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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 computationSun, 16 Dec 2012 13:19:37 -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/Dec/16/t1355682014kbs1y04ghtg4cjm.htm/, Retrieved Fri, 29 Mar 2024 08:10:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200539, Retrieved Fri, 29 Mar 2024 08:10:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96
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] [MR trend zonder b...] [2012-12-16 18:19:37] [447cab31e466d1c88f957d20e303ed40] [Current]
- R         [Multiple Regression] [Multiple Regressi...] [2012-12-19 13:13:34] [3ee3949b5f2daf713678f5a72e9e7041]
- R         [Multiple Regression] [Multiple Regressi...] [2012-12-19 13:43:26] [3ee3949b5f2daf713678f5a72e9e7041]
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Dataseries X:
41	38	13	12	14	12	53
39	32	16	11	18	11	86
30	35	19	15	11	14	66
31	33	15	6	12	12	67
34	37	14	13	16	21	76
35	29	13	10	18	12	78
39	31	19	12	14	22	53
34	36	15	14	14	11	80
36	35	14	12	15	10	74
37	38	15	6	15	13	76
38	31	16	10	17	10	79
36	34	16	12	19	8	54
38	35	16	12	10	15	67
39	38	16	11	16	14	54
33	37	17	15	18	10	87
32	33	15	12	14	14	58
36	32	15	10	14	14	75
38	38	20	12	17	11	88
39	38	18	11	14	10	64
32	32	16	12	16	13	57
32	33	16	11	18	7	66
31	31	16	12	11	14	68
39	38	19	13	14	12	54
37	39	16	11	12	14	56
39	32	17	9	17	11	86
41	32	17	13	9	9	80
36	35	16	10	16	11	76
33	37	15	14	14	15	69
33	33	16	12	15	14	78
34	33	14	10	11	13	67
31	28	15	12	16	9	80
27	32	12	8	13	15	54
37	31	14	10	17	10	71
34	37	16	12	15	11	84
34	30	14	12	14	13	74
32	33	7	7	16	8	71
29	31	10	6	9	20	63
36	33	14	12	15	12	71
29	31	16	10	17	10	76
35	33	16	10	13	10	69
37	32	16	10	15	9	74
34	33	14	12	16	14	75
38	32	20	15	16	8	54
35	33	14	10	12	14	52
38	28	14	10	12	11	69
37	35	11	12	11	13	68
38	39	14	13	15	9	65
33	34	15	11	15	11	75
36	38	16	11	17	15	74
38	32	14	12	13	11	75
32	38	16	14	16	10	72
32	30	14	10	14	14	67
32	33	12	12	11	18	63
34	38	16	13	12	14	62
32	32	9	5	12	11	63
37	32	14	6	15	12	76
39	34	16	12	16	13	74
29	34	16	12	15	9	67
37	36	15	11	12	10	73
35	34	16	10	12	15	70
30	28	12	7	8	20	53
38	34	16	12	13	12	77
34	35	16	14	11	12	77
31	35	14	11	14	14	52
34	31	16	12	15	13	54
35	37	17	13	10	11	80
36	35	18	14	11	17	66
30	27	18	11	12	12	73
39	40	12	12	15	13	63
35	37	16	12	15	14	69
38	36	10	8	14	13	67
31	38	14	11	16	15	54
34	39	18	14	15	13	81
38	41	18	14	15	10	69
34	27	16	12	13	11	84
39	30	17	9	12	19	80
37	37	16	13	17	13	70
34	31	16	11	13	17	69
28	31	13	12	15	13	77
37	27	16	12	13	9	54
33	36	16	12	15	11	79
37	38	20	12	16	10	30
35	37	16	12	15	9	71
37	33	15	12	16	12	73
32	34	15	11	15	12	72
33	31	16	10	14	13	77
38	39	14	9	15	13	75
33	34	16	12	14	12	69
29	32	16	12	13	15	54
33	33	15	12	7	22	70
31	36	12	9	17	13	73
36	32	17	15	13	15	54
35	41	16	12	15	13	77
32	28	15	12	14	15	82
29	30	13	12	13	10	80
39	36	16	10	16	11	80
37	35	16	13	12	16	69
35	31	16	9	14	11	78
37	34	16	12	17	11	81
32	36	14	10	15	10	76
38	36	16	14	17	10	76
37	35	16	11	12	16	73
36	37	20	15	16	12	85
32	28	15	11	11	11	66
33	39	16	11	15	16	79
40	32	13	12	9	19	68
38	35	17	12	16	11	76
41	39	16	12	15	16	71
36	35	16	11	10	15	54
43	42	12	7	10	24	46
30	34	16	12	15	14	82
31	33	16	14	11	15	74
32	41	17	11	13	11	88
32	33	13	11	14	15	38
37	34	12	10	18	12	76
37	32	18	13	16	10	86
33	40	14	13	14	14	54
34	40	14	8	14	13	70
33	35	13	11	14	9	69
38	36	16	12	14	15	90
33	37	13	11	12	15	54
31	27	16	13	14	14	76
38	39	13	12	15	11	89
37	38	16	14	15	8	76
33	31	15	13	15	11	73
31	33	16	15	13	11	79
39	32	15	10	17	8	90
44	39	17	11	17	10	74
33	36	15	9	19	11	81
35	33	12	11	15	13	72
32	33	16	10	13	11	71
28	32	10	11	9	20	66
40	37	16	8	15	10	77
27	30	12	11	15	15	65
37	38	14	12	15	12	74
32	29	15	12	16	14	82
28	22	13	9	11	23	54
34	35	15	11	14	14	63
30	35	11	10	11	16	54
35	34	12	8	15	11	64
31	35	8	9	13	12	69
32	34	16	8	15	10	54
30	34	15	9	16	14	84
30	35	17	15	14	12	86
31	23	16	11	15	12	77
40	31	10	8	16	11	89
32	27	18	13	16	12	76
36	36	13	12	11	13	60
32	31	16	12	12	11	75
35	32	13	9	9	19	73
38	39	10	7	16	12	85
42	37	15	13	13	17	79
34	38	16	9	16	9	71
35	39	16	6	12	12	72
35	34	14	8	9	19	69
33	31	10	8	13	18	78
36	32	17	15	13	15	54
32	37	13	6	14	14	69
33	36	15	9	19	11	81
34	32	16	11	13	9	84
32	35	12	8	12	18	84
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 time11 seconds
R Server'George Udny Yule' @ yule.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 & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200539&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200539&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200539&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 time11 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 6.03740337916085 + 0.107298238771915Connected[t] -0.0179858507377762Separate[t] + 0.533733913871116Software[t] + 0.0559016695036461Happiness[t] -0.0698967166353528Depression[t] + 0.0051268370936719Belonging[t] -0.00409234584985734t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  6.03740337916085 +  0.107298238771915Connected[t] -0.0179858507377762Separate[t] +  0.533733913871116Software[t] +  0.0559016695036461Happiness[t] -0.0698967166353528Depression[t] +  0.0051268370936719Belonging[t] -0.00409234584985734t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200539&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  6.03740337916085 +  0.107298238771915Connected[t] -0.0179858507377762Separate[t] +  0.533733913871116Software[t] +  0.0559016695036461Happiness[t] -0.0698967166353528Depression[t] +  0.0051268370936719Belonging[t] -0.00409234584985734t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200539&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200539&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] = + 6.03740337916085 + 0.107298238771915Connected[t] -0.0179858507377762Separate[t] + 0.533733913871116Software[t] + 0.0559016695036461Happiness[t] -0.0698967166353528Depression[t] + 0.0051268370936719Belonging[t] -0.00409234584985734t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.037403379160852.5823132.3380.0206740.010337
Connected0.1072982387719150.0471552.27550.0242580.012129
Separate-0.01798585073777620.044755-0.40190.6883330.344167
Software0.5337339138711160.0692847.703500
Happiness0.05590166950364610.0762580.73310.4646360.232318
Depression-0.06989671663535280.056017-1.24780.2140040.107002
Belonging0.00512683709367190.0146780.34930.727360.36368
t-0.004092345849857340.003249-1.25970.20970.10485

\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) & 6.03740337916085 & 2.582313 & 2.338 & 0.020674 & 0.010337 \tabularnewline
Connected & 0.107298238771915 & 0.047155 & 2.2755 & 0.024258 & 0.012129 \tabularnewline
Separate & -0.0179858507377762 & 0.044755 & -0.4019 & 0.688333 & 0.344167 \tabularnewline
Software & 0.533733913871116 & 0.069284 & 7.7035 & 0 & 0 \tabularnewline
Happiness & 0.0559016695036461 & 0.076258 & 0.7331 & 0.464636 & 0.232318 \tabularnewline
Depression & -0.0698967166353528 & 0.056017 & -1.2478 & 0.214004 & 0.107002 \tabularnewline
Belonging & 0.0051268370936719 & 0.014678 & 0.3493 & 0.72736 & 0.36368 \tabularnewline
t & -0.00409234584985734 & 0.003249 & -1.2597 & 0.2097 & 0.10485 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200539&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]6.03740337916085[/C][C]2.582313[/C][C]2.338[/C][C]0.020674[/C][C]0.010337[/C][/ROW]
[ROW][C]Connected[/C][C]0.107298238771915[/C][C]0.047155[/C][C]2.2755[/C][C]0.024258[/C][C]0.012129[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0179858507377762[/C][C]0.044755[/C][C]-0.4019[/C][C]0.688333[/C][C]0.344167[/C][/ROW]
[ROW][C]Software[/C][C]0.533733913871116[/C][C]0.069284[/C][C]7.7035[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0559016695036461[/C][C]0.076258[/C][C]0.7331[/C][C]0.464636[/C][C]0.232318[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0698967166353528[/C][C]0.056017[/C][C]-1.2478[/C][C]0.214004[/C][C]0.107002[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0051268370936719[/C][C]0.014678[/C][C]0.3493[/C][C]0.72736[/C][C]0.36368[/C][/ROW]
[ROW][C]t[/C][C]-0.00409234584985734[/C][C]0.003249[/C][C]-1.2597[/C][C]0.2097[/C][C]0.10485[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200539&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200539&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)6.037403379160852.5823132.3380.0206740.010337
Connected0.1072982387719150.0471552.27550.0242580.012129
Separate-0.01798585073777620.044755-0.40190.6883330.344167
Software0.5337339138711160.0692847.703500
Happiness0.05590166950364610.0762580.73310.4646360.232318
Depression-0.06989671663535280.056017-1.24780.2140040.107002
Belonging0.00512683709367190.0146780.34930.727360.36368
t-0.004092345849857340.003249-1.25970.20970.10485







Multiple Linear Regression - Regression Statistics
Multiple R0.600427258109706
R-squared0.360512892281139
Adjusted R-squared0.331445296475736
F-TEST (value)12.4025700197101
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value1.53166368477287e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84483623221262
Sum Squared Residuals524.126791447404

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.600427258109706 \tabularnewline
R-squared & 0.360512892281139 \tabularnewline
Adjusted R-squared & 0.331445296475736 \tabularnewline
F-TEST (value) & 12.4025700197101 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 1.53166368477287e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84483623221262 \tabularnewline
Sum Squared Residuals & 524.126791447404 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200539&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.600427258109706[/C][/ROW]
[ROW][C]R-squared[/C][C]0.360512892281139[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.331445296475736[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.4025700197101[/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]1.53166368477287e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.84483623221262[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]524.126791447404[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200539&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200539&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 R0.600427258109706
R-squared0.360512892281139
Adjusted R-squared0.331445296475736
F-TEST (value)12.4025700197101
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value1.53166368477287e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84483623221262
Sum Squared Residuals524.126791447404







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.3694686007688-3.36946860076883
21616.1876499866718-0.187649986671771
31916.59531301684082.4046869831592
41512.13170732626642.86829267373361
51415.7543814530184-1.75438145301844
61315.1513998731422-2.15139987314217
71915.55725183693673.44274816306326
81516.9014953557987-1.90149535579868
91416.1575548740652-2.15755487406516
101512.80496325582852.19503674417151
111615.50587975959380.494120240406196
121616.4241270566652-0.424127056665221
131615.69090217785890.309097822141133
141615.5450744561360.454925543864025
151717.6106900135167-0.610690013516745
161515.3181692699603-0.31816926996025
171514.7809441327860.219055867213982
182016.39504502843033.6049549715697
191815.74366462535752.25633537464247
201615.49635895584760.50364104415243
211615.51787201805130.482127981948723
221615.10585201999050.894147980009475
231916.60370126549292.3962987345071
241615.05821566552860.941784334471444
251714.97015653487922.02984346512083
261716.97741537673710.0225846232628859
271615.01268344808110.987316551918861
281516.35838227472-1.35838227472004
291615.53070542406110.469294575938897
301414.3563383198313-0.356338319831307
311515.8134924353742-0.813492435374194
321212.4529450032426-0.452945003242588
331415.2675352153757-1.26753521537565
341615.78604970310070.213950296899284
351415.6608948387042-1.66089483870422
36713.1654853046447-6.16548530464467
371011.0706490471844-1.07064904718437
381415.9196746013431-1.91967460134313
391614.41022941556961.58977058443045
401614.75446026320531.24553973679466
411615.19028448674810.809715513251905
421415.6253243250075-1.6253243250075
432017.98132924744142.01867075255857
441414.3154461131684-0.315446113168433
451415.0200241178217-1.02002411782168
461115.6493784659097-4.64937846590968
471416.7021879030267-2.70218790302673
481515.09554072693-0.0955407269299648
491615.1684893298170.831510670183045
501416.0817495054292-2.0817495054292
511616.6156416641287-0.615641664128703
521414.2034760776795-0.20347607767953
531214.7450947839315-2.74509478393154
541615.72976527475910.270234725240869
55911.5639372318229-2.56393723182291
561412.79452716779711.20547283220294
571616.1472143599231-0.14721435992312
581615.25793696373620.742063036263821
591515.3356842101307-0.33568421013071
601614.25436907988371.74563092011632
611211.56025241120380.439747588796209
621615.93702661130730.0629733886926509
631616.441419948367-0.441419948366996
641414.4139717924865-0.413971792486484
651615.47350354010090.526496459899067
661715.99611109265541.00388890734461
671816.23376825130421.76623174869576
681814.56984464944793.43015535055213
691215.8778942277641-3.87789422776414
701615.45943078496660.54056921503337
711013.6640247236302-3.66402472363019
721414.3794357700336-0.37943577003358
731816.50277039667121.49722960332876
741817.04006740921550.959932590784518
751615.6863186916270.313681308372965
761713.92797549484893.07202450515108
771616.3659416481689-0.36594164816889
781614.5720814810381.42791851896195
791314.8903385187259-1.8903385187259
801615.9737399991540.0262600008459599
811615.47876287465490.521237125345076
822015.74247515096624.25752484903375
831615.76596754628260.234032453717408
841515.9048802747126-0.9048802747126
851514.7515484637970.24845153620304
861614.27481379439061.72518620560941
871414.1752399179433-0.175239917943284
881615.30902139810580.690978601894243
891614.5692134228291.43078657717099
901514.23367054135840.76632945864156
911213.5632900801737-1.56329008017373
921716.90922579829620.0907742017038128
931615.40427484085350.595725159146466
941515.142042920973-0.142042920973031
951315.0634123968177-2.06341239681765
961615.05472779839360.945272201606362
971615.82574109812930.174258901870665
981614.05148847822941.94851152177061
991615.99232231911530.00767768088466241
1001414.2807584423478-0.280758442347825
1011617.1671945236212-1.16719452362121
1021614.75831888951251.2416811104875
1032017.31060784857972.6893921514203
1041514.59723801313510.402761986864935
1051614.44337152499711.55662847500286
1061315.2485063446279-2.24850634462791
1071715.96736008537861.03263991462139
1081615.78219961474460.217800385255376
1091614.48305770263991.51694229736012
1101212.299131271077-0.299131271076981
1111614.8757598456941.12424015430603
1121615.72990132569670.270098674303278
1131714.55118459596332.44881540403667
1141314.2109520042943-1.21095200429432
1151214.8197477251753-2.81974772517532
1161816.53208728761451.4679127123855
1171415.3994661882286-1.39946618822857
1181412.98592862192921.01407137807085
1191314.8401290620573-1.84012906205735
1201615.57655925235540.423440747644607
1211314.1878864736576-1.18788647365759
1221615.51101445708730.488985542912675
1231315.8406863615439-2.84068636154388
1241616.9577907230904-0.957790723090437
1251515.8916018022592-0.891601802259165
1261616.6233667886869-0.623366788686897
1271515.3166686703456-0.316668670345588
1281716.03507765029250.96492234970747
1291513.91498888445041.08501111554965
1301214.8373767509715-2.83737675097155
1311614.00051903210461.99948096789543
1321013.2406421825748-3.24064218257483
1331613.92377009809152.07622990190849
1341213.8408977166837-1.84089771668372
1351415.555466550271-1.55546655027102
1361515.1338786001839-0.133878600183892
1371312.17316227693820.826837723061755
1381514.48942812394320.510571876056824
1391113.1687689335099-2.16876893350985
1401213.2760444366432-1.27604443664318
141813.2024413286647-5.20244132866472
1421612.96459337432643.03540662567364
1431513.20975838057621.79024161942382
1441716.4283274356660.571672564334005
1451614.62218801761751.3778119823825
1461014.0260117044624-4.02601170446238
1471815.76760082189082.2323991781092
1481315.0656604029652-2.06566040296517
1491614.9949020148961.00509798510404
1501312.95638437722960.0436156227703758
1511013.0229287128859-3.0229287128859
1521516.1384548925762-1.13845489257621
1531613.80891917517322.19108082482681
1541611.86476748491714.13523251508285
1551412.3457096842591.65429031574102
1561012.5206233415716-2.52062334157161
1571716.64322331805550.356776681944539
1581311.51910448113311.4808955188669
1591513.79221850895461.20778149104537
1601614.85459956009991.14540043990013
1611212.2957793236577-0.295779323657688
1621312.60709015098320.392909849016844

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.3694686007688 & -3.36946860076883 \tabularnewline
2 & 16 & 16.1876499866718 & -0.187649986671771 \tabularnewline
3 & 19 & 16.5953130168408 & 2.4046869831592 \tabularnewline
4 & 15 & 12.1317073262664 & 2.86829267373361 \tabularnewline
5 & 14 & 15.7543814530184 & -1.75438145301844 \tabularnewline
6 & 13 & 15.1513998731422 & -2.15139987314217 \tabularnewline
7 & 19 & 15.5572518369367 & 3.44274816306326 \tabularnewline
8 & 15 & 16.9014953557987 & -1.90149535579868 \tabularnewline
9 & 14 & 16.1575548740652 & -2.15755487406516 \tabularnewline
10 & 15 & 12.8049632558285 & 2.19503674417151 \tabularnewline
11 & 16 & 15.5058797595938 & 0.494120240406196 \tabularnewline
12 & 16 & 16.4241270566652 & -0.424127056665221 \tabularnewline
13 & 16 & 15.6909021778589 & 0.309097822141133 \tabularnewline
14 & 16 & 15.545074456136 & 0.454925543864025 \tabularnewline
15 & 17 & 17.6106900135167 & -0.610690013516745 \tabularnewline
16 & 15 & 15.3181692699603 & -0.31816926996025 \tabularnewline
17 & 15 & 14.780944132786 & 0.219055867213982 \tabularnewline
18 & 20 & 16.3950450284303 & 3.6049549715697 \tabularnewline
19 & 18 & 15.7436646253575 & 2.25633537464247 \tabularnewline
20 & 16 & 15.4963589558476 & 0.50364104415243 \tabularnewline
21 & 16 & 15.5178720180513 & 0.482127981948723 \tabularnewline
22 & 16 & 15.1058520199905 & 0.894147980009475 \tabularnewline
23 & 19 & 16.6037012654929 & 2.3962987345071 \tabularnewline
24 & 16 & 15.0582156655286 & 0.941784334471444 \tabularnewline
25 & 17 & 14.9701565348792 & 2.02984346512083 \tabularnewline
26 & 17 & 16.9774153767371 & 0.0225846232628859 \tabularnewline
27 & 16 & 15.0126834480811 & 0.987316551918861 \tabularnewline
28 & 15 & 16.35838227472 & -1.35838227472004 \tabularnewline
29 & 16 & 15.5307054240611 & 0.469294575938897 \tabularnewline
30 & 14 & 14.3563383198313 & -0.356338319831307 \tabularnewline
31 & 15 & 15.8134924353742 & -0.813492435374194 \tabularnewline
32 & 12 & 12.4529450032426 & -0.452945003242588 \tabularnewline
33 & 14 & 15.2675352153757 & -1.26753521537565 \tabularnewline
34 & 16 & 15.7860497031007 & 0.213950296899284 \tabularnewline
35 & 14 & 15.6608948387042 & -1.66089483870422 \tabularnewline
36 & 7 & 13.1654853046447 & -6.16548530464467 \tabularnewline
37 & 10 & 11.0706490471844 & -1.07064904718437 \tabularnewline
38 & 14 & 15.9196746013431 & -1.91967460134313 \tabularnewline
39 & 16 & 14.4102294155696 & 1.58977058443045 \tabularnewline
40 & 16 & 14.7544602632053 & 1.24553973679466 \tabularnewline
41 & 16 & 15.1902844867481 & 0.809715513251905 \tabularnewline
42 & 14 & 15.6253243250075 & -1.6253243250075 \tabularnewline
43 & 20 & 17.9813292474414 & 2.01867075255857 \tabularnewline
44 & 14 & 14.3154461131684 & -0.315446113168433 \tabularnewline
45 & 14 & 15.0200241178217 & -1.02002411782168 \tabularnewline
46 & 11 & 15.6493784659097 & -4.64937846590968 \tabularnewline
47 & 14 & 16.7021879030267 & -2.70218790302673 \tabularnewline
48 & 15 & 15.09554072693 & -0.0955407269299648 \tabularnewline
49 & 16 & 15.168489329817 & 0.831510670183045 \tabularnewline
50 & 14 & 16.0817495054292 & -2.0817495054292 \tabularnewline
51 & 16 & 16.6156416641287 & -0.615641664128703 \tabularnewline
52 & 14 & 14.2034760776795 & -0.20347607767953 \tabularnewline
53 & 12 & 14.7450947839315 & -2.74509478393154 \tabularnewline
54 & 16 & 15.7297652747591 & 0.270234725240869 \tabularnewline
55 & 9 & 11.5639372318229 & -2.56393723182291 \tabularnewline
56 & 14 & 12.7945271677971 & 1.20547283220294 \tabularnewline
57 & 16 & 16.1472143599231 & -0.14721435992312 \tabularnewline
58 & 16 & 15.2579369637362 & 0.742063036263821 \tabularnewline
59 & 15 & 15.3356842101307 & -0.33568421013071 \tabularnewline
60 & 16 & 14.2543690798837 & 1.74563092011632 \tabularnewline
61 & 12 & 11.5602524112038 & 0.439747588796209 \tabularnewline
62 & 16 & 15.9370266113073 & 0.0629733886926509 \tabularnewline
63 & 16 & 16.441419948367 & -0.441419948366996 \tabularnewline
64 & 14 & 14.4139717924865 & -0.413971792486484 \tabularnewline
65 & 16 & 15.4735035401009 & 0.526496459899067 \tabularnewline
66 & 17 & 15.9961110926554 & 1.00388890734461 \tabularnewline
67 & 18 & 16.2337682513042 & 1.76623174869576 \tabularnewline
68 & 18 & 14.5698446494479 & 3.43015535055213 \tabularnewline
69 & 12 & 15.8778942277641 & -3.87789422776414 \tabularnewline
70 & 16 & 15.4594307849666 & 0.54056921503337 \tabularnewline
71 & 10 & 13.6640247236302 & -3.66402472363019 \tabularnewline
72 & 14 & 14.3794357700336 & -0.37943577003358 \tabularnewline
73 & 18 & 16.5027703966712 & 1.49722960332876 \tabularnewline
74 & 18 & 17.0400674092155 & 0.959932590784518 \tabularnewline
75 & 16 & 15.686318691627 & 0.313681308372965 \tabularnewline
76 & 17 & 13.9279754948489 & 3.07202450515108 \tabularnewline
77 & 16 & 16.3659416481689 & -0.36594164816889 \tabularnewline
78 & 16 & 14.572081481038 & 1.42791851896195 \tabularnewline
79 & 13 & 14.8903385187259 & -1.8903385187259 \tabularnewline
80 & 16 & 15.973739999154 & 0.0262600008459599 \tabularnewline
81 & 16 & 15.4787628746549 & 0.521237125345076 \tabularnewline
82 & 20 & 15.7424751509662 & 4.25752484903375 \tabularnewline
83 & 16 & 15.7659675462826 & 0.234032453717408 \tabularnewline
84 & 15 & 15.9048802747126 & -0.9048802747126 \tabularnewline
85 & 15 & 14.751548463797 & 0.24845153620304 \tabularnewline
86 & 16 & 14.2748137943906 & 1.72518620560941 \tabularnewline
87 & 14 & 14.1752399179433 & -0.175239917943284 \tabularnewline
88 & 16 & 15.3090213981058 & 0.690978601894243 \tabularnewline
89 & 16 & 14.569213422829 & 1.43078657717099 \tabularnewline
90 & 15 & 14.2336705413584 & 0.76632945864156 \tabularnewline
91 & 12 & 13.5632900801737 & -1.56329008017373 \tabularnewline
92 & 17 & 16.9092257982962 & 0.0907742017038128 \tabularnewline
93 & 16 & 15.4042748408535 & 0.595725159146466 \tabularnewline
94 & 15 & 15.142042920973 & -0.142042920973031 \tabularnewline
95 & 13 & 15.0634123968177 & -2.06341239681765 \tabularnewline
96 & 16 & 15.0547277983936 & 0.945272201606362 \tabularnewline
97 & 16 & 15.8257410981293 & 0.174258901870665 \tabularnewline
98 & 16 & 14.0514884782294 & 1.94851152177061 \tabularnewline
99 & 16 & 15.9923223191153 & 0.00767768088466241 \tabularnewline
100 & 14 & 14.2807584423478 & -0.280758442347825 \tabularnewline
101 & 16 & 17.1671945236212 & -1.16719452362121 \tabularnewline
102 & 16 & 14.7583188895125 & 1.2416811104875 \tabularnewline
103 & 20 & 17.3106078485797 & 2.6893921514203 \tabularnewline
104 & 15 & 14.5972380131351 & 0.402761986864935 \tabularnewline
105 & 16 & 14.4433715249971 & 1.55662847500286 \tabularnewline
106 & 13 & 15.2485063446279 & -2.24850634462791 \tabularnewline
107 & 17 & 15.9673600853786 & 1.03263991462139 \tabularnewline
108 & 16 & 15.7821996147446 & 0.217800385255376 \tabularnewline
109 & 16 & 14.4830577026399 & 1.51694229736012 \tabularnewline
110 & 12 & 12.299131271077 & -0.299131271076981 \tabularnewline
111 & 16 & 14.875759845694 & 1.12424015430603 \tabularnewline
112 & 16 & 15.7299013256967 & 0.270098674303278 \tabularnewline
113 & 17 & 14.5511845959633 & 2.44881540403667 \tabularnewline
114 & 13 & 14.2109520042943 & -1.21095200429432 \tabularnewline
115 & 12 & 14.8197477251753 & -2.81974772517532 \tabularnewline
116 & 18 & 16.5320872876145 & 1.4679127123855 \tabularnewline
117 & 14 & 15.3994661882286 & -1.39946618822857 \tabularnewline
118 & 14 & 12.9859286219292 & 1.01407137807085 \tabularnewline
119 & 13 & 14.8401290620573 & -1.84012906205735 \tabularnewline
120 & 16 & 15.5765592523554 & 0.423440747644607 \tabularnewline
121 & 13 & 14.1878864736576 & -1.18788647365759 \tabularnewline
122 & 16 & 15.5110144570873 & 0.488985542912675 \tabularnewline
123 & 13 & 15.8406863615439 & -2.84068636154388 \tabularnewline
124 & 16 & 16.9577907230904 & -0.957790723090437 \tabularnewline
125 & 15 & 15.8916018022592 & -0.891601802259165 \tabularnewline
126 & 16 & 16.6233667886869 & -0.623366788686897 \tabularnewline
127 & 15 & 15.3166686703456 & -0.316668670345588 \tabularnewline
128 & 17 & 16.0350776502925 & 0.96492234970747 \tabularnewline
129 & 15 & 13.9149888844504 & 1.08501111554965 \tabularnewline
130 & 12 & 14.8373767509715 & -2.83737675097155 \tabularnewline
131 & 16 & 14.0005190321046 & 1.99948096789543 \tabularnewline
132 & 10 & 13.2406421825748 & -3.24064218257483 \tabularnewline
133 & 16 & 13.9237700980915 & 2.07622990190849 \tabularnewline
134 & 12 & 13.8408977166837 & -1.84089771668372 \tabularnewline
135 & 14 & 15.555466550271 & -1.55546655027102 \tabularnewline
136 & 15 & 15.1338786001839 & -0.133878600183892 \tabularnewline
137 & 13 & 12.1731622769382 & 0.826837723061755 \tabularnewline
138 & 15 & 14.4894281239432 & 0.510571876056824 \tabularnewline
139 & 11 & 13.1687689335099 & -2.16876893350985 \tabularnewline
140 & 12 & 13.2760444366432 & -1.27604443664318 \tabularnewline
141 & 8 & 13.2024413286647 & -5.20244132866472 \tabularnewline
142 & 16 & 12.9645933743264 & 3.03540662567364 \tabularnewline
143 & 15 & 13.2097583805762 & 1.79024161942382 \tabularnewline
144 & 17 & 16.428327435666 & 0.571672564334005 \tabularnewline
145 & 16 & 14.6221880176175 & 1.3778119823825 \tabularnewline
146 & 10 & 14.0260117044624 & -4.02601170446238 \tabularnewline
147 & 18 & 15.7676008218908 & 2.2323991781092 \tabularnewline
148 & 13 & 15.0656604029652 & -2.06566040296517 \tabularnewline
149 & 16 & 14.994902014896 & 1.00509798510404 \tabularnewline
150 & 13 & 12.9563843772296 & 0.0436156227703758 \tabularnewline
151 & 10 & 13.0229287128859 & -3.0229287128859 \tabularnewline
152 & 15 & 16.1384548925762 & -1.13845489257621 \tabularnewline
153 & 16 & 13.8089191751732 & 2.19108082482681 \tabularnewline
154 & 16 & 11.8647674849171 & 4.13523251508285 \tabularnewline
155 & 14 & 12.345709684259 & 1.65429031574102 \tabularnewline
156 & 10 & 12.5206233415716 & -2.52062334157161 \tabularnewline
157 & 17 & 16.6432233180555 & 0.356776681944539 \tabularnewline
158 & 13 & 11.5191044811331 & 1.4808955188669 \tabularnewline
159 & 15 & 13.7922185089546 & 1.20778149104537 \tabularnewline
160 & 16 & 14.8545995600999 & 1.14540043990013 \tabularnewline
161 & 12 & 12.2957793236577 & -0.295779323657688 \tabularnewline
162 & 13 & 12.6070901509832 & 0.392909849016844 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200539&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]16.3694686007688[/C][C]-3.36946860076883[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.1876499866718[/C][C]-0.187649986671771[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5953130168408[/C][C]2.4046869831592[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.1317073262664[/C][C]2.86829267373361[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.7543814530184[/C][C]-1.75438145301844[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.1513998731422[/C][C]-2.15139987314217[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.5572518369367[/C][C]3.44274816306326[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9014953557987[/C][C]-1.90149535579868[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1575548740652[/C][C]-2.15755487406516[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.8049632558285[/C][C]2.19503674417151[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.5058797595938[/C][C]0.494120240406196[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.4241270566652[/C][C]-0.424127056665221[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6909021778589[/C][C]0.309097822141133[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.545074456136[/C][C]0.454925543864025[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.6106900135167[/C][C]-0.610690013516745[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.3181692699603[/C][C]-0.31816926996025[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.780944132786[/C][C]0.219055867213982[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3950450284303[/C][C]3.6049549715697[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.7436646253575[/C][C]2.25633537464247[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.4963589558476[/C][C]0.50364104415243[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.5178720180513[/C][C]0.482127981948723[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.1058520199905[/C][C]0.894147980009475[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.6037012654929[/C][C]2.3962987345071[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.0582156655286[/C][C]0.941784334471444[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.9701565348792[/C][C]2.02984346512083[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.9774153767371[/C][C]0.0225846232628859[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.0126834480811[/C][C]0.987316551918861[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.35838227472[/C][C]-1.35838227472004[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.5307054240611[/C][C]0.469294575938897[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.3563383198313[/C][C]-0.356338319831307[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.8134924353742[/C][C]-0.813492435374194[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4529450032426[/C][C]-0.452945003242588[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.2675352153757[/C][C]-1.26753521537565[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.7860497031007[/C][C]0.213950296899284[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6608948387042[/C][C]-1.66089483870422[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.1654853046447[/C][C]-6.16548530464467[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.0706490471844[/C][C]-1.07064904718437[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.9196746013431[/C][C]-1.91967460134313[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.4102294155696[/C][C]1.58977058443045[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7544602632053[/C][C]1.24553973679466[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1902844867481[/C][C]0.809715513251905[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.6253243250075[/C][C]-1.6253243250075[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.9813292474414[/C][C]2.01867075255857[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.3154461131684[/C][C]-0.315446113168433[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0200241178217[/C][C]-1.02002411782168[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6493784659097[/C][C]-4.64937846590968[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.7021879030267[/C][C]-2.70218790302673[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.09554072693[/C][C]-0.0955407269299648[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.168489329817[/C][C]0.831510670183045[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0817495054292[/C][C]-2.0817495054292[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6156416641287[/C][C]-0.615641664128703[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2034760776795[/C][C]-0.20347607767953[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.7450947839315[/C][C]-2.74509478393154[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.7297652747591[/C][C]0.270234725240869[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5639372318229[/C][C]-2.56393723182291[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.7945271677971[/C][C]1.20547283220294[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1472143599231[/C][C]-0.14721435992312[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.2579369637362[/C][C]0.742063036263821[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3356842101307[/C][C]-0.33568421013071[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2543690798837[/C][C]1.74563092011632[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5602524112038[/C][C]0.439747588796209[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9370266113073[/C][C]0.0629733886926509[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.441419948367[/C][C]-0.441419948366996[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4139717924865[/C][C]-0.413971792486484[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4735035401009[/C][C]0.526496459899067[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.9961110926554[/C][C]1.00388890734461[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.2337682513042[/C][C]1.76623174869576[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5698446494479[/C][C]3.43015535055213[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8778942277641[/C][C]-3.87789422776414[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4594307849666[/C][C]0.54056921503337[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.6640247236302[/C][C]-3.66402472363019[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3794357700336[/C][C]-0.37943577003358[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5027703966712[/C][C]1.49722960332876[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.0400674092155[/C][C]0.959932590784518[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.686318691627[/C][C]0.313681308372965[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.9279754948489[/C][C]3.07202450515108[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3659416481689[/C][C]-0.36594164816889[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.572081481038[/C][C]1.42791851896195[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8903385187259[/C][C]-1.8903385187259[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.973739999154[/C][C]0.0262600008459599[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4787628746549[/C][C]0.521237125345076[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.7424751509662[/C][C]4.25752484903375[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.7659675462826[/C][C]0.234032453717408[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9048802747126[/C][C]-0.9048802747126[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.751548463797[/C][C]0.24845153620304[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2748137943906[/C][C]1.72518620560941[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.1752399179433[/C][C]-0.175239917943284[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3090213981058[/C][C]0.690978601894243[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.569213422829[/C][C]1.43078657717099[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.2336705413584[/C][C]0.76632945864156[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.5632900801737[/C][C]-1.56329008017373[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9092257982962[/C][C]0.0907742017038128[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4042748408535[/C][C]0.595725159146466[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.142042920973[/C][C]-0.142042920973031[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.0634123968177[/C][C]-2.06341239681765[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.0547277983936[/C][C]0.945272201606362[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8257410981293[/C][C]0.174258901870665[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.0514884782294[/C][C]1.94851152177061[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.9923223191153[/C][C]0.00767768088466241[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.2807584423478[/C][C]-0.280758442347825[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.1671945236212[/C][C]-1.16719452362121[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7583188895125[/C][C]1.2416811104875[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3106078485797[/C][C]2.6893921514203[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.5972380131351[/C][C]0.402761986864935[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.4433715249971[/C][C]1.55662847500286[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.2485063446279[/C][C]-2.24850634462791[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9673600853786[/C][C]1.03263991462139[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.7821996147446[/C][C]0.217800385255376[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.4830577026399[/C][C]1.51694229736012[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.299131271077[/C][C]-0.299131271076981[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.875759845694[/C][C]1.12424015430603[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.7299013256967[/C][C]0.270098674303278[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5511845959633[/C][C]2.44881540403667[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.2109520042943[/C][C]-1.21095200429432[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.8197477251753[/C][C]-2.81974772517532[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.5320872876145[/C][C]1.4679127123855[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.3994661882286[/C][C]-1.39946618822857[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.9859286219292[/C][C]1.01407137807085[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.8401290620573[/C][C]-1.84012906205735[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.5765592523554[/C][C]0.423440747644607[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.1878864736576[/C][C]-1.18788647365759[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.5110144570873[/C][C]0.488985542912675[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8406863615439[/C][C]-2.84068636154388[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.9577907230904[/C][C]-0.957790723090437[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8916018022592[/C][C]-0.891601802259165[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.6233667886869[/C][C]-0.623366788686897[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.3166686703456[/C][C]-0.316668670345588[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0350776502925[/C][C]0.96492234970747[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.9149888844504[/C][C]1.08501111554965[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.8373767509715[/C][C]-2.83737675097155[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0005190321046[/C][C]1.99948096789543[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2406421825748[/C][C]-3.24064218257483[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9237700980915[/C][C]2.07622990190849[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8408977166837[/C][C]-1.84089771668372[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.555466550271[/C][C]-1.55546655027102[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.1338786001839[/C][C]-0.133878600183892[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.1731622769382[/C][C]0.826837723061755[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.4894281239432[/C][C]0.510571876056824[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.1687689335099[/C][C]-2.16876893350985[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.2760444366432[/C][C]-1.27604443664318[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2024413286647[/C][C]-5.20244132866472[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.9645933743264[/C][C]3.03540662567364[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.2097583805762[/C][C]1.79024161942382[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.428327435666[/C][C]0.571672564334005[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.6221880176175[/C][C]1.3778119823825[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.0260117044624[/C][C]-4.02601170446238[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.7676008218908[/C][C]2.2323991781092[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.0656604029652[/C][C]-2.06566040296517[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.994902014896[/C][C]1.00509798510404[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.9563843772296[/C][C]0.0436156227703758[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.0229287128859[/C][C]-3.0229287128859[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.1384548925762[/C][C]-1.13845489257621[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.8089191751732[/C][C]2.19108082482681[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8647674849171[/C][C]4.13523251508285[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.345709684259[/C][C]1.65429031574102[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.5206233415716[/C][C]-2.52062334157161[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.6432233180555[/C][C]0.356776681944539[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.5191044811331[/C][C]1.4808955188669[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.7922185089546[/C][C]1.20778149104537[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.8545995600999[/C][C]1.14540043990013[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.2957793236577[/C][C]-0.295779323657688[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6070901509832[/C][C]0.392909849016844[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200539&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200539&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
11316.3694686007688-3.36946860076883
21616.1876499866718-0.187649986671771
31916.59531301684082.4046869831592
41512.13170732626642.86829267373361
51415.7543814530184-1.75438145301844
61315.1513998731422-2.15139987314217
71915.55725183693673.44274816306326
81516.9014953557987-1.90149535579868
91416.1575548740652-2.15755487406516
101512.80496325582852.19503674417151
111615.50587975959380.494120240406196
121616.4241270566652-0.424127056665221
131615.69090217785890.309097822141133
141615.5450744561360.454925543864025
151717.6106900135167-0.610690013516745
161515.3181692699603-0.31816926996025
171514.7809441327860.219055867213982
182016.39504502843033.6049549715697
191815.74366462535752.25633537464247
201615.49635895584760.50364104415243
211615.51787201805130.482127981948723
221615.10585201999050.894147980009475
231916.60370126549292.3962987345071
241615.05821566552860.941784334471444
251714.97015653487922.02984346512083
261716.97741537673710.0225846232628859
271615.01268344808110.987316551918861
281516.35838227472-1.35838227472004
291615.53070542406110.469294575938897
301414.3563383198313-0.356338319831307
311515.8134924353742-0.813492435374194
321212.4529450032426-0.452945003242588
331415.2675352153757-1.26753521537565
341615.78604970310070.213950296899284
351415.6608948387042-1.66089483870422
36713.1654853046447-6.16548530464467
371011.0706490471844-1.07064904718437
381415.9196746013431-1.91967460134313
391614.41022941556961.58977058443045
401614.75446026320531.24553973679466
411615.19028448674810.809715513251905
421415.6253243250075-1.6253243250075
432017.98132924744142.01867075255857
441414.3154461131684-0.315446113168433
451415.0200241178217-1.02002411782168
461115.6493784659097-4.64937846590968
471416.7021879030267-2.70218790302673
481515.09554072693-0.0955407269299648
491615.1684893298170.831510670183045
501416.0817495054292-2.0817495054292
511616.6156416641287-0.615641664128703
521414.2034760776795-0.20347607767953
531214.7450947839315-2.74509478393154
541615.72976527475910.270234725240869
55911.5639372318229-2.56393723182291
561412.79452716779711.20547283220294
571616.1472143599231-0.14721435992312
581615.25793696373620.742063036263821
591515.3356842101307-0.33568421013071
601614.25436907988371.74563092011632
611211.56025241120380.439747588796209
621615.93702661130730.0629733886926509
631616.441419948367-0.441419948366996
641414.4139717924865-0.413971792486484
651615.47350354010090.526496459899067
661715.99611109265541.00388890734461
671816.23376825130421.76623174869576
681814.56984464944793.43015535055213
691215.8778942277641-3.87789422776414
701615.45943078496660.54056921503337
711013.6640247236302-3.66402472363019
721414.3794357700336-0.37943577003358
731816.50277039667121.49722960332876
741817.04006740921550.959932590784518
751615.6863186916270.313681308372965
761713.92797549484893.07202450515108
771616.3659416481689-0.36594164816889
781614.5720814810381.42791851896195
791314.8903385187259-1.8903385187259
801615.9737399991540.0262600008459599
811615.47876287465490.521237125345076
822015.74247515096624.25752484903375
831615.76596754628260.234032453717408
841515.9048802747126-0.9048802747126
851514.7515484637970.24845153620304
861614.27481379439061.72518620560941
871414.1752399179433-0.175239917943284
881615.30902139810580.690978601894243
891614.5692134228291.43078657717099
901514.23367054135840.76632945864156
911213.5632900801737-1.56329008017373
921716.90922579829620.0907742017038128
931615.40427484085350.595725159146466
941515.142042920973-0.142042920973031
951315.0634123968177-2.06341239681765
961615.05472779839360.945272201606362
971615.82574109812930.174258901870665
981614.05148847822941.94851152177061
991615.99232231911530.00767768088466241
1001414.2807584423478-0.280758442347825
1011617.1671945236212-1.16719452362121
1021614.75831888951251.2416811104875
1032017.31060784857972.6893921514203
1041514.59723801313510.402761986864935
1051614.44337152499711.55662847500286
1061315.2485063446279-2.24850634462791
1071715.96736008537861.03263991462139
1081615.78219961474460.217800385255376
1091614.48305770263991.51694229736012
1101212.299131271077-0.299131271076981
1111614.8757598456941.12424015430603
1121615.72990132569670.270098674303278
1131714.55118459596332.44881540403667
1141314.2109520042943-1.21095200429432
1151214.8197477251753-2.81974772517532
1161816.53208728761451.4679127123855
1171415.3994661882286-1.39946618822857
1181412.98592862192921.01407137807085
1191314.8401290620573-1.84012906205735
1201615.57655925235540.423440747644607
1211314.1878864736576-1.18788647365759
1221615.51101445708730.488985542912675
1231315.8406863615439-2.84068636154388
1241616.9577907230904-0.957790723090437
1251515.8916018022592-0.891601802259165
1261616.6233667886869-0.623366788686897
1271515.3166686703456-0.316668670345588
1281716.03507765029250.96492234970747
1291513.91498888445041.08501111554965
1301214.8373767509715-2.83737675097155
1311614.00051903210461.99948096789543
1321013.2406421825748-3.24064218257483
1331613.92377009809152.07622990190849
1341213.8408977166837-1.84089771668372
1351415.555466550271-1.55546655027102
1361515.1338786001839-0.133878600183892
1371312.17316227693820.826837723061755
1381514.48942812394320.510571876056824
1391113.1687689335099-2.16876893350985
1401213.2760444366432-1.27604443664318
141813.2024413286647-5.20244132866472
1421612.96459337432643.03540662567364
1431513.20975838057621.79024161942382
1441716.4283274356660.571672564334005
1451614.62218801761751.3778119823825
1461014.0260117044624-4.02601170446238
1471815.76760082189082.2323991781092
1481315.0656604029652-2.06566040296517
1491614.9949020148961.00509798510404
1501312.95638437722960.0436156227703758
1511013.0229287128859-3.0229287128859
1521516.1384548925762-1.13845489257621
1531613.80891917517322.19108082482681
1541611.86476748491714.13523251508285
1551412.3457096842591.65429031574102
1561012.5206233415716-2.52062334157161
1571716.64322331805550.356776681944539
1581311.51910448113311.4808955188669
1591513.79221850895461.20778149104537
1601614.85459956009991.14540043990013
1611212.2957793236577-0.295779323657688
1621312.60709015098320.392909849016844







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4486066592534970.8972133185069940.551393340746503
120.8555500736453980.2888998527092040.144449926354602
130.8270699585138110.3458600829723790.172930041486189
140.7471813236026220.5056373527947570.252818676397378
150.6978558710044620.6042882579910770.302144128995538
160.7338414342450510.5323171315098980.266158565754949
170.6725046578282680.6549906843434640.327495342171732
180.8701977711199710.2596044577600580.129802228880029
190.8437131190126010.3125737619747970.156286880987399
200.79302343345760.41395313308480.2069765665424
210.7297402726027720.5405194547944570.270259727397229
220.6901368694046510.6197262611906980.309863130595349
230.6569771559720330.6860456880559350.343022844027968
240.6332110499261110.7335779001477780.366788950073889
250.5760205405531330.8479589188937350.423979459446867
260.5272615918190170.9454768163619650.472738408180983
270.4767341302161760.9534682604323510.523265869783824
280.5268661386101410.9462677227797180.473133861389859
290.4683533367394320.9367066734788650.531646663260568
300.4818419834153410.9636839668306820.518158016584659
310.4280943154306240.8561886308612480.571905684569376
320.4260226992294430.8520453984588860.573977300770557
330.4124433253022350.8248866506044690.587556674697765
340.3544906089640270.7089812179280540.645509391035973
350.3397720838730650.6795441677461310.660227916126935
360.8389319914532660.3221360170934680.161068008546734
370.8220022388755680.3559955222488640.177997761124432
380.8059035228947120.3881929542105770.194096477105288
390.8379217691317520.3241564617364960.162078230868248
400.8246556961699740.3506886076600510.175344303830026
410.7984735603927160.4030528792145690.201526439607284
420.7770922073522570.4458155852954860.222907792647743
430.7963143524141480.4073712951717030.203685647585852
440.7583841966177870.4832316067644250.241615803382213
450.7259241716047650.5481516567904710.274075828395235
460.8832785166531370.2334429666937270.116721483346863
470.8917518624743260.2164962750513490.108248137525674
480.8706670298317650.258665940336470.129332970168235
490.8561719849744670.2876560300510660.143828015025533
500.8493351628481130.3013296743037740.150664837151887
510.8211236274255260.3577527451489480.178876372574474
520.7894814014381240.4210371971237520.210518598561876
530.8047844972955320.3904310054089350.195215502704467
540.7782745027933120.4434509944133750.221725497206687
550.7987812393877720.4024375212244550.201218760612228
560.7891519744402040.4216960511195920.210848025559796
570.7536831696206970.4926336607586050.246316830379303
580.7420654117012630.5158691765974730.257934588298737
590.7072486852396660.5855026295206690.292751314760334
600.714477516415470.5710449671690610.28552248358453
610.6798939409417170.6402121181165660.320106059058283
620.639003551022110.721992897955780.36099644897789
630.5993113155254170.8013773689491660.400688684474583
640.555854602225840.888290795548320.44414539777416
650.5179424467561850.964115106487630.482057553243815
660.4926711801393510.9853423602787020.507328819860649
670.487299933640960.974599867281920.51270006635904
680.6066203324781390.7867593350437230.393379667521861
690.7330287515111640.5339424969776730.266971248488836
700.6992848825206490.6014302349587030.300715117479351
710.8031724483397680.3936551033204640.196827551660232
720.7736727077918420.4526545844163160.226327292208158
730.7652459119390390.4695081761219230.234754088060961
740.7428999812591160.5142000374817680.257100018740884
750.704224111546390.5915517769072210.29577588845361
760.7539064386598060.4921871226803870.246093561340194
770.7170617669141260.5658764661717480.282938233085874
780.6957060572470930.6085878855058130.304293942752907
790.7019989070817530.5960021858364950.298001092918247
800.6631052581429870.6737894837140260.336894741857013
810.6239138376929340.7521723246141330.376086162307066
820.7867498019851760.4265003960296490.213250198014825
830.7520315398264620.4959369203470770.247968460173538
840.7247301256288390.5505397487423220.275269874371161
850.6850129325856610.6299741348286780.314987067414339
860.6711116958472540.6577766083054910.328888304152746
870.6286382627588070.7427234744823850.371361737241193
880.5871078995707360.8257842008585280.412892100429264
890.5640636477167310.8718727045665390.435936352283269
900.5262331513577960.9475336972844080.473766848642204
910.5166095901300650.966780819739870.483390409869935
920.4760840398903690.9521680797807370.523915960109631
930.4332180286803680.8664360573607360.566781971319632
940.388078987540820.776157975081640.61192101245918
950.414131208570830.828262417141660.58586879142917
960.3761539031965760.7523078063931530.623846096803424
970.3339756053986410.6679512107972820.666024394601359
980.3264350056208230.6528700112416460.673564994379177
990.2842733580005710.5685467160011410.715726641999429
1000.2498076882795310.4996153765590620.750192311720469
1010.2270075836441040.4540151672882080.772992416355896
1020.2069408422105770.4138816844211530.793059157789423
1030.249539172157470.4990783443149390.75046082784253
1040.2127262737610730.4254525475221460.787273726238927
1050.205812233853220.411624467706440.79418776614678
1060.2170214841487260.4340429682974520.782978515851274
1070.1952969269810370.3905938539620740.804703073018963
1080.1739292529005550.347858505801110.826070747099445
1090.1693812607914820.3387625215829640.830618739208518
1100.1659158416124810.3318316832249620.834084158387519
1110.1523708989595420.3047417979190850.847629101040458
1120.1314083922648470.2628167845296950.868591607735153
1130.1728677899257180.3457355798514350.827132210074282
1140.1485926353093310.2971852706186620.851407364690669
1150.168267404985250.33653480997050.83173259501475
1160.1721740893719710.3443481787439420.827825910628029
1170.1476768276109660.2953536552219320.852323172389034
1180.1453075418861410.2906150837722820.854692458113859
1190.1351655437422990.2703310874845990.864834456257701
1200.151349185376260.302698370752520.84865081462374
1210.1251440047353240.2502880094706490.874855995264676
1220.1116541925160630.2233083850321270.888345807483937
1230.1075628649705270.2151257299410550.892437135029473
1240.08514807150659420.1702961430131880.914851928493406
1250.06662187894200030.1332437578840010.933378121058
1260.05090346316113710.1018069263222740.949096536838863
1270.03751935549989360.07503871099978720.962480644500106
1280.03830487495778930.07660974991557860.961695125042211
1290.03726502248909390.07453004497818770.962734977510906
1300.0369644058933030.07392881178660610.963035594106697
1310.04241924033199030.08483848066398070.95758075966801
1320.04382966417951750.0876593283590350.956170335820482
1330.09727011491954130.1945402298390830.902729885080459
1340.09826125097755290.1965225019551060.901738749022447
1350.07749328776236530.1549865755247310.922506712237635
1360.05966367308612580.1193273461722520.940336326913874
1370.04620191181357090.09240382362714170.953798088186429
1380.04233600402430720.08467200804861430.957663995975693
1390.04138384639884830.08276769279769650.958616153601152
1400.02885385836979630.05770771673959250.971146141630204
1410.4440387556731160.8880775113462330.555961244326884
1420.3908925952100580.7817851904201170.609107404789942
1430.3464844509721330.6929689019442660.653515549027867
1440.2752041388124330.5504082776248660.724795861187567
1450.2269140001625120.4538280003250250.773085999837488
1460.2226673520807620.4453347041615250.777332647919238
1470.3326755380714040.6653510761428090.667324461928596
1480.7047239024428720.5905521951142550.295276097557128
1490.6094826169244840.7810347661510320.390517383075516
1500.4672857941824210.9345715883648420.532714205817579
1510.7059022443093520.5881955113812950.294097755690648

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.448606659253497 & 0.897213318506994 & 0.551393340746503 \tabularnewline
12 & 0.855550073645398 & 0.288899852709204 & 0.144449926354602 \tabularnewline
13 & 0.827069958513811 & 0.345860082972379 & 0.172930041486189 \tabularnewline
14 & 0.747181323602622 & 0.505637352794757 & 0.252818676397378 \tabularnewline
15 & 0.697855871004462 & 0.604288257991077 & 0.302144128995538 \tabularnewline
16 & 0.733841434245051 & 0.532317131509898 & 0.266158565754949 \tabularnewline
17 & 0.672504657828268 & 0.654990684343464 & 0.327495342171732 \tabularnewline
18 & 0.870197771119971 & 0.259604457760058 & 0.129802228880029 \tabularnewline
19 & 0.843713119012601 & 0.312573761974797 & 0.156286880987399 \tabularnewline
20 & 0.7930234334576 & 0.4139531330848 & 0.2069765665424 \tabularnewline
21 & 0.729740272602772 & 0.540519454794457 & 0.270259727397229 \tabularnewline
22 & 0.690136869404651 & 0.619726261190698 & 0.309863130595349 \tabularnewline
23 & 0.656977155972033 & 0.686045688055935 & 0.343022844027968 \tabularnewline
24 & 0.633211049926111 & 0.733577900147778 & 0.366788950073889 \tabularnewline
25 & 0.576020540553133 & 0.847958918893735 & 0.423979459446867 \tabularnewline
26 & 0.527261591819017 & 0.945476816361965 & 0.472738408180983 \tabularnewline
27 & 0.476734130216176 & 0.953468260432351 & 0.523265869783824 \tabularnewline
28 & 0.526866138610141 & 0.946267722779718 & 0.473133861389859 \tabularnewline
29 & 0.468353336739432 & 0.936706673478865 & 0.531646663260568 \tabularnewline
30 & 0.481841983415341 & 0.963683966830682 & 0.518158016584659 \tabularnewline
31 & 0.428094315430624 & 0.856188630861248 & 0.571905684569376 \tabularnewline
32 & 0.426022699229443 & 0.852045398458886 & 0.573977300770557 \tabularnewline
33 & 0.412443325302235 & 0.824886650604469 & 0.587556674697765 \tabularnewline
34 & 0.354490608964027 & 0.708981217928054 & 0.645509391035973 \tabularnewline
35 & 0.339772083873065 & 0.679544167746131 & 0.660227916126935 \tabularnewline
36 & 0.838931991453266 & 0.322136017093468 & 0.161068008546734 \tabularnewline
37 & 0.822002238875568 & 0.355995522248864 & 0.177997761124432 \tabularnewline
38 & 0.805903522894712 & 0.388192954210577 & 0.194096477105288 \tabularnewline
39 & 0.837921769131752 & 0.324156461736496 & 0.162078230868248 \tabularnewline
40 & 0.824655696169974 & 0.350688607660051 & 0.175344303830026 \tabularnewline
41 & 0.798473560392716 & 0.403052879214569 & 0.201526439607284 \tabularnewline
42 & 0.777092207352257 & 0.445815585295486 & 0.222907792647743 \tabularnewline
43 & 0.796314352414148 & 0.407371295171703 & 0.203685647585852 \tabularnewline
44 & 0.758384196617787 & 0.483231606764425 & 0.241615803382213 \tabularnewline
45 & 0.725924171604765 & 0.548151656790471 & 0.274075828395235 \tabularnewline
46 & 0.883278516653137 & 0.233442966693727 & 0.116721483346863 \tabularnewline
47 & 0.891751862474326 & 0.216496275051349 & 0.108248137525674 \tabularnewline
48 & 0.870667029831765 & 0.25866594033647 & 0.129332970168235 \tabularnewline
49 & 0.856171984974467 & 0.287656030051066 & 0.143828015025533 \tabularnewline
50 & 0.849335162848113 & 0.301329674303774 & 0.150664837151887 \tabularnewline
51 & 0.821123627425526 & 0.357752745148948 & 0.178876372574474 \tabularnewline
52 & 0.789481401438124 & 0.421037197123752 & 0.210518598561876 \tabularnewline
53 & 0.804784497295532 & 0.390431005408935 & 0.195215502704467 \tabularnewline
54 & 0.778274502793312 & 0.443450994413375 & 0.221725497206687 \tabularnewline
55 & 0.798781239387772 & 0.402437521224455 & 0.201218760612228 \tabularnewline
56 & 0.789151974440204 & 0.421696051119592 & 0.210848025559796 \tabularnewline
57 & 0.753683169620697 & 0.492633660758605 & 0.246316830379303 \tabularnewline
58 & 0.742065411701263 & 0.515869176597473 & 0.257934588298737 \tabularnewline
59 & 0.707248685239666 & 0.585502629520669 & 0.292751314760334 \tabularnewline
60 & 0.71447751641547 & 0.571044967169061 & 0.28552248358453 \tabularnewline
61 & 0.679893940941717 & 0.640212118116566 & 0.320106059058283 \tabularnewline
62 & 0.63900355102211 & 0.72199289795578 & 0.36099644897789 \tabularnewline
63 & 0.599311315525417 & 0.801377368949166 & 0.400688684474583 \tabularnewline
64 & 0.55585460222584 & 0.88829079554832 & 0.44414539777416 \tabularnewline
65 & 0.517942446756185 & 0.96411510648763 & 0.482057553243815 \tabularnewline
66 & 0.492671180139351 & 0.985342360278702 & 0.507328819860649 \tabularnewline
67 & 0.48729993364096 & 0.97459986728192 & 0.51270006635904 \tabularnewline
68 & 0.606620332478139 & 0.786759335043723 & 0.393379667521861 \tabularnewline
69 & 0.733028751511164 & 0.533942496977673 & 0.266971248488836 \tabularnewline
70 & 0.699284882520649 & 0.601430234958703 & 0.300715117479351 \tabularnewline
71 & 0.803172448339768 & 0.393655103320464 & 0.196827551660232 \tabularnewline
72 & 0.773672707791842 & 0.452654584416316 & 0.226327292208158 \tabularnewline
73 & 0.765245911939039 & 0.469508176121923 & 0.234754088060961 \tabularnewline
74 & 0.742899981259116 & 0.514200037481768 & 0.257100018740884 \tabularnewline
75 & 0.70422411154639 & 0.591551776907221 & 0.29577588845361 \tabularnewline
76 & 0.753906438659806 & 0.492187122680387 & 0.246093561340194 \tabularnewline
77 & 0.717061766914126 & 0.565876466171748 & 0.282938233085874 \tabularnewline
78 & 0.695706057247093 & 0.608587885505813 & 0.304293942752907 \tabularnewline
79 & 0.701998907081753 & 0.596002185836495 & 0.298001092918247 \tabularnewline
80 & 0.663105258142987 & 0.673789483714026 & 0.336894741857013 \tabularnewline
81 & 0.623913837692934 & 0.752172324614133 & 0.376086162307066 \tabularnewline
82 & 0.786749801985176 & 0.426500396029649 & 0.213250198014825 \tabularnewline
83 & 0.752031539826462 & 0.495936920347077 & 0.247968460173538 \tabularnewline
84 & 0.724730125628839 & 0.550539748742322 & 0.275269874371161 \tabularnewline
85 & 0.685012932585661 & 0.629974134828678 & 0.314987067414339 \tabularnewline
86 & 0.671111695847254 & 0.657776608305491 & 0.328888304152746 \tabularnewline
87 & 0.628638262758807 & 0.742723474482385 & 0.371361737241193 \tabularnewline
88 & 0.587107899570736 & 0.825784200858528 & 0.412892100429264 \tabularnewline
89 & 0.564063647716731 & 0.871872704566539 & 0.435936352283269 \tabularnewline
90 & 0.526233151357796 & 0.947533697284408 & 0.473766848642204 \tabularnewline
91 & 0.516609590130065 & 0.96678081973987 & 0.483390409869935 \tabularnewline
92 & 0.476084039890369 & 0.952168079780737 & 0.523915960109631 \tabularnewline
93 & 0.433218028680368 & 0.866436057360736 & 0.566781971319632 \tabularnewline
94 & 0.38807898754082 & 0.77615797508164 & 0.61192101245918 \tabularnewline
95 & 0.41413120857083 & 0.82826241714166 & 0.58586879142917 \tabularnewline
96 & 0.376153903196576 & 0.752307806393153 & 0.623846096803424 \tabularnewline
97 & 0.333975605398641 & 0.667951210797282 & 0.666024394601359 \tabularnewline
98 & 0.326435005620823 & 0.652870011241646 & 0.673564994379177 \tabularnewline
99 & 0.284273358000571 & 0.568546716001141 & 0.715726641999429 \tabularnewline
100 & 0.249807688279531 & 0.499615376559062 & 0.750192311720469 \tabularnewline
101 & 0.227007583644104 & 0.454015167288208 & 0.772992416355896 \tabularnewline
102 & 0.206940842210577 & 0.413881684421153 & 0.793059157789423 \tabularnewline
103 & 0.24953917215747 & 0.499078344314939 & 0.75046082784253 \tabularnewline
104 & 0.212726273761073 & 0.425452547522146 & 0.787273726238927 \tabularnewline
105 & 0.20581223385322 & 0.41162446770644 & 0.79418776614678 \tabularnewline
106 & 0.217021484148726 & 0.434042968297452 & 0.782978515851274 \tabularnewline
107 & 0.195296926981037 & 0.390593853962074 & 0.804703073018963 \tabularnewline
108 & 0.173929252900555 & 0.34785850580111 & 0.826070747099445 \tabularnewline
109 & 0.169381260791482 & 0.338762521582964 & 0.830618739208518 \tabularnewline
110 & 0.165915841612481 & 0.331831683224962 & 0.834084158387519 \tabularnewline
111 & 0.152370898959542 & 0.304741797919085 & 0.847629101040458 \tabularnewline
112 & 0.131408392264847 & 0.262816784529695 & 0.868591607735153 \tabularnewline
113 & 0.172867789925718 & 0.345735579851435 & 0.827132210074282 \tabularnewline
114 & 0.148592635309331 & 0.297185270618662 & 0.851407364690669 \tabularnewline
115 & 0.16826740498525 & 0.3365348099705 & 0.83173259501475 \tabularnewline
116 & 0.172174089371971 & 0.344348178743942 & 0.827825910628029 \tabularnewline
117 & 0.147676827610966 & 0.295353655221932 & 0.852323172389034 \tabularnewline
118 & 0.145307541886141 & 0.290615083772282 & 0.854692458113859 \tabularnewline
119 & 0.135165543742299 & 0.270331087484599 & 0.864834456257701 \tabularnewline
120 & 0.15134918537626 & 0.30269837075252 & 0.84865081462374 \tabularnewline
121 & 0.125144004735324 & 0.250288009470649 & 0.874855995264676 \tabularnewline
122 & 0.111654192516063 & 0.223308385032127 & 0.888345807483937 \tabularnewline
123 & 0.107562864970527 & 0.215125729941055 & 0.892437135029473 \tabularnewline
124 & 0.0851480715065942 & 0.170296143013188 & 0.914851928493406 \tabularnewline
125 & 0.0666218789420003 & 0.133243757884001 & 0.933378121058 \tabularnewline
126 & 0.0509034631611371 & 0.101806926322274 & 0.949096536838863 \tabularnewline
127 & 0.0375193554998936 & 0.0750387109997872 & 0.962480644500106 \tabularnewline
128 & 0.0383048749577893 & 0.0766097499155786 & 0.961695125042211 \tabularnewline
129 & 0.0372650224890939 & 0.0745300449781877 & 0.962734977510906 \tabularnewline
130 & 0.036964405893303 & 0.0739288117866061 & 0.963035594106697 \tabularnewline
131 & 0.0424192403319903 & 0.0848384806639807 & 0.95758075966801 \tabularnewline
132 & 0.0438296641795175 & 0.087659328359035 & 0.956170335820482 \tabularnewline
133 & 0.0972701149195413 & 0.194540229839083 & 0.902729885080459 \tabularnewline
134 & 0.0982612509775529 & 0.196522501955106 & 0.901738749022447 \tabularnewline
135 & 0.0774932877623653 & 0.154986575524731 & 0.922506712237635 \tabularnewline
136 & 0.0596636730861258 & 0.119327346172252 & 0.940336326913874 \tabularnewline
137 & 0.0462019118135709 & 0.0924038236271417 & 0.953798088186429 \tabularnewline
138 & 0.0423360040243072 & 0.0846720080486143 & 0.957663995975693 \tabularnewline
139 & 0.0413838463988483 & 0.0827676927976965 & 0.958616153601152 \tabularnewline
140 & 0.0288538583697963 & 0.0577077167395925 & 0.971146141630204 \tabularnewline
141 & 0.444038755673116 & 0.888077511346233 & 0.555961244326884 \tabularnewline
142 & 0.390892595210058 & 0.781785190420117 & 0.609107404789942 \tabularnewline
143 & 0.346484450972133 & 0.692968901944266 & 0.653515549027867 \tabularnewline
144 & 0.275204138812433 & 0.550408277624866 & 0.724795861187567 \tabularnewline
145 & 0.226914000162512 & 0.453828000325025 & 0.773085999837488 \tabularnewline
146 & 0.222667352080762 & 0.445334704161525 & 0.777332647919238 \tabularnewline
147 & 0.332675538071404 & 0.665351076142809 & 0.667324461928596 \tabularnewline
148 & 0.704723902442872 & 0.590552195114255 & 0.295276097557128 \tabularnewline
149 & 0.609482616924484 & 0.781034766151032 & 0.390517383075516 \tabularnewline
150 & 0.467285794182421 & 0.934571588364842 & 0.532714205817579 \tabularnewline
151 & 0.705902244309352 & 0.588195511381295 & 0.294097755690648 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200539&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]0.448606659253497[/C][C]0.897213318506994[/C][C]0.551393340746503[/C][/ROW]
[ROW][C]12[/C][C]0.855550073645398[/C][C]0.288899852709204[/C][C]0.144449926354602[/C][/ROW]
[ROW][C]13[/C][C]0.827069958513811[/C][C]0.345860082972379[/C][C]0.172930041486189[/C][/ROW]
[ROW][C]14[/C][C]0.747181323602622[/C][C]0.505637352794757[/C][C]0.252818676397378[/C][/ROW]
[ROW][C]15[/C][C]0.697855871004462[/C][C]0.604288257991077[/C][C]0.302144128995538[/C][/ROW]
[ROW][C]16[/C][C]0.733841434245051[/C][C]0.532317131509898[/C][C]0.266158565754949[/C][/ROW]
[ROW][C]17[/C][C]0.672504657828268[/C][C]0.654990684343464[/C][C]0.327495342171732[/C][/ROW]
[ROW][C]18[/C][C]0.870197771119971[/C][C]0.259604457760058[/C][C]0.129802228880029[/C][/ROW]
[ROW][C]19[/C][C]0.843713119012601[/C][C]0.312573761974797[/C][C]0.156286880987399[/C][/ROW]
[ROW][C]20[/C][C]0.7930234334576[/C][C]0.4139531330848[/C][C]0.2069765665424[/C][/ROW]
[ROW][C]21[/C][C]0.729740272602772[/C][C]0.540519454794457[/C][C]0.270259727397229[/C][/ROW]
[ROW][C]22[/C][C]0.690136869404651[/C][C]0.619726261190698[/C][C]0.309863130595349[/C][/ROW]
[ROW][C]23[/C][C]0.656977155972033[/C][C]0.686045688055935[/C][C]0.343022844027968[/C][/ROW]
[ROW][C]24[/C][C]0.633211049926111[/C][C]0.733577900147778[/C][C]0.366788950073889[/C][/ROW]
[ROW][C]25[/C][C]0.576020540553133[/C][C]0.847958918893735[/C][C]0.423979459446867[/C][/ROW]
[ROW][C]26[/C][C]0.527261591819017[/C][C]0.945476816361965[/C][C]0.472738408180983[/C][/ROW]
[ROW][C]27[/C][C]0.476734130216176[/C][C]0.953468260432351[/C][C]0.523265869783824[/C][/ROW]
[ROW][C]28[/C][C]0.526866138610141[/C][C]0.946267722779718[/C][C]0.473133861389859[/C][/ROW]
[ROW][C]29[/C][C]0.468353336739432[/C][C]0.936706673478865[/C][C]0.531646663260568[/C][/ROW]
[ROW][C]30[/C][C]0.481841983415341[/C][C]0.963683966830682[/C][C]0.518158016584659[/C][/ROW]
[ROW][C]31[/C][C]0.428094315430624[/C][C]0.856188630861248[/C][C]0.571905684569376[/C][/ROW]
[ROW][C]32[/C][C]0.426022699229443[/C][C]0.852045398458886[/C][C]0.573977300770557[/C][/ROW]
[ROW][C]33[/C][C]0.412443325302235[/C][C]0.824886650604469[/C][C]0.587556674697765[/C][/ROW]
[ROW][C]34[/C][C]0.354490608964027[/C][C]0.708981217928054[/C][C]0.645509391035973[/C][/ROW]
[ROW][C]35[/C][C]0.339772083873065[/C][C]0.679544167746131[/C][C]0.660227916126935[/C][/ROW]
[ROW][C]36[/C][C]0.838931991453266[/C][C]0.322136017093468[/C][C]0.161068008546734[/C][/ROW]
[ROW][C]37[/C][C]0.822002238875568[/C][C]0.355995522248864[/C][C]0.177997761124432[/C][/ROW]
[ROW][C]38[/C][C]0.805903522894712[/C][C]0.388192954210577[/C][C]0.194096477105288[/C][/ROW]
[ROW][C]39[/C][C]0.837921769131752[/C][C]0.324156461736496[/C][C]0.162078230868248[/C][/ROW]
[ROW][C]40[/C][C]0.824655696169974[/C][C]0.350688607660051[/C][C]0.175344303830026[/C][/ROW]
[ROW][C]41[/C][C]0.798473560392716[/C][C]0.403052879214569[/C][C]0.201526439607284[/C][/ROW]
[ROW][C]42[/C][C]0.777092207352257[/C][C]0.445815585295486[/C][C]0.222907792647743[/C][/ROW]
[ROW][C]43[/C][C]0.796314352414148[/C][C]0.407371295171703[/C][C]0.203685647585852[/C][/ROW]
[ROW][C]44[/C][C]0.758384196617787[/C][C]0.483231606764425[/C][C]0.241615803382213[/C][/ROW]
[ROW][C]45[/C][C]0.725924171604765[/C][C]0.548151656790471[/C][C]0.274075828395235[/C][/ROW]
[ROW][C]46[/C][C]0.883278516653137[/C][C]0.233442966693727[/C][C]0.116721483346863[/C][/ROW]
[ROW][C]47[/C][C]0.891751862474326[/C][C]0.216496275051349[/C][C]0.108248137525674[/C][/ROW]
[ROW][C]48[/C][C]0.870667029831765[/C][C]0.25866594033647[/C][C]0.129332970168235[/C][/ROW]
[ROW][C]49[/C][C]0.856171984974467[/C][C]0.287656030051066[/C][C]0.143828015025533[/C][/ROW]
[ROW][C]50[/C][C]0.849335162848113[/C][C]0.301329674303774[/C][C]0.150664837151887[/C][/ROW]
[ROW][C]51[/C][C]0.821123627425526[/C][C]0.357752745148948[/C][C]0.178876372574474[/C][/ROW]
[ROW][C]52[/C][C]0.789481401438124[/C][C]0.421037197123752[/C][C]0.210518598561876[/C][/ROW]
[ROW][C]53[/C][C]0.804784497295532[/C][C]0.390431005408935[/C][C]0.195215502704467[/C][/ROW]
[ROW][C]54[/C][C]0.778274502793312[/C][C]0.443450994413375[/C][C]0.221725497206687[/C][/ROW]
[ROW][C]55[/C][C]0.798781239387772[/C][C]0.402437521224455[/C][C]0.201218760612228[/C][/ROW]
[ROW][C]56[/C][C]0.789151974440204[/C][C]0.421696051119592[/C][C]0.210848025559796[/C][/ROW]
[ROW][C]57[/C][C]0.753683169620697[/C][C]0.492633660758605[/C][C]0.246316830379303[/C][/ROW]
[ROW][C]58[/C][C]0.742065411701263[/C][C]0.515869176597473[/C][C]0.257934588298737[/C][/ROW]
[ROW][C]59[/C][C]0.707248685239666[/C][C]0.585502629520669[/C][C]0.292751314760334[/C][/ROW]
[ROW][C]60[/C][C]0.71447751641547[/C][C]0.571044967169061[/C][C]0.28552248358453[/C][/ROW]
[ROW][C]61[/C][C]0.679893940941717[/C][C]0.640212118116566[/C][C]0.320106059058283[/C][/ROW]
[ROW][C]62[/C][C]0.63900355102211[/C][C]0.72199289795578[/C][C]0.36099644897789[/C][/ROW]
[ROW][C]63[/C][C]0.599311315525417[/C][C]0.801377368949166[/C][C]0.400688684474583[/C][/ROW]
[ROW][C]64[/C][C]0.55585460222584[/C][C]0.88829079554832[/C][C]0.44414539777416[/C][/ROW]
[ROW][C]65[/C][C]0.517942446756185[/C][C]0.96411510648763[/C][C]0.482057553243815[/C][/ROW]
[ROW][C]66[/C][C]0.492671180139351[/C][C]0.985342360278702[/C][C]0.507328819860649[/C][/ROW]
[ROW][C]67[/C][C]0.48729993364096[/C][C]0.97459986728192[/C][C]0.51270006635904[/C][/ROW]
[ROW][C]68[/C][C]0.606620332478139[/C][C]0.786759335043723[/C][C]0.393379667521861[/C][/ROW]
[ROW][C]69[/C][C]0.733028751511164[/C][C]0.533942496977673[/C][C]0.266971248488836[/C][/ROW]
[ROW][C]70[/C][C]0.699284882520649[/C][C]0.601430234958703[/C][C]0.300715117479351[/C][/ROW]
[ROW][C]71[/C][C]0.803172448339768[/C][C]0.393655103320464[/C][C]0.196827551660232[/C][/ROW]
[ROW][C]72[/C][C]0.773672707791842[/C][C]0.452654584416316[/C][C]0.226327292208158[/C][/ROW]
[ROW][C]73[/C][C]0.765245911939039[/C][C]0.469508176121923[/C][C]0.234754088060961[/C][/ROW]
[ROW][C]74[/C][C]0.742899981259116[/C][C]0.514200037481768[/C][C]0.257100018740884[/C][/ROW]
[ROW][C]75[/C][C]0.70422411154639[/C][C]0.591551776907221[/C][C]0.29577588845361[/C][/ROW]
[ROW][C]76[/C][C]0.753906438659806[/C][C]0.492187122680387[/C][C]0.246093561340194[/C][/ROW]
[ROW][C]77[/C][C]0.717061766914126[/C][C]0.565876466171748[/C][C]0.282938233085874[/C][/ROW]
[ROW][C]78[/C][C]0.695706057247093[/C][C]0.608587885505813[/C][C]0.304293942752907[/C][/ROW]
[ROW][C]79[/C][C]0.701998907081753[/C][C]0.596002185836495[/C][C]0.298001092918247[/C][/ROW]
[ROW][C]80[/C][C]0.663105258142987[/C][C]0.673789483714026[/C][C]0.336894741857013[/C][/ROW]
[ROW][C]81[/C][C]0.623913837692934[/C][C]0.752172324614133[/C][C]0.376086162307066[/C][/ROW]
[ROW][C]82[/C][C]0.786749801985176[/C][C]0.426500396029649[/C][C]0.213250198014825[/C][/ROW]
[ROW][C]83[/C][C]0.752031539826462[/C][C]0.495936920347077[/C][C]0.247968460173538[/C][/ROW]
[ROW][C]84[/C][C]0.724730125628839[/C][C]0.550539748742322[/C][C]0.275269874371161[/C][/ROW]
[ROW][C]85[/C][C]0.685012932585661[/C][C]0.629974134828678[/C][C]0.314987067414339[/C][/ROW]
[ROW][C]86[/C][C]0.671111695847254[/C][C]0.657776608305491[/C][C]0.328888304152746[/C][/ROW]
[ROW][C]87[/C][C]0.628638262758807[/C][C]0.742723474482385[/C][C]0.371361737241193[/C][/ROW]
[ROW][C]88[/C][C]0.587107899570736[/C][C]0.825784200858528[/C][C]0.412892100429264[/C][/ROW]
[ROW][C]89[/C][C]0.564063647716731[/C][C]0.871872704566539[/C][C]0.435936352283269[/C][/ROW]
[ROW][C]90[/C][C]0.526233151357796[/C][C]0.947533697284408[/C][C]0.473766848642204[/C][/ROW]
[ROW][C]91[/C][C]0.516609590130065[/C][C]0.96678081973987[/C][C]0.483390409869935[/C][/ROW]
[ROW][C]92[/C][C]0.476084039890369[/C][C]0.952168079780737[/C][C]0.523915960109631[/C][/ROW]
[ROW][C]93[/C][C]0.433218028680368[/C][C]0.866436057360736[/C][C]0.566781971319632[/C][/ROW]
[ROW][C]94[/C][C]0.38807898754082[/C][C]0.77615797508164[/C][C]0.61192101245918[/C][/ROW]
[ROW][C]95[/C][C]0.41413120857083[/C][C]0.82826241714166[/C][C]0.58586879142917[/C][/ROW]
[ROW][C]96[/C][C]0.376153903196576[/C][C]0.752307806393153[/C][C]0.623846096803424[/C][/ROW]
[ROW][C]97[/C][C]0.333975605398641[/C][C]0.667951210797282[/C][C]0.666024394601359[/C][/ROW]
[ROW][C]98[/C][C]0.326435005620823[/C][C]0.652870011241646[/C][C]0.673564994379177[/C][/ROW]
[ROW][C]99[/C][C]0.284273358000571[/C][C]0.568546716001141[/C][C]0.715726641999429[/C][/ROW]
[ROW][C]100[/C][C]0.249807688279531[/C][C]0.499615376559062[/C][C]0.750192311720469[/C][/ROW]
[ROW][C]101[/C][C]0.227007583644104[/C][C]0.454015167288208[/C][C]0.772992416355896[/C][/ROW]
[ROW][C]102[/C][C]0.206940842210577[/C][C]0.413881684421153[/C][C]0.793059157789423[/C][/ROW]
[ROW][C]103[/C][C]0.24953917215747[/C][C]0.499078344314939[/C][C]0.75046082784253[/C][/ROW]
[ROW][C]104[/C][C]0.212726273761073[/C][C]0.425452547522146[/C][C]0.787273726238927[/C][/ROW]
[ROW][C]105[/C][C]0.20581223385322[/C][C]0.41162446770644[/C][C]0.79418776614678[/C][/ROW]
[ROW][C]106[/C][C]0.217021484148726[/C][C]0.434042968297452[/C][C]0.782978515851274[/C][/ROW]
[ROW][C]107[/C][C]0.195296926981037[/C][C]0.390593853962074[/C][C]0.804703073018963[/C][/ROW]
[ROW][C]108[/C][C]0.173929252900555[/C][C]0.34785850580111[/C][C]0.826070747099445[/C][/ROW]
[ROW][C]109[/C][C]0.169381260791482[/C][C]0.338762521582964[/C][C]0.830618739208518[/C][/ROW]
[ROW][C]110[/C][C]0.165915841612481[/C][C]0.331831683224962[/C][C]0.834084158387519[/C][/ROW]
[ROW][C]111[/C][C]0.152370898959542[/C][C]0.304741797919085[/C][C]0.847629101040458[/C][/ROW]
[ROW][C]112[/C][C]0.131408392264847[/C][C]0.262816784529695[/C][C]0.868591607735153[/C][/ROW]
[ROW][C]113[/C][C]0.172867789925718[/C][C]0.345735579851435[/C][C]0.827132210074282[/C][/ROW]
[ROW][C]114[/C][C]0.148592635309331[/C][C]0.297185270618662[/C][C]0.851407364690669[/C][/ROW]
[ROW][C]115[/C][C]0.16826740498525[/C][C]0.3365348099705[/C][C]0.83173259501475[/C][/ROW]
[ROW][C]116[/C][C]0.172174089371971[/C][C]0.344348178743942[/C][C]0.827825910628029[/C][/ROW]
[ROW][C]117[/C][C]0.147676827610966[/C][C]0.295353655221932[/C][C]0.852323172389034[/C][/ROW]
[ROW][C]118[/C][C]0.145307541886141[/C][C]0.290615083772282[/C][C]0.854692458113859[/C][/ROW]
[ROW][C]119[/C][C]0.135165543742299[/C][C]0.270331087484599[/C][C]0.864834456257701[/C][/ROW]
[ROW][C]120[/C][C]0.15134918537626[/C][C]0.30269837075252[/C][C]0.84865081462374[/C][/ROW]
[ROW][C]121[/C][C]0.125144004735324[/C][C]0.250288009470649[/C][C]0.874855995264676[/C][/ROW]
[ROW][C]122[/C][C]0.111654192516063[/C][C]0.223308385032127[/C][C]0.888345807483937[/C][/ROW]
[ROW][C]123[/C][C]0.107562864970527[/C][C]0.215125729941055[/C][C]0.892437135029473[/C][/ROW]
[ROW][C]124[/C][C]0.0851480715065942[/C][C]0.170296143013188[/C][C]0.914851928493406[/C][/ROW]
[ROW][C]125[/C][C]0.0666218789420003[/C][C]0.133243757884001[/C][C]0.933378121058[/C][/ROW]
[ROW][C]126[/C][C]0.0509034631611371[/C][C]0.101806926322274[/C][C]0.949096536838863[/C][/ROW]
[ROW][C]127[/C][C]0.0375193554998936[/C][C]0.0750387109997872[/C][C]0.962480644500106[/C][/ROW]
[ROW][C]128[/C][C]0.0383048749577893[/C][C]0.0766097499155786[/C][C]0.961695125042211[/C][/ROW]
[ROW][C]129[/C][C]0.0372650224890939[/C][C]0.0745300449781877[/C][C]0.962734977510906[/C][/ROW]
[ROW][C]130[/C][C]0.036964405893303[/C][C]0.0739288117866061[/C][C]0.963035594106697[/C][/ROW]
[ROW][C]131[/C][C]0.0424192403319903[/C][C]0.0848384806639807[/C][C]0.95758075966801[/C][/ROW]
[ROW][C]132[/C][C]0.0438296641795175[/C][C]0.087659328359035[/C][C]0.956170335820482[/C][/ROW]
[ROW][C]133[/C][C]0.0972701149195413[/C][C]0.194540229839083[/C][C]0.902729885080459[/C][/ROW]
[ROW][C]134[/C][C]0.0982612509775529[/C][C]0.196522501955106[/C][C]0.901738749022447[/C][/ROW]
[ROW][C]135[/C][C]0.0774932877623653[/C][C]0.154986575524731[/C][C]0.922506712237635[/C][/ROW]
[ROW][C]136[/C][C]0.0596636730861258[/C][C]0.119327346172252[/C][C]0.940336326913874[/C][/ROW]
[ROW][C]137[/C][C]0.0462019118135709[/C][C]0.0924038236271417[/C][C]0.953798088186429[/C][/ROW]
[ROW][C]138[/C][C]0.0423360040243072[/C][C]0.0846720080486143[/C][C]0.957663995975693[/C][/ROW]
[ROW][C]139[/C][C]0.0413838463988483[/C][C]0.0827676927976965[/C][C]0.958616153601152[/C][/ROW]
[ROW][C]140[/C][C]0.0288538583697963[/C][C]0.0577077167395925[/C][C]0.971146141630204[/C][/ROW]
[ROW][C]141[/C][C]0.444038755673116[/C][C]0.888077511346233[/C][C]0.555961244326884[/C][/ROW]
[ROW][C]142[/C][C]0.390892595210058[/C][C]0.781785190420117[/C][C]0.609107404789942[/C][/ROW]
[ROW][C]143[/C][C]0.346484450972133[/C][C]0.692968901944266[/C][C]0.653515549027867[/C][/ROW]
[ROW][C]144[/C][C]0.275204138812433[/C][C]0.550408277624866[/C][C]0.724795861187567[/C][/ROW]
[ROW][C]145[/C][C]0.226914000162512[/C][C]0.453828000325025[/C][C]0.773085999837488[/C][/ROW]
[ROW][C]146[/C][C]0.222667352080762[/C][C]0.445334704161525[/C][C]0.777332647919238[/C][/ROW]
[ROW][C]147[/C][C]0.332675538071404[/C][C]0.665351076142809[/C][C]0.667324461928596[/C][/ROW]
[ROW][C]148[/C][C]0.704723902442872[/C][C]0.590552195114255[/C][C]0.295276097557128[/C][/ROW]
[ROW][C]149[/C][C]0.609482616924484[/C][C]0.781034766151032[/C][C]0.390517383075516[/C][/ROW]
[ROW][C]150[/C][C]0.467285794182421[/C][C]0.934571588364842[/C][C]0.532714205817579[/C][/ROW]
[ROW][C]151[/C][C]0.705902244309352[/C][C]0.588195511381295[/C][C]0.294097755690648[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200539&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200539&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
110.4486066592534970.8972133185069940.551393340746503
120.8555500736453980.2888998527092040.144449926354602
130.8270699585138110.3458600829723790.172930041486189
140.7471813236026220.5056373527947570.252818676397378
150.6978558710044620.6042882579910770.302144128995538
160.7338414342450510.5323171315098980.266158565754949
170.6725046578282680.6549906843434640.327495342171732
180.8701977711199710.2596044577600580.129802228880029
190.8437131190126010.3125737619747970.156286880987399
200.79302343345760.41395313308480.2069765665424
210.7297402726027720.5405194547944570.270259727397229
220.6901368694046510.6197262611906980.309863130595349
230.6569771559720330.6860456880559350.343022844027968
240.6332110499261110.7335779001477780.366788950073889
250.5760205405531330.8479589188937350.423979459446867
260.5272615918190170.9454768163619650.472738408180983
270.4767341302161760.9534682604323510.523265869783824
280.5268661386101410.9462677227797180.473133861389859
290.4683533367394320.9367066734788650.531646663260568
300.4818419834153410.9636839668306820.518158016584659
310.4280943154306240.8561886308612480.571905684569376
320.4260226992294430.8520453984588860.573977300770557
330.4124433253022350.8248866506044690.587556674697765
340.3544906089640270.7089812179280540.645509391035973
350.3397720838730650.6795441677461310.660227916126935
360.8389319914532660.3221360170934680.161068008546734
370.8220022388755680.3559955222488640.177997761124432
380.8059035228947120.3881929542105770.194096477105288
390.8379217691317520.3241564617364960.162078230868248
400.8246556961699740.3506886076600510.175344303830026
410.7984735603927160.4030528792145690.201526439607284
420.7770922073522570.4458155852954860.222907792647743
430.7963143524141480.4073712951717030.203685647585852
440.7583841966177870.4832316067644250.241615803382213
450.7259241716047650.5481516567904710.274075828395235
460.8832785166531370.2334429666937270.116721483346863
470.8917518624743260.2164962750513490.108248137525674
480.8706670298317650.258665940336470.129332970168235
490.8561719849744670.2876560300510660.143828015025533
500.8493351628481130.3013296743037740.150664837151887
510.8211236274255260.3577527451489480.178876372574474
520.7894814014381240.4210371971237520.210518598561876
530.8047844972955320.3904310054089350.195215502704467
540.7782745027933120.4434509944133750.221725497206687
550.7987812393877720.4024375212244550.201218760612228
560.7891519744402040.4216960511195920.210848025559796
570.7536831696206970.4926336607586050.246316830379303
580.7420654117012630.5158691765974730.257934588298737
590.7072486852396660.5855026295206690.292751314760334
600.714477516415470.5710449671690610.28552248358453
610.6798939409417170.6402121181165660.320106059058283
620.639003551022110.721992897955780.36099644897789
630.5993113155254170.8013773689491660.400688684474583
640.555854602225840.888290795548320.44414539777416
650.5179424467561850.964115106487630.482057553243815
660.4926711801393510.9853423602787020.507328819860649
670.487299933640960.974599867281920.51270006635904
680.6066203324781390.7867593350437230.393379667521861
690.7330287515111640.5339424969776730.266971248488836
700.6992848825206490.6014302349587030.300715117479351
710.8031724483397680.3936551033204640.196827551660232
720.7736727077918420.4526545844163160.226327292208158
730.7652459119390390.4695081761219230.234754088060961
740.7428999812591160.5142000374817680.257100018740884
750.704224111546390.5915517769072210.29577588845361
760.7539064386598060.4921871226803870.246093561340194
770.7170617669141260.5658764661717480.282938233085874
780.6957060572470930.6085878855058130.304293942752907
790.7019989070817530.5960021858364950.298001092918247
800.6631052581429870.6737894837140260.336894741857013
810.6239138376929340.7521723246141330.376086162307066
820.7867498019851760.4265003960296490.213250198014825
830.7520315398264620.4959369203470770.247968460173538
840.7247301256288390.5505397487423220.275269874371161
850.6850129325856610.6299741348286780.314987067414339
860.6711116958472540.6577766083054910.328888304152746
870.6286382627588070.7427234744823850.371361737241193
880.5871078995707360.8257842008585280.412892100429264
890.5640636477167310.8718727045665390.435936352283269
900.5262331513577960.9475336972844080.473766848642204
910.5166095901300650.966780819739870.483390409869935
920.4760840398903690.9521680797807370.523915960109631
930.4332180286803680.8664360573607360.566781971319632
940.388078987540820.776157975081640.61192101245918
950.414131208570830.828262417141660.58586879142917
960.3761539031965760.7523078063931530.623846096803424
970.3339756053986410.6679512107972820.666024394601359
980.3264350056208230.6528700112416460.673564994379177
990.2842733580005710.5685467160011410.715726641999429
1000.2498076882795310.4996153765590620.750192311720469
1010.2270075836441040.4540151672882080.772992416355896
1020.2069408422105770.4138816844211530.793059157789423
1030.249539172157470.4990783443149390.75046082784253
1040.2127262737610730.4254525475221460.787273726238927
1050.205812233853220.411624467706440.79418776614678
1060.2170214841487260.4340429682974520.782978515851274
1070.1952969269810370.3905938539620740.804703073018963
1080.1739292529005550.347858505801110.826070747099445
1090.1693812607914820.3387625215829640.830618739208518
1100.1659158416124810.3318316832249620.834084158387519
1110.1523708989595420.3047417979190850.847629101040458
1120.1314083922648470.2628167845296950.868591607735153
1130.1728677899257180.3457355798514350.827132210074282
1140.1485926353093310.2971852706186620.851407364690669
1150.168267404985250.33653480997050.83173259501475
1160.1721740893719710.3443481787439420.827825910628029
1170.1476768276109660.2953536552219320.852323172389034
1180.1453075418861410.2906150837722820.854692458113859
1190.1351655437422990.2703310874845990.864834456257701
1200.151349185376260.302698370752520.84865081462374
1210.1251440047353240.2502880094706490.874855995264676
1220.1116541925160630.2233083850321270.888345807483937
1230.1075628649705270.2151257299410550.892437135029473
1240.08514807150659420.1702961430131880.914851928493406
1250.06662187894200030.1332437578840010.933378121058
1260.05090346316113710.1018069263222740.949096536838863
1270.03751935549989360.07503871099978720.962480644500106
1280.03830487495778930.07660974991557860.961695125042211
1290.03726502248909390.07453004497818770.962734977510906
1300.0369644058933030.07392881178660610.963035594106697
1310.04241924033199030.08483848066398070.95758075966801
1320.04382966417951750.0876593283590350.956170335820482
1330.09727011491954130.1945402298390830.902729885080459
1340.09826125097755290.1965225019551060.901738749022447
1350.07749328776236530.1549865755247310.922506712237635
1360.05966367308612580.1193273461722520.940336326913874
1370.04620191181357090.09240382362714170.953798088186429
1380.04233600402430720.08467200804861430.957663995975693
1390.04138384639884830.08276769279769650.958616153601152
1400.02885385836979630.05770771673959250.971146141630204
1410.4440387556731160.8880775113462330.555961244326884
1420.3908925952100580.7817851904201170.609107404789942
1430.3464844509721330.6929689019442660.653515549027867
1440.2752041388124330.5504082776248660.724795861187567
1450.2269140001625120.4538280003250250.773085999837488
1460.2226673520807620.4453347041615250.777332647919238
1470.3326755380714040.6653510761428090.667324461928596
1480.7047239024428720.5905521951142550.295276097557128
1490.6094826169244840.7810347661510320.390517383075516
1500.4672857941824210.9345715883648420.532714205817579
1510.7059022443093520.5881955113812950.294097755690648







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level100.0709219858156028OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 10 & 0.0709219858156028 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200539&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.0709219858156028[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200539&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200539&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 level00OK
10% type I error level100.0709219858156028OK



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