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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 17 Nov 2009 08:02:21 -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/17/t1258470193s51uyvfj3lnf4dg.htm/, Retrieved Fri, 28 Nov 2025 18:01:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57370, Retrieved Fri, 28 Nov 2025 18:01:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact470

Tree of Dependent Computations

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]
-   PD      [Multiple Regression] [ws3] [2009-11-17 15:02:21] [94ba0ef70f5b330d175ff4daa1c9cd40] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
395.3	0
395.1	0
403.5	0
403.3	0
405.7	0
406.7	0
407.2	0
412.4	0
415.9	0
414.0	0
411.8	0
409.9	0
412.4	0
415.9	0
416.3	0
417.2	0
421.8	0
421.4	0
415.1	0
412.4	0
411.8	0
408.8	0
404.5	0
402.5	0
409.4	0
410.7	0
413.4	0
415.2	0
417.7	0
417.8	0
417.9	0
418.4	0
418.2	0
416.6	0
418.9	0
421.0	0
423.5	0
432.3	0
432.3	0
428.6	0
426.7	0
427.3	0
428.5	0
437.0	0
442.0	0
444.9	0
441.4	0
440.3	0
447.1	0
455.3	0
478.6	0
486.5	0
487.8	0
485.9	0
483.8	0
488.4	0
494.0	0
493.6	0
487.3	0
482.1	0
484.2	0
496.8	0
501.1	0
499.8	0
495.5	0
498.1	0
503.8	0
516.2	0
526.1	0
527.1	0
525.1	0
528.9	0
540.1	0
549.0	0
556.0	0
568.9	0
589.1	0
590.3	0
603.3	0
638.8	0
643.0	0
656.7	0
656.1	0
654.1	0
659.9	0
662.1	0
669.2	0
673.1	0
678.3	0
677.4	0
678.5	0
672.4	0
665.3	0
667.9	0
672.1	0
662.5	0
682.3	0
692.1	0
702.7	0
721.4	0
733.2	0
747.7	0
737.6	0
729.3	0
706.1	0
674.3	0
659.0	0
645.7	0
646.1	0
633.0	1
622.3	1
628.2	1
637.3	1
639.6	1
638.5	1
650.5	1
655.4	1

Tables (Output of Computation)





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=57370&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=57370&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57370&T=0

As an alternative you can also use a QR Code:  
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
Y[t] = + 334.951619017219 -71.0679068117941X[t] + 3.29705980435749t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  334.951619017219 -71.0679068117941X[t] +  3.29705980435749t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57370&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  334.951619017219 -71.0679068117941X[t] +  3.29705980435749t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57370&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57370&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 334.951619017219 -71.0679068117941X[t] + 3.29705980435749t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)334.9516190172197.33899645.6400
X-71.067906811794115.498156-4.58561.2e-056e-06
t3.297059804357490.11581728.467800

\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) & 334.951619017219 & 7.338996 & 45.64 & 0 & 0 \tabularnewline
X & -71.0679068117941 & 15.498156 & -4.5856 & 1.2e-05 & 6e-06 \tabularnewline
t & 3.29705980435749 & 0.115817 & 28.4678 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57370&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]334.951619017219[/C][C]7.338996[/C][C]45.64[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-71.0679068117941[/C][C]15.498156[/C][C]-4.5856[/C][C]1.2e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]t[/C][C]3.29705980435749[/C][C]0.115817[/C][C]28.4678[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57370&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57370&T=2

As an alternative you can also use a QR Code:  
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)334.9516190172197.33899645.6400
X-71.067906811794115.498156-4.58561.2e-056e-06
t3.297059804357490.11581728.467800






Multiple Linear Regression - Regression Statistics
Multiple R0.941323218705861
R-squared0.886089402074763
Adjusted R-squared0.884090970532215
F-TEST (value)443.392422111688
F-TEST (DF numerator)2
F-TEST (DF denominator)114
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation38.0529531418478
Sum Squared Residuals165075.105680986

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.941323218705861 \tabularnewline
R-squared & 0.886089402074763 \tabularnewline
Adjusted R-squared & 0.884090970532215 \tabularnewline
F-TEST (value) & 443.392422111688 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 114 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 38.0529531418478 \tabularnewline
Sum Squared Residuals & 165075.105680986 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57370&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.941323218705861[/C][/ROW]
[ROW][C]R-squared[/C][C]0.886089402074763[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.884090970532215[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]443.392422111688[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]114[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]38.0529531418478[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]165075.105680986[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57370&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57370&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.941323218705861
R-squared0.886089402074763
Adjusted R-squared0.884090970532215
F-TEST (value)443.392422111688
F-TEST (DF numerator)2
F-TEST (DF denominator)114
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation38.0529531418478
Sum Squared Residuals165075.105680986






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1395.3338.24867882157757.0513211784233
2395.1341.54573862593353.5542613740666
3403.5344.84279843029158.6572015697091
4403.3348.13985823464955.1601417653514
5405.7351.43691803900654.2630819609939
6406.7354.73397784336451.9660221566364
7407.2358.03103764772149.1689623522789
8412.4361.32809745207951.0719025479214
9415.9364.62515725643651.2748427435639
10414367.92221706079446.0777829392064
11411.8371.21927686515140.5807231348489
12409.9374.51633666950935.3836633304914
13412.4377.81339647386634.5866035261339
14415.9381.11045627822434.7895437217764
15416.3384.40751608258131.8924839174190
16417.2387.70457588693929.4954241130615
17421.8391.00163569129630.798364308704
18421.4394.29869549565427.1013045043465
19415.1397.59575530001117.504244699989
20412.4400.89281510436811.5071848956315
21411.8404.1898749087267.61012509127402
22408.8407.4869347130831.31306528691652
23404.5410.783994517441-6.28399451744097
24402.5414.081054321798-11.5810543217985
25409.4417.378114126156-7.97811412615599
26410.7420.675173930513-9.97517393051347
27413.4423.972233734871-10.5722337348710
28415.2427.269293539228-12.0692935392285
29417.7430.566353343586-12.8663533435859
30417.8433.863413147943-16.0634131479434
31417.9437.160472952301-19.2604729523009
32418.4440.457532756658-22.0575327566584
33418.2443.754592561016-25.5545925610159
34416.6447.051652365373-30.4516523653734
35418.9450.348712169731-31.4487121697309
36421453.645771974088-32.6457719740884
37423.5456.942831778446-33.4428317784459
38432.3460.239891582803-27.9398915828033
39432.3463.536951387161-31.2369513871608
40428.6466.834011191518-38.2340111915183
41426.7470.131070995876-43.4310709958759
42427.3473.428130800233-46.1281308002333
43428.5476.725190604591-48.2251906045908
44437480.022250408948-43.0222504089483
45442483.319310213306-41.3193102133058
46444.9486.616370017663-41.7163700176633
47441.4489.913429822021-48.5134298220208
48440.3493.210489626378-52.9104896263783
49447.1496.507549430736-49.4075494307357
50455.3499.804609235093-44.5046092350933
51478.6503.101669039451-24.5016690394507
52486.5506.398728843808-19.8987288438083
53487.8509.695788648166-21.8957886481657
54485.9512.992848452523-27.0928484525233
55483.8516.289908256881-32.4899082568807
56488.4519.586968061238-31.1869680612383
57494522.884027865596-28.8840278655957
58493.6526.181087669953-32.5810876699532
59487.3529.478147474311-42.1781474743107
60482.1532.775207278668-50.6752072786682
61484.2536.072267083026-51.8722670830257
62496.8539.369326887383-42.5693268873832
63501.1542.666386691741-41.5663866917407
64499.8545.963446496098-46.1634464960982
65495.5549.260506300456-53.7605063004557
66498.1552.557566104813-54.4575661048131
67503.8555.854625909171-52.0546259091706
68516.2559.151685713528-42.9516857135281
69526.1562.448745517886-36.3487455178856
70527.1565.745805322243-38.6458053222431
71525.1569.042865126601-43.9428651266006
72528.9572.339924930958-43.4399249309581
73540.1575.636984735316-35.5369847353156
74549578.934044539673-29.9340445396731
75556582.23110434403-26.2311043440306
76568.9585.528164148388-16.6281641483881
77589.1588.8252239527460.274776047254456
78590.3592.122283757103-1.82228375710310
79603.3595.419343561467.8806564385394
80638.8598.71640336581840.0835966341819
81643602.01346317017640.9865368298245
82656.7605.31052297453351.389477025467
83656.1608.60758277889147.4924172211095
84654.1611.90464258324842.195357416752
85659.9615.20170238760644.6982976123945
86662.1618.49876219196343.601237808037
87669.2621.7958219963247.4041780036796
88673.1625.09288180067848.007118199322
89678.3628.38994160503549.9100583949645
90677.4631.68700140939345.712998590607
91678.5634.9840612137543.5159387862495
92672.4638.28112101810834.118878981892
93665.3641.57818082246523.7218191775345
94667.9644.87524062682323.024759373177
95672.1648.1723004311823.9276995688196
96662.5651.46936023553811.0306397644621
97682.3654.76642003989527.5335799601045
98692.1658.06347984425334.0365201557471
99702.7661.3605396486141.3394603513896
100721.4664.65759945296856.7424005470321
101733.2667.95465925732565.2453407426747
102747.7671.25171906168376.4482809383172
103737.6674.5487788660463.0512211339596
104729.3677.84583867039851.4541613296021
105706.1681.14289847475524.9571015252447
106674.3684.439958279113-10.1399582791129
107659687.73701808347-28.7370180834703
108645.7691.034077887828-45.3340778878278
109646.1694.331137692185-48.2311376921853
110633626.5602906847496.43970931525123
111622.3629.857350489106-7.55735048910632
112628.2633.154410293464-4.95441029346372
113637.3636.4514700978210.8485299021787
114639.6639.748529902179-0.148529902178725
115638.5643.045589706536-4.54558970653624
116650.5646.3426495108944.15735048910627
117655.4649.6397093152515.76029068474875

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 395.3 & 338.248678821577 & 57.0513211784233 \tabularnewline
2 & 395.1 & 341.545738625933 & 53.5542613740666 \tabularnewline
3 & 403.5 & 344.842798430291 & 58.6572015697091 \tabularnewline
4 & 403.3 & 348.139858234649 & 55.1601417653514 \tabularnewline
5 & 405.7 & 351.436918039006 & 54.2630819609939 \tabularnewline
6 & 406.7 & 354.733977843364 & 51.9660221566364 \tabularnewline
7 & 407.2 & 358.031037647721 & 49.1689623522789 \tabularnewline
8 & 412.4 & 361.328097452079 & 51.0719025479214 \tabularnewline
9 & 415.9 & 364.625157256436 & 51.2748427435639 \tabularnewline
10 & 414 & 367.922217060794 & 46.0777829392064 \tabularnewline
11 & 411.8 & 371.219276865151 & 40.5807231348489 \tabularnewline
12 & 409.9 & 374.516336669509 & 35.3836633304914 \tabularnewline
13 & 412.4 & 377.813396473866 & 34.5866035261339 \tabularnewline
14 & 415.9 & 381.110456278224 & 34.7895437217764 \tabularnewline
15 & 416.3 & 384.407516082581 & 31.8924839174190 \tabularnewline
16 & 417.2 & 387.704575886939 & 29.4954241130615 \tabularnewline
17 & 421.8 & 391.001635691296 & 30.798364308704 \tabularnewline
18 & 421.4 & 394.298695495654 & 27.1013045043465 \tabularnewline
19 & 415.1 & 397.595755300011 & 17.504244699989 \tabularnewline
20 & 412.4 & 400.892815104368 & 11.5071848956315 \tabularnewline
21 & 411.8 & 404.189874908726 & 7.61012509127402 \tabularnewline
22 & 408.8 & 407.486934713083 & 1.31306528691652 \tabularnewline
23 & 404.5 & 410.783994517441 & -6.28399451744097 \tabularnewline
24 & 402.5 & 414.081054321798 & -11.5810543217985 \tabularnewline
25 & 409.4 & 417.378114126156 & -7.97811412615599 \tabularnewline
26 & 410.7 & 420.675173930513 & -9.97517393051347 \tabularnewline
27 & 413.4 & 423.972233734871 & -10.5722337348710 \tabularnewline
28 & 415.2 & 427.269293539228 & -12.0692935392285 \tabularnewline
29 & 417.7 & 430.566353343586 & -12.8663533435859 \tabularnewline
30 & 417.8 & 433.863413147943 & -16.0634131479434 \tabularnewline
31 & 417.9 & 437.160472952301 & -19.2604729523009 \tabularnewline
32 & 418.4 & 440.457532756658 & -22.0575327566584 \tabularnewline
33 & 418.2 & 443.754592561016 & -25.5545925610159 \tabularnewline
34 & 416.6 & 447.051652365373 & -30.4516523653734 \tabularnewline
35 & 418.9 & 450.348712169731 & -31.4487121697309 \tabularnewline
36 & 421 & 453.645771974088 & -32.6457719740884 \tabularnewline
37 & 423.5 & 456.942831778446 & -33.4428317784459 \tabularnewline
38 & 432.3 & 460.239891582803 & -27.9398915828033 \tabularnewline
39 & 432.3 & 463.536951387161 & -31.2369513871608 \tabularnewline
40 & 428.6 & 466.834011191518 & -38.2340111915183 \tabularnewline
41 & 426.7 & 470.131070995876 & -43.4310709958759 \tabularnewline
42 & 427.3 & 473.428130800233 & -46.1281308002333 \tabularnewline
43 & 428.5 & 476.725190604591 & -48.2251906045908 \tabularnewline
44 & 437 & 480.022250408948 & -43.0222504089483 \tabularnewline
45 & 442 & 483.319310213306 & -41.3193102133058 \tabularnewline
46 & 444.9 & 486.616370017663 & -41.7163700176633 \tabularnewline
47 & 441.4 & 489.913429822021 & -48.5134298220208 \tabularnewline
48 & 440.3 & 493.210489626378 & -52.9104896263783 \tabularnewline
49 & 447.1 & 496.507549430736 & -49.4075494307357 \tabularnewline
50 & 455.3 & 499.804609235093 & -44.5046092350933 \tabularnewline
51 & 478.6 & 503.101669039451 & -24.5016690394507 \tabularnewline
52 & 486.5 & 506.398728843808 & -19.8987288438083 \tabularnewline
53 & 487.8 & 509.695788648166 & -21.8957886481657 \tabularnewline
54 & 485.9 & 512.992848452523 & -27.0928484525233 \tabularnewline
55 & 483.8 & 516.289908256881 & -32.4899082568807 \tabularnewline
56 & 488.4 & 519.586968061238 & -31.1869680612383 \tabularnewline
57 & 494 & 522.884027865596 & -28.8840278655957 \tabularnewline
58 & 493.6 & 526.181087669953 & -32.5810876699532 \tabularnewline
59 & 487.3 & 529.478147474311 & -42.1781474743107 \tabularnewline
60 & 482.1 & 532.775207278668 & -50.6752072786682 \tabularnewline
61 & 484.2 & 536.072267083026 & -51.8722670830257 \tabularnewline
62 & 496.8 & 539.369326887383 & -42.5693268873832 \tabularnewline
63 & 501.1 & 542.666386691741 & -41.5663866917407 \tabularnewline
64 & 499.8 & 545.963446496098 & -46.1634464960982 \tabularnewline
65 & 495.5 & 549.260506300456 & -53.7605063004557 \tabularnewline
66 & 498.1 & 552.557566104813 & -54.4575661048131 \tabularnewline
67 & 503.8 & 555.854625909171 & -52.0546259091706 \tabularnewline
68 & 516.2 & 559.151685713528 & -42.9516857135281 \tabularnewline
69 & 526.1 & 562.448745517886 & -36.3487455178856 \tabularnewline
70 & 527.1 & 565.745805322243 & -38.6458053222431 \tabularnewline
71 & 525.1 & 569.042865126601 & -43.9428651266006 \tabularnewline
72 & 528.9 & 572.339924930958 & -43.4399249309581 \tabularnewline
73 & 540.1 & 575.636984735316 & -35.5369847353156 \tabularnewline
74 & 549 & 578.934044539673 & -29.9340445396731 \tabularnewline
75 & 556 & 582.23110434403 & -26.2311043440306 \tabularnewline
76 & 568.9 & 585.528164148388 & -16.6281641483881 \tabularnewline
77 & 589.1 & 588.825223952746 & 0.274776047254456 \tabularnewline
78 & 590.3 & 592.122283757103 & -1.82228375710310 \tabularnewline
79 & 603.3 & 595.41934356146 & 7.8806564385394 \tabularnewline
80 & 638.8 & 598.716403365818 & 40.0835966341819 \tabularnewline
81 & 643 & 602.013463170176 & 40.9865368298245 \tabularnewline
82 & 656.7 & 605.310522974533 & 51.389477025467 \tabularnewline
83 & 656.1 & 608.607582778891 & 47.4924172211095 \tabularnewline
84 & 654.1 & 611.904642583248 & 42.195357416752 \tabularnewline
85 & 659.9 & 615.201702387606 & 44.6982976123945 \tabularnewline
86 & 662.1 & 618.498762191963 & 43.601237808037 \tabularnewline
87 & 669.2 & 621.79582199632 & 47.4041780036796 \tabularnewline
88 & 673.1 & 625.092881800678 & 48.007118199322 \tabularnewline
89 & 678.3 & 628.389941605035 & 49.9100583949645 \tabularnewline
90 & 677.4 & 631.687001409393 & 45.712998590607 \tabularnewline
91 & 678.5 & 634.98406121375 & 43.5159387862495 \tabularnewline
92 & 672.4 & 638.281121018108 & 34.118878981892 \tabularnewline
93 & 665.3 & 641.578180822465 & 23.7218191775345 \tabularnewline
94 & 667.9 & 644.875240626823 & 23.024759373177 \tabularnewline
95 & 672.1 & 648.17230043118 & 23.9276995688196 \tabularnewline
96 & 662.5 & 651.469360235538 & 11.0306397644621 \tabularnewline
97 & 682.3 & 654.766420039895 & 27.5335799601045 \tabularnewline
98 & 692.1 & 658.063479844253 & 34.0365201557471 \tabularnewline
99 & 702.7 & 661.36053964861 & 41.3394603513896 \tabularnewline
100 & 721.4 & 664.657599452968 & 56.7424005470321 \tabularnewline
101 & 733.2 & 667.954659257325 & 65.2453407426747 \tabularnewline
102 & 747.7 & 671.251719061683 & 76.4482809383172 \tabularnewline
103 & 737.6 & 674.54877886604 & 63.0512211339596 \tabularnewline
104 & 729.3 & 677.845838670398 & 51.4541613296021 \tabularnewline
105 & 706.1 & 681.142898474755 & 24.9571015252447 \tabularnewline
106 & 674.3 & 684.439958279113 & -10.1399582791129 \tabularnewline
107 & 659 & 687.73701808347 & -28.7370180834703 \tabularnewline
108 & 645.7 & 691.034077887828 & -45.3340778878278 \tabularnewline
109 & 646.1 & 694.331137692185 & -48.2311376921853 \tabularnewline
110 & 633 & 626.560290684749 & 6.43970931525123 \tabularnewline
111 & 622.3 & 629.857350489106 & -7.55735048910632 \tabularnewline
112 & 628.2 & 633.154410293464 & -4.95441029346372 \tabularnewline
113 & 637.3 & 636.451470097821 & 0.8485299021787 \tabularnewline
114 & 639.6 & 639.748529902179 & -0.148529902178725 \tabularnewline
115 & 638.5 & 643.045589706536 & -4.54558970653624 \tabularnewline
116 & 650.5 & 646.342649510894 & 4.15735048910627 \tabularnewline
117 & 655.4 & 649.639709315251 & 5.76029068474875 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57370&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]395.3[/C][C]338.248678821577[/C][C]57.0513211784233[/C][/ROW]
[ROW][C]2[/C][C]395.1[/C][C]341.545738625933[/C][C]53.5542613740666[/C][/ROW]
[ROW][C]3[/C][C]403.5[/C][C]344.842798430291[/C][C]58.6572015697091[/C][/ROW]
[ROW][C]4[/C][C]403.3[/C][C]348.139858234649[/C][C]55.1601417653514[/C][/ROW]
[ROW][C]5[/C][C]405.7[/C][C]351.436918039006[/C][C]54.2630819609939[/C][/ROW]
[ROW][C]6[/C][C]406.7[/C][C]354.733977843364[/C][C]51.9660221566364[/C][/ROW]
[ROW][C]7[/C][C]407.2[/C][C]358.031037647721[/C][C]49.1689623522789[/C][/ROW]
[ROW][C]8[/C][C]412.4[/C][C]361.328097452079[/C][C]51.0719025479214[/C][/ROW]
[ROW][C]9[/C][C]415.9[/C][C]364.625157256436[/C][C]51.2748427435639[/C][/ROW]
[ROW][C]10[/C][C]414[/C][C]367.922217060794[/C][C]46.0777829392064[/C][/ROW]
[ROW][C]11[/C][C]411.8[/C][C]371.219276865151[/C][C]40.5807231348489[/C][/ROW]
[ROW][C]12[/C][C]409.9[/C][C]374.516336669509[/C][C]35.3836633304914[/C][/ROW]
[ROW][C]13[/C][C]412.4[/C][C]377.813396473866[/C][C]34.5866035261339[/C][/ROW]
[ROW][C]14[/C][C]415.9[/C][C]381.110456278224[/C][C]34.7895437217764[/C][/ROW]
[ROW][C]15[/C][C]416.3[/C][C]384.407516082581[/C][C]31.8924839174190[/C][/ROW]
[ROW][C]16[/C][C]417.2[/C][C]387.704575886939[/C][C]29.4954241130615[/C][/ROW]
[ROW][C]17[/C][C]421.8[/C][C]391.001635691296[/C][C]30.798364308704[/C][/ROW]
[ROW][C]18[/C][C]421.4[/C][C]394.298695495654[/C][C]27.1013045043465[/C][/ROW]
[ROW][C]19[/C][C]415.1[/C][C]397.595755300011[/C][C]17.504244699989[/C][/ROW]
[ROW][C]20[/C][C]412.4[/C][C]400.892815104368[/C][C]11.5071848956315[/C][/ROW]
[ROW][C]21[/C][C]411.8[/C][C]404.189874908726[/C][C]7.61012509127402[/C][/ROW]
[ROW][C]22[/C][C]408.8[/C][C]407.486934713083[/C][C]1.31306528691652[/C][/ROW]
[ROW][C]23[/C][C]404.5[/C][C]410.783994517441[/C][C]-6.28399451744097[/C][/ROW]
[ROW][C]24[/C][C]402.5[/C][C]414.081054321798[/C][C]-11.5810543217985[/C][/ROW]
[ROW][C]25[/C][C]409.4[/C][C]417.378114126156[/C][C]-7.97811412615599[/C][/ROW]
[ROW][C]26[/C][C]410.7[/C][C]420.675173930513[/C][C]-9.97517393051347[/C][/ROW]
[ROW][C]27[/C][C]413.4[/C][C]423.972233734871[/C][C]-10.5722337348710[/C][/ROW]
[ROW][C]28[/C][C]415.2[/C][C]427.269293539228[/C][C]-12.0692935392285[/C][/ROW]
[ROW][C]29[/C][C]417.7[/C][C]430.566353343586[/C][C]-12.8663533435859[/C][/ROW]
[ROW][C]30[/C][C]417.8[/C][C]433.863413147943[/C][C]-16.0634131479434[/C][/ROW]
[ROW][C]31[/C][C]417.9[/C][C]437.160472952301[/C][C]-19.2604729523009[/C][/ROW]
[ROW][C]32[/C][C]418.4[/C][C]440.457532756658[/C][C]-22.0575327566584[/C][/ROW]
[ROW][C]33[/C][C]418.2[/C][C]443.754592561016[/C][C]-25.5545925610159[/C][/ROW]
[ROW][C]34[/C][C]416.6[/C][C]447.051652365373[/C][C]-30.4516523653734[/C][/ROW]
[ROW][C]35[/C][C]418.9[/C][C]450.348712169731[/C][C]-31.4487121697309[/C][/ROW]
[ROW][C]36[/C][C]421[/C][C]453.645771974088[/C][C]-32.6457719740884[/C][/ROW]
[ROW][C]37[/C][C]423.5[/C][C]456.942831778446[/C][C]-33.4428317784459[/C][/ROW]
[ROW][C]38[/C][C]432.3[/C][C]460.239891582803[/C][C]-27.9398915828033[/C][/ROW]
[ROW][C]39[/C][C]432.3[/C][C]463.536951387161[/C][C]-31.2369513871608[/C][/ROW]
[ROW][C]40[/C][C]428.6[/C][C]466.834011191518[/C][C]-38.2340111915183[/C][/ROW]
[ROW][C]41[/C][C]426.7[/C][C]470.131070995876[/C][C]-43.4310709958759[/C][/ROW]
[ROW][C]42[/C][C]427.3[/C][C]473.428130800233[/C][C]-46.1281308002333[/C][/ROW]
[ROW][C]43[/C][C]428.5[/C][C]476.725190604591[/C][C]-48.2251906045908[/C][/ROW]
[ROW][C]44[/C][C]437[/C][C]480.022250408948[/C][C]-43.0222504089483[/C][/ROW]
[ROW][C]45[/C][C]442[/C][C]483.319310213306[/C][C]-41.3193102133058[/C][/ROW]
[ROW][C]46[/C][C]444.9[/C][C]486.616370017663[/C][C]-41.7163700176633[/C][/ROW]
[ROW][C]47[/C][C]441.4[/C][C]489.913429822021[/C][C]-48.5134298220208[/C][/ROW]
[ROW][C]48[/C][C]440.3[/C][C]493.210489626378[/C][C]-52.9104896263783[/C][/ROW]
[ROW][C]49[/C][C]447.1[/C][C]496.507549430736[/C][C]-49.4075494307357[/C][/ROW]
[ROW][C]50[/C][C]455.3[/C][C]499.804609235093[/C][C]-44.5046092350933[/C][/ROW]
[ROW][C]51[/C][C]478.6[/C][C]503.101669039451[/C][C]-24.5016690394507[/C][/ROW]
[ROW][C]52[/C][C]486.5[/C][C]506.398728843808[/C][C]-19.8987288438083[/C][/ROW]
[ROW][C]53[/C][C]487.8[/C][C]509.695788648166[/C][C]-21.8957886481657[/C][/ROW]
[ROW][C]54[/C][C]485.9[/C][C]512.992848452523[/C][C]-27.0928484525233[/C][/ROW]
[ROW][C]55[/C][C]483.8[/C][C]516.289908256881[/C][C]-32.4899082568807[/C][/ROW]
[ROW][C]56[/C][C]488.4[/C][C]519.586968061238[/C][C]-31.1869680612383[/C][/ROW]
[ROW][C]57[/C][C]494[/C][C]522.884027865596[/C][C]-28.8840278655957[/C][/ROW]
[ROW][C]58[/C][C]493.6[/C][C]526.181087669953[/C][C]-32.5810876699532[/C][/ROW]
[ROW][C]59[/C][C]487.3[/C][C]529.478147474311[/C][C]-42.1781474743107[/C][/ROW]
[ROW][C]60[/C][C]482.1[/C][C]532.775207278668[/C][C]-50.6752072786682[/C][/ROW]
[ROW][C]61[/C][C]484.2[/C][C]536.072267083026[/C][C]-51.8722670830257[/C][/ROW]
[ROW][C]62[/C][C]496.8[/C][C]539.369326887383[/C][C]-42.5693268873832[/C][/ROW]
[ROW][C]63[/C][C]501.1[/C][C]542.666386691741[/C][C]-41.5663866917407[/C][/ROW]
[ROW][C]64[/C][C]499.8[/C][C]545.963446496098[/C][C]-46.1634464960982[/C][/ROW]
[ROW][C]65[/C][C]495.5[/C][C]549.260506300456[/C][C]-53.7605063004557[/C][/ROW]
[ROW][C]66[/C][C]498.1[/C][C]552.557566104813[/C][C]-54.4575661048131[/C][/ROW]
[ROW][C]67[/C][C]503.8[/C][C]555.854625909171[/C][C]-52.0546259091706[/C][/ROW]
[ROW][C]68[/C][C]516.2[/C][C]559.151685713528[/C][C]-42.9516857135281[/C][/ROW]
[ROW][C]69[/C][C]526.1[/C][C]562.448745517886[/C][C]-36.3487455178856[/C][/ROW]
[ROW][C]70[/C][C]527.1[/C][C]565.745805322243[/C][C]-38.6458053222431[/C][/ROW]
[ROW][C]71[/C][C]525.1[/C][C]569.042865126601[/C][C]-43.9428651266006[/C][/ROW]
[ROW][C]72[/C][C]528.9[/C][C]572.339924930958[/C][C]-43.4399249309581[/C][/ROW]
[ROW][C]73[/C][C]540.1[/C][C]575.636984735316[/C][C]-35.5369847353156[/C][/ROW]
[ROW][C]74[/C][C]549[/C][C]578.934044539673[/C][C]-29.9340445396731[/C][/ROW]
[ROW][C]75[/C][C]556[/C][C]582.23110434403[/C][C]-26.2311043440306[/C][/ROW]
[ROW][C]76[/C][C]568.9[/C][C]585.528164148388[/C][C]-16.6281641483881[/C][/ROW]
[ROW][C]77[/C][C]589.1[/C][C]588.825223952746[/C][C]0.274776047254456[/C][/ROW]
[ROW][C]78[/C][C]590.3[/C][C]592.122283757103[/C][C]-1.82228375710310[/C][/ROW]
[ROW][C]79[/C][C]603.3[/C][C]595.41934356146[/C][C]7.8806564385394[/C][/ROW]
[ROW][C]80[/C][C]638.8[/C][C]598.716403365818[/C][C]40.0835966341819[/C][/ROW]
[ROW][C]81[/C][C]643[/C][C]602.013463170176[/C][C]40.9865368298245[/C][/ROW]
[ROW][C]82[/C][C]656.7[/C][C]605.310522974533[/C][C]51.389477025467[/C][/ROW]
[ROW][C]83[/C][C]656.1[/C][C]608.607582778891[/C][C]47.4924172211095[/C][/ROW]
[ROW][C]84[/C][C]654.1[/C][C]611.904642583248[/C][C]42.195357416752[/C][/ROW]
[ROW][C]85[/C][C]659.9[/C][C]615.201702387606[/C][C]44.6982976123945[/C][/ROW]
[ROW][C]86[/C][C]662.1[/C][C]618.498762191963[/C][C]43.601237808037[/C][/ROW]
[ROW][C]87[/C][C]669.2[/C][C]621.79582199632[/C][C]47.4041780036796[/C][/ROW]
[ROW][C]88[/C][C]673.1[/C][C]625.092881800678[/C][C]48.007118199322[/C][/ROW]
[ROW][C]89[/C][C]678.3[/C][C]628.389941605035[/C][C]49.9100583949645[/C][/ROW]
[ROW][C]90[/C][C]677.4[/C][C]631.687001409393[/C][C]45.712998590607[/C][/ROW]
[ROW][C]91[/C][C]678.5[/C][C]634.98406121375[/C][C]43.5159387862495[/C][/ROW]
[ROW][C]92[/C][C]672.4[/C][C]638.281121018108[/C][C]34.118878981892[/C][/ROW]
[ROW][C]93[/C][C]665.3[/C][C]641.578180822465[/C][C]23.7218191775345[/C][/ROW]
[ROW][C]94[/C][C]667.9[/C][C]644.875240626823[/C][C]23.024759373177[/C][/ROW]
[ROW][C]95[/C][C]672.1[/C][C]648.17230043118[/C][C]23.9276995688196[/C][/ROW]
[ROW][C]96[/C][C]662.5[/C][C]651.469360235538[/C][C]11.0306397644621[/C][/ROW]
[ROW][C]97[/C][C]682.3[/C][C]654.766420039895[/C][C]27.5335799601045[/C][/ROW]
[ROW][C]98[/C][C]692.1[/C][C]658.063479844253[/C][C]34.0365201557471[/C][/ROW]
[ROW][C]99[/C][C]702.7[/C][C]661.36053964861[/C][C]41.3394603513896[/C][/ROW]
[ROW][C]100[/C][C]721.4[/C][C]664.657599452968[/C][C]56.7424005470321[/C][/ROW]
[ROW][C]101[/C][C]733.2[/C][C]667.954659257325[/C][C]65.2453407426747[/C][/ROW]
[ROW][C]102[/C][C]747.7[/C][C]671.251719061683[/C][C]76.4482809383172[/C][/ROW]
[ROW][C]103[/C][C]737.6[/C][C]674.54877886604[/C][C]63.0512211339596[/C][/ROW]
[ROW][C]104[/C][C]729.3[/C][C]677.845838670398[/C][C]51.4541613296021[/C][/ROW]
[ROW][C]105[/C][C]706.1[/C][C]681.142898474755[/C][C]24.9571015252447[/C][/ROW]
[ROW][C]106[/C][C]674.3[/C][C]684.439958279113[/C][C]-10.1399582791129[/C][/ROW]
[ROW][C]107[/C][C]659[/C][C]687.73701808347[/C][C]-28.7370180834703[/C][/ROW]
[ROW][C]108[/C][C]645.7[/C][C]691.034077887828[/C][C]-45.3340778878278[/C][/ROW]
[ROW][C]109[/C][C]646.1[/C][C]694.331137692185[/C][C]-48.2311376921853[/C][/ROW]
[ROW][C]110[/C][C]633[/C][C]626.560290684749[/C][C]6.43970931525123[/C][/ROW]
[ROW][C]111[/C][C]622.3[/C][C]629.857350489106[/C][C]-7.55735048910632[/C][/ROW]
[ROW][C]112[/C][C]628.2[/C][C]633.154410293464[/C][C]-4.95441029346372[/C][/ROW]
[ROW][C]113[/C][C]637.3[/C][C]636.451470097821[/C][C]0.8485299021787[/C][/ROW]
[ROW][C]114[/C][C]639.6[/C][C]639.748529902179[/C][C]-0.148529902178725[/C][/ROW]
[ROW][C]115[/C][C]638.5[/C][C]643.045589706536[/C][C]-4.54558970653624[/C][/ROW]
[ROW][C]116[/C][C]650.5[/C][C]646.342649510894[/C][C]4.15735048910627[/C][/ROW]
[ROW][C]117[/C][C]655.4[/C][C]649.639709315251[/C][C]5.76029068474875[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57370&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57370&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1395.3338.24867882157757.0513211784233
2395.1341.54573862593353.5542613740666
3403.5344.84279843029158.6572015697091
4403.3348.13985823464955.1601417653514
5405.7351.43691803900654.2630819609939
6406.7354.73397784336451.9660221566364
7407.2358.03103764772149.1689623522789
8412.4361.32809745207951.0719025479214
9415.9364.62515725643651.2748427435639
10414367.92221706079446.0777829392064
11411.8371.21927686515140.5807231348489
12409.9374.51633666950935.3836633304914
13412.4377.81339647386634.5866035261339
14415.9381.11045627822434.7895437217764
15416.3384.40751608258131.8924839174190
16417.2387.70457588693929.4954241130615
17421.8391.00163569129630.798364308704
18421.4394.29869549565427.1013045043465
19415.1397.59575530001117.504244699989
20412.4400.89281510436811.5071848956315
21411.8404.1898749087267.61012509127402
22408.8407.4869347130831.31306528691652
23404.5410.783994517441-6.28399451744097
24402.5414.081054321798-11.5810543217985
25409.4417.378114126156-7.97811412615599
26410.7420.675173930513-9.97517393051347
27413.4423.972233734871-10.5722337348710
28415.2427.269293539228-12.0692935392285
29417.7430.566353343586-12.8663533435859
30417.8433.863413147943-16.0634131479434
31417.9437.160472952301-19.2604729523009
32418.4440.457532756658-22.0575327566584
33418.2443.754592561016-25.5545925610159
34416.6447.051652365373-30.4516523653734
35418.9450.348712169731-31.4487121697309
36421453.645771974088-32.6457719740884
37423.5456.942831778446-33.4428317784459
38432.3460.239891582803-27.9398915828033
39432.3463.536951387161-31.2369513871608
40428.6466.834011191518-38.2340111915183
41426.7470.131070995876-43.4310709958759
42427.3473.428130800233-46.1281308002333
43428.5476.725190604591-48.2251906045908
44437480.022250408948-43.0222504089483
45442483.319310213306-41.3193102133058
46444.9486.616370017663-41.7163700176633
47441.4489.913429822021-48.5134298220208
48440.3493.210489626378-52.9104896263783
49447.1496.507549430736-49.4075494307357
50455.3499.804609235093-44.5046092350933
51478.6503.101669039451-24.5016690394507
52486.5506.398728843808-19.8987288438083
53487.8509.695788648166-21.8957886481657
54485.9512.992848452523-27.0928484525233
55483.8516.289908256881-32.4899082568807
56488.4519.586968061238-31.1869680612383
57494522.884027865596-28.8840278655957
58493.6526.181087669953-32.5810876699532
59487.3529.478147474311-42.1781474743107
60482.1532.775207278668-50.6752072786682
61484.2536.072267083026-51.8722670830257
62496.8539.369326887383-42.5693268873832
63501.1542.666386691741-41.5663866917407
64499.8545.963446496098-46.1634464960982
65495.5549.260506300456-53.7605063004557
66498.1552.557566104813-54.4575661048131
67503.8555.854625909171-52.0546259091706
68516.2559.151685713528-42.9516857135281
69526.1562.448745517886-36.3487455178856
70527.1565.745805322243-38.6458053222431
71525.1569.042865126601-43.9428651266006
72528.9572.339924930958-43.4399249309581
73540.1575.636984735316-35.5369847353156
74549578.934044539673-29.9340445396731
75556582.23110434403-26.2311043440306
76568.9585.528164148388-16.6281641483881
77589.1588.8252239527460.274776047254456
78590.3592.122283757103-1.82228375710310
79603.3595.419343561467.8806564385394
80638.8598.71640336581840.0835966341819
81643602.01346317017640.9865368298245
82656.7605.31052297453351.389477025467
83656.1608.60758277889147.4924172211095
84654.1611.90464258324842.195357416752
85659.9615.20170238760644.6982976123945
86662.1618.49876219196343.601237808037
87669.2621.7958219963247.4041780036796
88673.1625.09288180067848.007118199322
89678.3628.38994160503549.9100583949645
90677.4631.68700140939345.712998590607
91678.5634.9840612137543.5159387862495
92672.4638.28112101810834.118878981892
93665.3641.57818082246523.7218191775345
94667.9644.87524062682323.024759373177
95672.1648.1723004311823.9276995688196
96662.5651.46936023553811.0306397644621
97682.3654.76642003989527.5335799601045
98692.1658.06347984425334.0365201557471
99702.7661.3605396486141.3394603513896
100721.4664.65759945296856.7424005470321
101733.2667.95465925732565.2453407426747
102747.7671.25171906168376.4482809383172
103737.6674.5487788660463.0512211339596
104729.3677.84583867039851.4541613296021
105706.1681.14289847475524.9571015252447
106674.3684.439958279113-10.1399582791129
107659687.73701808347-28.7370180834703
108645.7691.034077887828-45.3340778878278
109646.1694.331137692185-48.2311376921853
110633626.5602906847496.43970931525123
111622.3629.857350489106-7.55735048910632
112628.2633.154410293464-4.95441029346372
113637.3636.4514700978210.8485299021787
114639.6639.748529902179-0.148529902178725
115638.5643.045589706536-4.54558970653624
116650.5646.3426495108944.15735048910627
117655.4649.6397093152515.76029068474875






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.0004664964091127650.000932992818225530.999533503590887
74.67225613695761e-059.34451227391522e-050.99995327743863
83.26775694149021e-066.53551388298042e-060.999996732243059
92.65011113019184e-075.30022226038367e-070.999999734988887
103.34438975567698e-086.68877951135396e-080.999999966556102
112.4690428822045e-084.938085764409e-080.999999975309571
122.70635959672774e-085.41271919345548e-080.999999972936404
136.44626045788952e-091.28925209157790e-080.99999999355374
148.5947329924625e-101.7189465984925e-090.999999999140527
151.25420397619654e-102.50840795239307e-100.99999999987458
161.86994623996367e-113.73989247992735e-110.9999999999813
172.77581865162598e-125.55163730325196e-120.999999999997224
183.82549915356283e-137.65099830712566e-130.999999999999617
195.94414174728479e-131.18882834945696e-120.999999999999406
202.02226227465294e-124.04452454930588e-120.999999999997978
213.57476735045427e-127.14953470090854e-120.999999999996425
221.02949763125221e-112.05899526250443e-110.999999999989705
235.34240043491488e-111.06848008698298e-100.999999999946576
241.59524283069442e-103.19048566138884e-100.999999999840476
256.63304815564183e-111.32660963112837e-100.99999999993367
262.27221313087698e-114.54442626175395e-110.999999999977278
276.53825254359825e-121.30765050871965e-110.999999999993462
281.82190376380821e-123.64380752761642e-120.999999999998178
295.37709978123681e-131.07541995624736e-120.999999999999462
301.51133701808610e-133.02267403617219e-130.999999999999849
314.05372217462707e-148.10744434925415e-140.99999999999996
321.05374906795391e-142.10749813590781e-140.99999999999999
332.58553450998887e-155.17106901997775e-150.999999999999997
346.16509898392692e-161.23301979678538e-151
351.38805272018870e-162.77610544037741e-161
363.23664600003903e-176.47329200007806e-171
378.7779480060419e-181.75558960120838e-171
381.57074398685388e-173.14148797370776e-171
391.48496333492108e-172.96992666984216e-171
404.53382491913846e-189.06764983827692e-181
419.97167871601507e-191.99433574320301e-181
422.09745647098200e-194.19491294196401e-191
434.41084552149511e-208.82169104299021e-201
443.59826695025642e-207.19653390051284e-201
457.96624948624072e-201.59324989724814e-191
462.06310751595031e-194.12621503190063e-191
471.12357722407772e-192.24715444815545e-191
483.92590957765078e-207.85181915530156e-201
494.04772930607288e-208.09545861214576e-201
502.44662457244236e-194.89324914488472e-191
512.60828429831531e-155.21656859663062e-150.999999999999997
522.65340976645963e-125.30681953291926e-120.999999999997347
531.39129238181187e-102.78258476362374e-100.99999999986087
549.97890965677005e-101.99578193135401e-090.999999999002109
552.38804261368671e-094.77608522737342e-090.999999997611957
565.83623916318255e-091.16724783263651e-080.99999999416376
571.59099481347724e-083.18198962695449e-080.999999984090052
582.56700992173972e-085.13401984347944e-080.9999999743299
591.99142447851969e-083.98284895703937e-080.999999980085755
601.15028190178852e-082.30056380357704e-080.999999988497181
616.90485148372367e-091.38097029674473e-080.999999993095149
626.39580289518823e-091.27916057903765e-080.999999993604197
636.49757600376684e-091.29951520075337e-080.999999993502424
645.58544060450661e-091.11708812090132e-080.99999999441456
654.47232518507631e-098.94465037015262e-090.999999995527675
664.23437698258845e-098.4687539651769e-090.999999995765623
675.05696845564153e-091.01139369112831e-080.999999994943032
689.26254377702879e-091.85250875540576e-080.999999990737456
692.52651509139618e-085.05303018279236e-080.999999974734849
706.6931922376245e-081.3386384475249e-070.999999933068078
711.94243485314202e-073.88486970628405e-070.999999805756515
727.97343517219955e-071.59468703443991e-060.999999202656483
734.76919393781294e-069.53838787562587e-060.999995230806062
743.72908345343161e-057.45816690686321e-050.999962709165466
750.0003405525616818360.0006811051233636710.999659447438318
760.003109084560824190.006218169121648390.996890915439176
770.02210365041955510.04420730083911020.977896349580445
780.09597197939537250.1919439587907450.904028020604628
790.274136732345920.548273464691840.72586326765408
800.538320956726870.923358086546260.46167904327313
810.7213270279239760.5573459441520480.278672972076024
820.83962126338540.3207574732292010.160378736614600
830.8937913207645170.2124173584709650.106208679235483
840.9201623611527180.1596752776945640.079837638847282
850.9354493470880710.1291013058238580.0645506529119289
860.943378634484040.1132427310319220.0566213655159608
870.9473052029430280.1053895941139450.0526947970569723
880.9476657343526540.1046685312946930.0523342656473463
890.9454714584430540.1090570831138920.054528541556946
900.9392255419275950.121548916144810.0607744580724049
910.9294470018072950.141105996385410.070552998192705
920.916638526547130.1667229469057410.0833614734528705
930.9086925935775390.1826148128449230.0913074064224614
940.9041228402432690.1917543195134630.0958771597567314
950.9027797967852760.1944404064294480.0972202032147241
960.9360210409203680.1279579181592630.0639789590796315
970.94766003268770.1046799346245990.0523399673122997
980.9543833787871060.09123324242578730.0456166212128936
990.9535220567990180.09295588640196350.0464779432009817
1000.9375867357436450.1248265285127090.0624132642563546
1010.9180062755650440.1639874488699120.081993724434956
1020.9369993642127870.1260012715744270.0630006357872133
1030.9650151660517880.06996966789642320.0349848339482116
1040.9945542628695540.01089147426089180.00544573713044592
1050.999856237071050.0002875258579006120.000143762928950306
1060.9999864461367882.71077264242438e-051.35538632121219e-05
1070.9999953866832989.22663340315218e-064.61331670157609e-06
1080.999968147521296.37049574198638e-053.18524787099319e-05
1090.9997674373699170.0004651252601658740.000232562630082937
1100.9998772317370740.0002455365258528680.000122768262926434
1110.998599780777240.002800438445519670.00140021922275984

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.000466496409112765 & 0.00093299281822553 & 0.999533503590887 \tabularnewline
7 & 4.67225613695761e-05 & 9.34451227391522e-05 & 0.99995327743863 \tabularnewline
8 & 3.26775694149021e-06 & 6.53551388298042e-06 & 0.999996732243059 \tabularnewline
9 & 2.65011113019184e-07 & 5.30022226038367e-07 & 0.999999734988887 \tabularnewline
10 & 3.34438975567698e-08 & 6.68877951135396e-08 & 0.999999966556102 \tabularnewline
11 & 2.4690428822045e-08 & 4.938085764409e-08 & 0.999999975309571 \tabularnewline
12 & 2.70635959672774e-08 & 5.41271919345548e-08 & 0.999999972936404 \tabularnewline
13 & 6.44626045788952e-09 & 1.28925209157790e-08 & 0.99999999355374 \tabularnewline
14 & 8.5947329924625e-10 & 1.7189465984925e-09 & 0.999999999140527 \tabularnewline
15 & 1.25420397619654e-10 & 2.50840795239307e-10 & 0.99999999987458 \tabularnewline
16 & 1.86994623996367e-11 & 3.73989247992735e-11 & 0.9999999999813 \tabularnewline
17 & 2.77581865162598e-12 & 5.55163730325196e-12 & 0.999999999997224 \tabularnewline
18 & 3.82549915356283e-13 & 7.65099830712566e-13 & 0.999999999999617 \tabularnewline
19 & 5.94414174728479e-13 & 1.18882834945696e-12 & 0.999999999999406 \tabularnewline
20 & 2.02226227465294e-12 & 4.04452454930588e-12 & 0.999999999997978 \tabularnewline
21 & 3.57476735045427e-12 & 7.14953470090854e-12 & 0.999999999996425 \tabularnewline
22 & 1.02949763125221e-11 & 2.05899526250443e-11 & 0.999999999989705 \tabularnewline
23 & 5.34240043491488e-11 & 1.06848008698298e-10 & 0.999999999946576 \tabularnewline
24 & 1.59524283069442e-10 & 3.19048566138884e-10 & 0.999999999840476 \tabularnewline
25 & 6.63304815564183e-11 & 1.32660963112837e-10 & 0.99999999993367 \tabularnewline
26 & 2.27221313087698e-11 & 4.54442626175395e-11 & 0.999999999977278 \tabularnewline
27 & 6.53825254359825e-12 & 1.30765050871965e-11 & 0.999999999993462 \tabularnewline
28 & 1.82190376380821e-12 & 3.64380752761642e-12 & 0.999999999998178 \tabularnewline
29 & 5.37709978123681e-13 & 1.07541995624736e-12 & 0.999999999999462 \tabularnewline
30 & 1.51133701808610e-13 & 3.02267403617219e-13 & 0.999999999999849 \tabularnewline
31 & 4.05372217462707e-14 & 8.10744434925415e-14 & 0.99999999999996 \tabularnewline
32 & 1.05374906795391e-14 & 2.10749813590781e-14 & 0.99999999999999 \tabularnewline
33 & 2.58553450998887e-15 & 5.17106901997775e-15 & 0.999999999999997 \tabularnewline
34 & 6.16509898392692e-16 & 1.23301979678538e-15 & 1 \tabularnewline
35 & 1.38805272018870e-16 & 2.77610544037741e-16 & 1 \tabularnewline
36 & 3.23664600003903e-17 & 6.47329200007806e-17 & 1 \tabularnewline
37 & 8.7779480060419e-18 & 1.75558960120838e-17 & 1 \tabularnewline
38 & 1.57074398685388e-17 & 3.14148797370776e-17 & 1 \tabularnewline
39 & 1.48496333492108e-17 & 2.96992666984216e-17 & 1 \tabularnewline
40 & 4.53382491913846e-18 & 9.06764983827692e-18 & 1 \tabularnewline
41 & 9.97167871601507e-19 & 1.99433574320301e-18 & 1 \tabularnewline
42 & 2.09745647098200e-19 & 4.19491294196401e-19 & 1 \tabularnewline
43 & 4.41084552149511e-20 & 8.82169104299021e-20 & 1 \tabularnewline
44 & 3.59826695025642e-20 & 7.19653390051284e-20 & 1 \tabularnewline
45 & 7.96624948624072e-20 & 1.59324989724814e-19 & 1 \tabularnewline
46 & 2.06310751595031e-19 & 4.12621503190063e-19 & 1 \tabularnewline
47 & 1.12357722407772e-19 & 2.24715444815545e-19 & 1 \tabularnewline
48 & 3.92590957765078e-20 & 7.85181915530156e-20 & 1 \tabularnewline
49 & 4.04772930607288e-20 & 8.09545861214576e-20 & 1 \tabularnewline
50 & 2.44662457244236e-19 & 4.89324914488472e-19 & 1 \tabularnewline
51 & 2.60828429831531e-15 & 5.21656859663062e-15 & 0.999999999999997 \tabularnewline
52 & 2.65340976645963e-12 & 5.30681953291926e-12 & 0.999999999997347 \tabularnewline
53 & 1.39129238181187e-10 & 2.78258476362374e-10 & 0.99999999986087 \tabularnewline
54 & 9.97890965677005e-10 & 1.99578193135401e-09 & 0.999999999002109 \tabularnewline
55 & 2.38804261368671e-09 & 4.77608522737342e-09 & 0.999999997611957 \tabularnewline
56 & 5.83623916318255e-09 & 1.16724783263651e-08 & 0.99999999416376 \tabularnewline
57 & 1.59099481347724e-08 & 3.18198962695449e-08 & 0.999999984090052 \tabularnewline
58 & 2.56700992173972e-08 & 5.13401984347944e-08 & 0.9999999743299 \tabularnewline
59 & 1.99142447851969e-08 & 3.98284895703937e-08 & 0.999999980085755 \tabularnewline
60 & 1.15028190178852e-08 & 2.30056380357704e-08 & 0.999999988497181 \tabularnewline
61 & 6.90485148372367e-09 & 1.38097029674473e-08 & 0.999999993095149 \tabularnewline
62 & 6.39580289518823e-09 & 1.27916057903765e-08 & 0.999999993604197 \tabularnewline
63 & 6.49757600376684e-09 & 1.29951520075337e-08 & 0.999999993502424 \tabularnewline
64 & 5.58544060450661e-09 & 1.11708812090132e-08 & 0.99999999441456 \tabularnewline
65 & 4.47232518507631e-09 & 8.94465037015262e-09 & 0.999999995527675 \tabularnewline
66 & 4.23437698258845e-09 & 8.4687539651769e-09 & 0.999999995765623 \tabularnewline
67 & 5.05696845564153e-09 & 1.01139369112831e-08 & 0.999999994943032 \tabularnewline
68 & 9.26254377702879e-09 & 1.85250875540576e-08 & 0.999999990737456 \tabularnewline
69 & 2.52651509139618e-08 & 5.05303018279236e-08 & 0.999999974734849 \tabularnewline
70 & 6.6931922376245e-08 & 1.3386384475249e-07 & 0.999999933068078 \tabularnewline
71 & 1.94243485314202e-07 & 3.88486970628405e-07 & 0.999999805756515 \tabularnewline
72 & 7.97343517219955e-07 & 1.59468703443991e-06 & 0.999999202656483 \tabularnewline
73 & 4.76919393781294e-06 & 9.53838787562587e-06 & 0.999995230806062 \tabularnewline
74 & 3.72908345343161e-05 & 7.45816690686321e-05 & 0.999962709165466 \tabularnewline
75 & 0.000340552561681836 & 0.000681105123363671 & 0.999659447438318 \tabularnewline
76 & 0.00310908456082419 & 0.00621816912164839 & 0.996890915439176 \tabularnewline
77 & 0.0221036504195551 & 0.0442073008391102 & 0.977896349580445 \tabularnewline
78 & 0.0959719793953725 & 0.191943958790745 & 0.904028020604628 \tabularnewline
79 & 0.27413673234592 & 0.54827346469184 & 0.72586326765408 \tabularnewline
80 & 0.53832095672687 & 0.92335808654626 & 0.46167904327313 \tabularnewline
81 & 0.721327027923976 & 0.557345944152048 & 0.278672972076024 \tabularnewline
82 & 0.8396212633854 & 0.320757473229201 & 0.160378736614600 \tabularnewline
83 & 0.893791320764517 & 0.212417358470965 & 0.106208679235483 \tabularnewline
84 & 0.920162361152718 & 0.159675277694564 & 0.079837638847282 \tabularnewline
85 & 0.935449347088071 & 0.129101305823858 & 0.0645506529119289 \tabularnewline
86 & 0.94337863448404 & 0.113242731031922 & 0.0566213655159608 \tabularnewline
87 & 0.947305202943028 & 0.105389594113945 & 0.0526947970569723 \tabularnewline
88 & 0.947665734352654 & 0.104668531294693 & 0.0523342656473463 \tabularnewline
89 & 0.945471458443054 & 0.109057083113892 & 0.054528541556946 \tabularnewline
90 & 0.939225541927595 & 0.12154891614481 & 0.0607744580724049 \tabularnewline
91 & 0.929447001807295 & 0.14110599638541 & 0.070552998192705 \tabularnewline
92 & 0.91663852654713 & 0.166722946905741 & 0.0833614734528705 \tabularnewline
93 & 0.908692593577539 & 0.182614812844923 & 0.0913074064224614 \tabularnewline
94 & 0.904122840243269 & 0.191754319513463 & 0.0958771597567314 \tabularnewline
95 & 0.902779796785276 & 0.194440406429448 & 0.0972202032147241 \tabularnewline
96 & 0.936021040920368 & 0.127957918159263 & 0.0639789590796315 \tabularnewline
97 & 0.9476600326877 & 0.104679934624599 & 0.0523399673122997 \tabularnewline
98 & 0.954383378787106 & 0.0912332424257873 & 0.0456166212128936 \tabularnewline
99 & 0.953522056799018 & 0.0929558864019635 & 0.0464779432009817 \tabularnewline
100 & 0.937586735743645 & 0.124826528512709 & 0.0624132642563546 \tabularnewline
101 & 0.918006275565044 & 0.163987448869912 & 0.081993724434956 \tabularnewline
102 & 0.936999364212787 & 0.126001271574427 & 0.0630006357872133 \tabularnewline
103 & 0.965015166051788 & 0.0699696678964232 & 0.0349848339482116 \tabularnewline
104 & 0.994554262869554 & 0.0108914742608918 & 0.00544573713044592 \tabularnewline
105 & 0.99985623707105 & 0.000287525857900612 & 0.000143762928950306 \tabularnewline
106 & 0.999986446136788 & 2.71077264242438e-05 & 1.35538632121219e-05 \tabularnewline
107 & 0.999995386683298 & 9.22663340315218e-06 & 4.61331670157609e-06 \tabularnewline
108 & 0.99996814752129 & 6.37049574198638e-05 & 3.18524787099319e-05 \tabularnewline
109 & 0.999767437369917 & 0.000465125260165874 & 0.000232562630082937 \tabularnewline
110 & 0.999877231737074 & 0.000245536525852868 & 0.000122768262926434 \tabularnewline
111 & 0.99859978077724 & 0.00280043844551967 & 0.00140021922275984 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57370&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]0.000466496409112765[/C][C]0.00093299281822553[/C][C]0.999533503590887[/C][/ROW]
[ROW][C]7[/C][C]4.67225613695761e-05[/C][C]9.34451227391522e-05[/C][C]0.99995327743863[/C][/ROW]
[ROW][C]8[/C][C]3.26775694149021e-06[/C][C]6.53551388298042e-06[/C][C]0.999996732243059[/C][/ROW]
[ROW][C]9[/C][C]2.65011113019184e-07[/C][C]5.30022226038367e-07[/C][C]0.999999734988887[/C][/ROW]
[ROW][C]10[/C][C]3.34438975567698e-08[/C][C]6.68877951135396e-08[/C][C]0.999999966556102[/C][/ROW]
[ROW][C]11[/C][C]2.4690428822045e-08[/C][C]4.938085764409e-08[/C][C]0.999999975309571[/C][/ROW]
[ROW][C]12[/C][C]2.70635959672774e-08[/C][C]5.41271919345548e-08[/C][C]0.999999972936404[/C][/ROW]
[ROW][C]13[/C][C]6.44626045788952e-09[/C][C]1.28925209157790e-08[/C][C]0.99999999355374[/C][/ROW]
[ROW][C]14[/C][C]8.5947329924625e-10[/C][C]1.7189465984925e-09[/C][C]0.999999999140527[/C][/ROW]
[ROW][C]15[/C][C]1.25420397619654e-10[/C][C]2.50840795239307e-10[/C][C]0.99999999987458[/C][/ROW]
[ROW][C]16[/C][C]1.86994623996367e-11[/C][C]3.73989247992735e-11[/C][C]0.9999999999813[/C][/ROW]
[ROW][C]17[/C][C]2.77581865162598e-12[/C][C]5.55163730325196e-12[/C][C]0.999999999997224[/C][/ROW]
[ROW][C]18[/C][C]3.82549915356283e-13[/C][C]7.65099830712566e-13[/C][C]0.999999999999617[/C][/ROW]
[ROW][C]19[/C][C]5.94414174728479e-13[/C][C]1.18882834945696e-12[/C][C]0.999999999999406[/C][/ROW]
[ROW][C]20[/C][C]2.02226227465294e-12[/C][C]4.04452454930588e-12[/C][C]0.999999999997978[/C][/ROW]
[ROW][C]21[/C][C]3.57476735045427e-12[/C][C]7.14953470090854e-12[/C][C]0.999999999996425[/C][/ROW]
[ROW][C]22[/C][C]1.02949763125221e-11[/C][C]2.05899526250443e-11[/C][C]0.999999999989705[/C][/ROW]
[ROW][C]23[/C][C]5.34240043491488e-11[/C][C]1.06848008698298e-10[/C][C]0.999999999946576[/C][/ROW]
[ROW][C]24[/C][C]1.59524283069442e-10[/C][C]3.19048566138884e-10[/C][C]0.999999999840476[/C][/ROW]
[ROW][C]25[/C][C]6.63304815564183e-11[/C][C]1.32660963112837e-10[/C][C]0.99999999993367[/C][/ROW]
[ROW][C]26[/C][C]2.27221313087698e-11[/C][C]4.54442626175395e-11[/C][C]0.999999999977278[/C][/ROW]
[ROW][C]27[/C][C]6.53825254359825e-12[/C][C]1.30765050871965e-11[/C][C]0.999999999993462[/C][/ROW]
[ROW][C]28[/C][C]1.82190376380821e-12[/C][C]3.64380752761642e-12[/C][C]0.999999999998178[/C][/ROW]
[ROW][C]29[/C][C]5.37709978123681e-13[/C][C]1.07541995624736e-12[/C][C]0.999999999999462[/C][/ROW]
[ROW][C]30[/C][C]1.51133701808610e-13[/C][C]3.02267403617219e-13[/C][C]0.999999999999849[/C][/ROW]
[ROW][C]31[/C][C]4.05372217462707e-14[/C][C]8.10744434925415e-14[/C][C]0.99999999999996[/C][/ROW]
[ROW][C]32[/C][C]1.05374906795391e-14[/C][C]2.10749813590781e-14[/C][C]0.99999999999999[/C][/ROW]
[ROW][C]33[/C][C]2.58553450998887e-15[/C][C]5.17106901997775e-15[/C][C]0.999999999999997[/C][/ROW]
[ROW][C]34[/C][C]6.16509898392692e-16[/C][C]1.23301979678538e-15[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]1.38805272018870e-16[/C][C]2.77610544037741e-16[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]3.23664600003903e-17[/C][C]6.47329200007806e-17[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]8.7779480060419e-18[/C][C]1.75558960120838e-17[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]1.57074398685388e-17[/C][C]3.14148797370776e-17[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]1.48496333492108e-17[/C][C]2.96992666984216e-17[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]4.53382491913846e-18[/C][C]9.06764983827692e-18[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]9.97167871601507e-19[/C][C]1.99433574320301e-18[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]2.09745647098200e-19[/C][C]4.19491294196401e-19[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]4.41084552149511e-20[/C][C]8.82169104299021e-20[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]3.59826695025642e-20[/C][C]7.19653390051284e-20[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]7.96624948624072e-20[/C][C]1.59324989724814e-19[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]2.06310751595031e-19[/C][C]4.12621503190063e-19[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]1.12357722407772e-19[/C][C]2.24715444815545e-19[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]3.92590957765078e-20[/C][C]7.85181915530156e-20[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]4.04772930607288e-20[/C][C]8.09545861214576e-20[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]2.44662457244236e-19[/C][C]4.89324914488472e-19[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]2.60828429831531e-15[/C][C]5.21656859663062e-15[/C][C]0.999999999999997[/C][/ROW]
[ROW][C]52[/C][C]2.65340976645963e-12[/C][C]5.30681953291926e-12[/C][C]0.999999999997347[/C][/ROW]
[ROW][C]53[/C][C]1.39129238181187e-10[/C][C]2.78258476362374e-10[/C][C]0.99999999986087[/C][/ROW]
[ROW][C]54[/C][C]9.97890965677005e-10[/C][C]1.99578193135401e-09[/C][C]0.999999999002109[/C][/ROW]
[ROW][C]55[/C][C]2.38804261368671e-09[/C][C]4.77608522737342e-09[/C][C]0.999999997611957[/C][/ROW]
[ROW][C]56[/C][C]5.83623916318255e-09[/C][C]1.16724783263651e-08[/C][C]0.99999999416376[/C][/ROW]
[ROW][C]57[/C][C]1.59099481347724e-08[/C][C]3.18198962695449e-08[/C][C]0.999999984090052[/C][/ROW]
[ROW][C]58[/C][C]2.56700992173972e-08[/C][C]5.13401984347944e-08[/C][C]0.9999999743299[/C][/ROW]
[ROW][C]59[/C][C]1.99142447851969e-08[/C][C]3.98284895703937e-08[/C][C]0.999999980085755[/C][/ROW]
[ROW][C]60[/C][C]1.15028190178852e-08[/C][C]2.30056380357704e-08[/C][C]0.999999988497181[/C][/ROW]
[ROW][C]61[/C][C]6.90485148372367e-09[/C][C]1.38097029674473e-08[/C][C]0.999999993095149[/C][/ROW]
[ROW][C]62[/C][C]6.39580289518823e-09[/C][C]1.27916057903765e-08[/C][C]0.999999993604197[/C][/ROW]
[ROW][C]63[/C][C]6.49757600376684e-09[/C][C]1.29951520075337e-08[/C][C]0.999999993502424[/C][/ROW]
[ROW][C]64[/C][C]5.58544060450661e-09[/C][C]1.11708812090132e-08[/C][C]0.99999999441456[/C][/ROW]
[ROW][C]65[/C][C]4.47232518507631e-09[/C][C]8.94465037015262e-09[/C][C]0.999999995527675[/C][/ROW]
[ROW][C]66[/C][C]4.23437698258845e-09[/C][C]8.4687539651769e-09[/C][C]0.999999995765623[/C][/ROW]
[ROW][C]67[/C][C]5.05696845564153e-09[/C][C]1.01139369112831e-08[/C][C]0.999999994943032[/C][/ROW]
[ROW][C]68[/C][C]9.26254377702879e-09[/C][C]1.85250875540576e-08[/C][C]0.999999990737456[/C][/ROW]
[ROW][C]69[/C][C]2.52651509139618e-08[/C][C]5.05303018279236e-08[/C][C]0.999999974734849[/C][/ROW]
[ROW][C]70[/C][C]6.6931922376245e-08[/C][C]1.3386384475249e-07[/C][C]0.999999933068078[/C][/ROW]
[ROW][C]71[/C][C]1.94243485314202e-07[/C][C]3.88486970628405e-07[/C][C]0.999999805756515[/C][/ROW]
[ROW][C]72[/C][C]7.97343517219955e-07[/C][C]1.59468703443991e-06[/C][C]0.999999202656483[/C][/ROW]
[ROW][C]73[/C][C]4.76919393781294e-06[/C][C]9.53838787562587e-06[/C][C]0.999995230806062[/C][/ROW]
[ROW][C]74[/C][C]3.72908345343161e-05[/C][C]7.45816690686321e-05[/C][C]0.999962709165466[/C][/ROW]
[ROW][C]75[/C][C]0.000340552561681836[/C][C]0.000681105123363671[/C][C]0.999659447438318[/C][/ROW]
[ROW][C]76[/C][C]0.00310908456082419[/C][C]0.00621816912164839[/C][C]0.996890915439176[/C][/ROW]
[ROW][C]77[/C][C]0.0221036504195551[/C][C]0.0442073008391102[/C][C]0.977896349580445[/C][/ROW]
[ROW][C]78[/C][C]0.0959719793953725[/C][C]0.191943958790745[/C][C]0.904028020604628[/C][/ROW]
[ROW][C]79[/C][C]0.27413673234592[/C][C]0.54827346469184[/C][C]0.72586326765408[/C][/ROW]
[ROW][C]80[/C][C]0.53832095672687[/C][C]0.92335808654626[/C][C]0.46167904327313[/C][/ROW]
[ROW][C]81[/C][C]0.721327027923976[/C][C]0.557345944152048[/C][C]0.278672972076024[/C][/ROW]
[ROW][C]82[/C][C]0.8396212633854[/C][C]0.320757473229201[/C][C]0.160378736614600[/C][/ROW]
[ROW][C]83[/C][C]0.893791320764517[/C][C]0.212417358470965[/C][C]0.106208679235483[/C][/ROW]
[ROW][C]84[/C][C]0.920162361152718[/C][C]0.159675277694564[/C][C]0.079837638847282[/C][/ROW]
[ROW][C]85[/C][C]0.935449347088071[/C][C]0.129101305823858[/C][C]0.0645506529119289[/C][/ROW]
[ROW][C]86[/C][C]0.94337863448404[/C][C]0.113242731031922[/C][C]0.0566213655159608[/C][/ROW]
[ROW][C]87[/C][C]0.947305202943028[/C][C]0.105389594113945[/C][C]0.0526947970569723[/C][/ROW]
[ROW][C]88[/C][C]0.947665734352654[/C][C]0.104668531294693[/C][C]0.0523342656473463[/C][/ROW]
[ROW][C]89[/C][C]0.945471458443054[/C][C]0.109057083113892[/C][C]0.054528541556946[/C][/ROW]
[ROW][C]90[/C][C]0.939225541927595[/C][C]0.12154891614481[/C][C]0.0607744580724049[/C][/ROW]
[ROW][C]91[/C][C]0.929447001807295[/C][C]0.14110599638541[/C][C]0.070552998192705[/C][/ROW]
[ROW][C]92[/C][C]0.91663852654713[/C][C]0.166722946905741[/C][C]0.0833614734528705[/C][/ROW]
[ROW][C]93[/C][C]0.908692593577539[/C][C]0.182614812844923[/C][C]0.0913074064224614[/C][/ROW]
[ROW][C]94[/C][C]0.904122840243269[/C][C]0.191754319513463[/C][C]0.0958771597567314[/C][/ROW]
[ROW][C]95[/C][C]0.902779796785276[/C][C]0.194440406429448[/C][C]0.0972202032147241[/C][/ROW]
[ROW][C]96[/C][C]0.936021040920368[/C][C]0.127957918159263[/C][C]0.0639789590796315[/C][/ROW]
[ROW][C]97[/C][C]0.9476600326877[/C][C]0.104679934624599[/C][C]0.0523399673122997[/C][/ROW]
[ROW][C]98[/C][C]0.954383378787106[/C][C]0.0912332424257873[/C][C]0.0456166212128936[/C][/ROW]
[ROW][C]99[/C][C]0.953522056799018[/C][C]0.0929558864019635[/C][C]0.0464779432009817[/C][/ROW]
[ROW][C]100[/C][C]0.937586735743645[/C][C]0.124826528512709[/C][C]0.0624132642563546[/C][/ROW]
[ROW][C]101[/C][C]0.918006275565044[/C][C]0.163987448869912[/C][C]0.081993724434956[/C][/ROW]
[ROW][C]102[/C][C]0.936999364212787[/C][C]0.126001271574427[/C][C]0.0630006357872133[/C][/ROW]
[ROW][C]103[/C][C]0.965015166051788[/C][C]0.0699696678964232[/C][C]0.0349848339482116[/C][/ROW]
[ROW][C]104[/C][C]0.994554262869554[/C][C]0.0108914742608918[/C][C]0.00544573713044592[/C][/ROW]
[ROW][C]105[/C][C]0.99985623707105[/C][C]0.000287525857900612[/C][C]0.000143762928950306[/C][/ROW]
[ROW][C]106[/C][C]0.999986446136788[/C][C]2.71077264242438e-05[/C][C]1.35538632121219e-05[/C][/ROW]
[ROW][C]107[/C][C]0.999995386683298[/C][C]9.22663340315218e-06[/C][C]4.61331670157609e-06[/C][/ROW]
[ROW][C]108[/C][C]0.99996814752129[/C][C]6.37049574198638e-05[/C][C]3.18524787099319e-05[/C][/ROW]
[ROW][C]109[/C][C]0.999767437369917[/C][C]0.000465125260165874[/C][C]0.000232562630082937[/C][/ROW]
[ROW][C]110[/C][C]0.999877231737074[/C][C]0.000245536525852868[/C][C]0.000122768262926434[/C][/ROW]
[ROW][C]111[/C][C]0.99859978077724[/C][C]0.00280043844551967[/C][C]0.00140021922275984[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57370&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57370&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.0004664964091127650.000932992818225530.999533503590887
74.67225613695761e-059.34451227391522e-050.99995327743863
83.26775694149021e-066.53551388298042e-060.999996732243059
92.65011113019184e-075.30022226038367e-070.999999734988887
103.34438975567698e-086.68877951135396e-080.999999966556102
112.4690428822045e-084.938085764409e-080.999999975309571
122.70635959672774e-085.41271919345548e-080.999999972936404
136.44626045788952e-091.28925209157790e-080.99999999355374
148.5947329924625e-101.7189465984925e-090.999999999140527
151.25420397619654e-102.50840795239307e-100.99999999987458
161.86994623996367e-113.73989247992735e-110.9999999999813
172.77581865162598e-125.55163730325196e-120.999999999997224
183.82549915356283e-137.65099830712566e-130.999999999999617
195.94414174728479e-131.18882834945696e-120.999999999999406
202.02226227465294e-124.04452454930588e-120.999999999997978
213.57476735045427e-127.14953470090854e-120.999999999996425
221.02949763125221e-112.05899526250443e-110.999999999989705
235.34240043491488e-111.06848008698298e-100.999999999946576
241.59524283069442e-103.19048566138884e-100.999999999840476
256.63304815564183e-111.32660963112837e-100.99999999993367
262.27221313087698e-114.54442626175395e-110.999999999977278
276.53825254359825e-121.30765050871965e-110.999999999993462
281.82190376380821e-123.64380752761642e-120.999999999998178
295.37709978123681e-131.07541995624736e-120.999999999999462
301.51133701808610e-133.02267403617219e-130.999999999999849
314.05372217462707e-148.10744434925415e-140.99999999999996
321.05374906795391e-142.10749813590781e-140.99999999999999
332.58553450998887e-155.17106901997775e-150.999999999999997
346.16509898392692e-161.23301979678538e-151
351.38805272018870e-162.77610544037741e-161
363.23664600003903e-176.47329200007806e-171
378.7779480060419e-181.75558960120838e-171
381.57074398685388e-173.14148797370776e-171
391.48496333492108e-172.96992666984216e-171
404.53382491913846e-189.06764983827692e-181
419.97167871601507e-191.99433574320301e-181
422.09745647098200e-194.19491294196401e-191
434.41084552149511e-208.82169104299021e-201
443.59826695025642e-207.19653390051284e-201
457.96624948624072e-201.59324989724814e-191
462.06310751595031e-194.12621503190063e-191
471.12357722407772e-192.24715444815545e-191
483.92590957765078e-207.85181915530156e-201
494.04772930607288e-208.09545861214576e-201
502.44662457244236e-194.89324914488472e-191
512.60828429831531e-155.21656859663062e-150.999999999999997
522.65340976645963e-125.30681953291926e-120.999999999997347
531.39129238181187e-102.78258476362374e-100.99999999986087
549.97890965677005e-101.99578193135401e-090.999999999002109
552.38804261368671e-094.77608522737342e-090.999999997611957
565.83623916318255e-091.16724783263651e-080.99999999416376
571.59099481347724e-083.18198962695449e-080.999999984090052
582.56700992173972e-085.13401984347944e-080.9999999743299
591.99142447851969e-083.98284895703937e-080.999999980085755
601.15028190178852e-082.30056380357704e-080.999999988497181
616.90485148372367e-091.38097029674473e-080.999999993095149
626.39580289518823e-091.27916057903765e-080.999999993604197
636.49757600376684e-091.29951520075337e-080.999999993502424
645.58544060450661e-091.11708812090132e-080.99999999441456
654.47232518507631e-098.94465037015262e-090.999999995527675
664.23437698258845e-098.4687539651769e-090.999999995765623
675.05696845564153e-091.01139369112831e-080.999999994943032
689.26254377702879e-091.85250875540576e-080.999999990737456
692.52651509139618e-085.05303018279236e-080.999999974734849
706.6931922376245e-081.3386384475249e-070.999999933068078
711.94243485314202e-073.88486970628405e-070.999999805756515
727.97343517219955e-071.59468703443991e-060.999999202656483
734.76919393781294e-069.53838787562587e-060.999995230806062
743.72908345343161e-057.45816690686321e-050.999962709165466
750.0003405525616818360.0006811051233636710.999659447438318
760.003109084560824190.006218169121648390.996890915439176
770.02210365041955510.04420730083911020.977896349580445
780.09597197939537250.1919439587907450.904028020604628
790.274136732345920.548273464691840.72586326765408
800.538320956726870.923358086546260.46167904327313
810.7213270279239760.5573459441520480.278672972076024
820.83962126338540.3207574732292010.160378736614600
830.8937913207645170.2124173584709650.106208679235483
840.9201623611527180.1596752776945640.079837638847282
850.9354493470880710.1291013058238580.0645506529119289
860.943378634484040.1132427310319220.0566213655159608
870.9473052029430280.1053895941139450.0526947970569723
880.9476657343526540.1046685312946930.0523342656473463
890.9454714584430540.1090570831138920.054528541556946
900.9392255419275950.121548916144810.0607744580724049
910.9294470018072950.141105996385410.070552998192705
920.916638526547130.1667229469057410.0833614734528705
930.9086925935775390.1826148128449230.0913074064224614
940.9041228402432690.1917543195134630.0958771597567314
950.9027797967852760.1944404064294480.0972202032147241
960.9360210409203680.1279579181592630.0639789590796315
970.94766003268770.1046799346245990.0523399673122997
980.9543833787871060.09123324242578730.0456166212128936
990.9535220567990180.09295588640196350.0464779432009817
1000.9375867357436450.1248265285127090.0624132642563546
1010.9180062755650440.1639874488699120.081993724434956
1020.9369993642127870.1260012715744270.0630006357872133
1030.9650151660517880.06996966789642320.0349848339482116
1040.9945542628695540.01089147426089180.00544573713044592
1050.999856237071050.0002875258579006120.000143762928950306
1060.9999864461367882.71077264242438e-051.35538632121219e-05
1070.9999953866832989.22663340315218e-064.61331670157609e-06
1080.999968147521296.37049574198638e-053.18524787099319e-05
1090.9997674373699170.0004651252601658740.000232562630082937
1100.9998772317370740.0002455365258528680.000122768262926434
1110.998599780777240.002800438445519670.00140021922275984






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level780.735849056603774NOK
5% type I error level800.754716981132076NOK
10% type I error level830.783018867924528NOK

\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 & 78 & 0.735849056603774 & NOK \tabularnewline
5% type I error level & 80 & 0.754716981132076 & NOK \tabularnewline
10% type I error level & 83 & 0.783018867924528 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57370&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]78[/C][C]0.735849056603774[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]80[/C][C]0.754716981132076[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]83[/C][C]0.783018867924528[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57370&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57370&T=6

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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 level780.735849056603774NOK
5% type I error level800.754716981132076NOK
10% type I error level830.783018867924528NOK


Figures (Output of Computation)

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Input Parameters & R Code

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')
}