FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 12:07:59 -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/t1355677741r1zr72ym4lmk0e9.htm/, Retrieved Sat, 20 Apr 2024 10:40:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200496, Retrieved Sat, 20 Apr 2024 10:40:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact101

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [MR volgnummer] [2012-12-16 17:07:59] [447cab31e466d1c88f957d20e303ed40] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2000	1	41	38	13	12	14	12	53	32
2000	2	39	32	16	11	18	11	86	51
2000	3	30	35	19	15	11	14	66	42
2000	4	31	33	15	6	12	12	67	41
2000	5	34	37	14	13	16	21	76	46
2000	6	35	29	13	10	18	12	78	47
2000	7	39	31	19	12	14	22	53	37
2000	8	34	36	15	14	14	11	80	49
2000	9	36	35	14	12	15	10	74	45
2000	10	37	38	15	6	15	13	76	47
2000	11	38	31	16	10	17	10	79	49
2000	12	36	34	16	12	19	8	54	33
2000	13	38	35	16	12	10	15	67	42
2001	14	39	38	16	11	16	14	54	33
2001	15	33	37	17	15	18	10	87	53
2001	16	32	33	15	12	14	14	58	36
2001	17	36	32	15	10	14	14	75	45
2001	18	38	38	20	12	17	11	88	54
2001	19	39	38	18	11	14	10	64	41
2001	20	32	32	16	12	16	13	57	36
2001	21	32	33	16	11	18	7	66	41
2001	22	31	31	16	12	11	14	68	44
2001	23	39	38	19	13	14	12	54	33
2001	24	37	39	16	11	12	14	56	37
2001	25	39	32	17	9	17	11	86	52
2001	26	41	32	17	13	9	9	80	47
2002	27	36	35	16	10	16	11	76	43
2002	28	33	37	15	14	14	15	69	44
2002	29	33	33	16	12	15	14	78	45
2002	30	34	33	14	10	11	13	67	44
2002	31	31	28	15	12	16	9	80	49
2002	32	27	32	12	8	13	15	54	33
2002	33	37	31	14	10	17	10	71	43
2002	34	34	37	16	12	15	11	84	54
2002	35	34	30	14	12	14	13	74	42
2002	36	32	33	7	7	16	8	71	44
2002	37	29	31	10	6	9	20	63	37
2002	38	36	33	14	12	15	12	71	43
2002	39	29	31	16	10	17	10	76	46
2003	40	35	33	16	10	13	10	69	42
2003	41	37	32	16	10	15	9	74	45
2003	42	34	33	14	12	16	14	75	44
2003	43	38	32	20	15	16	8	54	33
2003	44	35	33	14	10	12	14	52	31
2003	45	38	28	14	10	12	11	69	42
2003	46	37	35	11	12	11	13	68	40
2003	47	38	39	14	13	15	9	65	43
2003	48	33	34	15	11	15	11	75	46
2003	49	36	38	16	11	17	15	74	42
2003	50	38	32	14	12	13	11	75	45
2003	51	32	38	16	14	16	10	72	44
2003	52	32	30	14	10	14	14	67	40
2004	53	32	33	12	12	11	18	63	37
2004	54	34	38	16	13	12	14	62	46
2004	55	32	32	9	5	12	11	63	36
2004	56	37	32	14	6	15	12	76	47
2004	57	39	34	16	12	16	13	74	45
2004	58	29	34	16	12	15	9	67	42
2004	59	37	36	15	11	12	10	73	43
2004	60	35	34	16	10	12	15	70	43
2004	61	30	28	12	7	8	20	53	32
2004	62	38	34	16	12	13	12	77	45
2004	63	34	35	16	14	11	12	77	45
2004	64	31	35	14	11	14	14	52	31
2004	65	34	31	16	12	15	13	54	33
2004	66	35	37	17	13	10	11	80	49
2005	67	36	35	18	14	11	17	66	42
2005	68	30	27	18	11	12	12	73	41
2005	69	39	40	12	12	15	13	63	38
2005	70	35	37	16	12	15	14	69	42
2005	71	38	36	10	8	14	13	67	44
2005	72	31	38	14	11	16	15	54	33
2005	73	34	39	18	14	15	13	81	48
2005	74	38	41	18	14	15	10	69	40
2005	75	34	27	16	12	13	11	84	50
2005	76	39	30	17	9	12	19	80	49
2005	77	37	37	16	13	17	13	70	43
2005	78	34	31	16	11	13	17	69	44
2005	79	28	31	13	12	15	13	77	47
2005	80	37	27	16	12	13	9	54	33
2006	81	33	36	16	12	15	11	79	46
2006	82	37	38	20	12	16	10	30	0
2006	83	35	37	16	12	15	9	71	45
2006	84	37	33	15	12	16	12	73	43
2006	85	32	34	15	11	15	12	72	44
2006	86	33	31	16	10	14	13	77	47
2006	87	38	39	14	9	15	13	75	45
2006	88	33	34	16	12	14	12	69	42
2006	89	29	32	16	12	13	15	54	33
2006	90	33	33	15	12	7	22	70	43
2006	91	31	36	12	9	17	13	73	46
2006	92	36	32	17	15	13	15	54	33
2006	93	35	41	16	12	15	13	77	46
2006	94	32	28	15	12	14	15	82	48
2007	95	29	30	13	12	13	10	80	47
2007	96	39	36	16	10	16	11	80	47
2007	97	37	35	16	13	12	16	69	43
2007	98	35	31	16	9	14	11	78	46
2007	99	37	34	16	12	17	11	81	48
2007	100	32	36	14	10	15	10	76	46
2007	101	38	36	16	14	17	10	76	45
2007	102	37	35	16	11	12	16	73	45
2007	103	36	37	20	15	16	12	85	52
2007	104	32	28	15	11	11	11	66	42
2007	105	33	39	16	11	15	16	79	47
2007	106	40	32	13	12	9	19	68	41
2007	107	38	35	17	12	16	11	76	47
2007	108	41	39	16	12	15	16	71	43
2008	109	36	35	16	11	10	15	54	33
2008	110	43	42	12	7	10	24	46	30
2008	111	30	34	16	12	15	14	82	49
2008	112	31	33	16	14	11	15	74	44
2008	113	32	41	17	11	13	11	88	55
2008	114	32	33	13	11	14	15	38	11
2008	115	37	34	12	10	18	12	76	47
2008	116	37	32	18	13	16	10	86	53
2008	117	33	40	14	13	14	14	54	33
2008	118	34	40	14	8	14	13	70	44
2008	119	33	35	13	11	14	9	69	42
2008	120	38	36	16	12	14	15	90	55
2008	121	33	37	13	11	12	15	54	33
2008	122	31	27	16	13	14	14	76	46
2009	123	38	39	13	12	15	11	89	54
2009	124	37	38	16	14	15	8	76	47
2009	125	33	31	15	13	15	11	73	45
2009	126	31	33	16	15	13	11	79	47
2009	127	39	32	15	10	17	8	90	55
2009	128	44	39	17	11	17	10	74	44
2009	129	33	36	15	9	19	11	81	53
2009	130	35	33	12	11	15	13	72	44
2009	131	32	33	16	10	13	11	71	42
2009	132	28	32	10	11	9	20	66	40
2009	133	40	37	16	8	15	10	77	46
2009	134	27	30	12	11	15	15	65	40
2009	135	37	38	14	12	15	12	74	46
2009	136	32	29	15	12	16	14	82	53
2010	137	28	22	13	9	11	23	54	33
2010	138	34	35	15	11	14	14	63	42
2010	139	30	35	11	10	11	16	54	35
2010	140	35	34	12	8	15	11	64	40
2010	141	31	35	8	9	13	12	69	41
2010	142	32	34	16	8	15	10	54	33
2010	143	30	34	15	9	16	14	84	51
2010	144	30	35	17	15	14	12	86	53
2010	145	31	23	16	11	15	12	77	46
2010	146	40	31	10	8	16	11	89	55
2010	147	32	27	18	13	16	12	76	47
2010	148	36	36	13	12	11	13	60	38
2010	149	32	31	16	12	12	11	75	46
2010	150	35	32	13	9	9	19	73	46
2011	151	38	39	10	7	16	12	85	53
2011	152	42	37	15	13	13	17	79	47
2011	153	34	38	16	9	16	9	71	41
2011	154	35	39	16	6	12	12	72	44
2011	155	35	34	14	8	9	19	69	43
2011	156	33	31	10	8	13	18	78	51
2011	157	36	32	17	15	13	15	54	33
2011	158	32	37	13	6	14	14	69	43
2011	159	33	36	15	9	19	11	81	53
2011	160	34	32	16	11	13	9	84	51
2011	161	32	35	12	8	12	18	84	50
2011	162	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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200496&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200496&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200496&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 192.32646202974 -0.0932944457947647Jaar[t] + 0.00253534923079148Volgnummer[t] + 0.105538702589016Connected[t] -0.0133280703286124Separate[t] + 0.529685111915017Software[t] + 0.0522729799854157Happiness[t] -0.0637883373720879Depression[t] + 0.0429223923795689Belonging[t] -0.0575356543912696Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  192.32646202974 -0.0932944457947647Jaar[t] +  0.00253534923079148Volgnummer[t] +  0.105538702589016Connected[t] -0.0133280703286124Separate[t] +  0.529685111915017Software[t] +  0.0522729799854157Happiness[t] -0.0637883373720879Depression[t] +  0.0429223923795689Belonging[t] -0.0575356543912696Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200496&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  192.32646202974 -0.0932944457947647Jaar[t] +  0.00253534923079148Volgnummer[t] +  0.105538702589016Connected[t] -0.0133280703286124Separate[t] +  0.529685111915017Software[t] +  0.0522729799854157Happiness[t] -0.0637883373720879Depression[t] +  0.0429223923795689Belonging[t] -0.0575356543912696Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200496&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200496&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] = + 192.32646202974 -0.0932944457947647Jaar[t] + 0.00253534923079148Volgnummer[t] + 0.105538702589016Connected[t] -0.0133280703286124Separate[t] + 0.529685111915017Software[t] + 0.0522729799854157Happiness[t] -0.0637883373720879Depression[t] + 0.0429223923795689Belonging[t] -0.0575356543912696Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)192.326462029741036.0971890.18560.8529860.426493
Jaar-0.09329444579476470.518185-0.180.857360.42868
Volgnummer0.002535349230791480.0376860.06730.9464510.473225
Connected0.1055387025890160.0473852.22720.0274010.0137
Separate-0.01332807032861240.045596-0.29230.770450.385225
Software0.5296851119150170.0697437.594800
Happiness0.05227297998541570.0766720.68180.4964180.248209
Depression-0.06378833737208790.056708-1.12490.2624220.131211
Belonging0.04292239237956890.0450380.9530.3420870.171043
Belonging_Final-0.05753565439126960.064609-0.89050.3745950.187297

\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) & 192.32646202974 & 1036.097189 & 0.1856 & 0.852986 & 0.426493 \tabularnewline
Jaar & -0.0932944457947647 & 0.518185 & -0.18 & 0.85736 & 0.42868 \tabularnewline
Volgnummer & 0.00253534923079148 & 0.037686 & 0.0673 & 0.946451 & 0.473225 \tabularnewline
Connected & 0.105538702589016 & 0.047385 & 2.2272 & 0.027401 & 0.0137 \tabularnewline
Separate & -0.0133280703286124 & 0.045596 & -0.2923 & 0.77045 & 0.385225 \tabularnewline
Software & 0.529685111915017 & 0.069743 & 7.5948 & 0 & 0 \tabularnewline
Happiness & 0.0522729799854157 & 0.076672 & 0.6818 & 0.496418 & 0.248209 \tabularnewline
Depression & -0.0637883373720879 & 0.056708 & -1.1249 & 0.262422 & 0.131211 \tabularnewline
Belonging & 0.0429223923795689 & 0.045038 & 0.953 & 0.342087 & 0.171043 \tabularnewline
Belonging_Final & -0.0575356543912696 & 0.064609 & -0.8905 & 0.374595 & 0.187297 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200496&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]192.32646202974[/C][C]1036.097189[/C][C]0.1856[/C][C]0.852986[/C][C]0.426493[/C][/ROW]
[ROW][C]Jaar[/C][C]-0.0932944457947647[/C][C]0.518185[/C][C]-0.18[/C][C]0.85736[/C][C]0.42868[/C][/ROW]
[ROW][C]Volgnummer[/C][C]0.00253534923079148[/C][C]0.037686[/C][C]0.0673[/C][C]0.946451[/C][C]0.473225[/C][/ROW]
[ROW][C]Connected[/C][C]0.105538702589016[/C][C]0.047385[/C][C]2.2272[/C][C]0.027401[/C][C]0.0137[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0133280703286124[/C][C]0.045596[/C][C]-0.2923[/C][C]0.77045[/C][C]0.385225[/C][/ROW]
[ROW][C]Software[/C][C]0.529685111915017[/C][C]0.069743[/C][C]7.5948[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0522729799854157[/C][C]0.076672[/C][C]0.6818[/C][C]0.496418[/C][C]0.248209[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0637883373720879[/C][C]0.056708[/C][C]-1.1249[/C][C]0.262422[/C][C]0.131211[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0429223923795689[/C][C]0.045038[/C][C]0.953[/C][C]0.342087[/C][C]0.171043[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0575356543912696[/C][C]0.064609[/C][C]-0.8905[/C][C]0.374595[/C][C]0.187297[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200496&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200496&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)192.326462029741036.0971890.18560.8529860.426493
Jaar-0.09329444579476470.518185-0.180.857360.42868
Volgnummer0.002535349230791480.0376860.06730.9464510.473225
Connected0.1055387025890160.0473852.22720.0274010.0137
Separate-0.01332807032861240.045596-0.29230.770450.385225
Software0.5296851119150170.0697437.594800
Happiness0.05227297998541570.0766720.68180.4964180.248209
Depression-0.06378833737208790.056708-1.12490.2624220.131211
Belonging0.04292239237956890.0450380.9530.3420870.171043
Belonging_Final-0.05753565439126960.064609-0.89050.3745950.187297






Multiple Linear Regression - Regression Statistics
Multiple R0.603200621443045
R-squared0.363850989709276
Adjusted R-squared0.326184271994694
F-TEST (value)9.65974769732629
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value1.34958710873434e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85208074720042
Sum Squared Residuals521.390870310871

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.603200621443045 \tabularnewline
R-squared & 0.363850989709276 \tabularnewline
Adjusted R-squared & 0.326184271994694 \tabularnewline
F-TEST (value) & 9.65974769732629 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 1.34958710873434e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85208074720042 \tabularnewline
Sum Squared Residuals & 521.390870310871 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200496&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.603200621443045[/C][/ROW]
[ROW][C]R-squared[/C][C]0.363850989709276[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.326184271994694[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.65974769732629[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]1.34958710873434e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85208074720042[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]521.390870310871[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200496&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200496&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 R0.603200621443045
R-squared0.363850989709276
Adjusted R-squared0.326184271994694
F-TEST (value)9.65974769732629
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value1.34958710873434e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85208074720042
Sum Squared Residuals521.390870310871






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.3170547930121-3.31705479301213
21616.2549378195265-0.254937819526474
31916.48847825204622.51152174795377
41512.13635013878852.86364986121146
51415.8436052409297-1.84360524092969
61315.1761986463208-2.17619864632084
71915.28892432984143.71107567015857
81516.9266444909602-1.92664449096021
91416.1828846725128-2.18288467251278
101512.8522723057172.14772769428302
111615.48199013794040.518009862059594
121616.3724673903235-0.37246739032346
131615.69594710434330.304052895656717
141615.47831339333910.521686606660916
151717.605110215184-0.605110215184028
161515.2354752836093-0.235475283609272
171514.8259830706260.17401692937405
182016.40735179037863.59264820962139
191815.61053621767612.38946378232391
201615.38435665582440.6156433441756
211615.42977806647470.570221933525306
221614.98392456577151.01607543422849
231916.58353247501962.41646752498041
241614.92587165739281.07412834260725
251715.05077754783321.94922245216681
261717.1226675024419-0.12266750244187
271615.17196257940250.828037420597534
281516.2322744173616-1.23227441736162
291615.67357901845920.32642098154082
301414.1623686020954-0.16236860209539
311515.7611294974257-0.761129497425708
321212.4344966115287-0.434496611528665
331415.2474750141503-1.24747501415034
341615.669560662760.330439337240045
351415.846746778461-1.84674677846099
36713.1294441128628-6.12944411286285
371011.2403339164082-1.24033391640817
381415.9552045061731-1.95520450617308
391614.4603824875471.539617512453
401614.69679341682631.30320658317367
411615.13407353763070.865926462369281
421415.6998242724917-1.6998242724917
432017.84114982071782.15885017928224
441414.302720012127-0.30272001212702
451414.9766653050329-0.976665305032891
461115.7320349448778-4.73203494487779
471416.4793529564157-2.47935295641567
481515.0905052063921-0.0905052063921116
491615.39295721774350.60704278225651
501416.1326003647915-2.13260036479145
511616.6306810549274-0.630681054927408
521414.2769318653351-0.276931865335081
531214.7945038858262-2.79450388582624
541615.21784444807980.782155551920211
55911.6614338671926-2.66143386719262
561412.73947734649811.26052265350191
571616.1222557983765-0.122255798376522
581615.14443470773690.855565292263081
591515.4143298476654-0.414329847665403
601614.25504995646121.74495004353878
611211.59598280002280.404017199977248
621616.065130416496-0.0651304164960116
631616.5870071489013-0.58700714890133
641414.4455526718206-0.445552671820646
651615.43453631538210.565463684617947
661716.05395056357080.94604943642922
671816.09645046534591.90354953465413
681814.71252989382053.28747010617947
691215.8577474059578-3.85774740595779
701615.44171555515860.558284444841393
711013.46605389867-3.46605389866999
721414.3440879074617-0.344087907461718
731816.61014010301411.38985989698593
741817.14475566063530.855244339364704
751615.75250400471850.247495995281517
761713.97695972607433.02304027392566
771616.3539465521984-0.353946552198415
781614.49576067560291.50423932439705
791314.9252204065366-1.92522040653659
801615.89980788664820.100192113351822
811615.56900693440.430993065599995
822016.62654514608673.37345485391334
831615.61356015780990.386439842190099
841515.942309254944-0.942309254944002
851514.72140688222980.278593117770169
861614.2657237144871.73427628551301
871414.2411424061278-0.241142406127762
881615.29826789608470.701732103915345
891614.53565158734281.46434841265718
901514.29825916924440.70174083075564
911213.5136626167341-1.51366261673408
921716.87108388890340.128916111096644
931615.53044671920120.469553280798842
941515.3093218733226-0.309321873322577
951315.1136501048415-2.11365010484149
961615.12526443674490.874735563255112
971615.7490684814590.250931518540971
981614.11228047423981.88771952576025
991616.0454791617202-0.0454791617202046
1001414.2939963578046-0.29399635780461
1011617.2105859845917-1.21058598459166
1021614.75899326451871.24100673548131
1032017.32463861540932.67536138459074
1041514.46848586572730.53151413427267
1051614.59041420599171.40958579400833
1061315.3227667957045-2.32276679570448
1071715.94862330033491.05137669966505
1081615.85877846483970.141221535160284
1091614.41205233563511.58794766436487
1101212.1964544508171-0.196454450817143
1111614.9333137544731.06668624552697
1121615.78550497605750.214495023942469
1131714.52561973397262.47438026602743
1141314.8173484505667-1.81734845056674
1151214.7647884148948-2.76478841489482
1161816.49007595274931.50992404725069
1171415.3813291512151-1.38132915121514
1181412.95863206060111.04136793939891
1191314.8386266605223-1.8386266605223
1201615.65588927293650.344110727063514
1211314.2037502379512-1.20375023795119
1221615.5525225317270.447477468272963
1231315.8522562553337-2.85225625533373
1241616.8580746880549-0.858074688054904
1251515.7970057268425-0.797005726842471
1261616.6590948395921-0.659094839592084
1271515.2831603333916-0.283160333391556
1281716.06833506066880.931664939331157
1291513.71395214831061.28604785168944
1301214.8217701009547-2.82177010095473
1311614.07318386167981.92681613832023
1321013.3138499733924-3.31384997339237
1331614.00560770976421.99439229023575
1341213.8296952843155-1.82969528431552
1351415.5431308259074-1.54313082590736
1361515.0032511586895-0.00325115868948473
1371312.10800457324630.891995426753724
1381514.2292720657690.770727934231032
1391113.012019927351-2.01201992735102
1401213.1657858946669-1.16578589466692
141813.2512654852917-5.2512654852917
1421612.89155447967673.10844552032327
1431513.26184715848581.73815284151421
1441716.4229692996280.577030700371982
1451614.64096077703931.35903922296071
1461014.0109736875882-4.01097368758818
1471815.709443053822.29055694617995
1481314.9904048426835-1.99040484268353
1491615.00082603849430.999173961505709
1501312.96462366572640.0353763342736232
1511012.9625623101176-2.9625623101176
1521516.2039382271053-1.20393822710529
1531613.89905586387932.1009441361207
1541611.87460500677334.12539499322673
1551412.32858210716891.67141789283114
1561012.3589208158072-2.35892081580717
1571716.5694093599310.43059064006902
1581311.50052419906581.49947580093417
1591513.60342373364481.39657626635522
1601614.8819575713621.118042428638
1611212.4755436067409-0.475543606740932
1621312.44198467742230.558015322577722

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.3170547930121 & -3.31705479301213 \tabularnewline
2 & 16 & 16.2549378195265 & -0.254937819526474 \tabularnewline
3 & 19 & 16.4884782520462 & 2.51152174795377 \tabularnewline
4 & 15 & 12.1363501387885 & 2.86364986121146 \tabularnewline
5 & 14 & 15.8436052409297 & -1.84360524092969 \tabularnewline
6 & 13 & 15.1761986463208 & -2.17619864632084 \tabularnewline
7 & 19 & 15.2889243298414 & 3.71107567015857 \tabularnewline
8 & 15 & 16.9266444909602 & -1.92664449096021 \tabularnewline
9 & 14 & 16.1828846725128 & -2.18288467251278 \tabularnewline
10 & 15 & 12.852272305717 & 2.14772769428302 \tabularnewline
11 & 16 & 15.4819901379404 & 0.518009862059594 \tabularnewline
12 & 16 & 16.3724673903235 & -0.37246739032346 \tabularnewline
13 & 16 & 15.6959471043433 & 0.304052895656717 \tabularnewline
14 & 16 & 15.4783133933391 & 0.521686606660916 \tabularnewline
15 & 17 & 17.605110215184 & -0.605110215184028 \tabularnewline
16 & 15 & 15.2354752836093 & -0.235475283609272 \tabularnewline
17 & 15 & 14.825983070626 & 0.17401692937405 \tabularnewline
18 & 20 & 16.4073517903786 & 3.59264820962139 \tabularnewline
19 & 18 & 15.6105362176761 & 2.38946378232391 \tabularnewline
20 & 16 & 15.3843566558244 & 0.6156433441756 \tabularnewline
21 & 16 & 15.4297780664747 & 0.570221933525306 \tabularnewline
22 & 16 & 14.9839245657715 & 1.01607543422849 \tabularnewline
23 & 19 & 16.5835324750196 & 2.41646752498041 \tabularnewline
24 & 16 & 14.9258716573928 & 1.07412834260725 \tabularnewline
25 & 17 & 15.0507775478332 & 1.94922245216681 \tabularnewline
26 & 17 & 17.1226675024419 & -0.12266750244187 \tabularnewline
27 & 16 & 15.1719625794025 & 0.828037420597534 \tabularnewline
28 & 15 & 16.2322744173616 & -1.23227441736162 \tabularnewline
29 & 16 & 15.6735790184592 & 0.32642098154082 \tabularnewline
30 & 14 & 14.1623686020954 & -0.16236860209539 \tabularnewline
31 & 15 & 15.7611294974257 & -0.761129497425708 \tabularnewline
32 & 12 & 12.4344966115287 & -0.434496611528665 \tabularnewline
33 & 14 & 15.2474750141503 & -1.24747501415034 \tabularnewline
34 & 16 & 15.66956066276 & 0.330439337240045 \tabularnewline
35 & 14 & 15.846746778461 & -1.84674677846099 \tabularnewline
36 & 7 & 13.1294441128628 & -6.12944411286285 \tabularnewline
37 & 10 & 11.2403339164082 & -1.24033391640817 \tabularnewline
38 & 14 & 15.9552045061731 & -1.95520450617308 \tabularnewline
39 & 16 & 14.460382487547 & 1.539617512453 \tabularnewline
40 & 16 & 14.6967934168263 & 1.30320658317367 \tabularnewline
41 & 16 & 15.1340735376307 & 0.865926462369281 \tabularnewline
42 & 14 & 15.6998242724917 & -1.6998242724917 \tabularnewline
43 & 20 & 17.8411498207178 & 2.15885017928224 \tabularnewline
44 & 14 & 14.302720012127 & -0.30272001212702 \tabularnewline
45 & 14 & 14.9766653050329 & -0.976665305032891 \tabularnewline
46 & 11 & 15.7320349448778 & -4.73203494487779 \tabularnewline
47 & 14 & 16.4793529564157 & -2.47935295641567 \tabularnewline
48 & 15 & 15.0905052063921 & -0.0905052063921116 \tabularnewline
49 & 16 & 15.3929572177435 & 0.60704278225651 \tabularnewline
50 & 14 & 16.1326003647915 & -2.13260036479145 \tabularnewline
51 & 16 & 16.6306810549274 & -0.630681054927408 \tabularnewline
52 & 14 & 14.2769318653351 & -0.276931865335081 \tabularnewline
53 & 12 & 14.7945038858262 & -2.79450388582624 \tabularnewline
54 & 16 & 15.2178444480798 & 0.782155551920211 \tabularnewline
55 & 9 & 11.6614338671926 & -2.66143386719262 \tabularnewline
56 & 14 & 12.7394773464981 & 1.26052265350191 \tabularnewline
57 & 16 & 16.1222557983765 & -0.122255798376522 \tabularnewline
58 & 16 & 15.1444347077369 & 0.855565292263081 \tabularnewline
59 & 15 & 15.4143298476654 & -0.414329847665403 \tabularnewline
60 & 16 & 14.2550499564612 & 1.74495004353878 \tabularnewline
61 & 12 & 11.5959828000228 & 0.404017199977248 \tabularnewline
62 & 16 & 16.065130416496 & -0.0651304164960116 \tabularnewline
63 & 16 & 16.5870071489013 & -0.58700714890133 \tabularnewline
64 & 14 & 14.4455526718206 & -0.445552671820646 \tabularnewline
65 & 16 & 15.4345363153821 & 0.565463684617947 \tabularnewline
66 & 17 & 16.0539505635708 & 0.94604943642922 \tabularnewline
67 & 18 & 16.0964504653459 & 1.90354953465413 \tabularnewline
68 & 18 & 14.7125298938205 & 3.28747010617947 \tabularnewline
69 & 12 & 15.8577474059578 & -3.85774740595779 \tabularnewline
70 & 16 & 15.4417155551586 & 0.558284444841393 \tabularnewline
71 & 10 & 13.46605389867 & -3.46605389866999 \tabularnewline
72 & 14 & 14.3440879074617 & -0.344087907461718 \tabularnewline
73 & 18 & 16.6101401030141 & 1.38985989698593 \tabularnewline
74 & 18 & 17.1447556606353 & 0.855244339364704 \tabularnewline
75 & 16 & 15.7525040047185 & 0.247495995281517 \tabularnewline
76 & 17 & 13.9769597260743 & 3.02304027392566 \tabularnewline
77 & 16 & 16.3539465521984 & -0.353946552198415 \tabularnewline
78 & 16 & 14.4957606756029 & 1.50423932439705 \tabularnewline
79 & 13 & 14.9252204065366 & -1.92522040653659 \tabularnewline
80 & 16 & 15.8998078866482 & 0.100192113351822 \tabularnewline
81 & 16 & 15.5690069344 & 0.430993065599995 \tabularnewline
82 & 20 & 16.6265451460867 & 3.37345485391334 \tabularnewline
83 & 16 & 15.6135601578099 & 0.386439842190099 \tabularnewline
84 & 15 & 15.942309254944 & -0.942309254944002 \tabularnewline
85 & 15 & 14.7214068822298 & 0.278593117770169 \tabularnewline
86 & 16 & 14.265723714487 & 1.73427628551301 \tabularnewline
87 & 14 & 14.2411424061278 & -0.241142406127762 \tabularnewline
88 & 16 & 15.2982678960847 & 0.701732103915345 \tabularnewline
89 & 16 & 14.5356515873428 & 1.46434841265718 \tabularnewline
90 & 15 & 14.2982591692444 & 0.70174083075564 \tabularnewline
91 & 12 & 13.5136626167341 & -1.51366261673408 \tabularnewline
92 & 17 & 16.8710838889034 & 0.128916111096644 \tabularnewline
93 & 16 & 15.5304467192012 & 0.469553280798842 \tabularnewline
94 & 15 & 15.3093218733226 & -0.309321873322577 \tabularnewline
95 & 13 & 15.1136501048415 & -2.11365010484149 \tabularnewline
96 & 16 & 15.1252644367449 & 0.874735563255112 \tabularnewline
97 & 16 & 15.749068481459 & 0.250931518540971 \tabularnewline
98 & 16 & 14.1122804742398 & 1.88771952576025 \tabularnewline
99 & 16 & 16.0454791617202 & -0.0454791617202046 \tabularnewline
100 & 14 & 14.2939963578046 & -0.29399635780461 \tabularnewline
101 & 16 & 17.2105859845917 & -1.21058598459166 \tabularnewline
102 & 16 & 14.7589932645187 & 1.24100673548131 \tabularnewline
103 & 20 & 17.3246386154093 & 2.67536138459074 \tabularnewline
104 & 15 & 14.4684858657273 & 0.53151413427267 \tabularnewline
105 & 16 & 14.5904142059917 & 1.40958579400833 \tabularnewline
106 & 13 & 15.3227667957045 & -2.32276679570448 \tabularnewline
107 & 17 & 15.9486233003349 & 1.05137669966505 \tabularnewline
108 & 16 & 15.8587784648397 & 0.141221535160284 \tabularnewline
109 & 16 & 14.4120523356351 & 1.58794766436487 \tabularnewline
110 & 12 & 12.1964544508171 & -0.196454450817143 \tabularnewline
111 & 16 & 14.933313754473 & 1.06668624552697 \tabularnewline
112 & 16 & 15.7855049760575 & 0.214495023942469 \tabularnewline
113 & 17 & 14.5256197339726 & 2.47438026602743 \tabularnewline
114 & 13 & 14.8173484505667 & -1.81734845056674 \tabularnewline
115 & 12 & 14.7647884148948 & -2.76478841489482 \tabularnewline
116 & 18 & 16.4900759527493 & 1.50992404725069 \tabularnewline
117 & 14 & 15.3813291512151 & -1.38132915121514 \tabularnewline
118 & 14 & 12.9586320606011 & 1.04136793939891 \tabularnewline
119 & 13 & 14.8386266605223 & -1.8386266605223 \tabularnewline
120 & 16 & 15.6558892729365 & 0.344110727063514 \tabularnewline
121 & 13 & 14.2037502379512 & -1.20375023795119 \tabularnewline
122 & 16 & 15.552522531727 & 0.447477468272963 \tabularnewline
123 & 13 & 15.8522562553337 & -2.85225625533373 \tabularnewline
124 & 16 & 16.8580746880549 & -0.858074688054904 \tabularnewline
125 & 15 & 15.7970057268425 & -0.797005726842471 \tabularnewline
126 & 16 & 16.6590948395921 & -0.659094839592084 \tabularnewline
127 & 15 & 15.2831603333916 & -0.283160333391556 \tabularnewline
128 & 17 & 16.0683350606688 & 0.931664939331157 \tabularnewline
129 & 15 & 13.7139521483106 & 1.28604785168944 \tabularnewline
130 & 12 & 14.8217701009547 & -2.82177010095473 \tabularnewline
131 & 16 & 14.0731838616798 & 1.92681613832023 \tabularnewline
132 & 10 & 13.3138499733924 & -3.31384997339237 \tabularnewline
133 & 16 & 14.0056077097642 & 1.99439229023575 \tabularnewline
134 & 12 & 13.8296952843155 & -1.82969528431552 \tabularnewline
135 & 14 & 15.5431308259074 & -1.54313082590736 \tabularnewline
136 & 15 & 15.0032511586895 & -0.00325115868948473 \tabularnewline
137 & 13 & 12.1080045732463 & 0.891995426753724 \tabularnewline
138 & 15 & 14.229272065769 & 0.770727934231032 \tabularnewline
139 & 11 & 13.012019927351 & -2.01201992735102 \tabularnewline
140 & 12 & 13.1657858946669 & -1.16578589466692 \tabularnewline
141 & 8 & 13.2512654852917 & -5.2512654852917 \tabularnewline
142 & 16 & 12.8915544796767 & 3.10844552032327 \tabularnewline
143 & 15 & 13.2618471584858 & 1.73815284151421 \tabularnewline
144 & 17 & 16.422969299628 & 0.577030700371982 \tabularnewline
145 & 16 & 14.6409607770393 & 1.35903922296071 \tabularnewline
146 & 10 & 14.0109736875882 & -4.01097368758818 \tabularnewline
147 & 18 & 15.70944305382 & 2.29055694617995 \tabularnewline
148 & 13 & 14.9904048426835 & -1.99040484268353 \tabularnewline
149 & 16 & 15.0008260384943 & 0.999173961505709 \tabularnewline
150 & 13 & 12.9646236657264 & 0.0353763342736232 \tabularnewline
151 & 10 & 12.9625623101176 & -2.9625623101176 \tabularnewline
152 & 15 & 16.2039382271053 & -1.20393822710529 \tabularnewline
153 & 16 & 13.8990558638793 & 2.1009441361207 \tabularnewline
154 & 16 & 11.8746050067733 & 4.12539499322673 \tabularnewline
155 & 14 & 12.3285821071689 & 1.67141789283114 \tabularnewline
156 & 10 & 12.3589208158072 & -2.35892081580717 \tabularnewline
157 & 17 & 16.569409359931 & 0.43059064006902 \tabularnewline
158 & 13 & 11.5005241990658 & 1.49947580093417 \tabularnewline
159 & 15 & 13.6034237336448 & 1.39657626635522 \tabularnewline
160 & 16 & 14.881957571362 & 1.118042428638 \tabularnewline
161 & 12 & 12.4755436067409 & -0.475543606740932 \tabularnewline
162 & 13 & 12.4419846774223 & 0.558015322577722 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200496&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]16.3170547930121[/C][C]-3.31705479301213[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.2549378195265[/C][C]-0.254937819526474[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.4884782520462[/C][C]2.51152174795377[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.1363501387885[/C][C]2.86364986121146[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.8436052409297[/C][C]-1.84360524092969[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.1761986463208[/C][C]-2.17619864632084[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.2889243298414[/C][C]3.71107567015857[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9266444909602[/C][C]-1.92664449096021[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1828846725128[/C][C]-2.18288467251278[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.852272305717[/C][C]2.14772769428302[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4819901379404[/C][C]0.518009862059594[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.3724673903235[/C][C]-0.37246739032346[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6959471043433[/C][C]0.304052895656717[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4783133933391[/C][C]0.521686606660916[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.605110215184[/C][C]-0.605110215184028[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2354752836093[/C][C]-0.235475283609272[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.825983070626[/C][C]0.17401692937405[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4073517903786[/C][C]3.59264820962139[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.6105362176761[/C][C]2.38946378232391[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3843566558244[/C][C]0.6156433441756[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.4297780664747[/C][C]0.570221933525306[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.9839245657715[/C][C]1.01607543422849[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.5835324750196[/C][C]2.41646752498041[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.9258716573928[/C][C]1.07412834260725[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.0507775478332[/C][C]1.94922245216681[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.1226675024419[/C][C]-0.12266750244187[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.1719625794025[/C][C]0.828037420597534[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.2322744173616[/C][C]-1.23227441736162[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.6735790184592[/C][C]0.32642098154082[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1623686020954[/C][C]-0.16236860209539[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7611294974257[/C][C]-0.761129497425708[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4344966115287[/C][C]-0.434496611528665[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.2474750141503[/C][C]-1.24747501415034[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.66956066276[/C][C]0.330439337240045[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.846746778461[/C][C]-1.84674677846099[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.1294441128628[/C][C]-6.12944411286285[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.2403339164082[/C][C]-1.24033391640817[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.9552045061731[/C][C]-1.95520450617308[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.460382487547[/C][C]1.539617512453[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6967934168263[/C][C]1.30320658317367[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1340735376307[/C][C]0.865926462369281[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.6998242724917[/C][C]-1.6998242724917[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.8411498207178[/C][C]2.15885017928224[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.302720012127[/C][C]-0.30272001212702[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9766653050329[/C][C]-0.976665305032891[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.7320349448778[/C][C]-4.73203494487779[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4793529564157[/C][C]-2.47935295641567[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0905052063921[/C][C]-0.0905052063921116[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.3929572177435[/C][C]0.60704278225651[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.1326003647915[/C][C]-2.13260036479145[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6306810549274[/C][C]-0.630681054927408[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2769318653351[/C][C]-0.276931865335081[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.7945038858262[/C][C]-2.79450388582624[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.2178444480798[/C][C]0.782155551920211[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.6614338671926[/C][C]-2.66143386719262[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.7394773464981[/C][C]1.26052265350191[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1222557983765[/C][C]-0.122255798376522[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.1444347077369[/C][C]0.855565292263081[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.4143298476654[/C][C]-0.414329847665403[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2550499564612[/C][C]1.74495004353878[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5959828000228[/C][C]0.404017199977248[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]16.065130416496[/C][C]-0.0651304164960116[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.5870071489013[/C][C]-0.58700714890133[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4455526718206[/C][C]-0.445552671820646[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4345363153821[/C][C]0.565463684617947[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.0539505635708[/C][C]0.94604943642922[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.0964504653459[/C][C]1.90354953465413[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.7125298938205[/C][C]3.28747010617947[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8577474059578[/C][C]-3.85774740595779[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4417155551586[/C][C]0.558284444841393[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.46605389867[/C][C]-3.46605389866999[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3440879074617[/C][C]-0.344087907461718[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.6101401030141[/C][C]1.38985989698593[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1447556606353[/C][C]0.855244339364704[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.7525040047185[/C][C]0.247495995281517[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.9769597260743[/C][C]3.02304027392566[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3539465521984[/C][C]-0.353946552198415[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4957606756029[/C][C]1.50423932439705[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.9252204065366[/C][C]-1.92522040653659[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.8998078866482[/C][C]0.100192113351822[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5690069344[/C][C]0.430993065599995[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.6265451460867[/C][C]3.37345485391334[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.6135601578099[/C][C]0.386439842190099[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.942309254944[/C][C]-0.942309254944002[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7214068822298[/C][C]0.278593117770169[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.265723714487[/C][C]1.73427628551301[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.2411424061278[/C][C]-0.241142406127762[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2982678960847[/C][C]0.701732103915345[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5356515873428[/C][C]1.46434841265718[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.2982591692444[/C][C]0.70174083075564[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.5136626167341[/C][C]-1.51366261673408[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.8710838889034[/C][C]0.128916111096644[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.5304467192012[/C][C]0.469553280798842[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.3093218733226[/C][C]-0.309321873322577[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1136501048415[/C][C]-2.11365010484149[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.1252644367449[/C][C]0.874735563255112[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.749068481459[/C][C]0.250931518540971[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1122804742398[/C][C]1.88771952576025[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.0454791617202[/C][C]-0.0454791617202046[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.2939963578046[/C][C]-0.29399635780461[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2105859845917[/C][C]-1.21058598459166[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7589932645187[/C][C]1.24100673548131[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3246386154093[/C][C]2.67536138459074[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.4684858657273[/C][C]0.53151413427267[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5904142059917[/C][C]1.40958579400833[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3227667957045[/C][C]-2.32276679570448[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9486233003349[/C][C]1.05137669966505[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8587784648397[/C][C]0.141221535160284[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.4120523356351[/C][C]1.58794766436487[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.1964544508171[/C][C]-0.196454450817143[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.933313754473[/C][C]1.06668624552697[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.7855049760575[/C][C]0.214495023942469[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5256197339726[/C][C]2.47438026602743[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.8173484505667[/C][C]-1.81734845056674[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.7647884148948[/C][C]-2.76478841489482[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.4900759527493[/C][C]1.50992404725069[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.3813291512151[/C][C]-1.38132915121514[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.9586320606011[/C][C]1.04136793939891[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.8386266605223[/C][C]-1.8386266605223[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6558892729365[/C][C]0.344110727063514[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.2037502379512[/C][C]-1.20375023795119[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.552522531727[/C][C]0.447477468272963[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8522562553337[/C][C]-2.85225625533373[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.8580746880549[/C][C]-0.858074688054904[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.7970057268425[/C][C]-0.797005726842471[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.6590948395921[/C][C]-0.659094839592084[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.2831603333916[/C][C]-0.283160333391556[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0683350606688[/C][C]0.931664939331157[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.7139521483106[/C][C]1.28604785168944[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.8217701009547[/C][C]-2.82177010095473[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0731838616798[/C][C]1.92681613832023[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3138499733924[/C][C]-3.31384997339237[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.0056077097642[/C][C]1.99439229023575[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8296952843155[/C][C]-1.82969528431552[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.5431308259074[/C][C]-1.54313082590736[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.0032511586895[/C][C]-0.00325115868948473[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.1080045732463[/C][C]0.891995426753724[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.229272065769[/C][C]0.770727934231032[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.012019927351[/C][C]-2.01201992735102[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.1657858946669[/C][C]-1.16578589466692[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2512654852917[/C][C]-5.2512654852917[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.8915544796767[/C][C]3.10844552032327[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.2618471584858[/C][C]1.73815284151421[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.422969299628[/C][C]0.577030700371982[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.6409607770393[/C][C]1.35903922296071[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.0109736875882[/C][C]-4.01097368758818[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.70944305382[/C][C]2.29055694617995[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.9904048426835[/C][C]-1.99040484268353[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.0008260384943[/C][C]0.999173961505709[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.9646236657264[/C][C]0.0353763342736232[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.9625623101176[/C][C]-2.9625623101176[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.2039382271053[/C][C]-1.20393822710529[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.8990558638793[/C][C]2.1009441361207[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8746050067733[/C][C]4.12539499322673[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.3285821071689[/C][C]1.67141789283114[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.3589208158072[/C][C]-2.35892081580717[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.569409359931[/C][C]0.43059064006902[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.5005241990658[/C][C]1.49947580093417[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.6034237336448[/C][C]1.39657626635522[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.881957571362[/C][C]1.118042428638[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.4755436067409[/C][C]-0.475543606740932[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.4419846774223[/C][C]0.558015322577722[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200496&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200496&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
11316.3170547930121-3.31705479301213
21616.2549378195265-0.254937819526474
31916.48847825204622.51152174795377
41512.13635013878852.86364986121146
51415.8436052409297-1.84360524092969
61315.1761986463208-2.17619864632084
71915.28892432984143.71107567015857
81516.9266444909602-1.92664449096021
91416.1828846725128-2.18288467251278
101512.8522723057172.14772769428302
111615.48199013794040.518009862059594
121616.3724673903235-0.37246739032346
131615.69594710434330.304052895656717
141615.47831339333910.521686606660916
151717.605110215184-0.605110215184028
161515.2354752836093-0.235475283609272
171514.8259830706260.17401692937405
182016.40735179037863.59264820962139
191815.61053621767612.38946378232391
201615.38435665582440.6156433441756
211615.42977806647470.570221933525306
221614.98392456577151.01607543422849
231916.58353247501962.41646752498041
241614.92587165739281.07412834260725
251715.05077754783321.94922245216681
261717.1226675024419-0.12266750244187
271615.17196257940250.828037420597534
281516.2322744173616-1.23227441736162
291615.67357901845920.32642098154082
301414.1623686020954-0.16236860209539
311515.7611294974257-0.761129497425708
321212.4344966115287-0.434496611528665
331415.2474750141503-1.24747501415034
341615.669560662760.330439337240045
351415.846746778461-1.84674677846099
36713.1294441128628-6.12944411286285
371011.2403339164082-1.24033391640817
381415.9552045061731-1.95520450617308
391614.4603824875471.539617512453
401614.69679341682631.30320658317367
411615.13407353763070.865926462369281
421415.6998242724917-1.6998242724917
432017.84114982071782.15885017928224
441414.302720012127-0.30272001212702
451414.9766653050329-0.976665305032891
461115.7320349448778-4.73203494487779
471416.4793529564157-2.47935295641567
481515.0905052063921-0.0905052063921116
491615.39295721774350.60704278225651
501416.1326003647915-2.13260036479145
511616.6306810549274-0.630681054927408
521414.2769318653351-0.276931865335081
531214.7945038858262-2.79450388582624
541615.21784444807980.782155551920211
55911.6614338671926-2.66143386719262
561412.73947734649811.26052265350191
571616.1222557983765-0.122255798376522
581615.14443470773690.855565292263081
591515.4143298476654-0.414329847665403
601614.25504995646121.74495004353878
611211.59598280002280.404017199977248
621616.065130416496-0.0651304164960116
631616.5870071489013-0.58700714890133
641414.4455526718206-0.445552671820646
651615.43453631538210.565463684617947
661716.05395056357080.94604943642922
671816.09645046534591.90354953465413
681814.71252989382053.28747010617947
691215.8577474059578-3.85774740595779
701615.44171555515860.558284444841393
711013.46605389867-3.46605389866999
721414.3440879074617-0.344087907461718
731816.61014010301411.38985989698593
741817.14475566063530.855244339364704
751615.75250400471850.247495995281517
761713.97695972607433.02304027392566
771616.3539465521984-0.353946552198415
781614.49576067560291.50423932439705
791314.9252204065366-1.92522040653659
801615.89980788664820.100192113351822
811615.56900693440.430993065599995
822016.62654514608673.37345485391334
831615.61356015780990.386439842190099
841515.942309254944-0.942309254944002
851514.72140688222980.278593117770169
861614.2657237144871.73427628551301
871414.2411424061278-0.241142406127762
881615.29826789608470.701732103915345
891614.53565158734281.46434841265718
901514.29825916924440.70174083075564
911213.5136626167341-1.51366261673408
921716.87108388890340.128916111096644
931615.53044671920120.469553280798842
941515.3093218733226-0.309321873322577
951315.1136501048415-2.11365010484149
961615.12526443674490.874735563255112
971615.7490684814590.250931518540971
981614.11228047423981.88771952576025
991616.0454791617202-0.0454791617202046
1001414.2939963578046-0.29399635780461
1011617.2105859845917-1.21058598459166
1021614.75899326451871.24100673548131
1032017.32463861540932.67536138459074
1041514.46848586572730.53151413427267
1051614.59041420599171.40958579400833
1061315.3227667957045-2.32276679570448
1071715.94862330033491.05137669966505
1081615.85877846483970.141221535160284
1091614.41205233563511.58794766436487
1101212.1964544508171-0.196454450817143
1111614.9333137544731.06668624552697
1121615.78550497605750.214495023942469
1131714.52561973397262.47438026602743
1141314.8173484505667-1.81734845056674
1151214.7647884148948-2.76478841489482
1161816.49007595274931.50992404725069
1171415.3813291512151-1.38132915121514
1181412.95863206060111.04136793939891
1191314.8386266605223-1.8386266605223
1201615.65588927293650.344110727063514
1211314.2037502379512-1.20375023795119
1221615.5525225317270.447477468272963
1231315.8522562553337-2.85225625533373
1241616.8580746880549-0.858074688054904
1251515.7970057268425-0.797005726842471
1261616.6590948395921-0.659094839592084
1271515.2831603333916-0.283160333391556
1281716.06833506066880.931664939331157
1291513.71395214831061.28604785168944
1301214.8217701009547-2.82177010095473
1311614.07318386167981.92681613832023
1321013.3138499733924-3.31384997339237
1331614.00560770976421.99439229023575
1341213.8296952843155-1.82969528431552
1351415.5431308259074-1.54313082590736
1361515.0032511586895-0.00325115868948473
1371312.10800457324630.891995426753724
1381514.2292720657690.770727934231032
1391113.012019927351-2.01201992735102
1401213.1657858946669-1.16578589466692
141813.2512654852917-5.2512654852917
1421612.89155447967673.10844552032327
1431513.26184715848581.73815284151421
1441716.4229692996280.577030700371982
1451614.64096077703931.35903922296071
1461014.0109736875882-4.01097368758818
1471815.709443053822.29055694617995
1481314.9904048426835-1.99040484268353
1491615.00082603849430.999173961505709
1501312.96462366572640.0353763342736232
1511012.9625623101176-2.9625623101176
1521516.2039382271053-1.20393822710529
1531613.89905586387932.1009441361207
1541611.87460500677334.12539499322673
1551412.32858210716891.67141789283114
1561012.3589208158072-2.35892081580717
1571716.5694093599310.43059064006902
1581311.50052419906581.49947580093417
1591513.60342373364481.39657626635522
1601614.8819575713621.118042428638
1611212.4755436067409-0.475543606740932
1621312.44198467742230.558015322577722






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.7344025528905120.5311948942189760.265597447109488
140.5872031319675340.8255937360649310.412796868032466
150.4689627142748230.9379254285496450.531037285725177
160.4016462209861090.8032924419722180.598353779013891
170.3047302819435460.6094605638870920.695269718056454
180.3865237422761720.7730474845523430.613476257723828
190.3274701900198060.6549403800396120.672529809980194
200.2466738990004760.4933477980009520.753326100999524
210.1899240995503270.3798481991006540.810075900449673
220.1913760613315740.3827521226631470.808623938668426
230.3808152824769450.7616305649538890.619184717523055
240.4384202989463220.8768405978926440.561579701053678
250.3933605375914690.7867210751829370.606639462408531
260.3302567885293110.6605135770586220.669743211470689
270.2813516184910790.5627032369821580.718648381508921
280.4351484988111270.8702969976222540.564851501188873
290.3771347888865220.7542695777730440.622865211113478
300.4948965337995690.9897930675991390.50510346620043
310.4416555085411880.8833110170823770.558344491458811
320.4078247382849660.8156494765699310.592175261715034
330.3941668352291840.7883336704583690.605833164770816
340.3625867434714960.7251734869429910.637413256528504
350.3161642167557930.6323284335115850.683835783244207
360.8607128560048830.2785742879902340.139287143995117
370.8324718114964340.3350563770071330.167528188503566
380.8124566105029910.3750867789940190.187543389497009
390.8456965413818870.3086069172362250.154303458618113
400.8317113361582590.3365773276834810.168288663841741
410.8009359096649680.3981281806700650.199064090335032
420.774512875319850.45097424936030.22548712468015
430.7994701032053350.4010597935893290.200529896794665
440.7589328673221880.4821342653556240.241067132677812
450.7319349434834790.5361301130330420.268065056516521
460.8715477125709330.2569045748581340.128452287429067
470.8951833123692520.2096333752614960.104816687630748
480.8734461583073170.2531076833853660.126553841692683
490.8729407852746710.2541184294506580.127059214725329
500.8659497415146970.2681005169706060.134050258485303
510.8397032931562680.3205934136874650.160296706843732
520.8108904149518360.3782191700963280.189109585048164
530.8213526651884510.3572946696230980.178647334811549
540.7924189610902130.4151620778195730.207581038909787
550.8094343703154950.381131259369010.190565629684505
560.7897721870340110.4204556259319790.210227812965989
570.7529382870942180.4941234258115640.247061712905782
580.7383387273220780.5233225453558440.261661272677922
590.7080041124461520.5839917751076970.291995887553848
600.7134069820922330.5731860358155350.286593017907767
610.6788799223171090.6422401553657820.321120077682891
620.6427327321860590.7145345356278830.357267267813941
630.6098431012248870.7803137975502250.390156898775113
640.565831983956610.8683360320867810.43416801604339
650.5264706146249030.9470587707501940.473529385375097
660.4957121361805290.9914242723610590.504287863819471
670.4972574106166630.9945148212333260.502742589383337
680.6444788058956880.7110423882086240.355521194104312
690.7486505950558420.5026988098883160.251349404944158
700.7163124908939510.5673750182120970.283687509106049
710.8174055333266370.3651889333467260.182594466673363
720.787021122118440.4259577557631190.21297887788156
730.7805704156730150.438859168653970.219429584326985
740.7634462024481260.4731075951037480.236553797551874
750.7257844931745820.5484310136508360.274215506825418
760.7631050246263260.4737899507473490.236894975373674
770.7261311870146440.5477376259707120.273868812985356
780.7046084588356820.5907830823286370.295391541164318
790.7158021194399260.5683957611201480.284197880560074
800.674984683099350.65003063380130.32501531690065
810.6384759769395070.7230480461209870.361524023060493
820.7626771285244190.4746457429511620.237322871475581
830.7275068103709230.5449863792581550.272493189629077
840.6972012272979860.6055975454040280.302798772702014
850.65589172694210.68821654611580.3441082730579
860.6431714337363730.7136571325272530.356828566263627
870.5988968139769060.8022063720461880.401103186023094
880.5565207308170730.8869585383658540.443479269182927
890.5330661727951140.9338676544097710.466933827204886
900.4937800566566720.9875601133133430.506219943343328
910.4827642365127710.9655284730255420.517235763487229
920.439430667785070.8788613355701410.56056933221493
930.3953920003832940.7907840007665880.604607999616706
940.3518621581642860.7037243163285730.648137841835714
950.3734718596350770.7469437192701550.626528140364923
960.336509490301710.673018980603420.66349050969829
970.2955433493590810.5910866987181630.704456650640918
980.2889091693941660.5778183387883330.711090830605834
990.2486704284812240.4973408569624470.751329571518776
1000.2158218475316440.4316436950632870.784178152468356
1010.1938229682799530.3876459365599060.806177031720047
1020.1755111115904460.3510222231808910.824488888409554
1030.2140581797471690.4281163594943380.785941820252831
1040.1806051243479070.3612102486958140.819394875652093
1050.1719312123700630.3438624247401260.828068787629937
1060.183025969744530.3660519394890610.81697403025547
1070.1627412434710470.3254824869420940.837258756528953
1080.1435386845972050.287077369194410.856461315402795
1090.1389514446431290.2779028892862570.861048555356871
1100.136026976520060.2720539530401190.86397302347994
1110.1237714382542280.2475428765084560.876228561745772
1120.1063910427664180.2127820855328350.893608957233582
1130.1481185533025240.2962371066050480.851881446697476
1140.1383988010932290.2767976021864580.861601198906771
1150.1557769690523370.3115539381046740.844223030947663
1160.164901473186560.329802946373120.83509852681344
1170.1408117957575190.2816235915150380.859188204242481
1180.1380106208008380.2760212416016770.861989379199162
1190.1280092743427510.2560185486855030.871990725657249
1200.1359437616834450.271887523366890.864056238316555
1210.1106050000565490.2212100001130980.889394999943451
1220.0942046181651440.1884092363302880.905795381834856
1230.09001535978010220.1800307195602040.909984640219898
1240.06963227912559070.1392645582511810.930367720874409
1250.0528661537079820.1057323074159640.947133846292018
1260.03964553642885160.07929107285770310.960354463571148
1270.02870400654243590.05740801308487170.971295993457564
1280.02689070687634230.05378141375268470.973109293123658
1290.02971876156903730.05943752313807460.970281238430963
1300.029320305927590.05864061185517990.97067969407241
1310.0316028206171330.0632056412342660.968397179382867
1320.03397922074918720.06795844149837430.966020779250813
1330.0678848897293130.1357697794586260.932115110270687
1340.07000582977161520.140011659543230.929994170228385
1350.05247339883657710.1049467976731540.947526601163423
1360.04312782368257740.08625564736515480.956872176317423
1370.0301237841917880.06024756838357610.969876215808212
1380.03543151509597270.07086303019194540.964568484904027
1390.02911965496026760.05823930992053520.970880345039732
1400.01887282337515370.03774564675030740.981127176624846
1410.4990106697953690.9980213395907380.500989330204631
1420.4345559037258990.8691118074517980.565444096274101
1430.3636358236548560.7272716473097130.636364176345144
1440.2767639411164250.5535278822328490.723236058883575
1450.198560144567780.3971202891355610.80143985543222
1460.2154493718225310.4308987436450630.784550628177469
1470.2223847401949120.4447694803898230.777615259805088
1480.4702472787015630.9404945574031250.529752721298437
1490.6145141340590630.7709717318818740.385485865940937

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.734402552890512 & 0.531194894218976 & 0.265597447109488 \tabularnewline
14 & 0.587203131967534 & 0.825593736064931 & 0.412796868032466 \tabularnewline
15 & 0.468962714274823 & 0.937925428549645 & 0.531037285725177 \tabularnewline
16 & 0.401646220986109 & 0.803292441972218 & 0.598353779013891 \tabularnewline
17 & 0.304730281943546 & 0.609460563887092 & 0.695269718056454 \tabularnewline
18 & 0.386523742276172 & 0.773047484552343 & 0.613476257723828 \tabularnewline
19 & 0.327470190019806 & 0.654940380039612 & 0.672529809980194 \tabularnewline
20 & 0.246673899000476 & 0.493347798000952 & 0.753326100999524 \tabularnewline
21 & 0.189924099550327 & 0.379848199100654 & 0.810075900449673 \tabularnewline
22 & 0.191376061331574 & 0.382752122663147 & 0.808623938668426 \tabularnewline
23 & 0.380815282476945 & 0.761630564953889 & 0.619184717523055 \tabularnewline
24 & 0.438420298946322 & 0.876840597892644 & 0.561579701053678 \tabularnewline
25 & 0.393360537591469 & 0.786721075182937 & 0.606639462408531 \tabularnewline
26 & 0.330256788529311 & 0.660513577058622 & 0.669743211470689 \tabularnewline
27 & 0.281351618491079 & 0.562703236982158 & 0.718648381508921 \tabularnewline
28 & 0.435148498811127 & 0.870296997622254 & 0.564851501188873 \tabularnewline
29 & 0.377134788886522 & 0.754269577773044 & 0.622865211113478 \tabularnewline
30 & 0.494896533799569 & 0.989793067599139 & 0.50510346620043 \tabularnewline
31 & 0.441655508541188 & 0.883311017082377 & 0.558344491458811 \tabularnewline
32 & 0.407824738284966 & 0.815649476569931 & 0.592175261715034 \tabularnewline
33 & 0.394166835229184 & 0.788333670458369 & 0.605833164770816 \tabularnewline
34 & 0.362586743471496 & 0.725173486942991 & 0.637413256528504 \tabularnewline
35 & 0.316164216755793 & 0.632328433511585 & 0.683835783244207 \tabularnewline
36 & 0.860712856004883 & 0.278574287990234 & 0.139287143995117 \tabularnewline
37 & 0.832471811496434 & 0.335056377007133 & 0.167528188503566 \tabularnewline
38 & 0.812456610502991 & 0.375086778994019 & 0.187543389497009 \tabularnewline
39 & 0.845696541381887 & 0.308606917236225 & 0.154303458618113 \tabularnewline
40 & 0.831711336158259 & 0.336577327683481 & 0.168288663841741 \tabularnewline
41 & 0.800935909664968 & 0.398128180670065 & 0.199064090335032 \tabularnewline
42 & 0.77451287531985 & 0.4509742493603 & 0.22548712468015 \tabularnewline
43 & 0.799470103205335 & 0.401059793589329 & 0.200529896794665 \tabularnewline
44 & 0.758932867322188 & 0.482134265355624 & 0.241067132677812 \tabularnewline
45 & 0.731934943483479 & 0.536130113033042 & 0.268065056516521 \tabularnewline
46 & 0.871547712570933 & 0.256904574858134 & 0.128452287429067 \tabularnewline
47 & 0.895183312369252 & 0.209633375261496 & 0.104816687630748 \tabularnewline
48 & 0.873446158307317 & 0.253107683385366 & 0.126553841692683 \tabularnewline
49 & 0.872940785274671 & 0.254118429450658 & 0.127059214725329 \tabularnewline
50 & 0.865949741514697 & 0.268100516970606 & 0.134050258485303 \tabularnewline
51 & 0.839703293156268 & 0.320593413687465 & 0.160296706843732 \tabularnewline
52 & 0.810890414951836 & 0.378219170096328 & 0.189109585048164 \tabularnewline
53 & 0.821352665188451 & 0.357294669623098 & 0.178647334811549 \tabularnewline
54 & 0.792418961090213 & 0.415162077819573 & 0.207581038909787 \tabularnewline
55 & 0.809434370315495 & 0.38113125936901 & 0.190565629684505 \tabularnewline
56 & 0.789772187034011 & 0.420455625931979 & 0.210227812965989 \tabularnewline
57 & 0.752938287094218 & 0.494123425811564 & 0.247061712905782 \tabularnewline
58 & 0.738338727322078 & 0.523322545355844 & 0.261661272677922 \tabularnewline
59 & 0.708004112446152 & 0.583991775107697 & 0.291995887553848 \tabularnewline
60 & 0.713406982092233 & 0.573186035815535 & 0.286593017907767 \tabularnewline
61 & 0.678879922317109 & 0.642240155365782 & 0.321120077682891 \tabularnewline
62 & 0.642732732186059 & 0.714534535627883 & 0.357267267813941 \tabularnewline
63 & 0.609843101224887 & 0.780313797550225 & 0.390156898775113 \tabularnewline
64 & 0.56583198395661 & 0.868336032086781 & 0.43416801604339 \tabularnewline
65 & 0.526470614624903 & 0.947058770750194 & 0.473529385375097 \tabularnewline
66 & 0.495712136180529 & 0.991424272361059 & 0.504287863819471 \tabularnewline
67 & 0.497257410616663 & 0.994514821233326 & 0.502742589383337 \tabularnewline
68 & 0.644478805895688 & 0.711042388208624 & 0.355521194104312 \tabularnewline
69 & 0.748650595055842 & 0.502698809888316 & 0.251349404944158 \tabularnewline
70 & 0.716312490893951 & 0.567375018212097 & 0.283687509106049 \tabularnewline
71 & 0.817405533326637 & 0.365188933346726 & 0.182594466673363 \tabularnewline
72 & 0.78702112211844 & 0.425957755763119 & 0.21297887788156 \tabularnewline
73 & 0.780570415673015 & 0.43885916865397 & 0.219429584326985 \tabularnewline
74 & 0.763446202448126 & 0.473107595103748 & 0.236553797551874 \tabularnewline
75 & 0.725784493174582 & 0.548431013650836 & 0.274215506825418 \tabularnewline
76 & 0.763105024626326 & 0.473789950747349 & 0.236894975373674 \tabularnewline
77 & 0.726131187014644 & 0.547737625970712 & 0.273868812985356 \tabularnewline
78 & 0.704608458835682 & 0.590783082328637 & 0.295391541164318 \tabularnewline
79 & 0.715802119439926 & 0.568395761120148 & 0.284197880560074 \tabularnewline
80 & 0.67498468309935 & 0.6500306338013 & 0.32501531690065 \tabularnewline
81 & 0.638475976939507 & 0.723048046120987 & 0.361524023060493 \tabularnewline
82 & 0.762677128524419 & 0.474645742951162 & 0.237322871475581 \tabularnewline
83 & 0.727506810370923 & 0.544986379258155 & 0.272493189629077 \tabularnewline
84 & 0.697201227297986 & 0.605597545404028 & 0.302798772702014 \tabularnewline
85 & 0.6558917269421 & 0.6882165461158 & 0.3441082730579 \tabularnewline
86 & 0.643171433736373 & 0.713657132527253 & 0.356828566263627 \tabularnewline
87 & 0.598896813976906 & 0.802206372046188 & 0.401103186023094 \tabularnewline
88 & 0.556520730817073 & 0.886958538365854 & 0.443479269182927 \tabularnewline
89 & 0.533066172795114 & 0.933867654409771 & 0.466933827204886 \tabularnewline
90 & 0.493780056656672 & 0.987560113313343 & 0.506219943343328 \tabularnewline
91 & 0.482764236512771 & 0.965528473025542 & 0.517235763487229 \tabularnewline
92 & 0.43943066778507 & 0.878861335570141 & 0.56056933221493 \tabularnewline
93 & 0.395392000383294 & 0.790784000766588 & 0.604607999616706 \tabularnewline
94 & 0.351862158164286 & 0.703724316328573 & 0.648137841835714 \tabularnewline
95 & 0.373471859635077 & 0.746943719270155 & 0.626528140364923 \tabularnewline
96 & 0.33650949030171 & 0.67301898060342 & 0.66349050969829 \tabularnewline
97 & 0.295543349359081 & 0.591086698718163 & 0.704456650640918 \tabularnewline
98 & 0.288909169394166 & 0.577818338788333 & 0.711090830605834 \tabularnewline
99 & 0.248670428481224 & 0.497340856962447 & 0.751329571518776 \tabularnewline
100 & 0.215821847531644 & 0.431643695063287 & 0.784178152468356 \tabularnewline
101 & 0.193822968279953 & 0.387645936559906 & 0.806177031720047 \tabularnewline
102 & 0.175511111590446 & 0.351022223180891 & 0.824488888409554 \tabularnewline
103 & 0.214058179747169 & 0.428116359494338 & 0.785941820252831 \tabularnewline
104 & 0.180605124347907 & 0.361210248695814 & 0.819394875652093 \tabularnewline
105 & 0.171931212370063 & 0.343862424740126 & 0.828068787629937 \tabularnewline
106 & 0.18302596974453 & 0.366051939489061 & 0.81697403025547 \tabularnewline
107 & 0.162741243471047 & 0.325482486942094 & 0.837258756528953 \tabularnewline
108 & 0.143538684597205 & 0.28707736919441 & 0.856461315402795 \tabularnewline
109 & 0.138951444643129 & 0.277902889286257 & 0.861048555356871 \tabularnewline
110 & 0.13602697652006 & 0.272053953040119 & 0.86397302347994 \tabularnewline
111 & 0.123771438254228 & 0.247542876508456 & 0.876228561745772 \tabularnewline
112 & 0.106391042766418 & 0.212782085532835 & 0.893608957233582 \tabularnewline
113 & 0.148118553302524 & 0.296237106605048 & 0.851881446697476 \tabularnewline
114 & 0.138398801093229 & 0.276797602186458 & 0.861601198906771 \tabularnewline
115 & 0.155776969052337 & 0.311553938104674 & 0.844223030947663 \tabularnewline
116 & 0.16490147318656 & 0.32980294637312 & 0.83509852681344 \tabularnewline
117 & 0.140811795757519 & 0.281623591515038 & 0.859188204242481 \tabularnewline
118 & 0.138010620800838 & 0.276021241601677 & 0.861989379199162 \tabularnewline
119 & 0.128009274342751 & 0.256018548685503 & 0.871990725657249 \tabularnewline
120 & 0.135943761683445 & 0.27188752336689 & 0.864056238316555 \tabularnewline
121 & 0.110605000056549 & 0.221210000113098 & 0.889394999943451 \tabularnewline
122 & 0.094204618165144 & 0.188409236330288 & 0.905795381834856 \tabularnewline
123 & 0.0900153597801022 & 0.180030719560204 & 0.909984640219898 \tabularnewline
124 & 0.0696322791255907 & 0.139264558251181 & 0.930367720874409 \tabularnewline
125 & 0.052866153707982 & 0.105732307415964 & 0.947133846292018 \tabularnewline
126 & 0.0396455364288516 & 0.0792910728577031 & 0.960354463571148 \tabularnewline
127 & 0.0287040065424359 & 0.0574080130848717 & 0.971295993457564 \tabularnewline
128 & 0.0268907068763423 & 0.0537814137526847 & 0.973109293123658 \tabularnewline
129 & 0.0297187615690373 & 0.0594375231380746 & 0.970281238430963 \tabularnewline
130 & 0.02932030592759 & 0.0586406118551799 & 0.97067969407241 \tabularnewline
131 & 0.031602820617133 & 0.063205641234266 & 0.968397179382867 \tabularnewline
132 & 0.0339792207491872 & 0.0679584414983743 & 0.966020779250813 \tabularnewline
133 & 0.067884889729313 & 0.135769779458626 & 0.932115110270687 \tabularnewline
134 & 0.0700058297716152 & 0.14001165954323 & 0.929994170228385 \tabularnewline
135 & 0.0524733988365771 & 0.104946797673154 & 0.947526601163423 \tabularnewline
136 & 0.0431278236825774 & 0.0862556473651548 & 0.956872176317423 \tabularnewline
137 & 0.030123784191788 & 0.0602475683835761 & 0.969876215808212 \tabularnewline
138 & 0.0354315150959727 & 0.0708630301919454 & 0.964568484904027 \tabularnewline
139 & 0.0291196549602676 & 0.0582393099205352 & 0.970880345039732 \tabularnewline
140 & 0.0188728233751537 & 0.0377456467503074 & 0.981127176624846 \tabularnewline
141 & 0.499010669795369 & 0.998021339590738 & 0.500989330204631 \tabularnewline
142 & 0.434555903725899 & 0.869111807451798 & 0.565444096274101 \tabularnewline
143 & 0.363635823654856 & 0.727271647309713 & 0.636364176345144 \tabularnewline
144 & 0.276763941116425 & 0.553527882232849 & 0.723236058883575 \tabularnewline
145 & 0.19856014456778 & 0.397120289135561 & 0.80143985543222 \tabularnewline
146 & 0.215449371822531 & 0.430898743645063 & 0.784550628177469 \tabularnewline
147 & 0.222384740194912 & 0.444769480389823 & 0.777615259805088 \tabularnewline
148 & 0.470247278701563 & 0.940494557403125 & 0.529752721298437 \tabularnewline
149 & 0.614514134059063 & 0.770971731881874 & 0.385485865940937 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200496&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]13[/C][C]0.734402552890512[/C][C]0.531194894218976[/C][C]0.265597447109488[/C][/ROW]
[ROW][C]14[/C][C]0.587203131967534[/C][C]0.825593736064931[/C][C]0.412796868032466[/C][/ROW]
[ROW][C]15[/C][C]0.468962714274823[/C][C]0.937925428549645[/C][C]0.531037285725177[/C][/ROW]
[ROW][C]16[/C][C]0.401646220986109[/C][C]0.803292441972218[/C][C]0.598353779013891[/C][/ROW]
[ROW][C]17[/C][C]0.304730281943546[/C][C]0.609460563887092[/C][C]0.695269718056454[/C][/ROW]
[ROW][C]18[/C][C]0.386523742276172[/C][C]0.773047484552343[/C][C]0.613476257723828[/C][/ROW]
[ROW][C]19[/C][C]0.327470190019806[/C][C]0.654940380039612[/C][C]0.672529809980194[/C][/ROW]
[ROW][C]20[/C][C]0.246673899000476[/C][C]0.493347798000952[/C][C]0.753326100999524[/C][/ROW]
[ROW][C]21[/C][C]0.189924099550327[/C][C]0.379848199100654[/C][C]0.810075900449673[/C][/ROW]
[ROW][C]22[/C][C]0.191376061331574[/C][C]0.382752122663147[/C][C]0.808623938668426[/C][/ROW]
[ROW][C]23[/C][C]0.380815282476945[/C][C]0.761630564953889[/C][C]0.619184717523055[/C][/ROW]
[ROW][C]24[/C][C]0.438420298946322[/C][C]0.876840597892644[/C][C]0.561579701053678[/C][/ROW]
[ROW][C]25[/C][C]0.393360537591469[/C][C]0.786721075182937[/C][C]0.606639462408531[/C][/ROW]
[ROW][C]26[/C][C]0.330256788529311[/C][C]0.660513577058622[/C][C]0.669743211470689[/C][/ROW]
[ROW][C]27[/C][C]0.281351618491079[/C][C]0.562703236982158[/C][C]0.718648381508921[/C][/ROW]
[ROW][C]28[/C][C]0.435148498811127[/C][C]0.870296997622254[/C][C]0.564851501188873[/C][/ROW]
[ROW][C]29[/C][C]0.377134788886522[/C][C]0.754269577773044[/C][C]0.622865211113478[/C][/ROW]
[ROW][C]30[/C][C]0.494896533799569[/C][C]0.989793067599139[/C][C]0.50510346620043[/C][/ROW]
[ROW][C]31[/C][C]0.441655508541188[/C][C]0.883311017082377[/C][C]0.558344491458811[/C][/ROW]
[ROW][C]32[/C][C]0.407824738284966[/C][C]0.815649476569931[/C][C]0.592175261715034[/C][/ROW]
[ROW][C]33[/C][C]0.394166835229184[/C][C]0.788333670458369[/C][C]0.605833164770816[/C][/ROW]
[ROW][C]34[/C][C]0.362586743471496[/C][C]0.725173486942991[/C][C]0.637413256528504[/C][/ROW]
[ROW][C]35[/C][C]0.316164216755793[/C][C]0.632328433511585[/C][C]0.683835783244207[/C][/ROW]
[ROW][C]36[/C][C]0.860712856004883[/C][C]0.278574287990234[/C][C]0.139287143995117[/C][/ROW]
[ROW][C]37[/C][C]0.832471811496434[/C][C]0.335056377007133[/C][C]0.167528188503566[/C][/ROW]
[ROW][C]38[/C][C]0.812456610502991[/C][C]0.375086778994019[/C][C]0.187543389497009[/C][/ROW]
[ROW][C]39[/C][C]0.845696541381887[/C][C]0.308606917236225[/C][C]0.154303458618113[/C][/ROW]
[ROW][C]40[/C][C]0.831711336158259[/C][C]0.336577327683481[/C][C]0.168288663841741[/C][/ROW]
[ROW][C]41[/C][C]0.800935909664968[/C][C]0.398128180670065[/C][C]0.199064090335032[/C][/ROW]
[ROW][C]42[/C][C]0.77451287531985[/C][C]0.4509742493603[/C][C]0.22548712468015[/C][/ROW]
[ROW][C]43[/C][C]0.799470103205335[/C][C]0.401059793589329[/C][C]0.200529896794665[/C][/ROW]
[ROW][C]44[/C][C]0.758932867322188[/C][C]0.482134265355624[/C][C]0.241067132677812[/C][/ROW]
[ROW][C]45[/C][C]0.731934943483479[/C][C]0.536130113033042[/C][C]0.268065056516521[/C][/ROW]
[ROW][C]46[/C][C]0.871547712570933[/C][C]0.256904574858134[/C][C]0.128452287429067[/C][/ROW]
[ROW][C]47[/C][C]0.895183312369252[/C][C]0.209633375261496[/C][C]0.104816687630748[/C][/ROW]
[ROW][C]48[/C][C]0.873446158307317[/C][C]0.253107683385366[/C][C]0.126553841692683[/C][/ROW]
[ROW][C]49[/C][C]0.872940785274671[/C][C]0.254118429450658[/C][C]0.127059214725329[/C][/ROW]
[ROW][C]50[/C][C]0.865949741514697[/C][C]0.268100516970606[/C][C]0.134050258485303[/C][/ROW]
[ROW][C]51[/C][C]0.839703293156268[/C][C]0.320593413687465[/C][C]0.160296706843732[/C][/ROW]
[ROW][C]52[/C][C]0.810890414951836[/C][C]0.378219170096328[/C][C]0.189109585048164[/C][/ROW]
[ROW][C]53[/C][C]0.821352665188451[/C][C]0.357294669623098[/C][C]0.178647334811549[/C][/ROW]
[ROW][C]54[/C][C]0.792418961090213[/C][C]0.415162077819573[/C][C]0.207581038909787[/C][/ROW]
[ROW][C]55[/C][C]0.809434370315495[/C][C]0.38113125936901[/C][C]0.190565629684505[/C][/ROW]
[ROW][C]56[/C][C]0.789772187034011[/C][C]0.420455625931979[/C][C]0.210227812965989[/C][/ROW]
[ROW][C]57[/C][C]0.752938287094218[/C][C]0.494123425811564[/C][C]0.247061712905782[/C][/ROW]
[ROW][C]58[/C][C]0.738338727322078[/C][C]0.523322545355844[/C][C]0.261661272677922[/C][/ROW]
[ROW][C]59[/C][C]0.708004112446152[/C][C]0.583991775107697[/C][C]0.291995887553848[/C][/ROW]
[ROW][C]60[/C][C]0.713406982092233[/C][C]0.573186035815535[/C][C]0.286593017907767[/C][/ROW]
[ROW][C]61[/C][C]0.678879922317109[/C][C]0.642240155365782[/C][C]0.321120077682891[/C][/ROW]
[ROW][C]62[/C][C]0.642732732186059[/C][C]0.714534535627883[/C][C]0.357267267813941[/C][/ROW]
[ROW][C]63[/C][C]0.609843101224887[/C][C]0.780313797550225[/C][C]0.390156898775113[/C][/ROW]
[ROW][C]64[/C][C]0.56583198395661[/C][C]0.868336032086781[/C][C]0.43416801604339[/C][/ROW]
[ROW][C]65[/C][C]0.526470614624903[/C][C]0.947058770750194[/C][C]0.473529385375097[/C][/ROW]
[ROW][C]66[/C][C]0.495712136180529[/C][C]0.991424272361059[/C][C]0.504287863819471[/C][/ROW]
[ROW][C]67[/C][C]0.497257410616663[/C][C]0.994514821233326[/C][C]0.502742589383337[/C][/ROW]
[ROW][C]68[/C][C]0.644478805895688[/C][C]0.711042388208624[/C][C]0.355521194104312[/C][/ROW]
[ROW][C]69[/C][C]0.748650595055842[/C][C]0.502698809888316[/C][C]0.251349404944158[/C][/ROW]
[ROW][C]70[/C][C]0.716312490893951[/C][C]0.567375018212097[/C][C]0.283687509106049[/C][/ROW]
[ROW][C]71[/C][C]0.817405533326637[/C][C]0.365188933346726[/C][C]0.182594466673363[/C][/ROW]
[ROW][C]72[/C][C]0.78702112211844[/C][C]0.425957755763119[/C][C]0.21297887788156[/C][/ROW]
[ROW][C]73[/C][C]0.780570415673015[/C][C]0.43885916865397[/C][C]0.219429584326985[/C][/ROW]
[ROW][C]74[/C][C]0.763446202448126[/C][C]0.473107595103748[/C][C]0.236553797551874[/C][/ROW]
[ROW][C]75[/C][C]0.725784493174582[/C][C]0.548431013650836[/C][C]0.274215506825418[/C][/ROW]
[ROW][C]76[/C][C]0.763105024626326[/C][C]0.473789950747349[/C][C]0.236894975373674[/C][/ROW]
[ROW][C]77[/C][C]0.726131187014644[/C][C]0.547737625970712[/C][C]0.273868812985356[/C][/ROW]
[ROW][C]78[/C][C]0.704608458835682[/C][C]0.590783082328637[/C][C]0.295391541164318[/C][/ROW]
[ROW][C]79[/C][C]0.715802119439926[/C][C]0.568395761120148[/C][C]0.284197880560074[/C][/ROW]
[ROW][C]80[/C][C]0.67498468309935[/C][C]0.6500306338013[/C][C]0.32501531690065[/C][/ROW]
[ROW][C]81[/C][C]0.638475976939507[/C][C]0.723048046120987[/C][C]0.361524023060493[/C][/ROW]
[ROW][C]82[/C][C]0.762677128524419[/C][C]0.474645742951162[/C][C]0.237322871475581[/C][/ROW]
[ROW][C]83[/C][C]0.727506810370923[/C][C]0.544986379258155[/C][C]0.272493189629077[/C][/ROW]
[ROW][C]84[/C][C]0.697201227297986[/C][C]0.605597545404028[/C][C]0.302798772702014[/C][/ROW]
[ROW][C]85[/C][C]0.6558917269421[/C][C]0.6882165461158[/C][C]0.3441082730579[/C][/ROW]
[ROW][C]86[/C][C]0.643171433736373[/C][C]0.713657132527253[/C][C]0.356828566263627[/C][/ROW]
[ROW][C]87[/C][C]0.598896813976906[/C][C]0.802206372046188[/C][C]0.401103186023094[/C][/ROW]
[ROW][C]88[/C][C]0.556520730817073[/C][C]0.886958538365854[/C][C]0.443479269182927[/C][/ROW]
[ROW][C]89[/C][C]0.533066172795114[/C][C]0.933867654409771[/C][C]0.466933827204886[/C][/ROW]
[ROW][C]90[/C][C]0.493780056656672[/C][C]0.987560113313343[/C][C]0.506219943343328[/C][/ROW]
[ROW][C]91[/C][C]0.482764236512771[/C][C]0.965528473025542[/C][C]0.517235763487229[/C][/ROW]
[ROW][C]92[/C][C]0.43943066778507[/C][C]0.878861335570141[/C][C]0.56056933221493[/C][/ROW]
[ROW][C]93[/C][C]0.395392000383294[/C][C]0.790784000766588[/C][C]0.604607999616706[/C][/ROW]
[ROW][C]94[/C][C]0.351862158164286[/C][C]0.703724316328573[/C][C]0.648137841835714[/C][/ROW]
[ROW][C]95[/C][C]0.373471859635077[/C][C]0.746943719270155[/C][C]0.626528140364923[/C][/ROW]
[ROW][C]96[/C][C]0.33650949030171[/C][C]0.67301898060342[/C][C]0.66349050969829[/C][/ROW]
[ROW][C]97[/C][C]0.295543349359081[/C][C]0.591086698718163[/C][C]0.704456650640918[/C][/ROW]
[ROW][C]98[/C][C]0.288909169394166[/C][C]0.577818338788333[/C][C]0.711090830605834[/C][/ROW]
[ROW][C]99[/C][C]0.248670428481224[/C][C]0.497340856962447[/C][C]0.751329571518776[/C][/ROW]
[ROW][C]100[/C][C]0.215821847531644[/C][C]0.431643695063287[/C][C]0.784178152468356[/C][/ROW]
[ROW][C]101[/C][C]0.193822968279953[/C][C]0.387645936559906[/C][C]0.806177031720047[/C][/ROW]
[ROW][C]102[/C][C]0.175511111590446[/C][C]0.351022223180891[/C][C]0.824488888409554[/C][/ROW]
[ROW][C]103[/C][C]0.214058179747169[/C][C]0.428116359494338[/C][C]0.785941820252831[/C][/ROW]
[ROW][C]104[/C][C]0.180605124347907[/C][C]0.361210248695814[/C][C]0.819394875652093[/C][/ROW]
[ROW][C]105[/C][C]0.171931212370063[/C][C]0.343862424740126[/C][C]0.828068787629937[/C][/ROW]
[ROW][C]106[/C][C]0.18302596974453[/C][C]0.366051939489061[/C][C]0.81697403025547[/C][/ROW]
[ROW][C]107[/C][C]0.162741243471047[/C][C]0.325482486942094[/C][C]0.837258756528953[/C][/ROW]
[ROW][C]108[/C][C]0.143538684597205[/C][C]0.28707736919441[/C][C]0.856461315402795[/C][/ROW]
[ROW][C]109[/C][C]0.138951444643129[/C][C]0.277902889286257[/C][C]0.861048555356871[/C][/ROW]
[ROW][C]110[/C][C]0.13602697652006[/C][C]0.272053953040119[/C][C]0.86397302347994[/C][/ROW]
[ROW][C]111[/C][C]0.123771438254228[/C][C]0.247542876508456[/C][C]0.876228561745772[/C][/ROW]
[ROW][C]112[/C][C]0.106391042766418[/C][C]0.212782085532835[/C][C]0.893608957233582[/C][/ROW]
[ROW][C]113[/C][C]0.148118553302524[/C][C]0.296237106605048[/C][C]0.851881446697476[/C][/ROW]
[ROW][C]114[/C][C]0.138398801093229[/C][C]0.276797602186458[/C][C]0.861601198906771[/C][/ROW]
[ROW][C]115[/C][C]0.155776969052337[/C][C]0.311553938104674[/C][C]0.844223030947663[/C][/ROW]
[ROW][C]116[/C][C]0.16490147318656[/C][C]0.32980294637312[/C][C]0.83509852681344[/C][/ROW]
[ROW][C]117[/C][C]0.140811795757519[/C][C]0.281623591515038[/C][C]0.859188204242481[/C][/ROW]
[ROW][C]118[/C][C]0.138010620800838[/C][C]0.276021241601677[/C][C]0.861989379199162[/C][/ROW]
[ROW][C]119[/C][C]0.128009274342751[/C][C]0.256018548685503[/C][C]0.871990725657249[/C][/ROW]
[ROW][C]120[/C][C]0.135943761683445[/C][C]0.27188752336689[/C][C]0.864056238316555[/C][/ROW]
[ROW][C]121[/C][C]0.110605000056549[/C][C]0.221210000113098[/C][C]0.889394999943451[/C][/ROW]
[ROW][C]122[/C][C]0.094204618165144[/C][C]0.188409236330288[/C][C]0.905795381834856[/C][/ROW]
[ROW][C]123[/C][C]0.0900153597801022[/C][C]0.180030719560204[/C][C]0.909984640219898[/C][/ROW]
[ROW][C]124[/C][C]0.0696322791255907[/C][C]0.139264558251181[/C][C]0.930367720874409[/C][/ROW]
[ROW][C]125[/C][C]0.052866153707982[/C][C]0.105732307415964[/C][C]0.947133846292018[/C][/ROW]
[ROW][C]126[/C][C]0.0396455364288516[/C][C]0.0792910728577031[/C][C]0.960354463571148[/C][/ROW]
[ROW][C]127[/C][C]0.0287040065424359[/C][C]0.0574080130848717[/C][C]0.971295993457564[/C][/ROW]
[ROW][C]128[/C][C]0.0268907068763423[/C][C]0.0537814137526847[/C][C]0.973109293123658[/C][/ROW]
[ROW][C]129[/C][C]0.0297187615690373[/C][C]0.0594375231380746[/C][C]0.970281238430963[/C][/ROW]
[ROW][C]130[/C][C]0.02932030592759[/C][C]0.0586406118551799[/C][C]0.97067969407241[/C][/ROW]
[ROW][C]131[/C][C]0.031602820617133[/C][C]0.063205641234266[/C][C]0.968397179382867[/C][/ROW]
[ROW][C]132[/C][C]0.0339792207491872[/C][C]0.0679584414983743[/C][C]0.966020779250813[/C][/ROW]
[ROW][C]133[/C][C]0.067884889729313[/C][C]0.135769779458626[/C][C]0.932115110270687[/C][/ROW]
[ROW][C]134[/C][C]0.0700058297716152[/C][C]0.14001165954323[/C][C]0.929994170228385[/C][/ROW]
[ROW][C]135[/C][C]0.0524733988365771[/C][C]0.104946797673154[/C][C]0.947526601163423[/C][/ROW]
[ROW][C]136[/C][C]0.0431278236825774[/C][C]0.0862556473651548[/C][C]0.956872176317423[/C][/ROW]
[ROW][C]137[/C][C]0.030123784191788[/C][C]0.0602475683835761[/C][C]0.969876215808212[/C][/ROW]
[ROW][C]138[/C][C]0.0354315150959727[/C][C]0.0708630301919454[/C][C]0.964568484904027[/C][/ROW]
[ROW][C]139[/C][C]0.0291196549602676[/C][C]0.0582393099205352[/C][C]0.970880345039732[/C][/ROW]
[ROW][C]140[/C][C]0.0188728233751537[/C][C]0.0377456467503074[/C][C]0.981127176624846[/C][/ROW]
[ROW][C]141[/C][C]0.499010669795369[/C][C]0.998021339590738[/C][C]0.500989330204631[/C][/ROW]
[ROW][C]142[/C][C]0.434555903725899[/C][C]0.869111807451798[/C][C]0.565444096274101[/C][/ROW]
[ROW][C]143[/C][C]0.363635823654856[/C][C]0.727271647309713[/C][C]0.636364176345144[/C][/ROW]
[ROW][C]144[/C][C]0.276763941116425[/C][C]0.553527882232849[/C][C]0.723236058883575[/C][/ROW]
[ROW][C]145[/C][C]0.19856014456778[/C][C]0.397120289135561[/C][C]0.80143985543222[/C][/ROW]
[ROW][C]146[/C][C]0.215449371822531[/C][C]0.430898743645063[/C][C]0.784550628177469[/C][/ROW]
[ROW][C]147[/C][C]0.222384740194912[/C][C]0.444769480389823[/C][C]0.777615259805088[/C][/ROW]
[ROW][C]148[/C][C]0.470247278701563[/C][C]0.940494557403125[/C][C]0.529752721298437[/C][/ROW]
[ROW][C]149[/C][C]0.614514134059063[/C][C]0.770971731881874[/C][C]0.385485865940937[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200496&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200496&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
130.7344025528905120.5311948942189760.265597447109488
140.5872031319675340.8255937360649310.412796868032466
150.4689627142748230.9379254285496450.531037285725177
160.4016462209861090.8032924419722180.598353779013891
170.3047302819435460.6094605638870920.695269718056454
180.3865237422761720.7730474845523430.613476257723828
190.3274701900198060.6549403800396120.672529809980194
200.2466738990004760.4933477980009520.753326100999524
210.1899240995503270.3798481991006540.810075900449673
220.1913760613315740.3827521226631470.808623938668426
230.3808152824769450.7616305649538890.619184717523055
240.4384202989463220.8768405978926440.561579701053678
250.3933605375914690.7867210751829370.606639462408531
260.3302567885293110.6605135770586220.669743211470689
270.2813516184910790.5627032369821580.718648381508921
280.4351484988111270.8702969976222540.564851501188873
290.3771347888865220.7542695777730440.622865211113478
300.4948965337995690.9897930675991390.50510346620043
310.4416555085411880.8833110170823770.558344491458811
320.4078247382849660.8156494765699310.592175261715034
330.3941668352291840.7883336704583690.605833164770816
340.3625867434714960.7251734869429910.637413256528504
350.3161642167557930.6323284335115850.683835783244207
360.8607128560048830.2785742879902340.139287143995117
370.8324718114964340.3350563770071330.167528188503566
380.8124566105029910.3750867789940190.187543389497009
390.8456965413818870.3086069172362250.154303458618113
400.8317113361582590.3365773276834810.168288663841741
410.8009359096649680.3981281806700650.199064090335032
420.774512875319850.45097424936030.22548712468015
430.7994701032053350.4010597935893290.200529896794665
440.7589328673221880.4821342653556240.241067132677812
450.7319349434834790.5361301130330420.268065056516521
460.8715477125709330.2569045748581340.128452287429067
470.8951833123692520.2096333752614960.104816687630748
480.8734461583073170.2531076833853660.126553841692683
490.8729407852746710.2541184294506580.127059214725329
500.8659497415146970.2681005169706060.134050258485303
510.8397032931562680.3205934136874650.160296706843732
520.8108904149518360.3782191700963280.189109585048164
530.8213526651884510.3572946696230980.178647334811549
540.7924189610902130.4151620778195730.207581038909787
550.8094343703154950.381131259369010.190565629684505
560.7897721870340110.4204556259319790.210227812965989
570.7529382870942180.4941234258115640.247061712905782
580.7383387273220780.5233225453558440.261661272677922
590.7080041124461520.5839917751076970.291995887553848
600.7134069820922330.5731860358155350.286593017907767
610.6788799223171090.6422401553657820.321120077682891
620.6427327321860590.7145345356278830.357267267813941
630.6098431012248870.7803137975502250.390156898775113
640.565831983956610.8683360320867810.43416801604339
650.5264706146249030.9470587707501940.473529385375097
660.4957121361805290.9914242723610590.504287863819471
670.4972574106166630.9945148212333260.502742589383337
680.6444788058956880.7110423882086240.355521194104312
690.7486505950558420.5026988098883160.251349404944158
700.7163124908939510.5673750182120970.283687509106049
710.8174055333266370.3651889333467260.182594466673363
720.787021122118440.4259577557631190.21297887788156
730.7805704156730150.438859168653970.219429584326985
740.7634462024481260.4731075951037480.236553797551874
750.7257844931745820.5484310136508360.274215506825418
760.7631050246263260.4737899507473490.236894975373674
770.7261311870146440.5477376259707120.273868812985356
780.7046084588356820.5907830823286370.295391541164318
790.7158021194399260.5683957611201480.284197880560074
800.674984683099350.65003063380130.32501531690065
810.6384759769395070.7230480461209870.361524023060493
820.7626771285244190.4746457429511620.237322871475581
830.7275068103709230.5449863792581550.272493189629077
840.6972012272979860.6055975454040280.302798772702014
850.65589172694210.68821654611580.3441082730579
860.6431714337363730.7136571325272530.356828566263627
870.5988968139769060.8022063720461880.401103186023094
880.5565207308170730.8869585383658540.443479269182927
890.5330661727951140.9338676544097710.466933827204886
900.4937800566566720.9875601133133430.506219943343328
910.4827642365127710.9655284730255420.517235763487229
920.439430667785070.8788613355701410.56056933221493
930.3953920003832940.7907840007665880.604607999616706
940.3518621581642860.7037243163285730.648137841835714
950.3734718596350770.7469437192701550.626528140364923
960.336509490301710.673018980603420.66349050969829
970.2955433493590810.5910866987181630.704456650640918
980.2889091693941660.5778183387883330.711090830605834
990.2486704284812240.4973408569624470.751329571518776
1000.2158218475316440.4316436950632870.784178152468356
1010.1938229682799530.3876459365599060.806177031720047
1020.1755111115904460.3510222231808910.824488888409554
1030.2140581797471690.4281163594943380.785941820252831
1040.1806051243479070.3612102486958140.819394875652093
1050.1719312123700630.3438624247401260.828068787629937
1060.183025969744530.3660519394890610.81697403025547
1070.1627412434710470.3254824869420940.837258756528953
1080.1435386845972050.287077369194410.856461315402795
1090.1389514446431290.2779028892862570.861048555356871
1100.136026976520060.2720539530401190.86397302347994
1110.1237714382542280.2475428765084560.876228561745772
1120.1063910427664180.2127820855328350.893608957233582
1130.1481185533025240.2962371066050480.851881446697476
1140.1383988010932290.2767976021864580.861601198906771
1150.1557769690523370.3115539381046740.844223030947663
1160.164901473186560.329802946373120.83509852681344
1170.1408117957575190.2816235915150380.859188204242481
1180.1380106208008380.2760212416016770.861989379199162
1190.1280092743427510.2560185486855030.871990725657249
1200.1359437616834450.271887523366890.864056238316555
1210.1106050000565490.2212100001130980.889394999943451
1220.0942046181651440.1884092363302880.905795381834856
1230.09001535978010220.1800307195602040.909984640219898
1240.06963227912559070.1392645582511810.930367720874409
1250.0528661537079820.1057323074159640.947133846292018
1260.03964553642885160.07929107285770310.960354463571148
1270.02870400654243590.05740801308487170.971295993457564
1280.02689070687634230.05378141375268470.973109293123658
1290.02971876156903730.05943752313807460.970281238430963
1300.029320305927590.05864061185517990.97067969407241
1310.0316028206171330.0632056412342660.968397179382867
1320.03397922074918720.06795844149837430.966020779250813
1330.0678848897293130.1357697794586260.932115110270687
1340.07000582977161520.140011659543230.929994170228385
1350.05247339883657710.1049467976731540.947526601163423
1360.04312782368257740.08625564736515480.956872176317423
1370.0301237841917880.06024756838357610.969876215808212
1380.03543151509597270.07086303019194540.964568484904027
1390.02911965496026760.05823930992053520.970880345039732
1400.01887282337515370.03774564675030740.981127176624846
1410.4990106697953690.9980213395907380.500989330204631
1420.4345559037258990.8691118074517980.565444096274101
1430.3636358236548560.7272716473097130.636364176345144
1440.2767639411164250.5535278822328490.723236058883575
1450.198560144567780.3971202891355610.80143985543222
1460.2154493718225310.4308987436450630.784550628177469
1470.2223847401949120.4447694803898230.777615259805088
1480.4702472787015630.9404945574031250.529752721298437
1490.6145141340590630.7709717318818740.385485865940937






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0072992700729927OK
10% type I error level120.0875912408759124OK

\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 & 0.0072992700729927 & OK \tabularnewline
10% type I error level & 12 & 0.0875912408759124 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200496&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]0.0072992700729927[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.0875912408759124[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200496&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200496&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 level10.0072992700729927OK
10% type I error level120.0875912408759124OK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 5 ; 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')
}