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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 22 Dec 2012 16:44:37 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/22/t1356212699hs2b338c2q3qwee.htm/, Retrieved Thu, 18 Apr 2024 17:16:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204621, Retrieved Thu, 18 Apr 2024 17:16:51 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact145

Tree of Dependent Computations

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] [] [2012-11-05 21:22:39] [43bd65bee76289cab2ce37423d405966]
-   P     [Multiple Regression] [] [2012-12-20 10:32:02] [43bd65bee76289cab2ce37423d405966]
- R           [Multiple Regression] [] [2012-12-22 21:44:37] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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
34	36	13	8	13	16	69	46

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204621&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204621&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204621&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 28.0319250600556 -0.0162764938429117Connected[t] + 0.00305274942489057Separate[t] -0.144083343591928Learning[t] -0.0466135490343518Software[t] -0.641378322065256Happiness[t] -0.121880686933211Belonging[t] + 0.130909410471364Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  28.0319250600556 -0.0162764938429117Connected[t] +  0.00305274942489057Separate[t] -0.144083343591928Learning[t] -0.0466135490343518Software[t] -0.641378322065256Happiness[t] -0.121880686933211Belonging[t] +  0.130909410471364Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204621&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  28.0319250600556 -0.0162764938429117Connected[t] +  0.00305274942489057Separate[t] -0.144083343591928Learning[t] -0.0466135490343518Software[t] -0.641378322065256Happiness[t] -0.121880686933211Belonging[t] +  0.130909410471364Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204621&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204621&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 28.0319250600556 -0.0162764938429117Connected[t] + 0.00305274942489057Separate[t] -0.144083343591928Learning[t] -0.0466135490343518Software[t] -0.641378322065256Happiness[t] -0.121880686933211Belonging[t] + 0.130909410471364Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)28.03192506005562.9917899.369600
Connected-0.01627649384291170.067978-0.23940.8110840.405542
Separate0.003052749424890570.0638060.04780.9619030.480951
Learning-0.1440833435919280.114073-1.26310.2084710.104236
Software-0.04661354903435180.116114-0.40140.6886470.344324
Happiness-0.6413783220652560.095854-6.691200
Belonging-0.1218806869332110.062732-1.94290.0538550.026928
Belonging_Final0.1309094104713640.0905921.4450.1504790.075239

\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) & 28.0319250600556 & 2.991789 & 9.3696 & 0 & 0 \tabularnewline
Connected & -0.0162764938429117 & 0.067978 & -0.2394 & 0.811084 & 0.405542 \tabularnewline
Separate & 0.00305274942489057 & 0.063806 & 0.0478 & 0.961903 & 0.480951 \tabularnewline
Learning & -0.144083343591928 & 0.114073 & -1.2631 & 0.208471 & 0.104236 \tabularnewline
Software & -0.0466135490343518 & 0.116114 & -0.4014 & 0.688647 & 0.344324 \tabularnewline
Happiness & -0.641378322065256 & 0.095854 & -6.6912 & 0 & 0 \tabularnewline
Belonging & -0.121880686933211 & 0.062732 & -1.9429 & 0.053855 & 0.026928 \tabularnewline
Belonging_Final & 0.130909410471364 & 0.090592 & 1.445 & 0.150479 & 0.075239 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204621&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]28.0319250600556[/C][C]2.991789[/C][C]9.3696[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0162764938429117[/C][C]0.067978[/C][C]-0.2394[/C][C]0.811084[/C][C]0.405542[/C][/ROW]
[ROW][C]Separate[/C][C]0.00305274942489057[/C][C]0.063806[/C][C]0.0478[/C][C]0.961903[/C][C]0.480951[/C][/ROW]
[ROW][C]Learning[/C][C]-0.144083343591928[/C][C]0.114073[/C][C]-1.2631[/C][C]0.208471[/C][C]0.104236[/C][/ROW]
[ROW][C]Software[/C][C]-0.0466135490343518[/C][C]0.116114[/C][C]-0.4014[/C][C]0.688647[/C][C]0.344324[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.641378322065256[/C][C]0.095854[/C][C]-6.6912[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.121880686933211[/C][C]0.062732[/C][C]-1.9429[/C][C]0.053855[/C][C]0.026928[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]0.130909410471364[/C][C]0.090592[/C][C]1.445[/C][C]0.150479[/C][C]0.075239[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204621&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204621&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)28.03192506005562.9917899.369600
Connected-0.01627649384291170.067978-0.23940.8110840.405542
Separate0.003052749424890570.0638060.04780.9619030.480951
Learning-0.1440833435919280.114073-1.26310.2084710.104236
Software-0.04661354903435180.116114-0.40140.6886470.344324
Happiness-0.6413783220652560.095854-6.691200
Belonging-0.1218806869332110.062732-1.94290.0538550.026928
Belonging_Final0.1309094104713640.0905921.4450.1504790.075239






Multiple Linear Regression - Regression Statistics
Multiple R0.581866339640286
R-squared0.338568437206384
Adjusted R-squared0.308503366170311
F-TEST (value)11.2611886664147
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value1.76930692319388e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.6328617115281
Sum Squared Residuals1067.52196197273

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.581866339640286 \tabularnewline
R-squared & 0.338568437206384 \tabularnewline
Adjusted R-squared & 0.308503366170311 \tabularnewline
F-TEST (value) & 11.2611886664147 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 1.76930692319388e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.6328617115281 \tabularnewline
Sum Squared Residuals & 1067.52196197273 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204621&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.581866339640286[/C][/ROW]
[ROW][C]R-squared[/C][C]0.338568437206384[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.308503366170311[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.2611886664147[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]1.76930692319388e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.6328617115281[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1067.52196197273[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204621&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204621&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.581866339640286
R-squared0.338568437206384
Adjusted R-squared0.308503366170311
F-TEST (value)11.2611886664147
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value1.76930692319388e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.6328617115281
Sum Squared Residuals1067.52196197273






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11213.7982754542448-1.79827545424476
2119.326578305538671.67342169446133
31414.6125980703651-0.612598070365088
41214.6919029738794-2.69190297387944
52111.46518057209869.53481942790137
61210.3127974660261.68720253397399
72213.59950618676158.40049381323847
81112.4593930578592-1.45939305785922
91012.2273659200577-2.22736592005769
101312.37390307217990.626096927820059
111010.619139908646-0.619139908646044
12810.2373340081958-2.23733400819577
131515.5739744326327-0.573974432632679
141412.17146403959671.82853596040328
15109.248901610000770.75109838999923
161413.5555676717420.444432328258013
171412.68684906139181.31315093860816
18119.528569552141781.47143044785822
191012.9945224109822-2.99452241098218
201312.24755562152790.752444378472132
21710.5720861458145-3.57208614581452
221415.1742587037778-1.17425870377776
231212.9287435548827-0.928743554882747
241415.0524593329875-1.05245933298749
251110.04800979255210.951990207447938
26915.0367662544933-6.03676625449332
271110.88546781406810.114532185931865
281513.16486280503551.83513719496448
291411.49440046781992.50559953218005
301315.6348091932846-2.63480919328457
31911.294270997927-2.29427099792702
321514.98877445682890.0112255431711478
331011.1131721223103-1.11317212231031
341111.9372355443196-0.937235544319606
351312.49330525127020.506694748729773
36813.1213718751578-5.12137187515782
372017.32678725271832.67321274728172
381212.3250836610648-0.325083661064816
391010.7385421826178-0.73854218261779
401013.5420291733181-3.54202917331815
41912.006991588825-3.00699158882496
421411.35964498942392.6403550105761
43811.4066364663852-3.40663646638517
441415.1035423452468-1.10354234524684
451114.4074809539141-3.40748095391407
461315.285589814494-2.28558981449403
47912.9955177424932-3.99551774249325
481112.1847015811421-1.18470158114211
491510.31948615463834.68051384536174
501113.3465306412945-2.3465306412945
511011.3917099997813-1.39170999978125
521413.21043132461450.789568675385497
531815.43345864451882.56654135548115
541415.4519095396656-1.45190953966564
551115.4166630365736-4.41666303657358
561212.4996699192225-0.499669919222525
571311.2459386798551.75406132014504
58912.5105185174677-3.51051851746771
591013.9008692132683-3.90086921326835
601514.19548896834640.804511031653555
612018.1722104134231.82778958657697
621212.820708079094-0.820708079094002
631214.0783963499523-2.07839634995235
641413.84538362630340.154616373696595
651312.82624203586790.17375796413205
661114.7701294635527-3.77012946355272
671714.70563599993392.2943640000661
681213.2932610736262-1.2932610736262
691312.9062885558030.0937114441969621
701412.17825642881851.82174357118151
711314.3242869724282-1.32428697242824
721512.58984267752392.41015732247613
731311.14213285588831.85786714411172
741011.498425338794-1.49842533879403
751112.6658270523158-1.66582705231576
761913.58735179421375.41264820578627
771310.8253619715062.17463802849404
781713.76740544021973.23259455978035
791312.38562697683640.614373023163554
80914.0479582007702-5.04795820077017
811111.5125874396328-0.512587439632772
821010.1861960447225-0.18619604472254
83912.3272232863662-3.32722328636616
841211.27958412769830.720415872301701
851212.3048013148419-0.304801314841931
861312.60659989698010.393400103019936
871312.22498389024130.775016109758725
881212.8430294902949-0.8430294902949
891514.19343389863790.806566101362088
902217.48273706245694.51726293754312
911311.70984192625811.29015807374189
921513.79557445104251.20442554895745
931311.73905957293781.26094042706218
941512.18608036387682.81391963612316
951013.2834123168995-3.28341231689952
961110.87580597611690.124194023883097
971614.14802876991571.85197123008433
981112.367870360924-1.36787036092402
991110.17667676835710.823323231642862
1001012.2758997795279-2.27589977952787
1011010.2899538785473-0.289953878547267
1021614.01555194119431.98444805880574
1031210.16343070522181.83656929477818
1041115.3214634072101-4.32146340721015
1051611.69926864741324.30073135258684
1061916.35310145210862.64689854789135
1071111.1392420266071-0.139242026607136
1081611.97385100121574.02614899878425
1091516.0594052052423-1.05940520524232
1102417.31194382887296.68805617112711
1111411.58239759292622.41760240707381
1121514.35585298295960.64414701704043
1131112.8106730420164-1.81067304201645
1141513.05522638484031.94477361515968
1151210.6833529429231.31664705707701
1161010.5223129730452-0.52231297304517
1171413.75292473474960.247075265250363
1181313.4596285103321-0.459628510332111
119913.3249458195299-4.32494581952992
1201511.91008043046043.08991956953958
1211515.2638335722661-0.263833572266107
1221412.47807251632511.52192748367488
1231111.7010816639075-0.701081663907469
124811.8569113363132-3.8569113363132
1251112.1951681981938-1.1951681981938
1261112.8098075865427-1.80980758654274
127810.1947684143828-2.19476841438281
1281010.3100624306707-0.310062430670654
129119.903602641499861.09639735850014
1301312.68516911466410.314830885335913
1311113.3470972809805-2.34709728098046
1322017.14013492142882.85986507857122
1331011.8319203021611-1.83192030216111
1341513.13574998377971.86425001622027
1351212.3511570849608-0.351157084960815
1361411.5609235215282.43907647847197
1372316.03403022024136.96596977975873
1381413.75178676010240.248213239897576
1391616.5445349341713-0.544534934171252
1401113.2794703647723-2.27947036477227
1411214.681611534838-2.68161153483798
1421013.0544074679659-3.05440746796585
1431411.24300070863222.75699929136778
1441211.9790195678740.0209804321260404
1451211.79582960769580.204170392304208
1461111.7523419961418-0.752341996141819
1471211.02178210163920.978217898360798
1481315.7299790451009-2.72997904510094
1491113.9252579002798-2.92525790027984
1501916.6194681861172.38053181388298
1511212.0816344550512-0.0816344550512346
1521712.88028759363094.11971240636909
153911.3213772127863-2.32137721278631
1541214.2843549482132-2.28435494821325
1551916.6228984067282.37710159327202
1561814.60746233361783.39253766638217
1571513.79557445104251.20442554895745
1581413.71130496786570.288695032134259
159119.903602641499861.09639735850014
160912.8586137589459-3.85861375894593
1611814.12696792797113.87303207202892
1621614.61657868616571.38342131383433

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 13.7982754542448 & -1.79827545424476 \tabularnewline
2 & 11 & 9.32657830553867 & 1.67342169446133 \tabularnewline
3 & 14 & 14.6125980703651 & -0.612598070365088 \tabularnewline
4 & 12 & 14.6919029738794 & -2.69190297387944 \tabularnewline
5 & 21 & 11.4651805720986 & 9.53481942790137 \tabularnewline
6 & 12 & 10.312797466026 & 1.68720253397399 \tabularnewline
7 & 22 & 13.5995061867615 & 8.40049381323847 \tabularnewline
8 & 11 & 12.4593930578592 & -1.45939305785922 \tabularnewline
9 & 10 & 12.2273659200577 & -2.22736592005769 \tabularnewline
10 & 13 & 12.3739030721799 & 0.626096927820059 \tabularnewline
11 & 10 & 10.619139908646 & -0.619139908646044 \tabularnewline
12 & 8 & 10.2373340081958 & -2.23733400819577 \tabularnewline
13 & 15 & 15.5739744326327 & -0.573974432632679 \tabularnewline
14 & 14 & 12.1714640395967 & 1.82853596040328 \tabularnewline
15 & 10 & 9.24890161000077 & 0.75109838999923 \tabularnewline
16 & 14 & 13.555567671742 & 0.444432328258013 \tabularnewline
17 & 14 & 12.6868490613918 & 1.31315093860816 \tabularnewline
18 & 11 & 9.52856955214178 & 1.47143044785822 \tabularnewline
19 & 10 & 12.9945224109822 & -2.99452241098218 \tabularnewline
20 & 13 & 12.2475556215279 & 0.752444378472132 \tabularnewline
21 & 7 & 10.5720861458145 & -3.57208614581452 \tabularnewline
22 & 14 & 15.1742587037778 & -1.17425870377776 \tabularnewline
23 & 12 & 12.9287435548827 & -0.928743554882747 \tabularnewline
24 & 14 & 15.0524593329875 & -1.05245933298749 \tabularnewline
25 & 11 & 10.0480097925521 & 0.951990207447938 \tabularnewline
26 & 9 & 15.0367662544933 & -6.03676625449332 \tabularnewline
27 & 11 & 10.8854678140681 & 0.114532185931865 \tabularnewline
28 & 15 & 13.1648628050355 & 1.83513719496448 \tabularnewline
29 & 14 & 11.4944004678199 & 2.50559953218005 \tabularnewline
30 & 13 & 15.6348091932846 & -2.63480919328457 \tabularnewline
31 & 9 & 11.294270997927 & -2.29427099792702 \tabularnewline
32 & 15 & 14.9887744568289 & 0.0112255431711478 \tabularnewline
33 & 10 & 11.1131721223103 & -1.11317212231031 \tabularnewline
34 & 11 & 11.9372355443196 & -0.937235544319606 \tabularnewline
35 & 13 & 12.4933052512702 & 0.506694748729773 \tabularnewline
36 & 8 & 13.1213718751578 & -5.12137187515782 \tabularnewline
37 & 20 & 17.3267872527183 & 2.67321274728172 \tabularnewline
38 & 12 & 12.3250836610648 & -0.325083661064816 \tabularnewline
39 & 10 & 10.7385421826178 & -0.73854218261779 \tabularnewline
40 & 10 & 13.5420291733181 & -3.54202917331815 \tabularnewline
41 & 9 & 12.006991588825 & -3.00699158882496 \tabularnewline
42 & 14 & 11.3596449894239 & 2.6403550105761 \tabularnewline
43 & 8 & 11.4066364663852 & -3.40663646638517 \tabularnewline
44 & 14 & 15.1035423452468 & -1.10354234524684 \tabularnewline
45 & 11 & 14.4074809539141 & -3.40748095391407 \tabularnewline
46 & 13 & 15.285589814494 & -2.28558981449403 \tabularnewline
47 & 9 & 12.9955177424932 & -3.99551774249325 \tabularnewline
48 & 11 & 12.1847015811421 & -1.18470158114211 \tabularnewline
49 & 15 & 10.3194861546383 & 4.68051384536174 \tabularnewline
50 & 11 & 13.3465306412945 & -2.3465306412945 \tabularnewline
51 & 10 & 11.3917099997813 & -1.39170999978125 \tabularnewline
52 & 14 & 13.2104313246145 & 0.789568675385497 \tabularnewline
53 & 18 & 15.4334586445188 & 2.56654135548115 \tabularnewline
54 & 14 & 15.4519095396656 & -1.45190953966564 \tabularnewline
55 & 11 & 15.4166630365736 & -4.41666303657358 \tabularnewline
56 & 12 & 12.4996699192225 & -0.499669919222525 \tabularnewline
57 & 13 & 11.245938679855 & 1.75406132014504 \tabularnewline
58 & 9 & 12.5105185174677 & -3.51051851746771 \tabularnewline
59 & 10 & 13.9008692132683 & -3.90086921326835 \tabularnewline
60 & 15 & 14.1954889683464 & 0.804511031653555 \tabularnewline
61 & 20 & 18.172210413423 & 1.82778958657697 \tabularnewline
62 & 12 & 12.820708079094 & -0.820708079094002 \tabularnewline
63 & 12 & 14.0783963499523 & -2.07839634995235 \tabularnewline
64 & 14 & 13.8453836263034 & 0.154616373696595 \tabularnewline
65 & 13 & 12.8262420358679 & 0.17375796413205 \tabularnewline
66 & 11 & 14.7701294635527 & -3.77012946355272 \tabularnewline
67 & 17 & 14.7056359999339 & 2.2943640000661 \tabularnewline
68 & 12 & 13.2932610736262 & -1.2932610736262 \tabularnewline
69 & 13 & 12.906288555803 & 0.0937114441969621 \tabularnewline
70 & 14 & 12.1782564288185 & 1.82174357118151 \tabularnewline
71 & 13 & 14.3242869724282 & -1.32428697242824 \tabularnewline
72 & 15 & 12.5898426775239 & 2.41015732247613 \tabularnewline
73 & 13 & 11.1421328558883 & 1.85786714411172 \tabularnewline
74 & 10 & 11.498425338794 & -1.49842533879403 \tabularnewline
75 & 11 & 12.6658270523158 & -1.66582705231576 \tabularnewline
76 & 19 & 13.5873517942137 & 5.41264820578627 \tabularnewline
77 & 13 & 10.825361971506 & 2.17463802849404 \tabularnewline
78 & 17 & 13.7674054402197 & 3.23259455978035 \tabularnewline
79 & 13 & 12.3856269768364 & 0.614373023163554 \tabularnewline
80 & 9 & 14.0479582007702 & -5.04795820077017 \tabularnewline
81 & 11 & 11.5125874396328 & -0.512587439632772 \tabularnewline
82 & 10 & 10.1861960447225 & -0.18619604472254 \tabularnewline
83 & 9 & 12.3272232863662 & -3.32722328636616 \tabularnewline
84 & 12 & 11.2795841276983 & 0.720415872301701 \tabularnewline
85 & 12 & 12.3048013148419 & -0.304801314841931 \tabularnewline
86 & 13 & 12.6065998969801 & 0.393400103019936 \tabularnewline
87 & 13 & 12.2249838902413 & 0.775016109758725 \tabularnewline
88 & 12 & 12.8430294902949 & -0.8430294902949 \tabularnewline
89 & 15 & 14.1934338986379 & 0.806566101362088 \tabularnewline
90 & 22 & 17.4827370624569 & 4.51726293754312 \tabularnewline
91 & 13 & 11.7098419262581 & 1.29015807374189 \tabularnewline
92 & 15 & 13.7955744510425 & 1.20442554895745 \tabularnewline
93 & 13 & 11.7390595729378 & 1.26094042706218 \tabularnewline
94 & 15 & 12.1860803638768 & 2.81391963612316 \tabularnewline
95 & 10 & 13.2834123168995 & -3.28341231689952 \tabularnewline
96 & 11 & 10.8758059761169 & 0.124194023883097 \tabularnewline
97 & 16 & 14.1480287699157 & 1.85197123008433 \tabularnewline
98 & 11 & 12.367870360924 & -1.36787036092402 \tabularnewline
99 & 11 & 10.1766767683571 & 0.823323231642862 \tabularnewline
100 & 10 & 12.2758997795279 & -2.27589977952787 \tabularnewline
101 & 10 & 10.2899538785473 & -0.289953878547267 \tabularnewline
102 & 16 & 14.0155519411943 & 1.98444805880574 \tabularnewline
103 & 12 & 10.1634307052218 & 1.83656929477818 \tabularnewline
104 & 11 & 15.3214634072101 & -4.32146340721015 \tabularnewline
105 & 16 & 11.6992686474132 & 4.30073135258684 \tabularnewline
106 & 19 & 16.3531014521086 & 2.64689854789135 \tabularnewline
107 & 11 & 11.1392420266071 & -0.139242026607136 \tabularnewline
108 & 16 & 11.9738510012157 & 4.02614899878425 \tabularnewline
109 & 15 & 16.0594052052423 & -1.05940520524232 \tabularnewline
110 & 24 & 17.3119438288729 & 6.68805617112711 \tabularnewline
111 & 14 & 11.5823975929262 & 2.41760240707381 \tabularnewline
112 & 15 & 14.3558529829596 & 0.64414701704043 \tabularnewline
113 & 11 & 12.8106730420164 & -1.81067304201645 \tabularnewline
114 & 15 & 13.0552263848403 & 1.94477361515968 \tabularnewline
115 & 12 & 10.683352942923 & 1.31664705707701 \tabularnewline
116 & 10 & 10.5223129730452 & -0.52231297304517 \tabularnewline
117 & 14 & 13.7529247347496 & 0.247075265250363 \tabularnewline
118 & 13 & 13.4596285103321 & -0.459628510332111 \tabularnewline
119 & 9 & 13.3249458195299 & -4.32494581952992 \tabularnewline
120 & 15 & 11.9100804304604 & 3.08991956953958 \tabularnewline
121 & 15 & 15.2638335722661 & -0.263833572266107 \tabularnewline
122 & 14 & 12.4780725163251 & 1.52192748367488 \tabularnewline
123 & 11 & 11.7010816639075 & -0.701081663907469 \tabularnewline
124 & 8 & 11.8569113363132 & -3.8569113363132 \tabularnewline
125 & 11 & 12.1951681981938 & -1.1951681981938 \tabularnewline
126 & 11 & 12.8098075865427 & -1.80980758654274 \tabularnewline
127 & 8 & 10.1947684143828 & -2.19476841438281 \tabularnewline
128 & 10 & 10.3100624306707 & -0.310062430670654 \tabularnewline
129 & 11 & 9.90360264149986 & 1.09639735850014 \tabularnewline
130 & 13 & 12.6851691146641 & 0.314830885335913 \tabularnewline
131 & 11 & 13.3470972809805 & -2.34709728098046 \tabularnewline
132 & 20 & 17.1401349214288 & 2.85986507857122 \tabularnewline
133 & 10 & 11.8319203021611 & -1.83192030216111 \tabularnewline
134 & 15 & 13.1357499837797 & 1.86425001622027 \tabularnewline
135 & 12 & 12.3511570849608 & -0.351157084960815 \tabularnewline
136 & 14 & 11.560923521528 & 2.43907647847197 \tabularnewline
137 & 23 & 16.0340302202413 & 6.96596977975873 \tabularnewline
138 & 14 & 13.7517867601024 & 0.248213239897576 \tabularnewline
139 & 16 & 16.5445349341713 & -0.544534934171252 \tabularnewline
140 & 11 & 13.2794703647723 & -2.27947036477227 \tabularnewline
141 & 12 & 14.681611534838 & -2.68161153483798 \tabularnewline
142 & 10 & 13.0544074679659 & -3.05440746796585 \tabularnewline
143 & 14 & 11.2430007086322 & 2.75699929136778 \tabularnewline
144 & 12 & 11.979019567874 & 0.0209804321260404 \tabularnewline
145 & 12 & 11.7958296076958 & 0.204170392304208 \tabularnewline
146 & 11 & 11.7523419961418 & -0.752341996141819 \tabularnewline
147 & 12 & 11.0217821016392 & 0.978217898360798 \tabularnewline
148 & 13 & 15.7299790451009 & -2.72997904510094 \tabularnewline
149 & 11 & 13.9252579002798 & -2.92525790027984 \tabularnewline
150 & 19 & 16.619468186117 & 2.38053181388298 \tabularnewline
151 & 12 & 12.0816344550512 & -0.0816344550512346 \tabularnewline
152 & 17 & 12.8802875936309 & 4.11971240636909 \tabularnewline
153 & 9 & 11.3213772127863 & -2.32137721278631 \tabularnewline
154 & 12 & 14.2843549482132 & -2.28435494821325 \tabularnewline
155 & 19 & 16.622898406728 & 2.37710159327202 \tabularnewline
156 & 18 & 14.6074623336178 & 3.39253766638217 \tabularnewline
157 & 15 & 13.7955744510425 & 1.20442554895745 \tabularnewline
158 & 14 & 13.7113049678657 & 0.288695032134259 \tabularnewline
159 & 11 & 9.90360264149986 & 1.09639735850014 \tabularnewline
160 & 9 & 12.8586137589459 & -3.85861375894593 \tabularnewline
161 & 18 & 14.1269679279711 & 3.87303207202892 \tabularnewline
162 & 16 & 14.6165786861657 & 1.38342131383433 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204621&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]12[/C][C]13.7982754542448[/C][C]-1.79827545424476[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]9.32657830553867[/C][C]1.67342169446133[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]14.6125980703651[/C][C]-0.612598070365088[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.6919029738794[/C][C]-2.69190297387944[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]11.4651805720986[/C][C]9.53481942790137[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.312797466026[/C][C]1.68720253397399[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]13.5995061867615[/C][C]8.40049381323847[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.4593930578592[/C][C]-1.45939305785922[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.2273659200577[/C][C]-2.22736592005769[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.3739030721799[/C][C]0.626096927820059[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.619139908646[/C][C]-0.619139908646044[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]10.2373340081958[/C][C]-2.23733400819577[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]15.5739744326327[/C][C]-0.573974432632679[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.1714640395967[/C][C]1.82853596040328[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]9.24890161000077[/C][C]0.75109838999923[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.555567671742[/C][C]0.444432328258013[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.6868490613918[/C][C]1.31315093860816[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]9.52856955214178[/C][C]1.47143044785822[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.9945224109822[/C][C]-2.99452241098218[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]12.2475556215279[/C][C]0.752444378472132[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]10.5720861458145[/C][C]-3.57208614581452[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]15.1742587037778[/C][C]-1.17425870377776[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.9287435548827[/C][C]-0.928743554882747[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]15.0524593329875[/C][C]-1.05245933298749[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]10.0480097925521[/C][C]0.951990207447938[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.0367662544933[/C][C]-6.03676625449332[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]10.8854678140681[/C][C]0.114532185931865[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.1648628050355[/C][C]1.83513719496448[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]11.4944004678199[/C][C]2.50559953218005[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]15.6348091932846[/C][C]-2.63480919328457[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.294270997927[/C][C]-2.29427099792702[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]14.9887744568289[/C][C]0.0112255431711478[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]11.1131721223103[/C][C]-1.11317212231031[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.9372355443196[/C][C]-0.937235544319606[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.4933052512702[/C][C]0.506694748729773[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]13.1213718751578[/C][C]-5.12137187515782[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]17.3267872527183[/C][C]2.67321274728172[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.3250836610648[/C][C]-0.325083661064816[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]10.7385421826178[/C][C]-0.73854218261779[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.5420291733181[/C][C]-3.54202917331815[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.006991588825[/C][C]-3.00699158882496[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]11.3596449894239[/C][C]2.6403550105761[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]11.4066364663852[/C][C]-3.40663646638517[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]15.1035423452468[/C][C]-1.10354234524684[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]14.4074809539141[/C][C]-3.40748095391407[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]15.285589814494[/C][C]-2.28558981449403[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.9955177424932[/C][C]-3.99551774249325[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.1847015811421[/C][C]-1.18470158114211[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]10.3194861546383[/C][C]4.68051384536174[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]13.3465306412945[/C][C]-2.3465306412945[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.3917099997813[/C][C]-1.39170999978125[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.2104313246145[/C][C]0.789568675385497[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]15.4334586445188[/C][C]2.56654135548115[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]15.4519095396656[/C][C]-1.45190953966564[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]15.4166630365736[/C][C]-4.41666303657358[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.4996699192225[/C][C]-0.499669919222525[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.245938679855[/C][C]1.75406132014504[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.5105185174677[/C][C]-3.51051851746771[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.9008692132683[/C][C]-3.90086921326835[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.1954889683464[/C][C]0.804511031653555[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]18.172210413423[/C][C]1.82778958657697[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.820708079094[/C][C]-0.820708079094002[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]14.0783963499523[/C][C]-2.07839634995235[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.8453836263034[/C][C]0.154616373696595[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]12.8262420358679[/C][C]0.17375796413205[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]14.7701294635527[/C][C]-3.77012946355272[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]14.7056359999339[/C][C]2.2943640000661[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.2932610736262[/C][C]-1.2932610736262[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.906288555803[/C][C]0.0937114441969621[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.1782564288185[/C][C]1.82174357118151[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]14.3242869724282[/C][C]-1.32428697242824[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]12.5898426775239[/C][C]2.41015732247613[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]11.1421328558883[/C][C]1.85786714411172[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.498425338794[/C][C]-1.49842533879403[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.6658270523158[/C][C]-1.66582705231576[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]13.5873517942137[/C][C]5.41264820578627[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.825361971506[/C][C]2.17463802849404[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]13.7674054402197[/C][C]3.23259455978035[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.3856269768364[/C][C]0.614373023163554[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]14.0479582007702[/C][C]-5.04795820077017[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]11.5125874396328[/C][C]-0.512587439632772[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]10.1861960447225[/C][C]-0.18619604472254[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.3272232863662[/C][C]-3.32722328636616[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]11.2795841276983[/C][C]0.720415872301701[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.3048013148419[/C][C]-0.304801314841931[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.6065998969801[/C][C]0.393400103019936[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]12.2249838902413[/C][C]0.775016109758725[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.8430294902949[/C][C]-0.8430294902949[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.1934338986379[/C][C]0.806566101362088[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]17.4827370624569[/C][C]4.51726293754312[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]11.7098419262581[/C][C]1.29015807374189[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.7955744510425[/C][C]1.20442554895745[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]11.7390595729378[/C][C]1.26094042706218[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.1860803638768[/C][C]2.81391963612316[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]13.2834123168995[/C][C]-3.28341231689952[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]10.8758059761169[/C][C]0.124194023883097[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.1480287699157[/C][C]1.85197123008433[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.367870360924[/C][C]-1.36787036092402[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]10.1766767683571[/C][C]0.823323231642862[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.2758997795279[/C][C]-2.27589977952787[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.2899538785473[/C][C]-0.289953878547267[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.0155519411943[/C][C]1.98444805880574[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]10.1634307052218[/C][C]1.83656929477818[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]15.3214634072101[/C][C]-4.32146340721015[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]11.6992686474132[/C][C]4.30073135258684[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]16.3531014521086[/C][C]2.64689854789135[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]11.1392420266071[/C][C]-0.139242026607136[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]11.9738510012157[/C][C]4.02614899878425[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]16.0594052052423[/C][C]-1.05940520524232[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]17.3119438288729[/C][C]6.68805617112711[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]11.5823975929262[/C][C]2.41760240707381[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]14.3558529829596[/C][C]0.64414701704043[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.8106730420164[/C][C]-1.81067304201645[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.0552263848403[/C][C]1.94477361515968[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]10.683352942923[/C][C]1.31664705707701[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]10.5223129730452[/C][C]-0.52231297304517[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.7529247347496[/C][C]0.247075265250363[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.4596285103321[/C][C]-0.459628510332111[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.3249458195299[/C][C]-4.32494581952992[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]11.9100804304604[/C][C]3.08991956953958[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]15.2638335722661[/C][C]-0.263833572266107[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.4780725163251[/C][C]1.52192748367488[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]11.7010816639075[/C][C]-0.701081663907469[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]11.8569113363132[/C][C]-3.8569113363132[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.1951681981938[/C][C]-1.1951681981938[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]12.8098075865427[/C][C]-1.80980758654274[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]10.1947684143828[/C][C]-2.19476841438281[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.3100624306707[/C][C]-0.310062430670654[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]9.90360264149986[/C][C]1.09639735850014[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.6851691146641[/C][C]0.314830885335913[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.3470972809805[/C][C]-2.34709728098046[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]17.1401349214288[/C][C]2.85986507857122[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.8319203021611[/C][C]-1.83192030216111[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.1357499837797[/C][C]1.86425001622027[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.3511570849608[/C][C]-0.351157084960815[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]11.560923521528[/C][C]2.43907647847197[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]16.0340302202413[/C][C]6.96596977975873[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.7517867601024[/C][C]0.248213239897576[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]16.5445349341713[/C][C]-0.544534934171252[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.2794703647723[/C][C]-2.27947036477227[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]14.681611534838[/C][C]-2.68161153483798[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]13.0544074679659[/C][C]-3.05440746796585[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]11.2430007086322[/C][C]2.75699929136778[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]11.979019567874[/C][C]0.0209804321260404[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.7958296076958[/C][C]0.204170392304208[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]11.7523419961418[/C][C]-0.752341996141819[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]11.0217821016392[/C][C]0.978217898360798[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.7299790451009[/C][C]-2.72997904510094[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]13.9252579002798[/C][C]-2.92525790027984[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]16.619468186117[/C][C]2.38053181388298[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]12.0816344550512[/C][C]-0.0816344550512346[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]12.8802875936309[/C][C]4.11971240636909[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.3213772127863[/C][C]-2.32137721278631[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.2843549482132[/C][C]-2.28435494821325[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]16.622898406728[/C][C]2.37710159327202[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]14.6074623336178[/C][C]3.39253766638217[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.7955744510425[/C][C]1.20442554895745[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.7113049678657[/C][C]0.288695032134259[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]9.90360264149986[/C][C]1.09639735850014[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]12.8586137589459[/C][C]-3.85861375894593[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]14.1269679279711[/C][C]3.87303207202892[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]14.6165786861657[/C][C]1.38342131383433[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204621&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204621&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11213.7982754542448-1.79827545424476
2119.326578305538671.67342169446133
31414.6125980703651-0.612598070365088
41214.6919029738794-2.69190297387944
52111.46518057209869.53481942790137
61210.3127974660261.68720253397399
72213.59950618676158.40049381323847
81112.4593930578592-1.45939305785922
91012.2273659200577-2.22736592005769
101312.37390307217990.626096927820059
111010.619139908646-0.619139908646044
12810.2373340081958-2.23733400819577
131515.5739744326327-0.573974432632679
141412.17146403959671.82853596040328
15109.248901610000770.75109838999923
161413.5555676717420.444432328258013
171412.68684906139181.31315093860816
18119.528569552141781.47143044785822
191012.9945224109822-2.99452241098218
201312.24755562152790.752444378472132
21710.5720861458145-3.57208614581452
221415.1742587037778-1.17425870377776
231212.9287435548827-0.928743554882747
241415.0524593329875-1.05245933298749
251110.04800979255210.951990207447938
26915.0367662544933-6.03676625449332
271110.88546781406810.114532185931865
281513.16486280503551.83513719496448
291411.49440046781992.50559953218005
301315.6348091932846-2.63480919328457
31911.294270997927-2.29427099792702
321514.98877445682890.0112255431711478
331011.1131721223103-1.11317212231031
341111.9372355443196-0.937235544319606
351312.49330525127020.506694748729773
36813.1213718751578-5.12137187515782
372017.32678725271832.67321274728172
381212.3250836610648-0.325083661064816
391010.7385421826178-0.73854218261779
401013.5420291733181-3.54202917331815
41912.006991588825-3.00699158882496
421411.35964498942392.6403550105761
43811.4066364663852-3.40663646638517
441415.1035423452468-1.10354234524684
451114.4074809539141-3.40748095391407
461315.285589814494-2.28558981449403
47912.9955177424932-3.99551774249325
481112.1847015811421-1.18470158114211
491510.31948615463834.68051384536174
501113.3465306412945-2.3465306412945
511011.3917099997813-1.39170999978125
521413.21043132461450.789568675385497
531815.43345864451882.56654135548115
541415.4519095396656-1.45190953966564
551115.4166630365736-4.41666303657358
561212.4996699192225-0.499669919222525
571311.2459386798551.75406132014504
58912.5105185174677-3.51051851746771
591013.9008692132683-3.90086921326835
601514.19548896834640.804511031653555
612018.1722104134231.82778958657697
621212.820708079094-0.820708079094002
631214.0783963499523-2.07839634995235
641413.84538362630340.154616373696595
651312.82624203586790.17375796413205
661114.7701294635527-3.77012946355272
671714.70563599993392.2943640000661
681213.2932610736262-1.2932610736262
691312.9062885558030.0937114441969621
701412.17825642881851.82174357118151
711314.3242869724282-1.32428697242824
721512.58984267752392.41015732247613
731311.14213285588831.85786714411172
741011.498425338794-1.49842533879403
751112.6658270523158-1.66582705231576
761913.58735179421375.41264820578627
771310.8253619715062.17463802849404
781713.76740544021973.23259455978035
791312.38562697683640.614373023163554
80914.0479582007702-5.04795820077017
811111.5125874396328-0.512587439632772
821010.1861960447225-0.18619604472254
83912.3272232863662-3.32722328636616
841211.27958412769830.720415872301701
851212.3048013148419-0.304801314841931
861312.60659989698010.393400103019936
871312.22498389024130.775016109758725
881212.8430294902949-0.8430294902949
891514.19343389863790.806566101362088
902217.48273706245694.51726293754312
911311.70984192625811.29015807374189
921513.79557445104251.20442554895745
931311.73905957293781.26094042706218
941512.18608036387682.81391963612316
951013.2834123168995-3.28341231689952
961110.87580597611690.124194023883097
971614.14802876991571.85197123008433
981112.367870360924-1.36787036092402
991110.17667676835710.823323231642862
1001012.2758997795279-2.27589977952787
1011010.2899538785473-0.289953878547267
1021614.01555194119431.98444805880574
1031210.16343070522181.83656929477818
1041115.3214634072101-4.32146340721015
1051611.69926864741324.30073135258684
1061916.35310145210862.64689854789135
1071111.1392420266071-0.139242026607136
1081611.97385100121574.02614899878425
1091516.0594052052423-1.05940520524232
1102417.31194382887296.68805617112711
1111411.58239759292622.41760240707381
1121514.35585298295960.64414701704043
1131112.8106730420164-1.81067304201645
1141513.05522638484031.94477361515968
1151210.6833529429231.31664705707701
1161010.5223129730452-0.52231297304517
1171413.75292473474960.247075265250363
1181313.4596285103321-0.459628510332111
119913.3249458195299-4.32494581952992
1201511.91008043046043.08991956953958
1211515.2638335722661-0.263833572266107
1221412.47807251632511.52192748367488
1231111.7010816639075-0.701081663907469
124811.8569113363132-3.8569113363132
1251112.1951681981938-1.1951681981938
1261112.8098075865427-1.80980758654274
127810.1947684143828-2.19476841438281
1281010.3100624306707-0.310062430670654
129119.903602641499861.09639735850014
1301312.68516911466410.314830885335913
1311113.3470972809805-2.34709728098046
1322017.14013492142882.85986507857122
1331011.8319203021611-1.83192030216111
1341513.13574998377971.86425001622027
1351212.3511570849608-0.351157084960815
1361411.5609235215282.43907647847197
1372316.03403022024136.96596977975873
1381413.75178676010240.248213239897576
1391616.5445349341713-0.544534934171252
1401113.2794703647723-2.27947036477227
1411214.681611534838-2.68161153483798
1421013.0544074679659-3.05440746796585
1431411.24300070863222.75699929136778
1441211.9790195678740.0209804321260404
1451211.79582960769580.204170392304208
1461111.7523419961418-0.752341996141819
1471211.02178210163920.978217898360798
1481315.7299790451009-2.72997904510094
1491113.9252579002798-2.92525790027984
1501916.6194681861172.38053181388298
1511212.0816344550512-0.0816344550512346
1521712.88028759363094.11971240636909
153911.3213772127863-2.32137721278631
1541214.2843549482132-2.28435494821325
1551916.6228984067282.37710159327202
1561814.60746233361783.39253766638217
1571513.79557445104251.20442554895745
1581413.71130496786570.288695032134259
159119.903602641499861.09639735850014
160912.8586137589459-3.85861375894593
1611814.12696792797113.87303207202892
1621614.61657868616571.38342131383433






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.8429695066158820.3140609867682370.157030493384118
120.9982685050272930.003462989945413890.00173149497270695
130.9960035521599060.007992895680188680.00399644784009434
140.9942481098499380.01150378030012480.00575189015006239
150.9923292860866660.01534142782666690.00767071391333347
160.9860333470983220.02793330580335590.0139666529016779
170.9850719472861320.02985610542773670.0149280527138684
180.9753405092159830.04931898156803430.0246594907840172
190.9850689791393940.02986204172121140.0149310208606057
200.9764225349509750.04715493009805090.0235774650490254
210.985505872936120.02898825412775980.0144941270638799
220.9823492837988220.03530143240235640.0176507162011782
230.9731634157918150.05367316841637070.0268365842081854
240.9648011381905010.07039772361899730.0351988618094986
250.9495257775127280.1009484449745430.0504742224872717
260.9638276900384810.07234461992303880.0361723099615194
270.9652104246896540.06957915062069240.0347895753103462
280.9525960563659510.09480788726809880.0474039436340494
290.9642074061073050.07158518778539090.0357925938926954
300.9620003693379140.07599926132417210.037999630662086
310.9611135942129730.07777281157405290.0388864057870265
320.9507950396257170.0984099207485650.0492049603742825
330.9379144726902210.1241710546195580.0620855273097791
340.9279386348134930.1441227303730140.0720613651865068
350.9129191789993730.1741616420012540.0870808210006272
360.9368603584085970.1262792831828060.0631396415914032
370.9656379396132930.06872412077341460.0343620603867073
380.9535170384925230.09296592301495480.0464829615074774
390.9416055322171290.1167889355657420.0583944677828708
400.9468938029482260.1062123941035470.0531061970517737
410.9464375241523020.1071249516953970.0535624758476983
420.9450533537771990.1098932924456010.0549466462228006
430.9555604557332720.08887908853345670.0444395442667284
440.9429195751229590.1141608497540830.0570804248770415
450.9415231770746520.1169536458506960.0584768229253481
460.9314911363323440.1370177273353120.0685088636676562
470.9461188669322780.1077622661354450.0538811330677223
480.9332672234335290.1334655531329410.0667327765664706
490.9525845013350130.09483099732997350.0474154986649867
500.9459995214318410.1080009571363190.0540004785681593
510.938611681640480.122776636719040.0613883183595198
520.9246481481371110.1507037037257780.0753518518628888
530.9289935089433820.1420129821132360.0710064910566179
540.9156464630475510.1687070739048980.084353536952449
550.9364430788149520.1271138423700960.0635569211850481
560.9219698682223050.156060263555390.0780301317776951
570.9126689035498160.1746621929003670.0873310964501835
580.9279433104894250.1441133790211490.0720566895105747
590.9422182101676060.1155635796647880.0577817898323938
600.930910206453570.1381795870928590.0690897935464297
610.9317393839283790.1365212321432430.0682606160716214
620.9162764880837890.1674470238324220.083723511916211
630.907713710660310.184572578679380.0922862893396901
640.8862470438857850.227505912228430.113752956114215
650.8615221062887960.2769557874224080.138477893711204
660.8825300892889840.2349398214220320.117469910711016
670.881481407308740.237037185382520.11851859269126
680.8658715615534050.2682568768931890.134128438446595
690.8400033456055670.3199933087888660.159996654394433
700.8243124074867670.3513751850264660.175687592513233
710.8030483145449560.3939033709100880.196951685455044
720.7956671076049990.4086657847900010.204332892395001
730.778226666880510.443546666238980.22177333311949
740.7576778131116660.4846443737766680.242322186888334
750.7382565452718790.5234869094562420.261743454728121
760.8509819766292790.2980360467414410.149018023370721
770.8436907029501330.3126185940997330.156309297049867
780.8594680906533130.2810638186933750.140531909346687
790.833861022839670.332277954320660.16613897716033
800.9082399320417490.1835201359165020.091760067958251
810.8882487945128450.2235024109743110.111751205487155
820.8656181696382210.2687636607235580.134381830361779
830.8809250019430630.2381499961138750.119074998056937
840.8578451878523880.2843096242952230.142154812147612
850.82993571700470.3401285659905990.170064282995299
860.7988317299393580.4023365401212850.201168270060642
870.7671779264486630.4656441471026750.232822073551337
880.7339555897967830.5320888204064350.266044410203217
890.6974653519978490.6050692960043020.302534648002151
900.7707010480154990.4585979039690030.229298951984501
910.744106899565930.511786200868140.25589310043407
920.7123360095598080.5753279808803840.287663990440192
930.6842855888111150.6314288223777690.315714411188885
940.6864534483056390.6270931033887220.313546551694361
950.7109046061578550.578190787684290.289095393842145
960.6684081049470170.6631837901059660.331591895052983
970.6431013962173790.7137972075652420.356898603782621
980.6143585505980210.7712828988039580.385641449401979
990.5715813742778830.8568372514442330.428418625722117
1000.5567378261267960.8865243477464080.443262173873204
1010.508496158484910.9830076830301810.49150384151509
1020.4824684284411650.9649368568823290.517531571558835
1030.4710202868902040.9420405737804070.528979713109796
1040.5947368820026430.8105262359947150.405263117997357
1050.7142465121214570.5715069757570870.285753487878543
1060.7029622728693560.5940754542612880.297037727130644
1070.65795189554520.68409620890960.3420481044548
1080.7372015623676340.5255968752647320.262798437632366
1090.7127889671690650.5744220656618690.287211032830935
1100.8946273239293150.210745352141370.105372676070685
1110.89647376186760.20705247626480.1035262381324
1120.8723904863439370.2552190273121260.127609513656063
1130.8511603678213610.2976792643572780.148839632178639
1140.8819856654285940.2360286691428120.118014334571406
1150.8664385204930180.2671229590139630.133561479506982
1160.8358811445410060.3282377109179870.164118855458994
1170.8305922681344990.3388154637310010.169407731865501
1180.7961743723257340.4076512553485330.203825627674266
1190.8446049311560950.310790137687810.155395068843905
1200.8683857385455810.2632285229088370.131614261454419
1210.837180567098490.325638865803020.16281943290151
1220.8067545171174620.3864909657650750.193245482882537
1230.7668956065694610.4662087868610780.233104393430539
1240.7774376014278120.4451247971443770.222562398572188
1250.7450422967531810.5099154064936380.254957703246819
1260.7169580495702980.5660839008594030.283041950429702
1270.7156905665314990.5686188669370020.284309433468501
1280.6982626510298870.6034746979402260.301737348970113
1290.6543830727964530.6912338544070950.345616927203547
1300.5946558016426030.8106883967147940.405344198357397
1310.5724742843490230.8550514313019550.427525715650977
1320.5346530133487190.9306939733025620.465346986651281
1330.4758667240464840.9517334480929690.524133275953516
1340.4321976226234130.8643952452468260.567802377376587
1350.3696320924222680.7392641848445360.630367907577732
1360.3190934386257910.6381868772515820.680906561374209
1370.6230209663723240.7539580672553520.376979033627676
1380.548770941917960.9024581161640810.45122905808204
1390.4731250452974550.9462500905949090.526874954702546
1400.4178877990159760.8357755980319520.582112200984024
1410.4062898677608320.8125797355216630.593710132239168
1420.3592432690871860.7184865381743720.640756730912814
1430.3687994224627930.7375988449255860.631200577537207
1440.2867186571463820.5734373142927640.713281342853618
1450.2143192384071690.4286384768143370.785680761592831
1460.3105507224151690.6211014448303380.689449277584831
1470.2256917761826410.4513835523652830.774308223817359
1480.4501917412602630.9003834825205270.549808258739737
1490.5280080683185870.9439838633628260.471991931681413
1500.3891580393770460.7783160787540920.610841960622954
1510.5446222381992050.9107555236015890.455377761800795

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.842969506615882 & 0.314060986768237 & 0.157030493384118 \tabularnewline
12 & 0.998268505027293 & 0.00346298994541389 & 0.00173149497270695 \tabularnewline
13 & 0.996003552159906 & 0.00799289568018868 & 0.00399644784009434 \tabularnewline
14 & 0.994248109849938 & 0.0115037803001248 & 0.00575189015006239 \tabularnewline
15 & 0.992329286086666 & 0.0153414278266669 & 0.00767071391333347 \tabularnewline
16 & 0.986033347098322 & 0.0279333058033559 & 0.0139666529016779 \tabularnewline
17 & 0.985071947286132 & 0.0298561054277367 & 0.0149280527138684 \tabularnewline
18 & 0.975340509215983 & 0.0493189815680343 & 0.0246594907840172 \tabularnewline
19 & 0.985068979139394 & 0.0298620417212114 & 0.0149310208606057 \tabularnewline
20 & 0.976422534950975 & 0.0471549300980509 & 0.0235774650490254 \tabularnewline
21 & 0.98550587293612 & 0.0289882541277598 & 0.0144941270638799 \tabularnewline
22 & 0.982349283798822 & 0.0353014324023564 & 0.0176507162011782 \tabularnewline
23 & 0.973163415791815 & 0.0536731684163707 & 0.0268365842081854 \tabularnewline
24 & 0.964801138190501 & 0.0703977236189973 & 0.0351988618094986 \tabularnewline
25 & 0.949525777512728 & 0.100948444974543 & 0.0504742224872717 \tabularnewline
26 & 0.963827690038481 & 0.0723446199230388 & 0.0361723099615194 \tabularnewline
27 & 0.965210424689654 & 0.0695791506206924 & 0.0347895753103462 \tabularnewline
28 & 0.952596056365951 & 0.0948078872680988 & 0.0474039436340494 \tabularnewline
29 & 0.964207406107305 & 0.0715851877853909 & 0.0357925938926954 \tabularnewline
30 & 0.962000369337914 & 0.0759992613241721 & 0.037999630662086 \tabularnewline
31 & 0.961113594212973 & 0.0777728115740529 & 0.0388864057870265 \tabularnewline
32 & 0.950795039625717 & 0.098409920748565 & 0.0492049603742825 \tabularnewline
33 & 0.937914472690221 & 0.124171054619558 & 0.0620855273097791 \tabularnewline
34 & 0.927938634813493 & 0.144122730373014 & 0.0720613651865068 \tabularnewline
35 & 0.912919178999373 & 0.174161642001254 & 0.0870808210006272 \tabularnewline
36 & 0.936860358408597 & 0.126279283182806 & 0.0631396415914032 \tabularnewline
37 & 0.965637939613293 & 0.0687241207734146 & 0.0343620603867073 \tabularnewline
38 & 0.953517038492523 & 0.0929659230149548 & 0.0464829615074774 \tabularnewline
39 & 0.941605532217129 & 0.116788935565742 & 0.0583944677828708 \tabularnewline
40 & 0.946893802948226 & 0.106212394103547 & 0.0531061970517737 \tabularnewline
41 & 0.946437524152302 & 0.107124951695397 & 0.0535624758476983 \tabularnewline
42 & 0.945053353777199 & 0.109893292445601 & 0.0549466462228006 \tabularnewline
43 & 0.955560455733272 & 0.0888790885334567 & 0.0444395442667284 \tabularnewline
44 & 0.942919575122959 & 0.114160849754083 & 0.0570804248770415 \tabularnewline
45 & 0.941523177074652 & 0.116953645850696 & 0.0584768229253481 \tabularnewline
46 & 0.931491136332344 & 0.137017727335312 & 0.0685088636676562 \tabularnewline
47 & 0.946118866932278 & 0.107762266135445 & 0.0538811330677223 \tabularnewline
48 & 0.933267223433529 & 0.133465553132941 & 0.0667327765664706 \tabularnewline
49 & 0.952584501335013 & 0.0948309973299735 & 0.0474154986649867 \tabularnewline
50 & 0.945999521431841 & 0.108000957136319 & 0.0540004785681593 \tabularnewline
51 & 0.93861168164048 & 0.12277663671904 & 0.0613883183595198 \tabularnewline
52 & 0.924648148137111 & 0.150703703725778 & 0.0753518518628888 \tabularnewline
53 & 0.928993508943382 & 0.142012982113236 & 0.0710064910566179 \tabularnewline
54 & 0.915646463047551 & 0.168707073904898 & 0.084353536952449 \tabularnewline
55 & 0.936443078814952 & 0.127113842370096 & 0.0635569211850481 \tabularnewline
56 & 0.921969868222305 & 0.15606026355539 & 0.0780301317776951 \tabularnewline
57 & 0.912668903549816 & 0.174662192900367 & 0.0873310964501835 \tabularnewline
58 & 0.927943310489425 & 0.144113379021149 & 0.0720566895105747 \tabularnewline
59 & 0.942218210167606 & 0.115563579664788 & 0.0577817898323938 \tabularnewline
60 & 0.93091020645357 & 0.138179587092859 & 0.0690897935464297 \tabularnewline
61 & 0.931739383928379 & 0.136521232143243 & 0.0682606160716214 \tabularnewline
62 & 0.916276488083789 & 0.167447023832422 & 0.083723511916211 \tabularnewline
63 & 0.90771371066031 & 0.18457257867938 & 0.0922862893396901 \tabularnewline
64 & 0.886247043885785 & 0.22750591222843 & 0.113752956114215 \tabularnewline
65 & 0.861522106288796 & 0.276955787422408 & 0.138477893711204 \tabularnewline
66 & 0.882530089288984 & 0.234939821422032 & 0.117469910711016 \tabularnewline
67 & 0.88148140730874 & 0.23703718538252 & 0.11851859269126 \tabularnewline
68 & 0.865871561553405 & 0.268256876893189 & 0.134128438446595 \tabularnewline
69 & 0.840003345605567 & 0.319993308788866 & 0.159996654394433 \tabularnewline
70 & 0.824312407486767 & 0.351375185026466 & 0.175687592513233 \tabularnewline
71 & 0.803048314544956 & 0.393903370910088 & 0.196951685455044 \tabularnewline
72 & 0.795667107604999 & 0.408665784790001 & 0.204332892395001 \tabularnewline
73 & 0.77822666688051 & 0.44354666623898 & 0.22177333311949 \tabularnewline
74 & 0.757677813111666 & 0.484644373776668 & 0.242322186888334 \tabularnewline
75 & 0.738256545271879 & 0.523486909456242 & 0.261743454728121 \tabularnewline
76 & 0.850981976629279 & 0.298036046741441 & 0.149018023370721 \tabularnewline
77 & 0.843690702950133 & 0.312618594099733 & 0.156309297049867 \tabularnewline
78 & 0.859468090653313 & 0.281063818693375 & 0.140531909346687 \tabularnewline
79 & 0.83386102283967 & 0.33227795432066 & 0.16613897716033 \tabularnewline
80 & 0.908239932041749 & 0.183520135916502 & 0.091760067958251 \tabularnewline
81 & 0.888248794512845 & 0.223502410974311 & 0.111751205487155 \tabularnewline
82 & 0.865618169638221 & 0.268763660723558 & 0.134381830361779 \tabularnewline
83 & 0.880925001943063 & 0.238149996113875 & 0.119074998056937 \tabularnewline
84 & 0.857845187852388 & 0.284309624295223 & 0.142154812147612 \tabularnewline
85 & 0.8299357170047 & 0.340128565990599 & 0.170064282995299 \tabularnewline
86 & 0.798831729939358 & 0.402336540121285 & 0.201168270060642 \tabularnewline
87 & 0.767177926448663 & 0.465644147102675 & 0.232822073551337 \tabularnewline
88 & 0.733955589796783 & 0.532088820406435 & 0.266044410203217 \tabularnewline
89 & 0.697465351997849 & 0.605069296004302 & 0.302534648002151 \tabularnewline
90 & 0.770701048015499 & 0.458597903969003 & 0.229298951984501 \tabularnewline
91 & 0.74410689956593 & 0.51178620086814 & 0.25589310043407 \tabularnewline
92 & 0.712336009559808 & 0.575327980880384 & 0.287663990440192 \tabularnewline
93 & 0.684285588811115 & 0.631428822377769 & 0.315714411188885 \tabularnewline
94 & 0.686453448305639 & 0.627093103388722 & 0.313546551694361 \tabularnewline
95 & 0.710904606157855 & 0.57819078768429 & 0.289095393842145 \tabularnewline
96 & 0.668408104947017 & 0.663183790105966 & 0.331591895052983 \tabularnewline
97 & 0.643101396217379 & 0.713797207565242 & 0.356898603782621 \tabularnewline
98 & 0.614358550598021 & 0.771282898803958 & 0.385641449401979 \tabularnewline
99 & 0.571581374277883 & 0.856837251444233 & 0.428418625722117 \tabularnewline
100 & 0.556737826126796 & 0.886524347746408 & 0.443262173873204 \tabularnewline
101 & 0.50849615848491 & 0.983007683030181 & 0.49150384151509 \tabularnewline
102 & 0.482468428441165 & 0.964936856882329 & 0.517531571558835 \tabularnewline
103 & 0.471020286890204 & 0.942040573780407 & 0.528979713109796 \tabularnewline
104 & 0.594736882002643 & 0.810526235994715 & 0.405263117997357 \tabularnewline
105 & 0.714246512121457 & 0.571506975757087 & 0.285753487878543 \tabularnewline
106 & 0.702962272869356 & 0.594075454261288 & 0.297037727130644 \tabularnewline
107 & 0.6579518955452 & 0.6840962089096 & 0.3420481044548 \tabularnewline
108 & 0.737201562367634 & 0.525596875264732 & 0.262798437632366 \tabularnewline
109 & 0.712788967169065 & 0.574422065661869 & 0.287211032830935 \tabularnewline
110 & 0.894627323929315 & 0.21074535214137 & 0.105372676070685 \tabularnewline
111 & 0.8964737618676 & 0.2070524762648 & 0.1035262381324 \tabularnewline
112 & 0.872390486343937 & 0.255219027312126 & 0.127609513656063 \tabularnewline
113 & 0.851160367821361 & 0.297679264357278 & 0.148839632178639 \tabularnewline
114 & 0.881985665428594 & 0.236028669142812 & 0.118014334571406 \tabularnewline
115 & 0.866438520493018 & 0.267122959013963 & 0.133561479506982 \tabularnewline
116 & 0.835881144541006 & 0.328237710917987 & 0.164118855458994 \tabularnewline
117 & 0.830592268134499 & 0.338815463731001 & 0.169407731865501 \tabularnewline
118 & 0.796174372325734 & 0.407651255348533 & 0.203825627674266 \tabularnewline
119 & 0.844604931156095 & 0.31079013768781 & 0.155395068843905 \tabularnewline
120 & 0.868385738545581 & 0.263228522908837 & 0.131614261454419 \tabularnewline
121 & 0.83718056709849 & 0.32563886580302 & 0.16281943290151 \tabularnewline
122 & 0.806754517117462 & 0.386490965765075 & 0.193245482882537 \tabularnewline
123 & 0.766895606569461 & 0.466208786861078 & 0.233104393430539 \tabularnewline
124 & 0.777437601427812 & 0.445124797144377 & 0.222562398572188 \tabularnewline
125 & 0.745042296753181 & 0.509915406493638 & 0.254957703246819 \tabularnewline
126 & 0.716958049570298 & 0.566083900859403 & 0.283041950429702 \tabularnewline
127 & 0.715690566531499 & 0.568618866937002 & 0.284309433468501 \tabularnewline
128 & 0.698262651029887 & 0.603474697940226 & 0.301737348970113 \tabularnewline
129 & 0.654383072796453 & 0.691233854407095 & 0.345616927203547 \tabularnewline
130 & 0.594655801642603 & 0.810688396714794 & 0.405344198357397 \tabularnewline
131 & 0.572474284349023 & 0.855051431301955 & 0.427525715650977 \tabularnewline
132 & 0.534653013348719 & 0.930693973302562 & 0.465346986651281 \tabularnewline
133 & 0.475866724046484 & 0.951733448092969 & 0.524133275953516 \tabularnewline
134 & 0.432197622623413 & 0.864395245246826 & 0.567802377376587 \tabularnewline
135 & 0.369632092422268 & 0.739264184844536 & 0.630367907577732 \tabularnewline
136 & 0.319093438625791 & 0.638186877251582 & 0.680906561374209 \tabularnewline
137 & 0.623020966372324 & 0.753958067255352 & 0.376979033627676 \tabularnewline
138 & 0.54877094191796 & 0.902458116164081 & 0.45122905808204 \tabularnewline
139 & 0.473125045297455 & 0.946250090594909 & 0.526874954702546 \tabularnewline
140 & 0.417887799015976 & 0.835775598031952 & 0.582112200984024 \tabularnewline
141 & 0.406289867760832 & 0.812579735521663 & 0.593710132239168 \tabularnewline
142 & 0.359243269087186 & 0.718486538174372 & 0.640756730912814 \tabularnewline
143 & 0.368799422462793 & 0.737598844925586 & 0.631200577537207 \tabularnewline
144 & 0.286718657146382 & 0.573437314292764 & 0.713281342853618 \tabularnewline
145 & 0.214319238407169 & 0.428638476814337 & 0.785680761592831 \tabularnewline
146 & 0.310550722415169 & 0.621101444830338 & 0.689449277584831 \tabularnewline
147 & 0.225691776182641 & 0.451383552365283 & 0.774308223817359 \tabularnewline
148 & 0.450191741260263 & 0.900383482520527 & 0.549808258739737 \tabularnewline
149 & 0.528008068318587 & 0.943983863362826 & 0.471991931681413 \tabularnewline
150 & 0.389158039377046 & 0.778316078754092 & 0.610841960622954 \tabularnewline
151 & 0.544622238199205 & 0.910755523601589 & 0.455377761800795 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204621&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]0.842969506615882[/C][C]0.314060986768237[/C][C]0.157030493384118[/C][/ROW]
[ROW][C]12[/C][C]0.998268505027293[/C][C]0.00346298994541389[/C][C]0.00173149497270695[/C][/ROW]
[ROW][C]13[/C][C]0.996003552159906[/C][C]0.00799289568018868[/C][C]0.00399644784009434[/C][/ROW]
[ROW][C]14[/C][C]0.994248109849938[/C][C]0.0115037803001248[/C][C]0.00575189015006239[/C][/ROW]
[ROW][C]15[/C][C]0.992329286086666[/C][C]0.0153414278266669[/C][C]0.00767071391333347[/C][/ROW]
[ROW][C]16[/C][C]0.986033347098322[/C][C]0.0279333058033559[/C][C]0.0139666529016779[/C][/ROW]
[ROW][C]17[/C][C]0.985071947286132[/C][C]0.0298561054277367[/C][C]0.0149280527138684[/C][/ROW]
[ROW][C]18[/C][C]0.975340509215983[/C][C]0.0493189815680343[/C][C]0.0246594907840172[/C][/ROW]
[ROW][C]19[/C][C]0.985068979139394[/C][C]0.0298620417212114[/C][C]0.0149310208606057[/C][/ROW]
[ROW][C]20[/C][C]0.976422534950975[/C][C]0.0471549300980509[/C][C]0.0235774650490254[/C][/ROW]
[ROW][C]21[/C][C]0.98550587293612[/C][C]0.0289882541277598[/C][C]0.0144941270638799[/C][/ROW]
[ROW][C]22[/C][C]0.982349283798822[/C][C]0.0353014324023564[/C][C]0.0176507162011782[/C][/ROW]
[ROW][C]23[/C][C]0.973163415791815[/C][C]0.0536731684163707[/C][C]0.0268365842081854[/C][/ROW]
[ROW][C]24[/C][C]0.964801138190501[/C][C]0.0703977236189973[/C][C]0.0351988618094986[/C][/ROW]
[ROW][C]25[/C][C]0.949525777512728[/C][C]0.100948444974543[/C][C]0.0504742224872717[/C][/ROW]
[ROW][C]26[/C][C]0.963827690038481[/C][C]0.0723446199230388[/C][C]0.0361723099615194[/C][/ROW]
[ROW][C]27[/C][C]0.965210424689654[/C][C]0.0695791506206924[/C][C]0.0347895753103462[/C][/ROW]
[ROW][C]28[/C][C]0.952596056365951[/C][C]0.0948078872680988[/C][C]0.0474039436340494[/C][/ROW]
[ROW][C]29[/C][C]0.964207406107305[/C][C]0.0715851877853909[/C][C]0.0357925938926954[/C][/ROW]
[ROW][C]30[/C][C]0.962000369337914[/C][C]0.0759992613241721[/C][C]0.037999630662086[/C][/ROW]
[ROW][C]31[/C][C]0.961113594212973[/C][C]0.0777728115740529[/C][C]0.0388864057870265[/C][/ROW]
[ROW][C]32[/C][C]0.950795039625717[/C][C]0.098409920748565[/C][C]0.0492049603742825[/C][/ROW]
[ROW][C]33[/C][C]0.937914472690221[/C][C]0.124171054619558[/C][C]0.0620855273097791[/C][/ROW]
[ROW][C]34[/C][C]0.927938634813493[/C][C]0.144122730373014[/C][C]0.0720613651865068[/C][/ROW]
[ROW][C]35[/C][C]0.912919178999373[/C][C]0.174161642001254[/C][C]0.0870808210006272[/C][/ROW]
[ROW][C]36[/C][C]0.936860358408597[/C][C]0.126279283182806[/C][C]0.0631396415914032[/C][/ROW]
[ROW][C]37[/C][C]0.965637939613293[/C][C]0.0687241207734146[/C][C]0.0343620603867073[/C][/ROW]
[ROW][C]38[/C][C]0.953517038492523[/C][C]0.0929659230149548[/C][C]0.0464829615074774[/C][/ROW]
[ROW][C]39[/C][C]0.941605532217129[/C][C]0.116788935565742[/C][C]0.0583944677828708[/C][/ROW]
[ROW][C]40[/C][C]0.946893802948226[/C][C]0.106212394103547[/C][C]0.0531061970517737[/C][/ROW]
[ROW][C]41[/C][C]0.946437524152302[/C][C]0.107124951695397[/C][C]0.0535624758476983[/C][/ROW]
[ROW][C]42[/C][C]0.945053353777199[/C][C]0.109893292445601[/C][C]0.0549466462228006[/C][/ROW]
[ROW][C]43[/C][C]0.955560455733272[/C][C]0.0888790885334567[/C][C]0.0444395442667284[/C][/ROW]
[ROW][C]44[/C][C]0.942919575122959[/C][C]0.114160849754083[/C][C]0.0570804248770415[/C][/ROW]
[ROW][C]45[/C][C]0.941523177074652[/C][C]0.116953645850696[/C][C]0.0584768229253481[/C][/ROW]
[ROW][C]46[/C][C]0.931491136332344[/C][C]0.137017727335312[/C][C]0.0685088636676562[/C][/ROW]
[ROW][C]47[/C][C]0.946118866932278[/C][C]0.107762266135445[/C][C]0.0538811330677223[/C][/ROW]
[ROW][C]48[/C][C]0.933267223433529[/C][C]0.133465553132941[/C][C]0.0667327765664706[/C][/ROW]
[ROW][C]49[/C][C]0.952584501335013[/C][C]0.0948309973299735[/C][C]0.0474154986649867[/C][/ROW]
[ROW][C]50[/C][C]0.945999521431841[/C][C]0.108000957136319[/C][C]0.0540004785681593[/C][/ROW]
[ROW][C]51[/C][C]0.93861168164048[/C][C]0.12277663671904[/C][C]0.0613883183595198[/C][/ROW]
[ROW][C]52[/C][C]0.924648148137111[/C][C]0.150703703725778[/C][C]0.0753518518628888[/C][/ROW]
[ROW][C]53[/C][C]0.928993508943382[/C][C]0.142012982113236[/C][C]0.0710064910566179[/C][/ROW]
[ROW][C]54[/C][C]0.915646463047551[/C][C]0.168707073904898[/C][C]0.084353536952449[/C][/ROW]
[ROW][C]55[/C][C]0.936443078814952[/C][C]0.127113842370096[/C][C]0.0635569211850481[/C][/ROW]
[ROW][C]56[/C][C]0.921969868222305[/C][C]0.15606026355539[/C][C]0.0780301317776951[/C][/ROW]
[ROW][C]57[/C][C]0.912668903549816[/C][C]0.174662192900367[/C][C]0.0873310964501835[/C][/ROW]
[ROW][C]58[/C][C]0.927943310489425[/C][C]0.144113379021149[/C][C]0.0720566895105747[/C][/ROW]
[ROW][C]59[/C][C]0.942218210167606[/C][C]0.115563579664788[/C][C]0.0577817898323938[/C][/ROW]
[ROW][C]60[/C][C]0.93091020645357[/C][C]0.138179587092859[/C][C]0.0690897935464297[/C][/ROW]
[ROW][C]61[/C][C]0.931739383928379[/C][C]0.136521232143243[/C][C]0.0682606160716214[/C][/ROW]
[ROW][C]62[/C][C]0.916276488083789[/C][C]0.167447023832422[/C][C]0.083723511916211[/C][/ROW]
[ROW][C]63[/C][C]0.90771371066031[/C][C]0.18457257867938[/C][C]0.0922862893396901[/C][/ROW]
[ROW][C]64[/C][C]0.886247043885785[/C][C]0.22750591222843[/C][C]0.113752956114215[/C][/ROW]
[ROW][C]65[/C][C]0.861522106288796[/C][C]0.276955787422408[/C][C]0.138477893711204[/C][/ROW]
[ROW][C]66[/C][C]0.882530089288984[/C][C]0.234939821422032[/C][C]0.117469910711016[/C][/ROW]
[ROW][C]67[/C][C]0.88148140730874[/C][C]0.23703718538252[/C][C]0.11851859269126[/C][/ROW]
[ROW][C]68[/C][C]0.865871561553405[/C][C]0.268256876893189[/C][C]0.134128438446595[/C][/ROW]
[ROW][C]69[/C][C]0.840003345605567[/C][C]0.319993308788866[/C][C]0.159996654394433[/C][/ROW]
[ROW][C]70[/C][C]0.824312407486767[/C][C]0.351375185026466[/C][C]0.175687592513233[/C][/ROW]
[ROW][C]71[/C][C]0.803048314544956[/C][C]0.393903370910088[/C][C]0.196951685455044[/C][/ROW]
[ROW][C]72[/C][C]0.795667107604999[/C][C]0.408665784790001[/C][C]0.204332892395001[/C][/ROW]
[ROW][C]73[/C][C]0.77822666688051[/C][C]0.44354666623898[/C][C]0.22177333311949[/C][/ROW]
[ROW][C]74[/C][C]0.757677813111666[/C][C]0.484644373776668[/C][C]0.242322186888334[/C][/ROW]
[ROW][C]75[/C][C]0.738256545271879[/C][C]0.523486909456242[/C][C]0.261743454728121[/C][/ROW]
[ROW][C]76[/C][C]0.850981976629279[/C][C]0.298036046741441[/C][C]0.149018023370721[/C][/ROW]
[ROW][C]77[/C][C]0.843690702950133[/C][C]0.312618594099733[/C][C]0.156309297049867[/C][/ROW]
[ROW][C]78[/C][C]0.859468090653313[/C][C]0.281063818693375[/C][C]0.140531909346687[/C][/ROW]
[ROW][C]79[/C][C]0.83386102283967[/C][C]0.33227795432066[/C][C]0.16613897716033[/C][/ROW]
[ROW][C]80[/C][C]0.908239932041749[/C][C]0.183520135916502[/C][C]0.091760067958251[/C][/ROW]
[ROW][C]81[/C][C]0.888248794512845[/C][C]0.223502410974311[/C][C]0.111751205487155[/C][/ROW]
[ROW][C]82[/C][C]0.865618169638221[/C][C]0.268763660723558[/C][C]0.134381830361779[/C][/ROW]
[ROW][C]83[/C][C]0.880925001943063[/C][C]0.238149996113875[/C][C]0.119074998056937[/C][/ROW]
[ROW][C]84[/C][C]0.857845187852388[/C][C]0.284309624295223[/C][C]0.142154812147612[/C][/ROW]
[ROW][C]85[/C][C]0.8299357170047[/C][C]0.340128565990599[/C][C]0.170064282995299[/C][/ROW]
[ROW][C]86[/C][C]0.798831729939358[/C][C]0.402336540121285[/C][C]0.201168270060642[/C][/ROW]
[ROW][C]87[/C][C]0.767177926448663[/C][C]0.465644147102675[/C][C]0.232822073551337[/C][/ROW]
[ROW][C]88[/C][C]0.733955589796783[/C][C]0.532088820406435[/C][C]0.266044410203217[/C][/ROW]
[ROW][C]89[/C][C]0.697465351997849[/C][C]0.605069296004302[/C][C]0.302534648002151[/C][/ROW]
[ROW][C]90[/C][C]0.770701048015499[/C][C]0.458597903969003[/C][C]0.229298951984501[/C][/ROW]
[ROW][C]91[/C][C]0.74410689956593[/C][C]0.51178620086814[/C][C]0.25589310043407[/C][/ROW]
[ROW][C]92[/C][C]0.712336009559808[/C][C]0.575327980880384[/C][C]0.287663990440192[/C][/ROW]
[ROW][C]93[/C][C]0.684285588811115[/C][C]0.631428822377769[/C][C]0.315714411188885[/C][/ROW]
[ROW][C]94[/C][C]0.686453448305639[/C][C]0.627093103388722[/C][C]0.313546551694361[/C][/ROW]
[ROW][C]95[/C][C]0.710904606157855[/C][C]0.57819078768429[/C][C]0.289095393842145[/C][/ROW]
[ROW][C]96[/C][C]0.668408104947017[/C][C]0.663183790105966[/C][C]0.331591895052983[/C][/ROW]
[ROW][C]97[/C][C]0.643101396217379[/C][C]0.713797207565242[/C][C]0.356898603782621[/C][/ROW]
[ROW][C]98[/C][C]0.614358550598021[/C][C]0.771282898803958[/C][C]0.385641449401979[/C][/ROW]
[ROW][C]99[/C][C]0.571581374277883[/C][C]0.856837251444233[/C][C]0.428418625722117[/C][/ROW]
[ROW][C]100[/C][C]0.556737826126796[/C][C]0.886524347746408[/C][C]0.443262173873204[/C][/ROW]
[ROW][C]101[/C][C]0.50849615848491[/C][C]0.983007683030181[/C][C]0.49150384151509[/C][/ROW]
[ROW][C]102[/C][C]0.482468428441165[/C][C]0.964936856882329[/C][C]0.517531571558835[/C][/ROW]
[ROW][C]103[/C][C]0.471020286890204[/C][C]0.942040573780407[/C][C]0.528979713109796[/C][/ROW]
[ROW][C]104[/C][C]0.594736882002643[/C][C]0.810526235994715[/C][C]0.405263117997357[/C][/ROW]
[ROW][C]105[/C][C]0.714246512121457[/C][C]0.571506975757087[/C][C]0.285753487878543[/C][/ROW]
[ROW][C]106[/C][C]0.702962272869356[/C][C]0.594075454261288[/C][C]0.297037727130644[/C][/ROW]
[ROW][C]107[/C][C]0.6579518955452[/C][C]0.6840962089096[/C][C]0.3420481044548[/C][/ROW]
[ROW][C]108[/C][C]0.737201562367634[/C][C]0.525596875264732[/C][C]0.262798437632366[/C][/ROW]
[ROW][C]109[/C][C]0.712788967169065[/C][C]0.574422065661869[/C][C]0.287211032830935[/C][/ROW]
[ROW][C]110[/C][C]0.894627323929315[/C][C]0.21074535214137[/C][C]0.105372676070685[/C][/ROW]
[ROW][C]111[/C][C]0.8964737618676[/C][C]0.2070524762648[/C][C]0.1035262381324[/C][/ROW]
[ROW][C]112[/C][C]0.872390486343937[/C][C]0.255219027312126[/C][C]0.127609513656063[/C][/ROW]
[ROW][C]113[/C][C]0.851160367821361[/C][C]0.297679264357278[/C][C]0.148839632178639[/C][/ROW]
[ROW][C]114[/C][C]0.881985665428594[/C][C]0.236028669142812[/C][C]0.118014334571406[/C][/ROW]
[ROW][C]115[/C][C]0.866438520493018[/C][C]0.267122959013963[/C][C]0.133561479506982[/C][/ROW]
[ROW][C]116[/C][C]0.835881144541006[/C][C]0.328237710917987[/C][C]0.164118855458994[/C][/ROW]
[ROW][C]117[/C][C]0.830592268134499[/C][C]0.338815463731001[/C][C]0.169407731865501[/C][/ROW]
[ROW][C]118[/C][C]0.796174372325734[/C][C]0.407651255348533[/C][C]0.203825627674266[/C][/ROW]
[ROW][C]119[/C][C]0.844604931156095[/C][C]0.31079013768781[/C][C]0.155395068843905[/C][/ROW]
[ROW][C]120[/C][C]0.868385738545581[/C][C]0.263228522908837[/C][C]0.131614261454419[/C][/ROW]
[ROW][C]121[/C][C]0.83718056709849[/C][C]0.32563886580302[/C][C]0.16281943290151[/C][/ROW]
[ROW][C]122[/C][C]0.806754517117462[/C][C]0.386490965765075[/C][C]0.193245482882537[/C][/ROW]
[ROW][C]123[/C][C]0.766895606569461[/C][C]0.466208786861078[/C][C]0.233104393430539[/C][/ROW]
[ROW][C]124[/C][C]0.777437601427812[/C][C]0.445124797144377[/C][C]0.222562398572188[/C][/ROW]
[ROW][C]125[/C][C]0.745042296753181[/C][C]0.509915406493638[/C][C]0.254957703246819[/C][/ROW]
[ROW][C]126[/C][C]0.716958049570298[/C][C]0.566083900859403[/C][C]0.283041950429702[/C][/ROW]
[ROW][C]127[/C][C]0.715690566531499[/C][C]0.568618866937002[/C][C]0.284309433468501[/C][/ROW]
[ROW][C]128[/C][C]0.698262651029887[/C][C]0.603474697940226[/C][C]0.301737348970113[/C][/ROW]
[ROW][C]129[/C][C]0.654383072796453[/C][C]0.691233854407095[/C][C]0.345616927203547[/C][/ROW]
[ROW][C]130[/C][C]0.594655801642603[/C][C]0.810688396714794[/C][C]0.405344198357397[/C][/ROW]
[ROW][C]131[/C][C]0.572474284349023[/C][C]0.855051431301955[/C][C]0.427525715650977[/C][/ROW]
[ROW][C]132[/C][C]0.534653013348719[/C][C]0.930693973302562[/C][C]0.465346986651281[/C][/ROW]
[ROW][C]133[/C][C]0.475866724046484[/C][C]0.951733448092969[/C][C]0.524133275953516[/C][/ROW]
[ROW][C]134[/C][C]0.432197622623413[/C][C]0.864395245246826[/C][C]0.567802377376587[/C][/ROW]
[ROW][C]135[/C][C]0.369632092422268[/C][C]0.739264184844536[/C][C]0.630367907577732[/C][/ROW]
[ROW][C]136[/C][C]0.319093438625791[/C][C]0.638186877251582[/C][C]0.680906561374209[/C][/ROW]
[ROW][C]137[/C][C]0.623020966372324[/C][C]0.753958067255352[/C][C]0.376979033627676[/C][/ROW]
[ROW][C]138[/C][C]0.54877094191796[/C][C]0.902458116164081[/C][C]0.45122905808204[/C][/ROW]
[ROW][C]139[/C][C]0.473125045297455[/C][C]0.946250090594909[/C][C]0.526874954702546[/C][/ROW]
[ROW][C]140[/C][C]0.417887799015976[/C][C]0.835775598031952[/C][C]0.582112200984024[/C][/ROW]
[ROW][C]141[/C][C]0.406289867760832[/C][C]0.812579735521663[/C][C]0.593710132239168[/C][/ROW]
[ROW][C]142[/C][C]0.359243269087186[/C][C]0.718486538174372[/C][C]0.640756730912814[/C][/ROW]
[ROW][C]143[/C][C]0.368799422462793[/C][C]0.737598844925586[/C][C]0.631200577537207[/C][/ROW]
[ROW][C]144[/C][C]0.286718657146382[/C][C]0.573437314292764[/C][C]0.713281342853618[/C][/ROW]
[ROW][C]145[/C][C]0.214319238407169[/C][C]0.428638476814337[/C][C]0.785680761592831[/C][/ROW]
[ROW][C]146[/C][C]0.310550722415169[/C][C]0.621101444830338[/C][C]0.689449277584831[/C][/ROW]
[ROW][C]147[/C][C]0.225691776182641[/C][C]0.451383552365283[/C][C]0.774308223817359[/C][/ROW]
[ROW][C]148[/C][C]0.450191741260263[/C][C]0.900383482520527[/C][C]0.549808258739737[/C][/ROW]
[ROW][C]149[/C][C]0.528008068318587[/C][C]0.943983863362826[/C][C]0.471991931681413[/C][/ROW]
[ROW][C]150[/C][C]0.389158039377046[/C][C]0.778316078754092[/C][C]0.610841960622954[/C][/ROW]
[ROW][C]151[/C][C]0.544622238199205[/C][C]0.910755523601589[/C][C]0.455377761800795[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204621&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204621&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.8429695066158820.3140609867682370.157030493384118
120.9982685050272930.003462989945413890.00173149497270695
130.9960035521599060.007992895680188680.00399644784009434
140.9942481098499380.01150378030012480.00575189015006239
150.9923292860866660.01534142782666690.00767071391333347
160.9860333470983220.02793330580335590.0139666529016779
170.9850719472861320.02985610542773670.0149280527138684
180.9753405092159830.04931898156803430.0246594907840172
190.9850689791393940.02986204172121140.0149310208606057
200.9764225349509750.04715493009805090.0235774650490254
210.985505872936120.02898825412775980.0144941270638799
220.9823492837988220.03530143240235640.0176507162011782
230.9731634157918150.05367316841637070.0268365842081854
240.9648011381905010.07039772361899730.0351988618094986
250.9495257775127280.1009484449745430.0504742224872717
260.9638276900384810.07234461992303880.0361723099615194
270.9652104246896540.06957915062069240.0347895753103462
280.9525960563659510.09480788726809880.0474039436340494
290.9642074061073050.07158518778539090.0357925938926954
300.9620003693379140.07599926132417210.037999630662086
310.9611135942129730.07777281157405290.0388864057870265
320.9507950396257170.0984099207485650.0492049603742825
330.9379144726902210.1241710546195580.0620855273097791
340.9279386348134930.1441227303730140.0720613651865068
350.9129191789993730.1741616420012540.0870808210006272
360.9368603584085970.1262792831828060.0631396415914032
370.9656379396132930.06872412077341460.0343620603867073
380.9535170384925230.09296592301495480.0464829615074774
390.9416055322171290.1167889355657420.0583944677828708
400.9468938029482260.1062123941035470.0531061970517737
410.9464375241523020.1071249516953970.0535624758476983
420.9450533537771990.1098932924456010.0549466462228006
430.9555604557332720.08887908853345670.0444395442667284
440.9429195751229590.1141608497540830.0570804248770415
450.9415231770746520.1169536458506960.0584768229253481
460.9314911363323440.1370177273353120.0685088636676562
470.9461188669322780.1077622661354450.0538811330677223
480.9332672234335290.1334655531329410.0667327765664706
490.9525845013350130.09483099732997350.0474154986649867
500.9459995214318410.1080009571363190.0540004785681593
510.938611681640480.122776636719040.0613883183595198
520.9246481481371110.1507037037257780.0753518518628888
530.9289935089433820.1420129821132360.0710064910566179
540.9156464630475510.1687070739048980.084353536952449
550.9364430788149520.1271138423700960.0635569211850481
560.9219698682223050.156060263555390.0780301317776951
570.9126689035498160.1746621929003670.0873310964501835
580.9279433104894250.1441133790211490.0720566895105747
590.9422182101676060.1155635796647880.0577817898323938
600.930910206453570.1381795870928590.0690897935464297
610.9317393839283790.1365212321432430.0682606160716214
620.9162764880837890.1674470238324220.083723511916211
630.907713710660310.184572578679380.0922862893396901
640.8862470438857850.227505912228430.113752956114215
650.8615221062887960.2769557874224080.138477893711204
660.8825300892889840.2349398214220320.117469910711016
670.881481407308740.237037185382520.11851859269126
680.8658715615534050.2682568768931890.134128438446595
690.8400033456055670.3199933087888660.159996654394433
700.8243124074867670.3513751850264660.175687592513233
710.8030483145449560.3939033709100880.196951685455044
720.7956671076049990.4086657847900010.204332892395001
730.778226666880510.443546666238980.22177333311949
740.7576778131116660.4846443737766680.242322186888334
750.7382565452718790.5234869094562420.261743454728121
760.8509819766292790.2980360467414410.149018023370721
770.8436907029501330.3126185940997330.156309297049867
780.8594680906533130.2810638186933750.140531909346687
790.833861022839670.332277954320660.16613897716033
800.9082399320417490.1835201359165020.091760067958251
810.8882487945128450.2235024109743110.111751205487155
820.8656181696382210.2687636607235580.134381830361779
830.8809250019430630.2381499961138750.119074998056937
840.8578451878523880.2843096242952230.142154812147612
850.82993571700470.3401285659905990.170064282995299
860.7988317299393580.4023365401212850.201168270060642
870.7671779264486630.4656441471026750.232822073551337
880.7339555897967830.5320888204064350.266044410203217
890.6974653519978490.6050692960043020.302534648002151
900.7707010480154990.4585979039690030.229298951984501
910.744106899565930.511786200868140.25589310043407
920.7123360095598080.5753279808803840.287663990440192
930.6842855888111150.6314288223777690.315714411188885
940.6864534483056390.6270931033887220.313546551694361
950.7109046061578550.578190787684290.289095393842145
960.6684081049470170.6631837901059660.331591895052983
970.6431013962173790.7137972075652420.356898603782621
980.6143585505980210.7712828988039580.385641449401979
990.5715813742778830.8568372514442330.428418625722117
1000.5567378261267960.8865243477464080.443262173873204
1010.508496158484910.9830076830301810.49150384151509
1020.4824684284411650.9649368568823290.517531571558835
1030.4710202868902040.9420405737804070.528979713109796
1040.5947368820026430.8105262359947150.405263117997357
1050.7142465121214570.5715069757570870.285753487878543
1060.7029622728693560.5940754542612880.297037727130644
1070.65795189554520.68409620890960.3420481044548
1080.7372015623676340.5255968752647320.262798437632366
1090.7127889671690650.5744220656618690.287211032830935
1100.8946273239293150.210745352141370.105372676070685
1110.89647376186760.20705247626480.1035262381324
1120.8723904863439370.2552190273121260.127609513656063
1130.8511603678213610.2976792643572780.148839632178639
1140.8819856654285940.2360286691428120.118014334571406
1150.8664385204930180.2671229590139630.133561479506982
1160.8358811445410060.3282377109179870.164118855458994
1170.8305922681344990.3388154637310010.169407731865501
1180.7961743723257340.4076512553485330.203825627674266
1190.8446049311560950.310790137687810.155395068843905
1200.8683857385455810.2632285229088370.131614261454419
1210.837180567098490.325638865803020.16281943290151
1220.8067545171174620.3864909657650750.193245482882537
1230.7668956065694610.4662087868610780.233104393430539
1240.7774376014278120.4451247971443770.222562398572188
1250.7450422967531810.5099154064936380.254957703246819
1260.7169580495702980.5660839008594030.283041950429702
1270.7156905665314990.5686188669370020.284309433468501
1280.6982626510298870.6034746979402260.301737348970113
1290.6543830727964530.6912338544070950.345616927203547
1300.5946558016426030.8106883967147940.405344198357397
1310.5724742843490230.8550514313019550.427525715650977
1320.5346530133487190.9306939733025620.465346986651281
1330.4758667240464840.9517334480929690.524133275953516
1340.4321976226234130.8643952452468260.567802377376587
1350.3696320924222680.7392641848445360.630367907577732
1360.3190934386257910.6381868772515820.680906561374209
1370.6230209663723240.7539580672553520.376979033627676
1380.548770941917960.9024581161640810.45122905808204
1390.4731250452974550.9462500905949090.526874954702546
1400.4178877990159760.8357755980319520.582112200984024
1410.4062898677608320.8125797355216630.593710132239168
1420.3592432690871860.7184865381743720.640756730912814
1430.3687994224627930.7375988449255860.631200577537207
1440.2867186571463820.5734373142927640.713281342853618
1450.2143192384071690.4286384768143370.785680761592831
1460.3105507224151690.6211014448303380.689449277584831
1470.2256917761826410.4513835523652830.774308223817359
1480.4501917412602630.9003834825205270.549808258739737
1490.5280080683185870.9439838633628260.471991931681413
1500.3891580393770460.7783160787540920.610841960622954
1510.5446222381992050.9107555236015890.455377761800795






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0141843971631206NOK
5% type I error level110.0780141843971631NOK
10% type I error level240.170212765957447NOK

\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 & 2 & 0.0141843971631206 & NOK \tabularnewline
5% type I error level & 11 & 0.0780141843971631 & NOK \tabularnewline
10% type I error level & 24 & 0.170212765957447 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204621&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]2[/C][C]0.0141843971631206[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]11[/C][C]0.0780141843971631[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]24[/C][C]0.170212765957447[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204621&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204621&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0141843971631206NOK
5% type I error level110.0780141843971631NOK
10% type I error level240.170212765957447NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}