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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 computationTue, 23 Nov 2010 19:46:06 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290541492yxt5q9y8yvk8lsn.htm/, Retrieved Thu, 31 Oct 2024 22:50:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99617, Retrieved Thu, 31 Oct 2024 22:50:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact198
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Tutorial WS7] [2010-11-23 19:46:06] [5fd8c857995b7937a45335fd5ccccdde] [Current]
-    D      [Multiple Regression] [Workshop 7 DMA] [2010-11-24 10:44:23] [74be16979710d4c4e7c6647856088456]
-   P         [Multiple Regression] [Verbetering] [2010-11-25 19:56:32] [c2a9e95daa10045f9fd6252038bcb219]
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Dataseries X:
11	12	24
7	8	25
17	8	30
10	8	19
12	9	22
12	7	22
11	4	25
11	11	23
12	7	17
13	7	21
14	12	19
16	10	19
11	10	15
10	8	16
11	8	23
15	4	27
9	9	22
11	8	14
17	7	22
17	11	23
11	9	23
18	11	21
14	13	19
10	8	18
11	8	20
15	9	23
15	6	25
13	9	19
16	9	24
13	6	22
9	6	25
18	16	26
18	5	29
12	7	32
17	9	25
9	6	29
9	6	28
12	5	17
18	12	28
12	7	29
18	10	26
14	9	25
15	8	14
16	5	25
10	8	26
11	8	20
14	10	18
9	6	32
12	8	25
17	7	25
5	4	23
12	8	21
12	8	20
6	4	15
24	20	30
12	8	24
12	8	26
14	6	24
7	4	22
13	8	14
12	9	24
13	6	24
14	7	24
8	9	24
11	5	19
9	5	31
11	8	22
13	8	27
10	6	19
11	8	25
12	7	20
9	7	21
15	9	27
18	11	23
15	6	25
12	8	20
13	6	21
14	9	22
10	8	23
13	6	25
13	10	25
11	8	17
13	8	19
16	10	25
8	5	19
16	7	20
11	5	26
9	8	23
16	14	27
12	7	17
14	8	17
8	6	19
9	5	17
15	6	22
11	10	21
21	12	32
14	9	21
18	12	21
12	7	18
13	8	18
15	10	23
12	6	19
19	10	20
15	10	21
11	10	20
11	5	17
10	7	18
13	10	19
15	11	22
12	6	15
12	7	14
16	12	18
9	11	24
18	11	35
8	11	29
13	5	21
17	8	25
9	6	20
15	9	22
8	4	13
7	4	26
12	7	17
14	11	25
6	6	20
8	7	19
17	8	21
10	4	22
11	8	24
14	9	21
11	8	26
13	11	24
12	8	16
11	5	23
9	4	18
12	8	16
20	10	26
12	6	19
13	9	21
12	9	21
12	13	22
9	9	23
15	10	29
24	20	21
7	5	21
17	11	23
11	6	27
17	9	25
11	7	21
12	9	10
14	10	20
11	9	26
16	8	24
21	7	29
14	6	19
20	13	24
13	6	19
11	8	24
15	10	22
19	16	17




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99617&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99617&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99617&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
PerStandards[t] = + 18.3823499874315 + 0.313043159644399ParExpectations[t] -0.0318454635433938ParCriticism[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PerStandards[t] =  +  18.3823499874315 +  0.313043159644399ParExpectations[t] -0.0318454635433938ParCriticism[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99617&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PerStandards[t] =  +  18.3823499874315 +  0.313043159644399ParExpectations[t] -0.0318454635433938ParCriticism[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99617&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99617&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
PerStandards[t] = + 18.3823499874315 + 0.313043159644399ParExpectations[t] -0.0318454635433938ParCriticism[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.38234998743151.30823814.051200
ParExpectations0.3130431596443990.1180362.65210.0088260.004413
ParCriticism-0.03184546354339380.150234-0.2120.8324050.416202

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 18.3823499874315 & 1.308238 & 14.0512 & 0 & 0 \tabularnewline
ParExpectations & 0.313043159644399 & 0.118036 & 2.6521 & 0.008826 & 0.004413 \tabularnewline
ParCriticism & -0.0318454635433938 & 0.150234 & -0.212 & 0.832405 & 0.416202 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99617&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]18.3823499874315[/C][C]1.308238[/C][C]14.0512[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]ParExpectations[/C][C]0.313043159644399[/C][C]0.118036[/C][C]2.6521[/C][C]0.008826[/C][C]0.004413[/C][/ROW]
[ROW][C]ParCriticism[/C][C]-0.0318454635433938[/C][C]0.150234[/C][C]-0.212[/C][C]0.832405[/C][C]0.416202[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99617&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99617&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)18.38234998743151.30823814.051200
ParExpectations0.3130431596443990.1180362.65210.0088260.004413
ParCriticism-0.03184546354339380.150234-0.2120.8324050.416202







Multiple Linear Regression - Regression Statistics
Multiple R0.244201249146055
R-squared0.0596342500844937
Adjusted R-squared0.0475782789317307
F-TEST (value)4.94644930125161
F-TEST (DF numerator)2
F-TEST (DF denominator)156
p-value0.00826324483165364
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.11541833268082
Sum Squared Residuals2642.1202162626

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.244201249146055 \tabularnewline
R-squared & 0.0596342500844937 \tabularnewline
Adjusted R-squared & 0.0475782789317307 \tabularnewline
F-TEST (value) & 4.94644930125161 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 0.00826324483165364 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.11541833268082 \tabularnewline
Sum Squared Residuals & 2642.1202162626 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99617&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.244201249146055[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0596342500844937[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0475782789317307[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.94644930125161[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]0.00826324483165364[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.11541833268082[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2642.1202162626[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99617&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99617&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 R0.244201249146055
R-squared0.0596342500844937
Adjusted R-squared0.0475782789317307
F-TEST (value)4.94644930125161
F-TEST (DF numerator)2
F-TEST (DF denominator)156
p-value0.00826324483165364
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.11541833268082
Sum Squared Residuals2642.1202162626







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12421.44367918099912.55632081900086
22520.31888839659514.68111160340486
33023.44931999303916.55068000696088
41921.2580178755283-2.25801787552833
52221.85225873127370.147741268726262
62221.91594965836050.0840503416394744
72521.69844288934633.30155711065369
82321.47552464454261.52447535545745
91721.9159496583605-4.91594965836053
102122.2289928180049-1.22899281800492
111922.3828086599324-3.38280865993235
121923.0725859063079-4.07258590630794
131521.5073701080859-6.50737010808595
141621.2580178755283-5.25801787552833
152321.57106103517271.42893896482727
162722.95061552792394.0493844720761
172220.91312925234051.08687074765946
181421.5710610351727-7.57106103517273
192223.4811654565825-1.48116545658252
202323.3537836024089-0.353783602408943
212321.53921557162931.46078442837066
222123.6668267620533-2.66682676205334
231922.3509631963890-3.35096319638896
241821.2580178755283-3.25801787552833
252021.5710610351727-1.57106103517273
262322.79138821020690.208611789793066
272522.88692460083712.11307539916289
281922.1653018909181-3.16530189091814
292423.10443136985130.895568630148668
302222.2608382815483-0.260838281548318
312521.00866564297073.99133435702928
322623.50759944433642.49240055566363
332923.85789954331375.1421004566863
343221.915949658360510.0840503416395
352523.41747452949571.58252547050427
362921.00866564297077.99133435702928
372821.00866564297076.99133435702928
381721.9796405854473-4.97964058544731
392823.63498129850994.36501870149005
402921.91594965836057.08405034163947
412623.69867222559672.30132777440326
422522.47834505056252.52165494943746
431422.8232336737503-8.82323367375033
442523.23181322402491.76818677597509
452621.25801787552834.74198212447167
462021.5710610351727-1.57106103517273
471822.4464995870191-4.44649958701914
483221.008665642970710.9913343570293
492521.88410419481713.11589580518287
502523.48116545658251.51883454341748
512319.82018393147993.17981606852008
522121.8841041948171-0.884104194817132
532021.8841041948171-1.88410419481713
541520.1332270911243-5.13322709112431
553025.25847654802924.74152345197081
562421.88410419481712.11589580518287
572621.88410419481714.11589580518287
582422.57388144119271.42611855880728
592220.44627025076871.55372974923129
601422.1971473544615-8.19714735446153
612421.85225873127372.14774126872626
622422.26083828154831.73916171845168
632422.54203597764931.45796402235068
642420.60008609269613.39991390730386
651921.6665974258029-2.66659742580291
663121.04051110651419.95948889348588
672221.57106103517270.428938964827266
682722.19714735446154.80285264553847
691921.3217088026151-2.32170880261512
702521.57106103517273.42893896482727
712021.9159496583605-1.91594965836053
722120.97682017942730.0231798205726697
732722.79138821020694.20861178979307
742323.6668267620533-0.666826762053342
752522.88692460083712.11307539916289
762021.8841041948171-1.88410419481713
772122.2608382815483-1.26083828154832
782222.4783450505625-0.478345050562535
792321.25801787552831.74198212447166
802522.26083828154832.73916171845168
812522.13345642737472.86654357262526
821721.5710610351727-4.57106103517273
831922.1971473544615-3.19714735446153
842523.07258590630791.92741409369206
851920.7274679468697-1.72746794686972
862023.1681222969381-3.16812229693812
872621.66659742580294.33340257419709
882320.94497471588392.05502528411606
892722.94520405213444.05479594786564
901721.9159496583605-4.91594965836053
911722.5101905141059-5.51019051410593
921920.6956224833263-1.69562248332633
931721.0405111065141-4.04051110651412
942222.8869246008371-0.886924600837115
952121.5073701080859-0.507370108085946
963224.57411077744317.42588922255686
972122.4783450505625-1.47834505056254
982123.6349812985099-2.63498129850995
991821.9159496583605-3.91594965836053
1001822.1971473544615-4.19714735446153
1012322.75954274666350.24045725333646
1021921.9477951219039-2.94779512190392
1032024.0117153852411-4.01171538524113
1042122.7595427466635-1.75954274666354
1052021.5073701080859-1.50737010808595
1061721.6665974258029-4.66659742580291
1071821.2898633390717-3.28986333907173
1081922.1334564273747-3.13345642737474
1092222.7276972831201-0.727697283120146
1101521.9477951219039-6.94779512190392
1111421.9159496583605-7.91594965836053
1121823.0088949792212-5.00889497922115
1132420.84943832525383.15056167474624
1143523.666826762053311.3331732379467
1152920.53639516560948.46360483439064
1162122.2926837450917-1.29268374509171
1172523.44931999303911.55068000696088
1182021.0086656429707-1.00866564297072
1192222.7913882102069-0.791388210206934
1201320.7593134104131-7.75931341041311
1212620.44627025076875.55372974923129
1221721.9159496583605-4.91594965836053
1232522.41465412347572.58534587652425
1242020.0695361640375-0.0695361640375286
1251920.6637770197829-1.66377701978293
1262123.4493199930391-2.44931999303912
1272221.38539972970190.614600270298090
1282421.57106103517272.42893896482727
1292122.4783450505625-1.47834505056254
1302621.57106103517274.42893896482727
1312422.10161096383131.89838903616865
1321621.8841041948171-5.88410419481713
1332321.66659742580291.33340257419709
1341821.0723565700575-3.07235657005751
1351621.8841041948171-5.88410419481713
1362624.32475854488551.67524145511447
1371921.9477951219039-2.94779512190392
1382122.1653018909181-1.16530189091814
1392121.8522587312737-0.852258731273738
1402221.72487687710020.275123122899836
1412320.91312925234052.08687074765946
1422922.75954274666356.24045725333646
1432125.2584765480292-4.25847654802919
1442120.41442478722530.585575212774679
1452323.3537836024089-0.353783602408943
1462721.63475196225955.36524803774048
1472523.41747452949571.58252547050427
1482121.6029064987161-0.602906498716127
1491021.8522587312737-11.8522587312737
1502022.4464995870191-2.44649958701914
1512621.53921557162934.46078442837066
1522423.13627683339470.863723166605274
1532924.73333809516014.26666190483989
1541922.5738814411927-3.57388144119272
1552424.2292221542553-0.229222154255351
1561922.2608382815483-3.26083828154832
1572421.57106103517272.42893896482727
1582222.7595427466635-0.75954274666354
1591723.8206426039808-6.82064260398077

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 21.4436791809991 & 2.55632081900086 \tabularnewline
2 & 25 & 20.3188883965951 & 4.68111160340486 \tabularnewline
3 & 30 & 23.4493199930391 & 6.55068000696088 \tabularnewline
4 & 19 & 21.2580178755283 & -2.25801787552833 \tabularnewline
5 & 22 & 21.8522587312737 & 0.147741268726262 \tabularnewline
6 & 22 & 21.9159496583605 & 0.0840503416394744 \tabularnewline
7 & 25 & 21.6984428893463 & 3.30155711065369 \tabularnewline
8 & 23 & 21.4755246445426 & 1.52447535545745 \tabularnewline
9 & 17 & 21.9159496583605 & -4.91594965836053 \tabularnewline
10 & 21 & 22.2289928180049 & -1.22899281800492 \tabularnewline
11 & 19 & 22.3828086599324 & -3.38280865993235 \tabularnewline
12 & 19 & 23.0725859063079 & -4.07258590630794 \tabularnewline
13 & 15 & 21.5073701080859 & -6.50737010808595 \tabularnewline
14 & 16 & 21.2580178755283 & -5.25801787552833 \tabularnewline
15 & 23 & 21.5710610351727 & 1.42893896482727 \tabularnewline
16 & 27 & 22.9506155279239 & 4.0493844720761 \tabularnewline
17 & 22 & 20.9131292523405 & 1.08687074765946 \tabularnewline
18 & 14 & 21.5710610351727 & -7.57106103517273 \tabularnewline
19 & 22 & 23.4811654565825 & -1.48116545658252 \tabularnewline
20 & 23 & 23.3537836024089 & -0.353783602408943 \tabularnewline
21 & 23 & 21.5392155716293 & 1.46078442837066 \tabularnewline
22 & 21 & 23.6668267620533 & -2.66682676205334 \tabularnewline
23 & 19 & 22.3509631963890 & -3.35096319638896 \tabularnewline
24 & 18 & 21.2580178755283 & -3.25801787552833 \tabularnewline
25 & 20 & 21.5710610351727 & -1.57106103517273 \tabularnewline
26 & 23 & 22.7913882102069 & 0.208611789793066 \tabularnewline
27 & 25 & 22.8869246008371 & 2.11307539916289 \tabularnewline
28 & 19 & 22.1653018909181 & -3.16530189091814 \tabularnewline
29 & 24 & 23.1044313698513 & 0.895568630148668 \tabularnewline
30 & 22 & 22.2608382815483 & -0.260838281548318 \tabularnewline
31 & 25 & 21.0086656429707 & 3.99133435702928 \tabularnewline
32 & 26 & 23.5075994443364 & 2.49240055566363 \tabularnewline
33 & 29 & 23.8578995433137 & 5.1421004566863 \tabularnewline
34 & 32 & 21.9159496583605 & 10.0840503416395 \tabularnewline
35 & 25 & 23.4174745294957 & 1.58252547050427 \tabularnewline
36 & 29 & 21.0086656429707 & 7.99133435702928 \tabularnewline
37 & 28 & 21.0086656429707 & 6.99133435702928 \tabularnewline
38 & 17 & 21.9796405854473 & -4.97964058544731 \tabularnewline
39 & 28 & 23.6349812985099 & 4.36501870149005 \tabularnewline
40 & 29 & 21.9159496583605 & 7.08405034163947 \tabularnewline
41 & 26 & 23.6986722255967 & 2.30132777440326 \tabularnewline
42 & 25 & 22.4783450505625 & 2.52165494943746 \tabularnewline
43 & 14 & 22.8232336737503 & -8.82323367375033 \tabularnewline
44 & 25 & 23.2318132240249 & 1.76818677597509 \tabularnewline
45 & 26 & 21.2580178755283 & 4.74198212447167 \tabularnewline
46 & 20 & 21.5710610351727 & -1.57106103517273 \tabularnewline
47 & 18 & 22.4464995870191 & -4.44649958701914 \tabularnewline
48 & 32 & 21.0086656429707 & 10.9913343570293 \tabularnewline
49 & 25 & 21.8841041948171 & 3.11589580518287 \tabularnewline
50 & 25 & 23.4811654565825 & 1.51883454341748 \tabularnewline
51 & 23 & 19.8201839314799 & 3.17981606852008 \tabularnewline
52 & 21 & 21.8841041948171 & -0.884104194817132 \tabularnewline
53 & 20 & 21.8841041948171 & -1.88410419481713 \tabularnewline
54 & 15 & 20.1332270911243 & -5.13322709112431 \tabularnewline
55 & 30 & 25.2584765480292 & 4.74152345197081 \tabularnewline
56 & 24 & 21.8841041948171 & 2.11589580518287 \tabularnewline
57 & 26 & 21.8841041948171 & 4.11589580518287 \tabularnewline
58 & 24 & 22.5738814411927 & 1.42611855880728 \tabularnewline
59 & 22 & 20.4462702507687 & 1.55372974923129 \tabularnewline
60 & 14 & 22.1971473544615 & -8.19714735446153 \tabularnewline
61 & 24 & 21.8522587312737 & 2.14774126872626 \tabularnewline
62 & 24 & 22.2608382815483 & 1.73916171845168 \tabularnewline
63 & 24 & 22.5420359776493 & 1.45796402235068 \tabularnewline
64 & 24 & 20.6000860926961 & 3.39991390730386 \tabularnewline
65 & 19 & 21.6665974258029 & -2.66659742580291 \tabularnewline
66 & 31 & 21.0405111065141 & 9.95948889348588 \tabularnewline
67 & 22 & 21.5710610351727 & 0.428938964827266 \tabularnewline
68 & 27 & 22.1971473544615 & 4.80285264553847 \tabularnewline
69 & 19 & 21.3217088026151 & -2.32170880261512 \tabularnewline
70 & 25 & 21.5710610351727 & 3.42893896482727 \tabularnewline
71 & 20 & 21.9159496583605 & -1.91594965836053 \tabularnewline
72 & 21 & 20.9768201794273 & 0.0231798205726697 \tabularnewline
73 & 27 & 22.7913882102069 & 4.20861178979307 \tabularnewline
74 & 23 & 23.6668267620533 & -0.666826762053342 \tabularnewline
75 & 25 & 22.8869246008371 & 2.11307539916289 \tabularnewline
76 & 20 & 21.8841041948171 & -1.88410419481713 \tabularnewline
77 & 21 & 22.2608382815483 & -1.26083828154832 \tabularnewline
78 & 22 & 22.4783450505625 & -0.478345050562535 \tabularnewline
79 & 23 & 21.2580178755283 & 1.74198212447166 \tabularnewline
80 & 25 & 22.2608382815483 & 2.73916171845168 \tabularnewline
81 & 25 & 22.1334564273747 & 2.86654357262526 \tabularnewline
82 & 17 & 21.5710610351727 & -4.57106103517273 \tabularnewline
83 & 19 & 22.1971473544615 & -3.19714735446153 \tabularnewline
84 & 25 & 23.0725859063079 & 1.92741409369206 \tabularnewline
85 & 19 & 20.7274679468697 & -1.72746794686972 \tabularnewline
86 & 20 & 23.1681222969381 & -3.16812229693812 \tabularnewline
87 & 26 & 21.6665974258029 & 4.33340257419709 \tabularnewline
88 & 23 & 20.9449747158839 & 2.05502528411606 \tabularnewline
89 & 27 & 22.9452040521344 & 4.05479594786564 \tabularnewline
90 & 17 & 21.9159496583605 & -4.91594965836053 \tabularnewline
91 & 17 & 22.5101905141059 & -5.51019051410593 \tabularnewline
92 & 19 & 20.6956224833263 & -1.69562248332633 \tabularnewline
93 & 17 & 21.0405111065141 & -4.04051110651412 \tabularnewline
94 & 22 & 22.8869246008371 & -0.886924600837115 \tabularnewline
95 & 21 & 21.5073701080859 & -0.507370108085946 \tabularnewline
96 & 32 & 24.5741107774431 & 7.42588922255686 \tabularnewline
97 & 21 & 22.4783450505625 & -1.47834505056254 \tabularnewline
98 & 21 & 23.6349812985099 & -2.63498129850995 \tabularnewline
99 & 18 & 21.9159496583605 & -3.91594965836053 \tabularnewline
100 & 18 & 22.1971473544615 & -4.19714735446153 \tabularnewline
101 & 23 & 22.7595427466635 & 0.24045725333646 \tabularnewline
102 & 19 & 21.9477951219039 & -2.94779512190392 \tabularnewline
103 & 20 & 24.0117153852411 & -4.01171538524113 \tabularnewline
104 & 21 & 22.7595427466635 & -1.75954274666354 \tabularnewline
105 & 20 & 21.5073701080859 & -1.50737010808595 \tabularnewline
106 & 17 & 21.6665974258029 & -4.66659742580291 \tabularnewline
107 & 18 & 21.2898633390717 & -3.28986333907173 \tabularnewline
108 & 19 & 22.1334564273747 & -3.13345642737474 \tabularnewline
109 & 22 & 22.7276972831201 & -0.727697283120146 \tabularnewline
110 & 15 & 21.9477951219039 & -6.94779512190392 \tabularnewline
111 & 14 & 21.9159496583605 & -7.91594965836053 \tabularnewline
112 & 18 & 23.0088949792212 & -5.00889497922115 \tabularnewline
113 & 24 & 20.8494383252538 & 3.15056167474624 \tabularnewline
114 & 35 & 23.6668267620533 & 11.3331732379467 \tabularnewline
115 & 29 & 20.5363951656094 & 8.46360483439064 \tabularnewline
116 & 21 & 22.2926837450917 & -1.29268374509171 \tabularnewline
117 & 25 & 23.4493199930391 & 1.55068000696088 \tabularnewline
118 & 20 & 21.0086656429707 & -1.00866564297072 \tabularnewline
119 & 22 & 22.7913882102069 & -0.791388210206934 \tabularnewline
120 & 13 & 20.7593134104131 & -7.75931341041311 \tabularnewline
121 & 26 & 20.4462702507687 & 5.55372974923129 \tabularnewline
122 & 17 & 21.9159496583605 & -4.91594965836053 \tabularnewline
123 & 25 & 22.4146541234757 & 2.58534587652425 \tabularnewline
124 & 20 & 20.0695361640375 & -0.0695361640375286 \tabularnewline
125 & 19 & 20.6637770197829 & -1.66377701978293 \tabularnewline
126 & 21 & 23.4493199930391 & -2.44931999303912 \tabularnewline
127 & 22 & 21.3853997297019 & 0.614600270298090 \tabularnewline
128 & 24 & 21.5710610351727 & 2.42893896482727 \tabularnewline
129 & 21 & 22.4783450505625 & -1.47834505056254 \tabularnewline
130 & 26 & 21.5710610351727 & 4.42893896482727 \tabularnewline
131 & 24 & 22.1016109638313 & 1.89838903616865 \tabularnewline
132 & 16 & 21.8841041948171 & -5.88410419481713 \tabularnewline
133 & 23 & 21.6665974258029 & 1.33340257419709 \tabularnewline
134 & 18 & 21.0723565700575 & -3.07235657005751 \tabularnewline
135 & 16 & 21.8841041948171 & -5.88410419481713 \tabularnewline
136 & 26 & 24.3247585448855 & 1.67524145511447 \tabularnewline
137 & 19 & 21.9477951219039 & -2.94779512190392 \tabularnewline
138 & 21 & 22.1653018909181 & -1.16530189091814 \tabularnewline
139 & 21 & 21.8522587312737 & -0.852258731273738 \tabularnewline
140 & 22 & 21.7248768771002 & 0.275123122899836 \tabularnewline
141 & 23 & 20.9131292523405 & 2.08687074765946 \tabularnewline
142 & 29 & 22.7595427466635 & 6.24045725333646 \tabularnewline
143 & 21 & 25.2584765480292 & -4.25847654802919 \tabularnewline
144 & 21 & 20.4144247872253 & 0.585575212774679 \tabularnewline
145 & 23 & 23.3537836024089 & -0.353783602408943 \tabularnewline
146 & 27 & 21.6347519622595 & 5.36524803774048 \tabularnewline
147 & 25 & 23.4174745294957 & 1.58252547050427 \tabularnewline
148 & 21 & 21.6029064987161 & -0.602906498716127 \tabularnewline
149 & 10 & 21.8522587312737 & -11.8522587312737 \tabularnewline
150 & 20 & 22.4464995870191 & -2.44649958701914 \tabularnewline
151 & 26 & 21.5392155716293 & 4.46078442837066 \tabularnewline
152 & 24 & 23.1362768333947 & 0.863723166605274 \tabularnewline
153 & 29 & 24.7333380951601 & 4.26666190483989 \tabularnewline
154 & 19 & 22.5738814411927 & -3.57388144119272 \tabularnewline
155 & 24 & 24.2292221542553 & -0.229222154255351 \tabularnewline
156 & 19 & 22.2608382815483 & -3.26083828154832 \tabularnewline
157 & 24 & 21.5710610351727 & 2.42893896482727 \tabularnewline
158 & 22 & 22.7595427466635 & -0.75954274666354 \tabularnewline
159 & 17 & 23.8206426039808 & -6.82064260398077 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99617&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]24[/C][C]21.4436791809991[/C][C]2.55632081900086[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]20.3188883965951[/C][C]4.68111160340486[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]23.4493199930391[/C][C]6.55068000696088[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]21.2580178755283[/C][C]-2.25801787552833[/C][/ROW]
[ROW][C]5[/C][C]22[/C][C]21.8522587312737[/C][C]0.147741268726262[/C][/ROW]
[ROW][C]6[/C][C]22[/C][C]21.9159496583605[/C][C]0.0840503416394744[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]21.6984428893463[/C][C]3.30155711065369[/C][/ROW]
[ROW][C]8[/C][C]23[/C][C]21.4755246445426[/C][C]1.52447535545745[/C][/ROW]
[ROW][C]9[/C][C]17[/C][C]21.9159496583605[/C][C]-4.91594965836053[/C][/ROW]
[ROW][C]10[/C][C]21[/C][C]22.2289928180049[/C][C]-1.22899281800492[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]22.3828086599324[/C][C]-3.38280865993235[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]23.0725859063079[/C][C]-4.07258590630794[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]21.5073701080859[/C][C]-6.50737010808595[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]21.2580178755283[/C][C]-5.25801787552833[/C][/ROW]
[ROW][C]15[/C][C]23[/C][C]21.5710610351727[/C][C]1.42893896482727[/C][/ROW]
[ROW][C]16[/C][C]27[/C][C]22.9506155279239[/C][C]4.0493844720761[/C][/ROW]
[ROW][C]17[/C][C]22[/C][C]20.9131292523405[/C][C]1.08687074765946[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]21.5710610351727[/C][C]-7.57106103517273[/C][/ROW]
[ROW][C]19[/C][C]22[/C][C]23.4811654565825[/C][C]-1.48116545658252[/C][/ROW]
[ROW][C]20[/C][C]23[/C][C]23.3537836024089[/C][C]-0.353783602408943[/C][/ROW]
[ROW][C]21[/C][C]23[/C][C]21.5392155716293[/C][C]1.46078442837066[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]23.6668267620533[/C][C]-2.66682676205334[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]22.3509631963890[/C][C]-3.35096319638896[/C][/ROW]
[ROW][C]24[/C][C]18[/C][C]21.2580178755283[/C][C]-3.25801787552833[/C][/ROW]
[ROW][C]25[/C][C]20[/C][C]21.5710610351727[/C][C]-1.57106103517273[/C][/ROW]
[ROW][C]26[/C][C]23[/C][C]22.7913882102069[/C][C]0.208611789793066[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]22.8869246008371[/C][C]2.11307539916289[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]22.1653018909181[/C][C]-3.16530189091814[/C][/ROW]
[ROW][C]29[/C][C]24[/C][C]23.1044313698513[/C][C]0.895568630148668[/C][/ROW]
[ROW][C]30[/C][C]22[/C][C]22.2608382815483[/C][C]-0.260838281548318[/C][/ROW]
[ROW][C]31[/C][C]25[/C][C]21.0086656429707[/C][C]3.99133435702928[/C][/ROW]
[ROW][C]32[/C][C]26[/C][C]23.5075994443364[/C][C]2.49240055566363[/C][/ROW]
[ROW][C]33[/C][C]29[/C][C]23.8578995433137[/C][C]5.1421004566863[/C][/ROW]
[ROW][C]34[/C][C]32[/C][C]21.9159496583605[/C][C]10.0840503416395[/C][/ROW]
[ROW][C]35[/C][C]25[/C][C]23.4174745294957[/C][C]1.58252547050427[/C][/ROW]
[ROW][C]36[/C][C]29[/C][C]21.0086656429707[/C][C]7.99133435702928[/C][/ROW]
[ROW][C]37[/C][C]28[/C][C]21.0086656429707[/C][C]6.99133435702928[/C][/ROW]
[ROW][C]38[/C][C]17[/C][C]21.9796405854473[/C][C]-4.97964058544731[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]23.6349812985099[/C][C]4.36501870149005[/C][/ROW]
[ROW][C]40[/C][C]29[/C][C]21.9159496583605[/C][C]7.08405034163947[/C][/ROW]
[ROW][C]41[/C][C]26[/C][C]23.6986722255967[/C][C]2.30132777440326[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]22.4783450505625[/C][C]2.52165494943746[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]22.8232336737503[/C][C]-8.82323367375033[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]23.2318132240249[/C][C]1.76818677597509[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]21.2580178755283[/C][C]4.74198212447167[/C][/ROW]
[ROW][C]46[/C][C]20[/C][C]21.5710610351727[/C][C]-1.57106103517273[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]22.4464995870191[/C][C]-4.44649958701914[/C][/ROW]
[ROW][C]48[/C][C]32[/C][C]21.0086656429707[/C][C]10.9913343570293[/C][/ROW]
[ROW][C]49[/C][C]25[/C][C]21.8841041948171[/C][C]3.11589580518287[/C][/ROW]
[ROW][C]50[/C][C]25[/C][C]23.4811654565825[/C][C]1.51883454341748[/C][/ROW]
[ROW][C]51[/C][C]23[/C][C]19.8201839314799[/C][C]3.17981606852008[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]21.8841041948171[/C][C]-0.884104194817132[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]21.8841041948171[/C][C]-1.88410419481713[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]20.1332270911243[/C][C]-5.13322709112431[/C][/ROW]
[ROW][C]55[/C][C]30[/C][C]25.2584765480292[/C][C]4.74152345197081[/C][/ROW]
[ROW][C]56[/C][C]24[/C][C]21.8841041948171[/C][C]2.11589580518287[/C][/ROW]
[ROW][C]57[/C][C]26[/C][C]21.8841041948171[/C][C]4.11589580518287[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]22.5738814411927[/C][C]1.42611855880728[/C][/ROW]
[ROW][C]59[/C][C]22[/C][C]20.4462702507687[/C][C]1.55372974923129[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]22.1971473544615[/C][C]-8.19714735446153[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]21.8522587312737[/C][C]2.14774126872626[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]22.2608382815483[/C][C]1.73916171845168[/C][/ROW]
[ROW][C]63[/C][C]24[/C][C]22.5420359776493[/C][C]1.45796402235068[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]20.6000860926961[/C][C]3.39991390730386[/C][/ROW]
[ROW][C]65[/C][C]19[/C][C]21.6665974258029[/C][C]-2.66659742580291[/C][/ROW]
[ROW][C]66[/C][C]31[/C][C]21.0405111065141[/C][C]9.95948889348588[/C][/ROW]
[ROW][C]67[/C][C]22[/C][C]21.5710610351727[/C][C]0.428938964827266[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]22.1971473544615[/C][C]4.80285264553847[/C][/ROW]
[ROW][C]69[/C][C]19[/C][C]21.3217088026151[/C][C]-2.32170880261512[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]21.5710610351727[/C][C]3.42893896482727[/C][/ROW]
[ROW][C]71[/C][C]20[/C][C]21.9159496583605[/C][C]-1.91594965836053[/C][/ROW]
[ROW][C]72[/C][C]21[/C][C]20.9768201794273[/C][C]0.0231798205726697[/C][/ROW]
[ROW][C]73[/C][C]27[/C][C]22.7913882102069[/C][C]4.20861178979307[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]23.6668267620533[/C][C]-0.666826762053342[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]22.8869246008371[/C][C]2.11307539916289[/C][/ROW]
[ROW][C]76[/C][C]20[/C][C]21.8841041948171[/C][C]-1.88410419481713[/C][/ROW]
[ROW][C]77[/C][C]21[/C][C]22.2608382815483[/C][C]-1.26083828154832[/C][/ROW]
[ROW][C]78[/C][C]22[/C][C]22.4783450505625[/C][C]-0.478345050562535[/C][/ROW]
[ROW][C]79[/C][C]23[/C][C]21.2580178755283[/C][C]1.74198212447166[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]22.2608382815483[/C][C]2.73916171845168[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]22.1334564273747[/C][C]2.86654357262526[/C][/ROW]
[ROW][C]82[/C][C]17[/C][C]21.5710610351727[/C][C]-4.57106103517273[/C][/ROW]
[ROW][C]83[/C][C]19[/C][C]22.1971473544615[/C][C]-3.19714735446153[/C][/ROW]
[ROW][C]84[/C][C]25[/C][C]23.0725859063079[/C][C]1.92741409369206[/C][/ROW]
[ROW][C]85[/C][C]19[/C][C]20.7274679468697[/C][C]-1.72746794686972[/C][/ROW]
[ROW][C]86[/C][C]20[/C][C]23.1681222969381[/C][C]-3.16812229693812[/C][/ROW]
[ROW][C]87[/C][C]26[/C][C]21.6665974258029[/C][C]4.33340257419709[/C][/ROW]
[ROW][C]88[/C][C]23[/C][C]20.9449747158839[/C][C]2.05502528411606[/C][/ROW]
[ROW][C]89[/C][C]27[/C][C]22.9452040521344[/C][C]4.05479594786564[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]21.9159496583605[/C][C]-4.91594965836053[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]22.5101905141059[/C][C]-5.51019051410593[/C][/ROW]
[ROW][C]92[/C][C]19[/C][C]20.6956224833263[/C][C]-1.69562248332633[/C][/ROW]
[ROW][C]93[/C][C]17[/C][C]21.0405111065141[/C][C]-4.04051110651412[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]22.8869246008371[/C][C]-0.886924600837115[/C][/ROW]
[ROW][C]95[/C][C]21[/C][C]21.5073701080859[/C][C]-0.507370108085946[/C][/ROW]
[ROW][C]96[/C][C]32[/C][C]24.5741107774431[/C][C]7.42588922255686[/C][/ROW]
[ROW][C]97[/C][C]21[/C][C]22.4783450505625[/C][C]-1.47834505056254[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]23.6349812985099[/C][C]-2.63498129850995[/C][/ROW]
[ROW][C]99[/C][C]18[/C][C]21.9159496583605[/C][C]-3.91594965836053[/C][/ROW]
[ROW][C]100[/C][C]18[/C][C]22.1971473544615[/C][C]-4.19714735446153[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]22.7595427466635[/C][C]0.24045725333646[/C][/ROW]
[ROW][C]102[/C][C]19[/C][C]21.9477951219039[/C][C]-2.94779512190392[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]24.0117153852411[/C][C]-4.01171538524113[/C][/ROW]
[ROW][C]104[/C][C]21[/C][C]22.7595427466635[/C][C]-1.75954274666354[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]21.5073701080859[/C][C]-1.50737010808595[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]21.6665974258029[/C][C]-4.66659742580291[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]21.2898633390717[/C][C]-3.28986333907173[/C][/ROW]
[ROW][C]108[/C][C]19[/C][C]22.1334564273747[/C][C]-3.13345642737474[/C][/ROW]
[ROW][C]109[/C][C]22[/C][C]22.7276972831201[/C][C]-0.727697283120146[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]21.9477951219039[/C][C]-6.94779512190392[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]21.9159496583605[/C][C]-7.91594965836053[/C][/ROW]
[ROW][C]112[/C][C]18[/C][C]23.0088949792212[/C][C]-5.00889497922115[/C][/ROW]
[ROW][C]113[/C][C]24[/C][C]20.8494383252538[/C][C]3.15056167474624[/C][/ROW]
[ROW][C]114[/C][C]35[/C][C]23.6668267620533[/C][C]11.3331732379467[/C][/ROW]
[ROW][C]115[/C][C]29[/C][C]20.5363951656094[/C][C]8.46360483439064[/C][/ROW]
[ROW][C]116[/C][C]21[/C][C]22.2926837450917[/C][C]-1.29268374509171[/C][/ROW]
[ROW][C]117[/C][C]25[/C][C]23.4493199930391[/C][C]1.55068000696088[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]21.0086656429707[/C][C]-1.00866564297072[/C][/ROW]
[ROW][C]119[/C][C]22[/C][C]22.7913882102069[/C][C]-0.791388210206934[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]20.7593134104131[/C][C]-7.75931341041311[/C][/ROW]
[ROW][C]121[/C][C]26[/C][C]20.4462702507687[/C][C]5.55372974923129[/C][/ROW]
[ROW][C]122[/C][C]17[/C][C]21.9159496583605[/C][C]-4.91594965836053[/C][/ROW]
[ROW][C]123[/C][C]25[/C][C]22.4146541234757[/C][C]2.58534587652425[/C][/ROW]
[ROW][C]124[/C][C]20[/C][C]20.0695361640375[/C][C]-0.0695361640375286[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]20.6637770197829[/C][C]-1.66377701978293[/C][/ROW]
[ROW][C]126[/C][C]21[/C][C]23.4493199930391[/C][C]-2.44931999303912[/C][/ROW]
[ROW][C]127[/C][C]22[/C][C]21.3853997297019[/C][C]0.614600270298090[/C][/ROW]
[ROW][C]128[/C][C]24[/C][C]21.5710610351727[/C][C]2.42893896482727[/C][/ROW]
[ROW][C]129[/C][C]21[/C][C]22.4783450505625[/C][C]-1.47834505056254[/C][/ROW]
[ROW][C]130[/C][C]26[/C][C]21.5710610351727[/C][C]4.42893896482727[/C][/ROW]
[ROW][C]131[/C][C]24[/C][C]22.1016109638313[/C][C]1.89838903616865[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]21.8841041948171[/C][C]-5.88410419481713[/C][/ROW]
[ROW][C]133[/C][C]23[/C][C]21.6665974258029[/C][C]1.33340257419709[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]21.0723565700575[/C][C]-3.07235657005751[/C][/ROW]
[ROW][C]135[/C][C]16[/C][C]21.8841041948171[/C][C]-5.88410419481713[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]24.3247585448855[/C][C]1.67524145511447[/C][/ROW]
[ROW][C]137[/C][C]19[/C][C]21.9477951219039[/C][C]-2.94779512190392[/C][/ROW]
[ROW][C]138[/C][C]21[/C][C]22.1653018909181[/C][C]-1.16530189091814[/C][/ROW]
[ROW][C]139[/C][C]21[/C][C]21.8522587312737[/C][C]-0.852258731273738[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]21.7248768771002[/C][C]0.275123122899836[/C][/ROW]
[ROW][C]141[/C][C]23[/C][C]20.9131292523405[/C][C]2.08687074765946[/C][/ROW]
[ROW][C]142[/C][C]29[/C][C]22.7595427466635[/C][C]6.24045725333646[/C][/ROW]
[ROW][C]143[/C][C]21[/C][C]25.2584765480292[/C][C]-4.25847654802919[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]20.4144247872253[/C][C]0.585575212774679[/C][/ROW]
[ROW][C]145[/C][C]23[/C][C]23.3537836024089[/C][C]-0.353783602408943[/C][/ROW]
[ROW][C]146[/C][C]27[/C][C]21.6347519622595[/C][C]5.36524803774048[/C][/ROW]
[ROW][C]147[/C][C]25[/C][C]23.4174745294957[/C][C]1.58252547050427[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]21.6029064987161[/C][C]-0.602906498716127[/C][/ROW]
[ROW][C]149[/C][C]10[/C][C]21.8522587312737[/C][C]-11.8522587312737[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]22.4464995870191[/C][C]-2.44649958701914[/C][/ROW]
[ROW][C]151[/C][C]26[/C][C]21.5392155716293[/C][C]4.46078442837066[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]23.1362768333947[/C][C]0.863723166605274[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]24.7333380951601[/C][C]4.26666190483989[/C][/ROW]
[ROW][C]154[/C][C]19[/C][C]22.5738814411927[/C][C]-3.57388144119272[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]24.2292221542553[/C][C]-0.229222154255351[/C][/ROW]
[ROW][C]156[/C][C]19[/C][C]22.2608382815483[/C][C]-3.26083828154832[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]21.5710610351727[/C][C]2.42893896482727[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]22.7595427466635[/C][C]-0.75954274666354[/C][/ROW]
[ROW][C]159[/C][C]17[/C][C]23.8206426039808[/C][C]-6.82064260398077[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99617&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99617&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
12421.44367918099912.55632081900086
22520.31888839659514.68111160340486
33023.44931999303916.55068000696088
41921.2580178755283-2.25801787552833
52221.85225873127370.147741268726262
62221.91594965836050.0840503416394744
72521.69844288934633.30155711065369
82321.47552464454261.52447535545745
91721.9159496583605-4.91594965836053
102122.2289928180049-1.22899281800492
111922.3828086599324-3.38280865993235
121923.0725859063079-4.07258590630794
131521.5073701080859-6.50737010808595
141621.2580178755283-5.25801787552833
152321.57106103517271.42893896482727
162722.95061552792394.0493844720761
172220.91312925234051.08687074765946
181421.5710610351727-7.57106103517273
192223.4811654565825-1.48116545658252
202323.3537836024089-0.353783602408943
212321.53921557162931.46078442837066
222123.6668267620533-2.66682676205334
231922.3509631963890-3.35096319638896
241821.2580178755283-3.25801787552833
252021.5710610351727-1.57106103517273
262322.79138821020690.208611789793066
272522.88692460083712.11307539916289
281922.1653018909181-3.16530189091814
292423.10443136985130.895568630148668
302222.2608382815483-0.260838281548318
312521.00866564297073.99133435702928
322623.50759944433642.49240055566363
332923.85789954331375.1421004566863
343221.915949658360510.0840503416395
352523.41747452949571.58252547050427
362921.00866564297077.99133435702928
372821.00866564297076.99133435702928
381721.9796405854473-4.97964058544731
392823.63498129850994.36501870149005
402921.91594965836057.08405034163947
412623.69867222559672.30132777440326
422522.47834505056252.52165494943746
431422.8232336737503-8.82323367375033
442523.23181322402491.76818677597509
452621.25801787552834.74198212447167
462021.5710610351727-1.57106103517273
471822.4464995870191-4.44649958701914
483221.008665642970710.9913343570293
492521.88410419481713.11589580518287
502523.48116545658251.51883454341748
512319.82018393147993.17981606852008
522121.8841041948171-0.884104194817132
532021.8841041948171-1.88410419481713
541520.1332270911243-5.13322709112431
553025.25847654802924.74152345197081
562421.88410419481712.11589580518287
572621.88410419481714.11589580518287
582422.57388144119271.42611855880728
592220.44627025076871.55372974923129
601422.1971473544615-8.19714735446153
612421.85225873127372.14774126872626
622422.26083828154831.73916171845168
632422.54203597764931.45796402235068
642420.60008609269613.39991390730386
651921.6665974258029-2.66659742580291
663121.04051110651419.95948889348588
672221.57106103517270.428938964827266
682722.19714735446154.80285264553847
691921.3217088026151-2.32170880261512
702521.57106103517273.42893896482727
712021.9159496583605-1.91594965836053
722120.97682017942730.0231798205726697
732722.79138821020694.20861178979307
742323.6668267620533-0.666826762053342
752522.88692460083712.11307539916289
762021.8841041948171-1.88410419481713
772122.2608382815483-1.26083828154832
782222.4783450505625-0.478345050562535
792321.25801787552831.74198212447166
802522.26083828154832.73916171845168
812522.13345642737472.86654357262526
821721.5710610351727-4.57106103517273
831922.1971473544615-3.19714735446153
842523.07258590630791.92741409369206
851920.7274679468697-1.72746794686972
862023.1681222969381-3.16812229693812
872621.66659742580294.33340257419709
882320.94497471588392.05502528411606
892722.94520405213444.05479594786564
901721.9159496583605-4.91594965836053
911722.5101905141059-5.51019051410593
921920.6956224833263-1.69562248332633
931721.0405111065141-4.04051110651412
942222.8869246008371-0.886924600837115
952121.5073701080859-0.507370108085946
963224.57411077744317.42588922255686
972122.4783450505625-1.47834505056254
982123.6349812985099-2.63498129850995
991821.9159496583605-3.91594965836053
1001822.1971473544615-4.19714735446153
1012322.75954274666350.24045725333646
1021921.9477951219039-2.94779512190392
1032024.0117153852411-4.01171538524113
1042122.7595427466635-1.75954274666354
1052021.5073701080859-1.50737010808595
1061721.6665974258029-4.66659742580291
1071821.2898633390717-3.28986333907173
1081922.1334564273747-3.13345642737474
1092222.7276972831201-0.727697283120146
1101521.9477951219039-6.94779512190392
1111421.9159496583605-7.91594965836053
1121823.0088949792212-5.00889497922115
1132420.84943832525383.15056167474624
1143523.666826762053311.3331732379467
1152920.53639516560948.46360483439064
1162122.2926837450917-1.29268374509171
1172523.44931999303911.55068000696088
1182021.0086656429707-1.00866564297072
1192222.7913882102069-0.791388210206934
1201320.7593134104131-7.75931341041311
1212620.44627025076875.55372974923129
1221721.9159496583605-4.91594965836053
1232522.41465412347572.58534587652425
1242020.0695361640375-0.0695361640375286
1251920.6637770197829-1.66377701978293
1262123.4493199930391-2.44931999303912
1272221.38539972970190.614600270298090
1282421.57106103517272.42893896482727
1292122.4783450505625-1.47834505056254
1302621.57106103517274.42893896482727
1312422.10161096383131.89838903616865
1321621.8841041948171-5.88410419481713
1332321.66659742580291.33340257419709
1341821.0723565700575-3.07235657005751
1351621.8841041948171-5.88410419481713
1362624.32475854488551.67524145511447
1371921.9477951219039-2.94779512190392
1382122.1653018909181-1.16530189091814
1392121.8522587312737-0.852258731273738
1402221.72487687710020.275123122899836
1412320.91312925234052.08687074765946
1422922.75954274666356.24045725333646
1432125.2584765480292-4.25847654802919
1442120.41442478722530.585575212774679
1452323.3537836024089-0.353783602408943
1462721.63475196225955.36524803774048
1472523.41747452949571.58252547050427
1482121.6029064987161-0.602906498716127
1491021.8522587312737-11.8522587312737
1502022.4464995870191-2.44649958701914
1512621.53921557162934.46078442837066
1522423.13627683339470.863723166605274
1532924.73333809516014.26666190483989
1541922.5738814411927-3.57388144119272
1552424.2292221542553-0.229222154255351
1561922.2608382815483-3.26083828154832
1572421.57106103517272.42893896482727
1582222.7595427466635-0.75954274666354
1591723.8206426039808-6.82064260398077







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.5680826324963410.8638347350073170.431917367503659
70.4227398078028730.8454796156057460.577260192197127
80.2816038402750790.5632076805501580.718396159724921
90.5195521125193620.9608957749612760.480447887480638
100.4438823037860130.8877646075720260.556117696213987
110.4579360955863420.9158721911726830.542063904413658
120.4443180941687480.8886361883374950.555681905831252
130.5676268051999360.8647463896001290.432373194800064
140.6083619828562470.7832760342875070.391638017143753
150.5365397018954510.9269205962090980.463460298104549
160.4819019853744830.9638039707489650.518098014625517
170.4141488968483050.828297793696610.585851103151695
180.584351473168920.8312970536621610.415648526831081
190.5216997678621120.9566004642757760.478300232137888
200.4515285538320580.9030571076641160.548471446167942
210.4001409520056030.8002819040112060.599859047994397
220.3409487876121270.6818975752242540.659051212387873
230.2863840020972540.5727680041945080.713615997902746
240.2584247367645970.5168494735291950.741575263235403
250.2098719532097550.4197439064195090.790128046790245
260.1669823780417190.3339647560834380.833017621958281
270.1346943922965940.2693887845931880.865305607703406
280.1143566147216880.2287132294433770.885643385278312
290.08974905146819260.1794981029363850.910250948531807
300.06714961792264340.1342992358452870.932850382077357
310.06621341572564360.1324268314512870.933786584274356
320.08447055689490690.1689411137898140.915529443105093
330.08506183234299930.1701236646859990.914938167657001
340.2785026813761760.5570053627523520.721497318623824
350.2358878041086050.4717756082172090.764112195891395
360.3528763555945250.7057527111890490.647123644405476
370.416340748471290.832681496942580.58365925152871
380.5006992351654760.9986015296690490.499300764834524
390.5236690756152410.9526618487695190.476330924384759
400.5989928850085780.8020142299828430.401007114991422
410.5596320920763710.8807358158472590.440367907923629
420.5220165281336280.9559669437327450.477983471866373
430.723447443427450.5531051131450990.276552556572550
440.6846610159402930.6306779681194140.315338984059707
450.6896459737637870.6207080524724270.310354026236213
460.6539553380122370.6920893239755260.346044661987763
470.658849435892860.6823011282142810.341150564107141
480.8468883579281270.3062232841437460.153111642071873
490.8293931075719880.3412137848560230.170606892428012
500.8003985133032480.3992029733935040.199601486696752
510.775915863306070.4481682733878590.224084136693929
520.7424624436350480.5150751127299040.257537556364952
530.7152849087927070.5694301824145860.284715091207293
540.7727375971059280.4545248057881440.227262402894072
550.8051827546026590.3896344907946820.194817245397341
560.7779243033860320.4441513932279360.222075696613968
570.7730059669568460.4539880660863090.226994033043154
580.7401523449913320.5196953100173360.259847655008668
590.7050854128152070.5898291743695860.294914587184793
600.8193420288875060.3613159422249890.180657971112494
610.7947229109550690.4105541780898630.205277089044931
620.7659089150373570.4681821699252870.234091084962643
630.7333796905276240.5332406189447510.266620309472376
640.7189928862216460.5620142275567070.281007113778354
650.7012963408082720.5974073183834550.298703659191728
660.856859417743160.2862811645136810.143140582256840
670.8299251806837210.3401496386325570.170074819316279
680.8395392255168720.3209215489662560.160460774483128
690.8226899409522320.3546201180955350.177310059047768
700.8133690614209710.3732618771580580.186630938579029
710.7905675622032990.4188648755934030.209432437796701
720.757344164232690.4853116715346190.242655835767309
730.7594463154304480.4811073691391050.240553684569552
740.7239272917417980.5521454165164050.276072708258202
750.6989995026832210.6020009946335570.301000497316779
760.6684744137212430.6630511725575140.331525586278757
770.6334648578459390.7330702843081230.366535142154061
780.5911621085528930.8176757828942130.408837891447107
790.555635879839160.8887282403216790.444364120160839
800.5363004040595190.9273991918809620.463699595940481
810.5142200620080050.971559875983990.485779937991995
820.5263270746637360.9473458506725280.473672925336264
830.5088030336966160.9823939326067680.491196966303384
840.475037766139130.950075532278260.52496223386087
850.4391928458467070.8783856916934140.560807154153293
860.4210967891974130.8421935783948270.578903210802587
870.4378660937856850.875732187571370.562133906214315
880.407029443080290.814058886160580.59297055691971
890.4052834811580340.8105669623160670.594716518841966
900.4211157351187910.8422314702375830.578884264881209
910.4526643815344320.9053287630688640.547335618465568
920.4142389077322860.8284778154645720.585761092267714
930.4071124394695180.8142248789390370.592887560530482
940.3655468196509870.7310936393019750.634453180349013
950.3232882187962770.6465764375925530.676711781203723
960.433200620728730.866401241457460.56679937927127
970.3926697196201150.785339439240230.607330280379885
980.3643247835349990.7286495670699980.635675216465001
990.3541244885243280.7082489770486560.645875511475672
1000.3496038258488760.6992076516977510.650396174151124
1010.3080751206901060.6161502413802110.691924879309894
1020.2833298981697490.5666597963394980.716670101830251
1030.2743853333514480.5487706667028970.725614666648552
1040.2410234155653030.4820468311306060.758976584434697
1050.2089134608384590.4178269216769170.791086539161541
1060.210812152727950.42162430545590.78918784727205
1070.1949389843644580.3898779687289150.805061015635542
1080.1786605895186790.3573211790373580.821339410481321
1090.1490528449030370.2981056898060730.850947155096963
1100.1958856007305390.3917712014610770.804114399269461
1110.2879671390716110.5759342781432220.712032860928389
1120.3043587105220820.6087174210441640.695641289477918
1130.2831137883909370.5662275767818740.716886211609063
1140.6149410788969980.7701178422060040.385058921103002
1150.7805491460926310.4389017078147370.219450853907369
1160.7440173418864150.511965316227170.255982658113585
1170.7075420215450960.5849159569098080.292457978454904
1180.6614693455872120.6770613088255760.338530654412788
1190.611623495577870.776753008844260.38837650442213
1200.7473755374660070.5052489250679850.252624462533993
1210.7801481912346820.4397036175306370.219851808765318
1220.7970638836270580.4058722327458840.202936116372942
1230.7856763616630440.4286472766739120.214323638336956
1240.7415104141775510.5169791716448980.258489585822449
1250.69787412089080.60425175821840.3021258791092
1260.6650480265328120.6699039469343760.334951973467188
1270.6100536481354070.7798927037291850.389946351864593
1280.5802535364843370.8394929270313250.419746463515663
1290.5247498353235430.9505003293529130.475250164676457
1300.5539729683522120.8920540632955760.446027031647788
1310.5320510529010960.9358978941978070.467948947098904
1320.5743608956486560.8512782087026870.425639104351344
1330.5150378181797260.9699243636405490.484962181820274
1340.4962104554921360.9924209109842710.503789544507864
1350.5562478178673340.8875043642653310.443752182132666
1360.4996390237759850.999278047551970.500360976224015
1370.4849936433676190.9699872867352370.515006356632381
1380.4187616868644930.8375233737289860.581238313135507
1390.3514034475296550.7028068950593090.648596552470345
1400.3071353873262620.6142707746525240.692864612673738
1410.2818324286966360.5636648573932720.718167571303364
1420.4123940771512420.8247881543024830.587605922848758
1430.3551635018731820.7103270037463640.644836498126818
1440.2825554019554090.5651108039108180.717444598044591
1450.2194332173343860.4388664346687730.780566782665614
1460.2608638953774650.521727790754930.739136104622535
1470.2038599533141220.4077199066282440.796140046685878
1480.1445681462167940.2891362924335880.855431853783206
1490.5720578144274820.8558843711450360.427942185572518
1500.4714205575112830.9428411150225660.528579442488717
1510.6118784906663380.7762430186673230.388121509333662
1520.4748408706437460.9496817412874920.525159129356254
1530.4846018219355820.9692036438711640.515398178064418

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.568082632496341 & 0.863834735007317 & 0.431917367503659 \tabularnewline
7 & 0.422739807802873 & 0.845479615605746 & 0.577260192197127 \tabularnewline
8 & 0.281603840275079 & 0.563207680550158 & 0.718396159724921 \tabularnewline
9 & 0.519552112519362 & 0.960895774961276 & 0.480447887480638 \tabularnewline
10 & 0.443882303786013 & 0.887764607572026 & 0.556117696213987 \tabularnewline
11 & 0.457936095586342 & 0.915872191172683 & 0.542063904413658 \tabularnewline
12 & 0.444318094168748 & 0.888636188337495 & 0.555681905831252 \tabularnewline
13 & 0.567626805199936 & 0.864746389600129 & 0.432373194800064 \tabularnewline
14 & 0.608361982856247 & 0.783276034287507 & 0.391638017143753 \tabularnewline
15 & 0.536539701895451 & 0.926920596209098 & 0.463460298104549 \tabularnewline
16 & 0.481901985374483 & 0.963803970748965 & 0.518098014625517 \tabularnewline
17 & 0.414148896848305 & 0.82829779369661 & 0.585851103151695 \tabularnewline
18 & 0.58435147316892 & 0.831297053662161 & 0.415648526831081 \tabularnewline
19 & 0.521699767862112 & 0.956600464275776 & 0.478300232137888 \tabularnewline
20 & 0.451528553832058 & 0.903057107664116 & 0.548471446167942 \tabularnewline
21 & 0.400140952005603 & 0.800281904011206 & 0.599859047994397 \tabularnewline
22 & 0.340948787612127 & 0.681897575224254 & 0.659051212387873 \tabularnewline
23 & 0.286384002097254 & 0.572768004194508 & 0.713615997902746 \tabularnewline
24 & 0.258424736764597 & 0.516849473529195 & 0.741575263235403 \tabularnewline
25 & 0.209871953209755 & 0.419743906419509 & 0.790128046790245 \tabularnewline
26 & 0.166982378041719 & 0.333964756083438 & 0.833017621958281 \tabularnewline
27 & 0.134694392296594 & 0.269388784593188 & 0.865305607703406 \tabularnewline
28 & 0.114356614721688 & 0.228713229443377 & 0.885643385278312 \tabularnewline
29 & 0.0897490514681926 & 0.179498102936385 & 0.910250948531807 \tabularnewline
30 & 0.0671496179226434 & 0.134299235845287 & 0.932850382077357 \tabularnewline
31 & 0.0662134157256436 & 0.132426831451287 & 0.933786584274356 \tabularnewline
32 & 0.0844705568949069 & 0.168941113789814 & 0.915529443105093 \tabularnewline
33 & 0.0850618323429993 & 0.170123664685999 & 0.914938167657001 \tabularnewline
34 & 0.278502681376176 & 0.557005362752352 & 0.721497318623824 \tabularnewline
35 & 0.235887804108605 & 0.471775608217209 & 0.764112195891395 \tabularnewline
36 & 0.352876355594525 & 0.705752711189049 & 0.647123644405476 \tabularnewline
37 & 0.41634074847129 & 0.83268149694258 & 0.58365925152871 \tabularnewline
38 & 0.500699235165476 & 0.998601529669049 & 0.499300764834524 \tabularnewline
39 & 0.523669075615241 & 0.952661848769519 & 0.476330924384759 \tabularnewline
40 & 0.598992885008578 & 0.802014229982843 & 0.401007114991422 \tabularnewline
41 & 0.559632092076371 & 0.880735815847259 & 0.440367907923629 \tabularnewline
42 & 0.522016528133628 & 0.955966943732745 & 0.477983471866373 \tabularnewline
43 & 0.72344744342745 & 0.553105113145099 & 0.276552556572550 \tabularnewline
44 & 0.684661015940293 & 0.630677968119414 & 0.315338984059707 \tabularnewline
45 & 0.689645973763787 & 0.620708052472427 & 0.310354026236213 \tabularnewline
46 & 0.653955338012237 & 0.692089323975526 & 0.346044661987763 \tabularnewline
47 & 0.65884943589286 & 0.682301128214281 & 0.341150564107141 \tabularnewline
48 & 0.846888357928127 & 0.306223284143746 & 0.153111642071873 \tabularnewline
49 & 0.829393107571988 & 0.341213784856023 & 0.170606892428012 \tabularnewline
50 & 0.800398513303248 & 0.399202973393504 & 0.199601486696752 \tabularnewline
51 & 0.77591586330607 & 0.448168273387859 & 0.224084136693929 \tabularnewline
52 & 0.742462443635048 & 0.515075112729904 & 0.257537556364952 \tabularnewline
53 & 0.715284908792707 & 0.569430182414586 & 0.284715091207293 \tabularnewline
54 & 0.772737597105928 & 0.454524805788144 & 0.227262402894072 \tabularnewline
55 & 0.805182754602659 & 0.389634490794682 & 0.194817245397341 \tabularnewline
56 & 0.777924303386032 & 0.444151393227936 & 0.222075696613968 \tabularnewline
57 & 0.773005966956846 & 0.453988066086309 & 0.226994033043154 \tabularnewline
58 & 0.740152344991332 & 0.519695310017336 & 0.259847655008668 \tabularnewline
59 & 0.705085412815207 & 0.589829174369586 & 0.294914587184793 \tabularnewline
60 & 0.819342028887506 & 0.361315942224989 & 0.180657971112494 \tabularnewline
61 & 0.794722910955069 & 0.410554178089863 & 0.205277089044931 \tabularnewline
62 & 0.765908915037357 & 0.468182169925287 & 0.234091084962643 \tabularnewline
63 & 0.733379690527624 & 0.533240618944751 & 0.266620309472376 \tabularnewline
64 & 0.718992886221646 & 0.562014227556707 & 0.281007113778354 \tabularnewline
65 & 0.701296340808272 & 0.597407318383455 & 0.298703659191728 \tabularnewline
66 & 0.85685941774316 & 0.286281164513681 & 0.143140582256840 \tabularnewline
67 & 0.829925180683721 & 0.340149638632557 & 0.170074819316279 \tabularnewline
68 & 0.839539225516872 & 0.320921548966256 & 0.160460774483128 \tabularnewline
69 & 0.822689940952232 & 0.354620118095535 & 0.177310059047768 \tabularnewline
70 & 0.813369061420971 & 0.373261877158058 & 0.186630938579029 \tabularnewline
71 & 0.790567562203299 & 0.418864875593403 & 0.209432437796701 \tabularnewline
72 & 0.75734416423269 & 0.485311671534619 & 0.242655835767309 \tabularnewline
73 & 0.759446315430448 & 0.481107369139105 & 0.240553684569552 \tabularnewline
74 & 0.723927291741798 & 0.552145416516405 & 0.276072708258202 \tabularnewline
75 & 0.698999502683221 & 0.602000994633557 & 0.301000497316779 \tabularnewline
76 & 0.668474413721243 & 0.663051172557514 & 0.331525586278757 \tabularnewline
77 & 0.633464857845939 & 0.733070284308123 & 0.366535142154061 \tabularnewline
78 & 0.591162108552893 & 0.817675782894213 & 0.408837891447107 \tabularnewline
79 & 0.55563587983916 & 0.888728240321679 & 0.444364120160839 \tabularnewline
80 & 0.536300404059519 & 0.927399191880962 & 0.463699595940481 \tabularnewline
81 & 0.514220062008005 & 0.97155987598399 & 0.485779937991995 \tabularnewline
82 & 0.526327074663736 & 0.947345850672528 & 0.473672925336264 \tabularnewline
83 & 0.508803033696616 & 0.982393932606768 & 0.491196966303384 \tabularnewline
84 & 0.47503776613913 & 0.95007553227826 & 0.52496223386087 \tabularnewline
85 & 0.439192845846707 & 0.878385691693414 & 0.560807154153293 \tabularnewline
86 & 0.421096789197413 & 0.842193578394827 & 0.578903210802587 \tabularnewline
87 & 0.437866093785685 & 0.87573218757137 & 0.562133906214315 \tabularnewline
88 & 0.40702944308029 & 0.81405888616058 & 0.59297055691971 \tabularnewline
89 & 0.405283481158034 & 0.810566962316067 & 0.594716518841966 \tabularnewline
90 & 0.421115735118791 & 0.842231470237583 & 0.578884264881209 \tabularnewline
91 & 0.452664381534432 & 0.905328763068864 & 0.547335618465568 \tabularnewline
92 & 0.414238907732286 & 0.828477815464572 & 0.585761092267714 \tabularnewline
93 & 0.407112439469518 & 0.814224878939037 & 0.592887560530482 \tabularnewline
94 & 0.365546819650987 & 0.731093639301975 & 0.634453180349013 \tabularnewline
95 & 0.323288218796277 & 0.646576437592553 & 0.676711781203723 \tabularnewline
96 & 0.43320062072873 & 0.86640124145746 & 0.56679937927127 \tabularnewline
97 & 0.392669719620115 & 0.78533943924023 & 0.607330280379885 \tabularnewline
98 & 0.364324783534999 & 0.728649567069998 & 0.635675216465001 \tabularnewline
99 & 0.354124488524328 & 0.708248977048656 & 0.645875511475672 \tabularnewline
100 & 0.349603825848876 & 0.699207651697751 & 0.650396174151124 \tabularnewline
101 & 0.308075120690106 & 0.616150241380211 & 0.691924879309894 \tabularnewline
102 & 0.283329898169749 & 0.566659796339498 & 0.716670101830251 \tabularnewline
103 & 0.274385333351448 & 0.548770666702897 & 0.725614666648552 \tabularnewline
104 & 0.241023415565303 & 0.482046831130606 & 0.758976584434697 \tabularnewline
105 & 0.208913460838459 & 0.417826921676917 & 0.791086539161541 \tabularnewline
106 & 0.21081215272795 & 0.4216243054559 & 0.78918784727205 \tabularnewline
107 & 0.194938984364458 & 0.389877968728915 & 0.805061015635542 \tabularnewline
108 & 0.178660589518679 & 0.357321179037358 & 0.821339410481321 \tabularnewline
109 & 0.149052844903037 & 0.298105689806073 & 0.850947155096963 \tabularnewline
110 & 0.195885600730539 & 0.391771201461077 & 0.804114399269461 \tabularnewline
111 & 0.287967139071611 & 0.575934278143222 & 0.712032860928389 \tabularnewline
112 & 0.304358710522082 & 0.608717421044164 & 0.695641289477918 \tabularnewline
113 & 0.283113788390937 & 0.566227576781874 & 0.716886211609063 \tabularnewline
114 & 0.614941078896998 & 0.770117842206004 & 0.385058921103002 \tabularnewline
115 & 0.780549146092631 & 0.438901707814737 & 0.219450853907369 \tabularnewline
116 & 0.744017341886415 & 0.51196531622717 & 0.255982658113585 \tabularnewline
117 & 0.707542021545096 & 0.584915956909808 & 0.292457978454904 \tabularnewline
118 & 0.661469345587212 & 0.677061308825576 & 0.338530654412788 \tabularnewline
119 & 0.61162349557787 & 0.77675300884426 & 0.38837650442213 \tabularnewline
120 & 0.747375537466007 & 0.505248925067985 & 0.252624462533993 \tabularnewline
121 & 0.780148191234682 & 0.439703617530637 & 0.219851808765318 \tabularnewline
122 & 0.797063883627058 & 0.405872232745884 & 0.202936116372942 \tabularnewline
123 & 0.785676361663044 & 0.428647276673912 & 0.214323638336956 \tabularnewline
124 & 0.741510414177551 & 0.516979171644898 & 0.258489585822449 \tabularnewline
125 & 0.6978741208908 & 0.6042517582184 & 0.3021258791092 \tabularnewline
126 & 0.665048026532812 & 0.669903946934376 & 0.334951973467188 \tabularnewline
127 & 0.610053648135407 & 0.779892703729185 & 0.389946351864593 \tabularnewline
128 & 0.580253536484337 & 0.839492927031325 & 0.419746463515663 \tabularnewline
129 & 0.524749835323543 & 0.950500329352913 & 0.475250164676457 \tabularnewline
130 & 0.553972968352212 & 0.892054063295576 & 0.446027031647788 \tabularnewline
131 & 0.532051052901096 & 0.935897894197807 & 0.467948947098904 \tabularnewline
132 & 0.574360895648656 & 0.851278208702687 & 0.425639104351344 \tabularnewline
133 & 0.515037818179726 & 0.969924363640549 & 0.484962181820274 \tabularnewline
134 & 0.496210455492136 & 0.992420910984271 & 0.503789544507864 \tabularnewline
135 & 0.556247817867334 & 0.887504364265331 & 0.443752182132666 \tabularnewline
136 & 0.499639023775985 & 0.99927804755197 & 0.500360976224015 \tabularnewline
137 & 0.484993643367619 & 0.969987286735237 & 0.515006356632381 \tabularnewline
138 & 0.418761686864493 & 0.837523373728986 & 0.581238313135507 \tabularnewline
139 & 0.351403447529655 & 0.702806895059309 & 0.648596552470345 \tabularnewline
140 & 0.307135387326262 & 0.614270774652524 & 0.692864612673738 \tabularnewline
141 & 0.281832428696636 & 0.563664857393272 & 0.718167571303364 \tabularnewline
142 & 0.412394077151242 & 0.824788154302483 & 0.587605922848758 \tabularnewline
143 & 0.355163501873182 & 0.710327003746364 & 0.644836498126818 \tabularnewline
144 & 0.282555401955409 & 0.565110803910818 & 0.717444598044591 \tabularnewline
145 & 0.219433217334386 & 0.438866434668773 & 0.780566782665614 \tabularnewline
146 & 0.260863895377465 & 0.52172779075493 & 0.739136104622535 \tabularnewline
147 & 0.203859953314122 & 0.407719906628244 & 0.796140046685878 \tabularnewline
148 & 0.144568146216794 & 0.289136292433588 & 0.855431853783206 \tabularnewline
149 & 0.572057814427482 & 0.855884371145036 & 0.427942185572518 \tabularnewline
150 & 0.471420557511283 & 0.942841115022566 & 0.528579442488717 \tabularnewline
151 & 0.611878490666338 & 0.776243018667323 & 0.388121509333662 \tabularnewline
152 & 0.474840870643746 & 0.949681741287492 & 0.525159129356254 \tabularnewline
153 & 0.484601821935582 & 0.969203643871164 & 0.515398178064418 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99617&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]6[/C][C]0.568082632496341[/C][C]0.863834735007317[/C][C]0.431917367503659[/C][/ROW]
[ROW][C]7[/C][C]0.422739807802873[/C][C]0.845479615605746[/C][C]0.577260192197127[/C][/ROW]
[ROW][C]8[/C][C]0.281603840275079[/C][C]0.563207680550158[/C][C]0.718396159724921[/C][/ROW]
[ROW][C]9[/C][C]0.519552112519362[/C][C]0.960895774961276[/C][C]0.480447887480638[/C][/ROW]
[ROW][C]10[/C][C]0.443882303786013[/C][C]0.887764607572026[/C][C]0.556117696213987[/C][/ROW]
[ROW][C]11[/C][C]0.457936095586342[/C][C]0.915872191172683[/C][C]0.542063904413658[/C][/ROW]
[ROW][C]12[/C][C]0.444318094168748[/C][C]0.888636188337495[/C][C]0.555681905831252[/C][/ROW]
[ROW][C]13[/C][C]0.567626805199936[/C][C]0.864746389600129[/C][C]0.432373194800064[/C][/ROW]
[ROW][C]14[/C][C]0.608361982856247[/C][C]0.783276034287507[/C][C]0.391638017143753[/C][/ROW]
[ROW][C]15[/C][C]0.536539701895451[/C][C]0.926920596209098[/C][C]0.463460298104549[/C][/ROW]
[ROW][C]16[/C][C]0.481901985374483[/C][C]0.963803970748965[/C][C]0.518098014625517[/C][/ROW]
[ROW][C]17[/C][C]0.414148896848305[/C][C]0.82829779369661[/C][C]0.585851103151695[/C][/ROW]
[ROW][C]18[/C][C]0.58435147316892[/C][C]0.831297053662161[/C][C]0.415648526831081[/C][/ROW]
[ROW][C]19[/C][C]0.521699767862112[/C][C]0.956600464275776[/C][C]0.478300232137888[/C][/ROW]
[ROW][C]20[/C][C]0.451528553832058[/C][C]0.903057107664116[/C][C]0.548471446167942[/C][/ROW]
[ROW][C]21[/C][C]0.400140952005603[/C][C]0.800281904011206[/C][C]0.599859047994397[/C][/ROW]
[ROW][C]22[/C][C]0.340948787612127[/C][C]0.681897575224254[/C][C]0.659051212387873[/C][/ROW]
[ROW][C]23[/C][C]0.286384002097254[/C][C]0.572768004194508[/C][C]0.713615997902746[/C][/ROW]
[ROW][C]24[/C][C]0.258424736764597[/C][C]0.516849473529195[/C][C]0.741575263235403[/C][/ROW]
[ROW][C]25[/C][C]0.209871953209755[/C][C]0.419743906419509[/C][C]0.790128046790245[/C][/ROW]
[ROW][C]26[/C][C]0.166982378041719[/C][C]0.333964756083438[/C][C]0.833017621958281[/C][/ROW]
[ROW][C]27[/C][C]0.134694392296594[/C][C]0.269388784593188[/C][C]0.865305607703406[/C][/ROW]
[ROW][C]28[/C][C]0.114356614721688[/C][C]0.228713229443377[/C][C]0.885643385278312[/C][/ROW]
[ROW][C]29[/C][C]0.0897490514681926[/C][C]0.179498102936385[/C][C]0.910250948531807[/C][/ROW]
[ROW][C]30[/C][C]0.0671496179226434[/C][C]0.134299235845287[/C][C]0.932850382077357[/C][/ROW]
[ROW][C]31[/C][C]0.0662134157256436[/C][C]0.132426831451287[/C][C]0.933786584274356[/C][/ROW]
[ROW][C]32[/C][C]0.0844705568949069[/C][C]0.168941113789814[/C][C]0.915529443105093[/C][/ROW]
[ROW][C]33[/C][C]0.0850618323429993[/C][C]0.170123664685999[/C][C]0.914938167657001[/C][/ROW]
[ROW][C]34[/C][C]0.278502681376176[/C][C]0.557005362752352[/C][C]0.721497318623824[/C][/ROW]
[ROW][C]35[/C][C]0.235887804108605[/C][C]0.471775608217209[/C][C]0.764112195891395[/C][/ROW]
[ROW][C]36[/C][C]0.352876355594525[/C][C]0.705752711189049[/C][C]0.647123644405476[/C][/ROW]
[ROW][C]37[/C][C]0.41634074847129[/C][C]0.83268149694258[/C][C]0.58365925152871[/C][/ROW]
[ROW][C]38[/C][C]0.500699235165476[/C][C]0.998601529669049[/C][C]0.499300764834524[/C][/ROW]
[ROW][C]39[/C][C]0.523669075615241[/C][C]0.952661848769519[/C][C]0.476330924384759[/C][/ROW]
[ROW][C]40[/C][C]0.598992885008578[/C][C]0.802014229982843[/C][C]0.401007114991422[/C][/ROW]
[ROW][C]41[/C][C]0.559632092076371[/C][C]0.880735815847259[/C][C]0.440367907923629[/C][/ROW]
[ROW][C]42[/C][C]0.522016528133628[/C][C]0.955966943732745[/C][C]0.477983471866373[/C][/ROW]
[ROW][C]43[/C][C]0.72344744342745[/C][C]0.553105113145099[/C][C]0.276552556572550[/C][/ROW]
[ROW][C]44[/C][C]0.684661015940293[/C][C]0.630677968119414[/C][C]0.315338984059707[/C][/ROW]
[ROW][C]45[/C][C]0.689645973763787[/C][C]0.620708052472427[/C][C]0.310354026236213[/C][/ROW]
[ROW][C]46[/C][C]0.653955338012237[/C][C]0.692089323975526[/C][C]0.346044661987763[/C][/ROW]
[ROW][C]47[/C][C]0.65884943589286[/C][C]0.682301128214281[/C][C]0.341150564107141[/C][/ROW]
[ROW][C]48[/C][C]0.846888357928127[/C][C]0.306223284143746[/C][C]0.153111642071873[/C][/ROW]
[ROW][C]49[/C][C]0.829393107571988[/C][C]0.341213784856023[/C][C]0.170606892428012[/C][/ROW]
[ROW][C]50[/C][C]0.800398513303248[/C][C]0.399202973393504[/C][C]0.199601486696752[/C][/ROW]
[ROW][C]51[/C][C]0.77591586330607[/C][C]0.448168273387859[/C][C]0.224084136693929[/C][/ROW]
[ROW][C]52[/C][C]0.742462443635048[/C][C]0.515075112729904[/C][C]0.257537556364952[/C][/ROW]
[ROW][C]53[/C][C]0.715284908792707[/C][C]0.569430182414586[/C][C]0.284715091207293[/C][/ROW]
[ROW][C]54[/C][C]0.772737597105928[/C][C]0.454524805788144[/C][C]0.227262402894072[/C][/ROW]
[ROW][C]55[/C][C]0.805182754602659[/C][C]0.389634490794682[/C][C]0.194817245397341[/C][/ROW]
[ROW][C]56[/C][C]0.777924303386032[/C][C]0.444151393227936[/C][C]0.222075696613968[/C][/ROW]
[ROW][C]57[/C][C]0.773005966956846[/C][C]0.453988066086309[/C][C]0.226994033043154[/C][/ROW]
[ROW][C]58[/C][C]0.740152344991332[/C][C]0.519695310017336[/C][C]0.259847655008668[/C][/ROW]
[ROW][C]59[/C][C]0.705085412815207[/C][C]0.589829174369586[/C][C]0.294914587184793[/C][/ROW]
[ROW][C]60[/C][C]0.819342028887506[/C][C]0.361315942224989[/C][C]0.180657971112494[/C][/ROW]
[ROW][C]61[/C][C]0.794722910955069[/C][C]0.410554178089863[/C][C]0.205277089044931[/C][/ROW]
[ROW][C]62[/C][C]0.765908915037357[/C][C]0.468182169925287[/C][C]0.234091084962643[/C][/ROW]
[ROW][C]63[/C][C]0.733379690527624[/C][C]0.533240618944751[/C][C]0.266620309472376[/C][/ROW]
[ROW][C]64[/C][C]0.718992886221646[/C][C]0.562014227556707[/C][C]0.281007113778354[/C][/ROW]
[ROW][C]65[/C][C]0.701296340808272[/C][C]0.597407318383455[/C][C]0.298703659191728[/C][/ROW]
[ROW][C]66[/C][C]0.85685941774316[/C][C]0.286281164513681[/C][C]0.143140582256840[/C][/ROW]
[ROW][C]67[/C][C]0.829925180683721[/C][C]0.340149638632557[/C][C]0.170074819316279[/C][/ROW]
[ROW][C]68[/C][C]0.839539225516872[/C][C]0.320921548966256[/C][C]0.160460774483128[/C][/ROW]
[ROW][C]69[/C][C]0.822689940952232[/C][C]0.354620118095535[/C][C]0.177310059047768[/C][/ROW]
[ROW][C]70[/C][C]0.813369061420971[/C][C]0.373261877158058[/C][C]0.186630938579029[/C][/ROW]
[ROW][C]71[/C][C]0.790567562203299[/C][C]0.418864875593403[/C][C]0.209432437796701[/C][/ROW]
[ROW][C]72[/C][C]0.75734416423269[/C][C]0.485311671534619[/C][C]0.242655835767309[/C][/ROW]
[ROW][C]73[/C][C]0.759446315430448[/C][C]0.481107369139105[/C][C]0.240553684569552[/C][/ROW]
[ROW][C]74[/C][C]0.723927291741798[/C][C]0.552145416516405[/C][C]0.276072708258202[/C][/ROW]
[ROW][C]75[/C][C]0.698999502683221[/C][C]0.602000994633557[/C][C]0.301000497316779[/C][/ROW]
[ROW][C]76[/C][C]0.668474413721243[/C][C]0.663051172557514[/C][C]0.331525586278757[/C][/ROW]
[ROW][C]77[/C][C]0.633464857845939[/C][C]0.733070284308123[/C][C]0.366535142154061[/C][/ROW]
[ROW][C]78[/C][C]0.591162108552893[/C][C]0.817675782894213[/C][C]0.408837891447107[/C][/ROW]
[ROW][C]79[/C][C]0.55563587983916[/C][C]0.888728240321679[/C][C]0.444364120160839[/C][/ROW]
[ROW][C]80[/C][C]0.536300404059519[/C][C]0.927399191880962[/C][C]0.463699595940481[/C][/ROW]
[ROW][C]81[/C][C]0.514220062008005[/C][C]0.97155987598399[/C][C]0.485779937991995[/C][/ROW]
[ROW][C]82[/C][C]0.526327074663736[/C][C]0.947345850672528[/C][C]0.473672925336264[/C][/ROW]
[ROW][C]83[/C][C]0.508803033696616[/C][C]0.982393932606768[/C][C]0.491196966303384[/C][/ROW]
[ROW][C]84[/C][C]0.47503776613913[/C][C]0.95007553227826[/C][C]0.52496223386087[/C][/ROW]
[ROW][C]85[/C][C]0.439192845846707[/C][C]0.878385691693414[/C][C]0.560807154153293[/C][/ROW]
[ROW][C]86[/C][C]0.421096789197413[/C][C]0.842193578394827[/C][C]0.578903210802587[/C][/ROW]
[ROW][C]87[/C][C]0.437866093785685[/C][C]0.87573218757137[/C][C]0.562133906214315[/C][/ROW]
[ROW][C]88[/C][C]0.40702944308029[/C][C]0.81405888616058[/C][C]0.59297055691971[/C][/ROW]
[ROW][C]89[/C][C]0.405283481158034[/C][C]0.810566962316067[/C][C]0.594716518841966[/C][/ROW]
[ROW][C]90[/C][C]0.421115735118791[/C][C]0.842231470237583[/C][C]0.578884264881209[/C][/ROW]
[ROW][C]91[/C][C]0.452664381534432[/C][C]0.905328763068864[/C][C]0.547335618465568[/C][/ROW]
[ROW][C]92[/C][C]0.414238907732286[/C][C]0.828477815464572[/C][C]0.585761092267714[/C][/ROW]
[ROW][C]93[/C][C]0.407112439469518[/C][C]0.814224878939037[/C][C]0.592887560530482[/C][/ROW]
[ROW][C]94[/C][C]0.365546819650987[/C][C]0.731093639301975[/C][C]0.634453180349013[/C][/ROW]
[ROW][C]95[/C][C]0.323288218796277[/C][C]0.646576437592553[/C][C]0.676711781203723[/C][/ROW]
[ROW][C]96[/C][C]0.43320062072873[/C][C]0.86640124145746[/C][C]0.56679937927127[/C][/ROW]
[ROW][C]97[/C][C]0.392669719620115[/C][C]0.78533943924023[/C][C]0.607330280379885[/C][/ROW]
[ROW][C]98[/C][C]0.364324783534999[/C][C]0.728649567069998[/C][C]0.635675216465001[/C][/ROW]
[ROW][C]99[/C][C]0.354124488524328[/C][C]0.708248977048656[/C][C]0.645875511475672[/C][/ROW]
[ROW][C]100[/C][C]0.349603825848876[/C][C]0.699207651697751[/C][C]0.650396174151124[/C][/ROW]
[ROW][C]101[/C][C]0.308075120690106[/C][C]0.616150241380211[/C][C]0.691924879309894[/C][/ROW]
[ROW][C]102[/C][C]0.283329898169749[/C][C]0.566659796339498[/C][C]0.716670101830251[/C][/ROW]
[ROW][C]103[/C][C]0.274385333351448[/C][C]0.548770666702897[/C][C]0.725614666648552[/C][/ROW]
[ROW][C]104[/C][C]0.241023415565303[/C][C]0.482046831130606[/C][C]0.758976584434697[/C][/ROW]
[ROW][C]105[/C][C]0.208913460838459[/C][C]0.417826921676917[/C][C]0.791086539161541[/C][/ROW]
[ROW][C]106[/C][C]0.21081215272795[/C][C]0.4216243054559[/C][C]0.78918784727205[/C][/ROW]
[ROW][C]107[/C][C]0.194938984364458[/C][C]0.389877968728915[/C][C]0.805061015635542[/C][/ROW]
[ROW][C]108[/C][C]0.178660589518679[/C][C]0.357321179037358[/C][C]0.821339410481321[/C][/ROW]
[ROW][C]109[/C][C]0.149052844903037[/C][C]0.298105689806073[/C][C]0.850947155096963[/C][/ROW]
[ROW][C]110[/C][C]0.195885600730539[/C][C]0.391771201461077[/C][C]0.804114399269461[/C][/ROW]
[ROW][C]111[/C][C]0.287967139071611[/C][C]0.575934278143222[/C][C]0.712032860928389[/C][/ROW]
[ROW][C]112[/C][C]0.304358710522082[/C][C]0.608717421044164[/C][C]0.695641289477918[/C][/ROW]
[ROW][C]113[/C][C]0.283113788390937[/C][C]0.566227576781874[/C][C]0.716886211609063[/C][/ROW]
[ROW][C]114[/C][C]0.614941078896998[/C][C]0.770117842206004[/C][C]0.385058921103002[/C][/ROW]
[ROW][C]115[/C][C]0.780549146092631[/C][C]0.438901707814737[/C][C]0.219450853907369[/C][/ROW]
[ROW][C]116[/C][C]0.744017341886415[/C][C]0.51196531622717[/C][C]0.255982658113585[/C][/ROW]
[ROW][C]117[/C][C]0.707542021545096[/C][C]0.584915956909808[/C][C]0.292457978454904[/C][/ROW]
[ROW][C]118[/C][C]0.661469345587212[/C][C]0.677061308825576[/C][C]0.338530654412788[/C][/ROW]
[ROW][C]119[/C][C]0.61162349557787[/C][C]0.77675300884426[/C][C]0.38837650442213[/C][/ROW]
[ROW][C]120[/C][C]0.747375537466007[/C][C]0.505248925067985[/C][C]0.252624462533993[/C][/ROW]
[ROW][C]121[/C][C]0.780148191234682[/C][C]0.439703617530637[/C][C]0.219851808765318[/C][/ROW]
[ROW][C]122[/C][C]0.797063883627058[/C][C]0.405872232745884[/C][C]0.202936116372942[/C][/ROW]
[ROW][C]123[/C][C]0.785676361663044[/C][C]0.428647276673912[/C][C]0.214323638336956[/C][/ROW]
[ROW][C]124[/C][C]0.741510414177551[/C][C]0.516979171644898[/C][C]0.258489585822449[/C][/ROW]
[ROW][C]125[/C][C]0.6978741208908[/C][C]0.6042517582184[/C][C]0.3021258791092[/C][/ROW]
[ROW][C]126[/C][C]0.665048026532812[/C][C]0.669903946934376[/C][C]0.334951973467188[/C][/ROW]
[ROW][C]127[/C][C]0.610053648135407[/C][C]0.779892703729185[/C][C]0.389946351864593[/C][/ROW]
[ROW][C]128[/C][C]0.580253536484337[/C][C]0.839492927031325[/C][C]0.419746463515663[/C][/ROW]
[ROW][C]129[/C][C]0.524749835323543[/C][C]0.950500329352913[/C][C]0.475250164676457[/C][/ROW]
[ROW][C]130[/C][C]0.553972968352212[/C][C]0.892054063295576[/C][C]0.446027031647788[/C][/ROW]
[ROW][C]131[/C][C]0.532051052901096[/C][C]0.935897894197807[/C][C]0.467948947098904[/C][/ROW]
[ROW][C]132[/C][C]0.574360895648656[/C][C]0.851278208702687[/C][C]0.425639104351344[/C][/ROW]
[ROW][C]133[/C][C]0.515037818179726[/C][C]0.969924363640549[/C][C]0.484962181820274[/C][/ROW]
[ROW][C]134[/C][C]0.496210455492136[/C][C]0.992420910984271[/C][C]0.503789544507864[/C][/ROW]
[ROW][C]135[/C][C]0.556247817867334[/C][C]0.887504364265331[/C][C]0.443752182132666[/C][/ROW]
[ROW][C]136[/C][C]0.499639023775985[/C][C]0.99927804755197[/C][C]0.500360976224015[/C][/ROW]
[ROW][C]137[/C][C]0.484993643367619[/C][C]0.969987286735237[/C][C]0.515006356632381[/C][/ROW]
[ROW][C]138[/C][C]0.418761686864493[/C][C]0.837523373728986[/C][C]0.581238313135507[/C][/ROW]
[ROW][C]139[/C][C]0.351403447529655[/C][C]0.702806895059309[/C][C]0.648596552470345[/C][/ROW]
[ROW][C]140[/C][C]0.307135387326262[/C][C]0.614270774652524[/C][C]0.692864612673738[/C][/ROW]
[ROW][C]141[/C][C]0.281832428696636[/C][C]0.563664857393272[/C][C]0.718167571303364[/C][/ROW]
[ROW][C]142[/C][C]0.412394077151242[/C][C]0.824788154302483[/C][C]0.587605922848758[/C][/ROW]
[ROW][C]143[/C][C]0.355163501873182[/C][C]0.710327003746364[/C][C]0.644836498126818[/C][/ROW]
[ROW][C]144[/C][C]0.282555401955409[/C][C]0.565110803910818[/C][C]0.717444598044591[/C][/ROW]
[ROW][C]145[/C][C]0.219433217334386[/C][C]0.438866434668773[/C][C]0.780566782665614[/C][/ROW]
[ROW][C]146[/C][C]0.260863895377465[/C][C]0.52172779075493[/C][C]0.739136104622535[/C][/ROW]
[ROW][C]147[/C][C]0.203859953314122[/C][C]0.407719906628244[/C][C]0.796140046685878[/C][/ROW]
[ROW][C]148[/C][C]0.144568146216794[/C][C]0.289136292433588[/C][C]0.855431853783206[/C][/ROW]
[ROW][C]149[/C][C]0.572057814427482[/C][C]0.855884371145036[/C][C]0.427942185572518[/C][/ROW]
[ROW][C]150[/C][C]0.471420557511283[/C][C]0.942841115022566[/C][C]0.528579442488717[/C][/ROW]
[ROW][C]151[/C][C]0.611878490666338[/C][C]0.776243018667323[/C][C]0.388121509333662[/C][/ROW]
[ROW][C]152[/C][C]0.474840870643746[/C][C]0.949681741287492[/C][C]0.525159129356254[/C][/ROW]
[ROW][C]153[/C][C]0.484601821935582[/C][C]0.969203643871164[/C][C]0.515398178064418[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99617&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99617&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
60.5680826324963410.8638347350073170.431917367503659
70.4227398078028730.8454796156057460.577260192197127
80.2816038402750790.5632076805501580.718396159724921
90.5195521125193620.9608957749612760.480447887480638
100.4438823037860130.8877646075720260.556117696213987
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280.1143566147216880.2287132294433770.885643385278312
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880.407029443080290.814058886160580.59297055691971
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980.3643247835349990.7286495670699980.635675216465001
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1000.3496038258488760.6992076516977510.650396174151124
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1020.2833298981697490.5666597963394980.716670101830251
1030.2743853333514480.5487706667028970.725614666648552
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1080.1786605895186790.3573211790373580.821339410481321
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1530.4846018219355820.9692036438711640.515398178064418







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99617&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99617&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99617&T=6

As an alternative you can also use a QR Code:  

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

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



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