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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 20 Dec 2011 05:59:07 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/20/t1324378790ppxld0h355hv2lq.htm/, Retrieved Mon, 06 May 2024 05:01:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157916, Retrieved Mon, 06 May 2024 05:01:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152

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]
-   PD    [Multiple Regression] [Paper Multiple Re...] [2011-12-20 10:59:07] [c18e83883fa784c15a15b4fbc0636edd] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14	1	7	41	38	13	12	53
18	1	5	39	32	16	11	86
11	1	5	30	35	19	15	66
12	0	5	31	33	15	6	67
16	1	8	34	37	14	13	76
18	1	6	35	29	13	10	78
14	1	5	39	31	19	12	53
14	1	6	34	36	15	14	80
15	1	5	36	35	14	12	74
15	1	4	37	38	15	6	76
17	0	6	38	31	16	10	79
19	1	5	36	34	16	12	54
10	0	5	38	35	16	12	67
16	1	6	39	38	16	11	54
18	1	7	33	37	17	15	87
14	0	6	32	33	15	12	58
14	0	7	36	32	15	10	75
17	1	6	38	38	20	12	88
14	0	8	39	38	18	11	64
16	1	7	32	32	16	12	57
18	0	5	32	33	16	11	66
11	1	5	31	31	16	12	68
14	1	7	39	38	19	13	54
12	1	7	37	39	16	11	56
17	0	5	39	32	17	9	86
9	1	4	41	32	17	13	80
16	0	10	36	35	16	10	76
14	1	6	33	37	15	14	69
15	1	5	33	33	16	12	78
11	0	5	34	33	14	10	67
16	1	5	31	28	15	12	80
13	0	5	27	32	12	8	54
17	1	6	37	31	14	10	71
15	1	5	34	37	16	12	84
14	0	5	34	30	14	12	74
16	0	5	32	33	7	7	71
9	0	5	29	31	10	6	63
15	0	5	36	33	14	12	71
17	1	5	29	31	16	10	76
13	0	5	35	33	16	10	69
15	0	5	37	32	16	10	74
16	1	7	34	33	14	12	75
16	0	5	38	32	20	15	54
12	0	6	35	33	14	10	52
12	1	7	38	28	14	10	69
11	1	7	37	35	11	12	68
15	1	5	38	39	14	13	65
15	1	5	33	34	15	11	75
17	1	4	36	38	16	11	74
13	0	5	38	32	14	12	75
16	1	4	32	38	16	14	72
14	0	5	32	30	14	10	67
11	0	5	32	33	12	12	63
12	1	7	34	38	16	13	62
12	0	5	32	32	9	5	63
15	1	5	37	32	14	6	76
16	1	6	39	34	16	12	74
15	1	4	29	34	16	12	67
12	0	6	37	36	15	11	73
12	1	6	35	34	16	10	70
8	0	5	30	28	12	7	53
13	0	7	38	34	16	12	77
11	1	6	34	35	16	14	77
14	1	8	31	35	14	11	52
15	1	7	34	31	16	12	54
10	0	5	35	37	17	13	80
11	1	6	36	35	18	14	66
12	0	6	30	27	18	11	73
15	1	5	39	40	12	12	63
15	0	5	35	37	16	12	69
14	0	5	38	36	10	8	67
16	1	5	31	38	14	11	54
15	1	4	34	39	18	14	81
15	0	6	38	41	18	14	69
13	0	6	34	27	16	12	84
12	1	6	39	30	17	9	80
17	1	6	37	37	16	13	70
13	1	7	34	31	16	11	69
15	0	5	28	31	13	12	77
13	0	7	37	27	16	12	54
15	0	6	33	36	16	12	79
16	0	5	37	38	20	12	30
15	1	5	35	37	16	12	71
16	0	4	37	33	15	12	73
15	1	8	32	34	15	11	72
14	1	8	33	31	16	10	77
15	0	5	38	39	14	9	75
14	1	5	33	34	16	12	69
13	1	6	29	32	16	12	54
7	1	4	33	33	15	12	70
17	1	5	31	36	12	9	73
13	1	5	36	32	17	15	54
15	1	5	35	41	16	12	77
14	1	5	32	28	15	12	82
13	1	6	29	30	13	12	80
16	1	6	39	36	16	10	80
12	1	5	37	35	16	13	69
14	1	6	35	31	16	9	78
17	0	5	37	34	16	12	81
15	0	7	32	36	14	10	76
17	1	5	38	36	16	14	76
12	0	6	37	35	16	11	73
16	1	6	36	37	20	15	85
11	0	6	32	28	15	11	66
15	1	4	33	39	16	11	79
9	0	5	40	32	13	12	68
16	1	5	38	35	17	12	76
15	0	7	41	39	16	12	71
10	0	6	36	35	16	11	54
10	1	9	43	42	12	7	46
15	1	6	30	34	16	12	82
11	1	6	31	33	16	14	74
13	1	5	32	41	17	11	88
14	0	6	32	33	13	11	38
18	1	5	37	34	12	10	76
16	0	8	37	32	18	13	86
14	1	7	33	40	14	13	54
14	1	5	34	40	14	8	70
14	1	7	33	35	13	11	69
14	1	6	38	36	16	12	90
12	1	6	33	37	13	11	54
14	1	9	31	27	16	13	76
15	1	7	38	39	13	12	89
15	1	6	37	38	16	14	76
15	1	5	33	31	15	13	73
13	1	5	31	33	16	15	79
17	0	6	39	32	15	10	90
17	1	6	44	39	17	11	74
19	1	7	33	36	15	9	81
15	1	5	35	33	12	11	72
13	0	5	32	33	16	10	71
9	0	5	28	32	10	11	66
15	1	6	40	37	16	8	77
15	0	4	27	30	12	11	65
15	0	5	37	38	14	12	74
16	1	7	32	29	15	12	82
11	0	5	28	22	13	9	54
14	0	7	34	35	15	11	63
11	1	7	30	35	11	10	54
15	1	6	35	34	12	8	64
13	0	5	31	35	8	9	69
15	1	8	32	34	16	8	54
16	0	5	30	34	15	9	84
14	1	5	30	35	17	15	86
15	0	5	31	23	16	11	77
16	1	6	40	31	10	8	89
16	1	4	32	27	18	13	76
11	0	5	36	36	13	12	60
12	0	5	32	31	16	12	75
9	0	7	35	32	13	9	73
16	1	6	38	39	10	7	85
13	1	7	42	37	15	13	79
16	0	10	34	38	16	9	71
12	1	6	35	39	16	6	72
9	1	8	35	34	14	8	69
13	1	4	33	31	10	8	78
13	1	5	36	32	17	15	54
14	1	6	32	37	13	6	69
19	1	7	33	36	15	9	81
13	1	7	34	32	16	11	84
12	1	6	32	35	12	8	84
13	1	6	34	36	13	8	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=157916&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=157916&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157916&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
Happiness[t] = + 4.44211579961638 + 0.508137945224857Gender[t] -0.00664894562959634Age[t] + 0.0314532808724505Connected[t] + 0.0715109518659967Separate[t] + 0.161229573882901Learning[t] -0.0527382589865435Software[t] + 0.0560342010760397`Belonging `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  4.44211579961638 +  0.508137945224857Gender[t] -0.00664894562959634Age[t] +  0.0314532808724505Connected[t] +  0.0715109518659967Separate[t] +  0.161229573882901Learning[t] -0.0527382589865435Software[t] +  0.0560342010760397`Belonging
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157916&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  4.44211579961638 +  0.508137945224857Gender[t] -0.00664894562959634Age[t] +  0.0314532808724505Connected[t] +  0.0715109518659967Separate[t] +  0.161229573882901Learning[t] -0.0527382589865435Software[t] +  0.0560342010760397`Belonging
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157916&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157916&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
Happiness[t] = + 4.44211579961638 + 0.508137945224857Gender[t] -0.00664894562959634Age[t] + 0.0314532808724505Connected[t] + 0.0715109518659967Separate[t] + 0.161229573882901Learning[t] -0.0527382589865435Software[t] + 0.0560342010760397`Belonging `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.442115799616382.6307441.68850.0933320.046666
Gender0.5081379452248570.3805081.33540.1837090.091855
Age-0.006648945629596340.1542-0.04310.9656630.482831
Connected0.03145328087245050.0576060.5460.5858530.292926
Separate0.07151095186599670.0546641.30820.1927620.096381
Learning0.1612295738829010.0950941.69550.0920060.046003
Software-0.05273825898654350.098899-0.53330.5946280.297314
`Belonging `0.05603420107603970.0166713.36120.0009790.000489

\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) & 4.44211579961638 & 2.630744 & 1.6885 & 0.093332 & 0.046666 \tabularnewline
Gender & 0.508137945224857 & 0.380508 & 1.3354 & 0.183709 & 0.091855 \tabularnewline
Age & -0.00664894562959634 & 0.1542 & -0.0431 & 0.965663 & 0.482831 \tabularnewline
Connected & 0.0314532808724505 & 0.057606 & 0.546 & 0.585853 & 0.292926 \tabularnewline
Separate & 0.0715109518659967 & 0.054664 & 1.3082 & 0.192762 & 0.096381 \tabularnewline
Learning & 0.161229573882901 & 0.095094 & 1.6955 & 0.092006 & 0.046003 \tabularnewline
Software & -0.0527382589865435 & 0.098899 & -0.5333 & 0.594628 & 0.297314 \tabularnewline
`Belonging
` & 0.0560342010760397 & 0.016671 & 3.3612 & 0.000979 & 0.000489 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157916&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]4.44211579961638[/C][C]2.630744[/C][C]1.6885[/C][C]0.093332[/C][C]0.046666[/C][/ROW]
[ROW][C]Gender[/C][C]0.508137945224857[/C][C]0.380508[/C][C]1.3354[/C][C]0.183709[/C][C]0.091855[/C][/ROW]
[ROW][C]Age[/C][C]-0.00664894562959634[/C][C]0.1542[/C][C]-0.0431[/C][C]0.965663[/C][C]0.482831[/C][/ROW]
[ROW][C]Connected[/C][C]0.0314532808724505[/C][C]0.057606[/C][C]0.546[/C][C]0.585853[/C][C]0.292926[/C][/ROW]
[ROW][C]Separate[/C][C]0.0715109518659967[/C][C]0.054664[/C][C]1.3082[/C][C]0.192762[/C][C]0.096381[/C][/ROW]
[ROW][C]Learning[/C][C]0.161229573882901[/C][C]0.095094[/C][C]1.6955[/C][C]0.092006[/C][C]0.046003[/C][/ROW]
[ROW][C]Software[/C][C]-0.0527382589865435[/C][C]0.098899[/C][C]-0.5333[/C][C]0.594628[/C][C]0.297314[/C][/ROW]
[ROW][C]`Belonging
`[/C][C]0.0560342010760397[/C][C]0.016671[/C][C]3.3612[/C][C]0.000979[/C][C]0.000489[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157916&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157916&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)4.442115799616382.6307441.68850.0933320.046666
Gender0.5081379452248570.3805081.33540.1837090.091855
Age-0.006648945629596340.1542-0.04310.9656630.482831
Connected0.03145328087245050.0576060.5460.5858530.292926
Separate0.07151095186599670.0546641.30820.1927620.096381
Learning0.1612295738829010.0950941.69550.0920060.046003
Software-0.05273825898654350.098899-0.53330.5946280.297314
`Belonging `0.05603420107603970.0166713.36120.0009790.000489






Multiple Linear Regression - Regression Statistics
Multiple R0.375015984639441
R-squared0.14063698873509
Adjusted R-squared0.101575033677594
F-TEST (value)3.60035713850174
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value0.001279420598848
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21571787838418
Sum Squared Residuals756.048480355062

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.375015984639441 \tabularnewline
R-squared & 0.14063698873509 \tabularnewline
Adjusted R-squared & 0.101575033677594 \tabularnewline
F-TEST (value) & 3.60035713850174 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 0.001279420598848 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.21571787838418 \tabularnewline
Sum Squared Residuals & 756.048480355062 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157916&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.375015984639441[/C][/ROW]
[ROW][C]R-squared[/C][C]0.14063698873509[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.101575033677594[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.60035713850174[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]0.001279420598848[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.21571787838418[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]756.048480355062[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157916&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157916&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 R0.375015984639441
R-squared0.14063698873509
Adjusted R-squared0.101575033677594
F-TEST (value)3.60035713850174
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value0.001279420598848
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21571787838418
Sum Squared Residuals756.048480355062






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.34364982178170.65635017821828
21815.25053105624462.74946894375542
31114.3340360481722-3.33403604817225
41213.6000897165112-1.60008971651117
51614.44259489782421.55740510217577
61814.02431206025673.97568793974329
71413.76084193153140.239158068468569
81414.7170099564179-0.717009956417947
91514.32309624956040.676903750439606
101515.1654588616147-0.165458861614672
111714.29327878410592.70672121589406
121913.45336042393945.54663957606059
131013.808084606314-3.80808460631396
141613.87985338737772.12014661262231
151815.41237897809342.58762102190657
161412.80415668815041.19584331184959
171413.90986785041040.0901321495896177
181716.34574297963570.654257020364348
191414.2412187094198-0.241218709419841
201613.33933010868652.66066989131346
211813.47304707525784.52695292474223
221113.8660399790437-2.86603997904372
231414.2514166454237-0.251416645423711
241213.9938772340213-1.99387723402127
251715.00909920287571.99090079712429
26915.0396344130727-6.03963441307265
271614.32171764407851.67828235592148
281414.1406914155751-0.140691415575055
291514.6323104552810.36768954471899
301113.3222669492995-2.32226694929945
311614.16268796247531.8373120375247
321312.08515578754510.914844212454931
331713.99923069208423.00076930791577
341515.2860127500737-0.286012750073686
351413.39449698326070.605503016739348
361612.5131049516383.48689504836197
37912.3638765773156-3.36387657731562
381513.50383379737541.49616620262458
391714.35688354388022.64311645611978
401313.7882477800898-0.788247780089784
411514.05981439534890.940185604651113
421614.15990409390031.84009590609965
431613.35181065529942.64818934470057
441212.5065582684017-0.506558268401709
451213.697433769577-1.69743376957701
461113.5213577110687-2.52135771106871
471514.11500055009840.88499944990162
481514.42722748902250.57277251097747
491714.91947545754032.08052454245967
501313.7193662115585-0.719366211558488
511614.52337915493881.47662084506119
521413.04482753195660.95517246804344
531112.6072879175115-1.6072879175115
541214.0587351280211-2.05873512802107
551212.4212560569026-0.421256056902605
561514.56851463090620.431485369093806
571614.6617553424481.33824465755205
581513.96828101745041.03171898254964
591214.0692072232378-2.06920722323779
601214.4172819326271-2.41728193262708
61811.8901758806089-3.89017588060894
621314.2836177739492-1.28361777394917
631114.6386259752067-3.63862597520673
641412.9658688436231.03413115637698
651513.16262311533731.83737688466268
661014.6936825963135-4.69368259631348
671114.407615472881-3.407615472881
681213.6891244119854-1.68912441198537
691513.83617549190551.16382450809451
701513.96881506958071.03118493041932
711413.12317115082870.876828849171275
721613.31241693826192.68758306173812
731515.4845636259999-0.484563625999872
741514.55955240382510.440447596174858
751314.0561163405593-1.05611634055927
761215.0313610922827-3.03136109228274
771714.53650657301032.46349342698966
781314.0558743904645-1.05587439046446
791513.28416127923721.71583872076284
801312.46280120526580.53719879473417
811514.38809062110060.611909378899413
821612.56281703675763.43718296324236
831514.58902141695760.41097858304238
841613.81523399991252.18476600008755
851514.20772476803320.792275231966829
861414.5187840315573-0.518784031557275
871514.37815765158010.621842348419906
881414.1995135974626-0.199513597462649
891313.0835166084707-0.0835166084706617
90714.0294562184194-7.02945621841939
911714.01706222518192.98293777481807
921313.3133533171307-0.313353317130683
931515.2112704308778-0.211270430877845
941414.3062096454998-0.306209645499834
951313.913695211067-0.913695211066998
961615.24645897060930.753541029390726
971214.3440994138319-2.34409941383191
981414.703760944624-0.703760944623952
991714.48959918864012.51040081135993
1001513.96490316157771.03509683842229
1011714.78656479511612.21343520488391
1021214.1589258452547-2.1589258452547
1031615.8850080858370.114991914162995
1041112.9476137964153-1.94761379641529
1051515.1767975721692-0.17679757216917
106913.2288037918882-4.22880379188821
1071614.98175993510611.01824006489392
1081514.39932716944030.600672830559733
1091013.0628227439375-3.06282274393749
1101013.3895246132489-3.38952461324894
1111514.82694942320420.173050576795782
1121114.2331416256293-3.23314162562927
1131315.9472546329664-2.94725463296637
1141411.41375177785042.58624822214964
1151814.17812435092623.82187564907379
1161614.87652234217871.12347765782125
1171413.39957099450650.600429005493505
1181414.6045606787875-0.604560678787491
1191413.82677619540730.173223804592707
1201415.6698711825241-1.66987118252413
1211213.1359340286283-1.13593402862829
1221413.94893573868310.0510642613168786
1231515.3380321697678-0.338032169767783
1241514.8914844723460.108515527653967
1251513.99514971329941.00485028670063
1261314.4672233176525-1.46722331765252
1271714.85138965479792.14861034520207
1281715.39054433900961.60945566099036
1291914.99863322592474.00136677407534
1301513.76683177402461.23316822597539
1311313.8059563396245-0.805956339624512
132912.3083455566046-3.30834555660456
1331515.2867971180927-0.286797118092689
1341512.40694426431952.59305573568052
1351514.0609444408060.939055559194023
1361614.36442270610661.63557729389336
1371111.5099908646539-0.509990864653911
1381413.33634547236450.663654527635549
1391112.6221824478701-1.62218244787009
1401513.54163494861231.45836505138768
1411312.56835822825530.431641771744692
1421513.5185534995071.48144650049301
1431614.43451402883781.56548597116223
1441415.1322609219272-1.13226092192724
1451513.34286048756181.65713951243821
1461614.56276437651181.43723562348822
1471614.33609289546941.66390710453064
1481112.9407608672541-1.94076086725408
1491213.7815947222236-1.78159472222359
150913.4966252786066-4.49662527860659
1511614.90054688437721.09945311562277
1521315.0302022675445-2.03020226754445
1531614.2459111915381.75408880846204
1541215.0978581300553-3.09785813005532
155914.1309672104991-5.13096721049913
1561313.7395130898274-0.739513089827379
1571313.3133533171307-0.313353317130683
1581414.2086850588292-0.20868505882915
1591914.99863322592474.00136677407534
1601314.9678983584711-1.96789835847105
1611214.6394700793818-2.63947007938176
1621314.094604150735-1.09460415073497

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.3436498217817 & 0.65635017821828 \tabularnewline
2 & 18 & 15.2505310562446 & 2.74946894375542 \tabularnewline
3 & 11 & 14.3340360481722 & -3.33403604817225 \tabularnewline
4 & 12 & 13.6000897165112 & -1.60008971651117 \tabularnewline
5 & 16 & 14.4425948978242 & 1.55740510217577 \tabularnewline
6 & 18 & 14.0243120602567 & 3.97568793974329 \tabularnewline
7 & 14 & 13.7608419315314 & 0.239158068468569 \tabularnewline
8 & 14 & 14.7170099564179 & -0.717009956417947 \tabularnewline
9 & 15 & 14.3230962495604 & 0.676903750439606 \tabularnewline
10 & 15 & 15.1654588616147 & -0.165458861614672 \tabularnewline
11 & 17 & 14.2932787841059 & 2.70672121589406 \tabularnewline
12 & 19 & 13.4533604239394 & 5.54663957606059 \tabularnewline
13 & 10 & 13.808084606314 & -3.80808460631396 \tabularnewline
14 & 16 & 13.8798533873777 & 2.12014661262231 \tabularnewline
15 & 18 & 15.4123789780934 & 2.58762102190657 \tabularnewline
16 & 14 & 12.8041566881504 & 1.19584331184959 \tabularnewline
17 & 14 & 13.9098678504104 & 0.0901321495896177 \tabularnewline
18 & 17 & 16.3457429796357 & 0.654257020364348 \tabularnewline
19 & 14 & 14.2412187094198 & -0.241218709419841 \tabularnewline
20 & 16 & 13.3393301086865 & 2.66066989131346 \tabularnewline
21 & 18 & 13.4730470752578 & 4.52695292474223 \tabularnewline
22 & 11 & 13.8660399790437 & -2.86603997904372 \tabularnewline
23 & 14 & 14.2514166454237 & -0.251416645423711 \tabularnewline
24 & 12 & 13.9938772340213 & -1.99387723402127 \tabularnewline
25 & 17 & 15.0090992028757 & 1.99090079712429 \tabularnewline
26 & 9 & 15.0396344130727 & -6.03963441307265 \tabularnewline
27 & 16 & 14.3217176440785 & 1.67828235592148 \tabularnewline
28 & 14 & 14.1406914155751 & -0.140691415575055 \tabularnewline
29 & 15 & 14.632310455281 & 0.36768954471899 \tabularnewline
30 & 11 & 13.3222669492995 & -2.32226694929945 \tabularnewline
31 & 16 & 14.1626879624753 & 1.8373120375247 \tabularnewline
32 & 13 & 12.0851557875451 & 0.914844212454931 \tabularnewline
33 & 17 & 13.9992306920842 & 3.00076930791577 \tabularnewline
34 & 15 & 15.2860127500737 & -0.286012750073686 \tabularnewline
35 & 14 & 13.3944969832607 & 0.605503016739348 \tabularnewline
36 & 16 & 12.513104951638 & 3.48689504836197 \tabularnewline
37 & 9 & 12.3638765773156 & -3.36387657731562 \tabularnewline
38 & 15 & 13.5038337973754 & 1.49616620262458 \tabularnewline
39 & 17 & 14.3568835438802 & 2.64311645611978 \tabularnewline
40 & 13 & 13.7882477800898 & -0.788247780089784 \tabularnewline
41 & 15 & 14.0598143953489 & 0.940185604651113 \tabularnewline
42 & 16 & 14.1599040939003 & 1.84009590609965 \tabularnewline
43 & 16 & 13.3518106552994 & 2.64818934470057 \tabularnewline
44 & 12 & 12.5065582684017 & -0.506558268401709 \tabularnewline
45 & 12 & 13.697433769577 & -1.69743376957701 \tabularnewline
46 & 11 & 13.5213577110687 & -2.52135771106871 \tabularnewline
47 & 15 & 14.1150005500984 & 0.88499944990162 \tabularnewline
48 & 15 & 14.4272274890225 & 0.57277251097747 \tabularnewline
49 & 17 & 14.9194754575403 & 2.08052454245967 \tabularnewline
50 & 13 & 13.7193662115585 & -0.719366211558488 \tabularnewline
51 & 16 & 14.5233791549388 & 1.47662084506119 \tabularnewline
52 & 14 & 13.0448275319566 & 0.95517246804344 \tabularnewline
53 & 11 & 12.6072879175115 & -1.6072879175115 \tabularnewline
54 & 12 & 14.0587351280211 & -2.05873512802107 \tabularnewline
55 & 12 & 12.4212560569026 & -0.421256056902605 \tabularnewline
56 & 15 & 14.5685146309062 & 0.431485369093806 \tabularnewline
57 & 16 & 14.661755342448 & 1.33824465755205 \tabularnewline
58 & 15 & 13.9682810174504 & 1.03171898254964 \tabularnewline
59 & 12 & 14.0692072232378 & -2.06920722323779 \tabularnewline
60 & 12 & 14.4172819326271 & -2.41728193262708 \tabularnewline
61 & 8 & 11.8901758806089 & -3.89017588060894 \tabularnewline
62 & 13 & 14.2836177739492 & -1.28361777394917 \tabularnewline
63 & 11 & 14.6386259752067 & -3.63862597520673 \tabularnewline
64 & 14 & 12.965868843623 & 1.03413115637698 \tabularnewline
65 & 15 & 13.1626231153373 & 1.83737688466268 \tabularnewline
66 & 10 & 14.6936825963135 & -4.69368259631348 \tabularnewline
67 & 11 & 14.407615472881 & -3.407615472881 \tabularnewline
68 & 12 & 13.6891244119854 & -1.68912441198537 \tabularnewline
69 & 15 & 13.8361754919055 & 1.16382450809451 \tabularnewline
70 & 15 & 13.9688150695807 & 1.03118493041932 \tabularnewline
71 & 14 & 13.1231711508287 & 0.876828849171275 \tabularnewline
72 & 16 & 13.3124169382619 & 2.68758306173812 \tabularnewline
73 & 15 & 15.4845636259999 & -0.484563625999872 \tabularnewline
74 & 15 & 14.5595524038251 & 0.440447596174858 \tabularnewline
75 & 13 & 14.0561163405593 & -1.05611634055927 \tabularnewline
76 & 12 & 15.0313610922827 & -3.03136109228274 \tabularnewline
77 & 17 & 14.5365065730103 & 2.46349342698966 \tabularnewline
78 & 13 & 14.0558743904645 & -1.05587439046446 \tabularnewline
79 & 15 & 13.2841612792372 & 1.71583872076284 \tabularnewline
80 & 13 & 12.4628012052658 & 0.53719879473417 \tabularnewline
81 & 15 & 14.3880906211006 & 0.611909378899413 \tabularnewline
82 & 16 & 12.5628170367576 & 3.43718296324236 \tabularnewline
83 & 15 & 14.5890214169576 & 0.41097858304238 \tabularnewline
84 & 16 & 13.8152339999125 & 2.18476600008755 \tabularnewline
85 & 15 & 14.2077247680332 & 0.792275231966829 \tabularnewline
86 & 14 & 14.5187840315573 & -0.518784031557275 \tabularnewline
87 & 15 & 14.3781576515801 & 0.621842348419906 \tabularnewline
88 & 14 & 14.1995135974626 & -0.199513597462649 \tabularnewline
89 & 13 & 13.0835166084707 & -0.0835166084706617 \tabularnewline
90 & 7 & 14.0294562184194 & -7.02945621841939 \tabularnewline
91 & 17 & 14.0170622251819 & 2.98293777481807 \tabularnewline
92 & 13 & 13.3133533171307 & -0.313353317130683 \tabularnewline
93 & 15 & 15.2112704308778 & -0.211270430877845 \tabularnewline
94 & 14 & 14.3062096454998 & -0.306209645499834 \tabularnewline
95 & 13 & 13.913695211067 & -0.913695211066998 \tabularnewline
96 & 16 & 15.2464589706093 & 0.753541029390726 \tabularnewline
97 & 12 & 14.3440994138319 & -2.34409941383191 \tabularnewline
98 & 14 & 14.703760944624 & -0.703760944623952 \tabularnewline
99 & 17 & 14.4895991886401 & 2.51040081135993 \tabularnewline
100 & 15 & 13.9649031615777 & 1.03509683842229 \tabularnewline
101 & 17 & 14.7865647951161 & 2.21343520488391 \tabularnewline
102 & 12 & 14.1589258452547 & -2.1589258452547 \tabularnewline
103 & 16 & 15.885008085837 & 0.114991914162995 \tabularnewline
104 & 11 & 12.9476137964153 & -1.94761379641529 \tabularnewline
105 & 15 & 15.1767975721692 & -0.17679757216917 \tabularnewline
106 & 9 & 13.2288037918882 & -4.22880379188821 \tabularnewline
107 & 16 & 14.9817599351061 & 1.01824006489392 \tabularnewline
108 & 15 & 14.3993271694403 & 0.600672830559733 \tabularnewline
109 & 10 & 13.0628227439375 & -3.06282274393749 \tabularnewline
110 & 10 & 13.3895246132489 & -3.38952461324894 \tabularnewline
111 & 15 & 14.8269494232042 & 0.173050576795782 \tabularnewline
112 & 11 & 14.2331416256293 & -3.23314162562927 \tabularnewline
113 & 13 & 15.9472546329664 & -2.94725463296637 \tabularnewline
114 & 14 & 11.4137517778504 & 2.58624822214964 \tabularnewline
115 & 18 & 14.1781243509262 & 3.82187564907379 \tabularnewline
116 & 16 & 14.8765223421787 & 1.12347765782125 \tabularnewline
117 & 14 & 13.3995709945065 & 0.600429005493505 \tabularnewline
118 & 14 & 14.6045606787875 & -0.604560678787491 \tabularnewline
119 & 14 & 13.8267761954073 & 0.173223804592707 \tabularnewline
120 & 14 & 15.6698711825241 & -1.66987118252413 \tabularnewline
121 & 12 & 13.1359340286283 & -1.13593402862829 \tabularnewline
122 & 14 & 13.9489357386831 & 0.0510642613168786 \tabularnewline
123 & 15 & 15.3380321697678 & -0.338032169767783 \tabularnewline
124 & 15 & 14.891484472346 & 0.108515527653967 \tabularnewline
125 & 15 & 13.9951497132994 & 1.00485028670063 \tabularnewline
126 & 13 & 14.4672233176525 & -1.46722331765252 \tabularnewline
127 & 17 & 14.8513896547979 & 2.14861034520207 \tabularnewline
128 & 17 & 15.3905443390096 & 1.60945566099036 \tabularnewline
129 & 19 & 14.9986332259247 & 4.00136677407534 \tabularnewline
130 & 15 & 13.7668317740246 & 1.23316822597539 \tabularnewline
131 & 13 & 13.8059563396245 & -0.805956339624512 \tabularnewline
132 & 9 & 12.3083455566046 & -3.30834555660456 \tabularnewline
133 & 15 & 15.2867971180927 & -0.286797118092689 \tabularnewline
134 & 15 & 12.4069442643195 & 2.59305573568052 \tabularnewline
135 & 15 & 14.060944440806 & 0.939055559194023 \tabularnewline
136 & 16 & 14.3644227061066 & 1.63557729389336 \tabularnewline
137 & 11 & 11.5099908646539 & -0.509990864653911 \tabularnewline
138 & 14 & 13.3363454723645 & 0.663654527635549 \tabularnewline
139 & 11 & 12.6221824478701 & -1.62218244787009 \tabularnewline
140 & 15 & 13.5416349486123 & 1.45836505138768 \tabularnewline
141 & 13 & 12.5683582282553 & 0.431641771744692 \tabularnewline
142 & 15 & 13.518553499507 & 1.48144650049301 \tabularnewline
143 & 16 & 14.4345140288378 & 1.56548597116223 \tabularnewline
144 & 14 & 15.1322609219272 & -1.13226092192724 \tabularnewline
145 & 15 & 13.3428604875618 & 1.65713951243821 \tabularnewline
146 & 16 & 14.5627643765118 & 1.43723562348822 \tabularnewline
147 & 16 & 14.3360928954694 & 1.66390710453064 \tabularnewline
148 & 11 & 12.9407608672541 & -1.94076086725408 \tabularnewline
149 & 12 & 13.7815947222236 & -1.78159472222359 \tabularnewline
150 & 9 & 13.4966252786066 & -4.49662527860659 \tabularnewline
151 & 16 & 14.9005468843772 & 1.09945311562277 \tabularnewline
152 & 13 & 15.0302022675445 & -2.03020226754445 \tabularnewline
153 & 16 & 14.245911191538 & 1.75408880846204 \tabularnewline
154 & 12 & 15.0978581300553 & -3.09785813005532 \tabularnewline
155 & 9 & 14.1309672104991 & -5.13096721049913 \tabularnewline
156 & 13 & 13.7395130898274 & -0.739513089827379 \tabularnewline
157 & 13 & 13.3133533171307 & -0.313353317130683 \tabularnewline
158 & 14 & 14.2086850588292 & -0.20868505882915 \tabularnewline
159 & 19 & 14.9986332259247 & 4.00136677407534 \tabularnewline
160 & 13 & 14.9678983584711 & -1.96789835847105 \tabularnewline
161 & 12 & 14.6394700793818 & -2.63947007938176 \tabularnewline
162 & 13 & 14.094604150735 & -1.09460415073497 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157916&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]14[/C][C]13.3436498217817[/C][C]0.65635017821828[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.2505310562446[/C][C]2.74946894375542[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]14.3340360481722[/C][C]-3.33403604817225[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.6000897165112[/C][C]-1.60008971651117[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]14.4425948978242[/C][C]1.55740510217577[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.0243120602567[/C][C]3.97568793974329[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]13.7608419315314[/C][C]0.239158068468569[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.7170099564179[/C][C]-0.717009956417947[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.3230962495604[/C][C]0.676903750439606[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]15.1654588616147[/C][C]-0.165458861614672[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]14.2932787841059[/C][C]2.70672121589406[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]13.4533604239394[/C][C]5.54663957606059[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.808084606314[/C][C]-3.80808460631396[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.8798533873777[/C][C]2.12014661262231[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.4123789780934[/C][C]2.58762102190657[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.8041566881504[/C][C]1.19584331184959[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.9098678504104[/C][C]0.0901321495896177[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]16.3457429796357[/C][C]0.654257020364348[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]14.2412187094198[/C][C]-0.241218709419841[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.3393301086865[/C][C]2.66066989131346[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]13.4730470752578[/C][C]4.52695292474223[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.8660399790437[/C][C]-2.86603997904372[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.2514166454237[/C][C]-0.251416645423711[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]13.9938772340213[/C][C]-1.99387723402127[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.0090992028757[/C][C]1.99090079712429[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.0396344130727[/C][C]-6.03963441307265[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.3217176440785[/C][C]1.67828235592148[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]14.1406914155751[/C][C]-0.140691415575055[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.632310455281[/C][C]0.36768954471899[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.3222669492995[/C][C]-2.32226694929945[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]14.1626879624753[/C][C]1.8373120375247[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.0851557875451[/C][C]0.914844212454931[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]13.9992306920842[/C][C]3.00076930791577[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]15.2860127500737[/C][C]-0.286012750073686[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.3944969832607[/C][C]0.605503016739348[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]12.513104951638[/C][C]3.48689504836197[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]12.3638765773156[/C][C]-3.36387657731562[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]13.5038337973754[/C][C]1.49616620262458[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]14.3568835438802[/C][C]2.64311645611978[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]13.7882477800898[/C][C]-0.788247780089784[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]14.0598143953489[/C][C]0.940185604651113[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.1599040939003[/C][C]1.84009590609965[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]13.3518106552994[/C][C]2.64818934470057[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.5065582684017[/C][C]-0.506558268401709[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]13.697433769577[/C][C]-1.69743376957701[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.5213577110687[/C][C]-2.52135771106871[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]14.1150005500984[/C][C]0.88499944990162[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.4272274890225[/C][C]0.57277251097747[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]14.9194754575403[/C][C]2.08052454245967[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]13.7193662115585[/C][C]-0.719366211558488[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]14.5233791549388[/C][C]1.47662084506119[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.0448275319566[/C][C]0.95517246804344[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]12.6072879175115[/C][C]-1.6072879175115[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]14.0587351280211[/C][C]-2.05873512802107[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]12.4212560569026[/C][C]-0.421256056902605[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.5685146309062[/C][C]0.431485369093806[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.661755342448[/C][C]1.33824465755205[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]13.9682810174504[/C][C]1.03171898254964[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.0692072232378[/C][C]-2.06920722323779[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]14.4172819326271[/C][C]-2.41728193262708[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]11.8901758806089[/C][C]-3.89017588060894[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.2836177739492[/C][C]-1.28361777394917[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.6386259752067[/C][C]-3.63862597520673[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.965868843623[/C][C]1.03413115637698[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.1626231153373[/C][C]1.83737688466268[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.6936825963135[/C][C]-4.69368259631348[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]14.407615472881[/C][C]-3.407615472881[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.6891244119854[/C][C]-1.68912441198537[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.8361754919055[/C][C]1.16382450809451[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.9688150695807[/C][C]1.03118493041932[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.1231711508287[/C][C]0.876828849171275[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]13.3124169382619[/C][C]2.68758306173812[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]15.4845636259999[/C][C]-0.484563625999872[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]14.5595524038251[/C][C]0.440447596174858[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.0561163405593[/C][C]-1.05611634055927[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]15.0313610922827[/C][C]-3.03136109228274[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.5365065730103[/C][C]2.46349342698966[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]14.0558743904645[/C][C]-1.05587439046446[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.2841612792372[/C][C]1.71583872076284[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]12.4628012052658[/C][C]0.53719879473417[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.3880906211006[/C][C]0.611909378899413[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]12.5628170367576[/C][C]3.43718296324236[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]14.5890214169576[/C][C]0.41097858304238[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]13.8152339999125[/C][C]2.18476600008755[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.2077247680332[/C][C]0.792275231966829[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.5187840315573[/C][C]-0.518784031557275[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]14.3781576515801[/C][C]0.621842348419906[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.1995135974626[/C][C]-0.199513597462649[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]13.0835166084707[/C][C]-0.0835166084706617[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]14.0294562184194[/C][C]-7.02945621841939[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.0170622251819[/C][C]2.98293777481807[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]13.3133533171307[/C][C]-0.313353317130683[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.2112704308778[/C][C]-0.211270430877845[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]14.3062096454998[/C][C]-0.306209645499834[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]13.913695211067[/C][C]-0.913695211066998[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.2464589706093[/C][C]0.753541029390726[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]14.3440994138319[/C][C]-2.34409941383191[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]14.703760944624[/C][C]-0.703760944623952[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]14.4895991886401[/C][C]2.51040081135993[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]13.9649031615777[/C][C]1.03509683842229[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]14.7865647951161[/C][C]2.21343520488391[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]14.1589258452547[/C][C]-2.1589258452547[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]15.885008085837[/C][C]0.114991914162995[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]12.9476137964153[/C][C]-1.94761379641529[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]15.1767975721692[/C][C]-0.17679757216917[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]13.2288037918882[/C][C]-4.22880379188821[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]14.9817599351061[/C][C]1.01824006489392[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]14.3993271694403[/C][C]0.600672830559733[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]13.0628227439375[/C][C]-3.06282274393749[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]13.3895246132489[/C][C]-3.38952461324894[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.8269494232042[/C][C]0.173050576795782[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]14.2331416256293[/C][C]-3.23314162562927[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.9472546329664[/C][C]-2.94725463296637[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]11.4137517778504[/C][C]2.58624822214964[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.1781243509262[/C][C]3.82187564907379[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]14.8765223421787[/C][C]1.12347765782125[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.3995709945065[/C][C]0.600429005493505[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.6045606787875[/C][C]-0.604560678787491[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.8267761954073[/C][C]0.173223804592707[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]15.6698711825241[/C][C]-1.66987118252413[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]13.1359340286283[/C][C]-1.13593402862829[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.9489357386831[/C][C]0.0510642613168786[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.3380321697678[/C][C]-0.338032169767783[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.891484472346[/C][C]0.108515527653967[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]13.9951497132994[/C][C]1.00485028670063[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.4672233176525[/C][C]-1.46722331765252[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]14.8513896547979[/C][C]2.14861034520207[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.3905443390096[/C][C]1.60945566099036[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]14.9986332259247[/C][C]4.00136677407534[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.7668317740246[/C][C]1.23316822597539[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]13.8059563396245[/C][C]-0.805956339624512[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]12.3083455566046[/C][C]-3.30834555660456[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]15.2867971180927[/C][C]-0.286797118092689[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.4069442643195[/C][C]2.59305573568052[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.060944440806[/C][C]0.939055559194023[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]14.3644227061066[/C][C]1.63557729389336[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]11.5099908646539[/C][C]-0.509990864653911[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.3363454723645[/C][C]0.663654527635549[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.6221824478701[/C][C]-1.62218244787009[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]13.5416349486123[/C][C]1.45836505138768[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]12.5683582282553[/C][C]0.431641771744692[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.518553499507[/C][C]1.48144650049301[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]14.4345140288378[/C][C]1.56548597116223[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]15.1322609219272[/C][C]-1.13226092192724[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]13.3428604875618[/C][C]1.65713951243821[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.5627643765118[/C][C]1.43723562348822[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.3360928954694[/C][C]1.66390710453064[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]12.9407608672541[/C][C]-1.94076086725408[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]13.7815947222236[/C][C]-1.78159472222359[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]13.4966252786066[/C][C]-4.49662527860659[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]14.9005468843772[/C][C]1.09945311562277[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]15.0302022675445[/C][C]-2.03020226754445[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.245911191538[/C][C]1.75408880846204[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]15.0978581300553[/C][C]-3.09785813005532[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]14.1309672104991[/C][C]-5.13096721049913[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]13.7395130898274[/C][C]-0.739513089827379[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]13.3133533171307[/C][C]-0.313353317130683[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]14.2086850588292[/C][C]-0.20868505882915[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]14.9986332259247[/C][C]4.00136677407534[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]14.9678983584711[/C][C]-1.96789835847105[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]14.6394700793818[/C][C]-2.63947007938176[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]14.094604150735[/C][C]-1.09460415073497[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157916&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157916&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
11413.34364982178170.65635017821828
21815.25053105624462.74946894375542
31114.3340360481722-3.33403604817225
41213.6000897165112-1.60008971651117
51614.44259489782421.55740510217577
61814.02431206025673.97568793974329
71413.76084193153140.239158068468569
81414.7170099564179-0.717009956417947
91514.32309624956040.676903750439606
101515.1654588616147-0.165458861614672
111714.29327878410592.70672121589406
121913.45336042393945.54663957606059
131013.808084606314-3.80808460631396
141613.87985338737772.12014661262231
151815.41237897809342.58762102190657
161412.80415668815041.19584331184959
171413.90986785041040.0901321495896177
181716.34574297963570.654257020364348
191414.2412187094198-0.241218709419841
201613.33933010868652.66066989131346
211813.47304707525784.52695292474223
221113.8660399790437-2.86603997904372
231414.2514166454237-0.251416645423711
241213.9938772340213-1.99387723402127
251715.00909920287571.99090079712429
26915.0396344130727-6.03963441307265
271614.32171764407851.67828235592148
281414.1406914155751-0.140691415575055
291514.6323104552810.36768954471899
301113.3222669492995-2.32226694929945
311614.16268796247531.8373120375247
321312.08515578754510.914844212454931
331713.99923069208423.00076930791577
341515.2860127500737-0.286012750073686
351413.39449698326070.605503016739348
361612.5131049516383.48689504836197
37912.3638765773156-3.36387657731562
381513.50383379737541.49616620262458
391714.35688354388022.64311645611978
401313.7882477800898-0.788247780089784
411514.05981439534890.940185604651113
421614.15990409390031.84009590609965
431613.35181065529942.64818934470057
441212.5065582684017-0.506558268401709
451213.697433769577-1.69743376957701
461113.5213577110687-2.52135771106871
471514.11500055009840.88499944990162
481514.42722748902250.57277251097747
491714.91947545754032.08052454245967
501313.7193662115585-0.719366211558488
511614.52337915493881.47662084506119
521413.04482753195660.95517246804344
531112.6072879175115-1.6072879175115
541214.0587351280211-2.05873512802107
551212.4212560569026-0.421256056902605
561514.56851463090620.431485369093806
571614.6617553424481.33824465755205
581513.96828101745041.03171898254964
591214.0692072232378-2.06920722323779
601214.4172819326271-2.41728193262708
61811.8901758806089-3.89017588060894
621314.2836177739492-1.28361777394917
631114.6386259752067-3.63862597520673
641412.9658688436231.03413115637698
651513.16262311533731.83737688466268
661014.6936825963135-4.69368259631348
671114.407615472881-3.407615472881
681213.6891244119854-1.68912441198537
691513.83617549190551.16382450809451
701513.96881506958071.03118493041932
711413.12317115082870.876828849171275
721613.31241693826192.68758306173812
731515.4845636259999-0.484563625999872
741514.55955240382510.440447596174858
751314.0561163405593-1.05611634055927
761215.0313610922827-3.03136109228274
771714.53650657301032.46349342698966
781314.0558743904645-1.05587439046446
791513.28416127923721.71583872076284
801312.46280120526580.53719879473417
811514.38809062110060.611909378899413
821612.56281703675763.43718296324236
831514.58902141695760.41097858304238
841613.81523399991252.18476600008755
851514.20772476803320.792275231966829
861414.5187840315573-0.518784031557275
871514.37815765158010.621842348419906
881414.1995135974626-0.199513597462649
891313.0835166084707-0.0835166084706617
90714.0294562184194-7.02945621841939
911714.01706222518192.98293777481807
921313.3133533171307-0.313353317130683
931515.2112704308778-0.211270430877845
941414.3062096454998-0.306209645499834
951313.913695211067-0.913695211066998
961615.24645897060930.753541029390726
971214.3440994138319-2.34409941383191
981414.703760944624-0.703760944623952
991714.48959918864012.51040081135993
1001513.96490316157771.03509683842229
1011714.78656479511612.21343520488391
1021214.1589258452547-2.1589258452547
1031615.8850080858370.114991914162995
1041112.9476137964153-1.94761379641529
1051515.1767975721692-0.17679757216917
106913.2288037918882-4.22880379188821
1071614.98175993510611.01824006489392
1081514.39932716944030.600672830559733
1091013.0628227439375-3.06282274393749
1101013.3895246132489-3.38952461324894
1111514.82694942320420.173050576795782
1121114.2331416256293-3.23314162562927
1131315.9472546329664-2.94725463296637
1141411.41375177785042.58624822214964
1151814.17812435092623.82187564907379
1161614.87652234217871.12347765782125
1171413.39957099450650.600429005493505
1181414.6045606787875-0.604560678787491
1191413.82677619540730.173223804592707
1201415.6698711825241-1.66987118252413
1211213.1359340286283-1.13593402862829
1221413.94893573868310.0510642613168786
1231515.3380321697678-0.338032169767783
1241514.8914844723460.108515527653967
1251513.99514971329941.00485028670063
1261314.4672233176525-1.46722331765252
1271714.85138965479792.14861034520207
1281715.39054433900961.60945566099036
1291914.99863322592474.00136677407534
1301513.76683177402461.23316822597539
1311313.8059563396245-0.805956339624512
132912.3083455566046-3.30834555660456
1331515.2867971180927-0.286797118092689
1341512.40694426431952.59305573568052
1351514.0609444408060.939055559194023
1361614.36442270610661.63557729389336
1371111.5099908646539-0.509990864653911
1381413.33634547236450.663654527635549
1391112.6221824478701-1.62218244787009
1401513.54163494861231.45836505138768
1411312.56835822825530.431641771744692
1421513.5185534995071.48144650049301
1431614.43451402883781.56548597116223
1441415.1322609219272-1.13226092192724
1451513.34286048756181.65713951243821
1461614.56276437651181.43723562348822
1471614.33609289546941.66390710453064
1481112.9407608672541-1.94076086725408
1491213.7815947222236-1.78159472222359
150913.4966252786066-4.49662527860659
1511614.90054688437721.09945311562277
1521315.0302022675445-2.03020226754445
1531614.2459111915381.75408880846204
1541215.0978581300553-3.09785813005532
155914.1309672104991-5.13096721049913
1561313.7395130898274-0.739513089827379
1571313.3133533171307-0.313353317130683
1581414.2086850588292-0.20868505882915
1591914.99863322592474.00136677407534
1601314.9678983584711-1.96789835847105
1611214.6394700793818-2.63947007938176
1621314.094604150735-1.09460415073497






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.02790919067919430.05581838135838850.972090809320806
120.8267582571571620.3464834856856760.173241742842838
130.8183075858312570.3633848283374870.181692414168743
140.7731967752424350.453606449515130.226803224757565
150.7973467407684910.4053065184630190.202653259231509
160.7887095325915530.4225809348168940.211290467408447
170.7376776868844220.5246446262311560.262322313115578
180.6626935549680570.6746128900638860.337306445031943
190.57535057607680.8492988478463990.4246494239232
200.5016324954648780.9967350090702440.498367504535122
210.8271105105648630.3457789788702740.172889489435137
220.91003147877320.17993704245360.0899685212267999
230.8793080459849330.2413839080301340.120691954015067
240.8765344098893130.2469311802213750.123465590110687
250.8456378256484490.3087243487031030.154362174351551
260.9769316981687090.04613660366258130.0230683018312907
270.9730341266942990.05393174661140150.0269658733057007
280.961183898147940.07763220370412010.03881610185206
290.9456974243306490.1086051513387020.0543025756693509
300.945470106058930.1090597878821390.0545298939410696
310.9291525201041370.1416949597917260.070847479895863
320.9067093124517420.1865813750965160.0932906875482582
330.8934873923292120.2130252153415770.106512607670788
340.8638244595350830.2723510809298340.136175540464917
350.8305070439385780.3389859121228430.169492956061422
360.8285950867393990.3428098265212020.171404913260601
370.9202242467129670.1595515065740660.0797757532870328
380.9098803306768230.1802393386463530.0901196693231767
390.9054138698147370.1891722603705260.0945861301852628
400.8812317992371280.2375364015257430.118768200762872
410.8571524680684630.2856950638630740.142847531931537
420.8350752143366770.3298495713266450.164924785663323
430.8645930641003640.2708138717992720.135406935899636
440.8405737774686530.3188524450626930.159426222531347
450.8891128236776170.2217743526447650.110887176322383
460.9084105263115570.1831789473768870.0915894736884433
470.8932004594954280.2135990810091430.106799540504572
480.8683095667713570.2633808664572870.131690433228644
490.868663547958930.2626729040821410.13133645204107
500.8437774469317290.3124451061365420.156222553068271
510.8259742731263950.3480514537472110.174025726873605
520.7954769199058470.4090461601883070.204523080094153
530.7786225397902420.4427549204195170.221377460209758
540.7797110096023930.4405779807952130.220288990397607
550.7437065647583770.5125868704832460.256293435241623
560.7047039864841730.5905920270316550.295296013515827
570.6728097319379010.6543805361241970.327190268062099
580.634453315330670.7310933693386590.36554668466933
590.6265626122219920.7468747755560170.373437387778008
600.6490327794420480.7019344411159040.350967220557952
610.7372279863150820.5255440273698350.262772013684918
620.7147866390405880.5704267219188240.285213360959412
630.7874637199362050.4250725601275910.212536280063795
640.7567293662119890.4865412675760220.243270633788011
650.7397999247321310.5204001505357380.260200075267869
660.8408155007299280.3183689985401450.159184499270072
670.8752216840291730.2495566319416530.124778315970827
680.8675712748063380.2648574503873240.132428725193662
690.8495837353136390.3008325293727230.150416264686361
700.8309213705305050.338157258938990.169078629469495
710.8043948723878280.3912102552243440.195605127612172
720.818128223559610.363743552880780.18187177644039
730.7867741841141960.4264516317716080.213225815885804
740.7557114198741140.4885771602517710.244288580125886
750.7262585977214890.5474828045570220.273741402278511
760.7627004861915540.4745990276168920.237299513808446
770.770213862846580.4595722743068410.22978613715342
780.7429020550412210.5141958899175590.257097944958779
790.7259385670675280.5481228658649430.274061432932472
800.6883310721487590.6233378557024810.311668927851241
810.6485606668859650.702878666228070.351439333114035
820.716782376194610.566435247610780.28321762380539
830.6787610444004510.6424779111990990.321238955599549
840.6806175609262180.6387648781475640.319382439073782
850.6430916205441860.7138167589116290.356908379455814
860.6009664356136990.7980671287726020.399033564386301
870.5591379946822470.8817240106355070.440862005317753
880.5135163621979240.9729672756041530.486483637802076
890.4702210183392030.9404420366784050.529778981660797
900.8231407800429330.3537184399141330.176859219957067
910.8469289778174430.3061420443651140.153071022182557
920.8175972883848390.3648054232303220.182402711615161
930.7849054839789230.4301890320421530.215094516021077
940.7497787635312870.5004424729374260.250221236468713
950.7173675501245920.5652648997508160.282632449875408
960.6799486212265160.6401027575469670.320051378773484
970.6777286157986710.6445427684026570.322271384201328
980.639173938274070.7216521234518590.36082606172593
990.6530118508347990.6939762983304020.346988149165201
1000.6208821172662790.7582357654674410.379117882733721
1010.6264178625992320.7471642748015350.373582137400768
1020.6169446077536920.7661107844926170.383055392246308
1030.569571809757370.860856380485260.43042819024263
1040.5558921799994660.8882156400010670.444107820000534
1050.5068601178444360.9862797643111280.493139882155564
1060.6323469100431440.7353061799137130.367653089956856
1070.5942438077210220.8115123845579550.405756192278978
1080.5509647172317090.8980705655365810.44903528276829
1090.5874969496467670.8250061007064660.412503050353233
1100.6496367897654190.7007264204691610.350363210234581
1110.6050655244432950.7898689511134110.394934475556705
1120.6429634408176270.7140731183647460.357036559182373
1130.6614643849459540.6770712301080920.338535615054046
1140.6930011102741710.6139977794516580.306998889725829
1150.7813359085739620.4373281828520760.218664091426038
1160.7454625563970930.5090748872058150.254537443602907
1170.7203614538377320.5592770923245370.279638546162268
1180.6741965511212980.6516068977574050.325803448878702
1190.6273941330585460.7452117338829090.372605866941454
1200.6164374110460180.7671251779079640.383562588953982
1210.5666555841335610.8666888317328770.433344415866439
1220.510134018987860.979731962024280.48986598101214
1230.4540386094859620.9080772189719250.545961390514038
1240.3981053931353980.7962107862707950.601894606864602
1250.3545144226451710.7090288452903410.645485577354829
1260.325129147386550.6502582947731010.67487085261345
1270.3014898623603050.6029797247206110.698510137639695
1280.2866243674822410.5732487349644820.713375632517759
1290.3894110743206860.7788221486413720.610588925679314
1300.359560881547960.7191217630959210.64043911845204
1310.3100832912943340.6201665825886670.689916708705666
1320.3701667893446710.7403335786893420.629833210655329
1330.3156350559116990.6312701118233980.684364944088301
1340.3112790219676710.6225580439353430.688720978032329
1350.2812036076358130.5624072152716250.718796392364187
1360.2451184509810040.4902369019620070.754881549018996
1370.1979203880219260.3958407760438520.802079611978074
1380.1658769914485510.3317539828971020.834123008551449
1390.1350571167472890.2701142334945780.864942883252711
1400.123872063734740.2477441274694810.87612793626526
1410.09669820908227360.1933964181645470.903301790917726
1420.1052147164246620.2104294328493240.894785283575338
1430.07999281638568660.1599856327713730.920007183614313
1440.07103455420817930.1420691084163590.928965445791821
1450.0724792006018770.1449584012037540.927520799398123
1460.1876699348625170.3753398697250340.812330065137483
1470.277188853991360.554377707982720.72281114600864
1480.2852580771413920.5705161542827840.714741922858608
1490.2210308989720230.4420617979440470.778969101027977
1500.1709282090588080.3418564181176160.829071790941192
1510.1243921959934270.2487843919868540.875607804006573

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0279091906791943 & 0.0558183813583885 & 0.972090809320806 \tabularnewline
12 & 0.826758257157162 & 0.346483485685676 & 0.173241742842838 \tabularnewline
13 & 0.818307585831257 & 0.363384828337487 & 0.181692414168743 \tabularnewline
14 & 0.773196775242435 & 0.45360644951513 & 0.226803224757565 \tabularnewline
15 & 0.797346740768491 & 0.405306518463019 & 0.202653259231509 \tabularnewline
16 & 0.788709532591553 & 0.422580934816894 & 0.211290467408447 \tabularnewline
17 & 0.737677686884422 & 0.524644626231156 & 0.262322313115578 \tabularnewline
18 & 0.662693554968057 & 0.674612890063886 & 0.337306445031943 \tabularnewline
19 & 0.5753505760768 & 0.849298847846399 & 0.4246494239232 \tabularnewline
20 & 0.501632495464878 & 0.996735009070244 & 0.498367504535122 \tabularnewline
21 & 0.827110510564863 & 0.345778978870274 & 0.172889489435137 \tabularnewline
22 & 0.9100314787732 & 0.1799370424536 & 0.0899685212267999 \tabularnewline
23 & 0.879308045984933 & 0.241383908030134 & 0.120691954015067 \tabularnewline
24 & 0.876534409889313 & 0.246931180221375 & 0.123465590110687 \tabularnewline
25 & 0.845637825648449 & 0.308724348703103 & 0.154362174351551 \tabularnewline
26 & 0.976931698168709 & 0.0461366036625813 & 0.0230683018312907 \tabularnewline
27 & 0.973034126694299 & 0.0539317466114015 & 0.0269658733057007 \tabularnewline
28 & 0.96118389814794 & 0.0776322037041201 & 0.03881610185206 \tabularnewline
29 & 0.945697424330649 & 0.108605151338702 & 0.0543025756693509 \tabularnewline
30 & 0.94547010605893 & 0.109059787882139 & 0.0545298939410696 \tabularnewline
31 & 0.929152520104137 & 0.141694959791726 & 0.070847479895863 \tabularnewline
32 & 0.906709312451742 & 0.186581375096516 & 0.0932906875482582 \tabularnewline
33 & 0.893487392329212 & 0.213025215341577 & 0.106512607670788 \tabularnewline
34 & 0.863824459535083 & 0.272351080929834 & 0.136175540464917 \tabularnewline
35 & 0.830507043938578 & 0.338985912122843 & 0.169492956061422 \tabularnewline
36 & 0.828595086739399 & 0.342809826521202 & 0.171404913260601 \tabularnewline
37 & 0.920224246712967 & 0.159551506574066 & 0.0797757532870328 \tabularnewline
38 & 0.909880330676823 & 0.180239338646353 & 0.0901196693231767 \tabularnewline
39 & 0.905413869814737 & 0.189172260370526 & 0.0945861301852628 \tabularnewline
40 & 0.881231799237128 & 0.237536401525743 & 0.118768200762872 \tabularnewline
41 & 0.857152468068463 & 0.285695063863074 & 0.142847531931537 \tabularnewline
42 & 0.835075214336677 & 0.329849571326645 & 0.164924785663323 \tabularnewline
43 & 0.864593064100364 & 0.270813871799272 & 0.135406935899636 \tabularnewline
44 & 0.840573777468653 & 0.318852445062693 & 0.159426222531347 \tabularnewline
45 & 0.889112823677617 & 0.221774352644765 & 0.110887176322383 \tabularnewline
46 & 0.908410526311557 & 0.183178947376887 & 0.0915894736884433 \tabularnewline
47 & 0.893200459495428 & 0.213599081009143 & 0.106799540504572 \tabularnewline
48 & 0.868309566771357 & 0.263380866457287 & 0.131690433228644 \tabularnewline
49 & 0.86866354795893 & 0.262672904082141 & 0.13133645204107 \tabularnewline
50 & 0.843777446931729 & 0.312445106136542 & 0.156222553068271 \tabularnewline
51 & 0.825974273126395 & 0.348051453747211 & 0.174025726873605 \tabularnewline
52 & 0.795476919905847 & 0.409046160188307 & 0.204523080094153 \tabularnewline
53 & 0.778622539790242 & 0.442754920419517 & 0.221377460209758 \tabularnewline
54 & 0.779711009602393 & 0.440577980795213 & 0.220288990397607 \tabularnewline
55 & 0.743706564758377 & 0.512586870483246 & 0.256293435241623 \tabularnewline
56 & 0.704703986484173 & 0.590592027031655 & 0.295296013515827 \tabularnewline
57 & 0.672809731937901 & 0.654380536124197 & 0.327190268062099 \tabularnewline
58 & 0.63445331533067 & 0.731093369338659 & 0.36554668466933 \tabularnewline
59 & 0.626562612221992 & 0.746874775556017 & 0.373437387778008 \tabularnewline
60 & 0.649032779442048 & 0.701934441115904 & 0.350967220557952 \tabularnewline
61 & 0.737227986315082 & 0.525544027369835 & 0.262772013684918 \tabularnewline
62 & 0.714786639040588 & 0.570426721918824 & 0.285213360959412 \tabularnewline
63 & 0.787463719936205 & 0.425072560127591 & 0.212536280063795 \tabularnewline
64 & 0.756729366211989 & 0.486541267576022 & 0.243270633788011 \tabularnewline
65 & 0.739799924732131 & 0.520400150535738 & 0.260200075267869 \tabularnewline
66 & 0.840815500729928 & 0.318368998540145 & 0.159184499270072 \tabularnewline
67 & 0.875221684029173 & 0.249556631941653 & 0.124778315970827 \tabularnewline
68 & 0.867571274806338 & 0.264857450387324 & 0.132428725193662 \tabularnewline
69 & 0.849583735313639 & 0.300832529372723 & 0.150416264686361 \tabularnewline
70 & 0.830921370530505 & 0.33815725893899 & 0.169078629469495 \tabularnewline
71 & 0.804394872387828 & 0.391210255224344 & 0.195605127612172 \tabularnewline
72 & 0.81812822355961 & 0.36374355288078 & 0.18187177644039 \tabularnewline
73 & 0.786774184114196 & 0.426451631771608 & 0.213225815885804 \tabularnewline
74 & 0.755711419874114 & 0.488577160251771 & 0.244288580125886 \tabularnewline
75 & 0.726258597721489 & 0.547482804557022 & 0.273741402278511 \tabularnewline
76 & 0.762700486191554 & 0.474599027616892 & 0.237299513808446 \tabularnewline
77 & 0.77021386284658 & 0.459572274306841 & 0.22978613715342 \tabularnewline
78 & 0.742902055041221 & 0.514195889917559 & 0.257097944958779 \tabularnewline
79 & 0.725938567067528 & 0.548122865864943 & 0.274061432932472 \tabularnewline
80 & 0.688331072148759 & 0.623337855702481 & 0.311668927851241 \tabularnewline
81 & 0.648560666885965 & 0.70287866622807 & 0.351439333114035 \tabularnewline
82 & 0.71678237619461 & 0.56643524761078 & 0.28321762380539 \tabularnewline
83 & 0.678761044400451 & 0.642477911199099 & 0.321238955599549 \tabularnewline
84 & 0.680617560926218 & 0.638764878147564 & 0.319382439073782 \tabularnewline
85 & 0.643091620544186 & 0.713816758911629 & 0.356908379455814 \tabularnewline
86 & 0.600966435613699 & 0.798067128772602 & 0.399033564386301 \tabularnewline
87 & 0.559137994682247 & 0.881724010635507 & 0.440862005317753 \tabularnewline
88 & 0.513516362197924 & 0.972967275604153 & 0.486483637802076 \tabularnewline
89 & 0.470221018339203 & 0.940442036678405 & 0.529778981660797 \tabularnewline
90 & 0.823140780042933 & 0.353718439914133 & 0.176859219957067 \tabularnewline
91 & 0.846928977817443 & 0.306142044365114 & 0.153071022182557 \tabularnewline
92 & 0.817597288384839 & 0.364805423230322 & 0.182402711615161 \tabularnewline
93 & 0.784905483978923 & 0.430189032042153 & 0.215094516021077 \tabularnewline
94 & 0.749778763531287 & 0.500442472937426 & 0.250221236468713 \tabularnewline
95 & 0.717367550124592 & 0.565264899750816 & 0.282632449875408 \tabularnewline
96 & 0.679948621226516 & 0.640102757546967 & 0.320051378773484 \tabularnewline
97 & 0.677728615798671 & 0.644542768402657 & 0.322271384201328 \tabularnewline
98 & 0.63917393827407 & 0.721652123451859 & 0.36082606172593 \tabularnewline
99 & 0.653011850834799 & 0.693976298330402 & 0.346988149165201 \tabularnewline
100 & 0.620882117266279 & 0.758235765467441 & 0.379117882733721 \tabularnewline
101 & 0.626417862599232 & 0.747164274801535 & 0.373582137400768 \tabularnewline
102 & 0.616944607753692 & 0.766110784492617 & 0.383055392246308 \tabularnewline
103 & 0.56957180975737 & 0.86085638048526 & 0.43042819024263 \tabularnewline
104 & 0.555892179999466 & 0.888215640001067 & 0.444107820000534 \tabularnewline
105 & 0.506860117844436 & 0.986279764311128 & 0.493139882155564 \tabularnewline
106 & 0.632346910043144 & 0.735306179913713 & 0.367653089956856 \tabularnewline
107 & 0.594243807721022 & 0.811512384557955 & 0.405756192278978 \tabularnewline
108 & 0.550964717231709 & 0.898070565536581 & 0.44903528276829 \tabularnewline
109 & 0.587496949646767 & 0.825006100706466 & 0.412503050353233 \tabularnewline
110 & 0.649636789765419 & 0.700726420469161 & 0.350363210234581 \tabularnewline
111 & 0.605065524443295 & 0.789868951113411 & 0.394934475556705 \tabularnewline
112 & 0.642963440817627 & 0.714073118364746 & 0.357036559182373 \tabularnewline
113 & 0.661464384945954 & 0.677071230108092 & 0.338535615054046 \tabularnewline
114 & 0.693001110274171 & 0.613997779451658 & 0.306998889725829 \tabularnewline
115 & 0.781335908573962 & 0.437328182852076 & 0.218664091426038 \tabularnewline
116 & 0.745462556397093 & 0.509074887205815 & 0.254537443602907 \tabularnewline
117 & 0.720361453837732 & 0.559277092324537 & 0.279638546162268 \tabularnewline
118 & 0.674196551121298 & 0.651606897757405 & 0.325803448878702 \tabularnewline
119 & 0.627394133058546 & 0.745211733882909 & 0.372605866941454 \tabularnewline
120 & 0.616437411046018 & 0.767125177907964 & 0.383562588953982 \tabularnewline
121 & 0.566655584133561 & 0.866688831732877 & 0.433344415866439 \tabularnewline
122 & 0.51013401898786 & 0.97973196202428 & 0.48986598101214 \tabularnewline
123 & 0.454038609485962 & 0.908077218971925 & 0.545961390514038 \tabularnewline
124 & 0.398105393135398 & 0.796210786270795 & 0.601894606864602 \tabularnewline
125 & 0.354514422645171 & 0.709028845290341 & 0.645485577354829 \tabularnewline
126 & 0.32512914738655 & 0.650258294773101 & 0.67487085261345 \tabularnewline
127 & 0.301489862360305 & 0.602979724720611 & 0.698510137639695 \tabularnewline
128 & 0.286624367482241 & 0.573248734964482 & 0.713375632517759 \tabularnewline
129 & 0.389411074320686 & 0.778822148641372 & 0.610588925679314 \tabularnewline
130 & 0.35956088154796 & 0.719121763095921 & 0.64043911845204 \tabularnewline
131 & 0.310083291294334 & 0.620166582588667 & 0.689916708705666 \tabularnewline
132 & 0.370166789344671 & 0.740333578689342 & 0.629833210655329 \tabularnewline
133 & 0.315635055911699 & 0.631270111823398 & 0.684364944088301 \tabularnewline
134 & 0.311279021967671 & 0.622558043935343 & 0.688720978032329 \tabularnewline
135 & 0.281203607635813 & 0.562407215271625 & 0.718796392364187 \tabularnewline
136 & 0.245118450981004 & 0.490236901962007 & 0.754881549018996 \tabularnewline
137 & 0.197920388021926 & 0.395840776043852 & 0.802079611978074 \tabularnewline
138 & 0.165876991448551 & 0.331753982897102 & 0.834123008551449 \tabularnewline
139 & 0.135057116747289 & 0.270114233494578 & 0.864942883252711 \tabularnewline
140 & 0.12387206373474 & 0.247744127469481 & 0.87612793626526 \tabularnewline
141 & 0.0966982090822736 & 0.193396418164547 & 0.903301790917726 \tabularnewline
142 & 0.105214716424662 & 0.210429432849324 & 0.894785283575338 \tabularnewline
143 & 0.0799928163856866 & 0.159985632771373 & 0.920007183614313 \tabularnewline
144 & 0.0710345542081793 & 0.142069108416359 & 0.928965445791821 \tabularnewline
145 & 0.072479200601877 & 0.144958401203754 & 0.927520799398123 \tabularnewline
146 & 0.187669934862517 & 0.375339869725034 & 0.812330065137483 \tabularnewline
147 & 0.27718885399136 & 0.55437770798272 & 0.72281114600864 \tabularnewline
148 & 0.285258077141392 & 0.570516154282784 & 0.714741922858608 \tabularnewline
149 & 0.221030898972023 & 0.442061797944047 & 0.778969101027977 \tabularnewline
150 & 0.170928209058808 & 0.341856418117616 & 0.829071790941192 \tabularnewline
151 & 0.124392195993427 & 0.248784391986854 & 0.875607804006573 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157916&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.0279091906791943[/C][C]0.0558183813583885[/C][C]0.972090809320806[/C][/ROW]
[ROW][C]12[/C][C]0.826758257157162[/C][C]0.346483485685676[/C][C]0.173241742842838[/C][/ROW]
[ROW][C]13[/C][C]0.818307585831257[/C][C]0.363384828337487[/C][C]0.181692414168743[/C][/ROW]
[ROW][C]14[/C][C]0.773196775242435[/C][C]0.45360644951513[/C][C]0.226803224757565[/C][/ROW]
[ROW][C]15[/C][C]0.797346740768491[/C][C]0.405306518463019[/C][C]0.202653259231509[/C][/ROW]
[ROW][C]16[/C][C]0.788709532591553[/C][C]0.422580934816894[/C][C]0.211290467408447[/C][/ROW]
[ROW][C]17[/C][C]0.737677686884422[/C][C]0.524644626231156[/C][C]0.262322313115578[/C][/ROW]
[ROW][C]18[/C][C]0.662693554968057[/C][C]0.674612890063886[/C][C]0.337306445031943[/C][/ROW]
[ROW][C]19[/C][C]0.5753505760768[/C][C]0.849298847846399[/C][C]0.4246494239232[/C][/ROW]
[ROW][C]20[/C][C]0.501632495464878[/C][C]0.996735009070244[/C][C]0.498367504535122[/C][/ROW]
[ROW][C]21[/C][C]0.827110510564863[/C][C]0.345778978870274[/C][C]0.172889489435137[/C][/ROW]
[ROW][C]22[/C][C]0.9100314787732[/C][C]0.1799370424536[/C][C]0.0899685212267999[/C][/ROW]
[ROW][C]23[/C][C]0.879308045984933[/C][C]0.241383908030134[/C][C]0.120691954015067[/C][/ROW]
[ROW][C]24[/C][C]0.876534409889313[/C][C]0.246931180221375[/C][C]0.123465590110687[/C][/ROW]
[ROW][C]25[/C][C]0.845637825648449[/C][C]0.308724348703103[/C][C]0.154362174351551[/C][/ROW]
[ROW][C]26[/C][C]0.976931698168709[/C][C]0.0461366036625813[/C][C]0.0230683018312907[/C][/ROW]
[ROW][C]27[/C][C]0.973034126694299[/C][C]0.0539317466114015[/C][C]0.0269658733057007[/C][/ROW]
[ROW][C]28[/C][C]0.96118389814794[/C][C]0.0776322037041201[/C][C]0.03881610185206[/C][/ROW]
[ROW][C]29[/C][C]0.945697424330649[/C][C]0.108605151338702[/C][C]0.0543025756693509[/C][/ROW]
[ROW][C]30[/C][C]0.94547010605893[/C][C]0.109059787882139[/C][C]0.0545298939410696[/C][/ROW]
[ROW][C]31[/C][C]0.929152520104137[/C][C]0.141694959791726[/C][C]0.070847479895863[/C][/ROW]
[ROW][C]32[/C][C]0.906709312451742[/C][C]0.186581375096516[/C][C]0.0932906875482582[/C][/ROW]
[ROW][C]33[/C][C]0.893487392329212[/C][C]0.213025215341577[/C][C]0.106512607670788[/C][/ROW]
[ROW][C]34[/C][C]0.863824459535083[/C][C]0.272351080929834[/C][C]0.136175540464917[/C][/ROW]
[ROW][C]35[/C][C]0.830507043938578[/C][C]0.338985912122843[/C][C]0.169492956061422[/C][/ROW]
[ROW][C]36[/C][C]0.828595086739399[/C][C]0.342809826521202[/C][C]0.171404913260601[/C][/ROW]
[ROW][C]37[/C][C]0.920224246712967[/C][C]0.159551506574066[/C][C]0.0797757532870328[/C][/ROW]
[ROW][C]38[/C][C]0.909880330676823[/C][C]0.180239338646353[/C][C]0.0901196693231767[/C][/ROW]
[ROW][C]39[/C][C]0.905413869814737[/C][C]0.189172260370526[/C][C]0.0945861301852628[/C][/ROW]
[ROW][C]40[/C][C]0.881231799237128[/C][C]0.237536401525743[/C][C]0.118768200762872[/C][/ROW]
[ROW][C]41[/C][C]0.857152468068463[/C][C]0.285695063863074[/C][C]0.142847531931537[/C][/ROW]
[ROW][C]42[/C][C]0.835075214336677[/C][C]0.329849571326645[/C][C]0.164924785663323[/C][/ROW]
[ROW][C]43[/C][C]0.864593064100364[/C][C]0.270813871799272[/C][C]0.135406935899636[/C][/ROW]
[ROW][C]44[/C][C]0.840573777468653[/C][C]0.318852445062693[/C][C]0.159426222531347[/C][/ROW]
[ROW][C]45[/C][C]0.889112823677617[/C][C]0.221774352644765[/C][C]0.110887176322383[/C][/ROW]
[ROW][C]46[/C][C]0.908410526311557[/C][C]0.183178947376887[/C][C]0.0915894736884433[/C][/ROW]
[ROW][C]47[/C][C]0.893200459495428[/C][C]0.213599081009143[/C][C]0.106799540504572[/C][/ROW]
[ROW][C]48[/C][C]0.868309566771357[/C][C]0.263380866457287[/C][C]0.131690433228644[/C][/ROW]
[ROW][C]49[/C][C]0.86866354795893[/C][C]0.262672904082141[/C][C]0.13133645204107[/C][/ROW]
[ROW][C]50[/C][C]0.843777446931729[/C][C]0.312445106136542[/C][C]0.156222553068271[/C][/ROW]
[ROW][C]51[/C][C]0.825974273126395[/C][C]0.348051453747211[/C][C]0.174025726873605[/C][/ROW]
[ROW][C]52[/C][C]0.795476919905847[/C][C]0.409046160188307[/C][C]0.204523080094153[/C][/ROW]
[ROW][C]53[/C][C]0.778622539790242[/C][C]0.442754920419517[/C][C]0.221377460209758[/C][/ROW]
[ROW][C]54[/C][C]0.779711009602393[/C][C]0.440577980795213[/C][C]0.220288990397607[/C][/ROW]
[ROW][C]55[/C][C]0.743706564758377[/C][C]0.512586870483246[/C][C]0.256293435241623[/C][/ROW]
[ROW][C]56[/C][C]0.704703986484173[/C][C]0.590592027031655[/C][C]0.295296013515827[/C][/ROW]
[ROW][C]57[/C][C]0.672809731937901[/C][C]0.654380536124197[/C][C]0.327190268062099[/C][/ROW]
[ROW][C]58[/C][C]0.63445331533067[/C][C]0.731093369338659[/C][C]0.36554668466933[/C][/ROW]
[ROW][C]59[/C][C]0.626562612221992[/C][C]0.746874775556017[/C][C]0.373437387778008[/C][/ROW]
[ROW][C]60[/C][C]0.649032779442048[/C][C]0.701934441115904[/C][C]0.350967220557952[/C][/ROW]
[ROW][C]61[/C][C]0.737227986315082[/C][C]0.525544027369835[/C][C]0.262772013684918[/C][/ROW]
[ROW][C]62[/C][C]0.714786639040588[/C][C]0.570426721918824[/C][C]0.285213360959412[/C][/ROW]
[ROW][C]63[/C][C]0.787463719936205[/C][C]0.425072560127591[/C][C]0.212536280063795[/C][/ROW]
[ROW][C]64[/C][C]0.756729366211989[/C][C]0.486541267576022[/C][C]0.243270633788011[/C][/ROW]
[ROW][C]65[/C][C]0.739799924732131[/C][C]0.520400150535738[/C][C]0.260200075267869[/C][/ROW]
[ROW][C]66[/C][C]0.840815500729928[/C][C]0.318368998540145[/C][C]0.159184499270072[/C][/ROW]
[ROW][C]67[/C][C]0.875221684029173[/C][C]0.249556631941653[/C][C]0.124778315970827[/C][/ROW]
[ROW][C]68[/C][C]0.867571274806338[/C][C]0.264857450387324[/C][C]0.132428725193662[/C][/ROW]
[ROW][C]69[/C][C]0.849583735313639[/C][C]0.300832529372723[/C][C]0.150416264686361[/C][/ROW]
[ROW][C]70[/C][C]0.830921370530505[/C][C]0.33815725893899[/C][C]0.169078629469495[/C][/ROW]
[ROW][C]71[/C][C]0.804394872387828[/C][C]0.391210255224344[/C][C]0.195605127612172[/C][/ROW]
[ROW][C]72[/C][C]0.81812822355961[/C][C]0.36374355288078[/C][C]0.18187177644039[/C][/ROW]
[ROW][C]73[/C][C]0.786774184114196[/C][C]0.426451631771608[/C][C]0.213225815885804[/C][/ROW]
[ROW][C]74[/C][C]0.755711419874114[/C][C]0.488577160251771[/C][C]0.244288580125886[/C][/ROW]
[ROW][C]75[/C][C]0.726258597721489[/C][C]0.547482804557022[/C][C]0.273741402278511[/C][/ROW]
[ROW][C]76[/C][C]0.762700486191554[/C][C]0.474599027616892[/C][C]0.237299513808446[/C][/ROW]
[ROW][C]77[/C][C]0.77021386284658[/C][C]0.459572274306841[/C][C]0.22978613715342[/C][/ROW]
[ROW][C]78[/C][C]0.742902055041221[/C][C]0.514195889917559[/C][C]0.257097944958779[/C][/ROW]
[ROW][C]79[/C][C]0.725938567067528[/C][C]0.548122865864943[/C][C]0.274061432932472[/C][/ROW]
[ROW][C]80[/C][C]0.688331072148759[/C][C]0.623337855702481[/C][C]0.311668927851241[/C][/ROW]
[ROW][C]81[/C][C]0.648560666885965[/C][C]0.70287866622807[/C][C]0.351439333114035[/C][/ROW]
[ROW][C]82[/C][C]0.71678237619461[/C][C]0.56643524761078[/C][C]0.28321762380539[/C][/ROW]
[ROW][C]83[/C][C]0.678761044400451[/C][C]0.642477911199099[/C][C]0.321238955599549[/C][/ROW]
[ROW][C]84[/C][C]0.680617560926218[/C][C]0.638764878147564[/C][C]0.319382439073782[/C][/ROW]
[ROW][C]85[/C][C]0.643091620544186[/C][C]0.713816758911629[/C][C]0.356908379455814[/C][/ROW]
[ROW][C]86[/C][C]0.600966435613699[/C][C]0.798067128772602[/C][C]0.399033564386301[/C][/ROW]
[ROW][C]87[/C][C]0.559137994682247[/C][C]0.881724010635507[/C][C]0.440862005317753[/C][/ROW]
[ROW][C]88[/C][C]0.513516362197924[/C][C]0.972967275604153[/C][C]0.486483637802076[/C][/ROW]
[ROW][C]89[/C][C]0.470221018339203[/C][C]0.940442036678405[/C][C]0.529778981660797[/C][/ROW]
[ROW][C]90[/C][C]0.823140780042933[/C][C]0.353718439914133[/C][C]0.176859219957067[/C][/ROW]
[ROW][C]91[/C][C]0.846928977817443[/C][C]0.306142044365114[/C][C]0.153071022182557[/C][/ROW]
[ROW][C]92[/C][C]0.817597288384839[/C][C]0.364805423230322[/C][C]0.182402711615161[/C][/ROW]
[ROW][C]93[/C][C]0.784905483978923[/C][C]0.430189032042153[/C][C]0.215094516021077[/C][/ROW]
[ROW][C]94[/C][C]0.749778763531287[/C][C]0.500442472937426[/C][C]0.250221236468713[/C][/ROW]
[ROW][C]95[/C][C]0.717367550124592[/C][C]0.565264899750816[/C][C]0.282632449875408[/C][/ROW]
[ROW][C]96[/C][C]0.679948621226516[/C][C]0.640102757546967[/C][C]0.320051378773484[/C][/ROW]
[ROW][C]97[/C][C]0.677728615798671[/C][C]0.644542768402657[/C][C]0.322271384201328[/C][/ROW]
[ROW][C]98[/C][C]0.63917393827407[/C][C]0.721652123451859[/C][C]0.36082606172593[/C][/ROW]
[ROW][C]99[/C][C]0.653011850834799[/C][C]0.693976298330402[/C][C]0.346988149165201[/C][/ROW]
[ROW][C]100[/C][C]0.620882117266279[/C][C]0.758235765467441[/C][C]0.379117882733721[/C][/ROW]
[ROW][C]101[/C][C]0.626417862599232[/C][C]0.747164274801535[/C][C]0.373582137400768[/C][/ROW]
[ROW][C]102[/C][C]0.616944607753692[/C][C]0.766110784492617[/C][C]0.383055392246308[/C][/ROW]
[ROW][C]103[/C][C]0.56957180975737[/C][C]0.86085638048526[/C][C]0.43042819024263[/C][/ROW]
[ROW][C]104[/C][C]0.555892179999466[/C][C]0.888215640001067[/C][C]0.444107820000534[/C][/ROW]
[ROW][C]105[/C][C]0.506860117844436[/C][C]0.986279764311128[/C][C]0.493139882155564[/C][/ROW]
[ROW][C]106[/C][C]0.632346910043144[/C][C]0.735306179913713[/C][C]0.367653089956856[/C][/ROW]
[ROW][C]107[/C][C]0.594243807721022[/C][C]0.811512384557955[/C][C]0.405756192278978[/C][/ROW]
[ROW][C]108[/C][C]0.550964717231709[/C][C]0.898070565536581[/C][C]0.44903528276829[/C][/ROW]
[ROW][C]109[/C][C]0.587496949646767[/C][C]0.825006100706466[/C][C]0.412503050353233[/C][/ROW]
[ROW][C]110[/C][C]0.649636789765419[/C][C]0.700726420469161[/C][C]0.350363210234581[/C][/ROW]
[ROW][C]111[/C][C]0.605065524443295[/C][C]0.789868951113411[/C][C]0.394934475556705[/C][/ROW]
[ROW][C]112[/C][C]0.642963440817627[/C][C]0.714073118364746[/C][C]0.357036559182373[/C][/ROW]
[ROW][C]113[/C][C]0.661464384945954[/C][C]0.677071230108092[/C][C]0.338535615054046[/C][/ROW]
[ROW][C]114[/C][C]0.693001110274171[/C][C]0.613997779451658[/C][C]0.306998889725829[/C][/ROW]
[ROW][C]115[/C][C]0.781335908573962[/C][C]0.437328182852076[/C][C]0.218664091426038[/C][/ROW]
[ROW][C]116[/C][C]0.745462556397093[/C][C]0.509074887205815[/C][C]0.254537443602907[/C][/ROW]
[ROW][C]117[/C][C]0.720361453837732[/C][C]0.559277092324537[/C][C]0.279638546162268[/C][/ROW]
[ROW][C]118[/C][C]0.674196551121298[/C][C]0.651606897757405[/C][C]0.325803448878702[/C][/ROW]
[ROW][C]119[/C][C]0.627394133058546[/C][C]0.745211733882909[/C][C]0.372605866941454[/C][/ROW]
[ROW][C]120[/C][C]0.616437411046018[/C][C]0.767125177907964[/C][C]0.383562588953982[/C][/ROW]
[ROW][C]121[/C][C]0.566655584133561[/C][C]0.866688831732877[/C][C]0.433344415866439[/C][/ROW]
[ROW][C]122[/C][C]0.51013401898786[/C][C]0.97973196202428[/C][C]0.48986598101214[/C][/ROW]
[ROW][C]123[/C][C]0.454038609485962[/C][C]0.908077218971925[/C][C]0.545961390514038[/C][/ROW]
[ROW][C]124[/C][C]0.398105393135398[/C][C]0.796210786270795[/C][C]0.601894606864602[/C][/ROW]
[ROW][C]125[/C][C]0.354514422645171[/C][C]0.709028845290341[/C][C]0.645485577354829[/C][/ROW]
[ROW][C]126[/C][C]0.32512914738655[/C][C]0.650258294773101[/C][C]0.67487085261345[/C][/ROW]
[ROW][C]127[/C][C]0.301489862360305[/C][C]0.602979724720611[/C][C]0.698510137639695[/C][/ROW]
[ROW][C]128[/C][C]0.286624367482241[/C][C]0.573248734964482[/C][C]0.713375632517759[/C][/ROW]
[ROW][C]129[/C][C]0.389411074320686[/C][C]0.778822148641372[/C][C]0.610588925679314[/C][/ROW]
[ROW][C]130[/C][C]0.35956088154796[/C][C]0.719121763095921[/C][C]0.64043911845204[/C][/ROW]
[ROW][C]131[/C][C]0.310083291294334[/C][C]0.620166582588667[/C][C]0.689916708705666[/C][/ROW]
[ROW][C]132[/C][C]0.370166789344671[/C][C]0.740333578689342[/C][C]0.629833210655329[/C][/ROW]
[ROW][C]133[/C][C]0.315635055911699[/C][C]0.631270111823398[/C][C]0.684364944088301[/C][/ROW]
[ROW][C]134[/C][C]0.311279021967671[/C][C]0.622558043935343[/C][C]0.688720978032329[/C][/ROW]
[ROW][C]135[/C][C]0.281203607635813[/C][C]0.562407215271625[/C][C]0.718796392364187[/C][/ROW]
[ROW][C]136[/C][C]0.245118450981004[/C][C]0.490236901962007[/C][C]0.754881549018996[/C][/ROW]
[ROW][C]137[/C][C]0.197920388021926[/C][C]0.395840776043852[/C][C]0.802079611978074[/C][/ROW]
[ROW][C]138[/C][C]0.165876991448551[/C][C]0.331753982897102[/C][C]0.834123008551449[/C][/ROW]
[ROW][C]139[/C][C]0.135057116747289[/C][C]0.270114233494578[/C][C]0.864942883252711[/C][/ROW]
[ROW][C]140[/C][C]0.12387206373474[/C][C]0.247744127469481[/C][C]0.87612793626526[/C][/ROW]
[ROW][C]141[/C][C]0.0966982090822736[/C][C]0.193396418164547[/C][C]0.903301790917726[/C][/ROW]
[ROW][C]142[/C][C]0.105214716424662[/C][C]0.210429432849324[/C][C]0.894785283575338[/C][/ROW]
[ROW][C]143[/C][C]0.0799928163856866[/C][C]0.159985632771373[/C][C]0.920007183614313[/C][/ROW]
[ROW][C]144[/C][C]0.0710345542081793[/C][C]0.142069108416359[/C][C]0.928965445791821[/C][/ROW]
[ROW][C]145[/C][C]0.072479200601877[/C][C]0.144958401203754[/C][C]0.927520799398123[/C][/ROW]
[ROW][C]146[/C][C]0.187669934862517[/C][C]0.375339869725034[/C][C]0.812330065137483[/C][/ROW]
[ROW][C]147[/C][C]0.27718885399136[/C][C]0.55437770798272[/C][C]0.72281114600864[/C][/ROW]
[ROW][C]148[/C][C]0.285258077141392[/C][C]0.570516154282784[/C][C]0.714741922858608[/C][/ROW]
[ROW][C]149[/C][C]0.221030898972023[/C][C]0.442061797944047[/C][C]0.778969101027977[/C][/ROW]
[ROW][C]150[/C][C]0.170928209058808[/C][C]0.341856418117616[/C][C]0.829071790941192[/C][/ROW]
[ROW][C]151[/C][C]0.124392195993427[/C][C]0.248784391986854[/C][C]0.875607804006573[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157916&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157916&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
110.02790919067919430.05581838135838850.972090809320806
120.8267582571571620.3464834856856760.173241742842838
130.8183075858312570.3633848283374870.181692414168743
140.7731967752424350.453606449515130.226803224757565
150.7973467407684910.4053065184630190.202653259231509
160.7887095325915530.4225809348168940.211290467408447
170.7376776868844220.5246446262311560.262322313115578
180.6626935549680570.6746128900638860.337306445031943
190.57535057607680.8492988478463990.4246494239232
200.5016324954648780.9967350090702440.498367504535122
210.8271105105648630.3457789788702740.172889489435137
220.91003147877320.17993704245360.0899685212267999
230.8793080459849330.2413839080301340.120691954015067
240.8765344098893130.2469311802213750.123465590110687
250.8456378256484490.3087243487031030.154362174351551
260.9769316981687090.04613660366258130.0230683018312907
270.9730341266942990.05393174661140150.0269658733057007
280.961183898147940.07763220370412010.03881610185206
290.9456974243306490.1086051513387020.0543025756693509
300.945470106058930.1090597878821390.0545298939410696
310.9291525201041370.1416949597917260.070847479895863
320.9067093124517420.1865813750965160.0932906875482582
330.8934873923292120.2130252153415770.106512607670788
340.8638244595350830.2723510809298340.136175540464917
350.8305070439385780.3389859121228430.169492956061422
360.8285950867393990.3428098265212020.171404913260601
370.9202242467129670.1595515065740660.0797757532870328
380.9098803306768230.1802393386463530.0901196693231767
390.9054138698147370.1891722603705260.0945861301852628
400.8812317992371280.2375364015257430.118768200762872
410.8571524680684630.2856950638630740.142847531931537
420.8350752143366770.3298495713266450.164924785663323
430.8645930641003640.2708138717992720.135406935899636
440.8405737774686530.3188524450626930.159426222531347
450.8891128236776170.2217743526447650.110887176322383
460.9084105263115570.1831789473768870.0915894736884433
470.8932004594954280.2135990810091430.106799540504572
480.8683095667713570.2633808664572870.131690433228644
490.868663547958930.2626729040821410.13133645204107
500.8437774469317290.3124451061365420.156222553068271
510.8259742731263950.3480514537472110.174025726873605
520.7954769199058470.4090461601883070.204523080094153
530.7786225397902420.4427549204195170.221377460209758
540.7797110096023930.4405779807952130.220288990397607
550.7437065647583770.5125868704832460.256293435241623
560.7047039864841730.5905920270316550.295296013515827
570.6728097319379010.6543805361241970.327190268062099
580.634453315330670.7310933693386590.36554668466933
590.6265626122219920.7468747755560170.373437387778008
600.6490327794420480.7019344411159040.350967220557952
610.7372279863150820.5255440273698350.262772013684918
620.7147866390405880.5704267219188240.285213360959412
630.7874637199362050.4250725601275910.212536280063795
640.7567293662119890.4865412675760220.243270633788011
650.7397999247321310.5204001505357380.260200075267869
660.8408155007299280.3183689985401450.159184499270072
670.8752216840291730.2495566319416530.124778315970827
680.8675712748063380.2648574503873240.132428725193662
690.8495837353136390.3008325293727230.150416264686361
700.8309213705305050.338157258938990.169078629469495
710.8043948723878280.3912102552243440.195605127612172
720.818128223559610.363743552880780.18187177644039
730.7867741841141960.4264516317716080.213225815885804
740.7557114198741140.4885771602517710.244288580125886
750.7262585977214890.5474828045570220.273741402278511
760.7627004861915540.4745990276168920.237299513808446
770.770213862846580.4595722743068410.22978613715342
780.7429020550412210.5141958899175590.257097944958779
790.7259385670675280.5481228658649430.274061432932472
800.6883310721487590.6233378557024810.311668927851241
810.6485606668859650.702878666228070.351439333114035
820.716782376194610.566435247610780.28321762380539
830.6787610444004510.6424779111990990.321238955599549
840.6806175609262180.6387648781475640.319382439073782
850.6430916205441860.7138167589116290.356908379455814
860.6009664356136990.7980671287726020.399033564386301
870.5591379946822470.8817240106355070.440862005317753
880.5135163621979240.9729672756041530.486483637802076
890.4702210183392030.9404420366784050.529778981660797
900.8231407800429330.3537184399141330.176859219957067
910.8469289778174430.3061420443651140.153071022182557
920.8175972883848390.3648054232303220.182402711615161
930.7849054839789230.4301890320421530.215094516021077
940.7497787635312870.5004424729374260.250221236468713
950.7173675501245920.5652648997508160.282632449875408
960.6799486212265160.6401027575469670.320051378773484
970.6777286157986710.6445427684026570.322271384201328
980.639173938274070.7216521234518590.36082606172593
990.6530118508347990.6939762983304020.346988149165201
1000.6208821172662790.7582357654674410.379117882733721
1010.6264178625992320.7471642748015350.373582137400768
1020.6169446077536920.7661107844926170.383055392246308
1030.569571809757370.860856380485260.43042819024263
1040.5558921799994660.8882156400010670.444107820000534
1050.5068601178444360.9862797643111280.493139882155564
1060.6323469100431440.7353061799137130.367653089956856
1070.5942438077210220.8115123845579550.405756192278978
1080.5509647172317090.8980705655365810.44903528276829
1090.5874969496467670.8250061007064660.412503050353233
1100.6496367897654190.7007264204691610.350363210234581
1110.6050655244432950.7898689511134110.394934475556705
1120.6429634408176270.7140731183647460.357036559182373
1130.6614643849459540.6770712301080920.338535615054046
1140.6930011102741710.6139977794516580.306998889725829
1150.7813359085739620.4373281828520760.218664091426038
1160.7454625563970930.5090748872058150.254537443602907
1170.7203614538377320.5592770923245370.279638546162268
1180.6741965511212980.6516068977574050.325803448878702
1190.6273941330585460.7452117338829090.372605866941454
1200.6164374110460180.7671251779079640.383562588953982
1210.5666555841335610.8666888317328770.433344415866439
1220.510134018987860.979731962024280.48986598101214
1230.4540386094859620.9080772189719250.545961390514038
1240.3981053931353980.7962107862707950.601894606864602
1250.3545144226451710.7090288452903410.645485577354829
1260.325129147386550.6502582947731010.67487085261345
1270.3014898623603050.6029797247206110.698510137639695
1280.2866243674822410.5732487349644820.713375632517759
1290.3894110743206860.7788221486413720.610588925679314
1300.359560881547960.7191217630959210.64043911845204
1310.3100832912943340.6201665825886670.689916708705666
1320.3701667893446710.7403335786893420.629833210655329
1330.3156350559116990.6312701118233980.684364944088301
1340.3112790219676710.6225580439353430.688720978032329
1350.2812036076358130.5624072152716250.718796392364187
1360.2451184509810040.4902369019620070.754881549018996
1370.1979203880219260.3958407760438520.802079611978074
1380.1658769914485510.3317539828971020.834123008551449
1390.1350571167472890.2701142334945780.864942883252711
1400.123872063734740.2477441274694810.87612793626526
1410.09669820908227360.1933964181645470.903301790917726
1420.1052147164246620.2104294328493240.894785283575338
1430.07999281638568660.1599856327713730.920007183614313
1440.07103455420817930.1420691084163590.928965445791821
1450.0724792006018770.1449584012037540.927520799398123
1460.1876699348625170.3753398697250340.812330065137483
1470.277188853991360.554377707982720.72281114600864
1480.2852580771413920.5705161542827840.714741922858608
1490.2210308989720230.4420617979440470.778969101027977
1500.1709282090588080.3418564181176160.829071790941192
1510.1243921959934270.2487843919868540.875607804006573






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00709219858156028OK
10% type I error level40.0283687943262411OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00709219858156028 & OK \tabularnewline
10% type I error level & 4 & 0.0283687943262411 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157916&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00709219858156028[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0283687943262411[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157916&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157916&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 level00OK
5% type I error level10.00709219858156028OK
10% type I error level40.0283687943262411OK


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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}