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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 04 Nov 2012 10:10:31 -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/04/t1352041850g37bhkevmco7vro.htm/, Retrieved Sun, 28 Apr 2024 08:11:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185832, Retrieved Sun, 28 Apr 2024 08:11:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Workshop 7] [2012-11-04 15:10:31] [38c0fff34b8aa23b45468de8b641bfee] [Current]
-   P     [Multiple Regression] [paper] [2012-12-21 17:27:28] [fa543719fe3f8358943b948de15add90]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185832&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185832&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185832&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 6.72660289825788 -0.145027925413223Month[t] + 0.111737350843465Connected[t] -0.0178041165883361Separate[t] + 0.543286550500503Software[t] + 0.0595543607156345Happines[t] -0.0671677203741168Depression[t] + 0.0393791446826237Belonging[t] -0.0558546409917482Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  6.72660289825788 -0.145027925413223Month[t] +  0.111737350843465Connected[t] -0.0178041165883361Separate[t] +  0.543286550500503Software[t] +  0.0595543607156345Happines[t] -0.0671677203741168Depression[t] +  0.0393791446826237Belonging[t] -0.0558546409917482Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185832&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  6.72660289825788 -0.145027925413223Month[t] +  0.111737350843465Connected[t] -0.0178041165883361Separate[t] +  0.543286550500503Software[t] +  0.0595543607156345Happines[t] -0.0671677203741168Depression[t] +  0.0393791446826237Belonging[t] -0.0558546409917482Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185832&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185832&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 6.72660289825788 -0.145027925413223Month[t] + 0.111737350843465Connected[t] -0.0178041165883361Separate[t] + 0.543286550500503Software[t] + 0.0595543607156345Happines[t] -0.0671677203741168Depression[t] + 0.0393791446826237Belonging[t] -0.0558546409917482Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.726602898257883.6668721.83440.0685330.034267
Month-0.1450279254132230.307834-0.47110.6382230.319112
Connected0.1117373508434650.0472652.3640.0193320.009666
Separate-0.01780411658833610.045333-0.39270.695060.34753
Software0.5432865505005030.0691437.857400
Happines0.05955436071563450.0765760.77770.4379350.218967
Depression-0.06716772037411680.057116-1.1760.2414250.120713
Belonging0.03937914468262370.0452990.86930.3860380.193019
Belonging_Final-0.05585464099174820.064579-0.86490.3884460.194223

\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) & 6.72660289825788 & 3.666872 & 1.8344 & 0.068533 & 0.034267 \tabularnewline
Month & -0.145027925413223 & 0.307834 & -0.4711 & 0.638223 & 0.319112 \tabularnewline
Connected & 0.111737350843465 & 0.047265 & 2.364 & 0.019332 & 0.009666 \tabularnewline
Separate & -0.0178041165883361 & 0.045333 & -0.3927 & 0.69506 & 0.34753 \tabularnewline
Software & 0.543286550500503 & 0.069143 & 7.8574 & 0 & 0 \tabularnewline
Happines & 0.0595543607156345 & 0.076576 & 0.7777 & 0.437935 & 0.218967 \tabularnewline
Depression & -0.0671677203741168 & 0.057116 & -1.176 & 0.241425 & 0.120713 \tabularnewline
Belonging & 0.0393791446826237 & 0.045299 & 0.8693 & 0.386038 & 0.193019 \tabularnewline
Belonging_Final & -0.0558546409917482 & 0.064579 & -0.8649 & 0.388446 & 0.194223 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185832&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]6.72660289825788[/C][C]3.666872[/C][C]1.8344[/C][C]0.068533[/C][C]0.034267[/C][/ROW]
[ROW][C]Month[/C][C]-0.145027925413223[/C][C]0.307834[/C][C]-0.4711[/C][C]0.638223[/C][C]0.319112[/C][/ROW]
[ROW][C]Connected[/C][C]0.111737350843465[/C][C]0.047265[/C][C]2.364[/C][C]0.019332[/C][C]0.009666[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0178041165883361[/C][C]0.045333[/C][C]-0.3927[/C][C]0.69506[/C][C]0.34753[/C][/ROW]
[ROW][C]Software[/C][C]0.543286550500503[/C][C]0.069143[/C][C]7.8574[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happines[/C][C]0.0595543607156345[/C][C]0.076576[/C][C]0.7777[/C][C]0.437935[/C][C]0.218967[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0671677203741168[/C][C]0.057116[/C][C]-1.176[/C][C]0.241425[/C][C]0.120713[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0393791446826237[/C][C]0.045299[/C][C]0.8693[/C][C]0.386038[/C][C]0.193019[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0558546409917482[/C][C]0.064579[/C][C]-0.8649[/C][C]0.388446[/C][C]0.194223[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185832&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185832&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.726602898257883.6668721.83440.0685330.034267
Month-0.1450279254132230.307834-0.47110.6382230.319112
Connected0.1117373508434650.0472652.3640.0193320.009666
Separate-0.01780411658833610.045333-0.39270.695060.34753
Software0.5432865505005030.0691437.857400
Happines0.05955436071563450.0765760.77770.4379350.218967
Depression-0.06716772037411680.057116-1.1760.2414250.120713
Belonging0.03937914468262370.0452990.86930.3860380.193019
Belonging_Final-0.05585464099174820.064579-0.86490.3884460.194223






Multiple Linear Regression - Regression Statistics
Multiple R0.598031762955037
R-squared0.357641989503109
Adjusted R-squared0.324054642549023
F-TEST (value)10.6481166848935
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value7.8017592386459e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85500523675172
Sum Squared Residuals526.479797541575

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.598031762955037 \tabularnewline
R-squared & 0.357641989503109 \tabularnewline
Adjusted R-squared & 0.324054642549023 \tabularnewline
F-TEST (value) & 10.6481166848935 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 7.8017592386459e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85500523675172 \tabularnewline
Sum Squared Residuals & 526.479797541575 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185832&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.598031762955037[/C][/ROW]
[ROW][C]R-squared[/C][C]0.357641989503109[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.324054642549023[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.6481166848935[/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]7.8017592386459e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85500523675172[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]526.479797541575[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185832&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185832&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.598031762955037
R-squared0.357641989503109
Adjusted R-squared0.324054642549023
F-TEST (value)10.6481166848935
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value7.8017592386459e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85500523675172
Sum Squared Residuals526.479797541575






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1729596917428-3.17295969174284
21616.0566818980054-0.056681898005418
31916.26750478179272.7324952182073
41511.81439499844663.18560500155345
51415.5902434948075-1.59024349480749
61314.9610759800279-1.96107598002795
71915.12316339747433.87683660252567
81516.6938552999615-1.69385529996147
91415.9624267941967-1.96242679419674
101512.52657833853162.47342166146843
111615.26313074211340.736869257886601
121616.2354565926443-0.235456592644348
131615.44420100063180.555798999368211
141615.37449622392930.625503776070708
151717.4652209950782-0.465220995078202
161515.0954858357912-0.0954858357911987
171514.64041994543130.35958005456874
182016.23304640380683.76695359619319
191815.47101270288672.52898729711326
201615.26018724950230.739812750497703
211615.29635072327430.7036492767257
221614.78764795486981.21235204513025
231916.47629604424732.52370395575274
241614.75033968818971.24966031181026
251714.8546997952972.14530020470297
261716.95221959087210.04778040912787
271615.0587079048860.941292095114009
281516.1417455644831-1.14174556448308
291615.5516686720770.448331327922961
301414.028467248914-0.0284672489139643
311515.6679472413163-0.667947241316262
321212.2647822593152-0.264782259315194
331415.1714880797594-1.17148807975943
341615.52727581685950.472724183140482
351415.7344790765887-1.73447907658874
36712.9662598799048-5.96625987990478
371010.976425670033-0.976425670032954
381415.8572714345608-1.8572714345608
391614.30692107344961.69307892655042
401614.65128405365981.34871594634021
411615.10817111417830.89182888582168
421415.66067759058-1.66067759058002
432017.94573609686262.05426390313744
441414.2680144027523-0.268014402752319
451414.9487946080421-0.948794608042119
461115.6774418779183-4.67744187791831
471416.4824362802448-2.48243628024482
481515.0180890910709-0.0180890910709306
491615.31656193646720.683438063532846
501416.0924165485259-2.09241654852591
511616.585288854401-0.585288854401007
521414.1933188227318-0.193318822731821
531214.7891929545692-2.7891929545692
541615.25308795241870.746912047581305
55911.5895742619802-2.58957426198021
561412.70057075843571.29942924156429
571616.1734941629088-0.173494162908752
581615.15714708545180.842852914548191
591515.4067405331053-0.406740533105331
601614.22161147817611.77838852182389
611211.51078931855880.4892106814412
621616.0683988843404-0.0683988843403711
631616.5711097439479-0.57110974394791
641414.4478520381336-0.447852038133557
651615.49133819598930.508661804010697
661715.86125661544031.13874338455966
671816.04811124981741.95188875018258
681814.61716204231813.38283795768186
691215.8198990726833-3.81989907268326
701615.3720506028290.627949397170966
711013.3690663582555-3.36906635825549
721414.2684017713942-0.268401771394229
731816.5158677304251.48413226957499
741817.10299945348710.897000546512945
751615.66459889986540.335401100134618
761713.84145559136893.15854440863112
771616.3086128005127-0.308612800512729
781614.3915302364781.60846976352203
791314.7996415193312-1.79964151933116
801615.9023009495250.0976990504749798
811615.5382560617120.461743938287966
822016.71605470917083.28394529082917
831615.61908357108960.380916428910379
841515.9622935100719-0.962293510071924
851514.68772794237580.312272057624249
861614.21220081183181.78779918816815
871414.1776734361759-0.177673436175863
881615.27676933093970.723230669060289
891614.51637523759091.48362476240909
901514.18954022246830.810459777531687
911213.4334201211109-1.43342012111089
921716.92839634499670.071603655003318
931615.45961645034380.540383549656197
941515.2471545534275-0.24715455342753
951315.1297148605019-2.12971486050191
961615.16518593017830.834814069821669
971615.80556692430630.194433075693745
981614.12195818944091.87804181055912
991616.1069714270756-0.106971427075588
1001414.2889758961938-0.288975896193808
1011617.3075095656796-1.30750956567963
1021614.76480112005221.23519887994775
1032017.37905731164242.62094268835763
1041514.49893733330510.501062666694898
1051614.54986391858421.45013608141581
1061315.3430686701328-2.34306867013278
1071716.00030921819370.999690781806287
1081615.89543468233840.10456531766158
1091614.52317471108281.47682528891724
1101212.2555824310139-0.255582431013856
1111614.98772259230861.01227740769141
1121615.86269204500250.13730795499751
1131714.5268233892132.47317661078701
1141314.9487867706446-1.94878677064459
1151214.8717438839947-2.87174388399473
1161816.61110208886561.38889791113438
1171415.4909003398483-1.49090033984834
1181412.96903792257621.03096207742384
1191314.9171818249732-1.91718182497322
1201615.69919639630040.300803603699614
1211314.271463146807-1.27146314680696
1221615.63911000393480.360889996065225
1231315.9904847850566-2.9904847850566
1241617.063681418993-1.06368141899296
1251515.9901429680502-0.990142968050223
1261616.8230899988686-0.823089998868603
1271515.4444142372619-0.444414237261911
1281716.27175802110040.728241978899593
1291513.83438965549611.16561034450393
1301214.9735763911204-2.97357639112037
1311614.18263464470731.8173653552927
1321013.3688625407631-3.36886254076307
1331614.11787663003481.88212336996519
1341213.946519044578-1.94651904457805
1351415.685533788122-1.68553378812199
1361515.1363536736858-0.136353673685839
1371312.29636891670510.703631083294902
1381514.45680570585040.543194294149639
1391113.1901414138795-2.19014141387948
1401213.3686334702848-1.36863347028476
141813.4019311414391-5.40193114143905
1421613.09778017824452.90221982175552
1431513.38446630890451.61553369109554
1441716.60865722201790.39134277798214
1451614.85702231543361.14297768456639
1461014.1869459371721-4.1869459371721
1471815.9484368759662.05156312403401
1481315.1995486095958-2.19954860959579
1491615.17935963293290.820640367067149
1501313.0721447828683-0.0721447828683166
1511013.1647767351569-3.16477673515686
1521516.4914049685477-1.49140496854767
1531614.14265537683891.85734462316106
1541612.0388235773153.96117642268495
1551412.64832526884911.35167473115089
1561012.5461953289603-2.54619532896025
1571716.92839634499670.071603655003318
1581311.66171024558821.33828975441176
1591513.83438965549611.16561034450393
1601615.11077256617970.889227433820318
1611212.5958166601353-0.595816660135297
1621312.62810844042540.371891559574603

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.1729596917428 & -3.17295969174284 \tabularnewline
2 & 16 & 16.0566818980054 & -0.056681898005418 \tabularnewline
3 & 19 & 16.2675047817927 & 2.7324952182073 \tabularnewline
4 & 15 & 11.8143949984466 & 3.18560500155345 \tabularnewline
5 & 14 & 15.5902434948075 & -1.59024349480749 \tabularnewline
6 & 13 & 14.9610759800279 & -1.96107598002795 \tabularnewline
7 & 19 & 15.1231633974743 & 3.87683660252567 \tabularnewline
8 & 15 & 16.6938552999615 & -1.69385529996147 \tabularnewline
9 & 14 & 15.9624267941967 & -1.96242679419674 \tabularnewline
10 & 15 & 12.5265783385316 & 2.47342166146843 \tabularnewline
11 & 16 & 15.2631307421134 & 0.736869257886601 \tabularnewline
12 & 16 & 16.2354565926443 & -0.235456592644348 \tabularnewline
13 & 16 & 15.4442010006318 & 0.555798999368211 \tabularnewline
14 & 16 & 15.3744962239293 & 0.625503776070708 \tabularnewline
15 & 17 & 17.4652209950782 & -0.465220995078202 \tabularnewline
16 & 15 & 15.0954858357912 & -0.0954858357911987 \tabularnewline
17 & 15 & 14.6404199454313 & 0.35958005456874 \tabularnewline
18 & 20 & 16.2330464038068 & 3.76695359619319 \tabularnewline
19 & 18 & 15.4710127028867 & 2.52898729711326 \tabularnewline
20 & 16 & 15.2601872495023 & 0.739812750497703 \tabularnewline
21 & 16 & 15.2963507232743 & 0.7036492767257 \tabularnewline
22 & 16 & 14.7876479548698 & 1.21235204513025 \tabularnewline
23 & 19 & 16.4762960442473 & 2.52370395575274 \tabularnewline
24 & 16 & 14.7503396881897 & 1.24966031181026 \tabularnewline
25 & 17 & 14.854699795297 & 2.14530020470297 \tabularnewline
26 & 17 & 16.9522195908721 & 0.04778040912787 \tabularnewline
27 & 16 & 15.058707904886 & 0.941292095114009 \tabularnewline
28 & 15 & 16.1417455644831 & -1.14174556448308 \tabularnewline
29 & 16 & 15.551668672077 & 0.448331327922961 \tabularnewline
30 & 14 & 14.028467248914 & -0.0284672489139643 \tabularnewline
31 & 15 & 15.6679472413163 & -0.667947241316262 \tabularnewline
32 & 12 & 12.2647822593152 & -0.264782259315194 \tabularnewline
33 & 14 & 15.1714880797594 & -1.17148807975943 \tabularnewline
34 & 16 & 15.5272758168595 & 0.472724183140482 \tabularnewline
35 & 14 & 15.7344790765887 & -1.73447907658874 \tabularnewline
36 & 7 & 12.9662598799048 & -5.96625987990478 \tabularnewline
37 & 10 & 10.976425670033 & -0.976425670032954 \tabularnewline
38 & 14 & 15.8572714345608 & -1.8572714345608 \tabularnewline
39 & 16 & 14.3069210734496 & 1.69307892655042 \tabularnewline
40 & 16 & 14.6512840536598 & 1.34871594634021 \tabularnewline
41 & 16 & 15.1081711141783 & 0.89182888582168 \tabularnewline
42 & 14 & 15.66067759058 & -1.66067759058002 \tabularnewline
43 & 20 & 17.9457360968626 & 2.05426390313744 \tabularnewline
44 & 14 & 14.2680144027523 & -0.268014402752319 \tabularnewline
45 & 14 & 14.9487946080421 & -0.948794608042119 \tabularnewline
46 & 11 & 15.6774418779183 & -4.67744187791831 \tabularnewline
47 & 14 & 16.4824362802448 & -2.48243628024482 \tabularnewline
48 & 15 & 15.0180890910709 & -0.0180890910709306 \tabularnewline
49 & 16 & 15.3165619364672 & 0.683438063532846 \tabularnewline
50 & 14 & 16.0924165485259 & -2.09241654852591 \tabularnewline
51 & 16 & 16.585288854401 & -0.585288854401007 \tabularnewline
52 & 14 & 14.1933188227318 & -0.193318822731821 \tabularnewline
53 & 12 & 14.7891929545692 & -2.7891929545692 \tabularnewline
54 & 16 & 15.2530879524187 & 0.746912047581305 \tabularnewline
55 & 9 & 11.5895742619802 & -2.58957426198021 \tabularnewline
56 & 14 & 12.7005707584357 & 1.29942924156429 \tabularnewline
57 & 16 & 16.1734941629088 & -0.173494162908752 \tabularnewline
58 & 16 & 15.1571470854518 & 0.842852914548191 \tabularnewline
59 & 15 & 15.4067405331053 & -0.406740533105331 \tabularnewline
60 & 16 & 14.2216114781761 & 1.77838852182389 \tabularnewline
61 & 12 & 11.5107893185588 & 0.4892106814412 \tabularnewline
62 & 16 & 16.0683988843404 & -0.0683988843403711 \tabularnewline
63 & 16 & 16.5711097439479 & -0.57110974394791 \tabularnewline
64 & 14 & 14.4478520381336 & -0.447852038133557 \tabularnewline
65 & 16 & 15.4913381959893 & 0.508661804010697 \tabularnewline
66 & 17 & 15.8612566154403 & 1.13874338455966 \tabularnewline
67 & 18 & 16.0481112498174 & 1.95188875018258 \tabularnewline
68 & 18 & 14.6171620423181 & 3.38283795768186 \tabularnewline
69 & 12 & 15.8198990726833 & -3.81989907268326 \tabularnewline
70 & 16 & 15.372050602829 & 0.627949397170966 \tabularnewline
71 & 10 & 13.3690663582555 & -3.36906635825549 \tabularnewline
72 & 14 & 14.2684017713942 & -0.268401771394229 \tabularnewline
73 & 18 & 16.515867730425 & 1.48413226957499 \tabularnewline
74 & 18 & 17.1029994534871 & 0.897000546512945 \tabularnewline
75 & 16 & 15.6645988998654 & 0.335401100134618 \tabularnewline
76 & 17 & 13.8414555913689 & 3.15854440863112 \tabularnewline
77 & 16 & 16.3086128005127 & -0.308612800512729 \tabularnewline
78 & 16 & 14.391530236478 & 1.60846976352203 \tabularnewline
79 & 13 & 14.7996415193312 & -1.79964151933116 \tabularnewline
80 & 16 & 15.902300949525 & 0.0976990504749798 \tabularnewline
81 & 16 & 15.538256061712 & 0.461743938287966 \tabularnewline
82 & 20 & 16.7160547091708 & 3.28394529082917 \tabularnewline
83 & 16 & 15.6190835710896 & 0.380916428910379 \tabularnewline
84 & 15 & 15.9622935100719 & -0.962293510071924 \tabularnewline
85 & 15 & 14.6877279423758 & 0.312272057624249 \tabularnewline
86 & 16 & 14.2122008118318 & 1.78779918816815 \tabularnewline
87 & 14 & 14.1776734361759 & -0.177673436175863 \tabularnewline
88 & 16 & 15.2767693309397 & 0.723230669060289 \tabularnewline
89 & 16 & 14.5163752375909 & 1.48362476240909 \tabularnewline
90 & 15 & 14.1895402224683 & 0.810459777531687 \tabularnewline
91 & 12 & 13.4334201211109 & -1.43342012111089 \tabularnewline
92 & 17 & 16.9283963449967 & 0.071603655003318 \tabularnewline
93 & 16 & 15.4596164503438 & 0.540383549656197 \tabularnewline
94 & 15 & 15.2471545534275 & -0.24715455342753 \tabularnewline
95 & 13 & 15.1297148605019 & -2.12971486050191 \tabularnewline
96 & 16 & 15.1651859301783 & 0.834814069821669 \tabularnewline
97 & 16 & 15.8055669243063 & 0.194433075693745 \tabularnewline
98 & 16 & 14.1219581894409 & 1.87804181055912 \tabularnewline
99 & 16 & 16.1069714270756 & -0.106971427075588 \tabularnewline
100 & 14 & 14.2889758961938 & -0.288975896193808 \tabularnewline
101 & 16 & 17.3075095656796 & -1.30750956567963 \tabularnewline
102 & 16 & 14.7648011200522 & 1.23519887994775 \tabularnewline
103 & 20 & 17.3790573116424 & 2.62094268835763 \tabularnewline
104 & 15 & 14.4989373333051 & 0.501062666694898 \tabularnewline
105 & 16 & 14.5498639185842 & 1.45013608141581 \tabularnewline
106 & 13 & 15.3430686701328 & -2.34306867013278 \tabularnewline
107 & 17 & 16.0003092181937 & 0.999690781806287 \tabularnewline
108 & 16 & 15.8954346823384 & 0.10456531766158 \tabularnewline
109 & 16 & 14.5231747110828 & 1.47682528891724 \tabularnewline
110 & 12 & 12.2555824310139 & -0.255582431013856 \tabularnewline
111 & 16 & 14.9877225923086 & 1.01227740769141 \tabularnewline
112 & 16 & 15.8626920450025 & 0.13730795499751 \tabularnewline
113 & 17 & 14.526823389213 & 2.47317661078701 \tabularnewline
114 & 13 & 14.9487867706446 & -1.94878677064459 \tabularnewline
115 & 12 & 14.8717438839947 & -2.87174388399473 \tabularnewline
116 & 18 & 16.6111020888656 & 1.38889791113438 \tabularnewline
117 & 14 & 15.4909003398483 & -1.49090033984834 \tabularnewline
118 & 14 & 12.9690379225762 & 1.03096207742384 \tabularnewline
119 & 13 & 14.9171818249732 & -1.91718182497322 \tabularnewline
120 & 16 & 15.6991963963004 & 0.300803603699614 \tabularnewline
121 & 13 & 14.271463146807 & -1.27146314680696 \tabularnewline
122 & 16 & 15.6391100039348 & 0.360889996065225 \tabularnewline
123 & 13 & 15.9904847850566 & -2.9904847850566 \tabularnewline
124 & 16 & 17.063681418993 & -1.06368141899296 \tabularnewline
125 & 15 & 15.9901429680502 & -0.990142968050223 \tabularnewline
126 & 16 & 16.8230899988686 & -0.823089998868603 \tabularnewline
127 & 15 & 15.4444142372619 & -0.444414237261911 \tabularnewline
128 & 17 & 16.2717580211004 & 0.728241978899593 \tabularnewline
129 & 15 & 13.8343896554961 & 1.16561034450393 \tabularnewline
130 & 12 & 14.9735763911204 & -2.97357639112037 \tabularnewline
131 & 16 & 14.1826346447073 & 1.8173653552927 \tabularnewline
132 & 10 & 13.3688625407631 & -3.36886254076307 \tabularnewline
133 & 16 & 14.1178766300348 & 1.88212336996519 \tabularnewline
134 & 12 & 13.946519044578 & -1.94651904457805 \tabularnewline
135 & 14 & 15.685533788122 & -1.68553378812199 \tabularnewline
136 & 15 & 15.1363536736858 & -0.136353673685839 \tabularnewline
137 & 13 & 12.2963689167051 & 0.703631083294902 \tabularnewline
138 & 15 & 14.4568057058504 & 0.543194294149639 \tabularnewline
139 & 11 & 13.1901414138795 & -2.19014141387948 \tabularnewline
140 & 12 & 13.3686334702848 & -1.36863347028476 \tabularnewline
141 & 8 & 13.4019311414391 & -5.40193114143905 \tabularnewline
142 & 16 & 13.0977801782445 & 2.90221982175552 \tabularnewline
143 & 15 & 13.3844663089045 & 1.61553369109554 \tabularnewline
144 & 17 & 16.6086572220179 & 0.39134277798214 \tabularnewline
145 & 16 & 14.8570223154336 & 1.14297768456639 \tabularnewline
146 & 10 & 14.1869459371721 & -4.1869459371721 \tabularnewline
147 & 18 & 15.948436875966 & 2.05156312403401 \tabularnewline
148 & 13 & 15.1995486095958 & -2.19954860959579 \tabularnewline
149 & 16 & 15.1793596329329 & 0.820640367067149 \tabularnewline
150 & 13 & 13.0721447828683 & -0.0721447828683166 \tabularnewline
151 & 10 & 13.1647767351569 & -3.16477673515686 \tabularnewline
152 & 15 & 16.4914049685477 & -1.49140496854767 \tabularnewline
153 & 16 & 14.1426553768389 & 1.85734462316106 \tabularnewline
154 & 16 & 12.038823577315 & 3.96117642268495 \tabularnewline
155 & 14 & 12.6483252688491 & 1.35167473115089 \tabularnewline
156 & 10 & 12.5461953289603 & -2.54619532896025 \tabularnewline
157 & 17 & 16.9283963449967 & 0.071603655003318 \tabularnewline
158 & 13 & 11.6617102455882 & 1.33828975441176 \tabularnewline
159 & 15 & 13.8343896554961 & 1.16561034450393 \tabularnewline
160 & 16 & 15.1107725661797 & 0.889227433820318 \tabularnewline
161 & 12 & 12.5958166601353 & -0.595816660135297 \tabularnewline
162 & 13 & 12.6281084404254 & 0.371891559574603 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185832&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]16.1729596917428[/C][C]-3.17295969174284[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.0566818980054[/C][C]-0.056681898005418[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.2675047817927[/C][C]2.7324952182073[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.8143949984466[/C][C]3.18560500155345[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.5902434948075[/C][C]-1.59024349480749[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.9610759800279[/C][C]-1.96107598002795[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.1231633974743[/C][C]3.87683660252567[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.6938552999615[/C][C]-1.69385529996147[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.9624267941967[/C][C]-1.96242679419674[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.5265783385316[/C][C]2.47342166146843[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.2631307421134[/C][C]0.736869257886601[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.2354565926443[/C][C]-0.235456592644348[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.4442010006318[/C][C]0.555798999368211[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3744962239293[/C][C]0.625503776070708[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.4652209950782[/C][C]-0.465220995078202[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0954858357912[/C][C]-0.0954858357911987[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.6404199454313[/C][C]0.35958005456874[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.2330464038068[/C][C]3.76695359619319[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.4710127028867[/C][C]2.52898729711326[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.2601872495023[/C][C]0.739812750497703[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2963507232743[/C][C]0.7036492767257[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.7876479548698[/C][C]1.21235204513025[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4762960442473[/C][C]2.52370395575274[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.7503396881897[/C][C]1.24966031181026[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.854699795297[/C][C]2.14530020470297[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.9522195908721[/C][C]0.04778040912787[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.058707904886[/C][C]0.941292095114009[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.1417455644831[/C][C]-1.14174556448308[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.551668672077[/C][C]0.448331327922961[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.028467248914[/C][C]-0.0284672489139643[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.6679472413163[/C][C]-0.667947241316262[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.2647822593152[/C][C]-0.264782259315194[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1714880797594[/C][C]-1.17148807975943[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5272758168595[/C][C]0.472724183140482[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.7344790765887[/C][C]-1.73447907658874[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.9662598799048[/C][C]-5.96625987990478[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.976425670033[/C][C]-0.976425670032954[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8572714345608[/C][C]-1.8572714345608[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.3069210734496[/C][C]1.69307892655042[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6512840536598[/C][C]1.34871594634021[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1081711141783[/C][C]0.89182888582168[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.66067759058[/C][C]-1.66067759058002[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.9457360968626[/C][C]2.05426390313744[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2680144027523[/C][C]-0.268014402752319[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9487946080421[/C][C]-0.948794608042119[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6774418779183[/C][C]-4.67744187791831[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4824362802448[/C][C]-2.48243628024482[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0180890910709[/C][C]-0.0180890910709306[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.3165619364672[/C][C]0.683438063532846[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0924165485259[/C][C]-2.09241654852591[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.585288854401[/C][C]-0.585288854401007[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1933188227318[/C][C]-0.193318822731821[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.7891929545692[/C][C]-2.7891929545692[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.2530879524187[/C][C]0.746912047581305[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5895742619802[/C][C]-2.58957426198021[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.7005707584357[/C][C]1.29942924156429[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1734941629088[/C][C]-0.173494162908752[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.1571470854518[/C][C]0.842852914548191[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.4067405331053[/C][C]-0.406740533105331[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2216114781761[/C][C]1.77838852182389[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5107893185588[/C][C]0.4892106814412[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]16.0683988843404[/C][C]-0.0683988843403711[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.5711097439479[/C][C]-0.57110974394791[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4478520381336[/C][C]-0.447852038133557[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4913381959893[/C][C]0.508661804010697[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.8612566154403[/C][C]1.13874338455966[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.0481112498174[/C][C]1.95188875018258[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.6171620423181[/C][C]3.38283795768186[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8198990726833[/C][C]-3.81989907268326[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.372050602829[/C][C]0.627949397170966[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.3690663582555[/C][C]-3.36906635825549[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.2684017713942[/C][C]-0.268401771394229[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.515867730425[/C][C]1.48413226957499[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1029994534871[/C][C]0.897000546512945[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6645988998654[/C][C]0.335401100134618[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8414555913689[/C][C]3.15854440863112[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3086128005127[/C][C]-0.308612800512729[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.391530236478[/C][C]1.60846976352203[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.7996415193312[/C][C]-1.79964151933116[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.902300949525[/C][C]0.0976990504749798[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.538256061712[/C][C]0.461743938287966[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.7160547091708[/C][C]3.28394529082917[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.6190835710896[/C][C]0.380916428910379[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9622935100719[/C][C]-0.962293510071924[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.6877279423758[/C][C]0.312272057624249[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2122008118318[/C][C]1.78779918816815[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.1776734361759[/C][C]-0.177673436175863[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2767693309397[/C][C]0.723230669060289[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5163752375909[/C][C]1.48362476240909[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.1895402224683[/C][C]0.810459777531687[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4334201211109[/C][C]-1.43342012111089[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9283963449967[/C][C]0.071603655003318[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4596164503438[/C][C]0.540383549656197[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2471545534275[/C][C]-0.24715455342753[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1297148605019[/C][C]-2.12971486050191[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.1651859301783[/C][C]0.834814069821669[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8055669243063[/C][C]0.194433075693745[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1219581894409[/C][C]1.87804181055912[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.1069714270756[/C][C]-0.106971427075588[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.2889758961938[/C][C]-0.288975896193808[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.3075095656796[/C][C]-1.30750956567963[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7648011200522[/C][C]1.23519887994775[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3790573116424[/C][C]2.62094268835763[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.4989373333051[/C][C]0.501062666694898[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5498639185842[/C][C]1.45013608141581[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3430686701328[/C][C]-2.34306867013278[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.0003092181937[/C][C]0.999690781806287[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8954346823384[/C][C]0.10456531766158[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5231747110828[/C][C]1.47682528891724[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.2555824310139[/C][C]-0.255582431013856[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.9877225923086[/C][C]1.01227740769141[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8626920450025[/C][C]0.13730795499751[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.526823389213[/C][C]2.47317661078701[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.9487867706446[/C][C]-1.94878677064459[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.8717438839947[/C][C]-2.87174388399473[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.6111020888656[/C][C]1.38889791113438[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.4909003398483[/C][C]-1.49090033984834[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.9690379225762[/C][C]1.03096207742384[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9171818249732[/C][C]-1.91718182497322[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6991963963004[/C][C]0.300803603699614[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.271463146807[/C][C]-1.27146314680696[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.6391100039348[/C][C]0.360889996065225[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.9904847850566[/C][C]-2.9904847850566[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.063681418993[/C][C]-1.06368141899296[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.9901429680502[/C][C]-0.990142968050223[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.8230899988686[/C][C]-0.823089998868603[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.4444142372619[/C][C]-0.444414237261911[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.2717580211004[/C][C]0.728241978899593[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.8343896554961[/C][C]1.16561034450393[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.9735763911204[/C][C]-2.97357639112037[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.1826346447073[/C][C]1.8173653552927[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3688625407631[/C][C]-3.36886254076307[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.1178766300348[/C][C]1.88212336996519[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.946519044578[/C][C]-1.94651904457805[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.685533788122[/C][C]-1.68553378812199[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.1363536736858[/C][C]-0.136353673685839[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.2963689167051[/C][C]0.703631083294902[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.4568057058504[/C][C]0.543194294149639[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.1901414138795[/C][C]-2.19014141387948[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.3686334702848[/C][C]-1.36863347028476[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.4019311414391[/C][C]-5.40193114143905[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.0977801782445[/C][C]2.90221982175552[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.3844663089045[/C][C]1.61553369109554[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.6086572220179[/C][C]0.39134277798214[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.8570223154336[/C][C]1.14297768456639[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.1869459371721[/C][C]-4.1869459371721[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.948436875966[/C][C]2.05156312403401[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.1995486095958[/C][C]-2.19954860959579[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.1793596329329[/C][C]0.820640367067149[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.0721447828683[/C][C]-0.0721447828683166[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.1647767351569[/C][C]-3.16477673515686[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.4914049685477[/C][C]-1.49140496854767[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.1426553768389[/C][C]1.85734462316106[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.038823577315[/C][C]3.96117642268495[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.6483252688491[/C][C]1.35167473115089[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.5461953289603[/C][C]-2.54619532896025[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.9283963449967[/C][C]0.071603655003318[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.6617102455882[/C][C]1.33828975441176[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.8343896554961[/C][C]1.16561034450393[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.1107725661797[/C][C]0.889227433820318[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.5958166601353[/C][C]-0.595816660135297[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6281084404254[/C][C]0.371891559574603[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185832&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185832&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1729596917428-3.17295969174284
21616.0566818980054-0.056681898005418
31916.26750478179272.7324952182073
41511.81439499844663.18560500155345
51415.5902434948075-1.59024349480749
61314.9610759800279-1.96107598002795
71915.12316339747433.87683660252567
81516.6938552999615-1.69385529996147
91415.9624267941967-1.96242679419674
101512.52657833853162.47342166146843
111615.26313074211340.736869257886601
121616.2354565926443-0.235456592644348
131615.44420100063180.555798999368211
141615.37449622392930.625503776070708
151717.4652209950782-0.465220995078202
161515.0954858357912-0.0954858357911987
171514.64041994543130.35958005456874
182016.23304640380683.76695359619319
191815.47101270288672.52898729711326
201615.26018724950230.739812750497703
211615.29635072327430.7036492767257
221614.78764795486981.21235204513025
231916.47629604424732.52370395575274
241614.75033968818971.24966031181026
251714.8546997952972.14530020470297
261716.95221959087210.04778040912787
271615.0587079048860.941292095114009
281516.1417455644831-1.14174556448308
291615.5516686720770.448331327922961
301414.028467248914-0.0284672489139643
311515.6679472413163-0.667947241316262
321212.2647822593152-0.264782259315194
331415.1714880797594-1.17148807975943
341615.52727581685950.472724183140482
351415.7344790765887-1.73447907658874
36712.9662598799048-5.96625987990478
371010.976425670033-0.976425670032954
381415.8572714345608-1.8572714345608
391614.30692107344961.69307892655042
401614.65128405365981.34871594634021
411615.10817111417830.89182888582168
421415.66067759058-1.66067759058002
432017.94573609686262.05426390313744
441414.2680144027523-0.268014402752319
451414.9487946080421-0.948794608042119
461115.6774418779183-4.67744187791831
471416.4824362802448-2.48243628024482
481515.0180890910709-0.0180890910709306
491615.31656193646720.683438063532846
501416.0924165485259-2.09241654852591
511616.585288854401-0.585288854401007
521414.1933188227318-0.193318822731821
531214.7891929545692-2.7891929545692
541615.25308795241870.746912047581305
55911.5895742619802-2.58957426198021
561412.70057075843571.29942924156429
571616.1734941629088-0.173494162908752
581615.15714708545180.842852914548191
591515.4067405331053-0.406740533105331
601614.22161147817611.77838852182389
611211.51078931855880.4892106814412
621616.0683988843404-0.0683988843403711
631616.5711097439479-0.57110974394791
641414.4478520381336-0.447852038133557
651615.49133819598930.508661804010697
661715.86125661544031.13874338455966
671816.04811124981741.95188875018258
681814.61716204231813.38283795768186
691215.8198990726833-3.81989907268326
701615.3720506028290.627949397170966
711013.3690663582555-3.36906635825549
721414.2684017713942-0.268401771394229
731816.5158677304251.48413226957499
741817.10299945348710.897000546512945
751615.66459889986540.335401100134618
761713.84145559136893.15854440863112
771616.3086128005127-0.308612800512729
781614.3915302364781.60846976352203
791314.7996415193312-1.79964151933116
801615.9023009495250.0976990504749798
811615.5382560617120.461743938287966
822016.71605470917083.28394529082917
831615.61908357108960.380916428910379
841515.9622935100719-0.962293510071924
851514.68772794237580.312272057624249
861614.21220081183181.78779918816815
871414.1776734361759-0.177673436175863
881615.27676933093970.723230669060289
891614.51637523759091.48362476240909
901514.18954022246830.810459777531687
911213.4334201211109-1.43342012111089
921716.92839634499670.071603655003318
931615.45961645034380.540383549656197
941515.2471545534275-0.24715455342753
951315.1297148605019-2.12971486050191
961615.16518593017830.834814069821669
971615.80556692430630.194433075693745
981614.12195818944091.87804181055912
991616.1069714270756-0.106971427075588
1001414.2889758961938-0.288975896193808
1011617.3075095656796-1.30750956567963
1021614.76480112005221.23519887994775
1032017.37905731164242.62094268835763
1041514.49893733330510.501062666694898
1051614.54986391858421.45013608141581
1061315.3430686701328-2.34306867013278
1071716.00030921819370.999690781806287
1081615.89543468233840.10456531766158
1091614.52317471108281.47682528891724
1101212.2555824310139-0.255582431013856
1111614.98772259230861.01227740769141
1121615.86269204500250.13730795499751
1131714.5268233892132.47317661078701
1141314.9487867706446-1.94878677064459
1151214.8717438839947-2.87174388399473
1161816.61110208886561.38889791113438
1171415.4909003398483-1.49090033984834
1181412.96903792257621.03096207742384
1191314.9171818249732-1.91718182497322
1201615.69919639630040.300803603699614
1211314.271463146807-1.27146314680696
1221615.63911000393480.360889996065225
1231315.9904847850566-2.9904847850566
1241617.063681418993-1.06368141899296
1251515.9901429680502-0.990142968050223
1261616.8230899988686-0.823089998868603
1271515.4444142372619-0.444414237261911
1281716.27175802110040.728241978899593
1291513.83438965549611.16561034450393
1301214.9735763911204-2.97357639112037
1311614.18263464470731.8173653552927
1321013.3688625407631-3.36886254076307
1331614.11787663003481.88212336996519
1341213.946519044578-1.94651904457805
1351415.685533788122-1.68553378812199
1361515.1363536736858-0.136353673685839
1371312.29636891670510.703631083294902
1381514.45680570585040.543194294149639
1391113.1901414138795-2.19014141387948
1401213.3686334702848-1.36863347028476
141813.4019311414391-5.40193114143905
1421613.09778017824452.90221982175552
1431513.38446630890451.61553369109554
1441716.60865722201790.39134277798214
1451614.85702231543361.14297768456639
1461014.1869459371721-4.1869459371721
1471815.9484368759662.05156312403401
1481315.1995486095958-2.19954860959579
1491615.17935963293290.820640367067149
1501313.0721447828683-0.0721447828683166
1511013.1647767351569-3.16477673515686
1521516.4914049685477-1.49140496854767
1531614.14265537683891.85734462316106
1541612.0388235773153.96117642268495
1551412.64832526884911.35167473115089
1561012.5461953289603-2.54619532896025
1571716.92839634499670.071603655003318
1581311.66171024558821.33828975441176
1591513.83438965549611.16561034450393
1601615.11077256617970.889227433820318
1611212.5958166601353-0.595816660135297
1621312.62810844042540.371891559574603






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.7592597089806430.4814805820387140.240740291019357
130.622102369037930.7557952619241410.37789763096207
140.5819533584519550.836093283096090.418046641548045
150.4619499282125650.923899856425130.538050071787435
160.3616626615278930.7233253230557860.638337338472107
170.2985582561633750.5971165123267490.701441743836625
180.5058889854103650.988222029179270.494111014589635
190.4217323191532920.8434646383065850.578267680846708
200.3325818636301220.6651637272602440.667418136369878
210.2626203866072290.5252407732144590.737379613392771
220.2394774631884740.4789549263769480.760522536811526
230.4363778377157470.8727556754314950.563622162284253
240.4716107947632930.9432215895265850.528389205236707
250.4601519358942560.9203038717885130.539848064105743
260.4194072640243490.8388145280486980.580592735975651
270.4796673166626920.9593346333253830.520332683337308
280.4827684812302410.9655369624604820.517231518769759
290.4629124881596060.9258249763192120.537087511840394
300.5039855880880130.9920288238239730.496014411911987
310.4431120666801810.8862241333603610.556887933319819
320.3938702143068150.7877404286136310.606129785693185
330.3672440243858360.7344880487716730.632755975614164
340.3326270359167980.6652540718335950.667372964083202
350.2854021969270070.5708043938540140.714597803072993
360.8513673602322050.2972652795355890.148632639767795
370.8261369102418150.347726179516370.173863089758185
380.819105164676210.361789670647580.18089483532379
390.8388931122265210.3222137755469570.161106887773479
400.8220827218587570.3558345562824860.177917278141243
410.7919081514205070.4161836971589850.208091848579493
420.7658118633684970.4683762732630070.234188136631503
430.79089457939540.41821084120920.2091054206046
440.7502982955385410.4994034089229170.249701704461459
450.7218159757030890.5563680485938220.278184024296911
460.8740055181208050.2519889637583890.125994481879195
470.9178554650713240.1642890698573520.0821445349286758
480.8961683181287470.2076633637425060.103831681871253
490.880863891267890.238272217464220.11913610873211
500.8797199773060890.2405600453878230.120280022693911
510.852953541881490.2940929162370190.14704645811851
520.8217747172855690.3564505654288620.178225282714431
530.8416143790053810.3167712419892390.158385620994619
540.8264878163970660.3470243672058690.173512183602934
550.8443640610939710.3112718778120580.155635938906029
560.8173472363017120.3653055273965760.182652763698288
570.7844007547887690.4311984904224630.215599245211231
580.7591743156567410.4816513686865170.240825684343259
590.7249390203162290.5501219593675420.275060979683771
600.7154227892333890.5691544215332220.284577210766611
610.6771140197151760.6457719605696490.322885980284824
620.6356647207139040.7286705585721910.364335279286096
630.6026170199187120.7947659601625760.397382980081288
640.5618516821147120.8762966357705750.438148317885288
650.5183642750903220.9632714498193560.481635724909678
660.4734648215293110.9469296430586220.526535178470689
670.4581036925889170.9162073851778340.541896307411083
680.5099629318790290.9800741362419430.490037068120971
690.7341719331537310.5316561336925380.265828066846269
700.6963360472929430.6073279054141130.303663952707057
710.8157421681826480.3685156636347030.184257831817352
720.782550900084970.4348981998300610.21744909991503
730.7724843162529260.4550313674941480.227515683747074
740.750846130166750.4983077396665010.249153869833251
750.7127322756733030.5745354486533950.287267724326697
760.7615944961484950.4768110077030110.238405503851505
770.7271953599282850.545609280143430.272804640071715
780.714073263741110.5718534725177790.28592673625889
790.7253122592801770.5493754814396470.274687740719823
800.6847882426955560.6304235146088870.315211757304444
810.6448373525552260.7103252948895480.355162647444774
820.7587462114820530.4825075770358930.241253788517947
830.7208160183937380.5583679632125240.279183981606262
840.6945992358092990.6108015283814030.305400764190701
850.6523273446463970.6953453107072070.347672655353603
860.642840480462020.7143190390759590.35715951953798
870.5989687388561390.8020625222877220.401031261143861
880.5575909142172080.8848181715655840.442409085782792
890.5375311942662180.9249376114675650.462468805733782
900.508082297933050.9838354041339010.49191770206695
910.4941266734446260.9882533468892510.505873326555374
920.456543026473650.9130860529473010.543456973526349
930.4130445935228640.8260891870457280.586955406477136
940.3715592446651470.7431184893302940.628440755334853
950.3895505933292680.7791011866585360.610449406670732
960.3531281010282910.7062562020565830.646871898971709
970.3155740055012920.6311480110025840.684425994498708
980.3134923723282810.6269847446565610.686507627671719
990.2727401626164870.5454803252329740.727259837383513
1000.2361422304279390.4722844608558780.763857769572061
1010.2162931812793290.4325863625586580.783706818720671
1020.2027441020283450.4054882040566910.797255897971655
1030.2429135217199870.4858270434399740.757086478280013
1040.2076908986587350.4153817973174710.792309101341265
1050.2001652634241890.4003305268483780.799834736575811
1060.2118073404019680.4236146808039360.788192659598032
1070.1898162225186620.3796324450373250.810183777481338
1080.1691478571153560.3382957142307130.830852142884644
1090.1651721932626730.3303443865253460.834827806737327
1100.1647071988576070.3294143977152140.835292801142393
1110.1441496401167510.2882992802335020.855850359883249
1120.1211800243147670.2423600486295330.878819975685233
1130.1410943194222480.2821886388444960.858905680577752
1140.132334013416530.264668026833060.86766598658347
1150.1745790232473720.3491580464947430.825420976752628
1160.1649906306451110.3299812612902230.835009369354889
1170.1452139856637830.2904279713275670.854786014336216
1180.1276685097785050.255337019557010.872331490221495
1190.1325131654021840.2650263308043680.867486834597816
1200.1272717610373420.2545435220746840.872728238962658
1210.1077571791440190.2155143582880380.892242820855981
1220.08638449362540360.1727689872508070.913615506374596
1230.09662417397096030.1932483479419210.90337582602904
1240.07831558741383140.1566311748276630.921684412586169
1250.06425459306675410.1285091861335080.935745406933246
1260.04971163296272310.09942326592544630.950288367037277
1270.03774755445900020.07549510891800050.962252445541
1280.02972704329741790.05945408659483580.970272956702582
1290.02298237344021180.04596474688042350.977017626559788
1300.03229339934377190.06458679868754370.967706600656228
1310.027119328641060.05423865728212010.97288067135894
1320.03673431465986150.0734686293197230.963265685340138
1330.03890140182579920.07780280365159840.961098598174201
1340.05573518917351680.1114703783470340.944264810826483
1350.04450332802966860.08900665605933730.955496671970331
1360.03067799515271530.06135599030543060.969322004847285
1370.02091030014372440.04182060028744880.979089699856276
1380.01457783370793640.02915566741587280.985422166292064
1390.02003196667099310.04006393334198610.979968033329007
1400.01649670925122340.03299341850244690.983503290748777
1410.6151225334896960.7697549330206090.384877466510304
1420.5510236307027950.8979527385944110.448976369297205
1430.4634562792694150.9269125585388310.536543720730585
1440.3912279672772170.7824559345544340.608772032722783
1450.3011150612450110.6022301224900220.698884938754989
1460.320309633424760.640619266849520.67969036657524
1470.2736042071416640.5472084142833270.726395792858336
1480.6632204255663560.6735591488672890.336779574433644
1490.6111520996084970.7776958007830060.388847900391503
1500.4779035798136840.9558071596273680.522096420186316

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.759259708980643 & 0.481480582038714 & 0.240740291019357 \tabularnewline
13 & 0.62210236903793 & 0.755795261924141 & 0.37789763096207 \tabularnewline
14 & 0.581953358451955 & 0.83609328309609 & 0.418046641548045 \tabularnewline
15 & 0.461949928212565 & 0.92389985642513 & 0.538050071787435 \tabularnewline
16 & 0.361662661527893 & 0.723325323055786 & 0.638337338472107 \tabularnewline
17 & 0.298558256163375 & 0.597116512326749 & 0.701441743836625 \tabularnewline
18 & 0.505888985410365 & 0.98822202917927 & 0.494111014589635 \tabularnewline
19 & 0.421732319153292 & 0.843464638306585 & 0.578267680846708 \tabularnewline
20 & 0.332581863630122 & 0.665163727260244 & 0.667418136369878 \tabularnewline
21 & 0.262620386607229 & 0.525240773214459 & 0.737379613392771 \tabularnewline
22 & 0.239477463188474 & 0.478954926376948 & 0.760522536811526 \tabularnewline
23 & 0.436377837715747 & 0.872755675431495 & 0.563622162284253 \tabularnewline
24 & 0.471610794763293 & 0.943221589526585 & 0.528389205236707 \tabularnewline
25 & 0.460151935894256 & 0.920303871788513 & 0.539848064105743 \tabularnewline
26 & 0.419407264024349 & 0.838814528048698 & 0.580592735975651 \tabularnewline
27 & 0.479667316662692 & 0.959334633325383 & 0.520332683337308 \tabularnewline
28 & 0.482768481230241 & 0.965536962460482 & 0.517231518769759 \tabularnewline
29 & 0.462912488159606 & 0.925824976319212 & 0.537087511840394 \tabularnewline
30 & 0.503985588088013 & 0.992028823823973 & 0.496014411911987 \tabularnewline
31 & 0.443112066680181 & 0.886224133360361 & 0.556887933319819 \tabularnewline
32 & 0.393870214306815 & 0.787740428613631 & 0.606129785693185 \tabularnewline
33 & 0.367244024385836 & 0.734488048771673 & 0.632755975614164 \tabularnewline
34 & 0.332627035916798 & 0.665254071833595 & 0.667372964083202 \tabularnewline
35 & 0.285402196927007 & 0.570804393854014 & 0.714597803072993 \tabularnewline
36 & 0.851367360232205 & 0.297265279535589 & 0.148632639767795 \tabularnewline
37 & 0.826136910241815 & 0.34772617951637 & 0.173863089758185 \tabularnewline
38 & 0.81910516467621 & 0.36178967064758 & 0.18089483532379 \tabularnewline
39 & 0.838893112226521 & 0.322213775546957 & 0.161106887773479 \tabularnewline
40 & 0.822082721858757 & 0.355834556282486 & 0.177917278141243 \tabularnewline
41 & 0.791908151420507 & 0.416183697158985 & 0.208091848579493 \tabularnewline
42 & 0.765811863368497 & 0.468376273263007 & 0.234188136631503 \tabularnewline
43 & 0.7908945793954 & 0.4182108412092 & 0.2091054206046 \tabularnewline
44 & 0.750298295538541 & 0.499403408922917 & 0.249701704461459 \tabularnewline
45 & 0.721815975703089 & 0.556368048593822 & 0.278184024296911 \tabularnewline
46 & 0.874005518120805 & 0.251988963758389 & 0.125994481879195 \tabularnewline
47 & 0.917855465071324 & 0.164289069857352 & 0.0821445349286758 \tabularnewline
48 & 0.896168318128747 & 0.207663363742506 & 0.103831681871253 \tabularnewline
49 & 0.88086389126789 & 0.23827221746422 & 0.11913610873211 \tabularnewline
50 & 0.879719977306089 & 0.240560045387823 & 0.120280022693911 \tabularnewline
51 & 0.85295354188149 & 0.294092916237019 & 0.14704645811851 \tabularnewline
52 & 0.821774717285569 & 0.356450565428862 & 0.178225282714431 \tabularnewline
53 & 0.841614379005381 & 0.316771241989239 & 0.158385620994619 \tabularnewline
54 & 0.826487816397066 & 0.347024367205869 & 0.173512183602934 \tabularnewline
55 & 0.844364061093971 & 0.311271877812058 & 0.155635938906029 \tabularnewline
56 & 0.817347236301712 & 0.365305527396576 & 0.182652763698288 \tabularnewline
57 & 0.784400754788769 & 0.431198490422463 & 0.215599245211231 \tabularnewline
58 & 0.759174315656741 & 0.481651368686517 & 0.240825684343259 \tabularnewline
59 & 0.724939020316229 & 0.550121959367542 & 0.275060979683771 \tabularnewline
60 & 0.715422789233389 & 0.569154421533222 & 0.284577210766611 \tabularnewline
61 & 0.677114019715176 & 0.645771960569649 & 0.322885980284824 \tabularnewline
62 & 0.635664720713904 & 0.728670558572191 & 0.364335279286096 \tabularnewline
63 & 0.602617019918712 & 0.794765960162576 & 0.397382980081288 \tabularnewline
64 & 0.561851682114712 & 0.876296635770575 & 0.438148317885288 \tabularnewline
65 & 0.518364275090322 & 0.963271449819356 & 0.481635724909678 \tabularnewline
66 & 0.473464821529311 & 0.946929643058622 & 0.526535178470689 \tabularnewline
67 & 0.458103692588917 & 0.916207385177834 & 0.541896307411083 \tabularnewline
68 & 0.509962931879029 & 0.980074136241943 & 0.490037068120971 \tabularnewline
69 & 0.734171933153731 & 0.531656133692538 & 0.265828066846269 \tabularnewline
70 & 0.696336047292943 & 0.607327905414113 & 0.303663952707057 \tabularnewline
71 & 0.815742168182648 & 0.368515663634703 & 0.184257831817352 \tabularnewline
72 & 0.78255090008497 & 0.434898199830061 & 0.21744909991503 \tabularnewline
73 & 0.772484316252926 & 0.455031367494148 & 0.227515683747074 \tabularnewline
74 & 0.75084613016675 & 0.498307739666501 & 0.249153869833251 \tabularnewline
75 & 0.712732275673303 & 0.574535448653395 & 0.287267724326697 \tabularnewline
76 & 0.761594496148495 & 0.476811007703011 & 0.238405503851505 \tabularnewline
77 & 0.727195359928285 & 0.54560928014343 & 0.272804640071715 \tabularnewline
78 & 0.71407326374111 & 0.571853472517779 & 0.28592673625889 \tabularnewline
79 & 0.725312259280177 & 0.549375481439647 & 0.274687740719823 \tabularnewline
80 & 0.684788242695556 & 0.630423514608887 & 0.315211757304444 \tabularnewline
81 & 0.644837352555226 & 0.710325294889548 & 0.355162647444774 \tabularnewline
82 & 0.758746211482053 & 0.482507577035893 & 0.241253788517947 \tabularnewline
83 & 0.720816018393738 & 0.558367963212524 & 0.279183981606262 \tabularnewline
84 & 0.694599235809299 & 0.610801528381403 & 0.305400764190701 \tabularnewline
85 & 0.652327344646397 & 0.695345310707207 & 0.347672655353603 \tabularnewline
86 & 0.64284048046202 & 0.714319039075959 & 0.35715951953798 \tabularnewline
87 & 0.598968738856139 & 0.802062522287722 & 0.401031261143861 \tabularnewline
88 & 0.557590914217208 & 0.884818171565584 & 0.442409085782792 \tabularnewline
89 & 0.537531194266218 & 0.924937611467565 & 0.462468805733782 \tabularnewline
90 & 0.50808229793305 & 0.983835404133901 & 0.49191770206695 \tabularnewline
91 & 0.494126673444626 & 0.988253346889251 & 0.505873326555374 \tabularnewline
92 & 0.45654302647365 & 0.913086052947301 & 0.543456973526349 \tabularnewline
93 & 0.413044593522864 & 0.826089187045728 & 0.586955406477136 \tabularnewline
94 & 0.371559244665147 & 0.743118489330294 & 0.628440755334853 \tabularnewline
95 & 0.389550593329268 & 0.779101186658536 & 0.610449406670732 \tabularnewline
96 & 0.353128101028291 & 0.706256202056583 & 0.646871898971709 \tabularnewline
97 & 0.315574005501292 & 0.631148011002584 & 0.684425994498708 \tabularnewline
98 & 0.313492372328281 & 0.626984744656561 & 0.686507627671719 \tabularnewline
99 & 0.272740162616487 & 0.545480325232974 & 0.727259837383513 \tabularnewline
100 & 0.236142230427939 & 0.472284460855878 & 0.763857769572061 \tabularnewline
101 & 0.216293181279329 & 0.432586362558658 & 0.783706818720671 \tabularnewline
102 & 0.202744102028345 & 0.405488204056691 & 0.797255897971655 \tabularnewline
103 & 0.242913521719987 & 0.485827043439974 & 0.757086478280013 \tabularnewline
104 & 0.207690898658735 & 0.415381797317471 & 0.792309101341265 \tabularnewline
105 & 0.200165263424189 & 0.400330526848378 & 0.799834736575811 \tabularnewline
106 & 0.211807340401968 & 0.423614680803936 & 0.788192659598032 \tabularnewline
107 & 0.189816222518662 & 0.379632445037325 & 0.810183777481338 \tabularnewline
108 & 0.169147857115356 & 0.338295714230713 & 0.830852142884644 \tabularnewline
109 & 0.165172193262673 & 0.330344386525346 & 0.834827806737327 \tabularnewline
110 & 0.164707198857607 & 0.329414397715214 & 0.835292801142393 \tabularnewline
111 & 0.144149640116751 & 0.288299280233502 & 0.855850359883249 \tabularnewline
112 & 0.121180024314767 & 0.242360048629533 & 0.878819975685233 \tabularnewline
113 & 0.141094319422248 & 0.282188638844496 & 0.858905680577752 \tabularnewline
114 & 0.13233401341653 & 0.26466802683306 & 0.86766598658347 \tabularnewline
115 & 0.174579023247372 & 0.349158046494743 & 0.825420976752628 \tabularnewline
116 & 0.164990630645111 & 0.329981261290223 & 0.835009369354889 \tabularnewline
117 & 0.145213985663783 & 0.290427971327567 & 0.854786014336216 \tabularnewline
118 & 0.127668509778505 & 0.25533701955701 & 0.872331490221495 \tabularnewline
119 & 0.132513165402184 & 0.265026330804368 & 0.867486834597816 \tabularnewline
120 & 0.127271761037342 & 0.254543522074684 & 0.872728238962658 \tabularnewline
121 & 0.107757179144019 & 0.215514358288038 & 0.892242820855981 \tabularnewline
122 & 0.0863844936254036 & 0.172768987250807 & 0.913615506374596 \tabularnewline
123 & 0.0966241739709603 & 0.193248347941921 & 0.90337582602904 \tabularnewline
124 & 0.0783155874138314 & 0.156631174827663 & 0.921684412586169 \tabularnewline
125 & 0.0642545930667541 & 0.128509186133508 & 0.935745406933246 \tabularnewline
126 & 0.0497116329627231 & 0.0994232659254463 & 0.950288367037277 \tabularnewline
127 & 0.0377475544590002 & 0.0754951089180005 & 0.962252445541 \tabularnewline
128 & 0.0297270432974179 & 0.0594540865948358 & 0.970272956702582 \tabularnewline
129 & 0.0229823734402118 & 0.0459647468804235 & 0.977017626559788 \tabularnewline
130 & 0.0322933993437719 & 0.0645867986875437 & 0.967706600656228 \tabularnewline
131 & 0.02711932864106 & 0.0542386572821201 & 0.97288067135894 \tabularnewline
132 & 0.0367343146598615 & 0.073468629319723 & 0.963265685340138 \tabularnewline
133 & 0.0389014018257992 & 0.0778028036515984 & 0.961098598174201 \tabularnewline
134 & 0.0557351891735168 & 0.111470378347034 & 0.944264810826483 \tabularnewline
135 & 0.0445033280296686 & 0.0890066560593373 & 0.955496671970331 \tabularnewline
136 & 0.0306779951527153 & 0.0613559903054306 & 0.969322004847285 \tabularnewline
137 & 0.0209103001437244 & 0.0418206002874488 & 0.979089699856276 \tabularnewline
138 & 0.0145778337079364 & 0.0291556674158728 & 0.985422166292064 \tabularnewline
139 & 0.0200319666709931 & 0.0400639333419861 & 0.979968033329007 \tabularnewline
140 & 0.0164967092512234 & 0.0329934185024469 & 0.983503290748777 \tabularnewline
141 & 0.615122533489696 & 0.769754933020609 & 0.384877466510304 \tabularnewline
142 & 0.551023630702795 & 0.897952738594411 & 0.448976369297205 \tabularnewline
143 & 0.463456279269415 & 0.926912558538831 & 0.536543720730585 \tabularnewline
144 & 0.391227967277217 & 0.782455934554434 & 0.608772032722783 \tabularnewline
145 & 0.301115061245011 & 0.602230122490022 & 0.698884938754989 \tabularnewline
146 & 0.32030963342476 & 0.64061926684952 & 0.67969036657524 \tabularnewline
147 & 0.273604207141664 & 0.547208414283327 & 0.726395792858336 \tabularnewline
148 & 0.663220425566356 & 0.673559148867289 & 0.336779574433644 \tabularnewline
149 & 0.611152099608497 & 0.777695800783006 & 0.388847900391503 \tabularnewline
150 & 0.477903579813684 & 0.955807159627368 & 0.522096420186316 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185832&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]0.759259708980643[/C][C]0.481480582038714[/C][C]0.240740291019357[/C][/ROW]
[ROW][C]13[/C][C]0.62210236903793[/C][C]0.755795261924141[/C][C]0.37789763096207[/C][/ROW]
[ROW][C]14[/C][C]0.581953358451955[/C][C]0.83609328309609[/C][C]0.418046641548045[/C][/ROW]
[ROW][C]15[/C][C]0.461949928212565[/C][C]0.92389985642513[/C][C]0.538050071787435[/C][/ROW]
[ROW][C]16[/C][C]0.361662661527893[/C][C]0.723325323055786[/C][C]0.638337338472107[/C][/ROW]
[ROW][C]17[/C][C]0.298558256163375[/C][C]0.597116512326749[/C][C]0.701441743836625[/C][/ROW]
[ROW][C]18[/C][C]0.505888985410365[/C][C]0.98822202917927[/C][C]0.494111014589635[/C][/ROW]
[ROW][C]19[/C][C]0.421732319153292[/C][C]0.843464638306585[/C][C]0.578267680846708[/C][/ROW]
[ROW][C]20[/C][C]0.332581863630122[/C][C]0.665163727260244[/C][C]0.667418136369878[/C][/ROW]
[ROW][C]21[/C][C]0.262620386607229[/C][C]0.525240773214459[/C][C]0.737379613392771[/C][/ROW]
[ROW][C]22[/C][C]0.239477463188474[/C][C]0.478954926376948[/C][C]0.760522536811526[/C][/ROW]
[ROW][C]23[/C][C]0.436377837715747[/C][C]0.872755675431495[/C][C]0.563622162284253[/C][/ROW]
[ROW][C]24[/C][C]0.471610794763293[/C][C]0.943221589526585[/C][C]0.528389205236707[/C][/ROW]
[ROW][C]25[/C][C]0.460151935894256[/C][C]0.920303871788513[/C][C]0.539848064105743[/C][/ROW]
[ROW][C]26[/C][C]0.419407264024349[/C][C]0.838814528048698[/C][C]0.580592735975651[/C][/ROW]
[ROW][C]27[/C][C]0.479667316662692[/C][C]0.959334633325383[/C][C]0.520332683337308[/C][/ROW]
[ROW][C]28[/C][C]0.482768481230241[/C][C]0.965536962460482[/C][C]0.517231518769759[/C][/ROW]
[ROW][C]29[/C][C]0.462912488159606[/C][C]0.925824976319212[/C][C]0.537087511840394[/C][/ROW]
[ROW][C]30[/C][C]0.503985588088013[/C][C]0.992028823823973[/C][C]0.496014411911987[/C][/ROW]
[ROW][C]31[/C][C]0.443112066680181[/C][C]0.886224133360361[/C][C]0.556887933319819[/C][/ROW]
[ROW][C]32[/C][C]0.393870214306815[/C][C]0.787740428613631[/C][C]0.606129785693185[/C][/ROW]
[ROW][C]33[/C][C]0.367244024385836[/C][C]0.734488048771673[/C][C]0.632755975614164[/C][/ROW]
[ROW][C]34[/C][C]0.332627035916798[/C][C]0.665254071833595[/C][C]0.667372964083202[/C][/ROW]
[ROW][C]35[/C][C]0.285402196927007[/C][C]0.570804393854014[/C][C]0.714597803072993[/C][/ROW]
[ROW][C]36[/C][C]0.851367360232205[/C][C]0.297265279535589[/C][C]0.148632639767795[/C][/ROW]
[ROW][C]37[/C][C]0.826136910241815[/C][C]0.34772617951637[/C][C]0.173863089758185[/C][/ROW]
[ROW][C]38[/C][C]0.81910516467621[/C][C]0.36178967064758[/C][C]0.18089483532379[/C][/ROW]
[ROW][C]39[/C][C]0.838893112226521[/C][C]0.322213775546957[/C][C]0.161106887773479[/C][/ROW]
[ROW][C]40[/C][C]0.822082721858757[/C][C]0.355834556282486[/C][C]0.177917278141243[/C][/ROW]
[ROW][C]41[/C][C]0.791908151420507[/C][C]0.416183697158985[/C][C]0.208091848579493[/C][/ROW]
[ROW][C]42[/C][C]0.765811863368497[/C][C]0.468376273263007[/C][C]0.234188136631503[/C][/ROW]
[ROW][C]43[/C][C]0.7908945793954[/C][C]0.4182108412092[/C][C]0.2091054206046[/C][/ROW]
[ROW][C]44[/C][C]0.750298295538541[/C][C]0.499403408922917[/C][C]0.249701704461459[/C][/ROW]
[ROW][C]45[/C][C]0.721815975703089[/C][C]0.556368048593822[/C][C]0.278184024296911[/C][/ROW]
[ROW][C]46[/C][C]0.874005518120805[/C][C]0.251988963758389[/C][C]0.125994481879195[/C][/ROW]
[ROW][C]47[/C][C]0.917855465071324[/C][C]0.164289069857352[/C][C]0.0821445349286758[/C][/ROW]
[ROW][C]48[/C][C]0.896168318128747[/C][C]0.207663363742506[/C][C]0.103831681871253[/C][/ROW]
[ROW][C]49[/C][C]0.88086389126789[/C][C]0.23827221746422[/C][C]0.11913610873211[/C][/ROW]
[ROW][C]50[/C][C]0.879719977306089[/C][C]0.240560045387823[/C][C]0.120280022693911[/C][/ROW]
[ROW][C]51[/C][C]0.85295354188149[/C][C]0.294092916237019[/C][C]0.14704645811851[/C][/ROW]
[ROW][C]52[/C][C]0.821774717285569[/C][C]0.356450565428862[/C][C]0.178225282714431[/C][/ROW]
[ROW][C]53[/C][C]0.841614379005381[/C][C]0.316771241989239[/C][C]0.158385620994619[/C][/ROW]
[ROW][C]54[/C][C]0.826487816397066[/C][C]0.347024367205869[/C][C]0.173512183602934[/C][/ROW]
[ROW][C]55[/C][C]0.844364061093971[/C][C]0.311271877812058[/C][C]0.155635938906029[/C][/ROW]
[ROW][C]56[/C][C]0.817347236301712[/C][C]0.365305527396576[/C][C]0.182652763698288[/C][/ROW]
[ROW][C]57[/C][C]0.784400754788769[/C][C]0.431198490422463[/C][C]0.215599245211231[/C][/ROW]
[ROW][C]58[/C][C]0.759174315656741[/C][C]0.481651368686517[/C][C]0.240825684343259[/C][/ROW]
[ROW][C]59[/C][C]0.724939020316229[/C][C]0.550121959367542[/C][C]0.275060979683771[/C][/ROW]
[ROW][C]60[/C][C]0.715422789233389[/C][C]0.569154421533222[/C][C]0.284577210766611[/C][/ROW]
[ROW][C]61[/C][C]0.677114019715176[/C][C]0.645771960569649[/C][C]0.322885980284824[/C][/ROW]
[ROW][C]62[/C][C]0.635664720713904[/C][C]0.728670558572191[/C][C]0.364335279286096[/C][/ROW]
[ROW][C]63[/C][C]0.602617019918712[/C][C]0.794765960162576[/C][C]0.397382980081288[/C][/ROW]
[ROW][C]64[/C][C]0.561851682114712[/C][C]0.876296635770575[/C][C]0.438148317885288[/C][/ROW]
[ROW][C]65[/C][C]0.518364275090322[/C][C]0.963271449819356[/C][C]0.481635724909678[/C][/ROW]
[ROW][C]66[/C][C]0.473464821529311[/C][C]0.946929643058622[/C][C]0.526535178470689[/C][/ROW]
[ROW][C]67[/C][C]0.458103692588917[/C][C]0.916207385177834[/C][C]0.541896307411083[/C][/ROW]
[ROW][C]68[/C][C]0.509962931879029[/C][C]0.980074136241943[/C][C]0.490037068120971[/C][/ROW]
[ROW][C]69[/C][C]0.734171933153731[/C][C]0.531656133692538[/C][C]0.265828066846269[/C][/ROW]
[ROW][C]70[/C][C]0.696336047292943[/C][C]0.607327905414113[/C][C]0.303663952707057[/C][/ROW]
[ROW][C]71[/C][C]0.815742168182648[/C][C]0.368515663634703[/C][C]0.184257831817352[/C][/ROW]
[ROW][C]72[/C][C]0.78255090008497[/C][C]0.434898199830061[/C][C]0.21744909991503[/C][/ROW]
[ROW][C]73[/C][C]0.772484316252926[/C][C]0.455031367494148[/C][C]0.227515683747074[/C][/ROW]
[ROW][C]74[/C][C]0.75084613016675[/C][C]0.498307739666501[/C][C]0.249153869833251[/C][/ROW]
[ROW][C]75[/C][C]0.712732275673303[/C][C]0.574535448653395[/C][C]0.287267724326697[/C][/ROW]
[ROW][C]76[/C][C]0.761594496148495[/C][C]0.476811007703011[/C][C]0.238405503851505[/C][/ROW]
[ROW][C]77[/C][C]0.727195359928285[/C][C]0.54560928014343[/C][C]0.272804640071715[/C][/ROW]
[ROW][C]78[/C][C]0.71407326374111[/C][C]0.571853472517779[/C][C]0.28592673625889[/C][/ROW]
[ROW][C]79[/C][C]0.725312259280177[/C][C]0.549375481439647[/C][C]0.274687740719823[/C][/ROW]
[ROW][C]80[/C][C]0.684788242695556[/C][C]0.630423514608887[/C][C]0.315211757304444[/C][/ROW]
[ROW][C]81[/C][C]0.644837352555226[/C][C]0.710325294889548[/C][C]0.355162647444774[/C][/ROW]
[ROW][C]82[/C][C]0.758746211482053[/C][C]0.482507577035893[/C][C]0.241253788517947[/C][/ROW]
[ROW][C]83[/C][C]0.720816018393738[/C][C]0.558367963212524[/C][C]0.279183981606262[/C][/ROW]
[ROW][C]84[/C][C]0.694599235809299[/C][C]0.610801528381403[/C][C]0.305400764190701[/C][/ROW]
[ROW][C]85[/C][C]0.652327344646397[/C][C]0.695345310707207[/C][C]0.347672655353603[/C][/ROW]
[ROW][C]86[/C][C]0.64284048046202[/C][C]0.714319039075959[/C][C]0.35715951953798[/C][/ROW]
[ROW][C]87[/C][C]0.598968738856139[/C][C]0.802062522287722[/C][C]0.401031261143861[/C][/ROW]
[ROW][C]88[/C][C]0.557590914217208[/C][C]0.884818171565584[/C][C]0.442409085782792[/C][/ROW]
[ROW][C]89[/C][C]0.537531194266218[/C][C]0.924937611467565[/C][C]0.462468805733782[/C][/ROW]
[ROW][C]90[/C][C]0.50808229793305[/C][C]0.983835404133901[/C][C]0.49191770206695[/C][/ROW]
[ROW][C]91[/C][C]0.494126673444626[/C][C]0.988253346889251[/C][C]0.505873326555374[/C][/ROW]
[ROW][C]92[/C][C]0.45654302647365[/C][C]0.913086052947301[/C][C]0.543456973526349[/C][/ROW]
[ROW][C]93[/C][C]0.413044593522864[/C][C]0.826089187045728[/C][C]0.586955406477136[/C][/ROW]
[ROW][C]94[/C][C]0.371559244665147[/C][C]0.743118489330294[/C][C]0.628440755334853[/C][/ROW]
[ROW][C]95[/C][C]0.389550593329268[/C][C]0.779101186658536[/C][C]0.610449406670732[/C][/ROW]
[ROW][C]96[/C][C]0.353128101028291[/C][C]0.706256202056583[/C][C]0.646871898971709[/C][/ROW]
[ROW][C]97[/C][C]0.315574005501292[/C][C]0.631148011002584[/C][C]0.684425994498708[/C][/ROW]
[ROW][C]98[/C][C]0.313492372328281[/C][C]0.626984744656561[/C][C]0.686507627671719[/C][/ROW]
[ROW][C]99[/C][C]0.272740162616487[/C][C]0.545480325232974[/C][C]0.727259837383513[/C][/ROW]
[ROW][C]100[/C][C]0.236142230427939[/C][C]0.472284460855878[/C][C]0.763857769572061[/C][/ROW]
[ROW][C]101[/C][C]0.216293181279329[/C][C]0.432586362558658[/C][C]0.783706818720671[/C][/ROW]
[ROW][C]102[/C][C]0.202744102028345[/C][C]0.405488204056691[/C][C]0.797255897971655[/C][/ROW]
[ROW][C]103[/C][C]0.242913521719987[/C][C]0.485827043439974[/C][C]0.757086478280013[/C][/ROW]
[ROW][C]104[/C][C]0.207690898658735[/C][C]0.415381797317471[/C][C]0.792309101341265[/C][/ROW]
[ROW][C]105[/C][C]0.200165263424189[/C][C]0.400330526848378[/C][C]0.799834736575811[/C][/ROW]
[ROW][C]106[/C][C]0.211807340401968[/C][C]0.423614680803936[/C][C]0.788192659598032[/C][/ROW]
[ROW][C]107[/C][C]0.189816222518662[/C][C]0.379632445037325[/C][C]0.810183777481338[/C][/ROW]
[ROW][C]108[/C][C]0.169147857115356[/C][C]0.338295714230713[/C][C]0.830852142884644[/C][/ROW]
[ROW][C]109[/C][C]0.165172193262673[/C][C]0.330344386525346[/C][C]0.834827806737327[/C][/ROW]
[ROW][C]110[/C][C]0.164707198857607[/C][C]0.329414397715214[/C][C]0.835292801142393[/C][/ROW]
[ROW][C]111[/C][C]0.144149640116751[/C][C]0.288299280233502[/C][C]0.855850359883249[/C][/ROW]
[ROW][C]112[/C][C]0.121180024314767[/C][C]0.242360048629533[/C][C]0.878819975685233[/C][/ROW]
[ROW][C]113[/C][C]0.141094319422248[/C][C]0.282188638844496[/C][C]0.858905680577752[/C][/ROW]
[ROW][C]114[/C][C]0.13233401341653[/C][C]0.26466802683306[/C][C]0.86766598658347[/C][/ROW]
[ROW][C]115[/C][C]0.174579023247372[/C][C]0.349158046494743[/C][C]0.825420976752628[/C][/ROW]
[ROW][C]116[/C][C]0.164990630645111[/C][C]0.329981261290223[/C][C]0.835009369354889[/C][/ROW]
[ROW][C]117[/C][C]0.145213985663783[/C][C]0.290427971327567[/C][C]0.854786014336216[/C][/ROW]
[ROW][C]118[/C][C]0.127668509778505[/C][C]0.25533701955701[/C][C]0.872331490221495[/C][/ROW]
[ROW][C]119[/C][C]0.132513165402184[/C][C]0.265026330804368[/C][C]0.867486834597816[/C][/ROW]
[ROW][C]120[/C][C]0.127271761037342[/C][C]0.254543522074684[/C][C]0.872728238962658[/C][/ROW]
[ROW][C]121[/C][C]0.107757179144019[/C][C]0.215514358288038[/C][C]0.892242820855981[/C][/ROW]
[ROW][C]122[/C][C]0.0863844936254036[/C][C]0.172768987250807[/C][C]0.913615506374596[/C][/ROW]
[ROW][C]123[/C][C]0.0966241739709603[/C][C]0.193248347941921[/C][C]0.90337582602904[/C][/ROW]
[ROW][C]124[/C][C]0.0783155874138314[/C][C]0.156631174827663[/C][C]0.921684412586169[/C][/ROW]
[ROW][C]125[/C][C]0.0642545930667541[/C][C]0.128509186133508[/C][C]0.935745406933246[/C][/ROW]
[ROW][C]126[/C][C]0.0497116329627231[/C][C]0.0994232659254463[/C][C]0.950288367037277[/C][/ROW]
[ROW][C]127[/C][C]0.0377475544590002[/C][C]0.0754951089180005[/C][C]0.962252445541[/C][/ROW]
[ROW][C]128[/C][C]0.0297270432974179[/C][C]0.0594540865948358[/C][C]0.970272956702582[/C][/ROW]
[ROW][C]129[/C][C]0.0229823734402118[/C][C]0.0459647468804235[/C][C]0.977017626559788[/C][/ROW]
[ROW][C]130[/C][C]0.0322933993437719[/C][C]0.0645867986875437[/C][C]0.967706600656228[/C][/ROW]
[ROW][C]131[/C][C]0.02711932864106[/C][C]0.0542386572821201[/C][C]0.97288067135894[/C][/ROW]
[ROW][C]132[/C][C]0.0367343146598615[/C][C]0.073468629319723[/C][C]0.963265685340138[/C][/ROW]
[ROW][C]133[/C][C]0.0389014018257992[/C][C]0.0778028036515984[/C][C]0.961098598174201[/C][/ROW]
[ROW][C]134[/C][C]0.0557351891735168[/C][C]0.111470378347034[/C][C]0.944264810826483[/C][/ROW]
[ROW][C]135[/C][C]0.0445033280296686[/C][C]0.0890066560593373[/C][C]0.955496671970331[/C][/ROW]
[ROW][C]136[/C][C]0.0306779951527153[/C][C]0.0613559903054306[/C][C]0.969322004847285[/C][/ROW]
[ROW][C]137[/C][C]0.0209103001437244[/C][C]0.0418206002874488[/C][C]0.979089699856276[/C][/ROW]
[ROW][C]138[/C][C]0.0145778337079364[/C][C]0.0291556674158728[/C][C]0.985422166292064[/C][/ROW]
[ROW][C]139[/C][C]0.0200319666709931[/C][C]0.0400639333419861[/C][C]0.979968033329007[/C][/ROW]
[ROW][C]140[/C][C]0.0164967092512234[/C][C]0.0329934185024469[/C][C]0.983503290748777[/C][/ROW]
[ROW][C]141[/C][C]0.615122533489696[/C][C]0.769754933020609[/C][C]0.384877466510304[/C][/ROW]
[ROW][C]142[/C][C]0.551023630702795[/C][C]0.897952738594411[/C][C]0.448976369297205[/C][/ROW]
[ROW][C]143[/C][C]0.463456279269415[/C][C]0.926912558538831[/C][C]0.536543720730585[/C][/ROW]
[ROW][C]144[/C][C]0.391227967277217[/C][C]0.782455934554434[/C][C]0.608772032722783[/C][/ROW]
[ROW][C]145[/C][C]0.301115061245011[/C][C]0.602230122490022[/C][C]0.698884938754989[/C][/ROW]
[ROW][C]146[/C][C]0.32030963342476[/C][C]0.64061926684952[/C][C]0.67969036657524[/C][/ROW]
[ROW][C]147[/C][C]0.273604207141664[/C][C]0.547208414283327[/C][C]0.726395792858336[/C][/ROW]
[ROW][C]148[/C][C]0.663220425566356[/C][C]0.673559148867289[/C][C]0.336779574433644[/C][/ROW]
[ROW][C]149[/C][C]0.611152099608497[/C][C]0.777695800783006[/C][C]0.388847900391503[/C][/ROW]
[ROW][C]150[/C][C]0.477903579813684[/C][C]0.955807159627368[/C][C]0.522096420186316[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185832&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185832&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.7592597089806430.4814805820387140.240740291019357
130.622102369037930.7557952619241410.37789763096207
140.5819533584519550.836093283096090.418046641548045
150.4619499282125650.923899856425130.538050071787435
160.3616626615278930.7233253230557860.638337338472107
170.2985582561633750.5971165123267490.701441743836625
180.5058889854103650.988222029179270.494111014589635
190.4217323191532920.8434646383065850.578267680846708
200.3325818636301220.6651637272602440.667418136369878
210.2626203866072290.5252407732144590.737379613392771
220.2394774631884740.4789549263769480.760522536811526
230.4363778377157470.8727556754314950.563622162284253
240.4716107947632930.9432215895265850.528389205236707
250.4601519358942560.9203038717885130.539848064105743
260.4194072640243490.8388145280486980.580592735975651
270.4796673166626920.9593346333253830.520332683337308
280.4827684812302410.9655369624604820.517231518769759
290.4629124881596060.9258249763192120.537087511840394
300.5039855880880130.9920288238239730.496014411911987
310.4431120666801810.8862241333603610.556887933319819
320.3938702143068150.7877404286136310.606129785693185
330.3672440243858360.7344880487716730.632755975614164
340.3326270359167980.6652540718335950.667372964083202
350.2854021969270070.5708043938540140.714597803072993
360.8513673602322050.2972652795355890.148632639767795
370.8261369102418150.347726179516370.173863089758185
380.819105164676210.361789670647580.18089483532379
390.8388931122265210.3222137755469570.161106887773479
400.8220827218587570.3558345562824860.177917278141243
410.7919081514205070.4161836971589850.208091848579493
420.7658118633684970.4683762732630070.234188136631503
430.79089457939540.41821084120920.2091054206046
440.7502982955385410.4994034089229170.249701704461459
450.7218159757030890.5563680485938220.278184024296911
460.8740055181208050.2519889637583890.125994481879195
470.9178554650713240.1642890698573520.0821445349286758
480.8961683181287470.2076633637425060.103831681871253
490.880863891267890.238272217464220.11913610873211
500.8797199773060890.2405600453878230.120280022693911
510.852953541881490.2940929162370190.14704645811851
520.8217747172855690.3564505654288620.178225282714431
530.8416143790053810.3167712419892390.158385620994619
540.8264878163970660.3470243672058690.173512183602934
550.8443640610939710.3112718778120580.155635938906029
560.8173472363017120.3653055273965760.182652763698288
570.7844007547887690.4311984904224630.215599245211231
580.7591743156567410.4816513686865170.240825684343259
590.7249390203162290.5501219593675420.275060979683771
600.7154227892333890.5691544215332220.284577210766611
610.6771140197151760.6457719605696490.322885980284824
620.6356647207139040.7286705585721910.364335279286096
630.6026170199187120.7947659601625760.397382980081288
640.5618516821147120.8762966357705750.438148317885288
650.5183642750903220.9632714498193560.481635724909678
660.4734648215293110.9469296430586220.526535178470689
670.4581036925889170.9162073851778340.541896307411083
680.5099629318790290.9800741362419430.490037068120971
690.7341719331537310.5316561336925380.265828066846269
700.6963360472929430.6073279054141130.303663952707057
710.8157421681826480.3685156636347030.184257831817352
720.782550900084970.4348981998300610.21744909991503
730.7724843162529260.4550313674941480.227515683747074
740.750846130166750.4983077396665010.249153869833251
750.7127322756733030.5745354486533950.287267724326697
760.7615944961484950.4768110077030110.238405503851505
770.7271953599282850.545609280143430.272804640071715
780.714073263741110.5718534725177790.28592673625889
790.7253122592801770.5493754814396470.274687740719823
800.6847882426955560.6304235146088870.315211757304444
810.6448373525552260.7103252948895480.355162647444774
820.7587462114820530.4825075770358930.241253788517947
830.7208160183937380.5583679632125240.279183981606262
840.6945992358092990.6108015283814030.305400764190701
850.6523273446463970.6953453107072070.347672655353603
860.642840480462020.7143190390759590.35715951953798
870.5989687388561390.8020625222877220.401031261143861
880.5575909142172080.8848181715655840.442409085782792
890.5375311942662180.9249376114675650.462468805733782
900.508082297933050.9838354041339010.49191770206695
910.4941266734446260.9882533468892510.505873326555374
920.456543026473650.9130860529473010.543456973526349
930.4130445935228640.8260891870457280.586955406477136
940.3715592446651470.7431184893302940.628440755334853
950.3895505933292680.7791011866585360.610449406670732
960.3531281010282910.7062562020565830.646871898971709
970.3155740055012920.6311480110025840.684425994498708
980.3134923723282810.6269847446565610.686507627671719
990.2727401626164870.5454803252329740.727259837383513
1000.2361422304279390.4722844608558780.763857769572061
1010.2162931812793290.4325863625586580.783706818720671
1020.2027441020283450.4054882040566910.797255897971655
1030.2429135217199870.4858270434399740.757086478280013
1040.2076908986587350.4153817973174710.792309101341265
1050.2001652634241890.4003305268483780.799834736575811
1060.2118073404019680.4236146808039360.788192659598032
1070.1898162225186620.3796324450373250.810183777481338
1080.1691478571153560.3382957142307130.830852142884644
1090.1651721932626730.3303443865253460.834827806737327
1100.1647071988576070.3294143977152140.835292801142393
1110.1441496401167510.2882992802335020.855850359883249
1120.1211800243147670.2423600486295330.878819975685233
1130.1410943194222480.2821886388444960.858905680577752
1140.132334013416530.264668026833060.86766598658347
1150.1745790232473720.3491580464947430.825420976752628
1160.1649906306451110.3299812612902230.835009369354889
1170.1452139856637830.2904279713275670.854786014336216
1180.1276685097785050.255337019557010.872331490221495
1190.1325131654021840.2650263308043680.867486834597816
1200.1272717610373420.2545435220746840.872728238962658
1210.1077571791440190.2155143582880380.892242820855981
1220.08638449362540360.1727689872508070.913615506374596
1230.09662417397096030.1932483479419210.90337582602904
1240.07831558741383140.1566311748276630.921684412586169
1250.06425459306675410.1285091861335080.935745406933246
1260.04971163296272310.09942326592544630.950288367037277
1270.03774755445900020.07549510891800050.962252445541
1280.02972704329741790.05945408659483580.970272956702582
1290.02298237344021180.04596474688042350.977017626559788
1300.03229339934377190.06458679868754370.967706600656228
1310.027119328641060.05423865728212010.97288067135894
1320.03673431465986150.0734686293197230.963265685340138
1330.03890140182579920.07780280365159840.961098598174201
1340.05573518917351680.1114703783470340.944264810826483
1350.04450332802966860.08900665605933730.955496671970331
1360.03067799515271530.06135599030543060.969322004847285
1370.02091030014372440.04182060028744880.979089699856276
1380.01457783370793640.02915566741587280.985422166292064
1390.02003196667099310.04006393334198610.979968033329007
1400.01649670925122340.03299341850244690.983503290748777
1410.6151225334896960.7697549330206090.384877466510304
1420.5510236307027950.8979527385944110.448976369297205
1430.4634562792694150.9269125585388310.536543720730585
1440.3912279672772170.7824559345544340.608772032722783
1450.3011150612450110.6022301224900220.698884938754989
1460.320309633424760.640619266849520.67969036657524
1470.2736042071416640.5472084142833270.726395792858336
1480.6632204255663560.6735591488672890.336779574433644
1490.6111520996084970.7776958007830060.388847900391503
1500.4779035798136840.9558071596273680.522096420186316






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.0359712230215827OK
10% type I error level140.100719424460432NOK

\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 & 5 & 0.0359712230215827 & OK \tabularnewline
10% type I error level & 14 & 0.100719424460432 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185832&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]5[/C][C]0.0359712230215827[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.100719424460432[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185832&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185832&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.0359712230215827OK
10% type I error level140.100719424460432NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 3 ; par2 = Include Quarterly Dummies ; par3 = 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')
}