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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 12 Nov 2014 14:23:59 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Nov/12/t141580299283ovdchbgiqw7zy.htm/, Retrieved Sun, 19 May 2024 14:06:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253882, Retrieved Sun, 19 May 2024 14:06:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact67

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- RMPD    [Multiple Regression] [] [2014-11-12 14:23:59] [baa7d013c3374cabca6c222951a47a9f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253882&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253882&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253882&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 27.8613571391079 -0.0183401869867956Connected[t] + 0.0105744763822826Separate[t] -0.156991363412531Learning[t] -0.0485940649756675Software[t] -0.657509864687547Happiness[t] -0.0361091310283581`Belonging\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  27.8613571391079 -0.0183401869867956Connected[t] +  0.0105744763822826Separate[t] -0.156991363412531Learning[t] -0.0485940649756675Software[t] -0.657509864687547Happiness[t] -0.0361091310283581`Belonging\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253882&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  27.8613571391079 -0.0183401869867956Connected[t] +  0.0105744763822826Separate[t] -0.156991363412531Learning[t] -0.0485940649756675Software[t] -0.657509864687547Happiness[t] -0.0361091310283581`Belonging\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253882&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253882&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 27.8613571391079 -0.0183401869867956Connected[t] + 0.0105744763822826Separate[t] -0.156991363412531Learning[t] -0.0485940649756675Software[t] -0.657509864687547Happiness[t] -0.0361091310283581`Belonging\r`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)27.86135713910792.9999359.287300
Connected-0.01834018698679560.068201-0.26890.7883530.394177
Separate0.01057447638228260.0638170.16570.8686080.434304
Learning-0.1569913634125310.114122-1.37560.1709150.085457
Software-0.04859406497566750.116512-0.41710.6772030.338601
Happiness-0.6575098646875470.095536-6.882300
`Belonging\r`-0.03610913102835810.020374-1.77230.0783060.039153

\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) & 27.8613571391079 & 2.999935 & 9.2873 & 0 & 0 \tabularnewline
Connected & -0.0183401869867956 & 0.068201 & -0.2689 & 0.788353 & 0.394177 \tabularnewline
Separate & 0.0105744763822826 & 0.063817 & 0.1657 & 0.868608 & 0.434304 \tabularnewline
Learning & -0.156991363412531 & 0.114122 & -1.3756 & 0.170915 & 0.085457 \tabularnewline
Software & -0.0485940649756675 & 0.116512 & -0.4171 & 0.677203 & 0.338601 \tabularnewline
Happiness & -0.657509864687547 & 0.095536 & -6.8823 & 0 & 0 \tabularnewline
`Belonging\r` & -0.0361091310283581 & 0.020374 & -1.7723 & 0.078306 & 0.039153 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253882&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]27.8613571391079[/C][C]2.999935[/C][C]9.2873[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0183401869867956[/C][C]0.068201[/C][C]-0.2689[/C][C]0.788353[/C][C]0.394177[/C][/ROW]
[ROW][C]Separate[/C][C]0.0105744763822826[/C][C]0.063817[/C][C]0.1657[/C][C]0.868608[/C][C]0.434304[/C][/ROW]
[ROW][C]Learning[/C][C]-0.156991363412531[/C][C]0.114122[/C][C]-1.3756[/C][C]0.170915[/C][C]0.085457[/C][/ROW]
[ROW][C]Software[/C][C]-0.0485940649756675[/C][C]0.116512[/C][C]-0.4171[/C][C]0.677203[/C][C]0.338601[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.657509864687547[/C][C]0.095536[/C][C]-6.8823[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Belonging\r`[/C][C]-0.0361091310283581[/C][C]0.020374[/C][C]-1.7723[/C][C]0.078306[/C][C]0.039153[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253882&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253882&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)27.86135713910792.9999359.287300
Connected-0.01834018698679560.068201-0.26890.7883530.394177
Separate0.01057447638228260.0638170.16570.8686080.434304
Learning-0.1569913634125310.114122-1.37560.1709150.085457
Software-0.04859406497566750.116512-0.41710.6772030.338601
Happiness-0.6575098646875470.095536-6.882300
`Belonging\r`-0.03610913102835810.020374-1.77230.0783060.039153






Multiple Linear Regression - Regression Statistics
Multiple R0.574107881560066
R-squared0.329599859669386
Adjusted R-squared0.303648886495298
F-TEST (value)12.7008670333214
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value1.2404077764927e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.6420872230313
Sum Squared Residuals1081.99685858631

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.574107881560066 \tabularnewline
R-squared & 0.329599859669386 \tabularnewline
Adjusted R-squared & 0.303648886495298 \tabularnewline
F-TEST (value) & 12.7008670333214 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 1.2404077764927e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.6420872230313 \tabularnewline
Sum Squared Residuals & 1081.99685858631 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253882&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.574107881560066[/C][/ROW]
[ROW][C]R-squared[/C][C]0.329599859669386[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.303648886495298[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.7008670333214[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]1.2404077764927e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.6420872230313[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1081.99685858631[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253882&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253882&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.574107881560066
R-squared0.329599859669386
Adjusted R-squared0.303648886495298
F-TEST (value)12.7008670333214
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value1.2404077764927e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.6420872230313
Sum Squared Residuals1081.99685858631






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11213.7683010209765-1.76830102097655
2119.497513728708461.50248627129154
31414.3537001639762-0.353700163976197
41214.6859040669401-2.68590406694006
52111.53499268208629.46500731791375
61210.34759225094891.65240774905108
72212.78901187979899.21098812020108
81112.4894159825774-1.48941598257743
91012.255485547068-2.25548554706803
101312.33122355361280.66877644638716
111010.4641472861747-0.464147286174695
12810.0230715056777-2.02307150567766
131515.4451356869056-0.445135686905615
141412.03147250928471.96852749071529
15109.272950478197090.727049521802911
161413.38596193997940.614038060020609
171412.78535961811921.21464038188083
18119.488032857993991.51196714200601
191012.6714182015512-2.67141820155116
201311.93948550183781.06051449816216
21710.3586521345655-3.35865213456547
221414.8376000945681-0.837600094568149
231212.7783300184709-0.778330018470876
241414.6365485563341-0.636548556334055
251110.09522035993480.904779640065188
26915.3408974297291-6.34089742972907
271111.30896282345-0.308962823450038
281512.91553108725852.08446891274155
291411.83093790432542.16906209567464
301315.2510084741771-2.25100847417708
31911.2420091330558-2.2420091330558
321514.93438513747230.0656148625276699
331011.0853432482134-1.08534324821342
341111.6382408366975-0.638240836697541
351312.89680340381780.103196596182248
36813.1004247394142-5.10042473941423
372017.60335842338782.39664157661218
381212.3426639873885-0.342663987388537
391010.7375363621409-0.737536362140931
401013.5314475689334-3.53144756893342
41911.9886273340607-2.98862733406066
421411.57739797256112.42260202743885
43811.164024124425-3.16402412442502
441415.1167953879281-1.11679538792811
451114.3950472175742-3.39504721757422
461315.5548136952392-2.55481369523916
47912.5374911929031-3.53749119290312
481112.1554252021809-1.15542520218091
491510.70680058499044.29319941500961
501113.4659923422943-2.46599234229432
511011.3641072647548-1.36410726475483
521413.28343582494120.716564175058814
531815.64891996913782.35108003086217
541414.3671517247907-0.367151724790675
551115.7919681731353-4.79196817313527
561212.4247680587317-0.424768058731675
571311.21839791821271.78160208178725
58912.3120735699668-3.31207356996676
591014.1479612631177-4.14796126311765
601514.16342277897490.836577221025113
612018.20931919042461.79068080957543
621213.1009403061771-1.10094030617711
631214.4027071299303-2.40270712993033
641413.84769129428910.152308705710901
651312.65806790925460.341932090745411
661114.8463010688737-3.84630106887371
671714.44924447044362.55075552955638
681213.7101981943471-1.71019819434708
691312.96452053615610.0354794638439449
701412.16153761513611.83846238486388
711313.9619951449156-0.961995144915568
721512.49217673200412.50782326799586
731311.35654632577081.64345367422922
741011.7376441029285-1.73764410292846
751112.8475158022498-1.84751580224981
761913.57827551677815.42172448322187
771310.72513431578342.27486568421659
781713.480044738183.51995526182002
791312.40857310776070.591426892239281
80913.8757691721402-4.87576917214016
811111.8265522024438-0.826552202443845
821012.2582125093131-2.25821250931311
83912.0893193530794-3.0893193530794
841211.43760431024490.562395689755053
851212.2820927822528-0.282092782252781
861312.6005960772280.39940392277197
871312.37077614252220.629223857477788
881212.8240044246504-0.824004424650408
891514.07536304994590.924636950054056
902217.53688123346514.46311876653487
911311.53861528178961.46138471821038
921513.64420818269881.35579181730116
931311.91496247243841.08503752756162
941512.46647041338742.53352958661262
951013.5863507806817-3.58635078068166
961111.1200802147585-0.120080214758501
971614.02764381748491.97235618251507
981112.5764006372017-1.57640063720175
991110.34480451030030.655195489699713
1001012.3643906392921-2.36439063929211
1011010.4309708012685-0.430970801268512
1021613.98039542332282.01960457667716
1031210.13419381843091.86580618156908
1041115.0653401688794-4.06534016887942
1051611.90686969656644.09313030343364
1061916.4691067076822.53089329231804
1071111.0181029561126-0.0181029561125804
1081612.00042718392323.99957281607681
1091515.9998288292235-0.999828829223529
1102417.05668361677166.9433163832284
1111411.75209641755462.24790358244541
1121514.54490613121120.45509386878877
1131112.7794050230251-1.77940502302506
1141514.47072135234730.529278647652716
1151210.5929938843561.40700611564401
1161010.4380429752807-0.438042975280748
1171413.69447691021890.30552308978113
1181313.3413609516567-0.341360951656683
119913.3541470562459-4.35414705624595
1201511.99516069088553.00483930911453
1211515.231952703811-0.231952703810982
1221412.48530548177381.51469451822615
1231111.8764574766107-0.876457476610727
124811.785479670395-3.78547967039497
1251112.0987319051394-1.09873190513944
1261113.0007466817187-2.00074668171868
127810.2161724976708-2.21617249767077
1281010.4136622020658-0.413662202065774
129119.427068039976481.57293196002352
1301312.68747183514770.312528164852296
1311113.5142498678371-2.51424986783709
1322017.28097536879352.71902463120652
1331011.9753398918779-1.97533989187795
1341513.05523381909371.94476618090627
1351212.2688687892281-0.26886878922808
1361411.16202516039462.83797483960543
1372315.91973448764967.0802655123504
1381413.23847892860420.761521071395758
1391616.2859109684951-0.28591096849508
1401113.1327015546839-2.13270155468386
1411214.9304822419211-2.93048224192108
1421012.9208479722777-2.9208479722777
1431411.32514184914992.67485815085014
1441211.97297067617150.0270293238285449
1451211.84657671048010.153423289519854
1461111.7630217770316-0.763021777031591
1471210.83796283858691.16203716141311
1481315.55861868001-2.55861868001004
1491113.9089861256953-2.9089861256953
1501916.52604418240112.47395581759885
1511212.0773285511525-0.077328551152544
1521713.09548202375693.90451797624308
153911.6065063466879-2.60650634668794
1541214.3384531587323-2.33845315873226
1551916.58323236084232.41676763915772
1561814.26113312131373.73886687868626
1571513.64420818269881.35579181730116
1581413.63660652087570.363393479124349
159119.427068039976481.57293196002352
160912.9489822491369-3.94898224913689
1611814.4486435655223.551356434478
1621614.1496734052561.85032659474401

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 13.7683010209765 & -1.76830102097655 \tabularnewline
2 & 11 & 9.49751372870846 & 1.50248627129154 \tabularnewline
3 & 14 & 14.3537001639762 & -0.353700163976197 \tabularnewline
4 & 12 & 14.6859040669401 & -2.68590406694006 \tabularnewline
5 & 21 & 11.5349926820862 & 9.46500731791375 \tabularnewline
6 & 12 & 10.3475922509489 & 1.65240774905108 \tabularnewline
7 & 22 & 12.7890118797989 & 9.21098812020108 \tabularnewline
8 & 11 & 12.4894159825774 & -1.48941598257743 \tabularnewline
9 & 10 & 12.255485547068 & -2.25548554706803 \tabularnewline
10 & 13 & 12.3312235536128 & 0.66877644638716 \tabularnewline
11 & 10 & 10.4641472861747 & -0.464147286174695 \tabularnewline
12 & 8 & 10.0230715056777 & -2.02307150567766 \tabularnewline
13 & 15 & 15.4451356869056 & -0.445135686905615 \tabularnewline
14 & 14 & 12.0314725092847 & 1.96852749071529 \tabularnewline
15 & 10 & 9.27295047819709 & 0.727049521802911 \tabularnewline
16 & 14 & 13.3859619399794 & 0.614038060020609 \tabularnewline
17 & 14 & 12.7853596181192 & 1.21464038188083 \tabularnewline
18 & 11 & 9.48803285799399 & 1.51196714200601 \tabularnewline
19 & 10 & 12.6714182015512 & -2.67141820155116 \tabularnewline
20 & 13 & 11.9394855018378 & 1.06051449816216 \tabularnewline
21 & 7 & 10.3586521345655 & -3.35865213456547 \tabularnewline
22 & 14 & 14.8376000945681 & -0.837600094568149 \tabularnewline
23 & 12 & 12.7783300184709 & -0.778330018470876 \tabularnewline
24 & 14 & 14.6365485563341 & -0.636548556334055 \tabularnewline
25 & 11 & 10.0952203599348 & 0.904779640065188 \tabularnewline
26 & 9 & 15.3408974297291 & -6.34089742972907 \tabularnewline
27 & 11 & 11.30896282345 & -0.308962823450038 \tabularnewline
28 & 15 & 12.9155310872585 & 2.08446891274155 \tabularnewline
29 & 14 & 11.8309379043254 & 2.16906209567464 \tabularnewline
30 & 13 & 15.2510084741771 & -2.25100847417708 \tabularnewline
31 & 9 & 11.2420091330558 & -2.2420091330558 \tabularnewline
32 & 15 & 14.9343851374723 & 0.0656148625276699 \tabularnewline
33 & 10 & 11.0853432482134 & -1.08534324821342 \tabularnewline
34 & 11 & 11.6382408366975 & -0.638240836697541 \tabularnewline
35 & 13 & 12.8968034038178 & 0.103196596182248 \tabularnewline
36 & 8 & 13.1004247394142 & -5.10042473941423 \tabularnewline
37 & 20 & 17.6033584233878 & 2.39664157661218 \tabularnewline
38 & 12 & 12.3426639873885 & -0.342663987388537 \tabularnewline
39 & 10 & 10.7375363621409 & -0.737536362140931 \tabularnewline
40 & 10 & 13.5314475689334 & -3.53144756893342 \tabularnewline
41 & 9 & 11.9886273340607 & -2.98862733406066 \tabularnewline
42 & 14 & 11.5773979725611 & 2.42260202743885 \tabularnewline
43 & 8 & 11.164024124425 & -3.16402412442502 \tabularnewline
44 & 14 & 15.1167953879281 & -1.11679538792811 \tabularnewline
45 & 11 & 14.3950472175742 & -3.39504721757422 \tabularnewline
46 & 13 & 15.5548136952392 & -2.55481369523916 \tabularnewline
47 & 9 & 12.5374911929031 & -3.53749119290312 \tabularnewline
48 & 11 & 12.1554252021809 & -1.15542520218091 \tabularnewline
49 & 15 & 10.7068005849904 & 4.29319941500961 \tabularnewline
50 & 11 & 13.4659923422943 & -2.46599234229432 \tabularnewline
51 & 10 & 11.3641072647548 & -1.36410726475483 \tabularnewline
52 & 14 & 13.2834358249412 & 0.716564175058814 \tabularnewline
53 & 18 & 15.6489199691378 & 2.35108003086217 \tabularnewline
54 & 14 & 14.3671517247907 & -0.367151724790675 \tabularnewline
55 & 11 & 15.7919681731353 & -4.79196817313527 \tabularnewline
56 & 12 & 12.4247680587317 & -0.424768058731675 \tabularnewline
57 & 13 & 11.2183979182127 & 1.78160208178725 \tabularnewline
58 & 9 & 12.3120735699668 & -3.31207356996676 \tabularnewline
59 & 10 & 14.1479612631177 & -4.14796126311765 \tabularnewline
60 & 15 & 14.1634227789749 & 0.836577221025113 \tabularnewline
61 & 20 & 18.2093191904246 & 1.79068080957543 \tabularnewline
62 & 12 & 13.1009403061771 & -1.10094030617711 \tabularnewline
63 & 12 & 14.4027071299303 & -2.40270712993033 \tabularnewline
64 & 14 & 13.8476912942891 & 0.152308705710901 \tabularnewline
65 & 13 & 12.6580679092546 & 0.341932090745411 \tabularnewline
66 & 11 & 14.8463010688737 & -3.84630106887371 \tabularnewline
67 & 17 & 14.4492444704436 & 2.55075552955638 \tabularnewline
68 & 12 & 13.7101981943471 & -1.71019819434708 \tabularnewline
69 & 13 & 12.9645205361561 & 0.0354794638439449 \tabularnewline
70 & 14 & 12.1615376151361 & 1.83846238486388 \tabularnewline
71 & 13 & 13.9619951449156 & -0.961995144915568 \tabularnewline
72 & 15 & 12.4921767320041 & 2.50782326799586 \tabularnewline
73 & 13 & 11.3565463257708 & 1.64345367422922 \tabularnewline
74 & 10 & 11.7376441029285 & -1.73764410292846 \tabularnewline
75 & 11 & 12.8475158022498 & -1.84751580224981 \tabularnewline
76 & 19 & 13.5782755167781 & 5.42172448322187 \tabularnewline
77 & 13 & 10.7251343157834 & 2.27486568421659 \tabularnewline
78 & 17 & 13.48004473818 & 3.51995526182002 \tabularnewline
79 & 13 & 12.4085731077607 & 0.591426892239281 \tabularnewline
80 & 9 & 13.8757691721402 & -4.87576917214016 \tabularnewline
81 & 11 & 11.8265522024438 & -0.826552202443845 \tabularnewline
82 & 10 & 12.2582125093131 & -2.25821250931311 \tabularnewline
83 & 9 & 12.0893193530794 & -3.0893193530794 \tabularnewline
84 & 12 & 11.4376043102449 & 0.562395689755053 \tabularnewline
85 & 12 & 12.2820927822528 & -0.282092782252781 \tabularnewline
86 & 13 & 12.600596077228 & 0.39940392277197 \tabularnewline
87 & 13 & 12.3707761425222 & 0.629223857477788 \tabularnewline
88 & 12 & 12.8240044246504 & -0.824004424650408 \tabularnewline
89 & 15 & 14.0753630499459 & 0.924636950054056 \tabularnewline
90 & 22 & 17.5368812334651 & 4.46311876653487 \tabularnewline
91 & 13 & 11.5386152817896 & 1.46138471821038 \tabularnewline
92 & 15 & 13.6442081826988 & 1.35579181730116 \tabularnewline
93 & 13 & 11.9149624724384 & 1.08503752756162 \tabularnewline
94 & 15 & 12.4664704133874 & 2.53352958661262 \tabularnewline
95 & 10 & 13.5863507806817 & -3.58635078068166 \tabularnewline
96 & 11 & 11.1200802147585 & -0.120080214758501 \tabularnewline
97 & 16 & 14.0276438174849 & 1.97235618251507 \tabularnewline
98 & 11 & 12.5764006372017 & -1.57640063720175 \tabularnewline
99 & 11 & 10.3448045103003 & 0.655195489699713 \tabularnewline
100 & 10 & 12.3643906392921 & -2.36439063929211 \tabularnewline
101 & 10 & 10.4309708012685 & -0.430970801268512 \tabularnewline
102 & 16 & 13.9803954233228 & 2.01960457667716 \tabularnewline
103 & 12 & 10.1341938184309 & 1.86580618156908 \tabularnewline
104 & 11 & 15.0653401688794 & -4.06534016887942 \tabularnewline
105 & 16 & 11.9068696965664 & 4.09313030343364 \tabularnewline
106 & 19 & 16.469106707682 & 2.53089329231804 \tabularnewline
107 & 11 & 11.0181029561126 & -0.0181029561125804 \tabularnewline
108 & 16 & 12.0004271839232 & 3.99957281607681 \tabularnewline
109 & 15 & 15.9998288292235 & -0.999828829223529 \tabularnewline
110 & 24 & 17.0566836167716 & 6.9433163832284 \tabularnewline
111 & 14 & 11.7520964175546 & 2.24790358244541 \tabularnewline
112 & 15 & 14.5449061312112 & 0.45509386878877 \tabularnewline
113 & 11 & 12.7794050230251 & -1.77940502302506 \tabularnewline
114 & 15 & 14.4707213523473 & 0.529278647652716 \tabularnewline
115 & 12 & 10.592993884356 & 1.40700611564401 \tabularnewline
116 & 10 & 10.4380429752807 & -0.438042975280748 \tabularnewline
117 & 14 & 13.6944769102189 & 0.30552308978113 \tabularnewline
118 & 13 & 13.3413609516567 & -0.341360951656683 \tabularnewline
119 & 9 & 13.3541470562459 & -4.35414705624595 \tabularnewline
120 & 15 & 11.9951606908855 & 3.00483930911453 \tabularnewline
121 & 15 & 15.231952703811 & -0.231952703810982 \tabularnewline
122 & 14 & 12.4853054817738 & 1.51469451822615 \tabularnewline
123 & 11 & 11.8764574766107 & -0.876457476610727 \tabularnewline
124 & 8 & 11.785479670395 & -3.78547967039497 \tabularnewline
125 & 11 & 12.0987319051394 & -1.09873190513944 \tabularnewline
126 & 11 & 13.0007466817187 & -2.00074668171868 \tabularnewline
127 & 8 & 10.2161724976708 & -2.21617249767077 \tabularnewline
128 & 10 & 10.4136622020658 & -0.413662202065774 \tabularnewline
129 & 11 & 9.42706803997648 & 1.57293196002352 \tabularnewline
130 & 13 & 12.6874718351477 & 0.312528164852296 \tabularnewline
131 & 11 & 13.5142498678371 & -2.51424986783709 \tabularnewline
132 & 20 & 17.2809753687935 & 2.71902463120652 \tabularnewline
133 & 10 & 11.9753398918779 & -1.97533989187795 \tabularnewline
134 & 15 & 13.0552338190937 & 1.94476618090627 \tabularnewline
135 & 12 & 12.2688687892281 & -0.26886878922808 \tabularnewline
136 & 14 & 11.1620251603946 & 2.83797483960543 \tabularnewline
137 & 23 & 15.9197344876496 & 7.0802655123504 \tabularnewline
138 & 14 & 13.2384789286042 & 0.761521071395758 \tabularnewline
139 & 16 & 16.2859109684951 & -0.28591096849508 \tabularnewline
140 & 11 & 13.1327015546839 & -2.13270155468386 \tabularnewline
141 & 12 & 14.9304822419211 & -2.93048224192108 \tabularnewline
142 & 10 & 12.9208479722777 & -2.9208479722777 \tabularnewline
143 & 14 & 11.3251418491499 & 2.67485815085014 \tabularnewline
144 & 12 & 11.9729706761715 & 0.0270293238285449 \tabularnewline
145 & 12 & 11.8465767104801 & 0.153423289519854 \tabularnewline
146 & 11 & 11.7630217770316 & -0.763021777031591 \tabularnewline
147 & 12 & 10.8379628385869 & 1.16203716141311 \tabularnewline
148 & 13 & 15.55861868001 & -2.55861868001004 \tabularnewline
149 & 11 & 13.9089861256953 & -2.9089861256953 \tabularnewline
150 & 19 & 16.5260441824011 & 2.47395581759885 \tabularnewline
151 & 12 & 12.0773285511525 & -0.077328551152544 \tabularnewline
152 & 17 & 13.0954820237569 & 3.90451797624308 \tabularnewline
153 & 9 & 11.6065063466879 & -2.60650634668794 \tabularnewline
154 & 12 & 14.3384531587323 & -2.33845315873226 \tabularnewline
155 & 19 & 16.5832323608423 & 2.41676763915772 \tabularnewline
156 & 18 & 14.2611331213137 & 3.73886687868626 \tabularnewline
157 & 15 & 13.6442081826988 & 1.35579181730116 \tabularnewline
158 & 14 & 13.6366065208757 & 0.363393479124349 \tabularnewline
159 & 11 & 9.42706803997648 & 1.57293196002352 \tabularnewline
160 & 9 & 12.9489822491369 & -3.94898224913689 \tabularnewline
161 & 18 & 14.448643565522 & 3.551356434478 \tabularnewline
162 & 16 & 14.149673405256 & 1.85032659474401 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253882&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]12[/C][C]13.7683010209765[/C][C]-1.76830102097655[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]9.49751372870846[/C][C]1.50248627129154[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]14.3537001639762[/C][C]-0.353700163976197[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.6859040669401[/C][C]-2.68590406694006[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]11.5349926820862[/C][C]9.46500731791375[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.3475922509489[/C][C]1.65240774905108[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]12.7890118797989[/C][C]9.21098812020108[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.4894159825774[/C][C]-1.48941598257743[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.255485547068[/C][C]-2.25548554706803[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.3312235536128[/C][C]0.66877644638716[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.4641472861747[/C][C]-0.464147286174695[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]10.0230715056777[/C][C]-2.02307150567766[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]15.4451356869056[/C][C]-0.445135686905615[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.0314725092847[/C][C]1.96852749071529[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]9.27295047819709[/C][C]0.727049521802911[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.3859619399794[/C][C]0.614038060020609[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.7853596181192[/C][C]1.21464038188083[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]9.48803285799399[/C][C]1.51196714200601[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.6714182015512[/C][C]-2.67141820155116[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.9394855018378[/C][C]1.06051449816216[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]10.3586521345655[/C][C]-3.35865213456547[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]14.8376000945681[/C][C]-0.837600094568149[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.7783300184709[/C][C]-0.778330018470876[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.6365485563341[/C][C]-0.636548556334055[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]10.0952203599348[/C][C]0.904779640065188[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.3408974297291[/C][C]-6.34089742972907[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.30896282345[/C][C]-0.308962823450038[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]12.9155310872585[/C][C]2.08446891274155[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]11.8309379043254[/C][C]2.16906209567464[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]15.2510084741771[/C][C]-2.25100847417708[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.2420091330558[/C][C]-2.2420091330558[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]14.9343851374723[/C][C]0.0656148625276699[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]11.0853432482134[/C][C]-1.08534324821342[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.6382408366975[/C][C]-0.638240836697541[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.8968034038178[/C][C]0.103196596182248[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]13.1004247394142[/C][C]-5.10042473941423[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]17.6033584233878[/C][C]2.39664157661218[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.3426639873885[/C][C]-0.342663987388537[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]10.7375363621409[/C][C]-0.737536362140931[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.5314475689334[/C][C]-3.53144756893342[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]11.9886273340607[/C][C]-2.98862733406066[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]11.5773979725611[/C][C]2.42260202743885[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]11.164024124425[/C][C]-3.16402412442502[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]15.1167953879281[/C][C]-1.11679538792811[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]14.3950472175742[/C][C]-3.39504721757422[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]15.5548136952392[/C][C]-2.55481369523916[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.5374911929031[/C][C]-3.53749119290312[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.1554252021809[/C][C]-1.15542520218091[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]10.7068005849904[/C][C]4.29319941500961[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]13.4659923422943[/C][C]-2.46599234229432[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.3641072647548[/C][C]-1.36410726475483[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.2834358249412[/C][C]0.716564175058814[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]15.6489199691378[/C][C]2.35108003086217[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.3671517247907[/C][C]-0.367151724790675[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]15.7919681731353[/C][C]-4.79196817313527[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.4247680587317[/C][C]-0.424768058731675[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.2183979182127[/C][C]1.78160208178725[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.3120735699668[/C][C]-3.31207356996676[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]14.1479612631177[/C][C]-4.14796126311765[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.1634227789749[/C][C]0.836577221025113[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]18.2093191904246[/C][C]1.79068080957543[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.1009403061771[/C][C]-1.10094030617711[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]14.4027071299303[/C][C]-2.40270712993033[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.8476912942891[/C][C]0.152308705710901[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]12.6580679092546[/C][C]0.341932090745411[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]14.8463010688737[/C][C]-3.84630106887371[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]14.4492444704436[/C][C]2.55075552955638[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.7101981943471[/C][C]-1.71019819434708[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.9645205361561[/C][C]0.0354794638439449[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.1615376151361[/C][C]1.83846238486388[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.9619951449156[/C][C]-0.961995144915568[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]12.4921767320041[/C][C]2.50782326799586[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]11.3565463257708[/C][C]1.64345367422922[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.7376441029285[/C][C]-1.73764410292846[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.8475158022498[/C][C]-1.84751580224981[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]13.5782755167781[/C][C]5.42172448322187[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.7251343157834[/C][C]2.27486568421659[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]13.48004473818[/C][C]3.51995526182002[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.4085731077607[/C][C]0.591426892239281[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]13.8757691721402[/C][C]-4.87576917214016[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]11.8265522024438[/C][C]-0.826552202443845[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]12.2582125093131[/C][C]-2.25821250931311[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.0893193530794[/C][C]-3.0893193530794[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]11.4376043102449[/C][C]0.562395689755053[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.2820927822528[/C][C]-0.282092782252781[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.600596077228[/C][C]0.39940392277197[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]12.3707761425222[/C][C]0.629223857477788[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.8240044246504[/C][C]-0.824004424650408[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.0753630499459[/C][C]0.924636950054056[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]17.5368812334651[/C][C]4.46311876653487[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]11.5386152817896[/C][C]1.46138471821038[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.6442081826988[/C][C]1.35579181730116[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]11.9149624724384[/C][C]1.08503752756162[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.4664704133874[/C][C]2.53352958661262[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]13.5863507806817[/C][C]-3.58635078068166[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.1200802147585[/C][C]-0.120080214758501[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.0276438174849[/C][C]1.97235618251507[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.5764006372017[/C][C]-1.57640063720175[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]10.3448045103003[/C][C]0.655195489699713[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.3643906392921[/C][C]-2.36439063929211[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.4309708012685[/C][C]-0.430970801268512[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.9803954233228[/C][C]2.01960457667716[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]10.1341938184309[/C][C]1.86580618156908[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]15.0653401688794[/C][C]-4.06534016887942[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]11.9068696965664[/C][C]4.09313030343364[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]16.469106707682[/C][C]2.53089329231804[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]11.0181029561126[/C][C]-0.0181029561125804[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]12.0004271839232[/C][C]3.99957281607681[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]15.9998288292235[/C][C]-0.999828829223529[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]17.0566836167716[/C][C]6.9433163832284[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]11.7520964175546[/C][C]2.24790358244541[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]14.5449061312112[/C][C]0.45509386878877[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.7794050230251[/C][C]-1.77940502302506[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]14.4707213523473[/C][C]0.529278647652716[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]10.592993884356[/C][C]1.40700611564401[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]10.4380429752807[/C][C]-0.438042975280748[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.6944769102189[/C][C]0.30552308978113[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.3413609516567[/C][C]-0.341360951656683[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.3541470562459[/C][C]-4.35414705624595[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]11.9951606908855[/C][C]3.00483930911453[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]15.231952703811[/C][C]-0.231952703810982[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.4853054817738[/C][C]1.51469451822615[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]11.8764574766107[/C][C]-0.876457476610727[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]11.785479670395[/C][C]-3.78547967039497[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.0987319051394[/C][C]-1.09873190513944[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.0007466817187[/C][C]-2.00074668171868[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]10.2161724976708[/C][C]-2.21617249767077[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.4136622020658[/C][C]-0.413662202065774[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]9.42706803997648[/C][C]1.57293196002352[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.6874718351477[/C][C]0.312528164852296[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.5142498678371[/C][C]-2.51424986783709[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]17.2809753687935[/C][C]2.71902463120652[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.9753398918779[/C][C]-1.97533989187795[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.0552338190937[/C][C]1.94476618090627[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.2688687892281[/C][C]-0.26886878922808[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]11.1620251603946[/C][C]2.83797483960543[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]15.9197344876496[/C][C]7.0802655123504[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.2384789286042[/C][C]0.761521071395758[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]16.2859109684951[/C][C]-0.28591096849508[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.1327015546839[/C][C]-2.13270155468386[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]14.9304822419211[/C][C]-2.93048224192108[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.9208479722777[/C][C]-2.9208479722777[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]11.3251418491499[/C][C]2.67485815085014[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]11.9729706761715[/C][C]0.0270293238285449[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.8465767104801[/C][C]0.153423289519854[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]11.7630217770316[/C][C]-0.763021777031591[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]10.8379628385869[/C][C]1.16203716141311[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.55861868001[/C][C]-2.55861868001004[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]13.9089861256953[/C][C]-2.9089861256953[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]16.5260441824011[/C][C]2.47395581759885[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]12.0773285511525[/C][C]-0.077328551152544[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]13.0954820237569[/C][C]3.90451797624308[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.6065063466879[/C][C]-2.60650634668794[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.3384531587323[/C][C]-2.33845315873226[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]16.5832323608423[/C][C]2.41676763915772[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]14.2611331213137[/C][C]3.73886687868626[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.6442081826988[/C][C]1.35579181730116[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.6366065208757[/C][C]0.363393479124349[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]9.42706803997648[/C][C]1.57293196002352[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]12.9489822491369[/C][C]-3.94898224913689[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]14.448643565522[/C][C]3.551356434478[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]14.149673405256[/C][C]1.85032659474401[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253882&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253882&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11213.7683010209765-1.76830102097655
2119.497513728708461.50248627129154
31414.3537001639762-0.353700163976197
41214.6859040669401-2.68590406694006
52111.53499268208629.46500731791375
61210.34759225094891.65240774905108
72212.78901187979899.21098812020108
81112.4894159825774-1.48941598257743
91012.255485547068-2.25548554706803
101312.33122355361280.66877644638716
111010.4641472861747-0.464147286174695
12810.0230715056777-2.02307150567766
131515.4451356869056-0.445135686905615
141412.03147250928471.96852749071529
15109.272950478197090.727049521802911
161413.38596193997940.614038060020609
171412.78535961811921.21464038188083
18119.488032857993991.51196714200601
191012.6714182015512-2.67141820155116
201311.93948550183781.06051449816216
21710.3586521345655-3.35865213456547
221414.8376000945681-0.837600094568149
231212.7783300184709-0.778330018470876
241414.6365485563341-0.636548556334055
251110.09522035993480.904779640065188
26915.3408974297291-6.34089742972907
271111.30896282345-0.308962823450038
281512.91553108725852.08446891274155
291411.83093790432542.16906209567464
301315.2510084741771-2.25100847417708
31911.2420091330558-2.2420091330558
321514.93438513747230.0656148625276699
331011.0853432482134-1.08534324821342
341111.6382408366975-0.638240836697541
351312.89680340381780.103196596182248
36813.1004247394142-5.10042473941423
372017.60335842338782.39664157661218
381212.3426639873885-0.342663987388537
391010.7375363621409-0.737536362140931
401013.5314475689334-3.53144756893342
41911.9886273340607-2.98862733406066
421411.57739797256112.42260202743885
43811.164024124425-3.16402412442502
441415.1167953879281-1.11679538792811
451114.3950472175742-3.39504721757422
461315.5548136952392-2.55481369523916
47912.5374911929031-3.53749119290312
481112.1554252021809-1.15542520218091
491510.70680058499044.29319941500961
501113.4659923422943-2.46599234229432
511011.3641072647548-1.36410726475483
521413.28343582494120.716564175058814
531815.64891996913782.35108003086217
541414.3671517247907-0.367151724790675
551115.7919681731353-4.79196817313527
561212.4247680587317-0.424768058731675
571311.21839791821271.78160208178725
58912.3120735699668-3.31207356996676
591014.1479612631177-4.14796126311765
601514.16342277897490.836577221025113
612018.20931919042461.79068080957543
621213.1009403061771-1.10094030617711
631214.4027071299303-2.40270712993033
641413.84769129428910.152308705710901
651312.65806790925460.341932090745411
661114.8463010688737-3.84630106887371
671714.44924447044362.55075552955638
681213.7101981943471-1.71019819434708
691312.96452053615610.0354794638439449
701412.16153761513611.83846238486388
711313.9619951449156-0.961995144915568
721512.49217673200412.50782326799586
731311.35654632577081.64345367422922
741011.7376441029285-1.73764410292846
751112.8475158022498-1.84751580224981
761913.57827551677815.42172448322187
771310.72513431578342.27486568421659
781713.480044738183.51995526182002
791312.40857310776070.591426892239281
80913.8757691721402-4.87576917214016
811111.8265522024438-0.826552202443845
821012.2582125093131-2.25821250931311
83912.0893193530794-3.0893193530794
841211.43760431024490.562395689755053
851212.2820927822528-0.282092782252781
861312.6005960772280.39940392277197
871312.37077614252220.629223857477788
881212.8240044246504-0.824004424650408
891514.07536304994590.924636950054056
902217.53688123346514.46311876653487
911311.53861528178961.46138471821038
921513.64420818269881.35579181730116
931311.91496247243841.08503752756162
941512.46647041338742.53352958661262
951013.5863507806817-3.58635078068166
961111.1200802147585-0.120080214758501
971614.02764381748491.97235618251507
981112.5764006372017-1.57640063720175
991110.34480451030030.655195489699713
1001012.3643906392921-2.36439063929211
1011010.4309708012685-0.430970801268512
1021613.98039542332282.01960457667716
1031210.13419381843091.86580618156908
1041115.0653401688794-4.06534016887942
1051611.90686969656644.09313030343364
1061916.4691067076822.53089329231804
1071111.0181029561126-0.0181029561125804
1081612.00042718392323.99957281607681
1091515.9998288292235-0.999828829223529
1102417.05668361677166.9433163832284
1111411.75209641755462.24790358244541
1121514.54490613121120.45509386878877
1131112.7794050230251-1.77940502302506
1141514.47072135234730.529278647652716
1151210.5929938843561.40700611564401
1161010.4380429752807-0.438042975280748
1171413.69447691021890.30552308978113
1181313.3413609516567-0.341360951656683
119913.3541470562459-4.35414705624595
1201511.99516069088553.00483930911453
1211515.231952703811-0.231952703810982
1221412.48530548177381.51469451822615
1231111.8764574766107-0.876457476610727
124811.785479670395-3.78547967039497
1251112.0987319051394-1.09873190513944
1261113.0007466817187-2.00074668171868
127810.2161724976708-2.21617249767077
1281010.4136622020658-0.413662202065774
129119.427068039976481.57293196002352
1301312.68747183514770.312528164852296
1311113.5142498678371-2.51424986783709
1322017.28097536879352.71902463120652
1331011.9753398918779-1.97533989187795
1341513.05523381909371.94476618090627
1351212.2688687892281-0.26886878922808
1361411.16202516039462.83797483960543
1372315.91973448764967.0802655123504
1381413.23847892860420.761521071395758
1391616.2859109684951-0.28591096849508
1401113.1327015546839-2.13270155468386
1411214.9304822419211-2.93048224192108
1421012.9208479722777-2.9208479722777
1431411.32514184914992.67485815085014
1441211.97297067617150.0270293238285449
1451211.84657671048010.153423289519854
1461111.7630217770316-0.763021777031591
1471210.83796283858691.16203716141311
1481315.55861868001-2.55861868001004
1491113.9089861256953-2.9089861256953
1501916.52604418240112.47395581759885
1511212.0773285511525-0.077328551152544
1521713.09548202375693.90451797624308
153911.6065063466879-2.60650634668794
1541214.3384531587323-2.33845315873226
1551916.58323236084232.41676763915772
1561814.26113312131373.73886687868626
1571513.64420818269881.35579181730116
1581413.63660652087570.363393479124349
159119.427068039976481.57293196002352
160912.9489822491369-3.94898224913689
1611814.4486435655223.551356434478
1621614.1496734052561.85032659474401






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8667207318705970.2665585362588060.133279268129403
110.8136022699416220.3727954601167550.186397730058378
120.9998591442005840.000281711598832640.00014085579941632
130.9996695669005740.0006608661988515630.000330433099425782
140.9992837445680260.001432510863948780.000716255431974388
150.9987028547514210.002594290497158010.00129714524857901
160.9974863568463680.005027286307263510.00251364315363175
170.9954854510977470.009029097804505930.00451454890225297
180.9927676720910390.01446465581792150.00723232790896074
190.993713856618780.01257228676244030.00628614338122013
200.9896798659060260.0206402681879480.010320134093974
210.9918711737137150.01625765257257060.00812882628628531
220.987640118224860.02471976355028020.0123598817751401
230.9832130089144810.03357398217103770.0167869910855189
240.9746902977010770.05061940459784660.0253097022989233
250.9638100022655490.07237999546890270.0361899977344513
260.9914189835428670.01716203291426680.00858101645713342
270.9870404527576050.02591909448479020.0129595472423951
280.9840354629071010.03192907418579790.015964537092899
290.9801558462132560.03968830757348890.0198441537867444
300.973896545735250.05220690852949910.0261034542647495
310.9721980213213720.05560395735725670.0278019786786284
320.9621282994597270.07574340108054520.0378717005402726
330.9513968928478590.09720621430428160.0486031071521408
340.9358060456251760.1283879087496490.0641939543748245
350.9162817915057420.1674364169885150.0837182084942576
360.9320156195441680.1359687609116630.0679843804558316
370.9577198503167270.08456029936654640.0422801496832732
380.9436440635684030.1127118728631930.0563559364315965
390.9304071605040140.1391856789919720.0695928394959859
400.9372655784735990.1254688430528020.0627344215264011
410.936783618770360.1264327624592790.0632163812296397
420.9332094444199570.1335811111600870.0667905555800433
430.9459419750162830.1081160499674330.0540580249837167
440.9313515654812980.1372968690374050.0686484345187024
450.9291414095376090.1417171809247820.0708585904623908
460.9197858728319110.1604282543361770.0802141271680885
470.9299695070028930.1400609859942140.0700304929971072
480.9143246937793160.1713506124413670.0856753062206836
490.9353427546162770.1293144907674470.0646572453837234
500.9277902092960480.1444195814079040.072209790703952
510.9182751857492560.1634496285014870.0817248142507436
520.9013473793572020.1973052412855970.0986526206427984
530.9073148138476150.1853703723047710.0926851861523853
540.8853509815409210.2292980369181590.11464901845908
550.9175710326101520.1648579347796960.0824289673898482
560.8998609645634450.200278070873110.100139035436555
570.8892293427064460.2215413145871090.110770657293554
580.9047305956800120.1905388086399750.0952694043199876
590.9243835268698310.1512329462603390.0756164731301693
600.9108119842955770.1783760314088460.0891880157044231
610.9121362811319030.1757274377361930.0878637188680967
620.894867950585920.2102640988281590.10513204941408
630.8872925485130280.2254149029739450.112707451486972
640.8628358505554330.2743282988891340.137164149444567
650.8351889908385330.3296220183229340.164811009161467
660.8597794907746250.280441018450750.140220509225375
670.8615577211743550.276884557651290.138442278825645
680.8475036486467260.3049927027065480.152496351353274
690.8195279577256980.3609440845486050.180472042274302
700.8036902803067590.3926194393864810.196309719693241
710.7783828912152040.4432342175695920.221617108784796
720.7746900074661320.4506199850677360.225309992533868
730.7551149225523580.4897701548952850.244885077447642
740.734615810248710.530768379502580.26538418975129
750.7185085404029160.5629829191941680.281491459597084
760.8368646174816120.3262707650367760.163135382518388
770.8319185339015430.3361629321969130.168081466098457
780.8540072582163780.2919854835672450.145992741783622
790.8281346656360940.3437306687278120.171865334363906
800.8988698791807190.2022602416385620.101130120819281
810.8785922283369530.2428155433260950.121407771663047
820.8758484870858180.2483030258283640.124151512914182
830.8844570521050110.2310858957899770.115542947894989
840.8612316067153930.2775367865692140.138768393284607
850.8338893694261150.332221261147770.166110630573885
860.8034881678306970.3930236643386060.196511832169303
870.7714234857444160.4571530285111690.228576514255584
880.7384989762843220.5230020474313560.261501023715678
890.703814744211140.5923705115777190.29618525578886
900.7729583053071620.4540833893856770.227041694692838
910.749774837350420.5004503252991590.250225162649579
920.720245073725090.559509852549820.27975492627491
930.6898966922210560.6202066155578890.310103307778944
940.6819542935399090.6360914129201820.318045706460091
950.7202637099614140.5594725800771720.279736290038586
960.6789242434414950.642151513117010.321075756558505
970.6570415820482160.6859168359035690.342958417951784
980.6347205490871970.7305589018256050.365279450912803
990.5911422189167660.8177155621664680.408857781083234
1000.5793106730660910.8413786538678190.420689326933909
1010.5322533217723110.9354933564553780.467746678227689
1020.5072933315909650.985413336818070.492706668409035
1030.4971354959315920.9942709918631830.502864504068408
1040.6022949190627570.7954101618744860.397705080937243
1050.702278030541980.5954439389160410.29772196945802
1060.6886354008344370.6227291983311270.311364599165563
1070.6433206971546380.7133586056907250.356679302845362
1080.7210613647290720.5578772705418560.278938635270928
1090.6926473035911540.6147053928176930.307352696408846
1100.9019452768117650.1961094463764710.0980547231882353
1110.899231602598320.2015367948033610.10076839740168
1120.8749707479767150.250058504046570.125029252023285
1130.8539451621790880.2921096756418230.146054837820912
1140.8239940580135260.3520118839729480.176005941986474
1150.7999475754974580.4001048490050840.200052424502542
1160.7604890306333110.4790219387333770.239510969366689
1170.7435132814454960.5129734371090090.256486718554504
1180.7031443204831370.5937113590337270.296855679516863
1190.7762683692509780.4474632614980440.223731630749022
1200.8079855250515940.3840289498968120.192014474948406
1210.7675850707685910.4648298584628190.232414929231409
1220.730101724846130.539796550307740.26989827515387
1230.6827716757628040.6344566484743910.317228324237196
1240.6868893226441890.6262213547116210.313110677355811
1250.6491724842365320.7016550315269360.350827515763468
1260.6354786399193550.7290427201612890.364521360080644
1270.6377703388629970.7244593222740070.362229661137003
1280.5967324065091970.8065351869816050.403267593490803
1290.583187567348210.8336248653035790.41681243265179
1300.5229186670058660.9541626659882680.477081332994134
1310.5278096007440810.9443807985118390.472190399255919
1320.4841034210412770.9682068420825540.515896578958723
1330.4351439307299960.8702878614599920.564856069270004
1340.3860997274965350.7721994549930710.613900272503465
1350.3267871374377690.6535742748755390.673212862562231
1360.3003689804667710.6007379609335410.699631019533229
1370.544619276393050.9107614472139010.45538072360695
1380.4918246165012380.9836492330024760.508175383498762
1390.4243114152899640.8486228305799280.575688584710036
1400.3727777098829920.7455554197659840.627222290117008
1410.4502840468993230.9005680937986450.549715953100677
1420.413738926129130.8274778522582590.58626107387087
1430.4074455793365210.8148911586730420.592554420663479
1440.325564488738250.6511289774764990.67443551126175
1450.2496153261433070.4992306522866130.750384673856693
1460.4021094853991160.8042189707982320.597890514600884
1470.3102149124960120.6204298249920250.689785087503988
1480.4748048407350540.9496096814701070.525195159264946
1490.4924024509454910.9848049018909820.507597549054509
1500.3719155188526520.7438310377053050.628084481147348
1510.6194596882732290.7610806234535410.380540311726771
1520.4657705310451630.9315410620903250.534229468954837

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.866720731870597 & 0.266558536258806 & 0.133279268129403 \tabularnewline
11 & 0.813602269941622 & 0.372795460116755 & 0.186397730058378 \tabularnewline
12 & 0.999859144200584 & 0.00028171159883264 & 0.00014085579941632 \tabularnewline
13 & 0.999669566900574 & 0.000660866198851563 & 0.000330433099425782 \tabularnewline
14 & 0.999283744568026 & 0.00143251086394878 & 0.000716255431974388 \tabularnewline
15 & 0.998702854751421 & 0.00259429049715801 & 0.00129714524857901 \tabularnewline
16 & 0.997486356846368 & 0.00502728630726351 & 0.00251364315363175 \tabularnewline
17 & 0.995485451097747 & 0.00902909780450593 & 0.00451454890225297 \tabularnewline
18 & 0.992767672091039 & 0.0144646558179215 & 0.00723232790896074 \tabularnewline
19 & 0.99371385661878 & 0.0125722867624403 & 0.00628614338122013 \tabularnewline
20 & 0.989679865906026 & 0.020640268187948 & 0.010320134093974 \tabularnewline
21 & 0.991871173713715 & 0.0162576525725706 & 0.00812882628628531 \tabularnewline
22 & 0.98764011822486 & 0.0247197635502802 & 0.0123598817751401 \tabularnewline
23 & 0.983213008914481 & 0.0335739821710377 & 0.0167869910855189 \tabularnewline
24 & 0.974690297701077 & 0.0506194045978466 & 0.0253097022989233 \tabularnewline
25 & 0.963810002265549 & 0.0723799954689027 & 0.0361899977344513 \tabularnewline
26 & 0.991418983542867 & 0.0171620329142668 & 0.00858101645713342 \tabularnewline
27 & 0.987040452757605 & 0.0259190944847902 & 0.0129595472423951 \tabularnewline
28 & 0.984035462907101 & 0.0319290741857979 & 0.015964537092899 \tabularnewline
29 & 0.980155846213256 & 0.0396883075734889 & 0.0198441537867444 \tabularnewline
30 & 0.97389654573525 & 0.0522069085294991 & 0.0261034542647495 \tabularnewline
31 & 0.972198021321372 & 0.0556039573572567 & 0.0278019786786284 \tabularnewline
32 & 0.962128299459727 & 0.0757434010805452 & 0.0378717005402726 \tabularnewline
33 & 0.951396892847859 & 0.0972062143042816 & 0.0486031071521408 \tabularnewline
34 & 0.935806045625176 & 0.128387908749649 & 0.0641939543748245 \tabularnewline
35 & 0.916281791505742 & 0.167436416988515 & 0.0837182084942576 \tabularnewline
36 & 0.932015619544168 & 0.135968760911663 & 0.0679843804558316 \tabularnewline
37 & 0.957719850316727 & 0.0845602993665464 & 0.0422801496832732 \tabularnewline
38 & 0.943644063568403 & 0.112711872863193 & 0.0563559364315965 \tabularnewline
39 & 0.930407160504014 & 0.139185678991972 & 0.0695928394959859 \tabularnewline
40 & 0.937265578473599 & 0.125468843052802 & 0.0627344215264011 \tabularnewline
41 & 0.93678361877036 & 0.126432762459279 & 0.0632163812296397 \tabularnewline
42 & 0.933209444419957 & 0.133581111160087 & 0.0667905555800433 \tabularnewline
43 & 0.945941975016283 & 0.108116049967433 & 0.0540580249837167 \tabularnewline
44 & 0.931351565481298 & 0.137296869037405 & 0.0686484345187024 \tabularnewline
45 & 0.929141409537609 & 0.141717180924782 & 0.0708585904623908 \tabularnewline
46 & 0.919785872831911 & 0.160428254336177 & 0.0802141271680885 \tabularnewline
47 & 0.929969507002893 & 0.140060985994214 & 0.0700304929971072 \tabularnewline
48 & 0.914324693779316 & 0.171350612441367 & 0.0856753062206836 \tabularnewline
49 & 0.935342754616277 & 0.129314490767447 & 0.0646572453837234 \tabularnewline
50 & 0.927790209296048 & 0.144419581407904 & 0.072209790703952 \tabularnewline
51 & 0.918275185749256 & 0.163449628501487 & 0.0817248142507436 \tabularnewline
52 & 0.901347379357202 & 0.197305241285597 & 0.0986526206427984 \tabularnewline
53 & 0.907314813847615 & 0.185370372304771 & 0.0926851861523853 \tabularnewline
54 & 0.885350981540921 & 0.229298036918159 & 0.11464901845908 \tabularnewline
55 & 0.917571032610152 & 0.164857934779696 & 0.0824289673898482 \tabularnewline
56 & 0.899860964563445 & 0.20027807087311 & 0.100139035436555 \tabularnewline
57 & 0.889229342706446 & 0.221541314587109 & 0.110770657293554 \tabularnewline
58 & 0.904730595680012 & 0.190538808639975 & 0.0952694043199876 \tabularnewline
59 & 0.924383526869831 & 0.151232946260339 & 0.0756164731301693 \tabularnewline
60 & 0.910811984295577 & 0.178376031408846 & 0.0891880157044231 \tabularnewline
61 & 0.912136281131903 & 0.175727437736193 & 0.0878637188680967 \tabularnewline
62 & 0.89486795058592 & 0.210264098828159 & 0.10513204941408 \tabularnewline
63 & 0.887292548513028 & 0.225414902973945 & 0.112707451486972 \tabularnewline
64 & 0.862835850555433 & 0.274328298889134 & 0.137164149444567 \tabularnewline
65 & 0.835188990838533 & 0.329622018322934 & 0.164811009161467 \tabularnewline
66 & 0.859779490774625 & 0.28044101845075 & 0.140220509225375 \tabularnewline
67 & 0.861557721174355 & 0.27688455765129 & 0.138442278825645 \tabularnewline
68 & 0.847503648646726 & 0.304992702706548 & 0.152496351353274 \tabularnewline
69 & 0.819527957725698 & 0.360944084548605 & 0.180472042274302 \tabularnewline
70 & 0.803690280306759 & 0.392619439386481 & 0.196309719693241 \tabularnewline
71 & 0.778382891215204 & 0.443234217569592 & 0.221617108784796 \tabularnewline
72 & 0.774690007466132 & 0.450619985067736 & 0.225309992533868 \tabularnewline
73 & 0.755114922552358 & 0.489770154895285 & 0.244885077447642 \tabularnewline
74 & 0.73461581024871 & 0.53076837950258 & 0.26538418975129 \tabularnewline
75 & 0.718508540402916 & 0.562982919194168 & 0.281491459597084 \tabularnewline
76 & 0.836864617481612 & 0.326270765036776 & 0.163135382518388 \tabularnewline
77 & 0.831918533901543 & 0.336162932196913 & 0.168081466098457 \tabularnewline
78 & 0.854007258216378 & 0.291985483567245 & 0.145992741783622 \tabularnewline
79 & 0.828134665636094 & 0.343730668727812 & 0.171865334363906 \tabularnewline
80 & 0.898869879180719 & 0.202260241638562 & 0.101130120819281 \tabularnewline
81 & 0.878592228336953 & 0.242815543326095 & 0.121407771663047 \tabularnewline
82 & 0.875848487085818 & 0.248303025828364 & 0.124151512914182 \tabularnewline
83 & 0.884457052105011 & 0.231085895789977 & 0.115542947894989 \tabularnewline
84 & 0.861231606715393 & 0.277536786569214 & 0.138768393284607 \tabularnewline
85 & 0.833889369426115 & 0.33222126114777 & 0.166110630573885 \tabularnewline
86 & 0.803488167830697 & 0.393023664338606 & 0.196511832169303 \tabularnewline
87 & 0.771423485744416 & 0.457153028511169 & 0.228576514255584 \tabularnewline
88 & 0.738498976284322 & 0.523002047431356 & 0.261501023715678 \tabularnewline
89 & 0.70381474421114 & 0.592370511577719 & 0.29618525578886 \tabularnewline
90 & 0.772958305307162 & 0.454083389385677 & 0.227041694692838 \tabularnewline
91 & 0.74977483735042 & 0.500450325299159 & 0.250225162649579 \tabularnewline
92 & 0.72024507372509 & 0.55950985254982 & 0.27975492627491 \tabularnewline
93 & 0.689896692221056 & 0.620206615557889 & 0.310103307778944 \tabularnewline
94 & 0.681954293539909 & 0.636091412920182 & 0.318045706460091 \tabularnewline
95 & 0.720263709961414 & 0.559472580077172 & 0.279736290038586 \tabularnewline
96 & 0.678924243441495 & 0.64215151311701 & 0.321075756558505 \tabularnewline
97 & 0.657041582048216 & 0.685916835903569 & 0.342958417951784 \tabularnewline
98 & 0.634720549087197 & 0.730558901825605 & 0.365279450912803 \tabularnewline
99 & 0.591142218916766 & 0.817715562166468 & 0.408857781083234 \tabularnewline
100 & 0.579310673066091 & 0.841378653867819 & 0.420689326933909 \tabularnewline
101 & 0.532253321772311 & 0.935493356455378 & 0.467746678227689 \tabularnewline
102 & 0.507293331590965 & 0.98541333681807 & 0.492706668409035 \tabularnewline
103 & 0.497135495931592 & 0.994270991863183 & 0.502864504068408 \tabularnewline
104 & 0.602294919062757 & 0.795410161874486 & 0.397705080937243 \tabularnewline
105 & 0.70227803054198 & 0.595443938916041 & 0.29772196945802 \tabularnewline
106 & 0.688635400834437 & 0.622729198331127 & 0.311364599165563 \tabularnewline
107 & 0.643320697154638 & 0.713358605690725 & 0.356679302845362 \tabularnewline
108 & 0.721061364729072 & 0.557877270541856 & 0.278938635270928 \tabularnewline
109 & 0.692647303591154 & 0.614705392817693 & 0.307352696408846 \tabularnewline
110 & 0.901945276811765 & 0.196109446376471 & 0.0980547231882353 \tabularnewline
111 & 0.89923160259832 & 0.201536794803361 & 0.10076839740168 \tabularnewline
112 & 0.874970747976715 & 0.25005850404657 & 0.125029252023285 \tabularnewline
113 & 0.853945162179088 & 0.292109675641823 & 0.146054837820912 \tabularnewline
114 & 0.823994058013526 & 0.352011883972948 & 0.176005941986474 \tabularnewline
115 & 0.799947575497458 & 0.400104849005084 & 0.200052424502542 \tabularnewline
116 & 0.760489030633311 & 0.479021938733377 & 0.239510969366689 \tabularnewline
117 & 0.743513281445496 & 0.512973437109009 & 0.256486718554504 \tabularnewline
118 & 0.703144320483137 & 0.593711359033727 & 0.296855679516863 \tabularnewline
119 & 0.776268369250978 & 0.447463261498044 & 0.223731630749022 \tabularnewline
120 & 0.807985525051594 & 0.384028949896812 & 0.192014474948406 \tabularnewline
121 & 0.767585070768591 & 0.464829858462819 & 0.232414929231409 \tabularnewline
122 & 0.73010172484613 & 0.53979655030774 & 0.26989827515387 \tabularnewline
123 & 0.682771675762804 & 0.634456648474391 & 0.317228324237196 \tabularnewline
124 & 0.686889322644189 & 0.626221354711621 & 0.313110677355811 \tabularnewline
125 & 0.649172484236532 & 0.701655031526936 & 0.350827515763468 \tabularnewline
126 & 0.635478639919355 & 0.729042720161289 & 0.364521360080644 \tabularnewline
127 & 0.637770338862997 & 0.724459322274007 & 0.362229661137003 \tabularnewline
128 & 0.596732406509197 & 0.806535186981605 & 0.403267593490803 \tabularnewline
129 & 0.58318756734821 & 0.833624865303579 & 0.41681243265179 \tabularnewline
130 & 0.522918667005866 & 0.954162665988268 & 0.477081332994134 \tabularnewline
131 & 0.527809600744081 & 0.944380798511839 & 0.472190399255919 \tabularnewline
132 & 0.484103421041277 & 0.968206842082554 & 0.515896578958723 \tabularnewline
133 & 0.435143930729996 & 0.870287861459992 & 0.564856069270004 \tabularnewline
134 & 0.386099727496535 & 0.772199454993071 & 0.613900272503465 \tabularnewline
135 & 0.326787137437769 & 0.653574274875539 & 0.673212862562231 \tabularnewline
136 & 0.300368980466771 & 0.600737960933541 & 0.699631019533229 \tabularnewline
137 & 0.54461927639305 & 0.910761447213901 & 0.45538072360695 \tabularnewline
138 & 0.491824616501238 & 0.983649233002476 & 0.508175383498762 \tabularnewline
139 & 0.424311415289964 & 0.848622830579928 & 0.575688584710036 \tabularnewline
140 & 0.372777709882992 & 0.745555419765984 & 0.627222290117008 \tabularnewline
141 & 0.450284046899323 & 0.900568093798645 & 0.549715953100677 \tabularnewline
142 & 0.41373892612913 & 0.827477852258259 & 0.58626107387087 \tabularnewline
143 & 0.407445579336521 & 0.814891158673042 & 0.592554420663479 \tabularnewline
144 & 0.32556448873825 & 0.651128977476499 & 0.67443551126175 \tabularnewline
145 & 0.249615326143307 & 0.499230652286613 & 0.750384673856693 \tabularnewline
146 & 0.402109485399116 & 0.804218970798232 & 0.597890514600884 \tabularnewline
147 & 0.310214912496012 & 0.620429824992025 & 0.689785087503988 \tabularnewline
148 & 0.474804840735054 & 0.949609681470107 & 0.525195159264946 \tabularnewline
149 & 0.492402450945491 & 0.984804901890982 & 0.507597549054509 \tabularnewline
150 & 0.371915518852652 & 0.743831037705305 & 0.628084481147348 \tabularnewline
151 & 0.619459688273229 & 0.761080623453541 & 0.380540311726771 \tabularnewline
152 & 0.465770531045163 & 0.931541062090325 & 0.534229468954837 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253882&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.866720731870597[/C][C]0.266558536258806[/C][C]0.133279268129403[/C][/ROW]
[ROW][C]11[/C][C]0.813602269941622[/C][C]0.372795460116755[/C][C]0.186397730058378[/C][/ROW]
[ROW][C]12[/C][C]0.999859144200584[/C][C]0.00028171159883264[/C][C]0.00014085579941632[/C][/ROW]
[ROW][C]13[/C][C]0.999669566900574[/C][C]0.000660866198851563[/C][C]0.000330433099425782[/C][/ROW]
[ROW][C]14[/C][C]0.999283744568026[/C][C]0.00143251086394878[/C][C]0.000716255431974388[/C][/ROW]
[ROW][C]15[/C][C]0.998702854751421[/C][C]0.00259429049715801[/C][C]0.00129714524857901[/C][/ROW]
[ROW][C]16[/C][C]0.997486356846368[/C][C]0.00502728630726351[/C][C]0.00251364315363175[/C][/ROW]
[ROW][C]17[/C][C]0.995485451097747[/C][C]0.00902909780450593[/C][C]0.00451454890225297[/C][/ROW]
[ROW][C]18[/C][C]0.992767672091039[/C][C]0.0144646558179215[/C][C]0.00723232790896074[/C][/ROW]
[ROW][C]19[/C][C]0.99371385661878[/C][C]0.0125722867624403[/C][C]0.00628614338122013[/C][/ROW]
[ROW][C]20[/C][C]0.989679865906026[/C][C]0.020640268187948[/C][C]0.010320134093974[/C][/ROW]
[ROW][C]21[/C][C]0.991871173713715[/C][C]0.0162576525725706[/C][C]0.00812882628628531[/C][/ROW]
[ROW][C]22[/C][C]0.98764011822486[/C][C]0.0247197635502802[/C][C]0.0123598817751401[/C][/ROW]
[ROW][C]23[/C][C]0.983213008914481[/C][C]0.0335739821710377[/C][C]0.0167869910855189[/C][/ROW]
[ROW][C]24[/C][C]0.974690297701077[/C][C]0.0506194045978466[/C][C]0.0253097022989233[/C][/ROW]
[ROW][C]25[/C][C]0.963810002265549[/C][C]0.0723799954689027[/C][C]0.0361899977344513[/C][/ROW]
[ROW][C]26[/C][C]0.991418983542867[/C][C]0.0171620329142668[/C][C]0.00858101645713342[/C][/ROW]
[ROW][C]27[/C][C]0.987040452757605[/C][C]0.0259190944847902[/C][C]0.0129595472423951[/C][/ROW]
[ROW][C]28[/C][C]0.984035462907101[/C][C]0.0319290741857979[/C][C]0.015964537092899[/C][/ROW]
[ROW][C]29[/C][C]0.980155846213256[/C][C]0.0396883075734889[/C][C]0.0198441537867444[/C][/ROW]
[ROW][C]30[/C][C]0.97389654573525[/C][C]0.0522069085294991[/C][C]0.0261034542647495[/C][/ROW]
[ROW][C]31[/C][C]0.972198021321372[/C][C]0.0556039573572567[/C][C]0.0278019786786284[/C][/ROW]
[ROW][C]32[/C][C]0.962128299459727[/C][C]0.0757434010805452[/C][C]0.0378717005402726[/C][/ROW]
[ROW][C]33[/C][C]0.951396892847859[/C][C]0.0972062143042816[/C][C]0.0486031071521408[/C][/ROW]
[ROW][C]34[/C][C]0.935806045625176[/C][C]0.128387908749649[/C][C]0.0641939543748245[/C][/ROW]
[ROW][C]35[/C][C]0.916281791505742[/C][C]0.167436416988515[/C][C]0.0837182084942576[/C][/ROW]
[ROW][C]36[/C][C]0.932015619544168[/C][C]0.135968760911663[/C][C]0.0679843804558316[/C][/ROW]
[ROW][C]37[/C][C]0.957719850316727[/C][C]0.0845602993665464[/C][C]0.0422801496832732[/C][/ROW]
[ROW][C]38[/C][C]0.943644063568403[/C][C]0.112711872863193[/C][C]0.0563559364315965[/C][/ROW]
[ROW][C]39[/C][C]0.930407160504014[/C][C]0.139185678991972[/C][C]0.0695928394959859[/C][/ROW]
[ROW][C]40[/C][C]0.937265578473599[/C][C]0.125468843052802[/C][C]0.0627344215264011[/C][/ROW]
[ROW][C]41[/C][C]0.93678361877036[/C][C]0.126432762459279[/C][C]0.0632163812296397[/C][/ROW]
[ROW][C]42[/C][C]0.933209444419957[/C][C]0.133581111160087[/C][C]0.0667905555800433[/C][/ROW]
[ROW][C]43[/C][C]0.945941975016283[/C][C]0.108116049967433[/C][C]0.0540580249837167[/C][/ROW]
[ROW][C]44[/C][C]0.931351565481298[/C][C]0.137296869037405[/C][C]0.0686484345187024[/C][/ROW]
[ROW][C]45[/C][C]0.929141409537609[/C][C]0.141717180924782[/C][C]0.0708585904623908[/C][/ROW]
[ROW][C]46[/C][C]0.919785872831911[/C][C]0.160428254336177[/C][C]0.0802141271680885[/C][/ROW]
[ROW][C]47[/C][C]0.929969507002893[/C][C]0.140060985994214[/C][C]0.0700304929971072[/C][/ROW]
[ROW][C]48[/C][C]0.914324693779316[/C][C]0.171350612441367[/C][C]0.0856753062206836[/C][/ROW]
[ROW][C]49[/C][C]0.935342754616277[/C][C]0.129314490767447[/C][C]0.0646572453837234[/C][/ROW]
[ROW][C]50[/C][C]0.927790209296048[/C][C]0.144419581407904[/C][C]0.072209790703952[/C][/ROW]
[ROW][C]51[/C][C]0.918275185749256[/C][C]0.163449628501487[/C][C]0.0817248142507436[/C][/ROW]
[ROW][C]52[/C][C]0.901347379357202[/C][C]0.197305241285597[/C][C]0.0986526206427984[/C][/ROW]
[ROW][C]53[/C][C]0.907314813847615[/C][C]0.185370372304771[/C][C]0.0926851861523853[/C][/ROW]
[ROW][C]54[/C][C]0.885350981540921[/C][C]0.229298036918159[/C][C]0.11464901845908[/C][/ROW]
[ROW][C]55[/C][C]0.917571032610152[/C][C]0.164857934779696[/C][C]0.0824289673898482[/C][/ROW]
[ROW][C]56[/C][C]0.899860964563445[/C][C]0.20027807087311[/C][C]0.100139035436555[/C][/ROW]
[ROW][C]57[/C][C]0.889229342706446[/C][C]0.221541314587109[/C][C]0.110770657293554[/C][/ROW]
[ROW][C]58[/C][C]0.904730595680012[/C][C]0.190538808639975[/C][C]0.0952694043199876[/C][/ROW]
[ROW][C]59[/C][C]0.924383526869831[/C][C]0.151232946260339[/C][C]0.0756164731301693[/C][/ROW]
[ROW][C]60[/C][C]0.910811984295577[/C][C]0.178376031408846[/C][C]0.0891880157044231[/C][/ROW]
[ROW][C]61[/C][C]0.912136281131903[/C][C]0.175727437736193[/C][C]0.0878637188680967[/C][/ROW]
[ROW][C]62[/C][C]0.89486795058592[/C][C]0.210264098828159[/C][C]0.10513204941408[/C][/ROW]
[ROW][C]63[/C][C]0.887292548513028[/C][C]0.225414902973945[/C][C]0.112707451486972[/C][/ROW]
[ROW][C]64[/C][C]0.862835850555433[/C][C]0.274328298889134[/C][C]0.137164149444567[/C][/ROW]
[ROW][C]65[/C][C]0.835188990838533[/C][C]0.329622018322934[/C][C]0.164811009161467[/C][/ROW]
[ROW][C]66[/C][C]0.859779490774625[/C][C]0.28044101845075[/C][C]0.140220509225375[/C][/ROW]
[ROW][C]67[/C][C]0.861557721174355[/C][C]0.27688455765129[/C][C]0.138442278825645[/C][/ROW]
[ROW][C]68[/C][C]0.847503648646726[/C][C]0.304992702706548[/C][C]0.152496351353274[/C][/ROW]
[ROW][C]69[/C][C]0.819527957725698[/C][C]0.360944084548605[/C][C]0.180472042274302[/C][/ROW]
[ROW][C]70[/C][C]0.803690280306759[/C][C]0.392619439386481[/C][C]0.196309719693241[/C][/ROW]
[ROW][C]71[/C][C]0.778382891215204[/C][C]0.443234217569592[/C][C]0.221617108784796[/C][/ROW]
[ROW][C]72[/C][C]0.774690007466132[/C][C]0.450619985067736[/C][C]0.225309992533868[/C][/ROW]
[ROW][C]73[/C][C]0.755114922552358[/C][C]0.489770154895285[/C][C]0.244885077447642[/C][/ROW]
[ROW][C]74[/C][C]0.73461581024871[/C][C]0.53076837950258[/C][C]0.26538418975129[/C][/ROW]
[ROW][C]75[/C][C]0.718508540402916[/C][C]0.562982919194168[/C][C]0.281491459597084[/C][/ROW]
[ROW][C]76[/C][C]0.836864617481612[/C][C]0.326270765036776[/C][C]0.163135382518388[/C][/ROW]
[ROW][C]77[/C][C]0.831918533901543[/C][C]0.336162932196913[/C][C]0.168081466098457[/C][/ROW]
[ROW][C]78[/C][C]0.854007258216378[/C][C]0.291985483567245[/C][C]0.145992741783622[/C][/ROW]
[ROW][C]79[/C][C]0.828134665636094[/C][C]0.343730668727812[/C][C]0.171865334363906[/C][/ROW]
[ROW][C]80[/C][C]0.898869879180719[/C][C]0.202260241638562[/C][C]0.101130120819281[/C][/ROW]
[ROW][C]81[/C][C]0.878592228336953[/C][C]0.242815543326095[/C][C]0.121407771663047[/C][/ROW]
[ROW][C]82[/C][C]0.875848487085818[/C][C]0.248303025828364[/C][C]0.124151512914182[/C][/ROW]
[ROW][C]83[/C][C]0.884457052105011[/C][C]0.231085895789977[/C][C]0.115542947894989[/C][/ROW]
[ROW][C]84[/C][C]0.861231606715393[/C][C]0.277536786569214[/C][C]0.138768393284607[/C][/ROW]
[ROW][C]85[/C][C]0.833889369426115[/C][C]0.33222126114777[/C][C]0.166110630573885[/C][/ROW]
[ROW][C]86[/C][C]0.803488167830697[/C][C]0.393023664338606[/C][C]0.196511832169303[/C][/ROW]
[ROW][C]87[/C][C]0.771423485744416[/C][C]0.457153028511169[/C][C]0.228576514255584[/C][/ROW]
[ROW][C]88[/C][C]0.738498976284322[/C][C]0.523002047431356[/C][C]0.261501023715678[/C][/ROW]
[ROW][C]89[/C][C]0.70381474421114[/C][C]0.592370511577719[/C][C]0.29618525578886[/C][/ROW]
[ROW][C]90[/C][C]0.772958305307162[/C][C]0.454083389385677[/C][C]0.227041694692838[/C][/ROW]
[ROW][C]91[/C][C]0.74977483735042[/C][C]0.500450325299159[/C][C]0.250225162649579[/C][/ROW]
[ROW][C]92[/C][C]0.72024507372509[/C][C]0.55950985254982[/C][C]0.27975492627491[/C][/ROW]
[ROW][C]93[/C][C]0.689896692221056[/C][C]0.620206615557889[/C][C]0.310103307778944[/C][/ROW]
[ROW][C]94[/C][C]0.681954293539909[/C][C]0.636091412920182[/C][C]0.318045706460091[/C][/ROW]
[ROW][C]95[/C][C]0.720263709961414[/C][C]0.559472580077172[/C][C]0.279736290038586[/C][/ROW]
[ROW][C]96[/C][C]0.678924243441495[/C][C]0.64215151311701[/C][C]0.321075756558505[/C][/ROW]
[ROW][C]97[/C][C]0.657041582048216[/C][C]0.685916835903569[/C][C]0.342958417951784[/C][/ROW]
[ROW][C]98[/C][C]0.634720549087197[/C][C]0.730558901825605[/C][C]0.365279450912803[/C][/ROW]
[ROW][C]99[/C][C]0.591142218916766[/C][C]0.817715562166468[/C][C]0.408857781083234[/C][/ROW]
[ROW][C]100[/C][C]0.579310673066091[/C][C]0.841378653867819[/C][C]0.420689326933909[/C][/ROW]
[ROW][C]101[/C][C]0.532253321772311[/C][C]0.935493356455378[/C][C]0.467746678227689[/C][/ROW]
[ROW][C]102[/C][C]0.507293331590965[/C][C]0.98541333681807[/C][C]0.492706668409035[/C][/ROW]
[ROW][C]103[/C][C]0.497135495931592[/C][C]0.994270991863183[/C][C]0.502864504068408[/C][/ROW]
[ROW][C]104[/C][C]0.602294919062757[/C][C]0.795410161874486[/C][C]0.397705080937243[/C][/ROW]
[ROW][C]105[/C][C]0.70227803054198[/C][C]0.595443938916041[/C][C]0.29772196945802[/C][/ROW]
[ROW][C]106[/C][C]0.688635400834437[/C][C]0.622729198331127[/C][C]0.311364599165563[/C][/ROW]
[ROW][C]107[/C][C]0.643320697154638[/C][C]0.713358605690725[/C][C]0.356679302845362[/C][/ROW]
[ROW][C]108[/C][C]0.721061364729072[/C][C]0.557877270541856[/C][C]0.278938635270928[/C][/ROW]
[ROW][C]109[/C][C]0.692647303591154[/C][C]0.614705392817693[/C][C]0.307352696408846[/C][/ROW]
[ROW][C]110[/C][C]0.901945276811765[/C][C]0.196109446376471[/C][C]0.0980547231882353[/C][/ROW]
[ROW][C]111[/C][C]0.89923160259832[/C][C]0.201536794803361[/C][C]0.10076839740168[/C][/ROW]
[ROW][C]112[/C][C]0.874970747976715[/C][C]0.25005850404657[/C][C]0.125029252023285[/C][/ROW]
[ROW][C]113[/C][C]0.853945162179088[/C][C]0.292109675641823[/C][C]0.146054837820912[/C][/ROW]
[ROW][C]114[/C][C]0.823994058013526[/C][C]0.352011883972948[/C][C]0.176005941986474[/C][/ROW]
[ROW][C]115[/C][C]0.799947575497458[/C][C]0.400104849005084[/C][C]0.200052424502542[/C][/ROW]
[ROW][C]116[/C][C]0.760489030633311[/C][C]0.479021938733377[/C][C]0.239510969366689[/C][/ROW]
[ROW][C]117[/C][C]0.743513281445496[/C][C]0.512973437109009[/C][C]0.256486718554504[/C][/ROW]
[ROW][C]118[/C][C]0.703144320483137[/C][C]0.593711359033727[/C][C]0.296855679516863[/C][/ROW]
[ROW][C]119[/C][C]0.776268369250978[/C][C]0.447463261498044[/C][C]0.223731630749022[/C][/ROW]
[ROW][C]120[/C][C]0.807985525051594[/C][C]0.384028949896812[/C][C]0.192014474948406[/C][/ROW]
[ROW][C]121[/C][C]0.767585070768591[/C][C]0.464829858462819[/C][C]0.232414929231409[/C][/ROW]
[ROW][C]122[/C][C]0.73010172484613[/C][C]0.53979655030774[/C][C]0.26989827515387[/C][/ROW]
[ROW][C]123[/C][C]0.682771675762804[/C][C]0.634456648474391[/C][C]0.317228324237196[/C][/ROW]
[ROW][C]124[/C][C]0.686889322644189[/C][C]0.626221354711621[/C][C]0.313110677355811[/C][/ROW]
[ROW][C]125[/C][C]0.649172484236532[/C][C]0.701655031526936[/C][C]0.350827515763468[/C][/ROW]
[ROW][C]126[/C][C]0.635478639919355[/C][C]0.729042720161289[/C][C]0.364521360080644[/C][/ROW]
[ROW][C]127[/C][C]0.637770338862997[/C][C]0.724459322274007[/C][C]0.362229661137003[/C][/ROW]
[ROW][C]128[/C][C]0.596732406509197[/C][C]0.806535186981605[/C][C]0.403267593490803[/C][/ROW]
[ROW][C]129[/C][C]0.58318756734821[/C][C]0.833624865303579[/C][C]0.41681243265179[/C][/ROW]
[ROW][C]130[/C][C]0.522918667005866[/C][C]0.954162665988268[/C][C]0.477081332994134[/C][/ROW]
[ROW][C]131[/C][C]0.527809600744081[/C][C]0.944380798511839[/C][C]0.472190399255919[/C][/ROW]
[ROW][C]132[/C][C]0.484103421041277[/C][C]0.968206842082554[/C][C]0.515896578958723[/C][/ROW]
[ROW][C]133[/C][C]0.435143930729996[/C][C]0.870287861459992[/C][C]0.564856069270004[/C][/ROW]
[ROW][C]134[/C][C]0.386099727496535[/C][C]0.772199454993071[/C][C]0.613900272503465[/C][/ROW]
[ROW][C]135[/C][C]0.326787137437769[/C][C]0.653574274875539[/C][C]0.673212862562231[/C][/ROW]
[ROW][C]136[/C][C]0.300368980466771[/C][C]0.600737960933541[/C][C]0.699631019533229[/C][/ROW]
[ROW][C]137[/C][C]0.54461927639305[/C][C]0.910761447213901[/C][C]0.45538072360695[/C][/ROW]
[ROW][C]138[/C][C]0.491824616501238[/C][C]0.983649233002476[/C][C]0.508175383498762[/C][/ROW]
[ROW][C]139[/C][C]0.424311415289964[/C][C]0.848622830579928[/C][C]0.575688584710036[/C][/ROW]
[ROW][C]140[/C][C]0.372777709882992[/C][C]0.745555419765984[/C][C]0.627222290117008[/C][/ROW]
[ROW][C]141[/C][C]0.450284046899323[/C][C]0.900568093798645[/C][C]0.549715953100677[/C][/ROW]
[ROW][C]142[/C][C]0.41373892612913[/C][C]0.827477852258259[/C][C]0.58626107387087[/C][/ROW]
[ROW][C]143[/C][C]0.407445579336521[/C][C]0.814891158673042[/C][C]0.592554420663479[/C][/ROW]
[ROW][C]144[/C][C]0.32556448873825[/C][C]0.651128977476499[/C][C]0.67443551126175[/C][/ROW]
[ROW][C]145[/C][C]0.249615326143307[/C][C]0.499230652286613[/C][C]0.750384673856693[/C][/ROW]
[ROW][C]146[/C][C]0.402109485399116[/C][C]0.804218970798232[/C][C]0.597890514600884[/C][/ROW]
[ROW][C]147[/C][C]0.310214912496012[/C][C]0.620429824992025[/C][C]0.689785087503988[/C][/ROW]
[ROW][C]148[/C][C]0.474804840735054[/C][C]0.949609681470107[/C][C]0.525195159264946[/C][/ROW]
[ROW][C]149[/C][C]0.492402450945491[/C][C]0.984804901890982[/C][C]0.507597549054509[/C][/ROW]
[ROW][C]150[/C][C]0.371915518852652[/C][C]0.743831037705305[/C][C]0.628084481147348[/C][/ROW]
[ROW][C]151[/C][C]0.619459688273229[/C][C]0.761080623453541[/C][C]0.380540311726771[/C][/ROW]
[ROW][C]152[/C][C]0.465770531045163[/C][C]0.931541062090325[/C][C]0.534229468954837[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253882&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253882&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8667207318705970.2665585362588060.133279268129403
110.8136022699416220.3727954601167550.186397730058378
120.9998591442005840.000281711598832640.00014085579941632
130.9996695669005740.0006608661988515630.000330433099425782
140.9992837445680260.001432510863948780.000716255431974388
150.9987028547514210.002594290497158010.00129714524857901
160.9974863568463680.005027286307263510.00251364315363175
170.9954854510977470.009029097804505930.00451454890225297
180.9927676720910390.01446465581792150.00723232790896074
190.993713856618780.01257228676244030.00628614338122013
200.9896798659060260.0206402681879480.010320134093974
210.9918711737137150.01625765257257060.00812882628628531
220.987640118224860.02471976355028020.0123598817751401
230.9832130089144810.03357398217103770.0167869910855189
240.9746902977010770.05061940459784660.0253097022989233
250.9638100022655490.07237999546890270.0361899977344513
260.9914189835428670.01716203291426680.00858101645713342
270.9870404527576050.02591909448479020.0129595472423951
280.9840354629071010.03192907418579790.015964537092899
290.9801558462132560.03968830757348890.0198441537867444
300.973896545735250.05220690852949910.0261034542647495
310.9721980213213720.05560395735725670.0278019786786284
320.9621282994597270.07574340108054520.0378717005402726
330.9513968928478590.09720621430428160.0486031071521408
340.9358060456251760.1283879087496490.0641939543748245
350.9162817915057420.1674364169885150.0837182084942576
360.9320156195441680.1359687609116630.0679843804558316
370.9577198503167270.08456029936654640.0422801496832732
380.9436440635684030.1127118728631930.0563559364315965
390.9304071605040140.1391856789919720.0695928394959859
400.9372655784735990.1254688430528020.0627344215264011
410.936783618770360.1264327624592790.0632163812296397
420.9332094444199570.1335811111600870.0667905555800433
430.9459419750162830.1081160499674330.0540580249837167
440.9313515654812980.1372968690374050.0686484345187024
450.9291414095376090.1417171809247820.0708585904623908
460.9197858728319110.1604282543361770.0802141271680885
470.9299695070028930.1400609859942140.0700304929971072
480.9143246937793160.1713506124413670.0856753062206836
490.9353427546162770.1293144907674470.0646572453837234
500.9277902092960480.1444195814079040.072209790703952
510.9182751857492560.1634496285014870.0817248142507436
520.9013473793572020.1973052412855970.0986526206427984
530.9073148138476150.1853703723047710.0926851861523853
540.8853509815409210.2292980369181590.11464901845908
550.9175710326101520.1648579347796960.0824289673898482
560.8998609645634450.200278070873110.100139035436555
570.8892293427064460.2215413145871090.110770657293554
580.9047305956800120.1905388086399750.0952694043199876
590.9243835268698310.1512329462603390.0756164731301693
600.9108119842955770.1783760314088460.0891880157044231
610.9121362811319030.1757274377361930.0878637188680967
620.894867950585920.2102640988281590.10513204941408
630.8872925485130280.2254149029739450.112707451486972
640.8628358505554330.2743282988891340.137164149444567
650.8351889908385330.3296220183229340.164811009161467
660.8597794907746250.280441018450750.140220509225375
670.8615577211743550.276884557651290.138442278825645
680.8475036486467260.3049927027065480.152496351353274
690.8195279577256980.3609440845486050.180472042274302
700.8036902803067590.3926194393864810.196309719693241
710.7783828912152040.4432342175695920.221617108784796
720.7746900074661320.4506199850677360.225309992533868
730.7551149225523580.4897701548952850.244885077447642
740.734615810248710.530768379502580.26538418975129
750.7185085404029160.5629829191941680.281491459597084
760.8368646174816120.3262707650367760.163135382518388
770.8319185339015430.3361629321969130.168081466098457
780.8540072582163780.2919854835672450.145992741783622
790.8281346656360940.3437306687278120.171865334363906
800.8988698791807190.2022602416385620.101130120819281
810.8785922283369530.2428155433260950.121407771663047
820.8758484870858180.2483030258283640.124151512914182
830.8844570521050110.2310858957899770.115542947894989
840.8612316067153930.2775367865692140.138768393284607
850.8338893694261150.332221261147770.166110630573885
860.8034881678306970.3930236643386060.196511832169303
870.7714234857444160.4571530285111690.228576514255584
880.7384989762843220.5230020474313560.261501023715678
890.703814744211140.5923705115777190.29618525578886
900.7729583053071620.4540833893856770.227041694692838
910.749774837350420.5004503252991590.250225162649579
920.720245073725090.559509852549820.27975492627491
930.6898966922210560.6202066155578890.310103307778944
940.6819542935399090.6360914129201820.318045706460091
950.7202637099614140.5594725800771720.279736290038586
960.6789242434414950.642151513117010.321075756558505
970.6570415820482160.6859168359035690.342958417951784
980.6347205490871970.7305589018256050.365279450912803
990.5911422189167660.8177155621664680.408857781083234
1000.5793106730660910.8413786538678190.420689326933909
1010.5322533217723110.9354933564553780.467746678227689
1020.5072933315909650.985413336818070.492706668409035
1030.4971354959315920.9942709918631830.502864504068408
1040.6022949190627570.7954101618744860.397705080937243
1050.702278030541980.5954439389160410.29772196945802
1060.6886354008344370.6227291983311270.311364599165563
1070.6433206971546380.7133586056907250.356679302845362
1080.7210613647290720.5578772705418560.278938635270928
1090.6926473035911540.6147053928176930.307352696408846
1100.9019452768117650.1961094463764710.0980547231882353
1110.899231602598320.2015367948033610.10076839740168
1120.8749707479767150.250058504046570.125029252023285
1130.8539451621790880.2921096756418230.146054837820912
1140.8239940580135260.3520118839729480.176005941986474
1150.7999475754974580.4001048490050840.200052424502542
1160.7604890306333110.4790219387333770.239510969366689
1170.7435132814454960.5129734371090090.256486718554504
1180.7031443204831370.5937113590337270.296855679516863
1190.7762683692509780.4474632614980440.223731630749022
1200.8079855250515940.3840289498968120.192014474948406
1210.7675850707685910.4648298584628190.232414929231409
1220.730101724846130.539796550307740.26989827515387
1230.6827716757628040.6344566484743910.317228324237196
1240.6868893226441890.6262213547116210.313110677355811
1250.6491724842365320.7016550315269360.350827515763468
1260.6354786399193550.7290427201612890.364521360080644
1270.6377703388629970.7244593222740070.362229661137003
1280.5967324065091970.8065351869816050.403267593490803
1290.583187567348210.8336248653035790.41681243265179
1300.5229186670058660.9541626659882680.477081332994134
1310.5278096007440810.9443807985118390.472190399255919
1320.4841034210412770.9682068420825540.515896578958723
1330.4351439307299960.8702878614599920.564856069270004
1340.3860997274965350.7721994549930710.613900272503465
1350.3267871374377690.6535742748755390.673212862562231
1360.3003689804667710.6007379609335410.699631019533229
1370.544619276393050.9107614472139010.45538072360695
1380.4918246165012380.9836492330024760.508175383498762
1390.4243114152899640.8486228305799280.575688584710036
1400.3727777098829920.7455554197659840.627222290117008
1410.4502840468993230.9005680937986450.549715953100677
1420.413738926129130.8274778522582590.58626107387087
1430.4074455793365210.8148911586730420.592554420663479
1440.325564488738250.6511289774764990.67443551126175
1450.2496153261433070.4992306522866130.750384673856693
1460.4021094853991160.8042189707982320.597890514600884
1470.3102149124960120.6204298249920250.689785087503988
1480.4748048407350540.9496096814701070.525195159264946
1490.4924024509454910.9848049018909820.507597549054509
1500.3719155188526520.7438310377053050.628084481147348
1510.6194596882732290.7610806234535410.380540311726771
1520.4657705310451630.9315410620903250.534229468954837






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level60.041958041958042NOK
5% type I error level160.111888111888112NOK
10% type I error level230.160839160839161NOK

\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 & 6 & 0.041958041958042 & NOK \tabularnewline
5% type I error level & 16 & 0.111888111888112 & NOK \tabularnewline
10% type I error level & 23 & 0.160839160839161 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253882&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]6[/C][C]0.041958041958042[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.111888111888112[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.160839160839161[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253882&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253882&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level60.041958041958042NOK
5% type I error level160.111888111888112NOK
10% type I error level230.160839160839161NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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