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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 computationSun, 16 Dec 2012 13:01:02 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/16/t1355680925icvvwnp73jijevs.htm/, Retrieved Thu, 25 Apr 2024 10:23:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200525, Retrieved Thu, 25 Apr 2024 10:23:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact84

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2000	41	38	13	12	14	12	53
2000	39	32	16	11	18	11	86
2000	30	35	19	15	11	14	66
2000	31	33	15	6	12	12	67
2000	34	37	14	13	16	21	76
2000	35	29	13	10	18	12	78
2000	39	31	19	12	14	22	53
2000	34	36	15	14	14	11	80
2000	36	35	14	12	15	10	74
2000	37	38	15	6	15	13	76
2000	38	31	16	10	17	10	79
2000	36	34	16	12	19	8	54
2000	38	35	16	12	10	15	67
2001	39	38	16	11	16	14	54
2001	33	37	17	15	18	10	87
2001	32	33	15	12	14	14	58
2001	36	32	15	10	14	14	75
2001	38	38	20	12	17	11	88
2001	39	38	18	11	14	10	64
2001	32	32	16	12	16	13	57
2001	32	33	16	11	18	7	66
2001	31	31	16	12	11	14	68
2001	39	38	19	13	14	12	54
2001	37	39	16	11	12	14	56
2001	39	32	17	9	17	11	86
2001	41	32	17	13	9	9	80
2002	36	35	16	10	16	11	76
2002	33	37	15	14	14	15	69
2002	33	33	16	12	15	14	78
2002	34	33	14	10	11	13	67
2002	31	28	15	12	16	9	80
2002	27	32	12	8	13	15	54
2002	37	31	14	10	17	10	71
2002	34	37	16	12	15	11	84
2002	34	30	14	12	14	13	74
2002	32	33	7	7	16	8	71
2002	29	31	10	6	9	20	63
2002	36	33	14	12	15	12	71
2002	29	31	16	10	17	10	76
2003	35	33	16	10	13	10	69
2003	37	32	16	10	15	9	74
2003	34	33	14	12	16	14	75
2003	38	32	20	15	16	8	54
2003	35	33	14	10	12	14	52
2003	38	28	14	10	12	11	69
2003	37	35	11	12	11	13	68
2003	38	39	14	13	15	9	65
2003	33	34	15	11	15	11	75
2003	36	38	16	11	17	15	74
2003	38	32	14	12	13	11	75
2003	32	38	16	14	16	10	72
2003	32	30	14	10	14	14	67
2004	32	33	12	12	11	18	63
2004	34	38	16	13	12	14	62
2004	32	32	9	5	12	11	63
2004	37	32	14	6	15	12	76
2004	39	34	16	12	16	13	74
2004	29	34	16	12	15	9	67
2004	37	36	15	11	12	10	73
2004	35	34	16	10	12	15	70
2004	30	28	12	7	8	20	53
2004	38	34	16	12	13	12	77
2004	34	35	16	14	11	12	77
2004	31	35	14	11	14	14	52
2004	34	31	16	12	15	13	54
2004	35	37	17	13	10	11	80
2005	36	35	18	14	11	17	66
2005	30	27	18	11	12	12	73
2005	39	40	12	12	15	13	63
2005	35	37	16	12	15	14	69
2005	38	36	10	8	14	13	67
2005	31	38	14	11	16	15	54
2005	34	39	18	14	15	13	81
2005	38	41	18	14	15	10	69
2005	34	27	16	12	13	11	84
2005	39	30	17	9	12	19	80
2005	37	37	16	13	17	13	70
2005	34	31	16	11	13	17	69
2005	28	31	13	12	15	13	77
2005	37	27	16	12	13	9	54
2006	33	36	16	12	15	11	79
2006	37	38	20	12	16	10	30
2006	35	37	16	12	15	9	71
2006	37	33	15	12	16	12	73
2006	32	34	15	11	15	12	72
2006	33	31	16	10	14	13	77
2006	38	39	14	9	15	13	75
2006	33	34	16	12	14	12	69
2006	29	32	16	12	13	15	54
2006	33	33	15	12	7	22	70
2006	31	36	12	9	17	13	73
2006	36	32	17	15	13	15	54
2006	35	41	16	12	15	13	77
2006	32	28	15	12	14	15	82
2007	29	30	13	12	13	10	80
2007	39	36	16	10	16	11	80
2007	37	35	16	13	12	16	69
2007	35	31	16	9	14	11	78
2007	37	34	16	12	17	11	81
2007	32	36	14	10	15	10	76
2007	38	36	16	14	17	10	76
2007	37	35	16	11	12	16	73
2007	36	37	20	15	16	12	85
2007	32	28	15	11	11	11	66
2007	33	39	16	11	15	16	79
2007	40	32	13	12	9	19	68
2007	38	35	17	12	16	11	76
2007	41	39	16	12	15	16	71
2008	36	35	16	11	10	15	54
2008	43	42	12	7	10	24	46
2008	30	34	16	12	15	14	82
2008	31	33	16	14	11	15	74
2008	32	41	17	11	13	11	88
2008	32	33	13	11	14	15	38
2008	37	34	12	10	18	12	76
2008	37	32	18	13	16	10	86
2008	33	40	14	13	14	14	54
2008	34	40	14	8	14	13	70
2008	33	35	13	11	14	9	69
2008	38	36	16	12	14	15	90
2008	33	37	13	11	12	15	54
2008	31	27	16	13	14	14	76
2009	38	39	13	12	15	11	89
2009	37	38	16	14	15	8	76
2009	33	31	15	13	15	11	73
2009	31	33	16	15	13	11	79
2009	39	32	15	10	17	8	90
2009	44	39	17	11	17	10	74
2009	33	36	15	9	19	11	81
2009	35	33	12	11	15	13	72
2009	32	33	16	10	13	11	71
2009	28	32	10	11	9	20	66
2009	40	37	16	8	15	10	77
2009	27	30	12	11	15	15	65
2009	37	38	14	12	15	12	74
2009	32	29	15	12	16	14	82
2010	28	22	13	9	11	23	54
2010	34	35	15	11	14	14	63
2010	30	35	11	10	11	16	54
2010	35	34	12	8	15	11	64
2010	31	35	8	9	13	12	69
2010	32	34	16	8	15	10	54
2010	30	34	15	9	16	14	84
2010	30	35	17	15	14	12	86
2010	31	23	16	11	15	12	77
2010	40	31	10	8	16	11	89
2010	32	27	18	13	16	12	76
2010	36	36	13	12	11	13	60
2010	32	31	16	12	12	11	75
2010	35	32	13	9	9	19	73
2011	38	39	10	7	16	12	85
2011	42	37	15	13	13	17	79
2011	34	38	16	9	16	9	71
2011	35	39	16	6	12	12	72
2011	35	34	14	8	9	19	69
2011	33	31	10	8	13	18	78
2011	36	32	17	15	13	15	54
2011	32	37	13	6	14	14	69
2011	33	36	15	9	19	11	81
2011	34	32	16	11	13	9	84
2011	32	35	12	8	12	18	84
2011	34	36	13	8	13	16	69

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 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=200525&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 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=200525&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200525&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 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
Learning[t] = + 118.587702458165 -0.056288591856722Jaar[t] + 0.107231629617173Connected[t] -0.0173315645800658Separate[t] + 0.533474933820667Software[t] + 0.0559979525339901Happiness[t] -0.0701374977101899Depression[t] + 0.00497559096325706`Belonging\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  118.587702458165 -0.056288591856722Jaar[t] +  0.107231629617173Connected[t] -0.0173315645800658Separate[t] +  0.533474933820667Software[t] +  0.0559979525339901Happiness[t] -0.0701374977101899Depression[t] +  0.00497559096325706`Belonging\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200525&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  118.587702458165 -0.056288591856722Jaar[t] +  0.107231629617173Connected[t] -0.0173315645800658Separate[t] +  0.533474933820667Software[t] +  0.0559979525339901Happiness[t] -0.0701374977101899Depression[t] +  0.00497559096325706`Belonging\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200525&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200525&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
Learning[t] = + 118.587702458165 -0.056288591856722Jaar[t] + 0.107231629617173Connected[t] -0.0173315645800658Separate[t] + 0.533474933820667Software[t] + 0.0559979525339901Happiness[t] -0.0701374977101899Depression[t] + 0.00497559096325706`Belonging\r`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)118.58770245816589.5333991.32450.1872970.093648
Jaar-0.0562885918567220.044646-1.26080.2092920.104646
Connected0.1072316296171730.047162.27380.0243620.012181
Separate-0.01733156458006580.04481-0.38680.6994510.349726
Software0.5334749338206670.069317.696900
Happiness0.05599795253399010.0762510.73440.463830.231915
Depression-0.07013749771018990.055997-1.25250.212280.10614
`Belonging\r`0.004975590963257060.0146540.33950.7346640.367332

\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) & 118.587702458165 & 89.533399 & 1.3245 & 0.187297 & 0.093648 \tabularnewline
Jaar & -0.056288591856722 & 0.044646 & -1.2608 & 0.209292 & 0.104646 \tabularnewline
Connected & 0.107231629617173 & 0.04716 & 2.2738 & 0.024362 & 0.012181 \tabularnewline
Separate & -0.0173315645800658 & 0.04481 & -0.3868 & 0.699451 & 0.349726 \tabularnewline
Software & 0.533474933820667 & 0.06931 & 7.6969 & 0 & 0 \tabularnewline
Happiness & 0.0559979525339901 & 0.076251 & 0.7344 & 0.46383 & 0.231915 \tabularnewline
Depression & -0.0701374977101899 & 0.055997 & -1.2525 & 0.21228 & 0.10614 \tabularnewline
`Belonging\r` & 0.00497559096325706 & 0.014654 & 0.3395 & 0.734664 & 0.367332 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200525&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]118.587702458165[/C][C]89.533399[/C][C]1.3245[/C][C]0.187297[/C][C]0.093648[/C][/ROW]
[ROW][C]Jaar[/C][C]-0.056288591856722[/C][C]0.044646[/C][C]-1.2608[/C][C]0.209292[/C][C]0.104646[/C][/ROW]
[ROW][C]Connected[/C][C]0.107231629617173[/C][C]0.04716[/C][C]2.2738[/C][C]0.024362[/C][C]0.012181[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0173315645800658[/C][C]0.04481[/C][C]-0.3868[/C][C]0.699451[/C][C]0.349726[/C][/ROW]
[ROW][C]Software[/C][C]0.533474933820667[/C][C]0.06931[/C][C]7.6969[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0559979525339901[/C][C]0.076251[/C][C]0.7344[/C][C]0.46383[/C][C]0.231915[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0701374977101899[/C][C]0.055997[/C][C]-1.2525[/C][C]0.21228[/C][C]0.10614[/C][/ROW]
[ROW][C]`Belonging\r`[/C][C]0.00497559096325706[/C][C]0.014654[/C][C]0.3395[/C][C]0.734664[/C][C]0.367332[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200525&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200525&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)118.58770245816589.5333991.32450.1872970.093648
Jaar-0.0562885918567220.044646-1.26080.2092920.104646
Connected0.1072316296171730.047162.27380.0243620.012181
Separate-0.01733156458006580.04481-0.38680.6994510.349726
Software0.5334749338206670.069317.696900
Happiness0.05599795253399010.0762510.73440.463830.231915
Depression-0.07013749771018990.055997-1.25250.212280.10614
`Belonging\r`0.004975590963257060.0146540.33950.7346640.367332






Multiple Linear Regression - Regression Statistics
Multiple R0.600437040999097
R-squared0.360524640203751
Adjusted R-squared0.331457578394831
F-TEST (value)12.4032020358215
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value1.52966528332854e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84481928652867
Sum Squared Residuals524.117162792017

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.600437040999097 \tabularnewline
R-squared & 0.360524640203751 \tabularnewline
Adjusted R-squared & 0.331457578394831 \tabularnewline
F-TEST (value) & 12.4032020358215 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 1.52966528332854e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84481928652867 \tabularnewline
Sum Squared Residuals & 524.117162792017 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200525&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.600437040999097[/C][/ROW]
[ROW][C]R-squared[/C][C]0.360524640203751[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.331457578394831[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.4032020358215[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]1.52966528332854e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.84481928652867[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]524.117162792017[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200525&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200525&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 R0.600437040999097
R-squared0.360524640203751
Adjusted R-squared0.331457578394831
F-TEST (value)12.4032020358215
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value1.52966528332854e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84481928652867
Sum Squared Residuals524.117162792017






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.3561429948372-3.35614299483716
21616.1705179988959-0.170517998895882
31916.58542839375022.41457160624984
41512.12729728705912.87270271294092
51415.7515251037486-1.75152510374857
61315.1501690149305-2.15016901493048
71915.56162571056113.43837428943892
81516.9116130380363-1.91161303803625
91416.172739898674-2.17273989867397
101512.82666592042292.17333407957712
111615.52645340847150.473546591528492
121616.4548264495452-0.454826449545233
131615.72169676994460.278303230055385
141615.5286127105360.471387289464011
151717.5931946303919-0.59319463039191
161515.3060305187218-0.306030518721824
171514.76992378050460.230076219495383
182016.39043655315483.60956344684522
191815.74692270594132.25307729405866
201615.50051989511680.499480104883197
211615.52731460671450.472685393285498
221615.11522407029540.884775929704617
231916.62384166852972.37615833147028
241615.08277725851210.917222741487869
251714.99128158686382.00871841313616
261717.0020824107498-0.00208241074976706
271614.98902448406951.0109755159305
281516.339191168689-1.33919116868898
291615.51248332828140.487516671718594
301414.3441792772356-0.344179277235646
311515.8013145149588-0.801314514958843
321212.4509777939794-0.450977793979427
331415.2668398674348-1.26683986743483
341615.79065473848840.209345261511572
351415.6659468329619-1.66594683296195
36713.1798708316132-6.17987083161323
371011.0859237701349-1.08592377013491
381415.9396240758105-1.9396240758105
391614.43386478531371.56613521468626
401614.72748189512121.27251810487884
411615.166288076530.833711923469972
421415.6044975456861-1.60449754568608
432017.96751800622962.03248199377042
441414.306348905371-0.306348905371041
451415.0096991566288-1.00969915662883
461115.6468479036749-4.6468479036749
471416.7078432368794-2.70784323687943
481515.1008739582647-0.100873958264749
491615.179712912060.820287087940032
501416.0931742642634-2.09317426426343
511616.6359495491437-0.635949549143724
521414.2232784797766-0.223278479776556
531214.7134988495252-2.71349884952521
541615.70635157209140.29364842790861
55911.5434663138659-2.54346631386593
561412.77563843818661.22436156181342
571616.1321974440821-0.132197444082085
581615.24960404947430.750395950525674
591515.3310412138983-0.331041213898293
601614.25215188856271.74784811143731
611211.56029398143290.43970601856706
621615.94203622746290.0579637725370968
631616.4507321069875-0.4507321069875
641414.4319415047741-0.431941504774144
651615.49252421793730.507475782062713
661716.01889199168980.981108008310155
671816.20348778521831.79651221478165
681814.53984030052163.46015969947843
691215.8611900116152-3.86119001161515
701615.4439742349560.556025765043991
711013.6532893163546-3.65328931635461
721414.3754678084615-0.375467808461528
731816.49887393308931.50112606691069
741817.04384272396940.956157276030641
751615.68310870365090.31689129634906
761713.92984705846613.07015294153393
771616.3790214217524-0.379021421752449
781614.58484866081.41515133919999
791314.9072844405324-1.90728444053244
801615.99581085902510.00418914097487342
811615.45072235120810.549277648791853
822015.72731723356134.27268276643871
831615.74832431357680.25167568642325
841515.8876504724613-0.887650472461294
851514.73971228247740.260287717522551
861614.26420617658631.73579382341374
871414.1742836448184-0.174283644818405
881615.30949412049150.690505879508472
891614.57418642106961.42581357893045
901514.2384406311950.761559368804978
911213.5777016543799-1.57770165437986
921716.92523262985180.0747673701482353
931615.42830161019530.57169838980473
941515.1605220677465-0.160522067746522
951315.0326138119686-2.0326138119686
961615.03184721291040.968152787089625
971615.80572952043540.194270479564641
981614.03415649652691.96584350347315
991615.97997049397470.0200295060252575
1001414.2754629869133-0.275462986913336
1011617.164748404967-1.16474840496702
1021614.75868201664711.24131798335295
1032017.31493588568822.68506411431178
1041514.60370521989580.396294780104191
1051614.45827664323961.54172335676039
1061315.2625622275106-2.26256222751061
1071715.98899465166161.01100534833843
1081615.80979988629160.190200113708395
1091614.45876715951351.54123284048652
1101212.2831256723928-0.283125672392799
1111614.85562768756251.14437231243748
1121615.71320671384890.286793286151116
1131714.54356519475792.45643480524213
1141314.2088861249288-1.20888612492877
1151214.8177145344842-2.8177145344842
1161816.53083746509131.4691625349087
1171415.4114936232491-1.41149362324912
1181413.00109753688530.99890246311474
1191314.8565229315079-1.85652293150792
1201615.59248687280160.407513127198355
1211314.2144050465698-1.21440504656981
1221615.53180370474730.468196295252714
1231315.8157759396162-2.81577593961622
1241616.9385555528287-0.938555552828674
1251515.8721357865794-0.872135786579435
1261616.6078169065379-0.607816906537854
1271515.3047626428143-0.304762642814324
1281716.03319032182790.966809678172097
1291513.91537476623851.08462523376155
1301214.8397354626287-2.83973546262868
1311614.00786913934561.99213086065437
1321013.2496418749337-3.24964187493372
1331613.94143299887912.05856700112087
1341213.8587729872683-1.85877298726831
1351415.5811045124201-1.58110451242007
1361515.1564581303745-0.156458130374461
1371312.14159538161460.858404618385351
1381514.47063634308120.529363656918842
1391113.1551857191001-2.15518571910014
1401213.2661607724442-1.26616077244421
141813.1961221752542-5.19612217525424
1421612.96484747167033.03515252832969
1431513.20857483684761.79142516315242
1441716.43032314747040.569676852529573
1451614.62285145063041.3771485493696
1461014.0347013408857-4.03470134088569
1471815.77872905113942.22127094886063
1481315.0884598387746-2.08845983877455
1491615.01709795560940.982902044390583
1501312.98199145720890.0180085427911352
1511013.0017821777702-3.00178217777021
1521516.1176865363906-1.11768653639057
1531613.79789131116792.20210868883211
1541611.85793786243974.14206213756028
1551412.33766243851831.66233756148168
1561012.5141034995396-2.51410349953963
1571716.64378967056820.356210329431845
1581311.52770023950621.47229976049384
1591513.8027975825251.19720241747499
1601614.865519391211.13448060879001
1611212.3114012048477-0.311401204847747
1621312.63017198300750.369828016992459

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.3561429948372 & -3.35614299483716 \tabularnewline
2 & 16 & 16.1705179988959 & -0.170517998895882 \tabularnewline
3 & 19 & 16.5854283937502 & 2.41457160624984 \tabularnewline
4 & 15 & 12.1272972870591 & 2.87270271294092 \tabularnewline
5 & 14 & 15.7515251037486 & -1.75152510374857 \tabularnewline
6 & 13 & 15.1501690149305 & -2.15016901493048 \tabularnewline
7 & 19 & 15.5616257105611 & 3.43837428943892 \tabularnewline
8 & 15 & 16.9116130380363 & -1.91161303803625 \tabularnewline
9 & 14 & 16.172739898674 & -2.17273989867397 \tabularnewline
10 & 15 & 12.8266659204229 & 2.17333407957712 \tabularnewline
11 & 16 & 15.5264534084715 & 0.473546591528492 \tabularnewline
12 & 16 & 16.4548264495452 & -0.454826449545233 \tabularnewline
13 & 16 & 15.7216967699446 & 0.278303230055385 \tabularnewline
14 & 16 & 15.528612710536 & 0.471387289464011 \tabularnewline
15 & 17 & 17.5931946303919 & -0.59319463039191 \tabularnewline
16 & 15 & 15.3060305187218 & -0.306030518721824 \tabularnewline
17 & 15 & 14.7699237805046 & 0.230076219495383 \tabularnewline
18 & 20 & 16.3904365531548 & 3.60956344684522 \tabularnewline
19 & 18 & 15.7469227059413 & 2.25307729405866 \tabularnewline
20 & 16 & 15.5005198951168 & 0.499480104883197 \tabularnewline
21 & 16 & 15.5273146067145 & 0.472685393285498 \tabularnewline
22 & 16 & 15.1152240702954 & 0.884775929704617 \tabularnewline
23 & 19 & 16.6238416685297 & 2.37615833147028 \tabularnewline
24 & 16 & 15.0827772585121 & 0.917222741487869 \tabularnewline
25 & 17 & 14.9912815868638 & 2.00871841313616 \tabularnewline
26 & 17 & 17.0020824107498 & -0.00208241074976706 \tabularnewline
27 & 16 & 14.9890244840695 & 1.0109755159305 \tabularnewline
28 & 15 & 16.339191168689 & -1.33919116868898 \tabularnewline
29 & 16 & 15.5124833282814 & 0.487516671718594 \tabularnewline
30 & 14 & 14.3441792772356 & -0.344179277235646 \tabularnewline
31 & 15 & 15.8013145149588 & -0.801314514958843 \tabularnewline
32 & 12 & 12.4509777939794 & -0.450977793979427 \tabularnewline
33 & 14 & 15.2668398674348 & -1.26683986743483 \tabularnewline
34 & 16 & 15.7906547384884 & 0.209345261511572 \tabularnewline
35 & 14 & 15.6659468329619 & -1.66594683296195 \tabularnewline
36 & 7 & 13.1798708316132 & -6.17987083161323 \tabularnewline
37 & 10 & 11.0859237701349 & -1.08592377013491 \tabularnewline
38 & 14 & 15.9396240758105 & -1.9396240758105 \tabularnewline
39 & 16 & 14.4338647853137 & 1.56613521468626 \tabularnewline
40 & 16 & 14.7274818951212 & 1.27251810487884 \tabularnewline
41 & 16 & 15.16628807653 & 0.833711923469972 \tabularnewline
42 & 14 & 15.6044975456861 & -1.60449754568608 \tabularnewline
43 & 20 & 17.9675180062296 & 2.03248199377042 \tabularnewline
44 & 14 & 14.306348905371 & -0.306348905371041 \tabularnewline
45 & 14 & 15.0096991566288 & -1.00969915662883 \tabularnewline
46 & 11 & 15.6468479036749 & -4.6468479036749 \tabularnewline
47 & 14 & 16.7078432368794 & -2.70784323687943 \tabularnewline
48 & 15 & 15.1008739582647 & -0.100873958264749 \tabularnewline
49 & 16 & 15.17971291206 & 0.820287087940032 \tabularnewline
50 & 14 & 16.0931742642634 & -2.09317426426343 \tabularnewline
51 & 16 & 16.6359495491437 & -0.635949549143724 \tabularnewline
52 & 14 & 14.2232784797766 & -0.223278479776556 \tabularnewline
53 & 12 & 14.7134988495252 & -2.71349884952521 \tabularnewline
54 & 16 & 15.7063515720914 & 0.29364842790861 \tabularnewline
55 & 9 & 11.5434663138659 & -2.54346631386593 \tabularnewline
56 & 14 & 12.7756384381866 & 1.22436156181342 \tabularnewline
57 & 16 & 16.1321974440821 & -0.132197444082085 \tabularnewline
58 & 16 & 15.2496040494743 & 0.750395950525674 \tabularnewline
59 & 15 & 15.3310412138983 & -0.331041213898293 \tabularnewline
60 & 16 & 14.2521518885627 & 1.74784811143731 \tabularnewline
61 & 12 & 11.5602939814329 & 0.43970601856706 \tabularnewline
62 & 16 & 15.9420362274629 & 0.0579637725370968 \tabularnewline
63 & 16 & 16.4507321069875 & -0.4507321069875 \tabularnewline
64 & 14 & 14.4319415047741 & -0.431941504774144 \tabularnewline
65 & 16 & 15.4925242179373 & 0.507475782062713 \tabularnewline
66 & 17 & 16.0188919916898 & 0.981108008310155 \tabularnewline
67 & 18 & 16.2034877852183 & 1.79651221478165 \tabularnewline
68 & 18 & 14.5398403005216 & 3.46015969947843 \tabularnewline
69 & 12 & 15.8611900116152 & -3.86119001161515 \tabularnewline
70 & 16 & 15.443974234956 & 0.556025765043991 \tabularnewline
71 & 10 & 13.6532893163546 & -3.65328931635461 \tabularnewline
72 & 14 & 14.3754678084615 & -0.375467808461528 \tabularnewline
73 & 18 & 16.4988739330893 & 1.50112606691069 \tabularnewline
74 & 18 & 17.0438427239694 & 0.956157276030641 \tabularnewline
75 & 16 & 15.6831087036509 & 0.31689129634906 \tabularnewline
76 & 17 & 13.9298470584661 & 3.07015294153393 \tabularnewline
77 & 16 & 16.3790214217524 & -0.379021421752449 \tabularnewline
78 & 16 & 14.5848486608 & 1.41515133919999 \tabularnewline
79 & 13 & 14.9072844405324 & -1.90728444053244 \tabularnewline
80 & 16 & 15.9958108590251 & 0.00418914097487342 \tabularnewline
81 & 16 & 15.4507223512081 & 0.549277648791853 \tabularnewline
82 & 20 & 15.7273172335613 & 4.27268276643871 \tabularnewline
83 & 16 & 15.7483243135768 & 0.25167568642325 \tabularnewline
84 & 15 & 15.8876504724613 & -0.887650472461294 \tabularnewline
85 & 15 & 14.7397122824774 & 0.260287717522551 \tabularnewline
86 & 16 & 14.2642061765863 & 1.73579382341374 \tabularnewline
87 & 14 & 14.1742836448184 & -0.174283644818405 \tabularnewline
88 & 16 & 15.3094941204915 & 0.690505879508472 \tabularnewline
89 & 16 & 14.5741864210696 & 1.42581357893045 \tabularnewline
90 & 15 & 14.238440631195 & 0.761559368804978 \tabularnewline
91 & 12 & 13.5777016543799 & -1.57770165437986 \tabularnewline
92 & 17 & 16.9252326298518 & 0.0747673701482353 \tabularnewline
93 & 16 & 15.4283016101953 & 0.57169838980473 \tabularnewline
94 & 15 & 15.1605220677465 & -0.160522067746522 \tabularnewline
95 & 13 & 15.0326138119686 & -2.0326138119686 \tabularnewline
96 & 16 & 15.0318472129104 & 0.968152787089625 \tabularnewline
97 & 16 & 15.8057295204354 & 0.194270479564641 \tabularnewline
98 & 16 & 14.0341564965269 & 1.96584350347315 \tabularnewline
99 & 16 & 15.9799704939747 & 0.0200295060252575 \tabularnewline
100 & 14 & 14.2754629869133 & -0.275462986913336 \tabularnewline
101 & 16 & 17.164748404967 & -1.16474840496702 \tabularnewline
102 & 16 & 14.7586820166471 & 1.24131798335295 \tabularnewline
103 & 20 & 17.3149358856882 & 2.68506411431178 \tabularnewline
104 & 15 & 14.6037052198958 & 0.396294780104191 \tabularnewline
105 & 16 & 14.4582766432396 & 1.54172335676039 \tabularnewline
106 & 13 & 15.2625622275106 & -2.26256222751061 \tabularnewline
107 & 17 & 15.9889946516616 & 1.01100534833843 \tabularnewline
108 & 16 & 15.8097998862916 & 0.190200113708395 \tabularnewline
109 & 16 & 14.4587671595135 & 1.54123284048652 \tabularnewline
110 & 12 & 12.2831256723928 & -0.283125672392799 \tabularnewline
111 & 16 & 14.8556276875625 & 1.14437231243748 \tabularnewline
112 & 16 & 15.7132067138489 & 0.286793286151116 \tabularnewline
113 & 17 & 14.5435651947579 & 2.45643480524213 \tabularnewline
114 & 13 & 14.2088861249288 & -1.20888612492877 \tabularnewline
115 & 12 & 14.8177145344842 & -2.8177145344842 \tabularnewline
116 & 18 & 16.5308374650913 & 1.4691625349087 \tabularnewline
117 & 14 & 15.4114936232491 & -1.41149362324912 \tabularnewline
118 & 14 & 13.0010975368853 & 0.99890246311474 \tabularnewline
119 & 13 & 14.8565229315079 & -1.85652293150792 \tabularnewline
120 & 16 & 15.5924868728016 & 0.407513127198355 \tabularnewline
121 & 13 & 14.2144050465698 & -1.21440504656981 \tabularnewline
122 & 16 & 15.5318037047473 & 0.468196295252714 \tabularnewline
123 & 13 & 15.8157759396162 & -2.81577593961622 \tabularnewline
124 & 16 & 16.9385555528287 & -0.938555552828674 \tabularnewline
125 & 15 & 15.8721357865794 & -0.872135786579435 \tabularnewline
126 & 16 & 16.6078169065379 & -0.607816906537854 \tabularnewline
127 & 15 & 15.3047626428143 & -0.304762642814324 \tabularnewline
128 & 17 & 16.0331903218279 & 0.966809678172097 \tabularnewline
129 & 15 & 13.9153747662385 & 1.08462523376155 \tabularnewline
130 & 12 & 14.8397354626287 & -2.83973546262868 \tabularnewline
131 & 16 & 14.0078691393456 & 1.99213086065437 \tabularnewline
132 & 10 & 13.2496418749337 & -3.24964187493372 \tabularnewline
133 & 16 & 13.9414329988791 & 2.05856700112087 \tabularnewline
134 & 12 & 13.8587729872683 & -1.85877298726831 \tabularnewline
135 & 14 & 15.5811045124201 & -1.58110451242007 \tabularnewline
136 & 15 & 15.1564581303745 & -0.156458130374461 \tabularnewline
137 & 13 & 12.1415953816146 & 0.858404618385351 \tabularnewline
138 & 15 & 14.4706363430812 & 0.529363656918842 \tabularnewline
139 & 11 & 13.1551857191001 & -2.15518571910014 \tabularnewline
140 & 12 & 13.2661607724442 & -1.26616077244421 \tabularnewline
141 & 8 & 13.1961221752542 & -5.19612217525424 \tabularnewline
142 & 16 & 12.9648474716703 & 3.03515252832969 \tabularnewline
143 & 15 & 13.2085748368476 & 1.79142516315242 \tabularnewline
144 & 17 & 16.4303231474704 & 0.569676852529573 \tabularnewline
145 & 16 & 14.6228514506304 & 1.3771485493696 \tabularnewline
146 & 10 & 14.0347013408857 & -4.03470134088569 \tabularnewline
147 & 18 & 15.7787290511394 & 2.22127094886063 \tabularnewline
148 & 13 & 15.0884598387746 & -2.08845983877455 \tabularnewline
149 & 16 & 15.0170979556094 & 0.982902044390583 \tabularnewline
150 & 13 & 12.9819914572089 & 0.0180085427911352 \tabularnewline
151 & 10 & 13.0017821777702 & -3.00178217777021 \tabularnewline
152 & 15 & 16.1176865363906 & -1.11768653639057 \tabularnewline
153 & 16 & 13.7978913111679 & 2.20210868883211 \tabularnewline
154 & 16 & 11.8579378624397 & 4.14206213756028 \tabularnewline
155 & 14 & 12.3376624385183 & 1.66233756148168 \tabularnewline
156 & 10 & 12.5141034995396 & -2.51410349953963 \tabularnewline
157 & 17 & 16.6437896705682 & 0.356210329431845 \tabularnewline
158 & 13 & 11.5277002395062 & 1.47229976049384 \tabularnewline
159 & 15 & 13.802797582525 & 1.19720241747499 \tabularnewline
160 & 16 & 14.86551939121 & 1.13448060879001 \tabularnewline
161 & 12 & 12.3114012048477 & -0.311401204847747 \tabularnewline
162 & 13 & 12.6301719830075 & 0.369828016992459 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200525&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.3561429948372[/C][C]-3.35614299483716[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.1705179988959[/C][C]-0.170517998895882[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5854283937502[/C][C]2.41457160624984[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.1272972870591[/C][C]2.87270271294092[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.7515251037486[/C][C]-1.75152510374857[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.1501690149305[/C][C]-2.15016901493048[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.5616257105611[/C][C]3.43837428943892[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9116130380363[/C][C]-1.91161303803625[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.172739898674[/C][C]-2.17273989867397[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.8266659204229[/C][C]2.17333407957712[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.5264534084715[/C][C]0.473546591528492[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.4548264495452[/C][C]-0.454826449545233[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.7216967699446[/C][C]0.278303230055385[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.528612710536[/C][C]0.471387289464011[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.5931946303919[/C][C]-0.59319463039191[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.3060305187218[/C][C]-0.306030518721824[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.7699237805046[/C][C]0.230076219495383[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3904365531548[/C][C]3.60956344684522[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.7469227059413[/C][C]2.25307729405866[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.5005198951168[/C][C]0.499480104883197[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.5273146067145[/C][C]0.472685393285498[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.1152240702954[/C][C]0.884775929704617[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.6238416685297[/C][C]2.37615833147028[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.0827772585121[/C][C]0.917222741487869[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.9912815868638[/C][C]2.00871841313616[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.0020824107498[/C][C]-0.00208241074976706[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9890244840695[/C][C]1.0109755159305[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.339191168689[/C][C]-1.33919116868898[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.5124833282814[/C][C]0.487516671718594[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.3441792772356[/C][C]-0.344179277235646[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.8013145149588[/C][C]-0.801314514958843[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4509777939794[/C][C]-0.450977793979427[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.2668398674348[/C][C]-1.26683986743483[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.7906547384884[/C][C]0.209345261511572[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6659468329619[/C][C]-1.66594683296195[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.1798708316132[/C][C]-6.17987083161323[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.0859237701349[/C][C]-1.08592377013491[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.9396240758105[/C][C]-1.9396240758105[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.4338647853137[/C][C]1.56613521468626[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7274818951212[/C][C]1.27251810487884[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.16628807653[/C][C]0.833711923469972[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.6044975456861[/C][C]-1.60449754568608[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.9675180062296[/C][C]2.03248199377042[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.306348905371[/C][C]-0.306348905371041[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0096991566288[/C][C]-1.00969915662883[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6468479036749[/C][C]-4.6468479036749[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.7078432368794[/C][C]-2.70784323687943[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1008739582647[/C][C]-0.100873958264749[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.17971291206[/C][C]0.820287087940032[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0931742642634[/C][C]-2.09317426426343[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6359495491437[/C][C]-0.635949549143724[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2232784797766[/C][C]-0.223278479776556[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.7134988495252[/C][C]-2.71349884952521[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.7063515720914[/C][C]0.29364842790861[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5434663138659[/C][C]-2.54346631386593[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.7756384381866[/C][C]1.22436156181342[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1321974440821[/C][C]-0.132197444082085[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.2496040494743[/C][C]0.750395950525674[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3310412138983[/C][C]-0.331041213898293[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2521518885627[/C][C]1.74784811143731[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5602939814329[/C][C]0.43970601856706[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9420362274629[/C][C]0.0579637725370968[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.4507321069875[/C][C]-0.4507321069875[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4319415047741[/C][C]-0.431941504774144[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4925242179373[/C][C]0.507475782062713[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.0188919916898[/C][C]0.981108008310155[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.2034877852183[/C][C]1.79651221478165[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5398403005216[/C][C]3.46015969947843[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8611900116152[/C][C]-3.86119001161515[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.443974234956[/C][C]0.556025765043991[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.6532893163546[/C][C]-3.65328931635461[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3754678084615[/C][C]-0.375467808461528[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.4988739330893[/C][C]1.50112606691069[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.0438427239694[/C][C]0.956157276030641[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6831087036509[/C][C]0.31689129634906[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.9298470584661[/C][C]3.07015294153393[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3790214217524[/C][C]-0.379021421752449[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.5848486608[/C][C]1.41515133919999[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.9072844405324[/C][C]-1.90728444053244[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.9958108590251[/C][C]0.00418914097487342[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4507223512081[/C][C]0.549277648791853[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.7273172335613[/C][C]4.27268276643871[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.7483243135768[/C][C]0.25167568642325[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.8876504724613[/C][C]-0.887650472461294[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7397122824774[/C][C]0.260287717522551[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2642061765863[/C][C]1.73579382341374[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.1742836448184[/C][C]-0.174283644818405[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3094941204915[/C][C]0.690505879508472[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5741864210696[/C][C]1.42581357893045[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.238440631195[/C][C]0.761559368804978[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.5777016543799[/C][C]-1.57770165437986[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9252326298518[/C][C]0.0747673701482353[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4283016101953[/C][C]0.57169838980473[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.1605220677465[/C][C]-0.160522067746522[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.0326138119686[/C][C]-2.0326138119686[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.0318472129104[/C][C]0.968152787089625[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8057295204354[/C][C]0.194270479564641[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.0341564965269[/C][C]1.96584350347315[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.9799704939747[/C][C]0.0200295060252575[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.2754629869133[/C][C]-0.275462986913336[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.164748404967[/C][C]-1.16474840496702[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7586820166471[/C][C]1.24131798335295[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3149358856882[/C][C]2.68506411431178[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.6037052198958[/C][C]0.396294780104191[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.4582766432396[/C][C]1.54172335676039[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.2625622275106[/C][C]-2.26256222751061[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9889946516616[/C][C]1.01100534833843[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8097998862916[/C][C]0.190200113708395[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.4587671595135[/C][C]1.54123284048652[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.2831256723928[/C][C]-0.283125672392799[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.8556276875625[/C][C]1.14437231243748[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.7132067138489[/C][C]0.286793286151116[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5435651947579[/C][C]2.45643480524213[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.2088861249288[/C][C]-1.20888612492877[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.8177145344842[/C][C]-2.8177145344842[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.5308374650913[/C][C]1.4691625349087[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.4114936232491[/C][C]-1.41149362324912[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0010975368853[/C][C]0.99890246311474[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.8565229315079[/C][C]-1.85652293150792[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.5924868728016[/C][C]0.407513127198355[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.2144050465698[/C][C]-1.21440504656981[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.5318037047473[/C][C]0.468196295252714[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8157759396162[/C][C]-2.81577593961622[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.9385555528287[/C][C]-0.938555552828674[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8721357865794[/C][C]-0.872135786579435[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.6078169065379[/C][C]-0.607816906537854[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.3047626428143[/C][C]-0.304762642814324[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0331903218279[/C][C]0.966809678172097[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.9153747662385[/C][C]1.08462523376155[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.8397354626287[/C][C]-2.83973546262868[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0078691393456[/C][C]1.99213086065437[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2496418749337[/C][C]-3.24964187493372[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9414329988791[/C][C]2.05856700112087[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8587729872683[/C][C]-1.85877298726831[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.5811045124201[/C][C]-1.58110451242007[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.1564581303745[/C][C]-0.156458130374461[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.1415953816146[/C][C]0.858404618385351[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.4706363430812[/C][C]0.529363656918842[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.1551857191001[/C][C]-2.15518571910014[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.2661607724442[/C][C]-1.26616077244421[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.1961221752542[/C][C]-5.19612217525424[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.9648474716703[/C][C]3.03515252832969[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.2085748368476[/C][C]1.79142516315242[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.4303231474704[/C][C]0.569676852529573[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.6228514506304[/C][C]1.3771485493696[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.0347013408857[/C][C]-4.03470134088569[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.7787290511394[/C][C]2.22127094886063[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.0884598387746[/C][C]-2.08845983877455[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.0170979556094[/C][C]0.982902044390583[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.9819914572089[/C][C]0.0180085427911352[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.0017821777702[/C][C]-3.00178217777021[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.1176865363906[/C][C]-1.11768653639057[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.7978913111679[/C][C]2.20210868883211[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8579378624397[/C][C]4.14206213756028[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.3376624385183[/C][C]1.66233756148168[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.5141034995396[/C][C]-2.51410349953963[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.6437896705682[/C][C]0.356210329431845[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.5277002395062[/C][C]1.47229976049384[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.802797582525[/C][C]1.19720241747499[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.86551939121[/C][C]1.13448060879001[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.3114012048477[/C][C]-0.311401204847747[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6301719830075[/C][C]0.369828016992459[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200525&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200525&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
11316.3561429948372-3.35614299483716
21616.1705179988959-0.170517998895882
31916.58542839375022.41457160624984
41512.12729728705912.87270271294092
51415.7515251037486-1.75152510374857
61315.1501690149305-2.15016901493048
71915.56162571056113.43837428943892
81516.9116130380363-1.91161303803625
91416.172739898674-2.17273989867397
101512.82666592042292.17333407957712
111615.52645340847150.473546591528492
121616.4548264495452-0.454826449545233
131615.72169676994460.278303230055385
141615.5286127105360.471387289464011
151717.5931946303919-0.59319463039191
161515.3060305187218-0.306030518721824
171514.76992378050460.230076219495383
182016.39043655315483.60956344684522
191815.74692270594132.25307729405866
201615.50051989511680.499480104883197
211615.52731460671450.472685393285498
221615.11522407029540.884775929704617
231916.62384166852972.37615833147028
241615.08277725851210.917222741487869
251714.99128158686382.00871841313616
261717.0020824107498-0.00208241074976706
271614.98902448406951.0109755159305
281516.339191168689-1.33919116868898
291615.51248332828140.487516671718594
301414.3441792772356-0.344179277235646
311515.8013145149588-0.801314514958843
321212.4509777939794-0.450977793979427
331415.2668398674348-1.26683986743483
341615.79065473848840.209345261511572
351415.6659468329619-1.66594683296195
36713.1798708316132-6.17987083161323
371011.0859237701349-1.08592377013491
381415.9396240758105-1.9396240758105
391614.43386478531371.56613521468626
401614.72748189512121.27251810487884
411615.166288076530.833711923469972
421415.6044975456861-1.60449754568608
432017.96751800622962.03248199377042
441414.306348905371-0.306348905371041
451415.0096991566288-1.00969915662883
461115.6468479036749-4.6468479036749
471416.7078432368794-2.70784323687943
481515.1008739582647-0.100873958264749
491615.179712912060.820287087940032
501416.0931742642634-2.09317426426343
511616.6359495491437-0.635949549143724
521414.2232784797766-0.223278479776556
531214.7134988495252-2.71349884952521
541615.70635157209140.29364842790861
55911.5434663138659-2.54346631386593
561412.77563843818661.22436156181342
571616.1321974440821-0.132197444082085
581615.24960404947430.750395950525674
591515.3310412138983-0.331041213898293
601614.25215188856271.74784811143731
611211.56029398143290.43970601856706
621615.94203622746290.0579637725370968
631616.4507321069875-0.4507321069875
641414.4319415047741-0.431941504774144
651615.49252421793730.507475782062713
661716.01889199168980.981108008310155
671816.20348778521831.79651221478165
681814.53984030052163.46015969947843
691215.8611900116152-3.86119001161515
701615.4439742349560.556025765043991
711013.6532893163546-3.65328931635461
721414.3754678084615-0.375467808461528
731816.49887393308931.50112606691069
741817.04384272396940.956157276030641
751615.68310870365090.31689129634906
761713.92984705846613.07015294153393
771616.3790214217524-0.379021421752449
781614.58484866081.41515133919999
791314.9072844405324-1.90728444053244
801615.99581085902510.00418914097487342
811615.45072235120810.549277648791853
822015.72731723356134.27268276643871
831615.74832431357680.25167568642325
841515.8876504724613-0.887650472461294
851514.73971228247740.260287717522551
861614.26420617658631.73579382341374
871414.1742836448184-0.174283644818405
881615.30949412049150.690505879508472
891614.57418642106961.42581357893045
901514.2384406311950.761559368804978
911213.5777016543799-1.57770165437986
921716.92523262985180.0747673701482353
931615.42830161019530.57169838980473
941515.1605220677465-0.160522067746522
951315.0326138119686-2.0326138119686
961615.03184721291040.968152787089625
971615.80572952043540.194270479564641
981614.03415649652691.96584350347315
991615.97997049397470.0200295060252575
1001414.2754629869133-0.275462986913336
1011617.164748404967-1.16474840496702
1021614.75868201664711.24131798335295
1032017.31493588568822.68506411431178
1041514.60370521989580.396294780104191
1051614.45827664323961.54172335676039
1061315.2625622275106-2.26256222751061
1071715.98899465166161.01100534833843
1081615.80979988629160.190200113708395
1091614.45876715951351.54123284048652
1101212.2831256723928-0.283125672392799
1111614.85562768756251.14437231243748
1121615.71320671384890.286793286151116
1131714.54356519475792.45643480524213
1141314.2088861249288-1.20888612492877
1151214.8177145344842-2.8177145344842
1161816.53083746509131.4691625349087
1171415.4114936232491-1.41149362324912
1181413.00109753688530.99890246311474
1191314.8565229315079-1.85652293150792
1201615.59248687280160.407513127198355
1211314.2144050465698-1.21440504656981
1221615.53180370474730.468196295252714
1231315.8157759396162-2.81577593961622
1241616.9385555528287-0.938555552828674
1251515.8721357865794-0.872135786579435
1261616.6078169065379-0.607816906537854
1271515.3047626428143-0.304762642814324
1281716.03319032182790.966809678172097
1291513.91537476623851.08462523376155
1301214.8397354626287-2.83973546262868
1311614.00786913934561.99213086065437
1321013.2496418749337-3.24964187493372
1331613.94143299887912.05856700112087
1341213.8587729872683-1.85877298726831
1351415.5811045124201-1.58110451242007
1361515.1564581303745-0.156458130374461
1371312.14159538161460.858404618385351
1381514.47063634308120.529363656918842
1391113.1551857191001-2.15518571910014
1401213.2661607724442-1.26616077244421
141813.1961221752542-5.19612217525424
1421612.96484747167033.03515252832969
1431513.20857483684761.79142516315242
1441716.43032314747040.569676852529573
1451614.62285145063041.3771485493696
1461014.0347013408857-4.03470134088569
1471815.77872905113942.22127094886063
1481315.0884598387746-2.08845983877455
1491615.01709795560940.982902044390583
1501312.98199145720890.0180085427911352
1511013.0017821777702-3.00178217777021
1521516.1176865363906-1.11768653639057
1531613.79789131116792.20210868883211
1541611.85793786243974.14206213756028
1551412.33766243851831.66233756148168
1561012.5141034995396-2.51410349953963
1571716.64378967056820.356210329431845
1581311.52770023950621.47229976049384
1591513.8027975825251.19720241747499
1601614.865519391211.13448060879001
1611212.3114012048477-0.311401204847747
1621312.63017198300750.369828016992459






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4485645128918450.897129025783690.551435487108155
120.9046754703688080.1906490592623850.0953245296311924
130.8453941334249070.3092117331501870.154605866575093
140.7622155048490310.4755689903019390.237784495150969
150.685869322926120.628261354147760.31413067707388
160.7473003140558490.5053993718883010.252699685944151
170.6828405152599180.6343189694801640.317159484740082
180.8860854659556740.2278290680886520.113914534044326
190.8654652197143510.2690695605712980.134534780285649
200.8153691949949350.369261610010130.184630805005065
210.7577187604936720.4845624790126570.242281239506328
220.7088691721199510.5822616557600980.291130827880049
230.6976018880934630.6047962238130740.302398111906537
240.6529605045371450.6940789909257110.347039495462855
250.6005317987191590.7989364025616810.399468201280841
260.5505498837231710.8989002325536580.449450116276829
270.5180240635762330.9639518728475330.481975936423767
280.5625997685980430.8748004628039150.437400231401957
290.5024047264774070.9951905470451860.497595273522593
300.5102017828876620.9795964342246750.489798217112338
310.4528073424513290.9056146849026580.547192657548671
320.4391792938989880.8783585877979770.560820706101012
330.416897985593160.833795971186320.58310201440684
340.357896775832210.715793551664420.64210322416779
350.3380268856454050.676053771290810.661973114354595
360.8375369723291810.3249260553416390.162463027670819
370.8227108618754760.3545782762490480.177289138124524
380.8105937191264790.3788125617470420.189406280873521
390.8341623899769060.3316752200461870.165837610023094
400.8256786542820070.3486426914359850.174321345717993
410.8007213224632610.3985573550734790.199278677536739
420.7796629817845240.4406740364309520.220337018215476
430.7980988544836030.4038022910327940.201901145516397
440.760619524729810.4787609505403790.23938047527019
450.7282128648135230.5435742703729550.271787135186477
460.8870903906854840.2258192186290330.112909609314516
470.8982541597874670.2034916804250670.101745840212533
480.8766398375127430.2467203249745140.123360162487257
490.8593848146392440.2812303707215130.140615185360756
500.8548315689863280.2903368620273440.145168431013672
510.8265327217233220.3469345565533550.173467278276677
520.7941802592269810.4116394815460380.205819740773019
530.8045489670679520.3909020658640950.195451032932048
540.7802057759323910.4395884481352180.219794224067609
550.7966154680678430.4067690638643130.203384531932156
560.7897167212763610.4205665574472780.210283278723639
570.7542883167840460.4914233664319090.245711683215954
580.7425850607145090.5148298785709810.257414939285491
590.7075921396149070.5848157207701860.292407860385093
600.7133536682810160.5732926634379690.286646331718984
610.6779678838810160.6440642322379670.322032116118984
620.6365259745344460.7269480509311070.363474025465554
630.5966258753417220.8067482493165560.403374124658278
640.5533626019950210.8932747960099590.446637398004979
650.5139255996116890.9721488007766220.486074400388311
660.4867338297020040.9734676594040080.513266170297996
670.4856110472248090.9712220944496170.514388952775191
680.6091910155081590.7816179689836820.390808984491841
690.7337703094532120.5324593810935770.266229690546788
700.7003258293422990.5993483413154030.299674170657701
710.8030515700947790.3938968598104430.196948429905221
720.7732617678082040.4534764643835930.226738232191796
730.7646643429817020.4706713140365960.235335657018298
740.7418624210372320.5162751579255360.258137578962768
750.7030508178461110.5938983643077780.296949182153889
760.7522981725771510.4954036548456980.247701827422849
770.7155660063600980.5688679872798040.284433993639902
780.6934476565346220.6131046869307560.306552343465378
790.7015748405583770.5968503188832450.298425159441623
800.6633209503957370.6733580992085260.336679049604263
810.6248258348233090.7503483303533830.375174165176691
820.7891014612583030.4217970774833930.210898538741697
830.7545631124340810.4908737751318370.245436887565919
840.7271225840067890.5457548319864220.272877415993211
850.6875249775672330.6249500448655340.312475022432767
860.6736095784475440.6527808431049110.326390421552456
870.6313998402081320.7372003195837350.368600159791868
880.5897574576810530.8204850846378940.410242542318947
890.5661054143970260.8677891712059470.433894585602974
900.5278189249651320.9443621500697350.472181075034867
910.5200632357214270.9598735285571470.479936764278573
920.4791118126894550.958223625378910.520888187310545
930.4356249667534550.871249933506910.564375033246545
940.3906353768164180.7812707536328350.609364623183582
950.4142307709228460.8284615418456920.585769229077154
960.37695667945770.7539133589154010.6230433205423
970.3351461867913290.6702923735826580.664853813208671
980.3280645336058170.6561290672116340.671935466394183
990.2858140774506910.5716281549013830.714185922549309
1000.2513458981403520.5026917962807040.748654101859648
1010.2284891082490670.4569782164981350.771510891750933
1020.2081073409490770.4162146818981530.791892659050923
1030.2503445327202830.5006890654405660.749655467279717
1040.2133742979576370.4267485959152750.786625702042362
1050.2059211500651410.4118423001302830.794078849934859
1060.217586678303820.4351733566076410.78241332169618
1070.1953438210061410.3906876420122830.804656178993859
1080.1740597703334280.3481195406668560.825940229666572
1090.1690758297030310.3381516594060620.830924170296969
1100.1644326541563040.3288653083126080.835567345843696
1110.1491477858358470.2982955716716950.850852214164153
1120.1269333570681810.2538667141363620.873066642931819
1130.159987728931010.319975457862020.84001227106899
1140.1374366500096080.2748733000192160.862563349990392
1150.1591785453419270.3183570906838540.840821454658073
1160.1592055233116610.3184110466233220.840794476688339
1170.1361775732190720.2723551464381440.863822426780928
1180.1312248495868090.2624496991736190.868775150413191
1190.122582622700950.2451652454018990.87741737729905
1200.1360250296941870.2720500593883730.863974970305813
1210.1122077317818690.2244154635637370.887792268218131
1220.1017228857279410.2034457714558810.898277114272059
1230.1014699197260220.2029398394520440.898530080273978
1240.08077704859754290.1615540971950860.919222951402457
1250.06391353051454540.1278270610290910.936086469485455
1260.04834892307748240.09669784615496490.951651076922518
1270.0356303601389840.07126072027796790.964369639861016
1280.03523792115902450.07047584231804890.964762078840976
1290.0330417074950970.0660834149901940.966958292504903
1300.03374159346569030.06748318693138050.96625840653431
1310.03657729002895720.07315458005791450.963422709971043
1320.03886151703830940.07772303407661870.961138482961691
1330.09051907975129030.1810381595025810.90948092024871
1340.08373382734765080.1674676546953020.916266172652349
1350.06963233076987590.1392646615397520.930367669230124
1360.06146944523549910.1229388904709980.938530554764501
1370.04417369357580910.08834738715161820.955826306424191
1380.03600231639653940.07200463279307880.963997683603461
1390.04361490421412770.08722980842825540.956385095785872
1400.03126021440904670.06252042881809330.968739785590953
1410.4924475741310170.9848951482620330.507552425868983
1420.444004031253010.888008062506020.55599596874699
1430.4265411257848080.8530822515696150.573458874215192
1440.3412233539803720.6824467079607440.658776646019628
1450.2760312515410830.5520625030821670.723968748458917
1460.2463186766047330.4926373532094650.753681323395267
1470.3815175344044830.7630350688089660.618482465595517
1480.6940074759813210.6119850480373580.305992524018679
1490.614061560505680.771876878988640.38593843949432
1500.4666828325330370.9333656650660740.533317167466963
1510.7059413856607780.5881172286784440.294058614339222

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.448564512891845 & 0.89712902578369 & 0.551435487108155 \tabularnewline
12 & 0.904675470368808 & 0.190649059262385 & 0.0953245296311924 \tabularnewline
13 & 0.845394133424907 & 0.309211733150187 & 0.154605866575093 \tabularnewline
14 & 0.762215504849031 & 0.475568990301939 & 0.237784495150969 \tabularnewline
15 & 0.68586932292612 & 0.62826135414776 & 0.31413067707388 \tabularnewline
16 & 0.747300314055849 & 0.505399371888301 & 0.252699685944151 \tabularnewline
17 & 0.682840515259918 & 0.634318969480164 & 0.317159484740082 \tabularnewline
18 & 0.886085465955674 & 0.227829068088652 & 0.113914534044326 \tabularnewline
19 & 0.865465219714351 & 0.269069560571298 & 0.134534780285649 \tabularnewline
20 & 0.815369194994935 & 0.36926161001013 & 0.184630805005065 \tabularnewline
21 & 0.757718760493672 & 0.484562479012657 & 0.242281239506328 \tabularnewline
22 & 0.708869172119951 & 0.582261655760098 & 0.291130827880049 \tabularnewline
23 & 0.697601888093463 & 0.604796223813074 & 0.302398111906537 \tabularnewline
24 & 0.652960504537145 & 0.694078990925711 & 0.347039495462855 \tabularnewline
25 & 0.600531798719159 & 0.798936402561681 & 0.399468201280841 \tabularnewline
26 & 0.550549883723171 & 0.898900232553658 & 0.449450116276829 \tabularnewline
27 & 0.518024063576233 & 0.963951872847533 & 0.481975936423767 \tabularnewline
28 & 0.562599768598043 & 0.874800462803915 & 0.437400231401957 \tabularnewline
29 & 0.502404726477407 & 0.995190547045186 & 0.497595273522593 \tabularnewline
30 & 0.510201782887662 & 0.979596434224675 & 0.489798217112338 \tabularnewline
31 & 0.452807342451329 & 0.905614684902658 & 0.547192657548671 \tabularnewline
32 & 0.439179293898988 & 0.878358587797977 & 0.560820706101012 \tabularnewline
33 & 0.41689798559316 & 0.83379597118632 & 0.58310201440684 \tabularnewline
34 & 0.35789677583221 & 0.71579355166442 & 0.64210322416779 \tabularnewline
35 & 0.338026885645405 & 0.67605377129081 & 0.661973114354595 \tabularnewline
36 & 0.837536972329181 & 0.324926055341639 & 0.162463027670819 \tabularnewline
37 & 0.822710861875476 & 0.354578276249048 & 0.177289138124524 \tabularnewline
38 & 0.810593719126479 & 0.378812561747042 & 0.189406280873521 \tabularnewline
39 & 0.834162389976906 & 0.331675220046187 & 0.165837610023094 \tabularnewline
40 & 0.825678654282007 & 0.348642691435985 & 0.174321345717993 \tabularnewline
41 & 0.800721322463261 & 0.398557355073479 & 0.199278677536739 \tabularnewline
42 & 0.779662981784524 & 0.440674036430952 & 0.220337018215476 \tabularnewline
43 & 0.798098854483603 & 0.403802291032794 & 0.201901145516397 \tabularnewline
44 & 0.76061952472981 & 0.478760950540379 & 0.23938047527019 \tabularnewline
45 & 0.728212864813523 & 0.543574270372955 & 0.271787135186477 \tabularnewline
46 & 0.887090390685484 & 0.225819218629033 & 0.112909609314516 \tabularnewline
47 & 0.898254159787467 & 0.203491680425067 & 0.101745840212533 \tabularnewline
48 & 0.876639837512743 & 0.246720324974514 & 0.123360162487257 \tabularnewline
49 & 0.859384814639244 & 0.281230370721513 & 0.140615185360756 \tabularnewline
50 & 0.854831568986328 & 0.290336862027344 & 0.145168431013672 \tabularnewline
51 & 0.826532721723322 & 0.346934556553355 & 0.173467278276677 \tabularnewline
52 & 0.794180259226981 & 0.411639481546038 & 0.205819740773019 \tabularnewline
53 & 0.804548967067952 & 0.390902065864095 & 0.195451032932048 \tabularnewline
54 & 0.780205775932391 & 0.439588448135218 & 0.219794224067609 \tabularnewline
55 & 0.796615468067843 & 0.406769063864313 & 0.203384531932156 \tabularnewline
56 & 0.789716721276361 & 0.420566557447278 & 0.210283278723639 \tabularnewline
57 & 0.754288316784046 & 0.491423366431909 & 0.245711683215954 \tabularnewline
58 & 0.742585060714509 & 0.514829878570981 & 0.257414939285491 \tabularnewline
59 & 0.707592139614907 & 0.584815720770186 & 0.292407860385093 \tabularnewline
60 & 0.713353668281016 & 0.573292663437969 & 0.286646331718984 \tabularnewline
61 & 0.677967883881016 & 0.644064232237967 & 0.322032116118984 \tabularnewline
62 & 0.636525974534446 & 0.726948050931107 & 0.363474025465554 \tabularnewline
63 & 0.596625875341722 & 0.806748249316556 & 0.403374124658278 \tabularnewline
64 & 0.553362601995021 & 0.893274796009959 & 0.446637398004979 \tabularnewline
65 & 0.513925599611689 & 0.972148800776622 & 0.486074400388311 \tabularnewline
66 & 0.486733829702004 & 0.973467659404008 & 0.513266170297996 \tabularnewline
67 & 0.485611047224809 & 0.971222094449617 & 0.514388952775191 \tabularnewline
68 & 0.609191015508159 & 0.781617968983682 & 0.390808984491841 \tabularnewline
69 & 0.733770309453212 & 0.532459381093577 & 0.266229690546788 \tabularnewline
70 & 0.700325829342299 & 0.599348341315403 & 0.299674170657701 \tabularnewline
71 & 0.803051570094779 & 0.393896859810443 & 0.196948429905221 \tabularnewline
72 & 0.773261767808204 & 0.453476464383593 & 0.226738232191796 \tabularnewline
73 & 0.764664342981702 & 0.470671314036596 & 0.235335657018298 \tabularnewline
74 & 0.741862421037232 & 0.516275157925536 & 0.258137578962768 \tabularnewline
75 & 0.703050817846111 & 0.593898364307778 & 0.296949182153889 \tabularnewline
76 & 0.752298172577151 & 0.495403654845698 & 0.247701827422849 \tabularnewline
77 & 0.715566006360098 & 0.568867987279804 & 0.284433993639902 \tabularnewline
78 & 0.693447656534622 & 0.613104686930756 & 0.306552343465378 \tabularnewline
79 & 0.701574840558377 & 0.596850318883245 & 0.298425159441623 \tabularnewline
80 & 0.663320950395737 & 0.673358099208526 & 0.336679049604263 \tabularnewline
81 & 0.624825834823309 & 0.750348330353383 & 0.375174165176691 \tabularnewline
82 & 0.789101461258303 & 0.421797077483393 & 0.210898538741697 \tabularnewline
83 & 0.754563112434081 & 0.490873775131837 & 0.245436887565919 \tabularnewline
84 & 0.727122584006789 & 0.545754831986422 & 0.272877415993211 \tabularnewline
85 & 0.687524977567233 & 0.624950044865534 & 0.312475022432767 \tabularnewline
86 & 0.673609578447544 & 0.652780843104911 & 0.326390421552456 \tabularnewline
87 & 0.631399840208132 & 0.737200319583735 & 0.368600159791868 \tabularnewline
88 & 0.589757457681053 & 0.820485084637894 & 0.410242542318947 \tabularnewline
89 & 0.566105414397026 & 0.867789171205947 & 0.433894585602974 \tabularnewline
90 & 0.527818924965132 & 0.944362150069735 & 0.472181075034867 \tabularnewline
91 & 0.520063235721427 & 0.959873528557147 & 0.479936764278573 \tabularnewline
92 & 0.479111812689455 & 0.95822362537891 & 0.520888187310545 \tabularnewline
93 & 0.435624966753455 & 0.87124993350691 & 0.564375033246545 \tabularnewline
94 & 0.390635376816418 & 0.781270753632835 & 0.609364623183582 \tabularnewline
95 & 0.414230770922846 & 0.828461541845692 & 0.585769229077154 \tabularnewline
96 & 0.3769566794577 & 0.753913358915401 & 0.6230433205423 \tabularnewline
97 & 0.335146186791329 & 0.670292373582658 & 0.664853813208671 \tabularnewline
98 & 0.328064533605817 & 0.656129067211634 & 0.671935466394183 \tabularnewline
99 & 0.285814077450691 & 0.571628154901383 & 0.714185922549309 \tabularnewline
100 & 0.251345898140352 & 0.502691796280704 & 0.748654101859648 \tabularnewline
101 & 0.228489108249067 & 0.456978216498135 & 0.771510891750933 \tabularnewline
102 & 0.208107340949077 & 0.416214681898153 & 0.791892659050923 \tabularnewline
103 & 0.250344532720283 & 0.500689065440566 & 0.749655467279717 \tabularnewline
104 & 0.213374297957637 & 0.426748595915275 & 0.786625702042362 \tabularnewline
105 & 0.205921150065141 & 0.411842300130283 & 0.794078849934859 \tabularnewline
106 & 0.21758667830382 & 0.435173356607641 & 0.78241332169618 \tabularnewline
107 & 0.195343821006141 & 0.390687642012283 & 0.804656178993859 \tabularnewline
108 & 0.174059770333428 & 0.348119540666856 & 0.825940229666572 \tabularnewline
109 & 0.169075829703031 & 0.338151659406062 & 0.830924170296969 \tabularnewline
110 & 0.164432654156304 & 0.328865308312608 & 0.835567345843696 \tabularnewline
111 & 0.149147785835847 & 0.298295571671695 & 0.850852214164153 \tabularnewline
112 & 0.126933357068181 & 0.253866714136362 & 0.873066642931819 \tabularnewline
113 & 0.15998772893101 & 0.31997545786202 & 0.84001227106899 \tabularnewline
114 & 0.137436650009608 & 0.274873300019216 & 0.862563349990392 \tabularnewline
115 & 0.159178545341927 & 0.318357090683854 & 0.840821454658073 \tabularnewline
116 & 0.159205523311661 & 0.318411046623322 & 0.840794476688339 \tabularnewline
117 & 0.136177573219072 & 0.272355146438144 & 0.863822426780928 \tabularnewline
118 & 0.131224849586809 & 0.262449699173619 & 0.868775150413191 \tabularnewline
119 & 0.12258262270095 & 0.245165245401899 & 0.87741737729905 \tabularnewline
120 & 0.136025029694187 & 0.272050059388373 & 0.863974970305813 \tabularnewline
121 & 0.112207731781869 & 0.224415463563737 & 0.887792268218131 \tabularnewline
122 & 0.101722885727941 & 0.203445771455881 & 0.898277114272059 \tabularnewline
123 & 0.101469919726022 & 0.202939839452044 & 0.898530080273978 \tabularnewline
124 & 0.0807770485975429 & 0.161554097195086 & 0.919222951402457 \tabularnewline
125 & 0.0639135305145454 & 0.127827061029091 & 0.936086469485455 \tabularnewline
126 & 0.0483489230774824 & 0.0966978461549649 & 0.951651076922518 \tabularnewline
127 & 0.035630360138984 & 0.0712607202779679 & 0.964369639861016 \tabularnewline
128 & 0.0352379211590245 & 0.0704758423180489 & 0.964762078840976 \tabularnewline
129 & 0.033041707495097 & 0.066083414990194 & 0.966958292504903 \tabularnewline
130 & 0.0337415934656903 & 0.0674831869313805 & 0.96625840653431 \tabularnewline
131 & 0.0365772900289572 & 0.0731545800579145 & 0.963422709971043 \tabularnewline
132 & 0.0388615170383094 & 0.0777230340766187 & 0.961138482961691 \tabularnewline
133 & 0.0905190797512903 & 0.181038159502581 & 0.90948092024871 \tabularnewline
134 & 0.0837338273476508 & 0.167467654695302 & 0.916266172652349 \tabularnewline
135 & 0.0696323307698759 & 0.139264661539752 & 0.930367669230124 \tabularnewline
136 & 0.0614694452354991 & 0.122938890470998 & 0.938530554764501 \tabularnewline
137 & 0.0441736935758091 & 0.0883473871516182 & 0.955826306424191 \tabularnewline
138 & 0.0360023163965394 & 0.0720046327930788 & 0.963997683603461 \tabularnewline
139 & 0.0436149042141277 & 0.0872298084282554 & 0.956385095785872 \tabularnewline
140 & 0.0312602144090467 & 0.0625204288180933 & 0.968739785590953 \tabularnewline
141 & 0.492447574131017 & 0.984895148262033 & 0.507552425868983 \tabularnewline
142 & 0.44400403125301 & 0.88800806250602 & 0.55599596874699 \tabularnewline
143 & 0.426541125784808 & 0.853082251569615 & 0.573458874215192 \tabularnewline
144 & 0.341223353980372 & 0.682446707960744 & 0.658776646019628 \tabularnewline
145 & 0.276031251541083 & 0.552062503082167 & 0.723968748458917 \tabularnewline
146 & 0.246318676604733 & 0.492637353209465 & 0.753681323395267 \tabularnewline
147 & 0.381517534404483 & 0.763035068808966 & 0.618482465595517 \tabularnewline
148 & 0.694007475981321 & 0.611985048037358 & 0.305992524018679 \tabularnewline
149 & 0.61406156050568 & 0.77187687898864 & 0.38593843949432 \tabularnewline
150 & 0.466682832533037 & 0.933365665066074 & 0.533317167466963 \tabularnewline
151 & 0.705941385660778 & 0.588117228678444 & 0.294058614339222 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200525&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]0.448564512891845[/C][C]0.89712902578369[/C][C]0.551435487108155[/C][/ROW]
[ROW][C]12[/C][C]0.904675470368808[/C][C]0.190649059262385[/C][C]0.0953245296311924[/C][/ROW]
[ROW][C]13[/C][C]0.845394133424907[/C][C]0.309211733150187[/C][C]0.154605866575093[/C][/ROW]
[ROW][C]14[/C][C]0.762215504849031[/C][C]0.475568990301939[/C][C]0.237784495150969[/C][/ROW]
[ROW][C]15[/C][C]0.68586932292612[/C][C]0.62826135414776[/C][C]0.31413067707388[/C][/ROW]
[ROW][C]16[/C][C]0.747300314055849[/C][C]0.505399371888301[/C][C]0.252699685944151[/C][/ROW]
[ROW][C]17[/C][C]0.682840515259918[/C][C]0.634318969480164[/C][C]0.317159484740082[/C][/ROW]
[ROW][C]18[/C][C]0.886085465955674[/C][C]0.227829068088652[/C][C]0.113914534044326[/C][/ROW]
[ROW][C]19[/C][C]0.865465219714351[/C][C]0.269069560571298[/C][C]0.134534780285649[/C][/ROW]
[ROW][C]20[/C][C]0.815369194994935[/C][C]0.36926161001013[/C][C]0.184630805005065[/C][/ROW]
[ROW][C]21[/C][C]0.757718760493672[/C][C]0.484562479012657[/C][C]0.242281239506328[/C][/ROW]
[ROW][C]22[/C][C]0.708869172119951[/C][C]0.582261655760098[/C][C]0.291130827880049[/C][/ROW]
[ROW][C]23[/C][C]0.697601888093463[/C][C]0.604796223813074[/C][C]0.302398111906537[/C][/ROW]
[ROW][C]24[/C][C]0.652960504537145[/C][C]0.694078990925711[/C][C]0.347039495462855[/C][/ROW]
[ROW][C]25[/C][C]0.600531798719159[/C][C]0.798936402561681[/C][C]0.399468201280841[/C][/ROW]
[ROW][C]26[/C][C]0.550549883723171[/C][C]0.898900232553658[/C][C]0.449450116276829[/C][/ROW]
[ROW][C]27[/C][C]0.518024063576233[/C][C]0.963951872847533[/C][C]0.481975936423767[/C][/ROW]
[ROW][C]28[/C][C]0.562599768598043[/C][C]0.874800462803915[/C][C]0.437400231401957[/C][/ROW]
[ROW][C]29[/C][C]0.502404726477407[/C][C]0.995190547045186[/C][C]0.497595273522593[/C][/ROW]
[ROW][C]30[/C][C]0.510201782887662[/C][C]0.979596434224675[/C][C]0.489798217112338[/C][/ROW]
[ROW][C]31[/C][C]0.452807342451329[/C][C]0.905614684902658[/C][C]0.547192657548671[/C][/ROW]
[ROW][C]32[/C][C]0.439179293898988[/C][C]0.878358587797977[/C][C]0.560820706101012[/C][/ROW]
[ROW][C]33[/C][C]0.41689798559316[/C][C]0.83379597118632[/C][C]0.58310201440684[/C][/ROW]
[ROW][C]34[/C][C]0.35789677583221[/C][C]0.71579355166442[/C][C]0.64210322416779[/C][/ROW]
[ROW][C]35[/C][C]0.338026885645405[/C][C]0.67605377129081[/C][C]0.661973114354595[/C][/ROW]
[ROW][C]36[/C][C]0.837536972329181[/C][C]0.324926055341639[/C][C]0.162463027670819[/C][/ROW]
[ROW][C]37[/C][C]0.822710861875476[/C][C]0.354578276249048[/C][C]0.177289138124524[/C][/ROW]
[ROW][C]38[/C][C]0.810593719126479[/C][C]0.378812561747042[/C][C]0.189406280873521[/C][/ROW]
[ROW][C]39[/C][C]0.834162389976906[/C][C]0.331675220046187[/C][C]0.165837610023094[/C][/ROW]
[ROW][C]40[/C][C]0.825678654282007[/C][C]0.348642691435985[/C][C]0.174321345717993[/C][/ROW]
[ROW][C]41[/C][C]0.800721322463261[/C][C]0.398557355073479[/C][C]0.199278677536739[/C][/ROW]
[ROW][C]42[/C][C]0.779662981784524[/C][C]0.440674036430952[/C][C]0.220337018215476[/C][/ROW]
[ROW][C]43[/C][C]0.798098854483603[/C][C]0.403802291032794[/C][C]0.201901145516397[/C][/ROW]
[ROW][C]44[/C][C]0.76061952472981[/C][C]0.478760950540379[/C][C]0.23938047527019[/C][/ROW]
[ROW][C]45[/C][C]0.728212864813523[/C][C]0.543574270372955[/C][C]0.271787135186477[/C][/ROW]
[ROW][C]46[/C][C]0.887090390685484[/C][C]0.225819218629033[/C][C]0.112909609314516[/C][/ROW]
[ROW][C]47[/C][C]0.898254159787467[/C][C]0.203491680425067[/C][C]0.101745840212533[/C][/ROW]
[ROW][C]48[/C][C]0.876639837512743[/C][C]0.246720324974514[/C][C]0.123360162487257[/C][/ROW]
[ROW][C]49[/C][C]0.859384814639244[/C][C]0.281230370721513[/C][C]0.140615185360756[/C][/ROW]
[ROW][C]50[/C][C]0.854831568986328[/C][C]0.290336862027344[/C][C]0.145168431013672[/C][/ROW]
[ROW][C]51[/C][C]0.826532721723322[/C][C]0.346934556553355[/C][C]0.173467278276677[/C][/ROW]
[ROW][C]52[/C][C]0.794180259226981[/C][C]0.411639481546038[/C][C]0.205819740773019[/C][/ROW]
[ROW][C]53[/C][C]0.804548967067952[/C][C]0.390902065864095[/C][C]0.195451032932048[/C][/ROW]
[ROW][C]54[/C][C]0.780205775932391[/C][C]0.439588448135218[/C][C]0.219794224067609[/C][/ROW]
[ROW][C]55[/C][C]0.796615468067843[/C][C]0.406769063864313[/C][C]0.203384531932156[/C][/ROW]
[ROW][C]56[/C][C]0.789716721276361[/C][C]0.420566557447278[/C][C]0.210283278723639[/C][/ROW]
[ROW][C]57[/C][C]0.754288316784046[/C][C]0.491423366431909[/C][C]0.245711683215954[/C][/ROW]
[ROW][C]58[/C][C]0.742585060714509[/C][C]0.514829878570981[/C][C]0.257414939285491[/C][/ROW]
[ROW][C]59[/C][C]0.707592139614907[/C][C]0.584815720770186[/C][C]0.292407860385093[/C][/ROW]
[ROW][C]60[/C][C]0.713353668281016[/C][C]0.573292663437969[/C][C]0.286646331718984[/C][/ROW]
[ROW][C]61[/C][C]0.677967883881016[/C][C]0.644064232237967[/C][C]0.322032116118984[/C][/ROW]
[ROW][C]62[/C][C]0.636525974534446[/C][C]0.726948050931107[/C][C]0.363474025465554[/C][/ROW]
[ROW][C]63[/C][C]0.596625875341722[/C][C]0.806748249316556[/C][C]0.403374124658278[/C][/ROW]
[ROW][C]64[/C][C]0.553362601995021[/C][C]0.893274796009959[/C][C]0.446637398004979[/C][/ROW]
[ROW][C]65[/C][C]0.513925599611689[/C][C]0.972148800776622[/C][C]0.486074400388311[/C][/ROW]
[ROW][C]66[/C][C]0.486733829702004[/C][C]0.973467659404008[/C][C]0.513266170297996[/C][/ROW]
[ROW][C]67[/C][C]0.485611047224809[/C][C]0.971222094449617[/C][C]0.514388952775191[/C][/ROW]
[ROW][C]68[/C][C]0.609191015508159[/C][C]0.781617968983682[/C][C]0.390808984491841[/C][/ROW]
[ROW][C]69[/C][C]0.733770309453212[/C][C]0.532459381093577[/C][C]0.266229690546788[/C][/ROW]
[ROW][C]70[/C][C]0.700325829342299[/C][C]0.599348341315403[/C][C]0.299674170657701[/C][/ROW]
[ROW][C]71[/C][C]0.803051570094779[/C][C]0.393896859810443[/C][C]0.196948429905221[/C][/ROW]
[ROW][C]72[/C][C]0.773261767808204[/C][C]0.453476464383593[/C][C]0.226738232191796[/C][/ROW]
[ROW][C]73[/C][C]0.764664342981702[/C][C]0.470671314036596[/C][C]0.235335657018298[/C][/ROW]
[ROW][C]74[/C][C]0.741862421037232[/C][C]0.516275157925536[/C][C]0.258137578962768[/C][/ROW]
[ROW][C]75[/C][C]0.703050817846111[/C][C]0.593898364307778[/C][C]0.296949182153889[/C][/ROW]
[ROW][C]76[/C][C]0.752298172577151[/C][C]0.495403654845698[/C][C]0.247701827422849[/C][/ROW]
[ROW][C]77[/C][C]0.715566006360098[/C][C]0.568867987279804[/C][C]0.284433993639902[/C][/ROW]
[ROW][C]78[/C][C]0.693447656534622[/C][C]0.613104686930756[/C][C]0.306552343465378[/C][/ROW]
[ROW][C]79[/C][C]0.701574840558377[/C][C]0.596850318883245[/C][C]0.298425159441623[/C][/ROW]
[ROW][C]80[/C][C]0.663320950395737[/C][C]0.673358099208526[/C][C]0.336679049604263[/C][/ROW]
[ROW][C]81[/C][C]0.624825834823309[/C][C]0.750348330353383[/C][C]0.375174165176691[/C][/ROW]
[ROW][C]82[/C][C]0.789101461258303[/C][C]0.421797077483393[/C][C]0.210898538741697[/C][/ROW]
[ROW][C]83[/C][C]0.754563112434081[/C][C]0.490873775131837[/C][C]0.245436887565919[/C][/ROW]
[ROW][C]84[/C][C]0.727122584006789[/C][C]0.545754831986422[/C][C]0.272877415993211[/C][/ROW]
[ROW][C]85[/C][C]0.687524977567233[/C][C]0.624950044865534[/C][C]0.312475022432767[/C][/ROW]
[ROW][C]86[/C][C]0.673609578447544[/C][C]0.652780843104911[/C][C]0.326390421552456[/C][/ROW]
[ROW][C]87[/C][C]0.631399840208132[/C][C]0.737200319583735[/C][C]0.368600159791868[/C][/ROW]
[ROW][C]88[/C][C]0.589757457681053[/C][C]0.820485084637894[/C][C]0.410242542318947[/C][/ROW]
[ROW][C]89[/C][C]0.566105414397026[/C][C]0.867789171205947[/C][C]0.433894585602974[/C][/ROW]
[ROW][C]90[/C][C]0.527818924965132[/C][C]0.944362150069735[/C][C]0.472181075034867[/C][/ROW]
[ROW][C]91[/C][C]0.520063235721427[/C][C]0.959873528557147[/C][C]0.479936764278573[/C][/ROW]
[ROW][C]92[/C][C]0.479111812689455[/C][C]0.95822362537891[/C][C]0.520888187310545[/C][/ROW]
[ROW][C]93[/C][C]0.435624966753455[/C][C]0.87124993350691[/C][C]0.564375033246545[/C][/ROW]
[ROW][C]94[/C][C]0.390635376816418[/C][C]0.781270753632835[/C][C]0.609364623183582[/C][/ROW]
[ROW][C]95[/C][C]0.414230770922846[/C][C]0.828461541845692[/C][C]0.585769229077154[/C][/ROW]
[ROW][C]96[/C][C]0.3769566794577[/C][C]0.753913358915401[/C][C]0.6230433205423[/C][/ROW]
[ROW][C]97[/C][C]0.335146186791329[/C][C]0.670292373582658[/C][C]0.664853813208671[/C][/ROW]
[ROW][C]98[/C][C]0.328064533605817[/C][C]0.656129067211634[/C][C]0.671935466394183[/C][/ROW]
[ROW][C]99[/C][C]0.285814077450691[/C][C]0.571628154901383[/C][C]0.714185922549309[/C][/ROW]
[ROW][C]100[/C][C]0.251345898140352[/C][C]0.502691796280704[/C][C]0.748654101859648[/C][/ROW]
[ROW][C]101[/C][C]0.228489108249067[/C][C]0.456978216498135[/C][C]0.771510891750933[/C][/ROW]
[ROW][C]102[/C][C]0.208107340949077[/C][C]0.416214681898153[/C][C]0.791892659050923[/C][/ROW]
[ROW][C]103[/C][C]0.250344532720283[/C][C]0.500689065440566[/C][C]0.749655467279717[/C][/ROW]
[ROW][C]104[/C][C]0.213374297957637[/C][C]0.426748595915275[/C][C]0.786625702042362[/C][/ROW]
[ROW][C]105[/C][C]0.205921150065141[/C][C]0.411842300130283[/C][C]0.794078849934859[/C][/ROW]
[ROW][C]106[/C][C]0.21758667830382[/C][C]0.435173356607641[/C][C]0.78241332169618[/C][/ROW]
[ROW][C]107[/C][C]0.195343821006141[/C][C]0.390687642012283[/C][C]0.804656178993859[/C][/ROW]
[ROW][C]108[/C][C]0.174059770333428[/C][C]0.348119540666856[/C][C]0.825940229666572[/C][/ROW]
[ROW][C]109[/C][C]0.169075829703031[/C][C]0.338151659406062[/C][C]0.830924170296969[/C][/ROW]
[ROW][C]110[/C][C]0.164432654156304[/C][C]0.328865308312608[/C][C]0.835567345843696[/C][/ROW]
[ROW][C]111[/C][C]0.149147785835847[/C][C]0.298295571671695[/C][C]0.850852214164153[/C][/ROW]
[ROW][C]112[/C][C]0.126933357068181[/C][C]0.253866714136362[/C][C]0.873066642931819[/C][/ROW]
[ROW][C]113[/C][C]0.15998772893101[/C][C]0.31997545786202[/C][C]0.84001227106899[/C][/ROW]
[ROW][C]114[/C][C]0.137436650009608[/C][C]0.274873300019216[/C][C]0.862563349990392[/C][/ROW]
[ROW][C]115[/C][C]0.159178545341927[/C][C]0.318357090683854[/C][C]0.840821454658073[/C][/ROW]
[ROW][C]116[/C][C]0.159205523311661[/C][C]0.318411046623322[/C][C]0.840794476688339[/C][/ROW]
[ROW][C]117[/C][C]0.136177573219072[/C][C]0.272355146438144[/C][C]0.863822426780928[/C][/ROW]
[ROW][C]118[/C][C]0.131224849586809[/C][C]0.262449699173619[/C][C]0.868775150413191[/C][/ROW]
[ROW][C]119[/C][C]0.12258262270095[/C][C]0.245165245401899[/C][C]0.87741737729905[/C][/ROW]
[ROW][C]120[/C][C]0.136025029694187[/C][C]0.272050059388373[/C][C]0.863974970305813[/C][/ROW]
[ROW][C]121[/C][C]0.112207731781869[/C][C]0.224415463563737[/C][C]0.887792268218131[/C][/ROW]
[ROW][C]122[/C][C]0.101722885727941[/C][C]0.203445771455881[/C][C]0.898277114272059[/C][/ROW]
[ROW][C]123[/C][C]0.101469919726022[/C][C]0.202939839452044[/C][C]0.898530080273978[/C][/ROW]
[ROW][C]124[/C][C]0.0807770485975429[/C][C]0.161554097195086[/C][C]0.919222951402457[/C][/ROW]
[ROW][C]125[/C][C]0.0639135305145454[/C][C]0.127827061029091[/C][C]0.936086469485455[/C][/ROW]
[ROW][C]126[/C][C]0.0483489230774824[/C][C]0.0966978461549649[/C][C]0.951651076922518[/C][/ROW]
[ROW][C]127[/C][C]0.035630360138984[/C][C]0.0712607202779679[/C][C]0.964369639861016[/C][/ROW]
[ROW][C]128[/C][C]0.0352379211590245[/C][C]0.0704758423180489[/C][C]0.964762078840976[/C][/ROW]
[ROW][C]129[/C][C]0.033041707495097[/C][C]0.066083414990194[/C][C]0.966958292504903[/C][/ROW]
[ROW][C]130[/C][C]0.0337415934656903[/C][C]0.0674831869313805[/C][C]0.96625840653431[/C][/ROW]
[ROW][C]131[/C][C]0.0365772900289572[/C][C]0.0731545800579145[/C][C]0.963422709971043[/C][/ROW]
[ROW][C]132[/C][C]0.0388615170383094[/C][C]0.0777230340766187[/C][C]0.961138482961691[/C][/ROW]
[ROW][C]133[/C][C]0.0905190797512903[/C][C]0.181038159502581[/C][C]0.90948092024871[/C][/ROW]
[ROW][C]134[/C][C]0.0837338273476508[/C][C]0.167467654695302[/C][C]0.916266172652349[/C][/ROW]
[ROW][C]135[/C][C]0.0696323307698759[/C][C]0.139264661539752[/C][C]0.930367669230124[/C][/ROW]
[ROW][C]136[/C][C]0.0614694452354991[/C][C]0.122938890470998[/C][C]0.938530554764501[/C][/ROW]
[ROW][C]137[/C][C]0.0441736935758091[/C][C]0.0883473871516182[/C][C]0.955826306424191[/C][/ROW]
[ROW][C]138[/C][C]0.0360023163965394[/C][C]0.0720046327930788[/C][C]0.963997683603461[/C][/ROW]
[ROW][C]139[/C][C]0.0436149042141277[/C][C]0.0872298084282554[/C][C]0.956385095785872[/C][/ROW]
[ROW][C]140[/C][C]0.0312602144090467[/C][C]0.0625204288180933[/C][C]0.968739785590953[/C][/ROW]
[ROW][C]141[/C][C]0.492447574131017[/C][C]0.984895148262033[/C][C]0.507552425868983[/C][/ROW]
[ROW][C]142[/C][C]0.44400403125301[/C][C]0.88800806250602[/C][C]0.55599596874699[/C][/ROW]
[ROW][C]143[/C][C]0.426541125784808[/C][C]0.853082251569615[/C][C]0.573458874215192[/C][/ROW]
[ROW][C]144[/C][C]0.341223353980372[/C][C]0.682446707960744[/C][C]0.658776646019628[/C][/ROW]
[ROW][C]145[/C][C]0.276031251541083[/C][C]0.552062503082167[/C][C]0.723968748458917[/C][/ROW]
[ROW][C]146[/C][C]0.246318676604733[/C][C]0.492637353209465[/C][C]0.753681323395267[/C][/ROW]
[ROW][C]147[/C][C]0.381517534404483[/C][C]0.763035068808966[/C][C]0.618482465595517[/C][/ROW]
[ROW][C]148[/C][C]0.694007475981321[/C][C]0.611985048037358[/C][C]0.305992524018679[/C][/ROW]
[ROW][C]149[/C][C]0.61406156050568[/C][C]0.77187687898864[/C][C]0.38593843949432[/C][/ROW]
[ROW][C]150[/C][C]0.466682832533037[/C][C]0.933365665066074[/C][C]0.533317167466963[/C][/ROW]
[ROW][C]151[/C][C]0.705941385660778[/C][C]0.588117228678444[/C][C]0.294058614339222[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200525&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200525&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
110.4485645128918450.897129025783690.551435487108155
120.9046754703688080.1906490592623850.0953245296311924
130.8453941334249070.3092117331501870.154605866575093
140.7622155048490310.4755689903019390.237784495150969
150.685869322926120.628261354147760.31413067707388
160.7473003140558490.5053993718883010.252699685944151
170.6828405152599180.6343189694801640.317159484740082
180.8860854659556740.2278290680886520.113914534044326
190.8654652197143510.2690695605712980.134534780285649
200.8153691949949350.369261610010130.184630805005065
210.7577187604936720.4845624790126570.242281239506328
220.7088691721199510.5822616557600980.291130827880049
230.6976018880934630.6047962238130740.302398111906537
240.6529605045371450.6940789909257110.347039495462855
250.6005317987191590.7989364025616810.399468201280841
260.5505498837231710.8989002325536580.449450116276829
270.5180240635762330.9639518728475330.481975936423767
280.5625997685980430.8748004628039150.437400231401957
290.5024047264774070.9951905470451860.497595273522593
300.5102017828876620.9795964342246750.489798217112338
310.4528073424513290.9056146849026580.547192657548671
320.4391792938989880.8783585877979770.560820706101012
330.416897985593160.833795971186320.58310201440684
340.357896775832210.715793551664420.64210322416779
350.3380268856454050.676053771290810.661973114354595
360.8375369723291810.3249260553416390.162463027670819
370.8227108618754760.3545782762490480.177289138124524
380.8105937191264790.3788125617470420.189406280873521
390.8341623899769060.3316752200461870.165837610023094
400.8256786542820070.3486426914359850.174321345717993
410.8007213224632610.3985573550734790.199278677536739
420.7796629817845240.4406740364309520.220337018215476
430.7980988544836030.4038022910327940.201901145516397
440.760619524729810.4787609505403790.23938047527019
450.7282128648135230.5435742703729550.271787135186477
460.8870903906854840.2258192186290330.112909609314516
470.8982541597874670.2034916804250670.101745840212533
480.8766398375127430.2467203249745140.123360162487257
490.8593848146392440.2812303707215130.140615185360756
500.8548315689863280.2903368620273440.145168431013672
510.8265327217233220.3469345565533550.173467278276677
520.7941802592269810.4116394815460380.205819740773019
530.8045489670679520.3909020658640950.195451032932048
540.7802057759323910.4395884481352180.219794224067609
550.7966154680678430.4067690638643130.203384531932156
560.7897167212763610.4205665574472780.210283278723639
570.7542883167840460.4914233664319090.245711683215954
580.7425850607145090.5148298785709810.257414939285491
590.7075921396149070.5848157207701860.292407860385093
600.7133536682810160.5732926634379690.286646331718984
610.6779678838810160.6440642322379670.322032116118984
620.6365259745344460.7269480509311070.363474025465554
630.5966258753417220.8067482493165560.403374124658278
640.5533626019950210.8932747960099590.446637398004979
650.5139255996116890.9721488007766220.486074400388311
660.4867338297020040.9734676594040080.513266170297996
670.4856110472248090.9712220944496170.514388952775191
680.6091910155081590.7816179689836820.390808984491841
690.7337703094532120.5324593810935770.266229690546788
700.7003258293422990.5993483413154030.299674170657701
710.8030515700947790.3938968598104430.196948429905221
720.7732617678082040.4534764643835930.226738232191796
730.7646643429817020.4706713140365960.235335657018298
740.7418624210372320.5162751579255360.258137578962768
750.7030508178461110.5938983643077780.296949182153889
760.7522981725771510.4954036548456980.247701827422849
770.7155660063600980.5688679872798040.284433993639902
780.6934476565346220.6131046869307560.306552343465378
790.7015748405583770.5968503188832450.298425159441623
800.6633209503957370.6733580992085260.336679049604263
810.6248258348233090.7503483303533830.375174165176691
820.7891014612583030.4217970774833930.210898538741697
830.7545631124340810.4908737751318370.245436887565919
840.7271225840067890.5457548319864220.272877415993211
850.6875249775672330.6249500448655340.312475022432767
860.6736095784475440.6527808431049110.326390421552456
870.6313998402081320.7372003195837350.368600159791868
880.5897574576810530.8204850846378940.410242542318947
890.5661054143970260.8677891712059470.433894585602974
900.5278189249651320.9443621500697350.472181075034867
910.5200632357214270.9598735285571470.479936764278573
920.4791118126894550.958223625378910.520888187310545
930.4356249667534550.871249933506910.564375033246545
940.3906353768164180.7812707536328350.609364623183582
950.4142307709228460.8284615418456920.585769229077154
960.37695667945770.7539133589154010.6230433205423
970.3351461867913290.6702923735826580.664853813208671
980.3280645336058170.6561290672116340.671935466394183
990.2858140774506910.5716281549013830.714185922549309
1000.2513458981403520.5026917962807040.748654101859648
1010.2284891082490670.4569782164981350.771510891750933
1020.2081073409490770.4162146818981530.791892659050923
1030.2503445327202830.5006890654405660.749655467279717
1040.2133742979576370.4267485959152750.786625702042362
1050.2059211500651410.4118423001302830.794078849934859
1060.217586678303820.4351733566076410.78241332169618
1070.1953438210061410.3906876420122830.804656178993859
1080.1740597703334280.3481195406668560.825940229666572
1090.1690758297030310.3381516594060620.830924170296969
1100.1644326541563040.3288653083126080.835567345843696
1110.1491477858358470.2982955716716950.850852214164153
1120.1269333570681810.2538667141363620.873066642931819
1130.159987728931010.319975457862020.84001227106899
1140.1374366500096080.2748733000192160.862563349990392
1150.1591785453419270.3183570906838540.840821454658073
1160.1592055233116610.3184110466233220.840794476688339
1170.1361775732190720.2723551464381440.863822426780928
1180.1312248495868090.2624496991736190.868775150413191
1190.122582622700950.2451652454018990.87741737729905
1200.1360250296941870.2720500593883730.863974970305813
1210.1122077317818690.2244154635637370.887792268218131
1220.1017228857279410.2034457714558810.898277114272059
1230.1014699197260220.2029398394520440.898530080273978
1240.08077704859754290.1615540971950860.919222951402457
1250.06391353051454540.1278270610290910.936086469485455
1260.04834892307748240.09669784615496490.951651076922518
1270.0356303601389840.07126072027796790.964369639861016
1280.03523792115902450.07047584231804890.964762078840976
1290.0330417074950970.0660834149901940.966958292504903
1300.03374159346569030.06748318693138050.96625840653431
1310.03657729002895720.07315458005791450.963422709971043
1320.03886151703830940.07772303407661870.961138482961691
1330.09051907975129030.1810381595025810.90948092024871
1340.08373382734765080.1674676546953020.916266172652349
1350.06963233076987590.1392646615397520.930367669230124
1360.06146944523549910.1229388904709980.938530554764501
1370.04417369357580910.08834738715161820.955826306424191
1380.03600231639653940.07200463279307880.963997683603461
1390.04361490421412770.08722980842825540.956385095785872
1400.03126021440904670.06252042881809330.968739785590953
1410.4924475741310170.9848951482620330.507552425868983
1420.444004031253010.888008062506020.55599596874699
1430.4265411257848080.8530822515696150.573458874215192
1440.3412233539803720.6824467079607440.658776646019628
1450.2760312515410830.5520625030821670.723968748458917
1460.2463186766047330.4926373532094650.753681323395267
1470.3815175344044830.7630350688089660.618482465595517
1480.6940074759813210.6119850480373580.305992524018679
1490.614061560505680.771876878988640.38593843949432
1500.4666828325330370.9333656650660740.533317167466963
1510.7059413856607780.5881172286784440.294058614339222






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level110.0780141843971631OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 11 & 0.0780141843971631 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200525&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.0780141843971631[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200525&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200525&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 level00OK
10% type I error level110.0780141843971631OK


Figures (Output of Computation)

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