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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 computationFri, 25 Nov 2011 04:30:15 -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/2011/Nov/25/t1322213519yupw4c1dmjpldm3.htm/, Retrieved Thu, 28 Mar 2024 07:54:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147266, Retrieved Thu, 28 Mar 2024 07:54:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple Regression ] [2011-11-25 09:30:15] [01668b8db120351c61467eadc96b2965] [Current]
- R P     [Multiple Regression] [MR - Perfectionis...] [2011-12-17 09:52:13] [ea9976aa04c7322b215e949114660791]
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Dataseries X:
24	14	11	12	24	26
25	11	7	8	25	23
17	6	17	8	30	25
18	12	10	8	19	23
18	8	12	9	22	19
16	10	12	7	22	29
20	10	11	4	25	25
16	11	11	11	23	21
18	16	12	7	17	22
17	11	13	7	21	25
23	13	14	12	19	24
30	12	16	10	19	18
23	8	11	10	15	22
18	12	10	8	16	15
15	11	11	8	23	22
12	4	15	4	27	28
21	9	9	9	22	20
15	8	11	8	14	12
20	8	17	7	22	24
31	14	17	11	23	20
27	15	11	9	23	21
34	16	18	11	21	20
21	9	14	13	19	21
31	14	10	8	18	23
19	11	11	8	20	28
16	8	15	9	23	24
20	9	15	6	25	24
21	9	13	9	19	24
22	9	16	9	24	23
17	9	13	6	22	23
24	10	9	6	25	29
25	16	18	16	26	24
26	11	18	5	29	18
25	8	12	7	32	25
17	9	17	9	25	21
32	16	9	6	29	26
33	11	9	6	28	22
13	16	12	5	17	22
32	12	18	12	28	22
25	12	12	7	29	23
29	14	18	10	26	30
22	9	14	9	25	23
18	10	15	8	14	17
17	9	16	5	25	23
20	10	10	8	26	23
15	12	11	8	20	25
20	14	14	10	18	24
33	14	9	6	32	24
29	10	12	8	25	23
23	14	17	7	25	21
26	16	5	4	23	24
18	9	12	8	21	24
20	10	12	8	20	28
11	6	6	4	15	16
28	8	24	20	30	20
26	13	12	8	24	29
22	10	12	8	26	27
17	8	14	6	24	22
12	7	7	4	22	28
14	15	13	8	14	16
17	9	12	9	24	25
21	10	13	6	24	24
19	12	14	7	24	28
18	13	8	9	24	24
10	10	11	5	19	23
29	11	9	5	31	30
31	8	11	8	22	24
19	9	13	8	27	21
9	13	10	6	19	25
20	11	11	8	25	25
28	8	12	7	20	22
19	9	9	7	21	23
30	9	15	9	27	26
29	15	18	11	23	23
26	9	15	6	25	25
23	10	12	8	20	21
13	14	13	6	21	25
21	12	14	9	22	24
19	12	10	8	23	29
28	11	13	6	25	22
23	14	13	10	25	27
18	6	11	8	17	26
21	12	13	8	19	22
20	8	16	10	25	24
23	14	8	5	19	27
21	11	16	7	20	24
21	10	11	5	26	24
15	14	9	8	23	29
28	12	16	14	27	22
19	10	12	7	17	21
26	14	14	8	17	24
10	5	8	6	19	24
16	11	9	5	17	23
22	10	15	6	22	20
19	9	11	10	21	27
31	10	21	12	32	26
31	16	14	9	21	25
29	13	18	12	21	21
19	9	12	7	18	21
22	10	13	8	18	19
23	10	15	10	23	21
15	7	12	6	19	21
20	9	19	10	20	16
18	8	15	10	21	22
23	14	11	10	20	29
25	14	11	5	17	15
21	8	10	7	18	17
24	9	13	10	19	15
25	14	15	11	22	21
17	14	12	6	15	21
13	8	12	7	14	19
28	8	16	12	18	24
21	8	9	11	24	20
25	7	18	11	35	17
9	6	8	11	29	23
16	8	13	5	21	24
19	6	17	8	25	14
17	11	9	6	20	19
25	14	15	9	22	24
20	11	8	4	13	13
29	11	7	4	26	22
14	11	12	7	17	16
22	14	14	11	25	19
15	8	6	6	20	25
19	20	8	7	19	25
20	11	17	8	21	23
15	8	10	4	22	24
20	11	11	8	24	26
18	10	14	9	21	26
33	14	11	8	26	25
22	11	13	11	24	18
16	9	12	8	16	21
17	9	11	5	23	26
16	8	9	4	18	23
21	10	12	8	16	23
26	13	20	10	26	22
18	13	12	6	19	20
18	12	13	9	21	13
17	8	12	9	21	24
22	13	12	13	22	15
30	14	9	9	23	14
30	12	15	10	29	22
24	14	24	20	21	10
21	15	7	5	21	24
21	13	17	11	23	22
29	16	11	6	27	24
31	9	17	9	25	19
20	9	11	7	21	20
16	9	12	9	10	13
22	8	14	10	20	20
20	7	11	9	26	22
28	16	16	8	24	24
38	11	21	7	29	29
22	9	14	6	19	12
20	11	20	13	24	20
17	9	13	6	19	21
28	14	11	8	24	24
22	13	15	10	22	22
31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.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=147266&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.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=147266&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147266&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 time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.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
Organization [t] = + 16.134380517971 -0.0706755178434467Expectations[t] + 0.21817366617141Concerns[t] -0.14895346558074Doubts[t] -0.255161534021278Criticism[t] + 0.422756959732833Standards[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Organization
[t] =  +  16.134380517971 -0.0706755178434467Expectations[t] +  0.21817366617141Concerns[t] -0.14895346558074Doubts[t] -0.255161534021278Criticism[t] +  0.422756959732833Standards[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147266&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Organization
[t] =  +  16.134380517971 -0.0706755178434467Expectations[t] +  0.21817366617141Concerns[t] -0.14895346558074Doubts[t] -0.255161534021278Criticism[t] +  0.422756959732833Standards[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147266&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147266&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
Organization [t] = + 16.134380517971 -0.0706755178434467Expectations[t] + 0.21817366617141Concerns[t] -0.14895346558074Doubts[t] -0.255161534021278Criticism[t] + 0.422756959732833Standards[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)16.1343805179712.0016118.060700
Expectations-0.07067551784344670.062922-1.12320.2631030.131551
Concerns0.218173666171410.1126221.93720.054560.02728
Doubts-0.148953465580740.104267-1.42860.1551640.077582
Criticism-0.2551615340212780.130412-1.95660.0522180.026109
Standards0.4227569597328330.0756165.590800

\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) & 16.134380517971 & 2.001611 & 8.0607 & 0 & 0 \tabularnewline
Expectations & -0.0706755178434467 & 0.062922 & -1.1232 & 0.263103 & 0.131551 \tabularnewline
Concerns & 0.21817366617141 & 0.112622 & 1.9372 & 0.05456 & 0.02728 \tabularnewline
Doubts & -0.14895346558074 & 0.104267 & -1.4286 & 0.155164 & 0.077582 \tabularnewline
Criticism & -0.255161534021278 & 0.130412 & -1.9566 & 0.052218 & 0.026109 \tabularnewline
Standards & 0.422756959732833 & 0.075616 & 5.5908 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147266&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]16.134380517971[/C][C]2.001611[/C][C]8.0607[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Expectations[/C][C]-0.0706755178434467[/C][C]0.062922[/C][C]-1.1232[/C][C]0.263103[/C][C]0.131551[/C][/ROW]
[ROW][C]Concerns[/C][C]0.21817366617141[/C][C]0.112622[/C][C]1.9372[/C][C]0.05456[/C][C]0.02728[/C][/ROW]
[ROW][C]Doubts[/C][C]-0.14895346558074[/C][C]0.104267[/C][C]-1.4286[/C][C]0.155164[/C][C]0.077582[/C][/ROW]
[ROW][C]Criticism[/C][C]-0.255161534021278[/C][C]0.130412[/C][C]-1.9566[/C][C]0.052218[/C][C]0.026109[/C][/ROW]
[ROW][C]Standards[/C][C]0.422756959732833[/C][C]0.075616[/C][C]5.5908[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147266&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147266&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)16.1343805179712.0016118.060700
Expectations-0.07067551784344670.062922-1.12320.2631030.131551
Concerns0.218173666171410.1126221.93720.054560.02728
Doubts-0.148953465580740.104267-1.42860.1551640.077582
Criticism-0.2551615340212780.130412-1.95660.0522180.026109
Standards0.4227569597328330.0756165.590800







Multiple Linear Regression - Regression Statistics
Multiple R0.47156352837492
R-squared0.222372161293404
Adjusted R-squared0.196959486825868
F-TEST (value)8.75044307428091
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value2.54750247341562e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.4993600467452
Sum Squared Residuals1873.56467272376

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.47156352837492 \tabularnewline
R-squared & 0.222372161293404 \tabularnewline
Adjusted R-squared & 0.196959486825868 \tabularnewline
F-TEST (value) & 8.75044307428091 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 2.54750247341562e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.4993600467452 \tabularnewline
Sum Squared Residuals & 1873.56467272376 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147266&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.47156352837492[/C][/ROW]
[ROW][C]R-squared[/C][C]0.222372161293404[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.196959486825868[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.75044307428091[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]2.54750247341562e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.4993600467452[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1873.56467272376[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147266&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147266&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 R0.47156352837492
R-squared0.222372161293404
Adjusted R-squared0.196959486825868
F-TEST (value)8.75044307428091
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value2.54750247341562e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.4993600467452
Sum Squared Residuals1873.56467272376







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12622.93833992007253.06166007992749
22324.2523603618557-1.25236036185575
32524.3511463166030.648853683396967
42321.98186049779211.01813950220794
51921.8243682471222-2.82436824712217
62922.91238968319446.08761031680557
72524.81239655866370.187603441336281
82122.6816276385943-1.68162763859431
92221.96629584587180.0337041541281641
102522.48817740620882.51182259379118
112420.23019657633823.76980342366183
121819.7297104221437-1.72971042214371
132218.40548387133463.59451612866544
141520.7135896185936-5.71358961859357
152223.5177877585016-1.51778775850159
162824.31845876152553.68154123847446
172022.2773757565055-2.27737575650546
181219.0584541223919-7.05845412239186
192421.44857295157412.55142704842587
202021.3822950759724-1.3822950759724
212123.2872146750446-2.28721467504459
222020.6121484697385-0.61214846973847
232119.24369141331811.75630858668185
242321.07666913843721.92333086156275
252821.96681480792936.0331851920707
262421.94161584579972.05838415420033
272423.48808596212680.511914037873207
282420.4132910149843.586708985016
292322.00953989906250.990460100937506
302322.72974856762010.270251432379885
312924.31727835040894.68272164959114
322422.08620525888731.91379474111273
331824.9997091636193-6.99970916361932
342526.0675322875889-1.06753228758893
352122.6367209824318-1.63672098243182
362626.7519440436211-0.751944043621079
372225.1676432351877-3.16764323518775
382222.8299965031316-0.829996503131624
392222.5849420248483-0.584942024848278
402325.6719560730761-2.67195607307607
413022.89812505929837.10187494070167
422322.73020378995680.269796210043192
431718.6869610388814-1.68696103888138
442323.8063205840977-0.80632058409767
452324.3634608478922-1.36346084789218
462522.46769054547452.5323094545255
472420.74796290434963.25203709565036
482427.5131920726333-3.51319207263331
492323.0067172964069-0.00671729640684946
502123.8138592742707-2.81385927427075
512425.7455923226503-1.74559232265028
522421.8749464875822.12505351241798
532821.52901215833376.47098784166629
541621.0929792851445-5.09297928514447
552019.90545028534740.0945497146525726
562923.45050788871865.54949211128141
572723.92420288104383.07579711895619
582223.2081353553336-1.20813535533363
592824.05082268602153.94917731397848
601620.3584383722737-4.35843837227369
612522.95873135060272.04126864939731
622423.51073408188340.489265918116595
632823.68431745031114.3156825496889
642424.3565643597678-0.356564359767844
652322.72744844467990.27255155532009
663026.97377771978133.02622228021869
672421.30970151475942.69029848524062
682124.1918592625548-3.19185926255483
692523.34643689259711.65356310740295
702524.009924088750.990075911249981
712220.78242221726461.21757778273541
722322.50629290050210.493707099497928
732622.86136010109423.13863989890584
742321.592866312251.40713368775004
752523.06403285506611.93596714493389
762121.3169856048034-0.316985604803363
772523.68056201011811.31943798988188
782422.1871294271161.81287057288401
792923.602212818885.39778718112005
802223.6569360828835-1.65693608288352
812723.64418853452993.35581146547012
822619.67835111571726.3216488842828
832221.32297354751950.677026452480496
842422.10031269428951.89968730571047
852723.1282217741433.87177822585703
862421.335857978362.66414202164002
872424.9093164665318-0.909316466531828
882924.47021568817734.5297843118227
892222.2324709996082-0.232470999608157
902120.58657833099990.413421669000072
912420.41147590559873.58852409440132
922421.82827897654382.1717210234562
932321.97396201548651.02603798451355
942022.2966377134128-2.29663771341281
952721.44290136727685.55709863272324
962623.4634376525382.53656234746198
972521.93031195363433.06968804636567
982120.05584352642020.944156473579804
992120.79116162456140.208838375438649
1001920.3931937376004-1.3931937376004
1012121.6280730192171-0.628073019217087
1022121.3154348573464-0.315434857346428
1031619.7578411650546-3.75784116505456
1042220.69958935662581.30041064337417
1052921.82831066702727.17168933297281
1061521.6944964222482-6.69449642224818
1071720.7295438538645-3.72954385386453
1081519.9461029274324-4.94610292743238
1092121.6814981544617-0.681498154461724
1102121.0102716459281-0.0102716459280739
1111919.3060132265193-0.306013226519289
1122418.06528676536965.93471323463043
1132022.3943929417572-2.39439294175716
1141725.2032625710465-8.20326257104646
1152325.0688900877806-2.0688900877806
1162422.41465499358061.5853450064194
1171422.096010482252-8.09601048225197
1181922.9163958428202-3.91639584282023
1192422.19182122250431.80817877749572
1201320.4043471047834-7.40434710478335
1212225.4130613862999-3.4130613862999
1221621.1581295863886-5.15812958638857
1231923.3107490527713-4.3107490527713
1242522.85008627673512.14991372326489
1252524.20964277450270.790357225497343
1262321.42517545633421.57482454366575
1272423.61010940192040.389890598079627
1282623.58716712901722.41283287098281
1292621.54005168857074.45994831142933
1302524.16842031503230.831579684967723
1311822.382424560105-4.38242456010498
1322119.90251272460471.09748727539525
1332623.70557399253572.2944260074643
1342321.99735951072631.00264048927367
1352319.76730880155893.23269119844108
1362222.5940710154958-0.594071015495776
1372022.4124503008445-2.41245030084455
1381322.1253524864942-9.12535248649423
1392421.47228680523282.52771319476722
1401521.6118883705203-6.61188837052032
1411423.1549213865043-9.15492138650432
1422224.1062334850528-2.10623348505278
1431017.6923817161542-7.69238171615418
1442424.4822138610477-0.482213861047675
1452221.87087658823550.129123411764547
1462425.8205497465243-1.82054974652427
1471921.6472637326236-2.64726373262357
1482022.1377104514971-2.13771045149714
1491317.1108094321865-4.11080943218647
1502020.1430837911-0.143083791099954
1512223.3048248497759-1.30482484977593
1522423.3678639892230.632136010777037
1532923.19441948471325.80558051528681
1541220.9591466336236-8.95914663362364
1552020.9707782686841-0.970778268684136
1562121.4614776884216-0.461477688421615
1572423.67628398478380.323716015216156
1582221.93051257584190.0694874241580689
1592017.70838604865042.29161395134965

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 22.9383399200725 & 3.06166007992749 \tabularnewline
2 & 23 & 24.2523603618557 & -1.25236036185575 \tabularnewline
3 & 25 & 24.351146316603 & 0.648853683396967 \tabularnewline
4 & 23 & 21.9818604977921 & 1.01813950220794 \tabularnewline
5 & 19 & 21.8243682471222 & -2.82436824712217 \tabularnewline
6 & 29 & 22.9123896831944 & 6.08761031680557 \tabularnewline
7 & 25 & 24.8123965586637 & 0.187603441336281 \tabularnewline
8 & 21 & 22.6816276385943 & -1.68162763859431 \tabularnewline
9 & 22 & 21.9662958458718 & 0.0337041541281641 \tabularnewline
10 & 25 & 22.4881774062088 & 2.51182259379118 \tabularnewline
11 & 24 & 20.2301965763382 & 3.76980342366183 \tabularnewline
12 & 18 & 19.7297104221437 & -1.72971042214371 \tabularnewline
13 & 22 & 18.4054838713346 & 3.59451612866544 \tabularnewline
14 & 15 & 20.7135896185936 & -5.71358961859357 \tabularnewline
15 & 22 & 23.5177877585016 & -1.51778775850159 \tabularnewline
16 & 28 & 24.3184587615255 & 3.68154123847446 \tabularnewline
17 & 20 & 22.2773757565055 & -2.27737575650546 \tabularnewline
18 & 12 & 19.0584541223919 & -7.05845412239186 \tabularnewline
19 & 24 & 21.4485729515741 & 2.55142704842587 \tabularnewline
20 & 20 & 21.3822950759724 & -1.3822950759724 \tabularnewline
21 & 21 & 23.2872146750446 & -2.28721467504459 \tabularnewline
22 & 20 & 20.6121484697385 & -0.61214846973847 \tabularnewline
23 & 21 & 19.2436914133181 & 1.75630858668185 \tabularnewline
24 & 23 & 21.0766691384372 & 1.92333086156275 \tabularnewline
25 & 28 & 21.9668148079293 & 6.0331851920707 \tabularnewline
26 & 24 & 21.9416158457997 & 2.05838415420033 \tabularnewline
27 & 24 & 23.4880859621268 & 0.511914037873207 \tabularnewline
28 & 24 & 20.413291014984 & 3.586708985016 \tabularnewline
29 & 23 & 22.0095398990625 & 0.990460100937506 \tabularnewline
30 & 23 & 22.7297485676201 & 0.270251432379885 \tabularnewline
31 & 29 & 24.3172783504089 & 4.68272164959114 \tabularnewline
32 & 24 & 22.0862052588873 & 1.91379474111273 \tabularnewline
33 & 18 & 24.9997091636193 & -6.99970916361932 \tabularnewline
34 & 25 & 26.0675322875889 & -1.06753228758893 \tabularnewline
35 & 21 & 22.6367209824318 & -1.63672098243182 \tabularnewline
36 & 26 & 26.7519440436211 & -0.751944043621079 \tabularnewline
37 & 22 & 25.1676432351877 & -3.16764323518775 \tabularnewline
38 & 22 & 22.8299965031316 & -0.829996503131624 \tabularnewline
39 & 22 & 22.5849420248483 & -0.584942024848278 \tabularnewline
40 & 23 & 25.6719560730761 & -2.67195607307607 \tabularnewline
41 & 30 & 22.8981250592983 & 7.10187494070167 \tabularnewline
42 & 23 & 22.7302037899568 & 0.269796210043192 \tabularnewline
43 & 17 & 18.6869610388814 & -1.68696103888138 \tabularnewline
44 & 23 & 23.8063205840977 & -0.80632058409767 \tabularnewline
45 & 23 & 24.3634608478922 & -1.36346084789218 \tabularnewline
46 & 25 & 22.4676905454745 & 2.5323094545255 \tabularnewline
47 & 24 & 20.7479629043496 & 3.25203709565036 \tabularnewline
48 & 24 & 27.5131920726333 & -3.51319207263331 \tabularnewline
49 & 23 & 23.0067172964069 & -0.00671729640684946 \tabularnewline
50 & 21 & 23.8138592742707 & -2.81385927427075 \tabularnewline
51 & 24 & 25.7455923226503 & -1.74559232265028 \tabularnewline
52 & 24 & 21.874946487582 & 2.12505351241798 \tabularnewline
53 & 28 & 21.5290121583337 & 6.47098784166629 \tabularnewline
54 & 16 & 21.0929792851445 & -5.09297928514447 \tabularnewline
55 & 20 & 19.9054502853474 & 0.0945497146525726 \tabularnewline
56 & 29 & 23.4505078887186 & 5.54949211128141 \tabularnewline
57 & 27 & 23.9242028810438 & 3.07579711895619 \tabularnewline
58 & 22 & 23.2081353553336 & -1.20813535533363 \tabularnewline
59 & 28 & 24.0508226860215 & 3.94917731397848 \tabularnewline
60 & 16 & 20.3584383722737 & -4.35843837227369 \tabularnewline
61 & 25 & 22.9587313506027 & 2.04126864939731 \tabularnewline
62 & 24 & 23.5107340818834 & 0.489265918116595 \tabularnewline
63 & 28 & 23.6843174503111 & 4.3156825496889 \tabularnewline
64 & 24 & 24.3565643597678 & -0.356564359767844 \tabularnewline
65 & 23 & 22.7274484446799 & 0.27255155532009 \tabularnewline
66 & 30 & 26.9737777197813 & 3.02622228021869 \tabularnewline
67 & 24 & 21.3097015147594 & 2.69029848524062 \tabularnewline
68 & 21 & 24.1918592625548 & -3.19185926255483 \tabularnewline
69 & 25 & 23.3464368925971 & 1.65356310740295 \tabularnewline
70 & 25 & 24.00992408875 & 0.990075911249981 \tabularnewline
71 & 22 & 20.7824222172646 & 1.21757778273541 \tabularnewline
72 & 23 & 22.5062929005021 & 0.493707099497928 \tabularnewline
73 & 26 & 22.8613601010942 & 3.13863989890584 \tabularnewline
74 & 23 & 21.59286631225 & 1.40713368775004 \tabularnewline
75 & 25 & 23.0640328550661 & 1.93596714493389 \tabularnewline
76 & 21 & 21.3169856048034 & -0.316985604803363 \tabularnewline
77 & 25 & 23.6805620101181 & 1.31943798988188 \tabularnewline
78 & 24 & 22.187129427116 & 1.81287057288401 \tabularnewline
79 & 29 & 23.60221281888 & 5.39778718112005 \tabularnewline
80 & 22 & 23.6569360828835 & -1.65693608288352 \tabularnewline
81 & 27 & 23.6441885345299 & 3.35581146547012 \tabularnewline
82 & 26 & 19.6783511157172 & 6.3216488842828 \tabularnewline
83 & 22 & 21.3229735475195 & 0.677026452480496 \tabularnewline
84 & 24 & 22.1003126942895 & 1.89968730571047 \tabularnewline
85 & 27 & 23.128221774143 & 3.87177822585703 \tabularnewline
86 & 24 & 21.33585797836 & 2.66414202164002 \tabularnewline
87 & 24 & 24.9093164665318 & -0.909316466531828 \tabularnewline
88 & 29 & 24.4702156881773 & 4.5297843118227 \tabularnewline
89 & 22 & 22.2324709996082 & -0.232470999608157 \tabularnewline
90 & 21 & 20.5865783309999 & 0.413421669000072 \tabularnewline
91 & 24 & 20.4114759055987 & 3.58852409440132 \tabularnewline
92 & 24 & 21.8282789765438 & 2.1717210234562 \tabularnewline
93 & 23 & 21.9739620154865 & 1.02603798451355 \tabularnewline
94 & 20 & 22.2966377134128 & -2.29663771341281 \tabularnewline
95 & 27 & 21.4429013672768 & 5.55709863272324 \tabularnewline
96 & 26 & 23.463437652538 & 2.53656234746198 \tabularnewline
97 & 25 & 21.9303119536343 & 3.06968804636567 \tabularnewline
98 & 21 & 20.0558435264202 & 0.944156473579804 \tabularnewline
99 & 21 & 20.7911616245614 & 0.208838375438649 \tabularnewline
100 & 19 & 20.3931937376004 & -1.3931937376004 \tabularnewline
101 & 21 & 21.6280730192171 & -0.628073019217087 \tabularnewline
102 & 21 & 21.3154348573464 & -0.315434857346428 \tabularnewline
103 & 16 & 19.7578411650546 & -3.75784116505456 \tabularnewline
104 & 22 & 20.6995893566258 & 1.30041064337417 \tabularnewline
105 & 29 & 21.8283106670272 & 7.17168933297281 \tabularnewline
106 & 15 & 21.6944964222482 & -6.69449642224818 \tabularnewline
107 & 17 & 20.7295438538645 & -3.72954385386453 \tabularnewline
108 & 15 & 19.9461029274324 & -4.94610292743238 \tabularnewline
109 & 21 & 21.6814981544617 & -0.681498154461724 \tabularnewline
110 & 21 & 21.0102716459281 & -0.0102716459280739 \tabularnewline
111 & 19 & 19.3060132265193 & -0.306013226519289 \tabularnewline
112 & 24 & 18.0652867653696 & 5.93471323463043 \tabularnewline
113 & 20 & 22.3943929417572 & -2.39439294175716 \tabularnewline
114 & 17 & 25.2032625710465 & -8.20326257104646 \tabularnewline
115 & 23 & 25.0688900877806 & -2.0688900877806 \tabularnewline
116 & 24 & 22.4146549935806 & 1.5853450064194 \tabularnewline
117 & 14 & 22.096010482252 & -8.09601048225197 \tabularnewline
118 & 19 & 22.9163958428202 & -3.91639584282023 \tabularnewline
119 & 24 & 22.1918212225043 & 1.80817877749572 \tabularnewline
120 & 13 & 20.4043471047834 & -7.40434710478335 \tabularnewline
121 & 22 & 25.4130613862999 & -3.4130613862999 \tabularnewline
122 & 16 & 21.1581295863886 & -5.15812958638857 \tabularnewline
123 & 19 & 23.3107490527713 & -4.3107490527713 \tabularnewline
124 & 25 & 22.8500862767351 & 2.14991372326489 \tabularnewline
125 & 25 & 24.2096427745027 & 0.790357225497343 \tabularnewline
126 & 23 & 21.4251754563342 & 1.57482454366575 \tabularnewline
127 & 24 & 23.6101094019204 & 0.389890598079627 \tabularnewline
128 & 26 & 23.5871671290172 & 2.41283287098281 \tabularnewline
129 & 26 & 21.5400516885707 & 4.45994831142933 \tabularnewline
130 & 25 & 24.1684203150323 & 0.831579684967723 \tabularnewline
131 & 18 & 22.382424560105 & -4.38242456010498 \tabularnewline
132 & 21 & 19.9025127246047 & 1.09748727539525 \tabularnewline
133 & 26 & 23.7055739925357 & 2.2944260074643 \tabularnewline
134 & 23 & 21.9973595107263 & 1.00264048927367 \tabularnewline
135 & 23 & 19.7673088015589 & 3.23269119844108 \tabularnewline
136 & 22 & 22.5940710154958 & -0.594071015495776 \tabularnewline
137 & 20 & 22.4124503008445 & -2.41245030084455 \tabularnewline
138 & 13 & 22.1253524864942 & -9.12535248649423 \tabularnewline
139 & 24 & 21.4722868052328 & 2.52771319476722 \tabularnewline
140 & 15 & 21.6118883705203 & -6.61188837052032 \tabularnewline
141 & 14 & 23.1549213865043 & -9.15492138650432 \tabularnewline
142 & 22 & 24.1062334850528 & -2.10623348505278 \tabularnewline
143 & 10 & 17.6923817161542 & -7.69238171615418 \tabularnewline
144 & 24 & 24.4822138610477 & -0.482213861047675 \tabularnewline
145 & 22 & 21.8708765882355 & 0.129123411764547 \tabularnewline
146 & 24 & 25.8205497465243 & -1.82054974652427 \tabularnewline
147 & 19 & 21.6472637326236 & -2.64726373262357 \tabularnewline
148 & 20 & 22.1377104514971 & -2.13771045149714 \tabularnewline
149 & 13 & 17.1108094321865 & -4.11080943218647 \tabularnewline
150 & 20 & 20.1430837911 & -0.143083791099954 \tabularnewline
151 & 22 & 23.3048248497759 & -1.30482484977593 \tabularnewline
152 & 24 & 23.367863989223 & 0.632136010777037 \tabularnewline
153 & 29 & 23.1944194847132 & 5.80558051528681 \tabularnewline
154 & 12 & 20.9591466336236 & -8.95914663362364 \tabularnewline
155 & 20 & 20.9707782686841 & -0.970778268684136 \tabularnewline
156 & 21 & 21.4614776884216 & -0.461477688421615 \tabularnewline
157 & 24 & 23.6762839847838 & 0.323716015216156 \tabularnewline
158 & 22 & 21.9305125758419 & 0.0694874241580689 \tabularnewline
159 & 20 & 17.7083860486504 & 2.29161395134965 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147266&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]26[/C][C]22.9383399200725[/C][C]3.06166007992749[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]24.2523603618557[/C][C]-1.25236036185575[/C][/ROW]
[ROW][C]3[/C][C]25[/C][C]24.351146316603[/C][C]0.648853683396967[/C][/ROW]
[ROW][C]4[/C][C]23[/C][C]21.9818604977921[/C][C]1.01813950220794[/C][/ROW]
[ROW][C]5[/C][C]19[/C][C]21.8243682471222[/C][C]-2.82436824712217[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]22.9123896831944[/C][C]6.08761031680557[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]24.8123965586637[/C][C]0.187603441336281[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]22.6816276385943[/C][C]-1.68162763859431[/C][/ROW]
[ROW][C]9[/C][C]22[/C][C]21.9662958458718[/C][C]0.0337041541281641[/C][/ROW]
[ROW][C]10[/C][C]25[/C][C]22.4881774062088[/C][C]2.51182259379118[/C][/ROW]
[ROW][C]11[/C][C]24[/C][C]20.2301965763382[/C][C]3.76980342366183[/C][/ROW]
[ROW][C]12[/C][C]18[/C][C]19.7297104221437[/C][C]-1.72971042214371[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]18.4054838713346[/C][C]3.59451612866544[/C][/ROW]
[ROW][C]14[/C][C]15[/C][C]20.7135896185936[/C][C]-5.71358961859357[/C][/ROW]
[ROW][C]15[/C][C]22[/C][C]23.5177877585016[/C][C]-1.51778775850159[/C][/ROW]
[ROW][C]16[/C][C]28[/C][C]24.3184587615255[/C][C]3.68154123847446[/C][/ROW]
[ROW][C]17[/C][C]20[/C][C]22.2773757565055[/C][C]-2.27737575650546[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]19.0584541223919[/C][C]-7.05845412239186[/C][/ROW]
[ROW][C]19[/C][C]24[/C][C]21.4485729515741[/C][C]2.55142704842587[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]21.3822950759724[/C][C]-1.3822950759724[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]23.2872146750446[/C][C]-2.28721467504459[/C][/ROW]
[ROW][C]22[/C][C]20[/C][C]20.6121484697385[/C][C]-0.61214846973847[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.2436914133181[/C][C]1.75630858668185[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]21.0766691384372[/C][C]1.92333086156275[/C][/ROW]
[ROW][C]25[/C][C]28[/C][C]21.9668148079293[/C][C]6.0331851920707[/C][/ROW]
[ROW][C]26[/C][C]24[/C][C]21.9416158457997[/C][C]2.05838415420033[/C][/ROW]
[ROW][C]27[/C][C]24[/C][C]23.4880859621268[/C][C]0.511914037873207[/C][/ROW]
[ROW][C]28[/C][C]24[/C][C]20.413291014984[/C][C]3.586708985016[/C][/ROW]
[ROW][C]29[/C][C]23[/C][C]22.0095398990625[/C][C]0.990460100937506[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]22.7297485676201[/C][C]0.270251432379885[/C][/ROW]
[ROW][C]31[/C][C]29[/C][C]24.3172783504089[/C][C]4.68272164959114[/C][/ROW]
[ROW][C]32[/C][C]24[/C][C]22.0862052588873[/C][C]1.91379474111273[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]24.9997091636193[/C][C]-6.99970916361932[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]26.0675322875889[/C][C]-1.06753228758893[/C][/ROW]
[ROW][C]35[/C][C]21[/C][C]22.6367209824318[/C][C]-1.63672098243182[/C][/ROW]
[ROW][C]36[/C][C]26[/C][C]26.7519440436211[/C][C]-0.751944043621079[/C][/ROW]
[ROW][C]37[/C][C]22[/C][C]25.1676432351877[/C][C]-3.16764323518775[/C][/ROW]
[ROW][C]38[/C][C]22[/C][C]22.8299965031316[/C][C]-0.829996503131624[/C][/ROW]
[ROW][C]39[/C][C]22[/C][C]22.5849420248483[/C][C]-0.584942024848278[/C][/ROW]
[ROW][C]40[/C][C]23[/C][C]25.6719560730761[/C][C]-2.67195607307607[/C][/ROW]
[ROW][C]41[/C][C]30[/C][C]22.8981250592983[/C][C]7.10187494070167[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]22.7302037899568[/C][C]0.269796210043192[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]18.6869610388814[/C][C]-1.68696103888138[/C][/ROW]
[ROW][C]44[/C][C]23[/C][C]23.8063205840977[/C][C]-0.80632058409767[/C][/ROW]
[ROW][C]45[/C][C]23[/C][C]24.3634608478922[/C][C]-1.36346084789218[/C][/ROW]
[ROW][C]46[/C][C]25[/C][C]22.4676905454745[/C][C]2.5323094545255[/C][/ROW]
[ROW][C]47[/C][C]24[/C][C]20.7479629043496[/C][C]3.25203709565036[/C][/ROW]
[ROW][C]48[/C][C]24[/C][C]27.5131920726333[/C][C]-3.51319207263331[/C][/ROW]
[ROW][C]49[/C][C]23[/C][C]23.0067172964069[/C][C]-0.00671729640684946[/C][/ROW]
[ROW][C]50[/C][C]21[/C][C]23.8138592742707[/C][C]-2.81385927427075[/C][/ROW]
[ROW][C]51[/C][C]24[/C][C]25.7455923226503[/C][C]-1.74559232265028[/C][/ROW]
[ROW][C]52[/C][C]24[/C][C]21.874946487582[/C][C]2.12505351241798[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]21.5290121583337[/C][C]6.47098784166629[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]21.0929792851445[/C][C]-5.09297928514447[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]19.9054502853474[/C][C]0.0945497146525726[/C][/ROW]
[ROW][C]56[/C][C]29[/C][C]23.4505078887186[/C][C]5.54949211128141[/C][/ROW]
[ROW][C]57[/C][C]27[/C][C]23.9242028810438[/C][C]3.07579711895619[/C][/ROW]
[ROW][C]58[/C][C]22[/C][C]23.2081353553336[/C][C]-1.20813535533363[/C][/ROW]
[ROW][C]59[/C][C]28[/C][C]24.0508226860215[/C][C]3.94917731397848[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]20.3584383722737[/C][C]-4.35843837227369[/C][/ROW]
[ROW][C]61[/C][C]25[/C][C]22.9587313506027[/C][C]2.04126864939731[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]23.5107340818834[/C][C]0.489265918116595[/C][/ROW]
[ROW][C]63[/C][C]28[/C][C]23.6843174503111[/C][C]4.3156825496889[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]24.3565643597678[/C][C]-0.356564359767844[/C][/ROW]
[ROW][C]65[/C][C]23[/C][C]22.7274484446799[/C][C]0.27255155532009[/C][/ROW]
[ROW][C]66[/C][C]30[/C][C]26.9737777197813[/C][C]3.02622228021869[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]21.3097015147594[/C][C]2.69029848524062[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]24.1918592625548[/C][C]-3.19185926255483[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]23.3464368925971[/C][C]1.65356310740295[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]24.00992408875[/C][C]0.990075911249981[/C][/ROW]
[ROW][C]71[/C][C]22[/C][C]20.7824222172646[/C][C]1.21757778273541[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]22.5062929005021[/C][C]0.493707099497928[/C][/ROW]
[ROW][C]73[/C][C]26[/C][C]22.8613601010942[/C][C]3.13863989890584[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]21.59286631225[/C][C]1.40713368775004[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]23.0640328550661[/C][C]1.93596714493389[/C][/ROW]
[ROW][C]76[/C][C]21[/C][C]21.3169856048034[/C][C]-0.316985604803363[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]23.6805620101181[/C][C]1.31943798988188[/C][/ROW]
[ROW][C]78[/C][C]24[/C][C]22.187129427116[/C][C]1.81287057288401[/C][/ROW]
[ROW][C]79[/C][C]29[/C][C]23.60221281888[/C][C]5.39778718112005[/C][/ROW]
[ROW][C]80[/C][C]22[/C][C]23.6569360828835[/C][C]-1.65693608288352[/C][/ROW]
[ROW][C]81[/C][C]27[/C][C]23.6441885345299[/C][C]3.35581146547012[/C][/ROW]
[ROW][C]82[/C][C]26[/C][C]19.6783511157172[/C][C]6.3216488842828[/C][/ROW]
[ROW][C]83[/C][C]22[/C][C]21.3229735475195[/C][C]0.677026452480496[/C][/ROW]
[ROW][C]84[/C][C]24[/C][C]22.1003126942895[/C][C]1.89968730571047[/C][/ROW]
[ROW][C]85[/C][C]27[/C][C]23.128221774143[/C][C]3.87177822585703[/C][/ROW]
[ROW][C]86[/C][C]24[/C][C]21.33585797836[/C][C]2.66414202164002[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]24.9093164665318[/C][C]-0.909316466531828[/C][/ROW]
[ROW][C]88[/C][C]29[/C][C]24.4702156881773[/C][C]4.5297843118227[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]22.2324709996082[/C][C]-0.232470999608157[/C][/ROW]
[ROW][C]90[/C][C]21[/C][C]20.5865783309999[/C][C]0.413421669000072[/C][/ROW]
[ROW][C]91[/C][C]24[/C][C]20.4114759055987[/C][C]3.58852409440132[/C][/ROW]
[ROW][C]92[/C][C]24[/C][C]21.8282789765438[/C][C]2.1717210234562[/C][/ROW]
[ROW][C]93[/C][C]23[/C][C]21.9739620154865[/C][C]1.02603798451355[/C][/ROW]
[ROW][C]94[/C][C]20[/C][C]22.2966377134128[/C][C]-2.29663771341281[/C][/ROW]
[ROW][C]95[/C][C]27[/C][C]21.4429013672768[/C][C]5.55709863272324[/C][/ROW]
[ROW][C]96[/C][C]26[/C][C]23.463437652538[/C][C]2.53656234746198[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]21.9303119536343[/C][C]3.06968804636567[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]20.0558435264202[/C][C]0.944156473579804[/C][/ROW]
[ROW][C]99[/C][C]21[/C][C]20.7911616245614[/C][C]0.208838375438649[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]20.3931937376004[/C][C]-1.3931937376004[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]21.6280730192171[/C][C]-0.628073019217087[/C][/ROW]
[ROW][C]102[/C][C]21[/C][C]21.3154348573464[/C][C]-0.315434857346428[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]19.7578411650546[/C][C]-3.75784116505456[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]20.6995893566258[/C][C]1.30041064337417[/C][/ROW]
[ROW][C]105[/C][C]29[/C][C]21.8283106670272[/C][C]7.17168933297281[/C][/ROW]
[ROW][C]106[/C][C]15[/C][C]21.6944964222482[/C][C]-6.69449642224818[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]20.7295438538645[/C][C]-3.72954385386453[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]19.9461029274324[/C][C]-4.94610292743238[/C][/ROW]
[ROW][C]109[/C][C]21[/C][C]21.6814981544617[/C][C]-0.681498154461724[/C][/ROW]
[ROW][C]110[/C][C]21[/C][C]21.0102716459281[/C][C]-0.0102716459280739[/C][/ROW]
[ROW][C]111[/C][C]19[/C][C]19.3060132265193[/C][C]-0.306013226519289[/C][/ROW]
[ROW][C]112[/C][C]24[/C][C]18.0652867653696[/C][C]5.93471323463043[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]22.3943929417572[/C][C]-2.39439294175716[/C][/ROW]
[ROW][C]114[/C][C]17[/C][C]25.2032625710465[/C][C]-8.20326257104646[/C][/ROW]
[ROW][C]115[/C][C]23[/C][C]25.0688900877806[/C][C]-2.0688900877806[/C][/ROW]
[ROW][C]116[/C][C]24[/C][C]22.4146549935806[/C][C]1.5853450064194[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]22.096010482252[/C][C]-8.09601048225197[/C][/ROW]
[ROW][C]118[/C][C]19[/C][C]22.9163958428202[/C][C]-3.91639584282023[/C][/ROW]
[ROW][C]119[/C][C]24[/C][C]22.1918212225043[/C][C]1.80817877749572[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]20.4043471047834[/C][C]-7.40434710478335[/C][/ROW]
[ROW][C]121[/C][C]22[/C][C]25.4130613862999[/C][C]-3.4130613862999[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]21.1581295863886[/C][C]-5.15812958638857[/C][/ROW]
[ROW][C]123[/C][C]19[/C][C]23.3107490527713[/C][C]-4.3107490527713[/C][/ROW]
[ROW][C]124[/C][C]25[/C][C]22.8500862767351[/C][C]2.14991372326489[/C][/ROW]
[ROW][C]125[/C][C]25[/C][C]24.2096427745027[/C][C]0.790357225497343[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]21.4251754563342[/C][C]1.57482454366575[/C][/ROW]
[ROW][C]127[/C][C]24[/C][C]23.6101094019204[/C][C]0.389890598079627[/C][/ROW]
[ROW][C]128[/C][C]26[/C][C]23.5871671290172[/C][C]2.41283287098281[/C][/ROW]
[ROW][C]129[/C][C]26[/C][C]21.5400516885707[/C][C]4.45994831142933[/C][/ROW]
[ROW][C]130[/C][C]25[/C][C]24.1684203150323[/C][C]0.831579684967723[/C][/ROW]
[ROW][C]131[/C][C]18[/C][C]22.382424560105[/C][C]-4.38242456010498[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]19.9025127246047[/C][C]1.09748727539525[/C][/ROW]
[ROW][C]133[/C][C]26[/C][C]23.7055739925357[/C][C]2.2944260074643[/C][/ROW]
[ROW][C]134[/C][C]23[/C][C]21.9973595107263[/C][C]1.00264048927367[/C][/ROW]
[ROW][C]135[/C][C]23[/C][C]19.7673088015589[/C][C]3.23269119844108[/C][/ROW]
[ROW][C]136[/C][C]22[/C][C]22.5940710154958[/C][C]-0.594071015495776[/C][/ROW]
[ROW][C]137[/C][C]20[/C][C]22.4124503008445[/C][C]-2.41245030084455[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]22.1253524864942[/C][C]-9.12535248649423[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]21.4722868052328[/C][C]2.52771319476722[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]21.6118883705203[/C][C]-6.61188837052032[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]23.1549213865043[/C][C]-9.15492138650432[/C][/ROW]
[ROW][C]142[/C][C]22[/C][C]24.1062334850528[/C][C]-2.10623348505278[/C][/ROW]
[ROW][C]143[/C][C]10[/C][C]17.6923817161542[/C][C]-7.69238171615418[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]24.4822138610477[/C][C]-0.482213861047675[/C][/ROW]
[ROW][C]145[/C][C]22[/C][C]21.8708765882355[/C][C]0.129123411764547[/C][/ROW]
[ROW][C]146[/C][C]24[/C][C]25.8205497465243[/C][C]-1.82054974652427[/C][/ROW]
[ROW][C]147[/C][C]19[/C][C]21.6472637326236[/C][C]-2.64726373262357[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]22.1377104514971[/C][C]-2.13771045149714[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]17.1108094321865[/C][C]-4.11080943218647[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]20.1430837911[/C][C]-0.143083791099954[/C][/ROW]
[ROW][C]151[/C][C]22[/C][C]23.3048248497759[/C][C]-1.30482484977593[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]23.367863989223[/C][C]0.632136010777037[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]23.1944194847132[/C][C]5.80558051528681[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]20.9591466336236[/C][C]-8.95914663362364[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]20.9707782686841[/C][C]-0.970778268684136[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]21.4614776884216[/C][C]-0.461477688421615[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]23.6762839847838[/C][C]0.323716015216156[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]21.9305125758419[/C][C]0.0694874241580689[/C][/ROW]
[ROW][C]159[/C][C]20[/C][C]17.7083860486504[/C][C]2.29161395134965[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147266&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147266&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
12622.93833992007253.06166007992749
22324.2523603618557-1.25236036185575
32524.3511463166030.648853683396967
42321.98186049779211.01813950220794
51921.8243682471222-2.82436824712217
62922.91238968319446.08761031680557
72524.81239655866370.187603441336281
82122.6816276385943-1.68162763859431
92221.96629584587180.0337041541281641
102522.48817740620882.51182259379118
112420.23019657633823.76980342366183
121819.7297104221437-1.72971042214371
132218.40548387133463.59451612866544
141520.7135896185936-5.71358961859357
152223.5177877585016-1.51778775850159
162824.31845876152553.68154123847446
172022.2773757565055-2.27737575650546
181219.0584541223919-7.05845412239186
192421.44857295157412.55142704842587
202021.3822950759724-1.3822950759724
212123.2872146750446-2.28721467504459
222020.6121484697385-0.61214846973847
232119.24369141331811.75630858668185
242321.07666913843721.92333086156275
252821.96681480792936.0331851920707
262421.94161584579972.05838415420033
272423.48808596212680.511914037873207
282420.4132910149843.586708985016
292322.00953989906250.990460100937506
302322.72974856762010.270251432379885
312924.31727835040894.68272164959114
322422.08620525888731.91379474111273
331824.9997091636193-6.99970916361932
342526.0675322875889-1.06753228758893
352122.6367209824318-1.63672098243182
362626.7519440436211-0.751944043621079
372225.1676432351877-3.16764323518775
382222.8299965031316-0.829996503131624
392222.5849420248483-0.584942024848278
402325.6719560730761-2.67195607307607
413022.89812505929837.10187494070167
422322.73020378995680.269796210043192
431718.6869610388814-1.68696103888138
442323.8063205840977-0.80632058409767
452324.3634608478922-1.36346084789218
462522.46769054547452.5323094545255
472420.74796290434963.25203709565036
482427.5131920726333-3.51319207263331
492323.0067172964069-0.00671729640684946
502123.8138592742707-2.81385927427075
512425.7455923226503-1.74559232265028
522421.8749464875822.12505351241798
532821.52901215833376.47098784166629
541621.0929792851445-5.09297928514447
552019.90545028534740.0945497146525726
562923.45050788871865.54949211128141
572723.92420288104383.07579711895619
582223.2081353553336-1.20813535533363
592824.05082268602153.94917731397848
601620.3584383722737-4.35843837227369
612522.95873135060272.04126864939731
622423.51073408188340.489265918116595
632823.68431745031114.3156825496889
642424.3565643597678-0.356564359767844
652322.72744844467990.27255155532009
663026.97377771978133.02622228021869
672421.30970151475942.69029848524062
682124.1918592625548-3.19185926255483
692523.34643689259711.65356310740295
702524.009924088750.990075911249981
712220.78242221726461.21757778273541
722322.50629290050210.493707099497928
732622.86136010109423.13863989890584
742321.592866312251.40713368775004
752523.06403285506611.93596714493389
762121.3169856048034-0.316985604803363
772523.68056201011811.31943798988188
782422.1871294271161.81287057288401
792923.602212818885.39778718112005
802223.6569360828835-1.65693608288352
812723.64418853452993.35581146547012
822619.67835111571726.3216488842828
832221.32297354751950.677026452480496
842422.10031269428951.89968730571047
852723.1282217741433.87177822585703
862421.335857978362.66414202164002
872424.9093164665318-0.909316466531828
882924.47021568817734.5297843118227
892222.2324709996082-0.232470999608157
902120.58657833099990.413421669000072
912420.41147590559873.58852409440132
922421.82827897654382.1717210234562
932321.97396201548651.02603798451355
942022.2966377134128-2.29663771341281
952721.44290136727685.55709863272324
962623.4634376525382.53656234746198
972521.93031195363433.06968804636567
982120.05584352642020.944156473579804
992120.79116162456140.208838375438649
1001920.3931937376004-1.3931937376004
1012121.6280730192171-0.628073019217087
1022121.3154348573464-0.315434857346428
1031619.7578411650546-3.75784116505456
1042220.69958935662581.30041064337417
1052921.82831066702727.17168933297281
1061521.6944964222482-6.69449642224818
1071720.7295438538645-3.72954385386453
1081519.9461029274324-4.94610292743238
1092121.6814981544617-0.681498154461724
1102121.0102716459281-0.0102716459280739
1111919.3060132265193-0.306013226519289
1122418.06528676536965.93471323463043
1132022.3943929417572-2.39439294175716
1141725.2032625710465-8.20326257104646
1152325.0688900877806-2.0688900877806
1162422.41465499358061.5853450064194
1171422.096010482252-8.09601048225197
1181922.9163958428202-3.91639584282023
1192422.19182122250431.80817877749572
1201320.4043471047834-7.40434710478335
1212225.4130613862999-3.4130613862999
1221621.1581295863886-5.15812958638857
1231923.3107490527713-4.3107490527713
1242522.85008627673512.14991372326489
1252524.20964277450270.790357225497343
1262321.42517545633421.57482454366575
1272423.61010940192040.389890598079627
1282623.58716712901722.41283287098281
1292621.54005168857074.45994831142933
1302524.16842031503230.831579684967723
1311822.382424560105-4.38242456010498
1322119.90251272460471.09748727539525
1332623.70557399253572.2944260074643
1342321.99735951072631.00264048927367
1352319.76730880155893.23269119844108
1362222.5940710154958-0.594071015495776
1372022.4124503008445-2.41245030084455
1381322.1253524864942-9.12535248649423
1392421.47228680523282.52771319476722
1401521.6118883705203-6.61188837052032
1411423.1549213865043-9.15492138650432
1422224.1062334850528-2.10623348505278
1431017.6923817161542-7.69238171615418
1442424.4822138610477-0.482213861047675
1452221.87087658823550.129123411764547
1462425.8205497465243-1.82054974652427
1471921.6472637326236-2.64726373262357
1482022.1377104514971-2.13771045149714
1491317.1108094321865-4.11080943218647
1502020.1430837911-0.143083791099954
1512223.3048248497759-1.30482484977593
1522423.3678639892230.632136010777037
1532923.19441948471325.80558051528681
1541220.9591466336236-8.95914663362364
1552020.9707782686841-0.970778268684136
1562121.4614776884216-0.461477688421615
1572423.67628398478380.323716015216156
1582221.93051257584190.0694874241580689
1592017.70838604865042.29161395134965







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.7253213230516890.5493573538966230.274678676948311
100.5986464109906280.8027071780187430.401353589009372
110.4893260815124580.9786521630249160.510673918487542
120.4448558931257080.8897117862514170.555144106874292
130.4529542350626250.9059084701252510.547045764937374
140.6934241689605640.6131516620788720.306575831039436
150.6248974246066110.7502051507867790.375102575393389
160.5729424577337620.8541150845324760.427057542266238
170.5069267630769460.9861464738461080.493073236923054
180.6370262446452050.725947510709590.362973755354795
190.5620112594482650.875977481103470.437988740551735
200.5671281596137130.8657436807725740.432871840386287
210.5193104347340460.9613791305319080.480689565265954
220.4501751285470530.9003502570941060.549824871452947
230.3911539944686660.7823079889373320.608846005531334
240.3917981325239540.7835962650479080.608201867476046
250.535610016044240.928779967911520.46438998395576
260.4730091818059330.9460183636118660.526990818194067
270.4094422667417830.8188845334835650.590557733258217
280.4027316330340330.8054632660680670.597268366965967
290.3418059785318420.6836119570636840.658194021468158
300.2850955636159930.5701911272319870.714904436384007
310.3093019969060440.6186039938120880.690698003093956
320.2607093695950430.5214187391900850.739290630404957
330.4851696443745190.9703392887490390.514830355625481
340.4387744161561080.8775488323122170.561225583843892
350.4039992529929890.8079985059859780.596000747007011
360.3483307111290370.6966614222580750.651669288870963
370.3256998806582530.6513997613165060.674300119341747
380.2763769321200350.5527538642400690.723623067879965
390.2315472767686260.4630945535372520.768452723231374
400.2082515204833970.4165030409667930.791748479516603
410.3597762817531760.7195525635063530.640223718246824
420.3096548314381420.6193096628762850.690345168561858
430.2778920360950290.5557840721900580.722107963904971
440.2360066800676720.4720133601353450.763993319932328
450.2034017647918770.4068035295837540.796598235208123
460.182515928463140.365031856926280.81748407153686
470.1702110977033430.3404221954066850.829788902296657
480.1572886639464290.3145773278928580.842711336053571
490.1282078965977020.2564157931954040.871792103402298
500.1187592016217540.2375184032435080.881240798378246
510.09794402906116590.1958880581223320.902055970938834
520.082903652089510.165807304179020.91709634791049
530.1389319756170410.2778639512340810.861068024382959
540.1704714021562140.3409428043124290.829528597843786
550.162698873296430.3253977465928610.83730112670357
560.2180702478481340.4361404956962690.781929752151866
570.2089855486336580.4179710972673170.791014451366342
580.1792270568938270.3584541137876530.820772943106174
590.1896994798377920.3793989596755840.810300520162208
600.2180149631526910.4360299263053830.781985036847309
610.191967614888960.383935229777920.80803238511104
620.1611003174793180.3222006349586370.838899682520682
630.1758507000633810.3517014001267610.824149299936619
640.1482046373359970.2964092746719940.851795362664003
650.1222079712085630.2444159424171250.877792028791437
660.1184502503507650.236900500701530.881549749649235
670.1082576603192060.2165153206384110.891742339680794
680.1085019288495910.2170038576991810.891498071150409
690.09211217258322560.1842243451664510.907887827416774
700.07513723641491190.1502744728298240.924862763585088
710.06109322106867040.1221864421373410.93890677893133
720.04826785448029330.09653570896058660.951732145519707
730.04535881619747580.09071763239495150.954641183802524
740.03638188762707370.07276377525414730.963618112372926
750.03030369776676680.06060739553353360.969696302233233
760.02328125942289910.04656251884579820.976718740577101
770.01828192098089190.03656384196178370.981718079019108
780.0146397126316490.02927942526329810.985360287368351
790.02194933684826360.04389867369652720.978050663151736
800.01752216151261680.03504432302523350.982477838487383
810.01714476519056130.03428953038112260.982855234809439
820.03084101231130630.06168202462261270.969158987688694
830.02381464956867610.04762929913735230.976185350431324
840.01990621709143680.03981243418287360.980093782908563
850.02189410335593110.04378820671186220.978105896644069
860.01947244916664390.03894489833328790.980527550833356
870.01484039730079640.02968079460159280.985159602699204
880.01943350331768890.03886700663537780.980566496682311
890.01533147971946570.03066295943893140.984668520280534
900.01150008661395750.02300017322791490.988499913386042
910.01162048896555180.02324097793110370.988379511034448
920.01018872277322660.02037744554645320.989811277226773
930.007838045464034960.01567609092806990.992161954535965
940.006501946671915760.01300389334383150.993498053328084
950.01198228179901930.02396456359803850.988017718200981
960.01089621894148880.02179243788297760.989103781058511
970.01040965815261170.02081931630522350.989590341847388
980.007997340608936080.01599468121787220.992002659391064
990.005925453757017730.01185090751403550.994074546242982
1000.004556769167675280.009113538335350570.995443230832325
1010.003383169856387410.006766339712774810.996616830143613
1020.002419194710520930.004838389421041850.997580805289479
1030.002601225399170810.005202450798341620.997398774600829
1040.002048888400231960.004097776800463930.997951111599768
1050.008725193417262120.01745038683452420.991274806582738
1060.01960001469256310.03920002938512610.980399985307437
1070.01953572455027240.03907144910054470.980464275449728
1080.0246994942341650.04939898846833010.975300505765835
1090.01917553290921240.03835106581842490.980824467090788
1100.01410168239392250.02820336478784490.985898317606077
1110.01028275208783190.02056550417566380.989717247912168
1120.02519172254264490.05038344508528980.974808277457355
1130.02298616611819310.04597233223638620.977013833881807
1140.06364683233832090.1272936646766420.936353167661679
1150.05462994370801090.1092598874160220.945370056291989
1160.04462449768458840.08924899536917690.955375502315412
1170.1293870327738970.2587740655477930.870612967226103
1180.1248358165085560.2496716330171120.875164183491444
1190.1114840173132070.2229680346264130.888515982686793
1200.192312214663350.38462442932670.80768778533665
1210.1843714475124840.3687428950249670.815628552487516
1220.2193672087559920.4387344175119830.780632791244008
1230.2131188919553990.4262377839107990.786881108044601
1240.2128450293108440.4256900586216880.787154970689156
1250.1948864589243870.3897729178487750.805113541075613
1260.1606852247092380.3213704494184760.839314775290762
1270.1274968038756050.2549936077512110.872503196124395
1280.1320290717841190.2640581435682380.867970928215881
1290.1873664260081740.3747328520163470.812633573991826
1300.1634486246872260.3268972493744510.836551375312774
1310.1448021004267170.2896042008534350.855197899573283
1320.1271100958706160.2542201917412320.872889904129384
1330.1178731028370160.2357462056740320.882126897162984
1340.09791589353630910.1958317870726180.902084106463691
1350.132789383519080.2655787670381590.86721061648092
1360.1009094895401080.2018189790802150.899090510459892
1370.07555835546564420.1511167109312880.924441644534356
1380.1852116197493410.3704232394986810.814788380250659
1390.2584735023210930.5169470046421860.741526497678907
1400.2391685034379870.4783370068759730.760831496562013
1410.4833561337163060.9667122674326120.516643866283694
1420.4353646171717640.8707292343435270.564635382828236
1430.7471173137231120.5057653725537770.252882686276888
1440.6969707961532350.606058407693530.303029203846765
1450.5979879813347150.804024037330570.402012018665285
1460.5351808653361350.9296382693277290.464819134663865
1470.558078277329840.8838434453403210.44192172267016
1480.4307892752804920.8615785505609850.569210724719508
1490.3379112269752260.6758224539504530.662088773024774
1500.2418616446089060.4837232892178130.758138355391094

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.725321323051689 & 0.549357353896623 & 0.274678676948311 \tabularnewline
10 & 0.598646410990628 & 0.802707178018743 & 0.401353589009372 \tabularnewline
11 & 0.489326081512458 & 0.978652163024916 & 0.510673918487542 \tabularnewline
12 & 0.444855893125708 & 0.889711786251417 & 0.555144106874292 \tabularnewline
13 & 0.452954235062625 & 0.905908470125251 & 0.547045764937374 \tabularnewline
14 & 0.693424168960564 & 0.613151662078872 & 0.306575831039436 \tabularnewline
15 & 0.624897424606611 & 0.750205150786779 & 0.375102575393389 \tabularnewline
16 & 0.572942457733762 & 0.854115084532476 & 0.427057542266238 \tabularnewline
17 & 0.506926763076946 & 0.986146473846108 & 0.493073236923054 \tabularnewline
18 & 0.637026244645205 & 0.72594751070959 & 0.362973755354795 \tabularnewline
19 & 0.562011259448265 & 0.87597748110347 & 0.437988740551735 \tabularnewline
20 & 0.567128159613713 & 0.865743680772574 & 0.432871840386287 \tabularnewline
21 & 0.519310434734046 & 0.961379130531908 & 0.480689565265954 \tabularnewline
22 & 0.450175128547053 & 0.900350257094106 & 0.549824871452947 \tabularnewline
23 & 0.391153994468666 & 0.782307988937332 & 0.608846005531334 \tabularnewline
24 & 0.391798132523954 & 0.783596265047908 & 0.608201867476046 \tabularnewline
25 & 0.53561001604424 & 0.92877996791152 & 0.46438998395576 \tabularnewline
26 & 0.473009181805933 & 0.946018363611866 & 0.526990818194067 \tabularnewline
27 & 0.409442266741783 & 0.818884533483565 & 0.590557733258217 \tabularnewline
28 & 0.402731633034033 & 0.805463266068067 & 0.597268366965967 \tabularnewline
29 & 0.341805978531842 & 0.683611957063684 & 0.658194021468158 \tabularnewline
30 & 0.285095563615993 & 0.570191127231987 & 0.714904436384007 \tabularnewline
31 & 0.309301996906044 & 0.618603993812088 & 0.690698003093956 \tabularnewline
32 & 0.260709369595043 & 0.521418739190085 & 0.739290630404957 \tabularnewline
33 & 0.485169644374519 & 0.970339288749039 & 0.514830355625481 \tabularnewline
34 & 0.438774416156108 & 0.877548832312217 & 0.561225583843892 \tabularnewline
35 & 0.403999252992989 & 0.807998505985978 & 0.596000747007011 \tabularnewline
36 & 0.348330711129037 & 0.696661422258075 & 0.651669288870963 \tabularnewline
37 & 0.325699880658253 & 0.651399761316506 & 0.674300119341747 \tabularnewline
38 & 0.276376932120035 & 0.552753864240069 & 0.723623067879965 \tabularnewline
39 & 0.231547276768626 & 0.463094553537252 & 0.768452723231374 \tabularnewline
40 & 0.208251520483397 & 0.416503040966793 & 0.791748479516603 \tabularnewline
41 & 0.359776281753176 & 0.719552563506353 & 0.640223718246824 \tabularnewline
42 & 0.309654831438142 & 0.619309662876285 & 0.690345168561858 \tabularnewline
43 & 0.277892036095029 & 0.555784072190058 & 0.722107963904971 \tabularnewline
44 & 0.236006680067672 & 0.472013360135345 & 0.763993319932328 \tabularnewline
45 & 0.203401764791877 & 0.406803529583754 & 0.796598235208123 \tabularnewline
46 & 0.18251592846314 & 0.36503185692628 & 0.81748407153686 \tabularnewline
47 & 0.170211097703343 & 0.340422195406685 & 0.829788902296657 \tabularnewline
48 & 0.157288663946429 & 0.314577327892858 & 0.842711336053571 \tabularnewline
49 & 0.128207896597702 & 0.256415793195404 & 0.871792103402298 \tabularnewline
50 & 0.118759201621754 & 0.237518403243508 & 0.881240798378246 \tabularnewline
51 & 0.0979440290611659 & 0.195888058122332 & 0.902055970938834 \tabularnewline
52 & 0.08290365208951 & 0.16580730417902 & 0.91709634791049 \tabularnewline
53 & 0.138931975617041 & 0.277863951234081 & 0.861068024382959 \tabularnewline
54 & 0.170471402156214 & 0.340942804312429 & 0.829528597843786 \tabularnewline
55 & 0.16269887329643 & 0.325397746592861 & 0.83730112670357 \tabularnewline
56 & 0.218070247848134 & 0.436140495696269 & 0.781929752151866 \tabularnewline
57 & 0.208985548633658 & 0.417971097267317 & 0.791014451366342 \tabularnewline
58 & 0.179227056893827 & 0.358454113787653 & 0.820772943106174 \tabularnewline
59 & 0.189699479837792 & 0.379398959675584 & 0.810300520162208 \tabularnewline
60 & 0.218014963152691 & 0.436029926305383 & 0.781985036847309 \tabularnewline
61 & 0.19196761488896 & 0.38393522977792 & 0.80803238511104 \tabularnewline
62 & 0.161100317479318 & 0.322200634958637 & 0.838899682520682 \tabularnewline
63 & 0.175850700063381 & 0.351701400126761 & 0.824149299936619 \tabularnewline
64 & 0.148204637335997 & 0.296409274671994 & 0.851795362664003 \tabularnewline
65 & 0.122207971208563 & 0.244415942417125 & 0.877792028791437 \tabularnewline
66 & 0.118450250350765 & 0.23690050070153 & 0.881549749649235 \tabularnewline
67 & 0.108257660319206 & 0.216515320638411 & 0.891742339680794 \tabularnewline
68 & 0.108501928849591 & 0.217003857699181 & 0.891498071150409 \tabularnewline
69 & 0.0921121725832256 & 0.184224345166451 & 0.907887827416774 \tabularnewline
70 & 0.0751372364149119 & 0.150274472829824 & 0.924862763585088 \tabularnewline
71 & 0.0610932210686704 & 0.122186442137341 & 0.93890677893133 \tabularnewline
72 & 0.0482678544802933 & 0.0965357089605866 & 0.951732145519707 \tabularnewline
73 & 0.0453588161974758 & 0.0907176323949515 & 0.954641183802524 \tabularnewline
74 & 0.0363818876270737 & 0.0727637752541473 & 0.963618112372926 \tabularnewline
75 & 0.0303036977667668 & 0.0606073955335336 & 0.969696302233233 \tabularnewline
76 & 0.0232812594228991 & 0.0465625188457982 & 0.976718740577101 \tabularnewline
77 & 0.0182819209808919 & 0.0365638419617837 & 0.981718079019108 \tabularnewline
78 & 0.014639712631649 & 0.0292794252632981 & 0.985360287368351 \tabularnewline
79 & 0.0219493368482636 & 0.0438986736965272 & 0.978050663151736 \tabularnewline
80 & 0.0175221615126168 & 0.0350443230252335 & 0.982477838487383 \tabularnewline
81 & 0.0171447651905613 & 0.0342895303811226 & 0.982855234809439 \tabularnewline
82 & 0.0308410123113063 & 0.0616820246226127 & 0.969158987688694 \tabularnewline
83 & 0.0238146495686761 & 0.0476292991373523 & 0.976185350431324 \tabularnewline
84 & 0.0199062170914368 & 0.0398124341828736 & 0.980093782908563 \tabularnewline
85 & 0.0218941033559311 & 0.0437882067118622 & 0.978105896644069 \tabularnewline
86 & 0.0194724491666439 & 0.0389448983332879 & 0.980527550833356 \tabularnewline
87 & 0.0148403973007964 & 0.0296807946015928 & 0.985159602699204 \tabularnewline
88 & 0.0194335033176889 & 0.0388670066353778 & 0.980566496682311 \tabularnewline
89 & 0.0153314797194657 & 0.0306629594389314 & 0.984668520280534 \tabularnewline
90 & 0.0115000866139575 & 0.0230001732279149 & 0.988499913386042 \tabularnewline
91 & 0.0116204889655518 & 0.0232409779311037 & 0.988379511034448 \tabularnewline
92 & 0.0101887227732266 & 0.0203774455464532 & 0.989811277226773 \tabularnewline
93 & 0.00783804546403496 & 0.0156760909280699 & 0.992161954535965 \tabularnewline
94 & 0.00650194667191576 & 0.0130038933438315 & 0.993498053328084 \tabularnewline
95 & 0.0119822817990193 & 0.0239645635980385 & 0.988017718200981 \tabularnewline
96 & 0.0108962189414888 & 0.0217924378829776 & 0.989103781058511 \tabularnewline
97 & 0.0104096581526117 & 0.0208193163052235 & 0.989590341847388 \tabularnewline
98 & 0.00799734060893608 & 0.0159946812178722 & 0.992002659391064 \tabularnewline
99 & 0.00592545375701773 & 0.0118509075140355 & 0.994074546242982 \tabularnewline
100 & 0.00455676916767528 & 0.00911353833535057 & 0.995443230832325 \tabularnewline
101 & 0.00338316985638741 & 0.00676633971277481 & 0.996616830143613 \tabularnewline
102 & 0.00241919471052093 & 0.00483838942104185 & 0.997580805289479 \tabularnewline
103 & 0.00260122539917081 & 0.00520245079834162 & 0.997398774600829 \tabularnewline
104 & 0.00204888840023196 & 0.00409777680046393 & 0.997951111599768 \tabularnewline
105 & 0.00872519341726212 & 0.0174503868345242 & 0.991274806582738 \tabularnewline
106 & 0.0196000146925631 & 0.0392000293851261 & 0.980399985307437 \tabularnewline
107 & 0.0195357245502724 & 0.0390714491005447 & 0.980464275449728 \tabularnewline
108 & 0.024699494234165 & 0.0493989884683301 & 0.975300505765835 \tabularnewline
109 & 0.0191755329092124 & 0.0383510658184249 & 0.980824467090788 \tabularnewline
110 & 0.0141016823939225 & 0.0282033647878449 & 0.985898317606077 \tabularnewline
111 & 0.0102827520878319 & 0.0205655041756638 & 0.989717247912168 \tabularnewline
112 & 0.0251917225426449 & 0.0503834450852898 & 0.974808277457355 \tabularnewline
113 & 0.0229861661181931 & 0.0459723322363862 & 0.977013833881807 \tabularnewline
114 & 0.0636468323383209 & 0.127293664676642 & 0.936353167661679 \tabularnewline
115 & 0.0546299437080109 & 0.109259887416022 & 0.945370056291989 \tabularnewline
116 & 0.0446244976845884 & 0.0892489953691769 & 0.955375502315412 \tabularnewline
117 & 0.129387032773897 & 0.258774065547793 & 0.870612967226103 \tabularnewline
118 & 0.124835816508556 & 0.249671633017112 & 0.875164183491444 \tabularnewline
119 & 0.111484017313207 & 0.222968034626413 & 0.888515982686793 \tabularnewline
120 & 0.19231221466335 & 0.3846244293267 & 0.80768778533665 \tabularnewline
121 & 0.184371447512484 & 0.368742895024967 & 0.815628552487516 \tabularnewline
122 & 0.219367208755992 & 0.438734417511983 & 0.780632791244008 \tabularnewline
123 & 0.213118891955399 & 0.426237783910799 & 0.786881108044601 \tabularnewline
124 & 0.212845029310844 & 0.425690058621688 & 0.787154970689156 \tabularnewline
125 & 0.194886458924387 & 0.389772917848775 & 0.805113541075613 \tabularnewline
126 & 0.160685224709238 & 0.321370449418476 & 0.839314775290762 \tabularnewline
127 & 0.127496803875605 & 0.254993607751211 & 0.872503196124395 \tabularnewline
128 & 0.132029071784119 & 0.264058143568238 & 0.867970928215881 \tabularnewline
129 & 0.187366426008174 & 0.374732852016347 & 0.812633573991826 \tabularnewline
130 & 0.163448624687226 & 0.326897249374451 & 0.836551375312774 \tabularnewline
131 & 0.144802100426717 & 0.289604200853435 & 0.855197899573283 \tabularnewline
132 & 0.127110095870616 & 0.254220191741232 & 0.872889904129384 \tabularnewline
133 & 0.117873102837016 & 0.235746205674032 & 0.882126897162984 \tabularnewline
134 & 0.0979158935363091 & 0.195831787072618 & 0.902084106463691 \tabularnewline
135 & 0.13278938351908 & 0.265578767038159 & 0.86721061648092 \tabularnewline
136 & 0.100909489540108 & 0.201818979080215 & 0.899090510459892 \tabularnewline
137 & 0.0755583554656442 & 0.151116710931288 & 0.924441644534356 \tabularnewline
138 & 0.185211619749341 & 0.370423239498681 & 0.814788380250659 \tabularnewline
139 & 0.258473502321093 & 0.516947004642186 & 0.741526497678907 \tabularnewline
140 & 0.239168503437987 & 0.478337006875973 & 0.760831496562013 \tabularnewline
141 & 0.483356133716306 & 0.966712267432612 & 0.516643866283694 \tabularnewline
142 & 0.435364617171764 & 0.870729234343527 & 0.564635382828236 \tabularnewline
143 & 0.747117313723112 & 0.505765372553777 & 0.252882686276888 \tabularnewline
144 & 0.696970796153235 & 0.60605840769353 & 0.303029203846765 \tabularnewline
145 & 0.597987981334715 & 0.80402403733057 & 0.402012018665285 \tabularnewline
146 & 0.535180865336135 & 0.929638269327729 & 0.464819134663865 \tabularnewline
147 & 0.55807827732984 & 0.883843445340321 & 0.44192172267016 \tabularnewline
148 & 0.430789275280492 & 0.861578550560985 & 0.569210724719508 \tabularnewline
149 & 0.337911226975226 & 0.675822453950453 & 0.662088773024774 \tabularnewline
150 & 0.241861644608906 & 0.483723289217813 & 0.758138355391094 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147266&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]0.725321323051689[/C][C]0.549357353896623[/C][C]0.274678676948311[/C][/ROW]
[ROW][C]10[/C][C]0.598646410990628[/C][C]0.802707178018743[/C][C]0.401353589009372[/C][/ROW]
[ROW][C]11[/C][C]0.489326081512458[/C][C]0.978652163024916[/C][C]0.510673918487542[/C][/ROW]
[ROW][C]12[/C][C]0.444855893125708[/C][C]0.889711786251417[/C][C]0.555144106874292[/C][/ROW]
[ROW][C]13[/C][C]0.452954235062625[/C][C]0.905908470125251[/C][C]0.547045764937374[/C][/ROW]
[ROW][C]14[/C][C]0.693424168960564[/C][C]0.613151662078872[/C][C]0.306575831039436[/C][/ROW]
[ROW][C]15[/C][C]0.624897424606611[/C][C]0.750205150786779[/C][C]0.375102575393389[/C][/ROW]
[ROW][C]16[/C][C]0.572942457733762[/C][C]0.854115084532476[/C][C]0.427057542266238[/C][/ROW]
[ROW][C]17[/C][C]0.506926763076946[/C][C]0.986146473846108[/C][C]0.493073236923054[/C][/ROW]
[ROW][C]18[/C][C]0.637026244645205[/C][C]0.72594751070959[/C][C]0.362973755354795[/C][/ROW]
[ROW][C]19[/C][C]0.562011259448265[/C][C]0.87597748110347[/C][C]0.437988740551735[/C][/ROW]
[ROW][C]20[/C][C]0.567128159613713[/C][C]0.865743680772574[/C][C]0.432871840386287[/C][/ROW]
[ROW][C]21[/C][C]0.519310434734046[/C][C]0.961379130531908[/C][C]0.480689565265954[/C][/ROW]
[ROW][C]22[/C][C]0.450175128547053[/C][C]0.900350257094106[/C][C]0.549824871452947[/C][/ROW]
[ROW][C]23[/C][C]0.391153994468666[/C][C]0.782307988937332[/C][C]0.608846005531334[/C][/ROW]
[ROW][C]24[/C][C]0.391798132523954[/C][C]0.783596265047908[/C][C]0.608201867476046[/C][/ROW]
[ROW][C]25[/C][C]0.53561001604424[/C][C]0.92877996791152[/C][C]0.46438998395576[/C][/ROW]
[ROW][C]26[/C][C]0.473009181805933[/C][C]0.946018363611866[/C][C]0.526990818194067[/C][/ROW]
[ROW][C]27[/C][C]0.409442266741783[/C][C]0.818884533483565[/C][C]0.590557733258217[/C][/ROW]
[ROW][C]28[/C][C]0.402731633034033[/C][C]0.805463266068067[/C][C]0.597268366965967[/C][/ROW]
[ROW][C]29[/C][C]0.341805978531842[/C][C]0.683611957063684[/C][C]0.658194021468158[/C][/ROW]
[ROW][C]30[/C][C]0.285095563615993[/C][C]0.570191127231987[/C][C]0.714904436384007[/C][/ROW]
[ROW][C]31[/C][C]0.309301996906044[/C][C]0.618603993812088[/C][C]0.690698003093956[/C][/ROW]
[ROW][C]32[/C][C]0.260709369595043[/C][C]0.521418739190085[/C][C]0.739290630404957[/C][/ROW]
[ROW][C]33[/C][C]0.485169644374519[/C][C]0.970339288749039[/C][C]0.514830355625481[/C][/ROW]
[ROW][C]34[/C][C]0.438774416156108[/C][C]0.877548832312217[/C][C]0.561225583843892[/C][/ROW]
[ROW][C]35[/C][C]0.403999252992989[/C][C]0.807998505985978[/C][C]0.596000747007011[/C][/ROW]
[ROW][C]36[/C][C]0.348330711129037[/C][C]0.696661422258075[/C][C]0.651669288870963[/C][/ROW]
[ROW][C]37[/C][C]0.325699880658253[/C][C]0.651399761316506[/C][C]0.674300119341747[/C][/ROW]
[ROW][C]38[/C][C]0.276376932120035[/C][C]0.552753864240069[/C][C]0.723623067879965[/C][/ROW]
[ROW][C]39[/C][C]0.231547276768626[/C][C]0.463094553537252[/C][C]0.768452723231374[/C][/ROW]
[ROW][C]40[/C][C]0.208251520483397[/C][C]0.416503040966793[/C][C]0.791748479516603[/C][/ROW]
[ROW][C]41[/C][C]0.359776281753176[/C][C]0.719552563506353[/C][C]0.640223718246824[/C][/ROW]
[ROW][C]42[/C][C]0.309654831438142[/C][C]0.619309662876285[/C][C]0.690345168561858[/C][/ROW]
[ROW][C]43[/C][C]0.277892036095029[/C][C]0.555784072190058[/C][C]0.722107963904971[/C][/ROW]
[ROW][C]44[/C][C]0.236006680067672[/C][C]0.472013360135345[/C][C]0.763993319932328[/C][/ROW]
[ROW][C]45[/C][C]0.203401764791877[/C][C]0.406803529583754[/C][C]0.796598235208123[/C][/ROW]
[ROW][C]46[/C][C]0.18251592846314[/C][C]0.36503185692628[/C][C]0.81748407153686[/C][/ROW]
[ROW][C]47[/C][C]0.170211097703343[/C][C]0.340422195406685[/C][C]0.829788902296657[/C][/ROW]
[ROW][C]48[/C][C]0.157288663946429[/C][C]0.314577327892858[/C][C]0.842711336053571[/C][/ROW]
[ROW][C]49[/C][C]0.128207896597702[/C][C]0.256415793195404[/C][C]0.871792103402298[/C][/ROW]
[ROW][C]50[/C][C]0.118759201621754[/C][C]0.237518403243508[/C][C]0.881240798378246[/C][/ROW]
[ROW][C]51[/C][C]0.0979440290611659[/C][C]0.195888058122332[/C][C]0.902055970938834[/C][/ROW]
[ROW][C]52[/C][C]0.08290365208951[/C][C]0.16580730417902[/C][C]0.91709634791049[/C][/ROW]
[ROW][C]53[/C][C]0.138931975617041[/C][C]0.277863951234081[/C][C]0.861068024382959[/C][/ROW]
[ROW][C]54[/C][C]0.170471402156214[/C][C]0.340942804312429[/C][C]0.829528597843786[/C][/ROW]
[ROW][C]55[/C][C]0.16269887329643[/C][C]0.325397746592861[/C][C]0.83730112670357[/C][/ROW]
[ROW][C]56[/C][C]0.218070247848134[/C][C]0.436140495696269[/C][C]0.781929752151866[/C][/ROW]
[ROW][C]57[/C][C]0.208985548633658[/C][C]0.417971097267317[/C][C]0.791014451366342[/C][/ROW]
[ROW][C]58[/C][C]0.179227056893827[/C][C]0.358454113787653[/C][C]0.820772943106174[/C][/ROW]
[ROW][C]59[/C][C]0.189699479837792[/C][C]0.379398959675584[/C][C]0.810300520162208[/C][/ROW]
[ROW][C]60[/C][C]0.218014963152691[/C][C]0.436029926305383[/C][C]0.781985036847309[/C][/ROW]
[ROW][C]61[/C][C]0.19196761488896[/C][C]0.38393522977792[/C][C]0.80803238511104[/C][/ROW]
[ROW][C]62[/C][C]0.161100317479318[/C][C]0.322200634958637[/C][C]0.838899682520682[/C][/ROW]
[ROW][C]63[/C][C]0.175850700063381[/C][C]0.351701400126761[/C][C]0.824149299936619[/C][/ROW]
[ROW][C]64[/C][C]0.148204637335997[/C][C]0.296409274671994[/C][C]0.851795362664003[/C][/ROW]
[ROW][C]65[/C][C]0.122207971208563[/C][C]0.244415942417125[/C][C]0.877792028791437[/C][/ROW]
[ROW][C]66[/C][C]0.118450250350765[/C][C]0.23690050070153[/C][C]0.881549749649235[/C][/ROW]
[ROW][C]67[/C][C]0.108257660319206[/C][C]0.216515320638411[/C][C]0.891742339680794[/C][/ROW]
[ROW][C]68[/C][C]0.108501928849591[/C][C]0.217003857699181[/C][C]0.891498071150409[/C][/ROW]
[ROW][C]69[/C][C]0.0921121725832256[/C][C]0.184224345166451[/C][C]0.907887827416774[/C][/ROW]
[ROW][C]70[/C][C]0.0751372364149119[/C][C]0.150274472829824[/C][C]0.924862763585088[/C][/ROW]
[ROW][C]71[/C][C]0.0610932210686704[/C][C]0.122186442137341[/C][C]0.93890677893133[/C][/ROW]
[ROW][C]72[/C][C]0.0482678544802933[/C][C]0.0965357089605866[/C][C]0.951732145519707[/C][/ROW]
[ROW][C]73[/C][C]0.0453588161974758[/C][C]0.0907176323949515[/C][C]0.954641183802524[/C][/ROW]
[ROW][C]74[/C][C]0.0363818876270737[/C][C]0.0727637752541473[/C][C]0.963618112372926[/C][/ROW]
[ROW][C]75[/C][C]0.0303036977667668[/C][C]0.0606073955335336[/C][C]0.969696302233233[/C][/ROW]
[ROW][C]76[/C][C]0.0232812594228991[/C][C]0.0465625188457982[/C][C]0.976718740577101[/C][/ROW]
[ROW][C]77[/C][C]0.0182819209808919[/C][C]0.0365638419617837[/C][C]0.981718079019108[/C][/ROW]
[ROW][C]78[/C][C]0.014639712631649[/C][C]0.0292794252632981[/C][C]0.985360287368351[/C][/ROW]
[ROW][C]79[/C][C]0.0219493368482636[/C][C]0.0438986736965272[/C][C]0.978050663151736[/C][/ROW]
[ROW][C]80[/C][C]0.0175221615126168[/C][C]0.0350443230252335[/C][C]0.982477838487383[/C][/ROW]
[ROW][C]81[/C][C]0.0171447651905613[/C][C]0.0342895303811226[/C][C]0.982855234809439[/C][/ROW]
[ROW][C]82[/C][C]0.0308410123113063[/C][C]0.0616820246226127[/C][C]0.969158987688694[/C][/ROW]
[ROW][C]83[/C][C]0.0238146495686761[/C][C]0.0476292991373523[/C][C]0.976185350431324[/C][/ROW]
[ROW][C]84[/C][C]0.0199062170914368[/C][C]0.0398124341828736[/C][C]0.980093782908563[/C][/ROW]
[ROW][C]85[/C][C]0.0218941033559311[/C][C]0.0437882067118622[/C][C]0.978105896644069[/C][/ROW]
[ROW][C]86[/C][C]0.0194724491666439[/C][C]0.0389448983332879[/C][C]0.980527550833356[/C][/ROW]
[ROW][C]87[/C][C]0.0148403973007964[/C][C]0.0296807946015928[/C][C]0.985159602699204[/C][/ROW]
[ROW][C]88[/C][C]0.0194335033176889[/C][C]0.0388670066353778[/C][C]0.980566496682311[/C][/ROW]
[ROW][C]89[/C][C]0.0153314797194657[/C][C]0.0306629594389314[/C][C]0.984668520280534[/C][/ROW]
[ROW][C]90[/C][C]0.0115000866139575[/C][C]0.0230001732279149[/C][C]0.988499913386042[/C][/ROW]
[ROW][C]91[/C][C]0.0116204889655518[/C][C]0.0232409779311037[/C][C]0.988379511034448[/C][/ROW]
[ROW][C]92[/C][C]0.0101887227732266[/C][C]0.0203774455464532[/C][C]0.989811277226773[/C][/ROW]
[ROW][C]93[/C][C]0.00783804546403496[/C][C]0.0156760909280699[/C][C]0.992161954535965[/C][/ROW]
[ROW][C]94[/C][C]0.00650194667191576[/C][C]0.0130038933438315[/C][C]0.993498053328084[/C][/ROW]
[ROW][C]95[/C][C]0.0119822817990193[/C][C]0.0239645635980385[/C][C]0.988017718200981[/C][/ROW]
[ROW][C]96[/C][C]0.0108962189414888[/C][C]0.0217924378829776[/C][C]0.989103781058511[/C][/ROW]
[ROW][C]97[/C][C]0.0104096581526117[/C][C]0.0208193163052235[/C][C]0.989590341847388[/C][/ROW]
[ROW][C]98[/C][C]0.00799734060893608[/C][C]0.0159946812178722[/C][C]0.992002659391064[/C][/ROW]
[ROW][C]99[/C][C]0.00592545375701773[/C][C]0.0118509075140355[/C][C]0.994074546242982[/C][/ROW]
[ROW][C]100[/C][C]0.00455676916767528[/C][C]0.00911353833535057[/C][C]0.995443230832325[/C][/ROW]
[ROW][C]101[/C][C]0.00338316985638741[/C][C]0.00676633971277481[/C][C]0.996616830143613[/C][/ROW]
[ROW][C]102[/C][C]0.00241919471052093[/C][C]0.00483838942104185[/C][C]0.997580805289479[/C][/ROW]
[ROW][C]103[/C][C]0.00260122539917081[/C][C]0.00520245079834162[/C][C]0.997398774600829[/C][/ROW]
[ROW][C]104[/C][C]0.00204888840023196[/C][C]0.00409777680046393[/C][C]0.997951111599768[/C][/ROW]
[ROW][C]105[/C][C]0.00872519341726212[/C][C]0.0174503868345242[/C][C]0.991274806582738[/C][/ROW]
[ROW][C]106[/C][C]0.0196000146925631[/C][C]0.0392000293851261[/C][C]0.980399985307437[/C][/ROW]
[ROW][C]107[/C][C]0.0195357245502724[/C][C]0.0390714491005447[/C][C]0.980464275449728[/C][/ROW]
[ROW][C]108[/C][C]0.024699494234165[/C][C]0.0493989884683301[/C][C]0.975300505765835[/C][/ROW]
[ROW][C]109[/C][C]0.0191755329092124[/C][C]0.0383510658184249[/C][C]0.980824467090788[/C][/ROW]
[ROW][C]110[/C][C]0.0141016823939225[/C][C]0.0282033647878449[/C][C]0.985898317606077[/C][/ROW]
[ROW][C]111[/C][C]0.0102827520878319[/C][C]0.0205655041756638[/C][C]0.989717247912168[/C][/ROW]
[ROW][C]112[/C][C]0.0251917225426449[/C][C]0.0503834450852898[/C][C]0.974808277457355[/C][/ROW]
[ROW][C]113[/C][C]0.0229861661181931[/C][C]0.0459723322363862[/C][C]0.977013833881807[/C][/ROW]
[ROW][C]114[/C][C]0.0636468323383209[/C][C]0.127293664676642[/C][C]0.936353167661679[/C][/ROW]
[ROW][C]115[/C][C]0.0546299437080109[/C][C]0.109259887416022[/C][C]0.945370056291989[/C][/ROW]
[ROW][C]116[/C][C]0.0446244976845884[/C][C]0.0892489953691769[/C][C]0.955375502315412[/C][/ROW]
[ROW][C]117[/C][C]0.129387032773897[/C][C]0.258774065547793[/C][C]0.870612967226103[/C][/ROW]
[ROW][C]118[/C][C]0.124835816508556[/C][C]0.249671633017112[/C][C]0.875164183491444[/C][/ROW]
[ROW][C]119[/C][C]0.111484017313207[/C][C]0.222968034626413[/C][C]0.888515982686793[/C][/ROW]
[ROW][C]120[/C][C]0.19231221466335[/C][C]0.3846244293267[/C][C]0.80768778533665[/C][/ROW]
[ROW][C]121[/C][C]0.184371447512484[/C][C]0.368742895024967[/C][C]0.815628552487516[/C][/ROW]
[ROW][C]122[/C][C]0.219367208755992[/C][C]0.438734417511983[/C][C]0.780632791244008[/C][/ROW]
[ROW][C]123[/C][C]0.213118891955399[/C][C]0.426237783910799[/C][C]0.786881108044601[/C][/ROW]
[ROW][C]124[/C][C]0.212845029310844[/C][C]0.425690058621688[/C][C]0.787154970689156[/C][/ROW]
[ROW][C]125[/C][C]0.194886458924387[/C][C]0.389772917848775[/C][C]0.805113541075613[/C][/ROW]
[ROW][C]126[/C][C]0.160685224709238[/C][C]0.321370449418476[/C][C]0.839314775290762[/C][/ROW]
[ROW][C]127[/C][C]0.127496803875605[/C][C]0.254993607751211[/C][C]0.872503196124395[/C][/ROW]
[ROW][C]128[/C][C]0.132029071784119[/C][C]0.264058143568238[/C][C]0.867970928215881[/C][/ROW]
[ROW][C]129[/C][C]0.187366426008174[/C][C]0.374732852016347[/C][C]0.812633573991826[/C][/ROW]
[ROW][C]130[/C][C]0.163448624687226[/C][C]0.326897249374451[/C][C]0.836551375312774[/C][/ROW]
[ROW][C]131[/C][C]0.144802100426717[/C][C]0.289604200853435[/C][C]0.855197899573283[/C][/ROW]
[ROW][C]132[/C][C]0.127110095870616[/C][C]0.254220191741232[/C][C]0.872889904129384[/C][/ROW]
[ROW][C]133[/C][C]0.117873102837016[/C][C]0.235746205674032[/C][C]0.882126897162984[/C][/ROW]
[ROW][C]134[/C][C]0.0979158935363091[/C][C]0.195831787072618[/C][C]0.902084106463691[/C][/ROW]
[ROW][C]135[/C][C]0.13278938351908[/C][C]0.265578767038159[/C][C]0.86721061648092[/C][/ROW]
[ROW][C]136[/C][C]0.100909489540108[/C][C]0.201818979080215[/C][C]0.899090510459892[/C][/ROW]
[ROW][C]137[/C][C]0.0755583554656442[/C][C]0.151116710931288[/C][C]0.924441644534356[/C][/ROW]
[ROW][C]138[/C][C]0.185211619749341[/C][C]0.370423239498681[/C][C]0.814788380250659[/C][/ROW]
[ROW][C]139[/C][C]0.258473502321093[/C][C]0.516947004642186[/C][C]0.741526497678907[/C][/ROW]
[ROW][C]140[/C][C]0.239168503437987[/C][C]0.478337006875973[/C][C]0.760831496562013[/C][/ROW]
[ROW][C]141[/C][C]0.483356133716306[/C][C]0.966712267432612[/C][C]0.516643866283694[/C][/ROW]
[ROW][C]142[/C][C]0.435364617171764[/C][C]0.870729234343527[/C][C]0.564635382828236[/C][/ROW]
[ROW][C]143[/C][C]0.747117313723112[/C][C]0.505765372553777[/C][C]0.252882686276888[/C][/ROW]
[ROW][C]144[/C][C]0.696970796153235[/C][C]0.60605840769353[/C][C]0.303029203846765[/C][/ROW]
[ROW][C]145[/C][C]0.597987981334715[/C][C]0.80402403733057[/C][C]0.402012018665285[/C][/ROW]
[ROW][C]146[/C][C]0.535180865336135[/C][C]0.929638269327729[/C][C]0.464819134663865[/C][/ROW]
[ROW][C]147[/C][C]0.55807827732984[/C][C]0.883843445340321[/C][C]0.44192172267016[/C][/ROW]
[ROW][C]148[/C][C]0.430789275280492[/C][C]0.861578550560985[/C][C]0.569210724719508[/C][/ROW]
[ROW][C]149[/C][C]0.337911226975226[/C][C]0.675822453950453[/C][C]0.662088773024774[/C][/ROW]
[ROW][C]150[/C][C]0.241861644608906[/C][C]0.483723289217813[/C][C]0.758138355391094[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147266&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147266&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
90.7253213230516890.5493573538966230.274678676948311
100.5986464109906280.8027071780187430.401353589009372
110.4893260815124580.9786521630249160.510673918487542
120.4448558931257080.8897117862514170.555144106874292
130.4529542350626250.9059084701252510.547045764937374
140.6934241689605640.6131516620788720.306575831039436
150.6248974246066110.7502051507867790.375102575393389
160.5729424577337620.8541150845324760.427057542266238
170.5069267630769460.9861464738461080.493073236923054
180.6370262446452050.725947510709590.362973755354795
190.5620112594482650.875977481103470.437988740551735
200.5671281596137130.8657436807725740.432871840386287
210.5193104347340460.9613791305319080.480689565265954
220.4501751285470530.9003502570941060.549824871452947
230.3911539944686660.7823079889373320.608846005531334
240.3917981325239540.7835962650479080.608201867476046
250.535610016044240.928779967911520.46438998395576
260.4730091818059330.9460183636118660.526990818194067
270.4094422667417830.8188845334835650.590557733258217
280.4027316330340330.8054632660680670.597268366965967
290.3418059785318420.6836119570636840.658194021468158
300.2850955636159930.5701911272319870.714904436384007
310.3093019969060440.6186039938120880.690698003093956
320.2607093695950430.5214187391900850.739290630404957
330.4851696443745190.9703392887490390.514830355625481
340.4387744161561080.8775488323122170.561225583843892
350.4039992529929890.8079985059859780.596000747007011
360.3483307111290370.6966614222580750.651669288870963
370.3256998806582530.6513997613165060.674300119341747
380.2763769321200350.5527538642400690.723623067879965
390.2315472767686260.4630945535372520.768452723231374
400.2082515204833970.4165030409667930.791748479516603
410.3597762817531760.7195525635063530.640223718246824
420.3096548314381420.6193096628762850.690345168561858
430.2778920360950290.5557840721900580.722107963904971
440.2360066800676720.4720133601353450.763993319932328
450.2034017647918770.4068035295837540.796598235208123
460.182515928463140.365031856926280.81748407153686
470.1702110977033430.3404221954066850.829788902296657
480.1572886639464290.3145773278928580.842711336053571
490.1282078965977020.2564157931954040.871792103402298
500.1187592016217540.2375184032435080.881240798378246
510.09794402906116590.1958880581223320.902055970938834
520.082903652089510.165807304179020.91709634791049
530.1389319756170410.2778639512340810.861068024382959
540.1704714021562140.3409428043124290.829528597843786
550.162698873296430.3253977465928610.83730112670357
560.2180702478481340.4361404956962690.781929752151866
570.2089855486336580.4179710972673170.791014451366342
580.1792270568938270.3584541137876530.820772943106174
590.1896994798377920.3793989596755840.810300520162208
600.2180149631526910.4360299263053830.781985036847309
610.191967614888960.383935229777920.80803238511104
620.1611003174793180.3222006349586370.838899682520682
630.1758507000633810.3517014001267610.824149299936619
640.1482046373359970.2964092746719940.851795362664003
650.1222079712085630.2444159424171250.877792028791437
660.1184502503507650.236900500701530.881549749649235
670.1082576603192060.2165153206384110.891742339680794
680.1085019288495910.2170038576991810.891498071150409
690.09211217258322560.1842243451664510.907887827416774
700.07513723641491190.1502744728298240.924862763585088
710.06109322106867040.1221864421373410.93890677893133
720.04826785448029330.09653570896058660.951732145519707
730.04535881619747580.09071763239495150.954641183802524
740.03638188762707370.07276377525414730.963618112372926
750.03030369776676680.06060739553353360.969696302233233
760.02328125942289910.04656251884579820.976718740577101
770.01828192098089190.03656384196178370.981718079019108
780.0146397126316490.02927942526329810.985360287368351
790.02194933684826360.04389867369652720.978050663151736
800.01752216151261680.03504432302523350.982477838487383
810.01714476519056130.03428953038112260.982855234809439
820.03084101231130630.06168202462261270.969158987688694
830.02381464956867610.04762929913735230.976185350431324
840.01990621709143680.03981243418287360.980093782908563
850.02189410335593110.04378820671186220.978105896644069
860.01947244916664390.03894489833328790.980527550833356
870.01484039730079640.02968079460159280.985159602699204
880.01943350331768890.03886700663537780.980566496682311
890.01533147971946570.03066295943893140.984668520280534
900.01150008661395750.02300017322791490.988499913386042
910.01162048896555180.02324097793110370.988379511034448
920.01018872277322660.02037744554645320.989811277226773
930.007838045464034960.01567609092806990.992161954535965
940.006501946671915760.01300389334383150.993498053328084
950.01198228179901930.02396456359803850.988017718200981
960.01089621894148880.02179243788297760.989103781058511
970.01040965815261170.02081931630522350.989590341847388
980.007997340608936080.01599468121787220.992002659391064
990.005925453757017730.01185090751403550.994074546242982
1000.004556769167675280.009113538335350570.995443230832325
1010.003383169856387410.006766339712774810.996616830143613
1020.002419194710520930.004838389421041850.997580805289479
1030.002601225399170810.005202450798341620.997398774600829
1040.002048888400231960.004097776800463930.997951111599768
1050.008725193417262120.01745038683452420.991274806582738
1060.01960001469256310.03920002938512610.980399985307437
1070.01953572455027240.03907144910054470.980464275449728
1080.0246994942341650.04939898846833010.975300505765835
1090.01917553290921240.03835106581842490.980824467090788
1100.01410168239392250.02820336478784490.985898317606077
1110.01028275208783190.02056550417566380.989717247912168
1120.02519172254264490.05038344508528980.974808277457355
1130.02298616611819310.04597233223638620.977013833881807
1140.06364683233832090.1272936646766420.936353167661679
1150.05462994370801090.1092598874160220.945370056291989
1160.04462449768458840.08924899536917690.955375502315412
1170.1293870327738970.2587740655477930.870612967226103
1180.1248358165085560.2496716330171120.875164183491444
1190.1114840173132070.2229680346264130.888515982686793
1200.192312214663350.38462442932670.80768778533665
1210.1843714475124840.3687428950249670.815628552487516
1220.2193672087559920.4387344175119830.780632791244008
1230.2131188919553990.4262377839107990.786881108044601
1240.2128450293108440.4256900586216880.787154970689156
1250.1948864589243870.3897729178487750.805113541075613
1260.1606852247092380.3213704494184760.839314775290762
1270.1274968038756050.2549936077512110.872503196124395
1280.1320290717841190.2640581435682380.867970928215881
1290.1873664260081740.3747328520163470.812633573991826
1300.1634486246872260.3268972493744510.836551375312774
1310.1448021004267170.2896042008534350.855197899573283
1320.1271100958706160.2542201917412320.872889904129384
1330.1178731028370160.2357462056740320.882126897162984
1340.09791589353630910.1958317870726180.902084106463691
1350.132789383519080.2655787670381590.86721061648092
1360.1009094895401080.2018189790802150.899090510459892
1370.07555835546564420.1511167109312880.924441644534356
1380.1852116197493410.3704232394986810.814788380250659
1390.2584735023210930.5169470046421860.741526497678907
1400.2391685034379870.4783370068759730.760831496562013
1410.4833561337163060.9667122674326120.516643866283694
1420.4353646171717640.8707292343435270.564635382828236
1430.7471173137231120.5057653725537770.252882686276888
1440.6969707961532350.606058407693530.303029203846765
1450.5979879813347150.804024037330570.402012018665285
1460.5351808653361350.9296382693277290.464819134663865
1470.558078277329840.8838434453403210.44192172267016
1480.4307892752804920.8615785505609850.569210724719508
1490.3379112269752260.6758224539504530.662088773024774
1500.2418616446089060.4837232892178130.758138355391094







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0352112676056338NOK
5% type I error level360.253521126760563NOK
10% type I error level430.302816901408451NOK

\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 & 5 & 0.0352112676056338 & NOK \tabularnewline
5% type I error level & 36 & 0.253521126760563 & NOK \tabularnewline
10% type I error level & 43 & 0.302816901408451 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147266&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]5[/C][C]0.0352112676056338[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.253521126760563[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]43[/C][C]0.302816901408451[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147266&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147266&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 level50.0352112676056338NOK
5% type I error level360.253521126760563NOK
10% type I error level430.302816901408451NOK



Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}