Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 07:02:05 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352116952fa67hcs3hnrbqfl.htm/, Retrieved Sun, 05 Feb 2023 23:58:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185994, Retrieved Sun, 05 Feb 2023 23:58:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2012-11-05 12:02:05] [6a75692d68b560ebf35260ff627da44a] [Current]
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Dataseries X:
13	12	14	12	53	32
16	11	18	11	86	51
19	15	11	14	66	42
15	6	12	12	67	41
14	13	16	21	76	46
13	10	18	12	78	47
19	12	14	22	53	37
15	14	14	11	80	49
14	12	15	10	74	45
15	6	15	13	76	47
16	10	17	10	79	49
16	12	19	8	54	33
16	12	10	15	67	42
16	11	16	14	54	33
17	15	18	10	87	53
15	12	14	14	58	36
15	10	14	14	75	45
20	12	17	11	88	54
18	11	14	10	64	41
16	12	16	13	57	36
16	11	18	7	66	41
16	12	11	14	68	44
19	13	14	12	54	33
16	11	12	14	56	37
17	9	17	11	86	52
17	13	9	9	80	47
16	10	16	11	76	43
15	14	14	15	69	44
16	12	15	14	78	45
14	10	11	13	67	44
15	12	16	9	80	49
12	8	13	15	54	33
14	10	17	10	71	43
16	12	15	11	84	54
14	12	14	13	74	42
7	7	16	8	71	44
10	6	9	20	63	37
14	12	15	12	71	43
16	10	17	10	76	46
16	10	13	10	69	42
16	10	15	9	74	45
14	12	16	14	75	44
20	15	16	8	54	33
14	10	12	14	52	31
14	10	12	11	69	42
11	12	11	13	68	40
14	13	15	9	65	43
15	11	15	11	75	46
16	11	17	15	74	42
14	12	13	11	75	45
16	14	16	10	72	44
14	10	14	14	67	40
12	12	11	18	63	37
16	13	12	14	62	46
9	5	12	11	63	36
14	6	15	12	76	47
16	12	16	13	74	45
16	12	15	9	67	42
15	11	12	10	73	43
16	10	12	15	70	43
12	7	8	20	53	32
16	12	13	12	77	45
16	14	11	12	77	45
14	11	14	14	52	31
16	12	15	13	54	33
17	13	10	11	80	49
18	14	11	17	66	42
18	11	12	12	73	41
12	12	15	13	63	38
16	12	15	14	69	42
10	8	14	13	67	44
14	11	16	15	54	33
18	14	15	13	81	48
18	14	15	10	69	40
16	12	13	11	84	50
17	9	12	19	80	49
16	13	17	13	70	43
16	11	13	17	69	44
13	12	15	13	77	47
16	12	13	9	54	33
16	12	15	11	79	46
20	12	16	10	30	0
16	12	15	9	71	45
15	12	16	12	73	43
15	11	15	12	72	44
16	10	14	13	77	47
14	9	15	13	75	45
16	12	14	12	69	42
16	12	13	15	54	33
15	12	7	22	70	43
12	9	17	13	73	46
17	15	13	15	54	33
16	12	15	13	77	46
15	12	14	15	82	48
13	12	13	10	80	47
16	10	16	11	80	47
16	13	12	16	69	43
16	9	14	11	78	46
16	12	17	11	81	48
14	10	15	10	76	46
16	14	17	10	76	45
16	11	12	16	73	45
20	15	16	12	85	52
15	11	11	11	66	42
16	11	15	16	79	47
13	12	9	19	68	41
17	12	16	11	76	47
16	12	15	16	71	43
16	11	10	15	54	33
12	7	10	24	46	30
16	12	15	14	82	49
16	14	11	15	74	44
17	11	13	11	88	55
13	11	14	15	38	11
12	10	18	12	76	47
18	13	16	10	86	53
14	13	14	14	54	33
14	8	14	13	70	44
13	11	14	9	69	42
16	12	14	15	90	55
13	11	12	15	54	33
16	13	14	14	76	46
13	12	15	11	89	54
16	14	15	8	76	47
15	13	15	11	73	45
16	15	13	11	79	47
15	10	17	8	90	55
17	11	17	10	74	44
15	9	19	11	81	53
12	11	15	13	72	44
16	10	13	11	71	42
10	11	9	20	66	40
16	8	15	10	77	46
12	11	15	15	65	40
14	12	15	12	74	46
15	12	16	14	82	53
13	9	11	23	54	33
15	11	14	14	63	42
11	10	11	16	54	35
12	8	15	11	64	40
8	9	13	12	69	41
16	8	15	10	54	33
15	9	16	14	84	51
17	15	14	12	86	53
16	11	15	12	77	46
10	8	16	11	89	55
18	13	16	12	76	47
13	12	11	13	60	38
16	12	12	11	75	46
13	9	9	19	73	46
10	7	16	12	85	53
15	13	13	17	79	47
16	9	16	9	71	41
16	6	12	12	72	44
14	8	9	19	69	43
10	8	13	18	78	51
17	15	13	15	54	33
13	6	14	14	69	43
15	9	19	11	81	53
16	11	13	9	84	51
12	8	12	18	84	50
13	8	13	16	69	46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=185994&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=185994&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185994&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 8.46131974364201 + 0.548818213339092Software[t] + 0.0710818062400159Hapiness[t] -0.0767771025235985Depression[t] + 0.0394093100647322Belonging[t] -0.0550446348229513`Belonging_Final\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  8.46131974364201 +  0.548818213339092Software[t] +  0.0710818062400159Hapiness[t] -0.0767771025235985Depression[t] +  0.0394093100647322Belonging[t] -0.0550446348229513`Belonging_Final\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185994&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  8.46131974364201 +  0.548818213339092Software[t] +  0.0710818062400159Hapiness[t] -0.0767771025235985Depression[t] +  0.0394093100647322Belonging[t] -0.0550446348229513`Belonging_Final\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185994&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185994&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 8.46131974364201 + 0.548818213339092Software[t] + 0.0710818062400159Hapiness[t] -0.0767771025235985Depression[t] + 0.0394093100647322Belonging[t] -0.0550446348229513`Belonging_Final\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.461319743642012.0412774.14515.6e-052.8e-05
Software0.5488182133390920.0696947.874700
Hapiness0.07108180624001590.07650.92920.3542370.177118
Depression-0.07677710252359850.057031-1.34620.1801790.09009
Belonging0.03940931006473220.0448490.87870.3809090.190455
`Belonging_Final\r`-0.05504463482295130.06457-0.85250.3952520.197626

\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) & 8.46131974364201 & 2.041277 & 4.1451 & 5.6e-05 & 2.8e-05 \tabularnewline
Software & 0.548818213339092 & 0.069694 & 7.8747 & 0 & 0 \tabularnewline
Hapiness & 0.0710818062400159 & 0.0765 & 0.9292 & 0.354237 & 0.177118 \tabularnewline
Depression & -0.0767771025235985 & 0.057031 & -1.3462 & 0.180179 & 0.09009 \tabularnewline
Belonging & 0.0394093100647322 & 0.044849 & 0.8787 & 0.380909 & 0.190455 \tabularnewline
`Belonging_Final\r` & -0.0550446348229513 & 0.06457 & -0.8525 & 0.395252 & 0.197626 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185994&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]8.46131974364201[/C][C]2.041277[/C][C]4.1451[/C][C]5.6e-05[/C][C]2.8e-05[/C][/ROW]
[ROW][C]Software[/C][C]0.548818213339092[/C][C]0.069694[/C][C]7.8747[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Hapiness[/C][C]0.0710818062400159[/C][C]0.0765[/C][C]0.9292[/C][C]0.354237[/C][C]0.177118[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0767771025235985[/C][C]0.057031[/C][C]-1.3462[/C][C]0.180179[/C][C]0.09009[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0394093100647322[/C][C]0.044849[/C][C]0.8787[/C][C]0.380909[/C][C]0.190455[/C][/ROW]
[ROW][C]`Belonging_Final\r`[/C][C]-0.0550446348229513[/C][C]0.06457[/C][C]-0.8525[/C][C]0.395252[/C][C]0.197626[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185994&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185994&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.461319743642012.0412774.14515.6e-052.8e-05
Software0.5488182133390920.0696947.874700
Hapiness0.07108180624001590.07650.92920.3542370.177118
Depression-0.07677710252359850.057031-1.34620.1801790.09009
Belonging0.03940931006473220.0448490.87870.3809090.190455
`Belonging_Final\r`-0.05504463482295130.06457-0.85250.3952520.197626







Multiple Linear Regression - Regression Statistics
Multiple R0.575493673973176
R-squared0.331192968783144
Adjusted R-squared0.309756845987732
F-TEST (value)15.4502272609685
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value2.48301379457416e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.87452138427473
Sum Squared Residuals548.157545536107

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.575493673973176 \tabularnewline
R-squared & 0.331192968783144 \tabularnewline
Adjusted R-squared & 0.309756845987732 \tabularnewline
F-TEST (value) & 15.4502272609685 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 2.48301379457416e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.87452138427473 \tabularnewline
Sum Squared Residuals & 548.157545536107 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185994&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.575493673973176[/C][/ROW]
[ROW][C]R-squared[/C][C]0.331192968783144[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.309756845987732[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.4502272609685[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]2.48301379457416e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.87452138427473[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]548.157545536107[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185994&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185994&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.575493673973176
R-squared0.331192968783144
Adjusted R-squared0.309756845987732
F-TEST (value)15.4502272609685
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value2.48301379457416e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.87452138427473
Sum Squared Residuals548.157545536107







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.4482234798846-2.44822347988455
21615.51516876452920.484831235470825
31916.68975317874662.31024682125345
41512.06947921486962.93052078513037
51415.5840006269588-1.58400062695878
61314.7944775074404-1.79447750744043
71914.40522928053384.59477071946622
81516.7509295888439-1.7509295888439
91415.7848747498327-1.78487474983274
101512.2303635127112.76963648728904
111614.80626994666641.19373005333355
121616.0951055964208-0.0951055964207731
131614.93484894003041.0651510599696
141614.872379349221.12762065077996
151717.716538760828-0.716538760827972
161515.2715372858692-0.271537285869178
171514.34845741688490.65154258311512
182015.90658988728894.09341011271114
191814.99106016889813.00893983110188
201615.45106869080810.548931309191924
211615.58453732155840.41546267844156
221615.01202788921280.987972110787158
231915.98140636846543.01859363153461
241614.44669220509761.55330779490236
251714.2914058967882.70859410321197
261716.11034581899780.889654181002173
271614.87045091664631.12954908335366
281516.2855419421522-1.28554194215221
291615.63540357999730.364596420002724
301413.95175925499350.0482407450064754
311515.9490109796929-0.949010979692943
321212.9359021879591-0.935902187959122
331414.8212632750863-0.821263275086295
341615.60678903454990.393210965450097
351415.6485955404908-1.64859554049078
36713.2022363990533-6.20223639905325
371011.3045582749937-1.30455827499367
381415.6231818842372-1.62318188423725
391614.85317592094111.1468240790589
401614.51316206481971.48683793518028
411614.76401542567821.23598457432184
421415.643302090866-1.64330209086605
432017.5283148177182.471685182282
441414.0705045604373-0.0705045604373259
451414.3653031560561-0.365303156056105
461115.3089835310282-4.30898353102824
471416.1658755447587-2.16587554475874
481515.1436441092118-0.143644109211832
491615.15946854082450.840531459175457
501415.6053433448938-1.60534334489384
511616.9298189974444-0.929818997444428
521414.3084061104818-0.308406110481779
531214.8931853725554-2.89318537255545
541615.28538277875770.714617221242347
55911.71502403791-2.71502403790996
561412.30714061523461.69285938476544
571615.6256252485020.374374751498038
581615.75092058637210.249079413627932
591515.0934910773548-0.0934910773547724
601614.04255942120351.95744057879651
611211.66342475556020.336575244439823
621615.60738486249970.392615137500291
631616.5628576766979-0.562857676697861
641414.7614863862564-0.761486386256449
651615.42689285884270.573107141157283
661715.91778415054471.08221584945526
671815.91060365783672.08939634216333
681815.05002614195352.94997385804652
691215.5063534753106-3.50635347531055
701615.44585369388350.55414630611646
711013.0673682470354-3.06736824703539
721414.7956022466964-0.795602246696444
731816.76291113492441.2370888650756
741816.9606878003021.03931219969798
751615.68480396136170.315196038638323
761713.25045808947963.74954191052038
771616.197977297468-0.197977297468043
781614.41445129084771.58554870915228
791315.5626821028102-2.56268210281024
801615.59183765645710.40816234354292
811615.85009956280990.149900437190147
822016.59895548025733.40104451974265
831615.74342392216210.256576077837857
841515.7730823106067-0.77308231060673
851515.0587283461399-0.0587283461399393
861614.3939638698921.60603613010796
871413.94749811230940.0525018876905973
881615.52832609269070.471673907309279
891615.13117504131550.868824958684511
901514.24734709901640.752652900983594
911213.955798469837-1.95579846983702
921716.77762968133280.222370318667236
931615.61772673763320.382273262366809
941515.4800480070237-0.480048007023736
951315.7690777280952-2.7690777280952
961614.80790961761351.19209038238653
971615.57282764863240.427172351367564
981614.09315380648781.90684619351217
991615.96099252577340.0390074742265541
1001414.7110123084611-0.711012308461071
1011617.1034934091204-1.10349340912042
1021614.52273919256731.47726080743272
1032017.39704695799422.60295304200577
1041514.7248116329610.275188367039016
1051614.86235120202981.13764879797018
1061314.6511126685837-1.65111266858367
1071715.74790880403271.25209119596728
1081615.31607347414290.683926525857144
1091614.36911140925631.63088859074365
1101211.33270405713860.667295942861408
1111615.57286228096440.4271377190356
1121616.2693430737558-0.26934307375582
1131715.01839981416681.98160018583324
1141315.2338716392856-2.23387163928563
1151214.715658887311-2.71565888731097
1161816.4373294116051.56267058839498
1171415.8278521634182-1.82785216341819
1181413.18559617722960.814403822770415
1191315.2098391869224-2.20983918692243
1201615.41001004378090.589989956219065
1211314.5112750217364-1.51127502173638
1221615.97927673214390.0207232678560639
1231315.8038355848736-2.80383558487356
1241617.0047947320417-1.00479473204168
1251516.2175065505835-1.2175065505835
1261617.2993459555241-1.29934595552414
1271515.063058753488-0.0630587534879887
1281715.43326478379661.56673521620337
1291514.18147832412140.818521675878554
1301214.9819512436163-2.98195124361634
1311614.51520358242561.48479641757442
1321014.0017433674145-4.00174336741447
1331613.65278519184762.34721480815238
1341214.7727104074078-2.77271040740782
1351415.5762759099626-1.57627590996259
1361515.4237655479126-0.42376554791261
1371312.72833996862940.271660031370606
1381514.5894978139160.410502186083961
1391113.7045086299878-2.70450862998777
1401213.3939548674202-1.39395486742021
141813.8658342812564-5.86583428125638
1421613.46195131305712.53804868694285
1431513.96621879767071.0337812023293
1441717.239248020756-0.239248020755979
1451615.14568562681770.854314373182302
1461013.6245999029343-3.62459990293426
1471816.21994991484821.78005008515179
1481315.1037983201563-2.10379832015629
1491615.47921690383090.520783096169124
1501312.92648140477530.0735185952246992
1511012.9514566164585-2.95145661645854
1521515.7410469137044-0.74104691370437
1531614.38822962767671.61177037232331
1541612.10139186072443.89860813927557
1551412.38515985564611.61484014435387
1561012.6605908951288-2.66059089512878
1571716.77762968133280.222370318667236
1581312.0268179727860.973182027213978
1591514.18147832412140.818521675878554
1601615.23449531824680.765504681753169
1611212.8810095841001-0.881009584100104
1621312.73468448370810.265315516291861

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.4482234798846 & -2.44822347988455 \tabularnewline
2 & 16 & 15.5151687645292 & 0.484831235470825 \tabularnewline
3 & 19 & 16.6897531787466 & 2.31024682125345 \tabularnewline
4 & 15 & 12.0694792148696 & 2.93052078513037 \tabularnewline
5 & 14 & 15.5840006269588 & -1.58400062695878 \tabularnewline
6 & 13 & 14.7944775074404 & -1.79447750744043 \tabularnewline
7 & 19 & 14.4052292805338 & 4.59477071946622 \tabularnewline
8 & 15 & 16.7509295888439 & -1.7509295888439 \tabularnewline
9 & 14 & 15.7848747498327 & -1.78487474983274 \tabularnewline
10 & 15 & 12.230363512711 & 2.76963648728904 \tabularnewline
11 & 16 & 14.8062699466664 & 1.19373005333355 \tabularnewline
12 & 16 & 16.0951055964208 & -0.0951055964207731 \tabularnewline
13 & 16 & 14.9348489400304 & 1.0651510599696 \tabularnewline
14 & 16 & 14.87237934922 & 1.12762065077996 \tabularnewline
15 & 17 & 17.716538760828 & -0.716538760827972 \tabularnewline
16 & 15 & 15.2715372858692 & -0.271537285869178 \tabularnewline
17 & 15 & 14.3484574168849 & 0.65154258311512 \tabularnewline
18 & 20 & 15.9065898872889 & 4.09341011271114 \tabularnewline
19 & 18 & 14.9910601688981 & 3.00893983110188 \tabularnewline
20 & 16 & 15.4510686908081 & 0.548931309191924 \tabularnewline
21 & 16 & 15.5845373215584 & 0.41546267844156 \tabularnewline
22 & 16 & 15.0120278892128 & 0.987972110787158 \tabularnewline
23 & 19 & 15.9814063684654 & 3.01859363153461 \tabularnewline
24 & 16 & 14.4466922050976 & 1.55330779490236 \tabularnewline
25 & 17 & 14.291405896788 & 2.70859410321197 \tabularnewline
26 & 17 & 16.1103458189978 & 0.889654181002173 \tabularnewline
27 & 16 & 14.8704509166463 & 1.12954908335366 \tabularnewline
28 & 15 & 16.2855419421522 & -1.28554194215221 \tabularnewline
29 & 16 & 15.6354035799973 & 0.364596420002724 \tabularnewline
30 & 14 & 13.9517592549935 & 0.0482407450064754 \tabularnewline
31 & 15 & 15.9490109796929 & -0.949010979692943 \tabularnewline
32 & 12 & 12.9359021879591 & -0.935902187959122 \tabularnewline
33 & 14 & 14.8212632750863 & -0.821263275086295 \tabularnewline
34 & 16 & 15.6067890345499 & 0.393210965450097 \tabularnewline
35 & 14 & 15.6485955404908 & -1.64859554049078 \tabularnewline
36 & 7 & 13.2022363990533 & -6.20223639905325 \tabularnewline
37 & 10 & 11.3045582749937 & -1.30455827499367 \tabularnewline
38 & 14 & 15.6231818842372 & -1.62318188423725 \tabularnewline
39 & 16 & 14.8531759209411 & 1.1468240790589 \tabularnewline
40 & 16 & 14.5131620648197 & 1.48683793518028 \tabularnewline
41 & 16 & 14.7640154256782 & 1.23598457432184 \tabularnewline
42 & 14 & 15.643302090866 & -1.64330209086605 \tabularnewline
43 & 20 & 17.528314817718 & 2.471685182282 \tabularnewline
44 & 14 & 14.0705045604373 & -0.0705045604373259 \tabularnewline
45 & 14 & 14.3653031560561 & -0.365303156056105 \tabularnewline
46 & 11 & 15.3089835310282 & -4.30898353102824 \tabularnewline
47 & 14 & 16.1658755447587 & -2.16587554475874 \tabularnewline
48 & 15 & 15.1436441092118 & -0.143644109211832 \tabularnewline
49 & 16 & 15.1594685408245 & 0.840531459175457 \tabularnewline
50 & 14 & 15.6053433448938 & -1.60534334489384 \tabularnewline
51 & 16 & 16.9298189974444 & -0.929818997444428 \tabularnewline
52 & 14 & 14.3084061104818 & -0.308406110481779 \tabularnewline
53 & 12 & 14.8931853725554 & -2.89318537255545 \tabularnewline
54 & 16 & 15.2853827787577 & 0.714617221242347 \tabularnewline
55 & 9 & 11.71502403791 & -2.71502403790996 \tabularnewline
56 & 14 & 12.3071406152346 & 1.69285938476544 \tabularnewline
57 & 16 & 15.625625248502 & 0.374374751498038 \tabularnewline
58 & 16 & 15.7509205863721 & 0.249079413627932 \tabularnewline
59 & 15 & 15.0934910773548 & -0.0934910773547724 \tabularnewline
60 & 16 & 14.0425594212035 & 1.95744057879651 \tabularnewline
61 & 12 & 11.6634247555602 & 0.336575244439823 \tabularnewline
62 & 16 & 15.6073848624997 & 0.392615137500291 \tabularnewline
63 & 16 & 16.5628576766979 & -0.562857676697861 \tabularnewline
64 & 14 & 14.7614863862564 & -0.761486386256449 \tabularnewline
65 & 16 & 15.4268928588427 & 0.573107141157283 \tabularnewline
66 & 17 & 15.9177841505447 & 1.08221584945526 \tabularnewline
67 & 18 & 15.9106036578367 & 2.08939634216333 \tabularnewline
68 & 18 & 15.0500261419535 & 2.94997385804652 \tabularnewline
69 & 12 & 15.5063534753106 & -3.50635347531055 \tabularnewline
70 & 16 & 15.4458536938835 & 0.55414630611646 \tabularnewline
71 & 10 & 13.0673682470354 & -3.06736824703539 \tabularnewline
72 & 14 & 14.7956022466964 & -0.795602246696444 \tabularnewline
73 & 18 & 16.7629111349244 & 1.2370888650756 \tabularnewline
74 & 18 & 16.960687800302 & 1.03931219969798 \tabularnewline
75 & 16 & 15.6848039613617 & 0.315196038638323 \tabularnewline
76 & 17 & 13.2504580894796 & 3.74954191052038 \tabularnewline
77 & 16 & 16.197977297468 & -0.197977297468043 \tabularnewline
78 & 16 & 14.4144512908477 & 1.58554870915228 \tabularnewline
79 & 13 & 15.5626821028102 & -2.56268210281024 \tabularnewline
80 & 16 & 15.5918376564571 & 0.40816234354292 \tabularnewline
81 & 16 & 15.8500995628099 & 0.149900437190147 \tabularnewline
82 & 20 & 16.5989554802573 & 3.40104451974265 \tabularnewline
83 & 16 & 15.7434239221621 & 0.256576077837857 \tabularnewline
84 & 15 & 15.7730823106067 & -0.77308231060673 \tabularnewline
85 & 15 & 15.0587283461399 & -0.0587283461399393 \tabularnewline
86 & 16 & 14.393963869892 & 1.60603613010796 \tabularnewline
87 & 14 & 13.9474981123094 & 0.0525018876905973 \tabularnewline
88 & 16 & 15.5283260926907 & 0.471673907309279 \tabularnewline
89 & 16 & 15.1311750413155 & 0.868824958684511 \tabularnewline
90 & 15 & 14.2473470990164 & 0.752652900983594 \tabularnewline
91 & 12 & 13.955798469837 & -1.95579846983702 \tabularnewline
92 & 17 & 16.7776296813328 & 0.222370318667236 \tabularnewline
93 & 16 & 15.6177267376332 & 0.382273262366809 \tabularnewline
94 & 15 & 15.4800480070237 & -0.480048007023736 \tabularnewline
95 & 13 & 15.7690777280952 & -2.7690777280952 \tabularnewline
96 & 16 & 14.8079096176135 & 1.19209038238653 \tabularnewline
97 & 16 & 15.5728276486324 & 0.427172351367564 \tabularnewline
98 & 16 & 14.0931538064878 & 1.90684619351217 \tabularnewline
99 & 16 & 15.9609925257734 & 0.0390074742265541 \tabularnewline
100 & 14 & 14.7110123084611 & -0.711012308461071 \tabularnewline
101 & 16 & 17.1034934091204 & -1.10349340912042 \tabularnewline
102 & 16 & 14.5227391925673 & 1.47726080743272 \tabularnewline
103 & 20 & 17.3970469579942 & 2.60295304200577 \tabularnewline
104 & 15 & 14.724811632961 & 0.275188367039016 \tabularnewline
105 & 16 & 14.8623512020298 & 1.13764879797018 \tabularnewline
106 & 13 & 14.6511126685837 & -1.65111266858367 \tabularnewline
107 & 17 & 15.7479088040327 & 1.25209119596728 \tabularnewline
108 & 16 & 15.3160734741429 & 0.683926525857144 \tabularnewline
109 & 16 & 14.3691114092563 & 1.63088859074365 \tabularnewline
110 & 12 & 11.3327040571386 & 0.667295942861408 \tabularnewline
111 & 16 & 15.5728622809644 & 0.4271377190356 \tabularnewline
112 & 16 & 16.2693430737558 & -0.26934307375582 \tabularnewline
113 & 17 & 15.0183998141668 & 1.98160018583324 \tabularnewline
114 & 13 & 15.2338716392856 & -2.23387163928563 \tabularnewline
115 & 12 & 14.715658887311 & -2.71565888731097 \tabularnewline
116 & 18 & 16.437329411605 & 1.56267058839498 \tabularnewline
117 & 14 & 15.8278521634182 & -1.82785216341819 \tabularnewline
118 & 14 & 13.1855961772296 & 0.814403822770415 \tabularnewline
119 & 13 & 15.2098391869224 & -2.20983918692243 \tabularnewline
120 & 16 & 15.4100100437809 & 0.589989956219065 \tabularnewline
121 & 13 & 14.5112750217364 & -1.51127502173638 \tabularnewline
122 & 16 & 15.9792767321439 & 0.0207232678560639 \tabularnewline
123 & 13 & 15.8038355848736 & -2.80383558487356 \tabularnewline
124 & 16 & 17.0047947320417 & -1.00479473204168 \tabularnewline
125 & 15 & 16.2175065505835 & -1.2175065505835 \tabularnewline
126 & 16 & 17.2993459555241 & -1.29934595552414 \tabularnewline
127 & 15 & 15.063058753488 & -0.0630587534879887 \tabularnewline
128 & 17 & 15.4332647837966 & 1.56673521620337 \tabularnewline
129 & 15 & 14.1814783241214 & 0.818521675878554 \tabularnewline
130 & 12 & 14.9819512436163 & -2.98195124361634 \tabularnewline
131 & 16 & 14.5152035824256 & 1.48479641757442 \tabularnewline
132 & 10 & 14.0017433674145 & -4.00174336741447 \tabularnewline
133 & 16 & 13.6527851918476 & 2.34721480815238 \tabularnewline
134 & 12 & 14.7727104074078 & -2.77271040740782 \tabularnewline
135 & 14 & 15.5762759099626 & -1.57627590996259 \tabularnewline
136 & 15 & 15.4237655479126 & -0.42376554791261 \tabularnewline
137 & 13 & 12.7283399686294 & 0.271660031370606 \tabularnewline
138 & 15 & 14.589497813916 & 0.410502186083961 \tabularnewline
139 & 11 & 13.7045086299878 & -2.70450862998777 \tabularnewline
140 & 12 & 13.3939548674202 & -1.39395486742021 \tabularnewline
141 & 8 & 13.8658342812564 & -5.86583428125638 \tabularnewline
142 & 16 & 13.4619513130571 & 2.53804868694285 \tabularnewline
143 & 15 & 13.9662187976707 & 1.0337812023293 \tabularnewline
144 & 17 & 17.239248020756 & -0.239248020755979 \tabularnewline
145 & 16 & 15.1456856268177 & 0.854314373182302 \tabularnewline
146 & 10 & 13.6245999029343 & -3.62459990293426 \tabularnewline
147 & 18 & 16.2199499148482 & 1.78005008515179 \tabularnewline
148 & 13 & 15.1037983201563 & -2.10379832015629 \tabularnewline
149 & 16 & 15.4792169038309 & 0.520783096169124 \tabularnewline
150 & 13 & 12.9264814047753 & 0.0735185952246992 \tabularnewline
151 & 10 & 12.9514566164585 & -2.95145661645854 \tabularnewline
152 & 15 & 15.7410469137044 & -0.74104691370437 \tabularnewline
153 & 16 & 14.3882296276767 & 1.61177037232331 \tabularnewline
154 & 16 & 12.1013918607244 & 3.89860813927557 \tabularnewline
155 & 14 & 12.3851598556461 & 1.61484014435387 \tabularnewline
156 & 10 & 12.6605908951288 & -2.66059089512878 \tabularnewline
157 & 17 & 16.7776296813328 & 0.222370318667236 \tabularnewline
158 & 13 & 12.026817972786 & 0.973182027213978 \tabularnewline
159 & 15 & 14.1814783241214 & 0.818521675878554 \tabularnewline
160 & 16 & 15.2344953182468 & 0.765504681753169 \tabularnewline
161 & 12 & 12.8810095841001 & -0.881009584100104 \tabularnewline
162 & 13 & 12.7346844837081 & 0.265315516291861 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185994&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]15.4482234798846[/C][C]-2.44822347988455[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.5151687645292[/C][C]0.484831235470825[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.6897531787466[/C][C]2.31024682125345[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.0694792148696[/C][C]2.93052078513037[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.5840006269588[/C][C]-1.58400062695878[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.7944775074404[/C][C]-1.79447750744043[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]14.4052292805338[/C][C]4.59477071946622[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.7509295888439[/C][C]-1.7509295888439[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.7848747498327[/C][C]-1.78487474983274[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.230363512711[/C][C]2.76963648728904[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]14.8062699466664[/C][C]1.19373005333355[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.0951055964208[/C][C]-0.0951055964207731[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]14.9348489400304[/C][C]1.0651510599696[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]14.87237934922[/C][C]1.12762065077996[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.716538760828[/C][C]-0.716538760827972[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2715372858692[/C][C]-0.271537285869178[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.3484574168849[/C][C]0.65154258311512[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]15.9065898872889[/C][C]4.09341011271114[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]14.9910601688981[/C][C]3.00893983110188[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.4510686908081[/C][C]0.548931309191924[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.5845373215584[/C][C]0.41546267844156[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.0120278892128[/C][C]0.987972110787158[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]15.9814063684654[/C][C]3.01859363153461[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.4466922050976[/C][C]1.55330779490236[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.291405896788[/C][C]2.70859410321197[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.1103458189978[/C][C]0.889654181002173[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.8704509166463[/C][C]1.12954908335366[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.2855419421522[/C][C]-1.28554194215221[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.6354035799973[/C][C]0.364596420002724[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.9517592549935[/C][C]0.0482407450064754[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.9490109796929[/C][C]-0.949010979692943[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.9359021879591[/C][C]-0.935902187959122[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.8212632750863[/C][C]-0.821263275086295[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.6067890345499[/C][C]0.393210965450097[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6485955404908[/C][C]-1.64859554049078[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.2022363990533[/C][C]-6.20223639905325[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.3045582749937[/C][C]-1.30455827499367[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.6231818842372[/C][C]-1.62318188423725[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.8531759209411[/C][C]1.1468240790589[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.5131620648197[/C][C]1.48683793518028[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.7640154256782[/C][C]1.23598457432184[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.643302090866[/C][C]-1.64330209086605[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.528314817718[/C][C]2.471685182282[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.0705045604373[/C][C]-0.0705045604373259[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.3653031560561[/C][C]-0.365303156056105[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.3089835310282[/C][C]-4.30898353102824[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.1658755447587[/C][C]-2.16587554475874[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1436441092118[/C][C]-0.143644109211832[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.1594685408245[/C][C]0.840531459175457[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.6053433448938[/C][C]-1.60534334489384[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.9298189974444[/C][C]-0.929818997444428[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.3084061104818[/C][C]-0.308406110481779[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.8931853725554[/C][C]-2.89318537255545[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.2853827787577[/C][C]0.714617221242347[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.71502403791[/C][C]-2.71502403790996[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.3071406152346[/C][C]1.69285938476544[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.625625248502[/C][C]0.374374751498038[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.7509205863721[/C][C]0.249079413627932[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.0934910773548[/C][C]-0.0934910773547724[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.0425594212035[/C][C]1.95744057879651[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.6634247555602[/C][C]0.336575244439823[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.6073848624997[/C][C]0.392615137500291[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.5628576766979[/C][C]-0.562857676697861[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.7614863862564[/C][C]-0.761486386256449[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4268928588427[/C][C]0.573107141157283[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.9177841505447[/C][C]1.08221584945526[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]15.9106036578367[/C][C]2.08939634216333[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]15.0500261419535[/C][C]2.94997385804652[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.5063534753106[/C][C]-3.50635347531055[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4458536938835[/C][C]0.55414630611646[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.0673682470354[/C][C]-3.06736824703539[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.7956022466964[/C][C]-0.795602246696444[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.7629111349244[/C][C]1.2370888650756[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.960687800302[/C][C]1.03931219969798[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6848039613617[/C][C]0.315196038638323[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.2504580894796[/C][C]3.74954191052038[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.197977297468[/C][C]-0.197977297468043[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4144512908477[/C][C]1.58554870915228[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.5626821028102[/C][C]-2.56268210281024[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.5918376564571[/C][C]0.40816234354292[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.8500995628099[/C][C]0.149900437190147[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.5989554802573[/C][C]3.40104451974265[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.7434239221621[/C][C]0.256576077837857[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.7730823106067[/C][C]-0.77308231060673[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]15.0587283461399[/C][C]-0.0587283461399393[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.393963869892[/C][C]1.60603613010796[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]13.9474981123094[/C][C]0.0525018876905973[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.5283260926907[/C][C]0.471673907309279[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]15.1311750413155[/C][C]0.868824958684511[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.2473470990164[/C][C]0.752652900983594[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.955798469837[/C][C]-1.95579846983702[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.7776296813328[/C][C]0.222370318667236[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.6177267376332[/C][C]0.382273262366809[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.4800480070237[/C][C]-0.480048007023736[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.7690777280952[/C][C]-2.7690777280952[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.8079096176135[/C][C]1.19209038238653[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.5728276486324[/C][C]0.427172351367564[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.0931538064878[/C][C]1.90684619351217[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.9609925257734[/C][C]0.0390074742265541[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.7110123084611[/C][C]-0.711012308461071[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.1034934091204[/C][C]-1.10349340912042[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.5227391925673[/C][C]1.47726080743272[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3970469579942[/C][C]2.60295304200577[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.724811632961[/C][C]0.275188367039016[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.8623512020298[/C][C]1.13764879797018[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]14.6511126685837[/C][C]-1.65111266858367[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.7479088040327[/C][C]1.25209119596728[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.3160734741429[/C][C]0.683926525857144[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.3691114092563[/C][C]1.63088859074365[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]11.3327040571386[/C][C]0.667295942861408[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.5728622809644[/C][C]0.4271377190356[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]16.2693430737558[/C][C]-0.26934307375582[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]15.0183998141668[/C][C]1.98160018583324[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]15.2338716392856[/C][C]-2.23387163928563[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.715658887311[/C][C]-2.71565888731097[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.437329411605[/C][C]1.56267058839498[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.8278521634182[/C][C]-1.82785216341819[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.1855961772296[/C][C]0.814403822770415[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.2098391869224[/C][C]-2.20983918692243[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.4100100437809[/C][C]0.589989956219065[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.5112750217364[/C][C]-1.51127502173638[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.9792767321439[/C][C]0.0207232678560639[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8038355848736[/C][C]-2.80383558487356[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.0047947320417[/C][C]-1.00479473204168[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.2175065505835[/C][C]-1.2175065505835[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]17.2993459555241[/C][C]-1.29934595552414[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.063058753488[/C][C]-0.0630587534879887[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.4332647837966[/C][C]1.56673521620337[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.1814783241214[/C][C]0.818521675878554[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.9819512436163[/C][C]-2.98195124361634[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.5152035824256[/C][C]1.48479641757442[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]14.0017433674145[/C][C]-4.00174336741447[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.6527851918476[/C][C]2.34721480815238[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]14.7727104074078[/C][C]-2.77271040740782[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.5762759099626[/C][C]-1.57627590996259[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.4237655479126[/C][C]-0.42376554791261[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.7283399686294[/C][C]0.271660031370606[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.589497813916[/C][C]0.410502186083961[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.7045086299878[/C][C]-2.70450862998777[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.3939548674202[/C][C]-1.39395486742021[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.8658342812564[/C][C]-5.86583428125638[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.4619513130571[/C][C]2.53804868694285[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.9662187976707[/C][C]1.0337812023293[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]17.239248020756[/C][C]-0.239248020755979[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]15.1456856268177[/C][C]0.854314373182302[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]13.6245999029343[/C][C]-3.62459990293426[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]16.2199499148482[/C][C]1.78005008515179[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.1037983201563[/C][C]-2.10379832015629[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.4792169038309[/C][C]0.520783096169124[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.9264814047753[/C][C]0.0735185952246992[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.9514566164585[/C][C]-2.95145661645854[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]15.7410469137044[/C][C]-0.74104691370437[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.3882296276767[/C][C]1.61177037232331[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.1013918607244[/C][C]3.89860813927557[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.3851598556461[/C][C]1.61484014435387[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.6605908951288[/C][C]-2.66059089512878[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.7776296813328[/C][C]0.222370318667236[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]12.026817972786[/C][C]0.973182027213978[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.1814783241214[/C][C]0.818521675878554[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.2344953182468[/C][C]0.765504681753169[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.8810095841001[/C][C]-0.881009584100104[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.7346844837081[/C][C]0.265315516291861[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185994&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185994&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.4482234798846-2.44822347988455
21615.51516876452920.484831235470825
31916.68975317874662.31024682125345
41512.06947921486962.93052078513037
51415.5840006269588-1.58400062695878
61314.7944775074404-1.79447750744043
71914.40522928053384.59477071946622
81516.7509295888439-1.7509295888439
91415.7848747498327-1.78487474983274
101512.2303635127112.76963648728904
111614.80626994666641.19373005333355
121616.0951055964208-0.0951055964207731
131614.93484894003041.0651510599696
141614.872379349221.12762065077996
151717.716538760828-0.716538760827972
161515.2715372858692-0.271537285869178
171514.34845741688490.65154258311512
182015.90658988728894.09341011271114
191814.99106016889813.00893983110188
201615.45106869080810.548931309191924
211615.58453732155840.41546267844156
221615.01202788921280.987972110787158
231915.98140636846543.01859363153461
241614.44669220509761.55330779490236
251714.2914058967882.70859410321197
261716.11034581899780.889654181002173
271614.87045091664631.12954908335366
281516.2855419421522-1.28554194215221
291615.63540357999730.364596420002724
301413.95175925499350.0482407450064754
311515.9490109796929-0.949010979692943
321212.9359021879591-0.935902187959122
331414.8212632750863-0.821263275086295
341615.60678903454990.393210965450097
351415.6485955404908-1.64859554049078
36713.2022363990533-6.20223639905325
371011.3045582749937-1.30455827499367
381415.6231818842372-1.62318188423725
391614.85317592094111.1468240790589
401614.51316206481971.48683793518028
411614.76401542567821.23598457432184
421415.643302090866-1.64330209086605
432017.5283148177182.471685182282
441414.0705045604373-0.0705045604373259
451414.3653031560561-0.365303156056105
461115.3089835310282-4.30898353102824
471416.1658755447587-2.16587554475874
481515.1436441092118-0.143644109211832
491615.15946854082450.840531459175457
501415.6053433448938-1.60534334489384
511616.9298189974444-0.929818997444428
521414.3084061104818-0.308406110481779
531214.8931853725554-2.89318537255545
541615.28538277875770.714617221242347
55911.71502403791-2.71502403790996
561412.30714061523461.69285938476544
571615.6256252485020.374374751498038
581615.75092058637210.249079413627932
591515.0934910773548-0.0934910773547724
601614.04255942120351.95744057879651
611211.66342475556020.336575244439823
621615.60738486249970.392615137500291
631616.5628576766979-0.562857676697861
641414.7614863862564-0.761486386256449
651615.42689285884270.573107141157283
661715.91778415054471.08221584945526
671815.91060365783672.08939634216333
681815.05002614195352.94997385804652
691215.5063534753106-3.50635347531055
701615.44585369388350.55414630611646
711013.0673682470354-3.06736824703539
721414.7956022466964-0.795602246696444
731816.76291113492441.2370888650756
741816.9606878003021.03931219969798
751615.68480396136170.315196038638323
761713.25045808947963.74954191052038
771616.197977297468-0.197977297468043
781614.41445129084771.58554870915228
791315.5626821028102-2.56268210281024
801615.59183765645710.40816234354292
811615.85009956280990.149900437190147
822016.59895548025733.40104451974265
831615.74342392216210.256576077837857
841515.7730823106067-0.77308231060673
851515.0587283461399-0.0587283461399393
861614.3939638698921.60603613010796
871413.94749811230940.0525018876905973
881615.52832609269070.471673907309279
891615.13117504131550.868824958684511
901514.24734709901640.752652900983594
911213.955798469837-1.95579846983702
921716.77762968133280.222370318667236
931615.61772673763320.382273262366809
941515.4800480070237-0.480048007023736
951315.7690777280952-2.7690777280952
961614.80790961761351.19209038238653
971615.57282764863240.427172351367564
981614.09315380648781.90684619351217
991615.96099252577340.0390074742265541
1001414.7110123084611-0.711012308461071
1011617.1034934091204-1.10349340912042
1021614.52273919256731.47726080743272
1032017.39704695799422.60295304200577
1041514.7248116329610.275188367039016
1051614.86235120202981.13764879797018
1061314.6511126685837-1.65111266858367
1071715.74790880403271.25209119596728
1081615.31607347414290.683926525857144
1091614.36911140925631.63088859074365
1101211.33270405713860.667295942861408
1111615.57286228096440.4271377190356
1121616.2693430737558-0.26934307375582
1131715.01839981416681.98160018583324
1141315.2338716392856-2.23387163928563
1151214.715658887311-2.71565888731097
1161816.4373294116051.56267058839498
1171415.8278521634182-1.82785216341819
1181413.18559617722960.814403822770415
1191315.2098391869224-2.20983918692243
1201615.41001004378090.589989956219065
1211314.5112750217364-1.51127502173638
1221615.97927673214390.0207232678560639
1231315.8038355848736-2.80383558487356
1241617.0047947320417-1.00479473204168
1251516.2175065505835-1.2175065505835
1261617.2993459555241-1.29934595552414
1271515.063058753488-0.0630587534879887
1281715.43326478379661.56673521620337
1291514.18147832412140.818521675878554
1301214.9819512436163-2.98195124361634
1311614.51520358242561.48479641757442
1321014.0017433674145-4.00174336741447
1331613.65278519184762.34721480815238
1341214.7727104074078-2.77271040740782
1351415.5762759099626-1.57627590996259
1361515.4237655479126-0.42376554791261
1371312.72833996862940.271660031370606
1381514.5894978139160.410502186083961
1391113.7045086299878-2.70450862998777
1401213.3939548674202-1.39395486742021
141813.8658342812564-5.86583428125638
1421613.46195131305712.53804868694285
1431513.96621879767071.0337812023293
1441717.239248020756-0.239248020755979
1451615.14568562681770.854314373182302
1461013.6245999029343-3.62459990293426
1471816.21994991484821.78005008515179
1481315.1037983201563-2.10379832015629
1491615.47921690383090.520783096169124
1501312.92648140477530.0735185952246992
1511012.9514566164585-2.95145661645854
1521515.7410469137044-0.74104691370437
1531614.38822962767671.61177037232331
1541612.10139186072443.89860813927557
1551412.38515985564611.61484014435387
1561012.6605908951288-2.66059089512878
1571716.77762968133280.222370318667236
1581312.0268179727860.973182027213978
1591514.18147832412140.818521675878554
1601615.23449531824680.765504681753169
1611212.8810095841001-0.881009584100104
1621312.73468448370810.265315516291861







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6488508781672250.702298243665550.351149121832775
100.505762071286340.988475857427320.49423792871366
110.3615641458746920.7231282917493840.638435854125308
120.3879682953853930.7759365907707860.612031704614607
130.2770654076121930.5541308152243870.722934592387807
140.2560306184395780.5120612368791560.743969381560422
150.1914112440082870.3828224880165740.808588755991713
160.1323873176701940.2647746353403880.867612682329806
170.1003436850398730.2006873700797460.899656314960127
180.2895652831455890.5791305662911790.710434716854411
190.2342653686925960.4685307373851920.765734631307404
200.175229856832560.350459713665120.82477014316744
210.1342347894368140.2684695788736290.865765210563186
220.1479646049423080.2959292098846160.852035395057692
230.3856843587982650.771368717596530.614315641201735
240.3625469241294830.7250938482589660.637453075870517
250.3658864633509280.7317729267018560.634113536649072
260.3573944161839750.7147888323679490.642605583816025
270.3917791854594470.7835583709188950.608220814540553
280.4103869747829380.8207739495658760.589613025217062
290.3678110463166010.7356220926332010.632188953683399
300.4247330540409560.8494661080819110.575266945959044
310.3965758499723540.7931516999447090.603424150027645
320.4296361301269340.8592722602538680.570363869873066
330.4003460374312260.8006920748624520.599653962568774
340.3623533071411910.7247066142823830.637646692858809
350.3275013554706780.6550027109413570.672498644529322
360.9080492358878370.1839015282243260.0919507641121628
370.9072660789489280.1854678421021440.0927339210510718
380.9001400843777160.1997198312445680.0998599156222842
390.884035575498960.2319288490020810.11596442450104
400.8713087007639320.2573825984721350.128691299236068
410.8522981198321420.2954037603357160.147701880167858
420.8372700432817850.325459913436430.162729956718215
430.8641886993287690.2716226013424620.135811300671231
440.8337246666636150.332550666672770.166275333336385
450.8024699319574960.3950601360850090.197530068042504
460.9028591123639720.1942817752720560.0971408876360282
470.9294117714884480.1411764570231030.0705882285115516
480.9110053158443580.1779893683112840.0889946841556419
490.8985870911849080.2028258176301840.101412908815092
500.888771144054950.2224577118900990.11122885594505
510.8684661436891130.2630677126217750.131533856310887
520.8406187632873070.3187624734253870.159381236712693
530.8663099582650930.2673800834698130.133690041734907
540.8574306597712430.2851386804575140.142569340228757
550.8758792984610240.2482414030779530.124120701538976
560.8632962142399630.2734075715200740.136703785760037
570.8362242068911430.3275515862177140.163775793108857
580.8055780549353240.3888438901293520.194421945064676
590.774542066563140.4509158668737210.22545793343686
600.7742441311371880.4515117377256230.225755868862812
610.737813983007110.5243720339857810.262186016992891
620.7047750019421990.5904499961156010.295224998057801
630.6672346477830710.6655307044338580.332765352216929
640.6258850039190470.7482299921619060.374114996080953
650.5888542731227750.822291453754450.411145726877225
660.5574588000462910.8850823999074190.442541199953709
670.5635355642776240.8729288714447520.436464435722376
680.6593216007581410.6813567984837190.340678399241859
690.7475647820588820.5048704358822350.252435217941118
700.7127511289183670.5744977421632660.287248871081633
710.7943602147775290.4112795704449420.205639785222471
720.7634217723836090.4731564552327820.236578227616391
730.742398450729950.5152030985400990.25760154927005
740.7213462737367680.5573074525264640.278653726263232
750.6815663499318090.6368673001363820.318433650068191
760.772366275191770.455267449616460.22763372480823
770.7370489301498980.5259021397002030.262951069850102
780.7253398376343740.5493203247312520.274660162365626
790.7622860897664850.4754278204670310.237713910233515
800.7298835022929580.5402329954140850.270116497707042
810.690747431249880.6185051375002390.309252568750119
820.8079955744880170.3840088510239650.192004425511983
830.7755522240284090.4488955519431820.224447775971591
840.7447443615904310.5105112768191380.255255638409569
850.7061518761752110.5876962476495780.293848123824789
860.6949689121728750.6100621756542510.305031087827125
870.653302206690860.693395586618280.34669779330914
880.6133722185818540.7732555628362910.386627781418146
890.5831345172908120.8337309654183750.416865482709188
900.5495827348586090.9008345302827810.450417265141391
910.5511328160619290.8977343678761420.448867183938071
920.5130606800221150.973878639955770.486939319977885
930.4700872072617280.9401744145234550.529912792738273
940.4275388381275520.8550776762551050.572461161872448
950.4781729186876680.9563458373753370.521827081312332
960.4510272997315190.9020545994630380.548972700268481
970.4116873611883740.8233747223767470.588312638811626
980.4118490666677750.823698133335550.588150933332225
990.3668788119586660.7337576239173320.633121188041334
1000.3284125403766580.6568250807533170.671587459623342
1010.2960359658120520.5920719316241040.703964034187948
1020.2849462842150830.5698925684301650.715053715784917
1030.3405914841053650.681182968210730.659408515894635
1040.2983125219974270.5966250439948530.701687478002573
1050.2837998805110470.5675997610220940.716200119488953
1060.2697512597321630.5395025194643270.730248740267837
1070.2539499752151710.5078999504303410.746050024784829
1080.2345614158643250.469122831728650.765438584135675
1090.2332058848631620.4664117697263230.766794115136838
1100.2193637336926510.4387274673853030.780636266307349
1110.192987089453780.385974178907560.80701291054622
1120.1652010610684910.3304021221369810.834798938931509
1130.1681094353439520.3362188706879040.831890564656048
1140.1615263269045380.3230526538090770.838473673095462
1150.1918808304372670.3837616608745350.808119169562733
1160.1920066151684410.3840132303368830.807993384831559
1170.1775922809330550.355184561866110.822407719066945
1180.1517629517150110.3035259034300210.848237048284989
1190.1594203675078150.318840735015630.840579632492185
1200.1468306322373850.2936612644747690.853169367762615
1210.1317186594976970.2634373189953940.868281340502303
1220.1099732522115930.2199465044231860.890026747788407
1230.1234656572775870.2469313145551750.876534342722413
1240.1015914231235260.2031828462470520.898408576876474
1250.08433404980517740.1686680996103550.915665950194823
1260.06878894926865140.1375778985373030.931211050731349
1270.05238687228762430.1047737445752490.947613127712376
1280.04525260732700790.09050521465401570.954747392672992
1290.03592696295620610.07185392591241220.964073037043794
1300.04762866541379690.09525733082759390.952371334586203
1310.03973076791251990.07946153582503990.96026923208748
1320.07033418550955760.1406683710191150.929665814490442
1330.07342168514390020.14684337028780.9265783148561
1340.0897299280502230.1794598561004460.910270071949777
1350.07710399657623460.1542079931524690.922896003423765
1360.05693005757899560.1138601151579910.943069942421004
1370.04099952696669840.08199905393339690.959000473033302
1380.02994532531146910.05989065062293810.970054674688531
1390.03896864352342420.07793728704684850.961031356476576
1400.03594561158925630.07189122317851270.964054388410744
1410.5285755801544430.9428488396911140.471424419845557
1420.4648627125294210.9297254250588430.535137287470579
1430.429264314824150.85852862964830.57073568517585
1440.3747310954218160.7494621908436330.625268904578184
1450.305070657188970.6101413143779410.694929342811029
1460.454542275235320.909084550470640.54545772476468
1470.5165184252061690.9669631495876630.483481574793831
1480.7614736269646090.4770527460707820.238526373035391
1490.6773634365940430.6452731268119140.322636563405957
1500.5605206755468770.8789586489062470.439479324453123
1510.7713016446182120.4573967107635750.228698355381788
1520.6983328017577970.6033343964844060.301667198242203
1530.590061997995530.8198760040089390.409938002004469

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.648850878167225 & 0.70229824366555 & 0.351149121832775 \tabularnewline
10 & 0.50576207128634 & 0.98847585742732 & 0.49423792871366 \tabularnewline
11 & 0.361564145874692 & 0.723128291749384 & 0.638435854125308 \tabularnewline
12 & 0.387968295385393 & 0.775936590770786 & 0.612031704614607 \tabularnewline
13 & 0.277065407612193 & 0.554130815224387 & 0.722934592387807 \tabularnewline
14 & 0.256030618439578 & 0.512061236879156 & 0.743969381560422 \tabularnewline
15 & 0.191411244008287 & 0.382822488016574 & 0.808588755991713 \tabularnewline
16 & 0.132387317670194 & 0.264774635340388 & 0.867612682329806 \tabularnewline
17 & 0.100343685039873 & 0.200687370079746 & 0.899656314960127 \tabularnewline
18 & 0.289565283145589 & 0.579130566291179 & 0.710434716854411 \tabularnewline
19 & 0.234265368692596 & 0.468530737385192 & 0.765734631307404 \tabularnewline
20 & 0.17522985683256 & 0.35045971366512 & 0.82477014316744 \tabularnewline
21 & 0.134234789436814 & 0.268469578873629 & 0.865765210563186 \tabularnewline
22 & 0.147964604942308 & 0.295929209884616 & 0.852035395057692 \tabularnewline
23 & 0.385684358798265 & 0.77136871759653 & 0.614315641201735 \tabularnewline
24 & 0.362546924129483 & 0.725093848258966 & 0.637453075870517 \tabularnewline
25 & 0.365886463350928 & 0.731772926701856 & 0.634113536649072 \tabularnewline
26 & 0.357394416183975 & 0.714788832367949 & 0.642605583816025 \tabularnewline
27 & 0.391779185459447 & 0.783558370918895 & 0.608220814540553 \tabularnewline
28 & 0.410386974782938 & 0.820773949565876 & 0.589613025217062 \tabularnewline
29 & 0.367811046316601 & 0.735622092633201 & 0.632188953683399 \tabularnewline
30 & 0.424733054040956 & 0.849466108081911 & 0.575266945959044 \tabularnewline
31 & 0.396575849972354 & 0.793151699944709 & 0.603424150027645 \tabularnewline
32 & 0.429636130126934 & 0.859272260253868 & 0.570363869873066 \tabularnewline
33 & 0.400346037431226 & 0.800692074862452 & 0.599653962568774 \tabularnewline
34 & 0.362353307141191 & 0.724706614282383 & 0.637646692858809 \tabularnewline
35 & 0.327501355470678 & 0.655002710941357 & 0.672498644529322 \tabularnewline
36 & 0.908049235887837 & 0.183901528224326 & 0.0919507641121628 \tabularnewline
37 & 0.907266078948928 & 0.185467842102144 & 0.0927339210510718 \tabularnewline
38 & 0.900140084377716 & 0.199719831244568 & 0.0998599156222842 \tabularnewline
39 & 0.88403557549896 & 0.231928849002081 & 0.11596442450104 \tabularnewline
40 & 0.871308700763932 & 0.257382598472135 & 0.128691299236068 \tabularnewline
41 & 0.852298119832142 & 0.295403760335716 & 0.147701880167858 \tabularnewline
42 & 0.837270043281785 & 0.32545991343643 & 0.162729956718215 \tabularnewline
43 & 0.864188699328769 & 0.271622601342462 & 0.135811300671231 \tabularnewline
44 & 0.833724666663615 & 0.33255066667277 & 0.166275333336385 \tabularnewline
45 & 0.802469931957496 & 0.395060136085009 & 0.197530068042504 \tabularnewline
46 & 0.902859112363972 & 0.194281775272056 & 0.0971408876360282 \tabularnewline
47 & 0.929411771488448 & 0.141176457023103 & 0.0705882285115516 \tabularnewline
48 & 0.911005315844358 & 0.177989368311284 & 0.0889946841556419 \tabularnewline
49 & 0.898587091184908 & 0.202825817630184 & 0.101412908815092 \tabularnewline
50 & 0.88877114405495 & 0.222457711890099 & 0.11122885594505 \tabularnewline
51 & 0.868466143689113 & 0.263067712621775 & 0.131533856310887 \tabularnewline
52 & 0.840618763287307 & 0.318762473425387 & 0.159381236712693 \tabularnewline
53 & 0.866309958265093 & 0.267380083469813 & 0.133690041734907 \tabularnewline
54 & 0.857430659771243 & 0.285138680457514 & 0.142569340228757 \tabularnewline
55 & 0.875879298461024 & 0.248241403077953 & 0.124120701538976 \tabularnewline
56 & 0.863296214239963 & 0.273407571520074 & 0.136703785760037 \tabularnewline
57 & 0.836224206891143 & 0.327551586217714 & 0.163775793108857 \tabularnewline
58 & 0.805578054935324 & 0.388843890129352 & 0.194421945064676 \tabularnewline
59 & 0.77454206656314 & 0.450915866873721 & 0.22545793343686 \tabularnewline
60 & 0.774244131137188 & 0.451511737725623 & 0.225755868862812 \tabularnewline
61 & 0.73781398300711 & 0.524372033985781 & 0.262186016992891 \tabularnewline
62 & 0.704775001942199 & 0.590449996115601 & 0.295224998057801 \tabularnewline
63 & 0.667234647783071 & 0.665530704433858 & 0.332765352216929 \tabularnewline
64 & 0.625885003919047 & 0.748229992161906 & 0.374114996080953 \tabularnewline
65 & 0.588854273122775 & 0.82229145375445 & 0.411145726877225 \tabularnewline
66 & 0.557458800046291 & 0.885082399907419 & 0.442541199953709 \tabularnewline
67 & 0.563535564277624 & 0.872928871444752 & 0.436464435722376 \tabularnewline
68 & 0.659321600758141 & 0.681356798483719 & 0.340678399241859 \tabularnewline
69 & 0.747564782058882 & 0.504870435882235 & 0.252435217941118 \tabularnewline
70 & 0.712751128918367 & 0.574497742163266 & 0.287248871081633 \tabularnewline
71 & 0.794360214777529 & 0.411279570444942 & 0.205639785222471 \tabularnewline
72 & 0.763421772383609 & 0.473156455232782 & 0.236578227616391 \tabularnewline
73 & 0.74239845072995 & 0.515203098540099 & 0.25760154927005 \tabularnewline
74 & 0.721346273736768 & 0.557307452526464 & 0.278653726263232 \tabularnewline
75 & 0.681566349931809 & 0.636867300136382 & 0.318433650068191 \tabularnewline
76 & 0.77236627519177 & 0.45526744961646 & 0.22763372480823 \tabularnewline
77 & 0.737048930149898 & 0.525902139700203 & 0.262951069850102 \tabularnewline
78 & 0.725339837634374 & 0.549320324731252 & 0.274660162365626 \tabularnewline
79 & 0.762286089766485 & 0.475427820467031 & 0.237713910233515 \tabularnewline
80 & 0.729883502292958 & 0.540232995414085 & 0.270116497707042 \tabularnewline
81 & 0.69074743124988 & 0.618505137500239 & 0.309252568750119 \tabularnewline
82 & 0.807995574488017 & 0.384008851023965 & 0.192004425511983 \tabularnewline
83 & 0.775552224028409 & 0.448895551943182 & 0.224447775971591 \tabularnewline
84 & 0.744744361590431 & 0.510511276819138 & 0.255255638409569 \tabularnewline
85 & 0.706151876175211 & 0.587696247649578 & 0.293848123824789 \tabularnewline
86 & 0.694968912172875 & 0.610062175654251 & 0.305031087827125 \tabularnewline
87 & 0.65330220669086 & 0.69339558661828 & 0.34669779330914 \tabularnewline
88 & 0.613372218581854 & 0.773255562836291 & 0.386627781418146 \tabularnewline
89 & 0.583134517290812 & 0.833730965418375 & 0.416865482709188 \tabularnewline
90 & 0.549582734858609 & 0.900834530282781 & 0.450417265141391 \tabularnewline
91 & 0.551132816061929 & 0.897734367876142 & 0.448867183938071 \tabularnewline
92 & 0.513060680022115 & 0.97387863995577 & 0.486939319977885 \tabularnewline
93 & 0.470087207261728 & 0.940174414523455 & 0.529912792738273 \tabularnewline
94 & 0.427538838127552 & 0.855077676255105 & 0.572461161872448 \tabularnewline
95 & 0.478172918687668 & 0.956345837375337 & 0.521827081312332 \tabularnewline
96 & 0.451027299731519 & 0.902054599463038 & 0.548972700268481 \tabularnewline
97 & 0.411687361188374 & 0.823374722376747 & 0.588312638811626 \tabularnewline
98 & 0.411849066667775 & 0.82369813333555 & 0.588150933332225 \tabularnewline
99 & 0.366878811958666 & 0.733757623917332 & 0.633121188041334 \tabularnewline
100 & 0.328412540376658 & 0.656825080753317 & 0.671587459623342 \tabularnewline
101 & 0.296035965812052 & 0.592071931624104 & 0.703964034187948 \tabularnewline
102 & 0.284946284215083 & 0.569892568430165 & 0.715053715784917 \tabularnewline
103 & 0.340591484105365 & 0.68118296821073 & 0.659408515894635 \tabularnewline
104 & 0.298312521997427 & 0.596625043994853 & 0.701687478002573 \tabularnewline
105 & 0.283799880511047 & 0.567599761022094 & 0.716200119488953 \tabularnewline
106 & 0.269751259732163 & 0.539502519464327 & 0.730248740267837 \tabularnewline
107 & 0.253949975215171 & 0.507899950430341 & 0.746050024784829 \tabularnewline
108 & 0.234561415864325 & 0.46912283172865 & 0.765438584135675 \tabularnewline
109 & 0.233205884863162 & 0.466411769726323 & 0.766794115136838 \tabularnewline
110 & 0.219363733692651 & 0.438727467385303 & 0.780636266307349 \tabularnewline
111 & 0.19298708945378 & 0.38597417890756 & 0.80701291054622 \tabularnewline
112 & 0.165201061068491 & 0.330402122136981 & 0.834798938931509 \tabularnewline
113 & 0.168109435343952 & 0.336218870687904 & 0.831890564656048 \tabularnewline
114 & 0.161526326904538 & 0.323052653809077 & 0.838473673095462 \tabularnewline
115 & 0.191880830437267 & 0.383761660874535 & 0.808119169562733 \tabularnewline
116 & 0.192006615168441 & 0.384013230336883 & 0.807993384831559 \tabularnewline
117 & 0.177592280933055 & 0.35518456186611 & 0.822407719066945 \tabularnewline
118 & 0.151762951715011 & 0.303525903430021 & 0.848237048284989 \tabularnewline
119 & 0.159420367507815 & 0.31884073501563 & 0.840579632492185 \tabularnewline
120 & 0.146830632237385 & 0.293661264474769 & 0.853169367762615 \tabularnewline
121 & 0.131718659497697 & 0.263437318995394 & 0.868281340502303 \tabularnewline
122 & 0.109973252211593 & 0.219946504423186 & 0.890026747788407 \tabularnewline
123 & 0.123465657277587 & 0.246931314555175 & 0.876534342722413 \tabularnewline
124 & 0.101591423123526 & 0.203182846247052 & 0.898408576876474 \tabularnewline
125 & 0.0843340498051774 & 0.168668099610355 & 0.915665950194823 \tabularnewline
126 & 0.0687889492686514 & 0.137577898537303 & 0.931211050731349 \tabularnewline
127 & 0.0523868722876243 & 0.104773744575249 & 0.947613127712376 \tabularnewline
128 & 0.0452526073270079 & 0.0905052146540157 & 0.954747392672992 \tabularnewline
129 & 0.0359269629562061 & 0.0718539259124122 & 0.964073037043794 \tabularnewline
130 & 0.0476286654137969 & 0.0952573308275939 & 0.952371334586203 \tabularnewline
131 & 0.0397307679125199 & 0.0794615358250399 & 0.96026923208748 \tabularnewline
132 & 0.0703341855095576 & 0.140668371019115 & 0.929665814490442 \tabularnewline
133 & 0.0734216851439002 & 0.1468433702878 & 0.9265783148561 \tabularnewline
134 & 0.089729928050223 & 0.179459856100446 & 0.910270071949777 \tabularnewline
135 & 0.0771039965762346 & 0.154207993152469 & 0.922896003423765 \tabularnewline
136 & 0.0569300575789956 & 0.113860115157991 & 0.943069942421004 \tabularnewline
137 & 0.0409995269666984 & 0.0819990539333969 & 0.959000473033302 \tabularnewline
138 & 0.0299453253114691 & 0.0598906506229381 & 0.970054674688531 \tabularnewline
139 & 0.0389686435234242 & 0.0779372870468485 & 0.961031356476576 \tabularnewline
140 & 0.0359456115892563 & 0.0718912231785127 & 0.964054388410744 \tabularnewline
141 & 0.528575580154443 & 0.942848839691114 & 0.471424419845557 \tabularnewline
142 & 0.464862712529421 & 0.929725425058843 & 0.535137287470579 \tabularnewline
143 & 0.42926431482415 & 0.8585286296483 & 0.57073568517585 \tabularnewline
144 & 0.374731095421816 & 0.749462190843633 & 0.625268904578184 \tabularnewline
145 & 0.30507065718897 & 0.610141314377941 & 0.694929342811029 \tabularnewline
146 & 0.45454227523532 & 0.90908455047064 & 0.54545772476468 \tabularnewline
147 & 0.516518425206169 & 0.966963149587663 & 0.483481574793831 \tabularnewline
148 & 0.761473626964609 & 0.477052746070782 & 0.238526373035391 \tabularnewline
149 & 0.677363436594043 & 0.645273126811914 & 0.322636563405957 \tabularnewline
150 & 0.560520675546877 & 0.878958648906247 & 0.439479324453123 \tabularnewline
151 & 0.771301644618212 & 0.457396710763575 & 0.228698355381788 \tabularnewline
152 & 0.698332801757797 & 0.603334396484406 & 0.301667198242203 \tabularnewline
153 & 0.59006199799553 & 0.819876004008939 & 0.409938002004469 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185994&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]9[/C][C]0.648850878167225[/C][C]0.70229824366555[/C][C]0.351149121832775[/C][/ROW]
[ROW][C]10[/C][C]0.50576207128634[/C][C]0.98847585742732[/C][C]0.49423792871366[/C][/ROW]
[ROW][C]11[/C][C]0.361564145874692[/C][C]0.723128291749384[/C][C]0.638435854125308[/C][/ROW]
[ROW][C]12[/C][C]0.387968295385393[/C][C]0.775936590770786[/C][C]0.612031704614607[/C][/ROW]
[ROW][C]13[/C][C]0.277065407612193[/C][C]0.554130815224387[/C][C]0.722934592387807[/C][/ROW]
[ROW][C]14[/C][C]0.256030618439578[/C][C]0.512061236879156[/C][C]0.743969381560422[/C][/ROW]
[ROW][C]15[/C][C]0.191411244008287[/C][C]0.382822488016574[/C][C]0.808588755991713[/C][/ROW]
[ROW][C]16[/C][C]0.132387317670194[/C][C]0.264774635340388[/C][C]0.867612682329806[/C][/ROW]
[ROW][C]17[/C][C]0.100343685039873[/C][C]0.200687370079746[/C][C]0.899656314960127[/C][/ROW]
[ROW][C]18[/C][C]0.289565283145589[/C][C]0.579130566291179[/C][C]0.710434716854411[/C][/ROW]
[ROW][C]19[/C][C]0.234265368692596[/C][C]0.468530737385192[/C][C]0.765734631307404[/C][/ROW]
[ROW][C]20[/C][C]0.17522985683256[/C][C]0.35045971366512[/C][C]0.82477014316744[/C][/ROW]
[ROW][C]21[/C][C]0.134234789436814[/C][C]0.268469578873629[/C][C]0.865765210563186[/C][/ROW]
[ROW][C]22[/C][C]0.147964604942308[/C][C]0.295929209884616[/C][C]0.852035395057692[/C][/ROW]
[ROW][C]23[/C][C]0.385684358798265[/C][C]0.77136871759653[/C][C]0.614315641201735[/C][/ROW]
[ROW][C]24[/C][C]0.362546924129483[/C][C]0.725093848258966[/C][C]0.637453075870517[/C][/ROW]
[ROW][C]25[/C][C]0.365886463350928[/C][C]0.731772926701856[/C][C]0.634113536649072[/C][/ROW]
[ROW][C]26[/C][C]0.357394416183975[/C][C]0.714788832367949[/C][C]0.642605583816025[/C][/ROW]
[ROW][C]27[/C][C]0.391779185459447[/C][C]0.783558370918895[/C][C]0.608220814540553[/C][/ROW]
[ROW][C]28[/C][C]0.410386974782938[/C][C]0.820773949565876[/C][C]0.589613025217062[/C][/ROW]
[ROW][C]29[/C][C]0.367811046316601[/C][C]0.735622092633201[/C][C]0.632188953683399[/C][/ROW]
[ROW][C]30[/C][C]0.424733054040956[/C][C]0.849466108081911[/C][C]0.575266945959044[/C][/ROW]
[ROW][C]31[/C][C]0.396575849972354[/C][C]0.793151699944709[/C][C]0.603424150027645[/C][/ROW]
[ROW][C]32[/C][C]0.429636130126934[/C][C]0.859272260253868[/C][C]0.570363869873066[/C][/ROW]
[ROW][C]33[/C][C]0.400346037431226[/C][C]0.800692074862452[/C][C]0.599653962568774[/C][/ROW]
[ROW][C]34[/C][C]0.362353307141191[/C][C]0.724706614282383[/C][C]0.637646692858809[/C][/ROW]
[ROW][C]35[/C][C]0.327501355470678[/C][C]0.655002710941357[/C][C]0.672498644529322[/C][/ROW]
[ROW][C]36[/C][C]0.908049235887837[/C][C]0.183901528224326[/C][C]0.0919507641121628[/C][/ROW]
[ROW][C]37[/C][C]0.907266078948928[/C][C]0.185467842102144[/C][C]0.0927339210510718[/C][/ROW]
[ROW][C]38[/C][C]0.900140084377716[/C][C]0.199719831244568[/C][C]0.0998599156222842[/C][/ROW]
[ROW][C]39[/C][C]0.88403557549896[/C][C]0.231928849002081[/C][C]0.11596442450104[/C][/ROW]
[ROW][C]40[/C][C]0.871308700763932[/C][C]0.257382598472135[/C][C]0.128691299236068[/C][/ROW]
[ROW][C]41[/C][C]0.852298119832142[/C][C]0.295403760335716[/C][C]0.147701880167858[/C][/ROW]
[ROW][C]42[/C][C]0.837270043281785[/C][C]0.32545991343643[/C][C]0.162729956718215[/C][/ROW]
[ROW][C]43[/C][C]0.864188699328769[/C][C]0.271622601342462[/C][C]0.135811300671231[/C][/ROW]
[ROW][C]44[/C][C]0.833724666663615[/C][C]0.33255066667277[/C][C]0.166275333336385[/C][/ROW]
[ROW][C]45[/C][C]0.802469931957496[/C][C]0.395060136085009[/C][C]0.197530068042504[/C][/ROW]
[ROW][C]46[/C][C]0.902859112363972[/C][C]0.194281775272056[/C][C]0.0971408876360282[/C][/ROW]
[ROW][C]47[/C][C]0.929411771488448[/C][C]0.141176457023103[/C][C]0.0705882285115516[/C][/ROW]
[ROW][C]48[/C][C]0.911005315844358[/C][C]0.177989368311284[/C][C]0.0889946841556419[/C][/ROW]
[ROW][C]49[/C][C]0.898587091184908[/C][C]0.202825817630184[/C][C]0.101412908815092[/C][/ROW]
[ROW][C]50[/C][C]0.88877114405495[/C][C]0.222457711890099[/C][C]0.11122885594505[/C][/ROW]
[ROW][C]51[/C][C]0.868466143689113[/C][C]0.263067712621775[/C][C]0.131533856310887[/C][/ROW]
[ROW][C]52[/C][C]0.840618763287307[/C][C]0.318762473425387[/C][C]0.159381236712693[/C][/ROW]
[ROW][C]53[/C][C]0.866309958265093[/C][C]0.267380083469813[/C][C]0.133690041734907[/C][/ROW]
[ROW][C]54[/C][C]0.857430659771243[/C][C]0.285138680457514[/C][C]0.142569340228757[/C][/ROW]
[ROW][C]55[/C][C]0.875879298461024[/C][C]0.248241403077953[/C][C]0.124120701538976[/C][/ROW]
[ROW][C]56[/C][C]0.863296214239963[/C][C]0.273407571520074[/C][C]0.136703785760037[/C][/ROW]
[ROW][C]57[/C][C]0.836224206891143[/C][C]0.327551586217714[/C][C]0.163775793108857[/C][/ROW]
[ROW][C]58[/C][C]0.805578054935324[/C][C]0.388843890129352[/C][C]0.194421945064676[/C][/ROW]
[ROW][C]59[/C][C]0.77454206656314[/C][C]0.450915866873721[/C][C]0.22545793343686[/C][/ROW]
[ROW][C]60[/C][C]0.774244131137188[/C][C]0.451511737725623[/C][C]0.225755868862812[/C][/ROW]
[ROW][C]61[/C][C]0.73781398300711[/C][C]0.524372033985781[/C][C]0.262186016992891[/C][/ROW]
[ROW][C]62[/C][C]0.704775001942199[/C][C]0.590449996115601[/C][C]0.295224998057801[/C][/ROW]
[ROW][C]63[/C][C]0.667234647783071[/C][C]0.665530704433858[/C][C]0.332765352216929[/C][/ROW]
[ROW][C]64[/C][C]0.625885003919047[/C][C]0.748229992161906[/C][C]0.374114996080953[/C][/ROW]
[ROW][C]65[/C][C]0.588854273122775[/C][C]0.82229145375445[/C][C]0.411145726877225[/C][/ROW]
[ROW][C]66[/C][C]0.557458800046291[/C][C]0.885082399907419[/C][C]0.442541199953709[/C][/ROW]
[ROW][C]67[/C][C]0.563535564277624[/C][C]0.872928871444752[/C][C]0.436464435722376[/C][/ROW]
[ROW][C]68[/C][C]0.659321600758141[/C][C]0.681356798483719[/C][C]0.340678399241859[/C][/ROW]
[ROW][C]69[/C][C]0.747564782058882[/C][C]0.504870435882235[/C][C]0.252435217941118[/C][/ROW]
[ROW][C]70[/C][C]0.712751128918367[/C][C]0.574497742163266[/C][C]0.287248871081633[/C][/ROW]
[ROW][C]71[/C][C]0.794360214777529[/C][C]0.411279570444942[/C][C]0.205639785222471[/C][/ROW]
[ROW][C]72[/C][C]0.763421772383609[/C][C]0.473156455232782[/C][C]0.236578227616391[/C][/ROW]
[ROW][C]73[/C][C]0.74239845072995[/C][C]0.515203098540099[/C][C]0.25760154927005[/C][/ROW]
[ROW][C]74[/C][C]0.721346273736768[/C][C]0.557307452526464[/C][C]0.278653726263232[/C][/ROW]
[ROW][C]75[/C][C]0.681566349931809[/C][C]0.636867300136382[/C][C]0.318433650068191[/C][/ROW]
[ROW][C]76[/C][C]0.77236627519177[/C][C]0.45526744961646[/C][C]0.22763372480823[/C][/ROW]
[ROW][C]77[/C][C]0.737048930149898[/C][C]0.525902139700203[/C][C]0.262951069850102[/C][/ROW]
[ROW][C]78[/C][C]0.725339837634374[/C][C]0.549320324731252[/C][C]0.274660162365626[/C][/ROW]
[ROW][C]79[/C][C]0.762286089766485[/C][C]0.475427820467031[/C][C]0.237713910233515[/C][/ROW]
[ROW][C]80[/C][C]0.729883502292958[/C][C]0.540232995414085[/C][C]0.270116497707042[/C][/ROW]
[ROW][C]81[/C][C]0.69074743124988[/C][C]0.618505137500239[/C][C]0.309252568750119[/C][/ROW]
[ROW][C]82[/C][C]0.807995574488017[/C][C]0.384008851023965[/C][C]0.192004425511983[/C][/ROW]
[ROW][C]83[/C][C]0.775552224028409[/C][C]0.448895551943182[/C][C]0.224447775971591[/C][/ROW]
[ROW][C]84[/C][C]0.744744361590431[/C][C]0.510511276819138[/C][C]0.255255638409569[/C][/ROW]
[ROW][C]85[/C][C]0.706151876175211[/C][C]0.587696247649578[/C][C]0.293848123824789[/C][/ROW]
[ROW][C]86[/C][C]0.694968912172875[/C][C]0.610062175654251[/C][C]0.305031087827125[/C][/ROW]
[ROW][C]87[/C][C]0.65330220669086[/C][C]0.69339558661828[/C][C]0.34669779330914[/C][/ROW]
[ROW][C]88[/C][C]0.613372218581854[/C][C]0.773255562836291[/C][C]0.386627781418146[/C][/ROW]
[ROW][C]89[/C][C]0.583134517290812[/C][C]0.833730965418375[/C][C]0.416865482709188[/C][/ROW]
[ROW][C]90[/C][C]0.549582734858609[/C][C]0.900834530282781[/C][C]0.450417265141391[/C][/ROW]
[ROW][C]91[/C][C]0.551132816061929[/C][C]0.897734367876142[/C][C]0.448867183938071[/C][/ROW]
[ROW][C]92[/C][C]0.513060680022115[/C][C]0.97387863995577[/C][C]0.486939319977885[/C][/ROW]
[ROW][C]93[/C][C]0.470087207261728[/C][C]0.940174414523455[/C][C]0.529912792738273[/C][/ROW]
[ROW][C]94[/C][C]0.427538838127552[/C][C]0.855077676255105[/C][C]0.572461161872448[/C][/ROW]
[ROW][C]95[/C][C]0.478172918687668[/C][C]0.956345837375337[/C][C]0.521827081312332[/C][/ROW]
[ROW][C]96[/C][C]0.451027299731519[/C][C]0.902054599463038[/C][C]0.548972700268481[/C][/ROW]
[ROW][C]97[/C][C]0.411687361188374[/C][C]0.823374722376747[/C][C]0.588312638811626[/C][/ROW]
[ROW][C]98[/C][C]0.411849066667775[/C][C]0.82369813333555[/C][C]0.588150933332225[/C][/ROW]
[ROW][C]99[/C][C]0.366878811958666[/C][C]0.733757623917332[/C][C]0.633121188041334[/C][/ROW]
[ROW][C]100[/C][C]0.328412540376658[/C][C]0.656825080753317[/C][C]0.671587459623342[/C][/ROW]
[ROW][C]101[/C][C]0.296035965812052[/C][C]0.592071931624104[/C][C]0.703964034187948[/C][/ROW]
[ROW][C]102[/C][C]0.284946284215083[/C][C]0.569892568430165[/C][C]0.715053715784917[/C][/ROW]
[ROW][C]103[/C][C]0.340591484105365[/C][C]0.68118296821073[/C][C]0.659408515894635[/C][/ROW]
[ROW][C]104[/C][C]0.298312521997427[/C][C]0.596625043994853[/C][C]0.701687478002573[/C][/ROW]
[ROW][C]105[/C][C]0.283799880511047[/C][C]0.567599761022094[/C][C]0.716200119488953[/C][/ROW]
[ROW][C]106[/C][C]0.269751259732163[/C][C]0.539502519464327[/C][C]0.730248740267837[/C][/ROW]
[ROW][C]107[/C][C]0.253949975215171[/C][C]0.507899950430341[/C][C]0.746050024784829[/C][/ROW]
[ROW][C]108[/C][C]0.234561415864325[/C][C]0.46912283172865[/C][C]0.765438584135675[/C][/ROW]
[ROW][C]109[/C][C]0.233205884863162[/C][C]0.466411769726323[/C][C]0.766794115136838[/C][/ROW]
[ROW][C]110[/C][C]0.219363733692651[/C][C]0.438727467385303[/C][C]0.780636266307349[/C][/ROW]
[ROW][C]111[/C][C]0.19298708945378[/C][C]0.38597417890756[/C][C]0.80701291054622[/C][/ROW]
[ROW][C]112[/C][C]0.165201061068491[/C][C]0.330402122136981[/C][C]0.834798938931509[/C][/ROW]
[ROW][C]113[/C][C]0.168109435343952[/C][C]0.336218870687904[/C][C]0.831890564656048[/C][/ROW]
[ROW][C]114[/C][C]0.161526326904538[/C][C]0.323052653809077[/C][C]0.838473673095462[/C][/ROW]
[ROW][C]115[/C][C]0.191880830437267[/C][C]0.383761660874535[/C][C]0.808119169562733[/C][/ROW]
[ROW][C]116[/C][C]0.192006615168441[/C][C]0.384013230336883[/C][C]0.807993384831559[/C][/ROW]
[ROW][C]117[/C][C]0.177592280933055[/C][C]0.35518456186611[/C][C]0.822407719066945[/C][/ROW]
[ROW][C]118[/C][C]0.151762951715011[/C][C]0.303525903430021[/C][C]0.848237048284989[/C][/ROW]
[ROW][C]119[/C][C]0.159420367507815[/C][C]0.31884073501563[/C][C]0.840579632492185[/C][/ROW]
[ROW][C]120[/C][C]0.146830632237385[/C][C]0.293661264474769[/C][C]0.853169367762615[/C][/ROW]
[ROW][C]121[/C][C]0.131718659497697[/C][C]0.263437318995394[/C][C]0.868281340502303[/C][/ROW]
[ROW][C]122[/C][C]0.109973252211593[/C][C]0.219946504423186[/C][C]0.890026747788407[/C][/ROW]
[ROW][C]123[/C][C]0.123465657277587[/C][C]0.246931314555175[/C][C]0.876534342722413[/C][/ROW]
[ROW][C]124[/C][C]0.101591423123526[/C][C]0.203182846247052[/C][C]0.898408576876474[/C][/ROW]
[ROW][C]125[/C][C]0.0843340498051774[/C][C]0.168668099610355[/C][C]0.915665950194823[/C][/ROW]
[ROW][C]126[/C][C]0.0687889492686514[/C][C]0.137577898537303[/C][C]0.931211050731349[/C][/ROW]
[ROW][C]127[/C][C]0.0523868722876243[/C][C]0.104773744575249[/C][C]0.947613127712376[/C][/ROW]
[ROW][C]128[/C][C]0.0452526073270079[/C][C]0.0905052146540157[/C][C]0.954747392672992[/C][/ROW]
[ROW][C]129[/C][C]0.0359269629562061[/C][C]0.0718539259124122[/C][C]0.964073037043794[/C][/ROW]
[ROW][C]130[/C][C]0.0476286654137969[/C][C]0.0952573308275939[/C][C]0.952371334586203[/C][/ROW]
[ROW][C]131[/C][C]0.0397307679125199[/C][C]0.0794615358250399[/C][C]0.96026923208748[/C][/ROW]
[ROW][C]132[/C][C]0.0703341855095576[/C][C]0.140668371019115[/C][C]0.929665814490442[/C][/ROW]
[ROW][C]133[/C][C]0.0734216851439002[/C][C]0.1468433702878[/C][C]0.9265783148561[/C][/ROW]
[ROW][C]134[/C][C]0.089729928050223[/C][C]0.179459856100446[/C][C]0.910270071949777[/C][/ROW]
[ROW][C]135[/C][C]0.0771039965762346[/C][C]0.154207993152469[/C][C]0.922896003423765[/C][/ROW]
[ROW][C]136[/C][C]0.0569300575789956[/C][C]0.113860115157991[/C][C]0.943069942421004[/C][/ROW]
[ROW][C]137[/C][C]0.0409995269666984[/C][C]0.0819990539333969[/C][C]0.959000473033302[/C][/ROW]
[ROW][C]138[/C][C]0.0299453253114691[/C][C]0.0598906506229381[/C][C]0.970054674688531[/C][/ROW]
[ROW][C]139[/C][C]0.0389686435234242[/C][C]0.0779372870468485[/C][C]0.961031356476576[/C][/ROW]
[ROW][C]140[/C][C]0.0359456115892563[/C][C]0.0718912231785127[/C][C]0.964054388410744[/C][/ROW]
[ROW][C]141[/C][C]0.528575580154443[/C][C]0.942848839691114[/C][C]0.471424419845557[/C][/ROW]
[ROW][C]142[/C][C]0.464862712529421[/C][C]0.929725425058843[/C][C]0.535137287470579[/C][/ROW]
[ROW][C]143[/C][C]0.42926431482415[/C][C]0.8585286296483[/C][C]0.57073568517585[/C][/ROW]
[ROW][C]144[/C][C]0.374731095421816[/C][C]0.749462190843633[/C][C]0.625268904578184[/C][/ROW]
[ROW][C]145[/C][C]0.30507065718897[/C][C]0.610141314377941[/C][C]0.694929342811029[/C][/ROW]
[ROW][C]146[/C][C]0.45454227523532[/C][C]0.90908455047064[/C][C]0.54545772476468[/C][/ROW]
[ROW][C]147[/C][C]0.516518425206169[/C][C]0.966963149587663[/C][C]0.483481574793831[/C][/ROW]
[ROW][C]148[/C][C]0.761473626964609[/C][C]0.477052746070782[/C][C]0.238526373035391[/C][/ROW]
[ROW][C]149[/C][C]0.677363436594043[/C][C]0.645273126811914[/C][C]0.322636563405957[/C][/ROW]
[ROW][C]150[/C][C]0.560520675546877[/C][C]0.878958648906247[/C][C]0.439479324453123[/C][/ROW]
[ROW][C]151[/C][C]0.771301644618212[/C][C]0.457396710763575[/C][C]0.228698355381788[/C][/ROW]
[ROW][C]152[/C][C]0.698332801757797[/C][C]0.603334396484406[/C][C]0.301667198242203[/C][/ROW]
[ROW][C]153[/C][C]0.59006199799553[/C][C]0.819876004008939[/C][C]0.409938002004469[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185994&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185994&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6488508781672250.702298243665550.351149121832775
100.505762071286340.988475857427320.49423792871366
110.3615641458746920.7231282917493840.638435854125308
120.3879682953853930.7759365907707860.612031704614607
130.2770654076121930.5541308152243870.722934592387807
140.2560306184395780.5120612368791560.743969381560422
150.1914112440082870.3828224880165740.808588755991713
160.1323873176701940.2647746353403880.867612682329806
170.1003436850398730.2006873700797460.899656314960127
180.2895652831455890.5791305662911790.710434716854411
190.2342653686925960.4685307373851920.765734631307404
200.175229856832560.350459713665120.82477014316744
210.1342347894368140.2684695788736290.865765210563186
220.1479646049423080.2959292098846160.852035395057692
230.3856843587982650.771368717596530.614315641201735
240.3625469241294830.7250938482589660.637453075870517
250.3658864633509280.7317729267018560.634113536649072
260.3573944161839750.7147888323679490.642605583816025
270.3917791854594470.7835583709188950.608220814540553
280.4103869747829380.8207739495658760.589613025217062
290.3678110463166010.7356220926332010.632188953683399
300.4247330540409560.8494661080819110.575266945959044
310.3965758499723540.7931516999447090.603424150027645
320.4296361301269340.8592722602538680.570363869873066
330.4003460374312260.8006920748624520.599653962568774
340.3623533071411910.7247066142823830.637646692858809
350.3275013554706780.6550027109413570.672498644529322
360.9080492358878370.1839015282243260.0919507641121628
370.9072660789489280.1854678421021440.0927339210510718
380.9001400843777160.1997198312445680.0998599156222842
390.884035575498960.2319288490020810.11596442450104
400.8713087007639320.2573825984721350.128691299236068
410.8522981198321420.2954037603357160.147701880167858
420.8372700432817850.325459913436430.162729956718215
430.8641886993287690.2716226013424620.135811300671231
440.8337246666636150.332550666672770.166275333336385
450.8024699319574960.3950601360850090.197530068042504
460.9028591123639720.1942817752720560.0971408876360282
470.9294117714884480.1411764570231030.0705882285115516
480.9110053158443580.1779893683112840.0889946841556419
490.8985870911849080.2028258176301840.101412908815092
500.888771144054950.2224577118900990.11122885594505
510.8684661436891130.2630677126217750.131533856310887
520.8406187632873070.3187624734253870.159381236712693
530.8663099582650930.2673800834698130.133690041734907
540.8574306597712430.2851386804575140.142569340228757
550.8758792984610240.2482414030779530.124120701538976
560.8632962142399630.2734075715200740.136703785760037
570.8362242068911430.3275515862177140.163775793108857
580.8055780549353240.3888438901293520.194421945064676
590.774542066563140.4509158668737210.22545793343686
600.7742441311371880.4515117377256230.225755868862812
610.737813983007110.5243720339857810.262186016992891
620.7047750019421990.5904499961156010.295224998057801
630.6672346477830710.6655307044338580.332765352216929
640.6258850039190470.7482299921619060.374114996080953
650.5888542731227750.822291453754450.411145726877225
660.5574588000462910.8850823999074190.442541199953709
670.5635355642776240.8729288714447520.436464435722376
680.6593216007581410.6813567984837190.340678399241859
690.7475647820588820.5048704358822350.252435217941118
700.7127511289183670.5744977421632660.287248871081633
710.7943602147775290.4112795704449420.205639785222471
720.7634217723836090.4731564552327820.236578227616391
730.742398450729950.5152030985400990.25760154927005
740.7213462737367680.5573074525264640.278653726263232
750.6815663499318090.6368673001363820.318433650068191
760.772366275191770.455267449616460.22763372480823
770.7370489301498980.5259021397002030.262951069850102
780.7253398376343740.5493203247312520.274660162365626
790.7622860897664850.4754278204670310.237713910233515
800.7298835022929580.5402329954140850.270116497707042
810.690747431249880.6185051375002390.309252568750119
820.8079955744880170.3840088510239650.192004425511983
830.7755522240284090.4488955519431820.224447775971591
840.7447443615904310.5105112768191380.255255638409569
850.7061518761752110.5876962476495780.293848123824789
860.6949689121728750.6100621756542510.305031087827125
870.653302206690860.693395586618280.34669779330914
880.6133722185818540.7732555628362910.386627781418146
890.5831345172908120.8337309654183750.416865482709188
900.5495827348586090.9008345302827810.450417265141391
910.5511328160619290.8977343678761420.448867183938071
920.5130606800221150.973878639955770.486939319977885
930.4700872072617280.9401744145234550.529912792738273
940.4275388381275520.8550776762551050.572461161872448
950.4781729186876680.9563458373753370.521827081312332
960.4510272997315190.9020545994630380.548972700268481
970.4116873611883740.8233747223767470.588312638811626
980.4118490666677750.823698133335550.588150933332225
990.3668788119586660.7337576239173320.633121188041334
1000.3284125403766580.6568250807533170.671587459623342
1010.2960359658120520.5920719316241040.703964034187948
1020.2849462842150830.5698925684301650.715053715784917
1030.3405914841053650.681182968210730.659408515894635
1040.2983125219974270.5966250439948530.701687478002573
1050.2837998805110470.5675997610220940.716200119488953
1060.2697512597321630.5395025194643270.730248740267837
1070.2539499752151710.5078999504303410.746050024784829
1080.2345614158643250.469122831728650.765438584135675
1090.2332058848631620.4664117697263230.766794115136838
1100.2193637336926510.4387274673853030.780636266307349
1110.192987089453780.385974178907560.80701291054622
1120.1652010610684910.3304021221369810.834798938931509
1130.1681094353439520.3362188706879040.831890564656048
1140.1615263269045380.3230526538090770.838473673095462
1150.1918808304372670.3837616608745350.808119169562733
1160.1920066151684410.3840132303368830.807993384831559
1170.1775922809330550.355184561866110.822407719066945
1180.1517629517150110.3035259034300210.848237048284989
1190.1594203675078150.318840735015630.840579632492185
1200.1468306322373850.2936612644747690.853169367762615
1210.1317186594976970.2634373189953940.868281340502303
1220.1099732522115930.2199465044231860.890026747788407
1230.1234656572775870.2469313145551750.876534342722413
1240.1015914231235260.2031828462470520.898408576876474
1250.08433404980517740.1686680996103550.915665950194823
1260.06878894926865140.1375778985373030.931211050731349
1270.05238687228762430.1047737445752490.947613127712376
1280.04525260732700790.09050521465401570.954747392672992
1290.03592696295620610.07185392591241220.964073037043794
1300.04762866541379690.09525733082759390.952371334586203
1310.03973076791251990.07946153582503990.96026923208748
1320.07033418550955760.1406683710191150.929665814490442
1330.07342168514390020.14684337028780.9265783148561
1340.0897299280502230.1794598561004460.910270071949777
1350.07710399657623460.1542079931524690.922896003423765
1360.05693005757899560.1138601151579910.943069942421004
1370.04099952696669840.08199905393339690.959000473033302
1380.02994532531146910.05989065062293810.970054674688531
1390.03896864352342420.07793728704684850.961031356476576
1400.03594561158925630.07189122317851270.964054388410744
1410.5285755801544430.9428488396911140.471424419845557
1420.4648627125294210.9297254250588430.535137287470579
1430.429264314824150.85852862964830.57073568517585
1440.3747310954218160.7494621908436330.625268904578184
1450.305070657188970.6101413143779410.694929342811029
1460.454542275235320.909084550470640.54545772476468
1470.5165184252061690.9669631495876630.483481574793831
1480.7614736269646090.4770527460707820.238526373035391
1490.6773634365940430.6452731268119140.322636563405957
1500.5605206755468770.8789586489062470.439479324453123
1510.7713016446182120.4573967107635750.228698355381788
1520.6983328017577970.6033343964844060.301667198242203
1530.590061997995530.8198760040089390.409938002004469







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 level80.0551724137931034OK

\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 & 8 & 0.0551724137931034 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185994&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]8[/C][C]0.0551724137931034[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185994&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185994&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level80.0551724137931034OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}