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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 computationThu, 06 Dec 2012 08:54:48 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/06/t1354802265ia3z5o6g2ax06xz.htm/, Retrieved Fri, 01 Nov 2024 00:10:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197084, Retrieved Fri, 01 Nov 2024 00:10:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [ws10] [2012-12-06 13:54:48] [0ce3a3cc7b36ec2616d0d876d7c7ef2d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	7	41	38	13	12	14	12
2	5	39	32	16	11	18	11
2	5	30	35	19	15	11	14
1	5	31	33	15	6	12	12
2	8	34	37	14	13	16	21
2	6	35	29	13	10	18	12
2	5	39	31	19	12	14	22
2	6	34	36	15	14	14	11
2	5	36	35	14	12	15	10
2	4	37	38	15	6	15	13
1	6	38	31	16	10	17	10
2	5	36	34	16	12	19	8
1	5	38	35	16	12	10	15
2	6	39	38	16	11	16	14
2	7	33	37	17	15	18	10
1	6	32	33	15	12	14	14
1	7	36	32	15	10	14	14
2	6	38	38	20	12	17	11
1	8	39	38	18	11	14	10
2	7	32	32	16	12	16	13
1	5	32	33	16	11	18	7
2	5	31	31	16	12	11	14
2	7	39	38	19	13	14	12
2	7	37	39	16	11	12	14
1	5	39	32	17	9	17	11
2	4	41	32	17	13	9	9
1	10	36	35	16	10	16	11
2	6	33	37	15	14	14	15
2	5	33	33	16	12	15	14
1	5	34	33	14	10	11	13
2	5	31	28	15	12	16	9
1	5	27	32	12	8	13	15
2	6	37	31	14	10	17	10
2	5	34	37	16	12	15	11
1	5	34	30	14	12	14	13
1	5	32	33	7	7	16	8
1	5	29	31	10	6	9	20
1	5	36	33	14	12	15	12
2	5	29	31	16	10	17	10
1	5	35	33	16	10	13	10
1	5	37	32	16	10	15	9
2	7	34	33	14	12	16	14
1	5	38	32	20	15	16	8
1	6	35	33	14	10	12	14
2	7	38	28	14	10	12	11
2	7	37	35	11	12	11	13
2	5	38	39	14	13	15	9
2	5	33	34	15	11	15	11
2	4	36	38	16	11	17	15
1	5	38	32	14	12	13	11
2	4	32	38	16	14	16	10
1	5	32	30	14	10	14	14
1	5	32	33	12	12	11	18
2	7	34	38	16	13	12	14
1	5	32	32	9	5	12	11
2	5	37	32	14	6	15	12
2	6	39	34	16	12	16	13
2	4	29	34	16	12	15	9
1	6	37	36	15	11	12	10
2	6	35	34	16	10	12	15
1	5	30	28	12	7	8	20
1	7	38	34	16	12	13	12
2	6	34	35	16	14	11	12
2	8	31	35	14	11	14	14
2	7	34	31	16	12	15	13
1	5	35	37	17	13	10	11
2	6	36	35	18	14	11	17
1	6	30	27	18	11	12	12
2	5	39	40	12	12	15	13
1	5	35	37	16	12	15	14
1	5	38	36	10	8	14	13
2	5	31	38	14	11	16	15
2	4	34	39	18	14	15	13
1	6	38	41	18	14	15	10
1	6	34	27	16	12	13	11
2	6	39	30	17	9	12	19
2	6	37	37	16	13	17	13
2	7	34	31	16	11	13	17
1	5	28	31	13	12	15	13
1	7	37	27	16	12	13	9
1	6	33	36	16	12	15	11
1	5	37	38	20	12	16	10
2	5	35	37	16	12	15	9
1	4	37	33	15	12	16	12
2	8	32	34	15	11	15	12
2	8	33	31	16	10	14	13
1	5	38	39	14	9	15	13
2	5	33	34	16	12	14	12
2	6	29	32	16	12	13	15
2	4	33	33	15	12	7	22
2	5	31	36	12	9	17	13
2	5	36	32	17	15	13	15
2	5	35	41	16	12	15	13
2	5	32	28	15	12	14	15
2	6	29	30	13	12	13	10
2	6	39	36	16	10	16	11
2	5	37	35	16	13	12	16
2	6	35	31	16	9	14	11
1	5	37	34	16	12	17	11
1	7	32	36	14	10	15	10
2	5	38	36	16	14	17	10
1	6	37	35	16	11	12	16
2	6	36	37	20	15	16	12
1	6	32	28	15	11	11	11
2	4	33	39	16	11	15	16
1	5	40	32	13	12	9	19
2	5	38	35	17	12	16	11
1	7	41	39	16	12	15	16
1	6	36	35	16	11	10	15
2	9	43	42	12	7	10	24
2	6	30	34	16	12	15	14
2	6	31	33	16	14	11	15
2	5	32	41	17	11	13	11
1	6	32	33	13	11	14	15
2	5	37	34	12	10	18	12
1	8	37	32	18	13	16	10
2	7	33	40	14	13	14	14
2	5	34	40	14	8	14	13
2	7	33	35	13	11	14	9
2	6	38	36	16	12	14	15
2	6	33	37	13	11	12	15
2	9	31	27	16	13	14	14
2	7	38	39	13	12	15	11
2	6	37	38	16	14	15	8
2	5	33	31	15	13	15	11
2	5	31	33	16	15	13	11
1	6	39	32	15	10	17	8
2	6	44	39	17	11	17	10
2	7	33	36	15	9	19	11
2	5	35	33	12	11	15	13
1	5	32	33	16	10	13	11
1	5	28	32	10	11	9	20
2	6	40	37	16	8	15	10
1	4	27	30	12	11	15	15
1	5	37	38	14	12	15	12
2	7	32	29	15	12	16	14
1	5	28	22	13	9	11	23
1	7	34	35	15	11	14	14
2	7	30	35	11	10	11	16
2	6	35	34	12	8	15	11
1	5	31	35	8	9	13	12
2	8	32	34	16	8	15	10
1	5	30	34	15	9	16	14
2	5	30	35	17	15	14	12
1	5	31	23	16	11	15	12
2	6	40	31	10	8	16	11
2	4	32	27	18	13	16	12
1	5	36	36	13	12	11	13
1	5	32	31	16	12	12	11
1	7	35	32	13	9	9	19
2	6	38	39	10	7	16	12
2	7	42	37	15	13	13	17
1	10	34	38	16	9	16	9
2	6	35	39	16	6	12	12
2	8	35	34	14	8	9	19
2	4	33	31	10	8	13	18
2	5	36	32	17	15	13	15
2	6	32	37	13	6	14	14
2	7	33	36	15	9	19	11
2	7	34	32	16	11	13	9
2	6	32	35	12	8	12	18
2	6	34	36	13	8	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197084&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197084&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197084&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 18.1821504311376 -0.331151943876252Gender[t] + 0.291464449218598Age[t] + 0.330159751154117Separate[t] + 0.312334883117665Learning[t] -0.108548398930014Software[t] + 0.068606745338049Happiness[t] -0.0307491690980014Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  18.1821504311376 -0.331151943876252Gender[t] +  0.291464449218598Age[t] +  0.330159751154117Separate[t] +  0.312334883117665Learning[t] -0.108548398930014Software[t] +  0.068606745338049Happiness[t] -0.0307491690980014Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197084&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  18.1821504311376 -0.331151943876252Gender[t] +  0.291464449218598Age[t] +  0.330159751154117Separate[t] +  0.312334883117665Learning[t] -0.108548398930014Software[t] +  0.068606745338049Happiness[t] -0.0307491690980014Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197084&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197084&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
Connected[t] = + 18.1821504311376 -0.331151943876252Gender[t] + 0.291464449218598Age[t] + 0.330159751154117Separate[t] + 0.312334883117665Learning[t] -0.108548398930014Software[t] + 0.068606745338049Happiness[t] -0.0307491690980014Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.18215043113763.9182294.64047e-064e-06
Gender-0.3311519438762520.541933-0.61110.5420630.271031
Age0.2914644492185980.2141811.36080.1755550.087777
Separate0.3301597511541170.0718134.59759e-064e-06
Learning0.3123348831176650.1330652.34720.0201860.010093
Software-0.1085483989300140.138629-0.7830.4348220.217411
Happiness0.0686067453380490.1295830.52940.597260.29863
Depression-0.03074916909800140.095391-0.32230.7476260.373813

\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) & 18.1821504311376 & 3.918229 & 4.6404 & 7e-06 & 4e-06 \tabularnewline
Gender & -0.331151943876252 & 0.541933 & -0.6111 & 0.542063 & 0.271031 \tabularnewline
Age & 0.291464449218598 & 0.214181 & 1.3608 & 0.175555 & 0.087777 \tabularnewline
Separate & 0.330159751154117 & 0.071813 & 4.5975 & 9e-06 & 4e-06 \tabularnewline
Learning & 0.312334883117665 & 0.133065 & 2.3472 & 0.020186 & 0.010093 \tabularnewline
Software & -0.108548398930014 & 0.138629 & -0.783 & 0.434822 & 0.217411 \tabularnewline
Happiness & 0.068606745338049 & 0.129583 & 0.5294 & 0.59726 & 0.29863 \tabularnewline
Depression & -0.0307491690980014 & 0.095391 & -0.3223 & 0.747626 & 0.373813 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197084&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]18.1821504311376[/C][C]3.918229[/C][C]4.6404[/C][C]7e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]Gender[/C][C]-0.331151943876252[/C][C]0.541933[/C][C]-0.6111[/C][C]0.542063[/C][C]0.271031[/C][/ROW]
[ROW][C]Age[/C][C]0.291464449218598[/C][C]0.214181[/C][C]1.3608[/C][C]0.175555[/C][C]0.087777[/C][/ROW]
[ROW][C]Separate[/C][C]0.330159751154117[/C][C]0.071813[/C][C]4.5975[/C][C]9e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]Learning[/C][C]0.312334883117665[/C][C]0.133065[/C][C]2.3472[/C][C]0.020186[/C][C]0.010093[/C][/ROW]
[ROW][C]Software[/C][C]-0.108548398930014[/C][C]0.138629[/C][C]-0.783[/C][C]0.434822[/C][C]0.217411[/C][/ROW]
[ROW][C]Happiness[/C][C]0.068606745338049[/C][C]0.129583[/C][C]0.5294[/C][C]0.59726[/C][C]0.29863[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0307491690980014[/C][C]0.095391[/C][C]-0.3223[/C][C]0.747626[/C][C]0.373813[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197084&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197084&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)18.18215043113763.9182294.64047e-064e-06
Gender-0.3311519438762520.541933-0.61110.5420630.271031
Age0.2914644492185980.2141811.36080.1755550.087777
Separate0.3301597511541170.0718134.59759e-064e-06
Learning0.3123348831176650.1330652.34720.0201860.010093
Software-0.1085483989300140.138629-0.7830.4348220.217411
Happiness0.0686067453380490.1295830.52940.597260.29863
Depression-0.03074916909800140.095391-0.32230.7476260.373813






Multiple Linear Regression - Regression Statistics
Multiple R0.436173226785665
R-squared0.19024708376462
Adjusted R-squared0.153440133026648
F-TEST (value)5.16878143802201
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value2.63203339517792e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.10541195036845
Sum Squared Residuals1485.11184074964

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.436173226785665 \tabularnewline
R-squared & 0.19024708376462 \tabularnewline
Adjusted R-squared & 0.153440133026648 \tabularnewline
F-TEST (value) & 5.16878143802201 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 2.63203339517792e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.10541195036845 \tabularnewline
Sum Squared Residuals & 1485.11184074964 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197084&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.436173226785665[/C][/ROW]
[ROW][C]R-squared[/C][C]0.19024708376462[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.153440133026648[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.16878143802201[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]2.63203339517792e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.10541195036845[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1485.11184074964[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197084&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197084&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 R0.436173226785665
R-squared0.19024708376462
Adjusted R-squared0.153440133026648
F-TEST (value)5.16878143802201
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value2.63203339517792e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.10541195036845
Sum Squared Residuals1485.11184074964






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14135.45544533069795.5445546693021
23934.24228712406914.75771287593086
33035.1630827065041-5.16308270650408
43134.6916162895056-3.69161628950562
53435.4810074817441-1.48100748174405
63532.68406690030442.31593309969559
73934.12791578190784.87208421809221
83434.9419835166443-0.941983516644307
93634.324477145451.67552285454999
103735.89486971909751.10513028090249
113834.60543458869983.39456541130016
123634.95491248007941.04508751992058
133834.78351928338133.21648071661866
143936.28524908224232.71475091775766
153336.3849052347725-3.38490523477254
163234.4075054976242-2.40750549762423
173634.58590699354871.41409300645126
183837.5868944684150.413105531584959
193937.8097828765071.19021712349297
203234.5179557947042-2.51795579470423
213235.0265954954915-3.02659549549152
223133.2310842493247-2.23108424932467
233937.22090623047381.77909376952619
243736.63244630126290.367553698737139
253935.0342640035853.96573599641496
264133.49009839026177.50990160973825
273636.9925754757547-0.992575475754654
283335.1491465914064-2.14914659140642
293334.1658307329851-1.1658307329851
303433.84573189623190.154268103768148
313132.4250496849249-1.4250496849249
322733.1837143291825-6.18371432918253
333733.64961287858833.35038712141174
343435.5787172448956-1.57871724489557
353432.84397608092361.15602391907638
363232.4818124833785-0.481812483378492
372932.0178087828109-3.01780878281091
383633.9338112488222.06618875117798
392933.982818195605-4.98281819560499
403534.69986266043730.300137339562716
413734.53766556905732.46233443094273
423434.192696610525-0.192696610525012
433835.34361902131392.65638097868609
443534.17505392169050.824946078309502
453832.57681517855635.42318482144374
463733.6037269058883.39627309411199
473835.56731692023452.43268307976554
483334.3844515072456-1.38445150724557
493635.74017776004520.259822239954806
503833.49718717608984.50281282391019
513235.4996716634071-3.49967166340711
523233.0303237096856-1.03032370968565
533232.8502194866465-0.850219486646487
543436.0851897522487-2.08518975224872
553232.6267448076735-0.626744807673533
563733.92378994737173.07621005262826
573934.88681084779394.11318915220614
582934.3582718804106-5.35827188041062
593735.49231633573251.5076836642675
603534.76798232610570.232017673894309
613031.4748441513158-1.47484415131582
623835.33435617397262.66564382602743
633434.6875892434957-0.687589243495706
643135.1158154703058-4.11581547030576
653434.1191892982121-0.11918929821206
663535.7706219462692-0.770621946269226
673635.1585131642410.841486835758971
683033.3963848865024-3.39638488650244
693935.25835862769133.74164137230874
703535.8176216814778-0.817621681477816
713834.00978865109773.99021134890228
723135.3383656976904-4.33836569769041
733436.2936469281645-2.2936469281645
743837.96029478008020.0397052199198107
753432.76252263577321.23747736422685
763933.74522992714495.2547700728551
773735.83734844766421.16265155233575
783433.9675275300740.0324724699260293
792832.9304076942981-4.93040769429812
803733.11548542318773.88451457681225
813335.8711738868363-2.8711738868363
823737.5887243868326-0.588724386832647
833535.6402155830916-0.640215583091571
843734.02328842805912.97671157194086
853235.2280956858034-3.22809568580336
863334.5591437999526-1.55914379995264
873836.20966578343881.79033421656124
883334.4888820769972-1.48888207699717
892933.9591727712755-4.95917277127548
903332.76718408516040.232815914839569
913134.4005783105409-3.40057831054093
923633.65439800838452.3456019916155
933536.837857911316-1.83785791131603
943232.1033411796608-0.103341179660796
952932.5155944651043-3.51559446510426
963935.82572548615813.17427451384187
973734.45028326215322.54971673784683
983534.14626163864150.853738361358542
993735.05660342598561.94339657401443
1003235.7858145367776-3.7858145367776
1013835.19942335565552.80057664434448
1023735.2899964531081.71000354689196
1033636.8317336060348-0.831733606034831
1043232.7516824120635-0.751682412063518
1053335.9023748514252-2.90237485142521
1064032.66443195883597.33556804116406
1073835.2993393710432.70066062895695
1084136.99937174402724.00062825597276
1093635.183532131530.816467868470052
1104336.94600333475576.05399666524431
1113034.7874549333578-4.78745493335781
1123133.9350222338935-2.93502223389347
1133237.1830260408836-5.18302604088362
1143233.8606349612209-1.86063496122092
1153733.73106632373873.26893367626127
1163735.74894106239841.2510589376016
1173336.2580529789977-3.25805297899772
1183436.2486152443086-2.2486152443086
1193334.6657619834595-1.6657619834595
1203835.348418521232.65158147877001
1213334.712908531285-1.71290853128503
1223133.1735748786667-2.17357487866672
1233835.8849609962882.115039003712
1243736.07549215470230.924507845297741
1253333.1768754559232-0.176875455923187
1263133.795219552813-2.79521955281296
1273934.68475779493234.3152422050677
1284437.11934713824426.88065286175582
1293336.1192236872032-3.11922368720322
1303533.05578876854241.94421123145755
1313234.6691134913393-2.66911349133928
1322831.805226539315-3.80522653931495
1334036.33512445893223.66487554106777
1342732.0434986715418-5.04349867154175
1353735.58461000459261.41538999540739
1363233.1843924890262-1.18439248902621
1372829.7026964583689-1.7026964583689
1383435.4678378480811-1.46783784808108
1393033.728576196454-3.72857619645403
1403534.06455650390120.935443496098787
1413132.9085531585382-1.90855315853821
1423235.9275741039071-3.92757410390707
1433034.9090594870259-4.90905948702589
1443034.8057315144789-4.80573151447891
1453131.3654319024462-0.365431902446195
1464032.51801422954167.48198577045842
1473232.5396342276812-0.539634227681164
1483634.30677946871651.69322053128349
1493233.723090445833-1.72309044583297
1503533.57300605406321.42699394593683
1513835.23709146860652.76290853139347
1524235.4190543560216.58094564397902
1533438.153101466343-4.15310146634302
1543536.9452221848903-1.94522218489034
1553534.61452134376140.385478656238567
1563331.51402091140421.48597908859577
1573633.65439800838452.3456019916155
1583235.4236131857092-3.4236131857092
1593336.1192236872032-3.11922368720322
1603434.5436806340121-0.543680634012099
1613233.9736518353552-1.97365183535517
1623434.746251553161-0.746251553161007

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 35.4554453306979 & 5.5445546693021 \tabularnewline
2 & 39 & 34.2422871240691 & 4.75771287593086 \tabularnewline
3 & 30 & 35.1630827065041 & -5.16308270650408 \tabularnewline
4 & 31 & 34.6916162895056 & -3.69161628950562 \tabularnewline
5 & 34 & 35.4810074817441 & -1.48100748174405 \tabularnewline
6 & 35 & 32.6840669003044 & 2.31593309969559 \tabularnewline
7 & 39 & 34.1279157819078 & 4.87208421809221 \tabularnewline
8 & 34 & 34.9419835166443 & -0.941983516644307 \tabularnewline
9 & 36 & 34.32447714545 & 1.67552285454999 \tabularnewline
10 & 37 & 35.8948697190975 & 1.10513028090249 \tabularnewline
11 & 38 & 34.6054345886998 & 3.39456541130016 \tabularnewline
12 & 36 & 34.9549124800794 & 1.04508751992058 \tabularnewline
13 & 38 & 34.7835192833813 & 3.21648071661866 \tabularnewline
14 & 39 & 36.2852490822423 & 2.71475091775766 \tabularnewline
15 & 33 & 36.3849052347725 & -3.38490523477254 \tabularnewline
16 & 32 & 34.4075054976242 & -2.40750549762423 \tabularnewline
17 & 36 & 34.5859069935487 & 1.41409300645126 \tabularnewline
18 & 38 & 37.586894468415 & 0.413105531584959 \tabularnewline
19 & 39 & 37.809782876507 & 1.19021712349297 \tabularnewline
20 & 32 & 34.5179557947042 & -2.51795579470423 \tabularnewline
21 & 32 & 35.0265954954915 & -3.02659549549152 \tabularnewline
22 & 31 & 33.2310842493247 & -2.23108424932467 \tabularnewline
23 & 39 & 37.2209062304738 & 1.77909376952619 \tabularnewline
24 & 37 & 36.6324463012629 & 0.367553698737139 \tabularnewline
25 & 39 & 35.034264003585 & 3.96573599641496 \tabularnewline
26 & 41 & 33.4900983902617 & 7.50990160973825 \tabularnewline
27 & 36 & 36.9925754757547 & -0.992575475754654 \tabularnewline
28 & 33 & 35.1491465914064 & -2.14914659140642 \tabularnewline
29 & 33 & 34.1658307329851 & -1.1658307329851 \tabularnewline
30 & 34 & 33.8457318962319 & 0.154268103768148 \tabularnewline
31 & 31 & 32.4250496849249 & -1.4250496849249 \tabularnewline
32 & 27 & 33.1837143291825 & -6.18371432918253 \tabularnewline
33 & 37 & 33.6496128785883 & 3.35038712141174 \tabularnewline
34 & 34 & 35.5787172448956 & -1.57871724489557 \tabularnewline
35 & 34 & 32.8439760809236 & 1.15602391907638 \tabularnewline
36 & 32 & 32.4818124833785 & -0.481812483378492 \tabularnewline
37 & 29 & 32.0178087828109 & -3.01780878281091 \tabularnewline
38 & 36 & 33.933811248822 & 2.06618875117798 \tabularnewline
39 & 29 & 33.982818195605 & -4.98281819560499 \tabularnewline
40 & 35 & 34.6998626604373 & 0.300137339562716 \tabularnewline
41 & 37 & 34.5376655690573 & 2.46233443094273 \tabularnewline
42 & 34 & 34.192696610525 & -0.192696610525012 \tabularnewline
43 & 38 & 35.3436190213139 & 2.65638097868609 \tabularnewline
44 & 35 & 34.1750539216905 & 0.824946078309502 \tabularnewline
45 & 38 & 32.5768151785563 & 5.42318482144374 \tabularnewline
46 & 37 & 33.603726905888 & 3.39627309411199 \tabularnewline
47 & 38 & 35.5673169202345 & 2.43268307976554 \tabularnewline
48 & 33 & 34.3844515072456 & -1.38445150724557 \tabularnewline
49 & 36 & 35.7401777600452 & 0.259822239954806 \tabularnewline
50 & 38 & 33.4971871760898 & 4.50281282391019 \tabularnewline
51 & 32 & 35.4996716634071 & -3.49967166340711 \tabularnewline
52 & 32 & 33.0303237096856 & -1.03032370968565 \tabularnewline
53 & 32 & 32.8502194866465 & -0.850219486646487 \tabularnewline
54 & 34 & 36.0851897522487 & -2.08518975224872 \tabularnewline
55 & 32 & 32.6267448076735 & -0.626744807673533 \tabularnewline
56 & 37 & 33.9237899473717 & 3.07621005262826 \tabularnewline
57 & 39 & 34.8868108477939 & 4.11318915220614 \tabularnewline
58 & 29 & 34.3582718804106 & -5.35827188041062 \tabularnewline
59 & 37 & 35.4923163357325 & 1.5076836642675 \tabularnewline
60 & 35 & 34.7679823261057 & 0.232017673894309 \tabularnewline
61 & 30 & 31.4748441513158 & -1.47484415131582 \tabularnewline
62 & 38 & 35.3343561739726 & 2.66564382602743 \tabularnewline
63 & 34 & 34.6875892434957 & -0.687589243495706 \tabularnewline
64 & 31 & 35.1158154703058 & -4.11581547030576 \tabularnewline
65 & 34 & 34.1191892982121 & -0.11918929821206 \tabularnewline
66 & 35 & 35.7706219462692 & -0.770621946269226 \tabularnewline
67 & 36 & 35.158513164241 & 0.841486835758971 \tabularnewline
68 & 30 & 33.3963848865024 & -3.39638488650244 \tabularnewline
69 & 39 & 35.2583586276913 & 3.74164137230874 \tabularnewline
70 & 35 & 35.8176216814778 & -0.817621681477816 \tabularnewline
71 & 38 & 34.0097886510977 & 3.99021134890228 \tabularnewline
72 & 31 & 35.3383656976904 & -4.33836569769041 \tabularnewline
73 & 34 & 36.2936469281645 & -2.2936469281645 \tabularnewline
74 & 38 & 37.9602947800802 & 0.0397052199198107 \tabularnewline
75 & 34 & 32.7625226357732 & 1.23747736422685 \tabularnewline
76 & 39 & 33.7452299271449 & 5.2547700728551 \tabularnewline
77 & 37 & 35.8373484476642 & 1.16265155233575 \tabularnewline
78 & 34 & 33.967527530074 & 0.0324724699260293 \tabularnewline
79 & 28 & 32.9304076942981 & -4.93040769429812 \tabularnewline
80 & 37 & 33.1154854231877 & 3.88451457681225 \tabularnewline
81 & 33 & 35.8711738868363 & -2.8711738868363 \tabularnewline
82 & 37 & 37.5887243868326 & -0.588724386832647 \tabularnewline
83 & 35 & 35.6402155830916 & -0.640215583091571 \tabularnewline
84 & 37 & 34.0232884280591 & 2.97671157194086 \tabularnewline
85 & 32 & 35.2280956858034 & -3.22809568580336 \tabularnewline
86 & 33 & 34.5591437999526 & -1.55914379995264 \tabularnewline
87 & 38 & 36.2096657834388 & 1.79033421656124 \tabularnewline
88 & 33 & 34.4888820769972 & -1.48888207699717 \tabularnewline
89 & 29 & 33.9591727712755 & -4.95917277127548 \tabularnewline
90 & 33 & 32.7671840851604 & 0.232815914839569 \tabularnewline
91 & 31 & 34.4005783105409 & -3.40057831054093 \tabularnewline
92 & 36 & 33.6543980083845 & 2.3456019916155 \tabularnewline
93 & 35 & 36.837857911316 & -1.83785791131603 \tabularnewline
94 & 32 & 32.1033411796608 & -0.103341179660796 \tabularnewline
95 & 29 & 32.5155944651043 & -3.51559446510426 \tabularnewline
96 & 39 & 35.8257254861581 & 3.17427451384187 \tabularnewline
97 & 37 & 34.4502832621532 & 2.54971673784683 \tabularnewline
98 & 35 & 34.1462616386415 & 0.853738361358542 \tabularnewline
99 & 37 & 35.0566034259856 & 1.94339657401443 \tabularnewline
100 & 32 & 35.7858145367776 & -3.7858145367776 \tabularnewline
101 & 38 & 35.1994233556555 & 2.80057664434448 \tabularnewline
102 & 37 & 35.289996453108 & 1.71000354689196 \tabularnewline
103 & 36 & 36.8317336060348 & -0.831733606034831 \tabularnewline
104 & 32 & 32.7516824120635 & -0.751682412063518 \tabularnewline
105 & 33 & 35.9023748514252 & -2.90237485142521 \tabularnewline
106 & 40 & 32.6644319588359 & 7.33556804116406 \tabularnewline
107 & 38 & 35.299339371043 & 2.70066062895695 \tabularnewline
108 & 41 & 36.9993717440272 & 4.00062825597276 \tabularnewline
109 & 36 & 35.18353213153 & 0.816467868470052 \tabularnewline
110 & 43 & 36.9460033347557 & 6.05399666524431 \tabularnewline
111 & 30 & 34.7874549333578 & -4.78745493335781 \tabularnewline
112 & 31 & 33.9350222338935 & -2.93502223389347 \tabularnewline
113 & 32 & 37.1830260408836 & -5.18302604088362 \tabularnewline
114 & 32 & 33.8606349612209 & -1.86063496122092 \tabularnewline
115 & 37 & 33.7310663237387 & 3.26893367626127 \tabularnewline
116 & 37 & 35.7489410623984 & 1.2510589376016 \tabularnewline
117 & 33 & 36.2580529789977 & -3.25805297899772 \tabularnewline
118 & 34 & 36.2486152443086 & -2.2486152443086 \tabularnewline
119 & 33 & 34.6657619834595 & -1.6657619834595 \tabularnewline
120 & 38 & 35.34841852123 & 2.65158147877001 \tabularnewline
121 & 33 & 34.712908531285 & -1.71290853128503 \tabularnewline
122 & 31 & 33.1735748786667 & -2.17357487866672 \tabularnewline
123 & 38 & 35.884960996288 & 2.115039003712 \tabularnewline
124 & 37 & 36.0754921547023 & 0.924507845297741 \tabularnewline
125 & 33 & 33.1768754559232 & -0.176875455923187 \tabularnewline
126 & 31 & 33.795219552813 & -2.79521955281296 \tabularnewline
127 & 39 & 34.6847577949323 & 4.3152422050677 \tabularnewline
128 & 44 & 37.1193471382442 & 6.88065286175582 \tabularnewline
129 & 33 & 36.1192236872032 & -3.11922368720322 \tabularnewline
130 & 35 & 33.0557887685424 & 1.94421123145755 \tabularnewline
131 & 32 & 34.6691134913393 & -2.66911349133928 \tabularnewline
132 & 28 & 31.805226539315 & -3.80522653931495 \tabularnewline
133 & 40 & 36.3351244589322 & 3.66487554106777 \tabularnewline
134 & 27 & 32.0434986715418 & -5.04349867154175 \tabularnewline
135 & 37 & 35.5846100045926 & 1.41538999540739 \tabularnewline
136 & 32 & 33.1843924890262 & -1.18439248902621 \tabularnewline
137 & 28 & 29.7026964583689 & -1.7026964583689 \tabularnewline
138 & 34 & 35.4678378480811 & -1.46783784808108 \tabularnewline
139 & 30 & 33.728576196454 & -3.72857619645403 \tabularnewline
140 & 35 & 34.0645565039012 & 0.935443496098787 \tabularnewline
141 & 31 & 32.9085531585382 & -1.90855315853821 \tabularnewline
142 & 32 & 35.9275741039071 & -3.92757410390707 \tabularnewline
143 & 30 & 34.9090594870259 & -4.90905948702589 \tabularnewline
144 & 30 & 34.8057315144789 & -4.80573151447891 \tabularnewline
145 & 31 & 31.3654319024462 & -0.365431902446195 \tabularnewline
146 & 40 & 32.5180142295416 & 7.48198577045842 \tabularnewline
147 & 32 & 32.5396342276812 & -0.539634227681164 \tabularnewline
148 & 36 & 34.3067794687165 & 1.69322053128349 \tabularnewline
149 & 32 & 33.723090445833 & -1.72309044583297 \tabularnewline
150 & 35 & 33.5730060540632 & 1.42699394593683 \tabularnewline
151 & 38 & 35.2370914686065 & 2.76290853139347 \tabularnewline
152 & 42 & 35.419054356021 & 6.58094564397902 \tabularnewline
153 & 34 & 38.153101466343 & -4.15310146634302 \tabularnewline
154 & 35 & 36.9452221848903 & -1.94522218489034 \tabularnewline
155 & 35 & 34.6145213437614 & 0.385478656238567 \tabularnewline
156 & 33 & 31.5140209114042 & 1.48597908859577 \tabularnewline
157 & 36 & 33.6543980083845 & 2.3456019916155 \tabularnewline
158 & 32 & 35.4236131857092 & -3.4236131857092 \tabularnewline
159 & 33 & 36.1192236872032 & -3.11922368720322 \tabularnewline
160 & 34 & 34.5436806340121 & -0.543680634012099 \tabularnewline
161 & 32 & 33.9736518353552 & -1.97365183535517 \tabularnewline
162 & 34 & 34.746251553161 & -0.746251553161007 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197084&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]41[/C][C]35.4554453306979[/C][C]5.5445546693021[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.2422871240691[/C][C]4.75771287593086[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]35.1630827065041[/C][C]-5.16308270650408[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.6916162895056[/C][C]-3.69161628950562[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]35.4810074817441[/C][C]-1.48100748174405[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]32.6840669003044[/C][C]2.31593309969559[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.1279157819078[/C][C]4.87208421809221[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]34.9419835166443[/C][C]-0.941983516644307[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.32447714545[/C][C]1.67552285454999[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]35.8948697190975[/C][C]1.10513028090249[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.6054345886998[/C][C]3.39456541130016[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]34.9549124800794[/C][C]1.04508751992058[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.7835192833813[/C][C]3.21648071661866[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]36.2852490822423[/C][C]2.71475091775766[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.3849052347725[/C][C]-3.38490523477254[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]34.4075054976242[/C][C]-2.40750549762423[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.5859069935487[/C][C]1.41409300645126[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]37.586894468415[/C][C]0.413105531584959[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]37.809782876507[/C][C]1.19021712349297[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]34.5179557947042[/C][C]-2.51795579470423[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]35.0265954954915[/C][C]-3.02659549549152[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.2310842493247[/C][C]-2.23108424932467[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]37.2209062304738[/C][C]1.77909376952619[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.6324463012629[/C][C]0.367553698737139[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.034264003585[/C][C]3.96573599641496[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]33.4900983902617[/C][C]7.50990160973825[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]36.9925754757547[/C][C]-0.992575475754654[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.1491465914064[/C][C]-2.14914659140642[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.1658307329851[/C][C]-1.1658307329851[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]33.8457318962319[/C][C]0.154268103768148[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]32.4250496849249[/C][C]-1.4250496849249[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]33.1837143291825[/C][C]-6.18371432918253[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.6496128785883[/C][C]3.35038712141174[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]35.5787172448956[/C][C]-1.57871724489557[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]32.8439760809236[/C][C]1.15602391907638[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]32.4818124833785[/C][C]-0.481812483378492[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.0178087828109[/C][C]-3.01780878281091[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]33.933811248822[/C][C]2.06618875117798[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]33.982818195605[/C][C]-4.98281819560499[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.6998626604373[/C][C]0.300137339562716[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.5376655690573[/C][C]2.46233443094273[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]34.192696610525[/C][C]-0.192696610525012[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]35.3436190213139[/C][C]2.65638097868609[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]34.1750539216905[/C][C]0.824946078309502[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]32.5768151785563[/C][C]5.42318482144374[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]33.603726905888[/C][C]3.39627309411199[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]35.5673169202345[/C][C]2.43268307976554[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.3844515072456[/C][C]-1.38445150724557[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]35.7401777600452[/C][C]0.259822239954806[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]33.4971871760898[/C][C]4.50281282391019[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]35.4996716634071[/C][C]-3.49967166340711[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]33.0303237096856[/C][C]-1.03032370968565[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]32.8502194866465[/C][C]-0.850219486646487[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]36.0851897522487[/C][C]-2.08518975224872[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.6267448076735[/C][C]-0.626744807673533[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]33.9237899473717[/C][C]3.07621005262826[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.8868108477939[/C][C]4.11318915220614[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.3582718804106[/C][C]-5.35827188041062[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.4923163357325[/C][C]1.5076836642675[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.7679823261057[/C][C]0.232017673894309[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.4748441513158[/C][C]-1.47484415131582[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]35.3343561739726[/C][C]2.66564382602743[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.6875892434957[/C][C]-0.687589243495706[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]35.1158154703058[/C][C]-4.11581547030576[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]34.1191892982121[/C][C]-0.11918929821206[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]35.7706219462692[/C][C]-0.770621946269226[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]35.158513164241[/C][C]0.841486835758971[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]33.3963848865024[/C][C]-3.39638488650244[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]35.2583586276913[/C][C]3.74164137230874[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.8176216814778[/C][C]-0.817621681477816[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]34.0097886510977[/C][C]3.99021134890228[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.3383656976904[/C][C]-4.33836569769041[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]36.2936469281645[/C][C]-2.2936469281645[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.9602947800802[/C][C]0.0397052199198107[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]32.7625226357732[/C][C]1.23747736422685[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]33.7452299271449[/C][C]5.2547700728551[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.8373484476642[/C][C]1.16265155233575[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.967527530074[/C][C]0.0324724699260293[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]32.9304076942981[/C][C]-4.93040769429812[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]33.1154854231877[/C][C]3.88451457681225[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.8711738868363[/C][C]-2.8711738868363[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]37.5887243868326[/C][C]-0.588724386832647[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.6402155830916[/C][C]-0.640215583091571[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.0232884280591[/C][C]2.97671157194086[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]35.2280956858034[/C][C]-3.22809568580336[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]34.5591437999526[/C][C]-1.55914379995264[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.2096657834388[/C][C]1.79033421656124[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.4888820769972[/C][C]-1.48888207699717[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]33.9591727712755[/C][C]-4.95917277127548[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]32.7671840851604[/C][C]0.232815914839569[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]34.4005783105409[/C][C]-3.40057831054093[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]33.6543980083845[/C][C]2.3456019916155[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]36.837857911316[/C][C]-1.83785791131603[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.1033411796608[/C][C]-0.103341179660796[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]32.5155944651043[/C][C]-3.51559446510426[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]35.8257254861581[/C][C]3.17427451384187[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.4502832621532[/C][C]2.54971673784683[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.1462616386415[/C][C]0.853738361358542[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.0566034259856[/C][C]1.94339657401443[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]35.7858145367776[/C][C]-3.7858145367776[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.1994233556555[/C][C]2.80057664434448[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]35.289996453108[/C][C]1.71000354689196[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]36.8317336060348[/C][C]-0.831733606034831[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]32.7516824120635[/C][C]-0.751682412063518[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]35.9023748514252[/C][C]-2.90237485142521[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]32.6644319588359[/C][C]7.33556804116406[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.299339371043[/C][C]2.70066062895695[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.9993717440272[/C][C]4.00062825597276[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]35.18353213153[/C][C]0.816467868470052[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]36.9460033347557[/C][C]6.05399666524431[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]34.7874549333578[/C][C]-4.78745493335781[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]33.9350222338935[/C][C]-2.93502223389347[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]37.1830260408836[/C][C]-5.18302604088362[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]33.8606349612209[/C][C]-1.86063496122092[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]33.7310663237387[/C][C]3.26893367626127[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]35.7489410623984[/C][C]1.2510589376016[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]36.2580529789977[/C][C]-3.25805297899772[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.2486152443086[/C][C]-2.2486152443086[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]34.6657619834595[/C][C]-1.6657619834595[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]35.34841852123[/C][C]2.65158147877001[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]34.712908531285[/C][C]-1.71290853128503[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]33.1735748786667[/C][C]-2.17357487866672[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]35.884960996288[/C][C]2.115039003712[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]36.0754921547023[/C][C]0.924507845297741[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]33.1768754559232[/C][C]-0.176875455923187[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]33.795219552813[/C][C]-2.79521955281296[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.6847577949323[/C][C]4.3152422050677[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]37.1193471382442[/C][C]6.88065286175582[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]36.1192236872032[/C][C]-3.11922368720322[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]33.0557887685424[/C][C]1.94421123145755[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.6691134913393[/C][C]-2.66911349133928[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]31.805226539315[/C][C]-3.80522653931495[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]36.3351244589322[/C][C]3.66487554106777[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]32.0434986715418[/C][C]-5.04349867154175[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]35.5846100045926[/C][C]1.41538999540739[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]33.1843924890262[/C][C]-1.18439248902621[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]29.7026964583689[/C][C]-1.7026964583689[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]35.4678378480811[/C][C]-1.46783784808108[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.728576196454[/C][C]-3.72857619645403[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]34.0645565039012[/C][C]0.935443496098787[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]32.9085531585382[/C][C]-1.90855315853821[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]35.9275741039071[/C][C]-3.92757410390707[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]34.9090594870259[/C][C]-4.90905948702589[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]34.8057315144789[/C][C]-4.80573151447891[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]31.3654319024462[/C][C]-0.365431902446195[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]32.5180142295416[/C][C]7.48198577045842[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]32.5396342276812[/C][C]-0.539634227681164[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]34.3067794687165[/C][C]1.69322053128349[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.723090445833[/C][C]-1.72309044583297[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.5730060540632[/C][C]1.42699394593683[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]35.2370914686065[/C][C]2.76290853139347[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]35.419054356021[/C][C]6.58094564397902[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]38.153101466343[/C][C]-4.15310146634302[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]36.9452221848903[/C][C]-1.94522218489034[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]34.6145213437614[/C][C]0.385478656238567[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]31.5140209114042[/C][C]1.48597908859577[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]33.6543980083845[/C][C]2.3456019916155[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]35.4236131857092[/C][C]-3.4236131857092[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]36.1192236872032[/C][C]-3.11922368720322[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]34.5436806340121[/C][C]-0.543680634012099[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]33.9736518353552[/C][C]-1.97365183535517[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.746251553161[/C][C]-0.746251553161007[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197084&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197084&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
14135.45544533069795.5445546693021
23934.24228712406914.75771287593086
33035.1630827065041-5.16308270650408
43134.6916162895056-3.69161628950562
53435.4810074817441-1.48100748174405
63532.68406690030442.31593309969559
73934.12791578190784.87208421809221
83434.9419835166443-0.941983516644307
93634.324477145451.67552285454999
103735.89486971909751.10513028090249
113834.60543458869983.39456541130016
123634.95491248007941.04508751992058
133834.78351928338133.21648071661866
143936.28524908224232.71475091775766
153336.3849052347725-3.38490523477254
163234.4075054976242-2.40750549762423
173634.58590699354871.41409300645126
183837.5868944684150.413105531584959
193937.8097828765071.19021712349297
203234.5179557947042-2.51795579470423
213235.0265954954915-3.02659549549152
223133.2310842493247-2.23108424932467
233937.22090623047381.77909376952619
243736.63244630126290.367553698737139
253935.0342640035853.96573599641496
264133.49009839026177.50990160973825
273636.9925754757547-0.992575475754654
283335.1491465914064-2.14914659140642
293334.1658307329851-1.1658307329851
303433.84573189623190.154268103768148
313132.4250496849249-1.4250496849249
322733.1837143291825-6.18371432918253
333733.64961287858833.35038712141174
343435.5787172448956-1.57871724489557
353432.84397608092361.15602391907638
363232.4818124833785-0.481812483378492
372932.0178087828109-3.01780878281091
383633.9338112488222.06618875117798
392933.982818195605-4.98281819560499
403534.69986266043730.300137339562716
413734.53766556905732.46233443094273
423434.192696610525-0.192696610525012
433835.34361902131392.65638097868609
443534.17505392169050.824946078309502
453832.57681517855635.42318482144374
463733.6037269058883.39627309411199
473835.56731692023452.43268307976554
483334.3844515072456-1.38445150724557
493635.74017776004520.259822239954806
503833.49718717608984.50281282391019
513235.4996716634071-3.49967166340711
523233.0303237096856-1.03032370968565
533232.8502194866465-0.850219486646487
543436.0851897522487-2.08518975224872
553232.6267448076735-0.626744807673533
563733.92378994737173.07621005262826
573934.88681084779394.11318915220614
582934.3582718804106-5.35827188041062
593735.49231633573251.5076836642675
603534.76798232610570.232017673894309
613031.4748441513158-1.47484415131582
623835.33435617397262.66564382602743
633434.6875892434957-0.687589243495706
643135.1158154703058-4.11581547030576
653434.1191892982121-0.11918929821206
663535.7706219462692-0.770621946269226
673635.1585131642410.841486835758971
683033.3963848865024-3.39638488650244
693935.25835862769133.74164137230874
703535.8176216814778-0.817621681477816
713834.00978865109773.99021134890228
723135.3383656976904-4.33836569769041
733436.2936469281645-2.2936469281645
743837.96029478008020.0397052199198107
753432.76252263577321.23747736422685
763933.74522992714495.2547700728551
773735.83734844766421.16265155233575
783433.9675275300740.0324724699260293
792832.9304076942981-4.93040769429812
803733.11548542318773.88451457681225
813335.8711738868363-2.8711738868363
823737.5887243868326-0.588724386832647
833535.6402155830916-0.640215583091571
843734.02328842805912.97671157194086
853235.2280956858034-3.22809568580336
863334.5591437999526-1.55914379995264
873836.20966578343881.79033421656124
883334.4888820769972-1.48888207699717
892933.9591727712755-4.95917277127548
903332.76718408516040.232815914839569
913134.4005783105409-3.40057831054093
923633.65439800838452.3456019916155
933536.837857911316-1.83785791131603
943232.1033411796608-0.103341179660796
952932.5155944651043-3.51559446510426
963935.82572548615813.17427451384187
973734.45028326215322.54971673784683
983534.14626163864150.853738361358542
993735.05660342598561.94339657401443
1003235.7858145367776-3.7858145367776
1013835.19942335565552.80057664434448
1023735.2899964531081.71000354689196
1033636.8317336060348-0.831733606034831
1043232.7516824120635-0.751682412063518
1053335.9023748514252-2.90237485142521
1064032.66443195883597.33556804116406
1073835.2993393710432.70066062895695
1084136.99937174402724.00062825597276
1093635.183532131530.816467868470052
1104336.94600333475576.05399666524431
1113034.7874549333578-4.78745493335781
1123133.9350222338935-2.93502223389347
1133237.1830260408836-5.18302604088362
1143233.8606349612209-1.86063496122092
1153733.73106632373873.26893367626127
1163735.74894106239841.2510589376016
1173336.2580529789977-3.25805297899772
1183436.2486152443086-2.2486152443086
1193334.6657619834595-1.6657619834595
1203835.348418521232.65158147877001
1213334.712908531285-1.71290853128503
1223133.1735748786667-2.17357487866672
1233835.8849609962882.115039003712
1243736.07549215470230.924507845297741
1253333.1768754559232-0.176875455923187
1263133.795219552813-2.79521955281296
1273934.68475779493234.3152422050677
1284437.11934713824426.88065286175582
1293336.1192236872032-3.11922368720322
1303533.05578876854241.94421123145755
1313234.6691134913393-2.66911349133928
1322831.805226539315-3.80522653931495
1334036.33512445893223.66487554106777
1342732.0434986715418-5.04349867154175
1353735.58461000459261.41538999540739
1363233.1843924890262-1.18439248902621
1372829.7026964583689-1.7026964583689
1383435.4678378480811-1.46783784808108
1393033.728576196454-3.72857619645403
1403534.06455650390120.935443496098787
1413132.9085531585382-1.90855315853821
1423235.9275741039071-3.92757410390707
1433034.9090594870259-4.90905948702589
1443034.8057315144789-4.80573151447891
1453131.3654319024462-0.365431902446195
1464032.51801422954167.48198577045842
1473232.5396342276812-0.539634227681164
1483634.30677946871651.69322053128349
1493233.723090445833-1.72309044583297
1503533.57300605406321.42699394593683
1513835.23709146860652.76290853139347
1524235.4190543560216.58094564397902
1533438.153101466343-4.15310146634302
1543536.9452221848903-1.94522218489034
1553534.61452134376140.385478656238567
1563331.51402091140421.48597908859577
1573633.65439800838452.3456019916155
1583235.4236131857092-3.4236131857092
1593336.1192236872032-3.11922368720322
1603434.5436806340121-0.543680634012099
1613233.9736518353552-1.97365183535517
1623434.746251553161-0.746251553161007






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.9487215221470950.1025569557058090.0512784778529046
120.9331875882168230.1336248235663540.0668124117831771
130.9226861779255110.1546276441489780.0773138220744892
140.8921057756727150.2157884486545690.107894224327285
150.8670059774217590.2659880451564820.132994022578241
160.8931273840533230.2137452318933550.106872615946677
170.8583408296234250.2833183407531510.141659170376575
180.8213768586616380.3572462826767240.178623141338362
190.8039390080373960.3921219839252080.196060991962604
200.7888594133279710.4222811733440570.211140586672029
210.8023372193959350.395325561208130.197662780604065
220.7616312551456050.4767374897087890.238368744854395
230.7302621773424060.5394756453151880.269737822657594
240.6644610893004030.6710778213991930.335538910699597
250.6549067887600920.6901864224798150.345093211239908
260.8712982636052420.2574034727895160.128701736394758
270.8332225533410070.3335548933179860.166777446658993
280.8078218036909610.3843563926180780.192178196309039
290.7807008740581560.4385982518836880.219299125941844
300.7305602189762170.5388795620475660.269439781023783
310.7106545518508490.5786908962983020.289345448149151
320.827553016799450.3448939664010990.17244698320055
330.8113918179769580.3772163640460850.188608182023042
340.7844834919506260.4310330160987480.215516508049374
350.7561804948054270.4876390103891450.243819505194573
360.709484561315550.5810308773688990.29051543868445
370.6861695211004220.6276609577991550.313830478899578
380.6706015777661160.6587968444677690.329398422233884
390.7804015324918570.4391969350162860.219598467508143
400.7366912323110920.5266175353778160.263308767688908
410.7104543761864990.5790912476270010.289545623813501
420.6613473109523210.6773053780953580.338652689047679
430.6291334141300090.7417331717399830.370866585869991
440.5822019033754410.8355961932491180.417798096624559
450.6413435716634150.7173128566731710.358656428336585
460.6405621289939710.7188757420120570.359437871006029
470.6176004791813280.7647990416373450.382399520818672
480.5854440580876210.8291118838247580.414555941912379
490.5375483414831210.9249033170337580.462451658516879
500.5802861851613770.8394276296772450.419713814838623
510.593037080836730.8139258383265390.406962919163269
520.5484835122844530.9030329754310950.451516487715547
530.4996341252075720.9992682504151450.500365874792428
540.4785446741832390.9570893483664780.521455325816761
550.4305867536282180.8611735072564360.569413246371782
560.42073937686270.8414787537254010.5792606231373
570.4464566581875490.8929133163750980.553543341812451
580.556815567979960.886368864040080.44318443202004
590.5183895746869680.9632208506260640.481610425313032
600.4712892288523260.9425784577046520.528710771147674
610.4346542781576870.8693085563153740.565345721842313
620.4139204770032830.8278409540065660.586079522996717
630.3750721952748870.7501443905497740.624927804725113
640.4228938445617980.8457876891235960.577106155438202
650.3798133010585750.759626602117150.620186698941425
660.3381504615256750.676300923051350.661849538474325
670.2978300247143520.5956600494287040.702169975285648
680.3268187514834010.6536375029668020.673181248516599
690.3500943468883220.7001886937766430.649905653111678
700.3089677859746250.6179355719492490.691032214025375
710.3367888299882760.6735776599765520.663211170011724
720.3759796051323860.7519592102647730.624020394867614
730.3525974791691750.705194958338350.647402520830825
740.3096632694180970.6193265388361940.690336730581903
750.2768251435452660.5536502870905330.723174856454734
760.348466842556030.6969336851120610.65153315744397
770.3106652000965790.6213304001931580.689334799903421
780.271015823774390.542031647548780.72898417622561
790.3239785246517590.6479570493035180.676021475348241
800.3542526239167120.7085052478334240.645747376083288
810.3445942916480990.6891885832961980.655405708351901
820.3030155103436310.6060310206872620.696984489656369
830.2658183193532860.5316366387065730.734181680646714
840.2685109887188680.5370219774377350.731489011281133
850.2773079542752190.5546159085504380.722692045724781
860.2517141229152020.5034282458304040.748285877084798
870.2291900684909520.4583801369819050.770809931509048
880.2021135575385910.4042271150771830.797886442461409
890.2531560865624490.5063121731248980.746843913437551
900.2178548572173090.4357097144346180.782145142782691
910.2255853618723130.4511707237446250.774414638127687
920.2092495911446770.4184991822893550.790750408855323
930.1889664216128640.3779328432257270.811033578387136
940.1587256635089130.3174513270178260.841274336491087
950.1622079957249530.3244159914499050.837792004275048
960.1630550948836810.3261101897673620.836944905116319
970.1533054544695810.3066109089391630.846694545530418
980.1332749456056590.2665498912113190.866725054394341
990.1186477775426480.2372955550852960.881352222457352
1000.1261321522793940.2522643045587890.873867847720606
1010.119926248073410.239852496146820.88007375192659
1020.1054543959906530.2109087919813060.894545604009347
1030.08601938160710120.1720387632142020.913980618392899
1040.07037071429546630.1407414285909330.929629285704534
1050.06825579145799570.1365115829159910.931744208542004
1060.1835762619078980.3671525238157950.816423738092102
1070.178324987260370.3566499745207410.82167501273963
1080.1964842268117450.3929684536234910.803515773188255
1090.1810667343566040.3621334687132080.818933265643396
1100.2944003416266160.5888006832532320.705599658373384
1110.3425661778703310.6851323557406630.657433822129669
1120.3234121368910150.646824273782030.676587863108985
1130.3812930791144260.7625861582288530.618706920885574
1140.3424800325461910.6849600650923810.657519967453809
1150.3287907424158890.6575814848317790.671209257584111
1160.3072736989355070.6145473978710140.692726301064493
1170.3147444056104830.6294888112209660.685255594389517
1180.2949511741157920.5899023482315840.705048825884208
1190.2663819891307870.5327639782615730.733618010869213
1200.254405358730160.508810717460320.74559464126984
1210.2268615145242670.4537230290485340.773138485475733
1220.1992750485423520.3985500970847030.800724951457648
1230.1706556883302520.3413113766605050.829344311669748
1240.1385731161491430.2771462322982870.861426883850857
1250.1107063280865440.2214126561730870.889293671913456
1260.1185598933709340.2371197867418680.881440106629066
1270.1782684092922030.3565368185844050.821731590707798
1280.3764998923348810.7529997846697610.623500107665119
1290.3511179651649920.7022359303299840.648882034835008
1300.3028030374332240.6056060748664490.697196962566776
1310.2591594069533590.5183188139067170.740840593046641
1320.2787162941419180.5574325882838360.721283705858082
1330.3733460505102630.7466921010205260.626653949489737
1340.4592838963306020.9185677926612040.540716103669398
1350.4366246593482470.8732493186964940.563375340651753
1360.3961110804634840.7922221609269690.603888919536516
1370.3614334975900760.7228669951801520.638566502409924
1380.2981021311681790.5962042623363580.701897868831821
1390.5569740962986590.8860518074026830.443025903701341
1400.4784184337095320.9568368674190640.521581566290468
1410.6104049729242550.779190054151490.389595027075745
1420.5718949033135840.8562101933728320.428105096686416
1430.5020210671878960.9959578656242090.497978932812104
1440.7699867035596970.4600265928806070.230013296440303
1450.7295407916568950.5409184166862090.270459208343105
1460.9257334885857150.1485330228285690.0742665114142847
1470.9104259867665660.1791480264668690.0895740132334343
1480.9065632435428580.1868735129142840.0934367564571421
1490.8469591308236830.3060817383526350.153040869176317
1500.8021299067507660.3957401864984680.197870093249234
1510.6535926665434730.6928146669130540.346407333456527

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.948721522147095 & 0.102556955705809 & 0.0512784778529046 \tabularnewline
12 & 0.933187588216823 & 0.133624823566354 & 0.0668124117831771 \tabularnewline
13 & 0.922686177925511 & 0.154627644148978 & 0.0773138220744892 \tabularnewline
14 & 0.892105775672715 & 0.215788448654569 & 0.107894224327285 \tabularnewline
15 & 0.867005977421759 & 0.265988045156482 & 0.132994022578241 \tabularnewline
16 & 0.893127384053323 & 0.213745231893355 & 0.106872615946677 \tabularnewline
17 & 0.858340829623425 & 0.283318340753151 & 0.141659170376575 \tabularnewline
18 & 0.821376858661638 & 0.357246282676724 & 0.178623141338362 \tabularnewline
19 & 0.803939008037396 & 0.392121983925208 & 0.196060991962604 \tabularnewline
20 & 0.788859413327971 & 0.422281173344057 & 0.211140586672029 \tabularnewline
21 & 0.802337219395935 & 0.39532556120813 & 0.197662780604065 \tabularnewline
22 & 0.761631255145605 & 0.476737489708789 & 0.238368744854395 \tabularnewline
23 & 0.730262177342406 & 0.539475645315188 & 0.269737822657594 \tabularnewline
24 & 0.664461089300403 & 0.671077821399193 & 0.335538910699597 \tabularnewline
25 & 0.654906788760092 & 0.690186422479815 & 0.345093211239908 \tabularnewline
26 & 0.871298263605242 & 0.257403472789516 & 0.128701736394758 \tabularnewline
27 & 0.833222553341007 & 0.333554893317986 & 0.166777446658993 \tabularnewline
28 & 0.807821803690961 & 0.384356392618078 & 0.192178196309039 \tabularnewline
29 & 0.780700874058156 & 0.438598251883688 & 0.219299125941844 \tabularnewline
30 & 0.730560218976217 & 0.538879562047566 & 0.269439781023783 \tabularnewline
31 & 0.710654551850849 & 0.578690896298302 & 0.289345448149151 \tabularnewline
32 & 0.82755301679945 & 0.344893966401099 & 0.17244698320055 \tabularnewline
33 & 0.811391817976958 & 0.377216364046085 & 0.188608182023042 \tabularnewline
34 & 0.784483491950626 & 0.431033016098748 & 0.215516508049374 \tabularnewline
35 & 0.756180494805427 & 0.487639010389145 & 0.243819505194573 \tabularnewline
36 & 0.70948456131555 & 0.581030877368899 & 0.29051543868445 \tabularnewline
37 & 0.686169521100422 & 0.627660957799155 & 0.313830478899578 \tabularnewline
38 & 0.670601577766116 & 0.658796844467769 & 0.329398422233884 \tabularnewline
39 & 0.780401532491857 & 0.439196935016286 & 0.219598467508143 \tabularnewline
40 & 0.736691232311092 & 0.526617535377816 & 0.263308767688908 \tabularnewline
41 & 0.710454376186499 & 0.579091247627001 & 0.289545623813501 \tabularnewline
42 & 0.661347310952321 & 0.677305378095358 & 0.338652689047679 \tabularnewline
43 & 0.629133414130009 & 0.741733171739983 & 0.370866585869991 \tabularnewline
44 & 0.582201903375441 & 0.835596193249118 & 0.417798096624559 \tabularnewline
45 & 0.641343571663415 & 0.717312856673171 & 0.358656428336585 \tabularnewline
46 & 0.640562128993971 & 0.718875742012057 & 0.359437871006029 \tabularnewline
47 & 0.617600479181328 & 0.764799041637345 & 0.382399520818672 \tabularnewline
48 & 0.585444058087621 & 0.829111883824758 & 0.414555941912379 \tabularnewline
49 & 0.537548341483121 & 0.924903317033758 & 0.462451658516879 \tabularnewline
50 & 0.580286185161377 & 0.839427629677245 & 0.419713814838623 \tabularnewline
51 & 0.59303708083673 & 0.813925838326539 & 0.406962919163269 \tabularnewline
52 & 0.548483512284453 & 0.903032975431095 & 0.451516487715547 \tabularnewline
53 & 0.499634125207572 & 0.999268250415145 & 0.500365874792428 \tabularnewline
54 & 0.478544674183239 & 0.957089348366478 & 0.521455325816761 \tabularnewline
55 & 0.430586753628218 & 0.861173507256436 & 0.569413246371782 \tabularnewline
56 & 0.4207393768627 & 0.841478753725401 & 0.5792606231373 \tabularnewline
57 & 0.446456658187549 & 0.892913316375098 & 0.553543341812451 \tabularnewline
58 & 0.55681556797996 & 0.88636886404008 & 0.44318443202004 \tabularnewline
59 & 0.518389574686968 & 0.963220850626064 & 0.481610425313032 \tabularnewline
60 & 0.471289228852326 & 0.942578457704652 & 0.528710771147674 \tabularnewline
61 & 0.434654278157687 & 0.869308556315374 & 0.565345721842313 \tabularnewline
62 & 0.413920477003283 & 0.827840954006566 & 0.586079522996717 \tabularnewline
63 & 0.375072195274887 & 0.750144390549774 & 0.624927804725113 \tabularnewline
64 & 0.422893844561798 & 0.845787689123596 & 0.577106155438202 \tabularnewline
65 & 0.379813301058575 & 0.75962660211715 & 0.620186698941425 \tabularnewline
66 & 0.338150461525675 & 0.67630092305135 & 0.661849538474325 \tabularnewline
67 & 0.297830024714352 & 0.595660049428704 & 0.702169975285648 \tabularnewline
68 & 0.326818751483401 & 0.653637502966802 & 0.673181248516599 \tabularnewline
69 & 0.350094346888322 & 0.700188693776643 & 0.649905653111678 \tabularnewline
70 & 0.308967785974625 & 0.617935571949249 & 0.691032214025375 \tabularnewline
71 & 0.336788829988276 & 0.673577659976552 & 0.663211170011724 \tabularnewline
72 & 0.375979605132386 & 0.751959210264773 & 0.624020394867614 \tabularnewline
73 & 0.352597479169175 & 0.70519495833835 & 0.647402520830825 \tabularnewline
74 & 0.309663269418097 & 0.619326538836194 & 0.690336730581903 \tabularnewline
75 & 0.276825143545266 & 0.553650287090533 & 0.723174856454734 \tabularnewline
76 & 0.34846684255603 & 0.696933685112061 & 0.65153315744397 \tabularnewline
77 & 0.310665200096579 & 0.621330400193158 & 0.689334799903421 \tabularnewline
78 & 0.27101582377439 & 0.54203164754878 & 0.72898417622561 \tabularnewline
79 & 0.323978524651759 & 0.647957049303518 & 0.676021475348241 \tabularnewline
80 & 0.354252623916712 & 0.708505247833424 & 0.645747376083288 \tabularnewline
81 & 0.344594291648099 & 0.689188583296198 & 0.655405708351901 \tabularnewline
82 & 0.303015510343631 & 0.606031020687262 & 0.696984489656369 \tabularnewline
83 & 0.265818319353286 & 0.531636638706573 & 0.734181680646714 \tabularnewline
84 & 0.268510988718868 & 0.537021977437735 & 0.731489011281133 \tabularnewline
85 & 0.277307954275219 & 0.554615908550438 & 0.722692045724781 \tabularnewline
86 & 0.251714122915202 & 0.503428245830404 & 0.748285877084798 \tabularnewline
87 & 0.229190068490952 & 0.458380136981905 & 0.770809931509048 \tabularnewline
88 & 0.202113557538591 & 0.404227115077183 & 0.797886442461409 \tabularnewline
89 & 0.253156086562449 & 0.506312173124898 & 0.746843913437551 \tabularnewline
90 & 0.217854857217309 & 0.435709714434618 & 0.782145142782691 \tabularnewline
91 & 0.225585361872313 & 0.451170723744625 & 0.774414638127687 \tabularnewline
92 & 0.209249591144677 & 0.418499182289355 & 0.790750408855323 \tabularnewline
93 & 0.188966421612864 & 0.377932843225727 & 0.811033578387136 \tabularnewline
94 & 0.158725663508913 & 0.317451327017826 & 0.841274336491087 \tabularnewline
95 & 0.162207995724953 & 0.324415991449905 & 0.837792004275048 \tabularnewline
96 & 0.163055094883681 & 0.326110189767362 & 0.836944905116319 \tabularnewline
97 & 0.153305454469581 & 0.306610908939163 & 0.846694545530418 \tabularnewline
98 & 0.133274945605659 & 0.266549891211319 & 0.866725054394341 \tabularnewline
99 & 0.118647777542648 & 0.237295555085296 & 0.881352222457352 \tabularnewline
100 & 0.126132152279394 & 0.252264304558789 & 0.873867847720606 \tabularnewline
101 & 0.11992624807341 & 0.23985249614682 & 0.88007375192659 \tabularnewline
102 & 0.105454395990653 & 0.210908791981306 & 0.894545604009347 \tabularnewline
103 & 0.0860193816071012 & 0.172038763214202 & 0.913980618392899 \tabularnewline
104 & 0.0703707142954663 & 0.140741428590933 & 0.929629285704534 \tabularnewline
105 & 0.0682557914579957 & 0.136511582915991 & 0.931744208542004 \tabularnewline
106 & 0.183576261907898 & 0.367152523815795 & 0.816423738092102 \tabularnewline
107 & 0.17832498726037 & 0.356649974520741 & 0.82167501273963 \tabularnewline
108 & 0.196484226811745 & 0.392968453623491 & 0.803515773188255 \tabularnewline
109 & 0.181066734356604 & 0.362133468713208 & 0.818933265643396 \tabularnewline
110 & 0.294400341626616 & 0.588800683253232 & 0.705599658373384 \tabularnewline
111 & 0.342566177870331 & 0.685132355740663 & 0.657433822129669 \tabularnewline
112 & 0.323412136891015 & 0.64682427378203 & 0.676587863108985 \tabularnewline
113 & 0.381293079114426 & 0.762586158228853 & 0.618706920885574 \tabularnewline
114 & 0.342480032546191 & 0.684960065092381 & 0.657519967453809 \tabularnewline
115 & 0.328790742415889 & 0.657581484831779 & 0.671209257584111 \tabularnewline
116 & 0.307273698935507 & 0.614547397871014 & 0.692726301064493 \tabularnewline
117 & 0.314744405610483 & 0.629488811220966 & 0.685255594389517 \tabularnewline
118 & 0.294951174115792 & 0.589902348231584 & 0.705048825884208 \tabularnewline
119 & 0.266381989130787 & 0.532763978261573 & 0.733618010869213 \tabularnewline
120 & 0.25440535873016 & 0.50881071746032 & 0.74559464126984 \tabularnewline
121 & 0.226861514524267 & 0.453723029048534 & 0.773138485475733 \tabularnewline
122 & 0.199275048542352 & 0.398550097084703 & 0.800724951457648 \tabularnewline
123 & 0.170655688330252 & 0.341311376660505 & 0.829344311669748 \tabularnewline
124 & 0.138573116149143 & 0.277146232298287 & 0.861426883850857 \tabularnewline
125 & 0.110706328086544 & 0.221412656173087 & 0.889293671913456 \tabularnewline
126 & 0.118559893370934 & 0.237119786741868 & 0.881440106629066 \tabularnewline
127 & 0.178268409292203 & 0.356536818584405 & 0.821731590707798 \tabularnewline
128 & 0.376499892334881 & 0.752999784669761 & 0.623500107665119 \tabularnewline
129 & 0.351117965164992 & 0.702235930329984 & 0.648882034835008 \tabularnewline
130 & 0.302803037433224 & 0.605606074866449 & 0.697196962566776 \tabularnewline
131 & 0.259159406953359 & 0.518318813906717 & 0.740840593046641 \tabularnewline
132 & 0.278716294141918 & 0.557432588283836 & 0.721283705858082 \tabularnewline
133 & 0.373346050510263 & 0.746692101020526 & 0.626653949489737 \tabularnewline
134 & 0.459283896330602 & 0.918567792661204 & 0.540716103669398 \tabularnewline
135 & 0.436624659348247 & 0.873249318696494 & 0.563375340651753 \tabularnewline
136 & 0.396111080463484 & 0.792222160926969 & 0.603888919536516 \tabularnewline
137 & 0.361433497590076 & 0.722866995180152 & 0.638566502409924 \tabularnewline
138 & 0.298102131168179 & 0.596204262336358 & 0.701897868831821 \tabularnewline
139 & 0.556974096298659 & 0.886051807402683 & 0.443025903701341 \tabularnewline
140 & 0.478418433709532 & 0.956836867419064 & 0.521581566290468 \tabularnewline
141 & 0.610404972924255 & 0.77919005415149 & 0.389595027075745 \tabularnewline
142 & 0.571894903313584 & 0.856210193372832 & 0.428105096686416 \tabularnewline
143 & 0.502021067187896 & 0.995957865624209 & 0.497978932812104 \tabularnewline
144 & 0.769986703559697 & 0.460026592880607 & 0.230013296440303 \tabularnewline
145 & 0.729540791656895 & 0.540918416686209 & 0.270459208343105 \tabularnewline
146 & 0.925733488585715 & 0.148533022828569 & 0.0742665114142847 \tabularnewline
147 & 0.910425986766566 & 0.179148026466869 & 0.0895740132334343 \tabularnewline
148 & 0.906563243542858 & 0.186873512914284 & 0.0934367564571421 \tabularnewline
149 & 0.846959130823683 & 0.306081738352635 & 0.153040869176317 \tabularnewline
150 & 0.802129906750766 & 0.395740186498468 & 0.197870093249234 \tabularnewline
151 & 0.653592666543473 & 0.692814666913054 & 0.346407333456527 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197084&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.948721522147095[/C][C]0.102556955705809[/C][C]0.0512784778529046[/C][/ROW]
[ROW][C]12[/C][C]0.933187588216823[/C][C]0.133624823566354[/C][C]0.0668124117831771[/C][/ROW]
[ROW][C]13[/C][C]0.922686177925511[/C][C]0.154627644148978[/C][C]0.0773138220744892[/C][/ROW]
[ROW][C]14[/C][C]0.892105775672715[/C][C]0.215788448654569[/C][C]0.107894224327285[/C][/ROW]
[ROW][C]15[/C][C]0.867005977421759[/C][C]0.265988045156482[/C][C]0.132994022578241[/C][/ROW]
[ROW][C]16[/C][C]0.893127384053323[/C][C]0.213745231893355[/C][C]0.106872615946677[/C][/ROW]
[ROW][C]17[/C][C]0.858340829623425[/C][C]0.283318340753151[/C][C]0.141659170376575[/C][/ROW]
[ROW][C]18[/C][C]0.821376858661638[/C][C]0.357246282676724[/C][C]0.178623141338362[/C][/ROW]
[ROW][C]19[/C][C]0.803939008037396[/C][C]0.392121983925208[/C][C]0.196060991962604[/C][/ROW]
[ROW][C]20[/C][C]0.788859413327971[/C][C]0.422281173344057[/C][C]0.211140586672029[/C][/ROW]
[ROW][C]21[/C][C]0.802337219395935[/C][C]0.39532556120813[/C][C]0.197662780604065[/C][/ROW]
[ROW][C]22[/C][C]0.761631255145605[/C][C]0.476737489708789[/C][C]0.238368744854395[/C][/ROW]
[ROW][C]23[/C][C]0.730262177342406[/C][C]0.539475645315188[/C][C]0.269737822657594[/C][/ROW]
[ROW][C]24[/C][C]0.664461089300403[/C][C]0.671077821399193[/C][C]0.335538910699597[/C][/ROW]
[ROW][C]25[/C][C]0.654906788760092[/C][C]0.690186422479815[/C][C]0.345093211239908[/C][/ROW]
[ROW][C]26[/C][C]0.871298263605242[/C][C]0.257403472789516[/C][C]0.128701736394758[/C][/ROW]
[ROW][C]27[/C][C]0.833222553341007[/C][C]0.333554893317986[/C][C]0.166777446658993[/C][/ROW]
[ROW][C]28[/C][C]0.807821803690961[/C][C]0.384356392618078[/C][C]0.192178196309039[/C][/ROW]
[ROW][C]29[/C][C]0.780700874058156[/C][C]0.438598251883688[/C][C]0.219299125941844[/C][/ROW]
[ROW][C]30[/C][C]0.730560218976217[/C][C]0.538879562047566[/C][C]0.269439781023783[/C][/ROW]
[ROW][C]31[/C][C]0.710654551850849[/C][C]0.578690896298302[/C][C]0.289345448149151[/C][/ROW]
[ROW][C]32[/C][C]0.82755301679945[/C][C]0.344893966401099[/C][C]0.17244698320055[/C][/ROW]
[ROW][C]33[/C][C]0.811391817976958[/C][C]0.377216364046085[/C][C]0.188608182023042[/C][/ROW]
[ROW][C]34[/C][C]0.784483491950626[/C][C]0.431033016098748[/C][C]0.215516508049374[/C][/ROW]
[ROW][C]35[/C][C]0.756180494805427[/C][C]0.487639010389145[/C][C]0.243819505194573[/C][/ROW]
[ROW][C]36[/C][C]0.70948456131555[/C][C]0.581030877368899[/C][C]0.29051543868445[/C][/ROW]
[ROW][C]37[/C][C]0.686169521100422[/C][C]0.627660957799155[/C][C]0.313830478899578[/C][/ROW]
[ROW][C]38[/C][C]0.670601577766116[/C][C]0.658796844467769[/C][C]0.329398422233884[/C][/ROW]
[ROW][C]39[/C][C]0.780401532491857[/C][C]0.439196935016286[/C][C]0.219598467508143[/C][/ROW]
[ROW][C]40[/C][C]0.736691232311092[/C][C]0.526617535377816[/C][C]0.263308767688908[/C][/ROW]
[ROW][C]41[/C][C]0.710454376186499[/C][C]0.579091247627001[/C][C]0.289545623813501[/C][/ROW]
[ROW][C]42[/C][C]0.661347310952321[/C][C]0.677305378095358[/C][C]0.338652689047679[/C][/ROW]
[ROW][C]43[/C][C]0.629133414130009[/C][C]0.741733171739983[/C][C]0.370866585869991[/C][/ROW]
[ROW][C]44[/C][C]0.582201903375441[/C][C]0.835596193249118[/C][C]0.417798096624559[/C][/ROW]
[ROW][C]45[/C][C]0.641343571663415[/C][C]0.717312856673171[/C][C]0.358656428336585[/C][/ROW]
[ROW][C]46[/C][C]0.640562128993971[/C][C]0.718875742012057[/C][C]0.359437871006029[/C][/ROW]
[ROW][C]47[/C][C]0.617600479181328[/C][C]0.764799041637345[/C][C]0.382399520818672[/C][/ROW]
[ROW][C]48[/C][C]0.585444058087621[/C][C]0.829111883824758[/C][C]0.414555941912379[/C][/ROW]
[ROW][C]49[/C][C]0.537548341483121[/C][C]0.924903317033758[/C][C]0.462451658516879[/C][/ROW]
[ROW][C]50[/C][C]0.580286185161377[/C][C]0.839427629677245[/C][C]0.419713814838623[/C][/ROW]
[ROW][C]51[/C][C]0.59303708083673[/C][C]0.813925838326539[/C][C]0.406962919163269[/C][/ROW]
[ROW][C]52[/C][C]0.548483512284453[/C][C]0.903032975431095[/C][C]0.451516487715547[/C][/ROW]
[ROW][C]53[/C][C]0.499634125207572[/C][C]0.999268250415145[/C][C]0.500365874792428[/C][/ROW]
[ROW][C]54[/C][C]0.478544674183239[/C][C]0.957089348366478[/C][C]0.521455325816761[/C][/ROW]
[ROW][C]55[/C][C]0.430586753628218[/C][C]0.861173507256436[/C][C]0.569413246371782[/C][/ROW]
[ROW][C]56[/C][C]0.4207393768627[/C][C]0.841478753725401[/C][C]0.5792606231373[/C][/ROW]
[ROW][C]57[/C][C]0.446456658187549[/C][C]0.892913316375098[/C][C]0.553543341812451[/C][/ROW]
[ROW][C]58[/C][C]0.55681556797996[/C][C]0.88636886404008[/C][C]0.44318443202004[/C][/ROW]
[ROW][C]59[/C][C]0.518389574686968[/C][C]0.963220850626064[/C][C]0.481610425313032[/C][/ROW]
[ROW][C]60[/C][C]0.471289228852326[/C][C]0.942578457704652[/C][C]0.528710771147674[/C][/ROW]
[ROW][C]61[/C][C]0.434654278157687[/C][C]0.869308556315374[/C][C]0.565345721842313[/C][/ROW]
[ROW][C]62[/C][C]0.413920477003283[/C][C]0.827840954006566[/C][C]0.586079522996717[/C][/ROW]
[ROW][C]63[/C][C]0.375072195274887[/C][C]0.750144390549774[/C][C]0.624927804725113[/C][/ROW]
[ROW][C]64[/C][C]0.422893844561798[/C][C]0.845787689123596[/C][C]0.577106155438202[/C][/ROW]
[ROW][C]65[/C][C]0.379813301058575[/C][C]0.75962660211715[/C][C]0.620186698941425[/C][/ROW]
[ROW][C]66[/C][C]0.338150461525675[/C][C]0.67630092305135[/C][C]0.661849538474325[/C][/ROW]
[ROW][C]67[/C][C]0.297830024714352[/C][C]0.595660049428704[/C][C]0.702169975285648[/C][/ROW]
[ROW][C]68[/C][C]0.326818751483401[/C][C]0.653637502966802[/C][C]0.673181248516599[/C][/ROW]
[ROW][C]69[/C][C]0.350094346888322[/C][C]0.700188693776643[/C][C]0.649905653111678[/C][/ROW]
[ROW][C]70[/C][C]0.308967785974625[/C][C]0.617935571949249[/C][C]0.691032214025375[/C][/ROW]
[ROW][C]71[/C][C]0.336788829988276[/C][C]0.673577659976552[/C][C]0.663211170011724[/C][/ROW]
[ROW][C]72[/C][C]0.375979605132386[/C][C]0.751959210264773[/C][C]0.624020394867614[/C][/ROW]
[ROW][C]73[/C][C]0.352597479169175[/C][C]0.70519495833835[/C][C]0.647402520830825[/C][/ROW]
[ROW][C]74[/C][C]0.309663269418097[/C][C]0.619326538836194[/C][C]0.690336730581903[/C][/ROW]
[ROW][C]75[/C][C]0.276825143545266[/C][C]0.553650287090533[/C][C]0.723174856454734[/C][/ROW]
[ROW][C]76[/C][C]0.34846684255603[/C][C]0.696933685112061[/C][C]0.65153315744397[/C][/ROW]
[ROW][C]77[/C][C]0.310665200096579[/C][C]0.621330400193158[/C][C]0.689334799903421[/C][/ROW]
[ROW][C]78[/C][C]0.27101582377439[/C][C]0.54203164754878[/C][C]0.72898417622561[/C][/ROW]
[ROW][C]79[/C][C]0.323978524651759[/C][C]0.647957049303518[/C][C]0.676021475348241[/C][/ROW]
[ROW][C]80[/C][C]0.354252623916712[/C][C]0.708505247833424[/C][C]0.645747376083288[/C][/ROW]
[ROW][C]81[/C][C]0.344594291648099[/C][C]0.689188583296198[/C][C]0.655405708351901[/C][/ROW]
[ROW][C]82[/C][C]0.303015510343631[/C][C]0.606031020687262[/C][C]0.696984489656369[/C][/ROW]
[ROW][C]83[/C][C]0.265818319353286[/C][C]0.531636638706573[/C][C]0.734181680646714[/C][/ROW]
[ROW][C]84[/C][C]0.268510988718868[/C][C]0.537021977437735[/C][C]0.731489011281133[/C][/ROW]
[ROW][C]85[/C][C]0.277307954275219[/C][C]0.554615908550438[/C][C]0.722692045724781[/C][/ROW]
[ROW][C]86[/C][C]0.251714122915202[/C][C]0.503428245830404[/C][C]0.748285877084798[/C][/ROW]
[ROW][C]87[/C][C]0.229190068490952[/C][C]0.458380136981905[/C][C]0.770809931509048[/C][/ROW]
[ROW][C]88[/C][C]0.202113557538591[/C][C]0.404227115077183[/C][C]0.797886442461409[/C][/ROW]
[ROW][C]89[/C][C]0.253156086562449[/C][C]0.506312173124898[/C][C]0.746843913437551[/C][/ROW]
[ROW][C]90[/C][C]0.217854857217309[/C][C]0.435709714434618[/C][C]0.782145142782691[/C][/ROW]
[ROW][C]91[/C][C]0.225585361872313[/C][C]0.451170723744625[/C][C]0.774414638127687[/C][/ROW]
[ROW][C]92[/C][C]0.209249591144677[/C][C]0.418499182289355[/C][C]0.790750408855323[/C][/ROW]
[ROW][C]93[/C][C]0.188966421612864[/C][C]0.377932843225727[/C][C]0.811033578387136[/C][/ROW]
[ROW][C]94[/C][C]0.158725663508913[/C][C]0.317451327017826[/C][C]0.841274336491087[/C][/ROW]
[ROW][C]95[/C][C]0.162207995724953[/C][C]0.324415991449905[/C][C]0.837792004275048[/C][/ROW]
[ROW][C]96[/C][C]0.163055094883681[/C][C]0.326110189767362[/C][C]0.836944905116319[/C][/ROW]
[ROW][C]97[/C][C]0.153305454469581[/C][C]0.306610908939163[/C][C]0.846694545530418[/C][/ROW]
[ROW][C]98[/C][C]0.133274945605659[/C][C]0.266549891211319[/C][C]0.866725054394341[/C][/ROW]
[ROW][C]99[/C][C]0.118647777542648[/C][C]0.237295555085296[/C][C]0.881352222457352[/C][/ROW]
[ROW][C]100[/C][C]0.126132152279394[/C][C]0.252264304558789[/C][C]0.873867847720606[/C][/ROW]
[ROW][C]101[/C][C]0.11992624807341[/C][C]0.23985249614682[/C][C]0.88007375192659[/C][/ROW]
[ROW][C]102[/C][C]0.105454395990653[/C][C]0.210908791981306[/C][C]0.894545604009347[/C][/ROW]
[ROW][C]103[/C][C]0.0860193816071012[/C][C]0.172038763214202[/C][C]0.913980618392899[/C][/ROW]
[ROW][C]104[/C][C]0.0703707142954663[/C][C]0.140741428590933[/C][C]0.929629285704534[/C][/ROW]
[ROW][C]105[/C][C]0.0682557914579957[/C][C]0.136511582915991[/C][C]0.931744208542004[/C][/ROW]
[ROW][C]106[/C][C]0.183576261907898[/C][C]0.367152523815795[/C][C]0.816423738092102[/C][/ROW]
[ROW][C]107[/C][C]0.17832498726037[/C][C]0.356649974520741[/C][C]0.82167501273963[/C][/ROW]
[ROW][C]108[/C][C]0.196484226811745[/C][C]0.392968453623491[/C][C]0.803515773188255[/C][/ROW]
[ROW][C]109[/C][C]0.181066734356604[/C][C]0.362133468713208[/C][C]0.818933265643396[/C][/ROW]
[ROW][C]110[/C][C]0.294400341626616[/C][C]0.588800683253232[/C][C]0.705599658373384[/C][/ROW]
[ROW][C]111[/C][C]0.342566177870331[/C][C]0.685132355740663[/C][C]0.657433822129669[/C][/ROW]
[ROW][C]112[/C][C]0.323412136891015[/C][C]0.64682427378203[/C][C]0.676587863108985[/C][/ROW]
[ROW][C]113[/C][C]0.381293079114426[/C][C]0.762586158228853[/C][C]0.618706920885574[/C][/ROW]
[ROW][C]114[/C][C]0.342480032546191[/C][C]0.684960065092381[/C][C]0.657519967453809[/C][/ROW]
[ROW][C]115[/C][C]0.328790742415889[/C][C]0.657581484831779[/C][C]0.671209257584111[/C][/ROW]
[ROW][C]116[/C][C]0.307273698935507[/C][C]0.614547397871014[/C][C]0.692726301064493[/C][/ROW]
[ROW][C]117[/C][C]0.314744405610483[/C][C]0.629488811220966[/C][C]0.685255594389517[/C][/ROW]
[ROW][C]118[/C][C]0.294951174115792[/C][C]0.589902348231584[/C][C]0.705048825884208[/C][/ROW]
[ROW][C]119[/C][C]0.266381989130787[/C][C]0.532763978261573[/C][C]0.733618010869213[/C][/ROW]
[ROW][C]120[/C][C]0.25440535873016[/C][C]0.50881071746032[/C][C]0.74559464126984[/C][/ROW]
[ROW][C]121[/C][C]0.226861514524267[/C][C]0.453723029048534[/C][C]0.773138485475733[/C][/ROW]
[ROW][C]122[/C][C]0.199275048542352[/C][C]0.398550097084703[/C][C]0.800724951457648[/C][/ROW]
[ROW][C]123[/C][C]0.170655688330252[/C][C]0.341311376660505[/C][C]0.829344311669748[/C][/ROW]
[ROW][C]124[/C][C]0.138573116149143[/C][C]0.277146232298287[/C][C]0.861426883850857[/C][/ROW]
[ROW][C]125[/C][C]0.110706328086544[/C][C]0.221412656173087[/C][C]0.889293671913456[/C][/ROW]
[ROW][C]126[/C][C]0.118559893370934[/C][C]0.237119786741868[/C][C]0.881440106629066[/C][/ROW]
[ROW][C]127[/C][C]0.178268409292203[/C][C]0.356536818584405[/C][C]0.821731590707798[/C][/ROW]
[ROW][C]128[/C][C]0.376499892334881[/C][C]0.752999784669761[/C][C]0.623500107665119[/C][/ROW]
[ROW][C]129[/C][C]0.351117965164992[/C][C]0.702235930329984[/C][C]0.648882034835008[/C][/ROW]
[ROW][C]130[/C][C]0.302803037433224[/C][C]0.605606074866449[/C][C]0.697196962566776[/C][/ROW]
[ROW][C]131[/C][C]0.259159406953359[/C][C]0.518318813906717[/C][C]0.740840593046641[/C][/ROW]
[ROW][C]132[/C][C]0.278716294141918[/C][C]0.557432588283836[/C][C]0.721283705858082[/C][/ROW]
[ROW][C]133[/C][C]0.373346050510263[/C][C]0.746692101020526[/C][C]0.626653949489737[/C][/ROW]
[ROW][C]134[/C][C]0.459283896330602[/C][C]0.918567792661204[/C][C]0.540716103669398[/C][/ROW]
[ROW][C]135[/C][C]0.436624659348247[/C][C]0.873249318696494[/C][C]0.563375340651753[/C][/ROW]
[ROW][C]136[/C][C]0.396111080463484[/C][C]0.792222160926969[/C][C]0.603888919536516[/C][/ROW]
[ROW][C]137[/C][C]0.361433497590076[/C][C]0.722866995180152[/C][C]0.638566502409924[/C][/ROW]
[ROW][C]138[/C][C]0.298102131168179[/C][C]0.596204262336358[/C][C]0.701897868831821[/C][/ROW]
[ROW][C]139[/C][C]0.556974096298659[/C][C]0.886051807402683[/C][C]0.443025903701341[/C][/ROW]
[ROW][C]140[/C][C]0.478418433709532[/C][C]0.956836867419064[/C][C]0.521581566290468[/C][/ROW]
[ROW][C]141[/C][C]0.610404972924255[/C][C]0.77919005415149[/C][C]0.389595027075745[/C][/ROW]
[ROW][C]142[/C][C]0.571894903313584[/C][C]0.856210193372832[/C][C]0.428105096686416[/C][/ROW]
[ROW][C]143[/C][C]0.502021067187896[/C][C]0.995957865624209[/C][C]0.497978932812104[/C][/ROW]
[ROW][C]144[/C][C]0.769986703559697[/C][C]0.460026592880607[/C][C]0.230013296440303[/C][/ROW]
[ROW][C]145[/C][C]0.729540791656895[/C][C]0.540918416686209[/C][C]0.270459208343105[/C][/ROW]
[ROW][C]146[/C][C]0.925733488585715[/C][C]0.148533022828569[/C][C]0.0742665114142847[/C][/ROW]
[ROW][C]147[/C][C]0.910425986766566[/C][C]0.179148026466869[/C][C]0.0895740132334343[/C][/ROW]
[ROW][C]148[/C][C]0.906563243542858[/C][C]0.186873512914284[/C][C]0.0934367564571421[/C][/ROW]
[ROW][C]149[/C][C]0.846959130823683[/C][C]0.306081738352635[/C][C]0.153040869176317[/C][/ROW]
[ROW][C]150[/C][C]0.802129906750766[/C][C]0.395740186498468[/C][C]0.197870093249234[/C][/ROW]
[ROW][C]151[/C][C]0.653592666543473[/C][C]0.692814666913054[/C][C]0.346407333456527[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197084&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197084&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.9487215221470950.1025569557058090.0512784778529046
120.9331875882168230.1336248235663540.0668124117831771
130.9226861779255110.1546276441489780.0773138220744892
140.8921057756727150.2157884486545690.107894224327285
150.8670059774217590.2659880451564820.132994022578241
160.8931273840533230.2137452318933550.106872615946677
170.8583408296234250.2833183407531510.141659170376575
180.8213768586616380.3572462826767240.178623141338362
190.8039390080373960.3921219839252080.196060991962604
200.7888594133279710.4222811733440570.211140586672029
210.8023372193959350.395325561208130.197662780604065
220.7616312551456050.4767374897087890.238368744854395
230.7302621773424060.5394756453151880.269737822657594
240.6644610893004030.6710778213991930.335538910699597
250.6549067887600920.6901864224798150.345093211239908
260.8712982636052420.2574034727895160.128701736394758
270.8332225533410070.3335548933179860.166777446658993
280.8078218036909610.3843563926180780.192178196309039
290.7807008740581560.4385982518836880.219299125941844
300.7305602189762170.5388795620475660.269439781023783
310.7106545518508490.5786908962983020.289345448149151
320.827553016799450.3448939664010990.17244698320055
330.8113918179769580.3772163640460850.188608182023042
340.7844834919506260.4310330160987480.215516508049374
350.7561804948054270.4876390103891450.243819505194573
360.709484561315550.5810308773688990.29051543868445
370.6861695211004220.6276609577991550.313830478899578
380.6706015777661160.6587968444677690.329398422233884
390.7804015324918570.4391969350162860.219598467508143
400.7366912323110920.5266175353778160.263308767688908
410.7104543761864990.5790912476270010.289545623813501
420.6613473109523210.6773053780953580.338652689047679
430.6291334141300090.7417331717399830.370866585869991
440.5822019033754410.8355961932491180.417798096624559
450.6413435716634150.7173128566731710.358656428336585
460.6405621289939710.7188757420120570.359437871006029
470.6176004791813280.7647990416373450.382399520818672
480.5854440580876210.8291118838247580.414555941912379
490.5375483414831210.9249033170337580.462451658516879
500.5802861851613770.8394276296772450.419713814838623
510.593037080836730.8139258383265390.406962919163269
520.5484835122844530.9030329754310950.451516487715547
530.4996341252075720.9992682504151450.500365874792428
540.4785446741832390.9570893483664780.521455325816761
550.4305867536282180.8611735072564360.569413246371782
560.42073937686270.8414787537254010.5792606231373
570.4464566581875490.8929133163750980.553543341812451
580.556815567979960.886368864040080.44318443202004
590.5183895746869680.9632208506260640.481610425313032
600.4712892288523260.9425784577046520.528710771147674
610.4346542781576870.8693085563153740.565345721842313
620.4139204770032830.8278409540065660.586079522996717
630.3750721952748870.7501443905497740.624927804725113
640.4228938445617980.8457876891235960.577106155438202
650.3798133010585750.759626602117150.620186698941425
660.3381504615256750.676300923051350.661849538474325
670.2978300247143520.5956600494287040.702169975285648
680.3268187514834010.6536375029668020.673181248516599
690.3500943468883220.7001886937766430.649905653111678
700.3089677859746250.6179355719492490.691032214025375
710.3367888299882760.6735776599765520.663211170011724
720.3759796051323860.7519592102647730.624020394867614
730.3525974791691750.705194958338350.647402520830825
740.3096632694180970.6193265388361940.690336730581903
750.2768251435452660.5536502870905330.723174856454734
760.348466842556030.6969336851120610.65153315744397
770.3106652000965790.6213304001931580.689334799903421
780.271015823774390.542031647548780.72898417622561
790.3239785246517590.6479570493035180.676021475348241
800.3542526239167120.7085052478334240.645747376083288
810.3445942916480990.6891885832961980.655405708351901
820.3030155103436310.6060310206872620.696984489656369
830.2658183193532860.5316366387065730.734181680646714
840.2685109887188680.5370219774377350.731489011281133
850.2773079542752190.5546159085504380.722692045724781
860.2517141229152020.5034282458304040.748285877084798
870.2291900684909520.4583801369819050.770809931509048
880.2021135575385910.4042271150771830.797886442461409
890.2531560865624490.5063121731248980.746843913437551
900.2178548572173090.4357097144346180.782145142782691
910.2255853618723130.4511707237446250.774414638127687
920.2092495911446770.4184991822893550.790750408855323
930.1889664216128640.3779328432257270.811033578387136
940.1587256635089130.3174513270178260.841274336491087
950.1622079957249530.3244159914499050.837792004275048
960.1630550948836810.3261101897673620.836944905116319
970.1533054544695810.3066109089391630.846694545530418
980.1332749456056590.2665498912113190.866725054394341
990.1186477775426480.2372955550852960.881352222457352
1000.1261321522793940.2522643045587890.873867847720606
1010.119926248073410.239852496146820.88007375192659
1020.1054543959906530.2109087919813060.894545604009347
1030.08601938160710120.1720387632142020.913980618392899
1040.07037071429546630.1407414285909330.929629285704534
1050.06825579145799570.1365115829159910.931744208542004
1060.1835762619078980.3671525238157950.816423738092102
1070.178324987260370.3566499745207410.82167501273963
1080.1964842268117450.3929684536234910.803515773188255
1090.1810667343566040.3621334687132080.818933265643396
1100.2944003416266160.5888006832532320.705599658373384
1110.3425661778703310.6851323557406630.657433822129669
1120.3234121368910150.646824273782030.676587863108985
1130.3812930791144260.7625861582288530.618706920885574
1140.3424800325461910.6849600650923810.657519967453809
1150.3287907424158890.6575814848317790.671209257584111
1160.3072736989355070.6145473978710140.692726301064493
1170.3147444056104830.6294888112209660.685255594389517
1180.2949511741157920.5899023482315840.705048825884208
1190.2663819891307870.5327639782615730.733618010869213
1200.254405358730160.508810717460320.74559464126984
1210.2268615145242670.4537230290485340.773138485475733
1220.1992750485423520.3985500970847030.800724951457648
1230.1706556883302520.3413113766605050.829344311669748
1240.1385731161491430.2771462322982870.861426883850857
1250.1107063280865440.2214126561730870.889293671913456
1260.1185598933709340.2371197867418680.881440106629066
1270.1782684092922030.3565368185844050.821731590707798
1280.3764998923348810.7529997846697610.623500107665119
1290.3511179651649920.7022359303299840.648882034835008
1300.3028030374332240.6056060748664490.697196962566776
1310.2591594069533590.5183188139067170.740840593046641
1320.2787162941419180.5574325882838360.721283705858082
1330.3733460505102630.7466921010205260.626653949489737
1340.4592838963306020.9185677926612040.540716103669398
1350.4366246593482470.8732493186964940.563375340651753
1360.3961110804634840.7922221609269690.603888919536516
1370.3614334975900760.7228669951801520.638566502409924
1380.2981021311681790.5962042623363580.701897868831821
1390.5569740962986590.8860518074026830.443025903701341
1400.4784184337095320.9568368674190640.521581566290468
1410.6104049729242550.779190054151490.389595027075745
1420.5718949033135840.8562101933728320.428105096686416
1430.5020210671878960.9959578656242090.497978932812104
1440.7699867035596970.4600265928806070.230013296440303
1450.7295407916568950.5409184166862090.270459208343105
1460.9257334885857150.1485330228285690.0742665114142847
1470.9104259867665660.1791480264668690.0895740132334343
1480.9065632435428580.1868735129142840.0934367564571421
1490.8469591308236830.3060817383526350.153040869176317
1500.8021299067507660.3957401864984680.197870093249234
1510.6535926665434730.6928146669130540.346407333456527






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

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197084&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197084&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197084&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 level00OK
10% type I error level00OK


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