455722 & 34.4911654427796 \tabularnewline
40 & 574 & 557.573604887274 & 16.4263951127264 \tabularnewline
41 & 573 & 549.239433903203 & 23.7605660967965 \tabularnewline
42 & 573 & 544.730064415532 & 28.2699355844685 \tabularnewline
43 & 620 & 569.034199534059 & 50.9658004659412 \tabularnewline
44 & 626 & 555.685288810267 & 70.3147111897333 \tabularnewline
45 & 620 & 543.016342796945 & 76.9836572030546 \tabularnewline
46 & 588 & 541.056840973539 & 46.9431590264612 \tabularnewline
47 & 566 & 543.602105357001 & 22.3978946429988 \tabularnewline
48 & 557 & 552.352047723509 & 4.64795227649065 \tabularnewline
49 & 561 & 548.437647357499 & 12.5623526425007 \tabularnewline
50 & 549 & 548.518039665505 & 0.481960334494919 \tabularnewline
51 & 532 & 536.529058362654 & -4.52905836265454 \tabularnewline
52 & 526 & 546.808921327722 & -20.808921327722 \tabularnewline
53 & 511 & 543.574485672183 & -32.5744856721827 \tabularnewline
54 & 499 & 536.345257342628 & -37.3452573426276 \tabularnewline
55 & 555 & 556.824590964757 & -1.82459096475674 \tabularnewline
56 & 565 & 546.365530260465 & 18.6344697395346 \tabularnewline
57 & 542 & 538.796319575675 & 3.20368042432503 \tabularnewline
58 & 527 & 529.867179469943 & -2.86717946994292 \tabularnewline
59 & 510 & 537.087201237892 & -27.0872012378919 \tabularnewline
60 & 514 & 547.877037735812 & -33.8770377358124 \tabularnewline
61 & 517 & 538.26793291961 & -21.2679329196096 \tabularnewline
62 & 508 & 535.968448740968 & -27.9684487409676 \tabularnewline
63 & 493 & 538.003739591577 & -45.0037395915769 \tabularnewline
64 & 490 & 531.114493617257 & -41.1144936172572 \tabularnewline
65 & 469 & 537.569555085926 & -68.5695550859265 \tabularnewline
66 & 478 & 531.275278233269 & -53.2752782332687 \tabularnewline
67 & 528 & 547.674823592573 & -19.6748235925732 \tabularnewline
68 & 534 & 543.930414404181 & -9.93041440418084 \tabularnewline
69 & 518 & 528.966587493021 & -10.9665874930206 \tabularnewline
70 & 506 & 529.386962156262 & -23.3869621562618 \tabularnewline
71 & 502 & 542.726666318448 & -40.7266663184478 \tabularnewline
72 & 516 & 546.546864534043 & -30.5468645340428 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57857&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]519[/C][C]580.446955050553[/C][C]-61.4469550505531[/C][/ROW]
[ROW][C]2[/C][C]517[/C][C]580.272360592135[/C][C]-63.272360592135[/C][/ROW]
[ROW][C]3[/C][C]510[/C][C]572.618154318536[/C][C]-62.6181543185356[/C][/ROW]
[ROW][C]4[/C][C]509[/C][C]574.398458402718[/C][C]-65.3984584027184[/C][/ROW]
[ROW][C]5[/C][C]501[/C][C]577.793678674269[/C][C]-76.7936786742692[/C][/ROW]
[ROW][C]6[/C][C]507[/C][C]571.839384176847[/C][C]-64.8393841768468[/C][/ROW]
[ROW][C]7[/C][C]569[/C][C]585.859053049503[/C][C]-16.8590530495035[/C][/ROW]
[ROW][C]8[/C][C]580[/C][C]583.219586515626[/C][C]-3.21958651562619[/C][/ROW]
[ROW][C]9[/C][C]578[/C][C]565.790887529009[/C][C]12.2091124709906[/C][/ROW]
[ROW][C]10[/C][C]565[/C][C]563.661394527985[/C][C]1.33860547201484[/C][/ROW]
[ROW][C]11[/C][C]547[/C][C]574.281239848288[/C][C]-27.281239848288[/C][/ROW]
[ROW][C]12[/C][C]555[/C][C]575.211588044382[/C][C]-20.2115880443822[/C][/ROW]
[ROW][C]13[/C][C]562[/C][C]576.226931829285[/C][C]-14.2269318292854[/C][/ROW]
[ROW][C]14[/C][C]561[/C][C]574.097438828261[/C][C]-13.0974388282611[/C][/ROW]
[ROW][C]15[/C][C]555[/C][C]560.238554571616[/C][C]-5.2385545716159[/C][/ROW]
[ROW][C]16[/C][C]544[/C][C]568.053545461227[/C][C]-24.0535454612268[/C][/ROW]
[ROW][C]17[/C][C]537[/C][C]572.213726032057[/C][C]-35.2137260320572[/C][/ROW]
[ROW][C]18[/C][C]543[/C][C]559.799766785162[/C][C]-16.7997667851624[/C][/ROW]
[ROW][C]19[/C][C]594[/C][C]579.684131285630[/C][C]14.3158687143704[/C][/ROW]
[ROW][C]20[/C][C]611[/C][C]571.179969123942[/C][C]39.8200308760582[/C][/ROW]
[ROW][C]21[/C][C]613[/C][C]555.961155446355[/C][C]57.0388445536449[/C][/ROW]
[ROW][C]22[/C][C]611[/C][C]558.676411007435[/C][C]52.3235889925649[/C][/ROW]
[ROW][C]23[/C][C]594[/C][C]563.601551877545[/C][C]30.3984481224548[/C][/ROW]
[ROW][C]24[/C][C]595[/C][C]565.041873606493[/C][C]29.9581263935075[/C][/ROW]
[ROW][C]25[/C][C]591[/C][C]570.476988009456[/C][C]20.5230119905443[/C][/ROW]
[ROW][C]26[/C][C]589[/C][C]568.857468541285[/C][C]20.1425314587155[/C][/ROW]
[ROW][C]27[/C][C]584[/C][C]560.86327991245[/C][C]23.1367200875502[/C][/ROW]
[ROW][C]28[/C][C]573[/C][C]561.963619286162[/C][C]11.0363807138383[/C][/ROW]
[ROW][C]29[/C][C]567[/C][C]565.443835146521[/C][C]1.55616485347860[/C][/ROW]
[ROW][C]30[/C][C]569[/C][C]551.499955301067[/C][C]17.5000446989326[/C][/ROW]
[ROW][C]31[/C][C]621[/C][C]579.033922794331[/C][C]41.9660772056692[/C][/ROW]
[ROW][C]32[/C][C]629[/C][C]565.939998836965[/C][C]63.0600011630347[/C][/ROW]
[ROW][C]33[/C][C]628[/C][C]550.551193981761[/C][C]77.4488060182392[/C][/ROW]
[ROW][C]34[/C][C]612[/C][C]556.241295151151[/C][C]55.7587048488495[/C][/ROW]
[ROW][C]35[/C][C]595[/C][C]553.261846262038[/C][C]41.7381537379622[/C][/ROW]
[ROW][C]36[/C][C]597[/C][C]558.271982720957[/C][C]38.7280172790433[/C][/ROW]
[ROW][C]37[/C][C]593[/C][C]560.562260337992[/C][C]32.4377396620075[/C][/ROW]
[ROW][C]38[/C][C]590[/C][C]558.602758514586[/C][C]31.3972414854141[/C][/ROW]
[ROW][C]39[/C][C]580[/C][C]545.50883455722[/C][C]34.4911654427796[/C][/ROW]
[ROW][C]40[/C][C]574[/C][C]557.573604887274[/C][C]16.4263951127264[/C][/ROW]
[ROW][C]41[/C][C]573[/C][C]549.239433903203[/C][C]23.7605660967965[/C][/ROW]
[ROW][C]42[/C][C]573[/C][C]544.730064415532[/C][C]28.2699355844685[/C][/ROW]
[ROW][C]43[/C][C]620[/C][C]569.034199534059[/C][C]50.9658004659412[/C][/ROW]
[ROW][C]44[/C][C]626[/C][C]555.685288810267[/C][C]70.3147111897333[/C][/ROW]
[ROW][C]45[/C][C]620[/C][C]543.016342796945[/C][C]76.9836572030546[/C][/ROW]
[ROW][C]46[/C][C]588[/C][C]541.056840973539[/C][C]46.9431590264612[/C][/ROW]
[ROW][C]47[/C][C]566[/C][C]543.602105357001[/C][C]22.3978946429988[/C][/ROW]
[ROW][C]48[/C][C]557[/C][C]552.352047723509[/C][C]4.64795227649065[/C][/ROW]
[ROW][C]49[/C][C]561[/C][C]548.437647357499[/C][C]12.5623526425007[/C][/ROW]
[ROW][C]50[/C][C]549[/C][C]548.518039665505[/C][C]0.481960334494919[/C][/ROW]
[ROW][C]51[/C][C]532[/C][C]536.529058362654[/C][C]-4.52905836265454[/C][/ROW]
[ROW][C]52[/C][C]526[/C][C]546.808921327722[/C][C]-20.808921327722[/C][/ROW]
[ROW][C]53[/C][C]511[/C][C]543.574485672183[/C][C]-32.5744856721827[/C][/ROW]
[ROW][C]54[/C][C]499[/C][C]536.345257342628[/C][C]-37.3452573426276[/C][/ROW]
[ROW][C]55[/C][C]555[/C][C]556.824590964757[/C][C]-1.82459096475674[/C][/ROW]
[ROW][C]56[/C][C]565[/C][C]546.365530260465[/C][C]18.6344697395346[/C][/ROW]
[ROW][C]57[/C][C]542[/C][C]538.796319575675[/C][C]3.20368042432503[/C][/ROW]
[ROW][C]58[/C][C]527[/C][C]529.867179469943[/C][C]-2.86717946994292[/C][/ROW]
[ROW][C]59[/C][C]510[/C][C]537.087201237892[/C][C]-27.0872012378919[/C][/ROW]
[ROW][C]60[/C][C]514[/C][C]547.877037735812[/C][C]-33.8770377358124[/C][/ROW]
[ROW][C]61[/C][C]517[/C][C]538.26793291961[/C][C]-21.2679329196096[/C][/ROW]
[ROW][C]62[/C][C]508[/C][C]535.968448740968[/C][C]-27.9684487409676[/C][/ROW]
[ROW][C]63[/C][C]493[/C][C]538.003739591577[/C][C]-45.0037395915769[/C][/ROW]
[ROW][C]64[/C][C]490[/C][C]531.114493617257[/C][C]-41.1144936172572[/C][/ROW]
[ROW][C]65[/C][C]469[/C][C]537.569555085926[/C][C]-68.5695550859265[/C][/ROW]
[ROW][C]66[/C][C]478[/C][C]531.275278233269[/C][C]-53.2752782332687[/C][/ROW]
[ROW][C]67[/C][C]528[/C][C]547.674823592573[/C][C]-19.6748235925732[/C][/ROW]
[ROW][C]68[/C][C]534[/C][C]543.930414404181[/C][C]-9.93041440418084[/C][/ROW]
[ROW][C]69[/C][C]518[/C][C]528.966587493021[/C][C]-10.9665874930206[/C][/ROW]
[ROW][C]70[/C][C]506[/C][C]529.386962156262[/C][C]-23.3869621562618[/C][/ROW]
[ROW][C]71[/C][C]502[/C][C]542.726666318448[/C][C]-40.7266663184478[/C][/ROW]
[ROW][C]72[/C][C]516[/C][C]546.546864534043[/C][C]-30.5468645340428[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57857&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57857&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
1519580.446955050553-61.4469550505531
2517580.272360592135-63.272360592135
3510572.618154318536-62.6181543185356
4509574.398458402718-65.3984584027184
5501577.793678674269-76.7936786742692
6507571.839384176847-64.8393841768468
7569585.859053049503-16.8590530495035
8580583.219586515626-3.21958651562619
9578565.79088752900912.2091124709906
10565563.6613945279851.33860547201484
11547574.281239848288-27.281239848288
12555575.211588044382-20.2115880443822
13562576.226931829285-14.2269318292854
14561574.097438828261-13.0974388282611
15555560.238554571616-5.2385545716159
16544568.053545461227-24.0535454612268
17537572.213726032057-35.2137260320572
18543559.799766785162-16.7997667851624
19594579.68413128563014.3158687143704
20611571.17996912394239.8200308760582
21613555.96115544635557.0388445536449
22611558.67641100743552.3235889925649
23594563.60155187754530.3984481224548
24595565.04187360649329.9581263935075
25591570.47698800945620.5230119905443
26589568.85746854128520.1425314587155
27584560.8632799124523.1367200875502
28573561.96361928616211.0363807138383
29567565.4438351465211.55616485347860
30569551.49995530106717.5000446989326
31621579.03392279433141.9660772056692
32629565.93999883696563.0600011630347
33628550.55119398176177.4488060182392
34612556.24129515115155.7587048488495
35595553.26184626203841.7381537379622
36597558.27198272095738.7280172790433
37593560.56226033799232.4377396620075
38590558.60275851458631.3972414854141
39580545.5088345572234.4911654427796
40574557.57360488727416.4263951127264
41573549.23943390320323.7605660967965
42573544.73006441553228.2699355844685
43620569.03419953405950.9658004659412
44626555.68528881026770.3147111897333
45620543.01634279694576.9836572030546
46588541.05684097353946.9431590264612
47566543.60210535700122.3978946429988
48557552.3520477235094.64795227649065
49561548.43764735749912.5623526425007
50549548.5180396655050.481960334494919
51532536.529058362654-4.52905836265454
52526546.808921327722-20.808921327722
53511543.574485672183-32.5744856721827
54499536.345257342628-37.3452573426276
55555556.824590964757-1.82459096475674
56565546.36553026046518.6344697395346
57542538.7963195756753.20368042432503
58527529.867179469943-2.86717946994292
59510537.087201237892-27.0872012378919
60514547.877037735812-33.8770377358124
61517538.26793291961-21.2679329196096
62508535.968448740968-27.9684487409676
63493538.003739591577-45.0037395915769
64490531.114493617257-41.1144936172572
65469537.569555085926-68.5695550859265
66478531.275278233269-53.2752782332687
67528547.674823592573-19.6748235925732
68534543.930414404181-9.93041440418084
69518528.966587493021-10.9665874930206
70506529.386962156262-23.3869621562618
71502542.726666318448-40.7266663184478
72516546.546864534043-30.5468645340428







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.002627026827348280.005254053654696550.997372973172652
70.05564121825814130.1112824365162830.944358781741859
80.04822450342753350.0964490068550670.951775496572467
90.1868981511655040.3737963023310090.813101848834496
100.1149098731283700.2298197462567410.88509012687163
110.1664148155329330.3328296310658660.833585184467067
120.1642130419074030.3284260838148060.835786958092597
130.1417356399794120.2834712799588240.858264360020588
140.1259484286986820.2518968573973640.874051571301318
150.1017059749304580.2034119498609170.898294025069542
160.1521738819666190.3043477639332380.847826118033381
170.3182580618079520.6365161236159050.681741938192048
180.3925695095241550.785139019048310.607430490475845
190.3753106755240490.7506213510480990.624689324475951
200.3999192823111470.7998385646222950.600080717688853
210.4651624826719650.930324965343930.534837517328036
220.4277740851059390.8555481702118780.572225914894061
230.3718670614463870.7437341228927740.628132938553613
240.3245991608686910.6491983217373820.67540083913131
250.320838148483090.641676296966180.67916185151691
260.3276755454414810.6553510908829620.672324454558519
270.3351684128131190.6703368256262390.664831587186881
280.440191797246960.880383594493920.55980820275304
290.6542574481482380.6914851037035240.345742551851762
300.7311829640336140.5376340719327710.268817035966386
310.7008199468427130.5983601063145730.299180053157287
320.6572414351369750.685517129726050.342758564863025
330.6627645122294390.6744709755411220.337235487770561
340.5995202941556570.8009594116886870.400479705844343
350.5601337319602910.8797325360794180.439866268039709
360.5275936178134170.9448127643731670.472406382186583
370.5216459351227910.9567081297544190.478354064877209
380.5182998887764170.9634002224471660.481700111223583
390.5045255817225790.9909488365548420.495474418277421
400.5869901752954210.8260196494091580.413009824704579
410.6002892862394790.7994214275210420.399710713760521
420.5806585938196960.8386828123606080.419341406180304
430.5144040935221550.971191812955690.485595906477845
440.5700504429952610.8598991140094770.429949557004739
450.805864679450890.3882706410982190.194135320549109
460.8903533869618570.2192932260762860.109646613038143
470.9197633748635760.1604732502728470.0802366251364237
480.9367468163674360.1265063672651280.0632531836325638
490.9463097047341660.1073805905316690.0536902952658345
500.9523635423266230.0952729153467530.0476364576733765
510.957947988084820.08410402383035850.0420520119151792
520.9656009855914550.06879802881709080.0343990144085454
530.9779133267520330.04417334649593430.0220866732479671
540.9863510828103340.02729783437933150.0136489171896657
550.9792251937749850.04154961245003010.0207748062250150
560.9841531083649050.031693783270190.015846891635095
570.9863923429248850.02721531415022970.0136076570751149
580.9906170176403630.01876596471927420.00938298235963712
590.9861168476845360.02776630463092750.0138831523154637
600.9772331549276140.04553369014477230.0227668450723862
610.9720102548185140.05597949036297270.0279897451814864
620.9637683397620570.07246332047588630.0362316602379432
630.9348125005290940.1303749989418120.0651874994709058
640.8835805787862320.2328388424275350.116419421213768
650.9094174480632660.1811651038734680.0905825519367341
660.9827391523894840.03452169522103220.0172608476105161

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.00262702682734828 & 0.00525405365469655 & 0.997372973172652 \tabularnewline
7 & 0.0556412182581413 & 0.111282436516283 & 0.944358781741859 \tabularnewline
8 & 0.0482245034275335 & 0.096449006855067 & 0.951775496572467 \tabularnewline
9 & 0.186898151165504 & 0.373796302331009 & 0.813101848834496 \tabularnewline
10 & 0.114909873128370 & 0.229819746256741 & 0.88509012687163 \tabularnewline
11 & 0.166414815532933 & 0.332829631065866 & 0.833585184467067 \tabularnewline
12 & 0.164213041907403 & 0.328426083814806 & 0.835786958092597 \tabularnewline
13 & 0.141735639979412 & 0.283471279958824 & 0.858264360020588 \tabularnewline
14 & 0.125948428698682 & 0.251896857397364 & 0.874051571301318 \tabularnewline
15 & 0.101705974930458 & 0.203411949860917 & 0.898294025069542 \tabularnewline
16 & 0.152173881966619 & 0.304347763933238 & 0.847826118033381 \tabularnewline
17 & 0.318258061807952 & 0.636516123615905 & 0.681741938192048 \tabularnewline
18 & 0.392569509524155 & 0.78513901904831 & 0.607430490475845 \tabularnewline
19 & 0.375310675524049 & 0.750621351048099 & 0.624689324475951 \tabularnewline
20 & 0.399919282311147 & 0.799838564622295 & 0.600080717688853 \tabularnewline
21 & 0.465162482671965 & 0.93032496534393 & 0.534837517328036 \tabularnewline
22 & 0.427774085105939 & 0.855548170211878 & 0.572225914894061 \tabularnewline
23 & 0.371867061446387 & 0.743734122892774 & 0.628132938553613 \tabularnewline
24 & 0.324599160868691 & 0.649198321737382 & 0.67540083913131 \tabularnewline
25 & 0.32083814848309 & 0.64167629696618 & 0.67916185151691 \tabularnewline
26 & 0.327675545441481 & 0.655351090882962 & 0.672324454558519 \tabularnewline
27 & 0.335168412813119 & 0.670336825626239 & 0.664831587186881 \tabularnewline
28 & 0.44019179724696 & 0.88038359449392 & 0.55980820275304 \tabularnewline
29 & 0.654257448148238 & 0.691485103703524 & 0.345742551851762 \tabularnewline
30 & 0.731182964033614 & 0.537634071932771 & 0.268817035966386 \tabularnewline
31 & 0.700819946842713 & 0.598360106314573 & 0.299180053157287 \tabularnewline
32 & 0.657241435136975 & 0.68551712972605 & 0.342758564863025 \tabularnewline
33 & 0.662764512229439 & 0.674470975541122 & 0.337235487770561 \tabularnewline
34 & 0.599520294155657 & 0.800959411688687 & 0.400479705844343 \tabularnewline
35 & 0.560133731960291 & 0.879732536079418 & 0.439866268039709 \tabularnewline
36 & 0.527593617813417 & 0.944812764373167 & 0.472406382186583 \tabularnewline
37 & 0.521645935122791 & 0.956708129754419 & 0.478354064877209 \tabularnewline
38 & 0.518299888776417 & 0.963400222447166 & 0.481700111223583 \tabularnewline
39 & 0.504525581722579 & 0.990948836554842 & 0.495474418277421 \tabularnewline
40 & 0.586990175295421 & 0.826019649409158 & 0.413009824704579 \tabularnewline
41 & 0.600289286239479 & 0.799421427521042 & 0.399710713760521 \tabularnewline
42 & 0.580658593819696 & 0.838682812360608 & 0.419341406180304 \tabularnewline
43 & 0.514404093522155 & 0.97119181295569 & 0.485595906477845 \tabularnewline
44 & 0.570050442995261 & 0.859899114009477 & 0.429949557004739 \tabularnewline
45 & 0.80586467945089 & 0.388270641098219 & 0.194135320549109 \tabularnewline
46 & 0.890353386961857 & 0.219293226076286 & 0.109646613038143 \tabularnewline
47 & 0.919763374863576 & 0.160473250272847 & 0.0802366251364237 \tabularnewline
48 & 0.936746816367436 & 0.126506367265128 & 0.0632531836325638 \tabularnewline
49 & 0.946309704734166 & 0.107380590531669 & 0.0536902952658345 \tabularnewline
50 & 0.952363542326623 & 0.095272915346753 & 0.0476364576733765 \tabularnewline
51 & 0.95794798808482 & 0.0841040238303585 & 0.0420520119151792 \tabularnewline
52 & 0.965600985591455 & 0.0687980288170908 & 0.0343990144085454 \tabularnewline
53 & 0.977913326752033 & 0.0441733464959343 & 0.0220866732479671 \tabularnewline
54 & 0.986351082810334 & 0.0272978343793315 & 0.0136489171896657 \tabularnewline
55 & 0.979225193774985 & 0.0415496124500301 & 0.0207748062250150 \tabularnewline
56 & 0.984153108364905 & 0.03169378327019 & 0.015846891635095 \tabularnewline
57 & 0.986392342924885 & 0.0272153141502297 & 0.0136076570751149 \tabularnewline
58 & 0.990617017640363 & 0.0187659647192742 & 0.00938298235963712 \tabularnewline
59 & 0.986116847684536 & 0.0277663046309275 & 0.0138831523154637 \tabularnewline
60 & 0.977233154927614 & 0.0455336901447723 & 0.0227668450723862 \tabularnewline
61 & 0.972010254818514 & 0.0559794903629727 & 0.0279897451814864 \tabularnewline
62 & 0.963768339762057 & 0.0724633204758863 & 0.0362316602379432 \tabularnewline
63 & 0.934812500529094 & 0.130374998941812 & 0.0651874994709058 \tabularnewline
64 & 0.883580578786232 & 0.232838842427535 & 0.116419421213768 \tabularnewline
65 & 0.909417448063266 & 0.181165103873468 & 0.0905825519367341 \tabularnewline
66 & 0.982739152389484 & 0.0345216952210322 & 0.0172608476105161 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57857&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]6[/C][C]0.00262702682734828[/C][C]0.00525405365469655[/C][C]0.997372973172652[/C][/ROW]
[ROW][C]7[/C][C]0.0556412182581413[/C][C]0.111282436516283[/C][C]0.944358781741859[/C][/ROW]
[ROW][C]8[/C][C]0.0482245034275335[/C][C]0.096449006855067[/C][C]0.951775496572467[/C][/ROW]
[ROW][C]9[/C][C]0.186898151165504[/C][C]0.373796302331009[/C][C]0.813101848834496[/C][/ROW]
[ROW][C]10[/C][C]0.114909873128370[/C][C]0.229819746256741[/C][C]0.88509012687163[/C][/ROW]
[ROW][C]11[/C][C]0.166414815532933[/C][C]0.332829631065866[/C][C]0.833585184467067[/C][/ROW]
[ROW][C]12[/C][C]0.164213041907403[/C][C]0.328426083814806[/C][C]0.835786958092597[/C][/ROW]
[ROW][C]13[/C][C]0.141735639979412[/C][C]0.283471279958824[/C][C]0.858264360020588[/C][/ROW]
[ROW][C]14[/C][C]0.125948428698682[/C][C]0.251896857397364[/C][C]0.874051571301318[/C][/ROW]
[ROW][C]15[/C][C]0.101705974930458[/C][C]0.203411949860917[/C][C]0.898294025069542[/C][/ROW]
[ROW][C]16[/C][C]0.152173881966619[/C][C]0.304347763933238[/C][C]0.847826118033381[/C][/ROW]
[ROW][C]17[/C][C]0.318258061807952[/C][C]0.636516123615905[/C][C]0.681741938192048[/C][/ROW]
[ROW][C]18[/C][C]0.392569509524155[/C][C]0.78513901904831[/C][C]0.607430490475845[/C][/ROW]
[ROW][C]19[/C][C]0.375310675524049[/C][C]0.750621351048099[/C][C]0.624689324475951[/C][/ROW]
[ROW][C]20[/C][C]0.399919282311147[/C][C]0.799838564622295[/C][C]0.600080717688853[/C][/ROW]
[ROW][C]21[/C][C]0.465162482671965[/C][C]0.93032496534393[/C][C]0.534837517328036[/C][/ROW]
[ROW][C]22[/C][C]0.427774085105939[/C][C]0.855548170211878[/C][C]0.572225914894061[/C][/ROW]
[ROW][C]23[/C][C]0.371867061446387[/C][C]0.743734122892774[/C][C]0.628132938553613[/C][/ROW]
[ROW][C]24[/C][C]0.324599160868691[/C][C]0.649198321737382[/C][C]0.67540083913131[/C][/ROW]
[ROW][C]25[/C][C]0.32083814848309[/C][C]0.64167629696618[/C][C]0.67916185151691[/C][/ROW]
[ROW][C]26[/C][C]0.327675545441481[/C][C]0.655351090882962[/C][C]0.672324454558519[/C][/ROW]
[ROW][C]27[/C][C]0.335168412813119[/C][C]0.670336825626239[/C][C]0.664831587186881[/C][/ROW]
[ROW][C]28[/C][C]0.44019179724696[/C][C]0.88038359449392[/C][C]0.55980820275304[/C][/ROW]
[ROW][C]29[/C][C]0.654257448148238[/C][C]0.691485103703524[/C][C]0.345742551851762[/C][/ROW]
[ROW][C]30[/C][C]0.731182964033614[/C][C]0.537634071932771[/C][C]0.268817035966386[/C][/ROW]
[ROW][C]31[/C][C]0.700819946842713[/C][C]0.598360106314573[/C][C]0.299180053157287[/C][/ROW]
[ROW][C]32[/C][C]0.657241435136975[/C][C]0.68551712972605[/C][C]0.342758564863025[/C][/ROW]
[ROW][C]33[/C][C]0.662764512229439[/C][C]0.674470975541122[/C][C]0.337235487770561[/C][/ROW]
[ROW][C]34[/C][C]0.599520294155657[/C][C]0.800959411688687[/C][C]0.400479705844343[/C][/ROW]
[ROW][C]35[/C][C]0.560133731960291[/C][C]0.879732536079418[/C][C]0.439866268039709[/C][/ROW]
[ROW][C]36[/C][C]0.527593617813417[/C][C]0.944812764373167[/C][C]0.472406382186583[/C][/ROW]
[ROW][C]37[/C][C]0.521645935122791[/C][C]0.956708129754419[/C][C]0.478354064877209[/C][/ROW]
[ROW][C]38[/C][C]0.518299888776417[/C][C]0.963400222447166[/C][C]0.481700111223583[/C][/ROW]
[ROW][C]39[/C][C]0.504525581722579[/C][C]0.990948836554842[/C][C]0.495474418277421[/C][/ROW]
[ROW][C]40[/C][C]0.586990175295421[/C][C]0.826019649409158[/C][C]0.413009824704579[/C][/ROW]
[ROW][C]41[/C][C]0.600289286239479[/C][C]0.799421427521042[/C][C]0.399710713760521[/C][/ROW]
[ROW][C]42[/C][C]0.580658593819696[/C][C]0.838682812360608[/C][C]0.419341406180304[/C][/ROW]
[ROW][C]43[/C][C]0.514404093522155[/C][C]0.97119181295569[/C][C]0.485595906477845[/C][/ROW]
[ROW][C]44[/C][C]0.570050442995261[/C][C]0.859899114009477[/C][C]0.429949557004739[/C][/ROW]
[ROW][C]45[/C][C]0.80586467945089[/C][C]0.388270641098219[/C][C]0.194135320549109[/C][/ROW]
[ROW][C]46[/C][C]0.890353386961857[/C][C]0.219293226076286[/C][C]0.109646613038143[/C][/ROW]
[ROW][C]47[/C][C]0.919763374863576[/C][C]0.160473250272847[/C][C]0.0802366251364237[/C][/ROW]
[ROW][C]48[/C][C]0.936746816367436[/C][C]0.126506367265128[/C][C]0.0632531836325638[/C][/ROW]
[ROW][C]49[/C][C]0.946309704734166[/C][C]0.107380590531669[/C][C]0.0536902952658345[/C][/ROW]
[ROW][C]50[/C][C]0.952363542326623[/C][C]0.095272915346753[/C][C]0.0476364576733765[/C][/ROW]
[ROW][C]51[/C][C]0.95794798808482[/C][C]0.0841040238303585[/C][C]0.0420520119151792[/C][/ROW]
[ROW][C]52[/C][C]0.965600985591455[/C][C]0.0687980288170908[/C][C]0.0343990144085454[/C][/ROW]
[ROW][C]53[/C][C]0.977913326752033[/C][C]0.0441733464959343[/C][C]0.0220866732479671[/C][/ROW]
[ROW][C]54[/C][C]0.986351082810334[/C][C]0.0272978343793315[/C][C]0.0136489171896657[/C][/ROW]
[ROW][C]55[/C][C]0.979225193774985[/C][C]0.0415496124500301[/C][C]0.0207748062250150[/C][/ROW]
[ROW][C]56[/C][C]0.984153108364905[/C][C]0.03169378327019[/C][C]0.015846891635095[/C][/ROW]
[ROW][C]57[/C][C]0.986392342924885[/C][C]0.0272153141502297[/C][C]0.0136076570751149[/C][/ROW]
[ROW][C]58[/C][C]0.990617017640363[/C][C]0.0187659647192742[/C][C]0.00938298235963712[/C][/ROW]
[ROW][C]59[/C][C]0.986116847684536[/C][C]0.0277663046309275[/C][C]0.0138831523154637[/C][/ROW]
[ROW][C]60[/C][C]0.977233154927614[/C][C]0.0455336901447723[/C][C]0.0227668450723862[/C][/ROW]
[ROW][C]61[/C][C]0.972010254818514[/C][C]0.0559794903629727[/C][C]0.0279897451814864[/C][/ROW]
[ROW][C]62[/C][C]0.963768339762057[/C][C]0.0724633204758863[/C][C]0.0362316602379432[/C][/ROW]
[ROW][C]63[/C][C]0.934812500529094[/C][C]0.130374998941812[/C][C]0.0651874994709058[/C][/ROW]
[ROW][C]64[/C][C]0.883580578786232[/C][C]0.232838842427535[/C][C]0.116419421213768[/C][/ROW]
[ROW][C]65[/C][C]0.909417448063266[/C][C]0.181165103873468[/C][C]0.0905825519367341[/C][/ROW]
[ROW][C]66[/C][C]0.982739152389484[/C][C]0.0345216952210322[/C][C]0.0172608476105161[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57857&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57857&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
60.002627026827348280.005254053654696550.997372973172652
70.05564121825814130.1112824365162830.944358781741859
80.04822450342753350.0964490068550670.951775496572467
90.1868981511655040.3737963023310090.813101848834496
100.1149098731283700.2298197462567410.88509012687163
110.1664148155329330.3328296310658660.833585184467067
120.1642130419074030.3284260838148060.835786958092597
130.1417356399794120.2834712799588240.858264360020588
140.1259484286986820.2518968573973640.874051571301318
150.1017059749304580.2034119498609170.898294025069542
160.1521738819666190.3043477639332380.847826118033381
170.3182580618079520.6365161236159050.681741938192048
180.3925695095241550.785139019048310.607430490475845
190.3753106755240490.7506213510480990.624689324475951
200.3999192823111470.7998385646222950.600080717688853
210.4651624826719650.930324965343930.534837517328036
220.4277740851059390.8555481702118780.572225914894061
230.3718670614463870.7437341228927740.628132938553613
240.3245991608686910.6491983217373820.67540083913131
250.320838148483090.641676296966180.67916185151691
260.3276755454414810.6553510908829620.672324454558519
270.3351684128131190.6703368256262390.664831587186881
280.440191797246960.880383594493920.55980820275304
290.6542574481482380.6914851037035240.345742551851762
300.7311829640336140.5376340719327710.268817035966386
310.7008199468427130.5983601063145730.299180053157287
320.6572414351369750.685517129726050.342758564863025
330.6627645122294390.6744709755411220.337235487770561
340.5995202941556570.8009594116886870.400479705844343
350.5601337319602910.8797325360794180.439866268039709
360.5275936178134170.9448127643731670.472406382186583
370.5216459351227910.9567081297544190.478354064877209
380.5182998887764170.9634002224471660.481700111223583
390.5045255817225790.9909488365548420.495474418277421
400.5869901752954210.8260196494091580.413009824704579
410.6002892862394790.7994214275210420.399710713760521
420.5806585938196960.8386828123606080.419341406180304
430.5144040935221550.971191812955690.485595906477845
440.5700504429952610.8598991140094770.429949557004739
450.805864679450890.3882706410982190.194135320549109
460.8903533869618570.2192932260762860.109646613038143
470.9197633748635760.1604732502728470.0802366251364237
480.9367468163674360.1265063672651280.0632531836325638
490.9463097047341660.1073805905316690.0536902952658345
500.9523635423266230.0952729153467530.0476364576733765
510.957947988084820.08410402383035850.0420520119151792
520.9656009855914550.06879802881709080.0343990144085454
530.9779133267520330.04417334649593430.0220866732479671
540.9863510828103340.02729783437933150.0136489171896657
550.9792251937749850.04154961245003010.0207748062250150
560.9841531083649050.031693783270190.015846891635095
570.9863923429248850.02721531415022970.0136076570751149
580.9906170176403630.01876596471927420.00938298235963712
590.9861168476845360.02776630463092750.0138831523154637
600.9772331549276140.04553369014477230.0227668450723862
610.9720102548185140.05597949036297270.0279897451814864
620.9637683397620570.07246332047588630.0362316602379432
630.9348125005290940.1303749989418120.0651874994709058
640.8835805787862320.2328388424275350.116419421213768
650.9094174480632660.1811651038734680.0905825519367341
660.9827391523894840.03452169522103220.0172608476105161







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0163934426229508NOK
5% type I error level100.163934426229508NOK
10% type I error level160.262295081967213NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 1 & 0.0163934426229508 & NOK \tabularnewline
5% type I error level & 10 & 0.163934426229508 & NOK \tabularnewline
10% type I error level & 16 & 0.262295081967213 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57857&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]1[/C][C]0.0163934426229508[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.163934426229508[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.262295081967213[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57857&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57857&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 level10.0163934426229508NOK
5% type I error level100.163934426229508NOK
10% type I error level160.262295081967213NOK



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