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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 13:38:37 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352140734wf190nlepas96vj.htm/, Retrieved Mon, 06 Feb 2023 00:10:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186208, Retrieved Mon, 06 Feb 2023 00:10:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact64
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [WS7 6] [2012-11-05 18:38:37] [0eae5e694d1d975eb250a0af7a7338a6] [Current]
- R PD      [Multiple Regression] [Paper 28] [2012-12-12 12:47:51] [cb178d3ebce11557293640a297e0ede2]
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Dataseries X:
09	41	38	13	12	14	12	53
09	39	32	16	11	18	11	86
09	30	35	19	15	11	14	66
09	31	33	15	6	12	12	67
09	34	37	14	13	16	21	76
09	35	29	13	10	18	12	78
09	39	31	19	12	14	22	53
09	34	36	15	14	14	11	80
09	36	35	14	12	15	10	74
09	37	38	15	6	15	13	76
09	38	31	16	10	17	10	79
09	36	34	16	12	19	8	54
09	38	35	16	12	10	15	67
09	39	38	16	11	16	14	54
09	33	37	17	15	18	10	87
09	32	33	15	12	14	14	58
09	36	32	15	10	14	14	75
09	38	38	20	12	17	11	88
09	39	38	18	11	14	10	64
09	32	32	16	12	16	13	57
09	32	33	16	11	18	7	66
09	31	31	16	12	11	14	68
09	39	38	19	13	14	12	54
09	37	39	16	11	12	14	56
09	39	32	17	9	17	11	86
09	41	32	17	13	9	9	80
09	36	35	16	10	16	11	76
09	33	37	15	14	14	15	69
09	33	33	16	12	15	14	78
09	34	33	14	10	11	13	67
09	31	28	15	12	16	9	80
09	27	32	12	8	13	15	54
09	37	31	14	10	17	10	71
09	34	37	16	12	15	11	84
09	34	30	14	12	14	13	74
09	32	33	7	7	16	8	71
09	29	31	10	6	9	20	63
09	36	33	14	12	15	12	71
09	29	31	16	10	17	10	76
09	35	33	16	10	13	10	69
09	37	32	16	10	15	9	74
09	34	33	14	12	16	14	75
09	38	32	20	15	16	8	54
09	35	33	14	10	12	14	52
09	38	28	14	10	12	11	69
09	37	35	11	12	11	13	68
09	38	39	14	13	15	9	65
09	33	34	15	11	15	11	75
09	36	38	16	11	17	15	74
09	38	32	14	12	13	11	75
09	32	38	16	14	16	10	72
09	32	30	14	10	14	14	67
09	32	33	12	12	11	18	63
09	34	38	16	13	12	14	62
09	32	32	9	5	12	11	63
09	37	32	14	6	15	12	76
09	39	34	16	12	16	13	74
09	29	34	16	12	15	9	67
09	37	36	15	11	12	10	73
09	35	34	16	10	12	15	70
09	30	28	12	7	8	20	53
09	38	34	16	12	13	12	77
09	34	35	16	14	11	12	77
10	31	35	14	11	14	14	52
10	34	31	16	12	15	13	54
10	35	37	17	13	10	11	80
10	36	35	18	14	11	17	66
10	30	27	18	11	12	12	73
10	39	40	12	12	15	13	63
10	35	37	16	12	15	14	69
10	38	36	10	8	14	13	67
10	31	38	14	11	16	15	54
10	34	39	18	14	15	13	81
10	38	41	18	14	15	10	69
10	34	27	16	12	13	11	84
10	39	30	17	9	12	19	80
10	37	37	16	13	17	13	70
10	34	31	16	11	13	17	69
10	28	31	13	12	15	13	77
10	37	27	16	12	13	9	54
10	33	36	16	12	15	11	79
10	37	38	20	12	16	10	30
10	35	37	16	12	15	9	71
10	37	33	15	12	16	12	73
10	32	34	15	11	15	12	72
10	33	31	16	10	14	13	77
10	38	39	14	9	15	13	75
10	33	34	16	12	14	12	69
10	29	32	16	12	13	15	54
10	33	33	15	12	7	22	70
10	31	36	12	9	17	13	73
10	36	32	17	15	13	15	54
10	35	41	16	12	15	13	77
10	32	28	15	12	14	15	82
10	29	30	13	12	13	10	80
10	39	36	16	10	16	11	80
10	37	35	16	13	12	16	69
10	35	31	16	9	14	11	78
10	37	34	16	12	17	11	81
10	32	36	14	10	15	10	76
10	38	36	16	14	17	10	76
10	37	35	16	11	12	16	73
10	36	37	20	15	16	12	85
10	32	28	15	11	11	11	66
10	33	39	16	11	15	16	79
10	40	32	13	12	9	19	68
10	38	35	17	12	16	11	76
10	41	39	16	12	15	16	71
10	36	35	16	11	10	15	54
10	43	42	12	7	10	24	46
10	30	34	16	12	15	14	82
10	31	33	16	14	11	15	74
10	32	41	17	11	13	11	88
10	32	33	13	11	14	15	38
10	37	34	12	10	18	12	76
10	37	32	18	13	16	10	86
10	33	40	14	13	14	14	54
10	34	40	14	8	14	13	70
10	33	35	13	11	14	9	69
10	38	36	16	12	14	15	90
10	33	37	13	11	12	15	54
10	31	27	16	13	14	14	76
10	38	39	13	12	15	11	89
10	37	38	16	14	15	8	76
10	33	31	15	13	15	11	73
10	31	33	16	15	13	11	79
10	39	32	15	10	17	8	90
10	44	39	17	11	17	10	74
10	33	36	15	9	19	11	81
10	35	33	12	11	15	13	72
10	32	33	16	10	13	11	71
10	28	32	10	11	9	20	66
10	40	37	16	8	15	10	77
10	27	30	12	11	15	15	65
10	37	38	14	12	15	12	74
10	32	29	15	12	16	14	82
10	28	22	13	9	11	23	54
10	34	35	15	11	14	14	63
10	30	35	11	10	11	16	54
10	35	34	12	8	15	11	64
10	31	35	8	9	13	12	69
10	32	34	16	8	15	10	54
10	30	34	15	9	16	14	84
10	30	35	17	15	14	12	86
10	31	23	16	11	15	12	77
10	40	31	10	8	16	11	89
10	32	27	18	13	16	12	76
10	36	36	13	12	11	13	60
10	32	31	16	12	12	11	75
10	35	32	13	9	9	19	73
10	38	39	10	7	16	12	85
10	42	37	15	13	13	17	79
10	34	38	16	9	16	9	71
10	35	39	16	6	12	12	72
10	35	34	14	8	9	19	69
09	33	31	10	8	13	18	78
10	36	32	17	15	13	15	54
10	32	37	13	6	14	14	69
10	33	36	15	9	19	11	81
10	34	32	16	11	13	9	84
10	32	35	12	8	12	18	84
10	34	36	13	8	13	16	69




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186208&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186208&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186208&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.71929234722637 + 0.00370823691652482Month[t] + 0.115337834651046Connected[t] -0.0237824859863305Separate[t] + 0.545418067796176Software[t] + 0.0629108169082979Happiness[t] -0.0769181848205586Depression[t] + 0.00132799497241473Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.71929234722637 +  0.00370823691652482Month[t] +  0.115337834651046Connected[t] -0.0237824859863305Separate[t] +  0.545418067796176Software[t] +  0.0629108169082979Happiness[t] -0.0769181848205586Depression[t] +  0.00132799497241473Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186208&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.71929234722637 +  0.00370823691652482Month[t] +  0.115337834651046Connected[t] -0.0237824859863305Separate[t] +  0.545418067796176Software[t] +  0.0629108169082979Happiness[t] -0.0769181848205586Depression[t] +  0.00132799497241473Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186208&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186208&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.71929234722637 + 0.00370823691652482Month[t] + 0.115337834651046Connected[t] -0.0237824859863305Separate[t] + 0.545418067796176Software[t] + 0.0629108169082979Happiness[t] -0.0769181848205586Depression[t] + 0.00132799497241473Belonging[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.719292347226373.7117651.54090.1254040.062702
Month0.003708236916524820.3053950.01210.9903280.495164
Connected0.1153378346510460.047232.44210.0157360.007868
Separate-0.02378248598633050.045119-0.52710.5988790.29944
Software0.5454180677961760.0690527.898600
Happiness0.06291081690829790.0764460.82290.4118140.205907
Depression-0.07691818482055860.056487-1.36170.1752850.087643
Belonging0.001327994972414730.0145620.09120.9274570.463728

\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) & 5.71929234722637 & 3.711765 & 1.5409 & 0.125404 & 0.062702 \tabularnewline
Month & 0.00370823691652482 & 0.305395 & 0.0121 & 0.990328 & 0.495164 \tabularnewline
Connected & 0.115337834651046 & 0.04723 & 2.4421 & 0.015736 & 0.007868 \tabularnewline
Separate & -0.0237824859863305 & 0.045119 & -0.5271 & 0.598879 & 0.29944 \tabularnewline
Software & 0.545418067796176 & 0.069052 & 7.8986 & 0 & 0 \tabularnewline
Happiness & 0.0629108169082979 & 0.076446 & 0.8229 & 0.411814 & 0.205907 \tabularnewline
Depression & -0.0769181848205586 & 0.056487 & -1.3617 & 0.175285 & 0.087643 \tabularnewline
Belonging & 0.00132799497241473 & 0.014562 & 0.0912 & 0.927457 & 0.463728 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186208&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]5.71929234722637[/C][C]3.711765[/C][C]1.5409[/C][C]0.125404[/C][C]0.062702[/C][/ROW]
[ROW][C]Month[/C][C]0.00370823691652482[/C][C]0.305395[/C][C]0.0121[/C][C]0.990328[/C][C]0.495164[/C][/ROW]
[ROW][C]Connected[/C][C]0.115337834651046[/C][C]0.04723[/C][C]2.4421[/C][C]0.015736[/C][C]0.007868[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0237824859863305[/C][C]0.045119[/C][C]-0.5271[/C][C]0.598879[/C][C]0.29944[/C][/ROW]
[ROW][C]Software[/C][C]0.545418067796176[/C][C]0.069052[/C][C]7.8986[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0629108169082979[/C][C]0.076446[/C][C]0.8229[/C][C]0.411814[/C][C]0.205907[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0769181848205586[/C][C]0.056487[/C][C]-1.3617[/C][C]0.175285[/C][C]0.087643[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00132799497241473[/C][C]0.014562[/C][C]0.0912[/C][C]0.927457[/C][C]0.463728[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186208&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186208&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.719292347226373.7117651.54090.1254040.062702
Month0.003708236916524820.3053950.01210.9903280.495164
Connected0.1153378346510460.047232.44210.0157360.007868
Separate-0.02378248598633050.045119-0.52710.5988790.29944
Software0.5454180677961760.0690527.898600
Happiness0.06291081690829790.0764460.82290.4118140.205907
Depression-0.07691818482055860.056487-1.36170.1752850.087643
Belonging0.001327994972414730.0145620.09120.9274570.463728







Multiple Linear Regression - Regression Statistics
Multiple R0.594915621478916
R-squared0.353924596679645
Adjusted R-squared0.324557532892356
F-TEST (value)12.0517529175946
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value3.22575299804839e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85431506425261
Sum Squared Residuals529.526591057182

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.594915621478916 \tabularnewline
R-squared & 0.353924596679645 \tabularnewline
Adjusted R-squared & 0.324557532892356 \tabularnewline
F-TEST (value) & 12.0517529175946 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 3.22575299804839e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85431506425261 \tabularnewline
Sum Squared Residuals & 529.526591057182 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186208&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.594915621478916[/C][/ROW]
[ROW][C]R-squared[/C][C]0.353924596679645[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.324557532892356[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.0517529175946[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]3.22575299804839e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85431506425261[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]529.526591057182[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186208&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186208&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.594915621478916
R-squared0.353924596679645
Adjusted R-squared0.324557532892356
F-TEST (value)12.0517529175946
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value3.22575299804839e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85431506425261
Sum Squared Residuals529.526591057182







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.150916998649-3.150916998649
21615.88990346401210.110096535987873
31916.26449759311042.73550240688962
41511.73671297109033.26328702890968
51415.3768545646713-1.37685456467127
61314.8669393709709-1.86693937097088
71915.31753688304563.68246311695439
81516.5947273127324-1.59472731273243
91415.8902103643229-1.89021036432286
101512.4335937697212.56640623027899
111615.25764145065660.742358549343408
121616.2929125881352-0.292912588135208
131615.41244506017380.587554939826231
141615.34813652069860.651863479301399
151717.3388824771519-0.33888247715188
161515.0845925219422-0.0845925219421735
171514.50146612547140.498533874528616
182016.11703395427583.88296604572419
191815.54326757588842.45673242411161
201615.30978683159320.690213168406757
211615.33986897530240.660131024697583
221614.8413671582631.15863284173696
231916.46698739211552.53301260788452
241614.84469108772181.15530891227818
251714.73615651151152.26384348848852
261716.79108631653850.208913683461481
271614.78802285076311.21197714923692
281516.1333263081163-1.13332630811625
291615.28940107294980.710598927050188
301414.1245697444993-0.12456974449931
311515.6277955645353-0.627795564535293
321212.2048725818701-0.204872581870055
331415.1316796562262-1.13167965622623
341615.54833148795170.4516685120483
351415.4847817535825-1.48478175358245
36712.9620968603426-5.96209686034263
371010.7442123645819-0.744212364581872
381415.7799549817372-1.77995498173716
391614.21561695387991.78438304612006
401614.59913975737351.40086024262654
411615.06297770616110.937022293838894
421415.4636657395919-1.46366573959191
432018.01867498207361.9813250179264
441414.2059802866519-0.205980286651874
451414.9242366895294-0.924236689529389
461115.5151824070446-4.51518240704455
471416.6361403875446-2.63614038754464
481514.94697108871170.0530289112882635
491615.01467554828150.985324451718502
501415.9908216679192-1.99082166791921
511616.5086025303155-0.50860253031551
521414.0770557990605-0.0770557990605449
531214.5948273067971-2.59482730679712
541615.62126417518190.378735824818148
55911.4020214288624-2.40202142886242
561412.65320687045961.34679312954045
571616.092162616709-0.0921626167089528
581615.17425022776550.825749772234471
591515.2462871994941-0.246287199494094
601614.12918352534851.87081647465155
611211.40012495835560.59987504164436
621615.86899450107080.131005498929184
631616.3488751782561-0.34887517825606
641414.3720119146043-0.372011914604327
651615.50105842197260.498941578027352
661715.89292956288431.1070704371157
671816.18406021567531.81593978432468
681814.50283659808923.49716340191084
691215.8756571761026-3.87565717610264
701615.41670308047140.583296919528627
711013.6161781771936-3.61617817719357
721414.3522238955862-0.352223895586202
731816.43749053392961.56250946607044
741817.06609551535380.933904484646224
751615.66404295091490.335957049085067
761713.84956218746023.15043781253978
771616.3968646311792-0.39686463117921
781614.54206590566391.45793409433614
791314.8395752984319-1.83957529843191
801616.1240529753367-0.124052975336746
811615.45384440134140.546155598658564
822015.94238801605354.05761198394651
831615.8039499945190.196050005481004
841515.9645678601579-0.964567860157861
851514.75443932123940.245560678760587
861614.26251751918651.73748248081351
871414.1637835637184-0.163783563718368
881615.34830042186110.651699578138907
891614.62092875927341.37907124072662
901514.16385333625620.836146663743833
911213.5509318229918-1.55093182299181
921717.0645478052192-0.0645478052192257
931615.40911528112590.590884718874072
941515.1621668833077-0.162166883307746
951315.0876125246316-2.08761252463161
961615.11927408553610.880725914463931
971615.89779296917630.102207030823709
981614.10293948530591.89706051469406
991616.0912383356797-0.0912383356797046
1001414.3206046310014-0.320604631001352
1011617.3201255439089-1.32012554390893
1021614.81226881347361.1877311865264
1032017.4062902246192.593709775381
1041514.71444118451030.285558815489725
1051614.45248795148351.54751204851653
1061315.3489208631333-2.34892086313326
1071716.1132428925740.886757107425952
1081615.90998473670870.0900152632913011
1091614.62279562535061.37720437464936
1101212.3791231716546-0.379123171654591
1111614.92862529981651.07137470018347
1121615.81939634381320.180603656186811
1131714.56030638989772.4396936101023
1141314.4394046067937-1.43940460679367
1151214.979754857313-2.97975485731302
1161816.70486871822291.29513128177712
1171415.5772672795119-1.57726727951195
1181413.06368087956130.936319120438693
1191315.0098544225403-2.00985442254026
1201615.67455796310270.325442036897306
1211314.3550387832414-1.35503878324143
1221615.68497981742530.315020182574723
1231315.9724660663618-2.97246606636182
1241617.1852374731097-1.18523747310974
1251516.1102069292348-1.11020692923478
1261616.8049487595703-0.804948759570269
1271515.5213493505755-0.521349350575512
1281716.30189490052280.698105099477149
1291514.07188945553090.928110544469072
1301215.0473171263583-3.04731712635832
1311614.18257229546111.81742770453889
1321013.3398746047591-3.33987460475915
1331614.13001668160341.86998331839655
1341214.0328295726609-2.03282957266093
1351415.7840726082903-1.78407260829032
1361515.3411242159586-0.34112421595857
1371312.3659944687160.634005531283958
1381514.7326333632540.267366636745974
1391113.3713431817359-2.37134318173592
1401213.5304928468453-1.53049284684526
141813.3946772462758-5.39467724627584
1421613.24811757798852.75188242201147
1431513.35793790328111.64206209671887
1441716.63733454984120.362665450158795
1451614.90734880730011.09265119269992
1461014.2746401692781-4.27464016927815
1471816.0799756555341.92002434446596
1481315.3691463635444-2.36914636354438
1491615.26337456600750.73662543399251
1501313.1426174613516-0.142617461351575
1511013.226056379579-3.22605637957902
1521516.4261897522707-1.42618975227075
1531614.09148628740141.90851371259861
1541612.06571760555513.93428239444488
1551412.54432244169311.45567755830692
1561012.7217994006389-2.72179940063894
1571717.0645478052192-0.0645478052192257
1581311.73527035283291.26472964716712
1591514.07188945553090.928110544469072
1601615.15354882282820.846451177171778
1611212.4600970118853-0.460097011885285
1621312.86381745716420.13618254283576

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.150916998649 & -3.150916998649 \tabularnewline
2 & 16 & 15.8899034640121 & 0.110096535987873 \tabularnewline
3 & 19 & 16.2644975931104 & 2.73550240688962 \tabularnewline
4 & 15 & 11.7367129710903 & 3.26328702890968 \tabularnewline
5 & 14 & 15.3768545646713 & -1.37685456467127 \tabularnewline
6 & 13 & 14.8669393709709 & -1.86693937097088 \tabularnewline
7 & 19 & 15.3175368830456 & 3.68246311695439 \tabularnewline
8 & 15 & 16.5947273127324 & -1.59472731273243 \tabularnewline
9 & 14 & 15.8902103643229 & -1.89021036432286 \tabularnewline
10 & 15 & 12.433593769721 & 2.56640623027899 \tabularnewline
11 & 16 & 15.2576414506566 & 0.742358549343408 \tabularnewline
12 & 16 & 16.2929125881352 & -0.292912588135208 \tabularnewline
13 & 16 & 15.4124450601738 & 0.587554939826231 \tabularnewline
14 & 16 & 15.3481365206986 & 0.651863479301399 \tabularnewline
15 & 17 & 17.3388824771519 & -0.33888247715188 \tabularnewline
16 & 15 & 15.0845925219422 & -0.0845925219421735 \tabularnewline
17 & 15 & 14.5014661254714 & 0.498533874528616 \tabularnewline
18 & 20 & 16.1170339542758 & 3.88296604572419 \tabularnewline
19 & 18 & 15.5432675758884 & 2.45673242411161 \tabularnewline
20 & 16 & 15.3097868315932 & 0.690213168406757 \tabularnewline
21 & 16 & 15.3398689753024 & 0.660131024697583 \tabularnewline
22 & 16 & 14.841367158263 & 1.15863284173696 \tabularnewline
23 & 19 & 16.4669873921155 & 2.53301260788452 \tabularnewline
24 & 16 & 14.8446910877218 & 1.15530891227818 \tabularnewline
25 & 17 & 14.7361565115115 & 2.26384348848852 \tabularnewline
26 & 17 & 16.7910863165385 & 0.208913683461481 \tabularnewline
27 & 16 & 14.7880228507631 & 1.21197714923692 \tabularnewline
28 & 15 & 16.1333263081163 & -1.13332630811625 \tabularnewline
29 & 16 & 15.2894010729498 & 0.710598927050188 \tabularnewline
30 & 14 & 14.1245697444993 & -0.12456974449931 \tabularnewline
31 & 15 & 15.6277955645353 & -0.627795564535293 \tabularnewline
32 & 12 & 12.2048725818701 & -0.204872581870055 \tabularnewline
33 & 14 & 15.1316796562262 & -1.13167965622623 \tabularnewline
34 & 16 & 15.5483314879517 & 0.4516685120483 \tabularnewline
35 & 14 & 15.4847817535825 & -1.48478175358245 \tabularnewline
36 & 7 & 12.9620968603426 & -5.96209686034263 \tabularnewline
37 & 10 & 10.7442123645819 & -0.744212364581872 \tabularnewline
38 & 14 & 15.7799549817372 & -1.77995498173716 \tabularnewline
39 & 16 & 14.2156169538799 & 1.78438304612006 \tabularnewline
40 & 16 & 14.5991397573735 & 1.40086024262654 \tabularnewline
41 & 16 & 15.0629777061611 & 0.937022293838894 \tabularnewline
42 & 14 & 15.4636657395919 & -1.46366573959191 \tabularnewline
43 & 20 & 18.0186749820736 & 1.9813250179264 \tabularnewline
44 & 14 & 14.2059802866519 & -0.205980286651874 \tabularnewline
45 & 14 & 14.9242366895294 & -0.924236689529389 \tabularnewline
46 & 11 & 15.5151824070446 & -4.51518240704455 \tabularnewline
47 & 14 & 16.6361403875446 & -2.63614038754464 \tabularnewline
48 & 15 & 14.9469710887117 & 0.0530289112882635 \tabularnewline
49 & 16 & 15.0146755482815 & 0.985324451718502 \tabularnewline
50 & 14 & 15.9908216679192 & -1.99082166791921 \tabularnewline
51 & 16 & 16.5086025303155 & -0.50860253031551 \tabularnewline
52 & 14 & 14.0770557990605 & -0.0770557990605449 \tabularnewline
53 & 12 & 14.5948273067971 & -2.59482730679712 \tabularnewline
54 & 16 & 15.6212641751819 & 0.378735824818148 \tabularnewline
55 & 9 & 11.4020214288624 & -2.40202142886242 \tabularnewline
56 & 14 & 12.6532068704596 & 1.34679312954045 \tabularnewline
57 & 16 & 16.092162616709 & -0.0921626167089528 \tabularnewline
58 & 16 & 15.1742502277655 & 0.825749772234471 \tabularnewline
59 & 15 & 15.2462871994941 & -0.246287199494094 \tabularnewline
60 & 16 & 14.1291835253485 & 1.87081647465155 \tabularnewline
61 & 12 & 11.4001249583556 & 0.59987504164436 \tabularnewline
62 & 16 & 15.8689945010708 & 0.131005498929184 \tabularnewline
63 & 16 & 16.3488751782561 & -0.34887517825606 \tabularnewline
64 & 14 & 14.3720119146043 & -0.372011914604327 \tabularnewline
65 & 16 & 15.5010584219726 & 0.498941578027352 \tabularnewline
66 & 17 & 15.8929295628843 & 1.1070704371157 \tabularnewline
67 & 18 & 16.1840602156753 & 1.81593978432468 \tabularnewline
68 & 18 & 14.5028365980892 & 3.49716340191084 \tabularnewline
69 & 12 & 15.8756571761026 & -3.87565717610264 \tabularnewline
70 & 16 & 15.4167030804714 & 0.583296919528627 \tabularnewline
71 & 10 & 13.6161781771936 & -3.61617817719357 \tabularnewline
72 & 14 & 14.3522238955862 & -0.352223895586202 \tabularnewline
73 & 18 & 16.4374905339296 & 1.56250946607044 \tabularnewline
74 & 18 & 17.0660955153538 & 0.933904484646224 \tabularnewline
75 & 16 & 15.6640429509149 & 0.335957049085067 \tabularnewline
76 & 17 & 13.8495621874602 & 3.15043781253978 \tabularnewline
77 & 16 & 16.3968646311792 & -0.39686463117921 \tabularnewline
78 & 16 & 14.5420659056639 & 1.45793409433614 \tabularnewline
79 & 13 & 14.8395752984319 & -1.83957529843191 \tabularnewline
80 & 16 & 16.1240529753367 & -0.124052975336746 \tabularnewline
81 & 16 & 15.4538444013414 & 0.546155598658564 \tabularnewline
82 & 20 & 15.9423880160535 & 4.05761198394651 \tabularnewline
83 & 16 & 15.803949994519 & 0.196050005481004 \tabularnewline
84 & 15 & 15.9645678601579 & -0.964567860157861 \tabularnewline
85 & 15 & 14.7544393212394 & 0.245560678760587 \tabularnewline
86 & 16 & 14.2625175191865 & 1.73748248081351 \tabularnewline
87 & 14 & 14.1637835637184 & -0.163783563718368 \tabularnewline
88 & 16 & 15.3483004218611 & 0.651699578138907 \tabularnewline
89 & 16 & 14.6209287592734 & 1.37907124072662 \tabularnewline
90 & 15 & 14.1638533362562 & 0.836146663743833 \tabularnewline
91 & 12 & 13.5509318229918 & -1.55093182299181 \tabularnewline
92 & 17 & 17.0645478052192 & -0.0645478052192257 \tabularnewline
93 & 16 & 15.4091152811259 & 0.590884718874072 \tabularnewline
94 & 15 & 15.1621668833077 & -0.162166883307746 \tabularnewline
95 & 13 & 15.0876125246316 & -2.08761252463161 \tabularnewline
96 & 16 & 15.1192740855361 & 0.880725914463931 \tabularnewline
97 & 16 & 15.8977929691763 & 0.102207030823709 \tabularnewline
98 & 16 & 14.1029394853059 & 1.89706051469406 \tabularnewline
99 & 16 & 16.0912383356797 & -0.0912383356797046 \tabularnewline
100 & 14 & 14.3206046310014 & -0.320604631001352 \tabularnewline
101 & 16 & 17.3201255439089 & -1.32012554390893 \tabularnewline
102 & 16 & 14.8122688134736 & 1.1877311865264 \tabularnewline
103 & 20 & 17.406290224619 & 2.593709775381 \tabularnewline
104 & 15 & 14.7144411845103 & 0.285558815489725 \tabularnewline
105 & 16 & 14.4524879514835 & 1.54751204851653 \tabularnewline
106 & 13 & 15.3489208631333 & -2.34892086313326 \tabularnewline
107 & 17 & 16.113242892574 & 0.886757107425952 \tabularnewline
108 & 16 & 15.9099847367087 & 0.0900152632913011 \tabularnewline
109 & 16 & 14.6227956253506 & 1.37720437464936 \tabularnewline
110 & 12 & 12.3791231716546 & -0.379123171654591 \tabularnewline
111 & 16 & 14.9286252998165 & 1.07137470018347 \tabularnewline
112 & 16 & 15.8193963438132 & 0.180603656186811 \tabularnewline
113 & 17 & 14.5603063898977 & 2.4396936101023 \tabularnewline
114 & 13 & 14.4394046067937 & -1.43940460679367 \tabularnewline
115 & 12 & 14.979754857313 & -2.97975485731302 \tabularnewline
116 & 18 & 16.7048687182229 & 1.29513128177712 \tabularnewline
117 & 14 & 15.5772672795119 & -1.57726727951195 \tabularnewline
118 & 14 & 13.0636808795613 & 0.936319120438693 \tabularnewline
119 & 13 & 15.0098544225403 & -2.00985442254026 \tabularnewline
120 & 16 & 15.6745579631027 & 0.325442036897306 \tabularnewline
121 & 13 & 14.3550387832414 & -1.35503878324143 \tabularnewline
122 & 16 & 15.6849798174253 & 0.315020182574723 \tabularnewline
123 & 13 & 15.9724660663618 & -2.97246606636182 \tabularnewline
124 & 16 & 17.1852374731097 & -1.18523747310974 \tabularnewline
125 & 15 & 16.1102069292348 & -1.11020692923478 \tabularnewline
126 & 16 & 16.8049487595703 & -0.804948759570269 \tabularnewline
127 & 15 & 15.5213493505755 & -0.521349350575512 \tabularnewline
128 & 17 & 16.3018949005228 & 0.698105099477149 \tabularnewline
129 & 15 & 14.0718894555309 & 0.928110544469072 \tabularnewline
130 & 12 & 15.0473171263583 & -3.04731712635832 \tabularnewline
131 & 16 & 14.1825722954611 & 1.81742770453889 \tabularnewline
132 & 10 & 13.3398746047591 & -3.33987460475915 \tabularnewline
133 & 16 & 14.1300166816034 & 1.86998331839655 \tabularnewline
134 & 12 & 14.0328295726609 & -2.03282957266093 \tabularnewline
135 & 14 & 15.7840726082903 & -1.78407260829032 \tabularnewline
136 & 15 & 15.3411242159586 & -0.34112421595857 \tabularnewline
137 & 13 & 12.365994468716 & 0.634005531283958 \tabularnewline
138 & 15 & 14.732633363254 & 0.267366636745974 \tabularnewline
139 & 11 & 13.3713431817359 & -2.37134318173592 \tabularnewline
140 & 12 & 13.5304928468453 & -1.53049284684526 \tabularnewline
141 & 8 & 13.3946772462758 & -5.39467724627584 \tabularnewline
142 & 16 & 13.2481175779885 & 2.75188242201147 \tabularnewline
143 & 15 & 13.3579379032811 & 1.64206209671887 \tabularnewline
144 & 17 & 16.6373345498412 & 0.362665450158795 \tabularnewline
145 & 16 & 14.9073488073001 & 1.09265119269992 \tabularnewline
146 & 10 & 14.2746401692781 & -4.27464016927815 \tabularnewline
147 & 18 & 16.079975655534 & 1.92002434446596 \tabularnewline
148 & 13 & 15.3691463635444 & -2.36914636354438 \tabularnewline
149 & 16 & 15.2633745660075 & 0.73662543399251 \tabularnewline
150 & 13 & 13.1426174613516 & -0.142617461351575 \tabularnewline
151 & 10 & 13.226056379579 & -3.22605637957902 \tabularnewline
152 & 15 & 16.4261897522707 & -1.42618975227075 \tabularnewline
153 & 16 & 14.0914862874014 & 1.90851371259861 \tabularnewline
154 & 16 & 12.0657176055551 & 3.93428239444488 \tabularnewline
155 & 14 & 12.5443224416931 & 1.45567755830692 \tabularnewline
156 & 10 & 12.7217994006389 & -2.72179940063894 \tabularnewline
157 & 17 & 17.0645478052192 & -0.0645478052192257 \tabularnewline
158 & 13 & 11.7352703528329 & 1.26472964716712 \tabularnewline
159 & 15 & 14.0718894555309 & 0.928110544469072 \tabularnewline
160 & 16 & 15.1535488228282 & 0.846451177171778 \tabularnewline
161 & 12 & 12.4600970118853 & -0.460097011885285 \tabularnewline
162 & 13 & 12.8638174571642 & 0.13618254283576 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186208&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]13[/C][C]16.150916998649[/C][C]-3.150916998649[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.8899034640121[/C][C]0.110096535987873[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.2644975931104[/C][C]2.73550240688962[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.7367129710903[/C][C]3.26328702890968[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.3768545646713[/C][C]-1.37685456467127[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8669393709709[/C][C]-1.86693937097088[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.3175368830456[/C][C]3.68246311695439[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.5947273127324[/C][C]-1.59472731273243[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.8902103643229[/C][C]-1.89021036432286[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.433593769721[/C][C]2.56640623027899[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.2576414506566[/C][C]0.742358549343408[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.2929125881352[/C][C]-0.292912588135208[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.4124450601738[/C][C]0.587554939826231[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3481365206986[/C][C]0.651863479301399[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.3388824771519[/C][C]-0.33888247715188[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0845925219422[/C][C]-0.0845925219421735[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.5014661254714[/C][C]0.498533874528616[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1170339542758[/C][C]3.88296604572419[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.5432675758884[/C][C]2.45673242411161[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3097868315932[/C][C]0.690213168406757[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3398689753024[/C][C]0.660131024697583[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.841367158263[/C][C]1.15863284173696[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4669873921155[/C][C]2.53301260788452[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8446910877218[/C][C]1.15530891227818[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.7361565115115[/C][C]2.26384348848852[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.7910863165385[/C][C]0.208913683461481[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.7880228507631[/C][C]1.21197714923692[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.1333263081163[/C][C]-1.13332630811625[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.2894010729498[/C][C]0.710598927050188[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1245697444993[/C][C]-0.12456974449931[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.6277955645353[/C][C]-0.627795564535293[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.2048725818701[/C][C]-0.204872581870055[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1316796562262[/C][C]-1.13167965622623[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5483314879517[/C][C]0.4516685120483[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.4847817535825[/C][C]-1.48478175358245[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.9620968603426[/C][C]-5.96209686034263[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.7442123645819[/C][C]-0.744212364581872[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7799549817372[/C][C]-1.77995498173716[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.2156169538799[/C][C]1.78438304612006[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.5991397573735[/C][C]1.40086024262654[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.0629777061611[/C][C]0.937022293838894[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.4636657395919[/C][C]-1.46366573959191[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]18.0186749820736[/C][C]1.9813250179264[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2059802866519[/C][C]-0.205980286651874[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9242366895294[/C][C]-0.924236689529389[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.5151824070446[/C][C]-4.51518240704455[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.6361403875446[/C][C]-2.63614038754464[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9469710887117[/C][C]0.0530289112882635[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.0146755482815[/C][C]0.985324451718502[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.9908216679192[/C][C]-1.99082166791921[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.5086025303155[/C][C]-0.50860253031551[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.0770557990605[/C][C]-0.0770557990605449[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.5948273067971[/C][C]-2.59482730679712[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.6212641751819[/C][C]0.378735824818148[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.4020214288624[/C][C]-2.40202142886242[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6532068704596[/C][C]1.34679312954045[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.092162616709[/C][C]-0.0921626167089528[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.1742502277655[/C][C]0.825749772234471[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.2462871994941[/C][C]-0.246287199494094[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1291835253485[/C][C]1.87081647465155[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4001249583556[/C][C]0.59987504164436[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.8689945010708[/C][C]0.131005498929184[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.3488751782561[/C][C]-0.34887517825606[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.3720119146043[/C][C]-0.372011914604327[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.5010584219726[/C][C]0.498941578027352[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.8929295628843[/C][C]1.1070704371157[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1840602156753[/C][C]1.81593978432468[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5028365980892[/C][C]3.49716340191084[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8756571761026[/C][C]-3.87565717610264[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4167030804714[/C][C]0.583296919528627[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.6161781771936[/C][C]-3.61617817719357[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3522238955862[/C][C]-0.352223895586202[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.4374905339296[/C][C]1.56250946607044[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.0660955153538[/C][C]0.933904484646224[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6640429509149[/C][C]0.335957049085067[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8495621874602[/C][C]3.15043781253978[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3968646311792[/C][C]-0.39686463117921[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.5420659056639[/C][C]1.45793409433614[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8395752984319[/C][C]-1.83957529843191[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.1240529753367[/C][C]-0.124052975336746[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4538444013414[/C][C]0.546155598658564[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.9423880160535[/C][C]4.05761198394651[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.803949994519[/C][C]0.196050005481004[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9645678601579[/C][C]-0.964567860157861[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7544393212394[/C][C]0.245560678760587[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2625175191865[/C][C]1.73748248081351[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.1637835637184[/C][C]-0.163783563718368[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3483004218611[/C][C]0.651699578138907[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.6209287592734[/C][C]1.37907124072662[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.1638533362562[/C][C]0.836146663743833[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.5509318229918[/C][C]-1.55093182299181[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.0645478052192[/C][C]-0.0645478052192257[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4091152811259[/C][C]0.590884718874072[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.1621668833077[/C][C]-0.162166883307746[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.0876125246316[/C][C]-2.08761252463161[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.1192740855361[/C][C]0.880725914463931[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8977929691763[/C][C]0.102207030823709[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1029394853059[/C][C]1.89706051469406[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.0912383356797[/C][C]-0.0912383356797046[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.3206046310014[/C][C]-0.320604631001352[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.3201255439089[/C][C]-1.32012554390893[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8122688134736[/C][C]1.1877311865264[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.406290224619[/C][C]2.593709775381[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.7144411845103[/C][C]0.285558815489725[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.4524879514835[/C][C]1.54751204851653[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3489208631333[/C][C]-2.34892086313326[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.113242892574[/C][C]0.886757107425952[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.9099847367087[/C][C]0.0900152632913011[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.6227956253506[/C][C]1.37720437464936[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.3791231716546[/C][C]-0.379123171654591[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.9286252998165[/C][C]1.07137470018347[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8193963438132[/C][C]0.180603656186811[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5603063898977[/C][C]2.4396936101023[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.4394046067937[/C][C]-1.43940460679367[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.979754857313[/C][C]-2.97975485731302[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.7048687182229[/C][C]1.29513128177712[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.5772672795119[/C][C]-1.57726727951195[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0636808795613[/C][C]0.936319120438693[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.0098544225403[/C][C]-2.00985442254026[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6745579631027[/C][C]0.325442036897306[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3550387832414[/C][C]-1.35503878324143[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.6849798174253[/C][C]0.315020182574723[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.9724660663618[/C][C]-2.97246606636182[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.1852374731097[/C][C]-1.18523747310974[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.1102069292348[/C][C]-1.11020692923478[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.8049487595703[/C][C]-0.804948759570269[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.5213493505755[/C][C]-0.521349350575512[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.3018949005228[/C][C]0.698105099477149[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.0718894555309[/C][C]0.928110544469072[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.0473171263583[/C][C]-3.04731712635832[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.1825722954611[/C][C]1.81742770453889[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3398746047591[/C][C]-3.33987460475915[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.1300166816034[/C][C]1.86998331839655[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]14.0328295726609[/C][C]-2.03282957266093[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.7840726082903[/C][C]-1.78407260829032[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.3411242159586[/C][C]-0.34112421595857[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.365994468716[/C][C]0.634005531283958[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.732633363254[/C][C]0.267366636745974[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.3713431817359[/C][C]-2.37134318173592[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.5304928468453[/C][C]-1.53049284684526[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.3946772462758[/C][C]-5.39467724627584[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.2481175779885[/C][C]2.75188242201147[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.3579379032811[/C][C]1.64206209671887[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.6373345498412[/C][C]0.362665450158795[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.9073488073001[/C][C]1.09265119269992[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.2746401692781[/C][C]-4.27464016927815[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]16.079975655534[/C][C]1.92002434446596[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.3691463635444[/C][C]-2.36914636354438[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.2633745660075[/C][C]0.73662543399251[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.1426174613516[/C][C]-0.142617461351575[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.226056379579[/C][C]-3.22605637957902[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.4261897522707[/C][C]-1.42618975227075[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.0914862874014[/C][C]1.90851371259861[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.0657176055551[/C][C]3.93428239444488[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.5443224416931[/C][C]1.45567755830692[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.7217994006389[/C][C]-2.72179940063894[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]17.0645478052192[/C][C]-0.0645478052192257[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7352703528329[/C][C]1.26472964716712[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.0718894555309[/C][C]0.928110544469072[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.1535488228282[/C][C]0.846451177171778[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.4600970118853[/C][C]-0.460097011885285[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.8638174571642[/C][C]0.13618254283576[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186208&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186208&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.150916998649-3.150916998649
21615.88990346401210.110096535987873
31916.26449759311042.73550240688962
41511.73671297109033.26328702890968
51415.3768545646713-1.37685456467127
61314.8669393709709-1.86693937097088
71915.31753688304563.68246311695439
81516.5947273127324-1.59472731273243
91415.8902103643229-1.89021036432286
101512.4335937697212.56640623027899
111615.25764145065660.742358549343408
121616.2929125881352-0.292912588135208
131615.41244506017380.587554939826231
141615.34813652069860.651863479301399
151717.3388824771519-0.33888247715188
161515.0845925219422-0.0845925219421735
171514.50146612547140.498533874528616
182016.11703395427583.88296604572419
191815.54326757588842.45673242411161
201615.30978683159320.690213168406757
211615.33986897530240.660131024697583
221614.8413671582631.15863284173696
231916.46698739211552.53301260788452
241614.84469108772181.15530891227818
251714.73615651151152.26384348848852
261716.79108631653850.208913683461481
271614.78802285076311.21197714923692
281516.1333263081163-1.13332630811625
291615.28940107294980.710598927050188
301414.1245697444993-0.12456974449931
311515.6277955645353-0.627795564535293
321212.2048725818701-0.204872581870055
331415.1316796562262-1.13167965622623
341615.54833148795170.4516685120483
351415.4847817535825-1.48478175358245
36712.9620968603426-5.96209686034263
371010.7442123645819-0.744212364581872
381415.7799549817372-1.77995498173716
391614.21561695387991.78438304612006
401614.59913975737351.40086024262654
411615.06297770616110.937022293838894
421415.4636657395919-1.46366573959191
432018.01867498207361.9813250179264
441414.2059802866519-0.205980286651874
451414.9242366895294-0.924236689529389
461115.5151824070446-4.51518240704455
471416.6361403875446-2.63614038754464
481514.94697108871170.0530289112882635
491615.01467554828150.985324451718502
501415.9908216679192-1.99082166791921
511616.5086025303155-0.50860253031551
521414.0770557990605-0.0770557990605449
531214.5948273067971-2.59482730679712
541615.62126417518190.378735824818148
55911.4020214288624-2.40202142886242
561412.65320687045961.34679312954045
571616.092162616709-0.0921626167089528
581615.17425022776550.825749772234471
591515.2462871994941-0.246287199494094
601614.12918352534851.87081647465155
611211.40012495835560.59987504164436
621615.86899450107080.131005498929184
631616.3488751782561-0.34887517825606
641414.3720119146043-0.372011914604327
651615.50105842197260.498941578027352
661715.89292956288431.1070704371157
671816.18406021567531.81593978432468
681814.50283659808923.49716340191084
691215.8756571761026-3.87565717610264
701615.41670308047140.583296919528627
711013.6161781771936-3.61617817719357
721414.3522238955862-0.352223895586202
731816.43749053392961.56250946607044
741817.06609551535380.933904484646224
751615.66404295091490.335957049085067
761713.84956218746023.15043781253978
771616.3968646311792-0.39686463117921
781614.54206590566391.45793409433614
791314.8395752984319-1.83957529843191
801616.1240529753367-0.124052975336746
811615.45384440134140.546155598658564
822015.94238801605354.05761198394651
831615.8039499945190.196050005481004
841515.9645678601579-0.964567860157861
851514.75443932123940.245560678760587
861614.26251751918651.73748248081351
871414.1637835637184-0.163783563718368
881615.34830042186110.651699578138907
891614.62092875927341.37907124072662
901514.16385333625620.836146663743833
911213.5509318229918-1.55093182299181
921717.0645478052192-0.0645478052192257
931615.40911528112590.590884718874072
941515.1621668833077-0.162166883307746
951315.0876125246316-2.08761252463161
961615.11927408553610.880725914463931
971615.89779296917630.102207030823709
981614.10293948530591.89706051469406
991616.0912383356797-0.0912383356797046
1001414.3206046310014-0.320604631001352
1011617.3201255439089-1.32012554390893
1021614.81226881347361.1877311865264
1032017.4062902246192.593709775381
1041514.71444118451030.285558815489725
1051614.45248795148351.54751204851653
1061315.3489208631333-2.34892086313326
1071716.1132428925740.886757107425952
1081615.90998473670870.0900152632913011
1091614.62279562535061.37720437464936
1101212.3791231716546-0.379123171654591
1111614.92862529981651.07137470018347
1121615.81939634381320.180603656186811
1131714.56030638989772.4396936101023
1141314.4394046067937-1.43940460679367
1151214.979754857313-2.97975485731302
1161816.70486871822291.29513128177712
1171415.5772672795119-1.57726727951195
1181413.06368087956130.936319120438693
1191315.0098544225403-2.00985442254026
1201615.67455796310270.325442036897306
1211314.3550387832414-1.35503878324143
1221615.68497981742530.315020182574723
1231315.9724660663618-2.97246606636182
1241617.1852374731097-1.18523747310974
1251516.1102069292348-1.11020692923478
1261616.8049487595703-0.804948759570269
1271515.5213493505755-0.521349350575512
1281716.30189490052280.698105099477149
1291514.07188945553090.928110544469072
1301215.0473171263583-3.04731712635832
1311614.18257229546111.81742770453889
1321013.3398746047591-3.33987460475915
1331614.13001668160341.86998331839655
1341214.0328295726609-2.03282957266093
1351415.7840726082903-1.78407260829032
1361515.3411242159586-0.34112421595857
1371312.3659944687160.634005531283958
1381514.7326333632540.267366636745974
1391113.3713431817359-2.37134318173592
1401213.5304928468453-1.53049284684526
141813.3946772462758-5.39467724627584
1421613.24811757798852.75188242201147
1431513.35793790328111.64206209671887
1441716.63733454984120.362665450158795
1451614.90734880730011.09265119269992
1461014.2746401692781-4.27464016927815
1471816.0799756555341.92002434446596
1481315.3691463635444-2.36914636354438
1491615.26337456600750.73662543399251
1501313.1426174613516-0.142617461351575
1511013.226056379579-3.22605637957902
1521516.4261897522707-1.42618975227075
1531614.09148628740141.90851371259861
1541612.06571760555513.93428239444488
1551412.54432244169311.45567755830692
1561012.7217994006389-2.72179940063894
1571717.0645478052192-0.0645478052192257
1581311.73527035283291.26472964716712
1591514.07188945553090.928110544469072
1601615.15354882282820.846451177171778
1611212.4600970118853-0.460097011885285
1621312.86381745716420.13618254283576







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.445664591661130.891329183322260.55433540833887
120.9025127380293830.1949745239412340.0974872619706172
130.8425689435349140.3148621129301720.157431056465086
140.7851611886626430.4296776226747150.214838811337357
150.7901334959893550.419733008021290.209866504010645
160.7572468894571880.4855062210856240.242753110542812
170.6806729930386670.6386540139226670.319327006961333
180.9166768193853260.1666463612293480.0833231806146739
190.9200165331628990.1599669336742020.0799834668371011
200.8886060233326020.2227879533347960.111393976667398
210.8528634350208650.294273129958270.147136564979135
220.8061516942757440.3876966114485120.193848305724256
230.8240390991738890.3519218016522220.175960900826111
240.7811118402388770.4377763195222460.218888159761123
250.769759010662360.4604819786752810.23024098933764
260.7158881921499410.5682236157001180.284111807850059
270.6628821184071690.6742357631856620.337117881592831
280.635323973432750.72935205313450.36467602656725
290.5747570203879340.8504859592241330.425242979612066
300.5518424724452450.8963150551095090.448157527554755
310.489273014480460.9785460289609210.51072698551954
320.4675839561028260.9351679122056520.532416043897174
330.4399862598110440.8799725196220870.560013740188956
340.3823058222688320.7646116445376640.617694177731168
350.3616989870642820.7233979741285640.638301012935718
360.8570639579759680.2858720840480630.142936042024032
370.8466991724194540.3066016551610920.153300827580546
380.8423787430988020.3152425138023970.157621256901198
390.8608149016650710.2783701966698580.139185098334929
400.8455879282510440.3088241434979120.154412071748956
410.8202035962536420.3595928074927150.179796403746358
420.8045501881364920.3908996237270160.195449811863508
430.8177988217611340.3644023564777320.182201178238866
440.785356433540090.429287132919820.21464356645991
450.7561872566607590.4876254866784830.243812743339241
460.9108544004382550.1782911991234910.0891455995617453
470.9277587055104760.1444825889790490.0722412944895244
480.9084424583085410.1831150833829190.0915575416914594
490.8918594153180410.2162811693639190.108140584681959
500.8898351640374230.2203296719251540.110164835962577
510.8645444119305560.2709111761388880.135455588069444
520.8351971008016060.3296057983967870.164802899198393
530.8569406819076150.286118636184770.143059318092385
540.8282396082693060.3435207834613870.171760391730694
550.8441058644212980.3117882711574030.155894135578702
560.8247636727876650.3504726544246690.175236327212334
570.7929317589748410.4141364820503180.207068241025159
580.7708071461430310.4583857077139380.229192853856969
590.7323775040114280.5352449919771440.267622495988572
600.7348367402758910.5303265194482180.265163259724109
610.7040243572051620.5919512855896770.295975642794838
620.6696916094197950.660616781160410.330308390580205
630.6348419783643020.7303160432713950.365158021635698
640.589190873607710.821618252784580.41080912639229
650.5472259261494750.905548147701050.452774073850525
660.5108703013433330.9782593973133350.489129698656667
670.4910829143599240.9821658287198470.508917085640076
680.5677928859962170.8644142280075670.432207114003783
690.7571391249075240.4857217501849530.242860875092476
700.7205772826962160.5588454346075680.279422717303784
710.8208058264076610.3583883471846770.179194173592339
720.7888674521145510.4222650957708990.211132547885449
730.7786892813882880.4426214372234240.221310718611712
740.7540154657564880.4919690684870240.245984534243512
750.7165829972476030.5668340055047950.283417002752397
760.7657685621598050.4684628756803890.234231437840195
770.7315059841489540.5369880317020930.268494015851046
780.7123393730623010.5753212538753990.287660626937699
790.7197646174608650.560470765078270.280235382539135
800.6791013295255190.6417973409489610.320898670474481
810.6388166831152890.7223666337694230.361183316884711
820.7987761536125250.402447692774950.201223846387475
830.7645677542779830.4708644914440330.235432245722017
840.7397206413090970.5205587173818060.260279358690903
850.7005088464569640.5989823070860710.299491153543036
860.6886947146749650.622610570650070.311305285325035
870.6470607466049370.7058785067901260.352939253395063
880.6077587478731880.7844825042536240.392241252126812
890.5893507026220530.8212985947558950.410649297377947
900.5536222436101820.8927555127796360.446377756389818
910.5440055937377720.9119888125244560.455994406262228
920.5089845592477680.9820308815044640.491015440752232
930.466198701212690.932397402425380.53380129878731
940.4220690173720620.8441380347441250.577930982627938
950.4399004695225020.8798009390450030.560099530477498
960.4030742045001420.8061484090002830.596925795499858
970.3633763836151490.7267527672302970.636623616384851
980.3586852460847560.7173704921695120.641314753915244
990.3156430379351960.6312860758703920.684356962064804
1000.2765002229577180.5530004459154350.723499777042282
1010.2522098356741040.5044196713482080.747790164325896
1020.2335116345854770.4670232691709550.766488365414523
1030.2833129743814620.5666259487629250.716687025618538
1040.2442688364493060.4885376728986130.755731163550694
1050.2352799971669950.4705599943339910.764720002833005
1060.252800305029460.5056006100589210.74719969497054
1070.2297320650963770.4594641301927530.770267934903623
1080.2063418145372050.4126836290744090.793658185462795
1090.1996949713957930.3993899427915860.800305028604207
1100.1860861696642310.3721723393284630.813913830335769
1110.1646835786200020.3293671572400030.835316421379998
1120.1385935360652830.2771870721305650.861406463934717
1130.1589829368567150.317965873713430.841017063143285
1140.139467532853810.278935065707620.86053246714619
1150.1809129315118570.3618258630237130.819087068488143
1160.1709268064887240.3418536129774480.829073193511276
1170.1487755566540880.2975511133081760.851224443345912
1180.1285654082164690.2571308164329370.871434591783531
1190.1311576653319510.2623153306639030.868842334668049
1200.1205889768628980.2411779537257960.879411023137102
1210.1020961197723780.2041922395447560.897903880227622
1220.08204332051258690.1640866410251740.917956679487413
1230.09184521092891330.1836904218578270.908154789071087
1240.07395729737672510.147914594753450.926042702623275
1250.06017848448796880.1203569689759380.939821515512031
1260.04588175221046320.09176350442092640.954118247789537
1270.03499970634754520.06999941269509030.965000293652455
1280.02903484314103250.0580696862820650.970965156858968
1290.02225442418928940.04450884837857880.977745575810711
1300.03043501042157160.06087002084314330.969564989578428
1310.02648555726779690.05297111453559390.973514442732203
1320.04092252108604130.08184504217208260.959077478913959
1330.045347697406450.09069539481290010.95465230259355
1340.06013881351144080.1202776270228820.939861186488559
1350.04793535102710830.09587070205421660.952064648972892
1360.03413393656211840.06826787312423690.965866063437882
1370.02368708313183330.04737416626366670.976312916868167
1380.01578479952442730.03156959904885460.984215200475573
1390.03159172051243390.06318344102486790.968408279487566
1400.02762746425863050.0552549285172610.97237253574137
1410.6413293062897220.7173413874205550.358670693710278
1420.5777594412579350.8444811174841290.422240558742065
1430.4970477286797370.9940954573594740.502952271320263
1440.4292835158871240.8585670317742480.570716484112876
1450.3425821375170590.6851642750341190.657417862482941
1460.4279572353901650.855914470780330.572042764609835
1470.3512920220659360.7025840441318720.648707977934064
1480.7031326543334260.5937346913331470.296867345666574
1490.6227642383502830.7544715232994350.377235761649717
1500.4770984361006990.9541968722013980.522901563899301
1510.8294970382506520.3410059234986950.170502961749347

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.44566459166113 & 0.89132918332226 & 0.55433540833887 \tabularnewline
12 & 0.902512738029383 & 0.194974523941234 & 0.0974872619706172 \tabularnewline
13 & 0.842568943534914 & 0.314862112930172 & 0.157431056465086 \tabularnewline
14 & 0.785161188662643 & 0.429677622674715 & 0.214838811337357 \tabularnewline
15 & 0.790133495989355 & 0.41973300802129 & 0.209866504010645 \tabularnewline
16 & 0.757246889457188 & 0.485506221085624 & 0.242753110542812 \tabularnewline
17 & 0.680672993038667 & 0.638654013922667 & 0.319327006961333 \tabularnewline
18 & 0.916676819385326 & 0.166646361229348 & 0.0833231806146739 \tabularnewline
19 & 0.920016533162899 & 0.159966933674202 & 0.0799834668371011 \tabularnewline
20 & 0.888606023332602 & 0.222787953334796 & 0.111393976667398 \tabularnewline
21 & 0.852863435020865 & 0.29427312995827 & 0.147136564979135 \tabularnewline
22 & 0.806151694275744 & 0.387696611448512 & 0.193848305724256 \tabularnewline
23 & 0.824039099173889 & 0.351921801652222 & 0.175960900826111 \tabularnewline
24 & 0.781111840238877 & 0.437776319522246 & 0.218888159761123 \tabularnewline
25 & 0.76975901066236 & 0.460481978675281 & 0.23024098933764 \tabularnewline
26 & 0.715888192149941 & 0.568223615700118 & 0.284111807850059 \tabularnewline
27 & 0.662882118407169 & 0.674235763185662 & 0.337117881592831 \tabularnewline
28 & 0.63532397343275 & 0.7293520531345 & 0.36467602656725 \tabularnewline
29 & 0.574757020387934 & 0.850485959224133 & 0.425242979612066 \tabularnewline
30 & 0.551842472445245 & 0.896315055109509 & 0.448157527554755 \tabularnewline
31 & 0.48927301448046 & 0.978546028960921 & 0.51072698551954 \tabularnewline
32 & 0.467583956102826 & 0.935167912205652 & 0.532416043897174 \tabularnewline
33 & 0.439986259811044 & 0.879972519622087 & 0.560013740188956 \tabularnewline
34 & 0.382305822268832 & 0.764611644537664 & 0.617694177731168 \tabularnewline
35 & 0.361698987064282 & 0.723397974128564 & 0.638301012935718 \tabularnewline
36 & 0.857063957975968 & 0.285872084048063 & 0.142936042024032 \tabularnewline
37 & 0.846699172419454 & 0.306601655161092 & 0.153300827580546 \tabularnewline
38 & 0.842378743098802 & 0.315242513802397 & 0.157621256901198 \tabularnewline
39 & 0.860814901665071 & 0.278370196669858 & 0.139185098334929 \tabularnewline
40 & 0.845587928251044 & 0.308824143497912 & 0.154412071748956 \tabularnewline
41 & 0.820203596253642 & 0.359592807492715 & 0.179796403746358 \tabularnewline
42 & 0.804550188136492 & 0.390899623727016 & 0.195449811863508 \tabularnewline
43 & 0.817798821761134 & 0.364402356477732 & 0.182201178238866 \tabularnewline
44 & 0.78535643354009 & 0.42928713291982 & 0.21464356645991 \tabularnewline
45 & 0.756187256660759 & 0.487625486678483 & 0.243812743339241 \tabularnewline
46 & 0.910854400438255 & 0.178291199123491 & 0.0891455995617453 \tabularnewline
47 & 0.927758705510476 & 0.144482588979049 & 0.0722412944895244 \tabularnewline
48 & 0.908442458308541 & 0.183115083382919 & 0.0915575416914594 \tabularnewline
49 & 0.891859415318041 & 0.216281169363919 & 0.108140584681959 \tabularnewline
50 & 0.889835164037423 & 0.220329671925154 & 0.110164835962577 \tabularnewline
51 & 0.864544411930556 & 0.270911176138888 & 0.135455588069444 \tabularnewline
52 & 0.835197100801606 & 0.329605798396787 & 0.164802899198393 \tabularnewline
53 & 0.856940681907615 & 0.28611863618477 & 0.143059318092385 \tabularnewline
54 & 0.828239608269306 & 0.343520783461387 & 0.171760391730694 \tabularnewline
55 & 0.844105864421298 & 0.311788271157403 & 0.155894135578702 \tabularnewline
56 & 0.824763672787665 & 0.350472654424669 & 0.175236327212334 \tabularnewline
57 & 0.792931758974841 & 0.414136482050318 & 0.207068241025159 \tabularnewline
58 & 0.770807146143031 & 0.458385707713938 & 0.229192853856969 \tabularnewline
59 & 0.732377504011428 & 0.535244991977144 & 0.267622495988572 \tabularnewline
60 & 0.734836740275891 & 0.530326519448218 & 0.265163259724109 \tabularnewline
61 & 0.704024357205162 & 0.591951285589677 & 0.295975642794838 \tabularnewline
62 & 0.669691609419795 & 0.66061678116041 & 0.330308390580205 \tabularnewline
63 & 0.634841978364302 & 0.730316043271395 & 0.365158021635698 \tabularnewline
64 & 0.58919087360771 & 0.82161825278458 & 0.41080912639229 \tabularnewline
65 & 0.547225926149475 & 0.90554814770105 & 0.452774073850525 \tabularnewline
66 & 0.510870301343333 & 0.978259397313335 & 0.489129698656667 \tabularnewline
67 & 0.491082914359924 & 0.982165828719847 & 0.508917085640076 \tabularnewline
68 & 0.567792885996217 & 0.864414228007567 & 0.432207114003783 \tabularnewline
69 & 0.757139124907524 & 0.485721750184953 & 0.242860875092476 \tabularnewline
70 & 0.720577282696216 & 0.558845434607568 & 0.279422717303784 \tabularnewline
71 & 0.820805826407661 & 0.358388347184677 & 0.179194173592339 \tabularnewline
72 & 0.788867452114551 & 0.422265095770899 & 0.211132547885449 \tabularnewline
73 & 0.778689281388288 & 0.442621437223424 & 0.221310718611712 \tabularnewline
74 & 0.754015465756488 & 0.491969068487024 & 0.245984534243512 \tabularnewline
75 & 0.716582997247603 & 0.566834005504795 & 0.283417002752397 \tabularnewline
76 & 0.765768562159805 & 0.468462875680389 & 0.234231437840195 \tabularnewline
77 & 0.731505984148954 & 0.536988031702093 & 0.268494015851046 \tabularnewline
78 & 0.712339373062301 & 0.575321253875399 & 0.287660626937699 \tabularnewline
79 & 0.719764617460865 & 0.56047076507827 & 0.280235382539135 \tabularnewline
80 & 0.679101329525519 & 0.641797340948961 & 0.320898670474481 \tabularnewline
81 & 0.638816683115289 & 0.722366633769423 & 0.361183316884711 \tabularnewline
82 & 0.798776153612525 & 0.40244769277495 & 0.201223846387475 \tabularnewline
83 & 0.764567754277983 & 0.470864491444033 & 0.235432245722017 \tabularnewline
84 & 0.739720641309097 & 0.520558717381806 & 0.260279358690903 \tabularnewline
85 & 0.700508846456964 & 0.598982307086071 & 0.299491153543036 \tabularnewline
86 & 0.688694714674965 & 0.62261057065007 & 0.311305285325035 \tabularnewline
87 & 0.647060746604937 & 0.705878506790126 & 0.352939253395063 \tabularnewline
88 & 0.607758747873188 & 0.784482504253624 & 0.392241252126812 \tabularnewline
89 & 0.589350702622053 & 0.821298594755895 & 0.410649297377947 \tabularnewline
90 & 0.553622243610182 & 0.892755512779636 & 0.446377756389818 \tabularnewline
91 & 0.544005593737772 & 0.911988812524456 & 0.455994406262228 \tabularnewline
92 & 0.508984559247768 & 0.982030881504464 & 0.491015440752232 \tabularnewline
93 & 0.46619870121269 & 0.93239740242538 & 0.53380129878731 \tabularnewline
94 & 0.422069017372062 & 0.844138034744125 & 0.577930982627938 \tabularnewline
95 & 0.439900469522502 & 0.879800939045003 & 0.560099530477498 \tabularnewline
96 & 0.403074204500142 & 0.806148409000283 & 0.596925795499858 \tabularnewline
97 & 0.363376383615149 & 0.726752767230297 & 0.636623616384851 \tabularnewline
98 & 0.358685246084756 & 0.717370492169512 & 0.641314753915244 \tabularnewline
99 & 0.315643037935196 & 0.631286075870392 & 0.684356962064804 \tabularnewline
100 & 0.276500222957718 & 0.553000445915435 & 0.723499777042282 \tabularnewline
101 & 0.252209835674104 & 0.504419671348208 & 0.747790164325896 \tabularnewline
102 & 0.233511634585477 & 0.467023269170955 & 0.766488365414523 \tabularnewline
103 & 0.283312974381462 & 0.566625948762925 & 0.716687025618538 \tabularnewline
104 & 0.244268836449306 & 0.488537672898613 & 0.755731163550694 \tabularnewline
105 & 0.235279997166995 & 0.470559994333991 & 0.764720002833005 \tabularnewline
106 & 0.25280030502946 & 0.505600610058921 & 0.74719969497054 \tabularnewline
107 & 0.229732065096377 & 0.459464130192753 & 0.770267934903623 \tabularnewline
108 & 0.206341814537205 & 0.412683629074409 & 0.793658185462795 \tabularnewline
109 & 0.199694971395793 & 0.399389942791586 & 0.800305028604207 \tabularnewline
110 & 0.186086169664231 & 0.372172339328463 & 0.813913830335769 \tabularnewline
111 & 0.164683578620002 & 0.329367157240003 & 0.835316421379998 \tabularnewline
112 & 0.138593536065283 & 0.277187072130565 & 0.861406463934717 \tabularnewline
113 & 0.158982936856715 & 0.31796587371343 & 0.841017063143285 \tabularnewline
114 & 0.13946753285381 & 0.27893506570762 & 0.86053246714619 \tabularnewline
115 & 0.180912931511857 & 0.361825863023713 & 0.819087068488143 \tabularnewline
116 & 0.170926806488724 & 0.341853612977448 & 0.829073193511276 \tabularnewline
117 & 0.148775556654088 & 0.297551113308176 & 0.851224443345912 \tabularnewline
118 & 0.128565408216469 & 0.257130816432937 & 0.871434591783531 \tabularnewline
119 & 0.131157665331951 & 0.262315330663903 & 0.868842334668049 \tabularnewline
120 & 0.120588976862898 & 0.241177953725796 & 0.879411023137102 \tabularnewline
121 & 0.102096119772378 & 0.204192239544756 & 0.897903880227622 \tabularnewline
122 & 0.0820433205125869 & 0.164086641025174 & 0.917956679487413 \tabularnewline
123 & 0.0918452109289133 & 0.183690421857827 & 0.908154789071087 \tabularnewline
124 & 0.0739572973767251 & 0.14791459475345 & 0.926042702623275 \tabularnewline
125 & 0.0601784844879688 & 0.120356968975938 & 0.939821515512031 \tabularnewline
126 & 0.0458817522104632 & 0.0917635044209264 & 0.954118247789537 \tabularnewline
127 & 0.0349997063475452 & 0.0699994126950903 & 0.965000293652455 \tabularnewline
128 & 0.0290348431410325 & 0.058069686282065 & 0.970965156858968 \tabularnewline
129 & 0.0222544241892894 & 0.0445088483785788 & 0.977745575810711 \tabularnewline
130 & 0.0304350104215716 & 0.0608700208431433 & 0.969564989578428 \tabularnewline
131 & 0.0264855572677969 & 0.0529711145355939 & 0.973514442732203 \tabularnewline
132 & 0.0409225210860413 & 0.0818450421720826 & 0.959077478913959 \tabularnewline
133 & 0.04534769740645 & 0.0906953948129001 & 0.95465230259355 \tabularnewline
134 & 0.0601388135114408 & 0.120277627022882 & 0.939861186488559 \tabularnewline
135 & 0.0479353510271083 & 0.0958707020542166 & 0.952064648972892 \tabularnewline
136 & 0.0341339365621184 & 0.0682678731242369 & 0.965866063437882 \tabularnewline
137 & 0.0236870831318333 & 0.0473741662636667 & 0.976312916868167 \tabularnewline
138 & 0.0157847995244273 & 0.0315695990488546 & 0.984215200475573 \tabularnewline
139 & 0.0315917205124339 & 0.0631834410248679 & 0.968408279487566 \tabularnewline
140 & 0.0276274642586305 & 0.055254928517261 & 0.97237253574137 \tabularnewline
141 & 0.641329306289722 & 0.717341387420555 & 0.358670693710278 \tabularnewline
142 & 0.577759441257935 & 0.844481117484129 & 0.422240558742065 \tabularnewline
143 & 0.497047728679737 & 0.994095457359474 & 0.502952271320263 \tabularnewline
144 & 0.429283515887124 & 0.858567031774248 & 0.570716484112876 \tabularnewline
145 & 0.342582137517059 & 0.685164275034119 & 0.657417862482941 \tabularnewline
146 & 0.427957235390165 & 0.85591447078033 & 0.572042764609835 \tabularnewline
147 & 0.351292022065936 & 0.702584044131872 & 0.648707977934064 \tabularnewline
148 & 0.703132654333426 & 0.593734691333147 & 0.296867345666574 \tabularnewline
149 & 0.622764238350283 & 0.754471523299435 & 0.377235761649717 \tabularnewline
150 & 0.477098436100699 & 0.954196872201398 & 0.522901563899301 \tabularnewline
151 & 0.829497038250652 & 0.341005923498695 & 0.170502961749347 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186208&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]11[/C][C]0.44566459166113[/C][C]0.89132918332226[/C][C]0.55433540833887[/C][/ROW]
[ROW][C]12[/C][C]0.902512738029383[/C][C]0.194974523941234[/C][C]0.0974872619706172[/C][/ROW]
[ROW][C]13[/C][C]0.842568943534914[/C][C]0.314862112930172[/C][C]0.157431056465086[/C][/ROW]
[ROW][C]14[/C][C]0.785161188662643[/C][C]0.429677622674715[/C][C]0.214838811337357[/C][/ROW]
[ROW][C]15[/C][C]0.790133495989355[/C][C]0.41973300802129[/C][C]0.209866504010645[/C][/ROW]
[ROW][C]16[/C][C]0.757246889457188[/C][C]0.485506221085624[/C][C]0.242753110542812[/C][/ROW]
[ROW][C]17[/C][C]0.680672993038667[/C][C]0.638654013922667[/C][C]0.319327006961333[/C][/ROW]
[ROW][C]18[/C][C]0.916676819385326[/C][C]0.166646361229348[/C][C]0.0833231806146739[/C][/ROW]
[ROW][C]19[/C][C]0.920016533162899[/C][C]0.159966933674202[/C][C]0.0799834668371011[/C][/ROW]
[ROW][C]20[/C][C]0.888606023332602[/C][C]0.222787953334796[/C][C]0.111393976667398[/C][/ROW]
[ROW][C]21[/C][C]0.852863435020865[/C][C]0.29427312995827[/C][C]0.147136564979135[/C][/ROW]
[ROW][C]22[/C][C]0.806151694275744[/C][C]0.387696611448512[/C][C]0.193848305724256[/C][/ROW]
[ROW][C]23[/C][C]0.824039099173889[/C][C]0.351921801652222[/C][C]0.175960900826111[/C][/ROW]
[ROW][C]24[/C][C]0.781111840238877[/C][C]0.437776319522246[/C][C]0.218888159761123[/C][/ROW]
[ROW][C]25[/C][C]0.76975901066236[/C][C]0.460481978675281[/C][C]0.23024098933764[/C][/ROW]
[ROW][C]26[/C][C]0.715888192149941[/C][C]0.568223615700118[/C][C]0.284111807850059[/C][/ROW]
[ROW][C]27[/C][C]0.662882118407169[/C][C]0.674235763185662[/C][C]0.337117881592831[/C][/ROW]
[ROW][C]28[/C][C]0.63532397343275[/C][C]0.7293520531345[/C][C]0.36467602656725[/C][/ROW]
[ROW][C]29[/C][C]0.574757020387934[/C][C]0.850485959224133[/C][C]0.425242979612066[/C][/ROW]
[ROW][C]30[/C][C]0.551842472445245[/C][C]0.896315055109509[/C][C]0.448157527554755[/C][/ROW]
[ROW][C]31[/C][C]0.48927301448046[/C][C]0.978546028960921[/C][C]0.51072698551954[/C][/ROW]
[ROW][C]32[/C][C]0.467583956102826[/C][C]0.935167912205652[/C][C]0.532416043897174[/C][/ROW]
[ROW][C]33[/C][C]0.439986259811044[/C][C]0.879972519622087[/C][C]0.560013740188956[/C][/ROW]
[ROW][C]34[/C][C]0.382305822268832[/C][C]0.764611644537664[/C][C]0.617694177731168[/C][/ROW]
[ROW][C]35[/C][C]0.361698987064282[/C][C]0.723397974128564[/C][C]0.638301012935718[/C][/ROW]
[ROW][C]36[/C][C]0.857063957975968[/C][C]0.285872084048063[/C][C]0.142936042024032[/C][/ROW]
[ROW][C]37[/C][C]0.846699172419454[/C][C]0.306601655161092[/C][C]0.153300827580546[/C][/ROW]
[ROW][C]38[/C][C]0.842378743098802[/C][C]0.315242513802397[/C][C]0.157621256901198[/C][/ROW]
[ROW][C]39[/C][C]0.860814901665071[/C][C]0.278370196669858[/C][C]0.139185098334929[/C][/ROW]
[ROW][C]40[/C][C]0.845587928251044[/C][C]0.308824143497912[/C][C]0.154412071748956[/C][/ROW]
[ROW][C]41[/C][C]0.820203596253642[/C][C]0.359592807492715[/C][C]0.179796403746358[/C][/ROW]
[ROW][C]42[/C][C]0.804550188136492[/C][C]0.390899623727016[/C][C]0.195449811863508[/C][/ROW]
[ROW][C]43[/C][C]0.817798821761134[/C][C]0.364402356477732[/C][C]0.182201178238866[/C][/ROW]
[ROW][C]44[/C][C]0.78535643354009[/C][C]0.42928713291982[/C][C]0.21464356645991[/C][/ROW]
[ROW][C]45[/C][C]0.756187256660759[/C][C]0.487625486678483[/C][C]0.243812743339241[/C][/ROW]
[ROW][C]46[/C][C]0.910854400438255[/C][C]0.178291199123491[/C][C]0.0891455995617453[/C][/ROW]
[ROW][C]47[/C][C]0.927758705510476[/C][C]0.144482588979049[/C][C]0.0722412944895244[/C][/ROW]
[ROW][C]48[/C][C]0.908442458308541[/C][C]0.183115083382919[/C][C]0.0915575416914594[/C][/ROW]
[ROW][C]49[/C][C]0.891859415318041[/C][C]0.216281169363919[/C][C]0.108140584681959[/C][/ROW]
[ROW][C]50[/C][C]0.889835164037423[/C][C]0.220329671925154[/C][C]0.110164835962577[/C][/ROW]
[ROW][C]51[/C][C]0.864544411930556[/C][C]0.270911176138888[/C][C]0.135455588069444[/C][/ROW]
[ROW][C]52[/C][C]0.835197100801606[/C][C]0.329605798396787[/C][C]0.164802899198393[/C][/ROW]
[ROW][C]53[/C][C]0.856940681907615[/C][C]0.28611863618477[/C][C]0.143059318092385[/C][/ROW]
[ROW][C]54[/C][C]0.828239608269306[/C][C]0.343520783461387[/C][C]0.171760391730694[/C][/ROW]
[ROW][C]55[/C][C]0.844105864421298[/C][C]0.311788271157403[/C][C]0.155894135578702[/C][/ROW]
[ROW][C]56[/C][C]0.824763672787665[/C][C]0.350472654424669[/C][C]0.175236327212334[/C][/ROW]
[ROW][C]57[/C][C]0.792931758974841[/C][C]0.414136482050318[/C][C]0.207068241025159[/C][/ROW]
[ROW][C]58[/C][C]0.770807146143031[/C][C]0.458385707713938[/C][C]0.229192853856969[/C][/ROW]
[ROW][C]59[/C][C]0.732377504011428[/C][C]0.535244991977144[/C][C]0.267622495988572[/C][/ROW]
[ROW][C]60[/C][C]0.734836740275891[/C][C]0.530326519448218[/C][C]0.265163259724109[/C][/ROW]
[ROW][C]61[/C][C]0.704024357205162[/C][C]0.591951285589677[/C][C]0.295975642794838[/C][/ROW]
[ROW][C]62[/C][C]0.669691609419795[/C][C]0.66061678116041[/C][C]0.330308390580205[/C][/ROW]
[ROW][C]63[/C][C]0.634841978364302[/C][C]0.730316043271395[/C][C]0.365158021635698[/C][/ROW]
[ROW][C]64[/C][C]0.58919087360771[/C][C]0.82161825278458[/C][C]0.41080912639229[/C][/ROW]
[ROW][C]65[/C][C]0.547225926149475[/C][C]0.90554814770105[/C][C]0.452774073850525[/C][/ROW]
[ROW][C]66[/C][C]0.510870301343333[/C][C]0.978259397313335[/C][C]0.489129698656667[/C][/ROW]
[ROW][C]67[/C][C]0.491082914359924[/C][C]0.982165828719847[/C][C]0.508917085640076[/C][/ROW]
[ROW][C]68[/C][C]0.567792885996217[/C][C]0.864414228007567[/C][C]0.432207114003783[/C][/ROW]
[ROW][C]69[/C][C]0.757139124907524[/C][C]0.485721750184953[/C][C]0.242860875092476[/C][/ROW]
[ROW][C]70[/C][C]0.720577282696216[/C][C]0.558845434607568[/C][C]0.279422717303784[/C][/ROW]
[ROW][C]71[/C][C]0.820805826407661[/C][C]0.358388347184677[/C][C]0.179194173592339[/C][/ROW]
[ROW][C]72[/C][C]0.788867452114551[/C][C]0.422265095770899[/C][C]0.211132547885449[/C][/ROW]
[ROW][C]73[/C][C]0.778689281388288[/C][C]0.442621437223424[/C][C]0.221310718611712[/C][/ROW]
[ROW][C]74[/C][C]0.754015465756488[/C][C]0.491969068487024[/C][C]0.245984534243512[/C][/ROW]
[ROW][C]75[/C][C]0.716582997247603[/C][C]0.566834005504795[/C][C]0.283417002752397[/C][/ROW]
[ROW][C]76[/C][C]0.765768562159805[/C][C]0.468462875680389[/C][C]0.234231437840195[/C][/ROW]
[ROW][C]77[/C][C]0.731505984148954[/C][C]0.536988031702093[/C][C]0.268494015851046[/C][/ROW]
[ROW][C]78[/C][C]0.712339373062301[/C][C]0.575321253875399[/C][C]0.287660626937699[/C][/ROW]
[ROW][C]79[/C][C]0.719764617460865[/C][C]0.56047076507827[/C][C]0.280235382539135[/C][/ROW]
[ROW][C]80[/C][C]0.679101329525519[/C][C]0.641797340948961[/C][C]0.320898670474481[/C][/ROW]
[ROW][C]81[/C][C]0.638816683115289[/C][C]0.722366633769423[/C][C]0.361183316884711[/C][/ROW]
[ROW][C]82[/C][C]0.798776153612525[/C][C]0.40244769277495[/C][C]0.201223846387475[/C][/ROW]
[ROW][C]83[/C][C]0.764567754277983[/C][C]0.470864491444033[/C][C]0.235432245722017[/C][/ROW]
[ROW][C]84[/C][C]0.739720641309097[/C][C]0.520558717381806[/C][C]0.260279358690903[/C][/ROW]
[ROW][C]85[/C][C]0.700508846456964[/C][C]0.598982307086071[/C][C]0.299491153543036[/C][/ROW]
[ROW][C]86[/C][C]0.688694714674965[/C][C]0.62261057065007[/C][C]0.311305285325035[/C][/ROW]
[ROW][C]87[/C][C]0.647060746604937[/C][C]0.705878506790126[/C][C]0.352939253395063[/C][/ROW]
[ROW][C]88[/C][C]0.607758747873188[/C][C]0.784482504253624[/C][C]0.392241252126812[/C][/ROW]
[ROW][C]89[/C][C]0.589350702622053[/C][C]0.821298594755895[/C][C]0.410649297377947[/C][/ROW]
[ROW][C]90[/C][C]0.553622243610182[/C][C]0.892755512779636[/C][C]0.446377756389818[/C][/ROW]
[ROW][C]91[/C][C]0.544005593737772[/C][C]0.911988812524456[/C][C]0.455994406262228[/C][/ROW]
[ROW][C]92[/C][C]0.508984559247768[/C][C]0.982030881504464[/C][C]0.491015440752232[/C][/ROW]
[ROW][C]93[/C][C]0.46619870121269[/C][C]0.93239740242538[/C][C]0.53380129878731[/C][/ROW]
[ROW][C]94[/C][C]0.422069017372062[/C][C]0.844138034744125[/C][C]0.577930982627938[/C][/ROW]
[ROW][C]95[/C][C]0.439900469522502[/C][C]0.879800939045003[/C][C]0.560099530477498[/C][/ROW]
[ROW][C]96[/C][C]0.403074204500142[/C][C]0.806148409000283[/C][C]0.596925795499858[/C][/ROW]
[ROW][C]97[/C][C]0.363376383615149[/C][C]0.726752767230297[/C][C]0.636623616384851[/C][/ROW]
[ROW][C]98[/C][C]0.358685246084756[/C][C]0.717370492169512[/C][C]0.641314753915244[/C][/ROW]
[ROW][C]99[/C][C]0.315643037935196[/C][C]0.631286075870392[/C][C]0.684356962064804[/C][/ROW]
[ROW][C]100[/C][C]0.276500222957718[/C][C]0.553000445915435[/C][C]0.723499777042282[/C][/ROW]
[ROW][C]101[/C][C]0.252209835674104[/C][C]0.504419671348208[/C][C]0.747790164325896[/C][/ROW]
[ROW][C]102[/C][C]0.233511634585477[/C][C]0.467023269170955[/C][C]0.766488365414523[/C][/ROW]
[ROW][C]103[/C][C]0.283312974381462[/C][C]0.566625948762925[/C][C]0.716687025618538[/C][/ROW]
[ROW][C]104[/C][C]0.244268836449306[/C][C]0.488537672898613[/C][C]0.755731163550694[/C][/ROW]
[ROW][C]105[/C][C]0.235279997166995[/C][C]0.470559994333991[/C][C]0.764720002833005[/C][/ROW]
[ROW][C]106[/C][C]0.25280030502946[/C][C]0.505600610058921[/C][C]0.74719969497054[/C][/ROW]
[ROW][C]107[/C][C]0.229732065096377[/C][C]0.459464130192753[/C][C]0.770267934903623[/C][/ROW]
[ROW][C]108[/C][C]0.206341814537205[/C][C]0.412683629074409[/C][C]0.793658185462795[/C][/ROW]
[ROW][C]109[/C][C]0.199694971395793[/C][C]0.399389942791586[/C][C]0.800305028604207[/C][/ROW]
[ROW][C]110[/C][C]0.186086169664231[/C][C]0.372172339328463[/C][C]0.813913830335769[/C][/ROW]
[ROW][C]111[/C][C]0.164683578620002[/C][C]0.329367157240003[/C][C]0.835316421379998[/C][/ROW]
[ROW][C]112[/C][C]0.138593536065283[/C][C]0.277187072130565[/C][C]0.861406463934717[/C][/ROW]
[ROW][C]113[/C][C]0.158982936856715[/C][C]0.31796587371343[/C][C]0.841017063143285[/C][/ROW]
[ROW][C]114[/C][C]0.13946753285381[/C][C]0.27893506570762[/C][C]0.86053246714619[/C][/ROW]
[ROW][C]115[/C][C]0.180912931511857[/C][C]0.361825863023713[/C][C]0.819087068488143[/C][/ROW]
[ROW][C]116[/C][C]0.170926806488724[/C][C]0.341853612977448[/C][C]0.829073193511276[/C][/ROW]
[ROW][C]117[/C][C]0.148775556654088[/C][C]0.297551113308176[/C][C]0.851224443345912[/C][/ROW]
[ROW][C]118[/C][C]0.128565408216469[/C][C]0.257130816432937[/C][C]0.871434591783531[/C][/ROW]
[ROW][C]119[/C][C]0.131157665331951[/C][C]0.262315330663903[/C][C]0.868842334668049[/C][/ROW]
[ROW][C]120[/C][C]0.120588976862898[/C][C]0.241177953725796[/C][C]0.879411023137102[/C][/ROW]
[ROW][C]121[/C][C]0.102096119772378[/C][C]0.204192239544756[/C][C]0.897903880227622[/C][/ROW]
[ROW][C]122[/C][C]0.0820433205125869[/C][C]0.164086641025174[/C][C]0.917956679487413[/C][/ROW]
[ROW][C]123[/C][C]0.0918452109289133[/C][C]0.183690421857827[/C][C]0.908154789071087[/C][/ROW]
[ROW][C]124[/C][C]0.0739572973767251[/C][C]0.14791459475345[/C][C]0.926042702623275[/C][/ROW]
[ROW][C]125[/C][C]0.0601784844879688[/C][C]0.120356968975938[/C][C]0.939821515512031[/C][/ROW]
[ROW][C]126[/C][C]0.0458817522104632[/C][C]0.0917635044209264[/C][C]0.954118247789537[/C][/ROW]
[ROW][C]127[/C][C]0.0349997063475452[/C][C]0.0699994126950903[/C][C]0.965000293652455[/C][/ROW]
[ROW][C]128[/C][C]0.0290348431410325[/C][C]0.058069686282065[/C][C]0.970965156858968[/C][/ROW]
[ROW][C]129[/C][C]0.0222544241892894[/C][C]0.0445088483785788[/C][C]0.977745575810711[/C][/ROW]
[ROW][C]130[/C][C]0.0304350104215716[/C][C]0.0608700208431433[/C][C]0.969564989578428[/C][/ROW]
[ROW][C]131[/C][C]0.0264855572677969[/C][C]0.0529711145355939[/C][C]0.973514442732203[/C][/ROW]
[ROW][C]132[/C][C]0.0409225210860413[/C][C]0.0818450421720826[/C][C]0.959077478913959[/C][/ROW]
[ROW][C]133[/C][C]0.04534769740645[/C][C]0.0906953948129001[/C][C]0.95465230259355[/C][/ROW]
[ROW][C]134[/C][C]0.0601388135114408[/C][C]0.120277627022882[/C][C]0.939861186488559[/C][/ROW]
[ROW][C]135[/C][C]0.0479353510271083[/C][C]0.0958707020542166[/C][C]0.952064648972892[/C][/ROW]
[ROW][C]136[/C][C]0.0341339365621184[/C][C]0.0682678731242369[/C][C]0.965866063437882[/C][/ROW]
[ROW][C]137[/C][C]0.0236870831318333[/C][C]0.0473741662636667[/C][C]0.976312916868167[/C][/ROW]
[ROW][C]138[/C][C]0.0157847995244273[/C][C]0.0315695990488546[/C][C]0.984215200475573[/C][/ROW]
[ROW][C]139[/C][C]0.0315917205124339[/C][C]0.0631834410248679[/C][C]0.968408279487566[/C][/ROW]
[ROW][C]140[/C][C]0.0276274642586305[/C][C]0.055254928517261[/C][C]0.97237253574137[/C][/ROW]
[ROW][C]141[/C][C]0.641329306289722[/C][C]0.717341387420555[/C][C]0.358670693710278[/C][/ROW]
[ROW][C]142[/C][C]0.577759441257935[/C][C]0.844481117484129[/C][C]0.422240558742065[/C][/ROW]
[ROW][C]143[/C][C]0.497047728679737[/C][C]0.994095457359474[/C][C]0.502952271320263[/C][/ROW]
[ROW][C]144[/C][C]0.429283515887124[/C][C]0.858567031774248[/C][C]0.570716484112876[/C][/ROW]
[ROW][C]145[/C][C]0.342582137517059[/C][C]0.685164275034119[/C][C]0.657417862482941[/C][/ROW]
[ROW][C]146[/C][C]0.427957235390165[/C][C]0.85591447078033[/C][C]0.572042764609835[/C][/ROW]
[ROW][C]147[/C][C]0.351292022065936[/C][C]0.702584044131872[/C][C]0.648707977934064[/C][/ROW]
[ROW][C]148[/C][C]0.703132654333426[/C][C]0.593734691333147[/C][C]0.296867345666574[/C][/ROW]
[ROW][C]149[/C][C]0.622764238350283[/C][C]0.754471523299435[/C][C]0.377235761649717[/C][/ROW]
[ROW][C]150[/C][C]0.477098436100699[/C][C]0.954196872201398[/C][C]0.522901563899301[/C][/ROW]
[ROW][C]151[/C][C]0.829497038250652[/C][C]0.341005923498695[/C][C]0.170502961749347[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186208&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186208&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.445664591661130.891329183322260.55433540833887
120.9025127380293830.1949745239412340.0974872619706172
130.8425689435349140.3148621129301720.157431056465086
140.7851611886626430.4296776226747150.214838811337357
150.7901334959893550.419733008021290.209866504010645
160.7572468894571880.4855062210856240.242753110542812
170.6806729930386670.6386540139226670.319327006961333
180.9166768193853260.1666463612293480.0833231806146739
190.9200165331628990.1599669336742020.0799834668371011
200.8886060233326020.2227879533347960.111393976667398
210.8528634350208650.294273129958270.147136564979135
220.8061516942757440.3876966114485120.193848305724256
230.8240390991738890.3519218016522220.175960900826111
240.7811118402388770.4377763195222460.218888159761123
250.769759010662360.4604819786752810.23024098933764
260.7158881921499410.5682236157001180.284111807850059
270.6628821184071690.6742357631856620.337117881592831
280.635323973432750.72935205313450.36467602656725
290.5747570203879340.8504859592241330.425242979612066
300.5518424724452450.8963150551095090.448157527554755
310.489273014480460.9785460289609210.51072698551954
320.4675839561028260.9351679122056520.532416043897174
330.4399862598110440.8799725196220870.560013740188956
340.3823058222688320.7646116445376640.617694177731168
350.3616989870642820.7233979741285640.638301012935718
360.8570639579759680.2858720840480630.142936042024032
370.8466991724194540.3066016551610920.153300827580546
380.8423787430988020.3152425138023970.157621256901198
390.8608149016650710.2783701966698580.139185098334929
400.8455879282510440.3088241434979120.154412071748956
410.8202035962536420.3595928074927150.179796403746358
420.8045501881364920.3908996237270160.195449811863508
430.8177988217611340.3644023564777320.182201178238866
440.785356433540090.429287132919820.21464356645991
450.7561872566607590.4876254866784830.243812743339241
460.9108544004382550.1782911991234910.0891455995617453
470.9277587055104760.1444825889790490.0722412944895244
480.9084424583085410.1831150833829190.0915575416914594
490.8918594153180410.2162811693639190.108140584681959
500.8898351640374230.2203296719251540.110164835962577
510.8645444119305560.2709111761388880.135455588069444
520.8351971008016060.3296057983967870.164802899198393
530.8569406819076150.286118636184770.143059318092385
540.8282396082693060.3435207834613870.171760391730694
550.8441058644212980.3117882711574030.155894135578702
560.8247636727876650.3504726544246690.175236327212334
570.7929317589748410.4141364820503180.207068241025159
580.7708071461430310.4583857077139380.229192853856969
590.7323775040114280.5352449919771440.267622495988572
600.7348367402758910.5303265194482180.265163259724109
610.7040243572051620.5919512855896770.295975642794838
620.6696916094197950.660616781160410.330308390580205
630.6348419783643020.7303160432713950.365158021635698
640.589190873607710.821618252784580.41080912639229
650.5472259261494750.905548147701050.452774073850525
660.5108703013433330.9782593973133350.489129698656667
670.4910829143599240.9821658287198470.508917085640076
680.5677928859962170.8644142280075670.432207114003783
690.7571391249075240.4857217501849530.242860875092476
700.7205772826962160.5588454346075680.279422717303784
710.8208058264076610.3583883471846770.179194173592339
720.7888674521145510.4222650957708990.211132547885449
730.7786892813882880.4426214372234240.221310718611712
740.7540154657564880.4919690684870240.245984534243512
750.7165829972476030.5668340055047950.283417002752397
760.7657685621598050.4684628756803890.234231437840195
770.7315059841489540.5369880317020930.268494015851046
780.7123393730623010.5753212538753990.287660626937699
790.7197646174608650.560470765078270.280235382539135
800.6791013295255190.6417973409489610.320898670474481
810.6388166831152890.7223666337694230.361183316884711
820.7987761536125250.402447692774950.201223846387475
830.7645677542779830.4708644914440330.235432245722017
840.7397206413090970.5205587173818060.260279358690903
850.7005088464569640.5989823070860710.299491153543036
860.6886947146749650.622610570650070.311305285325035
870.6470607466049370.7058785067901260.352939253395063
880.6077587478731880.7844825042536240.392241252126812
890.5893507026220530.8212985947558950.410649297377947
900.5536222436101820.8927555127796360.446377756389818
910.5440055937377720.9119888125244560.455994406262228
920.5089845592477680.9820308815044640.491015440752232
930.466198701212690.932397402425380.53380129878731
940.4220690173720620.8441380347441250.577930982627938
950.4399004695225020.8798009390450030.560099530477498
960.4030742045001420.8061484090002830.596925795499858
970.3633763836151490.7267527672302970.636623616384851
980.3586852460847560.7173704921695120.641314753915244
990.3156430379351960.6312860758703920.684356962064804
1000.2765002229577180.5530004459154350.723499777042282
1010.2522098356741040.5044196713482080.747790164325896
1020.2335116345854770.4670232691709550.766488365414523
1030.2833129743814620.5666259487629250.716687025618538
1040.2442688364493060.4885376728986130.755731163550694
1050.2352799971669950.4705599943339910.764720002833005
1060.252800305029460.5056006100589210.74719969497054
1070.2297320650963770.4594641301927530.770267934903623
1080.2063418145372050.4126836290744090.793658185462795
1090.1996949713957930.3993899427915860.800305028604207
1100.1860861696642310.3721723393284630.813913830335769
1110.1646835786200020.3293671572400030.835316421379998
1120.1385935360652830.2771870721305650.861406463934717
1130.1589829368567150.317965873713430.841017063143285
1140.139467532853810.278935065707620.86053246714619
1150.1809129315118570.3618258630237130.819087068488143
1160.1709268064887240.3418536129774480.829073193511276
1170.1487755566540880.2975511133081760.851224443345912
1180.1285654082164690.2571308164329370.871434591783531
1190.1311576653319510.2623153306639030.868842334668049
1200.1205889768628980.2411779537257960.879411023137102
1210.1020961197723780.2041922395447560.897903880227622
1220.08204332051258690.1640866410251740.917956679487413
1230.09184521092891330.1836904218578270.908154789071087
1240.07395729737672510.147914594753450.926042702623275
1250.06017848448796880.1203569689759380.939821515512031
1260.04588175221046320.09176350442092640.954118247789537
1270.03499970634754520.06999941269509030.965000293652455
1280.02903484314103250.0580696862820650.970965156858968
1290.02225442418928940.04450884837857880.977745575810711
1300.03043501042157160.06087002084314330.969564989578428
1310.02648555726779690.05297111453559390.973514442732203
1320.04092252108604130.08184504217208260.959077478913959
1330.045347697406450.09069539481290010.95465230259355
1340.06013881351144080.1202776270228820.939861186488559
1350.04793535102710830.09587070205421660.952064648972892
1360.03413393656211840.06826787312423690.965866063437882
1370.02368708313183330.04737416626366670.976312916868167
1380.01578479952442730.03156959904885460.984215200475573
1390.03159172051243390.06318344102486790.968408279487566
1400.02762746425863050.0552549285172610.97237253574137
1410.6413293062897220.7173413874205550.358670693710278
1420.5777594412579350.8444811174841290.422240558742065
1430.4970477286797370.9940954573594740.502952271320263
1440.4292835158871240.8585670317742480.570716484112876
1450.3425821375170590.6851642750341190.657417862482941
1460.4279572353901650.855914470780330.572042764609835
1470.3512920220659360.7025840441318720.648707977934064
1480.7031326543334260.5937346913331470.296867345666574
1490.6227642383502830.7544715232994350.377235761649717
1500.4770984361006990.9541968722013980.522901563899301
1510.8294970382506520.3410059234986950.170502961749347







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0212765957446809OK
10% type I error level140.099290780141844OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.0212765957446809 & OK \tabularnewline
10% type I error level & 14 & 0.099290780141844 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186208&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0212765957446809[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.099290780141844[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186208&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186208&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0212765957446809OK
10% type I error level140.099290780141844OK



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