Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 19:17:32 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t13521610960zj4b5cezrwdm4i.htm/, Retrieved Wed, 01 Feb 2023 16:21:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186406, Retrieved Wed, 01 Feb 2023 16:21:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D  [Multiple Regression] [WS7 Mra] [2012-11-05 22:20:25] [6e5c9f686e58f6d348ebade5a40c0120]
- R PD      [Multiple Regression] [WS7 age] [2012-11-06 00:17:32] [ae7a5a1cf44a58c30c60b8a64f459c03] [Current]
Feedback Forum

Post a new message
Dataseries X:
7	41	38	13	12	14	12	53	32
5	39	32	16	11	18	11	86	51
5	30	35	19	15	11	14	66	42
5	31	33	15	6	12	12	67	41
8	34	37	14	13	16	21	76	46
6	35	29	13	10	18	12	78	47
5	39	31	19	12	14	22	53	37
6	34	36	15	14	14	11	80	49
5	36	35	14	12	15	10	74	45
4	37	38	15	6	15	13	76	47
6	38	31	16	10	17	10	79	49
5	36	34	16	12	19	8	54	33
5	38	35	16	12	10	15	67	42
6	39	38	16	11	16	14	54	33
7	33	37	17	15	18	10	87	53
6	32	33	15	12	14	14	58	36
7	36	32	15	10	14	14	75	45
6	38	38	20	12	17	11	88	54
8	39	38	18	11	14	10	64	41
7	32	32	16	12	16	13	57	36
5	32	33	16	11	18	7	66	41
5	31	31	16	12	11	14	68	44
7	39	38	19	13	14	12	54	33
7	37	39	16	11	12	14	56	37
5	39	32	17	9	17	11	86	52
4	41	32	17	13	9	9	80	47
10	36	35	16	10	16	11	76	43
6	33	37	15	14	14	15	69	44
5	33	33	16	12	15	14	78	45
5	34	33	14	10	11	13	67	44
5	31	28	15	12	16	9	80	49
5	27	32	12	8	13	15	54	33
6	37	31	14	10	17	10	71	43
5	34	37	16	12	15	11	84	54
5	34	30	14	12	14	13	74	42
5	32	33	7	7	16	8	71	44
5	29	31	10	6	9	20	63	37
5	36	33	14	12	15	12	71	43
5	29	31	16	10	17	10	76	46
5	35	33	16	10	13	10	69	42
5	37	32	16	10	15	9	74	45
7	34	33	14	12	16	14	75	44
5	38	32	20	15	16	8	54	33
6	35	33	14	10	12	14	52	31
7	38	28	14	10	12	11	69	42
7	37	35	11	12	11	13	68	40
5	38	39	14	13	15	9	65	43
5	33	34	15	11	15	11	75	46
4	36	38	16	11	17	15	74	42
5	38	32	14	12	13	11	75	45
4	32	38	16	14	16	10	72	44
5	32	30	14	10	14	14	67	40
5	32	33	12	12	11	18	63	37
7	34	38	16	13	12	14	62	46
5	32	32	9	5	12	11	63	36
5	37	32	14	6	15	12	76	47
6	39	34	16	12	16	13	74	45
4	29	34	16	12	15	9	67	42
6	37	36	15	11	12	10	73	43
6	35	34	16	10	12	15	70	43
5	30	28	12	7	8	20	53	32
7	38	34	16	12	13	12	77	45
6	34	35	16	14	11	12	77	45
8	31	35	14	11	14	14	52	31
7	34	31	16	12	15	13	54	33
5	35	37	17	13	10	11	80	49
6	36	35	18	14	11	17	66	42
6	30	27	18	11	12	12	73	41
5	39	40	12	12	15	13	63	38
5	35	37	16	12	15	14	69	42
5	38	36	10	8	14	13	67	44
5	31	38	14	11	16	15	54	33
4	34	39	18	14	15	13	81	48
6	38	41	18	14	15	10	69	40
6	34	27	16	12	13	11	84	50
6	39	30	17	9	12	19	80	49
6	37	37	16	13	17	13	70	43
7	34	31	16	11	13	17	69	44
5	28	31	13	12	15	13	77	47
7	37	27	16	12	13	9	54	33
6	33	36	16	12	15	11	79	46
5	37	38	20	12	16	10	30	0
5	35	37	16	12	15	9	71	45
4	37	33	15	12	16	12	73	43
8	32	34	15	11	15	12	72	44
8	33	31	16	10	14	13	77	47
5	38	39	14	9	15	13	75	45
5	33	34	16	12	14	12	69	42
6	29	32	16	12	13	15	54	33
4	33	33	15	12	7	22	70	43
5	31	36	12	9	17	13	73	46
5	36	32	17	15	13	15	54	33
5	35	41	16	12	15	13	77	46
5	32	28	15	12	14	15	82	48
6	29	30	13	12	13	10	80	47
6	39	36	16	10	16	11	80	47
5	37	35	16	13	12	16	69	43
6	35	31	16	9	14	11	78	46
5	37	34	16	12	17	11	81	48
7	32	36	14	10	15	10	76	46
5	38	36	16	14	17	10	76	45
6	37	35	16	11	12	16	73	45
6	36	37	20	15	16	12	85	52
6	32	28	15	11	11	11	66	42
4	33	39	16	11	15	16	79	47
5	40	32	13	12	9	19	68	41
5	38	35	17	12	16	11	76	47
7	41	39	16	12	15	16	71	43
6	36	35	16	11	10	15	54	33
9	43	42	12	7	10	24	46	30
6	30	34	16	12	15	14	82	49
6	31	33	16	14	11	15	74	44
5	32	41	17	11	13	11	88	55
6	32	33	13	11	14	15	38	11
5	37	34	12	10	18	12	76	47
8	37	32	18	13	16	10	86	53
7	33	40	14	13	14	14	54	33
5	34	40	14	8	14	13	70	44
7	33	35	13	11	14	9	69	42
6	38	36	16	12	14	15	90	55
6	33	37	13	11	12	15	54	33
9	31	27	16	13	14	14	76	46
7	38	39	13	12	15	11	89	54
6	37	38	16	14	15	8	76	47
5	33	31	15	13	15	11	73	45
5	31	33	16	15	13	11	79	47
6	39	32	15	10	17	8	90	55
6	44	39	17	11	17	10	74	44
7	33	36	15	9	19	11	81	53
5	35	33	12	11	15	13	72	44
5	32	33	16	10	13	11	71	42
5	28	32	10	11	9	20	66	40
6	40	37	16	8	15	10	77	46
4	27	30	12	11	15	15	65	40
5	37	38	14	12	15	12	74	46
7	32	29	15	12	16	14	82	53
5	28	22	13	9	11	23	54	33
7	34	35	15	11	14	14	63	42
7	30	35	11	10	11	16	54	35
6	35	34	12	8	15	11	64	40
5	31	35	8	9	13	12	69	41
8	32	34	16	8	15	10	54	33
5	30	34	15	9	16	14	84	51
5	30	35	17	15	14	12	86	53
5	31	23	16	11	15	12	77	46
6	40	31	10	8	16	11	89	55
4	32	27	18	13	16	12	76	47
5	36	36	13	12	11	13	60	38
5	32	31	16	12	12	11	75	46
7	35	32	13	9	9	19	73	46
6	38	39	10	7	16	12	85	53
7	42	37	15	13	13	17	79	47
10	34	38	16	9	16	9	71	41
6	35	39	16	6	12	12	72	44
8	35	34	14	8	9	19	69	43
4	33	31	10	8	13	18	78	51
5	36	32	17	15	13	15	54	33
6	32	37	13	6	14	14	69	43
7	33	36	15	9	19	11	81	53
7	34	32	16	11	13	9	84	51
6	32	35	12	8	12	18	84	50
6	34	36	13	8	13	16	69	46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186406&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186406&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186406&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 4.95375411748306 + 0.133474500364509Age[t] + 0.107667989070285Connected[t] -0.0228646367819681Separate[t] + 0.54717958538811Software[t] + 0.0569493232045262Happiness[t] -0.0728576885272164Depression[t] + 0.0393567034692783Belonging[t] -0.056066633893206Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  4.95375411748306 +  0.133474500364509Age[t] +  0.107667989070285Connected[t] -0.0228646367819681Separate[t] +  0.54717958538811Software[t] +  0.0569493232045262Happiness[t] -0.0728576885272164Depression[t] +  0.0393567034692783Belonging[t] -0.056066633893206Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186406&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  4.95375411748306 +  0.133474500364509Age[t] +  0.107667989070285Connected[t] -0.0228646367819681Separate[t] +  0.54717958538811Software[t] +  0.0569493232045262Happiness[t] -0.0728576885272164Depression[t] +  0.0393567034692783Belonging[t] -0.056066633893206Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186406&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186406&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 4.95375411748306 + 0.133474500364509Age[t] + 0.107667989070285Connected[t] -0.0228646367819681Separate[t] + 0.54717958538811Software[t] + 0.0569493232045262Happiness[t] -0.0728576885272164Depression[t] + 0.0393567034692783Belonging[t] -0.056066633893206Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.953754117483062.6505971.86890.0635450.031773
Age0.1334745003645090.1282261.04090.2995480.149774
Connected0.1076679890702850.0472772.27740.0241480.012074
Separate-0.02286463678196810.044849-0.50980.6109140.305457
Software0.547179585388110.0690757.921500
Happiness0.05694932320452620.0764060.74530.4572060.228603
Depression-0.07285768852721640.056349-1.2930.1979660.098983
Belonging0.03935670346927830.0446520.88140.3794820.189741
Belonging_Final-0.0560666338932060.064047-0.87540.3827260.191363

\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) & 4.95375411748306 & 2.650597 & 1.8689 & 0.063545 & 0.031773 \tabularnewline
Age & 0.133474500364509 & 0.128226 & 1.0409 & 0.299548 & 0.149774 \tabularnewline
Connected & 0.107667989070285 & 0.047277 & 2.2774 & 0.024148 & 0.012074 \tabularnewline
Separate & -0.0228646367819681 & 0.044849 & -0.5098 & 0.610914 & 0.305457 \tabularnewline
Software & 0.54717958538811 & 0.069075 & 7.9215 & 0 & 0 \tabularnewline
Happiness & 0.0569493232045262 & 0.076406 & 0.7453 & 0.457206 & 0.228603 \tabularnewline
Depression & -0.0728576885272164 & 0.056349 & -1.293 & 0.197966 & 0.098983 \tabularnewline
Belonging & 0.0393567034692783 & 0.044652 & 0.8814 & 0.379482 & 0.189741 \tabularnewline
Belonging_Final & -0.056066633893206 & 0.064047 & -0.8754 & 0.382726 & 0.191363 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186406&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]4.95375411748306[/C][C]2.650597[/C][C]1.8689[/C][C]0.063545[/C][C]0.031773[/C][/ROW]
[ROW][C]Age[/C][C]0.133474500364509[/C][C]0.128226[/C][C]1.0409[/C][C]0.299548[/C][C]0.149774[/C][/ROW]
[ROW][C]Connected[/C][C]0.107667989070285[/C][C]0.047277[/C][C]2.2774[/C][C]0.024148[/C][C]0.012074[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0228646367819681[/C][C]0.044849[/C][C]-0.5098[/C][C]0.610914[/C][C]0.305457[/C][/ROW]
[ROW][C]Software[/C][C]0.54717958538811[/C][C]0.069075[/C][C]7.9215[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0569493232045262[/C][C]0.076406[/C][C]0.7453[/C][C]0.457206[/C][C]0.228603[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0728576885272164[/C][C]0.056349[/C][C]-1.293[/C][C]0.197966[/C][C]0.098983[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0393567034692783[/C][C]0.044652[/C][C]0.8814[/C][C]0.379482[/C][C]0.189741[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.056066633893206[/C][C]0.064047[/C][C]-0.8754[/C][C]0.382726[/C][C]0.191363[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186406&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186406&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.953754117483062.6505971.86890.0635450.031773
Age0.1334745003645090.1282261.04090.2995480.149774
Connected0.1076679890702850.0472772.27740.0241480.012074
Separate-0.02286463678196810.044849-0.50980.6109140.305457
Software0.547179585388110.0690757.921500
Happiness0.05694932320452620.0764060.74530.4572060.228603
Depression-0.07285768852721640.056349-1.2930.1979660.098983
Belonging0.03935670346927830.0446520.88140.3794820.189741
Belonging_Final-0.0560666338932060.064047-0.87540.3827260.191363







Multiple Linear Regression - Regression Statistics
Multiple R0.60102733147363
R-squared0.361233853178312
Adjusted R-squared0.327834316089596
F-TEST (value)10.8155347248917
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value5.22926146828695e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84981166385584
Sum Squared Residuals523.535888335781

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.60102733147363 \tabularnewline
R-squared & 0.361233853178312 \tabularnewline
Adjusted R-squared & 0.327834316089596 \tabularnewline
F-TEST (value) & 10.8155347248917 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 5.22926146828695e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84981166385584 \tabularnewline
Sum Squared Residuals & 523.535888335781 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186406&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.60102733147363[/C][/ROW]
[ROW][C]R-squared[/C][C]0.361233853178312[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.327834316089596[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.8155347248917[/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]5.22926146828695e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.84981166385584[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]523.535888335781[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186406&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186406&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.60102733147363
R-squared0.361233853178312
Adjusted R-squared0.327834316089596
F-TEST (value)10.8155347248917
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value5.22926146828695e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84981166385584
Sum Squared Residuals523.535888335781







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.2145332606848-3.21453326068478
21615.85641666897950.143583331020545
31916.10777650619342.89222349380662
41511.63464553795613.36535446204394
51415.74282681468-1.74282681467997
61314.917188757312-1.917188757312
71914.88339068455074.11660931544925
81516.6498271871816-1.64982718718163
91415.7781274574523-1.77812745745234
101512.1486565970542.85134340294595
111615.21045294049170.789547059508308
121616.0601703014398-0.0601703014397521
131615.23713135432890.762868645671134
141615.27001653572230.729983464277743
151717.5518340171394-0.551834017139444
161515.0531716473167-0.0531716473166555
171514.71028782390690.28971217609307
182016.14577912702443.85422087297561
191815.65953160769822.34046839230178
201615.35691041593010.643089584069876
211615.14483913236090.855160867639148
221614.63194442537941.36805557462065
231916.52966693750842.47033306249163
241614.79193999971191.20806000028814
251714.64904154110552.3509584588945
261716.65393510050260.346064899497415
271615.52607909790090.473920902099055
281516.0752732385245-1.07527323852448
291615.36684882357370.633151176426331
301413.8483609333080.151639066692009
311515.5415206665493-0.541520666549281
321212.096469572901-0.0964695729010146
331415.1243311270264-1.12433112702642
341615.33317184687450.666828153125453
351415.5697921761151-1.56979217611505
36712.7979480715389-5.79794807153892
371010.7781690772438-0.778169077243826
381415.6722045113404-1.67220451134042
391614.15809632976641.8419036702336
401614.47934730909391.52065269090605
411614.93288787461951.06711212538047
421415.7364116600629-1.73641166006287
432017.7919263396952.20807366030499
441414.2121107459926-0.212110745992598
451415.0538164492119-1.05381644921192
461115.7505670375022-4.75056703750225
471416.2799650989433-2.27996509894327
481514.84124092268420.1587590773158
491614.94668956680641.05331043319359
501415.9146577144718-1.91465771447182
511616.2740488113962-0.274048811396182
521414.0238756821725-0.023875682172512
531214.5981353066829-2.59813530668289
541615.3177003558360.682299644163996
55911.4137626225363-2.41376262253634
561412.49217660663761.50782339336242
571616.0958468194325-0.0958468194325485
581614.87940233629971.12059766370031
591515.3511543194206-0.351154319420611
601614.1520094764121.84799052358805
611211.33142737361680.668572626383154
621616.1417331600482-0.141733160048245
631616.5351825909878-0.535182590987796
641414.6637367486737-0.663736748673704
651615.58829149891990.411708501080082
661715.82617916089921.17382083910077
671816.12150629000981.87849370999016
681814.76967801769833.23032198230169
691215.7277778742621-3.72777787426209
701615.30471582504250.69528417495752
711013.2869277780806-3.28692777808059
721414.2023404342683-0.202340434268253
731816.32094155961931.67905844038071
741817.16765893816150.832341061838533
751615.80566060422250.194339395777492
761713.89269687165753.10730312834247
771616.370752293448-0.370752293448009
781614.60940009222781.3905999077722
791314.7956058690577-1.79560586905771
801616.1802861209585-0.180286120958458
811615.633592548750.366407451250005
822016.66545443192733.33454556807273
831615.57951777293750.420482227062499
841515.7820607301893-0.782060730189278
851515.0552019035588-0.0552019035588035
861614.58306082152191.41693917847809
871414.0812497701883-0.0812497701883377
881615.24673981109770.753260188902279
891614.63399839295851.36600160704147
901513.99219786938741.0078021306126
911213.3752863626195-1.37528636261954
921716.89573857225030.104261427749656
931615.37670205862320.623297941376774
941515.232923918879-0.232923918878977
951315.2823575248549-2.2823575248549
961615.22548070517610.774519294823916
971615.74033068157390.259669318426122
981614.22540692796231.77459307203773
991616.0569980637918-0.0569980637917588
1001414.5198274673874-0.519827467387382
1011617.2575700229348-1.25757002293477
1021614.82473955725291.17526044274713
1032017.4591026874772.54089731252305
1041514.64649416620140.353505833798553
1051614.33051497575771.66948502424232
1061315.2681045028644-2.26810450286443
1071715.94413520942241.05586479057764
1081616.0488748826202-0.0488748826201887
1091614.60105285110291.39894714889707
1101212.6040085538427-0.604008553842684
1111615.08761499824960.912385001750355
1121615.97733135524460.0226686447554006
1131714.46665926979282.53334073020715
1141315.0476661513459-2.04766615134591
1151214.8060136442397-2.80601364423966
1161816.98268913704041.01731086295962
1171415.6922143524683-1.69221435246829
1181412.88286738507941.11713261492059
1191315.1622176552383-2.16221765523831
1201615.75187644991120.248123550088765
1211314.3462182567372-1.34621825673719
1221616.1780488889348-0.178048888934765
1231316.1818470476672-3.18184704766716
1241617.157330723524-1.15733072352403
1251515.981547230761-0.981547230760973
1261616.8249494564529-0.824949456452891
1271515.5374956046373-0.537495604637266
1281716.30427301816540.695726981834596
1291514.03957255545440.960427444545604
1301214.9277893179309-2.92778931793088
1311614.16219906029441.83780093970557
1321013.3334045870603-3.33340458706034
1331614.16982977549621.83017022450376
1341213.8046190495834-1.80461904958344
1351415.6154195252291-1.61541952522908
1361515.3834314482963-0.383431448296266
1371312.28320336055230.716796639447694
1381514.66945698085680.330543019143156
1391113.4132981985485-2.41329819854852
1401213.4519887102221-1.45198871022213
141813.3661177506995-5.36611775069953
1421613.46769083482722.53230916517284
1431513.33613120407751.66386879592252
1441716.59474094942170.405259050578308
1451614.88327166755661.11672833244338
1461014.2587899674578-4.25878996745785
1471815.82189176575282.17810823424722
1481315.1503650509532-2.15036505095316
1491615.17849845973430.82150154026565
1501313.2716251499581-0.27162514995806
1511013.1952060750555-3.1952060750555
1521516.6532824878877-1.65328248788765
1531614.75603475152081.24396524847918
1541612.09018778957673.90981221042333
1551412.8229638791731.17703612082697
1561012.3486560513435-2.3486560513435
1571716.89573857225030.104261427749656
1581311.71909288876971.28090711123025
1591514.03957255545440.960427444545604
1601615.36728107845030.632718921549706
1611212.6517360473787-0.651736047378715
1621312.68078807252990.319211927470075

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.2145332606848 & -3.21453326068478 \tabularnewline
2 & 16 & 15.8564166689795 & 0.143583331020545 \tabularnewline
3 & 19 & 16.1077765061934 & 2.89222349380662 \tabularnewline
4 & 15 & 11.6346455379561 & 3.36535446204394 \tabularnewline
5 & 14 & 15.74282681468 & -1.74282681467997 \tabularnewline
6 & 13 & 14.917188757312 & -1.917188757312 \tabularnewline
7 & 19 & 14.8833906845507 & 4.11660931544925 \tabularnewline
8 & 15 & 16.6498271871816 & -1.64982718718163 \tabularnewline
9 & 14 & 15.7781274574523 & -1.77812745745234 \tabularnewline
10 & 15 & 12.148656597054 & 2.85134340294595 \tabularnewline
11 & 16 & 15.2104529404917 & 0.789547059508308 \tabularnewline
12 & 16 & 16.0601703014398 & -0.0601703014397521 \tabularnewline
13 & 16 & 15.2371313543289 & 0.762868645671134 \tabularnewline
14 & 16 & 15.2700165357223 & 0.729983464277743 \tabularnewline
15 & 17 & 17.5518340171394 & -0.551834017139444 \tabularnewline
16 & 15 & 15.0531716473167 & -0.0531716473166555 \tabularnewline
17 & 15 & 14.7102878239069 & 0.28971217609307 \tabularnewline
18 & 20 & 16.1457791270244 & 3.85422087297561 \tabularnewline
19 & 18 & 15.6595316076982 & 2.34046839230178 \tabularnewline
20 & 16 & 15.3569104159301 & 0.643089584069876 \tabularnewline
21 & 16 & 15.1448391323609 & 0.855160867639148 \tabularnewline
22 & 16 & 14.6319444253794 & 1.36805557462065 \tabularnewline
23 & 19 & 16.5296669375084 & 2.47033306249163 \tabularnewline
24 & 16 & 14.7919399997119 & 1.20806000028814 \tabularnewline
25 & 17 & 14.6490415411055 & 2.3509584588945 \tabularnewline
26 & 17 & 16.6539351005026 & 0.346064899497415 \tabularnewline
27 & 16 & 15.5260790979009 & 0.473920902099055 \tabularnewline
28 & 15 & 16.0752732385245 & -1.07527323852448 \tabularnewline
29 & 16 & 15.3668488235737 & 0.633151176426331 \tabularnewline
30 & 14 & 13.848360933308 & 0.151639066692009 \tabularnewline
31 & 15 & 15.5415206665493 & -0.541520666549281 \tabularnewline
32 & 12 & 12.096469572901 & -0.0964695729010146 \tabularnewline
33 & 14 & 15.1243311270264 & -1.12433112702642 \tabularnewline
34 & 16 & 15.3331718468745 & 0.666828153125453 \tabularnewline
35 & 14 & 15.5697921761151 & -1.56979217611505 \tabularnewline
36 & 7 & 12.7979480715389 & -5.79794807153892 \tabularnewline
37 & 10 & 10.7781690772438 & -0.778169077243826 \tabularnewline
38 & 14 & 15.6722045113404 & -1.67220451134042 \tabularnewline
39 & 16 & 14.1580963297664 & 1.8419036702336 \tabularnewline
40 & 16 & 14.4793473090939 & 1.52065269090605 \tabularnewline
41 & 16 & 14.9328878746195 & 1.06711212538047 \tabularnewline
42 & 14 & 15.7364116600629 & -1.73641166006287 \tabularnewline
43 & 20 & 17.791926339695 & 2.20807366030499 \tabularnewline
44 & 14 & 14.2121107459926 & -0.212110745992598 \tabularnewline
45 & 14 & 15.0538164492119 & -1.05381644921192 \tabularnewline
46 & 11 & 15.7505670375022 & -4.75056703750225 \tabularnewline
47 & 14 & 16.2799650989433 & -2.27996509894327 \tabularnewline
48 & 15 & 14.8412409226842 & 0.1587590773158 \tabularnewline
49 & 16 & 14.9466895668064 & 1.05331043319359 \tabularnewline
50 & 14 & 15.9146577144718 & -1.91465771447182 \tabularnewline
51 & 16 & 16.2740488113962 & -0.274048811396182 \tabularnewline
52 & 14 & 14.0238756821725 & -0.023875682172512 \tabularnewline
53 & 12 & 14.5981353066829 & -2.59813530668289 \tabularnewline
54 & 16 & 15.317700355836 & 0.682299644163996 \tabularnewline
55 & 9 & 11.4137626225363 & -2.41376262253634 \tabularnewline
56 & 14 & 12.4921766066376 & 1.50782339336242 \tabularnewline
57 & 16 & 16.0958468194325 & -0.0958468194325485 \tabularnewline
58 & 16 & 14.8794023362997 & 1.12059766370031 \tabularnewline
59 & 15 & 15.3511543194206 & -0.351154319420611 \tabularnewline
60 & 16 & 14.152009476412 & 1.84799052358805 \tabularnewline
61 & 12 & 11.3314273736168 & 0.668572626383154 \tabularnewline
62 & 16 & 16.1417331600482 & -0.141733160048245 \tabularnewline
63 & 16 & 16.5351825909878 & -0.535182590987796 \tabularnewline
64 & 14 & 14.6637367486737 & -0.663736748673704 \tabularnewline
65 & 16 & 15.5882914989199 & 0.411708501080082 \tabularnewline
66 & 17 & 15.8261791608992 & 1.17382083910077 \tabularnewline
67 & 18 & 16.1215062900098 & 1.87849370999016 \tabularnewline
68 & 18 & 14.7696780176983 & 3.23032198230169 \tabularnewline
69 & 12 & 15.7277778742621 & -3.72777787426209 \tabularnewline
70 & 16 & 15.3047158250425 & 0.69528417495752 \tabularnewline
71 & 10 & 13.2869277780806 & -3.28692777808059 \tabularnewline
72 & 14 & 14.2023404342683 & -0.202340434268253 \tabularnewline
73 & 18 & 16.3209415596193 & 1.67905844038071 \tabularnewline
74 & 18 & 17.1676589381615 & 0.832341061838533 \tabularnewline
75 & 16 & 15.8056606042225 & 0.194339395777492 \tabularnewline
76 & 17 & 13.8926968716575 & 3.10730312834247 \tabularnewline
77 & 16 & 16.370752293448 & -0.370752293448009 \tabularnewline
78 & 16 & 14.6094000922278 & 1.3905999077722 \tabularnewline
79 & 13 & 14.7956058690577 & -1.79560586905771 \tabularnewline
80 & 16 & 16.1802861209585 & -0.180286120958458 \tabularnewline
81 & 16 & 15.63359254875 & 0.366407451250005 \tabularnewline
82 & 20 & 16.6654544319273 & 3.33454556807273 \tabularnewline
83 & 16 & 15.5795177729375 & 0.420482227062499 \tabularnewline
84 & 15 & 15.7820607301893 & -0.782060730189278 \tabularnewline
85 & 15 & 15.0552019035588 & -0.0552019035588035 \tabularnewline
86 & 16 & 14.5830608215219 & 1.41693917847809 \tabularnewline
87 & 14 & 14.0812497701883 & -0.0812497701883377 \tabularnewline
88 & 16 & 15.2467398110977 & 0.753260188902279 \tabularnewline
89 & 16 & 14.6339983929585 & 1.36600160704147 \tabularnewline
90 & 15 & 13.9921978693874 & 1.0078021306126 \tabularnewline
91 & 12 & 13.3752863626195 & -1.37528636261954 \tabularnewline
92 & 17 & 16.8957385722503 & 0.104261427749656 \tabularnewline
93 & 16 & 15.3767020586232 & 0.623297941376774 \tabularnewline
94 & 15 & 15.232923918879 & -0.232923918878977 \tabularnewline
95 & 13 & 15.2823575248549 & -2.2823575248549 \tabularnewline
96 & 16 & 15.2254807051761 & 0.774519294823916 \tabularnewline
97 & 16 & 15.7403306815739 & 0.259669318426122 \tabularnewline
98 & 16 & 14.2254069279623 & 1.77459307203773 \tabularnewline
99 & 16 & 16.0569980637918 & -0.0569980637917588 \tabularnewline
100 & 14 & 14.5198274673874 & -0.519827467387382 \tabularnewline
101 & 16 & 17.2575700229348 & -1.25757002293477 \tabularnewline
102 & 16 & 14.8247395572529 & 1.17526044274713 \tabularnewline
103 & 20 & 17.459102687477 & 2.54089731252305 \tabularnewline
104 & 15 & 14.6464941662014 & 0.353505833798553 \tabularnewline
105 & 16 & 14.3305149757577 & 1.66948502424232 \tabularnewline
106 & 13 & 15.2681045028644 & -2.26810450286443 \tabularnewline
107 & 17 & 15.9441352094224 & 1.05586479057764 \tabularnewline
108 & 16 & 16.0488748826202 & -0.0488748826201887 \tabularnewline
109 & 16 & 14.6010528511029 & 1.39894714889707 \tabularnewline
110 & 12 & 12.6040085538427 & -0.604008553842684 \tabularnewline
111 & 16 & 15.0876149982496 & 0.912385001750355 \tabularnewline
112 & 16 & 15.9773313552446 & 0.0226686447554006 \tabularnewline
113 & 17 & 14.4666592697928 & 2.53334073020715 \tabularnewline
114 & 13 & 15.0476661513459 & -2.04766615134591 \tabularnewline
115 & 12 & 14.8060136442397 & -2.80601364423966 \tabularnewline
116 & 18 & 16.9826891370404 & 1.01731086295962 \tabularnewline
117 & 14 & 15.6922143524683 & -1.69221435246829 \tabularnewline
118 & 14 & 12.8828673850794 & 1.11713261492059 \tabularnewline
119 & 13 & 15.1622176552383 & -2.16221765523831 \tabularnewline
120 & 16 & 15.7518764499112 & 0.248123550088765 \tabularnewline
121 & 13 & 14.3462182567372 & -1.34621825673719 \tabularnewline
122 & 16 & 16.1780488889348 & -0.178048888934765 \tabularnewline
123 & 13 & 16.1818470476672 & -3.18184704766716 \tabularnewline
124 & 16 & 17.157330723524 & -1.15733072352403 \tabularnewline
125 & 15 & 15.981547230761 & -0.981547230760973 \tabularnewline
126 & 16 & 16.8249494564529 & -0.824949456452891 \tabularnewline
127 & 15 & 15.5374956046373 & -0.537495604637266 \tabularnewline
128 & 17 & 16.3042730181654 & 0.695726981834596 \tabularnewline
129 & 15 & 14.0395725554544 & 0.960427444545604 \tabularnewline
130 & 12 & 14.9277893179309 & -2.92778931793088 \tabularnewline
131 & 16 & 14.1621990602944 & 1.83780093970557 \tabularnewline
132 & 10 & 13.3334045870603 & -3.33340458706034 \tabularnewline
133 & 16 & 14.1698297754962 & 1.83017022450376 \tabularnewline
134 & 12 & 13.8046190495834 & -1.80461904958344 \tabularnewline
135 & 14 & 15.6154195252291 & -1.61541952522908 \tabularnewline
136 & 15 & 15.3834314482963 & -0.383431448296266 \tabularnewline
137 & 13 & 12.2832033605523 & 0.716796639447694 \tabularnewline
138 & 15 & 14.6694569808568 & 0.330543019143156 \tabularnewline
139 & 11 & 13.4132981985485 & -2.41329819854852 \tabularnewline
140 & 12 & 13.4519887102221 & -1.45198871022213 \tabularnewline
141 & 8 & 13.3661177506995 & -5.36611775069953 \tabularnewline
142 & 16 & 13.4676908348272 & 2.53230916517284 \tabularnewline
143 & 15 & 13.3361312040775 & 1.66386879592252 \tabularnewline
144 & 17 & 16.5947409494217 & 0.405259050578308 \tabularnewline
145 & 16 & 14.8832716675566 & 1.11672833244338 \tabularnewline
146 & 10 & 14.2587899674578 & -4.25878996745785 \tabularnewline
147 & 18 & 15.8218917657528 & 2.17810823424722 \tabularnewline
148 & 13 & 15.1503650509532 & -2.15036505095316 \tabularnewline
149 & 16 & 15.1784984597343 & 0.82150154026565 \tabularnewline
150 & 13 & 13.2716251499581 & -0.27162514995806 \tabularnewline
151 & 10 & 13.1952060750555 & -3.1952060750555 \tabularnewline
152 & 15 & 16.6532824878877 & -1.65328248788765 \tabularnewline
153 & 16 & 14.7560347515208 & 1.24396524847918 \tabularnewline
154 & 16 & 12.0901877895767 & 3.90981221042333 \tabularnewline
155 & 14 & 12.822963879173 & 1.17703612082697 \tabularnewline
156 & 10 & 12.3486560513435 & -2.3486560513435 \tabularnewline
157 & 17 & 16.8957385722503 & 0.104261427749656 \tabularnewline
158 & 13 & 11.7190928887697 & 1.28090711123025 \tabularnewline
159 & 15 & 14.0395725554544 & 0.960427444545604 \tabularnewline
160 & 16 & 15.3672810784503 & 0.632718921549706 \tabularnewline
161 & 12 & 12.6517360473787 & -0.651736047378715 \tabularnewline
162 & 13 & 12.6807880725299 & 0.319211927470075 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186406&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]16.2145332606848[/C][C]-3.21453326068478[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.8564166689795[/C][C]0.143583331020545[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.1077765061934[/C][C]2.89222349380662[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.6346455379561[/C][C]3.36535446204394[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.74282681468[/C][C]-1.74282681467997[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.917188757312[/C][C]-1.917188757312[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]14.8833906845507[/C][C]4.11660931544925[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.6498271871816[/C][C]-1.64982718718163[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.7781274574523[/C][C]-1.77812745745234[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.148656597054[/C][C]2.85134340294595[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.2104529404917[/C][C]0.789547059508308[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.0601703014398[/C][C]-0.0601703014397521[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.2371313543289[/C][C]0.762868645671134[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.2700165357223[/C][C]0.729983464277743[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.5518340171394[/C][C]-0.551834017139444[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0531716473167[/C][C]-0.0531716473166555[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.7102878239069[/C][C]0.28971217609307[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1457791270244[/C][C]3.85422087297561[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.6595316076982[/C][C]2.34046839230178[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3569104159301[/C][C]0.643089584069876[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.1448391323609[/C][C]0.855160867639148[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.6319444253794[/C][C]1.36805557462065[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.5296669375084[/C][C]2.47033306249163[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.7919399997119[/C][C]1.20806000028814[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.6490415411055[/C][C]2.3509584588945[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.6539351005026[/C][C]0.346064899497415[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.5260790979009[/C][C]0.473920902099055[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.0752732385245[/C][C]-1.07527323852448[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.3668488235737[/C][C]0.633151176426331[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.848360933308[/C][C]0.151639066692009[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.5415206665493[/C][C]-0.541520666549281[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.096469572901[/C][C]-0.0964695729010146[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1243311270264[/C][C]-1.12433112702642[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.3331718468745[/C][C]0.666828153125453[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.5697921761151[/C][C]-1.56979217611505[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.7979480715389[/C][C]-5.79794807153892[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.7781690772438[/C][C]-0.778169077243826[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.6722045113404[/C][C]-1.67220451134042[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.1580963297664[/C][C]1.8419036702336[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.4793473090939[/C][C]1.52065269090605[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.9328878746195[/C][C]1.06711212538047[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.7364116600629[/C][C]-1.73641166006287[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.791926339695[/C][C]2.20807366030499[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2121107459926[/C][C]-0.212110745992598[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0538164492119[/C][C]-1.05381644921192[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.7505670375022[/C][C]-4.75056703750225[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.2799650989433[/C][C]-2.27996509894327[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.8412409226842[/C][C]0.1587590773158[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.9466895668064[/C][C]1.05331043319359[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.9146577144718[/C][C]-1.91465771447182[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.2740488113962[/C][C]-0.274048811396182[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.0238756821725[/C][C]-0.023875682172512[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.5981353066829[/C][C]-2.59813530668289[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.317700355836[/C][C]0.682299644163996[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.4137626225363[/C][C]-2.41376262253634[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.4921766066376[/C][C]1.50782339336242[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0958468194325[/C][C]-0.0958468194325485[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.8794023362997[/C][C]1.12059766370031[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3511543194206[/C][C]-0.351154319420611[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.152009476412[/C][C]1.84799052358805[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.3314273736168[/C][C]0.668572626383154[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]16.1417331600482[/C][C]-0.141733160048245[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.5351825909878[/C][C]-0.535182590987796[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.6637367486737[/C][C]-0.663736748673704[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.5882914989199[/C][C]0.411708501080082[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.8261791608992[/C][C]1.17382083910077[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1215062900098[/C][C]1.87849370999016[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.7696780176983[/C][C]3.23032198230169[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.7277778742621[/C][C]-3.72777787426209[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.3047158250425[/C][C]0.69528417495752[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.2869277780806[/C][C]-3.28692777808059[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.2023404342683[/C][C]-0.202340434268253[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.3209415596193[/C][C]1.67905844038071[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1676589381615[/C][C]0.832341061838533[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.8056606042225[/C][C]0.194339395777492[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8926968716575[/C][C]3.10730312834247[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.370752293448[/C][C]-0.370752293448009[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.6094000922278[/C][C]1.3905999077722[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.7956058690577[/C][C]-1.79560586905771[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.1802861209585[/C][C]-0.180286120958458[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.63359254875[/C][C]0.366407451250005[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.6654544319273[/C][C]3.33454556807273[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.5795177729375[/C][C]0.420482227062499[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.7820607301893[/C][C]-0.782060730189278[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]15.0552019035588[/C][C]-0.0552019035588035[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.5830608215219[/C][C]1.41693917847809[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.0812497701883[/C][C]-0.0812497701883377[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2467398110977[/C][C]0.753260188902279[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.6339983929585[/C][C]1.36600160704147[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]13.9921978693874[/C][C]1.0078021306126[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.3752863626195[/C][C]-1.37528636261954[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.8957385722503[/C][C]0.104261427749656[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.3767020586232[/C][C]0.623297941376774[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.232923918879[/C][C]-0.232923918878977[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.2823575248549[/C][C]-2.2823575248549[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.2254807051761[/C][C]0.774519294823916[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.7403306815739[/C][C]0.259669318426122[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.2254069279623[/C][C]1.77459307203773[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.0569980637918[/C][C]-0.0569980637917588[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.5198274673874[/C][C]-0.519827467387382[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2575700229348[/C][C]-1.25757002293477[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8247395572529[/C][C]1.17526044274713[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.459102687477[/C][C]2.54089731252305[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.6464941662014[/C][C]0.353505833798553[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.3305149757577[/C][C]1.66948502424232[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.2681045028644[/C][C]-2.26810450286443[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9441352094224[/C][C]1.05586479057764[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]16.0488748826202[/C][C]-0.0488748826201887[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.6010528511029[/C][C]1.39894714889707[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.6040085538427[/C][C]-0.604008553842684[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.0876149982496[/C][C]0.912385001750355[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.9773313552446[/C][C]0.0226686447554006[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.4666592697928[/C][C]2.53334073020715[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]15.0476661513459[/C][C]-2.04766615134591[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.8060136442397[/C][C]-2.80601364423966[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.9826891370404[/C][C]1.01731086295962[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.6922143524683[/C][C]-1.69221435246829[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.8828673850794[/C][C]1.11713261492059[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.1622176552383[/C][C]-2.16221765523831[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.7518764499112[/C][C]0.248123550088765[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3462182567372[/C][C]-1.34621825673719[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]16.1780488889348[/C][C]-0.178048888934765[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]16.1818470476672[/C][C]-3.18184704766716[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.157330723524[/C][C]-1.15733072352403[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.981547230761[/C][C]-0.981547230760973[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.8249494564529[/C][C]-0.824949456452891[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.5374956046373[/C][C]-0.537495604637266[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.3042730181654[/C][C]0.695726981834596[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.0395725554544[/C][C]0.960427444545604[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.9277893179309[/C][C]-2.92778931793088[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.1621990602944[/C][C]1.83780093970557[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3334045870603[/C][C]-3.33340458706034[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.1698297754962[/C][C]1.83017022450376[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8046190495834[/C][C]-1.80461904958344[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.6154195252291[/C][C]-1.61541952522908[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.3834314482963[/C][C]-0.383431448296266[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.2832033605523[/C][C]0.716796639447694[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.6694569808568[/C][C]0.330543019143156[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.4132981985485[/C][C]-2.41329819854852[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.4519887102221[/C][C]-1.45198871022213[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.3661177506995[/C][C]-5.36611775069953[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.4676908348272[/C][C]2.53230916517284[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.3361312040775[/C][C]1.66386879592252[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.5947409494217[/C][C]0.405259050578308[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.8832716675566[/C][C]1.11672833244338[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.2587899674578[/C][C]-4.25878996745785[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.8218917657528[/C][C]2.17810823424722[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.1503650509532[/C][C]-2.15036505095316[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.1784984597343[/C][C]0.82150154026565[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.2716251499581[/C][C]-0.27162514995806[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.1952060750555[/C][C]-3.1952060750555[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.6532824878877[/C][C]-1.65328248788765[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.7560347515208[/C][C]1.24396524847918[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.0901877895767[/C][C]3.90981221042333[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.822963879173[/C][C]1.17703612082697[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.3486560513435[/C][C]-2.3486560513435[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.8957385722503[/C][C]0.104261427749656[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7190928887697[/C][C]1.28090711123025[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.0395725554544[/C][C]0.960427444545604[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.3672810784503[/C][C]0.632718921549706[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.6517360473787[/C][C]-0.651736047378715[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6807880725299[/C][C]0.319211927470075[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186406&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186406&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.2145332606848-3.21453326068478
21615.85641666897950.143583331020545
31916.10777650619342.89222349380662
41511.63464553795613.36535446204394
51415.74282681468-1.74282681467997
61314.917188757312-1.917188757312
71914.88339068455074.11660931544925
81516.6498271871816-1.64982718718163
91415.7781274574523-1.77812745745234
101512.1486565970542.85134340294595
111615.21045294049170.789547059508308
121616.0601703014398-0.0601703014397521
131615.23713135432890.762868645671134
141615.27001653572230.729983464277743
151717.5518340171394-0.551834017139444
161515.0531716473167-0.0531716473166555
171514.71028782390690.28971217609307
182016.14577912702443.85422087297561
191815.65953160769822.34046839230178
201615.35691041593010.643089584069876
211615.14483913236090.855160867639148
221614.63194442537941.36805557462065
231916.52966693750842.47033306249163
241614.79193999971191.20806000028814
251714.64904154110552.3509584588945
261716.65393510050260.346064899497415
271615.52607909790090.473920902099055
281516.0752732385245-1.07527323852448
291615.36684882357370.633151176426331
301413.8483609333080.151639066692009
311515.5415206665493-0.541520666549281
321212.096469572901-0.0964695729010146
331415.1243311270264-1.12433112702642
341615.33317184687450.666828153125453
351415.5697921761151-1.56979217611505
36712.7979480715389-5.79794807153892
371010.7781690772438-0.778169077243826
381415.6722045113404-1.67220451134042
391614.15809632976641.8419036702336
401614.47934730909391.52065269090605
411614.93288787461951.06711212538047
421415.7364116600629-1.73641166006287
432017.7919263396952.20807366030499
441414.2121107459926-0.212110745992598
451415.0538164492119-1.05381644921192
461115.7505670375022-4.75056703750225
471416.2799650989433-2.27996509894327
481514.84124092268420.1587590773158
491614.94668956680641.05331043319359
501415.9146577144718-1.91465771447182
511616.2740488113962-0.274048811396182
521414.0238756821725-0.023875682172512
531214.5981353066829-2.59813530668289
541615.3177003558360.682299644163996
55911.4137626225363-2.41376262253634
561412.49217660663761.50782339336242
571616.0958468194325-0.0958468194325485
581614.87940233629971.12059766370031
591515.3511543194206-0.351154319420611
601614.1520094764121.84799052358805
611211.33142737361680.668572626383154
621616.1417331600482-0.141733160048245
631616.5351825909878-0.535182590987796
641414.6637367486737-0.663736748673704
651615.58829149891990.411708501080082
661715.82617916089921.17382083910077
671816.12150629000981.87849370999016
681814.76967801769833.23032198230169
691215.7277778742621-3.72777787426209
701615.30471582504250.69528417495752
711013.2869277780806-3.28692777808059
721414.2023404342683-0.202340434268253
731816.32094155961931.67905844038071
741817.16765893816150.832341061838533
751615.80566060422250.194339395777492
761713.89269687165753.10730312834247
771616.370752293448-0.370752293448009
781614.60940009222781.3905999077722
791314.7956058690577-1.79560586905771
801616.1802861209585-0.180286120958458
811615.633592548750.366407451250005
822016.66545443192733.33454556807273
831615.57951777293750.420482227062499
841515.7820607301893-0.782060730189278
851515.0552019035588-0.0552019035588035
861614.58306082152191.41693917847809
871414.0812497701883-0.0812497701883377
881615.24673981109770.753260188902279
891614.63399839295851.36600160704147
901513.99219786938741.0078021306126
911213.3752863626195-1.37528636261954
921716.89573857225030.104261427749656
931615.37670205862320.623297941376774
941515.232923918879-0.232923918878977
951315.2823575248549-2.2823575248549
961615.22548070517610.774519294823916
971615.74033068157390.259669318426122
981614.22540692796231.77459307203773
991616.0569980637918-0.0569980637917588
1001414.5198274673874-0.519827467387382
1011617.2575700229348-1.25757002293477
1021614.82473955725291.17526044274713
1032017.4591026874772.54089731252305
1041514.64649416620140.353505833798553
1051614.33051497575771.66948502424232
1061315.2681045028644-2.26810450286443
1071715.94413520942241.05586479057764
1081616.0488748826202-0.0488748826201887
1091614.60105285110291.39894714889707
1101212.6040085538427-0.604008553842684
1111615.08761499824960.912385001750355
1121615.97733135524460.0226686447554006
1131714.46665926979282.53334073020715
1141315.0476661513459-2.04766615134591
1151214.8060136442397-2.80601364423966
1161816.98268913704041.01731086295962
1171415.6922143524683-1.69221435246829
1181412.88286738507941.11713261492059
1191315.1622176552383-2.16221765523831
1201615.75187644991120.248123550088765
1211314.3462182567372-1.34621825673719
1221616.1780488889348-0.178048888934765
1231316.1818470476672-3.18184704766716
1241617.157330723524-1.15733072352403
1251515.981547230761-0.981547230760973
1261616.8249494564529-0.824949456452891
1271515.5374956046373-0.537495604637266
1281716.30427301816540.695726981834596
1291514.03957255545440.960427444545604
1301214.9277893179309-2.92778931793088
1311614.16219906029441.83780093970557
1321013.3334045870603-3.33340458706034
1331614.16982977549621.83017022450376
1341213.8046190495834-1.80461904958344
1351415.6154195252291-1.61541952522908
1361515.3834314482963-0.383431448296266
1371312.28320336055230.716796639447694
1381514.66945698085680.330543019143156
1391113.4132981985485-2.41329819854852
1401213.4519887102221-1.45198871022213
141813.3661177506995-5.36611775069953
1421613.46769083482722.53230916517284
1431513.33613120407751.66386879592252
1441716.59474094942170.405259050578308
1451614.88327166755661.11672833244338
1461014.2587899674578-4.25878996745785
1471815.82189176575282.17810823424722
1481315.1503650509532-2.15036505095316
1491615.17849845973430.82150154026565
1501313.2716251499581-0.27162514995806
1511013.1952060750555-3.1952060750555
1521516.6532824878877-1.65328248788765
1531614.75603475152081.24396524847918
1541612.09018778957673.90981221042333
1551412.8229638791731.17703612082697
1561012.3486560513435-2.3486560513435
1571716.89573857225030.104261427749656
1581311.71909288876971.28090711123025
1591514.03957255545440.960427444545604
1601615.36728107845030.632718921549706
1611212.6517360473787-0.651736047378715
1621312.68078807252990.319211927470075







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.5974772008083850.805045598383230.402522799191615
130.4355291718055550.8710583436111110.564470828194445
140.3885599393405090.7771198786810190.611440060659491
150.3360916036569820.6721832073139640.663908396343018
160.2423945205563650.4847890411127290.757605479443635
170.2480569204808780.4961138409617560.751943079519122
180.5008681233866070.9982637532267870.499131876613393
190.424162643604290.8483252872085790.57583735639571
200.3350767183012920.6701534366025840.664923281698708
210.2681529628550480.5363059257100960.731847037144952
220.2467324015985250.493464803197050.753267598401475
230.4538164881821580.9076329763643150.546183511817842
240.4737786693387360.9475573386774720.526221330661264
250.4597795563949310.9195591127898620.540220443605069
260.4095959964160610.8191919928321220.590404003583939
270.4920053077143120.9840106154286240.507994692285688
280.4950178518267670.9900357036535350.504982148173233
290.481953936214890.963907872429780.51804606378511
300.5237288122065660.9525423755868670.476271187793434
310.4630656923361790.9261313846723570.536934307663821
320.4139422164578930.8278844329157860.586057783542107
330.3879410329705360.7758820659410730.612058967029464
340.3524057302852340.7048114605704670.647594269714767
350.3055380587789110.6110761175578220.694461941221089
360.8650427860002260.2699144279995470.134957213999774
370.8411662283126920.3176675433746160.158833771687308
380.8333139122226420.3333721755547160.166686087777358
390.8530020295874060.2939959408251890.146997970412594
400.8384578062520040.3230843874959910.161542193747996
410.810896828810510.378206342378980.18910317118949
420.7925862707416780.4148274585166430.207413729258322
430.8186356821772330.3627286356455340.181364317822767
440.7810318855006690.4379362289986620.218968114499331
450.75197176096120.4960564780776010.2480282390388
460.8976252088801760.2047495822396490.102374791119824
470.9363127956746450.127374408650710.0636872043253551
480.9184848596094350.1630302807811310.0815151403905654
490.903527877673630.1929442446527410.0964721223263705
500.9019054348627010.1961891302745990.0980945651372993
510.878626154123970.242747691752060.12137384587603
520.8509595854834690.2980808290330620.149040414516531
530.8661638952525120.2676722094949760.133836104747488
540.8506119749293180.2987760501413650.149388025070682
550.8570094827716750.285981034456650.142990517228325
560.8382773263587450.3234453472825090.161722673641255
570.8076906024179040.3846187951641920.192309397582096
580.7888669595962630.4222660808074730.211133040403737
590.7545824231584620.4908351536830770.245417576841538
600.7530060060152990.4939879879694020.246993993984701
610.7195475742698230.5609048514603550.280452425730177
620.6825945418938080.6348109162123840.317405458106192
630.6470671401425810.7058657197148370.352932859857419
640.6062099297959410.7875801404081190.393790070204059
650.5669337911578370.8661324176843260.433066208842163
660.5395253981357350.9209492037285290.460474601864265
670.5340951548430580.9318096903138840.465904845156942
680.6831001294593780.6337997410812450.316899870540622
690.781787094617160.4364258107656790.21821290538284
700.751933907402870.496132185194260.24806609259713
710.8362624920205230.3274750159589530.163737507979476
720.805215101592160.389569796815680.19478489840784
730.8017837326125930.3964325347748130.198216267387407
740.7835612528804280.4328774942391450.216438747119572
750.7472499514609970.5055000970780050.252750048539003
760.7947348908988770.4105302182022470.205265109101123
770.760931327422850.4781373451542990.23906867257715
780.7425007715273170.5149984569453670.257499228472683
790.7462032158632480.5075935682735050.253796784136752
800.7067930151635950.5864139696728110.293206984836405
810.6689170470972160.6621659058055690.331082952902784
820.7904932535362330.4190134929275340.209506746463767
830.7575965189096130.4848069621807740.242403481090387
840.7290479331087840.5419041337824330.270952066891216
850.6898194680291390.6203610639417230.310180531970861
860.6722329986343940.6555340027312120.327767001365606
870.6291612971517220.7416774056965560.370838702848278
880.5928756617241340.8142486765517320.407124338275866
890.5741890918609750.8516218162780510.425810908139025
900.549788431267750.90042313746450.45021156873225
910.5251164591092220.9497670817815570.474883540890778
920.4896757983147670.9793515966295350.510324201685233
930.450009924960640.9000198499212790.54999007503936
940.4063357904276770.8126715808553530.593664209572323
950.4306710802411320.8613421604822640.569328919758868
960.3952148128627890.7904296257255770.604785187137211
970.3594796140512140.7189592281024290.640520385948786
980.3583177489217540.7166354978435090.641682251078246
990.3162566876884640.6325133753769280.683743312311536
1000.280318906065730.5606378121314590.71968109393427
1010.2517624951797680.5035249903595350.748237504820232
1020.2363058476930360.4726116953860720.763694152306964
1030.2823896841430130.5647793682860260.717610315856987
1040.2436147253067080.4872294506134160.756385274693292
1050.2611156102080260.5222312204160520.738884389791974
1060.2673095874610410.5346191749220820.732690412538959
1070.2568635370898540.5137270741797070.743136462910146
1080.230688477832420.4613769556648410.76931152216758
1090.2293304316069260.4586608632138510.770669568393074
1100.2040630566807390.4081261133614780.795936943319261
1110.1799441227770590.3598882455541170.820055877222941
1120.1508266403594380.3016532807188760.849173359640562
1130.1915650332719410.3831300665438830.808434966728058
1140.1776865873911380.3553731747822770.822313412608862
1150.2026828486814080.4053656973628160.797317151318592
1160.1780264722918420.3560529445836840.821973527708158
1170.1592165157828920.3184330315657840.840783484217108
1180.1506449184177070.3012898368354150.849355081582293
1190.1661796069166730.3323592138333460.833820393083327
1200.156134150481410.3122683009628210.84386584951859
1210.1336312461955040.2672624923910070.866368753804496
1220.1128654953164510.2257309906329030.887134504683549
1230.139054726164030.278109452328060.86094527383597
1240.1143023912310270.2286047824620540.885697608768973
1250.09299567110007910.1859913422001580.907004328899921
1260.07292152548845820.1458430509769160.927078474511542
1270.05651305362206990.113026107244140.94348694637793
1280.04993889250064640.09987778500129270.950061107499354
1290.03841591666884170.07683183333768340.961584083331158
1300.04514920230057190.09029840460114370.954850797699428
1310.04269554692070660.08539109384141330.957304453079293
1320.06052435899102580.1210487179820520.939475641008974
1330.07918188565058910.1583637713011780.920818114349411
1340.08097933242743210.1619586648548640.919020667572568
1350.06163015990730410.1232603198146080.938369840092696
1360.0490162838660360.09803256773207190.950983716133964
1370.03403094235168610.06806188470337230.965969057648314
1380.02269193733183190.04538387466366390.977308062668168
1390.06297913127664220.1259582625532840.937020868723358
1400.0486449766152350.09728995323047010.951355023384765
1410.6229227221648040.7541545556703930.377077277835196
1420.5671224966235640.8657550067528720.432877503376436
1430.4880810082862970.9761620165725940.511918991713703
1440.3982477079113790.7964954158227570.601752292088621
1450.3061460984616150.6122921969232290.693853901538385
1460.3445916308412980.6891832616825960.655408369158702
1470.358906143161670.717812286323340.64109385683833
1480.7267662268250240.5464675463499510.273233773174976
1490.6249554797989420.7500890404021160.375044520201058
1500.4556300921291580.9112601842583150.544369907870842

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.597477200808385 & 0.80504559838323 & 0.402522799191615 \tabularnewline
13 & 0.435529171805555 & 0.871058343611111 & 0.564470828194445 \tabularnewline
14 & 0.388559939340509 & 0.777119878681019 & 0.611440060659491 \tabularnewline
15 & 0.336091603656982 & 0.672183207313964 & 0.663908396343018 \tabularnewline
16 & 0.242394520556365 & 0.484789041112729 & 0.757605479443635 \tabularnewline
17 & 0.248056920480878 & 0.496113840961756 & 0.751943079519122 \tabularnewline
18 & 0.500868123386607 & 0.998263753226787 & 0.499131876613393 \tabularnewline
19 & 0.42416264360429 & 0.848325287208579 & 0.57583735639571 \tabularnewline
20 & 0.335076718301292 & 0.670153436602584 & 0.664923281698708 \tabularnewline
21 & 0.268152962855048 & 0.536305925710096 & 0.731847037144952 \tabularnewline
22 & 0.246732401598525 & 0.49346480319705 & 0.753267598401475 \tabularnewline
23 & 0.453816488182158 & 0.907632976364315 & 0.546183511817842 \tabularnewline
24 & 0.473778669338736 & 0.947557338677472 & 0.526221330661264 \tabularnewline
25 & 0.459779556394931 & 0.919559112789862 & 0.540220443605069 \tabularnewline
26 & 0.409595996416061 & 0.819191992832122 & 0.590404003583939 \tabularnewline
27 & 0.492005307714312 & 0.984010615428624 & 0.507994692285688 \tabularnewline
28 & 0.495017851826767 & 0.990035703653535 & 0.504982148173233 \tabularnewline
29 & 0.48195393621489 & 0.96390787242978 & 0.51804606378511 \tabularnewline
30 & 0.523728812206566 & 0.952542375586867 & 0.476271187793434 \tabularnewline
31 & 0.463065692336179 & 0.926131384672357 & 0.536934307663821 \tabularnewline
32 & 0.413942216457893 & 0.827884432915786 & 0.586057783542107 \tabularnewline
33 & 0.387941032970536 & 0.775882065941073 & 0.612058967029464 \tabularnewline
34 & 0.352405730285234 & 0.704811460570467 & 0.647594269714767 \tabularnewline
35 & 0.305538058778911 & 0.611076117557822 & 0.694461941221089 \tabularnewline
36 & 0.865042786000226 & 0.269914427999547 & 0.134957213999774 \tabularnewline
37 & 0.841166228312692 & 0.317667543374616 & 0.158833771687308 \tabularnewline
38 & 0.833313912222642 & 0.333372175554716 & 0.166686087777358 \tabularnewline
39 & 0.853002029587406 & 0.293995940825189 & 0.146997970412594 \tabularnewline
40 & 0.838457806252004 & 0.323084387495991 & 0.161542193747996 \tabularnewline
41 & 0.81089682881051 & 0.37820634237898 & 0.18910317118949 \tabularnewline
42 & 0.792586270741678 & 0.414827458516643 & 0.207413729258322 \tabularnewline
43 & 0.818635682177233 & 0.362728635645534 & 0.181364317822767 \tabularnewline
44 & 0.781031885500669 & 0.437936228998662 & 0.218968114499331 \tabularnewline
45 & 0.7519717609612 & 0.496056478077601 & 0.2480282390388 \tabularnewline
46 & 0.897625208880176 & 0.204749582239649 & 0.102374791119824 \tabularnewline
47 & 0.936312795674645 & 0.12737440865071 & 0.0636872043253551 \tabularnewline
48 & 0.918484859609435 & 0.163030280781131 & 0.0815151403905654 \tabularnewline
49 & 0.90352787767363 & 0.192944244652741 & 0.0964721223263705 \tabularnewline
50 & 0.901905434862701 & 0.196189130274599 & 0.0980945651372993 \tabularnewline
51 & 0.87862615412397 & 0.24274769175206 & 0.12137384587603 \tabularnewline
52 & 0.850959585483469 & 0.298080829033062 & 0.149040414516531 \tabularnewline
53 & 0.866163895252512 & 0.267672209494976 & 0.133836104747488 \tabularnewline
54 & 0.850611974929318 & 0.298776050141365 & 0.149388025070682 \tabularnewline
55 & 0.857009482771675 & 0.28598103445665 & 0.142990517228325 \tabularnewline
56 & 0.838277326358745 & 0.323445347282509 & 0.161722673641255 \tabularnewline
57 & 0.807690602417904 & 0.384618795164192 & 0.192309397582096 \tabularnewline
58 & 0.788866959596263 & 0.422266080807473 & 0.211133040403737 \tabularnewline
59 & 0.754582423158462 & 0.490835153683077 & 0.245417576841538 \tabularnewline
60 & 0.753006006015299 & 0.493987987969402 & 0.246993993984701 \tabularnewline
61 & 0.719547574269823 & 0.560904851460355 & 0.280452425730177 \tabularnewline
62 & 0.682594541893808 & 0.634810916212384 & 0.317405458106192 \tabularnewline
63 & 0.647067140142581 & 0.705865719714837 & 0.352932859857419 \tabularnewline
64 & 0.606209929795941 & 0.787580140408119 & 0.393790070204059 \tabularnewline
65 & 0.566933791157837 & 0.866132417684326 & 0.433066208842163 \tabularnewline
66 & 0.539525398135735 & 0.920949203728529 & 0.460474601864265 \tabularnewline
67 & 0.534095154843058 & 0.931809690313884 & 0.465904845156942 \tabularnewline
68 & 0.683100129459378 & 0.633799741081245 & 0.316899870540622 \tabularnewline
69 & 0.78178709461716 & 0.436425810765679 & 0.21821290538284 \tabularnewline
70 & 0.75193390740287 & 0.49613218519426 & 0.24806609259713 \tabularnewline
71 & 0.836262492020523 & 0.327475015958953 & 0.163737507979476 \tabularnewline
72 & 0.80521510159216 & 0.38956979681568 & 0.19478489840784 \tabularnewline
73 & 0.801783732612593 & 0.396432534774813 & 0.198216267387407 \tabularnewline
74 & 0.783561252880428 & 0.432877494239145 & 0.216438747119572 \tabularnewline
75 & 0.747249951460997 & 0.505500097078005 & 0.252750048539003 \tabularnewline
76 & 0.794734890898877 & 0.410530218202247 & 0.205265109101123 \tabularnewline
77 & 0.76093132742285 & 0.478137345154299 & 0.23906867257715 \tabularnewline
78 & 0.742500771527317 & 0.514998456945367 & 0.257499228472683 \tabularnewline
79 & 0.746203215863248 & 0.507593568273505 & 0.253796784136752 \tabularnewline
80 & 0.706793015163595 & 0.586413969672811 & 0.293206984836405 \tabularnewline
81 & 0.668917047097216 & 0.662165905805569 & 0.331082952902784 \tabularnewline
82 & 0.790493253536233 & 0.419013492927534 & 0.209506746463767 \tabularnewline
83 & 0.757596518909613 & 0.484806962180774 & 0.242403481090387 \tabularnewline
84 & 0.729047933108784 & 0.541904133782433 & 0.270952066891216 \tabularnewline
85 & 0.689819468029139 & 0.620361063941723 & 0.310180531970861 \tabularnewline
86 & 0.672232998634394 & 0.655534002731212 & 0.327767001365606 \tabularnewline
87 & 0.629161297151722 & 0.741677405696556 & 0.370838702848278 \tabularnewline
88 & 0.592875661724134 & 0.814248676551732 & 0.407124338275866 \tabularnewline
89 & 0.574189091860975 & 0.851621816278051 & 0.425810908139025 \tabularnewline
90 & 0.54978843126775 & 0.9004231374645 & 0.45021156873225 \tabularnewline
91 & 0.525116459109222 & 0.949767081781557 & 0.474883540890778 \tabularnewline
92 & 0.489675798314767 & 0.979351596629535 & 0.510324201685233 \tabularnewline
93 & 0.45000992496064 & 0.900019849921279 & 0.54999007503936 \tabularnewline
94 & 0.406335790427677 & 0.812671580855353 & 0.593664209572323 \tabularnewline
95 & 0.430671080241132 & 0.861342160482264 & 0.569328919758868 \tabularnewline
96 & 0.395214812862789 & 0.790429625725577 & 0.604785187137211 \tabularnewline
97 & 0.359479614051214 & 0.718959228102429 & 0.640520385948786 \tabularnewline
98 & 0.358317748921754 & 0.716635497843509 & 0.641682251078246 \tabularnewline
99 & 0.316256687688464 & 0.632513375376928 & 0.683743312311536 \tabularnewline
100 & 0.28031890606573 & 0.560637812131459 & 0.71968109393427 \tabularnewline
101 & 0.251762495179768 & 0.503524990359535 & 0.748237504820232 \tabularnewline
102 & 0.236305847693036 & 0.472611695386072 & 0.763694152306964 \tabularnewline
103 & 0.282389684143013 & 0.564779368286026 & 0.717610315856987 \tabularnewline
104 & 0.243614725306708 & 0.487229450613416 & 0.756385274693292 \tabularnewline
105 & 0.261115610208026 & 0.522231220416052 & 0.738884389791974 \tabularnewline
106 & 0.267309587461041 & 0.534619174922082 & 0.732690412538959 \tabularnewline
107 & 0.256863537089854 & 0.513727074179707 & 0.743136462910146 \tabularnewline
108 & 0.23068847783242 & 0.461376955664841 & 0.76931152216758 \tabularnewline
109 & 0.229330431606926 & 0.458660863213851 & 0.770669568393074 \tabularnewline
110 & 0.204063056680739 & 0.408126113361478 & 0.795936943319261 \tabularnewline
111 & 0.179944122777059 & 0.359888245554117 & 0.820055877222941 \tabularnewline
112 & 0.150826640359438 & 0.301653280718876 & 0.849173359640562 \tabularnewline
113 & 0.191565033271941 & 0.383130066543883 & 0.808434966728058 \tabularnewline
114 & 0.177686587391138 & 0.355373174782277 & 0.822313412608862 \tabularnewline
115 & 0.202682848681408 & 0.405365697362816 & 0.797317151318592 \tabularnewline
116 & 0.178026472291842 & 0.356052944583684 & 0.821973527708158 \tabularnewline
117 & 0.159216515782892 & 0.318433031565784 & 0.840783484217108 \tabularnewline
118 & 0.150644918417707 & 0.301289836835415 & 0.849355081582293 \tabularnewline
119 & 0.166179606916673 & 0.332359213833346 & 0.833820393083327 \tabularnewline
120 & 0.15613415048141 & 0.312268300962821 & 0.84386584951859 \tabularnewline
121 & 0.133631246195504 & 0.267262492391007 & 0.866368753804496 \tabularnewline
122 & 0.112865495316451 & 0.225730990632903 & 0.887134504683549 \tabularnewline
123 & 0.13905472616403 & 0.27810945232806 & 0.86094527383597 \tabularnewline
124 & 0.114302391231027 & 0.228604782462054 & 0.885697608768973 \tabularnewline
125 & 0.0929956711000791 & 0.185991342200158 & 0.907004328899921 \tabularnewline
126 & 0.0729215254884582 & 0.145843050976916 & 0.927078474511542 \tabularnewline
127 & 0.0565130536220699 & 0.11302610724414 & 0.94348694637793 \tabularnewline
128 & 0.0499388925006464 & 0.0998777850012927 & 0.950061107499354 \tabularnewline
129 & 0.0384159166688417 & 0.0768318333376834 & 0.961584083331158 \tabularnewline
130 & 0.0451492023005719 & 0.0902984046011437 & 0.954850797699428 \tabularnewline
131 & 0.0426955469207066 & 0.0853910938414133 & 0.957304453079293 \tabularnewline
132 & 0.0605243589910258 & 0.121048717982052 & 0.939475641008974 \tabularnewline
133 & 0.0791818856505891 & 0.158363771301178 & 0.920818114349411 \tabularnewline
134 & 0.0809793324274321 & 0.161958664854864 & 0.919020667572568 \tabularnewline
135 & 0.0616301599073041 & 0.123260319814608 & 0.938369840092696 \tabularnewline
136 & 0.049016283866036 & 0.0980325677320719 & 0.950983716133964 \tabularnewline
137 & 0.0340309423516861 & 0.0680618847033723 & 0.965969057648314 \tabularnewline
138 & 0.0226919373318319 & 0.0453838746636639 & 0.977308062668168 \tabularnewline
139 & 0.0629791312766422 & 0.125958262553284 & 0.937020868723358 \tabularnewline
140 & 0.048644976615235 & 0.0972899532304701 & 0.951355023384765 \tabularnewline
141 & 0.622922722164804 & 0.754154555670393 & 0.377077277835196 \tabularnewline
142 & 0.567122496623564 & 0.865755006752872 & 0.432877503376436 \tabularnewline
143 & 0.488081008286297 & 0.976162016572594 & 0.511918991713703 \tabularnewline
144 & 0.398247707911379 & 0.796495415822757 & 0.601752292088621 \tabularnewline
145 & 0.306146098461615 & 0.612292196923229 & 0.693853901538385 \tabularnewline
146 & 0.344591630841298 & 0.689183261682596 & 0.655408369158702 \tabularnewline
147 & 0.35890614316167 & 0.71781228632334 & 0.64109385683833 \tabularnewline
148 & 0.726766226825024 & 0.546467546349951 & 0.273233773174976 \tabularnewline
149 & 0.624955479798942 & 0.750089040402116 & 0.375044520201058 \tabularnewline
150 & 0.455630092129158 & 0.911260184258315 & 0.544369907870842 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186406&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]0.597477200808385[/C][C]0.80504559838323[/C][C]0.402522799191615[/C][/ROW]
[ROW][C]13[/C][C]0.435529171805555[/C][C]0.871058343611111[/C][C]0.564470828194445[/C][/ROW]
[ROW][C]14[/C][C]0.388559939340509[/C][C]0.777119878681019[/C][C]0.611440060659491[/C][/ROW]
[ROW][C]15[/C][C]0.336091603656982[/C][C]0.672183207313964[/C][C]0.663908396343018[/C][/ROW]
[ROW][C]16[/C][C]0.242394520556365[/C][C]0.484789041112729[/C][C]0.757605479443635[/C][/ROW]
[ROW][C]17[/C][C]0.248056920480878[/C][C]0.496113840961756[/C][C]0.751943079519122[/C][/ROW]
[ROW][C]18[/C][C]0.500868123386607[/C][C]0.998263753226787[/C][C]0.499131876613393[/C][/ROW]
[ROW][C]19[/C][C]0.42416264360429[/C][C]0.848325287208579[/C][C]0.57583735639571[/C][/ROW]
[ROW][C]20[/C][C]0.335076718301292[/C][C]0.670153436602584[/C][C]0.664923281698708[/C][/ROW]
[ROW][C]21[/C][C]0.268152962855048[/C][C]0.536305925710096[/C][C]0.731847037144952[/C][/ROW]
[ROW][C]22[/C][C]0.246732401598525[/C][C]0.49346480319705[/C][C]0.753267598401475[/C][/ROW]
[ROW][C]23[/C][C]0.453816488182158[/C][C]0.907632976364315[/C][C]0.546183511817842[/C][/ROW]
[ROW][C]24[/C][C]0.473778669338736[/C][C]0.947557338677472[/C][C]0.526221330661264[/C][/ROW]
[ROW][C]25[/C][C]0.459779556394931[/C][C]0.919559112789862[/C][C]0.540220443605069[/C][/ROW]
[ROW][C]26[/C][C]0.409595996416061[/C][C]0.819191992832122[/C][C]0.590404003583939[/C][/ROW]
[ROW][C]27[/C][C]0.492005307714312[/C][C]0.984010615428624[/C][C]0.507994692285688[/C][/ROW]
[ROW][C]28[/C][C]0.495017851826767[/C][C]0.990035703653535[/C][C]0.504982148173233[/C][/ROW]
[ROW][C]29[/C][C]0.48195393621489[/C][C]0.96390787242978[/C][C]0.51804606378511[/C][/ROW]
[ROW][C]30[/C][C]0.523728812206566[/C][C]0.952542375586867[/C][C]0.476271187793434[/C][/ROW]
[ROW][C]31[/C][C]0.463065692336179[/C][C]0.926131384672357[/C][C]0.536934307663821[/C][/ROW]
[ROW][C]32[/C][C]0.413942216457893[/C][C]0.827884432915786[/C][C]0.586057783542107[/C][/ROW]
[ROW][C]33[/C][C]0.387941032970536[/C][C]0.775882065941073[/C][C]0.612058967029464[/C][/ROW]
[ROW][C]34[/C][C]0.352405730285234[/C][C]0.704811460570467[/C][C]0.647594269714767[/C][/ROW]
[ROW][C]35[/C][C]0.305538058778911[/C][C]0.611076117557822[/C][C]0.694461941221089[/C][/ROW]
[ROW][C]36[/C][C]0.865042786000226[/C][C]0.269914427999547[/C][C]0.134957213999774[/C][/ROW]
[ROW][C]37[/C][C]0.841166228312692[/C][C]0.317667543374616[/C][C]0.158833771687308[/C][/ROW]
[ROW][C]38[/C][C]0.833313912222642[/C][C]0.333372175554716[/C][C]0.166686087777358[/C][/ROW]
[ROW][C]39[/C][C]0.853002029587406[/C][C]0.293995940825189[/C][C]0.146997970412594[/C][/ROW]
[ROW][C]40[/C][C]0.838457806252004[/C][C]0.323084387495991[/C][C]0.161542193747996[/C][/ROW]
[ROW][C]41[/C][C]0.81089682881051[/C][C]0.37820634237898[/C][C]0.18910317118949[/C][/ROW]
[ROW][C]42[/C][C]0.792586270741678[/C][C]0.414827458516643[/C][C]0.207413729258322[/C][/ROW]
[ROW][C]43[/C][C]0.818635682177233[/C][C]0.362728635645534[/C][C]0.181364317822767[/C][/ROW]
[ROW][C]44[/C][C]0.781031885500669[/C][C]0.437936228998662[/C][C]0.218968114499331[/C][/ROW]
[ROW][C]45[/C][C]0.7519717609612[/C][C]0.496056478077601[/C][C]0.2480282390388[/C][/ROW]
[ROW][C]46[/C][C]0.897625208880176[/C][C]0.204749582239649[/C][C]0.102374791119824[/C][/ROW]
[ROW][C]47[/C][C]0.936312795674645[/C][C]0.12737440865071[/C][C]0.0636872043253551[/C][/ROW]
[ROW][C]48[/C][C]0.918484859609435[/C][C]0.163030280781131[/C][C]0.0815151403905654[/C][/ROW]
[ROW][C]49[/C][C]0.90352787767363[/C][C]0.192944244652741[/C][C]0.0964721223263705[/C][/ROW]
[ROW][C]50[/C][C]0.901905434862701[/C][C]0.196189130274599[/C][C]0.0980945651372993[/C][/ROW]
[ROW][C]51[/C][C]0.87862615412397[/C][C]0.24274769175206[/C][C]0.12137384587603[/C][/ROW]
[ROW][C]52[/C][C]0.850959585483469[/C][C]0.298080829033062[/C][C]0.149040414516531[/C][/ROW]
[ROW][C]53[/C][C]0.866163895252512[/C][C]0.267672209494976[/C][C]0.133836104747488[/C][/ROW]
[ROW][C]54[/C][C]0.850611974929318[/C][C]0.298776050141365[/C][C]0.149388025070682[/C][/ROW]
[ROW][C]55[/C][C]0.857009482771675[/C][C]0.28598103445665[/C][C]0.142990517228325[/C][/ROW]
[ROW][C]56[/C][C]0.838277326358745[/C][C]0.323445347282509[/C][C]0.161722673641255[/C][/ROW]
[ROW][C]57[/C][C]0.807690602417904[/C][C]0.384618795164192[/C][C]0.192309397582096[/C][/ROW]
[ROW][C]58[/C][C]0.788866959596263[/C][C]0.422266080807473[/C][C]0.211133040403737[/C][/ROW]
[ROW][C]59[/C][C]0.754582423158462[/C][C]0.490835153683077[/C][C]0.245417576841538[/C][/ROW]
[ROW][C]60[/C][C]0.753006006015299[/C][C]0.493987987969402[/C][C]0.246993993984701[/C][/ROW]
[ROW][C]61[/C][C]0.719547574269823[/C][C]0.560904851460355[/C][C]0.280452425730177[/C][/ROW]
[ROW][C]62[/C][C]0.682594541893808[/C][C]0.634810916212384[/C][C]0.317405458106192[/C][/ROW]
[ROW][C]63[/C][C]0.647067140142581[/C][C]0.705865719714837[/C][C]0.352932859857419[/C][/ROW]
[ROW][C]64[/C][C]0.606209929795941[/C][C]0.787580140408119[/C][C]0.393790070204059[/C][/ROW]
[ROW][C]65[/C][C]0.566933791157837[/C][C]0.866132417684326[/C][C]0.433066208842163[/C][/ROW]
[ROW][C]66[/C][C]0.539525398135735[/C][C]0.920949203728529[/C][C]0.460474601864265[/C][/ROW]
[ROW][C]67[/C][C]0.534095154843058[/C][C]0.931809690313884[/C][C]0.465904845156942[/C][/ROW]
[ROW][C]68[/C][C]0.683100129459378[/C][C]0.633799741081245[/C][C]0.316899870540622[/C][/ROW]
[ROW][C]69[/C][C]0.78178709461716[/C][C]0.436425810765679[/C][C]0.21821290538284[/C][/ROW]
[ROW][C]70[/C][C]0.75193390740287[/C][C]0.49613218519426[/C][C]0.24806609259713[/C][/ROW]
[ROW][C]71[/C][C]0.836262492020523[/C][C]0.327475015958953[/C][C]0.163737507979476[/C][/ROW]
[ROW][C]72[/C][C]0.80521510159216[/C][C]0.38956979681568[/C][C]0.19478489840784[/C][/ROW]
[ROW][C]73[/C][C]0.801783732612593[/C][C]0.396432534774813[/C][C]0.198216267387407[/C][/ROW]
[ROW][C]74[/C][C]0.783561252880428[/C][C]0.432877494239145[/C][C]0.216438747119572[/C][/ROW]
[ROW][C]75[/C][C]0.747249951460997[/C][C]0.505500097078005[/C][C]0.252750048539003[/C][/ROW]
[ROW][C]76[/C][C]0.794734890898877[/C][C]0.410530218202247[/C][C]0.205265109101123[/C][/ROW]
[ROW][C]77[/C][C]0.76093132742285[/C][C]0.478137345154299[/C][C]0.23906867257715[/C][/ROW]
[ROW][C]78[/C][C]0.742500771527317[/C][C]0.514998456945367[/C][C]0.257499228472683[/C][/ROW]
[ROW][C]79[/C][C]0.746203215863248[/C][C]0.507593568273505[/C][C]0.253796784136752[/C][/ROW]
[ROW][C]80[/C][C]0.706793015163595[/C][C]0.586413969672811[/C][C]0.293206984836405[/C][/ROW]
[ROW][C]81[/C][C]0.668917047097216[/C][C]0.662165905805569[/C][C]0.331082952902784[/C][/ROW]
[ROW][C]82[/C][C]0.790493253536233[/C][C]0.419013492927534[/C][C]0.209506746463767[/C][/ROW]
[ROW][C]83[/C][C]0.757596518909613[/C][C]0.484806962180774[/C][C]0.242403481090387[/C][/ROW]
[ROW][C]84[/C][C]0.729047933108784[/C][C]0.541904133782433[/C][C]0.270952066891216[/C][/ROW]
[ROW][C]85[/C][C]0.689819468029139[/C][C]0.620361063941723[/C][C]0.310180531970861[/C][/ROW]
[ROW][C]86[/C][C]0.672232998634394[/C][C]0.655534002731212[/C][C]0.327767001365606[/C][/ROW]
[ROW][C]87[/C][C]0.629161297151722[/C][C]0.741677405696556[/C][C]0.370838702848278[/C][/ROW]
[ROW][C]88[/C][C]0.592875661724134[/C][C]0.814248676551732[/C][C]0.407124338275866[/C][/ROW]
[ROW][C]89[/C][C]0.574189091860975[/C][C]0.851621816278051[/C][C]0.425810908139025[/C][/ROW]
[ROW][C]90[/C][C]0.54978843126775[/C][C]0.9004231374645[/C][C]0.45021156873225[/C][/ROW]
[ROW][C]91[/C][C]0.525116459109222[/C][C]0.949767081781557[/C][C]0.474883540890778[/C][/ROW]
[ROW][C]92[/C][C]0.489675798314767[/C][C]0.979351596629535[/C][C]0.510324201685233[/C][/ROW]
[ROW][C]93[/C][C]0.45000992496064[/C][C]0.900019849921279[/C][C]0.54999007503936[/C][/ROW]
[ROW][C]94[/C][C]0.406335790427677[/C][C]0.812671580855353[/C][C]0.593664209572323[/C][/ROW]
[ROW][C]95[/C][C]0.430671080241132[/C][C]0.861342160482264[/C][C]0.569328919758868[/C][/ROW]
[ROW][C]96[/C][C]0.395214812862789[/C][C]0.790429625725577[/C][C]0.604785187137211[/C][/ROW]
[ROW][C]97[/C][C]0.359479614051214[/C][C]0.718959228102429[/C][C]0.640520385948786[/C][/ROW]
[ROW][C]98[/C][C]0.358317748921754[/C][C]0.716635497843509[/C][C]0.641682251078246[/C][/ROW]
[ROW][C]99[/C][C]0.316256687688464[/C][C]0.632513375376928[/C][C]0.683743312311536[/C][/ROW]
[ROW][C]100[/C][C]0.28031890606573[/C][C]0.560637812131459[/C][C]0.71968109393427[/C][/ROW]
[ROW][C]101[/C][C]0.251762495179768[/C][C]0.503524990359535[/C][C]0.748237504820232[/C][/ROW]
[ROW][C]102[/C][C]0.236305847693036[/C][C]0.472611695386072[/C][C]0.763694152306964[/C][/ROW]
[ROW][C]103[/C][C]0.282389684143013[/C][C]0.564779368286026[/C][C]0.717610315856987[/C][/ROW]
[ROW][C]104[/C][C]0.243614725306708[/C][C]0.487229450613416[/C][C]0.756385274693292[/C][/ROW]
[ROW][C]105[/C][C]0.261115610208026[/C][C]0.522231220416052[/C][C]0.738884389791974[/C][/ROW]
[ROW][C]106[/C][C]0.267309587461041[/C][C]0.534619174922082[/C][C]0.732690412538959[/C][/ROW]
[ROW][C]107[/C][C]0.256863537089854[/C][C]0.513727074179707[/C][C]0.743136462910146[/C][/ROW]
[ROW][C]108[/C][C]0.23068847783242[/C][C]0.461376955664841[/C][C]0.76931152216758[/C][/ROW]
[ROW][C]109[/C][C]0.229330431606926[/C][C]0.458660863213851[/C][C]0.770669568393074[/C][/ROW]
[ROW][C]110[/C][C]0.204063056680739[/C][C]0.408126113361478[/C][C]0.795936943319261[/C][/ROW]
[ROW][C]111[/C][C]0.179944122777059[/C][C]0.359888245554117[/C][C]0.820055877222941[/C][/ROW]
[ROW][C]112[/C][C]0.150826640359438[/C][C]0.301653280718876[/C][C]0.849173359640562[/C][/ROW]
[ROW][C]113[/C][C]0.191565033271941[/C][C]0.383130066543883[/C][C]0.808434966728058[/C][/ROW]
[ROW][C]114[/C][C]0.177686587391138[/C][C]0.355373174782277[/C][C]0.822313412608862[/C][/ROW]
[ROW][C]115[/C][C]0.202682848681408[/C][C]0.405365697362816[/C][C]0.797317151318592[/C][/ROW]
[ROW][C]116[/C][C]0.178026472291842[/C][C]0.356052944583684[/C][C]0.821973527708158[/C][/ROW]
[ROW][C]117[/C][C]0.159216515782892[/C][C]0.318433031565784[/C][C]0.840783484217108[/C][/ROW]
[ROW][C]118[/C][C]0.150644918417707[/C][C]0.301289836835415[/C][C]0.849355081582293[/C][/ROW]
[ROW][C]119[/C][C]0.166179606916673[/C][C]0.332359213833346[/C][C]0.833820393083327[/C][/ROW]
[ROW][C]120[/C][C]0.15613415048141[/C][C]0.312268300962821[/C][C]0.84386584951859[/C][/ROW]
[ROW][C]121[/C][C]0.133631246195504[/C][C]0.267262492391007[/C][C]0.866368753804496[/C][/ROW]
[ROW][C]122[/C][C]0.112865495316451[/C][C]0.225730990632903[/C][C]0.887134504683549[/C][/ROW]
[ROW][C]123[/C][C]0.13905472616403[/C][C]0.27810945232806[/C][C]0.86094527383597[/C][/ROW]
[ROW][C]124[/C][C]0.114302391231027[/C][C]0.228604782462054[/C][C]0.885697608768973[/C][/ROW]
[ROW][C]125[/C][C]0.0929956711000791[/C][C]0.185991342200158[/C][C]0.907004328899921[/C][/ROW]
[ROW][C]126[/C][C]0.0729215254884582[/C][C]0.145843050976916[/C][C]0.927078474511542[/C][/ROW]
[ROW][C]127[/C][C]0.0565130536220699[/C][C]0.11302610724414[/C][C]0.94348694637793[/C][/ROW]
[ROW][C]128[/C][C]0.0499388925006464[/C][C]0.0998777850012927[/C][C]0.950061107499354[/C][/ROW]
[ROW][C]129[/C][C]0.0384159166688417[/C][C]0.0768318333376834[/C][C]0.961584083331158[/C][/ROW]
[ROW][C]130[/C][C]0.0451492023005719[/C][C]0.0902984046011437[/C][C]0.954850797699428[/C][/ROW]
[ROW][C]131[/C][C]0.0426955469207066[/C][C]0.0853910938414133[/C][C]0.957304453079293[/C][/ROW]
[ROW][C]132[/C][C]0.0605243589910258[/C][C]0.121048717982052[/C][C]0.939475641008974[/C][/ROW]
[ROW][C]133[/C][C]0.0791818856505891[/C][C]0.158363771301178[/C][C]0.920818114349411[/C][/ROW]
[ROW][C]134[/C][C]0.0809793324274321[/C][C]0.161958664854864[/C][C]0.919020667572568[/C][/ROW]
[ROW][C]135[/C][C]0.0616301599073041[/C][C]0.123260319814608[/C][C]0.938369840092696[/C][/ROW]
[ROW][C]136[/C][C]0.049016283866036[/C][C]0.0980325677320719[/C][C]0.950983716133964[/C][/ROW]
[ROW][C]137[/C][C]0.0340309423516861[/C][C]0.0680618847033723[/C][C]0.965969057648314[/C][/ROW]
[ROW][C]138[/C][C]0.0226919373318319[/C][C]0.0453838746636639[/C][C]0.977308062668168[/C][/ROW]
[ROW][C]139[/C][C]0.0629791312766422[/C][C]0.125958262553284[/C][C]0.937020868723358[/C][/ROW]
[ROW][C]140[/C][C]0.048644976615235[/C][C]0.0972899532304701[/C][C]0.951355023384765[/C][/ROW]
[ROW][C]141[/C][C]0.622922722164804[/C][C]0.754154555670393[/C][C]0.377077277835196[/C][/ROW]
[ROW][C]142[/C][C]0.567122496623564[/C][C]0.865755006752872[/C][C]0.432877503376436[/C][/ROW]
[ROW][C]143[/C][C]0.488081008286297[/C][C]0.976162016572594[/C][C]0.511918991713703[/C][/ROW]
[ROW][C]144[/C][C]0.398247707911379[/C][C]0.796495415822757[/C][C]0.601752292088621[/C][/ROW]
[ROW][C]145[/C][C]0.306146098461615[/C][C]0.612292196923229[/C][C]0.693853901538385[/C][/ROW]
[ROW][C]146[/C][C]0.344591630841298[/C][C]0.689183261682596[/C][C]0.655408369158702[/C][/ROW]
[ROW][C]147[/C][C]0.35890614316167[/C][C]0.71781228632334[/C][C]0.64109385683833[/C][/ROW]
[ROW][C]148[/C][C]0.726766226825024[/C][C]0.546467546349951[/C][C]0.273233773174976[/C][/ROW]
[ROW][C]149[/C][C]0.624955479798942[/C][C]0.750089040402116[/C][C]0.375044520201058[/C][/ROW]
[ROW][C]150[/C][C]0.455630092129158[/C][C]0.911260184258315[/C][C]0.544369907870842[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186406&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186406&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.5974772008083850.805045598383230.402522799191615
130.4355291718055550.8710583436111110.564470828194445
140.3885599393405090.7771198786810190.611440060659491
150.3360916036569820.6721832073139640.663908396343018
160.2423945205563650.4847890411127290.757605479443635
170.2480569204808780.4961138409617560.751943079519122
180.5008681233866070.9982637532267870.499131876613393
190.424162643604290.8483252872085790.57583735639571
200.3350767183012920.6701534366025840.664923281698708
210.2681529628550480.5363059257100960.731847037144952
220.2467324015985250.493464803197050.753267598401475
230.4538164881821580.9076329763643150.546183511817842
240.4737786693387360.9475573386774720.526221330661264
250.4597795563949310.9195591127898620.540220443605069
260.4095959964160610.8191919928321220.590404003583939
270.4920053077143120.9840106154286240.507994692285688
280.4950178518267670.9900357036535350.504982148173233
290.481953936214890.963907872429780.51804606378511
300.5237288122065660.9525423755868670.476271187793434
310.4630656923361790.9261313846723570.536934307663821
320.4139422164578930.8278844329157860.586057783542107
330.3879410329705360.7758820659410730.612058967029464
340.3524057302852340.7048114605704670.647594269714767
350.3055380587789110.6110761175578220.694461941221089
360.8650427860002260.2699144279995470.134957213999774
370.8411662283126920.3176675433746160.158833771687308
380.8333139122226420.3333721755547160.166686087777358
390.8530020295874060.2939959408251890.146997970412594
400.8384578062520040.3230843874959910.161542193747996
410.810896828810510.378206342378980.18910317118949
420.7925862707416780.4148274585166430.207413729258322
430.8186356821772330.3627286356455340.181364317822767
440.7810318855006690.4379362289986620.218968114499331
450.75197176096120.4960564780776010.2480282390388
460.8976252088801760.2047495822396490.102374791119824
470.9363127956746450.127374408650710.0636872043253551
480.9184848596094350.1630302807811310.0815151403905654
490.903527877673630.1929442446527410.0964721223263705
500.9019054348627010.1961891302745990.0980945651372993
510.878626154123970.242747691752060.12137384587603
520.8509595854834690.2980808290330620.149040414516531
530.8661638952525120.2676722094949760.133836104747488
540.8506119749293180.2987760501413650.149388025070682
550.8570094827716750.285981034456650.142990517228325
560.8382773263587450.3234453472825090.161722673641255
570.8076906024179040.3846187951641920.192309397582096
580.7888669595962630.4222660808074730.211133040403737
590.7545824231584620.4908351536830770.245417576841538
600.7530060060152990.4939879879694020.246993993984701
610.7195475742698230.5609048514603550.280452425730177
620.6825945418938080.6348109162123840.317405458106192
630.6470671401425810.7058657197148370.352932859857419
640.6062099297959410.7875801404081190.393790070204059
650.5669337911578370.8661324176843260.433066208842163
660.5395253981357350.9209492037285290.460474601864265
670.5340951548430580.9318096903138840.465904845156942
680.6831001294593780.6337997410812450.316899870540622
690.781787094617160.4364258107656790.21821290538284
700.751933907402870.496132185194260.24806609259713
710.8362624920205230.3274750159589530.163737507979476
720.805215101592160.389569796815680.19478489840784
730.8017837326125930.3964325347748130.198216267387407
740.7835612528804280.4328774942391450.216438747119572
750.7472499514609970.5055000970780050.252750048539003
760.7947348908988770.4105302182022470.205265109101123
770.760931327422850.4781373451542990.23906867257715
780.7425007715273170.5149984569453670.257499228472683
790.7462032158632480.5075935682735050.253796784136752
800.7067930151635950.5864139696728110.293206984836405
810.6689170470972160.6621659058055690.331082952902784
820.7904932535362330.4190134929275340.209506746463767
830.7575965189096130.4848069621807740.242403481090387
840.7290479331087840.5419041337824330.270952066891216
850.6898194680291390.6203610639417230.310180531970861
860.6722329986343940.6555340027312120.327767001365606
870.6291612971517220.7416774056965560.370838702848278
880.5928756617241340.8142486765517320.407124338275866
890.5741890918609750.8516218162780510.425810908139025
900.549788431267750.90042313746450.45021156873225
910.5251164591092220.9497670817815570.474883540890778
920.4896757983147670.9793515966295350.510324201685233
930.450009924960640.9000198499212790.54999007503936
940.4063357904276770.8126715808553530.593664209572323
950.4306710802411320.8613421604822640.569328919758868
960.3952148128627890.7904296257255770.604785187137211
970.3594796140512140.7189592281024290.640520385948786
980.3583177489217540.7166354978435090.641682251078246
990.3162566876884640.6325133753769280.683743312311536
1000.280318906065730.5606378121314590.71968109393427
1010.2517624951797680.5035249903595350.748237504820232
1020.2363058476930360.4726116953860720.763694152306964
1030.2823896841430130.5647793682860260.717610315856987
1040.2436147253067080.4872294506134160.756385274693292
1050.2611156102080260.5222312204160520.738884389791974
1060.2673095874610410.5346191749220820.732690412538959
1070.2568635370898540.5137270741797070.743136462910146
1080.230688477832420.4613769556648410.76931152216758
1090.2293304316069260.4586608632138510.770669568393074
1100.2040630566807390.4081261133614780.795936943319261
1110.1799441227770590.3598882455541170.820055877222941
1120.1508266403594380.3016532807188760.849173359640562
1130.1915650332719410.3831300665438830.808434966728058
1140.1776865873911380.3553731747822770.822313412608862
1150.2026828486814080.4053656973628160.797317151318592
1160.1780264722918420.3560529445836840.821973527708158
1170.1592165157828920.3184330315657840.840783484217108
1180.1506449184177070.3012898368354150.849355081582293
1190.1661796069166730.3323592138333460.833820393083327
1200.156134150481410.3122683009628210.84386584951859
1210.1336312461955040.2672624923910070.866368753804496
1220.1128654953164510.2257309906329030.887134504683549
1230.139054726164030.278109452328060.86094527383597
1240.1143023912310270.2286047824620540.885697608768973
1250.09299567110007910.1859913422001580.907004328899921
1260.07292152548845820.1458430509769160.927078474511542
1270.05651305362206990.113026107244140.94348694637793
1280.04993889250064640.09987778500129270.950061107499354
1290.03841591666884170.07683183333768340.961584083331158
1300.04514920230057190.09029840460114370.954850797699428
1310.04269554692070660.08539109384141330.957304453079293
1320.06052435899102580.1210487179820520.939475641008974
1330.07918188565058910.1583637713011780.920818114349411
1340.08097933242743210.1619586648548640.919020667572568
1350.06163015990730410.1232603198146080.938369840092696
1360.0490162838660360.09803256773207190.950983716133964
1370.03403094235168610.06806188470337230.965969057648314
1380.02269193733183190.04538387466366390.977308062668168
1390.06297913127664220.1259582625532840.937020868723358
1400.0486449766152350.09728995323047010.951355023384765
1410.6229227221648040.7541545556703930.377077277835196
1420.5671224966235640.8657550067528720.432877503376436
1430.4880810082862970.9761620165725940.511918991713703
1440.3982477079113790.7964954158227570.601752292088621
1450.3061460984616150.6122921969232290.693853901538385
1460.3445916308412980.6891832616825960.655408369158702
1470.358906143161670.717812286323340.64109385683833
1480.7267662268250240.5464675463499510.273233773174976
1490.6249554797989420.7500890404021160.375044520201058
1500.4556300921291580.9112601842583150.544369907870842







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00719424460431655OK
10% type I error level80.0575539568345324OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00719424460431655 & OK \tabularnewline
10% type I error level & 8 & 0.0575539568345324 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186406&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00719424460431655[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.0575539568345324[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186406&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186406&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00719424460431655OK
10% type I error level80.0575539568345324OK



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')
}