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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 16 Dec 2012 11:14:27 -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/t1355674516crwa8z5hrb10laq.htm/, Retrieved Fri, 19 Apr 2024 20:57:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200453, Retrieved Fri, 19 Apr 2024 20:57:29 +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] [] [2012-10-30 15:58:56] [b523afd25ea44f0db0ae0b1eedc88526]
- R PD    [Multiple Regression] [MR volgnummer] [2012-12-16 16:14:27] [447cab31e466d1c88f957d20e303ed40] [Current]
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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 time10 seconds
R Server'George Udny Yule' @ yule.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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200453&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200453&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200453&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 time10 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Separate[t] = -3301.73676468439 + 1.66067576515263jaar[t] -0.113057139298834volgnummer[t] + 0.384662783796655Connected[t] -0.0421525455342109Learning[t] + 0.127161673977428Software[t] + 0.219485002916961Happiness[t] -0.00315753915683208Depression[t] -0.125819805572958Belonging[t] + 0.145574738790249Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Separate[t] =  -3301.73676468439 +  1.66067576515263jaar[t] -0.113057139298834volgnummer[t] +  0.384662783796655Connected[t] -0.0421525455342109Learning[t] +  0.127161673977428Software[t] +  0.219485002916961Happiness[t] -0.00315753915683208Depression[t] -0.125819805572958Belonging[t] +  0.145574738790249Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200453&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Separate[t] =  -3301.73676468439 +  1.66067576515263jaar[t] -0.113057139298834volgnummer[t] +  0.384662783796655Connected[t] -0.0421525455342109Learning[t] +  0.127161673977428Software[t] +  0.219485002916961Happiness[t] -0.00315753915683208Depression[t] -0.125819805572958Belonging[t] +  0.145574738790249Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200453&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200453&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
Separate[t] = -3301.73676468439 + 1.66067576515263jaar[t] -0.113057139298834volgnummer[t] + 0.384662783796655Connected[t] -0.0421525455342109Learning[t] + 0.127161673977428Software[t] + 0.219485002916961Happiness[t] -0.00315753915683208Depression[t] -0.125819805572958Belonging[t] + 0.145574738790249Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-3301.736764684391823.235634-1.81090.0721280.036064
jaar1.660675765152630.911741.82140.0705070.035253
volgnummer-0.1130571392988340.066391-1.70290.0906320.045316
Connected0.3846627837966550.0797484.82353e-062e-06
Learning-0.04215254553421090.144206-0.29230.770450.385225
Software0.1271616739774280.145310.87510.3828980.191449
Happiness0.2194850029169610.1353961.62110.1070770.053538
Depression-0.003157539156832080.101268-0.03120.9751670.487583
Belonging-0.1258198055729580.079683-1.5790.1164140.058207
Belonging_Final0.1455747387902490.1145931.27040.2058970.102949

\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) & -3301.73676468439 & 1823.235634 & -1.8109 & 0.072128 & 0.036064 \tabularnewline
jaar & 1.66067576515263 & 0.91174 & 1.8214 & 0.070507 & 0.035253 \tabularnewline
volgnummer & -0.113057139298834 & 0.066391 & -1.7029 & 0.090632 & 0.045316 \tabularnewline
Connected & 0.384662783796655 & 0.079748 & 4.8235 & 3e-06 & 2e-06 \tabularnewline
Learning & -0.0421525455342109 & 0.144206 & -0.2923 & 0.77045 & 0.385225 \tabularnewline
Software & 0.127161673977428 & 0.14531 & 0.8751 & 0.382898 & 0.191449 \tabularnewline
Happiness & 0.219485002916961 & 0.135396 & 1.6211 & 0.107077 & 0.053538 \tabularnewline
Depression & -0.00315753915683208 & 0.101268 & -0.0312 & 0.975167 & 0.487583 \tabularnewline
Belonging & -0.125819805572958 & 0.079683 & -1.579 & 0.116414 & 0.058207 \tabularnewline
Belonging_Final & 0.145574738790249 & 0.114593 & 1.2704 & 0.205897 & 0.102949 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200453&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]-3301.73676468439[/C][C]1823.235634[/C][C]-1.8109[/C][C]0.072128[/C][C]0.036064[/C][/ROW]
[ROW][C]jaar[/C][C]1.66067576515263[/C][C]0.91174[/C][C]1.8214[/C][C]0.070507[/C][C]0.035253[/C][/ROW]
[ROW][C]volgnummer[/C][C]-0.113057139298834[/C][C]0.066391[/C][C]-1.7029[/C][C]0.090632[/C][C]0.045316[/C][/ROW]
[ROW][C]Connected[/C][C]0.384662783796655[/C][C]0.079748[/C][C]4.8235[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Learning[/C][C]-0.0421525455342109[/C][C]0.144206[/C][C]-0.2923[/C][C]0.77045[/C][C]0.385225[/C][/ROW]
[ROW][C]Software[/C][C]0.127161673977428[/C][C]0.14531[/C][C]0.8751[/C][C]0.382898[/C][C]0.191449[/C][/ROW]
[ROW][C]Happiness[/C][C]0.219485002916961[/C][C]0.135396[/C][C]1.6211[/C][C]0.107077[/C][C]0.053538[/C][/ROW]
[ROW][C]Depression[/C][C]-0.00315753915683208[/C][C]0.101268[/C][C]-0.0312[/C][C]0.975167[/C][C]0.487583[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.125819805572958[/C][C]0.079683[/C][C]-1.579[/C][C]0.116414[/C][C]0.058207[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]0.145574738790249[/C][C]0.114593[/C][C]1.2704[/C][C]0.205897[/C][C]0.102949[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200453&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200453&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)-3301.736764684391823.235634-1.81090.0721280.036064
jaar1.660675765152630.911741.82140.0705070.035253
volgnummer-0.1130571392988340.066391-1.70290.0906320.045316
Connected0.3846627837966550.0797484.82353e-062e-06
Learning-0.04215254553421090.144206-0.29230.770450.385225
Software0.1271616739774280.145310.87510.3828980.191449
Happiness0.2194850029169610.1353961.62110.1070770.053538
Depression-0.003157539156832080.101268-0.03120.9751670.487583
Belonging-0.1258198055729580.079683-1.5790.1164140.058207
Belonging_Final0.1455747387902490.1145931.27040.2058970.102949






Multiple Linear Regression - Regression Statistics
Multiple R0.433970305556809
R-squared0.18833022610507
Adjusted R-squared0.140270831598133
F-TEST (value)3.91869743756109
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value0.000167766893241938
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.29373141780235
Sum Squared Residuals1648.99733119798

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.433970305556809 \tabularnewline
R-squared & 0.18833022610507 \tabularnewline
Adjusted R-squared & 0.140270831598133 \tabularnewline
F-TEST (value) & 3.91869743756109 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 0.000167766893241938 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.29373141780235 \tabularnewline
Sum Squared Residuals & 1648.99733119798 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200453&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.433970305556809[/C][/ROW]
[ROW][C]R-squared[/C][C]0.18833022610507[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.140270831598133[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.91869743756109[/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]0.000167766893241938[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.29373141780235[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1648.99733119798[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200453&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200453&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 R0.433970305556809
R-squared0.18833022610507
Adjusted R-squared0.140270831598133
F-TEST (value)3.91869743756109
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value0.000167766893241938
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.29373141780235
Sum Squared Residuals1648.99733119798






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13837.27568112988950.724318870110459
23235.6346431163571-3.63464311635715
33532.10216580665322.89783419334679
43331.35233210435841.64766789564156
53733.77056518287683.22943481712324
62934.0641613368662-5.06416133686625
73136.2713957556889-5.27139575568892
83633.04245327323682.95754672676321
93533.88181331946121.11818668053884
103833.37833362352434.62166637647571
113134.4785661025637-3.47856610256365
123435.1120911464539-1.11209114645392
133533.44540695106171.55459304893829
143836.89607906672951.10392093327045
153734.15258072938492.8474192706151
163333.6411145093119-0.641114509311929
173233.9836211116168-1.98362111161681
183835.02589296308032.97410703691975
193835.72654828455282.27345171544723
203233.7146807571461-1.7146807571461
213333.5278726284394-0.527872628439371
223131.7839011900296-0.783901190029589
233835.5737755968722.42622440312799
243934.44890143849514.55109856150486
253234.3246185200208-2.32461852002084
263233.7670138386895-1.76701383868951
273534.50304627839790.496953721602142
283734.36151324449272.63848675550731
293333.1878192424122-0.187819242412207
303333.6530672800249-0.65306728002487
312832.8004639844716-4.80046398447164
323231.03128553124020.968714468759837
333135.1454128874085-4.14541288740852
343733.57192276285973.42807723714032
353032.8286718236452-2.82867182364524
363332.72891516152290.271084838477113
373129.62150014300521.37849985699476
383334.004502670925-1.00450267092496
393131.1130878786798-0.113087878679812
403334.3891828794954-1.3891828794954
413235.2952040412866-3.29520404128659
423334.2990897523907-1.29908975239066
433236.9130925222858-4.91309252228584
443334.3267588218718-1.32675882187175
452834.8395480833858-6.83954808338583
463534.33147939160970.668520608390335
473936.30854287486712.69145712513293
483433.14790700542340.852092994576597
493834.11654637159893.88345362840112
503234.4298761206965-2.42987612069648
513833.07235776134054.92764223865951
523032.1301589274429-2.13015892744291
533333.4118758328629-0.411875832862861
543835.69480336722742.30519663277259
553232.5181005112504-0.518100511250368
563234.8657183610795-2.86571836107952
573436.3774693394957-2.37746933949568
583432.65494393858061.34505606141937
593634.26322329865661.7367767013434
603433.57319809318750.426801906812466
612830.9678390557055-2.96783905570553
623434.3947639728919-0.394763972891936
633532.55840904052742.44159095947258
643532.75377234567242.24622765432756
653134.099712549181-3.09971254918096
663732.42309826159794.57690173840208
673535.3833826741572-0.383382674157233
682731.7898231310464-4.78982313104635
694036.9955793020533.00442069794701
703734.99948342799712.00051657200289
713636.1111448423501-0.111144842350079
723833.98531332954614.01468667045392
733933.81243578821325.18756421178678
744135.5927421581255.40725784187499
752732.8973383860705-5.89733838607048
763034.3969167656184-4.39691676561839
773735.56645317268411.43354682731594
783133.4259087101084-2.42590871010844
793131.1402601128581-0.14026011285807
802734.7921797270345-7.79217972703445
813633.98077861017122.01922138982883
823834.92913745471943.07086254528059
833735.39128868327391.6087113167261
843335.7569329538226-2.75693295382263
853433.64530976300930.354690236990656
863133.3325838344277-2.33258383442769
873935.27995916768553.7200408323145
883433.64263519357410.357364806425869
893232.3390937331834-0.339093733183352
903331.91045798174091.08954201825908
913633.05558057075212.9444194292479
923235.0318942782615-3.03189427826151
934133.63874303901077.36125696098927
942831.8501004623412-3.85010046234123
953032.2304033930964-2.23040339309635
963636.2384905768006-0.238490576800626
973535.6455840905303-0.645584090530304
983134.0136583555607-3.01365835556066
993435.6235568753999-1.62355687539992
1003633.31930464383862.68069535616142
1013636.2319610792046-0.231961079204643
1023534.61384530136990.386154698630053
1033734.85591756499872.14408243500126
1042832.7468867648304-4.74688676483037
1053932.93070840118056.06929159881954
1063234.9480968526271-2.94809685262705
1073535.3256492854293-0.325649285429279
1083936.22026041705732.77973958294271
1093535.3063252813602-0.306325281360215
1104238.08728749066653.91271250933347
1113432.80621979230281.19378020769716
1123332.72973598456330.270264015436738
1134132.86914907272728.13085092727284
1143333.0172587337318-0.017258733731762
1153436.0894569987879-2.08945699878788
1163235.2875650577077-3.28756505770772
1174034.46760580542755.53239419457246
1184033.69476585672056.30523414327954
1193533.46798398571131.53201601428875
1203634.51025525507061.48974474492939
1213733.36107890082083.6389210991792
1222732.1731253319398-5.1731253319398
1233936.17057746525642.82942253474363
1243836.42683017190031.57316982809968
1253134.7669500906395-3.76695009063953
1263333.1939988244769-0.193998824476859
1273236.2325808093561-4.23258080935613
1283938.49117385611050.5088261438895
1293634.84205431494071.15794568505935
1303334.8170542388555-1.81705423885549
1313332.65625229252460.343747707475373
1323230.81621265142681.18378734857317
1333735.52262460305931.47737539694066
1343031.579648016823-1.57964801682297
1353835.10661809845512.89338190154495
1362933.2537291461901-4.25372914619011
1372232.451133619235-10.451133619235
1383535.6807386997205-0.680738699720499
1393533.51906392495481.4809360750452
1403435.3962481568238-1.39624815682384
1413533.11465990438741.88534009561264
1423434.089867768054-0.0898677680540343
1433432.42940525799891.5705947420011
1443532.60186801041052.39813198958946
1452332.7398195860747-9.7398195860747
1463135.9831352765293-4.98313527652928
1472733.5592658954403-6.55926589544029
1483634.67082263133511.32917736866485
1493132.3957576282053-1.39575762820531
1503232.7495857441072-0.749585744107168
1513936.39101030917152.6089896908285
1523737.576039317414-0.576039317414015
1533834.65170600014573.34829399985427
1543933.96531840437075.03468159562928
1553433.74221659917510.257783400824921
1563133.9417612854098-2.94176128540982
1573235.9865590496005-3.98655904960048
1583733.15009873783693.84990126216307
1593634.77169166628091.22830833371909
1603233.2762642797119-1.2762642797119
1613531.78752913890563.21247086109438
1623633.93244323132992.06755676867011

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 38 & 37.2756811298895 & 0.724318870110459 \tabularnewline
2 & 32 & 35.6346431163571 & -3.63464311635715 \tabularnewline
3 & 35 & 32.1021658066532 & 2.89783419334679 \tabularnewline
4 & 33 & 31.3523321043584 & 1.64766789564156 \tabularnewline
5 & 37 & 33.7705651828768 & 3.22943481712324 \tabularnewline
6 & 29 & 34.0641613368662 & -5.06416133686625 \tabularnewline
7 & 31 & 36.2713957556889 & -5.27139575568892 \tabularnewline
8 & 36 & 33.0424532732368 & 2.95754672676321 \tabularnewline
9 & 35 & 33.8818133194612 & 1.11818668053884 \tabularnewline
10 & 38 & 33.3783336235243 & 4.62166637647571 \tabularnewline
11 & 31 & 34.4785661025637 & -3.47856610256365 \tabularnewline
12 & 34 & 35.1120911464539 & -1.11209114645392 \tabularnewline
13 & 35 & 33.4454069510617 & 1.55459304893829 \tabularnewline
14 & 38 & 36.8960790667295 & 1.10392093327045 \tabularnewline
15 & 37 & 34.1525807293849 & 2.8474192706151 \tabularnewline
16 & 33 & 33.6411145093119 & -0.641114509311929 \tabularnewline
17 & 32 & 33.9836211116168 & -1.98362111161681 \tabularnewline
18 & 38 & 35.0258929630803 & 2.97410703691975 \tabularnewline
19 & 38 & 35.7265482845528 & 2.27345171544723 \tabularnewline
20 & 32 & 33.7146807571461 & -1.7146807571461 \tabularnewline
21 & 33 & 33.5278726284394 & -0.527872628439371 \tabularnewline
22 & 31 & 31.7839011900296 & -0.783901190029589 \tabularnewline
23 & 38 & 35.573775596872 & 2.42622440312799 \tabularnewline
24 & 39 & 34.4489014384951 & 4.55109856150486 \tabularnewline
25 & 32 & 34.3246185200208 & -2.32461852002084 \tabularnewline
26 & 32 & 33.7670138386895 & -1.76701383868951 \tabularnewline
27 & 35 & 34.5030462783979 & 0.496953721602142 \tabularnewline
28 & 37 & 34.3615132444927 & 2.63848675550731 \tabularnewline
29 & 33 & 33.1878192424122 & -0.187819242412207 \tabularnewline
30 & 33 & 33.6530672800249 & -0.65306728002487 \tabularnewline
31 & 28 & 32.8004639844716 & -4.80046398447164 \tabularnewline
32 & 32 & 31.0312855312402 & 0.968714468759837 \tabularnewline
33 & 31 & 35.1454128874085 & -4.14541288740852 \tabularnewline
34 & 37 & 33.5719227628597 & 3.42807723714032 \tabularnewline
35 & 30 & 32.8286718236452 & -2.82867182364524 \tabularnewline
36 & 33 & 32.7289151615229 & 0.271084838477113 \tabularnewline
37 & 31 & 29.6215001430052 & 1.37849985699476 \tabularnewline
38 & 33 & 34.004502670925 & -1.00450267092496 \tabularnewline
39 & 31 & 31.1130878786798 & -0.113087878679812 \tabularnewline
40 & 33 & 34.3891828794954 & -1.3891828794954 \tabularnewline
41 & 32 & 35.2952040412866 & -3.29520404128659 \tabularnewline
42 & 33 & 34.2990897523907 & -1.29908975239066 \tabularnewline
43 & 32 & 36.9130925222858 & -4.91309252228584 \tabularnewline
44 & 33 & 34.3267588218718 & -1.32675882187175 \tabularnewline
45 & 28 & 34.8395480833858 & -6.83954808338583 \tabularnewline
46 & 35 & 34.3314793916097 & 0.668520608390335 \tabularnewline
47 & 39 & 36.3085428748671 & 2.69145712513293 \tabularnewline
48 & 34 & 33.1479070054234 & 0.852092994576597 \tabularnewline
49 & 38 & 34.1165463715989 & 3.88345362840112 \tabularnewline
50 & 32 & 34.4298761206965 & -2.42987612069648 \tabularnewline
51 & 38 & 33.0723577613405 & 4.92764223865951 \tabularnewline
52 & 30 & 32.1301589274429 & -2.13015892744291 \tabularnewline
53 & 33 & 33.4118758328629 & -0.411875832862861 \tabularnewline
54 & 38 & 35.6948033672274 & 2.30519663277259 \tabularnewline
55 & 32 & 32.5181005112504 & -0.518100511250368 \tabularnewline
56 & 32 & 34.8657183610795 & -2.86571836107952 \tabularnewline
57 & 34 & 36.3774693394957 & -2.37746933949568 \tabularnewline
58 & 34 & 32.6549439385806 & 1.34505606141937 \tabularnewline
59 & 36 & 34.2632232986566 & 1.7367767013434 \tabularnewline
60 & 34 & 33.5731980931875 & 0.426801906812466 \tabularnewline
61 & 28 & 30.9678390557055 & -2.96783905570553 \tabularnewline
62 & 34 & 34.3947639728919 & -0.394763972891936 \tabularnewline
63 & 35 & 32.5584090405274 & 2.44159095947258 \tabularnewline
64 & 35 & 32.7537723456724 & 2.24622765432756 \tabularnewline
65 & 31 & 34.099712549181 & -3.09971254918096 \tabularnewline
66 & 37 & 32.4230982615979 & 4.57690173840208 \tabularnewline
67 & 35 & 35.3833826741572 & -0.383382674157233 \tabularnewline
68 & 27 & 31.7898231310464 & -4.78982313104635 \tabularnewline
69 & 40 & 36.995579302053 & 3.00442069794701 \tabularnewline
70 & 37 & 34.9994834279971 & 2.00051657200289 \tabularnewline
71 & 36 & 36.1111448423501 & -0.111144842350079 \tabularnewline
72 & 38 & 33.9853133295461 & 4.01468667045392 \tabularnewline
73 & 39 & 33.8124357882132 & 5.18756421178678 \tabularnewline
74 & 41 & 35.592742158125 & 5.40725784187499 \tabularnewline
75 & 27 & 32.8973383860705 & -5.89733838607048 \tabularnewline
76 & 30 & 34.3969167656184 & -4.39691676561839 \tabularnewline
77 & 37 & 35.5664531726841 & 1.43354682731594 \tabularnewline
78 & 31 & 33.4259087101084 & -2.42590871010844 \tabularnewline
79 & 31 & 31.1402601128581 & -0.14026011285807 \tabularnewline
80 & 27 & 34.7921797270345 & -7.79217972703445 \tabularnewline
81 & 36 & 33.9807786101712 & 2.01922138982883 \tabularnewline
82 & 38 & 34.9291374547194 & 3.07086254528059 \tabularnewline
83 & 37 & 35.3912886832739 & 1.6087113167261 \tabularnewline
84 & 33 & 35.7569329538226 & -2.75693295382263 \tabularnewline
85 & 34 & 33.6453097630093 & 0.354690236990656 \tabularnewline
86 & 31 & 33.3325838344277 & -2.33258383442769 \tabularnewline
87 & 39 & 35.2799591676855 & 3.7200408323145 \tabularnewline
88 & 34 & 33.6426351935741 & 0.357364806425869 \tabularnewline
89 & 32 & 32.3390937331834 & -0.339093733183352 \tabularnewline
90 & 33 & 31.9104579817409 & 1.08954201825908 \tabularnewline
91 & 36 & 33.0555805707521 & 2.9444194292479 \tabularnewline
92 & 32 & 35.0318942782615 & -3.03189427826151 \tabularnewline
93 & 41 & 33.6387430390107 & 7.36125696098927 \tabularnewline
94 & 28 & 31.8501004623412 & -3.85010046234123 \tabularnewline
95 & 30 & 32.2304033930964 & -2.23040339309635 \tabularnewline
96 & 36 & 36.2384905768006 & -0.238490576800626 \tabularnewline
97 & 35 & 35.6455840905303 & -0.645584090530304 \tabularnewline
98 & 31 & 34.0136583555607 & -3.01365835556066 \tabularnewline
99 & 34 & 35.6235568753999 & -1.62355687539992 \tabularnewline
100 & 36 & 33.3193046438386 & 2.68069535616142 \tabularnewline
101 & 36 & 36.2319610792046 & -0.231961079204643 \tabularnewline
102 & 35 & 34.6138453013699 & 0.386154698630053 \tabularnewline
103 & 37 & 34.8559175649987 & 2.14408243500126 \tabularnewline
104 & 28 & 32.7468867648304 & -4.74688676483037 \tabularnewline
105 & 39 & 32.9307084011805 & 6.06929159881954 \tabularnewline
106 & 32 & 34.9480968526271 & -2.94809685262705 \tabularnewline
107 & 35 & 35.3256492854293 & -0.325649285429279 \tabularnewline
108 & 39 & 36.2202604170573 & 2.77973958294271 \tabularnewline
109 & 35 & 35.3063252813602 & -0.306325281360215 \tabularnewline
110 & 42 & 38.0872874906665 & 3.91271250933347 \tabularnewline
111 & 34 & 32.8062197923028 & 1.19378020769716 \tabularnewline
112 & 33 & 32.7297359845633 & 0.270264015436738 \tabularnewline
113 & 41 & 32.8691490727272 & 8.13085092727284 \tabularnewline
114 & 33 & 33.0172587337318 & -0.017258733731762 \tabularnewline
115 & 34 & 36.0894569987879 & -2.08945699878788 \tabularnewline
116 & 32 & 35.2875650577077 & -3.28756505770772 \tabularnewline
117 & 40 & 34.4676058054275 & 5.53239419457246 \tabularnewline
118 & 40 & 33.6947658567205 & 6.30523414327954 \tabularnewline
119 & 35 & 33.4679839857113 & 1.53201601428875 \tabularnewline
120 & 36 & 34.5102552550706 & 1.48974474492939 \tabularnewline
121 & 37 & 33.3610789008208 & 3.6389210991792 \tabularnewline
122 & 27 & 32.1731253319398 & -5.1731253319398 \tabularnewline
123 & 39 & 36.1705774652564 & 2.82942253474363 \tabularnewline
124 & 38 & 36.4268301719003 & 1.57316982809968 \tabularnewline
125 & 31 & 34.7669500906395 & -3.76695009063953 \tabularnewline
126 & 33 & 33.1939988244769 & -0.193998824476859 \tabularnewline
127 & 32 & 36.2325808093561 & -4.23258080935613 \tabularnewline
128 & 39 & 38.4911738561105 & 0.5088261438895 \tabularnewline
129 & 36 & 34.8420543149407 & 1.15794568505935 \tabularnewline
130 & 33 & 34.8170542388555 & -1.81705423885549 \tabularnewline
131 & 33 & 32.6562522925246 & 0.343747707475373 \tabularnewline
132 & 32 & 30.8162126514268 & 1.18378734857317 \tabularnewline
133 & 37 & 35.5226246030593 & 1.47737539694066 \tabularnewline
134 & 30 & 31.579648016823 & -1.57964801682297 \tabularnewline
135 & 38 & 35.1066180984551 & 2.89338190154495 \tabularnewline
136 & 29 & 33.2537291461901 & -4.25372914619011 \tabularnewline
137 & 22 & 32.451133619235 & -10.451133619235 \tabularnewline
138 & 35 & 35.6807386997205 & -0.680738699720499 \tabularnewline
139 & 35 & 33.5190639249548 & 1.4809360750452 \tabularnewline
140 & 34 & 35.3962481568238 & -1.39624815682384 \tabularnewline
141 & 35 & 33.1146599043874 & 1.88534009561264 \tabularnewline
142 & 34 & 34.089867768054 & -0.0898677680540343 \tabularnewline
143 & 34 & 32.4294052579989 & 1.5705947420011 \tabularnewline
144 & 35 & 32.6018680104105 & 2.39813198958946 \tabularnewline
145 & 23 & 32.7398195860747 & -9.7398195860747 \tabularnewline
146 & 31 & 35.9831352765293 & -4.98313527652928 \tabularnewline
147 & 27 & 33.5592658954403 & -6.55926589544029 \tabularnewline
148 & 36 & 34.6708226313351 & 1.32917736866485 \tabularnewline
149 & 31 & 32.3957576282053 & -1.39575762820531 \tabularnewline
150 & 32 & 32.7495857441072 & -0.749585744107168 \tabularnewline
151 & 39 & 36.3910103091715 & 2.6089896908285 \tabularnewline
152 & 37 & 37.576039317414 & -0.576039317414015 \tabularnewline
153 & 38 & 34.6517060001457 & 3.34829399985427 \tabularnewline
154 & 39 & 33.9653184043707 & 5.03468159562928 \tabularnewline
155 & 34 & 33.7422165991751 & 0.257783400824921 \tabularnewline
156 & 31 & 33.9417612854098 & -2.94176128540982 \tabularnewline
157 & 32 & 35.9865590496005 & -3.98655904960048 \tabularnewline
158 & 37 & 33.1500987378369 & 3.84990126216307 \tabularnewline
159 & 36 & 34.7716916662809 & 1.22830833371909 \tabularnewline
160 & 32 & 33.2762642797119 & -1.2762642797119 \tabularnewline
161 & 35 & 31.7875291389056 & 3.21247086109438 \tabularnewline
162 & 36 & 33.9324432313299 & 2.06755676867011 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200453&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]38[/C][C]37.2756811298895[/C][C]0.724318870110459[/C][/ROW]
[ROW][C]2[/C][C]32[/C][C]35.6346431163571[/C][C]-3.63464311635715[/C][/ROW]
[ROW][C]3[/C][C]35[/C][C]32.1021658066532[/C][C]2.89783419334679[/C][/ROW]
[ROW][C]4[/C][C]33[/C][C]31.3523321043584[/C][C]1.64766789564156[/C][/ROW]
[ROW][C]5[/C][C]37[/C][C]33.7705651828768[/C][C]3.22943481712324[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]34.0641613368662[/C][C]-5.06416133686625[/C][/ROW]
[ROW][C]7[/C][C]31[/C][C]36.2713957556889[/C][C]-5.27139575568892[/C][/ROW]
[ROW][C]8[/C][C]36[/C][C]33.0424532732368[/C][C]2.95754672676321[/C][/ROW]
[ROW][C]9[/C][C]35[/C][C]33.8818133194612[/C][C]1.11818668053884[/C][/ROW]
[ROW][C]10[/C][C]38[/C][C]33.3783336235243[/C][C]4.62166637647571[/C][/ROW]
[ROW][C]11[/C][C]31[/C][C]34.4785661025637[/C][C]-3.47856610256365[/C][/ROW]
[ROW][C]12[/C][C]34[/C][C]35.1120911464539[/C][C]-1.11209114645392[/C][/ROW]
[ROW][C]13[/C][C]35[/C][C]33.4454069510617[/C][C]1.55459304893829[/C][/ROW]
[ROW][C]14[/C][C]38[/C][C]36.8960790667295[/C][C]1.10392093327045[/C][/ROW]
[ROW][C]15[/C][C]37[/C][C]34.1525807293849[/C][C]2.8474192706151[/C][/ROW]
[ROW][C]16[/C][C]33[/C][C]33.6411145093119[/C][C]-0.641114509311929[/C][/ROW]
[ROW][C]17[/C][C]32[/C][C]33.9836211116168[/C][C]-1.98362111161681[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]35.0258929630803[/C][C]2.97410703691975[/C][/ROW]
[ROW][C]19[/C][C]38[/C][C]35.7265482845528[/C][C]2.27345171544723[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.7146807571461[/C][C]-1.7146807571461[/C][/ROW]
[ROW][C]21[/C][C]33[/C][C]33.5278726284394[/C][C]-0.527872628439371[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]31.7839011900296[/C][C]-0.783901190029589[/C][/ROW]
[ROW][C]23[/C][C]38[/C][C]35.573775596872[/C][C]2.42622440312799[/C][/ROW]
[ROW][C]24[/C][C]39[/C][C]34.4489014384951[/C][C]4.55109856150486[/C][/ROW]
[ROW][C]25[/C][C]32[/C][C]34.3246185200208[/C][C]-2.32461852002084[/C][/ROW]
[ROW][C]26[/C][C]32[/C][C]33.7670138386895[/C][C]-1.76701383868951[/C][/ROW]
[ROW][C]27[/C][C]35[/C][C]34.5030462783979[/C][C]0.496953721602142[/C][/ROW]
[ROW][C]28[/C][C]37[/C][C]34.3615132444927[/C][C]2.63848675550731[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]33.1878192424122[/C][C]-0.187819242412207[/C][/ROW]
[ROW][C]30[/C][C]33[/C][C]33.6530672800249[/C][C]-0.65306728002487[/C][/ROW]
[ROW][C]31[/C][C]28[/C][C]32.8004639844716[/C][C]-4.80046398447164[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]31.0312855312402[/C][C]0.968714468759837[/C][/ROW]
[ROW][C]33[/C][C]31[/C][C]35.1454128874085[/C][C]-4.14541288740852[/C][/ROW]
[ROW][C]34[/C][C]37[/C][C]33.5719227628597[/C][C]3.42807723714032[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]32.8286718236452[/C][C]-2.82867182364524[/C][/ROW]
[ROW][C]36[/C][C]33[/C][C]32.7289151615229[/C][C]0.271084838477113[/C][/ROW]
[ROW][C]37[/C][C]31[/C][C]29.6215001430052[/C][C]1.37849985699476[/C][/ROW]
[ROW][C]38[/C][C]33[/C][C]34.004502670925[/C][C]-1.00450267092496[/C][/ROW]
[ROW][C]39[/C][C]31[/C][C]31.1130878786798[/C][C]-0.113087878679812[/C][/ROW]
[ROW][C]40[/C][C]33[/C][C]34.3891828794954[/C][C]-1.3891828794954[/C][/ROW]
[ROW][C]41[/C][C]32[/C][C]35.2952040412866[/C][C]-3.29520404128659[/C][/ROW]
[ROW][C]42[/C][C]33[/C][C]34.2990897523907[/C][C]-1.29908975239066[/C][/ROW]
[ROW][C]43[/C][C]32[/C][C]36.9130925222858[/C][C]-4.91309252228584[/C][/ROW]
[ROW][C]44[/C][C]33[/C][C]34.3267588218718[/C][C]-1.32675882187175[/C][/ROW]
[ROW][C]45[/C][C]28[/C][C]34.8395480833858[/C][C]-6.83954808338583[/C][/ROW]
[ROW][C]46[/C][C]35[/C][C]34.3314793916097[/C][C]0.668520608390335[/C][/ROW]
[ROW][C]47[/C][C]39[/C][C]36.3085428748671[/C][C]2.69145712513293[/C][/ROW]
[ROW][C]48[/C][C]34[/C][C]33.1479070054234[/C][C]0.852092994576597[/C][/ROW]
[ROW][C]49[/C][C]38[/C][C]34.1165463715989[/C][C]3.88345362840112[/C][/ROW]
[ROW][C]50[/C][C]32[/C][C]34.4298761206965[/C][C]-2.42987612069648[/C][/ROW]
[ROW][C]51[/C][C]38[/C][C]33.0723577613405[/C][C]4.92764223865951[/C][/ROW]
[ROW][C]52[/C][C]30[/C][C]32.1301589274429[/C][C]-2.13015892744291[/C][/ROW]
[ROW][C]53[/C][C]33[/C][C]33.4118758328629[/C][C]-0.411875832862861[/C][/ROW]
[ROW][C]54[/C][C]38[/C][C]35.6948033672274[/C][C]2.30519663277259[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.5181005112504[/C][C]-0.518100511250368[/C][/ROW]
[ROW][C]56[/C][C]32[/C][C]34.8657183610795[/C][C]-2.86571836107952[/C][/ROW]
[ROW][C]57[/C][C]34[/C][C]36.3774693394957[/C][C]-2.37746933949568[/C][/ROW]
[ROW][C]58[/C][C]34[/C][C]32.6549439385806[/C][C]1.34505606141937[/C][/ROW]
[ROW][C]59[/C][C]36[/C][C]34.2632232986566[/C][C]1.7367767013434[/C][/ROW]
[ROW][C]60[/C][C]34[/C][C]33.5731980931875[/C][C]0.426801906812466[/C][/ROW]
[ROW][C]61[/C][C]28[/C][C]30.9678390557055[/C][C]-2.96783905570553[/C][/ROW]
[ROW][C]62[/C][C]34[/C][C]34.3947639728919[/C][C]-0.394763972891936[/C][/ROW]
[ROW][C]63[/C][C]35[/C][C]32.5584090405274[/C][C]2.44159095947258[/C][/ROW]
[ROW][C]64[/C][C]35[/C][C]32.7537723456724[/C][C]2.24622765432756[/C][/ROW]
[ROW][C]65[/C][C]31[/C][C]34.099712549181[/C][C]-3.09971254918096[/C][/ROW]
[ROW][C]66[/C][C]37[/C][C]32.4230982615979[/C][C]4.57690173840208[/C][/ROW]
[ROW][C]67[/C][C]35[/C][C]35.3833826741572[/C][C]-0.383382674157233[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]31.7898231310464[/C][C]-4.78982313104635[/C][/ROW]
[ROW][C]69[/C][C]40[/C][C]36.995579302053[/C][C]3.00442069794701[/C][/ROW]
[ROW][C]70[/C][C]37[/C][C]34.9994834279971[/C][C]2.00051657200289[/C][/ROW]
[ROW][C]71[/C][C]36[/C][C]36.1111448423501[/C][C]-0.111144842350079[/C][/ROW]
[ROW][C]72[/C][C]38[/C][C]33.9853133295461[/C][C]4.01468667045392[/C][/ROW]
[ROW][C]73[/C][C]39[/C][C]33.8124357882132[/C][C]5.18756421178678[/C][/ROW]
[ROW][C]74[/C][C]41[/C][C]35.592742158125[/C][C]5.40725784187499[/C][/ROW]
[ROW][C]75[/C][C]27[/C][C]32.8973383860705[/C][C]-5.89733838607048[/C][/ROW]
[ROW][C]76[/C][C]30[/C][C]34.3969167656184[/C][C]-4.39691676561839[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.5664531726841[/C][C]1.43354682731594[/C][/ROW]
[ROW][C]78[/C][C]31[/C][C]33.4259087101084[/C][C]-2.42590871010844[/C][/ROW]
[ROW][C]79[/C][C]31[/C][C]31.1402601128581[/C][C]-0.14026011285807[/C][/ROW]
[ROW][C]80[/C][C]27[/C][C]34.7921797270345[/C][C]-7.79217972703445[/C][/ROW]
[ROW][C]81[/C][C]36[/C][C]33.9807786101712[/C][C]2.01922138982883[/C][/ROW]
[ROW][C]82[/C][C]38[/C][C]34.9291374547194[/C][C]3.07086254528059[/C][/ROW]
[ROW][C]83[/C][C]37[/C][C]35.3912886832739[/C][C]1.6087113167261[/C][/ROW]
[ROW][C]84[/C][C]33[/C][C]35.7569329538226[/C][C]-2.75693295382263[/C][/ROW]
[ROW][C]85[/C][C]34[/C][C]33.6453097630093[/C][C]0.354690236990656[/C][/ROW]
[ROW][C]86[/C][C]31[/C][C]33.3325838344277[/C][C]-2.33258383442769[/C][/ROW]
[ROW][C]87[/C][C]39[/C][C]35.2799591676855[/C][C]3.7200408323145[/C][/ROW]
[ROW][C]88[/C][C]34[/C][C]33.6426351935741[/C][C]0.357364806425869[/C][/ROW]
[ROW][C]89[/C][C]32[/C][C]32.3390937331834[/C][C]-0.339093733183352[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]31.9104579817409[/C][C]1.08954201825908[/C][/ROW]
[ROW][C]91[/C][C]36[/C][C]33.0555805707521[/C][C]2.9444194292479[/C][/ROW]
[ROW][C]92[/C][C]32[/C][C]35.0318942782615[/C][C]-3.03189427826151[/C][/ROW]
[ROW][C]93[/C][C]41[/C][C]33.6387430390107[/C][C]7.36125696098927[/C][/ROW]
[ROW][C]94[/C][C]28[/C][C]31.8501004623412[/C][C]-3.85010046234123[/C][/ROW]
[ROW][C]95[/C][C]30[/C][C]32.2304033930964[/C][C]-2.23040339309635[/C][/ROW]
[ROW][C]96[/C][C]36[/C][C]36.2384905768006[/C][C]-0.238490576800626[/C][/ROW]
[ROW][C]97[/C][C]35[/C][C]35.6455840905303[/C][C]-0.645584090530304[/C][/ROW]
[ROW][C]98[/C][C]31[/C][C]34.0136583555607[/C][C]-3.01365835556066[/C][/ROW]
[ROW][C]99[/C][C]34[/C][C]35.6235568753999[/C][C]-1.62355687539992[/C][/ROW]
[ROW][C]100[/C][C]36[/C][C]33.3193046438386[/C][C]2.68069535616142[/C][/ROW]
[ROW][C]101[/C][C]36[/C][C]36.2319610792046[/C][C]-0.231961079204643[/C][/ROW]
[ROW][C]102[/C][C]35[/C][C]34.6138453013699[/C][C]0.386154698630053[/C][/ROW]
[ROW][C]103[/C][C]37[/C][C]34.8559175649987[/C][C]2.14408243500126[/C][/ROW]
[ROW][C]104[/C][C]28[/C][C]32.7468867648304[/C][C]-4.74688676483037[/C][/ROW]
[ROW][C]105[/C][C]39[/C][C]32.9307084011805[/C][C]6.06929159881954[/C][/ROW]
[ROW][C]106[/C][C]32[/C][C]34.9480968526271[/C][C]-2.94809685262705[/C][/ROW]
[ROW][C]107[/C][C]35[/C][C]35.3256492854293[/C][C]-0.325649285429279[/C][/ROW]
[ROW][C]108[/C][C]39[/C][C]36.2202604170573[/C][C]2.77973958294271[/C][/ROW]
[ROW][C]109[/C][C]35[/C][C]35.3063252813602[/C][C]-0.306325281360215[/C][/ROW]
[ROW][C]110[/C][C]42[/C][C]38.0872874906665[/C][C]3.91271250933347[/C][/ROW]
[ROW][C]111[/C][C]34[/C][C]32.8062197923028[/C][C]1.19378020769716[/C][/ROW]
[ROW][C]112[/C][C]33[/C][C]32.7297359845633[/C][C]0.270264015436738[/C][/ROW]
[ROW][C]113[/C][C]41[/C][C]32.8691490727272[/C][C]8.13085092727284[/C][/ROW]
[ROW][C]114[/C][C]33[/C][C]33.0172587337318[/C][C]-0.017258733731762[/C][/ROW]
[ROW][C]115[/C][C]34[/C][C]36.0894569987879[/C][C]-2.08945699878788[/C][/ROW]
[ROW][C]116[/C][C]32[/C][C]35.2875650577077[/C][C]-3.28756505770772[/C][/ROW]
[ROW][C]117[/C][C]40[/C][C]34.4676058054275[/C][C]5.53239419457246[/C][/ROW]
[ROW][C]118[/C][C]40[/C][C]33.6947658567205[/C][C]6.30523414327954[/C][/ROW]
[ROW][C]119[/C][C]35[/C][C]33.4679839857113[/C][C]1.53201601428875[/C][/ROW]
[ROW][C]120[/C][C]36[/C][C]34.5102552550706[/C][C]1.48974474492939[/C][/ROW]
[ROW][C]121[/C][C]37[/C][C]33.3610789008208[/C][C]3.6389210991792[/C][/ROW]
[ROW][C]122[/C][C]27[/C][C]32.1731253319398[/C][C]-5.1731253319398[/C][/ROW]
[ROW][C]123[/C][C]39[/C][C]36.1705774652564[/C][C]2.82942253474363[/C][/ROW]
[ROW][C]124[/C][C]38[/C][C]36.4268301719003[/C][C]1.57316982809968[/C][/ROW]
[ROW][C]125[/C][C]31[/C][C]34.7669500906395[/C][C]-3.76695009063953[/C][/ROW]
[ROW][C]126[/C][C]33[/C][C]33.1939988244769[/C][C]-0.193998824476859[/C][/ROW]
[ROW][C]127[/C][C]32[/C][C]36.2325808093561[/C][C]-4.23258080935613[/C][/ROW]
[ROW][C]128[/C][C]39[/C][C]38.4911738561105[/C][C]0.5088261438895[/C][/ROW]
[ROW][C]129[/C][C]36[/C][C]34.8420543149407[/C][C]1.15794568505935[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]34.8170542388555[/C][C]-1.81705423885549[/C][/ROW]
[ROW][C]131[/C][C]33[/C][C]32.6562522925246[/C][C]0.343747707475373[/C][/ROW]
[ROW][C]132[/C][C]32[/C][C]30.8162126514268[/C][C]1.18378734857317[/C][/ROW]
[ROW][C]133[/C][C]37[/C][C]35.5226246030593[/C][C]1.47737539694066[/C][/ROW]
[ROW][C]134[/C][C]30[/C][C]31.579648016823[/C][C]-1.57964801682297[/C][/ROW]
[ROW][C]135[/C][C]38[/C][C]35.1066180984551[/C][C]2.89338190154495[/C][/ROW]
[ROW][C]136[/C][C]29[/C][C]33.2537291461901[/C][C]-4.25372914619011[/C][/ROW]
[ROW][C]137[/C][C]22[/C][C]32.451133619235[/C][C]-10.451133619235[/C][/ROW]
[ROW][C]138[/C][C]35[/C][C]35.6807386997205[/C][C]-0.680738699720499[/C][/ROW]
[ROW][C]139[/C][C]35[/C][C]33.5190639249548[/C][C]1.4809360750452[/C][/ROW]
[ROW][C]140[/C][C]34[/C][C]35.3962481568238[/C][C]-1.39624815682384[/C][/ROW]
[ROW][C]141[/C][C]35[/C][C]33.1146599043874[/C][C]1.88534009561264[/C][/ROW]
[ROW][C]142[/C][C]34[/C][C]34.089867768054[/C][C]-0.0898677680540343[/C][/ROW]
[ROW][C]143[/C][C]34[/C][C]32.4294052579989[/C][C]1.5705947420011[/C][/ROW]
[ROW][C]144[/C][C]35[/C][C]32.6018680104105[/C][C]2.39813198958946[/C][/ROW]
[ROW][C]145[/C][C]23[/C][C]32.7398195860747[/C][C]-9.7398195860747[/C][/ROW]
[ROW][C]146[/C][C]31[/C][C]35.9831352765293[/C][C]-4.98313527652928[/C][/ROW]
[ROW][C]147[/C][C]27[/C][C]33.5592658954403[/C][C]-6.55926589544029[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]34.6708226313351[/C][C]1.32917736866485[/C][/ROW]
[ROW][C]149[/C][C]31[/C][C]32.3957576282053[/C][C]-1.39575762820531[/C][/ROW]
[ROW][C]150[/C][C]32[/C][C]32.7495857441072[/C][C]-0.749585744107168[/C][/ROW]
[ROW][C]151[/C][C]39[/C][C]36.3910103091715[/C][C]2.6089896908285[/C][/ROW]
[ROW][C]152[/C][C]37[/C][C]37.576039317414[/C][C]-0.576039317414015[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]34.6517060001457[/C][C]3.34829399985427[/C][/ROW]
[ROW][C]154[/C][C]39[/C][C]33.9653184043707[/C][C]5.03468159562928[/C][/ROW]
[ROW][C]155[/C][C]34[/C][C]33.7422165991751[/C][C]0.257783400824921[/C][/ROW]
[ROW][C]156[/C][C]31[/C][C]33.9417612854098[/C][C]-2.94176128540982[/C][/ROW]
[ROW][C]157[/C][C]32[/C][C]35.9865590496005[/C][C]-3.98655904960048[/C][/ROW]
[ROW][C]158[/C][C]37[/C][C]33.1500987378369[/C][C]3.84990126216307[/C][/ROW]
[ROW][C]159[/C][C]36[/C][C]34.7716916662809[/C][C]1.22830833371909[/C][/ROW]
[ROW][C]160[/C][C]32[/C][C]33.2762642797119[/C][C]-1.2762642797119[/C][/ROW]
[ROW][C]161[/C][C]35[/C][C]31.7875291389056[/C][C]3.21247086109438[/C][/ROW]
[ROW][C]162[/C][C]36[/C][C]33.9324432313299[/C][C]2.06755676867011[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200453&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200453&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
13837.27568112988950.724318870110459
23235.6346431163571-3.63464311635715
33532.10216580665322.89783419334679
43331.35233210435841.64766789564156
53733.77056518287683.22943481712324
62934.0641613368662-5.06416133686625
73136.2713957556889-5.27139575568892
83633.04245327323682.95754672676321
93533.88181331946121.11818668053884
103833.37833362352434.62166637647571
113134.4785661025637-3.47856610256365
123435.1120911464539-1.11209114645392
133533.44540695106171.55459304893829
143836.89607906672951.10392093327045
153734.15258072938492.8474192706151
163333.6411145093119-0.641114509311929
173233.9836211116168-1.98362111161681
183835.02589296308032.97410703691975
193835.72654828455282.27345171544723
203233.7146807571461-1.7146807571461
213333.5278726284394-0.527872628439371
223131.7839011900296-0.783901190029589
233835.5737755968722.42622440312799
243934.44890143849514.55109856150486
253234.3246185200208-2.32461852002084
263233.7670138386895-1.76701383868951
273534.50304627839790.496953721602142
283734.36151324449272.63848675550731
293333.1878192424122-0.187819242412207
303333.6530672800249-0.65306728002487
312832.8004639844716-4.80046398447164
323231.03128553124020.968714468759837
333135.1454128874085-4.14541288740852
343733.57192276285973.42807723714032
353032.8286718236452-2.82867182364524
363332.72891516152290.271084838477113
373129.62150014300521.37849985699476
383334.004502670925-1.00450267092496
393131.1130878786798-0.113087878679812
403334.3891828794954-1.3891828794954
413235.2952040412866-3.29520404128659
423334.2990897523907-1.29908975239066
433236.9130925222858-4.91309252228584
443334.3267588218718-1.32675882187175
452834.8395480833858-6.83954808338583
463534.33147939160970.668520608390335
473936.30854287486712.69145712513293
483433.14790700542340.852092994576597
493834.11654637159893.88345362840112
503234.4298761206965-2.42987612069648
513833.07235776134054.92764223865951
523032.1301589274429-2.13015892744291
533333.4118758328629-0.411875832862861
543835.69480336722742.30519663277259
553232.5181005112504-0.518100511250368
563234.8657183610795-2.86571836107952
573436.3774693394957-2.37746933949568
583432.65494393858061.34505606141937
593634.26322329865661.7367767013434
603433.57319809318750.426801906812466
612830.9678390557055-2.96783905570553
623434.3947639728919-0.394763972891936
633532.55840904052742.44159095947258
643532.75377234567242.24622765432756
653134.099712549181-3.09971254918096
663732.42309826159794.57690173840208
673535.3833826741572-0.383382674157233
682731.7898231310464-4.78982313104635
694036.9955793020533.00442069794701
703734.99948342799712.00051657200289
713636.1111448423501-0.111144842350079
723833.98531332954614.01468667045392
733933.81243578821325.18756421178678
744135.5927421581255.40725784187499
752732.8973383860705-5.89733838607048
763034.3969167656184-4.39691676561839
773735.56645317268411.43354682731594
783133.4259087101084-2.42590871010844
793131.1402601128581-0.14026011285807
802734.7921797270345-7.79217972703445
813633.98077861017122.01922138982883
823834.92913745471943.07086254528059
833735.39128868327391.6087113167261
843335.7569329538226-2.75693295382263
853433.64530976300930.354690236990656
863133.3325838344277-2.33258383442769
873935.27995916768553.7200408323145
883433.64263519357410.357364806425869
893232.3390937331834-0.339093733183352
903331.91045798174091.08954201825908
913633.05558057075212.9444194292479
923235.0318942782615-3.03189427826151
934133.63874303901077.36125696098927
942831.8501004623412-3.85010046234123
953032.2304033930964-2.23040339309635
963636.2384905768006-0.238490576800626
973535.6455840905303-0.645584090530304
983134.0136583555607-3.01365835556066
993435.6235568753999-1.62355687539992
1003633.31930464383862.68069535616142
1013636.2319610792046-0.231961079204643
1023534.61384530136990.386154698630053
1033734.85591756499872.14408243500126
1042832.7468867648304-4.74688676483037
1053932.93070840118056.06929159881954
1063234.9480968526271-2.94809685262705
1073535.3256492854293-0.325649285429279
1083936.22026041705732.77973958294271
1093535.3063252813602-0.306325281360215
1104238.08728749066653.91271250933347
1113432.80621979230281.19378020769716
1123332.72973598456330.270264015436738
1134132.86914907272728.13085092727284
1143333.0172587337318-0.017258733731762
1153436.0894569987879-2.08945699878788
1163235.2875650577077-3.28756505770772
1174034.46760580542755.53239419457246
1184033.69476585672056.30523414327954
1193533.46798398571131.53201601428875
1203634.51025525507061.48974474492939
1213733.36107890082083.6389210991792
1222732.1731253319398-5.1731253319398
1233936.17057746525642.82942253474363
1243836.42683017190031.57316982809968
1253134.7669500906395-3.76695009063953
1263333.1939988244769-0.193998824476859
1273236.2325808093561-4.23258080935613
1283938.49117385611050.5088261438895
1293634.84205431494071.15794568505935
1303334.8170542388555-1.81705423885549
1313332.65625229252460.343747707475373
1323230.81621265142681.18378734857317
1333735.52262460305931.47737539694066
1343031.579648016823-1.57964801682297
1353835.10661809845512.89338190154495
1362933.2537291461901-4.25372914619011
1372232.451133619235-10.451133619235
1383535.6807386997205-0.680738699720499
1393533.51906392495481.4809360750452
1403435.3962481568238-1.39624815682384
1413533.11465990438741.88534009561264
1423434.089867768054-0.0898677680540343
1433432.42940525799891.5705947420011
1443532.60186801041052.39813198958946
1452332.7398195860747-9.7398195860747
1463135.9831352765293-4.98313527652928
1472733.5592658954403-6.55926589544029
1483634.67082263133511.32917736866485
1493132.3957576282053-1.39575762820531
1503232.7495857441072-0.749585744107168
1513936.39101030917152.6089896908285
1523737.576039317414-0.576039317414015
1533834.65170600014573.34829399985427
1543933.96531840437075.03468159562928
1553433.74221659917510.257783400824921
1563133.9417612854098-2.94176128540982
1573235.9865590496005-3.98655904960048
1583733.15009873783693.84990126216307
1593634.77169166628091.22830833371909
1603233.2762642797119-1.2762642797119
1613531.78752913890563.21247086109438
1623633.93244323132992.06755676867011






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.8453787682847080.3092424634305840.154621231715292
140.7353706140652620.5292587718694750.264629385934738
150.6404087293905880.7191825412188240.359591270609412
160.6936096430089410.6127807139821170.306390356991059
170.740860662346070.5182786753078610.25913933765393
180.7027140729506140.5945718540987730.297285927049386
190.6437791878320960.7124416243358090.356220812167904
200.5735888946471440.8528222107057130.426411105352856
210.4809793874465120.9619587748930250.519020612553488
220.4273215951069960.8546431902139930.572678404893004
230.3494022111021250.6988044222042510.650597788897875
240.409945717959160.8198914359183210.59005428204084
250.3987658067870510.7975316135741030.601234193212949
260.5038182062572510.9923635874854990.496181793742749
270.4286927490809890.8573854981619780.571307250919011
280.371181818966670.742363637933340.62881818103333
290.3102933800212930.6205867600425850.689706619978707
300.2731867113509090.5463734227018180.726813288649091
310.3413087012206960.6826174024413910.658691298779304
320.2861208353872220.5722416707744450.713879164612778
330.2655594726942980.5311189453885960.734440527305702
340.2962419100839840.5924838201679690.703758089916016
350.2638438921046620.5276877842093240.736156107895338
360.234180452918970.4683609058379410.76581954708103
370.1955308416122530.3910616832245070.804469158387747
380.1559485161955270.3118970323910550.844051483804473
390.1227057796706460.2454115593412910.877294220329354
400.1001062336341230.2002124672682450.899893766365877
410.08917275193141740.1783455038628350.910827248068583
420.06841796818879920.1368359363775980.931582031811201
430.07157612015903450.1431522403180690.928423879840965
440.05419478379528030.1083895675905610.94580521620472
450.09815820103263070.1963164020652610.901841798967369
460.08518520590030170.1703704118006030.914814794099698
470.09615809406976770.1923161881395350.903841905930232
480.08012508255915760.1602501651183150.919874917440842
490.1255319640689920.2510639281379840.874468035931008
500.1093552422821410.2187104845642820.890644757717859
510.1378162663792260.2756325327584520.862183733620774
520.1244760538724860.2489521077449730.875523946127514
530.09922702397434320.1984540479486860.900772976025657
540.08328031688207020.166560633764140.91671968311793
550.06957208217159870.1391441643431970.930427917828401
560.06065571640171160.1213114328034230.939344283598288
570.05020661726087980.100413234521760.94979338273912
580.03936541248500680.07873082497001360.960634587514993
590.0356087931140060.07121758622801210.964391206885994
600.02682546670336120.05365093340672240.973174533296639
610.02754128137065230.05508256274130460.972458718629348
620.02077857457046160.04155714914092320.979221425429538
630.01698817967076730.03397635934153460.983011820329233
640.01436192597286840.02872385194573670.985638074027132
650.01402094786583150.02804189573166310.985979052134168
660.01568263916416410.03136527832832820.984317360835836
670.01149567209714660.02299134419429310.988504327902853
680.01575052086742630.03150104173485260.984249479132574
690.01854044408561330.03708088817122670.981459555914387
700.01611322789393520.03222645578787040.983886772106065
710.01199207757575730.02398415515151450.988007922424243
720.01438704875476880.02877409750953750.985612951245231
730.0208814241227220.0417628482454440.979118575877278
740.03479517123512670.06959034247025340.965204828764873
750.07445441336606150.1489088267321230.925545586633939
760.09311848106066690.1862369621213340.906881518939333
770.07722724908779810.1544544981755960.922772750912202
780.0744839023593210.1489678047186420.925516097640679
790.06167737653303610.1233547530660720.938322623466964
800.1765808578850420.3531617157700850.823419142114957
810.1590670834861120.3181341669722240.840932916513888
820.1654003303404860.3308006606809720.834599669659514
830.1435657038640370.2871314077280730.856434296135963
840.1362294372863580.2724588745727160.863770562713642
850.1121884915065170.2243769830130340.887811508493483
860.1065633687112680.2131267374225360.893436631288732
870.1160507031280530.2321014062561070.883949296871947
880.09449718970193810.1889943794038760.905502810298062
890.07712313706258980.154246274125180.92287686293741
900.06154086139123720.1230817227824740.938459138608763
910.05593176638486390.1118635327697280.944068233615136
920.05622880744751220.1124576148950240.943771192552488
930.1189253196480140.2378506392960280.881074680351986
940.1352201936729570.2704403873459140.864779806327043
950.1269024193502990.2538048387005980.873097580649701
960.1068182786995310.2136365573990620.893181721300469
970.08726652495214860.1745330499042970.912733475047851
980.101578654920790.2031573098415790.89842134507921
990.08927251596186120.1785450319237220.910727484038139
1000.07781230959019950.1556246191803990.922187690409801
1010.06169979748945370.1233995949789070.938300202510546
1020.04958105168303620.09916210336607240.950418948316964
1030.04279621654946790.08559243309893580.957203783450532
1040.08485427311312720.1697085462262540.915145726886873
1050.1320924182593420.2641848365186840.867907581740658
1060.1489050778661380.2978101557322760.851094922133862
1070.1250380376533360.2500760753066730.874961962346664
1080.1184138261228850.236827652245770.881586173877115
1090.1142697221336050.2285394442672090.885730277866395
1100.1106497048680880.2212994097361760.889350295131912
1110.09249319853679190.1849863970735840.907506801463208
1120.07299048630522960.1459809726104590.92700951369477
1130.1540912713685550.308182542737110.845908728631445
1140.1279187914693840.2558375829387670.872081208530616
1150.1108240997518750.221648199503750.889175900248125
1160.1078593413740850.215718682748170.892140658625915
1170.1806674507429250.3613349014858510.819332549257075
1180.2405574680559950.4811149361119910.759442531944005
1190.2009333711877190.4018667423754380.799066628812281
1200.1897812135537790.3795624271075570.810218786446221
1210.2306249095772930.4612498191545860.769375090422707
1220.2388236356470060.4776472712940110.761176364352995
1230.2285731456588510.4571462913177020.771426854341149
1240.2004283604277820.4008567208555640.799571639572218
1250.1882166312214870.3764332624429750.811783368778513
1260.1585181032042770.3170362064085550.841481896795723
1270.1930937667408390.3861875334816790.806906233259161
1280.1635981163648770.3271962327297540.836401883635123
1290.1460981987760370.2921963975520730.853901801223963
1300.1158622120470850.2317244240941710.884137787952915
1310.087724672974180.175449345948360.91227532702582
1320.08505840975946690.1701168195189340.914941590240533
1330.0676551348615270.1353102697230540.932344865138473
1340.06108880279852960.1221776055970590.93891119720147
1350.13954212594530.27908425189060.8604578740547
1360.1323811096383520.2647622192767030.867618890361648
1370.3586282286989160.7172564573978330.641371771301084
1380.2889180578337340.5778361156674680.711081942166266
1390.2262473560925460.4524947121850910.773752643907454
1400.174424690879310.348849381758620.82557530912069
1410.1284427950647490.2568855901294980.871557204935251
1420.08837409590173270.1767481918034650.911625904098267
1430.09095769688543510.181915393770870.909042303114565
1440.4346924038834110.8693848077668220.565307596116589
1450.6225668872307190.7548662255385620.377433112769281
1460.9671132790948970.06577344181020550.0328867209051028
1470.9655780949160490.06884381016790120.0344219050839506
1480.9729787535734420.05404249285311680.0270212464265584
1490.9929849644286630.01403007114267450.00701503557133725

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.845378768284708 & 0.309242463430584 & 0.154621231715292 \tabularnewline
14 & 0.735370614065262 & 0.529258771869475 & 0.264629385934738 \tabularnewline
15 & 0.640408729390588 & 0.719182541218824 & 0.359591270609412 \tabularnewline
16 & 0.693609643008941 & 0.612780713982117 & 0.306390356991059 \tabularnewline
17 & 0.74086066234607 & 0.518278675307861 & 0.25913933765393 \tabularnewline
18 & 0.702714072950614 & 0.594571854098773 & 0.297285927049386 \tabularnewline
19 & 0.643779187832096 & 0.712441624335809 & 0.356220812167904 \tabularnewline
20 & 0.573588894647144 & 0.852822210705713 & 0.426411105352856 \tabularnewline
21 & 0.480979387446512 & 0.961958774893025 & 0.519020612553488 \tabularnewline
22 & 0.427321595106996 & 0.854643190213993 & 0.572678404893004 \tabularnewline
23 & 0.349402211102125 & 0.698804422204251 & 0.650597788897875 \tabularnewline
24 & 0.40994571795916 & 0.819891435918321 & 0.59005428204084 \tabularnewline
25 & 0.398765806787051 & 0.797531613574103 & 0.601234193212949 \tabularnewline
26 & 0.503818206257251 & 0.992363587485499 & 0.496181793742749 \tabularnewline
27 & 0.428692749080989 & 0.857385498161978 & 0.571307250919011 \tabularnewline
28 & 0.37118181896667 & 0.74236363793334 & 0.62881818103333 \tabularnewline
29 & 0.310293380021293 & 0.620586760042585 & 0.689706619978707 \tabularnewline
30 & 0.273186711350909 & 0.546373422701818 & 0.726813288649091 \tabularnewline
31 & 0.341308701220696 & 0.682617402441391 & 0.658691298779304 \tabularnewline
32 & 0.286120835387222 & 0.572241670774445 & 0.713879164612778 \tabularnewline
33 & 0.265559472694298 & 0.531118945388596 & 0.734440527305702 \tabularnewline
34 & 0.296241910083984 & 0.592483820167969 & 0.703758089916016 \tabularnewline
35 & 0.263843892104662 & 0.527687784209324 & 0.736156107895338 \tabularnewline
36 & 0.23418045291897 & 0.468360905837941 & 0.76581954708103 \tabularnewline
37 & 0.195530841612253 & 0.391061683224507 & 0.804469158387747 \tabularnewline
38 & 0.155948516195527 & 0.311897032391055 & 0.844051483804473 \tabularnewline
39 & 0.122705779670646 & 0.245411559341291 & 0.877294220329354 \tabularnewline
40 & 0.100106233634123 & 0.200212467268245 & 0.899893766365877 \tabularnewline
41 & 0.0891727519314174 & 0.178345503862835 & 0.910827248068583 \tabularnewline
42 & 0.0684179681887992 & 0.136835936377598 & 0.931582031811201 \tabularnewline
43 & 0.0715761201590345 & 0.143152240318069 & 0.928423879840965 \tabularnewline
44 & 0.0541947837952803 & 0.108389567590561 & 0.94580521620472 \tabularnewline
45 & 0.0981582010326307 & 0.196316402065261 & 0.901841798967369 \tabularnewline
46 & 0.0851852059003017 & 0.170370411800603 & 0.914814794099698 \tabularnewline
47 & 0.0961580940697677 & 0.192316188139535 & 0.903841905930232 \tabularnewline
48 & 0.0801250825591576 & 0.160250165118315 & 0.919874917440842 \tabularnewline
49 & 0.125531964068992 & 0.251063928137984 & 0.874468035931008 \tabularnewline
50 & 0.109355242282141 & 0.218710484564282 & 0.890644757717859 \tabularnewline
51 & 0.137816266379226 & 0.275632532758452 & 0.862183733620774 \tabularnewline
52 & 0.124476053872486 & 0.248952107744973 & 0.875523946127514 \tabularnewline
53 & 0.0992270239743432 & 0.198454047948686 & 0.900772976025657 \tabularnewline
54 & 0.0832803168820702 & 0.16656063376414 & 0.91671968311793 \tabularnewline
55 & 0.0695720821715987 & 0.139144164343197 & 0.930427917828401 \tabularnewline
56 & 0.0606557164017116 & 0.121311432803423 & 0.939344283598288 \tabularnewline
57 & 0.0502066172608798 & 0.10041323452176 & 0.94979338273912 \tabularnewline
58 & 0.0393654124850068 & 0.0787308249700136 & 0.960634587514993 \tabularnewline
59 & 0.035608793114006 & 0.0712175862280121 & 0.964391206885994 \tabularnewline
60 & 0.0268254667033612 & 0.0536509334067224 & 0.973174533296639 \tabularnewline
61 & 0.0275412813706523 & 0.0550825627413046 & 0.972458718629348 \tabularnewline
62 & 0.0207785745704616 & 0.0415571491409232 & 0.979221425429538 \tabularnewline
63 & 0.0169881796707673 & 0.0339763593415346 & 0.983011820329233 \tabularnewline
64 & 0.0143619259728684 & 0.0287238519457367 & 0.985638074027132 \tabularnewline
65 & 0.0140209478658315 & 0.0280418957316631 & 0.985979052134168 \tabularnewline
66 & 0.0156826391641641 & 0.0313652783283282 & 0.984317360835836 \tabularnewline
67 & 0.0114956720971466 & 0.0229913441942931 & 0.988504327902853 \tabularnewline
68 & 0.0157505208674263 & 0.0315010417348526 & 0.984249479132574 \tabularnewline
69 & 0.0185404440856133 & 0.0370808881712267 & 0.981459555914387 \tabularnewline
70 & 0.0161132278939352 & 0.0322264557878704 & 0.983886772106065 \tabularnewline
71 & 0.0119920775757573 & 0.0239841551515145 & 0.988007922424243 \tabularnewline
72 & 0.0143870487547688 & 0.0287740975095375 & 0.985612951245231 \tabularnewline
73 & 0.020881424122722 & 0.041762848245444 & 0.979118575877278 \tabularnewline
74 & 0.0347951712351267 & 0.0695903424702534 & 0.965204828764873 \tabularnewline
75 & 0.0744544133660615 & 0.148908826732123 & 0.925545586633939 \tabularnewline
76 & 0.0931184810606669 & 0.186236962121334 & 0.906881518939333 \tabularnewline
77 & 0.0772272490877981 & 0.154454498175596 & 0.922772750912202 \tabularnewline
78 & 0.074483902359321 & 0.148967804718642 & 0.925516097640679 \tabularnewline
79 & 0.0616773765330361 & 0.123354753066072 & 0.938322623466964 \tabularnewline
80 & 0.176580857885042 & 0.353161715770085 & 0.823419142114957 \tabularnewline
81 & 0.159067083486112 & 0.318134166972224 & 0.840932916513888 \tabularnewline
82 & 0.165400330340486 & 0.330800660680972 & 0.834599669659514 \tabularnewline
83 & 0.143565703864037 & 0.287131407728073 & 0.856434296135963 \tabularnewline
84 & 0.136229437286358 & 0.272458874572716 & 0.863770562713642 \tabularnewline
85 & 0.112188491506517 & 0.224376983013034 & 0.887811508493483 \tabularnewline
86 & 0.106563368711268 & 0.213126737422536 & 0.893436631288732 \tabularnewline
87 & 0.116050703128053 & 0.232101406256107 & 0.883949296871947 \tabularnewline
88 & 0.0944971897019381 & 0.188994379403876 & 0.905502810298062 \tabularnewline
89 & 0.0771231370625898 & 0.15424627412518 & 0.92287686293741 \tabularnewline
90 & 0.0615408613912372 & 0.123081722782474 & 0.938459138608763 \tabularnewline
91 & 0.0559317663848639 & 0.111863532769728 & 0.944068233615136 \tabularnewline
92 & 0.0562288074475122 & 0.112457614895024 & 0.943771192552488 \tabularnewline
93 & 0.118925319648014 & 0.237850639296028 & 0.881074680351986 \tabularnewline
94 & 0.135220193672957 & 0.270440387345914 & 0.864779806327043 \tabularnewline
95 & 0.126902419350299 & 0.253804838700598 & 0.873097580649701 \tabularnewline
96 & 0.106818278699531 & 0.213636557399062 & 0.893181721300469 \tabularnewline
97 & 0.0872665249521486 & 0.174533049904297 & 0.912733475047851 \tabularnewline
98 & 0.10157865492079 & 0.203157309841579 & 0.89842134507921 \tabularnewline
99 & 0.0892725159618612 & 0.178545031923722 & 0.910727484038139 \tabularnewline
100 & 0.0778123095901995 & 0.155624619180399 & 0.922187690409801 \tabularnewline
101 & 0.0616997974894537 & 0.123399594978907 & 0.938300202510546 \tabularnewline
102 & 0.0495810516830362 & 0.0991621033660724 & 0.950418948316964 \tabularnewline
103 & 0.0427962165494679 & 0.0855924330989358 & 0.957203783450532 \tabularnewline
104 & 0.0848542731131272 & 0.169708546226254 & 0.915145726886873 \tabularnewline
105 & 0.132092418259342 & 0.264184836518684 & 0.867907581740658 \tabularnewline
106 & 0.148905077866138 & 0.297810155732276 & 0.851094922133862 \tabularnewline
107 & 0.125038037653336 & 0.250076075306673 & 0.874961962346664 \tabularnewline
108 & 0.118413826122885 & 0.23682765224577 & 0.881586173877115 \tabularnewline
109 & 0.114269722133605 & 0.228539444267209 & 0.885730277866395 \tabularnewline
110 & 0.110649704868088 & 0.221299409736176 & 0.889350295131912 \tabularnewline
111 & 0.0924931985367919 & 0.184986397073584 & 0.907506801463208 \tabularnewline
112 & 0.0729904863052296 & 0.145980972610459 & 0.92700951369477 \tabularnewline
113 & 0.154091271368555 & 0.30818254273711 & 0.845908728631445 \tabularnewline
114 & 0.127918791469384 & 0.255837582938767 & 0.872081208530616 \tabularnewline
115 & 0.110824099751875 & 0.22164819950375 & 0.889175900248125 \tabularnewline
116 & 0.107859341374085 & 0.21571868274817 & 0.892140658625915 \tabularnewline
117 & 0.180667450742925 & 0.361334901485851 & 0.819332549257075 \tabularnewline
118 & 0.240557468055995 & 0.481114936111991 & 0.759442531944005 \tabularnewline
119 & 0.200933371187719 & 0.401866742375438 & 0.799066628812281 \tabularnewline
120 & 0.189781213553779 & 0.379562427107557 & 0.810218786446221 \tabularnewline
121 & 0.230624909577293 & 0.461249819154586 & 0.769375090422707 \tabularnewline
122 & 0.238823635647006 & 0.477647271294011 & 0.761176364352995 \tabularnewline
123 & 0.228573145658851 & 0.457146291317702 & 0.771426854341149 \tabularnewline
124 & 0.200428360427782 & 0.400856720855564 & 0.799571639572218 \tabularnewline
125 & 0.188216631221487 & 0.376433262442975 & 0.811783368778513 \tabularnewline
126 & 0.158518103204277 & 0.317036206408555 & 0.841481896795723 \tabularnewline
127 & 0.193093766740839 & 0.386187533481679 & 0.806906233259161 \tabularnewline
128 & 0.163598116364877 & 0.327196232729754 & 0.836401883635123 \tabularnewline
129 & 0.146098198776037 & 0.292196397552073 & 0.853901801223963 \tabularnewline
130 & 0.115862212047085 & 0.231724424094171 & 0.884137787952915 \tabularnewline
131 & 0.08772467297418 & 0.17544934594836 & 0.91227532702582 \tabularnewline
132 & 0.0850584097594669 & 0.170116819518934 & 0.914941590240533 \tabularnewline
133 & 0.067655134861527 & 0.135310269723054 & 0.932344865138473 \tabularnewline
134 & 0.0610888027985296 & 0.122177605597059 & 0.93891119720147 \tabularnewline
135 & 0.1395421259453 & 0.2790842518906 & 0.8604578740547 \tabularnewline
136 & 0.132381109638352 & 0.264762219276703 & 0.867618890361648 \tabularnewline
137 & 0.358628228698916 & 0.717256457397833 & 0.641371771301084 \tabularnewline
138 & 0.288918057833734 & 0.577836115667468 & 0.711081942166266 \tabularnewline
139 & 0.226247356092546 & 0.452494712185091 & 0.773752643907454 \tabularnewline
140 & 0.17442469087931 & 0.34884938175862 & 0.82557530912069 \tabularnewline
141 & 0.128442795064749 & 0.256885590129498 & 0.871557204935251 \tabularnewline
142 & 0.0883740959017327 & 0.176748191803465 & 0.911625904098267 \tabularnewline
143 & 0.0909576968854351 & 0.18191539377087 & 0.909042303114565 \tabularnewline
144 & 0.434692403883411 & 0.869384807766822 & 0.565307596116589 \tabularnewline
145 & 0.622566887230719 & 0.754866225538562 & 0.377433112769281 \tabularnewline
146 & 0.967113279094897 & 0.0657734418102055 & 0.0328867209051028 \tabularnewline
147 & 0.965578094916049 & 0.0688438101679012 & 0.0344219050839506 \tabularnewline
148 & 0.972978753573442 & 0.0540424928531168 & 0.0270212464265584 \tabularnewline
149 & 0.992984964428663 & 0.0140300711426745 & 0.00701503557133725 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200453&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]0.845378768284708[/C][C]0.309242463430584[/C][C]0.154621231715292[/C][/ROW]
[ROW][C]14[/C][C]0.735370614065262[/C][C]0.529258771869475[/C][C]0.264629385934738[/C][/ROW]
[ROW][C]15[/C][C]0.640408729390588[/C][C]0.719182541218824[/C][C]0.359591270609412[/C][/ROW]
[ROW][C]16[/C][C]0.693609643008941[/C][C]0.612780713982117[/C][C]0.306390356991059[/C][/ROW]
[ROW][C]17[/C][C]0.74086066234607[/C][C]0.518278675307861[/C][C]0.25913933765393[/C][/ROW]
[ROW][C]18[/C][C]0.702714072950614[/C][C]0.594571854098773[/C][C]0.297285927049386[/C][/ROW]
[ROW][C]19[/C][C]0.643779187832096[/C][C]0.712441624335809[/C][C]0.356220812167904[/C][/ROW]
[ROW][C]20[/C][C]0.573588894647144[/C][C]0.852822210705713[/C][C]0.426411105352856[/C][/ROW]
[ROW][C]21[/C][C]0.480979387446512[/C][C]0.961958774893025[/C][C]0.519020612553488[/C][/ROW]
[ROW][C]22[/C][C]0.427321595106996[/C][C]0.854643190213993[/C][C]0.572678404893004[/C][/ROW]
[ROW][C]23[/C][C]0.349402211102125[/C][C]0.698804422204251[/C][C]0.650597788897875[/C][/ROW]
[ROW][C]24[/C][C]0.40994571795916[/C][C]0.819891435918321[/C][C]0.59005428204084[/C][/ROW]
[ROW][C]25[/C][C]0.398765806787051[/C][C]0.797531613574103[/C][C]0.601234193212949[/C][/ROW]
[ROW][C]26[/C][C]0.503818206257251[/C][C]0.992363587485499[/C][C]0.496181793742749[/C][/ROW]
[ROW][C]27[/C][C]0.428692749080989[/C][C]0.857385498161978[/C][C]0.571307250919011[/C][/ROW]
[ROW][C]28[/C][C]0.37118181896667[/C][C]0.74236363793334[/C][C]0.62881818103333[/C][/ROW]
[ROW][C]29[/C][C]0.310293380021293[/C][C]0.620586760042585[/C][C]0.689706619978707[/C][/ROW]
[ROW][C]30[/C][C]0.273186711350909[/C][C]0.546373422701818[/C][C]0.726813288649091[/C][/ROW]
[ROW][C]31[/C][C]0.341308701220696[/C][C]0.682617402441391[/C][C]0.658691298779304[/C][/ROW]
[ROW][C]32[/C][C]0.286120835387222[/C][C]0.572241670774445[/C][C]0.713879164612778[/C][/ROW]
[ROW][C]33[/C][C]0.265559472694298[/C][C]0.531118945388596[/C][C]0.734440527305702[/C][/ROW]
[ROW][C]34[/C][C]0.296241910083984[/C][C]0.592483820167969[/C][C]0.703758089916016[/C][/ROW]
[ROW][C]35[/C][C]0.263843892104662[/C][C]0.527687784209324[/C][C]0.736156107895338[/C][/ROW]
[ROW][C]36[/C][C]0.23418045291897[/C][C]0.468360905837941[/C][C]0.76581954708103[/C][/ROW]
[ROW][C]37[/C][C]0.195530841612253[/C][C]0.391061683224507[/C][C]0.804469158387747[/C][/ROW]
[ROW][C]38[/C][C]0.155948516195527[/C][C]0.311897032391055[/C][C]0.844051483804473[/C][/ROW]
[ROW][C]39[/C][C]0.122705779670646[/C][C]0.245411559341291[/C][C]0.877294220329354[/C][/ROW]
[ROW][C]40[/C][C]0.100106233634123[/C][C]0.200212467268245[/C][C]0.899893766365877[/C][/ROW]
[ROW][C]41[/C][C]0.0891727519314174[/C][C]0.178345503862835[/C][C]0.910827248068583[/C][/ROW]
[ROW][C]42[/C][C]0.0684179681887992[/C][C]0.136835936377598[/C][C]0.931582031811201[/C][/ROW]
[ROW][C]43[/C][C]0.0715761201590345[/C][C]0.143152240318069[/C][C]0.928423879840965[/C][/ROW]
[ROW][C]44[/C][C]0.0541947837952803[/C][C]0.108389567590561[/C][C]0.94580521620472[/C][/ROW]
[ROW][C]45[/C][C]0.0981582010326307[/C][C]0.196316402065261[/C][C]0.901841798967369[/C][/ROW]
[ROW][C]46[/C][C]0.0851852059003017[/C][C]0.170370411800603[/C][C]0.914814794099698[/C][/ROW]
[ROW][C]47[/C][C]0.0961580940697677[/C][C]0.192316188139535[/C][C]0.903841905930232[/C][/ROW]
[ROW][C]48[/C][C]0.0801250825591576[/C][C]0.160250165118315[/C][C]0.919874917440842[/C][/ROW]
[ROW][C]49[/C][C]0.125531964068992[/C][C]0.251063928137984[/C][C]0.874468035931008[/C][/ROW]
[ROW][C]50[/C][C]0.109355242282141[/C][C]0.218710484564282[/C][C]0.890644757717859[/C][/ROW]
[ROW][C]51[/C][C]0.137816266379226[/C][C]0.275632532758452[/C][C]0.862183733620774[/C][/ROW]
[ROW][C]52[/C][C]0.124476053872486[/C][C]0.248952107744973[/C][C]0.875523946127514[/C][/ROW]
[ROW][C]53[/C][C]0.0992270239743432[/C][C]0.198454047948686[/C][C]0.900772976025657[/C][/ROW]
[ROW][C]54[/C][C]0.0832803168820702[/C][C]0.16656063376414[/C][C]0.91671968311793[/C][/ROW]
[ROW][C]55[/C][C]0.0695720821715987[/C][C]0.139144164343197[/C][C]0.930427917828401[/C][/ROW]
[ROW][C]56[/C][C]0.0606557164017116[/C][C]0.121311432803423[/C][C]0.939344283598288[/C][/ROW]
[ROW][C]57[/C][C]0.0502066172608798[/C][C]0.10041323452176[/C][C]0.94979338273912[/C][/ROW]
[ROW][C]58[/C][C]0.0393654124850068[/C][C]0.0787308249700136[/C][C]0.960634587514993[/C][/ROW]
[ROW][C]59[/C][C]0.035608793114006[/C][C]0.0712175862280121[/C][C]0.964391206885994[/C][/ROW]
[ROW][C]60[/C][C]0.0268254667033612[/C][C]0.0536509334067224[/C][C]0.973174533296639[/C][/ROW]
[ROW][C]61[/C][C]0.0275412813706523[/C][C]0.0550825627413046[/C][C]0.972458718629348[/C][/ROW]
[ROW][C]62[/C][C]0.0207785745704616[/C][C]0.0415571491409232[/C][C]0.979221425429538[/C][/ROW]
[ROW][C]63[/C][C]0.0169881796707673[/C][C]0.0339763593415346[/C][C]0.983011820329233[/C][/ROW]
[ROW][C]64[/C][C]0.0143619259728684[/C][C]0.0287238519457367[/C][C]0.985638074027132[/C][/ROW]
[ROW][C]65[/C][C]0.0140209478658315[/C][C]0.0280418957316631[/C][C]0.985979052134168[/C][/ROW]
[ROW][C]66[/C][C]0.0156826391641641[/C][C]0.0313652783283282[/C][C]0.984317360835836[/C][/ROW]
[ROW][C]67[/C][C]0.0114956720971466[/C][C]0.0229913441942931[/C][C]0.988504327902853[/C][/ROW]
[ROW][C]68[/C][C]0.0157505208674263[/C][C]0.0315010417348526[/C][C]0.984249479132574[/C][/ROW]
[ROW][C]69[/C][C]0.0185404440856133[/C][C]0.0370808881712267[/C][C]0.981459555914387[/C][/ROW]
[ROW][C]70[/C][C]0.0161132278939352[/C][C]0.0322264557878704[/C][C]0.983886772106065[/C][/ROW]
[ROW][C]71[/C][C]0.0119920775757573[/C][C]0.0239841551515145[/C][C]0.988007922424243[/C][/ROW]
[ROW][C]72[/C][C]0.0143870487547688[/C][C]0.0287740975095375[/C][C]0.985612951245231[/C][/ROW]
[ROW][C]73[/C][C]0.020881424122722[/C][C]0.041762848245444[/C][C]0.979118575877278[/C][/ROW]
[ROW][C]74[/C][C]0.0347951712351267[/C][C]0.0695903424702534[/C][C]0.965204828764873[/C][/ROW]
[ROW][C]75[/C][C]0.0744544133660615[/C][C]0.148908826732123[/C][C]0.925545586633939[/C][/ROW]
[ROW][C]76[/C][C]0.0931184810606669[/C][C]0.186236962121334[/C][C]0.906881518939333[/C][/ROW]
[ROW][C]77[/C][C]0.0772272490877981[/C][C]0.154454498175596[/C][C]0.922772750912202[/C][/ROW]
[ROW][C]78[/C][C]0.074483902359321[/C][C]0.148967804718642[/C][C]0.925516097640679[/C][/ROW]
[ROW][C]79[/C][C]0.0616773765330361[/C][C]0.123354753066072[/C][C]0.938322623466964[/C][/ROW]
[ROW][C]80[/C][C]0.176580857885042[/C][C]0.353161715770085[/C][C]0.823419142114957[/C][/ROW]
[ROW][C]81[/C][C]0.159067083486112[/C][C]0.318134166972224[/C][C]0.840932916513888[/C][/ROW]
[ROW][C]82[/C][C]0.165400330340486[/C][C]0.330800660680972[/C][C]0.834599669659514[/C][/ROW]
[ROW][C]83[/C][C]0.143565703864037[/C][C]0.287131407728073[/C][C]0.856434296135963[/C][/ROW]
[ROW][C]84[/C][C]0.136229437286358[/C][C]0.272458874572716[/C][C]0.863770562713642[/C][/ROW]
[ROW][C]85[/C][C]0.112188491506517[/C][C]0.224376983013034[/C][C]0.887811508493483[/C][/ROW]
[ROW][C]86[/C][C]0.106563368711268[/C][C]0.213126737422536[/C][C]0.893436631288732[/C][/ROW]
[ROW][C]87[/C][C]0.116050703128053[/C][C]0.232101406256107[/C][C]0.883949296871947[/C][/ROW]
[ROW][C]88[/C][C]0.0944971897019381[/C][C]0.188994379403876[/C][C]0.905502810298062[/C][/ROW]
[ROW][C]89[/C][C]0.0771231370625898[/C][C]0.15424627412518[/C][C]0.92287686293741[/C][/ROW]
[ROW][C]90[/C][C]0.0615408613912372[/C][C]0.123081722782474[/C][C]0.938459138608763[/C][/ROW]
[ROW][C]91[/C][C]0.0559317663848639[/C][C]0.111863532769728[/C][C]0.944068233615136[/C][/ROW]
[ROW][C]92[/C][C]0.0562288074475122[/C][C]0.112457614895024[/C][C]0.943771192552488[/C][/ROW]
[ROW][C]93[/C][C]0.118925319648014[/C][C]0.237850639296028[/C][C]0.881074680351986[/C][/ROW]
[ROW][C]94[/C][C]0.135220193672957[/C][C]0.270440387345914[/C][C]0.864779806327043[/C][/ROW]
[ROW][C]95[/C][C]0.126902419350299[/C][C]0.253804838700598[/C][C]0.873097580649701[/C][/ROW]
[ROW][C]96[/C][C]0.106818278699531[/C][C]0.213636557399062[/C][C]0.893181721300469[/C][/ROW]
[ROW][C]97[/C][C]0.0872665249521486[/C][C]0.174533049904297[/C][C]0.912733475047851[/C][/ROW]
[ROW][C]98[/C][C]0.10157865492079[/C][C]0.203157309841579[/C][C]0.89842134507921[/C][/ROW]
[ROW][C]99[/C][C]0.0892725159618612[/C][C]0.178545031923722[/C][C]0.910727484038139[/C][/ROW]
[ROW][C]100[/C][C]0.0778123095901995[/C][C]0.155624619180399[/C][C]0.922187690409801[/C][/ROW]
[ROW][C]101[/C][C]0.0616997974894537[/C][C]0.123399594978907[/C][C]0.938300202510546[/C][/ROW]
[ROW][C]102[/C][C]0.0495810516830362[/C][C]0.0991621033660724[/C][C]0.950418948316964[/C][/ROW]
[ROW][C]103[/C][C]0.0427962165494679[/C][C]0.0855924330989358[/C][C]0.957203783450532[/C][/ROW]
[ROW][C]104[/C][C]0.0848542731131272[/C][C]0.169708546226254[/C][C]0.915145726886873[/C][/ROW]
[ROW][C]105[/C][C]0.132092418259342[/C][C]0.264184836518684[/C][C]0.867907581740658[/C][/ROW]
[ROW][C]106[/C][C]0.148905077866138[/C][C]0.297810155732276[/C][C]0.851094922133862[/C][/ROW]
[ROW][C]107[/C][C]0.125038037653336[/C][C]0.250076075306673[/C][C]0.874961962346664[/C][/ROW]
[ROW][C]108[/C][C]0.118413826122885[/C][C]0.23682765224577[/C][C]0.881586173877115[/C][/ROW]
[ROW][C]109[/C][C]0.114269722133605[/C][C]0.228539444267209[/C][C]0.885730277866395[/C][/ROW]
[ROW][C]110[/C][C]0.110649704868088[/C][C]0.221299409736176[/C][C]0.889350295131912[/C][/ROW]
[ROW][C]111[/C][C]0.0924931985367919[/C][C]0.184986397073584[/C][C]0.907506801463208[/C][/ROW]
[ROW][C]112[/C][C]0.0729904863052296[/C][C]0.145980972610459[/C][C]0.92700951369477[/C][/ROW]
[ROW][C]113[/C][C]0.154091271368555[/C][C]0.30818254273711[/C][C]0.845908728631445[/C][/ROW]
[ROW][C]114[/C][C]0.127918791469384[/C][C]0.255837582938767[/C][C]0.872081208530616[/C][/ROW]
[ROW][C]115[/C][C]0.110824099751875[/C][C]0.22164819950375[/C][C]0.889175900248125[/C][/ROW]
[ROW][C]116[/C][C]0.107859341374085[/C][C]0.21571868274817[/C][C]0.892140658625915[/C][/ROW]
[ROW][C]117[/C][C]0.180667450742925[/C][C]0.361334901485851[/C][C]0.819332549257075[/C][/ROW]
[ROW][C]118[/C][C]0.240557468055995[/C][C]0.481114936111991[/C][C]0.759442531944005[/C][/ROW]
[ROW][C]119[/C][C]0.200933371187719[/C][C]0.401866742375438[/C][C]0.799066628812281[/C][/ROW]
[ROW][C]120[/C][C]0.189781213553779[/C][C]0.379562427107557[/C][C]0.810218786446221[/C][/ROW]
[ROW][C]121[/C][C]0.230624909577293[/C][C]0.461249819154586[/C][C]0.769375090422707[/C][/ROW]
[ROW][C]122[/C][C]0.238823635647006[/C][C]0.477647271294011[/C][C]0.761176364352995[/C][/ROW]
[ROW][C]123[/C][C]0.228573145658851[/C][C]0.457146291317702[/C][C]0.771426854341149[/C][/ROW]
[ROW][C]124[/C][C]0.200428360427782[/C][C]0.400856720855564[/C][C]0.799571639572218[/C][/ROW]
[ROW][C]125[/C][C]0.188216631221487[/C][C]0.376433262442975[/C][C]0.811783368778513[/C][/ROW]
[ROW][C]126[/C][C]0.158518103204277[/C][C]0.317036206408555[/C][C]0.841481896795723[/C][/ROW]
[ROW][C]127[/C][C]0.193093766740839[/C][C]0.386187533481679[/C][C]0.806906233259161[/C][/ROW]
[ROW][C]128[/C][C]0.163598116364877[/C][C]0.327196232729754[/C][C]0.836401883635123[/C][/ROW]
[ROW][C]129[/C][C]0.146098198776037[/C][C]0.292196397552073[/C][C]0.853901801223963[/C][/ROW]
[ROW][C]130[/C][C]0.115862212047085[/C][C]0.231724424094171[/C][C]0.884137787952915[/C][/ROW]
[ROW][C]131[/C][C]0.08772467297418[/C][C]0.17544934594836[/C][C]0.91227532702582[/C][/ROW]
[ROW][C]132[/C][C]0.0850584097594669[/C][C]0.170116819518934[/C][C]0.914941590240533[/C][/ROW]
[ROW][C]133[/C][C]0.067655134861527[/C][C]0.135310269723054[/C][C]0.932344865138473[/C][/ROW]
[ROW][C]134[/C][C]0.0610888027985296[/C][C]0.122177605597059[/C][C]0.93891119720147[/C][/ROW]
[ROW][C]135[/C][C]0.1395421259453[/C][C]0.2790842518906[/C][C]0.8604578740547[/C][/ROW]
[ROW][C]136[/C][C]0.132381109638352[/C][C]0.264762219276703[/C][C]0.867618890361648[/C][/ROW]
[ROW][C]137[/C][C]0.358628228698916[/C][C]0.717256457397833[/C][C]0.641371771301084[/C][/ROW]
[ROW][C]138[/C][C]0.288918057833734[/C][C]0.577836115667468[/C][C]0.711081942166266[/C][/ROW]
[ROW][C]139[/C][C]0.226247356092546[/C][C]0.452494712185091[/C][C]0.773752643907454[/C][/ROW]
[ROW][C]140[/C][C]0.17442469087931[/C][C]0.34884938175862[/C][C]0.82557530912069[/C][/ROW]
[ROW][C]141[/C][C]0.128442795064749[/C][C]0.256885590129498[/C][C]0.871557204935251[/C][/ROW]
[ROW][C]142[/C][C]0.0883740959017327[/C][C]0.176748191803465[/C][C]0.911625904098267[/C][/ROW]
[ROW][C]143[/C][C]0.0909576968854351[/C][C]0.18191539377087[/C][C]0.909042303114565[/C][/ROW]
[ROW][C]144[/C][C]0.434692403883411[/C][C]0.869384807766822[/C][C]0.565307596116589[/C][/ROW]
[ROW][C]145[/C][C]0.622566887230719[/C][C]0.754866225538562[/C][C]0.377433112769281[/C][/ROW]
[ROW][C]146[/C][C]0.967113279094897[/C][C]0.0657734418102055[/C][C]0.0328867209051028[/C][/ROW]
[ROW][C]147[/C][C]0.965578094916049[/C][C]0.0688438101679012[/C][C]0.0344219050839506[/C][/ROW]
[ROW][C]148[/C][C]0.972978753573442[/C][C]0.0540424928531168[/C][C]0.0270212464265584[/C][/ROW]
[ROW][C]149[/C][C]0.992984964428663[/C][C]0.0140300711426745[/C][C]0.00701503557133725[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200453&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200453&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
130.8453787682847080.3092424634305840.154621231715292
140.7353706140652620.5292587718694750.264629385934738
150.6404087293905880.7191825412188240.359591270609412
160.6936096430089410.6127807139821170.306390356991059
170.740860662346070.5182786753078610.25913933765393
180.7027140729506140.5945718540987730.297285927049386
190.6437791878320960.7124416243358090.356220812167904
200.5735888946471440.8528222107057130.426411105352856
210.4809793874465120.9619587748930250.519020612553488
220.4273215951069960.8546431902139930.572678404893004
230.3494022111021250.6988044222042510.650597788897875
240.409945717959160.8198914359183210.59005428204084
250.3987658067870510.7975316135741030.601234193212949
260.5038182062572510.9923635874854990.496181793742749
270.4286927490809890.8573854981619780.571307250919011
280.371181818966670.742363637933340.62881818103333
290.3102933800212930.6205867600425850.689706619978707
300.2731867113509090.5463734227018180.726813288649091
310.3413087012206960.6826174024413910.658691298779304
320.2861208353872220.5722416707744450.713879164612778
330.2655594726942980.5311189453885960.734440527305702
340.2962419100839840.5924838201679690.703758089916016
350.2638438921046620.5276877842093240.736156107895338
360.234180452918970.4683609058379410.76581954708103
370.1955308416122530.3910616832245070.804469158387747
380.1559485161955270.3118970323910550.844051483804473
390.1227057796706460.2454115593412910.877294220329354
400.1001062336341230.2002124672682450.899893766365877
410.08917275193141740.1783455038628350.910827248068583
420.06841796818879920.1368359363775980.931582031811201
430.07157612015903450.1431522403180690.928423879840965
440.05419478379528030.1083895675905610.94580521620472
450.09815820103263070.1963164020652610.901841798967369
460.08518520590030170.1703704118006030.914814794099698
470.09615809406976770.1923161881395350.903841905930232
480.08012508255915760.1602501651183150.919874917440842
490.1255319640689920.2510639281379840.874468035931008
500.1093552422821410.2187104845642820.890644757717859
510.1378162663792260.2756325327584520.862183733620774
520.1244760538724860.2489521077449730.875523946127514
530.09922702397434320.1984540479486860.900772976025657
540.08328031688207020.166560633764140.91671968311793
550.06957208217159870.1391441643431970.930427917828401
560.06065571640171160.1213114328034230.939344283598288
570.05020661726087980.100413234521760.94979338273912
580.03936541248500680.07873082497001360.960634587514993
590.0356087931140060.07121758622801210.964391206885994
600.02682546670336120.05365093340672240.973174533296639
610.02754128137065230.05508256274130460.972458718629348
620.02077857457046160.04155714914092320.979221425429538
630.01698817967076730.03397635934153460.983011820329233
640.01436192597286840.02872385194573670.985638074027132
650.01402094786583150.02804189573166310.985979052134168
660.01568263916416410.03136527832832820.984317360835836
670.01149567209714660.02299134419429310.988504327902853
680.01575052086742630.03150104173485260.984249479132574
690.01854044408561330.03708088817122670.981459555914387
700.01611322789393520.03222645578787040.983886772106065
710.01199207757575730.02398415515151450.988007922424243
720.01438704875476880.02877409750953750.985612951245231
730.0208814241227220.0417628482454440.979118575877278
740.03479517123512670.06959034247025340.965204828764873
750.07445441336606150.1489088267321230.925545586633939
760.09311848106066690.1862369621213340.906881518939333
770.07722724908779810.1544544981755960.922772750912202
780.0744839023593210.1489678047186420.925516097640679
790.06167737653303610.1233547530660720.938322623466964
800.1765808578850420.3531617157700850.823419142114957
810.1590670834861120.3181341669722240.840932916513888
820.1654003303404860.3308006606809720.834599669659514
830.1435657038640370.2871314077280730.856434296135963
840.1362294372863580.2724588745727160.863770562713642
850.1121884915065170.2243769830130340.887811508493483
860.1065633687112680.2131267374225360.893436631288732
870.1160507031280530.2321014062561070.883949296871947
880.09449718970193810.1889943794038760.905502810298062
890.07712313706258980.154246274125180.92287686293741
900.06154086139123720.1230817227824740.938459138608763
910.05593176638486390.1118635327697280.944068233615136
920.05622880744751220.1124576148950240.943771192552488
930.1189253196480140.2378506392960280.881074680351986
940.1352201936729570.2704403873459140.864779806327043
950.1269024193502990.2538048387005980.873097580649701
960.1068182786995310.2136365573990620.893181721300469
970.08726652495214860.1745330499042970.912733475047851
980.101578654920790.2031573098415790.89842134507921
990.08927251596186120.1785450319237220.910727484038139
1000.07781230959019950.1556246191803990.922187690409801
1010.06169979748945370.1233995949789070.938300202510546
1020.04958105168303620.09916210336607240.950418948316964
1030.04279621654946790.08559243309893580.957203783450532
1040.08485427311312720.1697085462262540.915145726886873
1050.1320924182593420.2641848365186840.867907581740658
1060.1489050778661380.2978101557322760.851094922133862
1070.1250380376533360.2500760753066730.874961962346664
1080.1184138261228850.236827652245770.881586173877115
1090.1142697221336050.2285394442672090.885730277866395
1100.1106497048680880.2212994097361760.889350295131912
1110.09249319853679190.1849863970735840.907506801463208
1120.07299048630522960.1459809726104590.92700951369477
1130.1540912713685550.308182542737110.845908728631445
1140.1279187914693840.2558375829387670.872081208530616
1150.1108240997518750.221648199503750.889175900248125
1160.1078593413740850.215718682748170.892140658625915
1170.1806674507429250.3613349014858510.819332549257075
1180.2405574680559950.4811149361119910.759442531944005
1190.2009333711877190.4018667423754380.799066628812281
1200.1897812135537790.3795624271075570.810218786446221
1210.2306249095772930.4612498191545860.769375090422707
1220.2388236356470060.4776472712940110.761176364352995
1230.2285731456588510.4571462913177020.771426854341149
1240.2004283604277820.4008567208555640.799571639572218
1250.1882166312214870.3764332624429750.811783368778513
1260.1585181032042770.3170362064085550.841481896795723
1270.1930937667408390.3861875334816790.806906233259161
1280.1635981163648770.3271962327297540.836401883635123
1290.1460981987760370.2921963975520730.853901801223963
1300.1158622120470850.2317244240941710.884137787952915
1310.087724672974180.175449345948360.91227532702582
1320.08505840975946690.1701168195189340.914941590240533
1330.0676551348615270.1353102697230540.932344865138473
1340.06108880279852960.1221776055970590.93891119720147
1350.13954212594530.27908425189060.8604578740547
1360.1323811096383520.2647622192767030.867618890361648
1370.3586282286989160.7172564573978330.641371771301084
1380.2889180578337340.5778361156674680.711081942166266
1390.2262473560925460.4524947121850910.773752643907454
1400.174424690879310.348849381758620.82557530912069
1410.1284427950647490.2568855901294980.871557204935251
1420.08837409590173270.1767481918034650.911625904098267
1430.09095769688543510.181915393770870.909042303114565
1440.4346924038834110.8693848077668220.565307596116589
1450.6225668872307190.7548662255385620.377433112769281
1460.9671132790948970.06577344181020550.0328867209051028
1470.9655780949160490.06884381016790120.0344219050839506
1480.9729787535734420.05404249285311680.0270212464265584
1490.9929849644286630.01403007114267450.00701503557133725






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level130.0948905109489051NOK
10% type I error level230.167883211678832NOK

\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 & 13 & 0.0948905109489051 & NOK \tabularnewline
10% type I error level & 23 & 0.167883211678832 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200453&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]13[/C][C]0.0948905109489051[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.167883211678832[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200453&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200453&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 level130.0948905109489051NOK
10% type I error level230.167883211678832NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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