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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 10:55:33 -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/t1355673439k9nqbilo29t2olv.htm/, Retrieved Sat, 20 Apr 2024 07:36:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200441, Retrieved Sat, 20 Apr 2024 07:36:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact91

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-10-30 15:58:56] [b523afd25ea44f0db0ae0b1eedc88526]
- R PD    [Multiple Regression] [MR jaren] [2012-12-16 15:55:33] [447cab31e466d1c88f957d20e303ed40] [Current]
-           [Multiple Regression] [Multiple Regressi...] [2012-12-19 13:02:15] [3ee3949b5f2daf713678f5a72e9e7041]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2000	41	38	13	12	14	12	53	32
2000	39	32	16	11	18	11	86	51
2000	30	35	19	15	11	14	66	42
2000	31	33	15	6	12	12	67	41
2000	34	37	14	13	16	21	76	46
2000	35	29	13	10	18	12	78	47
2000	39	31	19	12	14	22	53	37
2000	34	36	15	14	14	11	80	49
2000	36	35	14	12	15	10	74	45
2000	37	38	15	6	15	13	76	47
2000	38	31	16	10	17	10	79	49
2000	36	34	16	12	19	8	54	33
2000	38	35	16	12	10	15	67	42
2001	39	38	16	11	16	14	54	33
2001	33	37	17	15	18	10	87	53
2001	32	33	15	12	14	14	58	36
2001	36	32	15	10	14	14	75	45
2001	38	38	20	12	17	11	88	54
2001	39	38	18	11	14	10	64	41
2001	32	32	16	12	16	13	57	36
2001	32	33	16	11	18	7	66	41
2001	31	31	16	12	11	14	68	44
2001	39	38	19	13	14	12	54	33
2001	37	39	16	11	12	14	56	37
2001	39	32	17	9	17	11	86	52
2001	41	32	17	13	9	9	80	47
2002	36	35	16	10	16	11	76	43
2002	33	37	15	14	14	15	69	44
2002	33	33	16	12	15	14	78	45
2002	34	33	14	10	11	13	67	44
2002	31	28	15	12	16	9	80	49
2002	27	32	12	8	13	15	54	33
2002	37	31	14	10	17	10	71	43
2002	34	37	16	12	15	11	84	54
2002	34	30	14	12	14	13	74	42
2002	32	33	7	7	16	8	71	44
2002	29	31	10	6	9	20	63	37
2002	36	33	14	12	15	12	71	43
2002	29	31	16	10	17	10	76	46
2003	35	33	16	10	13	10	69	42
2003	37	32	16	10	15	9	74	45
2003	34	33	14	12	16	14	75	44
2003	38	32	20	15	16	8	54	33
2003	35	33	14	10	12	14	52	31
2003	38	28	14	10	12	11	69	42
2003	37	35	11	12	11	13	68	40
2003	38	39	14	13	15	9	65	43
2003	33	34	15	11	15	11	75	46
2003	36	38	16	11	17	15	74	42
2003	38	32	14	12	13	11	75	45
2003	32	38	16	14	16	10	72	44
2003	32	30	14	10	14	14	67	40
2004	32	33	12	12	11	18	63	37
2004	34	38	16	13	12	14	62	46
2004	32	32	9	5	12	11	63	36
2004	37	32	14	6	15	12	76	47
2004	39	34	16	12	16	13	74	45
2004	29	34	16	12	15	9	67	42
2004	37	36	15	11	12	10	73	43
2004	35	34	16	10	12	15	70	43
2004	30	28	12	7	8	20	53	32
2004	38	34	16	12	13	12	77	45
2004	34	35	16	14	11	12	77	45
2004	31	35	14	11	14	14	52	31
2004	34	31	16	12	15	13	54	33
2004	35	37	17	13	10	11	80	49
2005	36	35	18	14	11	17	66	42
2005	30	27	18	11	12	12	73	41
2005	39	40	12	12	15	13	63	38
2005	35	37	16	12	15	14	69	42
2005	38	36	10	8	14	13	67	44
2005	31	38	14	11	16	15	54	33
2005	34	39	18	14	15	13	81	48
2005	38	41	18	14	15	10	69	40
2005	34	27	16	12	13	11	84	50
2005	39	30	17	9	12	19	80	49
2005	37	37	16	13	17	13	70	43
2005	34	31	16	11	13	17	69	44
2005	28	31	13	12	15	13	77	47
2005	37	27	16	12	13	9	54	33
2006	33	36	16	12	15	11	79	46
2006	37	38	20	12	16	10	30	0
2006	35	37	16	12	15	9	71	45
2006	37	33	15	12	16	12	73	43
2006	32	34	15	11	15	12	72	44
2006	33	31	16	10	14	13	77	47
2006	38	39	14	9	15	13	75	45
2006	33	34	16	12	14	12	69	42
2006	29	32	16	12	13	15	54	33
2006	33	33	15	12	7	22	70	43
2006	31	36	12	9	17	13	73	46
2006	36	32	17	15	13	15	54	33
2006	35	41	16	12	15	13	77	46
2006	32	28	15	12	14	15	82	48
2007	29	30	13	12	13	10	80	47
2007	39	36	16	10	16	11	80	47
2007	37	35	16	13	12	16	69	43
2007	35	31	16	9	14	11	78	46
2007	37	34	16	12	17	11	81	48
2007	32	36	14	10	15	10	76	46
2007	38	36	16	14	17	10	76	45
2007	37	35	16	11	12	16	73	45
2007	36	37	20	15	16	12	85	52
2007	32	28	15	11	11	11	66	42
2007	33	39	16	11	15	16	79	47
2007	40	32	13	12	9	19	68	41
2007	38	35	17	12	16	11	76	47
2007	41	39	16	12	15	16	71	43
2008	36	35	16	11	10	15	54	33
2008	43	42	12	7	10	24	46	30
2008	30	34	16	12	15	14	82	49
2008	31	33	16	14	11	15	74	44
2008	32	41	17	11	13	11	88	55
2008	32	33	13	11	14	15	38	11
2008	37	34	12	10	18	12	76	47
2008	37	32	18	13	16	10	86	53
2008	33	40	14	13	14	14	54	33
2008	34	40	14	8	14	13	70	44
2008	33	35	13	11	14	9	69	42
2008	38	36	16	12	14	15	90	55
2008	33	37	13	11	12	15	54	33
2008	31	27	16	13	14	14	76	46
2009	38	39	13	12	15	11	89	54
2009	37	38	16	14	15	8	76	47
2009	33	31	15	13	15	11	73	45
2009	31	33	16	15	13	11	79	47
2009	39	32	15	10	17	8	90	55
2009	44	39	17	11	17	10	74	44
2009	33	36	15	9	19	11	81	53
2009	35	33	12	11	15	13	72	44
2009	32	33	16	10	13	11	71	42
2009	28	32	10	11	9	20	66	40
2009	40	37	16	8	15	10	77	46
2009	27	30	12	11	15	15	65	40
2009	37	38	14	12	15	12	74	46
2009	32	29	15	12	16	14	82	53
2010	28	22	13	9	11	23	54	33
2010	34	35	15	11	14	14	63	42
2010	30	35	11	10	11	16	54	35
2010	35	34	12	8	15	11	64	40
2010	31	35	8	9	13	12	69	41
2010	32	34	16	8	15	10	54	33
2010	30	34	15	9	16	14	84	51
2010	30	35	17	15	14	12	86	53
2010	31	23	16	11	15	12	77	46
2010	40	31	10	8	16	11	89	55
2010	32	27	18	13	16	12	76	47
2010	36	36	13	12	11	13	60	38
2010	32	31	16	12	12	11	75	46
2010	35	32	13	9	9	19	73	46
2011	38	39	10	7	16	12	85	53
2011	42	37	15	13	13	17	79	47
2011	34	38	16	9	16	9	71	41
2011	35	39	16	6	12	12	72	44
2011	35	34	14	8	9	19	69	43
2011	33	31	10	8	13	18	78	51
2011	36	32	17	15	13	15	54	33
2011	32	37	13	6	14	14	69	43
2011	33	36	15	9	19	11	81	53
2011	34	32	16	11	13	9	84	51
2011	32	35	12	8	12	18	84	50
2011	34	36	13	8	13	16	69	46

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200441&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200441&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200441&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 time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 122.885849292047 -0.0585642533804576jaar[t] + 0.105572049440286Connected[t] -0.0137483003179505Separate[t] + 0.529844608156627Software[t] + 0.0522284083671098Happiness[t] -0.0636799587374268Depression[t] + 0.0426747922407322Belonging[t] -0.057003635801138Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  122.885849292047 -0.0585642533804576jaar[t] +  0.105572049440286Connected[t] -0.0137483003179505Separate[t] +  0.529844608156627Software[t] +  0.0522284083671098Happiness[t] -0.0636799587374268Depression[t] +  0.0426747922407322Belonging[t] -0.057003635801138Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200441&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  122.885849292047 -0.0585642533804576jaar[t] +  0.105572049440286Connected[t] -0.0137483003179505Separate[t] +  0.529844608156627Software[t] +  0.0522284083671098Happiness[t] -0.0636799587374268Depression[t] +  0.0426747922407322Belonging[t] -0.057003635801138Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200441&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200441&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] = + 122.885849292047 -0.0585642533804576jaar[t] + 0.105572049440286Connected[t] -0.0137483003179505Separate[t] + 0.529844608156627Software[t] + 0.0522284083671098Happiness[t] -0.0636799587374268Depression[t] + 0.0426747922407322Belonging[t] -0.057003635801138Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)122.88584929204789.7224431.36960.1728120.086406
jaar-0.05856425338045760.044748-1.30880.1925790.09629
Connected0.1055720494402860.0472282.23530.0268420.013421
Separate-0.01374830031795050.045019-0.30540.7604850.380243
Software0.5298446081566270.0694767.626300
Happiness0.05222840836710980.0764190.68340.4953580.247679
Depression-0.06367995873742680.0565-1.12710.2614760.130738
Belonging0.04267479224073220.0447410.95380.3416780.170839
Belonging_Final-0.0570036358011380.063914-0.89190.3738610.18693

\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) & 122.885849292047 & 89.722443 & 1.3696 & 0.172812 & 0.086406 \tabularnewline
jaar & -0.0585642533804576 & 0.044748 & -1.3088 & 0.192579 & 0.09629 \tabularnewline
Connected & 0.105572049440286 & 0.047228 & 2.2353 & 0.026842 & 0.013421 \tabularnewline
Separate & -0.0137483003179505 & 0.045019 & -0.3054 & 0.760485 & 0.380243 \tabularnewline
Software & 0.529844608156627 & 0.069476 & 7.6263 & 0 & 0 \tabularnewline
Happiness & 0.0522284083671098 & 0.076419 & 0.6834 & 0.495358 & 0.247679 \tabularnewline
Depression & -0.0636799587374268 & 0.0565 & -1.1271 & 0.261476 & 0.130738 \tabularnewline
Belonging & 0.0426747922407322 & 0.044741 & 0.9538 & 0.341678 & 0.170839 \tabularnewline
Belonging_Final & -0.057003635801138 & 0.063914 & -0.8919 & 0.373861 & 0.18693 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200441&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]122.885849292047[/C][C]89.722443[/C][C]1.3696[/C][C]0.172812[/C][C]0.086406[/C][/ROW]
[ROW][C]jaar[/C][C]-0.0585642533804576[/C][C]0.044748[/C][C]-1.3088[/C][C]0.192579[/C][C]0.09629[/C][/ROW]
[ROW][C]Connected[/C][C]0.105572049440286[/C][C]0.047228[/C][C]2.2353[/C][C]0.026842[/C][C]0.013421[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0137483003179505[/C][C]0.045019[/C][C]-0.3054[/C][C]0.760485[/C][C]0.380243[/C][/ROW]
[ROW][C]Software[/C][C]0.529844608156627[/C][C]0.069476[/C][C]7.6263[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0522284083671098[/C][C]0.076419[/C][C]0.6834[/C][C]0.495358[/C][C]0.247679[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0636799587374268[/C][C]0.0565[/C][C]-1.1271[/C][C]0.261476[/C][C]0.130738[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0426747922407322[/C][C]0.044741[/C][C]0.9538[/C][C]0.341678[/C][C]0.170839[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.057003635801138[/C][C]0.063914[/C][C]-0.8919[/C][C]0.373861[/C][C]0.18693[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200441&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200441&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)122.88584929204789.7224431.36960.1728120.086406
jaar-0.05856425338045760.044748-1.30880.1925790.09629
Connected0.1055720494402860.0472282.23530.0268420.013421
Separate-0.01374830031795050.045019-0.30540.7604850.380243
Software0.5298446081566270.0694767.626300
Happiness0.05222840836710980.0764190.68340.4953580.247679
Depression-0.06367995873742680.0565-1.12710.2614760.130738
Belonging0.04267479224073220.0447410.95380.3416780.170839
Belonging_Final-0.0570036358011380.063914-0.89190.3738610.18693






Multiple Linear Regression - Regression Statistics
Multiple R0.603184919776016
R-squared0.363832047445199
Adjusted R-squared0.330568363651484
F-TEST (value)10.9378158384834
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value3.90865118049533e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.8460457572347
Sum Squared Residuals521.406395484051

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.603184919776016 \tabularnewline
R-squared & 0.363832047445199 \tabularnewline
Adjusted R-squared & 0.330568363651484 \tabularnewline
F-TEST (value) & 10.9378158384834 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 3.90865118049533e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.8460457572347 \tabularnewline
Sum Squared Residuals & 521.406395484051 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200441&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.603184919776016[/C][/ROW]
[ROW][C]R-squared[/C][C]0.363832047445199[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.330568363651484[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.9378158384834[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]3.90865118049533e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.8460457572347[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]521.406395484051[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200441&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200441&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 R0.603184919776016
R-squared0.363832047445199
Adjusted R-squared0.330568363651484
F-TEST (value)10.9378158384834
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value3.90865118049533e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.8460457572347
Sum Squared Residuals521.406395484051






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.3261822993945-3.32618229939451
21616.2654760501931-0.265476050193111
31916.49635927951672.50364072048326
41512.14009321006712.85990678993288
51415.8455773702051-1.84557737020506
61315.1775243917705-2.17752439177046
71915.28945853635933.71054146364068
81516.9212013108792-1.9212013108792
91416.1742786506292-2.17427865062917
101512.83984058684272.16015941315725
111615.47054296920160.529457030798384
121616.3548482866894-0.354848286689419
131615.67816827570530.321831724294656
141615.48939739467550.510602605324498
151717.6164639105836-0.616463910583573
161515.2442106031652-0.244210603165172
171514.83299663081320.167003369186779
182016.41080482233223.58919517766777
191815.61037924888922.38962075111079
201615.3834208867140.616579113285963
211615.42541949855910.57458050144086
221614.98016876525771.01983123474232
231916.57198971172942.42801028827061
241614.91292640328551.08707359671455
251715.03799053633121.96200946366885
261717.1070151439375-0.107015143937504
271615.18536623326840.81463376673158
281516.245628083768-1.24562808376798
291615.68390993019650.316090069803486
301414.1721400097456-0.172140009745617
311515.7694705762369-0.76947057623685
321212.4365593416107-0.436559341610674
331415.2484658898814-1.24846588988138
341615.66854468581140.331455314188569
351415.8424901694015-1.84249016940145
36713.1217030908808-6.12170309088078
371011.2305076843024-1.23050768430241
381415.9432697219094-1.94326972190938
391614.44625254815931.55374745184066
401614.71400135483571.28599864516431
411615.14939358330610.850606416693896
421415.7121253937024-1.71212539370239
432017.85064482543312.14935517456693
441414.3086216372389-0.308621637238939
451414.9835506376418-0.983550637641762
461115.7331738558087-4.73317385580866
471416.4781954964263-2.4781954964263
481515.0877646320304-0.0877646320304247
491615.38456431182780.615435688172186
501416.1255129070913-2.1255129070913
511616.6186244677728-0.618624467772833
521414.2646963680069-0.264696368006893
531214.8134831083753-2.81348310837529
541615.23697104268860.763028957311424
55911.6733109069271-2.67331090692708
561412.7517533339661.24824666603395
571616.131674617901-0.131674617900974
581615.1507329117990.849267088201004
591515.4166480323333-0.416648032333259
601614.25673175552261.74326824447737
611211.59608258432350.403917415676484
621616.061121678819-0.0611216788189822
631616.5803175803189-0.58031758031892
641414.4345740103523-0.434574010352282
651615.42337864808530.576621351914705
661716.04000980485460.95999019514536
671816.11608582488731.88391417511274
681814.72946148985983.27053851014023
691215.8679948902056-3.86799489020562
701615.45130584490070.548694155099264
711013.4744865551996-3.47448655519962
721414.346884026894-0.346884026893954
731816.61168206195721.38831793804281
741817.1414451148150.85855488518497
751615.75389265531990.246107344680106
761713.97561056566933.02438943433072
771616.3461024838492-0.346102483849176
781614.48887502466281.51112497533725
791314.914851418384-1.91485141838403
801615.8867867625130.113213237487014
811615.58511904837270.414880951627293
822016.6269214396593.37307856034104
831615.62548006228550.374519937714537
841515.9521627506764-0.952162750676407
851514.72880275859140.271197241408582
861614.27222978752461.72777021247536
871414.2411451195138-0.241145119513763
881615.29797390270130.7020260972987
891614.53282485959591.46717514040408
901514.29499491351480.705005086485191
911213.5054892708372-1.50548927083723
921716.86136303014780.138636969852191
931615.51481214370720.485187856292791
941515.2966022632791-0.296602263279128
951315.1316506975816-2.13165069758161
961615.13819744012740.861802559872582
971615.76161386743560.2383861325644
981614.12200337038491.87799662961515
991616.0521387230027-0.052138723002704
1001414.2969491112539-0.296949111253939
1011617.2112202930575-1.21122029305752
1021614.7586165484831.241383451517
1032017.32163185573232.67836814426771
1041514.46545315054550.534546849454467
1051614.58006185639351.4199381436065
1061315.3133376866016-2.31333768660162
1071715.93536373835541.06463626164459
1081615.84109906535110.158900934648889
1091614.42692596470521.57307403529479
1101212.2068067167758-0.206806716775765
1111614.94474458703331.05525541296666
1121615.79478040197880.205219598021208
1131714.53041597364732.46958402635274
1141314.8123313128217-1.81233131282172
1151214.7660633775362-2.76606337753615
1161816.49072301098311.50927698901694
1171415.3737511233137-1.37375112331371
1181412.94953677274751.05046322725251
1191314.8282923636781-1.82829236367809
1201615.64529253793420.354707462065781
1211314.1871700324827-1.18717003248267
1221615.53913309244780.460866907552187
1231315.8667604704771-2.86676047047713
1241616.8699189653588-0.869918965358778
1251515.8089672803345-0.808967280334458
1261616.6676004622391-0.667600462239136
1271515.2900492552159-0.290049255215914
1281716.06839940883430.931600591165732
1291513.71513023110341.28486976889661
1301214.8198944883514-2.81989448835143
1311614.06756931197611.93243068802388
1321013.3074740709829-3.30747407098289
1331613.99363427562482.00636572437516
1341213.8184940738276-1.81849407382756
1351415.5271639654155-1.52716396541549
1361514.99027979928580.00972020071423856
1371312.12704848870980.872951511290203
1381514.24228735922590.757712640774068
1391113.0210617311734-2.02106173117335
1401213.1720242329367-1.17202423293669
141813.2540658929451-5.25406589294509
1421612.89126557155393.1087344284461
1431513.26165297704881.73834702295116
1441716.42121773929050.578782260709528
1451614.63957168872811.36042831127186
1461014.0051730584603-4.00517305846032
1471815.70238973353512.29761026646488
1481314.9765126660636-1.97651266606363
1491614.98664709293611.01335290706392
1501312.94860663698690.0513933630130955
1511012.975261839401-2.97526183940098
1521516.2150023293118-1.21500232931175
1531613.90404757272672.09595242727328
1541611.87804787253584.12195212746423
1551412.33301291325441.6669870867456
1561012.363751351291-2.36375135129104
1571716.56854176324550.431458236754478
1581311.49490448318911.50509551681088
1591513.59800172434251.40199827565752
1601614.87427730696451.12572269303551
1611212.4640100814574-0.464010081457368
1621312.42888686545550.571113134544478

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.3261822993945 & -3.32618229939451 \tabularnewline
2 & 16 & 16.2654760501931 & -0.265476050193111 \tabularnewline
3 & 19 & 16.4963592795167 & 2.50364072048326 \tabularnewline
4 & 15 & 12.1400932100671 & 2.85990678993288 \tabularnewline
5 & 14 & 15.8455773702051 & -1.84557737020506 \tabularnewline
6 & 13 & 15.1775243917705 & -2.17752439177046 \tabularnewline
7 & 19 & 15.2894585363593 & 3.71054146364068 \tabularnewline
8 & 15 & 16.9212013108792 & -1.9212013108792 \tabularnewline
9 & 14 & 16.1742786506292 & -2.17427865062917 \tabularnewline
10 & 15 & 12.8398405868427 & 2.16015941315725 \tabularnewline
11 & 16 & 15.4705429692016 & 0.529457030798384 \tabularnewline
12 & 16 & 16.3548482866894 & -0.354848286689419 \tabularnewline
13 & 16 & 15.6781682757053 & 0.321831724294656 \tabularnewline
14 & 16 & 15.4893973946755 & 0.510602605324498 \tabularnewline
15 & 17 & 17.6164639105836 & -0.616463910583573 \tabularnewline
16 & 15 & 15.2442106031652 & -0.244210603165172 \tabularnewline
17 & 15 & 14.8329966308132 & 0.167003369186779 \tabularnewline
18 & 20 & 16.4108048223322 & 3.58919517766777 \tabularnewline
19 & 18 & 15.6103792488892 & 2.38962075111079 \tabularnewline
20 & 16 & 15.383420886714 & 0.616579113285963 \tabularnewline
21 & 16 & 15.4254194985591 & 0.57458050144086 \tabularnewline
22 & 16 & 14.9801687652577 & 1.01983123474232 \tabularnewline
23 & 19 & 16.5719897117294 & 2.42801028827061 \tabularnewline
24 & 16 & 14.9129264032855 & 1.08707359671455 \tabularnewline
25 & 17 & 15.0379905363312 & 1.96200946366885 \tabularnewline
26 & 17 & 17.1070151439375 & -0.107015143937504 \tabularnewline
27 & 16 & 15.1853662332684 & 0.81463376673158 \tabularnewline
28 & 15 & 16.245628083768 & -1.24562808376798 \tabularnewline
29 & 16 & 15.6839099301965 & 0.316090069803486 \tabularnewline
30 & 14 & 14.1721400097456 & -0.172140009745617 \tabularnewline
31 & 15 & 15.7694705762369 & -0.76947057623685 \tabularnewline
32 & 12 & 12.4365593416107 & -0.436559341610674 \tabularnewline
33 & 14 & 15.2484658898814 & -1.24846588988138 \tabularnewline
34 & 16 & 15.6685446858114 & 0.331455314188569 \tabularnewline
35 & 14 & 15.8424901694015 & -1.84249016940145 \tabularnewline
36 & 7 & 13.1217030908808 & -6.12170309088078 \tabularnewline
37 & 10 & 11.2305076843024 & -1.23050768430241 \tabularnewline
38 & 14 & 15.9432697219094 & -1.94326972190938 \tabularnewline
39 & 16 & 14.4462525481593 & 1.55374745184066 \tabularnewline
40 & 16 & 14.7140013548357 & 1.28599864516431 \tabularnewline
41 & 16 & 15.1493935833061 & 0.850606416693896 \tabularnewline
42 & 14 & 15.7121253937024 & -1.71212539370239 \tabularnewline
43 & 20 & 17.8506448254331 & 2.14935517456693 \tabularnewline
44 & 14 & 14.3086216372389 & -0.308621637238939 \tabularnewline
45 & 14 & 14.9835506376418 & -0.983550637641762 \tabularnewline
46 & 11 & 15.7331738558087 & -4.73317385580866 \tabularnewline
47 & 14 & 16.4781954964263 & -2.4781954964263 \tabularnewline
48 & 15 & 15.0877646320304 & -0.0877646320304247 \tabularnewline
49 & 16 & 15.3845643118278 & 0.615435688172186 \tabularnewline
50 & 14 & 16.1255129070913 & -2.1255129070913 \tabularnewline
51 & 16 & 16.6186244677728 & -0.618624467772833 \tabularnewline
52 & 14 & 14.2646963680069 & -0.264696368006893 \tabularnewline
53 & 12 & 14.8134831083753 & -2.81348310837529 \tabularnewline
54 & 16 & 15.2369710426886 & 0.763028957311424 \tabularnewline
55 & 9 & 11.6733109069271 & -2.67331090692708 \tabularnewline
56 & 14 & 12.751753333966 & 1.24824666603395 \tabularnewline
57 & 16 & 16.131674617901 & -0.131674617900974 \tabularnewline
58 & 16 & 15.150732911799 & 0.849267088201004 \tabularnewline
59 & 15 & 15.4166480323333 & -0.416648032333259 \tabularnewline
60 & 16 & 14.2567317555226 & 1.74326824447737 \tabularnewline
61 & 12 & 11.5960825843235 & 0.403917415676484 \tabularnewline
62 & 16 & 16.061121678819 & -0.0611216788189822 \tabularnewline
63 & 16 & 16.5803175803189 & -0.58031758031892 \tabularnewline
64 & 14 & 14.4345740103523 & -0.434574010352282 \tabularnewline
65 & 16 & 15.4233786480853 & 0.576621351914705 \tabularnewline
66 & 17 & 16.0400098048546 & 0.95999019514536 \tabularnewline
67 & 18 & 16.1160858248873 & 1.88391417511274 \tabularnewline
68 & 18 & 14.7294614898598 & 3.27053851014023 \tabularnewline
69 & 12 & 15.8679948902056 & -3.86799489020562 \tabularnewline
70 & 16 & 15.4513058449007 & 0.548694155099264 \tabularnewline
71 & 10 & 13.4744865551996 & -3.47448655519962 \tabularnewline
72 & 14 & 14.346884026894 & -0.346884026893954 \tabularnewline
73 & 18 & 16.6116820619572 & 1.38831793804281 \tabularnewline
74 & 18 & 17.141445114815 & 0.85855488518497 \tabularnewline
75 & 16 & 15.7538926553199 & 0.246107344680106 \tabularnewline
76 & 17 & 13.9756105656693 & 3.02438943433072 \tabularnewline
77 & 16 & 16.3461024838492 & -0.346102483849176 \tabularnewline
78 & 16 & 14.4888750246628 & 1.51112497533725 \tabularnewline
79 & 13 & 14.914851418384 & -1.91485141838403 \tabularnewline
80 & 16 & 15.886786762513 & 0.113213237487014 \tabularnewline
81 & 16 & 15.5851190483727 & 0.414880951627293 \tabularnewline
82 & 20 & 16.626921439659 & 3.37307856034104 \tabularnewline
83 & 16 & 15.6254800622855 & 0.374519937714537 \tabularnewline
84 & 15 & 15.9521627506764 & -0.952162750676407 \tabularnewline
85 & 15 & 14.7288027585914 & 0.271197241408582 \tabularnewline
86 & 16 & 14.2722297875246 & 1.72777021247536 \tabularnewline
87 & 14 & 14.2411451195138 & -0.241145119513763 \tabularnewline
88 & 16 & 15.2979739027013 & 0.7020260972987 \tabularnewline
89 & 16 & 14.5328248595959 & 1.46717514040408 \tabularnewline
90 & 15 & 14.2949949135148 & 0.705005086485191 \tabularnewline
91 & 12 & 13.5054892708372 & -1.50548927083723 \tabularnewline
92 & 17 & 16.8613630301478 & 0.138636969852191 \tabularnewline
93 & 16 & 15.5148121437072 & 0.485187856292791 \tabularnewline
94 & 15 & 15.2966022632791 & -0.296602263279128 \tabularnewline
95 & 13 & 15.1316506975816 & -2.13165069758161 \tabularnewline
96 & 16 & 15.1381974401274 & 0.861802559872582 \tabularnewline
97 & 16 & 15.7616138674356 & 0.2383861325644 \tabularnewline
98 & 16 & 14.1220033703849 & 1.87799662961515 \tabularnewline
99 & 16 & 16.0521387230027 & -0.052138723002704 \tabularnewline
100 & 14 & 14.2969491112539 & -0.296949111253939 \tabularnewline
101 & 16 & 17.2112202930575 & -1.21122029305752 \tabularnewline
102 & 16 & 14.758616548483 & 1.241383451517 \tabularnewline
103 & 20 & 17.3216318557323 & 2.67836814426771 \tabularnewline
104 & 15 & 14.4654531505455 & 0.534546849454467 \tabularnewline
105 & 16 & 14.5800618563935 & 1.4199381436065 \tabularnewline
106 & 13 & 15.3133376866016 & -2.31333768660162 \tabularnewline
107 & 17 & 15.9353637383554 & 1.06463626164459 \tabularnewline
108 & 16 & 15.8410990653511 & 0.158900934648889 \tabularnewline
109 & 16 & 14.4269259647052 & 1.57307403529479 \tabularnewline
110 & 12 & 12.2068067167758 & -0.206806716775765 \tabularnewline
111 & 16 & 14.9447445870333 & 1.05525541296666 \tabularnewline
112 & 16 & 15.7947804019788 & 0.205219598021208 \tabularnewline
113 & 17 & 14.5304159736473 & 2.46958402635274 \tabularnewline
114 & 13 & 14.8123313128217 & -1.81233131282172 \tabularnewline
115 & 12 & 14.7660633775362 & -2.76606337753615 \tabularnewline
116 & 18 & 16.4907230109831 & 1.50927698901694 \tabularnewline
117 & 14 & 15.3737511233137 & -1.37375112331371 \tabularnewline
118 & 14 & 12.9495367727475 & 1.05046322725251 \tabularnewline
119 & 13 & 14.8282923636781 & -1.82829236367809 \tabularnewline
120 & 16 & 15.6452925379342 & 0.354707462065781 \tabularnewline
121 & 13 & 14.1871700324827 & -1.18717003248267 \tabularnewline
122 & 16 & 15.5391330924478 & 0.460866907552187 \tabularnewline
123 & 13 & 15.8667604704771 & -2.86676047047713 \tabularnewline
124 & 16 & 16.8699189653588 & -0.869918965358778 \tabularnewline
125 & 15 & 15.8089672803345 & -0.808967280334458 \tabularnewline
126 & 16 & 16.6676004622391 & -0.667600462239136 \tabularnewline
127 & 15 & 15.2900492552159 & -0.290049255215914 \tabularnewline
128 & 17 & 16.0683994088343 & 0.931600591165732 \tabularnewline
129 & 15 & 13.7151302311034 & 1.28486976889661 \tabularnewline
130 & 12 & 14.8198944883514 & -2.81989448835143 \tabularnewline
131 & 16 & 14.0675693119761 & 1.93243068802388 \tabularnewline
132 & 10 & 13.3074740709829 & -3.30747407098289 \tabularnewline
133 & 16 & 13.9936342756248 & 2.00636572437516 \tabularnewline
134 & 12 & 13.8184940738276 & -1.81849407382756 \tabularnewline
135 & 14 & 15.5271639654155 & -1.52716396541549 \tabularnewline
136 & 15 & 14.9902797992858 & 0.00972020071423856 \tabularnewline
137 & 13 & 12.1270484887098 & 0.872951511290203 \tabularnewline
138 & 15 & 14.2422873592259 & 0.757712640774068 \tabularnewline
139 & 11 & 13.0210617311734 & -2.02106173117335 \tabularnewline
140 & 12 & 13.1720242329367 & -1.17202423293669 \tabularnewline
141 & 8 & 13.2540658929451 & -5.25406589294509 \tabularnewline
142 & 16 & 12.8912655715539 & 3.1087344284461 \tabularnewline
143 & 15 & 13.2616529770488 & 1.73834702295116 \tabularnewline
144 & 17 & 16.4212177392905 & 0.578782260709528 \tabularnewline
145 & 16 & 14.6395716887281 & 1.36042831127186 \tabularnewline
146 & 10 & 14.0051730584603 & -4.00517305846032 \tabularnewline
147 & 18 & 15.7023897335351 & 2.29761026646488 \tabularnewline
148 & 13 & 14.9765126660636 & -1.97651266606363 \tabularnewline
149 & 16 & 14.9866470929361 & 1.01335290706392 \tabularnewline
150 & 13 & 12.9486066369869 & 0.0513933630130955 \tabularnewline
151 & 10 & 12.975261839401 & -2.97526183940098 \tabularnewline
152 & 15 & 16.2150023293118 & -1.21500232931175 \tabularnewline
153 & 16 & 13.9040475727267 & 2.09595242727328 \tabularnewline
154 & 16 & 11.8780478725358 & 4.12195212746423 \tabularnewline
155 & 14 & 12.3330129132544 & 1.6669870867456 \tabularnewline
156 & 10 & 12.363751351291 & -2.36375135129104 \tabularnewline
157 & 17 & 16.5685417632455 & 0.431458236754478 \tabularnewline
158 & 13 & 11.4949044831891 & 1.50509551681088 \tabularnewline
159 & 15 & 13.5980017243425 & 1.40199827565752 \tabularnewline
160 & 16 & 14.8742773069645 & 1.12572269303551 \tabularnewline
161 & 12 & 12.4640100814574 & -0.464010081457368 \tabularnewline
162 & 13 & 12.4288868654555 & 0.571113134544478 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200441&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]16.3261822993945[/C][C]-3.32618229939451[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.2654760501931[/C][C]-0.265476050193111[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.4963592795167[/C][C]2.50364072048326[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.1400932100671[/C][C]2.85990678993288[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.8455773702051[/C][C]-1.84557737020506[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.1775243917705[/C][C]-2.17752439177046[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.2894585363593[/C][C]3.71054146364068[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9212013108792[/C][C]-1.9212013108792[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1742786506292[/C][C]-2.17427865062917[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.8398405868427[/C][C]2.16015941315725[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4705429692016[/C][C]0.529457030798384[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.3548482866894[/C][C]-0.354848286689419[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6781682757053[/C][C]0.321831724294656[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4893973946755[/C][C]0.510602605324498[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.6164639105836[/C][C]-0.616463910583573[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2442106031652[/C][C]-0.244210603165172[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.8329966308132[/C][C]0.167003369186779[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4108048223322[/C][C]3.58919517766777[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.6103792488892[/C][C]2.38962075111079[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.383420886714[/C][C]0.616579113285963[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.4254194985591[/C][C]0.57458050144086[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.9801687652577[/C][C]1.01983123474232[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.5719897117294[/C][C]2.42801028827061[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.9129264032855[/C][C]1.08707359671455[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.0379905363312[/C][C]1.96200946366885[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.1070151439375[/C][C]-0.107015143937504[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.1853662332684[/C][C]0.81463376673158[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.245628083768[/C][C]-1.24562808376798[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.6839099301965[/C][C]0.316090069803486[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1721400097456[/C][C]-0.172140009745617[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7694705762369[/C][C]-0.76947057623685[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4365593416107[/C][C]-0.436559341610674[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.2484658898814[/C][C]-1.24846588988138[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.6685446858114[/C][C]0.331455314188569[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.8424901694015[/C][C]-1.84249016940145[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.1217030908808[/C][C]-6.12170309088078[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.2305076843024[/C][C]-1.23050768430241[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.9432697219094[/C][C]-1.94326972190938[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.4462525481593[/C][C]1.55374745184066[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7140013548357[/C][C]1.28599864516431[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1493935833061[/C][C]0.850606416693896[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.7121253937024[/C][C]-1.71212539370239[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.8506448254331[/C][C]2.14935517456693[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.3086216372389[/C][C]-0.308621637238939[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9835506376418[/C][C]-0.983550637641762[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.7331738558087[/C][C]-4.73317385580866[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4781954964263[/C][C]-2.4781954964263[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0877646320304[/C][C]-0.0877646320304247[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.3845643118278[/C][C]0.615435688172186[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.1255129070913[/C][C]-2.1255129070913[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6186244677728[/C][C]-0.618624467772833[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2646963680069[/C][C]-0.264696368006893[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.8134831083753[/C][C]-2.81348310837529[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.2369710426886[/C][C]0.763028957311424[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.6733109069271[/C][C]-2.67331090692708[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.751753333966[/C][C]1.24824666603395[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.131674617901[/C][C]-0.131674617900974[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.150732911799[/C][C]0.849267088201004[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.4166480323333[/C][C]-0.416648032333259[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2567317555226[/C][C]1.74326824447737[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5960825843235[/C][C]0.403917415676484[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]16.061121678819[/C][C]-0.0611216788189822[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.5803175803189[/C][C]-0.58031758031892[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4345740103523[/C][C]-0.434574010352282[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4233786480853[/C][C]0.576621351914705[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.0400098048546[/C][C]0.95999019514536[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1160858248873[/C][C]1.88391417511274[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.7294614898598[/C][C]3.27053851014023[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8679948902056[/C][C]-3.86799489020562[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4513058449007[/C][C]0.548694155099264[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.4744865551996[/C][C]-3.47448655519962[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.346884026894[/C][C]-0.346884026893954[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.6116820619572[/C][C]1.38831793804281[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.141445114815[/C][C]0.85855488518497[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.7538926553199[/C][C]0.246107344680106[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.9756105656693[/C][C]3.02438943433072[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3461024838492[/C][C]-0.346102483849176[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4888750246628[/C][C]1.51112497533725[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.914851418384[/C][C]-1.91485141838403[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.886786762513[/C][C]0.113213237487014[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5851190483727[/C][C]0.414880951627293[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.626921439659[/C][C]3.37307856034104[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.6254800622855[/C][C]0.374519937714537[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9521627506764[/C][C]-0.952162750676407[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7288027585914[/C][C]0.271197241408582[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2722297875246[/C][C]1.72777021247536[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.2411451195138[/C][C]-0.241145119513763[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2979739027013[/C][C]0.7020260972987[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5328248595959[/C][C]1.46717514040408[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.2949949135148[/C][C]0.705005086485191[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.5054892708372[/C][C]-1.50548927083723[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.8613630301478[/C][C]0.138636969852191[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.5148121437072[/C][C]0.485187856292791[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2966022632791[/C][C]-0.296602263279128[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1316506975816[/C][C]-2.13165069758161[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.1381974401274[/C][C]0.861802559872582[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.7616138674356[/C][C]0.2383861325644[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1220033703849[/C][C]1.87799662961515[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.0521387230027[/C][C]-0.052138723002704[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.2969491112539[/C][C]-0.296949111253939[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2112202930575[/C][C]-1.21122029305752[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.758616548483[/C][C]1.241383451517[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3216318557323[/C][C]2.67836814426771[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.4654531505455[/C][C]0.534546849454467[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5800618563935[/C][C]1.4199381436065[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3133376866016[/C][C]-2.31333768660162[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9353637383554[/C][C]1.06463626164459[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8410990653511[/C][C]0.158900934648889[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.4269259647052[/C][C]1.57307403529479[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.2068067167758[/C][C]-0.206806716775765[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.9447445870333[/C][C]1.05525541296666[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.7947804019788[/C][C]0.205219598021208[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5304159736473[/C][C]2.46958402635274[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.8123313128217[/C][C]-1.81233131282172[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.7660633775362[/C][C]-2.76606337753615[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.4907230109831[/C][C]1.50927698901694[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.3737511233137[/C][C]-1.37375112331371[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.9495367727475[/C][C]1.05046322725251[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.8282923636781[/C][C]-1.82829236367809[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6452925379342[/C][C]0.354707462065781[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.1871700324827[/C][C]-1.18717003248267[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.5391330924478[/C][C]0.460866907552187[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8667604704771[/C][C]-2.86676047047713[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.8699189653588[/C][C]-0.869918965358778[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8089672803345[/C][C]-0.808967280334458[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.6676004622391[/C][C]-0.667600462239136[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.2900492552159[/C][C]-0.290049255215914[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0683994088343[/C][C]0.931600591165732[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.7151302311034[/C][C]1.28486976889661[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.8198944883514[/C][C]-2.81989448835143[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0675693119761[/C][C]1.93243068802388[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3074740709829[/C][C]-3.30747407098289[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9936342756248[/C][C]2.00636572437516[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8184940738276[/C][C]-1.81849407382756[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.5271639654155[/C][C]-1.52716396541549[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.9902797992858[/C][C]0.00972020071423856[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.1270484887098[/C][C]0.872951511290203[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.2422873592259[/C][C]0.757712640774068[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.0210617311734[/C][C]-2.02106173117335[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.1720242329367[/C][C]-1.17202423293669[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2540658929451[/C][C]-5.25406589294509[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.8912655715539[/C][C]3.1087344284461[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.2616529770488[/C][C]1.73834702295116[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.4212177392905[/C][C]0.578782260709528[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.6395716887281[/C][C]1.36042831127186[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.0051730584603[/C][C]-4.00517305846032[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.7023897335351[/C][C]2.29761026646488[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.9765126660636[/C][C]-1.97651266606363[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.9866470929361[/C][C]1.01335290706392[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.9486066369869[/C][C]0.0513933630130955[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.975261839401[/C][C]-2.97526183940098[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.2150023293118[/C][C]-1.21500232931175[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.9040475727267[/C][C]2.09595242727328[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8780478725358[/C][C]4.12195212746423[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.3330129132544[/C][C]1.6669870867456[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.363751351291[/C][C]-2.36375135129104[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.5685417632455[/C][C]0.431458236754478[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.4949044831891[/C][C]1.50509551681088[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.5980017243425[/C][C]1.40199827565752[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.8742773069645[/C][C]1.12572269303551[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.4640100814574[/C][C]-0.464010081457368[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.4288868654555[/C][C]0.571113134544478[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200441&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200441&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
11316.3261822993945-3.32618229939451
21616.2654760501931-0.265476050193111
31916.49635927951672.50364072048326
41512.14009321006712.85990678993288
51415.8455773702051-1.84557737020506
61315.1775243917705-2.17752439177046
71915.28945853635933.71054146364068
81516.9212013108792-1.9212013108792
91416.1742786506292-2.17427865062917
101512.83984058684272.16015941315725
111615.47054296920160.529457030798384
121616.3548482866894-0.354848286689419
131615.67816827570530.321831724294656
141615.48939739467550.510602605324498
151717.6164639105836-0.616463910583573
161515.2442106031652-0.244210603165172
171514.83299663081320.167003369186779
182016.41080482233223.58919517766777
191815.61037924888922.38962075111079
201615.3834208867140.616579113285963
211615.42541949855910.57458050144086
221614.98016876525771.01983123474232
231916.57198971172942.42801028827061
241614.91292640328551.08707359671455
251715.03799053633121.96200946366885
261717.1070151439375-0.107015143937504
271615.18536623326840.81463376673158
281516.245628083768-1.24562808376798
291615.68390993019650.316090069803486
301414.1721400097456-0.172140009745617
311515.7694705762369-0.76947057623685
321212.4365593416107-0.436559341610674
331415.2484658898814-1.24846588988138
341615.66854468581140.331455314188569
351415.8424901694015-1.84249016940145
36713.1217030908808-6.12170309088078
371011.2305076843024-1.23050768430241
381415.9432697219094-1.94326972190938
391614.44625254815931.55374745184066
401614.71400135483571.28599864516431
411615.14939358330610.850606416693896
421415.7121253937024-1.71212539370239
432017.85064482543312.14935517456693
441414.3086216372389-0.308621637238939
451414.9835506376418-0.983550637641762
461115.7331738558087-4.73317385580866
471416.4781954964263-2.4781954964263
481515.0877646320304-0.0877646320304247
491615.38456431182780.615435688172186
501416.1255129070913-2.1255129070913
511616.6186244677728-0.618624467772833
521414.2646963680069-0.264696368006893
531214.8134831083753-2.81348310837529
541615.23697104268860.763028957311424
55911.6733109069271-2.67331090692708
561412.7517533339661.24824666603395
571616.131674617901-0.131674617900974
581615.1507329117990.849267088201004
591515.4166480323333-0.416648032333259
601614.25673175552261.74326824447737
611211.59608258432350.403917415676484
621616.061121678819-0.0611216788189822
631616.5803175803189-0.58031758031892
641414.4345740103523-0.434574010352282
651615.42337864808530.576621351914705
661716.04000980485460.95999019514536
671816.11608582488731.88391417511274
681814.72946148985983.27053851014023
691215.8679948902056-3.86799489020562
701615.45130584490070.548694155099264
711013.4744865551996-3.47448655519962
721414.346884026894-0.346884026893954
731816.61168206195721.38831793804281
741817.1414451148150.85855488518497
751615.75389265531990.246107344680106
761713.97561056566933.02438943433072
771616.3461024838492-0.346102483849176
781614.48887502466281.51112497533725
791314.914851418384-1.91485141838403
801615.8867867625130.113213237487014
811615.58511904837270.414880951627293
822016.6269214396593.37307856034104
831615.62548006228550.374519937714537
841515.9521627506764-0.952162750676407
851514.72880275859140.271197241408582
861614.27222978752461.72777021247536
871414.2411451195138-0.241145119513763
881615.29797390270130.7020260972987
891614.53282485959591.46717514040408
901514.29499491351480.705005086485191
911213.5054892708372-1.50548927083723
921716.86136303014780.138636969852191
931615.51481214370720.485187856292791
941515.2966022632791-0.296602263279128
951315.1316506975816-2.13165069758161
961615.13819744012740.861802559872582
971615.76161386743560.2383861325644
981614.12200337038491.87799662961515
991616.0521387230027-0.052138723002704
1001414.2969491112539-0.296949111253939
1011617.2112202930575-1.21122029305752
1021614.7586165484831.241383451517
1032017.32163185573232.67836814426771
1041514.46545315054550.534546849454467
1051614.58006185639351.4199381436065
1061315.3133376866016-2.31333768660162
1071715.93536373835541.06463626164459
1081615.84109906535110.158900934648889
1091614.42692596470521.57307403529479
1101212.2068067167758-0.206806716775765
1111614.94474458703331.05525541296666
1121615.79478040197880.205219598021208
1131714.53041597364732.46958402635274
1141314.8123313128217-1.81233131282172
1151214.7660633775362-2.76606337753615
1161816.49072301098311.50927698901694
1171415.3737511233137-1.37375112331371
1181412.94953677274751.05046322725251
1191314.8282923636781-1.82829236367809
1201615.64529253793420.354707462065781
1211314.1871700324827-1.18717003248267
1221615.53913309244780.460866907552187
1231315.8667604704771-2.86676047047713
1241616.8699189653588-0.869918965358778
1251515.8089672803345-0.808967280334458
1261616.6676004622391-0.667600462239136
1271515.2900492552159-0.290049255215914
1281716.06839940883430.931600591165732
1291513.71513023110341.28486976889661
1301214.8198944883514-2.81989448835143
1311614.06756931197611.93243068802388
1321013.3074740709829-3.30747407098289
1331613.99363427562482.00636572437516
1341213.8184940738276-1.81849407382756
1351415.5271639654155-1.52716396541549
1361514.99027979928580.00972020071423856
1371312.12704848870980.872951511290203
1381514.24228735922590.757712640774068
1391113.0210617311734-2.02106173117335
1401213.1720242329367-1.17202423293669
141813.2540658929451-5.25406589294509
1421612.89126557155393.1087344284461
1431513.26165297704881.73834702295116
1441716.42121773929050.578782260709528
1451614.63957168872811.36042831127186
1461014.0051730584603-4.00517305846032
1471815.70238973353512.29761026646488
1481314.9765126660636-1.97651266606363
1491614.98664709293611.01335290706392
1501312.94860663698690.0513933630130955
1511012.975261839401-2.97526183940098
1521516.2150023293118-1.21500232931175
1531613.90404757272672.09595242727328
1541611.87804787253584.12195212746423
1551412.33301291325441.6669870867456
1561012.363751351291-2.36375135129104
1571716.56854176324550.431458236754478
1581311.49490448318911.50509551681088
1591513.59800172434251.40199827565752
1601614.87427730696451.12572269303551
1611212.4640100814574-0.464010081457368
1621312.42888686545550.571113134544478






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.7623832218167350.4752335563665310.237616778183265
130.6258186588868330.7483626822263340.374181341113167
140.4831857187462710.9663714374925420.516814281253729
150.3712768121271770.7425536242543530.628723187872824
160.3433827037342660.6867654074685330.656617296265734
170.2499404226399710.4998808452799430.750059577360029
180.3334345463106590.6668690926213180.666565453689341
190.2793381989983170.5586763979966340.720661801001683
200.2165265681979090.4330531363958170.783473431802091
210.1707346626365110.3414693252730220.829265337363489
220.1914452185131670.3828904370263330.808554781486833
230.3162132606279390.6324265212558780.683786739372061
240.3842416185059170.7684832370118340.615758381494083
250.3353048466369420.6706096932738830.664695153363058
260.2759096285866570.5518192571733140.724090371413343
270.2337859347339330.4675718694678650.766214065266067
280.3799934850791620.7599869701583250.620006514920838
290.3265831935963120.6531663871926250.673416806403688
300.4631982383965470.9263964767930950.536801761603453
310.4129223676966310.8258447353932620.587077632303369
320.3714824433643610.7429648867287230.628517556635639
330.3574204706242080.7148409412484170.642579529375792
340.3251890613249140.6503781226498270.674810938675086
350.279985074469580.5599701489391610.720014925530419
360.842697111876440.314605776247120.15730288812356
370.815919840665830.3681603186683410.18408015933417
380.8021602086900450.3956795826199090.197839791309955
390.826780840823720.346438318352560.17321915917628
400.8165976558038260.3668046883923480.183402344196174
410.7875011729562750.424997654087450.212498827043725
420.7606368097771570.4787263804456850.239363190222843
430.7881314335749230.4237371328501540.211868566425077
440.7468486113013020.5063027773973960.253151388698698
450.7179569484261080.5640861031477830.282043051573892
460.8686513040436590.2626973919126820.131348695956341
470.8981598115329770.2036803769340450.101840188467023
480.8751057589415180.2497884821169640.124894241058482
490.8669941830435230.2660116339129530.133005816956477
500.8624791976299780.2750416047400440.137520802370022
510.8343486371685540.3313027256628920.165651362831446
520.8029852873962070.3940294252075870.197014712603793
530.812516557702810.3749668845943790.18748344229719
540.7825544309490260.4348911381019470.217445569050974
550.7996700942831720.4006598114336570.200329905716828
560.7808261494797190.4383477010405630.219173850520281
570.7434890714161370.5130218571677260.256510928583863
580.7288840427188370.5422319145623260.271115957281163
590.6982279973943420.6035440052113160.301772002605658
600.7029940638588120.5940118722823770.297005936141188
610.6678676765056120.6642646469887750.332132323494388
620.6313526241854750.7372947516290490.368647375814525
630.5984519436006650.803096112798670.401548056399335
640.5545422643832740.8909154712334510.445457735616726
650.5164419180759480.9671161638481050.483558081924052
660.4887384781210760.9774769562421510.511261521878924
670.4871256446052740.9742512892105480.512874355394726
680.6323163324551990.7353673350896010.367683667544801
690.7421793741122650.5156412517754710.257820625887735
700.7094416334404090.5811167331191830.290558366559591
710.8145393436031110.3709213127937770.185460656396889
720.7841769285631680.4316461428736650.215823071436832
730.7779436074809010.4441127850381980.222056392519099
740.7612181728440850.477563654311830.238781827155915
750.7237060587407070.5525878825185870.276293941259293
760.7630220548657360.4739558902685280.236977945134264
770.7259790260624260.5480419478751480.274020973937574
780.7057626262866240.5884747474267530.294237373713376
790.7152921965098760.5694156069802490.284707803490124
800.6745793824656990.6508412350686020.325420617534301
810.6381693619746820.7236612760506360.361830638025318
820.7638292582195310.4723414835609380.236170741780469
830.7291982866913980.5416034266172040.270801713308602
840.6997846644621290.6004306710757420.300215335537871
850.6589920704120270.6820158591759460.341007929587973
860.646207452261270.7075850954774590.35379254773873
870.6024636453218360.7950727093563270.397536354678164
880.5604188567376080.8791622865247840.439581143262392
890.5373609533552440.9252780932895120.462639046644756
900.4984567341698460.9969134683396920.501543265830154
910.4874946907258380.9749893814516770.512505309274162
920.4450358800675160.8900717601350320.554964119932484
930.4013606293282860.8027212586565720.598639370671714
940.3579381099381620.7158762198763240.642061890061838
950.3815694928174730.7631389856349470.618430507182527
960.3448519371293440.6897038742586880.655148062870656
970.3038009206250930.6076018412501850.696199079374907
980.2969527792683280.5939055585366550.703047220731672
990.2564034978481270.5128069956962540.743596502151873
1000.2236258501030090.4472517002060180.776374149896991
1010.201684441978510.4033688839570210.79831555802149
1020.1830354822306710.3660709644613410.816964517769329
1030.223291091082210.446582182164420.77670890891779
1040.1892388861989020.3784777723978050.810761113801097
1050.1814911613151610.3629823226303210.818508838684839
1060.1928770799906660.3857541599813330.807122920009333
1070.1726555200147660.3453110400295310.827344479985234
1080.1526716302982470.3053432605964930.847328369701753
1090.1483318529391220.2966637058782440.851668147060878
1100.1437853288685740.2875706577371490.856214671131426
1110.1287746009020290.2575492018040570.871225399097971
1120.1084812048781740.2169624097563470.891518795121826
1130.1392777504774970.2785555009549950.860722249522503
1140.1277814267235080.2555628534470170.872218573276492
1150.1473779342944470.2947558685888940.852622065705553
1160.1503399012107340.3006798024214690.849660098789266
1170.1275942248830590.2551884497661180.872405775116941
1180.1251693915463380.2503387830926770.874830608453661
1190.1148648484665290.2297296969330580.885135151533471
1200.126938782026010.253877564052020.87306121797399
1210.10348504478480.2069700895695990.8965149552152
1220.09242717605609520.184854352112190.907572823943905
1230.09287395551565360.1857479110313070.907126044484346
1240.07256884449482490.145137688989650.927431155505175
1250.05638172413572120.1127634482714420.943618275864279
1260.04223022377083170.08446044754166350.957769776229168
1270.03069062740057780.06138125480115570.969309372599422
1280.02851459374703450.0570291874940690.971485406252966
1290.02872433595906050.0574486719181210.971275664040939
1300.02958690618698340.05917381237396690.970413093813017
1310.03041450997499960.06082901994999920.969585490025
1320.03351093411048440.06702186822096880.966489065889516
1330.07213271455639070.1442654291127810.927867285443609
1340.06707386790172980.134147735803460.93292613209827
1350.053847669362090.107695338724180.94615233063791
1360.04856029024231970.09712058048463930.95143970975768
1370.03404964351413930.06809928702827870.965950356485861
1380.03043183602278080.06086367204556160.969568163977219
1390.03378698285408740.06757396570817470.966213017145913
1400.02330111799985250.0466022359997050.976698882000148
1410.5675490594829150.864901881034170.432450940517085
1420.5074441203113660.9851117593772690.492555879688634
1430.4416405408502840.8832810817005680.558359459149716
1440.3497521812206150.699504362441230.650247818779385
1450.2648558675477340.5297117350954690.735144132452266
1460.2673520273870330.5347040547740650.732647972612967
1470.2971510644260520.5943021288521050.702848935573948
1480.6096257209591380.7807485580817240.390374279040862
1490.538191045257270.9236179094854590.46180895474273
1500.3720962514799760.7441925029599520.627903748520024

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.762383221816735 & 0.475233556366531 & 0.237616778183265 \tabularnewline
13 & 0.625818658886833 & 0.748362682226334 & 0.374181341113167 \tabularnewline
14 & 0.483185718746271 & 0.966371437492542 & 0.516814281253729 \tabularnewline
15 & 0.371276812127177 & 0.742553624254353 & 0.628723187872824 \tabularnewline
16 & 0.343382703734266 & 0.686765407468533 & 0.656617296265734 \tabularnewline
17 & 0.249940422639971 & 0.499880845279943 & 0.750059577360029 \tabularnewline
18 & 0.333434546310659 & 0.666869092621318 & 0.666565453689341 \tabularnewline
19 & 0.279338198998317 & 0.558676397996634 & 0.720661801001683 \tabularnewline
20 & 0.216526568197909 & 0.433053136395817 & 0.783473431802091 \tabularnewline
21 & 0.170734662636511 & 0.341469325273022 & 0.829265337363489 \tabularnewline
22 & 0.191445218513167 & 0.382890437026333 & 0.808554781486833 \tabularnewline
23 & 0.316213260627939 & 0.632426521255878 & 0.683786739372061 \tabularnewline
24 & 0.384241618505917 & 0.768483237011834 & 0.615758381494083 \tabularnewline
25 & 0.335304846636942 & 0.670609693273883 & 0.664695153363058 \tabularnewline
26 & 0.275909628586657 & 0.551819257173314 & 0.724090371413343 \tabularnewline
27 & 0.233785934733933 & 0.467571869467865 & 0.766214065266067 \tabularnewline
28 & 0.379993485079162 & 0.759986970158325 & 0.620006514920838 \tabularnewline
29 & 0.326583193596312 & 0.653166387192625 & 0.673416806403688 \tabularnewline
30 & 0.463198238396547 & 0.926396476793095 & 0.536801761603453 \tabularnewline
31 & 0.412922367696631 & 0.825844735393262 & 0.587077632303369 \tabularnewline
32 & 0.371482443364361 & 0.742964886728723 & 0.628517556635639 \tabularnewline
33 & 0.357420470624208 & 0.714840941248417 & 0.642579529375792 \tabularnewline
34 & 0.325189061324914 & 0.650378122649827 & 0.674810938675086 \tabularnewline
35 & 0.27998507446958 & 0.559970148939161 & 0.720014925530419 \tabularnewline
36 & 0.84269711187644 & 0.31460577624712 & 0.15730288812356 \tabularnewline
37 & 0.81591984066583 & 0.368160318668341 & 0.18408015933417 \tabularnewline
38 & 0.802160208690045 & 0.395679582619909 & 0.197839791309955 \tabularnewline
39 & 0.82678084082372 & 0.34643831835256 & 0.17321915917628 \tabularnewline
40 & 0.816597655803826 & 0.366804688392348 & 0.183402344196174 \tabularnewline
41 & 0.787501172956275 & 0.42499765408745 & 0.212498827043725 \tabularnewline
42 & 0.760636809777157 & 0.478726380445685 & 0.239363190222843 \tabularnewline
43 & 0.788131433574923 & 0.423737132850154 & 0.211868566425077 \tabularnewline
44 & 0.746848611301302 & 0.506302777397396 & 0.253151388698698 \tabularnewline
45 & 0.717956948426108 & 0.564086103147783 & 0.282043051573892 \tabularnewline
46 & 0.868651304043659 & 0.262697391912682 & 0.131348695956341 \tabularnewline
47 & 0.898159811532977 & 0.203680376934045 & 0.101840188467023 \tabularnewline
48 & 0.875105758941518 & 0.249788482116964 & 0.124894241058482 \tabularnewline
49 & 0.866994183043523 & 0.266011633912953 & 0.133005816956477 \tabularnewline
50 & 0.862479197629978 & 0.275041604740044 & 0.137520802370022 \tabularnewline
51 & 0.834348637168554 & 0.331302725662892 & 0.165651362831446 \tabularnewline
52 & 0.802985287396207 & 0.394029425207587 & 0.197014712603793 \tabularnewline
53 & 0.81251655770281 & 0.374966884594379 & 0.18748344229719 \tabularnewline
54 & 0.782554430949026 & 0.434891138101947 & 0.217445569050974 \tabularnewline
55 & 0.799670094283172 & 0.400659811433657 & 0.200329905716828 \tabularnewline
56 & 0.780826149479719 & 0.438347701040563 & 0.219173850520281 \tabularnewline
57 & 0.743489071416137 & 0.513021857167726 & 0.256510928583863 \tabularnewline
58 & 0.728884042718837 & 0.542231914562326 & 0.271115957281163 \tabularnewline
59 & 0.698227997394342 & 0.603544005211316 & 0.301772002605658 \tabularnewline
60 & 0.702994063858812 & 0.594011872282377 & 0.297005936141188 \tabularnewline
61 & 0.667867676505612 & 0.664264646988775 & 0.332132323494388 \tabularnewline
62 & 0.631352624185475 & 0.737294751629049 & 0.368647375814525 \tabularnewline
63 & 0.598451943600665 & 0.80309611279867 & 0.401548056399335 \tabularnewline
64 & 0.554542264383274 & 0.890915471233451 & 0.445457735616726 \tabularnewline
65 & 0.516441918075948 & 0.967116163848105 & 0.483558081924052 \tabularnewline
66 & 0.488738478121076 & 0.977476956242151 & 0.511261521878924 \tabularnewline
67 & 0.487125644605274 & 0.974251289210548 & 0.512874355394726 \tabularnewline
68 & 0.632316332455199 & 0.735367335089601 & 0.367683667544801 \tabularnewline
69 & 0.742179374112265 & 0.515641251775471 & 0.257820625887735 \tabularnewline
70 & 0.709441633440409 & 0.581116733119183 & 0.290558366559591 \tabularnewline
71 & 0.814539343603111 & 0.370921312793777 & 0.185460656396889 \tabularnewline
72 & 0.784176928563168 & 0.431646142873665 & 0.215823071436832 \tabularnewline
73 & 0.777943607480901 & 0.444112785038198 & 0.222056392519099 \tabularnewline
74 & 0.761218172844085 & 0.47756365431183 & 0.238781827155915 \tabularnewline
75 & 0.723706058740707 & 0.552587882518587 & 0.276293941259293 \tabularnewline
76 & 0.763022054865736 & 0.473955890268528 & 0.236977945134264 \tabularnewline
77 & 0.725979026062426 & 0.548041947875148 & 0.274020973937574 \tabularnewline
78 & 0.705762626286624 & 0.588474747426753 & 0.294237373713376 \tabularnewline
79 & 0.715292196509876 & 0.569415606980249 & 0.284707803490124 \tabularnewline
80 & 0.674579382465699 & 0.650841235068602 & 0.325420617534301 \tabularnewline
81 & 0.638169361974682 & 0.723661276050636 & 0.361830638025318 \tabularnewline
82 & 0.763829258219531 & 0.472341483560938 & 0.236170741780469 \tabularnewline
83 & 0.729198286691398 & 0.541603426617204 & 0.270801713308602 \tabularnewline
84 & 0.699784664462129 & 0.600430671075742 & 0.300215335537871 \tabularnewline
85 & 0.658992070412027 & 0.682015859175946 & 0.341007929587973 \tabularnewline
86 & 0.64620745226127 & 0.707585095477459 & 0.35379254773873 \tabularnewline
87 & 0.602463645321836 & 0.795072709356327 & 0.397536354678164 \tabularnewline
88 & 0.560418856737608 & 0.879162286524784 & 0.439581143262392 \tabularnewline
89 & 0.537360953355244 & 0.925278093289512 & 0.462639046644756 \tabularnewline
90 & 0.498456734169846 & 0.996913468339692 & 0.501543265830154 \tabularnewline
91 & 0.487494690725838 & 0.974989381451677 & 0.512505309274162 \tabularnewline
92 & 0.445035880067516 & 0.890071760135032 & 0.554964119932484 \tabularnewline
93 & 0.401360629328286 & 0.802721258656572 & 0.598639370671714 \tabularnewline
94 & 0.357938109938162 & 0.715876219876324 & 0.642061890061838 \tabularnewline
95 & 0.381569492817473 & 0.763138985634947 & 0.618430507182527 \tabularnewline
96 & 0.344851937129344 & 0.689703874258688 & 0.655148062870656 \tabularnewline
97 & 0.303800920625093 & 0.607601841250185 & 0.696199079374907 \tabularnewline
98 & 0.296952779268328 & 0.593905558536655 & 0.703047220731672 \tabularnewline
99 & 0.256403497848127 & 0.512806995696254 & 0.743596502151873 \tabularnewline
100 & 0.223625850103009 & 0.447251700206018 & 0.776374149896991 \tabularnewline
101 & 0.20168444197851 & 0.403368883957021 & 0.79831555802149 \tabularnewline
102 & 0.183035482230671 & 0.366070964461341 & 0.816964517769329 \tabularnewline
103 & 0.22329109108221 & 0.44658218216442 & 0.77670890891779 \tabularnewline
104 & 0.189238886198902 & 0.378477772397805 & 0.810761113801097 \tabularnewline
105 & 0.181491161315161 & 0.362982322630321 & 0.818508838684839 \tabularnewline
106 & 0.192877079990666 & 0.385754159981333 & 0.807122920009333 \tabularnewline
107 & 0.172655520014766 & 0.345311040029531 & 0.827344479985234 \tabularnewline
108 & 0.152671630298247 & 0.305343260596493 & 0.847328369701753 \tabularnewline
109 & 0.148331852939122 & 0.296663705878244 & 0.851668147060878 \tabularnewline
110 & 0.143785328868574 & 0.287570657737149 & 0.856214671131426 \tabularnewline
111 & 0.128774600902029 & 0.257549201804057 & 0.871225399097971 \tabularnewline
112 & 0.108481204878174 & 0.216962409756347 & 0.891518795121826 \tabularnewline
113 & 0.139277750477497 & 0.278555500954995 & 0.860722249522503 \tabularnewline
114 & 0.127781426723508 & 0.255562853447017 & 0.872218573276492 \tabularnewline
115 & 0.147377934294447 & 0.294755868588894 & 0.852622065705553 \tabularnewline
116 & 0.150339901210734 & 0.300679802421469 & 0.849660098789266 \tabularnewline
117 & 0.127594224883059 & 0.255188449766118 & 0.872405775116941 \tabularnewline
118 & 0.125169391546338 & 0.250338783092677 & 0.874830608453661 \tabularnewline
119 & 0.114864848466529 & 0.229729696933058 & 0.885135151533471 \tabularnewline
120 & 0.12693878202601 & 0.25387756405202 & 0.87306121797399 \tabularnewline
121 & 0.1034850447848 & 0.206970089569599 & 0.8965149552152 \tabularnewline
122 & 0.0924271760560952 & 0.18485435211219 & 0.907572823943905 \tabularnewline
123 & 0.0928739555156536 & 0.185747911031307 & 0.907126044484346 \tabularnewline
124 & 0.0725688444948249 & 0.14513768898965 & 0.927431155505175 \tabularnewline
125 & 0.0563817241357212 & 0.112763448271442 & 0.943618275864279 \tabularnewline
126 & 0.0422302237708317 & 0.0844604475416635 & 0.957769776229168 \tabularnewline
127 & 0.0306906274005778 & 0.0613812548011557 & 0.969309372599422 \tabularnewline
128 & 0.0285145937470345 & 0.057029187494069 & 0.971485406252966 \tabularnewline
129 & 0.0287243359590605 & 0.057448671918121 & 0.971275664040939 \tabularnewline
130 & 0.0295869061869834 & 0.0591738123739669 & 0.970413093813017 \tabularnewline
131 & 0.0304145099749996 & 0.0608290199499992 & 0.969585490025 \tabularnewline
132 & 0.0335109341104844 & 0.0670218682209688 & 0.966489065889516 \tabularnewline
133 & 0.0721327145563907 & 0.144265429112781 & 0.927867285443609 \tabularnewline
134 & 0.0670738679017298 & 0.13414773580346 & 0.93292613209827 \tabularnewline
135 & 0.05384766936209 & 0.10769533872418 & 0.94615233063791 \tabularnewline
136 & 0.0485602902423197 & 0.0971205804846393 & 0.95143970975768 \tabularnewline
137 & 0.0340496435141393 & 0.0680992870282787 & 0.965950356485861 \tabularnewline
138 & 0.0304318360227808 & 0.0608636720455616 & 0.969568163977219 \tabularnewline
139 & 0.0337869828540874 & 0.0675739657081747 & 0.966213017145913 \tabularnewline
140 & 0.0233011179998525 & 0.046602235999705 & 0.976698882000148 \tabularnewline
141 & 0.567549059482915 & 0.86490188103417 & 0.432450940517085 \tabularnewline
142 & 0.507444120311366 & 0.985111759377269 & 0.492555879688634 \tabularnewline
143 & 0.441640540850284 & 0.883281081700568 & 0.558359459149716 \tabularnewline
144 & 0.349752181220615 & 0.69950436244123 & 0.650247818779385 \tabularnewline
145 & 0.264855867547734 & 0.529711735095469 & 0.735144132452266 \tabularnewline
146 & 0.267352027387033 & 0.534704054774065 & 0.732647972612967 \tabularnewline
147 & 0.297151064426052 & 0.594302128852105 & 0.702848935573948 \tabularnewline
148 & 0.609625720959138 & 0.780748558081724 & 0.390374279040862 \tabularnewline
149 & 0.53819104525727 & 0.923617909485459 & 0.46180895474273 \tabularnewline
150 & 0.372096251479976 & 0.744192502959952 & 0.627903748520024 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200441&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]12[/C][C]0.762383221816735[/C][C]0.475233556366531[/C][C]0.237616778183265[/C][/ROW]
[ROW][C]13[/C][C]0.625818658886833[/C][C]0.748362682226334[/C][C]0.374181341113167[/C][/ROW]
[ROW][C]14[/C][C]0.483185718746271[/C][C]0.966371437492542[/C][C]0.516814281253729[/C][/ROW]
[ROW][C]15[/C][C]0.371276812127177[/C][C]0.742553624254353[/C][C]0.628723187872824[/C][/ROW]
[ROW][C]16[/C][C]0.343382703734266[/C][C]0.686765407468533[/C][C]0.656617296265734[/C][/ROW]
[ROW][C]17[/C][C]0.249940422639971[/C][C]0.499880845279943[/C][C]0.750059577360029[/C][/ROW]
[ROW][C]18[/C][C]0.333434546310659[/C][C]0.666869092621318[/C][C]0.666565453689341[/C][/ROW]
[ROW][C]19[/C][C]0.279338198998317[/C][C]0.558676397996634[/C][C]0.720661801001683[/C][/ROW]
[ROW][C]20[/C][C]0.216526568197909[/C][C]0.433053136395817[/C][C]0.783473431802091[/C][/ROW]
[ROW][C]21[/C][C]0.170734662636511[/C][C]0.341469325273022[/C][C]0.829265337363489[/C][/ROW]
[ROW][C]22[/C][C]0.191445218513167[/C][C]0.382890437026333[/C][C]0.808554781486833[/C][/ROW]
[ROW][C]23[/C][C]0.316213260627939[/C][C]0.632426521255878[/C][C]0.683786739372061[/C][/ROW]
[ROW][C]24[/C][C]0.384241618505917[/C][C]0.768483237011834[/C][C]0.615758381494083[/C][/ROW]
[ROW][C]25[/C][C]0.335304846636942[/C][C]0.670609693273883[/C][C]0.664695153363058[/C][/ROW]
[ROW][C]26[/C][C]0.275909628586657[/C][C]0.551819257173314[/C][C]0.724090371413343[/C][/ROW]
[ROW][C]27[/C][C]0.233785934733933[/C][C]0.467571869467865[/C][C]0.766214065266067[/C][/ROW]
[ROW][C]28[/C][C]0.379993485079162[/C][C]0.759986970158325[/C][C]0.620006514920838[/C][/ROW]
[ROW][C]29[/C][C]0.326583193596312[/C][C]0.653166387192625[/C][C]0.673416806403688[/C][/ROW]
[ROW][C]30[/C][C]0.463198238396547[/C][C]0.926396476793095[/C][C]0.536801761603453[/C][/ROW]
[ROW][C]31[/C][C]0.412922367696631[/C][C]0.825844735393262[/C][C]0.587077632303369[/C][/ROW]
[ROW][C]32[/C][C]0.371482443364361[/C][C]0.742964886728723[/C][C]0.628517556635639[/C][/ROW]
[ROW][C]33[/C][C]0.357420470624208[/C][C]0.714840941248417[/C][C]0.642579529375792[/C][/ROW]
[ROW][C]34[/C][C]0.325189061324914[/C][C]0.650378122649827[/C][C]0.674810938675086[/C][/ROW]
[ROW][C]35[/C][C]0.27998507446958[/C][C]0.559970148939161[/C][C]0.720014925530419[/C][/ROW]
[ROW][C]36[/C][C]0.84269711187644[/C][C]0.31460577624712[/C][C]0.15730288812356[/C][/ROW]
[ROW][C]37[/C][C]0.81591984066583[/C][C]0.368160318668341[/C][C]0.18408015933417[/C][/ROW]
[ROW][C]38[/C][C]0.802160208690045[/C][C]0.395679582619909[/C][C]0.197839791309955[/C][/ROW]
[ROW][C]39[/C][C]0.82678084082372[/C][C]0.34643831835256[/C][C]0.17321915917628[/C][/ROW]
[ROW][C]40[/C][C]0.816597655803826[/C][C]0.366804688392348[/C][C]0.183402344196174[/C][/ROW]
[ROW][C]41[/C][C]0.787501172956275[/C][C]0.42499765408745[/C][C]0.212498827043725[/C][/ROW]
[ROW][C]42[/C][C]0.760636809777157[/C][C]0.478726380445685[/C][C]0.239363190222843[/C][/ROW]
[ROW][C]43[/C][C]0.788131433574923[/C][C]0.423737132850154[/C][C]0.211868566425077[/C][/ROW]
[ROW][C]44[/C][C]0.746848611301302[/C][C]0.506302777397396[/C][C]0.253151388698698[/C][/ROW]
[ROW][C]45[/C][C]0.717956948426108[/C][C]0.564086103147783[/C][C]0.282043051573892[/C][/ROW]
[ROW][C]46[/C][C]0.868651304043659[/C][C]0.262697391912682[/C][C]0.131348695956341[/C][/ROW]
[ROW][C]47[/C][C]0.898159811532977[/C][C]0.203680376934045[/C][C]0.101840188467023[/C][/ROW]
[ROW][C]48[/C][C]0.875105758941518[/C][C]0.249788482116964[/C][C]0.124894241058482[/C][/ROW]
[ROW][C]49[/C][C]0.866994183043523[/C][C]0.266011633912953[/C][C]0.133005816956477[/C][/ROW]
[ROW][C]50[/C][C]0.862479197629978[/C][C]0.275041604740044[/C][C]0.137520802370022[/C][/ROW]
[ROW][C]51[/C][C]0.834348637168554[/C][C]0.331302725662892[/C][C]0.165651362831446[/C][/ROW]
[ROW][C]52[/C][C]0.802985287396207[/C][C]0.394029425207587[/C][C]0.197014712603793[/C][/ROW]
[ROW][C]53[/C][C]0.81251655770281[/C][C]0.374966884594379[/C][C]0.18748344229719[/C][/ROW]
[ROW][C]54[/C][C]0.782554430949026[/C][C]0.434891138101947[/C][C]0.217445569050974[/C][/ROW]
[ROW][C]55[/C][C]0.799670094283172[/C][C]0.400659811433657[/C][C]0.200329905716828[/C][/ROW]
[ROW][C]56[/C][C]0.780826149479719[/C][C]0.438347701040563[/C][C]0.219173850520281[/C][/ROW]
[ROW][C]57[/C][C]0.743489071416137[/C][C]0.513021857167726[/C][C]0.256510928583863[/C][/ROW]
[ROW][C]58[/C][C]0.728884042718837[/C][C]0.542231914562326[/C][C]0.271115957281163[/C][/ROW]
[ROW][C]59[/C][C]0.698227997394342[/C][C]0.603544005211316[/C][C]0.301772002605658[/C][/ROW]
[ROW][C]60[/C][C]0.702994063858812[/C][C]0.594011872282377[/C][C]0.297005936141188[/C][/ROW]
[ROW][C]61[/C][C]0.667867676505612[/C][C]0.664264646988775[/C][C]0.332132323494388[/C][/ROW]
[ROW][C]62[/C][C]0.631352624185475[/C][C]0.737294751629049[/C][C]0.368647375814525[/C][/ROW]
[ROW][C]63[/C][C]0.598451943600665[/C][C]0.80309611279867[/C][C]0.401548056399335[/C][/ROW]
[ROW][C]64[/C][C]0.554542264383274[/C][C]0.890915471233451[/C][C]0.445457735616726[/C][/ROW]
[ROW][C]65[/C][C]0.516441918075948[/C][C]0.967116163848105[/C][C]0.483558081924052[/C][/ROW]
[ROW][C]66[/C][C]0.488738478121076[/C][C]0.977476956242151[/C][C]0.511261521878924[/C][/ROW]
[ROW][C]67[/C][C]0.487125644605274[/C][C]0.974251289210548[/C][C]0.512874355394726[/C][/ROW]
[ROW][C]68[/C][C]0.632316332455199[/C][C]0.735367335089601[/C][C]0.367683667544801[/C][/ROW]
[ROW][C]69[/C][C]0.742179374112265[/C][C]0.515641251775471[/C][C]0.257820625887735[/C][/ROW]
[ROW][C]70[/C][C]0.709441633440409[/C][C]0.581116733119183[/C][C]0.290558366559591[/C][/ROW]
[ROW][C]71[/C][C]0.814539343603111[/C][C]0.370921312793777[/C][C]0.185460656396889[/C][/ROW]
[ROW][C]72[/C][C]0.784176928563168[/C][C]0.431646142873665[/C][C]0.215823071436832[/C][/ROW]
[ROW][C]73[/C][C]0.777943607480901[/C][C]0.444112785038198[/C][C]0.222056392519099[/C][/ROW]
[ROW][C]74[/C][C]0.761218172844085[/C][C]0.47756365431183[/C][C]0.238781827155915[/C][/ROW]
[ROW][C]75[/C][C]0.723706058740707[/C][C]0.552587882518587[/C][C]0.276293941259293[/C][/ROW]
[ROW][C]76[/C][C]0.763022054865736[/C][C]0.473955890268528[/C][C]0.236977945134264[/C][/ROW]
[ROW][C]77[/C][C]0.725979026062426[/C][C]0.548041947875148[/C][C]0.274020973937574[/C][/ROW]
[ROW][C]78[/C][C]0.705762626286624[/C][C]0.588474747426753[/C][C]0.294237373713376[/C][/ROW]
[ROW][C]79[/C][C]0.715292196509876[/C][C]0.569415606980249[/C][C]0.284707803490124[/C][/ROW]
[ROW][C]80[/C][C]0.674579382465699[/C][C]0.650841235068602[/C][C]0.325420617534301[/C][/ROW]
[ROW][C]81[/C][C]0.638169361974682[/C][C]0.723661276050636[/C][C]0.361830638025318[/C][/ROW]
[ROW][C]82[/C][C]0.763829258219531[/C][C]0.472341483560938[/C][C]0.236170741780469[/C][/ROW]
[ROW][C]83[/C][C]0.729198286691398[/C][C]0.541603426617204[/C][C]0.270801713308602[/C][/ROW]
[ROW][C]84[/C][C]0.699784664462129[/C][C]0.600430671075742[/C][C]0.300215335537871[/C][/ROW]
[ROW][C]85[/C][C]0.658992070412027[/C][C]0.682015859175946[/C][C]0.341007929587973[/C][/ROW]
[ROW][C]86[/C][C]0.64620745226127[/C][C]0.707585095477459[/C][C]0.35379254773873[/C][/ROW]
[ROW][C]87[/C][C]0.602463645321836[/C][C]0.795072709356327[/C][C]0.397536354678164[/C][/ROW]
[ROW][C]88[/C][C]0.560418856737608[/C][C]0.879162286524784[/C][C]0.439581143262392[/C][/ROW]
[ROW][C]89[/C][C]0.537360953355244[/C][C]0.925278093289512[/C][C]0.462639046644756[/C][/ROW]
[ROW][C]90[/C][C]0.498456734169846[/C][C]0.996913468339692[/C][C]0.501543265830154[/C][/ROW]
[ROW][C]91[/C][C]0.487494690725838[/C][C]0.974989381451677[/C][C]0.512505309274162[/C][/ROW]
[ROW][C]92[/C][C]0.445035880067516[/C][C]0.890071760135032[/C][C]0.554964119932484[/C][/ROW]
[ROW][C]93[/C][C]0.401360629328286[/C][C]0.802721258656572[/C][C]0.598639370671714[/C][/ROW]
[ROW][C]94[/C][C]0.357938109938162[/C][C]0.715876219876324[/C][C]0.642061890061838[/C][/ROW]
[ROW][C]95[/C][C]0.381569492817473[/C][C]0.763138985634947[/C][C]0.618430507182527[/C][/ROW]
[ROW][C]96[/C][C]0.344851937129344[/C][C]0.689703874258688[/C][C]0.655148062870656[/C][/ROW]
[ROW][C]97[/C][C]0.303800920625093[/C][C]0.607601841250185[/C][C]0.696199079374907[/C][/ROW]
[ROW][C]98[/C][C]0.296952779268328[/C][C]0.593905558536655[/C][C]0.703047220731672[/C][/ROW]
[ROW][C]99[/C][C]0.256403497848127[/C][C]0.512806995696254[/C][C]0.743596502151873[/C][/ROW]
[ROW][C]100[/C][C]0.223625850103009[/C][C]0.447251700206018[/C][C]0.776374149896991[/C][/ROW]
[ROW][C]101[/C][C]0.20168444197851[/C][C]0.403368883957021[/C][C]0.79831555802149[/C][/ROW]
[ROW][C]102[/C][C]0.183035482230671[/C][C]0.366070964461341[/C][C]0.816964517769329[/C][/ROW]
[ROW][C]103[/C][C]0.22329109108221[/C][C]0.44658218216442[/C][C]0.77670890891779[/C][/ROW]
[ROW][C]104[/C][C]0.189238886198902[/C][C]0.378477772397805[/C][C]0.810761113801097[/C][/ROW]
[ROW][C]105[/C][C]0.181491161315161[/C][C]0.362982322630321[/C][C]0.818508838684839[/C][/ROW]
[ROW][C]106[/C][C]0.192877079990666[/C][C]0.385754159981333[/C][C]0.807122920009333[/C][/ROW]
[ROW][C]107[/C][C]0.172655520014766[/C][C]0.345311040029531[/C][C]0.827344479985234[/C][/ROW]
[ROW][C]108[/C][C]0.152671630298247[/C][C]0.305343260596493[/C][C]0.847328369701753[/C][/ROW]
[ROW][C]109[/C][C]0.148331852939122[/C][C]0.296663705878244[/C][C]0.851668147060878[/C][/ROW]
[ROW][C]110[/C][C]0.143785328868574[/C][C]0.287570657737149[/C][C]0.856214671131426[/C][/ROW]
[ROW][C]111[/C][C]0.128774600902029[/C][C]0.257549201804057[/C][C]0.871225399097971[/C][/ROW]
[ROW][C]112[/C][C]0.108481204878174[/C][C]0.216962409756347[/C][C]0.891518795121826[/C][/ROW]
[ROW][C]113[/C][C]0.139277750477497[/C][C]0.278555500954995[/C][C]0.860722249522503[/C][/ROW]
[ROW][C]114[/C][C]0.127781426723508[/C][C]0.255562853447017[/C][C]0.872218573276492[/C][/ROW]
[ROW][C]115[/C][C]0.147377934294447[/C][C]0.294755868588894[/C][C]0.852622065705553[/C][/ROW]
[ROW][C]116[/C][C]0.150339901210734[/C][C]0.300679802421469[/C][C]0.849660098789266[/C][/ROW]
[ROW][C]117[/C][C]0.127594224883059[/C][C]0.255188449766118[/C][C]0.872405775116941[/C][/ROW]
[ROW][C]118[/C][C]0.125169391546338[/C][C]0.250338783092677[/C][C]0.874830608453661[/C][/ROW]
[ROW][C]119[/C][C]0.114864848466529[/C][C]0.229729696933058[/C][C]0.885135151533471[/C][/ROW]
[ROW][C]120[/C][C]0.12693878202601[/C][C]0.25387756405202[/C][C]0.87306121797399[/C][/ROW]
[ROW][C]121[/C][C]0.1034850447848[/C][C]0.206970089569599[/C][C]0.8965149552152[/C][/ROW]
[ROW][C]122[/C][C]0.0924271760560952[/C][C]0.18485435211219[/C][C]0.907572823943905[/C][/ROW]
[ROW][C]123[/C][C]0.0928739555156536[/C][C]0.185747911031307[/C][C]0.907126044484346[/C][/ROW]
[ROW][C]124[/C][C]0.0725688444948249[/C][C]0.14513768898965[/C][C]0.927431155505175[/C][/ROW]
[ROW][C]125[/C][C]0.0563817241357212[/C][C]0.112763448271442[/C][C]0.943618275864279[/C][/ROW]
[ROW][C]126[/C][C]0.0422302237708317[/C][C]0.0844604475416635[/C][C]0.957769776229168[/C][/ROW]
[ROW][C]127[/C][C]0.0306906274005778[/C][C]0.0613812548011557[/C][C]0.969309372599422[/C][/ROW]
[ROW][C]128[/C][C]0.0285145937470345[/C][C]0.057029187494069[/C][C]0.971485406252966[/C][/ROW]
[ROW][C]129[/C][C]0.0287243359590605[/C][C]0.057448671918121[/C][C]0.971275664040939[/C][/ROW]
[ROW][C]130[/C][C]0.0295869061869834[/C][C]0.0591738123739669[/C][C]0.970413093813017[/C][/ROW]
[ROW][C]131[/C][C]0.0304145099749996[/C][C]0.0608290199499992[/C][C]0.969585490025[/C][/ROW]
[ROW][C]132[/C][C]0.0335109341104844[/C][C]0.0670218682209688[/C][C]0.966489065889516[/C][/ROW]
[ROW][C]133[/C][C]0.0721327145563907[/C][C]0.144265429112781[/C][C]0.927867285443609[/C][/ROW]
[ROW][C]134[/C][C]0.0670738679017298[/C][C]0.13414773580346[/C][C]0.93292613209827[/C][/ROW]
[ROW][C]135[/C][C]0.05384766936209[/C][C]0.10769533872418[/C][C]0.94615233063791[/C][/ROW]
[ROW][C]136[/C][C]0.0485602902423197[/C][C]0.0971205804846393[/C][C]0.95143970975768[/C][/ROW]
[ROW][C]137[/C][C]0.0340496435141393[/C][C]0.0680992870282787[/C][C]0.965950356485861[/C][/ROW]
[ROW][C]138[/C][C]0.0304318360227808[/C][C]0.0608636720455616[/C][C]0.969568163977219[/C][/ROW]
[ROW][C]139[/C][C]0.0337869828540874[/C][C]0.0675739657081747[/C][C]0.966213017145913[/C][/ROW]
[ROW][C]140[/C][C]0.0233011179998525[/C][C]0.046602235999705[/C][C]0.976698882000148[/C][/ROW]
[ROW][C]141[/C][C]0.567549059482915[/C][C]0.86490188103417[/C][C]0.432450940517085[/C][/ROW]
[ROW][C]142[/C][C]0.507444120311366[/C][C]0.985111759377269[/C][C]0.492555879688634[/C][/ROW]
[ROW][C]143[/C][C]0.441640540850284[/C][C]0.883281081700568[/C][C]0.558359459149716[/C][/ROW]
[ROW][C]144[/C][C]0.349752181220615[/C][C]0.69950436244123[/C][C]0.650247818779385[/C][/ROW]
[ROW][C]145[/C][C]0.264855867547734[/C][C]0.529711735095469[/C][C]0.735144132452266[/C][/ROW]
[ROW][C]146[/C][C]0.267352027387033[/C][C]0.534704054774065[/C][C]0.732647972612967[/C][/ROW]
[ROW][C]147[/C][C]0.297151064426052[/C][C]0.594302128852105[/C][C]0.702848935573948[/C][/ROW]
[ROW][C]148[/C][C]0.609625720959138[/C][C]0.780748558081724[/C][C]0.390374279040862[/C][/ROW]
[ROW][C]149[/C][C]0.53819104525727[/C][C]0.923617909485459[/C][C]0.46180895474273[/C][/ROW]
[ROW][C]150[/C][C]0.372096251479976[/C][C]0.744192502959952[/C][C]0.627903748520024[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200441&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200441&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
120.7623832218167350.4752335563665310.237616778183265
130.6258186588868330.7483626822263340.374181341113167
140.4831857187462710.9663714374925420.516814281253729
150.3712768121271770.7425536242543530.628723187872824
160.3433827037342660.6867654074685330.656617296265734
170.2499404226399710.4998808452799430.750059577360029
180.3334345463106590.6668690926213180.666565453689341
190.2793381989983170.5586763979966340.720661801001683
200.2165265681979090.4330531363958170.783473431802091
210.1707346626365110.3414693252730220.829265337363489
220.1914452185131670.3828904370263330.808554781486833
230.3162132606279390.6324265212558780.683786739372061
240.3842416185059170.7684832370118340.615758381494083
250.3353048466369420.6706096932738830.664695153363058
260.2759096285866570.5518192571733140.724090371413343
270.2337859347339330.4675718694678650.766214065266067
280.3799934850791620.7599869701583250.620006514920838
290.3265831935963120.6531663871926250.673416806403688
300.4631982383965470.9263964767930950.536801761603453
310.4129223676966310.8258447353932620.587077632303369
320.3714824433643610.7429648867287230.628517556635639
330.3574204706242080.7148409412484170.642579529375792
340.3251890613249140.6503781226498270.674810938675086
350.279985074469580.5599701489391610.720014925530419
360.842697111876440.314605776247120.15730288812356
370.815919840665830.3681603186683410.18408015933417
380.8021602086900450.3956795826199090.197839791309955
390.826780840823720.346438318352560.17321915917628
400.8165976558038260.3668046883923480.183402344196174
410.7875011729562750.424997654087450.212498827043725
420.7606368097771570.4787263804456850.239363190222843
430.7881314335749230.4237371328501540.211868566425077
440.7468486113013020.5063027773973960.253151388698698
450.7179569484261080.5640861031477830.282043051573892
460.8686513040436590.2626973919126820.131348695956341
470.8981598115329770.2036803769340450.101840188467023
480.8751057589415180.2497884821169640.124894241058482
490.8669941830435230.2660116339129530.133005816956477
500.8624791976299780.2750416047400440.137520802370022
510.8343486371685540.3313027256628920.165651362831446
520.8029852873962070.3940294252075870.197014712603793
530.812516557702810.3749668845943790.18748344229719
540.7825544309490260.4348911381019470.217445569050974
550.7996700942831720.4006598114336570.200329905716828
560.7808261494797190.4383477010405630.219173850520281
570.7434890714161370.5130218571677260.256510928583863
580.7288840427188370.5422319145623260.271115957281163
590.6982279973943420.6035440052113160.301772002605658
600.7029940638588120.5940118722823770.297005936141188
610.6678676765056120.6642646469887750.332132323494388
620.6313526241854750.7372947516290490.368647375814525
630.5984519436006650.803096112798670.401548056399335
640.5545422643832740.8909154712334510.445457735616726
650.5164419180759480.9671161638481050.483558081924052
660.4887384781210760.9774769562421510.511261521878924
670.4871256446052740.9742512892105480.512874355394726
680.6323163324551990.7353673350896010.367683667544801
690.7421793741122650.5156412517754710.257820625887735
700.7094416334404090.5811167331191830.290558366559591
710.8145393436031110.3709213127937770.185460656396889
720.7841769285631680.4316461428736650.215823071436832
730.7779436074809010.4441127850381980.222056392519099
740.7612181728440850.477563654311830.238781827155915
750.7237060587407070.5525878825185870.276293941259293
760.7630220548657360.4739558902685280.236977945134264
770.7259790260624260.5480419478751480.274020973937574
780.7057626262866240.5884747474267530.294237373713376
790.7152921965098760.5694156069802490.284707803490124
800.6745793824656990.6508412350686020.325420617534301
810.6381693619746820.7236612760506360.361830638025318
820.7638292582195310.4723414835609380.236170741780469
830.7291982866913980.5416034266172040.270801713308602
840.6997846644621290.6004306710757420.300215335537871
850.6589920704120270.6820158591759460.341007929587973
860.646207452261270.7075850954774590.35379254773873
870.6024636453218360.7950727093563270.397536354678164
880.5604188567376080.8791622865247840.439581143262392
890.5373609533552440.9252780932895120.462639046644756
900.4984567341698460.9969134683396920.501543265830154
910.4874946907258380.9749893814516770.512505309274162
920.4450358800675160.8900717601350320.554964119932484
930.4013606293282860.8027212586565720.598639370671714
940.3579381099381620.7158762198763240.642061890061838
950.3815694928174730.7631389856349470.618430507182527
960.3448519371293440.6897038742586880.655148062870656
970.3038009206250930.6076018412501850.696199079374907
980.2969527792683280.5939055585366550.703047220731672
990.2564034978481270.5128069956962540.743596502151873
1000.2236258501030090.4472517002060180.776374149896991
1010.201684441978510.4033688839570210.79831555802149
1020.1830354822306710.3660709644613410.816964517769329
1030.223291091082210.446582182164420.77670890891779
1040.1892388861989020.3784777723978050.810761113801097
1050.1814911613151610.3629823226303210.818508838684839
1060.1928770799906660.3857541599813330.807122920009333
1070.1726555200147660.3453110400295310.827344479985234
1080.1526716302982470.3053432605964930.847328369701753
1090.1483318529391220.2966637058782440.851668147060878
1100.1437853288685740.2875706577371490.856214671131426
1110.1287746009020290.2575492018040570.871225399097971
1120.1084812048781740.2169624097563470.891518795121826
1130.1392777504774970.2785555009549950.860722249522503
1140.1277814267235080.2555628534470170.872218573276492
1150.1473779342944470.2947558685888940.852622065705553
1160.1503399012107340.3006798024214690.849660098789266
1170.1275942248830590.2551884497661180.872405775116941
1180.1251693915463380.2503387830926770.874830608453661
1190.1148648484665290.2297296969330580.885135151533471
1200.126938782026010.253877564052020.87306121797399
1210.10348504478480.2069700895695990.8965149552152
1220.09242717605609520.184854352112190.907572823943905
1230.09287395551565360.1857479110313070.907126044484346
1240.07256884449482490.145137688989650.927431155505175
1250.05638172413572120.1127634482714420.943618275864279
1260.04223022377083170.08446044754166350.957769776229168
1270.03069062740057780.06138125480115570.969309372599422
1280.02851459374703450.0570291874940690.971485406252966
1290.02872433595906050.0574486719181210.971275664040939
1300.02958690618698340.05917381237396690.970413093813017
1310.03041450997499960.06082901994999920.969585490025
1320.03351093411048440.06702186822096880.966489065889516
1330.07213271455639070.1442654291127810.927867285443609
1340.06707386790172980.134147735803460.93292613209827
1350.053847669362090.107695338724180.94615233063791
1360.04856029024231970.09712058048463930.95143970975768
1370.03404964351413930.06809928702827870.965950356485861
1380.03043183602278080.06086367204556160.969568163977219
1390.03378698285408740.06757396570817470.966213017145913
1400.02330111799985250.0466022359997050.976698882000148
1410.5675490594829150.864901881034170.432450940517085
1420.5074441203113660.9851117593772690.492555879688634
1430.4416405408502840.8832810817005680.558359459149716
1440.3497521812206150.699504362441230.650247818779385
1450.2648558675477340.5297117350954690.735144132452266
1460.2673520273870330.5347040547740650.732647972612967
1470.2971510644260520.5943021288521050.702848935573948
1480.6096257209591380.7807485580817240.390374279040862
1490.538191045257270.9236179094854590.46180895474273
1500.3720962514799760.7441925029599520.627903748520024






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00719424460431655OK
10% type I error level120.0863309352517986OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00719424460431655 & OK \tabularnewline
10% type I error level & 12 & 0.0863309352517986 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200441&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00719424460431655[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.0863309352517986[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200441&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200441&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00719424460431655OK
10% type I error level120.0863309352517986OK


Figures (Output of Computation)

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

Parameters (Session):
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')
}