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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 12 Dec 2012 07:36:12 -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/12/t1355315823r51f5ymjzy6kdgv.htm/, Retrieved Mon, 29 Apr 2024 15:28:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198847, Retrieved Mon, 29 Apr 2024 15:28:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact82

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 3] [2012-11-04 16:26:17] [cb178d3ebce11557293640a297e0ede2]
- R         [Multiple Regression] [Paper 26] [2012-12-12 12:36:12] [0eae5e694d1d975eb250a0af7a7338a6] [Current]
-   P         [Multiple Regression] [Paper 27] [2012-12-12 12:39:16] [cb178d3ebce11557293640a297e0ede2]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
09	41	38	13	12	14	12	53	32
09	39	32	16	11	18	11	86	51
09	30	35	19	15	11	14	66	42
09	31	33	15	6	12	12	67	41
09	34	37	14	13	16	21	76	46
09	35	29	13	10	18	12	78	47
09	39	31	19	12	14	22	53	37
09	34	36	15	14	14	11	80	49
09	36	35	14	12	15	10	74	45
09	37	38	15	6	15	13	76	47
09	38	31	16	10	17	10	79	49
09	36	34	16	12	19	8	54	33
09	38	35	16	12	10	15	67	42
09	39	38	16	11	16	14	54	33
09	33	37	17	15	18	10	87	53
09	32	33	15	12	14	14	58	36
09	36	32	15	10	14	14	75	45
09	38	38	20	12	17	11	88	54
09	39	38	18	11	14	10	64	41
09	32	32	16	12	16	13	57	36
09	32	33	16	11	18	7	66	41
09	31	31	16	12	11	14	68	44
09	39	38	19	13	14	12	54	33
09	37	39	16	11	12	14	56	37
09	39	32	17	9	17	11	86	52
09	41	32	17	13	9	9	80	47
09	36	35	16	10	16	11	76	43
09	33	37	15	14	14	15	69	44
09	33	33	16	12	15	14	78	45
09	34	33	14	10	11	13	67	44
09	31	28	15	12	16	9	80	49
09	27	32	12	8	13	15	54	33
09	37	31	14	10	17	10	71	43
09	34	37	16	12	15	11	84	54
09	34	30	14	12	14	13	74	42
09	32	33	7	7	16	8	71	44
09	29	31	10	6	9	20	63	37
09	36	33	14	12	15	12	71	43
09	29	31	16	10	17	10	76	46
09	35	33	16	10	13	10	69	42
09	37	32	16	10	15	9	74	45
09	34	33	14	12	16	14	75	44
09	38	32	20	15	16	8	54	33
09	35	33	14	10	12	14	52	31
09	38	28	14	10	12	11	69	42
09	37	35	11	12	11	13	68	40
09	38	39	14	13	15	9	65	43
09	33	34	15	11	15	11	75	46
09	36	38	16	11	17	15	74	42
09	38	32	14	12	13	11	75	45
09	32	38	16	14	16	10	72	44
09	32	30	14	10	14	14	67	40
09	32	33	12	12	11	18	63	37
09	34	38	16	13	12	14	62	46
09	32	32	9	5	12	11	63	36
09	37	32	14	6	15	12	76	47
09	39	34	16	12	16	13	74	45
09	29	34	16	12	15	9	67	42
09	37	36	15	11	12	10	73	43
09	35	34	16	10	12	15	70	43
09	30	28	12	7	8	20	53	32
09	38	34	16	12	13	12	77	45
09	34	35	16	14	11	12	77	45
10	31	35	14	11	14	14	52	31
10	34	31	16	12	15	13	54	33
10	35	37	17	13	10	11	80	49
10	36	35	18	14	11	17	66	42
10	30	27	18	11	12	12	73	41
10	39	40	12	12	15	13	63	38
10	35	37	16	12	15	14	69	42
10	38	36	10	8	14	13	67	44
10	31	38	14	11	16	15	54	33
10	34	39	18	14	15	13	81	48
10	38	41	18	14	15	10	69	40
10	34	27	16	12	13	11	84	50
10	39	30	17	9	12	19	80	49
10	37	37	16	13	17	13	70	43
10	34	31	16	11	13	17	69	44
10	28	31	13	12	15	13	77	47
10	37	27	16	12	13	9	54	33
10	33	36	16	12	15	11	79	46
10	37	38	20	12	16	10	30	0
10	35	37	16	12	15	9	71	45
10	37	33	15	12	16	12	73	43
10	32	34	15	11	15	12	72	44
10	33	31	16	10	14	13	77	47
10	38	39	14	9	15	13	75	45
10	33	34	16	12	14	12	69	42
10	29	32	16	12	13	15	54	33
10	33	33	15	12	7	22	70	43
10	31	36	12	9	17	13	73	46
10	36	32	17	15	13	15	54	33
10	35	41	16	12	15	13	77	46
10	32	28	15	12	14	15	82	48
10	29	30	13	12	13	10	80	47
10	39	36	16	10	16	11	80	47
10	37	35	16	13	12	16	69	43
10	35	31	16	9	14	11	78	46
10	37	34	16	12	17	11	81	48
10	32	36	14	10	15	10	76	46
10	38	36	16	14	17	10	76	45
10	37	35	16	11	12	16	73	45
10	36	37	20	15	16	12	85	52
10	32	28	15	11	11	11	66	42
10	33	39	16	11	15	16	79	47
10	40	32	13	12	9	19	68	41
10	38	35	17	12	16	11	76	47
10	41	39	16	12	15	16	71	43
10	36	35	16	11	10	15	54	33
10	43	42	12	7	10	24	46	30
10	30	34	16	12	15	14	82	49
10	31	33	16	14	11	15	74	44
10	32	41	17	11	13	11	88	55
10	32	33	13	11	14	15	38	11
10	37	34	12	10	18	12	76	47
10	37	32	18	13	16	10	86	53
10	33	40	14	13	14	14	54	33
10	34	40	14	8	14	13	70	44
10	33	35	13	11	14	9	69	42
10	38	36	16	12	14	15	90	55
10	33	37	13	11	12	15	54	33
10	31	27	16	13	14	14	76	46
10	38	39	13	12	15	11	89	54
10	37	38	16	14	15	8	76	47
10	33	31	15	13	15	11	73	45
10	31	33	16	15	13	11	79	47
10	39	32	15	10	17	8	90	55
10	44	39	17	11	17	10	74	44
10	33	36	15	9	19	11	81	53
10	35	33	12	11	15	13	72	44
10	32	33	16	10	13	11	71	42
10	28	32	10	11	9	20	66	40
10	40	37	16	8	15	10	77	46
10	27	30	12	11	15	15	65	40
10	37	38	14	12	15	12	74	46
10	32	29	15	12	16	14	82	53
10	28	22	13	9	11	23	54	33
10	34	35	15	11	14	14	63	42
10	30	35	11	10	11	16	54	35
10	35	34	12	8	15	11	64	40
10	31	35	8	9	13	12	69	41
10	32	34	16	8	15	10	54	33
10	30	34	15	9	16	14	84	51
10	30	35	17	15	14	12	86	53
10	31	23	16	11	15	12	77	46
10	40	31	10	8	16	11	89	55
10	32	27	18	13	16	12	76	47
10	36	36	13	12	11	13	60	38
10	32	31	16	12	12	11	75	46
10	35	32	13	9	9	19	73	46
10	38	39	10	7	16	12	85	53
10	42	37	15	13	13	17	79	47
10	34	38	16	9	16	9	71	41
10	35	39	16	6	12	12	72	44
10	35	34	14	8	9	19	69	43
09	33	31	10	8	13	18	78	51
10	36	32	17	15	13	15	54	33
10	32	37	13	6	14	14	69	43
10	33	36	15	9	19	11	81	53
10	34	32	16	11	13	9	84	51
10	32	35	12	8	12	18	84	50
10	34	36	13	8	13	16	69	46

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 13 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198847&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198847&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198847&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 time13 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.75297936491273 -0.0282568887487843month[t] + 0.113576967565404Connected[t] -0.0200904826801223Separate[t] + 0.542745611256127Software[t] + 0.0596364628099267Happiness[t] -0.0704181393331173Depression[t] + 0.036427902180297Belonging[t] -0.0529848894217246Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.75297936491273 -0.0282568887487843month[t] +  0.113576967565404Connected[t] -0.0200904826801223Separate[t] +  0.542745611256127Software[t] +  0.0596364628099267Happiness[t] -0.0704181393331173Depression[t] +  0.036427902180297Belonging[t] -0.0529848894217246Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198847&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.75297936491273 -0.0282568887487843month[t] +  0.113576967565404Connected[t] -0.0200904826801223Separate[t] +  0.542745611256127Software[t] +  0.0596364628099267Happiness[t] -0.0704181393331173Depression[t] +  0.036427902180297Belonging[t] -0.0529848894217246Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198847&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198847&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.75297936491273 -0.0282568887487843month[t] + 0.113576967565404Connected[t] -0.0200904826801223Separate[t] + 0.542745611256127Software[t] + 0.0596364628099267Happiness[t] -0.0704181393331173Depression[t] + 0.036427902180297Belonging[t] -0.0529848894217246Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.752979364912733.7159641.54820.1236450.061823
month-0.02825688874878430.308202-0.09170.927070.463535
Connected0.1135769675654040.0473292.39970.017610.008805
Separate-0.02009048268012230.045392-0.44260.6586780.329339
Software0.5427456112561270.0692037.842800
Happiness0.05963646280992670.0766320.77820.4376440.218822
Depression-0.07041813933311730.057102-1.23320.2193920.109696
Belonging0.0364279021802970.0452630.80480.4221790.211089
Belonging_Final-0.05298488942172460.064685-0.81910.4139940.206997

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 5.75297936491273 & 3.715964 & 1.5482 & 0.123645 & 0.061823 \tabularnewline
month & -0.0282568887487843 & 0.308202 & -0.0917 & 0.92707 & 0.463535 \tabularnewline
Connected & 0.113576967565404 & 0.047329 & 2.3997 & 0.01761 & 0.008805 \tabularnewline
Separate & -0.0200904826801223 & 0.045392 & -0.4426 & 0.658678 & 0.329339 \tabularnewline
Software & 0.542745611256127 & 0.069203 & 7.8428 & 0 & 0 \tabularnewline
Happiness & 0.0596364628099267 & 0.076632 & 0.7782 & 0.437644 & 0.218822 \tabularnewline
Depression & -0.0704181393331173 & 0.057102 & -1.2332 & 0.219392 & 0.109696 \tabularnewline
Belonging & 0.036427902180297 & 0.045263 & 0.8048 & 0.422179 & 0.211089 \tabularnewline
Belonging_Final & -0.0529848894217246 & 0.064685 & -0.8191 & 0.413994 & 0.206997 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198847&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]5.75297936491273[/C][C]3.715964[/C][C]1.5482[/C][C]0.123645[/C][C]0.061823[/C][/ROW]
[ROW][C]month[/C][C]-0.0282568887487843[/C][C]0.308202[/C][C]-0.0917[/C][C]0.92707[/C][C]0.463535[/C][/ROW]
[ROW][C]Connected[/C][C]0.113576967565404[/C][C]0.047329[/C][C]2.3997[/C][C]0.01761[/C][C]0.008805[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0200904826801223[/C][C]0.045392[/C][C]-0.4426[/C][C]0.658678[/C][C]0.329339[/C][/ROW]
[ROW][C]Software[/C][C]0.542745611256127[/C][C]0.069203[/C][C]7.8428[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0596364628099267[/C][C]0.076632[/C][C]0.7782[/C][C]0.437644[/C][C]0.218822[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0704181393331173[/C][C]0.057102[/C][C]-1.2332[/C][C]0.219392[/C][C]0.109696[/C][/ROW]
[ROW][C]Belonging[/C][C]0.036427902180297[/C][C]0.045263[/C][C]0.8048[/C][C]0.422179[/C][C]0.211089[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0529848894217246[/C][C]0.064685[/C][C]-0.8191[/C][C]0.413994[/C][C]0.206997[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198847&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198847&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)5.752979364912733.7159641.54820.1236450.061823
month-0.02825688874878430.308202-0.09170.927070.463535
Connected0.1135769675654040.0473292.39970.017610.008805
Separate-0.02009048268012230.045392-0.44260.6586780.329339
Software0.5427456112561270.0692037.842800
Happiness0.05963646280992670.0766320.77820.4376440.218822
Depression-0.07041813933311730.057102-1.23320.2193920.109696
Belonging0.0364279021802970.0452630.80480.4221790.211089
Belonging_Final-0.05298488942172460.064685-0.81910.4139940.206997






Multiple Linear Regression - Regression Statistics
Multiple R0.597281724924451
R-squared0.356745458928728
Adjusted R-squared0.323111234558988
F-TEST (value)10.6066206554086
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value8.61755111714047e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85629928851646
Sum Squared Residuals527.21459842765

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.597281724924451 \tabularnewline
R-squared & 0.356745458928728 \tabularnewline
Adjusted R-squared & 0.323111234558988 \tabularnewline
F-TEST (value) & 10.6066206554086 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 8.61755111714047e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85629928851646 \tabularnewline
Sum Squared Residuals & 527.21459842765 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198847&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.597281724924451[/C][/ROW]
[ROW][C]R-squared[/C][C]0.356745458928728[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.323111234558988[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.6066206554086[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]8.61755111714047e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85629928851646[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]527.21459842765[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198847&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198847&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 R0.597281724924451
R-squared0.356745458928728
Adjusted R-squared0.323111234558988
F-TEST (value)10.6066206554086
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value8.61755111714047e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85629928851646
Sum Squared Residuals527.21459842765






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1298871909862-3.12988719098625
21615.98490240418990.0150975958101213
31916.19301699660612.80698300339387
41511.75194996130483.24805003869518
51415.4792474818291-1.47924748182913
61314.8982185716239-1.89821857162391
71915.07426079417653.92573920582352
81516.6137489839327-1.61374898393271
91415.8989289259795-1.89892892597955
101512.45139238548562.54860761451438
111615.2104264481530.789573551846969
121616.2056621680204-0.205662168020368
131615.37976920339820.62023079660177
141615.32186730431150.678132695688522
151717.3748468930913-0.374846893091318
161515.0375105708466-0.0375105708465517
171514.56882803354560.431171966454438
182016.14779282508543.85220717491464
191815.42466684245332.57533315754673
201615.21086421629930.789135783700652
211615.25273655649570.747263443504294
221614.72560508664081.27439491335921
231916.42892187987012.57107812012989
241614.69699328193451.30300671806546
251714.7867898294462.21321017055403
261716.89502781981490.10497218018505
271614.98148161014811.01851838985185
281516.0626264994801-1.06262649948007
291615.46241804003230.5375819599677
301413.97465403861730.0253459613826816
311515.6083599244513-0.608359924451314
321212.2019222280761-0.201922228076089
331415.1233355996756-1.1233355996756
341615.44859090316280.551409096837173
351415.6602911917052-1.66029119170525
36712.9152478891545-5.91524788915454
371010.8889504374052-0.888950437405246
381415.7949596849761-1.79495968497612
391614.23790470178871.76209529821132
401614.5975839330061.40241606699403
411615.05770425840620.942295741593818
421415.5793326532885-1.57933265328847
432017.922324513852.07767548614998
441414.2198343594375-0.219834359437496
451414.9087126469597-0.908712646959749
461115.6090626583327-4.60906265833273
471416.4370033401999-2.43700334019989
481514.94856776813280.0514322318672124
491615.22204876390250.777951236097505
501416.033091146378-2.03309114637805
511616.5096063780609-0.509606378060878
521414.1282023583104-0.128202358310439
531214.7060832465641-2.70608324656406
541615.2035474927170.796452507283037
55911.5325027780148-2.53250277801477
561412.64235342089951.35764657910053
571616.1081323561665-0.108132356166457
581615.09835812803810.901641871961943
591515.3403022878421-0.340302287842074
601614.14920930360891.85079069639109
611211.44655342676290.55344657323713
621615.99534784604530.00465215395471908
631616.4871677899959-0.487167789995944
641414.3591071719428-0.359107171942833
651615.41988624427580.58011375572415
661715.89768711757341.10231288242662
671816.09222188399431.90777811600571
681814.66295447043383.33704552956622
691215.8698834104958-3.86988341049582
701615.41205670433630.587943295663665
711013.4338517380073-3.43385173800731
721414.3145765357063-0.314576535706347
731816.53343362288931.46656637711072
741817.14555923500020.85444076499977
751615.71390547328230.286094526717688
761713.87757373252633.12242626747375
771616.3550903284348-0.355090328434813
781614.43977989913291.56022010086712
791314.8344777371261-1.83447773712611
801616.0033787094052-0.00337870940516579
811615.56858713400110.431412865998926
822016.76510634761033.23489365238973
831615.67804853709730.321951462902658
841516.0127720309633-1.01277203096326
851514.7330018447880.266998155211959
861614.2572348896311.74276511036905
871414.2144006920536-0.214400692053646
881615.32637403310220.673625966897797
891614.57180171948261.42819828051738
901514.20827089554020.791729104459763
911213.4730655989737-1.47306559897372
921716.99507732620880.00492267379117193
931615.48159657270440.518403427295559
941515.2777389354317-0.277738935431694
951315.169410386292-2.16941038629203
961615.20763719244970.792362807550259
971615.84940665956580.150593340434225
981614.17189228377391.82810771622611
991616.1492349207599-0.149234920759932
1001414.3306533767156-0.330653376715642
1011617.3553554421742-1.35535544217415
1021614.80365726693131.19634273306874
1032017.40734078781382.59265921218621
1041514.57281939472390.427180605276148
1051614.56049448861731.43950551138273
1061315.3870414692802-2.38704146928025
1071716.05393032135550.946069678644474
1081615.93237218064110.0676278193588523
1091614.58491404471421.41508595528583
1101212.3021051907114-0.302105190711376
1111615.00711181694150.992888183058528
1121615.89080772879270.109192271207325
1131714.54352633098622.45647366901377
1141314.9921541234457-1.9921541234457
1151214.9238074002447-2.92380740024473
1161816.66015823769241.33984176230764
1171415.5381859417024-1.53818594170243
1181413.00846564356610.991534356433905
1191314.9747923571653-1.97479235716531
1201615.71900587087340.280994129126558
1211314.3232751022776-1.32327510227757
1221615.68481856689730.315181433102675
1231316.0166004302169-3.0166004302169
1241617.1171910834514-1.11719108345143
1251516.0462026349978-1.04620263499776
1261616.8576836656374-0.857683665637436
1271515.4992899104087-0.499289910408683
1281716.3284380508190.671561949181044
1291513.88085772988091.11914227011908
1301215.0234050908313-3.02340509083126
1311614.23103380658851.76896619341155
1321013.3910831929673-3.39108319296729
1331614.17011531422681.82988468577321
1341213.991168762107-1.99116876210698
1351415.7301563886678-1.73015638866784
1361515.1824150705959-0.18241507059592
1371312.34827470466530.651725295334732
1381514.51771121498330.482288785016659
1391113.243955172699-2.24395517269898
1401213.4364303932935-1.43643039329351
141813.4442412081347-5.44424120813469
1421613.17273283407952.82726716592048
1431513.40539747150021.59460252849984
1441716.63023003492030.369769965079671
1451614.9165899187621.08341008123798
1461014.2401473551524-4.24014735515238
1471816.00551984932711.99448015067291
1481315.2616848807395-2.2616848807395
1491615.23084158268530.769158417314687
1501313.1081348614777-0.108134861477708
1511013.1993639781136-3.19936397811361
1521516.5186683196254-1.51866831962543
1531614.18772027358031.81227972641974
1541612.08064288937323.91935711062677
1551412.53845134440531.46154865559468
1561012.6127617408854-2.61276174088537
1571716.99507732620880.00492267379117193
1581311.70223078187171.29776921812829
1591513.88085772988091.11914227011908
1601615.15855883787010.841441162129917
1611212.6024817935443-0.60248179354427
1621312.67553901245360.32446098754644

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.1298871909862 & -3.12988719098625 \tabularnewline
2 & 16 & 15.9849024041899 & 0.0150975958101213 \tabularnewline
3 & 19 & 16.1930169966061 & 2.80698300339387 \tabularnewline
4 & 15 & 11.7519499613048 & 3.24805003869518 \tabularnewline
5 & 14 & 15.4792474818291 & -1.47924748182913 \tabularnewline
6 & 13 & 14.8982185716239 & -1.89821857162391 \tabularnewline
7 & 19 & 15.0742607941765 & 3.92573920582352 \tabularnewline
8 & 15 & 16.6137489839327 & -1.61374898393271 \tabularnewline
9 & 14 & 15.8989289259795 & -1.89892892597955 \tabularnewline
10 & 15 & 12.4513923854856 & 2.54860761451438 \tabularnewline
11 & 16 & 15.210426448153 & 0.789573551846969 \tabularnewline
12 & 16 & 16.2056621680204 & -0.205662168020368 \tabularnewline
13 & 16 & 15.3797692033982 & 0.62023079660177 \tabularnewline
14 & 16 & 15.3218673043115 & 0.678132695688522 \tabularnewline
15 & 17 & 17.3748468930913 & -0.374846893091318 \tabularnewline
16 & 15 & 15.0375105708466 & -0.0375105708465517 \tabularnewline
17 & 15 & 14.5688280335456 & 0.431171966454438 \tabularnewline
18 & 20 & 16.1477928250854 & 3.85220717491464 \tabularnewline
19 & 18 & 15.4246668424533 & 2.57533315754673 \tabularnewline
20 & 16 & 15.2108642162993 & 0.789135783700652 \tabularnewline
21 & 16 & 15.2527365564957 & 0.747263443504294 \tabularnewline
22 & 16 & 14.7256050866408 & 1.27439491335921 \tabularnewline
23 & 19 & 16.4289218798701 & 2.57107812012989 \tabularnewline
24 & 16 & 14.6969932819345 & 1.30300671806546 \tabularnewline
25 & 17 & 14.786789829446 & 2.21321017055403 \tabularnewline
26 & 17 & 16.8950278198149 & 0.10497218018505 \tabularnewline
27 & 16 & 14.9814816101481 & 1.01851838985185 \tabularnewline
28 & 15 & 16.0626264994801 & -1.06262649948007 \tabularnewline
29 & 16 & 15.4624180400323 & 0.5375819599677 \tabularnewline
30 & 14 & 13.9746540386173 & 0.0253459613826816 \tabularnewline
31 & 15 & 15.6083599244513 & -0.608359924451314 \tabularnewline
32 & 12 & 12.2019222280761 & -0.201922228076089 \tabularnewline
33 & 14 & 15.1233355996756 & -1.1233355996756 \tabularnewline
34 & 16 & 15.4485909031628 & 0.551409096837173 \tabularnewline
35 & 14 & 15.6602911917052 & -1.66029119170525 \tabularnewline
36 & 7 & 12.9152478891545 & -5.91524788915454 \tabularnewline
37 & 10 & 10.8889504374052 & -0.888950437405246 \tabularnewline
38 & 14 & 15.7949596849761 & -1.79495968497612 \tabularnewline
39 & 16 & 14.2379047017887 & 1.76209529821132 \tabularnewline
40 & 16 & 14.597583933006 & 1.40241606699403 \tabularnewline
41 & 16 & 15.0577042584062 & 0.942295741593818 \tabularnewline
42 & 14 & 15.5793326532885 & -1.57933265328847 \tabularnewline
43 & 20 & 17.92232451385 & 2.07767548614998 \tabularnewline
44 & 14 & 14.2198343594375 & -0.219834359437496 \tabularnewline
45 & 14 & 14.9087126469597 & -0.908712646959749 \tabularnewline
46 & 11 & 15.6090626583327 & -4.60906265833273 \tabularnewline
47 & 14 & 16.4370033401999 & -2.43700334019989 \tabularnewline
48 & 15 & 14.9485677681328 & 0.0514322318672124 \tabularnewline
49 & 16 & 15.2220487639025 & 0.777951236097505 \tabularnewline
50 & 14 & 16.033091146378 & -2.03309114637805 \tabularnewline
51 & 16 & 16.5096063780609 & -0.509606378060878 \tabularnewline
52 & 14 & 14.1282023583104 & -0.128202358310439 \tabularnewline
53 & 12 & 14.7060832465641 & -2.70608324656406 \tabularnewline
54 & 16 & 15.203547492717 & 0.796452507283037 \tabularnewline
55 & 9 & 11.5325027780148 & -2.53250277801477 \tabularnewline
56 & 14 & 12.6423534208995 & 1.35764657910053 \tabularnewline
57 & 16 & 16.1081323561665 & -0.108132356166457 \tabularnewline
58 & 16 & 15.0983581280381 & 0.901641871961943 \tabularnewline
59 & 15 & 15.3403022878421 & -0.340302287842074 \tabularnewline
60 & 16 & 14.1492093036089 & 1.85079069639109 \tabularnewline
61 & 12 & 11.4465534267629 & 0.55344657323713 \tabularnewline
62 & 16 & 15.9953478460453 & 0.00465215395471908 \tabularnewline
63 & 16 & 16.4871677899959 & -0.487167789995944 \tabularnewline
64 & 14 & 14.3591071719428 & -0.359107171942833 \tabularnewline
65 & 16 & 15.4198862442758 & 0.58011375572415 \tabularnewline
66 & 17 & 15.8976871175734 & 1.10231288242662 \tabularnewline
67 & 18 & 16.0922218839943 & 1.90777811600571 \tabularnewline
68 & 18 & 14.6629544704338 & 3.33704552956622 \tabularnewline
69 & 12 & 15.8698834104958 & -3.86988341049582 \tabularnewline
70 & 16 & 15.4120567043363 & 0.587943295663665 \tabularnewline
71 & 10 & 13.4338517380073 & -3.43385173800731 \tabularnewline
72 & 14 & 14.3145765357063 & -0.314576535706347 \tabularnewline
73 & 18 & 16.5334336228893 & 1.46656637711072 \tabularnewline
74 & 18 & 17.1455592350002 & 0.85444076499977 \tabularnewline
75 & 16 & 15.7139054732823 & 0.286094526717688 \tabularnewline
76 & 17 & 13.8775737325263 & 3.12242626747375 \tabularnewline
77 & 16 & 16.3550903284348 & -0.355090328434813 \tabularnewline
78 & 16 & 14.4397798991329 & 1.56022010086712 \tabularnewline
79 & 13 & 14.8344777371261 & -1.83447773712611 \tabularnewline
80 & 16 & 16.0033787094052 & -0.00337870940516579 \tabularnewline
81 & 16 & 15.5685871340011 & 0.431412865998926 \tabularnewline
82 & 20 & 16.7651063476103 & 3.23489365238973 \tabularnewline
83 & 16 & 15.6780485370973 & 0.321951462902658 \tabularnewline
84 & 15 & 16.0127720309633 & -1.01277203096326 \tabularnewline
85 & 15 & 14.733001844788 & 0.266998155211959 \tabularnewline
86 & 16 & 14.257234889631 & 1.74276511036905 \tabularnewline
87 & 14 & 14.2144006920536 & -0.214400692053646 \tabularnewline
88 & 16 & 15.3263740331022 & 0.673625966897797 \tabularnewline
89 & 16 & 14.5718017194826 & 1.42819828051738 \tabularnewline
90 & 15 & 14.2082708955402 & 0.791729104459763 \tabularnewline
91 & 12 & 13.4730655989737 & -1.47306559897372 \tabularnewline
92 & 17 & 16.9950773262088 & 0.00492267379117193 \tabularnewline
93 & 16 & 15.4815965727044 & 0.518403427295559 \tabularnewline
94 & 15 & 15.2777389354317 & -0.277738935431694 \tabularnewline
95 & 13 & 15.169410386292 & -2.16941038629203 \tabularnewline
96 & 16 & 15.2076371924497 & 0.792362807550259 \tabularnewline
97 & 16 & 15.8494066595658 & 0.150593340434225 \tabularnewline
98 & 16 & 14.1718922837739 & 1.82810771622611 \tabularnewline
99 & 16 & 16.1492349207599 & -0.149234920759932 \tabularnewline
100 & 14 & 14.3306533767156 & -0.330653376715642 \tabularnewline
101 & 16 & 17.3553554421742 & -1.35535544217415 \tabularnewline
102 & 16 & 14.8036572669313 & 1.19634273306874 \tabularnewline
103 & 20 & 17.4073407878138 & 2.59265921218621 \tabularnewline
104 & 15 & 14.5728193947239 & 0.427180605276148 \tabularnewline
105 & 16 & 14.5604944886173 & 1.43950551138273 \tabularnewline
106 & 13 & 15.3870414692802 & -2.38704146928025 \tabularnewline
107 & 17 & 16.0539303213555 & 0.946069678644474 \tabularnewline
108 & 16 & 15.9323721806411 & 0.0676278193588523 \tabularnewline
109 & 16 & 14.5849140447142 & 1.41508595528583 \tabularnewline
110 & 12 & 12.3021051907114 & -0.302105190711376 \tabularnewline
111 & 16 & 15.0071118169415 & 0.992888183058528 \tabularnewline
112 & 16 & 15.8908077287927 & 0.109192271207325 \tabularnewline
113 & 17 & 14.5435263309862 & 2.45647366901377 \tabularnewline
114 & 13 & 14.9921541234457 & -1.9921541234457 \tabularnewline
115 & 12 & 14.9238074002447 & -2.92380740024473 \tabularnewline
116 & 18 & 16.6601582376924 & 1.33984176230764 \tabularnewline
117 & 14 & 15.5381859417024 & -1.53818594170243 \tabularnewline
118 & 14 & 13.0084656435661 & 0.991534356433905 \tabularnewline
119 & 13 & 14.9747923571653 & -1.97479235716531 \tabularnewline
120 & 16 & 15.7190058708734 & 0.280994129126558 \tabularnewline
121 & 13 & 14.3232751022776 & -1.32327510227757 \tabularnewline
122 & 16 & 15.6848185668973 & 0.315181433102675 \tabularnewline
123 & 13 & 16.0166004302169 & -3.0166004302169 \tabularnewline
124 & 16 & 17.1171910834514 & -1.11719108345143 \tabularnewline
125 & 15 & 16.0462026349978 & -1.04620263499776 \tabularnewline
126 & 16 & 16.8576836656374 & -0.857683665637436 \tabularnewline
127 & 15 & 15.4992899104087 & -0.499289910408683 \tabularnewline
128 & 17 & 16.328438050819 & 0.671561949181044 \tabularnewline
129 & 15 & 13.8808577298809 & 1.11914227011908 \tabularnewline
130 & 12 & 15.0234050908313 & -3.02340509083126 \tabularnewline
131 & 16 & 14.2310338065885 & 1.76896619341155 \tabularnewline
132 & 10 & 13.3910831929673 & -3.39108319296729 \tabularnewline
133 & 16 & 14.1701153142268 & 1.82988468577321 \tabularnewline
134 & 12 & 13.991168762107 & -1.99116876210698 \tabularnewline
135 & 14 & 15.7301563886678 & -1.73015638866784 \tabularnewline
136 & 15 & 15.1824150705959 & -0.18241507059592 \tabularnewline
137 & 13 & 12.3482747046653 & 0.651725295334732 \tabularnewline
138 & 15 & 14.5177112149833 & 0.482288785016659 \tabularnewline
139 & 11 & 13.243955172699 & -2.24395517269898 \tabularnewline
140 & 12 & 13.4364303932935 & -1.43643039329351 \tabularnewline
141 & 8 & 13.4442412081347 & -5.44424120813469 \tabularnewline
142 & 16 & 13.1727328340795 & 2.82726716592048 \tabularnewline
143 & 15 & 13.4053974715002 & 1.59460252849984 \tabularnewline
144 & 17 & 16.6302300349203 & 0.369769965079671 \tabularnewline
145 & 16 & 14.916589918762 & 1.08341008123798 \tabularnewline
146 & 10 & 14.2401473551524 & -4.24014735515238 \tabularnewline
147 & 18 & 16.0055198493271 & 1.99448015067291 \tabularnewline
148 & 13 & 15.2616848807395 & -2.2616848807395 \tabularnewline
149 & 16 & 15.2308415826853 & 0.769158417314687 \tabularnewline
150 & 13 & 13.1081348614777 & -0.108134861477708 \tabularnewline
151 & 10 & 13.1993639781136 & -3.19936397811361 \tabularnewline
152 & 15 & 16.5186683196254 & -1.51866831962543 \tabularnewline
153 & 16 & 14.1877202735803 & 1.81227972641974 \tabularnewline
154 & 16 & 12.0806428893732 & 3.91935711062677 \tabularnewline
155 & 14 & 12.5384513444053 & 1.46154865559468 \tabularnewline
156 & 10 & 12.6127617408854 & -2.61276174088537 \tabularnewline
157 & 17 & 16.9950773262088 & 0.00492267379117193 \tabularnewline
158 & 13 & 11.7022307818717 & 1.29776921812829 \tabularnewline
159 & 15 & 13.8808577298809 & 1.11914227011908 \tabularnewline
160 & 16 & 15.1585588378701 & 0.841441162129917 \tabularnewline
161 & 12 & 12.6024817935443 & -0.60248179354427 \tabularnewline
162 & 13 & 12.6755390124536 & 0.32446098754644 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198847&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.1298871909862[/C][C]-3.12988719098625[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.9849024041899[/C][C]0.0150975958101213[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.1930169966061[/C][C]2.80698300339387[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.7519499613048[/C][C]3.24805003869518[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.4792474818291[/C][C]-1.47924748182913[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8982185716239[/C][C]-1.89821857162391[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.0742607941765[/C][C]3.92573920582352[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.6137489839327[/C][C]-1.61374898393271[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.8989289259795[/C][C]-1.89892892597955[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.4513923854856[/C][C]2.54860761451438[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.210426448153[/C][C]0.789573551846969[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.2056621680204[/C][C]-0.205662168020368[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.3797692033982[/C][C]0.62023079660177[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3218673043115[/C][C]0.678132695688522[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.3748468930913[/C][C]-0.374846893091318[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0375105708466[/C][C]-0.0375105708465517[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.5688280335456[/C][C]0.431171966454438[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1477928250854[/C][C]3.85220717491464[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.4246668424533[/C][C]2.57533315754673[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.2108642162993[/C][C]0.789135783700652[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2527365564957[/C][C]0.747263443504294[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.7256050866408[/C][C]1.27439491335921[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4289218798701[/C][C]2.57107812012989[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.6969932819345[/C][C]1.30300671806546[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.786789829446[/C][C]2.21321017055403[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.8950278198149[/C][C]0.10497218018505[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9814816101481[/C][C]1.01851838985185[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.0626264994801[/C][C]-1.06262649948007[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.4624180400323[/C][C]0.5375819599677[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.9746540386173[/C][C]0.0253459613826816[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.6083599244513[/C][C]-0.608359924451314[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.2019222280761[/C][C]-0.201922228076089[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1233355996756[/C][C]-1.1233355996756[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.4485909031628[/C][C]0.551409096837173[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6602911917052[/C][C]-1.66029119170525[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.9152478891545[/C][C]-5.91524788915454[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.8889504374052[/C][C]-0.888950437405246[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7949596849761[/C][C]-1.79495968497612[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.2379047017887[/C][C]1.76209529821132[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.597583933006[/C][C]1.40241606699403[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.0577042584062[/C][C]0.942295741593818[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.5793326532885[/C][C]-1.57933265328847[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.92232451385[/C][C]2.07767548614998[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2198343594375[/C][C]-0.219834359437496[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9087126469597[/C][C]-0.908712646959749[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6090626583327[/C][C]-4.60906265833273[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4370033401999[/C][C]-2.43700334019989[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9485677681328[/C][C]0.0514322318672124[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.2220487639025[/C][C]0.777951236097505[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.033091146378[/C][C]-2.03309114637805[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.5096063780609[/C][C]-0.509606378060878[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1282023583104[/C][C]-0.128202358310439[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.7060832465641[/C][C]-2.70608324656406[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.203547492717[/C][C]0.796452507283037[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5325027780148[/C][C]-2.53250277801477[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6423534208995[/C][C]1.35764657910053[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1081323561665[/C][C]-0.108132356166457[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.0983581280381[/C][C]0.901641871961943[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3403022878421[/C][C]-0.340302287842074[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1492093036089[/C][C]1.85079069639109[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4465534267629[/C][C]0.55344657323713[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9953478460453[/C][C]0.00465215395471908[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.4871677899959[/C][C]-0.487167789995944[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.3591071719428[/C][C]-0.359107171942833[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4198862442758[/C][C]0.58011375572415[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.8976871175734[/C][C]1.10231288242662[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.0922218839943[/C][C]1.90777811600571[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.6629544704338[/C][C]3.33704552956622[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8698834104958[/C][C]-3.86988341049582[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4120567043363[/C][C]0.587943295663665[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.4338517380073[/C][C]-3.43385173800731[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3145765357063[/C][C]-0.314576535706347[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5334336228893[/C][C]1.46656637711072[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1455592350002[/C][C]0.85444076499977[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.7139054732823[/C][C]0.286094526717688[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8775737325263[/C][C]3.12242626747375[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3550903284348[/C][C]-0.355090328434813[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4397798991329[/C][C]1.56022010086712[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8344777371261[/C][C]-1.83447773712611[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.0033787094052[/C][C]-0.00337870940516579[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5685871340011[/C][C]0.431412865998926[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.7651063476103[/C][C]3.23489365238973[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.6780485370973[/C][C]0.321951462902658[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]16.0127720309633[/C][C]-1.01277203096326[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.733001844788[/C][C]0.266998155211959[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.257234889631[/C][C]1.74276511036905[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.2144006920536[/C][C]-0.214400692053646[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3263740331022[/C][C]0.673625966897797[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5718017194826[/C][C]1.42819828051738[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.2082708955402[/C][C]0.791729104459763[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4730655989737[/C][C]-1.47306559897372[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9950773262088[/C][C]0.00492267379117193[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4815965727044[/C][C]0.518403427295559[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2777389354317[/C][C]-0.277738935431694[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.169410386292[/C][C]-2.16941038629203[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.2076371924497[/C][C]0.792362807550259[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8494066595658[/C][C]0.150593340434225[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1718922837739[/C][C]1.82810771622611[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.1492349207599[/C][C]-0.149234920759932[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.3306533767156[/C][C]-0.330653376715642[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.3553554421742[/C][C]-1.35535544217415[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8036572669313[/C][C]1.19634273306874[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.4073407878138[/C][C]2.59265921218621[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.5728193947239[/C][C]0.427180605276148[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5604944886173[/C][C]1.43950551138273[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3870414692802[/C][C]-2.38704146928025[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.0539303213555[/C][C]0.946069678644474[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.9323721806411[/C][C]0.0676278193588523[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5849140447142[/C][C]1.41508595528583[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.3021051907114[/C][C]-0.302105190711376[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.0071118169415[/C][C]0.992888183058528[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8908077287927[/C][C]0.109192271207325[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5435263309862[/C][C]2.45647366901377[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.9921541234457[/C][C]-1.9921541234457[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.9238074002447[/C][C]-2.92380740024473[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.6601582376924[/C][C]1.33984176230764[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.5381859417024[/C][C]-1.53818594170243[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0084656435661[/C][C]0.991534356433905[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9747923571653[/C][C]-1.97479235716531[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.7190058708734[/C][C]0.280994129126558[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3232751022776[/C][C]-1.32327510227757[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.6848185668973[/C][C]0.315181433102675[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]16.0166004302169[/C][C]-3.0166004302169[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.1171910834514[/C][C]-1.11719108345143[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.0462026349978[/C][C]-1.04620263499776[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.8576836656374[/C][C]-0.857683665637436[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.4992899104087[/C][C]-0.499289910408683[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.328438050819[/C][C]0.671561949181044[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.8808577298809[/C][C]1.11914227011908[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.0234050908313[/C][C]-3.02340509083126[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.2310338065885[/C][C]1.76896619341155[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3910831929673[/C][C]-3.39108319296729[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.1701153142268[/C][C]1.82988468577321[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.991168762107[/C][C]-1.99116876210698[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.7301563886678[/C][C]-1.73015638866784[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.1824150705959[/C][C]-0.18241507059592[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.3482747046653[/C][C]0.651725295334732[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.5177112149833[/C][C]0.482288785016659[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.243955172699[/C][C]-2.24395517269898[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.4364303932935[/C][C]-1.43643039329351[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.4442412081347[/C][C]-5.44424120813469[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.1727328340795[/C][C]2.82726716592048[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.4053974715002[/C][C]1.59460252849984[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.6302300349203[/C][C]0.369769965079671[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.916589918762[/C][C]1.08341008123798[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.2401473551524[/C][C]-4.24014735515238[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]16.0055198493271[/C][C]1.99448015067291[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.2616848807395[/C][C]-2.2616848807395[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.2308415826853[/C][C]0.769158417314687[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.1081348614777[/C][C]-0.108134861477708[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.1993639781136[/C][C]-3.19936397811361[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.5186683196254[/C][C]-1.51866831962543[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.1877202735803[/C][C]1.81227972641974[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.0806428893732[/C][C]3.91935711062677[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.5384513444053[/C][C]1.46154865559468[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.6127617408854[/C][C]-2.61276174088537[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.9950773262088[/C][C]0.00492267379117193[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7022307818717[/C][C]1.29776921812829[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.8808577298809[/C][C]1.11914227011908[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.1585588378701[/C][C]0.841441162129917[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.6024817935443[/C][C]-0.60248179354427[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6755390124536[/C][C]0.32446098754644[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198847&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198847&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1298871909862-3.12988719098625
21615.98490240418990.0150975958101213
31916.19301699660612.80698300339387
41511.75194996130483.24805003869518
51415.4792474818291-1.47924748182913
61314.8982185716239-1.89821857162391
71915.07426079417653.92573920582352
81516.6137489839327-1.61374898393271
91415.8989289259795-1.89892892597955
101512.45139238548562.54860761451438
111615.2104264481530.789573551846969
121616.2056621680204-0.205662168020368
131615.37976920339820.62023079660177
141615.32186730431150.678132695688522
151717.3748468930913-0.374846893091318
161515.0375105708466-0.0375105708465517
171514.56882803354560.431171966454438
182016.14779282508543.85220717491464
191815.42466684245332.57533315754673
201615.21086421629930.789135783700652
211615.25273655649570.747263443504294
221614.72560508664081.27439491335921
231916.42892187987012.57107812012989
241614.69699328193451.30300671806546
251714.7867898294462.21321017055403
261716.89502781981490.10497218018505
271614.98148161014811.01851838985185
281516.0626264994801-1.06262649948007
291615.46241804003230.5375819599677
301413.97465403861730.0253459613826816
311515.6083599244513-0.608359924451314
321212.2019222280761-0.201922228076089
331415.1233355996756-1.1233355996756
341615.44859090316280.551409096837173
351415.6602911917052-1.66029119170525
36712.9152478891545-5.91524788915454
371010.8889504374052-0.888950437405246
381415.7949596849761-1.79495968497612
391614.23790470178871.76209529821132
401614.5975839330061.40241606699403
411615.05770425840620.942295741593818
421415.5793326532885-1.57933265328847
432017.922324513852.07767548614998
441414.2198343594375-0.219834359437496
451414.9087126469597-0.908712646959749
461115.6090626583327-4.60906265833273
471416.4370033401999-2.43700334019989
481514.94856776813280.0514322318672124
491615.22204876390250.777951236097505
501416.033091146378-2.03309114637805
511616.5096063780609-0.509606378060878
521414.1282023583104-0.128202358310439
531214.7060832465641-2.70608324656406
541615.2035474927170.796452507283037
55911.5325027780148-2.53250277801477
561412.64235342089951.35764657910053
571616.1081323561665-0.108132356166457
581615.09835812803810.901641871961943
591515.3403022878421-0.340302287842074
601614.14920930360891.85079069639109
611211.44655342676290.55344657323713
621615.99534784604530.00465215395471908
631616.4871677899959-0.487167789995944
641414.3591071719428-0.359107171942833
651615.41988624427580.58011375572415
661715.89768711757341.10231288242662
671816.09222188399431.90777811600571
681814.66295447043383.33704552956622
691215.8698834104958-3.86988341049582
701615.41205670433630.587943295663665
711013.4338517380073-3.43385173800731
721414.3145765357063-0.314576535706347
731816.53343362288931.46656637711072
741817.14555923500020.85444076499977
751615.71390547328230.286094526717688
761713.87757373252633.12242626747375
771616.3550903284348-0.355090328434813
781614.43977989913291.56022010086712
791314.8344777371261-1.83447773712611
801616.0033787094052-0.00337870940516579
811615.56858713400110.431412865998926
822016.76510634761033.23489365238973
831615.67804853709730.321951462902658
841516.0127720309633-1.01277203096326
851514.7330018447880.266998155211959
861614.2572348896311.74276511036905
871414.2144006920536-0.214400692053646
881615.32637403310220.673625966897797
891614.57180171948261.42819828051738
901514.20827089554020.791729104459763
911213.4730655989737-1.47306559897372
921716.99507732620880.00492267379117193
931615.48159657270440.518403427295559
941515.2777389354317-0.277738935431694
951315.169410386292-2.16941038629203
961615.20763719244970.792362807550259
971615.84940665956580.150593340434225
981614.17189228377391.82810771622611
991616.1492349207599-0.149234920759932
1001414.3306533767156-0.330653376715642
1011617.3553554421742-1.35535544217415
1021614.80365726693131.19634273306874
1032017.40734078781382.59265921218621
1041514.57281939472390.427180605276148
1051614.56049448861731.43950551138273
1061315.3870414692802-2.38704146928025
1071716.05393032135550.946069678644474
1081615.93237218064110.0676278193588523
1091614.58491404471421.41508595528583
1101212.3021051907114-0.302105190711376
1111615.00711181694150.992888183058528
1121615.89080772879270.109192271207325
1131714.54352633098622.45647366901377
1141314.9921541234457-1.9921541234457
1151214.9238074002447-2.92380740024473
1161816.66015823769241.33984176230764
1171415.5381859417024-1.53818594170243
1181413.00846564356610.991534356433905
1191314.9747923571653-1.97479235716531
1201615.71900587087340.280994129126558
1211314.3232751022776-1.32327510227757
1221615.68481856689730.315181433102675
1231316.0166004302169-3.0166004302169
1241617.1171910834514-1.11719108345143
1251516.0462026349978-1.04620263499776
1261616.8576836656374-0.857683665637436
1271515.4992899104087-0.499289910408683
1281716.3284380508190.671561949181044
1291513.88085772988091.11914227011908
1301215.0234050908313-3.02340509083126
1311614.23103380658851.76896619341155
1321013.3910831929673-3.39108319296729
1331614.17011531422681.82988468577321
1341213.991168762107-1.99116876210698
1351415.7301563886678-1.73015638866784
1361515.1824150705959-0.18241507059592
1371312.34827470466530.651725295334732
1381514.51771121498330.482288785016659
1391113.243955172699-2.24395517269898
1401213.4364303932935-1.43643039329351
141813.4442412081347-5.44424120813469
1421613.17273283407952.82726716592048
1431513.40539747150021.59460252849984
1441716.63023003492030.369769965079671
1451614.9165899187621.08341008123798
1461014.2401473551524-4.24014735515238
1471816.00551984932711.99448015067291
1481315.2616848807395-2.2616848807395
1491615.23084158268530.769158417314687
1501313.1081348614777-0.108134861477708
1511013.1993639781136-3.19936397811361
1521516.5186683196254-1.51866831962543
1531614.18772027358031.81227972641974
1541612.08064288937323.91935711062677
1551412.53845134440531.46154865559468
1561012.6127617408854-2.61276174088537
1571716.99507732620880.00492267379117193
1581311.70223078187171.29776921812829
1591513.88085772988091.11914227011908
1601615.15855883787010.841441162129917
1611212.6024817935443-0.60248179354427
1621312.67553901245360.32446098754644






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.7589096926116930.4821806147766130.241090307388307
130.6217866319861170.7564267360277670.378213368013884
140.5817604633576610.8364790732846780.418239536642339
150.4615293343706210.9230586687412430.538470665629379
160.3612640996222090.7225281992444180.638735900377791
170.2983012322782810.5966024645565630.701698767721719
180.5072883538987520.9854232922024960.492711646101248
190.4239848248375410.8479696496750830.576015175162459
200.3350827812435850.670165562487170.664917218756415
210.2651830399946330.5303660799892670.734816960005367
220.2425303152754240.4850606305508480.757469684724576
230.4424122948676840.8848245897353680.557587705132316
240.47938518106490.95877036212980.5206148189351
250.4697576070582470.9395152141164940.530242392941753
260.4292180118477150.858436023695430.570781988152285
270.4911559651315960.9823119302631920.508844034868404
280.4940359970896120.9880719941792250.505964002910388
290.4751966141377230.9503932282754460.524803385862277
300.5171950803310690.9656098393378620.482804919668931
310.4561000929218430.9122001858436850.543899907078157
320.4069814149228540.8139628298457070.593018585077146
330.3798105563575320.7596211127150640.620189443642468
340.3461093236535370.6922186473070750.653890676346463
350.2968381748862410.5936763497724820.703161825113759
360.8572282157235040.2855435685529930.142771784276496
370.8320993562649020.3358012874701960.167900643735098
380.8239553263350710.3520893473298580.176044673664929
390.8451355118589170.3097289762821660.154864488141083
400.8302492525519530.3395014948960950.169750747448047
410.8022997953494410.3954004093011170.197700204650559
420.7749978799748950.450004240050210.225002120025105
430.8033087228518490.3933825542963020.196691277148151
440.7647895336080330.4704209327839340.235210466391967
450.7367748802166280.5264502395667450.263225119783372
460.8789939758621370.2420120482757260.121006024137863
470.9199774638068760.1600450723862490.0800225361931244
480.89891973908290.20216052183420.1010802609171
490.8856210052197990.2287579895604020.114378994780201
500.8818018655384890.2363962689230220.118198134461511
510.8545606257897510.2908787484204980.145439374210249
520.8234937715431810.3530124569136370.176506228456819
530.8368602412050710.3262795175898580.163139758794929
540.8250862724728120.3498274550543770.174913727527189
550.8367586849558460.3264826300883070.163241315044154
560.8122095318701470.3755809362597060.187790468129853
570.7789792122919770.4420415754160470.221020787708023
580.7579322737031240.4841354525937510.242067726296876
590.7195429830853610.5609140338292780.280457016914639
600.7250448264130720.5499103471738550.274955173586928
610.6947847745424920.6104304509150160.305215225457508
620.6596070662285640.6807858675428710.340392933771436
630.622368290642650.7552634187146990.37763170935735
640.5754351583637050.8491296832725890.424564841636295
650.5339040656214530.9321918687570930.466095934378547
660.4937792314455480.9875584628910960.506220768554452
670.479489572051320.958979144102640.52051042794868
680.565922969491580.868154061016840.43407703050842
690.7458423367494010.5083153265011980.254157663250599
700.7087892737725680.5824214524548640.291210726227432
710.8298527389336390.3402945221327220.170147261066361
720.7980916473552070.4038167052895860.201908352644793
730.788618597967480.422762804065040.21138140203252
740.7690958967164740.4618082065670520.230904103283526
750.7324201646008350.535159670798330.267579835399165
760.7696581267412030.4606837465175940.230341873258797
770.7357620037286170.5284759925427660.264237996271383
780.7194097647101430.5611804705797140.280590235289857
790.7313526619627170.5372946760745650.268647338037283
800.6913414909151680.6173170181696640.308658509084832
810.6515956433573730.6968087132852540.348404356642627
820.7834744894327910.4330510211344190.216525510567209
830.7479266605177250.5041466789645490.252073339482275
840.7220962464230370.5558075071539270.277903753576963
850.6814794645188870.6370410709622260.318520535481113
860.6699380507035960.6601238985928080.330061949296404
870.6270988626996550.7458022746006910.372901137300345
880.5870093894096580.8259812211806830.412990610590342
890.5683752801377930.8632494397244140.431624719862207
900.5321725988837950.935654802232410.467827401116205
910.5195216543990450.960956691201910.480478345600955
920.4828039755107640.9656079510215270.517196024489236
930.4397135780787110.8794271561574220.560286421921289
940.3970608745513590.7941217491027180.602939125448641
950.4153692833487580.8307385666975160.584630716651242
960.3785267579043080.7570535158086160.621473242095692
970.3387911172116410.6775822344232830.661208882788359
980.3341394707494690.6682789414989380.665860529250531
990.2922960606194640.5845921212389290.707703939380536
1000.2541147574880670.5082295149761330.745885242511933
1010.2306478814517430.4612957629034850.769352118548257
1020.212699226195820.425398452391640.78730077380418
1030.2600357622068080.5200715244136160.739964237793192
1040.2225427430455020.4450854860910040.777457256954498
1050.2142465938260430.4284931876520860.785753406173957
1060.2316113530388370.4632227060776740.768388646961163
1070.2094758179423110.4189516358846230.790524182057688
1080.1874408313998310.3748816627996620.812559168600169
1090.1802820381290060.3605640762580120.819717961870994
1100.1657517053321950.331503410664390.834248294667805
1110.1459097472642780.2918194945285550.854090252735722
1120.1220753271297980.2441506542595950.877924672870202
1130.1416349263638320.2832698527276630.858365073636168
1140.1266260465186840.2532520930373680.873373953481316
1150.1640356763952320.3280713527904650.835964323604768
1160.1549986119791240.3099972239582490.845001388020876
1170.1338326545281850.2676653090563710.866167345471815
1180.1152045923480220.2304091846960440.884795407651978
1190.1167404814279050.233480962855810.883259518572095
1200.1063601381296160.2127202762592320.893639861870384
1210.0893193920606830.1786387841213660.910680607939317
1220.07082410950440670.1416482190088130.929175890495593
1230.08007885715887260.1601577143177450.919921142841127
1240.06342101150065670.1268420230013130.936578988499343
1250.05075822751167550.1015164550233510.949241772488325
1260.03831061503239960.07662123006479920.9616893849676
1270.02874174712828550.0574834942565710.971258252871715
1280.0231951261651820.0463902523303640.976804873834818
1290.01782681606847570.03565363213695140.982173183931524
1300.02468543115282250.0493708623056450.975314568847177
1310.02084969414930910.04169938829861810.979150305850691
1320.0334009420652840.0668018841305680.966599057934716
1330.03563862131189920.07127724262379850.964361378688101
1340.04817252675439590.09634505350879170.951827473245604
1350.03766077709416730.07532155418833460.962339222905833
1360.02586005294501860.05172010589003720.974139947054981
1370.01769039769539890.03538079539079780.982309602304601
1380.01179784947869060.02359569895738120.988202150521309
1390.02238603038039780.04477206076079570.977613969619602
1400.01900351086525230.03800702173050460.980996489134748
1410.6443720508324050.711255898335190.355627949167595
1420.5789529575920250.842094084815950.421047042407975
1430.4909354425211330.9818708850422650.509064557478867
1440.4070963304675020.8141926609350050.592903669532498
1450.3151343759792270.6302687519584550.684865624020773
1460.4502214272947040.9004428545894080.549778572705296
1470.3441176218095180.6882352436190350.655882378190482
1480.6049836206688690.7900327586622610.395016379331131
1490.5454855701325420.9090288597349160.454514429867458
1500.3880212206841550.7760424413683090.611978779315845

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.758909692611693 & 0.482180614776613 & 0.241090307388307 \tabularnewline
13 & 0.621786631986117 & 0.756426736027767 & 0.378213368013884 \tabularnewline
14 & 0.581760463357661 & 0.836479073284678 & 0.418239536642339 \tabularnewline
15 & 0.461529334370621 & 0.923058668741243 & 0.538470665629379 \tabularnewline
16 & 0.361264099622209 & 0.722528199244418 & 0.638735900377791 \tabularnewline
17 & 0.298301232278281 & 0.596602464556563 & 0.701698767721719 \tabularnewline
18 & 0.507288353898752 & 0.985423292202496 & 0.492711646101248 \tabularnewline
19 & 0.423984824837541 & 0.847969649675083 & 0.576015175162459 \tabularnewline
20 & 0.335082781243585 & 0.67016556248717 & 0.664917218756415 \tabularnewline
21 & 0.265183039994633 & 0.530366079989267 & 0.734816960005367 \tabularnewline
22 & 0.242530315275424 & 0.485060630550848 & 0.757469684724576 \tabularnewline
23 & 0.442412294867684 & 0.884824589735368 & 0.557587705132316 \tabularnewline
24 & 0.4793851810649 & 0.9587703621298 & 0.5206148189351 \tabularnewline
25 & 0.469757607058247 & 0.939515214116494 & 0.530242392941753 \tabularnewline
26 & 0.429218011847715 & 0.85843602369543 & 0.570781988152285 \tabularnewline
27 & 0.491155965131596 & 0.982311930263192 & 0.508844034868404 \tabularnewline
28 & 0.494035997089612 & 0.988071994179225 & 0.505964002910388 \tabularnewline
29 & 0.475196614137723 & 0.950393228275446 & 0.524803385862277 \tabularnewline
30 & 0.517195080331069 & 0.965609839337862 & 0.482804919668931 \tabularnewline
31 & 0.456100092921843 & 0.912200185843685 & 0.543899907078157 \tabularnewline
32 & 0.406981414922854 & 0.813962829845707 & 0.593018585077146 \tabularnewline
33 & 0.379810556357532 & 0.759621112715064 & 0.620189443642468 \tabularnewline
34 & 0.346109323653537 & 0.692218647307075 & 0.653890676346463 \tabularnewline
35 & 0.296838174886241 & 0.593676349772482 & 0.703161825113759 \tabularnewline
36 & 0.857228215723504 & 0.285543568552993 & 0.142771784276496 \tabularnewline
37 & 0.832099356264902 & 0.335801287470196 & 0.167900643735098 \tabularnewline
38 & 0.823955326335071 & 0.352089347329858 & 0.176044673664929 \tabularnewline
39 & 0.845135511858917 & 0.309728976282166 & 0.154864488141083 \tabularnewline
40 & 0.830249252551953 & 0.339501494896095 & 0.169750747448047 \tabularnewline
41 & 0.802299795349441 & 0.395400409301117 & 0.197700204650559 \tabularnewline
42 & 0.774997879974895 & 0.45000424005021 & 0.225002120025105 \tabularnewline
43 & 0.803308722851849 & 0.393382554296302 & 0.196691277148151 \tabularnewline
44 & 0.764789533608033 & 0.470420932783934 & 0.235210466391967 \tabularnewline
45 & 0.736774880216628 & 0.526450239566745 & 0.263225119783372 \tabularnewline
46 & 0.878993975862137 & 0.242012048275726 & 0.121006024137863 \tabularnewline
47 & 0.919977463806876 & 0.160045072386249 & 0.0800225361931244 \tabularnewline
48 & 0.8989197390829 & 0.2021605218342 & 0.1010802609171 \tabularnewline
49 & 0.885621005219799 & 0.228757989560402 & 0.114378994780201 \tabularnewline
50 & 0.881801865538489 & 0.236396268923022 & 0.118198134461511 \tabularnewline
51 & 0.854560625789751 & 0.290878748420498 & 0.145439374210249 \tabularnewline
52 & 0.823493771543181 & 0.353012456913637 & 0.176506228456819 \tabularnewline
53 & 0.836860241205071 & 0.326279517589858 & 0.163139758794929 \tabularnewline
54 & 0.825086272472812 & 0.349827455054377 & 0.174913727527189 \tabularnewline
55 & 0.836758684955846 & 0.326482630088307 & 0.163241315044154 \tabularnewline
56 & 0.812209531870147 & 0.375580936259706 & 0.187790468129853 \tabularnewline
57 & 0.778979212291977 & 0.442041575416047 & 0.221020787708023 \tabularnewline
58 & 0.757932273703124 & 0.484135452593751 & 0.242067726296876 \tabularnewline
59 & 0.719542983085361 & 0.560914033829278 & 0.280457016914639 \tabularnewline
60 & 0.725044826413072 & 0.549910347173855 & 0.274955173586928 \tabularnewline
61 & 0.694784774542492 & 0.610430450915016 & 0.305215225457508 \tabularnewline
62 & 0.659607066228564 & 0.680785867542871 & 0.340392933771436 \tabularnewline
63 & 0.62236829064265 & 0.755263418714699 & 0.37763170935735 \tabularnewline
64 & 0.575435158363705 & 0.849129683272589 & 0.424564841636295 \tabularnewline
65 & 0.533904065621453 & 0.932191868757093 & 0.466095934378547 \tabularnewline
66 & 0.493779231445548 & 0.987558462891096 & 0.506220768554452 \tabularnewline
67 & 0.47948957205132 & 0.95897914410264 & 0.52051042794868 \tabularnewline
68 & 0.56592296949158 & 0.86815406101684 & 0.43407703050842 \tabularnewline
69 & 0.745842336749401 & 0.508315326501198 & 0.254157663250599 \tabularnewline
70 & 0.708789273772568 & 0.582421452454864 & 0.291210726227432 \tabularnewline
71 & 0.829852738933639 & 0.340294522132722 & 0.170147261066361 \tabularnewline
72 & 0.798091647355207 & 0.403816705289586 & 0.201908352644793 \tabularnewline
73 & 0.78861859796748 & 0.42276280406504 & 0.21138140203252 \tabularnewline
74 & 0.769095896716474 & 0.461808206567052 & 0.230904103283526 \tabularnewline
75 & 0.732420164600835 & 0.53515967079833 & 0.267579835399165 \tabularnewline
76 & 0.769658126741203 & 0.460683746517594 & 0.230341873258797 \tabularnewline
77 & 0.735762003728617 & 0.528475992542766 & 0.264237996271383 \tabularnewline
78 & 0.719409764710143 & 0.561180470579714 & 0.280590235289857 \tabularnewline
79 & 0.731352661962717 & 0.537294676074565 & 0.268647338037283 \tabularnewline
80 & 0.691341490915168 & 0.617317018169664 & 0.308658509084832 \tabularnewline
81 & 0.651595643357373 & 0.696808713285254 & 0.348404356642627 \tabularnewline
82 & 0.783474489432791 & 0.433051021134419 & 0.216525510567209 \tabularnewline
83 & 0.747926660517725 & 0.504146678964549 & 0.252073339482275 \tabularnewline
84 & 0.722096246423037 & 0.555807507153927 & 0.277903753576963 \tabularnewline
85 & 0.681479464518887 & 0.637041070962226 & 0.318520535481113 \tabularnewline
86 & 0.669938050703596 & 0.660123898592808 & 0.330061949296404 \tabularnewline
87 & 0.627098862699655 & 0.745802274600691 & 0.372901137300345 \tabularnewline
88 & 0.587009389409658 & 0.825981221180683 & 0.412990610590342 \tabularnewline
89 & 0.568375280137793 & 0.863249439724414 & 0.431624719862207 \tabularnewline
90 & 0.532172598883795 & 0.93565480223241 & 0.467827401116205 \tabularnewline
91 & 0.519521654399045 & 0.96095669120191 & 0.480478345600955 \tabularnewline
92 & 0.482803975510764 & 0.965607951021527 & 0.517196024489236 \tabularnewline
93 & 0.439713578078711 & 0.879427156157422 & 0.560286421921289 \tabularnewline
94 & 0.397060874551359 & 0.794121749102718 & 0.602939125448641 \tabularnewline
95 & 0.415369283348758 & 0.830738566697516 & 0.584630716651242 \tabularnewline
96 & 0.378526757904308 & 0.757053515808616 & 0.621473242095692 \tabularnewline
97 & 0.338791117211641 & 0.677582234423283 & 0.661208882788359 \tabularnewline
98 & 0.334139470749469 & 0.668278941498938 & 0.665860529250531 \tabularnewline
99 & 0.292296060619464 & 0.584592121238929 & 0.707703939380536 \tabularnewline
100 & 0.254114757488067 & 0.508229514976133 & 0.745885242511933 \tabularnewline
101 & 0.230647881451743 & 0.461295762903485 & 0.769352118548257 \tabularnewline
102 & 0.21269922619582 & 0.42539845239164 & 0.78730077380418 \tabularnewline
103 & 0.260035762206808 & 0.520071524413616 & 0.739964237793192 \tabularnewline
104 & 0.222542743045502 & 0.445085486091004 & 0.777457256954498 \tabularnewline
105 & 0.214246593826043 & 0.428493187652086 & 0.785753406173957 \tabularnewline
106 & 0.231611353038837 & 0.463222706077674 & 0.768388646961163 \tabularnewline
107 & 0.209475817942311 & 0.418951635884623 & 0.790524182057688 \tabularnewline
108 & 0.187440831399831 & 0.374881662799662 & 0.812559168600169 \tabularnewline
109 & 0.180282038129006 & 0.360564076258012 & 0.819717961870994 \tabularnewline
110 & 0.165751705332195 & 0.33150341066439 & 0.834248294667805 \tabularnewline
111 & 0.145909747264278 & 0.291819494528555 & 0.854090252735722 \tabularnewline
112 & 0.122075327129798 & 0.244150654259595 & 0.877924672870202 \tabularnewline
113 & 0.141634926363832 & 0.283269852727663 & 0.858365073636168 \tabularnewline
114 & 0.126626046518684 & 0.253252093037368 & 0.873373953481316 \tabularnewline
115 & 0.164035676395232 & 0.328071352790465 & 0.835964323604768 \tabularnewline
116 & 0.154998611979124 & 0.309997223958249 & 0.845001388020876 \tabularnewline
117 & 0.133832654528185 & 0.267665309056371 & 0.866167345471815 \tabularnewline
118 & 0.115204592348022 & 0.230409184696044 & 0.884795407651978 \tabularnewline
119 & 0.116740481427905 & 0.23348096285581 & 0.883259518572095 \tabularnewline
120 & 0.106360138129616 & 0.212720276259232 & 0.893639861870384 \tabularnewline
121 & 0.089319392060683 & 0.178638784121366 & 0.910680607939317 \tabularnewline
122 & 0.0708241095044067 & 0.141648219008813 & 0.929175890495593 \tabularnewline
123 & 0.0800788571588726 & 0.160157714317745 & 0.919921142841127 \tabularnewline
124 & 0.0634210115006567 & 0.126842023001313 & 0.936578988499343 \tabularnewline
125 & 0.0507582275116755 & 0.101516455023351 & 0.949241772488325 \tabularnewline
126 & 0.0383106150323996 & 0.0766212300647992 & 0.9616893849676 \tabularnewline
127 & 0.0287417471282855 & 0.057483494256571 & 0.971258252871715 \tabularnewline
128 & 0.023195126165182 & 0.046390252330364 & 0.976804873834818 \tabularnewline
129 & 0.0178268160684757 & 0.0356536321369514 & 0.982173183931524 \tabularnewline
130 & 0.0246854311528225 & 0.049370862305645 & 0.975314568847177 \tabularnewline
131 & 0.0208496941493091 & 0.0416993882986181 & 0.979150305850691 \tabularnewline
132 & 0.033400942065284 & 0.066801884130568 & 0.966599057934716 \tabularnewline
133 & 0.0356386213118992 & 0.0712772426237985 & 0.964361378688101 \tabularnewline
134 & 0.0481725267543959 & 0.0963450535087917 & 0.951827473245604 \tabularnewline
135 & 0.0376607770941673 & 0.0753215541883346 & 0.962339222905833 \tabularnewline
136 & 0.0258600529450186 & 0.0517201058900372 & 0.974139947054981 \tabularnewline
137 & 0.0176903976953989 & 0.0353807953907978 & 0.982309602304601 \tabularnewline
138 & 0.0117978494786906 & 0.0235956989573812 & 0.988202150521309 \tabularnewline
139 & 0.0223860303803978 & 0.0447720607607957 & 0.977613969619602 \tabularnewline
140 & 0.0190035108652523 & 0.0380070217305046 & 0.980996489134748 \tabularnewline
141 & 0.644372050832405 & 0.71125589833519 & 0.355627949167595 \tabularnewline
142 & 0.578952957592025 & 0.84209408481595 & 0.421047042407975 \tabularnewline
143 & 0.490935442521133 & 0.981870885042265 & 0.509064557478867 \tabularnewline
144 & 0.407096330467502 & 0.814192660935005 & 0.592903669532498 \tabularnewline
145 & 0.315134375979227 & 0.630268751958455 & 0.684865624020773 \tabularnewline
146 & 0.450221427294704 & 0.900442854589408 & 0.549778572705296 \tabularnewline
147 & 0.344117621809518 & 0.688235243619035 & 0.655882378190482 \tabularnewline
148 & 0.604983620668869 & 0.790032758662261 & 0.395016379331131 \tabularnewline
149 & 0.545485570132542 & 0.909028859734916 & 0.454514429867458 \tabularnewline
150 & 0.388021220684155 & 0.776042441368309 & 0.611978779315845 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198847&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.758909692611693[/C][C]0.482180614776613[/C][C]0.241090307388307[/C][/ROW]
[ROW][C]13[/C][C]0.621786631986117[/C][C]0.756426736027767[/C][C]0.378213368013884[/C][/ROW]
[ROW][C]14[/C][C]0.581760463357661[/C][C]0.836479073284678[/C][C]0.418239536642339[/C][/ROW]
[ROW][C]15[/C][C]0.461529334370621[/C][C]0.923058668741243[/C][C]0.538470665629379[/C][/ROW]
[ROW][C]16[/C][C]0.361264099622209[/C][C]0.722528199244418[/C][C]0.638735900377791[/C][/ROW]
[ROW][C]17[/C][C]0.298301232278281[/C][C]0.596602464556563[/C][C]0.701698767721719[/C][/ROW]
[ROW][C]18[/C][C]0.507288353898752[/C][C]0.985423292202496[/C][C]0.492711646101248[/C][/ROW]
[ROW][C]19[/C][C]0.423984824837541[/C][C]0.847969649675083[/C][C]0.576015175162459[/C][/ROW]
[ROW][C]20[/C][C]0.335082781243585[/C][C]0.67016556248717[/C][C]0.664917218756415[/C][/ROW]
[ROW][C]21[/C][C]0.265183039994633[/C][C]0.530366079989267[/C][C]0.734816960005367[/C][/ROW]
[ROW][C]22[/C][C]0.242530315275424[/C][C]0.485060630550848[/C][C]0.757469684724576[/C][/ROW]
[ROW][C]23[/C][C]0.442412294867684[/C][C]0.884824589735368[/C][C]0.557587705132316[/C][/ROW]
[ROW][C]24[/C][C]0.4793851810649[/C][C]0.9587703621298[/C][C]0.5206148189351[/C][/ROW]
[ROW][C]25[/C][C]0.469757607058247[/C][C]0.939515214116494[/C][C]0.530242392941753[/C][/ROW]
[ROW][C]26[/C][C]0.429218011847715[/C][C]0.85843602369543[/C][C]0.570781988152285[/C][/ROW]
[ROW][C]27[/C][C]0.491155965131596[/C][C]0.982311930263192[/C][C]0.508844034868404[/C][/ROW]
[ROW][C]28[/C][C]0.494035997089612[/C][C]0.988071994179225[/C][C]0.505964002910388[/C][/ROW]
[ROW][C]29[/C][C]0.475196614137723[/C][C]0.950393228275446[/C][C]0.524803385862277[/C][/ROW]
[ROW][C]30[/C][C]0.517195080331069[/C][C]0.965609839337862[/C][C]0.482804919668931[/C][/ROW]
[ROW][C]31[/C][C]0.456100092921843[/C][C]0.912200185843685[/C][C]0.543899907078157[/C][/ROW]
[ROW][C]32[/C][C]0.406981414922854[/C][C]0.813962829845707[/C][C]0.593018585077146[/C][/ROW]
[ROW][C]33[/C][C]0.379810556357532[/C][C]0.759621112715064[/C][C]0.620189443642468[/C][/ROW]
[ROW][C]34[/C][C]0.346109323653537[/C][C]0.692218647307075[/C][C]0.653890676346463[/C][/ROW]
[ROW][C]35[/C][C]0.296838174886241[/C][C]0.593676349772482[/C][C]0.703161825113759[/C][/ROW]
[ROW][C]36[/C][C]0.857228215723504[/C][C]0.285543568552993[/C][C]0.142771784276496[/C][/ROW]
[ROW][C]37[/C][C]0.832099356264902[/C][C]0.335801287470196[/C][C]0.167900643735098[/C][/ROW]
[ROW][C]38[/C][C]0.823955326335071[/C][C]0.352089347329858[/C][C]0.176044673664929[/C][/ROW]
[ROW][C]39[/C][C]0.845135511858917[/C][C]0.309728976282166[/C][C]0.154864488141083[/C][/ROW]
[ROW][C]40[/C][C]0.830249252551953[/C][C]0.339501494896095[/C][C]0.169750747448047[/C][/ROW]
[ROW][C]41[/C][C]0.802299795349441[/C][C]0.395400409301117[/C][C]0.197700204650559[/C][/ROW]
[ROW][C]42[/C][C]0.774997879974895[/C][C]0.45000424005021[/C][C]0.225002120025105[/C][/ROW]
[ROW][C]43[/C][C]0.803308722851849[/C][C]0.393382554296302[/C][C]0.196691277148151[/C][/ROW]
[ROW][C]44[/C][C]0.764789533608033[/C][C]0.470420932783934[/C][C]0.235210466391967[/C][/ROW]
[ROW][C]45[/C][C]0.736774880216628[/C][C]0.526450239566745[/C][C]0.263225119783372[/C][/ROW]
[ROW][C]46[/C][C]0.878993975862137[/C][C]0.242012048275726[/C][C]0.121006024137863[/C][/ROW]
[ROW][C]47[/C][C]0.919977463806876[/C][C]0.160045072386249[/C][C]0.0800225361931244[/C][/ROW]
[ROW][C]48[/C][C]0.8989197390829[/C][C]0.2021605218342[/C][C]0.1010802609171[/C][/ROW]
[ROW][C]49[/C][C]0.885621005219799[/C][C]0.228757989560402[/C][C]0.114378994780201[/C][/ROW]
[ROW][C]50[/C][C]0.881801865538489[/C][C]0.236396268923022[/C][C]0.118198134461511[/C][/ROW]
[ROW][C]51[/C][C]0.854560625789751[/C][C]0.290878748420498[/C][C]0.145439374210249[/C][/ROW]
[ROW][C]52[/C][C]0.823493771543181[/C][C]0.353012456913637[/C][C]0.176506228456819[/C][/ROW]
[ROW][C]53[/C][C]0.836860241205071[/C][C]0.326279517589858[/C][C]0.163139758794929[/C][/ROW]
[ROW][C]54[/C][C]0.825086272472812[/C][C]0.349827455054377[/C][C]0.174913727527189[/C][/ROW]
[ROW][C]55[/C][C]0.836758684955846[/C][C]0.326482630088307[/C][C]0.163241315044154[/C][/ROW]
[ROW][C]56[/C][C]0.812209531870147[/C][C]0.375580936259706[/C][C]0.187790468129853[/C][/ROW]
[ROW][C]57[/C][C]0.778979212291977[/C][C]0.442041575416047[/C][C]0.221020787708023[/C][/ROW]
[ROW][C]58[/C][C]0.757932273703124[/C][C]0.484135452593751[/C][C]0.242067726296876[/C][/ROW]
[ROW][C]59[/C][C]0.719542983085361[/C][C]0.560914033829278[/C][C]0.280457016914639[/C][/ROW]
[ROW][C]60[/C][C]0.725044826413072[/C][C]0.549910347173855[/C][C]0.274955173586928[/C][/ROW]
[ROW][C]61[/C][C]0.694784774542492[/C][C]0.610430450915016[/C][C]0.305215225457508[/C][/ROW]
[ROW][C]62[/C][C]0.659607066228564[/C][C]0.680785867542871[/C][C]0.340392933771436[/C][/ROW]
[ROW][C]63[/C][C]0.62236829064265[/C][C]0.755263418714699[/C][C]0.37763170935735[/C][/ROW]
[ROW][C]64[/C][C]0.575435158363705[/C][C]0.849129683272589[/C][C]0.424564841636295[/C][/ROW]
[ROW][C]65[/C][C]0.533904065621453[/C][C]0.932191868757093[/C][C]0.466095934378547[/C][/ROW]
[ROW][C]66[/C][C]0.493779231445548[/C][C]0.987558462891096[/C][C]0.506220768554452[/C][/ROW]
[ROW][C]67[/C][C]0.47948957205132[/C][C]0.95897914410264[/C][C]0.52051042794868[/C][/ROW]
[ROW][C]68[/C][C]0.56592296949158[/C][C]0.86815406101684[/C][C]0.43407703050842[/C][/ROW]
[ROW][C]69[/C][C]0.745842336749401[/C][C]0.508315326501198[/C][C]0.254157663250599[/C][/ROW]
[ROW][C]70[/C][C]0.708789273772568[/C][C]0.582421452454864[/C][C]0.291210726227432[/C][/ROW]
[ROW][C]71[/C][C]0.829852738933639[/C][C]0.340294522132722[/C][C]0.170147261066361[/C][/ROW]
[ROW][C]72[/C][C]0.798091647355207[/C][C]0.403816705289586[/C][C]0.201908352644793[/C][/ROW]
[ROW][C]73[/C][C]0.78861859796748[/C][C]0.42276280406504[/C][C]0.21138140203252[/C][/ROW]
[ROW][C]74[/C][C]0.769095896716474[/C][C]0.461808206567052[/C][C]0.230904103283526[/C][/ROW]
[ROW][C]75[/C][C]0.732420164600835[/C][C]0.53515967079833[/C][C]0.267579835399165[/C][/ROW]
[ROW][C]76[/C][C]0.769658126741203[/C][C]0.460683746517594[/C][C]0.230341873258797[/C][/ROW]
[ROW][C]77[/C][C]0.735762003728617[/C][C]0.528475992542766[/C][C]0.264237996271383[/C][/ROW]
[ROW][C]78[/C][C]0.719409764710143[/C][C]0.561180470579714[/C][C]0.280590235289857[/C][/ROW]
[ROW][C]79[/C][C]0.731352661962717[/C][C]0.537294676074565[/C][C]0.268647338037283[/C][/ROW]
[ROW][C]80[/C][C]0.691341490915168[/C][C]0.617317018169664[/C][C]0.308658509084832[/C][/ROW]
[ROW][C]81[/C][C]0.651595643357373[/C][C]0.696808713285254[/C][C]0.348404356642627[/C][/ROW]
[ROW][C]82[/C][C]0.783474489432791[/C][C]0.433051021134419[/C][C]0.216525510567209[/C][/ROW]
[ROW][C]83[/C][C]0.747926660517725[/C][C]0.504146678964549[/C][C]0.252073339482275[/C][/ROW]
[ROW][C]84[/C][C]0.722096246423037[/C][C]0.555807507153927[/C][C]0.277903753576963[/C][/ROW]
[ROW][C]85[/C][C]0.681479464518887[/C][C]0.637041070962226[/C][C]0.318520535481113[/C][/ROW]
[ROW][C]86[/C][C]0.669938050703596[/C][C]0.660123898592808[/C][C]0.330061949296404[/C][/ROW]
[ROW][C]87[/C][C]0.627098862699655[/C][C]0.745802274600691[/C][C]0.372901137300345[/C][/ROW]
[ROW][C]88[/C][C]0.587009389409658[/C][C]0.825981221180683[/C][C]0.412990610590342[/C][/ROW]
[ROW][C]89[/C][C]0.568375280137793[/C][C]0.863249439724414[/C][C]0.431624719862207[/C][/ROW]
[ROW][C]90[/C][C]0.532172598883795[/C][C]0.93565480223241[/C][C]0.467827401116205[/C][/ROW]
[ROW][C]91[/C][C]0.519521654399045[/C][C]0.96095669120191[/C][C]0.480478345600955[/C][/ROW]
[ROW][C]92[/C][C]0.482803975510764[/C][C]0.965607951021527[/C][C]0.517196024489236[/C][/ROW]
[ROW][C]93[/C][C]0.439713578078711[/C][C]0.879427156157422[/C][C]0.560286421921289[/C][/ROW]
[ROW][C]94[/C][C]0.397060874551359[/C][C]0.794121749102718[/C][C]0.602939125448641[/C][/ROW]
[ROW][C]95[/C][C]0.415369283348758[/C][C]0.830738566697516[/C][C]0.584630716651242[/C][/ROW]
[ROW][C]96[/C][C]0.378526757904308[/C][C]0.757053515808616[/C][C]0.621473242095692[/C][/ROW]
[ROW][C]97[/C][C]0.338791117211641[/C][C]0.677582234423283[/C][C]0.661208882788359[/C][/ROW]
[ROW][C]98[/C][C]0.334139470749469[/C][C]0.668278941498938[/C][C]0.665860529250531[/C][/ROW]
[ROW][C]99[/C][C]0.292296060619464[/C][C]0.584592121238929[/C][C]0.707703939380536[/C][/ROW]
[ROW][C]100[/C][C]0.254114757488067[/C][C]0.508229514976133[/C][C]0.745885242511933[/C][/ROW]
[ROW][C]101[/C][C]0.230647881451743[/C][C]0.461295762903485[/C][C]0.769352118548257[/C][/ROW]
[ROW][C]102[/C][C]0.21269922619582[/C][C]0.42539845239164[/C][C]0.78730077380418[/C][/ROW]
[ROW][C]103[/C][C]0.260035762206808[/C][C]0.520071524413616[/C][C]0.739964237793192[/C][/ROW]
[ROW][C]104[/C][C]0.222542743045502[/C][C]0.445085486091004[/C][C]0.777457256954498[/C][/ROW]
[ROW][C]105[/C][C]0.214246593826043[/C][C]0.428493187652086[/C][C]0.785753406173957[/C][/ROW]
[ROW][C]106[/C][C]0.231611353038837[/C][C]0.463222706077674[/C][C]0.768388646961163[/C][/ROW]
[ROW][C]107[/C][C]0.209475817942311[/C][C]0.418951635884623[/C][C]0.790524182057688[/C][/ROW]
[ROW][C]108[/C][C]0.187440831399831[/C][C]0.374881662799662[/C][C]0.812559168600169[/C][/ROW]
[ROW][C]109[/C][C]0.180282038129006[/C][C]0.360564076258012[/C][C]0.819717961870994[/C][/ROW]
[ROW][C]110[/C][C]0.165751705332195[/C][C]0.33150341066439[/C][C]0.834248294667805[/C][/ROW]
[ROW][C]111[/C][C]0.145909747264278[/C][C]0.291819494528555[/C][C]0.854090252735722[/C][/ROW]
[ROW][C]112[/C][C]0.122075327129798[/C][C]0.244150654259595[/C][C]0.877924672870202[/C][/ROW]
[ROW][C]113[/C][C]0.141634926363832[/C][C]0.283269852727663[/C][C]0.858365073636168[/C][/ROW]
[ROW][C]114[/C][C]0.126626046518684[/C][C]0.253252093037368[/C][C]0.873373953481316[/C][/ROW]
[ROW][C]115[/C][C]0.164035676395232[/C][C]0.328071352790465[/C][C]0.835964323604768[/C][/ROW]
[ROW][C]116[/C][C]0.154998611979124[/C][C]0.309997223958249[/C][C]0.845001388020876[/C][/ROW]
[ROW][C]117[/C][C]0.133832654528185[/C][C]0.267665309056371[/C][C]0.866167345471815[/C][/ROW]
[ROW][C]118[/C][C]0.115204592348022[/C][C]0.230409184696044[/C][C]0.884795407651978[/C][/ROW]
[ROW][C]119[/C][C]0.116740481427905[/C][C]0.23348096285581[/C][C]0.883259518572095[/C][/ROW]
[ROW][C]120[/C][C]0.106360138129616[/C][C]0.212720276259232[/C][C]0.893639861870384[/C][/ROW]
[ROW][C]121[/C][C]0.089319392060683[/C][C]0.178638784121366[/C][C]0.910680607939317[/C][/ROW]
[ROW][C]122[/C][C]0.0708241095044067[/C][C]0.141648219008813[/C][C]0.929175890495593[/C][/ROW]
[ROW][C]123[/C][C]0.0800788571588726[/C][C]0.160157714317745[/C][C]0.919921142841127[/C][/ROW]
[ROW][C]124[/C][C]0.0634210115006567[/C][C]0.126842023001313[/C][C]0.936578988499343[/C][/ROW]
[ROW][C]125[/C][C]0.0507582275116755[/C][C]0.101516455023351[/C][C]0.949241772488325[/C][/ROW]
[ROW][C]126[/C][C]0.0383106150323996[/C][C]0.0766212300647992[/C][C]0.9616893849676[/C][/ROW]
[ROW][C]127[/C][C]0.0287417471282855[/C][C]0.057483494256571[/C][C]0.971258252871715[/C][/ROW]
[ROW][C]128[/C][C]0.023195126165182[/C][C]0.046390252330364[/C][C]0.976804873834818[/C][/ROW]
[ROW][C]129[/C][C]0.0178268160684757[/C][C]0.0356536321369514[/C][C]0.982173183931524[/C][/ROW]
[ROW][C]130[/C][C]0.0246854311528225[/C][C]0.049370862305645[/C][C]0.975314568847177[/C][/ROW]
[ROW][C]131[/C][C]0.0208496941493091[/C][C]0.0416993882986181[/C][C]0.979150305850691[/C][/ROW]
[ROW][C]132[/C][C]0.033400942065284[/C][C]0.066801884130568[/C][C]0.966599057934716[/C][/ROW]
[ROW][C]133[/C][C]0.0356386213118992[/C][C]0.0712772426237985[/C][C]0.964361378688101[/C][/ROW]
[ROW][C]134[/C][C]0.0481725267543959[/C][C]0.0963450535087917[/C][C]0.951827473245604[/C][/ROW]
[ROW][C]135[/C][C]0.0376607770941673[/C][C]0.0753215541883346[/C][C]0.962339222905833[/C][/ROW]
[ROW][C]136[/C][C]0.0258600529450186[/C][C]0.0517201058900372[/C][C]0.974139947054981[/C][/ROW]
[ROW][C]137[/C][C]0.0176903976953989[/C][C]0.0353807953907978[/C][C]0.982309602304601[/C][/ROW]
[ROW][C]138[/C][C]0.0117978494786906[/C][C]0.0235956989573812[/C][C]0.988202150521309[/C][/ROW]
[ROW][C]139[/C][C]0.0223860303803978[/C][C]0.0447720607607957[/C][C]0.977613969619602[/C][/ROW]
[ROW][C]140[/C][C]0.0190035108652523[/C][C]0.0380070217305046[/C][C]0.980996489134748[/C][/ROW]
[ROW][C]141[/C][C]0.644372050832405[/C][C]0.71125589833519[/C][C]0.355627949167595[/C][/ROW]
[ROW][C]142[/C][C]0.578952957592025[/C][C]0.84209408481595[/C][C]0.421047042407975[/C][/ROW]
[ROW][C]143[/C][C]0.490935442521133[/C][C]0.981870885042265[/C][C]0.509064557478867[/C][/ROW]
[ROW][C]144[/C][C]0.407096330467502[/C][C]0.814192660935005[/C][C]0.592903669532498[/C][/ROW]
[ROW][C]145[/C][C]0.315134375979227[/C][C]0.630268751958455[/C][C]0.684865624020773[/C][/ROW]
[ROW][C]146[/C][C]0.450221427294704[/C][C]0.900442854589408[/C][C]0.549778572705296[/C][/ROW]
[ROW][C]147[/C][C]0.344117621809518[/C][C]0.688235243619035[/C][C]0.655882378190482[/C][/ROW]
[ROW][C]148[/C][C]0.604983620668869[/C][C]0.790032758662261[/C][C]0.395016379331131[/C][/ROW]
[ROW][C]149[/C][C]0.545485570132542[/C][C]0.909028859734916[/C][C]0.454514429867458[/C][/ROW]
[ROW][C]150[/C][C]0.388021220684155[/C][C]0.776042441368309[/C][C]0.611978779315845[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198847&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198847&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
120.7589096926116930.4821806147766130.241090307388307
130.6217866319861170.7564267360277670.378213368013884
140.5817604633576610.8364790732846780.418239536642339
150.4615293343706210.9230586687412430.538470665629379
160.3612640996222090.7225281992444180.638735900377791
170.2983012322782810.5966024645565630.701698767721719
180.5072883538987520.9854232922024960.492711646101248
190.4239848248375410.8479696496750830.576015175162459
200.3350827812435850.670165562487170.664917218756415
210.2651830399946330.5303660799892670.734816960005367
220.2425303152754240.4850606305508480.757469684724576
230.4424122948676840.8848245897353680.557587705132316
240.47938518106490.95877036212980.5206148189351
250.4697576070582470.9395152141164940.530242392941753
260.4292180118477150.858436023695430.570781988152285
270.4911559651315960.9823119302631920.508844034868404
280.4940359970896120.9880719941792250.505964002910388
290.4751966141377230.9503932282754460.524803385862277
300.5171950803310690.9656098393378620.482804919668931
310.4561000929218430.9122001858436850.543899907078157
320.4069814149228540.8139628298457070.593018585077146
330.3798105563575320.7596211127150640.620189443642468
340.3461093236535370.6922186473070750.653890676346463
350.2968381748862410.5936763497724820.703161825113759
360.8572282157235040.2855435685529930.142771784276496
370.8320993562649020.3358012874701960.167900643735098
380.8239553263350710.3520893473298580.176044673664929
390.8451355118589170.3097289762821660.154864488141083
400.8302492525519530.3395014948960950.169750747448047
410.8022997953494410.3954004093011170.197700204650559
420.7749978799748950.450004240050210.225002120025105
430.8033087228518490.3933825542963020.196691277148151
440.7647895336080330.4704209327839340.235210466391967
450.7367748802166280.5264502395667450.263225119783372
460.8789939758621370.2420120482757260.121006024137863
470.9199774638068760.1600450723862490.0800225361931244
480.89891973908290.20216052183420.1010802609171
490.8856210052197990.2287579895604020.114378994780201
500.8818018655384890.2363962689230220.118198134461511
510.8545606257897510.2908787484204980.145439374210249
520.8234937715431810.3530124569136370.176506228456819
530.8368602412050710.3262795175898580.163139758794929
540.8250862724728120.3498274550543770.174913727527189
550.8367586849558460.3264826300883070.163241315044154
560.8122095318701470.3755809362597060.187790468129853
570.7789792122919770.4420415754160470.221020787708023
580.7579322737031240.4841354525937510.242067726296876
590.7195429830853610.5609140338292780.280457016914639
600.7250448264130720.5499103471738550.274955173586928
610.6947847745424920.6104304509150160.305215225457508
620.6596070662285640.6807858675428710.340392933771436
630.622368290642650.7552634187146990.37763170935735
640.5754351583637050.8491296832725890.424564841636295
650.5339040656214530.9321918687570930.466095934378547
660.4937792314455480.9875584628910960.506220768554452
670.479489572051320.958979144102640.52051042794868
680.565922969491580.868154061016840.43407703050842
690.7458423367494010.5083153265011980.254157663250599
700.7087892737725680.5824214524548640.291210726227432
710.8298527389336390.3402945221327220.170147261066361
720.7980916473552070.4038167052895860.201908352644793
730.788618597967480.422762804065040.21138140203252
740.7690958967164740.4618082065670520.230904103283526
750.7324201646008350.535159670798330.267579835399165
760.7696581267412030.4606837465175940.230341873258797
770.7357620037286170.5284759925427660.264237996271383
780.7194097647101430.5611804705797140.280590235289857
790.7313526619627170.5372946760745650.268647338037283
800.6913414909151680.6173170181696640.308658509084832
810.6515956433573730.6968087132852540.348404356642627
820.7834744894327910.4330510211344190.216525510567209
830.7479266605177250.5041466789645490.252073339482275
840.7220962464230370.5558075071539270.277903753576963
850.6814794645188870.6370410709622260.318520535481113
860.6699380507035960.6601238985928080.330061949296404
870.6270988626996550.7458022746006910.372901137300345
880.5870093894096580.8259812211806830.412990610590342
890.5683752801377930.8632494397244140.431624719862207
900.5321725988837950.935654802232410.467827401116205
910.5195216543990450.960956691201910.480478345600955
920.4828039755107640.9656079510215270.517196024489236
930.4397135780787110.8794271561574220.560286421921289
940.3970608745513590.7941217491027180.602939125448641
950.4153692833487580.8307385666975160.584630716651242
960.3785267579043080.7570535158086160.621473242095692
970.3387911172116410.6775822344232830.661208882788359
980.3341394707494690.6682789414989380.665860529250531
990.2922960606194640.5845921212389290.707703939380536
1000.2541147574880670.5082295149761330.745885242511933
1010.2306478814517430.4612957629034850.769352118548257
1020.212699226195820.425398452391640.78730077380418
1030.2600357622068080.5200715244136160.739964237793192
1040.2225427430455020.4450854860910040.777457256954498
1050.2142465938260430.4284931876520860.785753406173957
1060.2316113530388370.4632227060776740.768388646961163
1070.2094758179423110.4189516358846230.790524182057688
1080.1874408313998310.3748816627996620.812559168600169
1090.1802820381290060.3605640762580120.819717961870994
1100.1657517053321950.331503410664390.834248294667805
1110.1459097472642780.2918194945285550.854090252735722
1120.1220753271297980.2441506542595950.877924672870202
1130.1416349263638320.2832698527276630.858365073636168
1140.1266260465186840.2532520930373680.873373953481316
1150.1640356763952320.3280713527904650.835964323604768
1160.1549986119791240.3099972239582490.845001388020876
1170.1338326545281850.2676653090563710.866167345471815
1180.1152045923480220.2304091846960440.884795407651978
1190.1167404814279050.233480962855810.883259518572095
1200.1063601381296160.2127202762592320.893639861870384
1210.0893193920606830.1786387841213660.910680607939317
1220.07082410950440670.1416482190088130.929175890495593
1230.08007885715887260.1601577143177450.919921142841127
1240.06342101150065670.1268420230013130.936578988499343
1250.05075822751167550.1015164550233510.949241772488325
1260.03831061503239960.07662123006479920.9616893849676
1270.02874174712828550.0574834942565710.971258252871715
1280.0231951261651820.0463902523303640.976804873834818
1290.01782681606847570.03565363213695140.982173183931524
1300.02468543115282250.0493708623056450.975314568847177
1310.02084969414930910.04169938829861810.979150305850691
1320.0334009420652840.0668018841305680.966599057934716
1330.03563862131189920.07127724262379850.964361378688101
1340.04817252675439590.09634505350879170.951827473245604
1350.03766077709416730.07532155418833460.962339222905833
1360.02586005294501860.05172010589003720.974139947054981
1370.01769039769539890.03538079539079780.982309602304601
1380.01179784947869060.02359569895738120.988202150521309
1390.02238603038039780.04477206076079570.977613969619602
1400.01900351086525230.03800702173050460.980996489134748
1410.6443720508324050.711255898335190.355627949167595
1420.5789529575920250.842094084815950.421047042407975
1430.4909354425211330.9818708850422650.509064557478867
1440.4070963304675020.8141926609350050.592903669532498
1450.3151343759792270.6302687519584550.684865624020773
1460.4502214272947040.9004428545894080.549778572705296
1470.3441176218095180.6882352436190350.655882378190482
1480.6049836206688690.7900327586622610.395016379331131
1490.5454855701325420.9090288597349160.454514429867458
1500.3880212206841550.7760424413683090.611978779315845






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level80.0575539568345324NOK
10% type I error level150.107913669064748NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 8 & 0.0575539568345324 & NOK \tabularnewline
10% type I error level & 15 & 0.107913669064748 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198847&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.0575539568345324[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.107913669064748[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198847&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198847&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level80.0575539568345324NOK
10% type I error level150.107913669064748NOK


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