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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 computationMon, 05 Nov 2012 16:15:28 -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/Nov/05/t13521501638zbykn4yew1294b.htm/, Retrieved Sun, 05 Feb 2023 23:57:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186301, Retrieved Sun, 05 Feb 2023 23:57:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [WS 7] [2012-11-05 21:15:28] [4e0a07d67ff6ab1ee99ce2372e43edac] [Current]
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Dataseries X:
41	38	13	12	14	12	53	32
39	32	16	11	18	11	86	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	6	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	7	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	9	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	28	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	7	7	16	8	71	44
29	31	10	6	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	12	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	32	14	6	15	12	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	77	45
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
37	38	20	12	16	10	30	0
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	10	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	82	49
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	70	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
33	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	82	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	8	9	13	12	69	41
32	34	16	8	15	10	54	33
30	34	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
35	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	35	12	8	12	18	84	50




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186301&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186301&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186301&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 time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.50659192614105 + 0.114210321866415Connected[t] -0.0210540558574558Separate[t] + 0.543771350648278Software[t] + 0.0596331308591448Happiness[t] -0.071570896534173Depression[t] + 0.0365925912634606Belonging[t] -0.0535457450767283Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.50659192614105 +  0.114210321866415Connected[t] -0.0210540558574558Separate[t] +  0.543771350648278Software[t] +  0.0596331308591448Happiness[t] -0.071570896534173Depression[t] +  0.0365925912634606Belonging[t] -0.0535457450767283Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186301&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.50659192614105 +  0.114210321866415Connected[t] -0.0210540558574558Separate[t] +  0.543771350648278Software[t] +  0.0596331308591448Happiness[t] -0.071570896534173Depression[t] +  0.0365925912634606Belonging[t] -0.0535457450767283Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186301&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186301&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
Learning[t] = + 5.50659192614105 + 0.114210321866415Connected[t] -0.0210540558574558Separate[t] + 0.543771350648278Software[t] + 0.0596331308591448Happiness[t] -0.071570896534173Depression[t] + 0.0365925912634606Belonging[t] -0.0535457450767283Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.506591926141052.6051652.11370.0361610.018081
Connected0.1142103218664150.0470472.42760.0163610.00818
Separate-0.02105405585745580.045009-0.46780.6406150.320308
Software0.5437713506482780.0695247.821300
Happiness0.05963313085914480.0766240.77830.4376210.218811
Depression-0.0715708965341730.056568-1.26520.207720.10386
Belonging0.03659259126346060.0449320.81440.416680.20834
Belonging_Final-0.05354574507672830.064611-0.82870.4085440.204272

\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) & 5.50659192614105 & 2.605165 & 2.1137 & 0.036161 & 0.018081 \tabularnewline
Connected & 0.114210321866415 & 0.047047 & 2.4276 & 0.016361 & 0.00818 \tabularnewline
Separate & -0.0210540558574558 & 0.045009 & -0.4678 & 0.640615 & 0.320308 \tabularnewline
Software & 0.543771350648278 & 0.069524 & 7.8213 & 0 & 0 \tabularnewline
Happiness & 0.0596331308591448 & 0.076624 & 0.7783 & 0.437621 & 0.218811 \tabularnewline
Depression & -0.071570896534173 & 0.056568 & -1.2652 & 0.20772 & 0.10386 \tabularnewline
Belonging & 0.0365925912634606 & 0.044932 & 0.8144 & 0.41668 & 0.20834 \tabularnewline
Belonging_Final & -0.0535457450767283 & 0.064611 & -0.8287 & 0.408544 & 0.204272 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186301&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]5.50659192614105[/C][C]2.605165[/C][C]2.1137[/C][C]0.036161[/C][C]0.018081[/C][/ROW]
[ROW][C]Connected[/C][C]0.114210321866415[/C][C]0.047047[/C][C]2.4276[/C][C]0.016361[/C][C]0.00818[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0210540558574558[/C][C]0.045009[/C][C]-0.4678[/C][C]0.640615[/C][C]0.320308[/C][/ROW]
[ROW][C]Software[/C][C]0.543771350648278[/C][C]0.069524[/C][C]7.8213[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0596331308591448[/C][C]0.076624[/C][C]0.7783[/C][C]0.437621[/C][C]0.218811[/C][/ROW]
[ROW][C]Depression[/C][C]-0.071570896534173[/C][C]0.056568[/C][C]-1.2652[/C][C]0.20772[/C][C]0.10386[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0365925912634606[/C][C]0.044932[/C][C]0.8144[/C][C]0.41668[/C][C]0.20834[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0535457450767283[/C][C]0.064611[/C][C]-0.8287[/C][C]0.408544[/C][C]0.204272[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186301&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186301&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)5.506591926141052.6051652.11370.0361610.018081
Connected0.1142103218664150.0470472.42760.0163610.00818
Separate-0.02105405585745580.045009-0.46780.6406150.320308
Software0.5437713506482780.0695247.821300
Happiness0.05963313085914480.0766240.77830.4376210.218811
Depression-0.0715708965341730.056568-1.26520.207720.10386
Belonging0.03659259126346060.0449320.81440.416680.20834
Belonging_Final-0.05354574507672830.064611-0.82870.4085440.204272







Multiple Linear Regression - Regression Statistics
Multiple R0.594824581821767
R-squared0.35381628313944
Adjusted R-squared0.324252322237322
F-TEST (value)11.9678240784746
F-TEST (DF numerator)7
F-TEST (DF denominator)153
p-value3.9993564016072e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85617046327028
Sum Squared Residuals527.141424673702

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.594824581821767 \tabularnewline
R-squared & 0.35381628313944 \tabularnewline
Adjusted R-squared & 0.324252322237322 \tabularnewline
F-TEST (value) & 11.9678240784746 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 3.9993564016072e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85617046327028 \tabularnewline
Sum Squared Residuals & 527.141424673702 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186301&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.594824581821767[/C][/ROW]
[ROW][C]R-squared[/C][C]0.35381628313944[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.324252322237322[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.9678240784746[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]3.9993564016072e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85617046327028[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]527.141424673702[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186301&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186301&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 R0.594824581821767
R-squared0.35381628313944
Adjusted R-squared0.324252322237322
F-TEST (value)11.9678240784746
F-TEST (DF numerator)7
F-TEST (DF denominator)153
p-value3.9993564016072e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85617046327028
Sum Squared Residuals527.141424673702







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1163737759861-3.11637377598614
21615.97079589195690.0292041080431209
31916.17274150498472.8272584950153
41511.72803104299923.2719689570008
51415.4488442903231-1.4488442903231
61314.8832167750804-1.88321677508036
71915.05189383253013.94810616746987
81516.5958455302759-1.59584553027593
91415.8836089886891-1.88360898868913
101512.42341004186452.57658995813555
111615.19674939228390.803250607716097
121616.197034476703-0.197034476703035
131615.36049859184130.639501408158673
141615.30335309644170.696646903558294
151717.3564210817084-0.356421081708398
161515.0193893414177-0.0193893414177254
171514.54990432923260.450095670767438
182016.12694740203163.8730525979684
191815.40793037288092.59206962711911
201615.19468796426420.805312035735817
211615.24015879466930.759841205330719
221614.70594769070961.29405230929043
231916.41477132908832.58522867091168
241614.67434807563481.32565192436515
251714.77007431472452.22992568527555
261716.88783028504850.112169714951543
271614.96440519439791.03559480560208
281516.0395077879009-1.03950778790094
291615.44317291372190.556827086278057
301413.95390614856810.0460938514319424
311515.5965123650264-0.596512365026409
321212.177369228133-0.17736922813302
331415.1110728107702-1.11107281077017
341615.42552354365130.574476456348738
351415.6467500390121-1.6467500390121
36712.8965619549108-5.89656195491083
371010.8580645613835-0.858064561383539
381415.7798890236988-1.77988902369877
391614.2197159569261.78028404307403
401614.58237209443571.41762790556434
411615.04500967336550.954990326634475
421415.5607843377338-1.56078433773385
432017.91997789151822.08002210848182
441414.203384521805-0.203384521805004
451414.8990693119289-0.899069311928865
461115.5927472753193-4.59274727531932
471416.4209138249569-2.42091382495689
481514.92973667795160.070263322048382
491615.19872448474610.80127551525391
501416.0209472330053-2.02094723300532
511616.4911419283566-0.491141928356606
521414.1101591487578-0.11015914875777
531214.6836235739442-2.68362357394417
541615.17795770907980.822042290920176
55911.5124533269388-2.51245332693877
561412.62130527354341.37869472645664
571616.0922144514025-0.0922144514024526
581615.08125078440180.91874921559819
591515.3245934103624-0.324593410362369
601614.12687727123491.87312272876507
611211.42137808336050.578621916639511
621615.98045340728320.0195465927168422
631616.4708345035383-0.470834503538308
641414.367472734991-0.367472734990999
651615.43538899443510.564611005564872
661715.90669792221.09330207779997
671816.09951939593241.90048060406761
681814.67855735709973.32144264290032
691215.8785586970376-3.8785586970376
701615.4186812478840.581318752116011
711013.4389419597422-3.43894195974223
721414.3180996249762-0.318099624976213
731816.53931303683451.46068696316545
741817.15801376764010.841986232359885
751615.73098082081440.269019179185556
761713.88253128751973.11746871248033
771616.3647572467043-0.364757246704292
781614.44595346903981.55404653096023
791314.842116231222-1.84211623122204
801616.0292535078826-0.0292535078825983
811615.57777028193880.422229718061173
822016.79377478670283.20622521329717
831615.68908367785160.31091632214841
841516.0269176589512-1.02691765895125
851514.74126917591410.258730824085895
861614.26599200839841.73400799160159
871414.2183792587082-0.218379258708241
881615.33693143393270.663068566067272
891614.5808752744591.41912472554104
901514.20789145462120.792108545378828
911213.4746045073303-1.47460450733034
921717.0116615794687-0.0116615794686995
931615.48459367078910.51540632921089
941515.2887619735731-0.288761973573146
951315.1826048106205-2.18260481062052
961615.21816948888660.781830511113362
971615.85739442325750.1426055767425
981614.18392143089151.8160785691085
991616.1620796352112-0.162079635211153
1001414.3378103815196-0.337810381519647
1011617.3709697221063-1.37096972210627
1021614.80913059686131.19086940313867
1032017.41700455507082.58299544492918
1041514.58816782672910.411832173270873
1051614.55943653597061.44056346402941
1061315.3963030224909-2.39630302249088
1071716.06618555912040.933814440879608
1081615.93833271174940.0616672882505854
1091614.59451461672581.40548538327421
1101212.2952815125103-0.295281512510332
1111615.01167527701220.988324722987829
1121615.89936693133780.100633068662198
1131714.54267368409922.45732631590085
1141315.0108388958843-2.01083889588427
1151214.933181956999-2.93318195699899
1161816.6751310941831.32486890581697
1171415.5442594931066-1.54425949310657
1181413.00766222263710.992337777362873
1191314.9868187170295-1.98681871702951
1201615.72351197248260.276488027517423
1211314.3290418011299-1.32904180112992
1221615.69848389731930.301516102680669
1231316.0232196757193-3.02321967571931
1241617.1314353297215-1.13143532972154
1251516.0608021093703-1.06080210937035
1261616.8710138509282-0.871013850928179
1271515.5142914847989-0.514291484798904
1281716.34211599633740.657884003662637
1291513.88335372042041.1166462795796
1301215.0333833008366-3.03338330083663
1311614.24135541482921.75864458517084
1321013.3907974754613-3.39079747546125
1331614.17948879056041.82051120943958
1341213.997755941872-1.99775594187202
1351415.7379696048381-1.7379696048381
1361515.1908163505845-0.190816350584511
1371312.35405802523060.645941974769403
1381514.52361900864430.476380991355701
1391113.2464520790505-2.24645207905053
1401213.4455992363019-1.44559923630192
141813.4500552966152-5.45005529661519
1421613.18343347013932.81656652986066
1431513.40608804829991.59391195170005
1441716.63763132005570.362368679944322
1451614.93452493464351.06547506535645
1461014.2510747495011-4.25107474950109
1471816.02155652889571.97844347110435
1481315.2718336576412-2.27183365764122
1491615.24356048172840.756439518271586
1501313.1091715921476-0.10917159214763
1511013.1996005368258-3.19960053682578
1521516.5261430875273-1.52614308752733
1531614.19632135934891.80367864065106
1541612.08087371640723.91912628359275
1551412.53755899996081.46244100003922
1561012.5833713045284-2.58337130452839
1571717.0116615794687-0.0116615794686995
1581311.70024330245921.2997566975408
1591513.88335372042041.1166462795796
1601615.17153523887050.828464761129495
1611212.5984129210305-0.5984129210305

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.1163737759861 & -3.11637377598614 \tabularnewline
2 & 16 & 15.9707958919569 & 0.0292041080431209 \tabularnewline
3 & 19 & 16.1727415049847 & 2.8272584950153 \tabularnewline
4 & 15 & 11.7280310429992 & 3.2719689570008 \tabularnewline
5 & 14 & 15.4488442903231 & -1.4488442903231 \tabularnewline
6 & 13 & 14.8832167750804 & -1.88321677508036 \tabularnewline
7 & 19 & 15.0518938325301 & 3.94810616746987 \tabularnewline
8 & 15 & 16.5958455302759 & -1.59584553027593 \tabularnewline
9 & 14 & 15.8836089886891 & -1.88360898868913 \tabularnewline
10 & 15 & 12.4234100418645 & 2.57658995813555 \tabularnewline
11 & 16 & 15.1967493922839 & 0.803250607716097 \tabularnewline
12 & 16 & 16.197034476703 & -0.197034476703035 \tabularnewline
13 & 16 & 15.3604985918413 & 0.639501408158673 \tabularnewline
14 & 16 & 15.3033530964417 & 0.696646903558294 \tabularnewline
15 & 17 & 17.3564210817084 & -0.356421081708398 \tabularnewline
16 & 15 & 15.0193893414177 & -0.0193893414177254 \tabularnewline
17 & 15 & 14.5499043292326 & 0.450095670767438 \tabularnewline
18 & 20 & 16.1269474020316 & 3.8730525979684 \tabularnewline
19 & 18 & 15.4079303728809 & 2.59206962711911 \tabularnewline
20 & 16 & 15.1946879642642 & 0.805312035735817 \tabularnewline
21 & 16 & 15.2401587946693 & 0.759841205330719 \tabularnewline
22 & 16 & 14.7059476907096 & 1.29405230929043 \tabularnewline
23 & 19 & 16.4147713290883 & 2.58522867091168 \tabularnewline
24 & 16 & 14.6743480756348 & 1.32565192436515 \tabularnewline
25 & 17 & 14.7700743147245 & 2.22992568527555 \tabularnewline
26 & 17 & 16.8878302850485 & 0.112169714951543 \tabularnewline
27 & 16 & 14.9644051943979 & 1.03559480560208 \tabularnewline
28 & 15 & 16.0395077879009 & -1.03950778790094 \tabularnewline
29 & 16 & 15.4431729137219 & 0.556827086278057 \tabularnewline
30 & 14 & 13.9539061485681 & 0.0460938514319424 \tabularnewline
31 & 15 & 15.5965123650264 & -0.596512365026409 \tabularnewline
32 & 12 & 12.177369228133 & -0.17736922813302 \tabularnewline
33 & 14 & 15.1110728107702 & -1.11107281077017 \tabularnewline
34 & 16 & 15.4255235436513 & 0.574476456348738 \tabularnewline
35 & 14 & 15.6467500390121 & -1.6467500390121 \tabularnewline
36 & 7 & 12.8965619549108 & -5.89656195491083 \tabularnewline
37 & 10 & 10.8580645613835 & -0.858064561383539 \tabularnewline
38 & 14 & 15.7798890236988 & -1.77988902369877 \tabularnewline
39 & 16 & 14.219715956926 & 1.78028404307403 \tabularnewline
40 & 16 & 14.5823720944357 & 1.41762790556434 \tabularnewline
41 & 16 & 15.0450096733655 & 0.954990326634475 \tabularnewline
42 & 14 & 15.5607843377338 & -1.56078433773385 \tabularnewline
43 & 20 & 17.9199778915182 & 2.08002210848182 \tabularnewline
44 & 14 & 14.203384521805 & -0.203384521805004 \tabularnewline
45 & 14 & 14.8990693119289 & -0.899069311928865 \tabularnewline
46 & 11 & 15.5927472753193 & -4.59274727531932 \tabularnewline
47 & 14 & 16.4209138249569 & -2.42091382495689 \tabularnewline
48 & 15 & 14.9297366779516 & 0.070263322048382 \tabularnewline
49 & 16 & 15.1987244847461 & 0.80127551525391 \tabularnewline
50 & 14 & 16.0209472330053 & -2.02094723300532 \tabularnewline
51 & 16 & 16.4911419283566 & -0.491141928356606 \tabularnewline
52 & 14 & 14.1101591487578 & -0.11015914875777 \tabularnewline
53 & 12 & 14.6836235739442 & -2.68362357394417 \tabularnewline
54 & 16 & 15.1779577090798 & 0.822042290920176 \tabularnewline
55 & 9 & 11.5124533269388 & -2.51245332693877 \tabularnewline
56 & 14 & 12.6213052735434 & 1.37869472645664 \tabularnewline
57 & 16 & 16.0922144514025 & -0.0922144514024526 \tabularnewline
58 & 16 & 15.0812507844018 & 0.91874921559819 \tabularnewline
59 & 15 & 15.3245934103624 & -0.324593410362369 \tabularnewline
60 & 16 & 14.1268772712349 & 1.87312272876507 \tabularnewline
61 & 12 & 11.4213780833605 & 0.578621916639511 \tabularnewline
62 & 16 & 15.9804534072832 & 0.0195465927168422 \tabularnewline
63 & 16 & 16.4708345035383 & -0.470834503538308 \tabularnewline
64 & 14 & 14.367472734991 & -0.367472734990999 \tabularnewline
65 & 16 & 15.4353889944351 & 0.564611005564872 \tabularnewline
66 & 17 & 15.9066979222 & 1.09330207779997 \tabularnewline
67 & 18 & 16.0995193959324 & 1.90048060406761 \tabularnewline
68 & 18 & 14.6785573570997 & 3.32144264290032 \tabularnewline
69 & 12 & 15.8785586970376 & -3.8785586970376 \tabularnewline
70 & 16 & 15.418681247884 & 0.581318752116011 \tabularnewline
71 & 10 & 13.4389419597422 & -3.43894195974223 \tabularnewline
72 & 14 & 14.3180996249762 & -0.318099624976213 \tabularnewline
73 & 18 & 16.5393130368345 & 1.46068696316545 \tabularnewline
74 & 18 & 17.1580137676401 & 0.841986232359885 \tabularnewline
75 & 16 & 15.7309808208144 & 0.269019179185556 \tabularnewline
76 & 17 & 13.8825312875197 & 3.11746871248033 \tabularnewline
77 & 16 & 16.3647572467043 & -0.364757246704292 \tabularnewline
78 & 16 & 14.4459534690398 & 1.55404653096023 \tabularnewline
79 & 13 & 14.842116231222 & -1.84211623122204 \tabularnewline
80 & 16 & 16.0292535078826 & -0.0292535078825983 \tabularnewline
81 & 16 & 15.5777702819388 & 0.422229718061173 \tabularnewline
82 & 20 & 16.7937747867028 & 3.20622521329717 \tabularnewline
83 & 16 & 15.6890836778516 & 0.31091632214841 \tabularnewline
84 & 15 & 16.0269176589512 & -1.02691765895125 \tabularnewline
85 & 15 & 14.7412691759141 & 0.258730824085895 \tabularnewline
86 & 16 & 14.2659920083984 & 1.73400799160159 \tabularnewline
87 & 14 & 14.2183792587082 & -0.218379258708241 \tabularnewline
88 & 16 & 15.3369314339327 & 0.663068566067272 \tabularnewline
89 & 16 & 14.580875274459 & 1.41912472554104 \tabularnewline
90 & 15 & 14.2078914546212 & 0.792108545378828 \tabularnewline
91 & 12 & 13.4746045073303 & -1.47460450733034 \tabularnewline
92 & 17 & 17.0116615794687 & -0.0116615794686995 \tabularnewline
93 & 16 & 15.4845936707891 & 0.51540632921089 \tabularnewline
94 & 15 & 15.2887619735731 & -0.288761973573146 \tabularnewline
95 & 13 & 15.1826048106205 & -2.18260481062052 \tabularnewline
96 & 16 & 15.2181694888866 & 0.781830511113362 \tabularnewline
97 & 16 & 15.8573944232575 & 0.1426055767425 \tabularnewline
98 & 16 & 14.1839214308915 & 1.8160785691085 \tabularnewline
99 & 16 & 16.1620796352112 & -0.162079635211153 \tabularnewline
100 & 14 & 14.3378103815196 & -0.337810381519647 \tabularnewline
101 & 16 & 17.3709697221063 & -1.37096972210627 \tabularnewline
102 & 16 & 14.8091305968613 & 1.19086940313867 \tabularnewline
103 & 20 & 17.4170045550708 & 2.58299544492918 \tabularnewline
104 & 15 & 14.5881678267291 & 0.411832173270873 \tabularnewline
105 & 16 & 14.5594365359706 & 1.44056346402941 \tabularnewline
106 & 13 & 15.3963030224909 & -2.39630302249088 \tabularnewline
107 & 17 & 16.0661855591204 & 0.933814440879608 \tabularnewline
108 & 16 & 15.9383327117494 & 0.0616672882505854 \tabularnewline
109 & 16 & 14.5945146167258 & 1.40548538327421 \tabularnewline
110 & 12 & 12.2952815125103 & -0.295281512510332 \tabularnewline
111 & 16 & 15.0116752770122 & 0.988324722987829 \tabularnewline
112 & 16 & 15.8993669313378 & 0.100633068662198 \tabularnewline
113 & 17 & 14.5426736840992 & 2.45732631590085 \tabularnewline
114 & 13 & 15.0108388958843 & -2.01083889588427 \tabularnewline
115 & 12 & 14.933181956999 & -2.93318195699899 \tabularnewline
116 & 18 & 16.675131094183 & 1.32486890581697 \tabularnewline
117 & 14 & 15.5442594931066 & -1.54425949310657 \tabularnewline
118 & 14 & 13.0076622226371 & 0.992337777362873 \tabularnewline
119 & 13 & 14.9868187170295 & -1.98681871702951 \tabularnewline
120 & 16 & 15.7235119724826 & 0.276488027517423 \tabularnewline
121 & 13 & 14.3290418011299 & -1.32904180112992 \tabularnewline
122 & 16 & 15.6984838973193 & 0.301516102680669 \tabularnewline
123 & 13 & 16.0232196757193 & -3.02321967571931 \tabularnewline
124 & 16 & 17.1314353297215 & -1.13143532972154 \tabularnewline
125 & 15 & 16.0608021093703 & -1.06080210937035 \tabularnewline
126 & 16 & 16.8710138509282 & -0.871013850928179 \tabularnewline
127 & 15 & 15.5142914847989 & -0.514291484798904 \tabularnewline
128 & 17 & 16.3421159963374 & 0.657884003662637 \tabularnewline
129 & 15 & 13.8833537204204 & 1.1166462795796 \tabularnewline
130 & 12 & 15.0333833008366 & -3.03338330083663 \tabularnewline
131 & 16 & 14.2413554148292 & 1.75864458517084 \tabularnewline
132 & 10 & 13.3907974754613 & -3.39079747546125 \tabularnewline
133 & 16 & 14.1794887905604 & 1.82051120943958 \tabularnewline
134 & 12 & 13.997755941872 & -1.99775594187202 \tabularnewline
135 & 14 & 15.7379696048381 & -1.7379696048381 \tabularnewline
136 & 15 & 15.1908163505845 & -0.190816350584511 \tabularnewline
137 & 13 & 12.3540580252306 & 0.645941974769403 \tabularnewline
138 & 15 & 14.5236190086443 & 0.476380991355701 \tabularnewline
139 & 11 & 13.2464520790505 & -2.24645207905053 \tabularnewline
140 & 12 & 13.4455992363019 & -1.44559923630192 \tabularnewline
141 & 8 & 13.4500552966152 & -5.45005529661519 \tabularnewline
142 & 16 & 13.1834334701393 & 2.81656652986066 \tabularnewline
143 & 15 & 13.4060880482999 & 1.59391195170005 \tabularnewline
144 & 17 & 16.6376313200557 & 0.362368679944322 \tabularnewline
145 & 16 & 14.9345249346435 & 1.06547506535645 \tabularnewline
146 & 10 & 14.2510747495011 & -4.25107474950109 \tabularnewline
147 & 18 & 16.0215565288957 & 1.97844347110435 \tabularnewline
148 & 13 & 15.2718336576412 & -2.27183365764122 \tabularnewline
149 & 16 & 15.2435604817284 & 0.756439518271586 \tabularnewline
150 & 13 & 13.1091715921476 & -0.10917159214763 \tabularnewline
151 & 10 & 13.1996005368258 & -3.19960053682578 \tabularnewline
152 & 15 & 16.5261430875273 & -1.52614308752733 \tabularnewline
153 & 16 & 14.1963213593489 & 1.80367864065106 \tabularnewline
154 & 16 & 12.0808737164072 & 3.91912628359275 \tabularnewline
155 & 14 & 12.5375589999608 & 1.46244100003922 \tabularnewline
156 & 10 & 12.5833713045284 & -2.58337130452839 \tabularnewline
157 & 17 & 17.0116615794687 & -0.0116615794686995 \tabularnewline
158 & 13 & 11.7002433024592 & 1.2997566975408 \tabularnewline
159 & 15 & 13.8833537204204 & 1.1166462795796 \tabularnewline
160 & 16 & 15.1715352388705 & 0.828464761129495 \tabularnewline
161 & 12 & 12.5984129210305 & -0.5984129210305 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186301&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.1163737759861[/C][C]-3.11637377598614[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.9707958919569[/C][C]0.0292041080431209[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.1727415049847[/C][C]2.8272584950153[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.7280310429992[/C][C]3.2719689570008[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.4488442903231[/C][C]-1.4488442903231[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8832167750804[/C][C]-1.88321677508036[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.0518938325301[/C][C]3.94810616746987[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.5958455302759[/C][C]-1.59584553027593[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.8836089886891[/C][C]-1.88360898868913[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.4234100418645[/C][C]2.57658995813555[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.1967493922839[/C][C]0.803250607716097[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.197034476703[/C][C]-0.197034476703035[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.3604985918413[/C][C]0.639501408158673[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3033530964417[/C][C]0.696646903558294[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.3564210817084[/C][C]-0.356421081708398[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0193893414177[/C][C]-0.0193893414177254[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.5499043292326[/C][C]0.450095670767438[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1269474020316[/C][C]3.8730525979684[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.4079303728809[/C][C]2.59206962711911[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.1946879642642[/C][C]0.805312035735817[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2401587946693[/C][C]0.759841205330719[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.7059476907096[/C][C]1.29405230929043[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4147713290883[/C][C]2.58522867091168[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.6743480756348[/C][C]1.32565192436515[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.7700743147245[/C][C]2.22992568527555[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.8878302850485[/C][C]0.112169714951543[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9644051943979[/C][C]1.03559480560208[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.0395077879009[/C][C]-1.03950778790094[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.4431729137219[/C][C]0.556827086278057[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.9539061485681[/C][C]0.0460938514319424[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.5965123650264[/C][C]-0.596512365026409[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.177369228133[/C][C]-0.17736922813302[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1110728107702[/C][C]-1.11107281077017[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.4255235436513[/C][C]0.574476456348738[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6467500390121[/C][C]-1.6467500390121[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.8965619549108[/C][C]-5.89656195491083[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.8580645613835[/C][C]-0.858064561383539[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7798890236988[/C][C]-1.77988902369877[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.219715956926[/C][C]1.78028404307403[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.5823720944357[/C][C]1.41762790556434[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.0450096733655[/C][C]0.954990326634475[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.5607843377338[/C][C]-1.56078433773385[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.9199778915182[/C][C]2.08002210848182[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.203384521805[/C][C]-0.203384521805004[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.8990693119289[/C][C]-0.899069311928865[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.5927472753193[/C][C]-4.59274727531932[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4209138249569[/C][C]-2.42091382495689[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9297366779516[/C][C]0.070263322048382[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.1987244847461[/C][C]0.80127551525391[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0209472330053[/C][C]-2.02094723300532[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.4911419283566[/C][C]-0.491141928356606[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1101591487578[/C][C]-0.11015914875777[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6836235739442[/C][C]-2.68362357394417[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.1779577090798[/C][C]0.822042290920176[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5124533269388[/C][C]-2.51245332693877[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6213052735434[/C][C]1.37869472645664[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0922144514025[/C][C]-0.0922144514024526[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.0812507844018[/C][C]0.91874921559819[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3245934103624[/C][C]-0.324593410362369[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1268772712349[/C][C]1.87312272876507[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4213780833605[/C][C]0.578621916639511[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9804534072832[/C][C]0.0195465927168422[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.4708345035383[/C][C]-0.470834503538308[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.367472734991[/C][C]-0.367472734990999[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4353889944351[/C][C]0.564611005564872[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.9066979222[/C][C]1.09330207779997[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.0995193959324[/C][C]1.90048060406761[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.6785573570997[/C][C]3.32144264290032[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8785586970376[/C][C]-3.8785586970376[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.418681247884[/C][C]0.581318752116011[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.4389419597422[/C][C]-3.43894195974223[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3180996249762[/C][C]-0.318099624976213[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5393130368345[/C][C]1.46068696316545[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1580137676401[/C][C]0.841986232359885[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.7309808208144[/C][C]0.269019179185556[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8825312875197[/C][C]3.11746871248033[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3647572467043[/C][C]-0.364757246704292[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4459534690398[/C][C]1.55404653096023[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.842116231222[/C][C]-1.84211623122204[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.0292535078826[/C][C]-0.0292535078825983[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5777702819388[/C][C]0.422229718061173[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.7937747867028[/C][C]3.20622521329717[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.6890836778516[/C][C]0.31091632214841[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]16.0269176589512[/C][C]-1.02691765895125[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7412691759141[/C][C]0.258730824085895[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2659920083984[/C][C]1.73400799160159[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.2183792587082[/C][C]-0.218379258708241[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3369314339327[/C][C]0.663068566067272[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.580875274459[/C][C]1.41912472554104[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.2078914546212[/C][C]0.792108545378828[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4746045073303[/C][C]-1.47460450733034[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.0116615794687[/C][C]-0.0116615794686995[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4845936707891[/C][C]0.51540632921089[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2887619735731[/C][C]-0.288761973573146[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1826048106205[/C][C]-2.18260481062052[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.2181694888866[/C][C]0.781830511113362[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8573944232575[/C][C]0.1426055767425[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1839214308915[/C][C]1.8160785691085[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.1620796352112[/C][C]-0.162079635211153[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.3378103815196[/C][C]-0.337810381519647[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.3709697221063[/C][C]-1.37096972210627[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8091305968613[/C][C]1.19086940313867[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.4170045550708[/C][C]2.58299544492918[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.5881678267291[/C][C]0.411832173270873[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5594365359706[/C][C]1.44056346402941[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3963030224909[/C][C]-2.39630302249088[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.0661855591204[/C][C]0.933814440879608[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.9383327117494[/C][C]0.0616672882505854[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5945146167258[/C][C]1.40548538327421[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.2952815125103[/C][C]-0.295281512510332[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.0116752770122[/C][C]0.988324722987829[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8993669313378[/C][C]0.100633068662198[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5426736840992[/C][C]2.45732631590085[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]15.0108388958843[/C][C]-2.01083889588427[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.933181956999[/C][C]-2.93318195699899[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.675131094183[/C][C]1.32486890581697[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.5442594931066[/C][C]-1.54425949310657[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0076622226371[/C][C]0.992337777362873[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9868187170295[/C][C]-1.98681871702951[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.7235119724826[/C][C]0.276488027517423[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3290418011299[/C][C]-1.32904180112992[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.6984838973193[/C][C]0.301516102680669[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]16.0232196757193[/C][C]-3.02321967571931[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.1314353297215[/C][C]-1.13143532972154[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.0608021093703[/C][C]-1.06080210937035[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.8710138509282[/C][C]-0.871013850928179[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.5142914847989[/C][C]-0.514291484798904[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.3421159963374[/C][C]0.657884003662637[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.8833537204204[/C][C]1.1166462795796[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.0333833008366[/C][C]-3.03338330083663[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.2413554148292[/C][C]1.75864458517084[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3907974754613[/C][C]-3.39079747546125[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.1794887905604[/C][C]1.82051120943958[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.997755941872[/C][C]-1.99775594187202[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.7379696048381[/C][C]-1.7379696048381[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.1908163505845[/C][C]-0.190816350584511[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.3540580252306[/C][C]0.645941974769403[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.5236190086443[/C][C]0.476380991355701[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.2464520790505[/C][C]-2.24645207905053[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.4455992363019[/C][C]-1.44559923630192[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.4500552966152[/C][C]-5.45005529661519[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.1834334701393[/C][C]2.81656652986066[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.4060880482999[/C][C]1.59391195170005[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.6376313200557[/C][C]0.362368679944322[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.9345249346435[/C][C]1.06547506535645[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.2510747495011[/C][C]-4.25107474950109[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]16.0215565288957[/C][C]1.97844347110435[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.2718336576412[/C][C]-2.27183365764122[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.2435604817284[/C][C]0.756439518271586[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.1091715921476[/C][C]-0.10917159214763[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.1996005368258[/C][C]-3.19960053682578[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.5261430875273[/C][C]-1.52614308752733[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.1963213593489[/C][C]1.80367864065106[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.0808737164072[/C][C]3.91912628359275[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.5375589999608[/C][C]1.46244100003922[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.5833713045284[/C][C]-2.58337130452839[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]17.0116615794687[/C][C]-0.0116615794686995[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7002433024592[/C][C]1.2997566975408[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.8833537204204[/C][C]1.1166462795796[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.1715352388705[/C][C]0.828464761129495[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.5984129210305[/C][C]-0.5984129210305[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186301&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186301&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
11316.1163737759861-3.11637377598614
21615.97079589195690.0292041080431209
31916.17274150498472.8272584950153
41511.72803104299923.2719689570008
51415.4488442903231-1.4488442903231
61314.8832167750804-1.88321677508036
71915.05189383253013.94810616746987
81516.5958455302759-1.59584553027593
91415.8836089886891-1.88360898868913
101512.42341004186452.57658995813555
111615.19674939228390.803250607716097
121616.197034476703-0.197034476703035
131615.36049859184130.639501408158673
141615.30335309644170.696646903558294
151717.3564210817084-0.356421081708398
161515.0193893414177-0.0193893414177254
171514.54990432923260.450095670767438
182016.12694740203163.8730525979684
191815.40793037288092.59206962711911
201615.19468796426420.805312035735817
211615.24015879466930.759841205330719
221614.70594769070961.29405230929043
231916.41477132908832.58522867091168
241614.67434807563481.32565192436515
251714.77007431472452.22992568527555
261716.88783028504850.112169714951543
271614.96440519439791.03559480560208
281516.0395077879009-1.03950778790094
291615.44317291372190.556827086278057
301413.95390614856810.0460938514319424
311515.5965123650264-0.596512365026409
321212.177369228133-0.17736922813302
331415.1110728107702-1.11107281077017
341615.42552354365130.574476456348738
351415.6467500390121-1.6467500390121
36712.8965619549108-5.89656195491083
371010.8580645613835-0.858064561383539
381415.7798890236988-1.77988902369877
391614.2197159569261.78028404307403
401614.58237209443571.41762790556434
411615.04500967336550.954990326634475
421415.5607843377338-1.56078433773385
432017.91997789151822.08002210848182
441414.203384521805-0.203384521805004
451414.8990693119289-0.899069311928865
461115.5927472753193-4.59274727531932
471416.4209138249569-2.42091382495689
481514.92973667795160.070263322048382
491615.19872448474610.80127551525391
501416.0209472330053-2.02094723300532
511616.4911419283566-0.491141928356606
521414.1101591487578-0.11015914875777
531214.6836235739442-2.68362357394417
541615.17795770907980.822042290920176
55911.5124533269388-2.51245332693877
561412.62130527354341.37869472645664
571616.0922144514025-0.0922144514024526
581615.08125078440180.91874921559819
591515.3245934103624-0.324593410362369
601614.12687727123491.87312272876507
611211.42137808336050.578621916639511
621615.98045340728320.0195465927168422
631616.4708345035383-0.470834503538308
641414.367472734991-0.367472734990999
651615.43538899443510.564611005564872
661715.90669792221.09330207779997
671816.09951939593241.90048060406761
681814.67855735709973.32144264290032
691215.8785586970376-3.8785586970376
701615.4186812478840.581318752116011
711013.4389419597422-3.43894195974223
721414.3180996249762-0.318099624976213
731816.53931303683451.46068696316545
741817.15801376764010.841986232359885
751615.73098082081440.269019179185556
761713.88253128751973.11746871248033
771616.3647572467043-0.364757246704292
781614.44595346903981.55404653096023
791314.842116231222-1.84211623122204
801616.0292535078826-0.0292535078825983
811615.57777028193880.422229718061173
822016.79377478670283.20622521329717
831615.68908367785160.31091632214841
841516.0269176589512-1.02691765895125
851514.74126917591410.258730824085895
861614.26599200839841.73400799160159
871414.2183792587082-0.218379258708241
881615.33693143393270.663068566067272
891614.5808752744591.41912472554104
901514.20789145462120.792108545378828
911213.4746045073303-1.47460450733034
921717.0116615794687-0.0116615794686995
931615.48459367078910.51540632921089
941515.2887619735731-0.288761973573146
951315.1826048106205-2.18260481062052
961615.21816948888660.781830511113362
971615.85739442325750.1426055767425
981614.18392143089151.8160785691085
991616.1620796352112-0.162079635211153
1001414.3378103815196-0.337810381519647
1011617.3709697221063-1.37096972210627
1021614.80913059686131.19086940313867
1032017.41700455507082.58299544492918
1041514.58816782672910.411832173270873
1051614.55943653597061.44056346402941
1061315.3963030224909-2.39630302249088
1071716.06618555912040.933814440879608
1081615.93833271174940.0616672882505854
1091614.59451461672581.40548538327421
1101212.2952815125103-0.295281512510332
1111615.01167527701220.988324722987829
1121615.89936693133780.100633068662198
1131714.54267368409922.45732631590085
1141315.0108388958843-2.01083889588427
1151214.933181956999-2.93318195699899
1161816.6751310941831.32486890581697
1171415.5442594931066-1.54425949310657
1181413.00766222263710.992337777362873
1191314.9868187170295-1.98681871702951
1201615.72351197248260.276488027517423
1211314.3290418011299-1.32904180112992
1221615.69848389731930.301516102680669
1231316.0232196757193-3.02321967571931
1241617.1314353297215-1.13143532972154
1251516.0608021093703-1.06080210937035
1261616.8710138509282-0.871013850928179
1271515.5142914847989-0.514291484798904
1281716.34211599633740.657884003662637
1291513.88335372042041.1166462795796
1301215.0333833008366-3.03338330083663
1311614.24135541482921.75864458517084
1321013.3907974754613-3.39079747546125
1331614.17948879056041.82051120943958
1341213.997755941872-1.99775594187202
1351415.7379696048381-1.7379696048381
1361515.1908163505845-0.190816350584511
1371312.35405802523060.645941974769403
1381514.52361900864430.476380991355701
1391113.2464520790505-2.24645207905053
1401213.4455992363019-1.44559923630192
141813.4500552966152-5.45005529661519
1421613.18343347013932.81656652986066
1431513.40608804829991.59391195170005
1441716.63763132005570.362368679944322
1451614.93452493464351.06547506535645
1461014.2510747495011-4.25107474950109
1471816.02155652889571.97844347110435
1481315.2718336576412-2.27183365764122
1491615.24356048172840.756439518271586
1501313.1091715921476-0.10917159214763
1511013.1996005368258-3.19960053682578
1521516.5261430875273-1.52614308752733
1531614.19632135934891.80367864065106
1541612.08087371640723.91912628359275
1551412.53755899996081.46244100003922
1561012.5833713045284-2.58337130452839
1571717.0116615794687-0.0116615794686995
1581311.70024330245921.2997566975408
1591513.88335372042041.1166462795796
1601615.17153523887050.828464761129495
1611212.5984129210305-0.5984129210305







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.389151031034470.778302062068940.61084896896553
120.6221400278953450.7557199442093110.377859972104655
130.4830330586879750.9660661173759490.516966941312025
140.4563873897633860.9127747795267720.543612610236614
150.347784746012440.6955694920248810.65221525398756
160.2636006141806020.5272012283612040.736399385819398
170.2146523244587250.429304648917450.785347675541275
180.4141166250245160.8282332500490310.585883374975484
190.3372706034614720.6745412069229440.662729396538528
200.2585422286366250.517084457273250.741457771363375
210.1994993802161650.3989987604323290.800500619783835
220.1818104323799140.3636208647598280.818189567620086
230.3641589067842930.7283178135685860.635841093215707
240.4002190568606080.8004381137212150.599780943139392
250.3891382064419520.7782764128839030.610861793558048
260.3516284032716040.7032568065432090.648371596728396
270.4106395281051190.8212790562102370.589360471894881
280.4165826719030860.8331653438061720.583417328096914
290.3977145230912480.7954290461824960.602285476908752
300.4392798064871250.878559612974250.560720193512875
310.3814312688311360.7628625376622720.618568731168864
320.3351362336602370.6702724673204730.664863766339763
330.31190187006120.62380374012240.6880981299388
340.2790560863859210.5581121727718420.720943913614079
350.2386719565960210.4773439131920410.761328043403979
360.8224274913061710.3551450173876580.177572508693829
370.7952395479571240.4095209040857520.204760452042876
380.7889679792164740.4220640415670530.211032020783526
390.8107575435522530.3784849128954940.189242456447747
400.7917950614451010.4164098771097970.208204938554899
410.7581681512388910.4836636975222180.241831848761109
420.7325188157156110.5349623685687780.267481184284389
430.7579360332903820.4841279334192360.242063966709618
440.7142803555987770.5714392888024460.285719644401223
450.6850514132119180.6298971735761640.314948586788082
460.8544946603721460.2910106792557080.145505339627854
470.9039512562814770.1920974874370470.0960487437185234
480.8799262833320630.2401474333358740.120073716667937
490.8632422280871830.2735155438256350.136757771912818
500.8621829320828980.2756341358342040.137817067917102
510.8328155886596830.3343688226806350.167184411340317
520.7991686628016590.4016626743966820.200831337198341
530.8166392375066910.3667215249866180.183360762493309
540.8035465648446170.3929068703107660.196453435155383
550.8173964771203190.3652070457593620.182603522879681
560.7913819265403670.4172361469192650.208618073459633
570.7560780491457630.4878439017084740.243921950854237
580.7320281896738370.5359436206523270.267971810326163
590.6933294236448930.6133411527102150.306670576355107
600.6896650871396970.6206698257206050.310334912860303
610.6509438590209140.6981122819581710.349056140979086
620.607684556620050.7846308867598990.39231544337995
630.5676201311271430.8647597377457150.432379868872857
640.5197765018159270.9604469963681470.480223498184073
650.480843543515110.961687087030220.51915645648489
660.4515505933839870.9031011867679730.548449406616013
670.4487131494156050.8974262988312110.551286850584395
680.5978279763167050.804344047366590.402172023683295
690.7148975484226590.5702049031546810.285102451577341
700.6790685820252020.6418628359495960.320931417974798
710.7889143306085490.4221713387829030.211085669391451
720.7530627836124740.4938744327750520.246937216387526
730.7428780993456360.5142438013087280.257121900654364
740.7223358600110460.5553282799779070.277664139988954
750.6816035544329830.6367928911340350.318396445567017
760.7344092565765410.5311814868469170.265590743423459
770.6959493066467280.6081013867065440.304050693353272
780.6827011261382820.6345977477234360.317298873861718
790.6883755005224990.6232489989550020.311624499477501
800.645667743154650.70866451369070.35433225684535
810.6054466533022020.7891066933955960.394553346697798
820.7298426559013430.5403146881973150.270157344098657
830.6912880172623030.6174239654753940.308711982737697
840.6604437108934380.6791125782131240.339556289106562
850.6171998150579860.7656003698840270.382800184942014
860.6104467121168750.779106575766250.389553287883125
870.5647090312058290.8705819375883410.435290968794171
880.5242696869440330.9514606261119340.475730313055967
890.5074392756181510.9851214487636980.492560724381849
900.4733564492068980.9467128984137950.526643550793102
910.4538063559405420.9076127118810850.546193644059458
920.4156602724114280.8313205448228570.584339727588572
930.3740581934273040.7481163868546080.625941806572696
940.3327240022730970.6654480045461930.667275997726903
950.3483967017504370.6967934035008740.651603298249563
960.314133713405840.6282674268116790.68586628659416
970.2775253007613640.5550506015227280.722474699238636
980.2757933401455720.5515866802911450.724206659854428
990.2374312876646220.4748625753292430.762568712335378
1000.2034842265959570.4069684531919130.796515773404043
1010.1830328849305020.3660657698610050.816967115069498
1020.1691383221431290.3382766442862590.830861677856871
1030.2091568440087610.4183136880175220.790843155991239
1040.1770246593601870.3540493187203750.822975340639813
1050.1708923943267390.3417847886534780.829107605673261
1060.1821985461526270.3643970923052530.817801453847373
1070.1634410846538730.3268821693077450.836558915346127
1080.1444885490046390.2889770980092780.855511450995361
1090.1386131559626590.2772263119253180.861386844037341
1100.1311313235304620.2622626470609250.868868676469538
1110.1146239164653790.2292478329307580.885376083534621
1120.09435659126318430.1887131825263690.905643408736816
1130.1107632044476370.2215264088952740.889236795552363
1140.1033917468070030.2067834936140070.896608253192997
1150.1325597813085420.2651195626170830.867440218691458
1160.125713269427920.2514265388558410.87428673057208
1170.1081199164770450.2162398329540890.891880083522955
1180.09390651832119830.1878130366423970.906093481678802
1190.09620798055566050.1924159611113210.90379201944434
1200.08902007022581290.1780401404516260.910979929774187
1210.07437699155664030.1487539831132810.92562300844336
1220.05832018787089090.1166403757417820.941679812129109
1230.06629604433916660.1325920886783330.933703955660833
1240.0522822855082640.1045645710165280.947717714491736
1250.04164680770285030.08329361540570060.95835319229715
1260.03149672685263070.06299345370526140.968503273147369
1270.02333923421818610.04667846843637210.976660765781814
1280.01803777210710560.03607554421421130.981962227892894
1290.01457642906764940.02915285813529880.985423570932351
1300.02056959311808250.04113918623616490.979430406881918
1310.01665939167500620.03331878335001240.983340608324994
1320.02631093245380840.05262186490761670.973689067546192
1330.02686983964923770.05373967929847550.973130160350762
1340.03639629216320210.07279258432640420.963603707836798
1350.02824785693372020.05649571386744040.97175214306628
1360.01900451005462820.03800902010925630.980995489945372
1370.01243659648686330.02487319297372660.987563403513137
1380.008880796603433930.01776159320686790.991119203396566
1390.01312267559675880.02624535119351750.986877324403241
1400.0105737005050230.02114740101004610.989426299494977
1410.5540837090524460.8918325818951090.445916290947554
1420.4875033277632810.9750066555265610.512496672236719
1430.4016580788664410.8033161577328820.598341921133559
1440.3219391861895580.6438783723791150.678060813810442
1450.2395501708974240.4791003417948480.760449829102576
1460.2948784524436020.5897569048872050.705121547556398
1470.2373639379192480.4747278758384960.762636062080752
1480.7382418603086780.5235162793826450.261758139691322
1490.7816581795188410.4366836409623180.218341820481159
1500.6247995158335480.7504009683329040.375200484166452

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.38915103103447 & 0.77830206206894 & 0.61084896896553 \tabularnewline
12 & 0.622140027895345 & 0.755719944209311 & 0.377859972104655 \tabularnewline
13 & 0.483033058687975 & 0.966066117375949 & 0.516966941312025 \tabularnewline
14 & 0.456387389763386 & 0.912774779526772 & 0.543612610236614 \tabularnewline
15 & 0.34778474601244 & 0.695569492024881 & 0.65221525398756 \tabularnewline
16 & 0.263600614180602 & 0.527201228361204 & 0.736399385819398 \tabularnewline
17 & 0.214652324458725 & 0.42930464891745 & 0.785347675541275 \tabularnewline
18 & 0.414116625024516 & 0.828233250049031 & 0.585883374975484 \tabularnewline
19 & 0.337270603461472 & 0.674541206922944 & 0.662729396538528 \tabularnewline
20 & 0.258542228636625 & 0.51708445727325 & 0.741457771363375 \tabularnewline
21 & 0.199499380216165 & 0.398998760432329 & 0.800500619783835 \tabularnewline
22 & 0.181810432379914 & 0.363620864759828 & 0.818189567620086 \tabularnewline
23 & 0.364158906784293 & 0.728317813568586 & 0.635841093215707 \tabularnewline
24 & 0.400219056860608 & 0.800438113721215 & 0.599780943139392 \tabularnewline
25 & 0.389138206441952 & 0.778276412883903 & 0.610861793558048 \tabularnewline
26 & 0.351628403271604 & 0.703256806543209 & 0.648371596728396 \tabularnewline
27 & 0.410639528105119 & 0.821279056210237 & 0.589360471894881 \tabularnewline
28 & 0.416582671903086 & 0.833165343806172 & 0.583417328096914 \tabularnewline
29 & 0.397714523091248 & 0.795429046182496 & 0.602285476908752 \tabularnewline
30 & 0.439279806487125 & 0.87855961297425 & 0.560720193512875 \tabularnewline
31 & 0.381431268831136 & 0.762862537662272 & 0.618568731168864 \tabularnewline
32 & 0.335136233660237 & 0.670272467320473 & 0.664863766339763 \tabularnewline
33 & 0.3119018700612 & 0.6238037401224 & 0.6880981299388 \tabularnewline
34 & 0.279056086385921 & 0.558112172771842 & 0.720943913614079 \tabularnewline
35 & 0.238671956596021 & 0.477343913192041 & 0.761328043403979 \tabularnewline
36 & 0.822427491306171 & 0.355145017387658 & 0.177572508693829 \tabularnewline
37 & 0.795239547957124 & 0.409520904085752 & 0.204760452042876 \tabularnewline
38 & 0.788967979216474 & 0.422064041567053 & 0.211032020783526 \tabularnewline
39 & 0.810757543552253 & 0.378484912895494 & 0.189242456447747 \tabularnewline
40 & 0.791795061445101 & 0.416409877109797 & 0.208204938554899 \tabularnewline
41 & 0.758168151238891 & 0.483663697522218 & 0.241831848761109 \tabularnewline
42 & 0.732518815715611 & 0.534962368568778 & 0.267481184284389 \tabularnewline
43 & 0.757936033290382 & 0.484127933419236 & 0.242063966709618 \tabularnewline
44 & 0.714280355598777 & 0.571439288802446 & 0.285719644401223 \tabularnewline
45 & 0.685051413211918 & 0.629897173576164 & 0.314948586788082 \tabularnewline
46 & 0.854494660372146 & 0.291010679255708 & 0.145505339627854 \tabularnewline
47 & 0.903951256281477 & 0.192097487437047 & 0.0960487437185234 \tabularnewline
48 & 0.879926283332063 & 0.240147433335874 & 0.120073716667937 \tabularnewline
49 & 0.863242228087183 & 0.273515543825635 & 0.136757771912818 \tabularnewline
50 & 0.862182932082898 & 0.275634135834204 & 0.137817067917102 \tabularnewline
51 & 0.832815588659683 & 0.334368822680635 & 0.167184411340317 \tabularnewline
52 & 0.799168662801659 & 0.401662674396682 & 0.200831337198341 \tabularnewline
53 & 0.816639237506691 & 0.366721524986618 & 0.183360762493309 \tabularnewline
54 & 0.803546564844617 & 0.392906870310766 & 0.196453435155383 \tabularnewline
55 & 0.817396477120319 & 0.365207045759362 & 0.182603522879681 \tabularnewline
56 & 0.791381926540367 & 0.417236146919265 & 0.208618073459633 \tabularnewline
57 & 0.756078049145763 & 0.487843901708474 & 0.243921950854237 \tabularnewline
58 & 0.732028189673837 & 0.535943620652327 & 0.267971810326163 \tabularnewline
59 & 0.693329423644893 & 0.613341152710215 & 0.306670576355107 \tabularnewline
60 & 0.689665087139697 & 0.620669825720605 & 0.310334912860303 \tabularnewline
61 & 0.650943859020914 & 0.698112281958171 & 0.349056140979086 \tabularnewline
62 & 0.60768455662005 & 0.784630886759899 & 0.39231544337995 \tabularnewline
63 & 0.567620131127143 & 0.864759737745715 & 0.432379868872857 \tabularnewline
64 & 0.519776501815927 & 0.960446996368147 & 0.480223498184073 \tabularnewline
65 & 0.48084354351511 & 0.96168708703022 & 0.51915645648489 \tabularnewline
66 & 0.451550593383987 & 0.903101186767973 & 0.548449406616013 \tabularnewline
67 & 0.448713149415605 & 0.897426298831211 & 0.551286850584395 \tabularnewline
68 & 0.597827976316705 & 0.80434404736659 & 0.402172023683295 \tabularnewline
69 & 0.714897548422659 & 0.570204903154681 & 0.285102451577341 \tabularnewline
70 & 0.679068582025202 & 0.641862835949596 & 0.320931417974798 \tabularnewline
71 & 0.788914330608549 & 0.422171338782903 & 0.211085669391451 \tabularnewline
72 & 0.753062783612474 & 0.493874432775052 & 0.246937216387526 \tabularnewline
73 & 0.742878099345636 & 0.514243801308728 & 0.257121900654364 \tabularnewline
74 & 0.722335860011046 & 0.555328279977907 & 0.277664139988954 \tabularnewline
75 & 0.681603554432983 & 0.636792891134035 & 0.318396445567017 \tabularnewline
76 & 0.734409256576541 & 0.531181486846917 & 0.265590743423459 \tabularnewline
77 & 0.695949306646728 & 0.608101386706544 & 0.304050693353272 \tabularnewline
78 & 0.682701126138282 & 0.634597747723436 & 0.317298873861718 \tabularnewline
79 & 0.688375500522499 & 0.623248998955002 & 0.311624499477501 \tabularnewline
80 & 0.64566774315465 & 0.7086645136907 & 0.35433225684535 \tabularnewline
81 & 0.605446653302202 & 0.789106693395596 & 0.394553346697798 \tabularnewline
82 & 0.729842655901343 & 0.540314688197315 & 0.270157344098657 \tabularnewline
83 & 0.691288017262303 & 0.617423965475394 & 0.308711982737697 \tabularnewline
84 & 0.660443710893438 & 0.679112578213124 & 0.339556289106562 \tabularnewline
85 & 0.617199815057986 & 0.765600369884027 & 0.382800184942014 \tabularnewline
86 & 0.610446712116875 & 0.77910657576625 & 0.389553287883125 \tabularnewline
87 & 0.564709031205829 & 0.870581937588341 & 0.435290968794171 \tabularnewline
88 & 0.524269686944033 & 0.951460626111934 & 0.475730313055967 \tabularnewline
89 & 0.507439275618151 & 0.985121448763698 & 0.492560724381849 \tabularnewline
90 & 0.473356449206898 & 0.946712898413795 & 0.526643550793102 \tabularnewline
91 & 0.453806355940542 & 0.907612711881085 & 0.546193644059458 \tabularnewline
92 & 0.415660272411428 & 0.831320544822857 & 0.584339727588572 \tabularnewline
93 & 0.374058193427304 & 0.748116386854608 & 0.625941806572696 \tabularnewline
94 & 0.332724002273097 & 0.665448004546193 & 0.667275997726903 \tabularnewline
95 & 0.348396701750437 & 0.696793403500874 & 0.651603298249563 \tabularnewline
96 & 0.31413371340584 & 0.628267426811679 & 0.68586628659416 \tabularnewline
97 & 0.277525300761364 & 0.555050601522728 & 0.722474699238636 \tabularnewline
98 & 0.275793340145572 & 0.551586680291145 & 0.724206659854428 \tabularnewline
99 & 0.237431287664622 & 0.474862575329243 & 0.762568712335378 \tabularnewline
100 & 0.203484226595957 & 0.406968453191913 & 0.796515773404043 \tabularnewline
101 & 0.183032884930502 & 0.366065769861005 & 0.816967115069498 \tabularnewline
102 & 0.169138322143129 & 0.338276644286259 & 0.830861677856871 \tabularnewline
103 & 0.209156844008761 & 0.418313688017522 & 0.790843155991239 \tabularnewline
104 & 0.177024659360187 & 0.354049318720375 & 0.822975340639813 \tabularnewline
105 & 0.170892394326739 & 0.341784788653478 & 0.829107605673261 \tabularnewline
106 & 0.182198546152627 & 0.364397092305253 & 0.817801453847373 \tabularnewline
107 & 0.163441084653873 & 0.326882169307745 & 0.836558915346127 \tabularnewline
108 & 0.144488549004639 & 0.288977098009278 & 0.855511450995361 \tabularnewline
109 & 0.138613155962659 & 0.277226311925318 & 0.861386844037341 \tabularnewline
110 & 0.131131323530462 & 0.262262647060925 & 0.868868676469538 \tabularnewline
111 & 0.114623916465379 & 0.229247832930758 & 0.885376083534621 \tabularnewline
112 & 0.0943565912631843 & 0.188713182526369 & 0.905643408736816 \tabularnewline
113 & 0.110763204447637 & 0.221526408895274 & 0.889236795552363 \tabularnewline
114 & 0.103391746807003 & 0.206783493614007 & 0.896608253192997 \tabularnewline
115 & 0.132559781308542 & 0.265119562617083 & 0.867440218691458 \tabularnewline
116 & 0.12571326942792 & 0.251426538855841 & 0.87428673057208 \tabularnewline
117 & 0.108119916477045 & 0.216239832954089 & 0.891880083522955 \tabularnewline
118 & 0.0939065183211983 & 0.187813036642397 & 0.906093481678802 \tabularnewline
119 & 0.0962079805556605 & 0.192415961111321 & 0.90379201944434 \tabularnewline
120 & 0.0890200702258129 & 0.178040140451626 & 0.910979929774187 \tabularnewline
121 & 0.0743769915566403 & 0.148753983113281 & 0.92562300844336 \tabularnewline
122 & 0.0583201878708909 & 0.116640375741782 & 0.941679812129109 \tabularnewline
123 & 0.0662960443391666 & 0.132592088678333 & 0.933703955660833 \tabularnewline
124 & 0.052282285508264 & 0.104564571016528 & 0.947717714491736 \tabularnewline
125 & 0.0416468077028503 & 0.0832936154057006 & 0.95835319229715 \tabularnewline
126 & 0.0314967268526307 & 0.0629934537052614 & 0.968503273147369 \tabularnewline
127 & 0.0233392342181861 & 0.0466784684363721 & 0.976660765781814 \tabularnewline
128 & 0.0180377721071056 & 0.0360755442142113 & 0.981962227892894 \tabularnewline
129 & 0.0145764290676494 & 0.0291528581352988 & 0.985423570932351 \tabularnewline
130 & 0.0205695931180825 & 0.0411391862361649 & 0.979430406881918 \tabularnewline
131 & 0.0166593916750062 & 0.0333187833500124 & 0.983340608324994 \tabularnewline
132 & 0.0263109324538084 & 0.0526218649076167 & 0.973689067546192 \tabularnewline
133 & 0.0268698396492377 & 0.0537396792984755 & 0.973130160350762 \tabularnewline
134 & 0.0363962921632021 & 0.0727925843264042 & 0.963603707836798 \tabularnewline
135 & 0.0282478569337202 & 0.0564957138674404 & 0.97175214306628 \tabularnewline
136 & 0.0190045100546282 & 0.0380090201092563 & 0.980995489945372 \tabularnewline
137 & 0.0124365964868633 & 0.0248731929737266 & 0.987563403513137 \tabularnewline
138 & 0.00888079660343393 & 0.0177615932068679 & 0.991119203396566 \tabularnewline
139 & 0.0131226755967588 & 0.0262453511935175 & 0.986877324403241 \tabularnewline
140 & 0.010573700505023 & 0.0211474010100461 & 0.989426299494977 \tabularnewline
141 & 0.554083709052446 & 0.891832581895109 & 0.445916290947554 \tabularnewline
142 & 0.487503327763281 & 0.975006655526561 & 0.512496672236719 \tabularnewline
143 & 0.401658078866441 & 0.803316157732882 & 0.598341921133559 \tabularnewline
144 & 0.321939186189558 & 0.643878372379115 & 0.678060813810442 \tabularnewline
145 & 0.239550170897424 & 0.479100341794848 & 0.760449829102576 \tabularnewline
146 & 0.294878452443602 & 0.589756904887205 & 0.705121547556398 \tabularnewline
147 & 0.237363937919248 & 0.474727875838496 & 0.762636062080752 \tabularnewline
148 & 0.738241860308678 & 0.523516279382645 & 0.261758139691322 \tabularnewline
149 & 0.781658179518841 & 0.436683640962318 & 0.218341820481159 \tabularnewline
150 & 0.624799515833548 & 0.750400968332904 & 0.375200484166452 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186301&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]11[/C][C]0.38915103103447[/C][C]0.77830206206894[/C][C]0.61084896896553[/C][/ROW]
[ROW][C]12[/C][C]0.622140027895345[/C][C]0.755719944209311[/C][C]0.377859972104655[/C][/ROW]
[ROW][C]13[/C][C]0.483033058687975[/C][C]0.966066117375949[/C][C]0.516966941312025[/C][/ROW]
[ROW][C]14[/C][C]0.456387389763386[/C][C]0.912774779526772[/C][C]0.543612610236614[/C][/ROW]
[ROW][C]15[/C][C]0.34778474601244[/C][C]0.695569492024881[/C][C]0.65221525398756[/C][/ROW]
[ROW][C]16[/C][C]0.263600614180602[/C][C]0.527201228361204[/C][C]0.736399385819398[/C][/ROW]
[ROW][C]17[/C][C]0.214652324458725[/C][C]0.42930464891745[/C][C]0.785347675541275[/C][/ROW]
[ROW][C]18[/C][C]0.414116625024516[/C][C]0.828233250049031[/C][C]0.585883374975484[/C][/ROW]
[ROW][C]19[/C][C]0.337270603461472[/C][C]0.674541206922944[/C][C]0.662729396538528[/C][/ROW]
[ROW][C]20[/C][C]0.258542228636625[/C][C]0.51708445727325[/C][C]0.741457771363375[/C][/ROW]
[ROW][C]21[/C][C]0.199499380216165[/C][C]0.398998760432329[/C][C]0.800500619783835[/C][/ROW]
[ROW][C]22[/C][C]0.181810432379914[/C][C]0.363620864759828[/C][C]0.818189567620086[/C][/ROW]
[ROW][C]23[/C][C]0.364158906784293[/C][C]0.728317813568586[/C][C]0.635841093215707[/C][/ROW]
[ROW][C]24[/C][C]0.400219056860608[/C][C]0.800438113721215[/C][C]0.599780943139392[/C][/ROW]
[ROW][C]25[/C][C]0.389138206441952[/C][C]0.778276412883903[/C][C]0.610861793558048[/C][/ROW]
[ROW][C]26[/C][C]0.351628403271604[/C][C]0.703256806543209[/C][C]0.648371596728396[/C][/ROW]
[ROW][C]27[/C][C]0.410639528105119[/C][C]0.821279056210237[/C][C]0.589360471894881[/C][/ROW]
[ROW][C]28[/C][C]0.416582671903086[/C][C]0.833165343806172[/C][C]0.583417328096914[/C][/ROW]
[ROW][C]29[/C][C]0.397714523091248[/C][C]0.795429046182496[/C][C]0.602285476908752[/C][/ROW]
[ROW][C]30[/C][C]0.439279806487125[/C][C]0.87855961297425[/C][C]0.560720193512875[/C][/ROW]
[ROW][C]31[/C][C]0.381431268831136[/C][C]0.762862537662272[/C][C]0.618568731168864[/C][/ROW]
[ROW][C]32[/C][C]0.335136233660237[/C][C]0.670272467320473[/C][C]0.664863766339763[/C][/ROW]
[ROW][C]33[/C][C]0.3119018700612[/C][C]0.6238037401224[/C][C]0.6880981299388[/C][/ROW]
[ROW][C]34[/C][C]0.279056086385921[/C][C]0.558112172771842[/C][C]0.720943913614079[/C][/ROW]
[ROW][C]35[/C][C]0.238671956596021[/C][C]0.477343913192041[/C][C]0.761328043403979[/C][/ROW]
[ROW][C]36[/C][C]0.822427491306171[/C][C]0.355145017387658[/C][C]0.177572508693829[/C][/ROW]
[ROW][C]37[/C][C]0.795239547957124[/C][C]0.409520904085752[/C][C]0.204760452042876[/C][/ROW]
[ROW][C]38[/C][C]0.788967979216474[/C][C]0.422064041567053[/C][C]0.211032020783526[/C][/ROW]
[ROW][C]39[/C][C]0.810757543552253[/C][C]0.378484912895494[/C][C]0.189242456447747[/C][/ROW]
[ROW][C]40[/C][C]0.791795061445101[/C][C]0.416409877109797[/C][C]0.208204938554899[/C][/ROW]
[ROW][C]41[/C][C]0.758168151238891[/C][C]0.483663697522218[/C][C]0.241831848761109[/C][/ROW]
[ROW][C]42[/C][C]0.732518815715611[/C][C]0.534962368568778[/C][C]0.267481184284389[/C][/ROW]
[ROW][C]43[/C][C]0.757936033290382[/C][C]0.484127933419236[/C][C]0.242063966709618[/C][/ROW]
[ROW][C]44[/C][C]0.714280355598777[/C][C]0.571439288802446[/C][C]0.285719644401223[/C][/ROW]
[ROW][C]45[/C][C]0.685051413211918[/C][C]0.629897173576164[/C][C]0.314948586788082[/C][/ROW]
[ROW][C]46[/C][C]0.854494660372146[/C][C]0.291010679255708[/C][C]0.145505339627854[/C][/ROW]
[ROW][C]47[/C][C]0.903951256281477[/C][C]0.192097487437047[/C][C]0.0960487437185234[/C][/ROW]
[ROW][C]48[/C][C]0.879926283332063[/C][C]0.240147433335874[/C][C]0.120073716667937[/C][/ROW]
[ROW][C]49[/C][C]0.863242228087183[/C][C]0.273515543825635[/C][C]0.136757771912818[/C][/ROW]
[ROW][C]50[/C][C]0.862182932082898[/C][C]0.275634135834204[/C][C]0.137817067917102[/C][/ROW]
[ROW][C]51[/C][C]0.832815588659683[/C][C]0.334368822680635[/C][C]0.167184411340317[/C][/ROW]
[ROW][C]52[/C][C]0.799168662801659[/C][C]0.401662674396682[/C][C]0.200831337198341[/C][/ROW]
[ROW][C]53[/C][C]0.816639237506691[/C][C]0.366721524986618[/C][C]0.183360762493309[/C][/ROW]
[ROW][C]54[/C][C]0.803546564844617[/C][C]0.392906870310766[/C][C]0.196453435155383[/C][/ROW]
[ROW][C]55[/C][C]0.817396477120319[/C][C]0.365207045759362[/C][C]0.182603522879681[/C][/ROW]
[ROW][C]56[/C][C]0.791381926540367[/C][C]0.417236146919265[/C][C]0.208618073459633[/C][/ROW]
[ROW][C]57[/C][C]0.756078049145763[/C][C]0.487843901708474[/C][C]0.243921950854237[/C][/ROW]
[ROW][C]58[/C][C]0.732028189673837[/C][C]0.535943620652327[/C][C]0.267971810326163[/C][/ROW]
[ROW][C]59[/C][C]0.693329423644893[/C][C]0.613341152710215[/C][C]0.306670576355107[/C][/ROW]
[ROW][C]60[/C][C]0.689665087139697[/C][C]0.620669825720605[/C][C]0.310334912860303[/C][/ROW]
[ROW][C]61[/C][C]0.650943859020914[/C][C]0.698112281958171[/C][C]0.349056140979086[/C][/ROW]
[ROW][C]62[/C][C]0.60768455662005[/C][C]0.784630886759899[/C][C]0.39231544337995[/C][/ROW]
[ROW][C]63[/C][C]0.567620131127143[/C][C]0.864759737745715[/C][C]0.432379868872857[/C][/ROW]
[ROW][C]64[/C][C]0.519776501815927[/C][C]0.960446996368147[/C][C]0.480223498184073[/C][/ROW]
[ROW][C]65[/C][C]0.48084354351511[/C][C]0.96168708703022[/C][C]0.51915645648489[/C][/ROW]
[ROW][C]66[/C][C]0.451550593383987[/C][C]0.903101186767973[/C][C]0.548449406616013[/C][/ROW]
[ROW][C]67[/C][C]0.448713149415605[/C][C]0.897426298831211[/C][C]0.551286850584395[/C][/ROW]
[ROW][C]68[/C][C]0.597827976316705[/C][C]0.80434404736659[/C][C]0.402172023683295[/C][/ROW]
[ROW][C]69[/C][C]0.714897548422659[/C][C]0.570204903154681[/C][C]0.285102451577341[/C][/ROW]
[ROW][C]70[/C][C]0.679068582025202[/C][C]0.641862835949596[/C][C]0.320931417974798[/C][/ROW]
[ROW][C]71[/C][C]0.788914330608549[/C][C]0.422171338782903[/C][C]0.211085669391451[/C][/ROW]
[ROW][C]72[/C][C]0.753062783612474[/C][C]0.493874432775052[/C][C]0.246937216387526[/C][/ROW]
[ROW][C]73[/C][C]0.742878099345636[/C][C]0.514243801308728[/C][C]0.257121900654364[/C][/ROW]
[ROW][C]74[/C][C]0.722335860011046[/C][C]0.555328279977907[/C][C]0.277664139988954[/C][/ROW]
[ROW][C]75[/C][C]0.681603554432983[/C][C]0.636792891134035[/C][C]0.318396445567017[/C][/ROW]
[ROW][C]76[/C][C]0.734409256576541[/C][C]0.531181486846917[/C][C]0.265590743423459[/C][/ROW]
[ROW][C]77[/C][C]0.695949306646728[/C][C]0.608101386706544[/C][C]0.304050693353272[/C][/ROW]
[ROW][C]78[/C][C]0.682701126138282[/C][C]0.634597747723436[/C][C]0.317298873861718[/C][/ROW]
[ROW][C]79[/C][C]0.688375500522499[/C][C]0.623248998955002[/C][C]0.311624499477501[/C][/ROW]
[ROW][C]80[/C][C]0.64566774315465[/C][C]0.7086645136907[/C][C]0.35433225684535[/C][/ROW]
[ROW][C]81[/C][C]0.605446653302202[/C][C]0.789106693395596[/C][C]0.394553346697798[/C][/ROW]
[ROW][C]82[/C][C]0.729842655901343[/C][C]0.540314688197315[/C][C]0.270157344098657[/C][/ROW]
[ROW][C]83[/C][C]0.691288017262303[/C][C]0.617423965475394[/C][C]0.308711982737697[/C][/ROW]
[ROW][C]84[/C][C]0.660443710893438[/C][C]0.679112578213124[/C][C]0.339556289106562[/C][/ROW]
[ROW][C]85[/C][C]0.617199815057986[/C][C]0.765600369884027[/C][C]0.382800184942014[/C][/ROW]
[ROW][C]86[/C][C]0.610446712116875[/C][C]0.77910657576625[/C][C]0.389553287883125[/C][/ROW]
[ROW][C]87[/C][C]0.564709031205829[/C][C]0.870581937588341[/C][C]0.435290968794171[/C][/ROW]
[ROW][C]88[/C][C]0.524269686944033[/C][C]0.951460626111934[/C][C]0.475730313055967[/C][/ROW]
[ROW][C]89[/C][C]0.507439275618151[/C][C]0.985121448763698[/C][C]0.492560724381849[/C][/ROW]
[ROW][C]90[/C][C]0.473356449206898[/C][C]0.946712898413795[/C][C]0.526643550793102[/C][/ROW]
[ROW][C]91[/C][C]0.453806355940542[/C][C]0.907612711881085[/C][C]0.546193644059458[/C][/ROW]
[ROW][C]92[/C][C]0.415660272411428[/C][C]0.831320544822857[/C][C]0.584339727588572[/C][/ROW]
[ROW][C]93[/C][C]0.374058193427304[/C][C]0.748116386854608[/C][C]0.625941806572696[/C][/ROW]
[ROW][C]94[/C][C]0.332724002273097[/C][C]0.665448004546193[/C][C]0.667275997726903[/C][/ROW]
[ROW][C]95[/C][C]0.348396701750437[/C][C]0.696793403500874[/C][C]0.651603298249563[/C][/ROW]
[ROW][C]96[/C][C]0.31413371340584[/C][C]0.628267426811679[/C][C]0.68586628659416[/C][/ROW]
[ROW][C]97[/C][C]0.277525300761364[/C][C]0.555050601522728[/C][C]0.722474699238636[/C][/ROW]
[ROW][C]98[/C][C]0.275793340145572[/C][C]0.551586680291145[/C][C]0.724206659854428[/C][/ROW]
[ROW][C]99[/C][C]0.237431287664622[/C][C]0.474862575329243[/C][C]0.762568712335378[/C][/ROW]
[ROW][C]100[/C][C]0.203484226595957[/C][C]0.406968453191913[/C][C]0.796515773404043[/C][/ROW]
[ROW][C]101[/C][C]0.183032884930502[/C][C]0.366065769861005[/C][C]0.816967115069498[/C][/ROW]
[ROW][C]102[/C][C]0.169138322143129[/C][C]0.338276644286259[/C][C]0.830861677856871[/C][/ROW]
[ROW][C]103[/C][C]0.209156844008761[/C][C]0.418313688017522[/C][C]0.790843155991239[/C][/ROW]
[ROW][C]104[/C][C]0.177024659360187[/C][C]0.354049318720375[/C][C]0.822975340639813[/C][/ROW]
[ROW][C]105[/C][C]0.170892394326739[/C][C]0.341784788653478[/C][C]0.829107605673261[/C][/ROW]
[ROW][C]106[/C][C]0.182198546152627[/C][C]0.364397092305253[/C][C]0.817801453847373[/C][/ROW]
[ROW][C]107[/C][C]0.163441084653873[/C][C]0.326882169307745[/C][C]0.836558915346127[/C][/ROW]
[ROW][C]108[/C][C]0.144488549004639[/C][C]0.288977098009278[/C][C]0.855511450995361[/C][/ROW]
[ROW][C]109[/C][C]0.138613155962659[/C][C]0.277226311925318[/C][C]0.861386844037341[/C][/ROW]
[ROW][C]110[/C][C]0.131131323530462[/C][C]0.262262647060925[/C][C]0.868868676469538[/C][/ROW]
[ROW][C]111[/C][C]0.114623916465379[/C][C]0.229247832930758[/C][C]0.885376083534621[/C][/ROW]
[ROW][C]112[/C][C]0.0943565912631843[/C][C]0.188713182526369[/C][C]0.905643408736816[/C][/ROW]
[ROW][C]113[/C][C]0.110763204447637[/C][C]0.221526408895274[/C][C]0.889236795552363[/C][/ROW]
[ROW][C]114[/C][C]0.103391746807003[/C][C]0.206783493614007[/C][C]0.896608253192997[/C][/ROW]
[ROW][C]115[/C][C]0.132559781308542[/C][C]0.265119562617083[/C][C]0.867440218691458[/C][/ROW]
[ROW][C]116[/C][C]0.12571326942792[/C][C]0.251426538855841[/C][C]0.87428673057208[/C][/ROW]
[ROW][C]117[/C][C]0.108119916477045[/C][C]0.216239832954089[/C][C]0.891880083522955[/C][/ROW]
[ROW][C]118[/C][C]0.0939065183211983[/C][C]0.187813036642397[/C][C]0.906093481678802[/C][/ROW]
[ROW][C]119[/C][C]0.0962079805556605[/C][C]0.192415961111321[/C][C]0.90379201944434[/C][/ROW]
[ROW][C]120[/C][C]0.0890200702258129[/C][C]0.178040140451626[/C][C]0.910979929774187[/C][/ROW]
[ROW][C]121[/C][C]0.0743769915566403[/C][C]0.148753983113281[/C][C]0.92562300844336[/C][/ROW]
[ROW][C]122[/C][C]0.0583201878708909[/C][C]0.116640375741782[/C][C]0.941679812129109[/C][/ROW]
[ROW][C]123[/C][C]0.0662960443391666[/C][C]0.132592088678333[/C][C]0.933703955660833[/C][/ROW]
[ROW][C]124[/C][C]0.052282285508264[/C][C]0.104564571016528[/C][C]0.947717714491736[/C][/ROW]
[ROW][C]125[/C][C]0.0416468077028503[/C][C]0.0832936154057006[/C][C]0.95835319229715[/C][/ROW]
[ROW][C]126[/C][C]0.0314967268526307[/C][C]0.0629934537052614[/C][C]0.968503273147369[/C][/ROW]
[ROW][C]127[/C][C]0.0233392342181861[/C][C]0.0466784684363721[/C][C]0.976660765781814[/C][/ROW]
[ROW][C]128[/C][C]0.0180377721071056[/C][C]0.0360755442142113[/C][C]0.981962227892894[/C][/ROW]
[ROW][C]129[/C][C]0.0145764290676494[/C][C]0.0291528581352988[/C][C]0.985423570932351[/C][/ROW]
[ROW][C]130[/C][C]0.0205695931180825[/C][C]0.0411391862361649[/C][C]0.979430406881918[/C][/ROW]
[ROW][C]131[/C][C]0.0166593916750062[/C][C]0.0333187833500124[/C][C]0.983340608324994[/C][/ROW]
[ROW][C]132[/C][C]0.0263109324538084[/C][C]0.0526218649076167[/C][C]0.973689067546192[/C][/ROW]
[ROW][C]133[/C][C]0.0268698396492377[/C][C]0.0537396792984755[/C][C]0.973130160350762[/C][/ROW]
[ROW][C]134[/C][C]0.0363962921632021[/C][C]0.0727925843264042[/C][C]0.963603707836798[/C][/ROW]
[ROW][C]135[/C][C]0.0282478569337202[/C][C]0.0564957138674404[/C][C]0.97175214306628[/C][/ROW]
[ROW][C]136[/C][C]0.0190045100546282[/C][C]0.0380090201092563[/C][C]0.980995489945372[/C][/ROW]
[ROW][C]137[/C][C]0.0124365964868633[/C][C]0.0248731929737266[/C][C]0.987563403513137[/C][/ROW]
[ROW][C]138[/C][C]0.00888079660343393[/C][C]0.0177615932068679[/C][C]0.991119203396566[/C][/ROW]
[ROW][C]139[/C][C]0.0131226755967588[/C][C]0.0262453511935175[/C][C]0.986877324403241[/C][/ROW]
[ROW][C]140[/C][C]0.010573700505023[/C][C]0.0211474010100461[/C][C]0.989426299494977[/C][/ROW]
[ROW][C]141[/C][C]0.554083709052446[/C][C]0.891832581895109[/C][C]0.445916290947554[/C][/ROW]
[ROW][C]142[/C][C]0.487503327763281[/C][C]0.975006655526561[/C][C]0.512496672236719[/C][/ROW]
[ROW][C]143[/C][C]0.401658078866441[/C][C]0.803316157732882[/C][C]0.598341921133559[/C][/ROW]
[ROW][C]144[/C][C]0.321939186189558[/C][C]0.643878372379115[/C][C]0.678060813810442[/C][/ROW]
[ROW][C]145[/C][C]0.239550170897424[/C][C]0.479100341794848[/C][C]0.760449829102576[/C][/ROW]
[ROW][C]146[/C][C]0.294878452443602[/C][C]0.589756904887205[/C][C]0.705121547556398[/C][/ROW]
[ROW][C]147[/C][C]0.237363937919248[/C][C]0.474727875838496[/C][C]0.762636062080752[/C][/ROW]
[ROW][C]148[/C][C]0.738241860308678[/C][C]0.523516279382645[/C][C]0.261758139691322[/C][/ROW]
[ROW][C]149[/C][C]0.781658179518841[/C][C]0.436683640962318[/C][C]0.218341820481159[/C][/ROW]
[ROW][C]150[/C][C]0.624799515833548[/C][C]0.750400968332904[/C][C]0.375200484166452[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186301&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186301&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
110.389151031034470.778302062068940.61084896896553
120.6221400278953450.7557199442093110.377859972104655
130.4830330586879750.9660661173759490.516966941312025
140.4563873897633860.9127747795267720.543612610236614
150.347784746012440.6955694920248810.65221525398756
160.2636006141806020.5272012283612040.736399385819398
170.2146523244587250.429304648917450.785347675541275
180.4141166250245160.8282332500490310.585883374975484
190.3372706034614720.6745412069229440.662729396538528
200.2585422286366250.517084457273250.741457771363375
210.1994993802161650.3989987604323290.800500619783835
220.1818104323799140.3636208647598280.818189567620086
230.3641589067842930.7283178135685860.635841093215707
240.4002190568606080.8004381137212150.599780943139392
250.3891382064419520.7782764128839030.610861793558048
260.3516284032716040.7032568065432090.648371596728396
270.4106395281051190.8212790562102370.589360471894881
280.4165826719030860.8331653438061720.583417328096914
290.3977145230912480.7954290461824960.602285476908752
300.4392798064871250.878559612974250.560720193512875
310.3814312688311360.7628625376622720.618568731168864
320.3351362336602370.6702724673204730.664863766339763
330.31190187006120.62380374012240.6880981299388
340.2790560863859210.5581121727718420.720943913614079
350.2386719565960210.4773439131920410.761328043403979
360.8224274913061710.3551450173876580.177572508693829
370.7952395479571240.4095209040857520.204760452042876
380.7889679792164740.4220640415670530.211032020783526
390.8107575435522530.3784849128954940.189242456447747
400.7917950614451010.4164098771097970.208204938554899
410.7581681512388910.4836636975222180.241831848761109
420.7325188157156110.5349623685687780.267481184284389
430.7579360332903820.4841279334192360.242063966709618
440.7142803555987770.5714392888024460.285719644401223
450.6850514132119180.6298971735761640.314948586788082
460.8544946603721460.2910106792557080.145505339627854
470.9039512562814770.1920974874370470.0960487437185234
480.8799262833320630.2401474333358740.120073716667937
490.8632422280871830.2735155438256350.136757771912818
500.8621829320828980.2756341358342040.137817067917102
510.8328155886596830.3343688226806350.167184411340317
520.7991686628016590.4016626743966820.200831337198341
530.8166392375066910.3667215249866180.183360762493309
540.8035465648446170.3929068703107660.196453435155383
550.8173964771203190.3652070457593620.182603522879681
560.7913819265403670.4172361469192650.208618073459633
570.7560780491457630.4878439017084740.243921950854237
580.7320281896738370.5359436206523270.267971810326163
590.6933294236448930.6133411527102150.306670576355107
600.6896650871396970.6206698257206050.310334912860303
610.6509438590209140.6981122819581710.349056140979086
620.607684556620050.7846308867598990.39231544337995
630.5676201311271430.8647597377457150.432379868872857
640.5197765018159270.9604469963681470.480223498184073
650.480843543515110.961687087030220.51915645648489
660.4515505933839870.9031011867679730.548449406616013
670.4487131494156050.8974262988312110.551286850584395
680.5978279763167050.804344047366590.402172023683295
690.7148975484226590.5702049031546810.285102451577341
700.6790685820252020.6418628359495960.320931417974798
710.7889143306085490.4221713387829030.211085669391451
720.7530627836124740.4938744327750520.246937216387526
730.7428780993456360.5142438013087280.257121900654364
740.7223358600110460.5553282799779070.277664139988954
750.6816035544329830.6367928911340350.318396445567017
760.7344092565765410.5311814868469170.265590743423459
770.6959493066467280.6081013867065440.304050693353272
780.6827011261382820.6345977477234360.317298873861718
790.6883755005224990.6232489989550020.311624499477501
800.645667743154650.70866451369070.35433225684535
810.6054466533022020.7891066933955960.394553346697798
820.7298426559013430.5403146881973150.270157344098657
830.6912880172623030.6174239654753940.308711982737697
840.6604437108934380.6791125782131240.339556289106562
850.6171998150579860.7656003698840270.382800184942014
860.6104467121168750.779106575766250.389553287883125
870.5647090312058290.8705819375883410.435290968794171
880.5242696869440330.9514606261119340.475730313055967
890.5074392756181510.9851214487636980.492560724381849
900.4733564492068980.9467128984137950.526643550793102
910.4538063559405420.9076127118810850.546193644059458
920.4156602724114280.8313205448228570.584339727588572
930.3740581934273040.7481163868546080.625941806572696
940.3327240022730970.6654480045461930.667275997726903
950.3483967017504370.6967934035008740.651603298249563
960.314133713405840.6282674268116790.68586628659416
970.2775253007613640.5550506015227280.722474699238636
980.2757933401455720.5515866802911450.724206659854428
990.2374312876646220.4748625753292430.762568712335378
1000.2034842265959570.4069684531919130.796515773404043
1010.1830328849305020.3660657698610050.816967115069498
1020.1691383221431290.3382766442862590.830861677856871
1030.2091568440087610.4183136880175220.790843155991239
1040.1770246593601870.3540493187203750.822975340639813
1050.1708923943267390.3417847886534780.829107605673261
1060.1821985461526270.3643970923052530.817801453847373
1070.1634410846538730.3268821693077450.836558915346127
1080.1444885490046390.2889770980092780.855511450995361
1090.1386131559626590.2772263119253180.861386844037341
1100.1311313235304620.2622626470609250.868868676469538
1110.1146239164653790.2292478329307580.885376083534621
1120.09435659126318430.1887131825263690.905643408736816
1130.1107632044476370.2215264088952740.889236795552363
1140.1033917468070030.2067834936140070.896608253192997
1150.1325597813085420.2651195626170830.867440218691458
1160.125713269427920.2514265388558410.87428673057208
1170.1081199164770450.2162398329540890.891880083522955
1180.09390651832119830.1878130366423970.906093481678802
1190.09620798055566050.1924159611113210.90379201944434
1200.08902007022581290.1780401404516260.910979929774187
1210.07437699155664030.1487539831132810.92562300844336
1220.05832018787089090.1166403757417820.941679812129109
1230.06629604433916660.1325920886783330.933703955660833
1240.0522822855082640.1045645710165280.947717714491736
1250.04164680770285030.08329361540570060.95835319229715
1260.03149672685263070.06299345370526140.968503273147369
1270.02333923421818610.04667846843637210.976660765781814
1280.01803777210710560.03607554421421130.981962227892894
1290.01457642906764940.02915285813529880.985423570932351
1300.02056959311808250.04113918623616490.979430406881918
1310.01665939167500620.03331878335001240.983340608324994
1320.02631093245380840.05262186490761670.973689067546192
1330.02686983964923770.05373967929847550.973130160350762
1340.03639629216320210.07279258432640420.963603707836798
1350.02824785693372020.05649571386744040.97175214306628
1360.01900451005462820.03800902010925630.980995489945372
1370.01243659648686330.02487319297372660.987563403513137
1380.008880796603433930.01776159320686790.991119203396566
1390.01312267559675880.02624535119351750.986877324403241
1400.0105737005050230.02114740101004610.989426299494977
1410.5540837090524460.8918325818951090.445916290947554
1420.4875033277632810.9750066555265610.512496672236719
1430.4016580788664410.8033161577328820.598341921133559
1440.3219391861895580.6438783723791150.678060813810442
1450.2395501708974240.4791003417948480.760449829102576
1460.2948784524436020.5897569048872050.705121547556398
1470.2373639379192480.4747278758384960.762636062080752
1480.7382418603086780.5235162793826450.261758139691322
1490.7816581795188410.4366836409623180.218341820481159
1500.6247995158335480.7504009683329040.375200484166452







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level100.0714285714285714NOK
10% type I error level160.114285714285714NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 10 & 0.0714285714285714 & NOK \tabularnewline
10% type I error level & 16 & 0.114285714285714 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186301&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.0714285714285714[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.114285714285714[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186301&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186301&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 level00OK
5% type I error level100.0714285714285714NOK
10% type I error level160.114285714285714NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}