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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 12:27:28 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356110864ousarwiosbg75hw.htm/, Retrieved Thu, 28 Mar 2024 11:34:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204000, Retrieved Thu, 28 Mar 2024 11:34:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact64
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 15:10:31] [9d44b52ac7f20a3e9be7c3c8470fe2cd]
-   P     [Multiple Regression] [paper] [2012-12-21 17:27:28] [97e5c69206415429213a02c19f23a896] [Current]
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Dataseries X:
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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204000&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204000&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204000&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 3.26166669547301 + 0.147083088454563Month[t] -0.0453946858294687Connected[t] + 0.0306926204872215Separate[t] + 0.529196646395567Learning[t] -0.040478334179682Happines[t] -0.0263389868788488Depression[t] + 0.000182229060028198Belonging[t] -0.00250539691115257Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Software[t] =  +  3.26166669547301 +  0.147083088454563Month[t] -0.0453946858294687Connected[t] +  0.0306926204872215Separate[t] +  0.529196646395567Learning[t] -0.040478334179682Happines[t] -0.0263389868788488Depression[t] +  0.000182229060028198Belonging[t] -0.00250539691115257Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204000&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Software[t] =  +  3.26166669547301 +  0.147083088454563Month[t] -0.0453946858294687Connected[t] +  0.0306926204872215Separate[t] +  0.529196646395567Learning[t] -0.040478334179682Happines[t] -0.0263389868788488Depression[t] +  0.000182229060028198Belonging[t] -0.00250539691115257Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204000&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204000&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Software[t] = + 3.26166669547301 + 0.147083088454563Month[t] -0.0453946858294687Connected[t] + 0.0306926204872215Separate[t] + 0.529196646395567Learning[t] -0.040478334179682Happines[t] -0.0263389868788488Depression[t] + 0.000182229060028198Belonging[t] -0.00250539691115257Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.261666695473013.6490780.89380.3728150.186407
Month0.1470830884545630.3038040.48410.628980.31449
Connected-0.04539468582946870.047351-0.95870.3392270.169614
Separate0.03069262048722150.0446950.68670.4933080.246654
Learning0.5291966463955670.067357.857400
Happines-0.0404783341796820.075655-0.5350.5933980.296699
Depression-0.02633898687884880.056585-0.46550.6422490.321125
Belonging0.0001822290600281980.0448180.00410.9967610.498381
Belonging_Final-0.002505396911152570.063891-0.03920.9687710.484386

\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) & 3.26166669547301 & 3.649078 & 0.8938 & 0.372815 & 0.186407 \tabularnewline
Month & 0.147083088454563 & 0.303804 & 0.4841 & 0.62898 & 0.31449 \tabularnewline
Connected & -0.0453946858294687 & 0.047351 & -0.9587 & 0.339227 & 0.169614 \tabularnewline
Separate & 0.0306926204872215 & 0.044695 & 0.6867 & 0.493308 & 0.246654 \tabularnewline
Learning & 0.529196646395567 & 0.06735 & 7.8574 & 0 & 0 \tabularnewline
Happines & -0.040478334179682 & 0.075655 & -0.535 & 0.593398 & 0.296699 \tabularnewline
Depression & -0.0263389868788488 & 0.056585 & -0.4655 & 0.642249 & 0.321125 \tabularnewline
Belonging & 0.000182229060028198 & 0.044818 & 0.0041 & 0.996761 & 0.498381 \tabularnewline
Belonging_Final & -0.00250539691115257 & 0.063891 & -0.0392 & 0.968771 & 0.484386 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204000&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]3.26166669547301[/C][C]3.649078[/C][C]0.8938[/C][C]0.372815[/C][C]0.186407[/C][/ROW]
[ROW][C]Month[/C][C]0.147083088454563[/C][C]0.303804[/C][C]0.4841[/C][C]0.62898[/C][C]0.31449[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0453946858294687[/C][C]0.047351[/C][C]-0.9587[/C][C]0.339227[/C][C]0.169614[/C][/ROW]
[ROW][C]Separate[/C][C]0.0306926204872215[/C][C]0.044695[/C][C]0.6867[/C][C]0.493308[/C][C]0.246654[/C][/ROW]
[ROW][C]Learning[/C][C]0.529196646395567[/C][C]0.06735[/C][C]7.8574[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happines[/C][C]-0.040478334179682[/C][C]0.075655[/C][C]-0.535[/C][C]0.593398[/C][C]0.296699[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0263389868788488[/C][C]0.056585[/C][C]-0.4655[/C][C]0.642249[/C][C]0.321125[/C][/ROW]
[ROW][C]Belonging[/C][C]0.000182229060028198[/C][C]0.044818[/C][C]0.0041[/C][C]0.996761[/C][C]0.498381[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.00250539691115257[/C][C]0.063891[/C][C]-0.0392[/C][C]0.968771[/C][C]0.484386[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204000&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204000&T=2

As an alternative you can also use a 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)3.261666695473013.6490780.89380.3728150.186407
Month0.1470830884545630.3038040.48410.628980.31449
Connected-0.04539468582946870.047351-0.95870.3392270.169614
Separate0.03069262048722150.0446950.68670.4933080.246654
Learning0.5291966463955670.067357.857400
Happines-0.0404783341796820.075655-0.5350.5933980.296699
Depression-0.02633898687884880.056585-0.46550.6422490.321125
Belonging0.0001822290600281980.0448180.00410.9967610.498381
Belonging_Final-0.002505396911152570.063891-0.03920.9687710.484386







Multiple Linear Regression - Regression Statistics
Multiple R0.552797073768428
R-squared0.305584604766937
Adjusted R-squared0.269275303055405
F-TEST (value)8.41615207019739
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value1.92469518101035e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83079283761049
Sum Squared Residuals512.825769379617

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.552797073768428 \tabularnewline
R-squared & 0.305584604766937 \tabularnewline
Adjusted R-squared & 0.269275303055405 \tabularnewline
F-TEST (value) & 8.41615207019739 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 1.92469518101035e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.83079283761049 \tabularnewline
Sum Squared Residuals & 512.825769379617 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204000&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.552797073768428[/C][/ROW]
[ROW][C]R-squared[/C][C]0.305584604766937[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.269275303055405[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.41615207019739[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]1.92469518101035e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.83079283761049[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]512.825769379617[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204000&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204000&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.552797073768428
R-squared0.305584604766937
Adjusted R-squared0.269275303055405
F-TEST (value)8.41615207019739
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value1.92469518101035e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83079283761049
Sum Squared Residuals512.825769379617







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1129.816829272175522.18317072782448
21111.133889527927-0.133889527926988
31513.44534487066161.55465512933839
4611.2366656238246-5.23666562382462
51310.28420426024572.71579573975434
6109.618025238882030.381974761117967
71212.5920333254531-0.592033325453062
81411.12026754880852.87973245119149
91210.46437777625041.5356222237496
10610.9565943019394-4.95659430193936
111011.2191441047299-1.21914410472987
121211.40926326732650.590736732673456
131211.50891902120.491080978799982
141111.359250773053-0.359250773052977
151512.1104278138522.88957218614796
161211.06852321889360.931476781106397
171010.8368011769086-0.836801176908613
181213.5135531238283-1.51355312382833
191112.5857357970299-1.58573579702993
201211.51222733426140.487772665738596
211111.6091102846468-0.609110284646846
221211.68494342799430.315056572005697
231313.0804753543567-0.0804753543567389
241111.6329889723933-0.63298897239331
25911.7010591115911-2.70105911159108
261311.99820799732291.00179200267711
271011.4613289999254-1.46132899992536
281411.10152137250532.89847862749474
291211.49294285428020.50705714571979
301010.577908076508-0.577908076508027
311210.98263194779751.01736805220252
3289.6981407101612-1.6981407101612
331010.2197200467549-0.219720046754897
341211.6278804131960.372119586804031
351210.37068160974981.62931839025018
3676.894353103133940.105646896866055
3768.54010230144404-2.54010230144404
381210.35477866816051.64522133183952
391011.6346657807485-1.63466578074846
401011.5943422276892-1.59434222768924
411011.4116375086292-1.41163750862924
421210.3506352512111.64936474878901
431513.49531024281451.50468975718552
441010.4955327935646-0.495532793564588
451010.2604411222744-0.260441122274422
46128.9257231375123.074276862488
471310.52606860570122.47393139429876
481111.0704037049172-0.0704037049171738
491111.409713518483-0.409713518483043
501210.33631045367031.66368954632966
511411.75809027843252.24190972156752
521010.438867184932-0.438867184932015
53129.495418083119542.50458191688046
541311.71702521155431.28297478844571
5558.02353549432903-3.02353549432903
56610.2695809194993-4.26958091949932
571211.23639909624970.763600903750327
581211.84242082355270.157579176447303
591111.105135924605-0.105135924605038
601011.5314950801108-1.53149508011077
6179.5122060750792-2.5122060750792
621211.43011445867710.569885541322879
631411.72334249084162.27665750915842
641110.65754010949750.342459890502458
651211.43819317984830.561806820151699
661312.47295520143070.527044798569309
671412.71184623710721.28815376289276
681112.8336709887322-1.8336709887322
69129.506862914942742.49313708505726
701211.67788318221790.322116817782075
7188.39726869498507-0.397268694985067
721110.78475906847330.215240931526693
731412.85654975594521.14345024405476
741412.83322964080721.16677035919276
751211.55901555278230.440984447217708
76911.7848595513121-2.78485955131211
771311.53015296122731.46984703877265
781111.5360510590246-0.536051059024577
791210.23917015571751.76082984428251
801211.51263434474030.487365655259669
811211.80879759698190.191202403018141
821213.8975703668915-1.8975703668915
831211.80242638399880.197573616001232
841210.94554984112151.05445015887849
851111.2410065989646-0.241006598964577
861011.6402649999365-1.64026499993653
87910.5646072434184-1.56460724341839
881211.76975100035260.230248999647422
891211.87122101253910.128778987460905
901211.2274970360880.772502963912049
9199.6480723665813-0.648072366581299
921512.08265485812842.91734514187162
931211.81842889588130.181571104118724
941211.01011295254010.989887047459944
951210.32360316557681.67639683442321
961011.4936279799742-1.49362797997424
971311.59196020145471.40803979854526
98911.6048412280065-2.60484122800647
991211.48023060862790.519769391372071
1001010.821591289719-0.821591289718959
1011411.52916519608512.47083480391493
1021111.5876783238725-0.587678323872548
1031513.73933641739771.26066358260234
1041111.249020619101-0.249020619100961
1051111.7666751271381-0.766675127138127
106129.823354949982752.17664505001725
1071212.0367977754719-0.0367977754719416
1081211.41208139566680.587918604333238
1091111.7669710757335-0.766971075733525
11079.31627750909912-2.31627750909912
1111211.79760994930590.202390050694097
1121411.86816614490462.13183385509537
1131112.5969001893422-1.59690018934225
1141110.18986436925640.810135630743566
115109.298220953598140.701779046401857
1161312.53244014224750.467559857752472
1171310.8626505930272.13734940697297
118810.8189511930142-2.81895119301418
1191110.29187064228960.708129357710355
1201211.49640250195870.50359749804126
1211110.29599376665030.70400623334969
1221311.58426807061891.41573192938115
1231210.06809120542051.93190879457946
1241411.76456897118372.23543102881626
1251311.12754987070121.87245012929883
1261511.88596037862733.11403962137266
1271010.8619785933977-0.861978593397714
1281111.8802125277566-0.880212527756584
129911.1005142936096-2.10051429360955
130119.46020099492341.5397990050766
1311011.8516348448734-1.85163484487341
132118.756303192661922.24369680733808
133811.5477019454216-3.54770194542159
134119.68734863056421.3126513694358
1351210.60296066966831.3970393303317
1361210.9736609069911.026339093009
13799.89234432763822-0.892344327638222
1381111.1720809404969-0.172080940496897
139109.321527843851670.678472156148332
14089.5521353443327-1.5521353443327
14197.700643552425041.29935644757496
142811.84716046206-3.84716046206001
143911.2232886330284-2.22328863302841
1441512.44136285272162.55863714727843
1451111.473879457308-0.473879457308012
14688.1011871995863-0.101187199586297
1471312.56648258606770.4335174139323
1481210.21083978641651.78916021358353
1491211.82143526667420.178564733325847
150910.0387125398745-1.03871253987447
15178.41546042584635-1.41546042584635
1521310.82215874878342.17784125121659
153911.8480569437804-2.84805694378039
154611.9089172928469-5.90891729284689
155810.4889986132833-2.48899861328326
15688.36402916276398-0.364029162763983
1571512.08265485812842.91734514187162
158610.2644502377882-4.26445023778816
159911.1005142936096-2.10051429360955
1601111.7626512320649-0.762651232064944
16189.63466472878448-1.63466472878448
162810.1232524153305-2.12325241533053

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 9.81682927217552 & 2.18317072782448 \tabularnewline
2 & 11 & 11.133889527927 & -0.133889527926988 \tabularnewline
3 & 15 & 13.4453448706616 & 1.55465512933839 \tabularnewline
4 & 6 & 11.2366656238246 & -5.23666562382462 \tabularnewline
5 & 13 & 10.2842042602457 & 2.71579573975434 \tabularnewline
6 & 10 & 9.61802523888203 & 0.381974761117967 \tabularnewline
7 & 12 & 12.5920333254531 & -0.592033325453062 \tabularnewline
8 & 14 & 11.1202675488085 & 2.87973245119149 \tabularnewline
9 & 12 & 10.4643777762504 & 1.5356222237496 \tabularnewline
10 & 6 & 10.9565943019394 & -4.95659430193936 \tabularnewline
11 & 10 & 11.2191441047299 & -1.21914410472987 \tabularnewline
12 & 12 & 11.4092632673265 & 0.590736732673456 \tabularnewline
13 & 12 & 11.5089190212 & 0.491080978799982 \tabularnewline
14 & 11 & 11.359250773053 & -0.359250773052977 \tabularnewline
15 & 15 & 12.110427813852 & 2.88957218614796 \tabularnewline
16 & 12 & 11.0685232188936 & 0.931476781106397 \tabularnewline
17 & 10 & 10.8368011769086 & -0.836801176908613 \tabularnewline
18 & 12 & 13.5135531238283 & -1.51355312382833 \tabularnewline
19 & 11 & 12.5857357970299 & -1.58573579702993 \tabularnewline
20 & 12 & 11.5122273342614 & 0.487772665738596 \tabularnewline
21 & 11 & 11.6091102846468 & -0.609110284646846 \tabularnewline
22 & 12 & 11.6849434279943 & 0.315056572005697 \tabularnewline
23 & 13 & 13.0804753543567 & -0.0804753543567389 \tabularnewline
24 & 11 & 11.6329889723933 & -0.63298897239331 \tabularnewline
25 & 9 & 11.7010591115911 & -2.70105911159108 \tabularnewline
26 & 13 & 11.9982079973229 & 1.00179200267711 \tabularnewline
27 & 10 & 11.4613289999254 & -1.46132899992536 \tabularnewline
28 & 14 & 11.1015213725053 & 2.89847862749474 \tabularnewline
29 & 12 & 11.4929428542802 & 0.50705714571979 \tabularnewline
30 & 10 & 10.577908076508 & -0.577908076508027 \tabularnewline
31 & 12 & 10.9826319477975 & 1.01736805220252 \tabularnewline
32 & 8 & 9.6981407101612 & -1.6981407101612 \tabularnewline
33 & 10 & 10.2197200467549 & -0.219720046754897 \tabularnewline
34 & 12 & 11.627880413196 & 0.372119586804031 \tabularnewline
35 & 12 & 10.3706816097498 & 1.62931839025018 \tabularnewline
36 & 7 & 6.89435310313394 & 0.105646896866055 \tabularnewline
37 & 6 & 8.54010230144404 & -2.54010230144404 \tabularnewline
38 & 12 & 10.3547786681605 & 1.64522133183952 \tabularnewline
39 & 10 & 11.6346657807485 & -1.63466578074846 \tabularnewline
40 & 10 & 11.5943422276892 & -1.59434222768924 \tabularnewline
41 & 10 & 11.4116375086292 & -1.41163750862924 \tabularnewline
42 & 12 & 10.350635251211 & 1.64936474878901 \tabularnewline
43 & 15 & 13.4953102428145 & 1.50468975718552 \tabularnewline
44 & 10 & 10.4955327935646 & -0.495532793564588 \tabularnewline
45 & 10 & 10.2604411222744 & -0.260441122274422 \tabularnewline
46 & 12 & 8.925723137512 & 3.074276862488 \tabularnewline
47 & 13 & 10.5260686057012 & 2.47393139429876 \tabularnewline
48 & 11 & 11.0704037049172 & -0.0704037049171738 \tabularnewline
49 & 11 & 11.409713518483 & -0.409713518483043 \tabularnewline
50 & 12 & 10.3363104536703 & 1.66368954632966 \tabularnewline
51 & 14 & 11.7580902784325 & 2.24190972156752 \tabularnewline
52 & 10 & 10.438867184932 & -0.438867184932015 \tabularnewline
53 & 12 & 9.49541808311954 & 2.50458191688046 \tabularnewline
54 & 13 & 11.7170252115543 & 1.28297478844571 \tabularnewline
55 & 5 & 8.02353549432903 & -3.02353549432903 \tabularnewline
56 & 6 & 10.2695809194993 & -4.26958091949932 \tabularnewline
57 & 12 & 11.2363990962497 & 0.763600903750327 \tabularnewline
58 & 12 & 11.8424208235527 & 0.157579176447303 \tabularnewline
59 & 11 & 11.105135924605 & -0.105135924605038 \tabularnewline
60 & 10 & 11.5314950801108 & -1.53149508011077 \tabularnewline
61 & 7 & 9.5122060750792 & -2.5122060750792 \tabularnewline
62 & 12 & 11.4301144586771 & 0.569885541322879 \tabularnewline
63 & 14 & 11.7233424908416 & 2.27665750915842 \tabularnewline
64 & 11 & 10.6575401094975 & 0.342459890502458 \tabularnewline
65 & 12 & 11.4381931798483 & 0.561806820151699 \tabularnewline
66 & 13 & 12.4729552014307 & 0.527044798569309 \tabularnewline
67 & 14 & 12.7118462371072 & 1.28815376289276 \tabularnewline
68 & 11 & 12.8336709887322 & -1.8336709887322 \tabularnewline
69 & 12 & 9.50686291494274 & 2.49313708505726 \tabularnewline
70 & 12 & 11.6778831822179 & 0.322116817782075 \tabularnewline
71 & 8 & 8.39726869498507 & -0.397268694985067 \tabularnewline
72 & 11 & 10.7847590684733 & 0.215240931526693 \tabularnewline
73 & 14 & 12.8565497559452 & 1.14345024405476 \tabularnewline
74 & 14 & 12.8332296408072 & 1.16677035919276 \tabularnewline
75 & 12 & 11.5590155527823 & 0.440984447217708 \tabularnewline
76 & 9 & 11.7848595513121 & -2.78485955131211 \tabularnewline
77 & 13 & 11.5301529612273 & 1.46984703877265 \tabularnewline
78 & 11 & 11.5360510590246 & -0.536051059024577 \tabularnewline
79 & 12 & 10.2391701557175 & 1.76082984428251 \tabularnewline
80 & 12 & 11.5126343447403 & 0.487365655259669 \tabularnewline
81 & 12 & 11.8087975969819 & 0.191202403018141 \tabularnewline
82 & 12 & 13.8975703668915 & -1.8975703668915 \tabularnewline
83 & 12 & 11.8024263839988 & 0.197573616001232 \tabularnewline
84 & 12 & 10.9455498411215 & 1.05445015887849 \tabularnewline
85 & 11 & 11.2410065989646 & -0.241006598964577 \tabularnewline
86 & 10 & 11.6402649999365 & -1.64026499993653 \tabularnewline
87 & 9 & 10.5646072434184 & -1.56460724341839 \tabularnewline
88 & 12 & 11.7697510003526 & 0.230248999647422 \tabularnewline
89 & 12 & 11.8712210125391 & 0.128778987460905 \tabularnewline
90 & 12 & 11.227497036088 & 0.772502963912049 \tabularnewline
91 & 9 & 9.6480723665813 & -0.648072366581299 \tabularnewline
92 & 15 & 12.0826548581284 & 2.91734514187162 \tabularnewline
93 & 12 & 11.8184288958813 & 0.181571104118724 \tabularnewline
94 & 12 & 11.0101129525401 & 0.989887047459944 \tabularnewline
95 & 12 & 10.3236031655768 & 1.67639683442321 \tabularnewline
96 & 10 & 11.4936279799742 & -1.49362797997424 \tabularnewline
97 & 13 & 11.5919602014547 & 1.40803979854526 \tabularnewline
98 & 9 & 11.6048412280065 & -2.60484122800647 \tabularnewline
99 & 12 & 11.4802306086279 & 0.519769391372071 \tabularnewline
100 & 10 & 10.821591289719 & -0.821591289718959 \tabularnewline
101 & 14 & 11.5291651960851 & 2.47083480391493 \tabularnewline
102 & 11 & 11.5876783238725 & -0.587678323872548 \tabularnewline
103 & 15 & 13.7393364173977 & 1.26066358260234 \tabularnewline
104 & 11 & 11.249020619101 & -0.249020619100961 \tabularnewline
105 & 11 & 11.7666751271381 & -0.766675127138127 \tabularnewline
106 & 12 & 9.82335494998275 & 2.17664505001725 \tabularnewline
107 & 12 & 12.0367977754719 & -0.0367977754719416 \tabularnewline
108 & 12 & 11.4120813956668 & 0.587918604333238 \tabularnewline
109 & 11 & 11.7669710757335 & -0.766971075733525 \tabularnewline
110 & 7 & 9.31627750909912 & -2.31627750909912 \tabularnewline
111 & 12 & 11.7976099493059 & 0.202390050694097 \tabularnewline
112 & 14 & 11.8681661449046 & 2.13183385509537 \tabularnewline
113 & 11 & 12.5969001893422 & -1.59690018934225 \tabularnewline
114 & 11 & 10.1898643692564 & 0.810135630743566 \tabularnewline
115 & 10 & 9.29822095359814 & 0.701779046401857 \tabularnewline
116 & 13 & 12.5324401422475 & 0.467559857752472 \tabularnewline
117 & 13 & 10.862650593027 & 2.13734940697297 \tabularnewline
118 & 8 & 10.8189511930142 & -2.81895119301418 \tabularnewline
119 & 11 & 10.2918706422896 & 0.708129357710355 \tabularnewline
120 & 12 & 11.4964025019587 & 0.50359749804126 \tabularnewline
121 & 11 & 10.2959937666503 & 0.70400623334969 \tabularnewline
122 & 13 & 11.5842680706189 & 1.41573192938115 \tabularnewline
123 & 12 & 10.0680912054205 & 1.93190879457946 \tabularnewline
124 & 14 & 11.7645689711837 & 2.23543102881626 \tabularnewline
125 & 13 & 11.1275498707012 & 1.87245012929883 \tabularnewline
126 & 15 & 11.8859603786273 & 3.11403962137266 \tabularnewline
127 & 10 & 10.8619785933977 & -0.861978593397714 \tabularnewline
128 & 11 & 11.8802125277566 & -0.880212527756584 \tabularnewline
129 & 9 & 11.1005142936096 & -2.10051429360955 \tabularnewline
130 & 11 & 9.4602009949234 & 1.5397990050766 \tabularnewline
131 & 10 & 11.8516348448734 & -1.85163484487341 \tabularnewline
132 & 11 & 8.75630319266192 & 2.24369680733808 \tabularnewline
133 & 8 & 11.5477019454216 & -3.54770194542159 \tabularnewline
134 & 11 & 9.6873486305642 & 1.3126513694358 \tabularnewline
135 & 12 & 10.6029606696683 & 1.3970393303317 \tabularnewline
136 & 12 & 10.973660906991 & 1.026339093009 \tabularnewline
137 & 9 & 9.89234432763822 & -0.892344327638222 \tabularnewline
138 & 11 & 11.1720809404969 & -0.172080940496897 \tabularnewline
139 & 10 & 9.32152784385167 & 0.678472156148332 \tabularnewline
140 & 8 & 9.5521353443327 & -1.5521353443327 \tabularnewline
141 & 9 & 7.70064355242504 & 1.29935644757496 \tabularnewline
142 & 8 & 11.84716046206 & -3.84716046206001 \tabularnewline
143 & 9 & 11.2232886330284 & -2.22328863302841 \tabularnewline
144 & 15 & 12.4413628527216 & 2.55863714727843 \tabularnewline
145 & 11 & 11.473879457308 & -0.473879457308012 \tabularnewline
146 & 8 & 8.1011871995863 & -0.101187199586297 \tabularnewline
147 & 13 & 12.5664825860677 & 0.4335174139323 \tabularnewline
148 & 12 & 10.2108397864165 & 1.78916021358353 \tabularnewline
149 & 12 & 11.8214352666742 & 0.178564733325847 \tabularnewline
150 & 9 & 10.0387125398745 & -1.03871253987447 \tabularnewline
151 & 7 & 8.41546042584635 & -1.41546042584635 \tabularnewline
152 & 13 & 10.8221587487834 & 2.17784125121659 \tabularnewline
153 & 9 & 11.8480569437804 & -2.84805694378039 \tabularnewline
154 & 6 & 11.9089172928469 & -5.90891729284689 \tabularnewline
155 & 8 & 10.4889986132833 & -2.48899861328326 \tabularnewline
156 & 8 & 8.36402916276398 & -0.364029162763983 \tabularnewline
157 & 15 & 12.0826548581284 & 2.91734514187162 \tabularnewline
158 & 6 & 10.2644502377882 & -4.26445023778816 \tabularnewline
159 & 9 & 11.1005142936096 & -2.10051429360955 \tabularnewline
160 & 11 & 11.7626512320649 & -0.762651232064944 \tabularnewline
161 & 8 & 9.63466472878448 & -1.63466472878448 \tabularnewline
162 & 8 & 10.1232524153305 & -2.12325241533053 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204000&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]12[/C][C]9.81682927217552[/C][C]2.18317072782448[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.133889527927[/C][C]-0.133889527926988[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.4453448706616[/C][C]1.55465512933839[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]11.2366656238246[/C][C]-5.23666562382462[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]10.2842042602457[/C][C]2.71579573975434[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]9.61802523888203[/C][C]0.381974761117967[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]12.5920333254531[/C][C]-0.592033325453062[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]11.1202675488085[/C][C]2.87973245119149[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.4643777762504[/C][C]1.5356222237496[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]10.9565943019394[/C][C]-4.95659430193936[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]11.2191441047299[/C][C]-1.21914410472987[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.4092632673265[/C][C]0.590736732673456[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.5089190212[/C][C]0.491080978799982[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.359250773053[/C][C]-0.359250773052977[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]12.110427813852[/C][C]2.88957218614796[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]11.0685232188936[/C][C]0.931476781106397[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.8368011769086[/C][C]-0.836801176908613[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]13.5135531238283[/C][C]-1.51355312382833[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.5857357970299[/C][C]-1.58573579702993[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.5122273342614[/C][C]0.487772665738596[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.6091102846468[/C][C]-0.609110284646846[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.6849434279943[/C][C]0.315056572005697[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]13.0804753543567[/C][C]-0.0804753543567389[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.6329889723933[/C][C]-0.63298897239331[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]11.7010591115911[/C][C]-2.70105911159108[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]11.9982079973229[/C][C]1.00179200267711[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]11.4613289999254[/C][C]-1.46132899992536[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]11.1015213725053[/C][C]2.89847862749474[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.4929428542802[/C][C]0.50705714571979[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]10.577908076508[/C][C]-0.577908076508027[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]10.9826319477975[/C][C]1.01736805220252[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]9.6981407101612[/C][C]-1.6981407101612[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.2197200467549[/C][C]-0.219720046754897[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]11.627880413196[/C][C]0.372119586804031[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.3706816097498[/C][C]1.62931839025018[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]6.89435310313394[/C][C]0.105646896866055[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]8.54010230144404[/C][C]-2.54010230144404[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.3547786681605[/C][C]1.64522133183952[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.6346657807485[/C][C]-1.63466578074846[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.5943422276892[/C][C]-1.59434222768924[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.4116375086292[/C][C]-1.41163750862924[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.350635251211[/C][C]1.64936474878901[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.4953102428145[/C][C]1.50468975718552[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.4955327935646[/C][C]-0.495532793564588[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.2604411222744[/C][C]-0.260441122274422[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]8.925723137512[/C][C]3.074276862488[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.5260686057012[/C][C]2.47393139429876[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.0704037049172[/C][C]-0.0704037049171738[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.409713518483[/C][C]-0.409713518483043[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.3363104536703[/C][C]1.66368954632966[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]11.7580902784325[/C][C]2.24190972156752[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]10.438867184932[/C][C]-0.438867184932015[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]9.49541808311954[/C][C]2.50458191688046[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.7170252115543[/C][C]1.28297478844571[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]8.02353549432903[/C][C]-3.02353549432903[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.2695809194993[/C][C]-4.26958091949932[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.2363990962497[/C][C]0.763600903750327[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.8424208235527[/C][C]0.157579176447303[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]11.105135924605[/C][C]-0.105135924605038[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]11.5314950801108[/C][C]-1.53149508011077[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.5122060750792[/C][C]-2.5122060750792[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.4301144586771[/C][C]0.569885541322879[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.7233424908416[/C][C]2.27665750915842[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.6575401094975[/C][C]0.342459890502458[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.4381931798483[/C][C]0.561806820151699[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.4729552014307[/C][C]0.527044798569309[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]12.7118462371072[/C][C]1.28815376289276[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.8336709887322[/C][C]-1.8336709887322[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]9.50686291494274[/C][C]2.49313708505726[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.6778831822179[/C][C]0.322116817782075[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.39726869498507[/C][C]-0.397268694985067[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]10.7847590684733[/C][C]0.215240931526693[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.8565497559452[/C][C]1.14345024405476[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]12.8332296408072[/C][C]1.16677035919276[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.5590155527823[/C][C]0.440984447217708[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]11.7848595513121[/C][C]-2.78485955131211[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.5301529612273[/C][C]1.46984703877265[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.5360510590246[/C][C]-0.536051059024577[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]10.2391701557175[/C][C]1.76082984428251[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.5126343447403[/C][C]0.487365655259669[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.8087975969819[/C][C]0.191202403018141[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.8975703668915[/C][C]-1.8975703668915[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.8024263839988[/C][C]0.197573616001232[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.9455498411215[/C][C]1.05445015887849[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]11.2410065989646[/C][C]-0.241006598964577[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.6402649999365[/C][C]-1.64026499993653[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]10.5646072434184[/C][C]-1.56460724341839[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.7697510003526[/C][C]0.230248999647422[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.8712210125391[/C][C]0.128778987460905[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.227497036088[/C][C]0.772502963912049[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]9.6480723665813[/C][C]-0.648072366581299[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]12.0826548581284[/C][C]2.91734514187162[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.8184288958813[/C][C]0.181571104118724[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]11.0101129525401[/C][C]0.989887047459944[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]10.3236031655768[/C][C]1.67639683442321[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.4936279799742[/C][C]-1.49362797997424[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.5919602014547[/C][C]1.40803979854526[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]11.6048412280065[/C][C]-2.60484122800647[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.4802306086279[/C][C]0.519769391372071[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.821591289719[/C][C]-0.821591289718959[/C][/ROW]
[ROW][C]101[/C][C]14[/C][C]11.5291651960851[/C][C]2.47083480391493[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.5876783238725[/C][C]-0.587678323872548[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.7393364173977[/C][C]1.26066358260234[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.249020619101[/C][C]-0.249020619100961[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.7666751271381[/C][C]-0.766675127138127[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]9.82335494998275[/C][C]2.17664505001725[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]12.0367977754719[/C][C]-0.0367977754719416[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]11.4120813956668[/C][C]0.587918604333238[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.7669710757335[/C][C]-0.766971075733525[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]9.31627750909912[/C][C]-2.31627750909912[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.7976099493059[/C][C]0.202390050694097[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]11.8681661449046[/C][C]2.13183385509537[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.5969001893422[/C][C]-1.59690018934225[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]10.1898643692564[/C][C]0.810135630743566[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]9.29822095359814[/C][C]0.701779046401857[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.5324401422475[/C][C]0.467559857752472[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]10.862650593027[/C][C]2.13734940697297[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]10.8189511930142[/C][C]-2.81895119301418[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]10.2918706422896[/C][C]0.708129357710355[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.4964025019587[/C][C]0.50359749804126[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]10.2959937666503[/C][C]0.70400623334969[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]11.5842680706189[/C][C]1.41573192938115[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.0680912054205[/C][C]1.93190879457946[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]11.7645689711837[/C][C]2.23543102881626[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]11.1275498707012[/C][C]1.87245012929883[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]11.8859603786273[/C][C]3.11403962137266[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.8619785933977[/C][C]-0.861978593397714[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.8802125277566[/C][C]-0.880212527756584[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]11.1005142936096[/C][C]-2.10051429360955[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]9.4602009949234[/C][C]1.5397990050766[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]11.8516348448734[/C][C]-1.85163484487341[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]8.75630319266192[/C][C]2.24369680733808[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]11.5477019454216[/C][C]-3.54770194542159[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]9.6873486305642[/C][C]1.3126513694358[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.6029606696683[/C][C]1.3970393303317[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.973660906991[/C][C]1.026339093009[/C][/ROW]
[ROW][C]137[/C][C]9[/C][C]9.89234432763822[/C][C]-0.892344327638222[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.1720809404969[/C][C]-0.172080940496897[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]9.32152784385167[/C][C]0.678472156148332[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]9.5521353443327[/C][C]-1.5521353443327[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.70064355242504[/C][C]1.29935644757496[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]11.84716046206[/C][C]-3.84716046206001[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]11.2232886330284[/C][C]-2.22328863302841[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]12.4413628527216[/C][C]2.55863714727843[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.473879457308[/C][C]-0.473879457308012[/C][/ROW]
[ROW][C]146[/C][C]8[/C][C]8.1011871995863[/C][C]-0.101187199586297[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]12.5664825860677[/C][C]0.4335174139323[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]10.2108397864165[/C][C]1.78916021358353[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.8214352666742[/C][C]0.178564733325847[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]10.0387125398745[/C][C]-1.03871253987447[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]8.41546042584635[/C][C]-1.41546042584635[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]10.8221587487834[/C][C]2.17784125121659[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.8480569437804[/C][C]-2.84805694378039[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]11.9089172928469[/C][C]-5.90891729284689[/C][/ROW]
[ROW][C]155[/C][C]8[/C][C]10.4889986132833[/C][C]-2.48899861328326[/C][/ROW]
[ROW][C]156[/C][C]8[/C][C]8.36402916276398[/C][C]-0.364029162763983[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]12.0826548581284[/C][C]2.91734514187162[/C][/ROW]
[ROW][C]158[/C][C]6[/C][C]10.2644502377882[/C][C]-4.26445023778816[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]11.1005142936096[/C][C]-2.10051429360955[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]11.7626512320649[/C][C]-0.762651232064944[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]9.63466472878448[/C][C]-1.63466472878448[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]10.1232524153305[/C][C]-2.12325241533053[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204000&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204000&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1129.816829272175522.18317072782448
21111.133889527927-0.133889527926988
31513.44534487066161.55465512933839
4611.2366656238246-5.23666562382462
51310.28420426024572.71579573975434
6109.618025238882030.381974761117967
71212.5920333254531-0.592033325453062
81411.12026754880852.87973245119149
91210.46437777625041.5356222237496
10610.9565943019394-4.95659430193936
111011.2191441047299-1.21914410472987
121211.40926326732650.590736732673456
131211.50891902120.491080978799982
141111.359250773053-0.359250773052977
151512.1104278138522.88957218614796
161211.06852321889360.931476781106397
171010.8368011769086-0.836801176908613
181213.5135531238283-1.51355312382833
191112.5857357970299-1.58573579702993
201211.51222733426140.487772665738596
211111.6091102846468-0.609110284646846
221211.68494342799430.315056572005697
231313.0804753543567-0.0804753543567389
241111.6329889723933-0.63298897239331
25911.7010591115911-2.70105911159108
261311.99820799732291.00179200267711
271011.4613289999254-1.46132899992536
281411.10152137250532.89847862749474
291211.49294285428020.50705714571979
301010.577908076508-0.577908076508027
311210.98263194779751.01736805220252
3289.6981407101612-1.6981407101612
331010.2197200467549-0.219720046754897
341211.6278804131960.372119586804031
351210.37068160974981.62931839025018
3676.894353103133940.105646896866055
3768.54010230144404-2.54010230144404
381210.35477866816051.64522133183952
391011.6346657807485-1.63466578074846
401011.5943422276892-1.59434222768924
411011.4116375086292-1.41163750862924
421210.3506352512111.64936474878901
431513.49531024281451.50468975718552
441010.4955327935646-0.495532793564588
451010.2604411222744-0.260441122274422
46128.9257231375123.074276862488
471310.52606860570122.47393139429876
481111.0704037049172-0.0704037049171738
491111.409713518483-0.409713518483043
501210.33631045367031.66368954632966
511411.75809027843252.24190972156752
521010.438867184932-0.438867184932015
53129.495418083119542.50458191688046
541311.71702521155431.28297478844571
5558.02353549432903-3.02353549432903
56610.2695809194993-4.26958091949932
571211.23639909624970.763600903750327
581211.84242082355270.157579176447303
591111.105135924605-0.105135924605038
601011.5314950801108-1.53149508011077
6179.5122060750792-2.5122060750792
621211.43011445867710.569885541322879
631411.72334249084162.27665750915842
641110.65754010949750.342459890502458
651211.43819317984830.561806820151699
661312.47295520143070.527044798569309
671412.71184623710721.28815376289276
681112.8336709887322-1.8336709887322
69129.506862914942742.49313708505726
701211.67788318221790.322116817782075
7188.39726869498507-0.397268694985067
721110.78475906847330.215240931526693
731412.85654975594521.14345024405476
741412.83322964080721.16677035919276
751211.55901555278230.440984447217708
76911.7848595513121-2.78485955131211
771311.53015296122731.46984703877265
781111.5360510590246-0.536051059024577
791210.23917015571751.76082984428251
801211.51263434474030.487365655259669
811211.80879759698190.191202403018141
821213.8975703668915-1.8975703668915
831211.80242638399880.197573616001232
841210.94554984112151.05445015887849
851111.2410065989646-0.241006598964577
861011.6402649999365-1.64026499993653
87910.5646072434184-1.56460724341839
881211.76975100035260.230248999647422
891211.87122101253910.128778987460905
901211.2274970360880.772502963912049
9199.6480723665813-0.648072366581299
921512.08265485812842.91734514187162
931211.81842889588130.181571104118724
941211.01011295254010.989887047459944
951210.32360316557681.67639683442321
961011.4936279799742-1.49362797997424
971311.59196020145471.40803979854526
98911.6048412280065-2.60484122800647
991211.48023060862790.519769391372071
1001010.821591289719-0.821591289718959
1011411.52916519608512.47083480391493
1021111.5876783238725-0.587678323872548
1031513.73933641739771.26066358260234
1041111.249020619101-0.249020619100961
1051111.7666751271381-0.766675127138127
106129.823354949982752.17664505001725
1071212.0367977754719-0.0367977754719416
1081211.41208139566680.587918604333238
1091111.7669710757335-0.766971075733525
11079.31627750909912-2.31627750909912
1111211.79760994930590.202390050694097
1121411.86816614490462.13183385509537
1131112.5969001893422-1.59690018934225
1141110.18986436925640.810135630743566
115109.298220953598140.701779046401857
1161312.53244014224750.467559857752472
1171310.8626505930272.13734940697297
118810.8189511930142-2.81895119301418
1191110.29187064228960.708129357710355
1201211.49640250195870.50359749804126
1211110.29599376665030.70400623334969
1221311.58426807061891.41573192938115
1231210.06809120542051.93190879457946
1241411.76456897118372.23543102881626
1251311.12754987070121.87245012929883
1261511.88596037862733.11403962137266
1271010.8619785933977-0.861978593397714
1281111.8802125277566-0.880212527756584
129911.1005142936096-2.10051429360955
130119.46020099492341.5397990050766
1311011.8516348448734-1.85163484487341
132118.756303192661922.24369680733808
133811.5477019454216-3.54770194542159
134119.68734863056421.3126513694358
1351210.60296066966831.3970393303317
1361210.9736609069911.026339093009
13799.89234432763822-0.892344327638222
1381111.1720809404969-0.172080940496897
139109.321527843851670.678472156148332
14089.5521353443327-1.5521353443327
14197.700643552425041.29935644757496
142811.84716046206-3.84716046206001
143911.2232886330284-2.22328863302841
1441512.44136285272162.55863714727843
1451111.473879457308-0.473879457308012
14688.1011871995863-0.101187199586297
1471312.56648258606770.4335174139323
1481210.21083978641651.78916021358353
1491211.82143526667420.178564733325847
150910.0387125398745-1.03871253987447
15178.41546042584635-1.41546042584635
1521310.82215874878342.17784125121659
153911.8480569437804-2.84805694378039
154611.9089172928469-5.90891729284689
155810.4889986132833-2.48899861328326
15688.36402916276398-0.364029162763983
1571512.08265485812842.91734514187162
158610.2644502377882-4.26445023778816
159911.1005142936096-2.10051429360955
1601111.7626512320649-0.762651232064944
16189.63466472878448-1.63466472878448
162810.1232524153305-2.12325241533053







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.9999060111163570.0001879777672869569.39888836434778e-05
130.9997382286464990.0005235427070024810.000261771353501241
140.9995232041636530.0009535916726942510.000476795836347126
150.9994258046000050.001148390799990020.000574195399995009
160.9988926322023010.002214735595397470.00110736779769873
170.9978241903405680.004351619318863170.00217580965943158
180.9971664680623940.005667063875212530.00283353193760627
190.9952834872399940.009433025520012540.00471651276000627
200.9916891968071610.01662160638567740.00831080319283869
210.9866333045858740.02673339082825230.0133666954141262
220.9802984721162870.03940305576742550.0197015278837127
230.9697863116135710.06042737677285830.0302136883864292
240.9558010600308230.08839787993835390.0441989399691769
250.9534815390264630.09303692194707320.0465184609735366
260.9562855876173350.08742882476532970.0437144123826648
270.9561252466646490.08774950667070160.0438747533353508
280.9614713320401630.07705733591967410.0385286679598371
290.9459590253252760.1080819493494490.0540409746747243
300.9283694140221410.1432611719557180.0716305859778592
310.9137970911162190.1724058177675620.0862029088837812
320.9273964634396820.1452070731206360.0726035365603179
330.9038161157020820.1923677685958370.0961838842979183
340.8753509810716030.2492980378567940.124649018928397
350.8629215418470260.2741569163059480.137078458152974
360.8288709526833450.342258094633310.171129047316655
370.8644456889018510.2711086221962970.135554311098149
380.8564517192025250.2870965615949510.143548280797475
390.8456219969615490.3087560060769020.154378003038451
400.8279144612886370.3441710774227260.172085538711363
410.8049807659057170.3900384681885670.195019234094283
420.7893087092427650.421382581514470.210691290757235
430.7871570575662820.4256858848674360.212842942433718
440.7472583066540190.5054833866919620.252741693345981
450.705047384885120.589905230229760.29495261511488
460.7641955485434160.4716089029131680.235804451456584
470.7751322573729170.4497354852541650.224867742627083
480.7336377717953770.5327244564092460.266362228204623
490.6968322103015570.6063355793968860.303167789698443
500.686167572597310.6276648548053810.31383242740269
510.6989565780854680.6020868438290650.301043421914532
520.6527350749698250.6945298500603490.347264925030175
530.6909953539757010.6180092920485970.309004646024299
540.6686788894432930.6626422211134130.331321110556707
550.7372314584838570.5255370830322860.262768541516143
560.8654640496485370.2690719007029260.134535950351463
570.8426999455693340.3146001088613330.157300054430666
580.8124901771901350.3750196456197290.187509822809865
590.7781115964649270.4437768070701460.221888403535073
600.7606764133236260.4786471733527480.239323586676374
610.7720839682018080.4558320635963840.227916031798192
620.7369227484324540.5261545031350930.263077251567546
630.756668080016510.4866638399669790.24333191998349
640.7240544288735250.551891142252950.275945571126475
650.7147178478508720.5705643042982550.285282152149128
660.6739546793149430.6520906413701130.326045320685057
670.6445048608904880.7109902782190250.355495139109513
680.637823271553930.724353456892140.36217672844607
690.645339386462630.709321227074740.35466061353737
700.6063467903609280.7873064192781430.393653209639072
710.5714294176454640.8571411647090710.428570582354536
720.5309557274918920.9380885450162160.469044272508108
730.499689317021780.999378634043560.50031068297822
740.47466916962010.9493383392401990.5253308303799
750.4413509845671820.8827019691343640.558649015432818
760.5072982916906510.9854034166186970.492701708309349
770.4943006228430960.9886012456861920.505699377156904
780.4516937958066280.9033875916132560.548306204193372
790.4492475501288270.8984951002576530.550752449871174
800.4146955676491730.8293911352983470.585304432350827
810.3733847289024360.7467694578048710.626615271097564
820.3810012295200360.7620024590400730.618998770479964
830.3455543401577940.6911086803155880.654445659842206
840.3129712087088540.6259424174177090.687028791291146
850.2749560352757470.5499120705514940.725043964724253
860.2716062235904450.5432124471808890.728393776409555
870.2759942552920540.5519885105841080.724005744707946
880.2383929131954860.4767858263909720.761607086804514
890.2040771033797790.4081542067595580.795922896620221
900.1774216018708310.3548432037416630.822578398129169
910.1557355169983710.3114710339967430.844264483001629
920.2074501871280760.4149003742561510.792549812871924
930.1809462019284870.3618924038569740.819053798071513
940.1605166457690110.3210332915380210.839483354230989
950.1495788912777590.2991577825555170.850421108722241
960.1448160205722350.289632041144470.855183979427765
970.1317223638047430.2634447276094850.868277636195257
980.1733407886142850.346681577228570.826659211385715
990.1452898711612450.2905797423224890.854710128838755
1000.1256260059808290.2512520119616580.874373994019171
1010.1469631750566440.2939263501132880.853036824943356
1020.1247723251457760.2495446502915520.875227674854224
1030.1176162992857590.2352325985715170.882383700714241
1040.09802446344852990.196048926897060.90197553655147
1050.08247123975631940.1649424795126390.917528760243681
1060.0786061252802770.1572122505605540.921393874719723
1070.06284409925538150.1256881985107630.937155900744619
1080.05402347736540350.1080469547308070.945976522634597
1090.04405612455145280.08811224910290570.955943875448547
1100.04857702038412310.09715404076824630.951422979615877
1110.03782051956780670.07564103913561340.962179480432193
1120.03876032100280160.07752064200560320.961239678997198
1130.03529450038052490.07058900076104990.964705499619475
1140.02772946141096250.0554589228219250.972270538589037
1150.02201450411639640.04402900823279290.977985495883604
1160.0166029282948360.0332058565896720.983397071705164
1170.02496523135160420.04993046270320840.975034768648396
1180.02959289885730820.05918579771461640.970407101142692
1190.02283362285356830.04566724570713650.977166377146432
1200.01704158137875790.03408316275751580.982958418621242
1210.01371652215035050.02743304430070110.986283477849649
1220.01105649182563690.02211298365127380.988943508174363
1230.01256405780053220.02512811560106450.987435942199468
1240.01939784705985040.03879569411970070.98060215294015
1250.02027555776951560.04055111553903120.979724442230484
1260.04599622325322320.09199244650644640.954003776746777
1270.03510691429045620.07021382858091230.964893085709544
1280.02683699199446310.05367398398892630.973163008005537
1290.02267838134244250.0453567626848850.977321618657558
1300.02151408418198830.04302816836397660.978485915818012
1310.0175622088624490.0351244177248980.982437791137551
1320.01795156996605340.03590313993210680.982048430033947
1330.02886020725416460.05772041450832910.971139792745835
1340.03394527666408480.06789055332816970.966054723335915
1350.03696444917961080.07392889835922160.963035550820389
1360.03044086386952540.06088172773905070.969559136130475
1370.02940519761493560.05881039522987120.970594802385064
1380.02108632751395650.0421726550279130.978913672486044
1390.01900061533554170.03800123067108350.980999384664458
1400.01308491657352610.02616983314705220.986915083426474
1410.04639358236727380.09278716473454760.953606417632726
1420.04696750199125280.09393500398250560.953032498008747
1430.03295152094029580.06590304188059160.967048479059704
1440.325179927305010.650359854610020.67482007269499
1450.3418411479641750.683682295928350.658158852035825
1460.5969360242464790.8061279515070430.403063975753521
1470.5851256965853710.8297486068292580.414874303414629
1480.8688366993498320.2623266013003360.131163300650168
1490.86802391015080.26395217969840.1319760898492
1500.7708910666924360.4582178666151280.229108933307564

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.999906011116357 & 0.000187977767286956 & 9.39888836434778e-05 \tabularnewline
13 & 0.999738228646499 & 0.000523542707002481 & 0.000261771353501241 \tabularnewline
14 & 0.999523204163653 & 0.000953591672694251 & 0.000476795836347126 \tabularnewline
15 & 0.999425804600005 & 0.00114839079999002 & 0.000574195399995009 \tabularnewline
16 & 0.998892632202301 & 0.00221473559539747 & 0.00110736779769873 \tabularnewline
17 & 0.997824190340568 & 0.00435161931886317 & 0.00217580965943158 \tabularnewline
18 & 0.997166468062394 & 0.00566706387521253 & 0.00283353193760627 \tabularnewline
19 & 0.995283487239994 & 0.00943302552001254 & 0.00471651276000627 \tabularnewline
20 & 0.991689196807161 & 0.0166216063856774 & 0.00831080319283869 \tabularnewline
21 & 0.986633304585874 & 0.0267333908282523 & 0.0133666954141262 \tabularnewline
22 & 0.980298472116287 & 0.0394030557674255 & 0.0197015278837127 \tabularnewline
23 & 0.969786311613571 & 0.0604273767728583 & 0.0302136883864292 \tabularnewline
24 & 0.955801060030823 & 0.0883978799383539 & 0.0441989399691769 \tabularnewline
25 & 0.953481539026463 & 0.0930369219470732 & 0.0465184609735366 \tabularnewline
26 & 0.956285587617335 & 0.0874288247653297 & 0.0437144123826648 \tabularnewline
27 & 0.956125246664649 & 0.0877495066707016 & 0.0438747533353508 \tabularnewline
28 & 0.961471332040163 & 0.0770573359196741 & 0.0385286679598371 \tabularnewline
29 & 0.945959025325276 & 0.108081949349449 & 0.0540409746747243 \tabularnewline
30 & 0.928369414022141 & 0.143261171955718 & 0.0716305859778592 \tabularnewline
31 & 0.913797091116219 & 0.172405817767562 & 0.0862029088837812 \tabularnewline
32 & 0.927396463439682 & 0.145207073120636 & 0.0726035365603179 \tabularnewline
33 & 0.903816115702082 & 0.192367768595837 & 0.0961838842979183 \tabularnewline
34 & 0.875350981071603 & 0.249298037856794 & 0.124649018928397 \tabularnewline
35 & 0.862921541847026 & 0.274156916305948 & 0.137078458152974 \tabularnewline
36 & 0.828870952683345 & 0.34225809463331 & 0.171129047316655 \tabularnewline
37 & 0.864445688901851 & 0.271108622196297 & 0.135554311098149 \tabularnewline
38 & 0.856451719202525 & 0.287096561594951 & 0.143548280797475 \tabularnewline
39 & 0.845621996961549 & 0.308756006076902 & 0.154378003038451 \tabularnewline
40 & 0.827914461288637 & 0.344171077422726 & 0.172085538711363 \tabularnewline
41 & 0.804980765905717 & 0.390038468188567 & 0.195019234094283 \tabularnewline
42 & 0.789308709242765 & 0.42138258151447 & 0.210691290757235 \tabularnewline
43 & 0.787157057566282 & 0.425685884867436 & 0.212842942433718 \tabularnewline
44 & 0.747258306654019 & 0.505483386691962 & 0.252741693345981 \tabularnewline
45 & 0.70504738488512 & 0.58990523022976 & 0.29495261511488 \tabularnewline
46 & 0.764195548543416 & 0.471608902913168 & 0.235804451456584 \tabularnewline
47 & 0.775132257372917 & 0.449735485254165 & 0.224867742627083 \tabularnewline
48 & 0.733637771795377 & 0.532724456409246 & 0.266362228204623 \tabularnewline
49 & 0.696832210301557 & 0.606335579396886 & 0.303167789698443 \tabularnewline
50 & 0.68616757259731 & 0.627664854805381 & 0.31383242740269 \tabularnewline
51 & 0.698956578085468 & 0.602086843829065 & 0.301043421914532 \tabularnewline
52 & 0.652735074969825 & 0.694529850060349 & 0.347264925030175 \tabularnewline
53 & 0.690995353975701 & 0.618009292048597 & 0.309004646024299 \tabularnewline
54 & 0.668678889443293 & 0.662642221113413 & 0.331321110556707 \tabularnewline
55 & 0.737231458483857 & 0.525537083032286 & 0.262768541516143 \tabularnewline
56 & 0.865464049648537 & 0.269071900702926 & 0.134535950351463 \tabularnewline
57 & 0.842699945569334 & 0.314600108861333 & 0.157300054430666 \tabularnewline
58 & 0.812490177190135 & 0.375019645619729 & 0.187509822809865 \tabularnewline
59 & 0.778111596464927 & 0.443776807070146 & 0.221888403535073 \tabularnewline
60 & 0.760676413323626 & 0.478647173352748 & 0.239323586676374 \tabularnewline
61 & 0.772083968201808 & 0.455832063596384 & 0.227916031798192 \tabularnewline
62 & 0.736922748432454 & 0.526154503135093 & 0.263077251567546 \tabularnewline
63 & 0.75666808001651 & 0.486663839966979 & 0.24333191998349 \tabularnewline
64 & 0.724054428873525 & 0.55189114225295 & 0.275945571126475 \tabularnewline
65 & 0.714717847850872 & 0.570564304298255 & 0.285282152149128 \tabularnewline
66 & 0.673954679314943 & 0.652090641370113 & 0.326045320685057 \tabularnewline
67 & 0.644504860890488 & 0.710990278219025 & 0.355495139109513 \tabularnewline
68 & 0.63782327155393 & 0.72435345689214 & 0.36217672844607 \tabularnewline
69 & 0.64533938646263 & 0.70932122707474 & 0.35466061353737 \tabularnewline
70 & 0.606346790360928 & 0.787306419278143 & 0.393653209639072 \tabularnewline
71 & 0.571429417645464 & 0.857141164709071 & 0.428570582354536 \tabularnewline
72 & 0.530955727491892 & 0.938088545016216 & 0.469044272508108 \tabularnewline
73 & 0.49968931702178 & 0.99937863404356 & 0.50031068297822 \tabularnewline
74 & 0.4746691696201 & 0.949338339240199 & 0.5253308303799 \tabularnewline
75 & 0.441350984567182 & 0.882701969134364 & 0.558649015432818 \tabularnewline
76 & 0.507298291690651 & 0.985403416618697 & 0.492701708309349 \tabularnewline
77 & 0.494300622843096 & 0.988601245686192 & 0.505699377156904 \tabularnewline
78 & 0.451693795806628 & 0.903387591613256 & 0.548306204193372 \tabularnewline
79 & 0.449247550128827 & 0.898495100257653 & 0.550752449871174 \tabularnewline
80 & 0.414695567649173 & 0.829391135298347 & 0.585304432350827 \tabularnewline
81 & 0.373384728902436 & 0.746769457804871 & 0.626615271097564 \tabularnewline
82 & 0.381001229520036 & 0.762002459040073 & 0.618998770479964 \tabularnewline
83 & 0.345554340157794 & 0.691108680315588 & 0.654445659842206 \tabularnewline
84 & 0.312971208708854 & 0.625942417417709 & 0.687028791291146 \tabularnewline
85 & 0.274956035275747 & 0.549912070551494 & 0.725043964724253 \tabularnewline
86 & 0.271606223590445 & 0.543212447180889 & 0.728393776409555 \tabularnewline
87 & 0.275994255292054 & 0.551988510584108 & 0.724005744707946 \tabularnewline
88 & 0.238392913195486 & 0.476785826390972 & 0.761607086804514 \tabularnewline
89 & 0.204077103379779 & 0.408154206759558 & 0.795922896620221 \tabularnewline
90 & 0.177421601870831 & 0.354843203741663 & 0.822578398129169 \tabularnewline
91 & 0.155735516998371 & 0.311471033996743 & 0.844264483001629 \tabularnewline
92 & 0.207450187128076 & 0.414900374256151 & 0.792549812871924 \tabularnewline
93 & 0.180946201928487 & 0.361892403856974 & 0.819053798071513 \tabularnewline
94 & 0.160516645769011 & 0.321033291538021 & 0.839483354230989 \tabularnewline
95 & 0.149578891277759 & 0.299157782555517 & 0.850421108722241 \tabularnewline
96 & 0.144816020572235 & 0.28963204114447 & 0.855183979427765 \tabularnewline
97 & 0.131722363804743 & 0.263444727609485 & 0.868277636195257 \tabularnewline
98 & 0.173340788614285 & 0.34668157722857 & 0.826659211385715 \tabularnewline
99 & 0.145289871161245 & 0.290579742322489 & 0.854710128838755 \tabularnewline
100 & 0.125626005980829 & 0.251252011961658 & 0.874373994019171 \tabularnewline
101 & 0.146963175056644 & 0.293926350113288 & 0.853036824943356 \tabularnewline
102 & 0.124772325145776 & 0.249544650291552 & 0.875227674854224 \tabularnewline
103 & 0.117616299285759 & 0.235232598571517 & 0.882383700714241 \tabularnewline
104 & 0.0980244634485299 & 0.19604892689706 & 0.90197553655147 \tabularnewline
105 & 0.0824712397563194 & 0.164942479512639 & 0.917528760243681 \tabularnewline
106 & 0.078606125280277 & 0.157212250560554 & 0.921393874719723 \tabularnewline
107 & 0.0628440992553815 & 0.125688198510763 & 0.937155900744619 \tabularnewline
108 & 0.0540234773654035 & 0.108046954730807 & 0.945976522634597 \tabularnewline
109 & 0.0440561245514528 & 0.0881122491029057 & 0.955943875448547 \tabularnewline
110 & 0.0485770203841231 & 0.0971540407682463 & 0.951422979615877 \tabularnewline
111 & 0.0378205195678067 & 0.0756410391356134 & 0.962179480432193 \tabularnewline
112 & 0.0387603210028016 & 0.0775206420056032 & 0.961239678997198 \tabularnewline
113 & 0.0352945003805249 & 0.0705890007610499 & 0.964705499619475 \tabularnewline
114 & 0.0277294614109625 & 0.055458922821925 & 0.972270538589037 \tabularnewline
115 & 0.0220145041163964 & 0.0440290082327929 & 0.977985495883604 \tabularnewline
116 & 0.016602928294836 & 0.033205856589672 & 0.983397071705164 \tabularnewline
117 & 0.0249652313516042 & 0.0499304627032084 & 0.975034768648396 \tabularnewline
118 & 0.0295928988573082 & 0.0591857977146164 & 0.970407101142692 \tabularnewline
119 & 0.0228336228535683 & 0.0456672457071365 & 0.977166377146432 \tabularnewline
120 & 0.0170415813787579 & 0.0340831627575158 & 0.982958418621242 \tabularnewline
121 & 0.0137165221503505 & 0.0274330443007011 & 0.986283477849649 \tabularnewline
122 & 0.0110564918256369 & 0.0221129836512738 & 0.988943508174363 \tabularnewline
123 & 0.0125640578005322 & 0.0251281156010645 & 0.987435942199468 \tabularnewline
124 & 0.0193978470598504 & 0.0387956941197007 & 0.98060215294015 \tabularnewline
125 & 0.0202755577695156 & 0.0405511155390312 & 0.979724442230484 \tabularnewline
126 & 0.0459962232532232 & 0.0919924465064464 & 0.954003776746777 \tabularnewline
127 & 0.0351069142904562 & 0.0702138285809123 & 0.964893085709544 \tabularnewline
128 & 0.0268369919944631 & 0.0536739839889263 & 0.973163008005537 \tabularnewline
129 & 0.0226783813424425 & 0.045356762684885 & 0.977321618657558 \tabularnewline
130 & 0.0215140841819883 & 0.0430281683639766 & 0.978485915818012 \tabularnewline
131 & 0.017562208862449 & 0.035124417724898 & 0.982437791137551 \tabularnewline
132 & 0.0179515699660534 & 0.0359031399321068 & 0.982048430033947 \tabularnewline
133 & 0.0288602072541646 & 0.0577204145083291 & 0.971139792745835 \tabularnewline
134 & 0.0339452766640848 & 0.0678905533281697 & 0.966054723335915 \tabularnewline
135 & 0.0369644491796108 & 0.0739288983592216 & 0.963035550820389 \tabularnewline
136 & 0.0304408638695254 & 0.0608817277390507 & 0.969559136130475 \tabularnewline
137 & 0.0294051976149356 & 0.0588103952298712 & 0.970594802385064 \tabularnewline
138 & 0.0210863275139565 & 0.042172655027913 & 0.978913672486044 \tabularnewline
139 & 0.0190006153355417 & 0.0380012306710835 & 0.980999384664458 \tabularnewline
140 & 0.0130849165735261 & 0.0261698331470522 & 0.986915083426474 \tabularnewline
141 & 0.0463935823672738 & 0.0927871647345476 & 0.953606417632726 \tabularnewline
142 & 0.0469675019912528 & 0.0939350039825056 & 0.953032498008747 \tabularnewline
143 & 0.0329515209402958 & 0.0659030418805916 & 0.967048479059704 \tabularnewline
144 & 0.32517992730501 & 0.65035985461002 & 0.67482007269499 \tabularnewline
145 & 0.341841147964175 & 0.68368229592835 & 0.658158852035825 \tabularnewline
146 & 0.596936024246479 & 0.806127951507043 & 0.403063975753521 \tabularnewline
147 & 0.585125696585371 & 0.829748606829258 & 0.414874303414629 \tabularnewline
148 & 0.868836699349832 & 0.262326601300336 & 0.131163300650168 \tabularnewline
149 & 0.8680239101508 & 0.2639521796984 & 0.1319760898492 \tabularnewline
150 & 0.770891066692436 & 0.458217866615128 & 0.229108933307564 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204000&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.999906011116357[/C][C]0.000187977767286956[/C][C]9.39888836434778e-05[/C][/ROW]
[ROW][C]13[/C][C]0.999738228646499[/C][C]0.000523542707002481[/C][C]0.000261771353501241[/C][/ROW]
[ROW][C]14[/C][C]0.999523204163653[/C][C]0.000953591672694251[/C][C]0.000476795836347126[/C][/ROW]
[ROW][C]15[/C][C]0.999425804600005[/C][C]0.00114839079999002[/C][C]0.000574195399995009[/C][/ROW]
[ROW][C]16[/C][C]0.998892632202301[/C][C]0.00221473559539747[/C][C]0.00110736779769873[/C][/ROW]
[ROW][C]17[/C][C]0.997824190340568[/C][C]0.00435161931886317[/C][C]0.00217580965943158[/C][/ROW]
[ROW][C]18[/C][C]0.997166468062394[/C][C]0.00566706387521253[/C][C]0.00283353193760627[/C][/ROW]
[ROW][C]19[/C][C]0.995283487239994[/C][C]0.00943302552001254[/C][C]0.00471651276000627[/C][/ROW]
[ROW][C]20[/C][C]0.991689196807161[/C][C]0.0166216063856774[/C][C]0.00831080319283869[/C][/ROW]
[ROW][C]21[/C][C]0.986633304585874[/C][C]0.0267333908282523[/C][C]0.0133666954141262[/C][/ROW]
[ROW][C]22[/C][C]0.980298472116287[/C][C]0.0394030557674255[/C][C]0.0197015278837127[/C][/ROW]
[ROW][C]23[/C][C]0.969786311613571[/C][C]0.0604273767728583[/C][C]0.0302136883864292[/C][/ROW]
[ROW][C]24[/C][C]0.955801060030823[/C][C]0.0883978799383539[/C][C]0.0441989399691769[/C][/ROW]
[ROW][C]25[/C][C]0.953481539026463[/C][C]0.0930369219470732[/C][C]0.0465184609735366[/C][/ROW]
[ROW][C]26[/C][C]0.956285587617335[/C][C]0.0874288247653297[/C][C]0.0437144123826648[/C][/ROW]
[ROW][C]27[/C][C]0.956125246664649[/C][C]0.0877495066707016[/C][C]0.0438747533353508[/C][/ROW]
[ROW][C]28[/C][C]0.961471332040163[/C][C]0.0770573359196741[/C][C]0.0385286679598371[/C][/ROW]
[ROW][C]29[/C][C]0.945959025325276[/C][C]0.108081949349449[/C][C]0.0540409746747243[/C][/ROW]
[ROW][C]30[/C][C]0.928369414022141[/C][C]0.143261171955718[/C][C]0.0716305859778592[/C][/ROW]
[ROW][C]31[/C][C]0.913797091116219[/C][C]0.172405817767562[/C][C]0.0862029088837812[/C][/ROW]
[ROW][C]32[/C][C]0.927396463439682[/C][C]0.145207073120636[/C][C]0.0726035365603179[/C][/ROW]
[ROW][C]33[/C][C]0.903816115702082[/C][C]0.192367768595837[/C][C]0.0961838842979183[/C][/ROW]
[ROW][C]34[/C][C]0.875350981071603[/C][C]0.249298037856794[/C][C]0.124649018928397[/C][/ROW]
[ROW][C]35[/C][C]0.862921541847026[/C][C]0.274156916305948[/C][C]0.137078458152974[/C][/ROW]
[ROW][C]36[/C][C]0.828870952683345[/C][C]0.34225809463331[/C][C]0.171129047316655[/C][/ROW]
[ROW][C]37[/C][C]0.864445688901851[/C][C]0.271108622196297[/C][C]0.135554311098149[/C][/ROW]
[ROW][C]38[/C][C]0.856451719202525[/C][C]0.287096561594951[/C][C]0.143548280797475[/C][/ROW]
[ROW][C]39[/C][C]0.845621996961549[/C][C]0.308756006076902[/C][C]0.154378003038451[/C][/ROW]
[ROW][C]40[/C][C]0.827914461288637[/C][C]0.344171077422726[/C][C]0.172085538711363[/C][/ROW]
[ROW][C]41[/C][C]0.804980765905717[/C][C]0.390038468188567[/C][C]0.195019234094283[/C][/ROW]
[ROW][C]42[/C][C]0.789308709242765[/C][C]0.42138258151447[/C][C]0.210691290757235[/C][/ROW]
[ROW][C]43[/C][C]0.787157057566282[/C][C]0.425685884867436[/C][C]0.212842942433718[/C][/ROW]
[ROW][C]44[/C][C]0.747258306654019[/C][C]0.505483386691962[/C][C]0.252741693345981[/C][/ROW]
[ROW][C]45[/C][C]0.70504738488512[/C][C]0.58990523022976[/C][C]0.29495261511488[/C][/ROW]
[ROW][C]46[/C][C]0.764195548543416[/C][C]0.471608902913168[/C][C]0.235804451456584[/C][/ROW]
[ROW][C]47[/C][C]0.775132257372917[/C][C]0.449735485254165[/C][C]0.224867742627083[/C][/ROW]
[ROW][C]48[/C][C]0.733637771795377[/C][C]0.532724456409246[/C][C]0.266362228204623[/C][/ROW]
[ROW][C]49[/C][C]0.696832210301557[/C][C]0.606335579396886[/C][C]0.303167789698443[/C][/ROW]
[ROW][C]50[/C][C]0.68616757259731[/C][C]0.627664854805381[/C][C]0.31383242740269[/C][/ROW]
[ROW][C]51[/C][C]0.698956578085468[/C][C]0.602086843829065[/C][C]0.301043421914532[/C][/ROW]
[ROW][C]52[/C][C]0.652735074969825[/C][C]0.694529850060349[/C][C]0.347264925030175[/C][/ROW]
[ROW][C]53[/C][C]0.690995353975701[/C][C]0.618009292048597[/C][C]0.309004646024299[/C][/ROW]
[ROW][C]54[/C][C]0.668678889443293[/C][C]0.662642221113413[/C][C]0.331321110556707[/C][/ROW]
[ROW][C]55[/C][C]0.737231458483857[/C][C]0.525537083032286[/C][C]0.262768541516143[/C][/ROW]
[ROW][C]56[/C][C]0.865464049648537[/C][C]0.269071900702926[/C][C]0.134535950351463[/C][/ROW]
[ROW][C]57[/C][C]0.842699945569334[/C][C]0.314600108861333[/C][C]0.157300054430666[/C][/ROW]
[ROW][C]58[/C][C]0.812490177190135[/C][C]0.375019645619729[/C][C]0.187509822809865[/C][/ROW]
[ROW][C]59[/C][C]0.778111596464927[/C][C]0.443776807070146[/C][C]0.221888403535073[/C][/ROW]
[ROW][C]60[/C][C]0.760676413323626[/C][C]0.478647173352748[/C][C]0.239323586676374[/C][/ROW]
[ROW][C]61[/C][C]0.772083968201808[/C][C]0.455832063596384[/C][C]0.227916031798192[/C][/ROW]
[ROW][C]62[/C][C]0.736922748432454[/C][C]0.526154503135093[/C][C]0.263077251567546[/C][/ROW]
[ROW][C]63[/C][C]0.75666808001651[/C][C]0.486663839966979[/C][C]0.24333191998349[/C][/ROW]
[ROW][C]64[/C][C]0.724054428873525[/C][C]0.55189114225295[/C][C]0.275945571126475[/C][/ROW]
[ROW][C]65[/C][C]0.714717847850872[/C][C]0.570564304298255[/C][C]0.285282152149128[/C][/ROW]
[ROW][C]66[/C][C]0.673954679314943[/C][C]0.652090641370113[/C][C]0.326045320685057[/C][/ROW]
[ROW][C]67[/C][C]0.644504860890488[/C][C]0.710990278219025[/C][C]0.355495139109513[/C][/ROW]
[ROW][C]68[/C][C]0.63782327155393[/C][C]0.72435345689214[/C][C]0.36217672844607[/C][/ROW]
[ROW][C]69[/C][C]0.64533938646263[/C][C]0.70932122707474[/C][C]0.35466061353737[/C][/ROW]
[ROW][C]70[/C][C]0.606346790360928[/C][C]0.787306419278143[/C][C]0.393653209639072[/C][/ROW]
[ROW][C]71[/C][C]0.571429417645464[/C][C]0.857141164709071[/C][C]0.428570582354536[/C][/ROW]
[ROW][C]72[/C][C]0.530955727491892[/C][C]0.938088545016216[/C][C]0.469044272508108[/C][/ROW]
[ROW][C]73[/C][C]0.49968931702178[/C][C]0.99937863404356[/C][C]0.50031068297822[/C][/ROW]
[ROW][C]74[/C][C]0.4746691696201[/C][C]0.949338339240199[/C][C]0.5253308303799[/C][/ROW]
[ROW][C]75[/C][C]0.441350984567182[/C][C]0.882701969134364[/C][C]0.558649015432818[/C][/ROW]
[ROW][C]76[/C][C]0.507298291690651[/C][C]0.985403416618697[/C][C]0.492701708309349[/C][/ROW]
[ROW][C]77[/C][C]0.494300622843096[/C][C]0.988601245686192[/C][C]0.505699377156904[/C][/ROW]
[ROW][C]78[/C][C]0.451693795806628[/C][C]0.903387591613256[/C][C]0.548306204193372[/C][/ROW]
[ROW][C]79[/C][C]0.449247550128827[/C][C]0.898495100257653[/C][C]0.550752449871174[/C][/ROW]
[ROW][C]80[/C][C]0.414695567649173[/C][C]0.829391135298347[/C][C]0.585304432350827[/C][/ROW]
[ROW][C]81[/C][C]0.373384728902436[/C][C]0.746769457804871[/C][C]0.626615271097564[/C][/ROW]
[ROW][C]82[/C][C]0.381001229520036[/C][C]0.762002459040073[/C][C]0.618998770479964[/C][/ROW]
[ROW][C]83[/C][C]0.345554340157794[/C][C]0.691108680315588[/C][C]0.654445659842206[/C][/ROW]
[ROW][C]84[/C][C]0.312971208708854[/C][C]0.625942417417709[/C][C]0.687028791291146[/C][/ROW]
[ROW][C]85[/C][C]0.274956035275747[/C][C]0.549912070551494[/C][C]0.725043964724253[/C][/ROW]
[ROW][C]86[/C][C]0.271606223590445[/C][C]0.543212447180889[/C][C]0.728393776409555[/C][/ROW]
[ROW][C]87[/C][C]0.275994255292054[/C][C]0.551988510584108[/C][C]0.724005744707946[/C][/ROW]
[ROW][C]88[/C][C]0.238392913195486[/C][C]0.476785826390972[/C][C]0.761607086804514[/C][/ROW]
[ROW][C]89[/C][C]0.204077103379779[/C][C]0.408154206759558[/C][C]0.795922896620221[/C][/ROW]
[ROW][C]90[/C][C]0.177421601870831[/C][C]0.354843203741663[/C][C]0.822578398129169[/C][/ROW]
[ROW][C]91[/C][C]0.155735516998371[/C][C]0.311471033996743[/C][C]0.844264483001629[/C][/ROW]
[ROW][C]92[/C][C]0.207450187128076[/C][C]0.414900374256151[/C][C]0.792549812871924[/C][/ROW]
[ROW][C]93[/C][C]0.180946201928487[/C][C]0.361892403856974[/C][C]0.819053798071513[/C][/ROW]
[ROW][C]94[/C][C]0.160516645769011[/C][C]0.321033291538021[/C][C]0.839483354230989[/C][/ROW]
[ROW][C]95[/C][C]0.149578891277759[/C][C]0.299157782555517[/C][C]0.850421108722241[/C][/ROW]
[ROW][C]96[/C][C]0.144816020572235[/C][C]0.28963204114447[/C][C]0.855183979427765[/C][/ROW]
[ROW][C]97[/C][C]0.131722363804743[/C][C]0.263444727609485[/C][C]0.868277636195257[/C][/ROW]
[ROW][C]98[/C][C]0.173340788614285[/C][C]0.34668157722857[/C][C]0.826659211385715[/C][/ROW]
[ROW][C]99[/C][C]0.145289871161245[/C][C]0.290579742322489[/C][C]0.854710128838755[/C][/ROW]
[ROW][C]100[/C][C]0.125626005980829[/C][C]0.251252011961658[/C][C]0.874373994019171[/C][/ROW]
[ROW][C]101[/C][C]0.146963175056644[/C][C]0.293926350113288[/C][C]0.853036824943356[/C][/ROW]
[ROW][C]102[/C][C]0.124772325145776[/C][C]0.249544650291552[/C][C]0.875227674854224[/C][/ROW]
[ROW][C]103[/C][C]0.117616299285759[/C][C]0.235232598571517[/C][C]0.882383700714241[/C][/ROW]
[ROW][C]104[/C][C]0.0980244634485299[/C][C]0.19604892689706[/C][C]0.90197553655147[/C][/ROW]
[ROW][C]105[/C][C]0.0824712397563194[/C][C]0.164942479512639[/C][C]0.917528760243681[/C][/ROW]
[ROW][C]106[/C][C]0.078606125280277[/C][C]0.157212250560554[/C][C]0.921393874719723[/C][/ROW]
[ROW][C]107[/C][C]0.0628440992553815[/C][C]0.125688198510763[/C][C]0.937155900744619[/C][/ROW]
[ROW][C]108[/C][C]0.0540234773654035[/C][C]0.108046954730807[/C][C]0.945976522634597[/C][/ROW]
[ROW][C]109[/C][C]0.0440561245514528[/C][C]0.0881122491029057[/C][C]0.955943875448547[/C][/ROW]
[ROW][C]110[/C][C]0.0485770203841231[/C][C]0.0971540407682463[/C][C]0.951422979615877[/C][/ROW]
[ROW][C]111[/C][C]0.0378205195678067[/C][C]0.0756410391356134[/C][C]0.962179480432193[/C][/ROW]
[ROW][C]112[/C][C]0.0387603210028016[/C][C]0.0775206420056032[/C][C]0.961239678997198[/C][/ROW]
[ROW][C]113[/C][C]0.0352945003805249[/C][C]0.0705890007610499[/C][C]0.964705499619475[/C][/ROW]
[ROW][C]114[/C][C]0.0277294614109625[/C][C]0.055458922821925[/C][C]0.972270538589037[/C][/ROW]
[ROW][C]115[/C][C]0.0220145041163964[/C][C]0.0440290082327929[/C][C]0.977985495883604[/C][/ROW]
[ROW][C]116[/C][C]0.016602928294836[/C][C]0.033205856589672[/C][C]0.983397071705164[/C][/ROW]
[ROW][C]117[/C][C]0.0249652313516042[/C][C]0.0499304627032084[/C][C]0.975034768648396[/C][/ROW]
[ROW][C]118[/C][C]0.0295928988573082[/C][C]0.0591857977146164[/C][C]0.970407101142692[/C][/ROW]
[ROW][C]119[/C][C]0.0228336228535683[/C][C]0.0456672457071365[/C][C]0.977166377146432[/C][/ROW]
[ROW][C]120[/C][C]0.0170415813787579[/C][C]0.0340831627575158[/C][C]0.982958418621242[/C][/ROW]
[ROW][C]121[/C][C]0.0137165221503505[/C][C]0.0274330443007011[/C][C]0.986283477849649[/C][/ROW]
[ROW][C]122[/C][C]0.0110564918256369[/C][C]0.0221129836512738[/C][C]0.988943508174363[/C][/ROW]
[ROW][C]123[/C][C]0.0125640578005322[/C][C]0.0251281156010645[/C][C]0.987435942199468[/C][/ROW]
[ROW][C]124[/C][C]0.0193978470598504[/C][C]0.0387956941197007[/C][C]0.98060215294015[/C][/ROW]
[ROW][C]125[/C][C]0.0202755577695156[/C][C]0.0405511155390312[/C][C]0.979724442230484[/C][/ROW]
[ROW][C]126[/C][C]0.0459962232532232[/C][C]0.0919924465064464[/C][C]0.954003776746777[/C][/ROW]
[ROW][C]127[/C][C]0.0351069142904562[/C][C]0.0702138285809123[/C][C]0.964893085709544[/C][/ROW]
[ROW][C]128[/C][C]0.0268369919944631[/C][C]0.0536739839889263[/C][C]0.973163008005537[/C][/ROW]
[ROW][C]129[/C][C]0.0226783813424425[/C][C]0.045356762684885[/C][C]0.977321618657558[/C][/ROW]
[ROW][C]130[/C][C]0.0215140841819883[/C][C]0.0430281683639766[/C][C]0.978485915818012[/C][/ROW]
[ROW][C]131[/C][C]0.017562208862449[/C][C]0.035124417724898[/C][C]0.982437791137551[/C][/ROW]
[ROW][C]132[/C][C]0.0179515699660534[/C][C]0.0359031399321068[/C][C]0.982048430033947[/C][/ROW]
[ROW][C]133[/C][C]0.0288602072541646[/C][C]0.0577204145083291[/C][C]0.971139792745835[/C][/ROW]
[ROW][C]134[/C][C]0.0339452766640848[/C][C]0.0678905533281697[/C][C]0.966054723335915[/C][/ROW]
[ROW][C]135[/C][C]0.0369644491796108[/C][C]0.0739288983592216[/C][C]0.963035550820389[/C][/ROW]
[ROW][C]136[/C][C]0.0304408638695254[/C][C]0.0608817277390507[/C][C]0.969559136130475[/C][/ROW]
[ROW][C]137[/C][C]0.0294051976149356[/C][C]0.0588103952298712[/C][C]0.970594802385064[/C][/ROW]
[ROW][C]138[/C][C]0.0210863275139565[/C][C]0.042172655027913[/C][C]0.978913672486044[/C][/ROW]
[ROW][C]139[/C][C]0.0190006153355417[/C][C]0.0380012306710835[/C][C]0.980999384664458[/C][/ROW]
[ROW][C]140[/C][C]0.0130849165735261[/C][C]0.0261698331470522[/C][C]0.986915083426474[/C][/ROW]
[ROW][C]141[/C][C]0.0463935823672738[/C][C]0.0927871647345476[/C][C]0.953606417632726[/C][/ROW]
[ROW][C]142[/C][C]0.0469675019912528[/C][C]0.0939350039825056[/C][C]0.953032498008747[/C][/ROW]
[ROW][C]143[/C][C]0.0329515209402958[/C][C]0.0659030418805916[/C][C]0.967048479059704[/C][/ROW]
[ROW][C]144[/C][C]0.32517992730501[/C][C]0.65035985461002[/C][C]0.67482007269499[/C][/ROW]
[ROW][C]145[/C][C]0.341841147964175[/C][C]0.68368229592835[/C][C]0.658158852035825[/C][/ROW]
[ROW][C]146[/C][C]0.596936024246479[/C][C]0.806127951507043[/C][C]0.403063975753521[/C][/ROW]
[ROW][C]147[/C][C]0.585125696585371[/C][C]0.829748606829258[/C][C]0.414874303414629[/C][/ROW]
[ROW][C]148[/C][C]0.868836699349832[/C][C]0.262326601300336[/C][C]0.131163300650168[/C][/ROW]
[ROW][C]149[/C][C]0.8680239101508[/C][C]0.2639521796984[/C][C]0.1319760898492[/C][/ROW]
[ROW][C]150[/C][C]0.770891066692436[/C][C]0.458217866615128[/C][C]0.229108933307564[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204000&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204000&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.9999060111163570.0001879777672869569.39888836434778e-05
130.9997382286464990.0005235427070024810.000261771353501241
140.9995232041636530.0009535916726942510.000476795836347126
150.9994258046000050.001148390799990020.000574195399995009
160.9988926322023010.002214735595397470.00110736779769873
170.9978241903405680.004351619318863170.00217580965943158
180.9971664680623940.005667063875212530.00283353193760627
190.9952834872399940.009433025520012540.00471651276000627
200.9916891968071610.01662160638567740.00831080319283869
210.9866333045858740.02673339082825230.0133666954141262
220.9802984721162870.03940305576742550.0197015278837127
230.9697863116135710.06042737677285830.0302136883864292
240.9558010600308230.08839787993835390.0441989399691769
250.9534815390264630.09303692194707320.0465184609735366
260.9562855876173350.08742882476532970.0437144123826648
270.9561252466646490.08774950667070160.0438747533353508
280.9614713320401630.07705733591967410.0385286679598371
290.9459590253252760.1080819493494490.0540409746747243
300.9283694140221410.1432611719557180.0716305859778592
310.9137970911162190.1724058177675620.0862029088837812
320.9273964634396820.1452070731206360.0726035365603179
330.9038161157020820.1923677685958370.0961838842979183
340.8753509810716030.2492980378567940.124649018928397
350.8629215418470260.2741569163059480.137078458152974
360.8288709526833450.342258094633310.171129047316655
370.8644456889018510.2711086221962970.135554311098149
380.8564517192025250.2870965615949510.143548280797475
390.8456219969615490.3087560060769020.154378003038451
400.8279144612886370.3441710774227260.172085538711363
410.8049807659057170.3900384681885670.195019234094283
420.7893087092427650.421382581514470.210691290757235
430.7871570575662820.4256858848674360.212842942433718
440.7472583066540190.5054833866919620.252741693345981
450.705047384885120.589905230229760.29495261511488
460.7641955485434160.4716089029131680.235804451456584
470.7751322573729170.4497354852541650.224867742627083
480.7336377717953770.5327244564092460.266362228204623
490.6968322103015570.6063355793968860.303167789698443
500.686167572597310.6276648548053810.31383242740269
510.6989565780854680.6020868438290650.301043421914532
520.6527350749698250.6945298500603490.347264925030175
530.6909953539757010.6180092920485970.309004646024299
540.6686788894432930.6626422211134130.331321110556707
550.7372314584838570.5255370830322860.262768541516143
560.8654640496485370.2690719007029260.134535950351463
570.8426999455693340.3146001088613330.157300054430666
580.8124901771901350.3750196456197290.187509822809865
590.7781115964649270.4437768070701460.221888403535073
600.7606764133236260.4786471733527480.239323586676374
610.7720839682018080.4558320635963840.227916031798192
620.7369227484324540.5261545031350930.263077251567546
630.756668080016510.4866638399669790.24333191998349
640.7240544288735250.551891142252950.275945571126475
650.7147178478508720.5705643042982550.285282152149128
660.6739546793149430.6520906413701130.326045320685057
670.6445048608904880.7109902782190250.355495139109513
680.637823271553930.724353456892140.36217672844607
690.645339386462630.709321227074740.35466061353737
700.6063467903609280.7873064192781430.393653209639072
710.5714294176454640.8571411647090710.428570582354536
720.5309557274918920.9380885450162160.469044272508108
730.499689317021780.999378634043560.50031068297822
740.47466916962010.9493383392401990.5253308303799
750.4413509845671820.8827019691343640.558649015432818
760.5072982916906510.9854034166186970.492701708309349
770.4943006228430960.9886012456861920.505699377156904
780.4516937958066280.9033875916132560.548306204193372
790.4492475501288270.8984951002576530.550752449871174
800.4146955676491730.8293911352983470.585304432350827
810.3733847289024360.7467694578048710.626615271097564
820.3810012295200360.7620024590400730.618998770479964
830.3455543401577940.6911086803155880.654445659842206
840.3129712087088540.6259424174177090.687028791291146
850.2749560352757470.5499120705514940.725043964724253
860.2716062235904450.5432124471808890.728393776409555
870.2759942552920540.5519885105841080.724005744707946
880.2383929131954860.4767858263909720.761607086804514
890.2040771033797790.4081542067595580.795922896620221
900.1774216018708310.3548432037416630.822578398129169
910.1557355169983710.3114710339967430.844264483001629
920.2074501871280760.4149003742561510.792549812871924
930.1809462019284870.3618924038569740.819053798071513
940.1605166457690110.3210332915380210.839483354230989
950.1495788912777590.2991577825555170.850421108722241
960.1448160205722350.289632041144470.855183979427765
970.1317223638047430.2634447276094850.868277636195257
980.1733407886142850.346681577228570.826659211385715
990.1452898711612450.2905797423224890.854710128838755
1000.1256260059808290.2512520119616580.874373994019171
1010.1469631750566440.2939263501132880.853036824943356
1020.1247723251457760.2495446502915520.875227674854224
1030.1176162992857590.2352325985715170.882383700714241
1040.09802446344852990.196048926897060.90197553655147
1050.08247123975631940.1649424795126390.917528760243681
1060.0786061252802770.1572122505605540.921393874719723
1070.06284409925538150.1256881985107630.937155900744619
1080.05402347736540350.1080469547308070.945976522634597
1090.04405612455145280.08811224910290570.955943875448547
1100.04857702038412310.09715404076824630.951422979615877
1110.03782051956780670.07564103913561340.962179480432193
1120.03876032100280160.07752064200560320.961239678997198
1130.03529450038052490.07058900076104990.964705499619475
1140.02772946141096250.0554589228219250.972270538589037
1150.02201450411639640.04402900823279290.977985495883604
1160.0166029282948360.0332058565896720.983397071705164
1170.02496523135160420.04993046270320840.975034768648396
1180.02959289885730820.05918579771461640.970407101142692
1190.02283362285356830.04566724570713650.977166377146432
1200.01704158137875790.03408316275751580.982958418621242
1210.01371652215035050.02743304430070110.986283477849649
1220.01105649182563690.02211298365127380.988943508174363
1230.01256405780053220.02512811560106450.987435942199468
1240.01939784705985040.03879569411970070.98060215294015
1250.02027555776951560.04055111553903120.979724442230484
1260.04599622325322320.09199244650644640.954003776746777
1270.03510691429045620.07021382858091230.964893085709544
1280.02683699199446310.05367398398892630.973163008005537
1290.02267838134244250.0453567626848850.977321618657558
1300.02151408418198830.04302816836397660.978485915818012
1310.0175622088624490.0351244177248980.982437791137551
1320.01795156996605340.03590313993210680.982048430033947
1330.02886020725416460.05772041450832910.971139792745835
1340.03394527666408480.06789055332816970.966054723335915
1350.03696444917961080.07392889835922160.963035550820389
1360.03044086386952540.06088172773905070.969559136130475
1370.02940519761493560.05881039522987120.970594802385064
1380.02108632751395650.0421726550279130.978913672486044
1390.01900061533554170.03800123067108350.980999384664458
1400.01308491657352610.02616983314705220.986915083426474
1410.04639358236727380.09278716473454760.953606417632726
1420.04696750199125280.09393500398250560.953032498008747
1430.03295152094029580.06590304188059160.967048479059704
1440.325179927305010.650359854610020.67482007269499
1450.3418411479641750.683682295928350.658158852035825
1460.5969360242464790.8061279515070430.403063975753521
1470.5851256965853710.8297486068292580.414874303414629
1480.8688366993498320.2623266013003360.131163300650168
1490.86802391015080.26395217969840.1319760898492
1500.7708910666924360.4582178666151280.229108933307564







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0575539568345324NOK
5% type I error level280.201438848920863NOK
10% type I error level520.37410071942446NOK

\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 & 8 & 0.0575539568345324 & NOK \tabularnewline
5% type I error level & 28 & 0.201438848920863 & NOK \tabularnewline
10% type I error level & 52 & 0.37410071942446 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204000&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]8[/C][C]0.0575539568345324[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]28[/C][C]0.201438848920863[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]52[/C][C]0.37410071942446[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204000&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204000&T=6

As an alternative you can also use a 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 level80.0575539568345324NOK
5% type I error level280.201438848920863NOK
10% type I error level520.37410071942446NOK



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