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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, 04 Nov 2012 10:35:18 -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/Nov/04/t13520433555rkedz5a7uls7k1.htm/, Retrieved Thu, 02 May 2024 20:15:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185842, Retrieved Thu, 02 May 2024 20:15:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Workshop 7] [2012-11-04 15:35:18] [38c0fff34b8aa23b45468de8b641bfee] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	41	38	13	12	14	12	53	32
9	39	32	16	11	18	11	86	51
9	30	35	19	15	11	14	66	42
9	31	33	15	6	12	12	67	41
9	34	37	14	13	16	21	76	46
9	35	29	13	10	18	12	78	47
9	39	31	19	12	14	22	53	37
9	34	36	15	14	14	11	80	49
9	36	35	14	12	15	10	74	45
9	37	38	15	6	15	13	76	47
9	38	31	16	10	17	10	79	49
9	36	34	16	12	19	8	54	33
9	38	35	16	12	10	15	67	42
9	39	38	16	11	16	14	54	33
9	33	37	17	15	18	10	87	53
9	32	33	15	12	14	14	58	36
9	36	32	15	10	14	14	75	45
9	38	38	20	12	17	11	88	54
9	39	38	18	11	14	10	64	41
9	32	32	16	12	16	13	57	36
9	32	33	16	11	18	7	66	41
9	31	31	16	12	11	14	68	44
9	39	38	19	13	14	12	54	33
9	37	39	16	11	12	14	56	37
9	39	32	17	9	17	11	86	52
9	41	32	17	13	9	9	80	47
9	36	35	16	10	16	11	76	43
9	33	37	15	14	14	15	69	44
9	33	33	16	12	15	14	78	45
9	34	33	14	10	11	13	67	44
9	31	28	15	12	16	9	80	49
9	27	32	12	8	13	15	54	33
9	37	31	14	10	17	10	71	43
9	34	37	16	12	15	11	84	54
9	34	30	14	12	14	13	74	42
9	32	33	7	7	16	8	71	44
9	29	31	10	6	9	20	63	37
9	36	33	14	12	15	12	71	43
9	29	31	16	10	17	10	76	46
9	35	33	16	10	13	10	69	42
9	37	32	16	10	15	9	74	45
9	34	33	14	12	16	14	75	44
9	38	32	20	15	16	8	54	33
9	35	33	14	10	12	14	52	31
9	38	28	14	10	12	11	69	42
9	37	35	11	12	11	13	68	40
9	38	39	14	13	15	9	65	43
9	33	34	15	11	15	11	75	46
9	36	38	16	11	17	15	74	42
9	38	32	14	12	13	11	75	45
9	32	38	16	14	16	10	72	44
9	32	30	14	10	14	14	67	40
9	32	33	12	12	11	18	63	37
9	34	38	16	13	12	14	62	46
9	32	32	9	5	12	11	63	36
9	37	32	14	6	15	12	76	47
9	39	34	16	12	16	13	74	45
9	29	34	16	12	15	9	67	42
9	37	36	15	11	12	10	73	43
9	35	34	16	10	12	15	70	43
9	30	28	12	7	8	20	53	32
9	38	34	16	12	13	12	77	45
9	34	35	16	14	11	12	77	45
9	31	35	14	11	14	14	52	31
9	34	31	16	12	15	13	54	33
10	35	37	17	13	10	11	80	49
10	36	35	18	14	11	17	66	42
10	30	27	18	11	12	12	73	41
10	39	40	12	12	15	13	63	38
10	35	37	16	12	15	14	69	42
10	38	36	10	8	14	13	67	44
10	31	38	14	11	16	15	54	33
10	34	39	18	14	15	13	81	48
10	38	41	18	14	15	10	69	40
10	34	27	16	12	13	11	84	50
10	39	30	17	9	12	19	80	49
10	37	37	16	13	17	13	70	43
10	34	31	16	11	13	17	69	44
10	28	31	13	12	15	13	77	47
10	37	27	16	12	13	9	54	33
10	33	36	16	12	15	11	79	46
10	37	38	20	12	16	10	30	0
10	35	37	16	12	15	9	71	45
10	37	33	15	12	16	12	73	43
10	32	34	15	11	15	12	72	44
10	33	31	16	10	14	13	77	47
10	38	39	14	9	15	13	75	45
10	33	34	16	12	14	12	69	42
10	29	32	16	12	13	15	54	33
10	33	33	15	12	7	22	70	43
10	31	36	12	9	17	13	73	46
10	36	32	17	15	13	15	54	33
10	35	41	16	12	15	13	77	46
10	32	28	15	12	14	15	82	48
10	29	30	13	12	13	10	80	47
10	39	36	16	10	16	11	80	47
10	37	35	16	13	12	16	69	43
10	35	31	16	9	14	11	78	46
10	37	34	16	12	17	11	81	48
10	32	36	14	10	15	10	76	46
10	38	36	16	14	17	10	76	45
10	37	35	16	11	12	16	73	45
10	36	37	20	15	16	12	85	52
10	32	28	15	11	11	11	66	42
10	33	39	16	11	15	16	79	47
10	40	32	13	12	9	19	68	41
10	38	35	17	12	16	11	76	47
10	41	39	16	12	15	16	71	43
10	36	35	16	11	10	15	54	33
10	43	42	12	7	10	24	46	30
10	30	34	16	12	15	14	82	49
10	31	33	16	14	11	15	74	44
10	32	41	17	11	13	11	88	55
10	32	33	13	11	14	15	38	11
10	37	34	12	10	18	12	76	47
10	37	32	18	13	16	10	86	53
10	33	40	14	13	14	14	54	33
10	34	40	14	8	14	13	70	44
10	33	35	13	11	14	9	69	42
10	38	36	16	12	14	15	90	55
10	33	37	13	11	12	15	54	33
10	31	27	16	13	14	14	76	46
10	38	39	13	12	15	11	89	54
10	37	38	16	14	15	8	76	47
10	33	31	15	13	15	11	73	45
10	31	33	16	15	13	11	79	47
10	39	32	15	10	17	8	90	55
10	44	39	17	11	17	10	74	44
10	33	36	15	9	19	11	81	53
10	35	33	12	11	15	13	72	44
10	32	33	16	10	13	11	71	42
10	28	32	10	11	9	20	66	40
10	40	37	16	8	15	10	77	46
10	27	30	12	11	15	15	65	40
10	37	38	14	12	15	12	74	46
10	32	29	15	12	16	14	82	53
10	28	22	13	9	11	23	54	33
10	34	35	15	11	14	14	63	42
10	30	35	11	10	11	16	54	35
10	35	34	12	8	15	11	64	40
10	31	35	8	9	13	12	69	41
10	32	34	16	8	15	10	54	33
10	30	34	15	9	16	14	84	51
10	30	35	17	15	14	12	86	53
10	31	23	16	11	15	12	77	46
10	40	31	10	8	16	11	89	55
10	32	27	18	13	16	12	76	47
10	36	36	13	12	11	13	60	38
10	32	31	16	12	12	11	75	46
10	35	32	13	9	9	19	73	46
10	38	39	10	7	16	12	85	53
10	42	37	15	13	13	17	79	47
10	34	38	16	9	16	9	71	41
10	35	39	16	6	12	12	72	44
9	35	34	14	8	9	19	69	43
10	33	31	10	8	13	18	78	51
10	36	32	17	15	13	15	54	33
10	32	37	13	6	14	14	69	43
10	33	36	15	9	19	11	81	53
10	34	32	16	11	13	9	84	51
10	32	35	12	8	12	18	84	50
10	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 time14 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 14 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185842&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]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185842&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185842&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 time14 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 1.01513523734579 + 0.611262473290177Month[t] + 0.104826952846486Connected[t] -0.018730625721894Separate[t] + 0.511453166676237Software[t] + 0.0433287462643628Happines[t] -0.07071885192344Depression[t] + 0.0351556714609746Belonging[t] -0.045703230204992Belonging_Final[t] -0.00963745644843234t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  1.01513523734579 +  0.611262473290177Month[t] +  0.104826952846486Connected[t] -0.018730625721894Separate[t] +  0.511453166676237Software[t] +  0.0433287462643628Happines[t] -0.07071885192344Depression[t] +  0.0351556714609746Belonging[t] -0.045703230204992Belonging_Final[t] -0.00963745644843234t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185842&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  1.01513523734579 +  0.611262473290177Month[t] +  0.104826952846486Connected[t] -0.018730625721894Separate[t] +  0.511453166676237Software[t] +  0.0433287462643628Happines[t] -0.07071885192344Depression[t] +  0.0351556714609746Belonging[t] -0.045703230204992Belonging_Final[t] -0.00963745644843234t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185842&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185842&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 1.01513523734579 + 0.611262473290177Month[t] + 0.104826952846486Connected[t] -0.018730625721894Separate[t] + 0.511453166676237Software[t] + 0.0433287462643628Happines[t] -0.07071885192344Depression[t] + 0.0351556714609746Belonging[t] -0.045703230204992Belonging_Final[t] -0.00963745644843234t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.015135237345795.0477050.20110.8408830.420441
Month0.6112624732901770.5543331.10270.2719010.13595
Connected0.1048269528464860.0471972.2210.0278280.013914
Separate-0.0187306257218940.04509-0.41540.6784340.339217
Software0.5114531666762370.0714657.156700
Happines0.04332874626436280.0768020.56420.5734740.286737
Depression-0.070718851923440.056847-1.2440.2154040.107702
Belonging0.03515567146097460.0451270.7790.4371640.218582
Belonging_Final-0.0457032302049920.064527-0.70830.4798530.239926
t-0.009637456448432340.005889-1.63660.1037870.051894

\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) & 1.01513523734579 & 5.047705 & 0.2011 & 0.840883 & 0.420441 \tabularnewline
Month & 0.611262473290177 & 0.554333 & 1.1027 & 0.271901 & 0.13595 \tabularnewline
Connected & 0.104826952846486 & 0.047197 & 2.221 & 0.027828 & 0.013914 \tabularnewline
Separate & -0.018730625721894 & 0.04509 & -0.4154 & 0.678434 & 0.339217 \tabularnewline
Software & 0.511453166676237 & 0.071465 & 7.1567 & 0 & 0 \tabularnewline
Happines & 0.0433287462643628 & 0.076802 & 0.5642 & 0.573474 & 0.286737 \tabularnewline
Depression & -0.07071885192344 & 0.056847 & -1.244 & 0.215404 & 0.107702 \tabularnewline
Belonging & 0.0351556714609746 & 0.045127 & 0.779 & 0.437164 & 0.218582 \tabularnewline
Belonging_Final & -0.045703230204992 & 0.064527 & -0.7083 & 0.479853 & 0.239926 \tabularnewline
t & -0.00963745644843234 & 0.005889 & -1.6366 & 0.103787 & 0.051894 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185842&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]1.01513523734579[/C][C]5.047705[/C][C]0.2011[/C][C]0.840883[/C][C]0.420441[/C][/ROW]
[ROW][C]Month[/C][C]0.611262473290177[/C][C]0.554333[/C][C]1.1027[/C][C]0.271901[/C][C]0.13595[/C][/ROW]
[ROW][C]Connected[/C][C]0.104826952846486[/C][C]0.047197[/C][C]2.221[/C][C]0.027828[/C][C]0.013914[/C][/ROW]
[ROW][C]Separate[/C][C]-0.018730625721894[/C][C]0.04509[/C][C]-0.4154[/C][C]0.678434[/C][C]0.339217[/C][/ROW]
[ROW][C]Software[/C][C]0.511453166676237[/C][C]0.071465[/C][C]7.1567[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happines[/C][C]0.0433287462643628[/C][C]0.076802[/C][C]0.5642[/C][C]0.573474[/C][C]0.286737[/C][/ROW]
[ROW][C]Depression[/C][C]-0.07071885192344[/C][C]0.056847[/C][C]-1.244[/C][C]0.215404[/C][C]0.107702[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0351556714609746[/C][C]0.045127[/C][C]0.779[/C][C]0.437164[/C][C]0.218582[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.045703230204992[/C][C]0.064527[/C][C]-0.7083[/C][C]0.479853[/C][C]0.239926[/C][/ROW]
[ROW][C]t[/C][C]-0.00963745644843234[/C][C]0.005889[/C][C]-1.6366[/C][C]0.103787[/C][C]0.051894[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185842&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185842&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)1.015135237345795.0477050.20110.8408830.420441
Month0.6112624732901770.5543331.10270.2719010.13595
Connected0.1048269528464860.0471972.2210.0278280.013914
Separate-0.0187306257218940.04509-0.41540.6784340.339217
Software0.5114531666762370.0714657.156700
Happines0.04332874626436280.0768020.56420.5734740.286737
Depression-0.070718851923440.056847-1.2440.2154040.107702
Belonging0.03515567146097460.0451270.7790.4371640.218582
Belonging_Final-0.0457032302049920.064527-0.70830.4798530.239926
t-0.009637456448432340.005889-1.63660.1037870.051894






Multiple Linear Regression - Regression Statistics
Multiple R0.607260220849627
R-squared0.368764975826338
Adjusted R-squared0.331389217816056
F-TEST (value)9.8664213238133
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value7.83240139412555e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84491360342345
Sum Squared Residuals517.363343022729

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.607260220849627 \tabularnewline
R-squared & 0.368764975826338 \tabularnewline
Adjusted R-squared & 0.331389217816056 \tabularnewline
F-TEST (value) & 9.8664213238133 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 7.83240139412555e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84491360342345 \tabularnewline
Sum Squared Residuals & 517.363343022729 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185842&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.607260220849627[/C][/ROW]
[ROW][C]R-squared[/C][C]0.368764975826338[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.331389217816056[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.8664213238133[/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]7.83240139412555e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.84491360342345[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]517.363343022729[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185842&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185842&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 R0.607260220849627
R-squared0.368764975826338
Adjusted R-squared0.331389217816056
F-TEST (value)9.8664213238133
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value7.83240139412555e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84491360342345
Sum Squared Residuals517.363343022729






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.3891627753895-3.38916277538949
21616.3066116222014-0.306611622201408
31916.53591024267842.46408975732156
41512.33110784221142.66889215778864
51415.7659311180188-1.76593111801877
61315.2243413927199-2.22434139271991
71915.31709384079983.68290615920019
81516.8912465630062-1.8912465630062
91416.1730137948621-2.17301379486209
101512.91004074077872.08995925922132
111615.49503188620690.504968113793074
121616.3229100733834-0.322910073383425
131615.66489987421050.335100125789532
141615.47744099912850.522559000871523
151717.5189905723628-0.51899057236278
161515.2463392142921-0.246339214292112
171514.83815120459070.161848795409314
182016.33652768456753.66347231543251
191815.61140250502122.38859749497884
201615.34874068721150.651259312788532
211615.40777493455810.592225065441919
221614.97709340837551.02290659162446
231916.46839043576322.53160956423678
241614.86686534027381.13313465972618
251714.97301181406562.02698818593436
261717.0312307860062-0.0312307860062147
271615.11096094311070.889039056889315
281516.1338682127297-1.13386821272965
291615.56099233694790.439007663052094
301414.1896702109937-0.189670210993745
311515.7101380749511-0.710138074951083
321212.423362512485-0.423362512484983
331415.1711549010371-1.17115490103708
341615.55447101735590.445528982644105
351415.6880635386996-1.68806353869963
36713.0986927433644-6.09869274336438
371011.1873323059594-1.18733230595937
381415.7754905514815-1.77549055148152
391614.31338320626451.68661679373551
401614.65865445098691.34134554901311
411615.07344653709540.926553462904615
421415.5240973180512-1.5240973180512
432017.67563734185442.32436265814559
441414.1989895886534-0.198989588653378
451414.9045535577059-0.904553557705852
461115.5533654405479-4.55336544054792
471416.2986992884879-2.29869928848794
481514.9086831832120.0913168167880356
491615.09004341660940.909956583390625
501415.9215031903438-1.92150319034382
511616.3343679023762-0.33436790237618
521414.0662644482906-0.0662644482905594
531214.6069668063131-2.60696680631315
541615.10450270427670.895497295723323
55911.610314292338-2.61031429233795
561412.64982035040561.3501796495944
571615.87479956009270.12520043990727
581614.9474592269971.05254077300302
591515.1920486830447-0.192048683044745
601614.03970413167081.96029586832923
611211.46213603803620.537863961963754
621615.76798495251740.232015047482643
631616.2565578997848-0.256557899784834
641414.14758205606-0.147582056059961
651615.13175360841460.868246391585431
661716.34486473767820.655135262321792
671816.43572749790131.56427250209869
681815.10132976643382.89867023356623
691216.1479102807702-4.14791028077021
701615.7325591461240.267440853876042
711013.8759928095591-3.87599280955907
721414.6203965237142-0.620396523714247
731816.8026324340661.19736756593395
741817.33075587743830.66924412256167
751616.0540594617705-0.0540594617705282
761714.27400637506932.72599362493065
771616.5330328090623-0.53303280906229
781614.76134262063611.23865737936389
791315.1478651950803-2.1478651950803
801616.1840755115703-0.18407551157033
811615.81652419478020.183475805219778
822016.68250158430363.31749841569638
831615.89406812421850.105931875781489
841516.1618970701766-1.1618970701766
851514.97375340916730.0262465908327215
861614.53830268455691.46169731544313
871414.4559256836419-0.45592568364188
881615.50373185910740.496268140892598
891614.74075654061241.25924345938756
901514.48215027010350.517849729896525
911213.7104219844902-1.71042198449021
921716.97999234122130.0200076587787424
931615.60512644771370.394873552286292
941515.4241117138941-0.424111713894085
951315.3481895480983-2.34818954809829
961615.31079891930050.689201080699473
971615.91378897298430.086211027015677
981614.34315055170531.65684944829473
991616.1653814165789-0.165381416578865
1001414.4709310736016-0.470931073601562
1011617.2684287236707-1.26842872367072
1021614.89191158282361.1080884171764
1032017.34393442763782.65606557236219
1041514.68090178948860.319098210511447
1051614.61828270635371.38171729364627
1061315.4003794283629-2.40037942836294
1071716.00097421875180.999025781248245
1081615.84100667558870.158993324411263
1091614.58416479893421.41583520106581
1101212.3507836172692-0.350783617269173
1111615.00658166222920.993418337770791
1121615.84664506005790.153354939942097
1131714.61660681907332.38339318092669
1141314.7704262629415-1.77042626294155
1151214.8308105472925-2.83081054729251
1161816.52511138701451.47488861298547
1171415.3658713305312-1.36587133053117
1181413.03426905659220.965730943407844
1191314.8869434725782-1.88694347257819
1201615.61397731779150.386022682208536
1211314.2032307040985-1.20323070409848
1221615.53081105645720.469188943542799
1231315.8656247839826-2.86562478398262
1241616.8678527719747-0.867852771974668
1251515.8323516077741-0.832351607774051
1261616.6313754033685-0.63137540336847
1271515.3283764472912-0.328376447291193
1281716.02201962673060.97798037326939
1291513.74327050177121.25672949822879
1301214.8025605013257-2.80256050132574
1311614.07802001992881.92197998007123
1321013.2851019952292-3.28510199522923
1331613.98502934596182.01497065403821
1341213.7768724466721-1.77687244667206
1351415.4314508972787-1.43145089727872
1361514.92946811076520.0705318892347616
1371312.17387012751520.826129872484818
1381514.24413046452140.755869535478587
1391113.035829655657-2.035829655657
1401213.1961010640699-1.19610106406986
141813.2325771198375-5.23257711983754
1421612.90143004138223.09856995861783
1431513.1860571846271.81394281537301
1441716.26009319634420.739906803655792
1451614.58108784925061.41891215074942
1461013.9552750464912-3.95527504649123
1471815.57709356426352.42290643573651
1481314.8682108662518-1.8682108662518
1491614.87939440741281.12060559258718
1501312.86509928665070.134900713349316
1511012.9162006087473-2.91620060874735
1521516.0117560692399-1.01175606923989
1531613.78767076131552.21232923868451
1541611.84234457198124.15765542801884
1551411.65322211776942.3467778822306
1561012.2961941445736-2.29619414457358
1571716.35355767207320.646442327926844
1581311.41223114359571.58776885640433
1591513.45414680831821.54585319168176
1601614.725503842011.27449615799004
1611212.2815659193038-0.281565919303831
1621312.30309604184310.696903958156932

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.3891627753895 & -3.38916277538949 \tabularnewline
2 & 16 & 16.3066116222014 & -0.306611622201408 \tabularnewline
3 & 19 & 16.5359102426784 & 2.46408975732156 \tabularnewline
4 & 15 & 12.3311078422114 & 2.66889215778864 \tabularnewline
5 & 14 & 15.7659311180188 & -1.76593111801877 \tabularnewline
6 & 13 & 15.2243413927199 & -2.22434139271991 \tabularnewline
7 & 19 & 15.3170938407998 & 3.68290615920019 \tabularnewline
8 & 15 & 16.8912465630062 & -1.8912465630062 \tabularnewline
9 & 14 & 16.1730137948621 & -2.17301379486209 \tabularnewline
10 & 15 & 12.9100407407787 & 2.08995925922132 \tabularnewline
11 & 16 & 15.4950318862069 & 0.504968113793074 \tabularnewline
12 & 16 & 16.3229100733834 & -0.322910073383425 \tabularnewline
13 & 16 & 15.6648998742105 & 0.335100125789532 \tabularnewline
14 & 16 & 15.4774409991285 & 0.522559000871523 \tabularnewline
15 & 17 & 17.5189905723628 & -0.51899057236278 \tabularnewline
16 & 15 & 15.2463392142921 & -0.246339214292112 \tabularnewline
17 & 15 & 14.8381512045907 & 0.161848795409314 \tabularnewline
18 & 20 & 16.3365276845675 & 3.66347231543251 \tabularnewline
19 & 18 & 15.6114025050212 & 2.38859749497884 \tabularnewline
20 & 16 & 15.3487406872115 & 0.651259312788532 \tabularnewline
21 & 16 & 15.4077749345581 & 0.592225065441919 \tabularnewline
22 & 16 & 14.9770934083755 & 1.02290659162446 \tabularnewline
23 & 19 & 16.4683904357632 & 2.53160956423678 \tabularnewline
24 & 16 & 14.8668653402738 & 1.13313465972618 \tabularnewline
25 & 17 & 14.9730118140656 & 2.02698818593436 \tabularnewline
26 & 17 & 17.0312307860062 & -0.0312307860062147 \tabularnewline
27 & 16 & 15.1109609431107 & 0.889039056889315 \tabularnewline
28 & 15 & 16.1338682127297 & -1.13386821272965 \tabularnewline
29 & 16 & 15.5609923369479 & 0.439007663052094 \tabularnewline
30 & 14 & 14.1896702109937 & -0.189670210993745 \tabularnewline
31 & 15 & 15.7101380749511 & -0.710138074951083 \tabularnewline
32 & 12 & 12.423362512485 & -0.423362512484983 \tabularnewline
33 & 14 & 15.1711549010371 & -1.17115490103708 \tabularnewline
34 & 16 & 15.5544710173559 & 0.445528982644105 \tabularnewline
35 & 14 & 15.6880635386996 & -1.68806353869963 \tabularnewline
36 & 7 & 13.0986927433644 & -6.09869274336438 \tabularnewline
37 & 10 & 11.1873323059594 & -1.18733230595937 \tabularnewline
38 & 14 & 15.7754905514815 & -1.77549055148152 \tabularnewline
39 & 16 & 14.3133832062645 & 1.68661679373551 \tabularnewline
40 & 16 & 14.6586544509869 & 1.34134554901311 \tabularnewline
41 & 16 & 15.0734465370954 & 0.926553462904615 \tabularnewline
42 & 14 & 15.5240973180512 & -1.5240973180512 \tabularnewline
43 & 20 & 17.6756373418544 & 2.32436265814559 \tabularnewline
44 & 14 & 14.1989895886534 & -0.198989588653378 \tabularnewline
45 & 14 & 14.9045535577059 & -0.904553557705852 \tabularnewline
46 & 11 & 15.5533654405479 & -4.55336544054792 \tabularnewline
47 & 14 & 16.2986992884879 & -2.29869928848794 \tabularnewline
48 & 15 & 14.908683183212 & 0.0913168167880356 \tabularnewline
49 & 16 & 15.0900434166094 & 0.909956583390625 \tabularnewline
50 & 14 & 15.9215031903438 & -1.92150319034382 \tabularnewline
51 & 16 & 16.3343679023762 & -0.33436790237618 \tabularnewline
52 & 14 & 14.0662644482906 & -0.0662644482905594 \tabularnewline
53 & 12 & 14.6069668063131 & -2.60696680631315 \tabularnewline
54 & 16 & 15.1045027042767 & 0.895497295723323 \tabularnewline
55 & 9 & 11.610314292338 & -2.61031429233795 \tabularnewline
56 & 14 & 12.6498203504056 & 1.3501796495944 \tabularnewline
57 & 16 & 15.8747995600927 & 0.12520043990727 \tabularnewline
58 & 16 & 14.947459226997 & 1.05254077300302 \tabularnewline
59 & 15 & 15.1920486830447 & -0.192048683044745 \tabularnewline
60 & 16 & 14.0397041316708 & 1.96029586832923 \tabularnewline
61 & 12 & 11.4621360380362 & 0.537863961963754 \tabularnewline
62 & 16 & 15.7679849525174 & 0.232015047482643 \tabularnewline
63 & 16 & 16.2565578997848 & -0.256557899784834 \tabularnewline
64 & 14 & 14.14758205606 & -0.147582056059961 \tabularnewline
65 & 16 & 15.1317536084146 & 0.868246391585431 \tabularnewline
66 & 17 & 16.3448647376782 & 0.655135262321792 \tabularnewline
67 & 18 & 16.4357274979013 & 1.56427250209869 \tabularnewline
68 & 18 & 15.1013297664338 & 2.89867023356623 \tabularnewline
69 & 12 & 16.1479102807702 & -4.14791028077021 \tabularnewline
70 & 16 & 15.732559146124 & 0.267440853876042 \tabularnewline
71 & 10 & 13.8759928095591 & -3.87599280955907 \tabularnewline
72 & 14 & 14.6203965237142 & -0.620396523714247 \tabularnewline
73 & 18 & 16.802632434066 & 1.19736756593395 \tabularnewline
74 & 18 & 17.3307558774383 & 0.66924412256167 \tabularnewline
75 & 16 & 16.0540594617705 & -0.0540594617705282 \tabularnewline
76 & 17 & 14.2740063750693 & 2.72599362493065 \tabularnewline
77 & 16 & 16.5330328090623 & -0.53303280906229 \tabularnewline
78 & 16 & 14.7613426206361 & 1.23865737936389 \tabularnewline
79 & 13 & 15.1478651950803 & -2.1478651950803 \tabularnewline
80 & 16 & 16.1840755115703 & -0.18407551157033 \tabularnewline
81 & 16 & 15.8165241947802 & 0.183475805219778 \tabularnewline
82 & 20 & 16.6825015843036 & 3.31749841569638 \tabularnewline
83 & 16 & 15.8940681242185 & 0.105931875781489 \tabularnewline
84 & 15 & 16.1618970701766 & -1.1618970701766 \tabularnewline
85 & 15 & 14.9737534091673 & 0.0262465908327215 \tabularnewline
86 & 16 & 14.5383026845569 & 1.46169731544313 \tabularnewline
87 & 14 & 14.4559256836419 & -0.45592568364188 \tabularnewline
88 & 16 & 15.5037318591074 & 0.496268140892598 \tabularnewline
89 & 16 & 14.7407565406124 & 1.25924345938756 \tabularnewline
90 & 15 & 14.4821502701035 & 0.517849729896525 \tabularnewline
91 & 12 & 13.7104219844902 & -1.71042198449021 \tabularnewline
92 & 17 & 16.9799923412213 & 0.0200076587787424 \tabularnewline
93 & 16 & 15.6051264477137 & 0.394873552286292 \tabularnewline
94 & 15 & 15.4241117138941 & -0.424111713894085 \tabularnewline
95 & 13 & 15.3481895480983 & -2.34818954809829 \tabularnewline
96 & 16 & 15.3107989193005 & 0.689201080699473 \tabularnewline
97 & 16 & 15.9137889729843 & 0.086211027015677 \tabularnewline
98 & 16 & 14.3431505517053 & 1.65684944829473 \tabularnewline
99 & 16 & 16.1653814165789 & -0.165381416578865 \tabularnewline
100 & 14 & 14.4709310736016 & -0.470931073601562 \tabularnewline
101 & 16 & 17.2684287236707 & -1.26842872367072 \tabularnewline
102 & 16 & 14.8919115828236 & 1.1080884171764 \tabularnewline
103 & 20 & 17.3439344276378 & 2.65606557236219 \tabularnewline
104 & 15 & 14.6809017894886 & 0.319098210511447 \tabularnewline
105 & 16 & 14.6182827063537 & 1.38171729364627 \tabularnewline
106 & 13 & 15.4003794283629 & -2.40037942836294 \tabularnewline
107 & 17 & 16.0009742187518 & 0.999025781248245 \tabularnewline
108 & 16 & 15.8410066755887 & 0.158993324411263 \tabularnewline
109 & 16 & 14.5841647989342 & 1.41583520106581 \tabularnewline
110 & 12 & 12.3507836172692 & -0.350783617269173 \tabularnewline
111 & 16 & 15.0065816622292 & 0.993418337770791 \tabularnewline
112 & 16 & 15.8466450600579 & 0.153354939942097 \tabularnewline
113 & 17 & 14.6166068190733 & 2.38339318092669 \tabularnewline
114 & 13 & 14.7704262629415 & -1.77042626294155 \tabularnewline
115 & 12 & 14.8308105472925 & -2.83081054729251 \tabularnewline
116 & 18 & 16.5251113870145 & 1.47488861298547 \tabularnewline
117 & 14 & 15.3658713305312 & -1.36587133053117 \tabularnewline
118 & 14 & 13.0342690565922 & 0.965730943407844 \tabularnewline
119 & 13 & 14.8869434725782 & -1.88694347257819 \tabularnewline
120 & 16 & 15.6139773177915 & 0.386022682208536 \tabularnewline
121 & 13 & 14.2032307040985 & -1.20323070409848 \tabularnewline
122 & 16 & 15.5308110564572 & 0.469188943542799 \tabularnewline
123 & 13 & 15.8656247839826 & -2.86562478398262 \tabularnewline
124 & 16 & 16.8678527719747 & -0.867852771974668 \tabularnewline
125 & 15 & 15.8323516077741 & -0.832351607774051 \tabularnewline
126 & 16 & 16.6313754033685 & -0.63137540336847 \tabularnewline
127 & 15 & 15.3283764472912 & -0.328376447291193 \tabularnewline
128 & 17 & 16.0220196267306 & 0.97798037326939 \tabularnewline
129 & 15 & 13.7432705017712 & 1.25672949822879 \tabularnewline
130 & 12 & 14.8025605013257 & -2.80256050132574 \tabularnewline
131 & 16 & 14.0780200199288 & 1.92197998007123 \tabularnewline
132 & 10 & 13.2851019952292 & -3.28510199522923 \tabularnewline
133 & 16 & 13.9850293459618 & 2.01497065403821 \tabularnewline
134 & 12 & 13.7768724466721 & -1.77687244667206 \tabularnewline
135 & 14 & 15.4314508972787 & -1.43145089727872 \tabularnewline
136 & 15 & 14.9294681107652 & 0.0705318892347616 \tabularnewline
137 & 13 & 12.1738701275152 & 0.826129872484818 \tabularnewline
138 & 15 & 14.2441304645214 & 0.755869535478587 \tabularnewline
139 & 11 & 13.035829655657 & -2.035829655657 \tabularnewline
140 & 12 & 13.1961010640699 & -1.19610106406986 \tabularnewline
141 & 8 & 13.2325771198375 & -5.23257711983754 \tabularnewline
142 & 16 & 12.9014300413822 & 3.09856995861783 \tabularnewline
143 & 15 & 13.186057184627 & 1.81394281537301 \tabularnewline
144 & 17 & 16.2600931963442 & 0.739906803655792 \tabularnewline
145 & 16 & 14.5810878492506 & 1.41891215074942 \tabularnewline
146 & 10 & 13.9552750464912 & -3.95527504649123 \tabularnewline
147 & 18 & 15.5770935642635 & 2.42290643573651 \tabularnewline
148 & 13 & 14.8682108662518 & -1.8682108662518 \tabularnewline
149 & 16 & 14.8793944074128 & 1.12060559258718 \tabularnewline
150 & 13 & 12.8650992866507 & 0.134900713349316 \tabularnewline
151 & 10 & 12.9162006087473 & -2.91620060874735 \tabularnewline
152 & 15 & 16.0117560692399 & -1.01175606923989 \tabularnewline
153 & 16 & 13.7876707613155 & 2.21232923868451 \tabularnewline
154 & 16 & 11.8423445719812 & 4.15765542801884 \tabularnewline
155 & 14 & 11.6532221177694 & 2.3467778822306 \tabularnewline
156 & 10 & 12.2961941445736 & -2.29619414457358 \tabularnewline
157 & 17 & 16.3535576720732 & 0.646442327926844 \tabularnewline
158 & 13 & 11.4122311435957 & 1.58776885640433 \tabularnewline
159 & 15 & 13.4541468083182 & 1.54585319168176 \tabularnewline
160 & 16 & 14.72550384201 & 1.27449615799004 \tabularnewline
161 & 12 & 12.2815659193038 & -0.281565919303831 \tabularnewline
162 & 13 & 12.3030960418431 & 0.696903958156932 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185842&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.3891627753895[/C][C]-3.38916277538949[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.3066116222014[/C][C]-0.306611622201408[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5359102426784[/C][C]2.46408975732156[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.3311078422114[/C][C]2.66889215778864[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.7659311180188[/C][C]-1.76593111801877[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.2243413927199[/C][C]-2.22434139271991[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.3170938407998[/C][C]3.68290615920019[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.8912465630062[/C][C]-1.8912465630062[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1730137948621[/C][C]-2.17301379486209[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.9100407407787[/C][C]2.08995925922132[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4950318862069[/C][C]0.504968113793074[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.3229100733834[/C][C]-0.322910073383425[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6648998742105[/C][C]0.335100125789532[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4774409991285[/C][C]0.522559000871523[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.5189905723628[/C][C]-0.51899057236278[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2463392142921[/C][C]-0.246339214292112[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.8381512045907[/C][C]0.161848795409314[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3365276845675[/C][C]3.66347231543251[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.6114025050212[/C][C]2.38859749497884[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3487406872115[/C][C]0.651259312788532[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.4077749345581[/C][C]0.592225065441919[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.9770934083755[/C][C]1.02290659162446[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4683904357632[/C][C]2.53160956423678[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8668653402738[/C][C]1.13313465972618[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.9730118140656[/C][C]2.02698818593436[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.0312307860062[/C][C]-0.0312307860062147[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.1109609431107[/C][C]0.889039056889315[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.1338682127297[/C][C]-1.13386821272965[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.5609923369479[/C][C]0.439007663052094[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1896702109937[/C][C]-0.189670210993745[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7101380749511[/C][C]-0.710138074951083[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.423362512485[/C][C]-0.423362512484983[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1711549010371[/C][C]-1.17115490103708[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5544710173559[/C][C]0.445528982644105[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6880635386996[/C][C]-1.68806353869963[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.0986927433644[/C][C]-6.09869274336438[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.1873323059594[/C][C]-1.18733230595937[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7754905514815[/C][C]-1.77549055148152[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.3133832062645[/C][C]1.68661679373551[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6586544509869[/C][C]1.34134554901311[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.0734465370954[/C][C]0.926553462904615[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.5240973180512[/C][C]-1.5240973180512[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.6756373418544[/C][C]2.32436265814559[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1989895886534[/C][C]-0.198989588653378[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9045535577059[/C][C]-0.904553557705852[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.5533654405479[/C][C]-4.55336544054792[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.2986992884879[/C][C]-2.29869928848794[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.908683183212[/C][C]0.0913168167880356[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.0900434166094[/C][C]0.909956583390625[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.9215031903438[/C][C]-1.92150319034382[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.3343679023762[/C][C]-0.33436790237618[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.0662644482906[/C][C]-0.0662644482905594[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6069668063131[/C][C]-2.60696680631315[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.1045027042767[/C][C]0.895497295723323[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.610314292338[/C][C]-2.61031429233795[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6498203504056[/C][C]1.3501796495944[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.8747995600927[/C][C]0.12520043990727[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.947459226997[/C][C]1.05254077300302[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.1920486830447[/C][C]-0.192048683044745[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.0397041316708[/C][C]1.96029586832923[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4621360380362[/C][C]0.537863961963754[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.7679849525174[/C][C]0.232015047482643[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.2565578997848[/C][C]-0.256557899784834[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.14758205606[/C][C]-0.147582056059961[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.1317536084146[/C][C]0.868246391585431[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.3448647376782[/C][C]0.655135262321792[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.4357274979013[/C][C]1.56427250209869[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]15.1013297664338[/C][C]2.89867023356623[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]16.1479102807702[/C][C]-4.14791028077021[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.732559146124[/C][C]0.267440853876042[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.8759928095591[/C][C]-3.87599280955907[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.6203965237142[/C][C]-0.620396523714247[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.802632434066[/C][C]1.19736756593395[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.3307558774383[/C][C]0.66924412256167[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]16.0540594617705[/C][C]-0.0540594617705282[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]14.2740063750693[/C][C]2.72599362493065[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.5330328090623[/C][C]-0.53303280906229[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.7613426206361[/C][C]1.23865737936389[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.1478651950803[/C][C]-2.1478651950803[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.1840755115703[/C][C]-0.18407551157033[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.8165241947802[/C][C]0.183475805219778[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.6825015843036[/C][C]3.31749841569638[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.8940681242185[/C][C]0.105931875781489[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]16.1618970701766[/C][C]-1.1618970701766[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.9737534091673[/C][C]0.0262465908327215[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.5383026845569[/C][C]1.46169731544313[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.4559256836419[/C][C]-0.45592568364188[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.5037318591074[/C][C]0.496268140892598[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.7407565406124[/C][C]1.25924345938756[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.4821502701035[/C][C]0.517849729896525[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.7104219844902[/C][C]-1.71042198449021[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9799923412213[/C][C]0.0200076587787424[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.6051264477137[/C][C]0.394873552286292[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.4241117138941[/C][C]-0.424111713894085[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.3481895480983[/C][C]-2.34818954809829[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.3107989193005[/C][C]0.689201080699473[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.9137889729843[/C][C]0.086211027015677[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.3431505517053[/C][C]1.65684944829473[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.1653814165789[/C][C]-0.165381416578865[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.4709310736016[/C][C]-0.470931073601562[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2684287236707[/C][C]-1.26842872367072[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8919115828236[/C][C]1.1080884171764[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3439344276378[/C][C]2.65606557236219[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.6809017894886[/C][C]0.319098210511447[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.6182827063537[/C][C]1.38171729364627[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.4003794283629[/C][C]-2.40037942836294[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.0009742187518[/C][C]0.999025781248245[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8410066755887[/C][C]0.158993324411263[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5841647989342[/C][C]1.41583520106581[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.3507836172692[/C][C]-0.350783617269173[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.0065816622292[/C][C]0.993418337770791[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8466450600579[/C][C]0.153354939942097[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.6166068190733[/C][C]2.38339318092669[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.7704262629415[/C][C]-1.77042626294155[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.8308105472925[/C][C]-2.83081054729251[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.5251113870145[/C][C]1.47488861298547[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.3658713305312[/C][C]-1.36587133053117[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0342690565922[/C][C]0.965730943407844[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.8869434725782[/C][C]-1.88694347257819[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6139773177915[/C][C]0.386022682208536[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.2032307040985[/C][C]-1.20323070409848[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.5308110564572[/C][C]0.469188943542799[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8656247839826[/C][C]-2.86562478398262[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.8678527719747[/C][C]-0.867852771974668[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8323516077741[/C][C]-0.832351607774051[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.6313754033685[/C][C]-0.63137540336847[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.3283764472912[/C][C]-0.328376447291193[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0220196267306[/C][C]0.97798037326939[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.7432705017712[/C][C]1.25672949822879[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.8025605013257[/C][C]-2.80256050132574[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0780200199288[/C][C]1.92197998007123[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2851019952292[/C][C]-3.28510199522923[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9850293459618[/C][C]2.01497065403821[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.7768724466721[/C][C]-1.77687244667206[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.4314508972787[/C][C]-1.43145089727872[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.9294681107652[/C][C]0.0705318892347616[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.1738701275152[/C][C]0.826129872484818[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.2441304645214[/C][C]0.755869535478587[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.035829655657[/C][C]-2.035829655657[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.1961010640699[/C][C]-1.19610106406986[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2325771198375[/C][C]-5.23257711983754[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.9014300413822[/C][C]3.09856995861783[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.186057184627[/C][C]1.81394281537301[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.2600931963442[/C][C]0.739906803655792[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.5810878492506[/C][C]1.41891215074942[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]13.9552750464912[/C][C]-3.95527504649123[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.5770935642635[/C][C]2.42290643573651[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.8682108662518[/C][C]-1.8682108662518[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.8793944074128[/C][C]1.12060559258718[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.8650992866507[/C][C]0.134900713349316[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.9162006087473[/C][C]-2.91620060874735[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.0117560692399[/C][C]-1.01175606923989[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.7876707613155[/C][C]2.21232923868451[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8423445719812[/C][C]4.15765542801884[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]11.6532221177694[/C][C]2.3467778822306[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.2961941445736[/C][C]-2.29619414457358[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.3535576720732[/C][C]0.646442327926844[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.4122311435957[/C][C]1.58776885640433[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.4541468083182[/C][C]1.54585319168176[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.72550384201[/C][C]1.27449615799004[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.2815659193038[/C][C]-0.281565919303831[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.3030960418431[/C][C]0.696903958156932[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185842&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185842&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.3891627753895-3.38916277538949
21616.3066116222014-0.306611622201408
31916.53591024267842.46408975732156
41512.33110784221142.66889215778864
51415.7659311180188-1.76593111801877
61315.2243413927199-2.22434139271991
71915.31709384079983.68290615920019
81516.8912465630062-1.8912465630062
91416.1730137948621-2.17301379486209
101512.91004074077872.08995925922132
111615.49503188620690.504968113793074
121616.3229100733834-0.322910073383425
131615.66489987421050.335100125789532
141615.47744099912850.522559000871523
151717.5189905723628-0.51899057236278
161515.2463392142921-0.246339214292112
171514.83815120459070.161848795409314
182016.33652768456753.66347231543251
191815.61140250502122.38859749497884
201615.34874068721150.651259312788532
211615.40777493455810.592225065441919
221614.97709340837551.02290659162446
231916.46839043576322.53160956423678
241614.86686534027381.13313465972618
251714.97301181406562.02698818593436
261717.0312307860062-0.0312307860062147
271615.11096094311070.889039056889315
281516.1338682127297-1.13386821272965
291615.56099233694790.439007663052094
301414.1896702109937-0.189670210993745
311515.7101380749511-0.710138074951083
321212.423362512485-0.423362512484983
331415.1711549010371-1.17115490103708
341615.55447101735590.445528982644105
351415.6880635386996-1.68806353869963
36713.0986927433644-6.09869274336438
371011.1873323059594-1.18733230595937
381415.7754905514815-1.77549055148152
391614.31338320626451.68661679373551
401614.65865445098691.34134554901311
411615.07344653709540.926553462904615
421415.5240973180512-1.5240973180512
432017.67563734185442.32436265814559
441414.1989895886534-0.198989588653378
451414.9045535577059-0.904553557705852
461115.5533654405479-4.55336544054792
471416.2986992884879-2.29869928848794
481514.9086831832120.0913168167880356
491615.09004341660940.909956583390625
501415.9215031903438-1.92150319034382
511616.3343679023762-0.33436790237618
521414.0662644482906-0.0662644482905594
531214.6069668063131-2.60696680631315
541615.10450270427670.895497295723323
55911.610314292338-2.61031429233795
561412.64982035040561.3501796495944
571615.87479956009270.12520043990727
581614.9474592269971.05254077300302
591515.1920486830447-0.192048683044745
601614.03970413167081.96029586832923
611211.46213603803620.537863961963754
621615.76798495251740.232015047482643
631616.2565578997848-0.256557899784834
641414.14758205606-0.147582056059961
651615.13175360841460.868246391585431
661716.34486473767820.655135262321792
671816.43572749790131.56427250209869
681815.10132976643382.89867023356623
691216.1479102807702-4.14791028077021
701615.7325591461240.267440853876042
711013.8759928095591-3.87599280955907
721414.6203965237142-0.620396523714247
731816.8026324340661.19736756593395
741817.33075587743830.66924412256167
751616.0540594617705-0.0540594617705282
761714.27400637506932.72599362493065
771616.5330328090623-0.53303280906229
781614.76134262063611.23865737936389
791315.1478651950803-2.1478651950803
801616.1840755115703-0.18407551157033
811615.81652419478020.183475805219778
822016.68250158430363.31749841569638
831615.89406812421850.105931875781489
841516.1618970701766-1.1618970701766
851514.97375340916730.0262465908327215
861614.53830268455691.46169731544313
871414.4559256836419-0.45592568364188
881615.50373185910740.496268140892598
891614.74075654061241.25924345938756
901514.48215027010350.517849729896525
911213.7104219844902-1.71042198449021
921716.97999234122130.0200076587787424
931615.60512644771370.394873552286292
941515.4241117138941-0.424111713894085
951315.3481895480983-2.34818954809829
961615.31079891930050.689201080699473
971615.91378897298430.086211027015677
981614.34315055170531.65684944829473
991616.1653814165789-0.165381416578865
1001414.4709310736016-0.470931073601562
1011617.2684287236707-1.26842872367072
1021614.89191158282361.1080884171764
1032017.34393442763782.65606557236219
1041514.68090178948860.319098210511447
1051614.61828270635371.38171729364627
1061315.4003794283629-2.40037942836294
1071716.00097421875180.999025781248245
1081615.84100667558870.158993324411263
1091614.58416479893421.41583520106581
1101212.3507836172692-0.350783617269173
1111615.00658166222920.993418337770791
1121615.84664506005790.153354939942097
1131714.61660681907332.38339318092669
1141314.7704262629415-1.77042626294155
1151214.8308105472925-2.83081054729251
1161816.52511138701451.47488861298547
1171415.3658713305312-1.36587133053117
1181413.03426905659220.965730943407844
1191314.8869434725782-1.88694347257819
1201615.61397731779150.386022682208536
1211314.2032307040985-1.20323070409848
1221615.53081105645720.469188943542799
1231315.8656247839826-2.86562478398262
1241616.8678527719747-0.867852771974668
1251515.8323516077741-0.832351607774051
1261616.6313754033685-0.63137540336847
1271515.3283764472912-0.328376447291193
1281716.02201962673060.97798037326939
1291513.74327050177121.25672949822879
1301214.8025605013257-2.80256050132574
1311614.07802001992881.92197998007123
1321013.2851019952292-3.28510199522923
1331613.98502934596182.01497065403821
1341213.7768724466721-1.77687244667206
1351415.4314508972787-1.43145089727872
1361514.92946811076520.0705318892347616
1371312.17387012751520.826129872484818
1381514.24413046452140.755869535478587
1391113.035829655657-2.035829655657
1401213.1961010640699-1.19610106406986
141813.2325771198375-5.23257711983754
1421612.90143004138223.09856995861783
1431513.1860571846271.81394281537301
1441716.26009319634420.739906803655792
1451614.58108784925061.41891215074942
1461013.9552750464912-3.95527504649123
1471815.57709356426352.42290643573651
1481314.8682108662518-1.8682108662518
1491614.87939440741281.12060559258718
1501312.86509928665070.134900713349316
1511012.9162006087473-2.91620060874735
1521516.0117560692399-1.01175606923989
1531613.78767076131552.21232923868451
1541611.84234457198124.15765542801884
1551411.65322211776942.3467778822306
1561012.2961941445736-2.29619414457358
1571716.35355767207320.646442327926844
1581311.41223114359571.58776885640433
1591513.45414680831821.54585319168176
1601614.725503842011.27449615799004
1611212.2815659193038-0.281565919303831
1621312.30309604184310.696903958156932






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.7367732013661120.5264535972677760.263226798633888
140.7114746242529390.5770507514941220.288525375747061
150.5897117728669920.8205764542660160.410288227133008
160.4766629697016140.9533259394032280.523337030298386
170.3981966870762930.7963933741525860.601803312923707
180.5541870052733820.8916259894532360.445812994726618
190.4684147226272980.9368294452545970.531585277372702
200.3797107366799520.7594214733599050.620289263320048
210.3090315076544540.6180630153089090.690968492345546
220.3011750189433930.6023500378867850.698824981056607
230.4286136171588910.8572272343177810.571386382841109
240.5014321875059980.9971356249880030.498567812494002
250.4473558507586720.8947117015173440.552644149241328
260.3789704375384720.7579408750769440.621029562461528
270.3503142983499170.7006285966998340.649685701650083
280.4540141409932390.9080282819864780.545985859006761
290.3965024041735860.7930048083471720.603497595826414
300.506477643739170.987044712521660.49352235626083
310.4553930253245850.910786050649170.544606974675415
320.4179115989781720.8358231979563450.582088401021828
330.4056816032991370.8113632065982740.594318396700863
340.3742123724307840.7484247448615680.625787627569216
350.3248795285646880.6497590571293770.675120471435312
360.8669153014294290.2661693971411420.133084698570571
370.840449366791810.3191012664163810.15955063320819
380.8217865793171740.3564268413656520.178213420682826
390.8521916515983220.2956166968033560.147808348401678
400.8405550594491630.3188898811016740.159444940550837
410.8141932323536320.3716135352927350.185806767646368
420.785588755155240.428822489689520.21441124484476
430.8165298047185430.3669403905629150.183470195281458
440.7779149233256230.4441701533487540.222085076674377
450.7489272809867760.5021454380264480.251072719013224
460.8794736168495620.2410527663008770.120526383150438
470.9011025382292670.1977949235414670.0988974617707334
480.8794467708703350.2411064582593290.120553229129665
490.8768358992015880.2463282015968250.123164100798412
500.8683815885628770.2632368228742450.131618411437123
510.8407994079065220.3184011841869560.159200592093478
520.8107553216630730.3784893566738530.189244678336927
530.8211570457345010.3576859085309980.178842954265499
540.7918611181442520.4162777637114960.208138881855748
550.8102736422437150.379452715512570.189726357756285
560.7895793102742770.4208413794514470.210420689725723
570.752836028020120.4943279439597610.24716397197988
580.7379024926725180.5241950146549640.262097507327482
590.708238504231950.58352299153610.29176149576805
600.7130309842719110.5739380314561780.286969015728089
610.6786806263082460.6426387473835070.321319373691754
620.643118638042440.713762723915120.35688136195756
630.6152289205762380.7695421588475230.384771079423762
640.5801231327312280.8397537345375450.419876867268772
650.5451715723642350.909656855271530.454828427635765
660.4972200835589870.9944401671179750.502779916441013
670.473366916728680.9467338334573610.52663308327132
680.5090810350883460.9818379298233070.490918964911654
690.7331670080164910.5336659839670170.266832991983509
700.6929723403170930.6140553193658150.307027659682907
710.8255160851072530.3489678297854950.174483914892747
720.79539044065930.4092191186813990.2046095593407
730.7830368107672030.4339263784655940.216963189232797
740.7603548521567040.4792902956865920.239645147843296
750.722801296428390.554397407143220.27719870357161
760.7572748540021410.4854502919957190.242725145997859
770.7213887389174430.5572225221651130.278611261082557
780.6993438204994230.6013123590011530.300656179500577
790.7179299002676810.5641401994646380.282070099732319
800.6767814171692890.6464371656614210.323218582830711
810.6368349603869670.7263300792260670.363165039613033
820.7467451043675090.5065097912649820.253254895632491
830.7086797991611750.5826404016776510.291320200838825
840.6820229642453090.6359540715093820.317977035754691
850.6389936197990390.7220127604019220.361006380200961
860.6207337276919390.7585325446161220.379266272308061
870.5759588764547460.8480822470905080.424041123545254
880.5315010391657250.936997921668550.468498960834275
890.5044371194204920.9911257611590150.495562880579508
900.468976540265930.9379530805318610.53102345973407
910.4609265998016180.9218531996032360.539073400198382
920.4184749143654840.8369498287309680.581525085634516
930.3758940713337190.7517881426674370.624105928666281
940.3330914789768730.6661829579537470.666908521023126
950.3641479839736080.7282959679472160.635852016026392
960.3271492502892140.6542985005784290.672850749710786
970.2864622357055680.5729244714111360.713537764294432
980.2784193755956780.5568387511913550.721580624404322
990.2388682659841470.4777365319682950.761131734015853
1000.2079728625467390.4159457250934770.792027137453261
1010.1874747690309790.3749495380619590.812525230969021
1020.1715175664917290.3430351329834580.828482433508271
1030.2119798214486940.4239596428973870.788020178551306
1040.1785909853740010.3571819707480010.821409014625999
1050.1723380822665680.3446761645331360.827661917733432
1060.1778496019050680.3556992038101370.822150398094932
1070.1588549355671730.3177098711343450.841145064432827
1080.1391111642265290.2782223284530590.860888835773471
1090.1379667615959440.2759335231918870.862033238404056
1100.1427473391712340.2854946783424680.857252660828766
1110.1288715230856420.2577430461712840.871128476914358
1120.1106695627504230.2213391255008460.889330437249577
1130.153345158636020.306690317272040.84665484136398
1140.142496609346170.284993218692340.85750339065383
1150.1614783313066310.3229566626132620.838521668693369
1160.1702171209401840.3404342418803690.829782879059816
1170.1440809986681770.2881619973363540.855919001331823
1180.1490637811837280.2981275623674570.850936218816272
1190.1359816667413460.2719633334826920.864018333258654
1200.1592419215154160.3184838430308310.840758078484584
1210.1300832130135120.2601664260270250.869916786986488
1220.1148713479269250.2297426958538510.885128652073075
1230.107228448420530.214456896841060.89277155157947
1240.08331279908655890.1666255981731180.916687200913441
1250.06396517014252770.1279303402850550.936034829857472
1260.04778531913879940.09557063827759880.952214680861201
1270.03481012455513910.06962024911027820.965189875444861
1280.03310519983268040.06621039966536090.96689480016732
1290.0342286687508770.0684573375017540.965771331249123
1300.03376221092893220.06752442185786450.966237789071068
1310.03744821033290660.07489642066581320.962551789667093
1320.03606847368872670.07213694737745330.963931526311273
1330.07903071612970360.1580614322594070.920969283870296
1340.08625887553260020.17251775106520.9137411244674
1350.06474094582918370.1294818916583670.935259054170816
1360.0510285649114410.1020571298228820.948971435088559
1370.03909856856137770.07819713712275540.960901431438622
1380.04665704937623320.09331409875246630.953342950623767
1390.03614626252918930.07229252505837850.963853737470811
1400.02355737841566340.04711475683132690.976442621584337
1410.5144044483614370.9711911032771260.485595551638563
1420.4530739245679420.9061478491358840.546926075432058
1430.3807568021636540.7615136043273070.619243197836346
1440.293112825316630.5862256506332610.70688717468337
1450.2146869167547310.4293738335094630.785313083245269
1460.2318300586009920.4636601172019840.768169941399008
1470.2472784022701530.4945568045403060.752721597729847
1480.5072183526617810.9855632946764390.492781647338219
1490.5437961327942160.9124077344115680.456203867205784

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.736773201366112 & 0.526453597267776 & 0.263226798633888 \tabularnewline
14 & 0.711474624252939 & 0.577050751494122 & 0.288525375747061 \tabularnewline
15 & 0.589711772866992 & 0.820576454266016 & 0.410288227133008 \tabularnewline
16 & 0.476662969701614 & 0.953325939403228 & 0.523337030298386 \tabularnewline
17 & 0.398196687076293 & 0.796393374152586 & 0.601803312923707 \tabularnewline
18 & 0.554187005273382 & 0.891625989453236 & 0.445812994726618 \tabularnewline
19 & 0.468414722627298 & 0.936829445254597 & 0.531585277372702 \tabularnewline
20 & 0.379710736679952 & 0.759421473359905 & 0.620289263320048 \tabularnewline
21 & 0.309031507654454 & 0.618063015308909 & 0.690968492345546 \tabularnewline
22 & 0.301175018943393 & 0.602350037886785 & 0.698824981056607 \tabularnewline
23 & 0.428613617158891 & 0.857227234317781 & 0.571386382841109 \tabularnewline
24 & 0.501432187505998 & 0.997135624988003 & 0.498567812494002 \tabularnewline
25 & 0.447355850758672 & 0.894711701517344 & 0.552644149241328 \tabularnewline
26 & 0.378970437538472 & 0.757940875076944 & 0.621029562461528 \tabularnewline
27 & 0.350314298349917 & 0.700628596699834 & 0.649685701650083 \tabularnewline
28 & 0.454014140993239 & 0.908028281986478 & 0.545985859006761 \tabularnewline
29 & 0.396502404173586 & 0.793004808347172 & 0.603497595826414 \tabularnewline
30 & 0.50647764373917 & 0.98704471252166 & 0.49352235626083 \tabularnewline
31 & 0.455393025324585 & 0.91078605064917 & 0.544606974675415 \tabularnewline
32 & 0.417911598978172 & 0.835823197956345 & 0.582088401021828 \tabularnewline
33 & 0.405681603299137 & 0.811363206598274 & 0.594318396700863 \tabularnewline
34 & 0.374212372430784 & 0.748424744861568 & 0.625787627569216 \tabularnewline
35 & 0.324879528564688 & 0.649759057129377 & 0.675120471435312 \tabularnewline
36 & 0.866915301429429 & 0.266169397141142 & 0.133084698570571 \tabularnewline
37 & 0.84044936679181 & 0.319101266416381 & 0.15955063320819 \tabularnewline
38 & 0.821786579317174 & 0.356426841365652 & 0.178213420682826 \tabularnewline
39 & 0.852191651598322 & 0.295616696803356 & 0.147808348401678 \tabularnewline
40 & 0.840555059449163 & 0.318889881101674 & 0.159444940550837 \tabularnewline
41 & 0.814193232353632 & 0.371613535292735 & 0.185806767646368 \tabularnewline
42 & 0.78558875515524 & 0.42882248968952 & 0.21441124484476 \tabularnewline
43 & 0.816529804718543 & 0.366940390562915 & 0.183470195281458 \tabularnewline
44 & 0.777914923325623 & 0.444170153348754 & 0.222085076674377 \tabularnewline
45 & 0.748927280986776 & 0.502145438026448 & 0.251072719013224 \tabularnewline
46 & 0.879473616849562 & 0.241052766300877 & 0.120526383150438 \tabularnewline
47 & 0.901102538229267 & 0.197794923541467 & 0.0988974617707334 \tabularnewline
48 & 0.879446770870335 & 0.241106458259329 & 0.120553229129665 \tabularnewline
49 & 0.876835899201588 & 0.246328201596825 & 0.123164100798412 \tabularnewline
50 & 0.868381588562877 & 0.263236822874245 & 0.131618411437123 \tabularnewline
51 & 0.840799407906522 & 0.318401184186956 & 0.159200592093478 \tabularnewline
52 & 0.810755321663073 & 0.378489356673853 & 0.189244678336927 \tabularnewline
53 & 0.821157045734501 & 0.357685908530998 & 0.178842954265499 \tabularnewline
54 & 0.791861118144252 & 0.416277763711496 & 0.208138881855748 \tabularnewline
55 & 0.810273642243715 & 0.37945271551257 & 0.189726357756285 \tabularnewline
56 & 0.789579310274277 & 0.420841379451447 & 0.210420689725723 \tabularnewline
57 & 0.75283602802012 & 0.494327943959761 & 0.24716397197988 \tabularnewline
58 & 0.737902492672518 & 0.524195014654964 & 0.262097507327482 \tabularnewline
59 & 0.70823850423195 & 0.5835229915361 & 0.29176149576805 \tabularnewline
60 & 0.713030984271911 & 0.573938031456178 & 0.286969015728089 \tabularnewline
61 & 0.678680626308246 & 0.642638747383507 & 0.321319373691754 \tabularnewline
62 & 0.64311863804244 & 0.71376272391512 & 0.35688136195756 \tabularnewline
63 & 0.615228920576238 & 0.769542158847523 & 0.384771079423762 \tabularnewline
64 & 0.580123132731228 & 0.839753734537545 & 0.419876867268772 \tabularnewline
65 & 0.545171572364235 & 0.90965685527153 & 0.454828427635765 \tabularnewline
66 & 0.497220083558987 & 0.994440167117975 & 0.502779916441013 \tabularnewline
67 & 0.47336691672868 & 0.946733833457361 & 0.52663308327132 \tabularnewline
68 & 0.509081035088346 & 0.981837929823307 & 0.490918964911654 \tabularnewline
69 & 0.733167008016491 & 0.533665983967017 & 0.266832991983509 \tabularnewline
70 & 0.692972340317093 & 0.614055319365815 & 0.307027659682907 \tabularnewline
71 & 0.825516085107253 & 0.348967829785495 & 0.174483914892747 \tabularnewline
72 & 0.7953904406593 & 0.409219118681399 & 0.2046095593407 \tabularnewline
73 & 0.783036810767203 & 0.433926378465594 & 0.216963189232797 \tabularnewline
74 & 0.760354852156704 & 0.479290295686592 & 0.239645147843296 \tabularnewline
75 & 0.72280129642839 & 0.55439740714322 & 0.27719870357161 \tabularnewline
76 & 0.757274854002141 & 0.485450291995719 & 0.242725145997859 \tabularnewline
77 & 0.721388738917443 & 0.557222522165113 & 0.278611261082557 \tabularnewline
78 & 0.699343820499423 & 0.601312359001153 & 0.300656179500577 \tabularnewline
79 & 0.717929900267681 & 0.564140199464638 & 0.282070099732319 \tabularnewline
80 & 0.676781417169289 & 0.646437165661421 & 0.323218582830711 \tabularnewline
81 & 0.636834960386967 & 0.726330079226067 & 0.363165039613033 \tabularnewline
82 & 0.746745104367509 & 0.506509791264982 & 0.253254895632491 \tabularnewline
83 & 0.708679799161175 & 0.582640401677651 & 0.291320200838825 \tabularnewline
84 & 0.682022964245309 & 0.635954071509382 & 0.317977035754691 \tabularnewline
85 & 0.638993619799039 & 0.722012760401922 & 0.361006380200961 \tabularnewline
86 & 0.620733727691939 & 0.758532544616122 & 0.379266272308061 \tabularnewline
87 & 0.575958876454746 & 0.848082247090508 & 0.424041123545254 \tabularnewline
88 & 0.531501039165725 & 0.93699792166855 & 0.468498960834275 \tabularnewline
89 & 0.504437119420492 & 0.991125761159015 & 0.495562880579508 \tabularnewline
90 & 0.46897654026593 & 0.937953080531861 & 0.53102345973407 \tabularnewline
91 & 0.460926599801618 & 0.921853199603236 & 0.539073400198382 \tabularnewline
92 & 0.418474914365484 & 0.836949828730968 & 0.581525085634516 \tabularnewline
93 & 0.375894071333719 & 0.751788142667437 & 0.624105928666281 \tabularnewline
94 & 0.333091478976873 & 0.666182957953747 & 0.666908521023126 \tabularnewline
95 & 0.364147983973608 & 0.728295967947216 & 0.635852016026392 \tabularnewline
96 & 0.327149250289214 & 0.654298500578429 & 0.672850749710786 \tabularnewline
97 & 0.286462235705568 & 0.572924471411136 & 0.713537764294432 \tabularnewline
98 & 0.278419375595678 & 0.556838751191355 & 0.721580624404322 \tabularnewline
99 & 0.238868265984147 & 0.477736531968295 & 0.761131734015853 \tabularnewline
100 & 0.207972862546739 & 0.415945725093477 & 0.792027137453261 \tabularnewline
101 & 0.187474769030979 & 0.374949538061959 & 0.812525230969021 \tabularnewline
102 & 0.171517566491729 & 0.343035132983458 & 0.828482433508271 \tabularnewline
103 & 0.211979821448694 & 0.423959642897387 & 0.788020178551306 \tabularnewline
104 & 0.178590985374001 & 0.357181970748001 & 0.821409014625999 \tabularnewline
105 & 0.172338082266568 & 0.344676164533136 & 0.827661917733432 \tabularnewline
106 & 0.177849601905068 & 0.355699203810137 & 0.822150398094932 \tabularnewline
107 & 0.158854935567173 & 0.317709871134345 & 0.841145064432827 \tabularnewline
108 & 0.139111164226529 & 0.278222328453059 & 0.860888835773471 \tabularnewline
109 & 0.137966761595944 & 0.275933523191887 & 0.862033238404056 \tabularnewline
110 & 0.142747339171234 & 0.285494678342468 & 0.857252660828766 \tabularnewline
111 & 0.128871523085642 & 0.257743046171284 & 0.871128476914358 \tabularnewline
112 & 0.110669562750423 & 0.221339125500846 & 0.889330437249577 \tabularnewline
113 & 0.15334515863602 & 0.30669031727204 & 0.84665484136398 \tabularnewline
114 & 0.14249660934617 & 0.28499321869234 & 0.85750339065383 \tabularnewline
115 & 0.161478331306631 & 0.322956662613262 & 0.838521668693369 \tabularnewline
116 & 0.170217120940184 & 0.340434241880369 & 0.829782879059816 \tabularnewline
117 & 0.144080998668177 & 0.288161997336354 & 0.855919001331823 \tabularnewline
118 & 0.149063781183728 & 0.298127562367457 & 0.850936218816272 \tabularnewline
119 & 0.135981666741346 & 0.271963333482692 & 0.864018333258654 \tabularnewline
120 & 0.159241921515416 & 0.318483843030831 & 0.840758078484584 \tabularnewline
121 & 0.130083213013512 & 0.260166426027025 & 0.869916786986488 \tabularnewline
122 & 0.114871347926925 & 0.229742695853851 & 0.885128652073075 \tabularnewline
123 & 0.10722844842053 & 0.21445689684106 & 0.89277155157947 \tabularnewline
124 & 0.0833127990865589 & 0.166625598173118 & 0.916687200913441 \tabularnewline
125 & 0.0639651701425277 & 0.127930340285055 & 0.936034829857472 \tabularnewline
126 & 0.0477853191387994 & 0.0955706382775988 & 0.952214680861201 \tabularnewline
127 & 0.0348101245551391 & 0.0696202491102782 & 0.965189875444861 \tabularnewline
128 & 0.0331051998326804 & 0.0662103996653609 & 0.96689480016732 \tabularnewline
129 & 0.034228668750877 & 0.068457337501754 & 0.965771331249123 \tabularnewline
130 & 0.0337622109289322 & 0.0675244218578645 & 0.966237789071068 \tabularnewline
131 & 0.0374482103329066 & 0.0748964206658132 & 0.962551789667093 \tabularnewline
132 & 0.0360684736887267 & 0.0721369473774533 & 0.963931526311273 \tabularnewline
133 & 0.0790307161297036 & 0.158061432259407 & 0.920969283870296 \tabularnewline
134 & 0.0862588755326002 & 0.1725177510652 & 0.9137411244674 \tabularnewline
135 & 0.0647409458291837 & 0.129481891658367 & 0.935259054170816 \tabularnewline
136 & 0.051028564911441 & 0.102057129822882 & 0.948971435088559 \tabularnewline
137 & 0.0390985685613777 & 0.0781971371227554 & 0.960901431438622 \tabularnewline
138 & 0.0466570493762332 & 0.0933140987524663 & 0.953342950623767 \tabularnewline
139 & 0.0361462625291893 & 0.0722925250583785 & 0.963853737470811 \tabularnewline
140 & 0.0235573784156634 & 0.0471147568313269 & 0.976442621584337 \tabularnewline
141 & 0.514404448361437 & 0.971191103277126 & 0.485595551638563 \tabularnewline
142 & 0.453073924567942 & 0.906147849135884 & 0.546926075432058 \tabularnewline
143 & 0.380756802163654 & 0.761513604327307 & 0.619243197836346 \tabularnewline
144 & 0.29311282531663 & 0.586225650633261 & 0.70688717468337 \tabularnewline
145 & 0.214686916754731 & 0.429373833509463 & 0.785313083245269 \tabularnewline
146 & 0.231830058600992 & 0.463660117201984 & 0.768169941399008 \tabularnewline
147 & 0.247278402270153 & 0.494556804540306 & 0.752721597729847 \tabularnewline
148 & 0.507218352661781 & 0.985563294676439 & 0.492781647338219 \tabularnewline
149 & 0.543796132794216 & 0.912407734411568 & 0.456203867205784 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185842&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]0.736773201366112[/C][C]0.526453597267776[/C][C]0.263226798633888[/C][/ROW]
[ROW][C]14[/C][C]0.711474624252939[/C][C]0.577050751494122[/C][C]0.288525375747061[/C][/ROW]
[ROW][C]15[/C][C]0.589711772866992[/C][C]0.820576454266016[/C][C]0.410288227133008[/C][/ROW]
[ROW][C]16[/C][C]0.476662969701614[/C][C]0.953325939403228[/C][C]0.523337030298386[/C][/ROW]
[ROW][C]17[/C][C]0.398196687076293[/C][C]0.796393374152586[/C][C]0.601803312923707[/C][/ROW]
[ROW][C]18[/C][C]0.554187005273382[/C][C]0.891625989453236[/C][C]0.445812994726618[/C][/ROW]
[ROW][C]19[/C][C]0.468414722627298[/C][C]0.936829445254597[/C][C]0.531585277372702[/C][/ROW]
[ROW][C]20[/C][C]0.379710736679952[/C][C]0.759421473359905[/C][C]0.620289263320048[/C][/ROW]
[ROW][C]21[/C][C]0.309031507654454[/C][C]0.618063015308909[/C][C]0.690968492345546[/C][/ROW]
[ROW][C]22[/C][C]0.301175018943393[/C][C]0.602350037886785[/C][C]0.698824981056607[/C][/ROW]
[ROW][C]23[/C][C]0.428613617158891[/C][C]0.857227234317781[/C][C]0.571386382841109[/C][/ROW]
[ROW][C]24[/C][C]0.501432187505998[/C][C]0.997135624988003[/C][C]0.498567812494002[/C][/ROW]
[ROW][C]25[/C][C]0.447355850758672[/C][C]0.894711701517344[/C][C]0.552644149241328[/C][/ROW]
[ROW][C]26[/C][C]0.378970437538472[/C][C]0.757940875076944[/C][C]0.621029562461528[/C][/ROW]
[ROW][C]27[/C][C]0.350314298349917[/C][C]0.700628596699834[/C][C]0.649685701650083[/C][/ROW]
[ROW][C]28[/C][C]0.454014140993239[/C][C]0.908028281986478[/C][C]0.545985859006761[/C][/ROW]
[ROW][C]29[/C][C]0.396502404173586[/C][C]0.793004808347172[/C][C]0.603497595826414[/C][/ROW]
[ROW][C]30[/C][C]0.50647764373917[/C][C]0.98704471252166[/C][C]0.49352235626083[/C][/ROW]
[ROW][C]31[/C][C]0.455393025324585[/C][C]0.91078605064917[/C][C]0.544606974675415[/C][/ROW]
[ROW][C]32[/C][C]0.417911598978172[/C][C]0.835823197956345[/C][C]0.582088401021828[/C][/ROW]
[ROW][C]33[/C][C]0.405681603299137[/C][C]0.811363206598274[/C][C]0.594318396700863[/C][/ROW]
[ROW][C]34[/C][C]0.374212372430784[/C][C]0.748424744861568[/C][C]0.625787627569216[/C][/ROW]
[ROW][C]35[/C][C]0.324879528564688[/C][C]0.649759057129377[/C][C]0.675120471435312[/C][/ROW]
[ROW][C]36[/C][C]0.866915301429429[/C][C]0.266169397141142[/C][C]0.133084698570571[/C][/ROW]
[ROW][C]37[/C][C]0.84044936679181[/C][C]0.319101266416381[/C][C]0.15955063320819[/C][/ROW]
[ROW][C]38[/C][C]0.821786579317174[/C][C]0.356426841365652[/C][C]0.178213420682826[/C][/ROW]
[ROW][C]39[/C][C]0.852191651598322[/C][C]0.295616696803356[/C][C]0.147808348401678[/C][/ROW]
[ROW][C]40[/C][C]0.840555059449163[/C][C]0.318889881101674[/C][C]0.159444940550837[/C][/ROW]
[ROW][C]41[/C][C]0.814193232353632[/C][C]0.371613535292735[/C][C]0.185806767646368[/C][/ROW]
[ROW][C]42[/C][C]0.78558875515524[/C][C]0.42882248968952[/C][C]0.21441124484476[/C][/ROW]
[ROW][C]43[/C][C]0.816529804718543[/C][C]0.366940390562915[/C][C]0.183470195281458[/C][/ROW]
[ROW][C]44[/C][C]0.777914923325623[/C][C]0.444170153348754[/C][C]0.222085076674377[/C][/ROW]
[ROW][C]45[/C][C]0.748927280986776[/C][C]0.502145438026448[/C][C]0.251072719013224[/C][/ROW]
[ROW][C]46[/C][C]0.879473616849562[/C][C]0.241052766300877[/C][C]0.120526383150438[/C][/ROW]
[ROW][C]47[/C][C]0.901102538229267[/C][C]0.197794923541467[/C][C]0.0988974617707334[/C][/ROW]
[ROW][C]48[/C][C]0.879446770870335[/C][C]0.241106458259329[/C][C]0.120553229129665[/C][/ROW]
[ROW][C]49[/C][C]0.876835899201588[/C][C]0.246328201596825[/C][C]0.123164100798412[/C][/ROW]
[ROW][C]50[/C][C]0.868381588562877[/C][C]0.263236822874245[/C][C]0.131618411437123[/C][/ROW]
[ROW][C]51[/C][C]0.840799407906522[/C][C]0.318401184186956[/C][C]0.159200592093478[/C][/ROW]
[ROW][C]52[/C][C]0.810755321663073[/C][C]0.378489356673853[/C][C]0.189244678336927[/C][/ROW]
[ROW][C]53[/C][C]0.821157045734501[/C][C]0.357685908530998[/C][C]0.178842954265499[/C][/ROW]
[ROW][C]54[/C][C]0.791861118144252[/C][C]0.416277763711496[/C][C]0.208138881855748[/C][/ROW]
[ROW][C]55[/C][C]0.810273642243715[/C][C]0.37945271551257[/C][C]0.189726357756285[/C][/ROW]
[ROW][C]56[/C][C]0.789579310274277[/C][C]0.420841379451447[/C][C]0.210420689725723[/C][/ROW]
[ROW][C]57[/C][C]0.75283602802012[/C][C]0.494327943959761[/C][C]0.24716397197988[/C][/ROW]
[ROW][C]58[/C][C]0.737902492672518[/C][C]0.524195014654964[/C][C]0.262097507327482[/C][/ROW]
[ROW][C]59[/C][C]0.70823850423195[/C][C]0.5835229915361[/C][C]0.29176149576805[/C][/ROW]
[ROW][C]60[/C][C]0.713030984271911[/C][C]0.573938031456178[/C][C]0.286969015728089[/C][/ROW]
[ROW][C]61[/C][C]0.678680626308246[/C][C]0.642638747383507[/C][C]0.321319373691754[/C][/ROW]
[ROW][C]62[/C][C]0.64311863804244[/C][C]0.71376272391512[/C][C]0.35688136195756[/C][/ROW]
[ROW][C]63[/C][C]0.615228920576238[/C][C]0.769542158847523[/C][C]0.384771079423762[/C][/ROW]
[ROW][C]64[/C][C]0.580123132731228[/C][C]0.839753734537545[/C][C]0.419876867268772[/C][/ROW]
[ROW][C]65[/C][C]0.545171572364235[/C][C]0.90965685527153[/C][C]0.454828427635765[/C][/ROW]
[ROW][C]66[/C][C]0.497220083558987[/C][C]0.994440167117975[/C][C]0.502779916441013[/C][/ROW]
[ROW][C]67[/C][C]0.47336691672868[/C][C]0.946733833457361[/C][C]0.52663308327132[/C][/ROW]
[ROW][C]68[/C][C]0.509081035088346[/C][C]0.981837929823307[/C][C]0.490918964911654[/C][/ROW]
[ROW][C]69[/C][C]0.733167008016491[/C][C]0.533665983967017[/C][C]0.266832991983509[/C][/ROW]
[ROW][C]70[/C][C]0.692972340317093[/C][C]0.614055319365815[/C][C]0.307027659682907[/C][/ROW]
[ROW][C]71[/C][C]0.825516085107253[/C][C]0.348967829785495[/C][C]0.174483914892747[/C][/ROW]
[ROW][C]72[/C][C]0.7953904406593[/C][C]0.409219118681399[/C][C]0.2046095593407[/C][/ROW]
[ROW][C]73[/C][C]0.783036810767203[/C][C]0.433926378465594[/C][C]0.216963189232797[/C][/ROW]
[ROW][C]74[/C][C]0.760354852156704[/C][C]0.479290295686592[/C][C]0.239645147843296[/C][/ROW]
[ROW][C]75[/C][C]0.72280129642839[/C][C]0.55439740714322[/C][C]0.27719870357161[/C][/ROW]
[ROW][C]76[/C][C]0.757274854002141[/C][C]0.485450291995719[/C][C]0.242725145997859[/C][/ROW]
[ROW][C]77[/C][C]0.721388738917443[/C][C]0.557222522165113[/C][C]0.278611261082557[/C][/ROW]
[ROW][C]78[/C][C]0.699343820499423[/C][C]0.601312359001153[/C][C]0.300656179500577[/C][/ROW]
[ROW][C]79[/C][C]0.717929900267681[/C][C]0.564140199464638[/C][C]0.282070099732319[/C][/ROW]
[ROW][C]80[/C][C]0.676781417169289[/C][C]0.646437165661421[/C][C]0.323218582830711[/C][/ROW]
[ROW][C]81[/C][C]0.636834960386967[/C][C]0.726330079226067[/C][C]0.363165039613033[/C][/ROW]
[ROW][C]82[/C][C]0.746745104367509[/C][C]0.506509791264982[/C][C]0.253254895632491[/C][/ROW]
[ROW][C]83[/C][C]0.708679799161175[/C][C]0.582640401677651[/C][C]0.291320200838825[/C][/ROW]
[ROW][C]84[/C][C]0.682022964245309[/C][C]0.635954071509382[/C][C]0.317977035754691[/C][/ROW]
[ROW][C]85[/C][C]0.638993619799039[/C][C]0.722012760401922[/C][C]0.361006380200961[/C][/ROW]
[ROW][C]86[/C][C]0.620733727691939[/C][C]0.758532544616122[/C][C]0.379266272308061[/C][/ROW]
[ROW][C]87[/C][C]0.575958876454746[/C][C]0.848082247090508[/C][C]0.424041123545254[/C][/ROW]
[ROW][C]88[/C][C]0.531501039165725[/C][C]0.93699792166855[/C][C]0.468498960834275[/C][/ROW]
[ROW][C]89[/C][C]0.504437119420492[/C][C]0.991125761159015[/C][C]0.495562880579508[/C][/ROW]
[ROW][C]90[/C][C]0.46897654026593[/C][C]0.937953080531861[/C][C]0.53102345973407[/C][/ROW]
[ROW][C]91[/C][C]0.460926599801618[/C][C]0.921853199603236[/C][C]0.539073400198382[/C][/ROW]
[ROW][C]92[/C][C]0.418474914365484[/C][C]0.836949828730968[/C][C]0.581525085634516[/C][/ROW]
[ROW][C]93[/C][C]0.375894071333719[/C][C]0.751788142667437[/C][C]0.624105928666281[/C][/ROW]
[ROW][C]94[/C][C]0.333091478976873[/C][C]0.666182957953747[/C][C]0.666908521023126[/C][/ROW]
[ROW][C]95[/C][C]0.364147983973608[/C][C]0.728295967947216[/C][C]0.635852016026392[/C][/ROW]
[ROW][C]96[/C][C]0.327149250289214[/C][C]0.654298500578429[/C][C]0.672850749710786[/C][/ROW]
[ROW][C]97[/C][C]0.286462235705568[/C][C]0.572924471411136[/C][C]0.713537764294432[/C][/ROW]
[ROW][C]98[/C][C]0.278419375595678[/C][C]0.556838751191355[/C][C]0.721580624404322[/C][/ROW]
[ROW][C]99[/C][C]0.238868265984147[/C][C]0.477736531968295[/C][C]0.761131734015853[/C][/ROW]
[ROW][C]100[/C][C]0.207972862546739[/C][C]0.415945725093477[/C][C]0.792027137453261[/C][/ROW]
[ROW][C]101[/C][C]0.187474769030979[/C][C]0.374949538061959[/C][C]0.812525230969021[/C][/ROW]
[ROW][C]102[/C][C]0.171517566491729[/C][C]0.343035132983458[/C][C]0.828482433508271[/C][/ROW]
[ROW][C]103[/C][C]0.211979821448694[/C][C]0.423959642897387[/C][C]0.788020178551306[/C][/ROW]
[ROW][C]104[/C][C]0.178590985374001[/C][C]0.357181970748001[/C][C]0.821409014625999[/C][/ROW]
[ROW][C]105[/C][C]0.172338082266568[/C][C]0.344676164533136[/C][C]0.827661917733432[/C][/ROW]
[ROW][C]106[/C][C]0.177849601905068[/C][C]0.355699203810137[/C][C]0.822150398094932[/C][/ROW]
[ROW][C]107[/C][C]0.158854935567173[/C][C]0.317709871134345[/C][C]0.841145064432827[/C][/ROW]
[ROW][C]108[/C][C]0.139111164226529[/C][C]0.278222328453059[/C][C]0.860888835773471[/C][/ROW]
[ROW][C]109[/C][C]0.137966761595944[/C][C]0.275933523191887[/C][C]0.862033238404056[/C][/ROW]
[ROW][C]110[/C][C]0.142747339171234[/C][C]0.285494678342468[/C][C]0.857252660828766[/C][/ROW]
[ROW][C]111[/C][C]0.128871523085642[/C][C]0.257743046171284[/C][C]0.871128476914358[/C][/ROW]
[ROW][C]112[/C][C]0.110669562750423[/C][C]0.221339125500846[/C][C]0.889330437249577[/C][/ROW]
[ROW][C]113[/C][C]0.15334515863602[/C][C]0.30669031727204[/C][C]0.84665484136398[/C][/ROW]
[ROW][C]114[/C][C]0.14249660934617[/C][C]0.28499321869234[/C][C]0.85750339065383[/C][/ROW]
[ROW][C]115[/C][C]0.161478331306631[/C][C]0.322956662613262[/C][C]0.838521668693369[/C][/ROW]
[ROW][C]116[/C][C]0.170217120940184[/C][C]0.340434241880369[/C][C]0.829782879059816[/C][/ROW]
[ROW][C]117[/C][C]0.144080998668177[/C][C]0.288161997336354[/C][C]0.855919001331823[/C][/ROW]
[ROW][C]118[/C][C]0.149063781183728[/C][C]0.298127562367457[/C][C]0.850936218816272[/C][/ROW]
[ROW][C]119[/C][C]0.135981666741346[/C][C]0.271963333482692[/C][C]0.864018333258654[/C][/ROW]
[ROW][C]120[/C][C]0.159241921515416[/C][C]0.318483843030831[/C][C]0.840758078484584[/C][/ROW]
[ROW][C]121[/C][C]0.130083213013512[/C][C]0.260166426027025[/C][C]0.869916786986488[/C][/ROW]
[ROW][C]122[/C][C]0.114871347926925[/C][C]0.229742695853851[/C][C]0.885128652073075[/C][/ROW]
[ROW][C]123[/C][C]0.10722844842053[/C][C]0.21445689684106[/C][C]0.89277155157947[/C][/ROW]
[ROW][C]124[/C][C]0.0833127990865589[/C][C]0.166625598173118[/C][C]0.916687200913441[/C][/ROW]
[ROW][C]125[/C][C]0.0639651701425277[/C][C]0.127930340285055[/C][C]0.936034829857472[/C][/ROW]
[ROW][C]126[/C][C]0.0477853191387994[/C][C]0.0955706382775988[/C][C]0.952214680861201[/C][/ROW]
[ROW][C]127[/C][C]0.0348101245551391[/C][C]0.0696202491102782[/C][C]0.965189875444861[/C][/ROW]
[ROW][C]128[/C][C]0.0331051998326804[/C][C]0.0662103996653609[/C][C]0.96689480016732[/C][/ROW]
[ROW][C]129[/C][C]0.034228668750877[/C][C]0.068457337501754[/C][C]0.965771331249123[/C][/ROW]
[ROW][C]130[/C][C]0.0337622109289322[/C][C]0.0675244218578645[/C][C]0.966237789071068[/C][/ROW]
[ROW][C]131[/C][C]0.0374482103329066[/C][C]0.0748964206658132[/C][C]0.962551789667093[/C][/ROW]
[ROW][C]132[/C][C]0.0360684736887267[/C][C]0.0721369473774533[/C][C]0.963931526311273[/C][/ROW]
[ROW][C]133[/C][C]0.0790307161297036[/C][C]0.158061432259407[/C][C]0.920969283870296[/C][/ROW]
[ROW][C]134[/C][C]0.0862588755326002[/C][C]0.1725177510652[/C][C]0.9137411244674[/C][/ROW]
[ROW][C]135[/C][C]0.0647409458291837[/C][C]0.129481891658367[/C][C]0.935259054170816[/C][/ROW]
[ROW][C]136[/C][C]0.051028564911441[/C][C]0.102057129822882[/C][C]0.948971435088559[/C][/ROW]
[ROW][C]137[/C][C]0.0390985685613777[/C][C]0.0781971371227554[/C][C]0.960901431438622[/C][/ROW]
[ROW][C]138[/C][C]0.0466570493762332[/C][C]0.0933140987524663[/C][C]0.953342950623767[/C][/ROW]
[ROW][C]139[/C][C]0.0361462625291893[/C][C]0.0722925250583785[/C][C]0.963853737470811[/C][/ROW]
[ROW][C]140[/C][C]0.0235573784156634[/C][C]0.0471147568313269[/C][C]0.976442621584337[/C][/ROW]
[ROW][C]141[/C][C]0.514404448361437[/C][C]0.971191103277126[/C][C]0.485595551638563[/C][/ROW]
[ROW][C]142[/C][C]0.453073924567942[/C][C]0.906147849135884[/C][C]0.546926075432058[/C][/ROW]
[ROW][C]143[/C][C]0.380756802163654[/C][C]0.761513604327307[/C][C]0.619243197836346[/C][/ROW]
[ROW][C]144[/C][C]0.29311282531663[/C][C]0.586225650633261[/C][C]0.70688717468337[/C][/ROW]
[ROW][C]145[/C][C]0.214686916754731[/C][C]0.429373833509463[/C][C]0.785313083245269[/C][/ROW]
[ROW][C]146[/C][C]0.231830058600992[/C][C]0.463660117201984[/C][C]0.768169941399008[/C][/ROW]
[ROW][C]147[/C][C]0.247278402270153[/C][C]0.494556804540306[/C][C]0.752721597729847[/C][/ROW]
[ROW][C]148[/C][C]0.507218352661781[/C][C]0.985563294676439[/C][C]0.492781647338219[/C][/ROW]
[ROW][C]149[/C][C]0.543796132794216[/C][C]0.912407734411568[/C][C]0.456203867205784[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185842&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185842&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
130.7367732013661120.5264535972677760.263226798633888
140.7114746242529390.5770507514941220.288525375747061
150.5897117728669920.8205764542660160.410288227133008
160.4766629697016140.9533259394032280.523337030298386
170.3981966870762930.7963933741525860.601803312923707
180.5541870052733820.8916259894532360.445812994726618
190.4684147226272980.9368294452545970.531585277372702
200.3797107366799520.7594214733599050.620289263320048
210.3090315076544540.6180630153089090.690968492345546
220.3011750189433930.6023500378867850.698824981056607
230.4286136171588910.8572272343177810.571386382841109
240.5014321875059980.9971356249880030.498567812494002
250.4473558507586720.8947117015173440.552644149241328
260.3789704375384720.7579408750769440.621029562461528
270.3503142983499170.7006285966998340.649685701650083
280.4540141409932390.9080282819864780.545985859006761
290.3965024041735860.7930048083471720.603497595826414
300.506477643739170.987044712521660.49352235626083
310.4553930253245850.910786050649170.544606974675415
320.4179115989781720.8358231979563450.582088401021828
330.4056816032991370.8113632065982740.594318396700863
340.3742123724307840.7484247448615680.625787627569216
350.3248795285646880.6497590571293770.675120471435312
360.8669153014294290.2661693971411420.133084698570571
370.840449366791810.3191012664163810.15955063320819
380.8217865793171740.3564268413656520.178213420682826
390.8521916515983220.2956166968033560.147808348401678
400.8405550594491630.3188898811016740.159444940550837
410.8141932323536320.3716135352927350.185806767646368
420.785588755155240.428822489689520.21441124484476
430.8165298047185430.3669403905629150.183470195281458
440.7779149233256230.4441701533487540.222085076674377
450.7489272809867760.5021454380264480.251072719013224
460.8794736168495620.2410527663008770.120526383150438
470.9011025382292670.1977949235414670.0988974617707334
480.8794467708703350.2411064582593290.120553229129665
490.8768358992015880.2463282015968250.123164100798412
500.8683815885628770.2632368228742450.131618411437123
510.8407994079065220.3184011841869560.159200592093478
520.8107553216630730.3784893566738530.189244678336927
530.8211570457345010.3576859085309980.178842954265499
540.7918611181442520.4162777637114960.208138881855748
550.8102736422437150.379452715512570.189726357756285
560.7895793102742770.4208413794514470.210420689725723
570.752836028020120.4943279439597610.24716397197988
580.7379024926725180.5241950146549640.262097507327482
590.708238504231950.58352299153610.29176149576805
600.7130309842719110.5739380314561780.286969015728089
610.6786806263082460.6426387473835070.321319373691754
620.643118638042440.713762723915120.35688136195756
630.6152289205762380.7695421588475230.384771079423762
640.5801231327312280.8397537345375450.419876867268772
650.5451715723642350.909656855271530.454828427635765
660.4972200835589870.9944401671179750.502779916441013
670.473366916728680.9467338334573610.52663308327132
680.5090810350883460.9818379298233070.490918964911654
690.7331670080164910.5336659839670170.266832991983509
700.6929723403170930.6140553193658150.307027659682907
710.8255160851072530.3489678297854950.174483914892747
720.79539044065930.4092191186813990.2046095593407
730.7830368107672030.4339263784655940.216963189232797
740.7603548521567040.4792902956865920.239645147843296
750.722801296428390.554397407143220.27719870357161
760.7572748540021410.4854502919957190.242725145997859
770.7213887389174430.5572225221651130.278611261082557
780.6993438204994230.6013123590011530.300656179500577
790.7179299002676810.5641401994646380.282070099732319
800.6767814171692890.6464371656614210.323218582830711
810.6368349603869670.7263300792260670.363165039613033
820.7467451043675090.5065097912649820.253254895632491
830.7086797991611750.5826404016776510.291320200838825
840.6820229642453090.6359540715093820.317977035754691
850.6389936197990390.7220127604019220.361006380200961
860.6207337276919390.7585325446161220.379266272308061
870.5759588764547460.8480822470905080.424041123545254
880.5315010391657250.936997921668550.468498960834275
890.5044371194204920.9911257611590150.495562880579508
900.468976540265930.9379530805318610.53102345973407
910.4609265998016180.9218531996032360.539073400198382
920.4184749143654840.8369498287309680.581525085634516
930.3758940713337190.7517881426674370.624105928666281
940.3330914789768730.6661829579537470.666908521023126
950.3641479839736080.7282959679472160.635852016026392
960.3271492502892140.6542985005784290.672850749710786
970.2864622357055680.5729244714111360.713537764294432
980.2784193755956780.5568387511913550.721580624404322
990.2388682659841470.4777365319682950.761131734015853
1000.2079728625467390.4159457250934770.792027137453261
1010.1874747690309790.3749495380619590.812525230969021
1020.1715175664917290.3430351329834580.828482433508271
1030.2119798214486940.4239596428973870.788020178551306
1040.1785909853740010.3571819707480010.821409014625999
1050.1723380822665680.3446761645331360.827661917733432
1060.1778496019050680.3556992038101370.822150398094932
1070.1588549355671730.3177098711343450.841145064432827
1080.1391111642265290.2782223284530590.860888835773471
1090.1379667615959440.2759335231918870.862033238404056
1100.1427473391712340.2854946783424680.857252660828766
1110.1288715230856420.2577430461712840.871128476914358
1120.1106695627504230.2213391255008460.889330437249577
1130.153345158636020.306690317272040.84665484136398
1140.142496609346170.284993218692340.85750339065383
1150.1614783313066310.3229566626132620.838521668693369
1160.1702171209401840.3404342418803690.829782879059816
1170.1440809986681770.2881619973363540.855919001331823
1180.1490637811837280.2981275623674570.850936218816272
1190.1359816667413460.2719633334826920.864018333258654
1200.1592419215154160.3184838430308310.840758078484584
1210.1300832130135120.2601664260270250.869916786986488
1220.1148713479269250.2297426958538510.885128652073075
1230.107228448420530.214456896841060.89277155157947
1240.08331279908655890.1666255981731180.916687200913441
1250.06396517014252770.1279303402850550.936034829857472
1260.04778531913879940.09557063827759880.952214680861201
1270.03481012455513910.06962024911027820.965189875444861
1280.03310519983268040.06621039966536090.96689480016732
1290.0342286687508770.0684573375017540.965771331249123
1300.03376221092893220.06752442185786450.966237789071068
1310.03744821033290660.07489642066581320.962551789667093
1320.03606847368872670.07213694737745330.963931526311273
1330.07903071612970360.1580614322594070.920969283870296
1340.08625887553260020.17251775106520.9137411244674
1350.06474094582918370.1294818916583670.935259054170816
1360.0510285649114410.1020571298228820.948971435088559
1370.03909856856137770.07819713712275540.960901431438622
1380.04665704937623320.09331409875246630.953342950623767
1390.03614626252918930.07229252505837850.963853737470811
1400.02355737841566340.04711475683132690.976442621584337
1410.5144044483614370.9711911032771260.485595551638563
1420.4530739245679420.9061478491358840.546926075432058
1430.3807568021636540.7615136043273070.619243197836346
1440.293112825316630.5862256506332610.70688717468337
1450.2146869167547310.4293738335094630.785313083245269
1460.2318300586009920.4636601172019840.768169941399008
1470.2472784022701530.4945568045403060.752721597729847
1480.5072183526617810.9855632946764390.492781647338219
1490.5437961327942160.9124077344115680.456203867205784






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0072992700729927OK
10% type I error level110.0802919708029197OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0072992700729927 & OK \tabularnewline
10% type I error level & 11 & 0.0802919708029197 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185842&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0072992700729927[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.0802919708029197[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185842&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185842&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0072992700729927OK
10% type I error level110.0802919708029197OK


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):
par1 = 3 ; par2 = Include Quarterly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}