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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 08:40:23 -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/t1258472553he7xlzplxx9kwad.htm/, Retrieved Fri, 27 Jun 2025 01:55:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57371, Retrieved Fri, 27 Jun 2025 01:55:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact333

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 15:40:23] [94ba0ef70f5b330d175ff4daa1c9cd40] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57371&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]9 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=57371&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57371&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 time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 17.0254941272264 -2.97333759398567X[t] + 1.41303661442087Y1[t] -0.370434738549315Y2[t] -0.0226275979163081Y3[t] -0.0678756241040444Y4[t] + 0.163198807114589t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  17.0254941272264 -2.97333759398567X[t] +  1.41303661442087Y1[t] -0.370434738549315Y2[t] -0.0226275979163081Y3[t] -0.0678756241040444Y4[t] +  0.163198807114589t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57371&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  17.0254941272264 -2.97333759398567X[t] +  1.41303661442087Y1[t] -0.370434738549315Y2[t] -0.0226275979163081Y3[t] -0.0678756241040444Y4[t] +  0.163198807114589t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57371&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57371&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] = + 17.0254941272264 -2.97333759398567X[t] + 1.41303661442087Y1[t] -0.370434738549315Y2[t] -0.0226275979163081Y3[t] -0.0678756241040444Y4[t] + 0.163198807114589t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.02549412722646.6014482.57910.0112780.005639
X-2.973337593985673.315291-0.89690.3718280.185914
Y11.413036614420870.09664814.620400
Y2-0.3704347385493150.168032-2.20460.0296470.014824
Y3-0.02262759791630810.168536-0.13430.8934520.446726
Y4-0.06787562410404440.097449-0.69650.4876240.243812
t0.1631988071145890.0692082.35810.0202030.010101

\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) & 17.0254941272264 & 6.601448 & 2.5791 & 0.011278 & 0.005639 \tabularnewline
X & -2.97333759398567 & 3.315291 & -0.8969 & 0.371828 & 0.185914 \tabularnewline
Y1 & 1.41303661442087 & 0.096648 & 14.6204 & 0 & 0 \tabularnewline
Y2 & -0.370434738549315 & 0.168032 & -2.2046 & 0.029647 & 0.014824 \tabularnewline
Y3 & -0.0226275979163081 & 0.168536 & -0.1343 & 0.893452 & 0.446726 \tabularnewline
Y4 & -0.0678756241040444 & 0.097449 & -0.6965 & 0.487624 & 0.243812 \tabularnewline
t & 0.163198807114589 & 0.069208 & 2.3581 & 0.020203 & 0.010101 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57371&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]17.0254941272264[/C][C]6.601448[/C][C]2.5791[/C][C]0.011278[/C][C]0.005639[/C][/ROW]
[ROW][C]X[/C][C]-2.97333759398567[/C][C]3.315291[/C][C]-0.8969[/C][C]0.371828[/C][C]0.185914[/C][/ROW]
[ROW][C]Y1[/C][C]1.41303661442087[/C][C]0.096648[/C][C]14.6204[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.370434738549315[/C][C]0.168032[/C][C]-2.2046[/C][C]0.029647[/C][C]0.014824[/C][/ROW]
[ROW][C]Y3[/C][C]-0.0226275979163081[/C][C]0.168536[/C][C]-0.1343[/C][C]0.893452[/C][C]0.446726[/C][/ROW]
[ROW][C]Y4[/C][C]-0.0678756241040444[/C][C]0.097449[/C][C]-0.6965[/C][C]0.487624[/C][C]0.243812[/C][/ROW]
[ROW][C]t[/C][C]0.163198807114589[/C][C]0.069208[/C][C]2.3581[/C][C]0.020203[/C][C]0.010101[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57371&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57371&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)17.02549412722646.6014482.57910.0112780.005639
X-2.973337593985673.315291-0.89690.3718280.185914
Y11.413036614420870.09664814.620400
Y2-0.3704347385493150.168032-2.20460.0296470.014824
Y3-0.02262759791630810.168536-0.13430.8934520.446726
Y4-0.06787562410404440.097449-0.69650.4876240.243812
t0.1631988071145890.0692082.35810.0202030.010101






Multiple Linear Regression - Regression Statistics
Multiple R0.997970467716217
R-squared0.995945054433724
Adjusted R-squared0.995715529212992
F-TEST (value)4339.153018805
F-TEST (DF numerator)6
F-TEST (DF denominator)106
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.27639098080917
Sum Squared Residuals5612.2617647937

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.997970467716217 \tabularnewline
R-squared & 0.995945054433724 \tabularnewline
Adjusted R-squared & 0.995715529212992 \tabularnewline
F-TEST (value) & 4339.153018805 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.27639098080917 \tabularnewline
Sum Squared Residuals & 5612.2617647937 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57371&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.997970467716217[/C][/ROW]
[ROW][C]R-squared[/C][C]0.995945054433724[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.995715529212992[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4339.153018805[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]106[/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]7.27639098080917[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5612.2617647937[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57371&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57371&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 R0.997970467716217
R-squared0.995945054433724
Adjusted R-squared0.995715529212992
F-TEST (value)4339.153018805
F-TEST (DF numerator)6
F-TEST (DF denominator)106
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.27639098080917
Sum Squared Residuals5612.2617647937






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1405.7401.8245443805683.87545561943187
2406.7405.2766213123251.42337868767535
3407.2405.3981836384511.80181636154907
4412.4405.8567349040486.54326509595179
5415.9412.9969776411112.90302235888937
6414416.10035453518-2.1003545351796
7411.8412.130660868755-0.330660868755126
8409.9409.4568552893390.443144710660566
9412.4407.555668705544.84433129446036
10415.9412.1340294531643.76597054683632
11416.3416.509088373448-0.209088373447860
12417.2416.0133749324151.18662506758483
13421.8417.0512471441224.74875285587844
14421.4423.134407389347-1.73440738934716
15415.1420.9808766656-5.88087666560015
16412.4412.2249436851740.175056314825548
17411.8410.6035056545011.19649434549879
18408.8411.088760403561-2.28876040356085
19404.5407.723821156772-3.22382115677194
20402.5403.119107481355-0.619107481355438
21409.4402.1577106036027.24228939639828
22410.7413.112657070671-2.41265707067113
23413.4412.8939241600230.506075839977374
24415.2416.370377488545-1.17037748854498
25417.7417.5791107239250.120889276075128
26417.8420.458785711994-2.65878571199355
27417.9419.613207472847-1.71320747284674
28418.4419.70192134937-1.30192134937036
29418.2420.362643169789-2.16264316978871
30416.6420.048966962542-3.44896696254241
31418.9418.0072927729250.892707227074969
32421421.983759082418-0.983759082417645
33423.5424.31211416264-0.812114162639592
34432.3427.2865490782125.01345092178829
35432.3438.754751354793-6.45475135479311
36428.6435.459016657264-6.85901665726445
37426.7430.025168069098-3.32516806909827
38427.3428.27690034933-0.976900349330005
39428.5430.075469240631-1.57546924063121
40437432.0061833871474.99381661285283
41442443.851058857628-1.85105885762786
42444.9447.862866967216-2.96286696721563
43441.4449.997912932191-8.59791293219065
44440.3443.451142052573-3.15114205257325
45447.1442.951524014274.14847598572999
46455.3453.0132072946562.28679270534389
47478.6462.50680515995916.0931948400415
48486.5492.476987747659-5.97698774765852
49487.8494.524945853678-6.72494585367764
50485.9492.514854675897-6.61485467589668
51483.8487.751458690334-3.95145869033436
52488.4485.0854733026963.31452669730423
53494492.4813076118061.51869238819444
54493.6499.029993303772-5.42999330377211
55487.3496.591994789446-9.29199478944569
56482.1487.562294401919-5.46229440191858
57484.2482.3403892110891.85961078891073
58496.8487.5669296654589.23307033454159
59501.1505.301756804343-4.20175680434267
60499.8507.178970637462-7.37897063746242
61495.5503.484705925704-7.98470592570384
62498.1497.1008809161720.99911908382831
63503.8502.2683949901861.53160500981355
64516.2509.7083091616476.49169083835291
65526.1525.5147174069140.585282593085601
66527.1534.76813400799-7.66813400799054
67525.1532.009592246333-6.90959224633251
68528.9527.9106121277940.989387872205504
69540.1533.4896232702616.61037672973944
70549548.048559724130.95144027586986
71556556.688681703964-0.688681703964186
72568.9562.9349111706785.96508882932204
73589.1577.77164652255611.328353477444
74590.3600.937090573746-10.6370905737459
75603.3594.546126217628.7538737823794
76638.8611.30160629709627.4983937029043
77643655.413712590609-12.4137125906087
78656.7647.9856224379538.71437756204665
79656.1664.265934121345-8.16593412134524
80654.1656.00173447474-1.90173447473958
81659.9652.9660451834526.93395481654834
82662.1661.149406339830.950593660169812
83669.2662.358744785386.84125521462021
84673.1671.7440583103681.35594168963245
85678.3674.3445539348043.95544606519593
86677.4680.10086533833-2.70086533833003
87678.5676.4959059889972.00409401100289
88672.4678.164457893499-5.76445789349856
89665.3668.968066733025-3.66806673302521
90667.9661.3945551868886.5054448131119
91672.1667.9251009959724.17489900402786
92662.5673.634620515667-11.1346205156667
93682.3659.0999270989923.2000729010099
94692.1690.5259118277921.57408817220781
95702.7697.1344089517155.5655910482855
96721.4708.84911498656312.5508850134370
97733.2729.9438024378853.25619756211501
98747.7738.9486700301618.7513299698388
99737.6754.087152134959-16.4871521349586
100729.3733.0710976013-3.77109760129934
101706.1724.118450833854-18.0184508338545
102674.3693.81815070581-19.5181507058105
103659658.5142239748420.485776025158301
104645.7649.926115218907-4.2261152189072
105646.1639.2578506469816.8421493530187
106633644.444355623212-11.4443556232125
107622.3627.288044987073-4.98804498707281
108628.2618.07814185629710.1218581437027
109637.3630.8111796740356.48882032596509
110639.6642.778732688406-3.17873268840567
111638.5643.413725938097-4.9137259380967
112650.5640.5642072474339.93579275256742
113655.4657.421611985447-2.02161198544754

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 405.7 & 401.824544380568 & 3.87545561943187 \tabularnewline
2 & 406.7 & 405.276621312325 & 1.42337868767535 \tabularnewline
3 & 407.2 & 405.398183638451 & 1.80181636154907 \tabularnewline
4 & 412.4 & 405.856734904048 & 6.54326509595179 \tabularnewline
5 & 415.9 & 412.996977641111 & 2.90302235888937 \tabularnewline
6 & 414 & 416.10035453518 & -2.1003545351796 \tabularnewline
7 & 411.8 & 412.130660868755 & -0.330660868755126 \tabularnewline
8 & 409.9 & 409.456855289339 & 0.443144710660566 \tabularnewline
9 & 412.4 & 407.55566870554 & 4.84433129446036 \tabularnewline
10 & 415.9 & 412.134029453164 & 3.76597054683632 \tabularnewline
11 & 416.3 & 416.509088373448 & -0.209088373447860 \tabularnewline
12 & 417.2 & 416.013374932415 & 1.18662506758483 \tabularnewline
13 & 421.8 & 417.051247144122 & 4.74875285587844 \tabularnewline
14 & 421.4 & 423.134407389347 & -1.73440738934716 \tabularnewline
15 & 415.1 & 420.9808766656 & -5.88087666560015 \tabularnewline
16 & 412.4 & 412.224943685174 & 0.175056314825548 \tabularnewline
17 & 411.8 & 410.603505654501 & 1.19649434549879 \tabularnewline
18 & 408.8 & 411.088760403561 & -2.28876040356085 \tabularnewline
19 & 404.5 & 407.723821156772 & -3.22382115677194 \tabularnewline
20 & 402.5 & 403.119107481355 & -0.619107481355438 \tabularnewline
21 & 409.4 & 402.157710603602 & 7.24228939639828 \tabularnewline
22 & 410.7 & 413.112657070671 & -2.41265707067113 \tabularnewline
23 & 413.4 & 412.893924160023 & 0.506075839977374 \tabularnewline
24 & 415.2 & 416.370377488545 & -1.17037748854498 \tabularnewline
25 & 417.7 & 417.579110723925 & 0.120889276075128 \tabularnewline
26 & 417.8 & 420.458785711994 & -2.65878571199355 \tabularnewline
27 & 417.9 & 419.613207472847 & -1.71320747284674 \tabularnewline
28 & 418.4 & 419.70192134937 & -1.30192134937036 \tabularnewline
29 & 418.2 & 420.362643169789 & -2.16264316978871 \tabularnewline
30 & 416.6 & 420.048966962542 & -3.44896696254241 \tabularnewline
31 & 418.9 & 418.007292772925 & 0.892707227074969 \tabularnewline
32 & 421 & 421.983759082418 & -0.983759082417645 \tabularnewline
33 & 423.5 & 424.31211416264 & -0.812114162639592 \tabularnewline
34 & 432.3 & 427.286549078212 & 5.01345092178829 \tabularnewline
35 & 432.3 & 438.754751354793 & -6.45475135479311 \tabularnewline
36 & 428.6 & 435.459016657264 & -6.85901665726445 \tabularnewline
37 & 426.7 & 430.025168069098 & -3.32516806909827 \tabularnewline
38 & 427.3 & 428.27690034933 & -0.976900349330005 \tabularnewline
39 & 428.5 & 430.075469240631 & -1.57546924063121 \tabularnewline
40 & 437 & 432.006183387147 & 4.99381661285283 \tabularnewline
41 & 442 & 443.851058857628 & -1.85105885762786 \tabularnewline
42 & 444.9 & 447.862866967216 & -2.96286696721563 \tabularnewline
43 & 441.4 & 449.997912932191 & -8.59791293219065 \tabularnewline
44 & 440.3 & 443.451142052573 & -3.15114205257325 \tabularnewline
45 & 447.1 & 442.95152401427 & 4.14847598572999 \tabularnewline
46 & 455.3 & 453.013207294656 & 2.28679270534389 \tabularnewline
47 & 478.6 & 462.506805159959 & 16.0931948400415 \tabularnewline
48 & 486.5 & 492.476987747659 & -5.97698774765852 \tabularnewline
49 & 487.8 & 494.524945853678 & -6.72494585367764 \tabularnewline
50 & 485.9 & 492.514854675897 & -6.61485467589668 \tabularnewline
51 & 483.8 & 487.751458690334 & -3.95145869033436 \tabularnewline
52 & 488.4 & 485.085473302696 & 3.31452669730423 \tabularnewline
53 & 494 & 492.481307611806 & 1.51869238819444 \tabularnewline
54 & 493.6 & 499.029993303772 & -5.42999330377211 \tabularnewline
55 & 487.3 & 496.591994789446 & -9.29199478944569 \tabularnewline
56 & 482.1 & 487.562294401919 & -5.46229440191858 \tabularnewline
57 & 484.2 & 482.340389211089 & 1.85961078891073 \tabularnewline
58 & 496.8 & 487.566929665458 & 9.23307033454159 \tabularnewline
59 & 501.1 & 505.301756804343 & -4.20175680434267 \tabularnewline
60 & 499.8 & 507.178970637462 & -7.37897063746242 \tabularnewline
61 & 495.5 & 503.484705925704 & -7.98470592570384 \tabularnewline
62 & 498.1 & 497.100880916172 & 0.99911908382831 \tabularnewline
63 & 503.8 & 502.268394990186 & 1.53160500981355 \tabularnewline
64 & 516.2 & 509.708309161647 & 6.49169083835291 \tabularnewline
65 & 526.1 & 525.514717406914 & 0.585282593085601 \tabularnewline
66 & 527.1 & 534.76813400799 & -7.66813400799054 \tabularnewline
67 & 525.1 & 532.009592246333 & -6.90959224633251 \tabularnewline
68 & 528.9 & 527.910612127794 & 0.989387872205504 \tabularnewline
69 & 540.1 & 533.489623270261 & 6.61037672973944 \tabularnewline
70 & 549 & 548.04855972413 & 0.95144027586986 \tabularnewline
71 & 556 & 556.688681703964 & -0.688681703964186 \tabularnewline
72 & 568.9 & 562.934911170678 & 5.96508882932204 \tabularnewline
73 & 589.1 & 577.771646522556 & 11.328353477444 \tabularnewline
74 & 590.3 & 600.937090573746 & -10.6370905737459 \tabularnewline
75 & 603.3 & 594.54612621762 & 8.7538737823794 \tabularnewline
76 & 638.8 & 611.301606297096 & 27.4983937029043 \tabularnewline
77 & 643 & 655.413712590609 & -12.4137125906087 \tabularnewline
78 & 656.7 & 647.985622437953 & 8.71437756204665 \tabularnewline
79 & 656.1 & 664.265934121345 & -8.16593412134524 \tabularnewline
80 & 654.1 & 656.00173447474 & -1.90173447473958 \tabularnewline
81 & 659.9 & 652.966045183452 & 6.93395481654834 \tabularnewline
82 & 662.1 & 661.14940633983 & 0.950593660169812 \tabularnewline
83 & 669.2 & 662.35874478538 & 6.84125521462021 \tabularnewline
84 & 673.1 & 671.744058310368 & 1.35594168963245 \tabularnewline
85 & 678.3 & 674.344553934804 & 3.95544606519593 \tabularnewline
86 & 677.4 & 680.10086533833 & -2.70086533833003 \tabularnewline
87 & 678.5 & 676.495905988997 & 2.00409401100289 \tabularnewline
88 & 672.4 & 678.164457893499 & -5.76445789349856 \tabularnewline
89 & 665.3 & 668.968066733025 & -3.66806673302521 \tabularnewline
90 & 667.9 & 661.394555186888 & 6.5054448131119 \tabularnewline
91 & 672.1 & 667.925100995972 & 4.17489900402786 \tabularnewline
92 & 662.5 & 673.634620515667 & -11.1346205156667 \tabularnewline
93 & 682.3 & 659.09992709899 & 23.2000729010099 \tabularnewline
94 & 692.1 & 690.525911827792 & 1.57408817220781 \tabularnewline
95 & 702.7 & 697.134408951715 & 5.5655910482855 \tabularnewline
96 & 721.4 & 708.849114986563 & 12.5508850134370 \tabularnewline
97 & 733.2 & 729.943802437885 & 3.25619756211501 \tabularnewline
98 & 747.7 & 738.948670030161 & 8.7513299698388 \tabularnewline
99 & 737.6 & 754.087152134959 & -16.4871521349586 \tabularnewline
100 & 729.3 & 733.0710976013 & -3.77109760129934 \tabularnewline
101 & 706.1 & 724.118450833854 & -18.0184508338545 \tabularnewline
102 & 674.3 & 693.81815070581 & -19.5181507058105 \tabularnewline
103 & 659 & 658.514223974842 & 0.485776025158301 \tabularnewline
104 & 645.7 & 649.926115218907 & -4.2261152189072 \tabularnewline
105 & 646.1 & 639.257850646981 & 6.8421493530187 \tabularnewline
106 & 633 & 644.444355623212 & -11.4443556232125 \tabularnewline
107 & 622.3 & 627.288044987073 & -4.98804498707281 \tabularnewline
108 & 628.2 & 618.078141856297 & 10.1218581437027 \tabularnewline
109 & 637.3 & 630.811179674035 & 6.48882032596509 \tabularnewline
110 & 639.6 & 642.778732688406 & -3.17873268840567 \tabularnewline
111 & 638.5 & 643.413725938097 & -4.9137259380967 \tabularnewline
112 & 650.5 & 640.564207247433 & 9.93579275256742 \tabularnewline
113 & 655.4 & 657.421611985447 & -2.02161198544754 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57371&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]405.7[/C][C]401.824544380568[/C][C]3.87545561943187[/C][/ROW]
[ROW][C]2[/C][C]406.7[/C][C]405.276621312325[/C][C]1.42337868767535[/C][/ROW]
[ROW][C]3[/C][C]407.2[/C][C]405.398183638451[/C][C]1.80181636154907[/C][/ROW]
[ROW][C]4[/C][C]412.4[/C][C]405.856734904048[/C][C]6.54326509595179[/C][/ROW]
[ROW][C]5[/C][C]415.9[/C][C]412.996977641111[/C][C]2.90302235888937[/C][/ROW]
[ROW][C]6[/C][C]414[/C][C]416.10035453518[/C][C]-2.1003545351796[/C][/ROW]
[ROW][C]7[/C][C]411.8[/C][C]412.130660868755[/C][C]-0.330660868755126[/C][/ROW]
[ROW][C]8[/C][C]409.9[/C][C]409.456855289339[/C][C]0.443144710660566[/C][/ROW]
[ROW][C]9[/C][C]412.4[/C][C]407.55566870554[/C][C]4.84433129446036[/C][/ROW]
[ROW][C]10[/C][C]415.9[/C][C]412.134029453164[/C][C]3.76597054683632[/C][/ROW]
[ROW][C]11[/C][C]416.3[/C][C]416.509088373448[/C][C]-0.209088373447860[/C][/ROW]
[ROW][C]12[/C][C]417.2[/C][C]416.013374932415[/C][C]1.18662506758483[/C][/ROW]
[ROW][C]13[/C][C]421.8[/C][C]417.051247144122[/C][C]4.74875285587844[/C][/ROW]
[ROW][C]14[/C][C]421.4[/C][C]423.134407389347[/C][C]-1.73440738934716[/C][/ROW]
[ROW][C]15[/C][C]415.1[/C][C]420.9808766656[/C][C]-5.88087666560015[/C][/ROW]
[ROW][C]16[/C][C]412.4[/C][C]412.224943685174[/C][C]0.175056314825548[/C][/ROW]
[ROW][C]17[/C][C]411.8[/C][C]410.603505654501[/C][C]1.19649434549879[/C][/ROW]
[ROW][C]18[/C][C]408.8[/C][C]411.088760403561[/C][C]-2.28876040356085[/C][/ROW]
[ROW][C]19[/C][C]404.5[/C][C]407.723821156772[/C][C]-3.22382115677194[/C][/ROW]
[ROW][C]20[/C][C]402.5[/C][C]403.119107481355[/C][C]-0.619107481355438[/C][/ROW]
[ROW][C]21[/C][C]409.4[/C][C]402.157710603602[/C][C]7.24228939639828[/C][/ROW]
[ROW][C]22[/C][C]410.7[/C][C]413.112657070671[/C][C]-2.41265707067113[/C][/ROW]
[ROW][C]23[/C][C]413.4[/C][C]412.893924160023[/C][C]0.506075839977374[/C][/ROW]
[ROW][C]24[/C][C]415.2[/C][C]416.370377488545[/C][C]-1.17037748854498[/C][/ROW]
[ROW][C]25[/C][C]417.7[/C][C]417.579110723925[/C][C]0.120889276075128[/C][/ROW]
[ROW][C]26[/C][C]417.8[/C][C]420.458785711994[/C][C]-2.65878571199355[/C][/ROW]
[ROW][C]27[/C][C]417.9[/C][C]419.613207472847[/C][C]-1.71320747284674[/C][/ROW]
[ROW][C]28[/C][C]418.4[/C][C]419.70192134937[/C][C]-1.30192134937036[/C][/ROW]
[ROW][C]29[/C][C]418.2[/C][C]420.362643169789[/C][C]-2.16264316978871[/C][/ROW]
[ROW][C]30[/C][C]416.6[/C][C]420.048966962542[/C][C]-3.44896696254241[/C][/ROW]
[ROW][C]31[/C][C]418.9[/C][C]418.007292772925[/C][C]0.892707227074969[/C][/ROW]
[ROW][C]32[/C][C]421[/C][C]421.983759082418[/C][C]-0.983759082417645[/C][/ROW]
[ROW][C]33[/C][C]423.5[/C][C]424.31211416264[/C][C]-0.812114162639592[/C][/ROW]
[ROW][C]34[/C][C]432.3[/C][C]427.286549078212[/C][C]5.01345092178829[/C][/ROW]
[ROW][C]35[/C][C]432.3[/C][C]438.754751354793[/C][C]-6.45475135479311[/C][/ROW]
[ROW][C]36[/C][C]428.6[/C][C]435.459016657264[/C][C]-6.85901665726445[/C][/ROW]
[ROW][C]37[/C][C]426.7[/C][C]430.025168069098[/C][C]-3.32516806909827[/C][/ROW]
[ROW][C]38[/C][C]427.3[/C][C]428.27690034933[/C][C]-0.976900349330005[/C][/ROW]
[ROW][C]39[/C][C]428.5[/C][C]430.075469240631[/C][C]-1.57546924063121[/C][/ROW]
[ROW][C]40[/C][C]437[/C][C]432.006183387147[/C][C]4.99381661285283[/C][/ROW]
[ROW][C]41[/C][C]442[/C][C]443.851058857628[/C][C]-1.85105885762786[/C][/ROW]
[ROW][C]42[/C][C]444.9[/C][C]447.862866967216[/C][C]-2.96286696721563[/C][/ROW]
[ROW][C]43[/C][C]441.4[/C][C]449.997912932191[/C][C]-8.59791293219065[/C][/ROW]
[ROW][C]44[/C][C]440.3[/C][C]443.451142052573[/C][C]-3.15114205257325[/C][/ROW]
[ROW][C]45[/C][C]447.1[/C][C]442.95152401427[/C][C]4.14847598572999[/C][/ROW]
[ROW][C]46[/C][C]455.3[/C][C]453.013207294656[/C][C]2.28679270534389[/C][/ROW]
[ROW][C]47[/C][C]478.6[/C][C]462.506805159959[/C][C]16.0931948400415[/C][/ROW]
[ROW][C]48[/C][C]486.5[/C][C]492.476987747659[/C][C]-5.97698774765852[/C][/ROW]
[ROW][C]49[/C][C]487.8[/C][C]494.524945853678[/C][C]-6.72494585367764[/C][/ROW]
[ROW][C]50[/C][C]485.9[/C][C]492.514854675897[/C][C]-6.61485467589668[/C][/ROW]
[ROW][C]51[/C][C]483.8[/C][C]487.751458690334[/C][C]-3.95145869033436[/C][/ROW]
[ROW][C]52[/C][C]488.4[/C][C]485.085473302696[/C][C]3.31452669730423[/C][/ROW]
[ROW][C]53[/C][C]494[/C][C]492.481307611806[/C][C]1.51869238819444[/C][/ROW]
[ROW][C]54[/C][C]493.6[/C][C]499.029993303772[/C][C]-5.42999330377211[/C][/ROW]
[ROW][C]55[/C][C]487.3[/C][C]496.591994789446[/C][C]-9.29199478944569[/C][/ROW]
[ROW][C]56[/C][C]482.1[/C][C]487.562294401919[/C][C]-5.46229440191858[/C][/ROW]
[ROW][C]57[/C][C]484.2[/C][C]482.340389211089[/C][C]1.85961078891073[/C][/ROW]
[ROW][C]58[/C][C]496.8[/C][C]487.566929665458[/C][C]9.23307033454159[/C][/ROW]
[ROW][C]59[/C][C]501.1[/C][C]505.301756804343[/C][C]-4.20175680434267[/C][/ROW]
[ROW][C]60[/C][C]499.8[/C][C]507.178970637462[/C][C]-7.37897063746242[/C][/ROW]
[ROW][C]61[/C][C]495.5[/C][C]503.484705925704[/C][C]-7.98470592570384[/C][/ROW]
[ROW][C]62[/C][C]498.1[/C][C]497.100880916172[/C][C]0.99911908382831[/C][/ROW]
[ROW][C]63[/C][C]503.8[/C][C]502.268394990186[/C][C]1.53160500981355[/C][/ROW]
[ROW][C]64[/C][C]516.2[/C][C]509.708309161647[/C][C]6.49169083835291[/C][/ROW]
[ROW][C]65[/C][C]526.1[/C][C]525.514717406914[/C][C]0.585282593085601[/C][/ROW]
[ROW][C]66[/C][C]527.1[/C][C]534.76813400799[/C][C]-7.66813400799054[/C][/ROW]
[ROW][C]67[/C][C]525.1[/C][C]532.009592246333[/C][C]-6.90959224633251[/C][/ROW]
[ROW][C]68[/C][C]528.9[/C][C]527.910612127794[/C][C]0.989387872205504[/C][/ROW]
[ROW][C]69[/C][C]540.1[/C][C]533.489623270261[/C][C]6.61037672973944[/C][/ROW]
[ROW][C]70[/C][C]549[/C][C]548.04855972413[/C][C]0.95144027586986[/C][/ROW]
[ROW][C]71[/C][C]556[/C][C]556.688681703964[/C][C]-0.688681703964186[/C][/ROW]
[ROW][C]72[/C][C]568.9[/C][C]562.934911170678[/C][C]5.96508882932204[/C][/ROW]
[ROW][C]73[/C][C]589.1[/C][C]577.771646522556[/C][C]11.328353477444[/C][/ROW]
[ROW][C]74[/C][C]590.3[/C][C]600.937090573746[/C][C]-10.6370905737459[/C][/ROW]
[ROW][C]75[/C][C]603.3[/C][C]594.54612621762[/C][C]8.7538737823794[/C][/ROW]
[ROW][C]76[/C][C]638.8[/C][C]611.301606297096[/C][C]27.4983937029043[/C][/ROW]
[ROW][C]77[/C][C]643[/C][C]655.413712590609[/C][C]-12.4137125906087[/C][/ROW]
[ROW][C]78[/C][C]656.7[/C][C]647.985622437953[/C][C]8.71437756204665[/C][/ROW]
[ROW][C]79[/C][C]656.1[/C][C]664.265934121345[/C][C]-8.16593412134524[/C][/ROW]
[ROW][C]80[/C][C]654.1[/C][C]656.00173447474[/C][C]-1.90173447473958[/C][/ROW]
[ROW][C]81[/C][C]659.9[/C][C]652.966045183452[/C][C]6.93395481654834[/C][/ROW]
[ROW][C]82[/C][C]662.1[/C][C]661.14940633983[/C][C]0.950593660169812[/C][/ROW]
[ROW][C]83[/C][C]669.2[/C][C]662.35874478538[/C][C]6.84125521462021[/C][/ROW]
[ROW][C]84[/C][C]673.1[/C][C]671.744058310368[/C][C]1.35594168963245[/C][/ROW]
[ROW][C]85[/C][C]678.3[/C][C]674.344553934804[/C][C]3.95544606519593[/C][/ROW]
[ROW][C]86[/C][C]677.4[/C][C]680.10086533833[/C][C]-2.70086533833003[/C][/ROW]
[ROW][C]87[/C][C]678.5[/C][C]676.495905988997[/C][C]2.00409401100289[/C][/ROW]
[ROW][C]88[/C][C]672.4[/C][C]678.164457893499[/C][C]-5.76445789349856[/C][/ROW]
[ROW][C]89[/C][C]665.3[/C][C]668.968066733025[/C][C]-3.66806673302521[/C][/ROW]
[ROW][C]90[/C][C]667.9[/C][C]661.394555186888[/C][C]6.5054448131119[/C][/ROW]
[ROW][C]91[/C][C]672.1[/C][C]667.925100995972[/C][C]4.17489900402786[/C][/ROW]
[ROW][C]92[/C][C]662.5[/C][C]673.634620515667[/C][C]-11.1346205156667[/C][/ROW]
[ROW][C]93[/C][C]682.3[/C][C]659.09992709899[/C][C]23.2000729010099[/C][/ROW]
[ROW][C]94[/C][C]692.1[/C][C]690.525911827792[/C][C]1.57408817220781[/C][/ROW]
[ROW][C]95[/C][C]702.7[/C][C]697.134408951715[/C][C]5.5655910482855[/C][/ROW]
[ROW][C]96[/C][C]721.4[/C][C]708.849114986563[/C][C]12.5508850134370[/C][/ROW]
[ROW][C]97[/C][C]733.2[/C][C]729.943802437885[/C][C]3.25619756211501[/C][/ROW]
[ROW][C]98[/C][C]747.7[/C][C]738.948670030161[/C][C]8.7513299698388[/C][/ROW]
[ROW][C]99[/C][C]737.6[/C][C]754.087152134959[/C][C]-16.4871521349586[/C][/ROW]
[ROW][C]100[/C][C]729.3[/C][C]733.0710976013[/C][C]-3.77109760129934[/C][/ROW]
[ROW][C]101[/C][C]706.1[/C][C]724.118450833854[/C][C]-18.0184508338545[/C][/ROW]
[ROW][C]102[/C][C]674.3[/C][C]693.81815070581[/C][C]-19.5181507058105[/C][/ROW]
[ROW][C]103[/C][C]659[/C][C]658.514223974842[/C][C]0.485776025158301[/C][/ROW]
[ROW][C]104[/C][C]645.7[/C][C]649.926115218907[/C][C]-4.2261152189072[/C][/ROW]
[ROW][C]105[/C][C]646.1[/C][C]639.257850646981[/C][C]6.8421493530187[/C][/ROW]
[ROW][C]106[/C][C]633[/C][C]644.444355623212[/C][C]-11.4443556232125[/C][/ROW]
[ROW][C]107[/C][C]622.3[/C][C]627.288044987073[/C][C]-4.98804498707281[/C][/ROW]
[ROW][C]108[/C][C]628.2[/C][C]618.078141856297[/C][C]10.1218581437027[/C][/ROW]
[ROW][C]109[/C][C]637.3[/C][C]630.811179674035[/C][C]6.48882032596509[/C][/ROW]
[ROW][C]110[/C][C]639.6[/C][C]642.778732688406[/C][C]-3.17873268840567[/C][/ROW]
[ROW][C]111[/C][C]638.5[/C][C]643.413725938097[/C][C]-4.9137259380967[/C][/ROW]
[ROW][C]112[/C][C]650.5[/C][C]640.564207247433[/C][C]9.93579275256742[/C][/ROW]
[ROW][C]113[/C][C]655.4[/C][C]657.421611985447[/C][C]-2.02161198544754[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57371&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57371&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
1405.7401.8245443805683.87545561943187
2406.7405.2766213123251.42337868767535
3407.2405.3981836384511.80181636154907
4412.4405.8567349040486.54326509595179
5415.9412.9969776411112.90302235888937
6414416.10035453518-2.1003545351796
7411.8412.130660868755-0.330660868755126
8409.9409.4568552893390.443144710660566
9412.4407.555668705544.84433129446036
10415.9412.1340294531643.76597054683632
11416.3416.509088373448-0.209088373447860
12417.2416.0133749324151.18662506758483
13421.8417.0512471441224.74875285587844
14421.4423.134407389347-1.73440738934716
15415.1420.9808766656-5.88087666560015
16412.4412.2249436851740.175056314825548
17411.8410.6035056545011.19649434549879
18408.8411.088760403561-2.28876040356085
19404.5407.723821156772-3.22382115677194
20402.5403.119107481355-0.619107481355438
21409.4402.1577106036027.24228939639828
22410.7413.112657070671-2.41265707067113
23413.4412.8939241600230.506075839977374
24415.2416.370377488545-1.17037748854498
25417.7417.5791107239250.120889276075128
26417.8420.458785711994-2.65878571199355
27417.9419.613207472847-1.71320747284674
28418.4419.70192134937-1.30192134937036
29418.2420.362643169789-2.16264316978871
30416.6420.048966962542-3.44896696254241
31418.9418.0072927729250.892707227074969
32421421.983759082418-0.983759082417645
33423.5424.31211416264-0.812114162639592
34432.3427.2865490782125.01345092178829
35432.3438.754751354793-6.45475135479311
36428.6435.459016657264-6.85901665726445
37426.7430.025168069098-3.32516806909827
38427.3428.27690034933-0.976900349330005
39428.5430.075469240631-1.57546924063121
40437432.0061833871474.99381661285283
41442443.851058857628-1.85105885762786
42444.9447.862866967216-2.96286696721563
43441.4449.997912932191-8.59791293219065
44440.3443.451142052573-3.15114205257325
45447.1442.951524014274.14847598572999
46455.3453.0132072946562.28679270534389
47478.6462.50680515995916.0931948400415
48486.5492.476987747659-5.97698774765852
49487.8494.524945853678-6.72494585367764
50485.9492.514854675897-6.61485467589668
51483.8487.751458690334-3.95145869033436
52488.4485.0854733026963.31452669730423
53494492.4813076118061.51869238819444
54493.6499.029993303772-5.42999330377211
55487.3496.591994789446-9.29199478944569
56482.1487.562294401919-5.46229440191858
57484.2482.3403892110891.85961078891073
58496.8487.5669296654589.23307033454159
59501.1505.301756804343-4.20175680434267
60499.8507.178970637462-7.37897063746242
61495.5503.484705925704-7.98470592570384
62498.1497.1008809161720.99911908382831
63503.8502.2683949901861.53160500981355
64516.2509.7083091616476.49169083835291
65526.1525.5147174069140.585282593085601
66527.1534.76813400799-7.66813400799054
67525.1532.009592246333-6.90959224633251
68528.9527.9106121277940.989387872205504
69540.1533.4896232702616.61037672973944
70549548.048559724130.95144027586986
71556556.688681703964-0.688681703964186
72568.9562.9349111706785.96508882932204
73589.1577.77164652255611.328353477444
74590.3600.937090573746-10.6370905737459
75603.3594.546126217628.7538737823794
76638.8611.30160629709627.4983937029043
77643655.413712590609-12.4137125906087
78656.7647.9856224379538.71437756204665
79656.1664.265934121345-8.16593412134524
80654.1656.00173447474-1.90173447473958
81659.9652.9660451834526.93395481654834
82662.1661.149406339830.950593660169812
83669.2662.358744785386.84125521462021
84673.1671.7440583103681.35594168963245
85678.3674.3445539348043.95544606519593
86677.4680.10086533833-2.70086533833003
87678.5676.4959059889972.00409401100289
88672.4678.164457893499-5.76445789349856
89665.3668.968066733025-3.66806673302521
90667.9661.3945551868886.5054448131119
91672.1667.9251009959724.17489900402786
92662.5673.634620515667-11.1346205156667
93682.3659.0999270989923.2000729010099
94692.1690.5259118277921.57408817220781
95702.7697.1344089517155.5655910482855
96721.4708.84911498656312.5508850134370
97733.2729.9438024378853.25619756211501
98747.7738.9486700301618.7513299698388
99737.6754.087152134959-16.4871521349586
100729.3733.0710976013-3.77109760129934
101706.1724.118450833854-18.0184508338545
102674.3693.81815070581-19.5181507058105
103659658.5142239748420.485776025158301
104645.7649.926115218907-4.2261152189072
105646.1639.2578506469816.8421493530187
106633644.444355623212-11.4443556232125
107622.3627.288044987073-4.98804498707281
108628.2618.07814185629710.1218581437027
109637.3630.8111796740356.48882032596509
110639.6642.778732688406-3.17873268840567
111638.5643.413725938097-4.9137259380967
112650.5640.5642072474339.93579275256742
113655.4657.421611985447-2.02161198544754






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.05327201463947070.1065440292789410.94672798536053
110.01652367602938210.03304735205876430.983476323970618
120.005648206057246970.01129641211449390.994351793942753
130.00431178648236020.00862357296472040.99568821351764
140.001456244284165340.002912488568330680.998543755715835
150.001496523857606210.002993047715212430.998503476142394
160.0005472342177990260.001094468435598050.9994527657822
170.0004250353112435940.0008500706224871880.999574964688756
180.0004179726542522950.000835945308504590.999582027345748
190.0001907467881291470.0003814935762582950.99980925321187
206.38627785698852e-050.0001277255571397700.99993613722143
210.0001494196198713250.000298839239742650.999850580380129
227.50041848527208e-050.0001500083697054420.999924995815147
235.13559479874637e-050.0001027118959749270.999948644052013
241.86608872214456e-053.73217744428912e-050.999981339112779
258.67637480874332e-061.73527496174866e-050.999991323625191
263.00552095746989e-066.01104191493977e-060.999996994479043
271.07778191072874e-062.15556382145748e-060.99999892221809
283.75309826427624e-077.50619652855249e-070.999999624690174
291.2125394178203e-072.4250788356406e-070.999999878746058
303.86348245074370e-087.72696490148741e-080.999999961365176
312.28301951754785e-084.56603903509570e-080.999999977169805
327.9097420867311e-091.58194841734622e-080.999999992090258
333.79559636741921e-097.59119273483842e-090.999999996204404
344.68148204914326e-089.36296409828652e-080.99999995318518
352.11885750837688e-084.23771501675376e-080.999999978811425
367.72114613779455e-091.54422922755891e-080.999999992278854
372.52651635098144e-095.05303270196289e-090.999999997473484
381.03685776461038e-092.07371552922076e-090.999999998963142
393.99284355974862e-107.98568711949725e-100.999999999600716
404.37612120623795e-098.75224241247591e-090.999999995623879
411.81180819039469e-093.62361638078937e-090.999999998188192
428.26637666212131e-101.65327533242426e-090.999999999173362
434.88249334042997e-109.76498668085993e-100.99999999951175
441.93863335889733e-103.87726671779465e-100.999999999806137
455.03063835514618e-101.00612767102924e-090.999999999496936
463.88639550646753e-107.77279101293506e-100.99999999961136
471.83966241515281e-063.67932483030561e-060.999998160337585
481.50603034866150e-063.01206069732299e-060.999998493969651
498.28950826127074e-071.65790165225415e-060.999999171049174
504.27765254061355e-078.5553050812271e-070.999999572234746
512.38473642953617e-074.76947285907233e-070.999999761526357
521.27741584388530e-072.55483168777060e-070.999999872258416
536.22207638783779e-081.24441527756756e-070.999999937779236
545.75404165003672e-081.15080833000734e-070.999999942459584
557.54332259129016e-081.50866451825803e-070.999999924566774
564.23991443181684e-088.47982886363368e-080.999999957600856
572.04788544157639e-084.09577088315279e-080.999999979521146
583.70346482775592e-087.40692965551185e-080.999999962965352
592.65806501537123e-085.31613003074245e-080.99999997341935
601.89134759333784e-083.78269518667568e-080.999999981086524
611.6962144823663e-083.3924289647326e-080.999999983037855
629.25130273445294e-091.85026054689059e-080.999999990748697
634.02602988655916e-098.05205977311832e-090.99999999597397
644.24061109482202e-098.48122218964405e-090.999999995759389
652.14749453614351e-094.29498907228703e-090.999999997852505
662.47084276148594e-094.94168552297187e-090.999999997529157
674.01096077768932e-098.02192155537864e-090.99999999598904
683.93597895746357e-097.87195791492715e-090.999999996064021
693.0156687393205e-096.031337478641e-090.999999996984331
701.71008198864545e-093.4201639772909e-090.999999998289918
712.12223151849986e-094.24446303699972e-090.999999997877768
727.62566033566779e-091.52513206713356e-080.99999999237434
732.243017201485e-084.48603440297e-080.999999977569828
742.01837987634177e-064.03675975268355e-060.999997981620124
751.91228249219319e-053.82456498438637e-050.999980877175078
760.0007759056840353550.001551811368070710.999224094315965
770.01300356788944020.02600713577888040.98699643211056
780.01793464347752020.03586928695504040.98206535652248
790.1133845226042810.2267690452085610.886615477395719
800.1534071089866370.3068142179732740.846592891013363
810.1205386034748430.2410772069496860.879461396525157
820.1005551658614740.2011103317229480.899444834138526
830.07926718155886440.1585343631177290.920732818441136
840.06068482415739430.1213696483147890.939315175842606
850.0437849749625980.0875699499251960.956215025037402
860.03926792686344440.07853585372688890.960732073136556
870.02732978453836060.05465956907672130.97267021546164
880.03365257155923720.06730514311847430.966347428440763
890.03927171187781470.07854342375562950.960728288122185
900.02688398619632120.05376797239264250.973116013803679
910.01731336511884210.03462673023768430.982686634881158
920.2072064923595260.4144129847190510.792793507640474
930.2411961628128950.482392325625790.758803837187105
940.2849703227504220.5699406455008450.715029677249578
950.2643632543364830.5287265086729650.735636745663517
960.2745849246269570.5491698492539140.725415075373043
970.2209564020870380.4419128041740760.779043597912962
980.3478632992961020.6957265985922030.652136700703898
990.3610572250513620.7221144501027240.638942774948638
1000.7300221435705480.5399557128589050.269977856429452
1010.71518003052880.5696399389424020.284819969471201
1020.6875017046696930.6249965906606140.312498295330307
1030.8217324391620050.356535121675990.178267560837995

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0532720146394707 & 0.106544029278941 & 0.94672798536053 \tabularnewline
11 & 0.0165236760293821 & 0.0330473520587643 & 0.983476323970618 \tabularnewline
12 & 0.00564820605724697 & 0.0112964121144939 & 0.994351793942753 \tabularnewline
13 & 0.0043117864823602 & 0.0086235729647204 & 0.99568821351764 \tabularnewline
14 & 0.00145624428416534 & 0.00291248856833068 & 0.998543755715835 \tabularnewline
15 & 0.00149652385760621 & 0.00299304771521243 & 0.998503476142394 \tabularnewline
16 & 0.000547234217799026 & 0.00109446843559805 & 0.9994527657822 \tabularnewline
17 & 0.000425035311243594 & 0.000850070622487188 & 0.999574964688756 \tabularnewline
18 & 0.000417972654252295 & 0.00083594530850459 & 0.999582027345748 \tabularnewline
19 & 0.000190746788129147 & 0.000381493576258295 & 0.99980925321187 \tabularnewline
20 & 6.38627785698852e-05 & 0.000127725557139770 & 0.99993613722143 \tabularnewline
21 & 0.000149419619871325 & 0.00029883923974265 & 0.999850580380129 \tabularnewline
22 & 7.50041848527208e-05 & 0.000150008369705442 & 0.999924995815147 \tabularnewline
23 & 5.13559479874637e-05 & 0.000102711895974927 & 0.999948644052013 \tabularnewline
24 & 1.86608872214456e-05 & 3.73217744428912e-05 & 0.999981339112779 \tabularnewline
25 & 8.67637480874332e-06 & 1.73527496174866e-05 & 0.999991323625191 \tabularnewline
26 & 3.00552095746989e-06 & 6.01104191493977e-06 & 0.999996994479043 \tabularnewline
27 & 1.07778191072874e-06 & 2.15556382145748e-06 & 0.99999892221809 \tabularnewline
28 & 3.75309826427624e-07 & 7.50619652855249e-07 & 0.999999624690174 \tabularnewline
29 & 1.2125394178203e-07 & 2.4250788356406e-07 & 0.999999878746058 \tabularnewline
30 & 3.86348245074370e-08 & 7.72696490148741e-08 & 0.999999961365176 \tabularnewline
31 & 2.28301951754785e-08 & 4.56603903509570e-08 & 0.999999977169805 \tabularnewline
32 & 7.9097420867311e-09 & 1.58194841734622e-08 & 0.999999992090258 \tabularnewline
33 & 3.79559636741921e-09 & 7.59119273483842e-09 & 0.999999996204404 \tabularnewline
34 & 4.68148204914326e-08 & 9.36296409828652e-08 & 0.99999995318518 \tabularnewline
35 & 2.11885750837688e-08 & 4.23771501675376e-08 & 0.999999978811425 \tabularnewline
36 & 7.72114613779455e-09 & 1.54422922755891e-08 & 0.999999992278854 \tabularnewline
37 & 2.52651635098144e-09 & 5.05303270196289e-09 & 0.999999997473484 \tabularnewline
38 & 1.03685776461038e-09 & 2.07371552922076e-09 & 0.999999998963142 \tabularnewline
39 & 3.99284355974862e-10 & 7.98568711949725e-10 & 0.999999999600716 \tabularnewline
40 & 4.37612120623795e-09 & 8.75224241247591e-09 & 0.999999995623879 \tabularnewline
41 & 1.81180819039469e-09 & 3.62361638078937e-09 & 0.999999998188192 \tabularnewline
42 & 8.26637666212131e-10 & 1.65327533242426e-09 & 0.999999999173362 \tabularnewline
43 & 4.88249334042997e-10 & 9.76498668085993e-10 & 0.99999999951175 \tabularnewline
44 & 1.93863335889733e-10 & 3.87726671779465e-10 & 0.999999999806137 \tabularnewline
45 & 5.03063835514618e-10 & 1.00612767102924e-09 & 0.999999999496936 \tabularnewline
46 & 3.88639550646753e-10 & 7.77279101293506e-10 & 0.99999999961136 \tabularnewline
47 & 1.83966241515281e-06 & 3.67932483030561e-06 & 0.999998160337585 \tabularnewline
48 & 1.50603034866150e-06 & 3.01206069732299e-06 & 0.999998493969651 \tabularnewline
49 & 8.28950826127074e-07 & 1.65790165225415e-06 & 0.999999171049174 \tabularnewline
50 & 4.27765254061355e-07 & 8.5553050812271e-07 & 0.999999572234746 \tabularnewline
51 & 2.38473642953617e-07 & 4.76947285907233e-07 & 0.999999761526357 \tabularnewline
52 & 1.27741584388530e-07 & 2.55483168777060e-07 & 0.999999872258416 \tabularnewline
53 & 6.22207638783779e-08 & 1.24441527756756e-07 & 0.999999937779236 \tabularnewline
54 & 5.75404165003672e-08 & 1.15080833000734e-07 & 0.999999942459584 \tabularnewline
55 & 7.54332259129016e-08 & 1.50866451825803e-07 & 0.999999924566774 \tabularnewline
56 & 4.23991443181684e-08 & 8.47982886363368e-08 & 0.999999957600856 \tabularnewline
57 & 2.04788544157639e-08 & 4.09577088315279e-08 & 0.999999979521146 \tabularnewline
58 & 3.70346482775592e-08 & 7.40692965551185e-08 & 0.999999962965352 \tabularnewline
59 & 2.65806501537123e-08 & 5.31613003074245e-08 & 0.99999997341935 \tabularnewline
60 & 1.89134759333784e-08 & 3.78269518667568e-08 & 0.999999981086524 \tabularnewline
61 & 1.6962144823663e-08 & 3.3924289647326e-08 & 0.999999983037855 \tabularnewline
62 & 9.25130273445294e-09 & 1.85026054689059e-08 & 0.999999990748697 \tabularnewline
63 & 4.02602988655916e-09 & 8.05205977311832e-09 & 0.99999999597397 \tabularnewline
64 & 4.24061109482202e-09 & 8.48122218964405e-09 & 0.999999995759389 \tabularnewline
65 & 2.14749453614351e-09 & 4.29498907228703e-09 & 0.999999997852505 \tabularnewline
66 & 2.47084276148594e-09 & 4.94168552297187e-09 & 0.999999997529157 \tabularnewline
67 & 4.01096077768932e-09 & 8.02192155537864e-09 & 0.99999999598904 \tabularnewline
68 & 3.93597895746357e-09 & 7.87195791492715e-09 & 0.999999996064021 \tabularnewline
69 & 3.0156687393205e-09 & 6.031337478641e-09 & 0.999999996984331 \tabularnewline
70 & 1.71008198864545e-09 & 3.4201639772909e-09 & 0.999999998289918 \tabularnewline
71 & 2.12223151849986e-09 & 4.24446303699972e-09 & 0.999999997877768 \tabularnewline
72 & 7.62566033566779e-09 & 1.52513206713356e-08 & 0.99999999237434 \tabularnewline
73 & 2.243017201485e-08 & 4.48603440297e-08 & 0.999999977569828 \tabularnewline
74 & 2.01837987634177e-06 & 4.03675975268355e-06 & 0.999997981620124 \tabularnewline
75 & 1.91228249219319e-05 & 3.82456498438637e-05 & 0.999980877175078 \tabularnewline
76 & 0.000775905684035355 & 0.00155181136807071 & 0.999224094315965 \tabularnewline
77 & 0.0130035678894402 & 0.0260071357788804 & 0.98699643211056 \tabularnewline
78 & 0.0179346434775202 & 0.0358692869550404 & 0.98206535652248 \tabularnewline
79 & 0.113384522604281 & 0.226769045208561 & 0.886615477395719 \tabularnewline
80 & 0.153407108986637 & 0.306814217973274 & 0.846592891013363 \tabularnewline
81 & 0.120538603474843 & 0.241077206949686 & 0.879461396525157 \tabularnewline
82 & 0.100555165861474 & 0.201110331722948 & 0.899444834138526 \tabularnewline
83 & 0.0792671815588644 & 0.158534363117729 & 0.920732818441136 \tabularnewline
84 & 0.0606848241573943 & 0.121369648314789 & 0.939315175842606 \tabularnewline
85 & 0.043784974962598 & 0.087569949925196 & 0.956215025037402 \tabularnewline
86 & 0.0392679268634444 & 0.0785358537268889 & 0.960732073136556 \tabularnewline
87 & 0.0273297845383606 & 0.0546595690767213 & 0.97267021546164 \tabularnewline
88 & 0.0336525715592372 & 0.0673051431184743 & 0.966347428440763 \tabularnewline
89 & 0.0392717118778147 & 0.0785434237556295 & 0.960728288122185 \tabularnewline
90 & 0.0268839861963212 & 0.0537679723926425 & 0.973116013803679 \tabularnewline
91 & 0.0173133651188421 & 0.0346267302376843 & 0.982686634881158 \tabularnewline
92 & 0.207206492359526 & 0.414412984719051 & 0.792793507640474 \tabularnewline
93 & 0.241196162812895 & 0.48239232562579 & 0.758803837187105 \tabularnewline
94 & 0.284970322750422 & 0.569940645500845 & 0.715029677249578 \tabularnewline
95 & 0.264363254336483 & 0.528726508672965 & 0.735636745663517 \tabularnewline
96 & 0.274584924626957 & 0.549169849253914 & 0.725415075373043 \tabularnewline
97 & 0.220956402087038 & 0.441912804174076 & 0.779043597912962 \tabularnewline
98 & 0.347863299296102 & 0.695726598592203 & 0.652136700703898 \tabularnewline
99 & 0.361057225051362 & 0.722114450102724 & 0.638942774948638 \tabularnewline
100 & 0.730022143570548 & 0.539955712858905 & 0.269977856429452 \tabularnewline
101 & 0.7151800305288 & 0.569639938942402 & 0.284819969471201 \tabularnewline
102 & 0.687501704669693 & 0.624996590660614 & 0.312498295330307 \tabularnewline
103 & 0.821732439162005 & 0.35653512167599 & 0.178267560837995 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57371&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]10[/C][C]0.0532720146394707[/C][C]0.106544029278941[/C][C]0.94672798536053[/C][/ROW]
[ROW][C]11[/C][C]0.0165236760293821[/C][C]0.0330473520587643[/C][C]0.983476323970618[/C][/ROW]
[ROW][C]12[/C][C]0.00564820605724697[/C][C]0.0112964121144939[/C][C]0.994351793942753[/C][/ROW]
[ROW][C]13[/C][C]0.0043117864823602[/C][C]0.0086235729647204[/C][C]0.99568821351764[/C][/ROW]
[ROW][C]14[/C][C]0.00145624428416534[/C][C]0.00291248856833068[/C][C]0.998543755715835[/C][/ROW]
[ROW][C]15[/C][C]0.00149652385760621[/C][C]0.00299304771521243[/C][C]0.998503476142394[/C][/ROW]
[ROW][C]16[/C][C]0.000547234217799026[/C][C]0.00109446843559805[/C][C]0.9994527657822[/C][/ROW]
[ROW][C]17[/C][C]0.000425035311243594[/C][C]0.000850070622487188[/C][C]0.999574964688756[/C][/ROW]
[ROW][C]18[/C][C]0.000417972654252295[/C][C]0.00083594530850459[/C][C]0.999582027345748[/C][/ROW]
[ROW][C]19[/C][C]0.000190746788129147[/C][C]0.000381493576258295[/C][C]0.99980925321187[/C][/ROW]
[ROW][C]20[/C][C]6.38627785698852e-05[/C][C]0.000127725557139770[/C][C]0.99993613722143[/C][/ROW]
[ROW][C]21[/C][C]0.000149419619871325[/C][C]0.00029883923974265[/C][C]0.999850580380129[/C][/ROW]
[ROW][C]22[/C][C]7.50041848527208e-05[/C][C]0.000150008369705442[/C][C]0.999924995815147[/C][/ROW]
[ROW][C]23[/C][C]5.13559479874637e-05[/C][C]0.000102711895974927[/C][C]0.999948644052013[/C][/ROW]
[ROW][C]24[/C][C]1.86608872214456e-05[/C][C]3.73217744428912e-05[/C][C]0.999981339112779[/C][/ROW]
[ROW][C]25[/C][C]8.67637480874332e-06[/C][C]1.73527496174866e-05[/C][C]0.999991323625191[/C][/ROW]
[ROW][C]26[/C][C]3.00552095746989e-06[/C][C]6.01104191493977e-06[/C][C]0.999996994479043[/C][/ROW]
[ROW][C]27[/C][C]1.07778191072874e-06[/C][C]2.15556382145748e-06[/C][C]0.99999892221809[/C][/ROW]
[ROW][C]28[/C][C]3.75309826427624e-07[/C][C]7.50619652855249e-07[/C][C]0.999999624690174[/C][/ROW]
[ROW][C]29[/C][C]1.2125394178203e-07[/C][C]2.4250788356406e-07[/C][C]0.999999878746058[/C][/ROW]
[ROW][C]30[/C][C]3.86348245074370e-08[/C][C]7.72696490148741e-08[/C][C]0.999999961365176[/C][/ROW]
[ROW][C]31[/C][C]2.28301951754785e-08[/C][C]4.56603903509570e-08[/C][C]0.999999977169805[/C][/ROW]
[ROW][C]32[/C][C]7.9097420867311e-09[/C][C]1.58194841734622e-08[/C][C]0.999999992090258[/C][/ROW]
[ROW][C]33[/C][C]3.79559636741921e-09[/C][C]7.59119273483842e-09[/C][C]0.999999996204404[/C][/ROW]
[ROW][C]34[/C][C]4.68148204914326e-08[/C][C]9.36296409828652e-08[/C][C]0.99999995318518[/C][/ROW]
[ROW][C]35[/C][C]2.11885750837688e-08[/C][C]4.23771501675376e-08[/C][C]0.999999978811425[/C][/ROW]
[ROW][C]36[/C][C]7.72114613779455e-09[/C][C]1.54422922755891e-08[/C][C]0.999999992278854[/C][/ROW]
[ROW][C]37[/C][C]2.52651635098144e-09[/C][C]5.05303270196289e-09[/C][C]0.999999997473484[/C][/ROW]
[ROW][C]38[/C][C]1.03685776461038e-09[/C][C]2.07371552922076e-09[/C][C]0.999999998963142[/C][/ROW]
[ROW][C]39[/C][C]3.99284355974862e-10[/C][C]7.98568711949725e-10[/C][C]0.999999999600716[/C][/ROW]
[ROW][C]40[/C][C]4.37612120623795e-09[/C][C]8.75224241247591e-09[/C][C]0.999999995623879[/C][/ROW]
[ROW][C]41[/C][C]1.81180819039469e-09[/C][C]3.62361638078937e-09[/C][C]0.999999998188192[/C][/ROW]
[ROW][C]42[/C][C]8.26637666212131e-10[/C][C]1.65327533242426e-09[/C][C]0.999999999173362[/C][/ROW]
[ROW][C]43[/C][C]4.88249334042997e-10[/C][C]9.76498668085993e-10[/C][C]0.99999999951175[/C][/ROW]
[ROW][C]44[/C][C]1.93863335889733e-10[/C][C]3.87726671779465e-10[/C][C]0.999999999806137[/C][/ROW]
[ROW][C]45[/C][C]5.03063835514618e-10[/C][C]1.00612767102924e-09[/C][C]0.999999999496936[/C][/ROW]
[ROW][C]46[/C][C]3.88639550646753e-10[/C][C]7.77279101293506e-10[/C][C]0.99999999961136[/C][/ROW]
[ROW][C]47[/C][C]1.83966241515281e-06[/C][C]3.67932483030561e-06[/C][C]0.999998160337585[/C][/ROW]
[ROW][C]48[/C][C]1.50603034866150e-06[/C][C]3.01206069732299e-06[/C][C]0.999998493969651[/C][/ROW]
[ROW][C]49[/C][C]8.28950826127074e-07[/C][C]1.65790165225415e-06[/C][C]0.999999171049174[/C][/ROW]
[ROW][C]50[/C][C]4.27765254061355e-07[/C][C]8.5553050812271e-07[/C][C]0.999999572234746[/C][/ROW]
[ROW][C]51[/C][C]2.38473642953617e-07[/C][C]4.76947285907233e-07[/C][C]0.999999761526357[/C][/ROW]
[ROW][C]52[/C][C]1.27741584388530e-07[/C][C]2.55483168777060e-07[/C][C]0.999999872258416[/C][/ROW]
[ROW][C]53[/C][C]6.22207638783779e-08[/C][C]1.24441527756756e-07[/C][C]0.999999937779236[/C][/ROW]
[ROW][C]54[/C][C]5.75404165003672e-08[/C][C]1.15080833000734e-07[/C][C]0.999999942459584[/C][/ROW]
[ROW][C]55[/C][C]7.54332259129016e-08[/C][C]1.50866451825803e-07[/C][C]0.999999924566774[/C][/ROW]
[ROW][C]56[/C][C]4.23991443181684e-08[/C][C]8.47982886363368e-08[/C][C]0.999999957600856[/C][/ROW]
[ROW][C]57[/C][C]2.04788544157639e-08[/C][C]4.09577088315279e-08[/C][C]0.999999979521146[/C][/ROW]
[ROW][C]58[/C][C]3.70346482775592e-08[/C][C]7.40692965551185e-08[/C][C]0.999999962965352[/C][/ROW]
[ROW][C]59[/C][C]2.65806501537123e-08[/C][C]5.31613003074245e-08[/C][C]0.99999997341935[/C][/ROW]
[ROW][C]60[/C][C]1.89134759333784e-08[/C][C]3.78269518667568e-08[/C][C]0.999999981086524[/C][/ROW]
[ROW][C]61[/C][C]1.6962144823663e-08[/C][C]3.3924289647326e-08[/C][C]0.999999983037855[/C][/ROW]
[ROW][C]62[/C][C]9.25130273445294e-09[/C][C]1.85026054689059e-08[/C][C]0.999999990748697[/C][/ROW]
[ROW][C]63[/C][C]4.02602988655916e-09[/C][C]8.05205977311832e-09[/C][C]0.99999999597397[/C][/ROW]
[ROW][C]64[/C][C]4.24061109482202e-09[/C][C]8.48122218964405e-09[/C][C]0.999999995759389[/C][/ROW]
[ROW][C]65[/C][C]2.14749453614351e-09[/C][C]4.29498907228703e-09[/C][C]0.999999997852505[/C][/ROW]
[ROW][C]66[/C][C]2.47084276148594e-09[/C][C]4.94168552297187e-09[/C][C]0.999999997529157[/C][/ROW]
[ROW][C]67[/C][C]4.01096077768932e-09[/C][C]8.02192155537864e-09[/C][C]0.99999999598904[/C][/ROW]
[ROW][C]68[/C][C]3.93597895746357e-09[/C][C]7.87195791492715e-09[/C][C]0.999999996064021[/C][/ROW]
[ROW][C]69[/C][C]3.0156687393205e-09[/C][C]6.031337478641e-09[/C][C]0.999999996984331[/C][/ROW]
[ROW][C]70[/C][C]1.71008198864545e-09[/C][C]3.4201639772909e-09[/C][C]0.999999998289918[/C][/ROW]
[ROW][C]71[/C][C]2.12223151849986e-09[/C][C]4.24446303699972e-09[/C][C]0.999999997877768[/C][/ROW]
[ROW][C]72[/C][C]7.62566033566779e-09[/C][C]1.52513206713356e-08[/C][C]0.99999999237434[/C][/ROW]
[ROW][C]73[/C][C]2.243017201485e-08[/C][C]4.48603440297e-08[/C][C]0.999999977569828[/C][/ROW]
[ROW][C]74[/C][C]2.01837987634177e-06[/C][C]4.03675975268355e-06[/C][C]0.999997981620124[/C][/ROW]
[ROW][C]75[/C][C]1.91228249219319e-05[/C][C]3.82456498438637e-05[/C][C]0.999980877175078[/C][/ROW]
[ROW][C]76[/C][C]0.000775905684035355[/C][C]0.00155181136807071[/C][C]0.999224094315965[/C][/ROW]
[ROW][C]77[/C][C]0.0130035678894402[/C][C]0.0260071357788804[/C][C]0.98699643211056[/C][/ROW]
[ROW][C]78[/C][C]0.0179346434775202[/C][C]0.0358692869550404[/C][C]0.98206535652248[/C][/ROW]
[ROW][C]79[/C][C]0.113384522604281[/C][C]0.226769045208561[/C][C]0.886615477395719[/C][/ROW]
[ROW][C]80[/C][C]0.153407108986637[/C][C]0.306814217973274[/C][C]0.846592891013363[/C][/ROW]
[ROW][C]81[/C][C]0.120538603474843[/C][C]0.241077206949686[/C][C]0.879461396525157[/C][/ROW]
[ROW][C]82[/C][C]0.100555165861474[/C][C]0.201110331722948[/C][C]0.899444834138526[/C][/ROW]
[ROW][C]83[/C][C]0.0792671815588644[/C][C]0.158534363117729[/C][C]0.920732818441136[/C][/ROW]
[ROW][C]84[/C][C]0.0606848241573943[/C][C]0.121369648314789[/C][C]0.939315175842606[/C][/ROW]
[ROW][C]85[/C][C]0.043784974962598[/C][C]0.087569949925196[/C][C]0.956215025037402[/C][/ROW]
[ROW][C]86[/C][C]0.0392679268634444[/C][C]0.0785358537268889[/C][C]0.960732073136556[/C][/ROW]
[ROW][C]87[/C][C]0.0273297845383606[/C][C]0.0546595690767213[/C][C]0.97267021546164[/C][/ROW]
[ROW][C]88[/C][C]0.0336525715592372[/C][C]0.0673051431184743[/C][C]0.966347428440763[/C][/ROW]
[ROW][C]89[/C][C]0.0392717118778147[/C][C]0.0785434237556295[/C][C]0.960728288122185[/C][/ROW]
[ROW][C]90[/C][C]0.0268839861963212[/C][C]0.0537679723926425[/C][C]0.973116013803679[/C][/ROW]
[ROW][C]91[/C][C]0.0173133651188421[/C][C]0.0346267302376843[/C][C]0.982686634881158[/C][/ROW]
[ROW][C]92[/C][C]0.207206492359526[/C][C]0.414412984719051[/C][C]0.792793507640474[/C][/ROW]
[ROW][C]93[/C][C]0.241196162812895[/C][C]0.48239232562579[/C][C]0.758803837187105[/C][/ROW]
[ROW][C]94[/C][C]0.284970322750422[/C][C]0.569940645500845[/C][C]0.715029677249578[/C][/ROW]
[ROW][C]95[/C][C]0.264363254336483[/C][C]0.528726508672965[/C][C]0.735636745663517[/C][/ROW]
[ROW][C]96[/C][C]0.274584924626957[/C][C]0.549169849253914[/C][C]0.725415075373043[/C][/ROW]
[ROW][C]97[/C][C]0.220956402087038[/C][C]0.441912804174076[/C][C]0.779043597912962[/C][/ROW]
[ROW][C]98[/C][C]0.347863299296102[/C][C]0.695726598592203[/C][C]0.652136700703898[/C][/ROW]
[ROW][C]99[/C][C]0.361057225051362[/C][C]0.722114450102724[/C][C]0.638942774948638[/C][/ROW]
[ROW][C]100[/C][C]0.730022143570548[/C][C]0.539955712858905[/C][C]0.269977856429452[/C][/ROW]
[ROW][C]101[/C][C]0.7151800305288[/C][C]0.569639938942402[/C][C]0.284819969471201[/C][/ROW]
[ROW][C]102[/C][C]0.687501704669693[/C][C]0.624996590660614[/C][C]0.312498295330307[/C][/ROW]
[ROW][C]103[/C][C]0.821732439162005[/C][C]0.35653512167599[/C][C]0.178267560837995[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57371&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57371&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
100.05327201463947070.1065440292789410.94672798536053
110.01652367602938210.03304735205876430.983476323970618
120.005648206057246970.01129641211449390.994351793942753
130.00431178648236020.00862357296472040.99568821351764
140.001456244284165340.002912488568330680.998543755715835
150.001496523857606210.002993047715212430.998503476142394
160.0005472342177990260.001094468435598050.9994527657822
170.0004250353112435940.0008500706224871880.999574964688756
180.0004179726542522950.000835945308504590.999582027345748
190.0001907467881291470.0003814935762582950.99980925321187
206.38627785698852e-050.0001277255571397700.99993613722143
210.0001494196198713250.000298839239742650.999850580380129
227.50041848527208e-050.0001500083697054420.999924995815147
235.13559479874637e-050.0001027118959749270.999948644052013
241.86608872214456e-053.73217744428912e-050.999981339112779
258.67637480874332e-061.73527496174866e-050.999991323625191
263.00552095746989e-066.01104191493977e-060.999996994479043
271.07778191072874e-062.15556382145748e-060.99999892221809
283.75309826427624e-077.50619652855249e-070.999999624690174
291.2125394178203e-072.4250788356406e-070.999999878746058
303.86348245074370e-087.72696490148741e-080.999999961365176
312.28301951754785e-084.56603903509570e-080.999999977169805
327.9097420867311e-091.58194841734622e-080.999999992090258
333.79559636741921e-097.59119273483842e-090.999999996204404
344.68148204914326e-089.36296409828652e-080.99999995318518
352.11885750837688e-084.23771501675376e-080.999999978811425
367.72114613779455e-091.54422922755891e-080.999999992278854
372.52651635098144e-095.05303270196289e-090.999999997473484
381.03685776461038e-092.07371552922076e-090.999999998963142
393.99284355974862e-107.98568711949725e-100.999999999600716
404.37612120623795e-098.75224241247591e-090.999999995623879
411.81180819039469e-093.62361638078937e-090.999999998188192
428.26637666212131e-101.65327533242426e-090.999999999173362
434.88249334042997e-109.76498668085993e-100.99999999951175
441.93863335889733e-103.87726671779465e-100.999999999806137
455.03063835514618e-101.00612767102924e-090.999999999496936
463.88639550646753e-107.77279101293506e-100.99999999961136
471.83966241515281e-063.67932483030561e-060.999998160337585
481.50603034866150e-063.01206069732299e-060.999998493969651
498.28950826127074e-071.65790165225415e-060.999999171049174
504.27765254061355e-078.5553050812271e-070.999999572234746
512.38473642953617e-074.76947285907233e-070.999999761526357
521.27741584388530e-072.55483168777060e-070.999999872258416
536.22207638783779e-081.24441527756756e-070.999999937779236
545.75404165003672e-081.15080833000734e-070.999999942459584
557.54332259129016e-081.50866451825803e-070.999999924566774
564.23991443181684e-088.47982886363368e-080.999999957600856
572.04788544157639e-084.09577088315279e-080.999999979521146
583.70346482775592e-087.40692965551185e-080.999999962965352
592.65806501537123e-085.31613003074245e-080.99999997341935
601.89134759333784e-083.78269518667568e-080.999999981086524
611.6962144823663e-083.3924289647326e-080.999999983037855
629.25130273445294e-091.85026054689059e-080.999999990748697
634.02602988655916e-098.05205977311832e-090.99999999597397
644.24061109482202e-098.48122218964405e-090.999999995759389
652.14749453614351e-094.29498907228703e-090.999999997852505
662.47084276148594e-094.94168552297187e-090.999999997529157
674.01096077768932e-098.02192155537864e-090.99999999598904
683.93597895746357e-097.87195791492715e-090.999999996064021
693.0156687393205e-096.031337478641e-090.999999996984331
701.71008198864545e-093.4201639772909e-090.999999998289918
712.12223151849986e-094.24446303699972e-090.999999997877768
727.62566033566779e-091.52513206713356e-080.99999999237434
732.243017201485e-084.48603440297e-080.999999977569828
742.01837987634177e-064.03675975268355e-060.999997981620124
751.91228249219319e-053.82456498438637e-050.999980877175078
760.0007759056840353550.001551811368070710.999224094315965
770.01300356788944020.02600713577888040.98699643211056
780.01793464347752020.03586928695504040.98206535652248
790.1133845226042810.2267690452085610.886615477395719
800.1534071089866370.3068142179732740.846592891013363
810.1205386034748430.2410772069496860.879461396525157
820.1005551658614740.2011103317229480.899444834138526
830.07926718155886440.1585343631177290.920732818441136
840.06068482415739430.1213696483147890.939315175842606
850.0437849749625980.0875699499251960.956215025037402
860.03926792686344440.07853585372688890.960732073136556
870.02732978453836060.05465956907672130.97267021546164
880.03365257155923720.06730514311847430.966347428440763
890.03927171187781470.07854342375562950.960728288122185
900.02688398619632120.05376797239264250.973116013803679
910.01731336511884210.03462673023768430.982686634881158
920.2072064923595260.4144129847190510.792793507640474
930.2411961628128950.482392325625790.758803837187105
940.2849703227504220.5699406455008450.715029677249578
950.2643632543364830.5287265086729650.735636745663517
960.2745849246269570.5491698492539140.725415075373043
970.2209564020870380.4419128041740760.779043597912962
980.3478632992961020.6957265985922030.652136700703898
990.3610572250513620.7221144501027240.638942774948638
1000.7300221435705480.5399557128589050.269977856429452
1010.71518003052880.5696399389424020.284819969471201
1020.6875017046696930.6249965906606140.312498295330307
1030.8217324391620050.356535121675990.178267560837995






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level640.680851063829787NOK
5% type I error level690.73404255319149NOK
10% type I error level750.797872340425532NOK

\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 & 64 & 0.680851063829787 & NOK \tabularnewline
5% type I error level & 69 & 0.73404255319149 & NOK \tabularnewline
10% type I error level & 75 & 0.797872340425532 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57371&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]64[/C][C]0.680851063829787[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]69[/C][C]0.73404255319149[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]75[/C][C]0.797872340425532[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57371&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57371&T=6

As an alternative you can also use a QR Code:  
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 level640.680851063829787NOK
5% type I error level690.73404255319149NOK
10% type I error level750.797872340425532NOK


Figures (Output of Computation)

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Figure 10
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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')
}