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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 12 Dec 2010 10:42:03 +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/Dec/12/t1292150871zrpy0qij51kw5cp.htm/, Retrieved Tue, 07 May 2024 07:46:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108365, Retrieved Tue, 07 May 2024 07:46:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Workshop 10 (2)] [2010-12-12 10:42:03] [c9b1b69acb8f4b2b921fdfd5091a94b7] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	15	42	14	13
12	18	51	8	13
15	11	42	12	16
12	16	46	7	12
10	12	41	10	11
12	17	49	7	12
15	15	47	16	18
9	19	33	11	11
12	16	33	14	14
11	18	47	6	9
11	10	42	16	14
11	14	32	11	12
15	18	53	16	11
7	18	41	12	12
11	14	41	7	13
11	14	33	13	11
10	12	37	11	12
14	16	43	15	16
10	15	45	7	9
6	13	33	9	11
11	16	49	7	13
15	14	42	14	15
11	9	43	15	10
12	9	37	7	11
14	17	43	15	13
15	13	42	17	16
9	15	43	15	15
13	17	46	14	14
13	16	33	14	14
16	12	42	8	14
13	11	40	8	8
12	16	44	14	13
14	17	42	14	15
11	17	52	8	13
9	16	44	11	11
16	13	45	16	15
12	12	46	10	15
10	12	36	8	9
13	16	45	14	13
16	14	49	16	16
14	12	43	13	13
15	12	43	5	11
5	14	37	8	12
8	8	32	10	12
11	15	45	8	12
16	14	45	13	14
17	11	45	15	14
9	13	45	6	8
9	14	31	12	13
13	15	33	16	16
10	16	44	5	13
6	10	49	15	11
12	11	44	12	14
8	12	41	8	13
14	14	44	13	13
12	15	38	14	13
11	16	33	12	12
16	9	47	16	16
8	11	37	10	15
15	15	48	15	15
7	15	40	8	12
16	13	50	16	14
14	17	54	19	12
16	17	43	14	15
9	15	54	6	12
14	13	44	13	13
11	15	47	15	12
13	13	33	7	12
15	15	45	13	13
5	10	33	4	5
15	15	44	14	13
13	14	47	13	13
11	15	45	11	14
11	16	43	14	17
12	13	33	12	13
12	7	43	15	13
12	13	33	14	12
12	15	46	13	13
14	13	47	8	14
6	16	47	6	11
7	16	0	7	12
14	14	42	13	12
14	12	43	13	16
10	11	44	11	12
13	15	46	5	12
12	14	36	12	12
9	11	42	8	10
12	14	44	11	15
16	15	47	14	15
10	9	41	9	12
14	15	47	10	16
10	17	46	13	15
16	16	47	16	16
15	14	46	16	13
12	15	46	11	12
10	16	36	8	11
8	10	30	4	13
8	17	48	7	10
11	15	45	14	15
13	15	49	11	13
16	13	55	15	15
14	14	11	17	18
11	16	52	5	13
4	11	33	4	10
14	18	47	10	16
9	14	33	11	13
14	14	44	15	15
8	14	42	10	14
8	14	55	9	15
11	15	42	12	14
12	14	46	15	13
11	15	43	7	13
14	15	46	13	15
15	15	47	12	16
16	12	33	14	14
16	19	53	14	14
14	13	42	15	14
14	15	43	12	12
12	15	44	12	13
14	17	55	16	12
8	9	40	9	12
13	15	40	15	14
16	15	46	15	14
12	16	53	6	14
16	12	49	14	16
12	17	44	15	13
11	11	35	10	14
4	15	40	6	4
16	11	44	14	16
15	15	46	12	13
10	17	45	8	16
13	14	53	11	15
15	12	45	13	14
12	14	48	9	13
14	15	46	15	14
7	16	55	13	12
19	16	47	15	15
12	14	43	14	14
12	11	38	16	13
13	12	46	14	14
15	16	53	14	16
8	14	40	10	6
12	13	47	10	13
10	13	47	4	13
8	14	42	8	14
10	16	53	15	15
15	16	43	16	14
16	12	44	12	15
13	11	42	12	13
16	13	51	15	16
9	15	54	9	12
14	13	41	12	15
14	16	51	14	12
12	13	51	11	14

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \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=108365&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/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=108365&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108365&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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
Y1[t] = -0.996170178492618 -0.0268429787707296X1[t] + 0.0790401730395211X2[t] + 0.323763854841615X3[t] + 0.474394463844188`X4 `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y1[t] =  -0.996170178492618 -0.0268429787707296X1[t] +  0.0790401730395211X2[t] +  0.323763854841615X3[t] +  0.474394463844188`X4
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108365&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y1[t] =  -0.996170178492618 -0.0268429787707296X1[t] +  0.0790401730395211X2[t] +  0.323763854841615X3[t] +  0.474394463844188`X4
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108365&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108365&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Y1[t] = -0.996170178492618 -0.0268429787707296X1[t] + 0.0790401730395211X2[t] + 0.323763854841615X3[t] + 0.474394463844188`X4 `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.9961701784926181.607037-0.61990.5362840.268142
X1-0.02684297877072960.077845-0.34480.7307120.365356
X20.07904017303952110.0250263.15840.0019210.000961
X30.3237638548416150.0588115.505200
`X4 `0.4743944638441880.0939955.0471e-061e-06

\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) & -0.996170178492618 & 1.607037 & -0.6199 & 0.536284 & 0.268142 \tabularnewline
X1 & -0.0268429787707296 & 0.077845 & -0.3448 & 0.730712 & 0.365356 \tabularnewline
X2 & 0.0790401730395211 & 0.025026 & 3.1584 & 0.001921 & 0.000961 \tabularnewline
X3 & 0.323763854841615 & 0.058811 & 5.5052 & 0 & 0 \tabularnewline
`X4
` & 0.474394463844188 & 0.093995 & 5.047 & 1e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108365&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]-0.996170178492618[/C][C]1.607037[/C][C]-0.6199[/C][C]0.536284[/C][C]0.268142[/C][/ROW]
[ROW][C]X1[/C][C]-0.0268429787707296[/C][C]0.077845[/C][C]-0.3448[/C][C]0.730712[/C][C]0.365356[/C][/ROW]
[ROW][C]X2[/C][C]0.0790401730395211[/C][C]0.025026[/C][C]3.1584[/C][C]0.001921[/C][C]0.000961[/C][/ROW]
[ROW][C]X3[/C][C]0.323763854841615[/C][C]0.058811[/C][C]5.5052[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`X4
`[/C][C]0.474394463844188[/C][C]0.093995[/C][C]5.047[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108365&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108365&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.9961701784926181.607037-0.61990.5362840.268142
X1-0.02684297877072960.077845-0.34480.7307120.365356
X20.07904017303952110.0250263.15840.0019210.000961
X30.3237638548416150.0588115.505200
`X4 `0.4743944638441880.0939955.0471e-061e-06






Multiple Linear Regression - Regression Statistics
Multiple R0.6876699174452
R-squared0.472889915359088
Adjusted R-squared0.458739309060003
F-TEST (value)33.4183500949828
F-TEST (DF numerator)4
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.16086383895017
Sum Squared Residuals695.730547041885

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.6876699174452 \tabularnewline
R-squared & 0.472889915359088 \tabularnewline
Adjusted R-squared & 0.458739309060003 \tabularnewline
F-TEST (value) & 33.4183500949828 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.16086383895017 \tabularnewline
Sum Squared Residuals & 695.730547041885 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108365&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.6876699174452[/C][/ROW]
[ROW][C]R-squared[/C][C]0.472889915359088[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.458739309060003[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]33.4183500949828[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.16086383895017[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]695.730547041885[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108365&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108365&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.6876699174452
R-squared0.472889915359088
Adjusted R-squared0.458739309060003
F-TEST (value)33.4183500949828
F-TEST (DF numerator)4
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.16086383895017
Sum Squared Residuals695.730547041885






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11312.62069440536340.37930559463656
21211.30894389735720.691056102642809
31513.50372200229561.49627799770435
41210.16927067101531.83072932898474
51010.3783388215812-0.378338821581222
61210.37954821136311.62045178863692
71516.0353952994652-1.03539529946516
899.88188044071155-0.881880440711552
91212.3568843330811-0.35688433308115
10118.447677640139132.55232235986087
111113.8768314727444-2.87683147274445
121110.41144962536990.588550374630133
131513.10834615448081.89165384551922
14711.3392031224843-4.33920312248425
151110.30215022720330.697849772796714
161110.66362304424840.336376955751568
171010.8603364481089-0.860336448108933
181414.4198388460064-0.419838846006353
19108.693890085213891.30610991478611
2069.3954106036527-3.3954106036527
211110.8807856539780.119214346022004
221513.59632631182251.40367368817751
231111.7613729143363-0.761372914336336
24129.171415501210482.82858449878953
251412.96981247570311.03018752429694
261515.0688553189623-0.0688553189622511
27913.9722873609329-4.9722873609329
281313.3575636038242-0.357563603824196
291312.35688433308110.64311566691885
301611.23303467647014.76696532352993
31138.255430526096634.74456947390337
321212.7519317726717-0.751931772671696
331413.51579737551030.484202624489701
341111.4148270491674-0.414827049167445
35910.8318512804585-1.83185128045848
361614.50781751939501.49218248060499
371212.6711175421556-0.671117542155571
38108.3868213190121.61317868098800
391312.83097194571120.169028054288782
401615.27152969662660.728470303373445
411412.45649965987351.54350034012652
42158.917599893452186.08240010654782
4359.83535892604263-4.83535892604263
44810.2487436431526-2.24874364315263
451110.44083733158810.559162668411931
461613.03528851225522.96471148774475
471713.76334515825073.23665484174933
4897.949417724069551.05058227593045
49911.1305677710161-2.13056777101615
501313.9800439492235-0.980043949223485
51109.838057079097160.161942920902841
52612.6831654376469-6.68316543764692
531212.7130134206863-0.713013420686302
54810.6796000395864-2.67960003958636
551412.48185387537151.51814612462846
561212.3045337132053-0.304533713205298
571110.76056769570950.239432304290455
581615.24766424440120.752335755598839
59811.9865989635706-3.98659896357061
601514.36748822613050.632511773869499
61710.0456364663905-3.04563646639046
621614.42862392074841.57137607925157
631414.6599153346601-0.659915334660067
641613.59483754854982.40516245145018
65910.5046711792605-1.50467117926053
661412.50869685414231.49130314585773
671112.8652646615584-1.86526466155842
68139.222277357813663.77772264218634
691512.53405106964032.46594893035967
7055.01075348269169-0.0107534826916893
711512.77877475144242.22122524855757
721312.71897439449010.281025605509896
731112.3609178238013-1.36091782380129
741114.5704694550089-3.57046945500892
751211.31549109586590.684508904134079
761213.2382422634104-1.23824226341036
771211.48862434170500.511375658295036
781212.6130912426799-0.613091242679853
791411.60139256289692.39860743710305
8069.45015252536896-3.45015252536896
8176.533422711197270.466577288802734
821411.84937906544832.15062093455169
831413.87968305140600.120316948593959
841011.4404606381563-1.44046063815631
85139.548585940102743.45141405989726
861211.05137417236960.948625827630432
8799.36229979986405-0.362299799864049
881212.7831150933767-0.783115093376685
891613.96468419824942.03531580175064
901010.6094983668960-0.609498366895977
911413.14402324272710.855976757272909
921013.5081942128268-3.50819421282677
931615.05976339300610.940236606993947
941513.61122578597541.38877421402457
951211.49116906915240.508830930847565
96109.228238331617460.77176166838254
9788.56878867432662-0.568788674326625
9889.35171911063518-1.35171911063518
991113.8066038521703-2.80660385217032
1001312.20268405211520.797315947884813
1011614.97445539494861.02554460505139
1021413.54055590365470.459444096345263
1031110.47037846341330.529621536586671
10447.3558828231419-3.3558828231419
1051413.06349430641490.936505693585098
106910.9648842622536-1.96488426225358
1071414.0781705127431-0.0781705127431459
108811.8268764286118-3.82687642861184
109813.0050292871282-5.00502928712819
1101112.4475611595243-1.44756115952434
1111213.2874619311338-1.28746193113381
1121110.43338759451160.566612405488402
1131413.56188017036820.438119829631772
1141513.79155095241031.20844904758968
1151612.46425624816413.53574375183593
1161613.85715885755942.14284114244061
1171413.47253868159060.527461318409354
1181411.57781240487552.42218759512451
1191212.1312470417592-0.131247041759196
1201413.76766394317470.232336056825257
121810.5304581938565-2.53045819385646
1221313.2607723779701-0.260772377970143
1231613.73501341620732.26498658379273
1241211.34757695513870.652423044861346
1251614.67768794448481.32231205551522
1261213.0488526487426-1.04885264874258
1271111.3541241536474-0.354124153647380
12845.60295304595373-1.60295304595373
1291614.30933005805791.69066994194209
1301512.28932738783822.71067261216176
1311012.2847292294234-2.28472922942336
1321313.4944766507324-0.494476650732376
1331513.08897446979671.91102553020329
1341211.50295914816320.497040851836835
1351413.73501341620730.264986583792729
136712.8232153574206-5.82321535742063
1371914.26160507432034.73839492567975
1381213.2009720210178-1.20097202101782
1391213.0594333379714-1.05943333797145
1401313.4917784976778-0.491778497677845
1411514.88647672155990.113523278440050
14287.87364037177930.126359628220702
1431211.7745258087360.225474191264012
144109.83194267968630.168057320313702
145811.1793487189286-3.17934871892861
1461014.7358461125574-4.73584611255738
1471513.79481377315961.20518622684041
1481613.16056490575982.83943509424024
1491312.08053861076310.919461389236928
1501615.13268916663470.867310833365288
151911.4759627437854-2.47596274378538
1521412.89660140787051.10339859212953
1531412.83081852010421.16918147989584
1541212.8888448195799-0.888844819579876

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 12.6206944053634 & 0.37930559463656 \tabularnewline
2 & 12 & 11.3089438973572 & 0.691056102642809 \tabularnewline
3 & 15 & 13.5037220022956 & 1.49627799770435 \tabularnewline
4 & 12 & 10.1692706710153 & 1.83072932898474 \tabularnewline
5 & 10 & 10.3783388215812 & -0.378338821581222 \tabularnewline
6 & 12 & 10.3795482113631 & 1.62045178863692 \tabularnewline
7 & 15 & 16.0353952994652 & -1.03539529946516 \tabularnewline
8 & 9 & 9.88188044071155 & -0.881880440711552 \tabularnewline
9 & 12 & 12.3568843330811 & -0.35688433308115 \tabularnewline
10 & 11 & 8.44767764013913 & 2.55232235986087 \tabularnewline
11 & 11 & 13.8768314727444 & -2.87683147274445 \tabularnewline
12 & 11 & 10.4114496253699 & 0.588550374630133 \tabularnewline
13 & 15 & 13.1083461544808 & 1.89165384551922 \tabularnewline
14 & 7 & 11.3392031224843 & -4.33920312248425 \tabularnewline
15 & 11 & 10.3021502272033 & 0.697849772796714 \tabularnewline
16 & 11 & 10.6636230442484 & 0.336376955751568 \tabularnewline
17 & 10 & 10.8603364481089 & -0.860336448108933 \tabularnewline
18 & 14 & 14.4198388460064 & -0.419838846006353 \tabularnewline
19 & 10 & 8.69389008521389 & 1.30610991478611 \tabularnewline
20 & 6 & 9.3954106036527 & -3.3954106036527 \tabularnewline
21 & 11 & 10.880785653978 & 0.119214346022004 \tabularnewline
22 & 15 & 13.5963263118225 & 1.40367368817751 \tabularnewline
23 & 11 & 11.7613729143363 & -0.761372914336336 \tabularnewline
24 & 12 & 9.17141550121048 & 2.82858449878953 \tabularnewline
25 & 14 & 12.9698124757031 & 1.03018752429694 \tabularnewline
26 & 15 & 15.0688553189623 & -0.0688553189622511 \tabularnewline
27 & 9 & 13.9722873609329 & -4.9722873609329 \tabularnewline
28 & 13 & 13.3575636038242 & -0.357563603824196 \tabularnewline
29 & 13 & 12.3568843330811 & 0.64311566691885 \tabularnewline
30 & 16 & 11.2330346764701 & 4.76696532352993 \tabularnewline
31 & 13 & 8.25543052609663 & 4.74456947390337 \tabularnewline
32 & 12 & 12.7519317726717 & -0.751931772671696 \tabularnewline
33 & 14 & 13.5157973755103 & 0.484202624489701 \tabularnewline
34 & 11 & 11.4148270491674 & -0.414827049167445 \tabularnewline
35 & 9 & 10.8318512804585 & -1.83185128045848 \tabularnewline
36 & 16 & 14.5078175193950 & 1.49218248060499 \tabularnewline
37 & 12 & 12.6711175421556 & -0.671117542155571 \tabularnewline
38 & 10 & 8.386821319012 & 1.61317868098800 \tabularnewline
39 & 13 & 12.8309719457112 & 0.169028054288782 \tabularnewline
40 & 16 & 15.2715296966266 & 0.728470303373445 \tabularnewline
41 & 14 & 12.4564996598735 & 1.54350034012652 \tabularnewline
42 & 15 & 8.91759989345218 & 6.08240010654782 \tabularnewline
43 & 5 & 9.83535892604263 & -4.83535892604263 \tabularnewline
44 & 8 & 10.2487436431526 & -2.24874364315263 \tabularnewline
45 & 11 & 10.4408373315881 & 0.559162668411931 \tabularnewline
46 & 16 & 13.0352885122552 & 2.96471148774475 \tabularnewline
47 & 17 & 13.7633451582507 & 3.23665484174933 \tabularnewline
48 & 9 & 7.94941772406955 & 1.05058227593045 \tabularnewline
49 & 9 & 11.1305677710161 & -2.13056777101615 \tabularnewline
50 & 13 & 13.9800439492235 & -0.980043949223485 \tabularnewline
51 & 10 & 9.83805707909716 & 0.161942920902841 \tabularnewline
52 & 6 & 12.6831654376469 & -6.68316543764692 \tabularnewline
53 & 12 & 12.7130134206863 & -0.713013420686302 \tabularnewline
54 & 8 & 10.6796000395864 & -2.67960003958636 \tabularnewline
55 & 14 & 12.4818538753715 & 1.51814612462846 \tabularnewline
56 & 12 & 12.3045337132053 & -0.304533713205298 \tabularnewline
57 & 11 & 10.7605676957095 & 0.239432304290455 \tabularnewline
58 & 16 & 15.2476642444012 & 0.752335755598839 \tabularnewline
59 & 8 & 11.9865989635706 & -3.98659896357061 \tabularnewline
60 & 15 & 14.3674882261305 & 0.632511773869499 \tabularnewline
61 & 7 & 10.0456364663905 & -3.04563646639046 \tabularnewline
62 & 16 & 14.4286239207484 & 1.57137607925157 \tabularnewline
63 & 14 & 14.6599153346601 & -0.659915334660067 \tabularnewline
64 & 16 & 13.5948375485498 & 2.40516245145018 \tabularnewline
65 & 9 & 10.5046711792605 & -1.50467117926053 \tabularnewline
66 & 14 & 12.5086968541423 & 1.49130314585773 \tabularnewline
67 & 11 & 12.8652646615584 & -1.86526466155842 \tabularnewline
68 & 13 & 9.22227735781366 & 3.77772264218634 \tabularnewline
69 & 15 & 12.5340510696403 & 2.46594893035967 \tabularnewline
70 & 5 & 5.01075348269169 & -0.0107534826916893 \tabularnewline
71 & 15 & 12.7787747514424 & 2.22122524855757 \tabularnewline
72 & 13 & 12.7189743944901 & 0.281025605509896 \tabularnewline
73 & 11 & 12.3609178238013 & -1.36091782380129 \tabularnewline
74 & 11 & 14.5704694550089 & -3.57046945500892 \tabularnewline
75 & 12 & 11.3154910958659 & 0.684508904134079 \tabularnewline
76 & 12 & 13.2382422634104 & -1.23824226341036 \tabularnewline
77 & 12 & 11.4886243417050 & 0.511375658295036 \tabularnewline
78 & 12 & 12.6130912426799 & -0.613091242679853 \tabularnewline
79 & 14 & 11.6013925628969 & 2.39860743710305 \tabularnewline
80 & 6 & 9.45015252536896 & -3.45015252536896 \tabularnewline
81 & 7 & 6.53342271119727 & 0.466577288802734 \tabularnewline
82 & 14 & 11.8493790654483 & 2.15062093455169 \tabularnewline
83 & 14 & 13.8796830514060 & 0.120316948593959 \tabularnewline
84 & 10 & 11.4404606381563 & -1.44046063815631 \tabularnewline
85 & 13 & 9.54858594010274 & 3.45141405989726 \tabularnewline
86 & 12 & 11.0513741723696 & 0.948625827630432 \tabularnewline
87 & 9 & 9.36229979986405 & -0.362299799864049 \tabularnewline
88 & 12 & 12.7831150933767 & -0.783115093376685 \tabularnewline
89 & 16 & 13.9646841982494 & 2.03531580175064 \tabularnewline
90 & 10 & 10.6094983668960 & -0.609498366895977 \tabularnewline
91 & 14 & 13.1440232427271 & 0.855976757272909 \tabularnewline
92 & 10 & 13.5081942128268 & -3.50819421282677 \tabularnewline
93 & 16 & 15.0597633930061 & 0.940236606993947 \tabularnewline
94 & 15 & 13.6112257859754 & 1.38877421402457 \tabularnewline
95 & 12 & 11.4911690691524 & 0.508830930847565 \tabularnewline
96 & 10 & 9.22823833161746 & 0.77176166838254 \tabularnewline
97 & 8 & 8.56878867432662 & -0.568788674326625 \tabularnewline
98 & 8 & 9.35171911063518 & -1.35171911063518 \tabularnewline
99 & 11 & 13.8066038521703 & -2.80660385217032 \tabularnewline
100 & 13 & 12.2026840521152 & 0.797315947884813 \tabularnewline
101 & 16 & 14.9744553949486 & 1.02554460505139 \tabularnewline
102 & 14 & 13.5405559036547 & 0.459444096345263 \tabularnewline
103 & 11 & 10.4703784634133 & 0.529621536586671 \tabularnewline
104 & 4 & 7.3558828231419 & -3.3558828231419 \tabularnewline
105 & 14 & 13.0634943064149 & 0.936505693585098 \tabularnewline
106 & 9 & 10.9648842622536 & -1.96488426225358 \tabularnewline
107 & 14 & 14.0781705127431 & -0.0781705127431459 \tabularnewline
108 & 8 & 11.8268764286118 & -3.82687642861184 \tabularnewline
109 & 8 & 13.0050292871282 & -5.00502928712819 \tabularnewline
110 & 11 & 12.4475611595243 & -1.44756115952434 \tabularnewline
111 & 12 & 13.2874619311338 & -1.28746193113381 \tabularnewline
112 & 11 & 10.4333875945116 & 0.566612405488402 \tabularnewline
113 & 14 & 13.5618801703682 & 0.438119829631772 \tabularnewline
114 & 15 & 13.7915509524103 & 1.20844904758968 \tabularnewline
115 & 16 & 12.4642562481641 & 3.53574375183593 \tabularnewline
116 & 16 & 13.8571588575594 & 2.14284114244061 \tabularnewline
117 & 14 & 13.4725386815906 & 0.527461318409354 \tabularnewline
118 & 14 & 11.5778124048755 & 2.42218759512451 \tabularnewline
119 & 12 & 12.1312470417592 & -0.131247041759196 \tabularnewline
120 & 14 & 13.7676639431747 & 0.232336056825257 \tabularnewline
121 & 8 & 10.5304581938565 & -2.53045819385646 \tabularnewline
122 & 13 & 13.2607723779701 & -0.260772377970143 \tabularnewline
123 & 16 & 13.7350134162073 & 2.26498658379273 \tabularnewline
124 & 12 & 11.3475769551387 & 0.652423044861346 \tabularnewline
125 & 16 & 14.6776879444848 & 1.32231205551522 \tabularnewline
126 & 12 & 13.0488526487426 & -1.04885264874258 \tabularnewline
127 & 11 & 11.3541241536474 & -0.354124153647380 \tabularnewline
128 & 4 & 5.60295304595373 & -1.60295304595373 \tabularnewline
129 & 16 & 14.3093300580579 & 1.69066994194209 \tabularnewline
130 & 15 & 12.2893273878382 & 2.71067261216176 \tabularnewline
131 & 10 & 12.2847292294234 & -2.28472922942336 \tabularnewline
132 & 13 & 13.4944766507324 & -0.494476650732376 \tabularnewline
133 & 15 & 13.0889744697967 & 1.91102553020329 \tabularnewline
134 & 12 & 11.5029591481632 & 0.497040851836835 \tabularnewline
135 & 14 & 13.7350134162073 & 0.264986583792729 \tabularnewline
136 & 7 & 12.8232153574206 & -5.82321535742063 \tabularnewline
137 & 19 & 14.2616050743203 & 4.73839492567975 \tabularnewline
138 & 12 & 13.2009720210178 & -1.20097202101782 \tabularnewline
139 & 12 & 13.0594333379714 & -1.05943333797145 \tabularnewline
140 & 13 & 13.4917784976778 & -0.491778497677845 \tabularnewline
141 & 15 & 14.8864767215599 & 0.113523278440050 \tabularnewline
142 & 8 & 7.8736403717793 & 0.126359628220702 \tabularnewline
143 & 12 & 11.774525808736 & 0.225474191264012 \tabularnewline
144 & 10 & 9.8319426796863 & 0.168057320313702 \tabularnewline
145 & 8 & 11.1793487189286 & -3.17934871892861 \tabularnewline
146 & 10 & 14.7358461125574 & -4.73584611255738 \tabularnewline
147 & 15 & 13.7948137731596 & 1.20518622684041 \tabularnewline
148 & 16 & 13.1605649057598 & 2.83943509424024 \tabularnewline
149 & 13 & 12.0805386107631 & 0.919461389236928 \tabularnewline
150 & 16 & 15.1326891666347 & 0.867310833365288 \tabularnewline
151 & 9 & 11.4759627437854 & -2.47596274378538 \tabularnewline
152 & 14 & 12.8966014078705 & 1.10339859212953 \tabularnewline
153 & 14 & 12.8308185201042 & 1.16918147989584 \tabularnewline
154 & 12 & 12.8888448195799 & -0.888844819579876 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108365&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]12.6206944053634[/C][C]0.37930559463656[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.3089438973572[/C][C]0.691056102642809[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.5037220022956[/C][C]1.49627799770435[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.1692706710153[/C][C]1.83072932898474[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.3783388215812[/C][C]-0.378338821581222[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.3795482113631[/C][C]1.62045178863692[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.0353952994652[/C][C]-1.03539529946516[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]9.88188044071155[/C][C]-0.881880440711552[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.3568843330811[/C][C]-0.35688433308115[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]8.44767764013913[/C][C]2.55232235986087[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.8768314727444[/C][C]-2.87683147274445[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]10.4114496253699[/C][C]0.588550374630133[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]13.1083461544808[/C][C]1.89165384551922[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.3392031224843[/C][C]-4.33920312248425[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]10.3021502272033[/C][C]0.697849772796714[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.6636230442484[/C][C]0.336376955751568[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.8603364481089[/C][C]-0.860336448108933[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.4198388460064[/C][C]-0.419838846006353[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.69389008521389[/C][C]1.30610991478611[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.3954106036527[/C][C]-3.3954106036527[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]10.880785653978[/C][C]0.119214346022004[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]13.5963263118225[/C][C]1.40367368817751[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.7613729143363[/C][C]-0.761372914336336[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.17141550121048[/C][C]2.82858449878953[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]12.9698124757031[/C][C]1.03018752429694[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]15.0688553189623[/C][C]-0.0688553189622511[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]13.9722873609329[/C][C]-4.9722873609329[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]13.3575636038242[/C][C]-0.357563603824196[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]12.3568843330811[/C][C]0.64311566691885[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]11.2330346764701[/C][C]4.76696532352993[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.25543052609663[/C][C]4.74456947390337[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.7519317726717[/C][C]-0.751931772671696[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]13.5157973755103[/C][C]0.484202624489701[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.4148270491674[/C][C]-0.414827049167445[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.8318512804585[/C][C]-1.83185128045848[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.5078175193950[/C][C]1.49218248060499[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.6711175421556[/C][C]-0.671117542155571[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]8.386821319012[/C][C]1.61317868098800[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]12.8309719457112[/C][C]0.169028054288782[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.2715296966266[/C][C]0.728470303373445[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.4564996598735[/C][C]1.54350034012652[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.91759989345218[/C][C]6.08240010654782[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.83535892604263[/C][C]-4.83535892604263[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.2487436431526[/C][C]-2.24874364315263[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]10.4408373315881[/C][C]0.559162668411931[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.0352885122552[/C][C]2.96471148774475[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]13.7633451582507[/C][C]3.23665484174933[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]7.94941772406955[/C][C]1.05058227593045[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.1305677710161[/C][C]-2.13056777101615[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]13.9800439492235[/C][C]-0.980043949223485[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]9.83805707909716[/C][C]0.161942920902841[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.6831654376469[/C][C]-6.68316543764692[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.7130134206863[/C][C]-0.713013420686302[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.6796000395864[/C][C]-2.67960003958636[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]12.4818538753715[/C][C]1.51814612462846[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.3045337132053[/C][C]-0.304533713205298[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.7605676957095[/C][C]0.239432304290455[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.2476642444012[/C][C]0.752335755598839[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]11.9865989635706[/C][C]-3.98659896357061[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.3674882261305[/C][C]0.632511773869499[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]10.0456364663905[/C][C]-3.04563646639046[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.4286239207484[/C][C]1.57137607925157[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]14.6599153346601[/C][C]-0.659915334660067[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.5948375485498[/C][C]2.40516245145018[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.5046711792605[/C][C]-1.50467117926053[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.5086968541423[/C][C]1.49130314585773[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.8652646615584[/C][C]-1.86526466155842[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]9.22227735781366[/C][C]3.77772264218634[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]12.5340510696403[/C][C]2.46594893035967[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.01075348269169[/C][C]-0.0107534826916893[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.7787747514424[/C][C]2.22122524855757[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.7189743944901[/C][C]0.281025605509896[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.3609178238013[/C][C]-1.36091782380129[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.5704694550089[/C][C]-3.57046945500892[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.3154910958659[/C][C]0.684508904134079[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.2382422634104[/C][C]-1.23824226341036[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]11.4886243417050[/C][C]0.511375658295036[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]12.6130912426799[/C][C]-0.613091242679853[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]11.6013925628969[/C][C]2.39860743710305[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]9.45015252536896[/C][C]-3.45015252536896[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]6.53342271119727[/C][C]0.466577288802734[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]11.8493790654483[/C][C]2.15062093455169[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.8796830514060[/C][C]0.120316948593959[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.4404606381563[/C][C]-1.44046063815631[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]9.54858594010274[/C][C]3.45141405989726[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]11.0513741723696[/C][C]0.948625827630432[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.36229979986405[/C][C]-0.362299799864049[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.7831150933767[/C][C]-0.783115093376685[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]13.9646841982494[/C][C]2.03531580175064[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.6094983668960[/C][C]-0.609498366895977[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.1440232427271[/C][C]0.855976757272909[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.5081942128268[/C][C]-3.50819421282677[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.0597633930061[/C][C]0.940236606993947[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.6112257859754[/C][C]1.38877421402457[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.4911690691524[/C][C]0.508830930847565[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.22823833161746[/C][C]0.77176166838254[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]8.56878867432662[/C][C]-0.568788674326625[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]9.35171911063518[/C][C]-1.35171911063518[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]13.8066038521703[/C][C]-2.80660385217032[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.2026840521152[/C][C]0.797315947884813[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.9744553949486[/C][C]1.02554460505139[/C][/ROW]
[ROW][C]102[/C][C]14[/C][C]13.5405559036547[/C][C]0.459444096345263[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]10.4703784634133[/C][C]0.529621536586671[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]7.3558828231419[/C][C]-3.3558828231419[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]13.0634943064149[/C][C]0.936505693585098[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]10.9648842622536[/C][C]-1.96488426225358[/C][/ROW]
[ROW][C]107[/C][C]14[/C][C]14.0781705127431[/C][C]-0.0781705127431459[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]11.8268764286118[/C][C]-3.82687642861184[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]13.0050292871282[/C][C]-5.00502928712819[/C][/ROW]
[ROW][C]110[/C][C]11[/C][C]12.4475611595243[/C][C]-1.44756115952434[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]13.2874619311338[/C][C]-1.28746193113381[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]10.4333875945116[/C][C]0.566612405488402[/C][/ROW]
[ROW][C]113[/C][C]14[/C][C]13.5618801703682[/C][C]0.438119829631772[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.7915509524103[/C][C]1.20844904758968[/C][/ROW]
[ROW][C]115[/C][C]16[/C][C]12.4642562481641[/C][C]3.53574375183593[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.8571588575594[/C][C]2.14284114244061[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.4725386815906[/C][C]0.527461318409354[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]11.5778124048755[/C][C]2.42218759512451[/C][/ROW]
[ROW][C]119[/C][C]12[/C][C]12.1312470417592[/C][C]-0.131247041759196[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]13.7676639431747[/C][C]0.232336056825257[/C][/ROW]
[ROW][C]121[/C][C]8[/C][C]10.5304581938565[/C][C]-2.53045819385646[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]13.2607723779701[/C][C]-0.260772377970143[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]13.7350134162073[/C][C]2.26498658379273[/C][/ROW]
[ROW][C]124[/C][C]12[/C][C]11.3475769551387[/C][C]0.652423044861346[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]14.6776879444848[/C][C]1.32231205551522[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]13.0488526487426[/C][C]-1.04885264874258[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]11.3541241536474[/C][C]-0.354124153647380[/C][/ROW]
[ROW][C]128[/C][C]4[/C][C]5.60295304595373[/C][C]-1.60295304595373[/C][/ROW]
[ROW][C]129[/C][C]16[/C][C]14.3093300580579[/C][C]1.69066994194209[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]12.2893273878382[/C][C]2.71067261216176[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]12.2847292294234[/C][C]-2.28472922942336[/C][/ROW]
[ROW][C]132[/C][C]13[/C][C]13.4944766507324[/C][C]-0.494476650732376[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]13.0889744697967[/C][C]1.91102553020329[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]11.5029591481632[/C][C]0.497040851836835[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]13.7350134162073[/C][C]0.264986583792729[/C][/ROW]
[ROW][C]136[/C][C]7[/C][C]12.8232153574206[/C][C]-5.82321535742063[/C][/ROW]
[ROW][C]137[/C][C]19[/C][C]14.2616050743203[/C][C]4.73839492567975[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]13.2009720210178[/C][C]-1.20097202101782[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.0594333379714[/C][C]-1.05943333797145[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]13.4917784976778[/C][C]-0.491778497677845[/C][/ROW]
[ROW][C]141[/C][C]15[/C][C]14.8864767215599[/C][C]0.113523278440050[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]7.8736403717793[/C][C]0.126359628220702[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]11.774525808736[/C][C]0.225474191264012[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]9.8319426796863[/C][C]0.168057320313702[/C][/ROW]
[ROW][C]145[/C][C]8[/C][C]11.1793487189286[/C][C]-3.17934871892861[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.7358461125574[/C][C]-4.73584611255738[/C][/ROW]
[ROW][C]147[/C][C]15[/C][C]13.7948137731596[/C][C]1.20518622684041[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]13.1605649057598[/C][C]2.83943509424024[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]12.0805386107631[/C][C]0.919461389236928[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]15.1326891666347[/C][C]0.867310833365288[/C][/ROW]
[ROW][C]151[/C][C]9[/C][C]11.4759627437854[/C][C]-2.47596274378538[/C][/ROW]
[ROW][C]152[/C][C]14[/C][C]12.8966014078705[/C][C]1.10339859212953[/C][/ROW]
[ROW][C]153[/C][C]14[/C][C]12.8308185201042[/C][C]1.16918147989584[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]12.8888448195799[/C][C]-0.888844819579876[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108365&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108365&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11312.62069440536340.37930559463656
21211.30894389735720.691056102642809
31513.50372200229561.49627799770435
41210.16927067101531.83072932898474
51010.3783388215812-0.378338821581222
61210.37954821136311.62045178863692
71516.0353952994652-1.03539529946516
899.88188044071155-0.881880440711552
91212.3568843330811-0.35688433308115
10118.447677640139132.55232235986087
111113.8768314727444-2.87683147274445
121110.41144962536990.588550374630133
131513.10834615448081.89165384551922
14711.3392031224843-4.33920312248425
151110.30215022720330.697849772796714
161110.66362304424840.336376955751568
171010.8603364481089-0.860336448108933
181414.4198388460064-0.419838846006353
19108.693890085213891.30610991478611
2069.3954106036527-3.3954106036527
211110.8807856539780.119214346022004
221513.59632631182251.40367368817751
231111.7613729143363-0.761372914336336
24129.171415501210482.82858449878953
251412.96981247570311.03018752429694
261515.0688553189623-0.0688553189622511
27913.9722873609329-4.9722873609329
281313.3575636038242-0.357563603824196
291312.35688433308110.64311566691885
301611.23303467647014.76696532352993
31138.255430526096634.74456947390337
321212.7519317726717-0.751931772671696
331413.51579737551030.484202624489701
341111.4148270491674-0.414827049167445
35910.8318512804585-1.83185128045848
361614.50781751939501.49218248060499
371212.6711175421556-0.671117542155571
38108.3868213190121.61317868098800
391312.83097194571120.169028054288782
401615.27152969662660.728470303373445
411412.45649965987351.54350034012652
42158.917599893452186.08240010654782
4359.83535892604263-4.83535892604263
44810.2487436431526-2.24874364315263
451110.44083733158810.559162668411931
461613.03528851225522.96471148774475
471713.76334515825073.23665484174933
4897.949417724069551.05058227593045
49911.1305677710161-2.13056777101615
501313.9800439492235-0.980043949223485
51109.838057079097160.161942920902841
52612.6831654376469-6.68316543764692
531212.7130134206863-0.713013420686302
54810.6796000395864-2.67960003958636
551412.48185387537151.51814612462846
561212.3045337132053-0.304533713205298
571110.76056769570950.239432304290455
581615.24766424440120.752335755598839
59811.9865989635706-3.98659896357061
601514.36748822613050.632511773869499
61710.0456364663905-3.04563646639046
621614.42862392074841.57137607925157
631414.6599153346601-0.659915334660067
641613.59483754854982.40516245145018
65910.5046711792605-1.50467117926053
661412.50869685414231.49130314585773
671112.8652646615584-1.86526466155842
68139.222277357813663.77772264218634
691512.53405106964032.46594893035967
7055.01075348269169-0.0107534826916893
711512.77877475144242.22122524855757
721312.71897439449010.281025605509896
731112.3609178238013-1.36091782380129
741114.5704694550089-3.57046945500892
751211.31549109586590.684508904134079
761213.2382422634104-1.23824226341036
771211.48862434170500.511375658295036
781212.6130912426799-0.613091242679853
791411.60139256289692.39860743710305
8069.45015252536896-3.45015252536896
8176.533422711197270.466577288802734
821411.84937906544832.15062093455169
831413.87968305140600.120316948593959
841011.4404606381563-1.44046063815631
85139.548585940102743.45141405989726
861211.05137417236960.948625827630432
8799.36229979986405-0.362299799864049
881212.7831150933767-0.783115093376685
891613.96468419824942.03531580175064
901010.6094983668960-0.609498366895977
911413.14402324272710.855976757272909
921013.5081942128268-3.50819421282677
931615.05976339300610.940236606993947
941513.61122578597541.38877421402457
951211.49116906915240.508830930847565
96109.228238331617460.77176166838254
9788.56878867432662-0.568788674326625
9889.35171911063518-1.35171911063518
991113.8066038521703-2.80660385217032
1001312.20268405211520.797315947884813
1011614.97445539494861.02554460505139
1021413.54055590365470.459444096345263
1031110.47037846341330.529621536586671
10447.3558828231419-3.3558828231419
1051413.06349430641490.936505693585098
106910.9648842622536-1.96488426225358
1071414.0781705127431-0.0781705127431459
108811.8268764286118-3.82687642861184
109813.0050292871282-5.00502928712819
1101112.4475611595243-1.44756115952434
1111213.2874619311338-1.28746193113381
1121110.43338759451160.566612405488402
1131413.56188017036820.438119829631772
1141513.79155095241031.20844904758968
1151612.46425624816413.53574375183593
1161613.85715885755942.14284114244061
1171413.47253868159060.527461318409354
1181411.57781240487552.42218759512451
1191212.1312470417592-0.131247041759196
1201413.76766394317470.232336056825257
121810.5304581938565-2.53045819385646
1221313.2607723779701-0.260772377970143
1231613.73501341620732.26498658379273
1241211.34757695513870.652423044861346
1251614.67768794448481.32231205551522
1261213.0488526487426-1.04885264874258
1271111.3541241536474-0.354124153647380
12845.60295304595373-1.60295304595373
1291614.30933005805791.69066994194209
1301512.28932738783822.71067261216176
1311012.2847292294234-2.28472922942336
1321313.4944766507324-0.494476650732376
1331513.08897446979671.91102553020329
1341211.50295914816320.497040851836835
1351413.73501341620730.264986583792729
136712.8232153574206-5.82321535742063
1371914.26160507432034.73839492567975
1381213.2009720210178-1.20097202101782
1391213.0594333379714-1.05943333797145
1401313.4917784976778-0.491778497677845
1411514.88647672155990.113523278440050
14287.87364037177930.126359628220702
1431211.7745258087360.225474191264012
144109.83194267968630.168057320313702
145811.1793487189286-3.17934871892861
1461014.7358461125574-4.73584611255738
1471513.79481377315961.20518622684041
1481613.16056490575982.83943509424024
1491312.08053861076310.919461389236928
1501615.13268916663470.867310833365288
151911.4759627437854-2.47596274378538
1521412.89660140787051.10339859212953
1531412.83081852010421.16918147989584
1541212.8888448195799-0.888844819579876






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1222955286431410.2445910572862820.877704471356859
90.04803625668720070.09607251337440130.9519637433128
100.03972195243210370.07944390486420750.960278047567896
110.04100068551002720.08200137102005450.958999314489973
120.01827497683168800.03654995366337590.981725023168312
130.05188765613737710.1037753122747540.948112343862623
140.3502654606478520.7005309212957050.649734539352148
150.2626358885029770.5252717770059530.737364111497023
160.2115993976038590.4231987952077190.78840060239614
170.1594512529416210.3189025058832420.840548747058379
180.1118878941980790.2237757883961590.88811210580192
190.07600209046241920.1520041809248380.92399790953758
200.1331141018851910.2662282037703820.866885898114809
210.1061308650030530.2122617300061060.893869134996947
220.1036227664711580.2072455329423150.896377233528842
230.07367584897678360.1473516979535670.926324151023216
240.09411726630800950.1882345326160190.90588273369199
250.08246749828517360.1649349965703470.917532501714826
260.06018334695157530.1203666939031510.939816653048425
270.2029522115245170.4059044230490330.797047788475483
280.1583890110648930.3167780221297860.841610988935107
290.1517019623579050.3034039247158110.848298037642095
300.2781959837678210.5563919675356420.721804016232179
310.3985351057502480.7970702115004950.601464894249752
320.3429338308527110.6858676617054220.657066169147289
330.3047043329122590.6094086658245190.695295667087741
340.2867982079210880.5735964158421750.713201792078912
350.2874326315730070.5748652631460130.712567368426993
360.2752414562588550.550482912517710.724758543741145
370.2544290809147620.5088581618295250.745570919085238
380.2194606180964590.4389212361929180.780539381903541
390.1809771303274860.3619542606549730.819022869672514
400.1531098925533830.3062197851067670.846890107446617
410.13265579166510.26531158333020.8673442083349
420.2929637128758340.5859274257516680.707036287124166
430.58171293697530.8365741260493990.418287063024699
440.6076526398657220.7846947202685550.392347360134277
450.5616140155475390.8767719689049220.438385984452461
460.598660721819780.802678556360440.40133927818022
470.6383784900827850.723243019834430.361621509917215
480.6126480126625660.7747039746748680.387351987337434
490.5882472363762750.8235055272474510.411752763623725
500.5573816503149670.8852366993700660.442618349685033
510.5144870827547720.9710258344904560.485512917245228
520.9106590957835420.1786818084329160.089340904216458
530.893812686544610.2123746269107800.106187313455390
540.9151865644208920.1696268711582150.0848134355791076
550.9051145431434510.1897709137130990.0948854568565493
560.8841961551009950.2316076897980110.115803844899005
570.8618078586140690.2763842827718630.138192141385931
580.8361924669967150.3276150660065690.163807533003285
590.8990653379804230.2018693240391530.100934662019577
600.8777499443408060.2445001113183880.122250055659194
610.9022367797606570.1955264404786860.0977632202393432
620.88920697633040.2215860473392020.110793023669601
630.8693286532284960.2613426935430090.130671346771504
640.8772163060910280.2455673878179440.122783693908972
650.880331249291420.2393375014171590.119668750708580
660.8663534366049290.2672931267901420.133646563395071
670.8616277619467890.2767444761064220.138372238053211
680.9120616306164190.1758767387671620.0879383693835812
690.9168563868364030.1662872263271940.083143613163597
700.8998487630802670.2003024738394660.100151236919733
710.9006848492280960.1986303015438080.0993151507719041
720.8786985534764710.2426028930470570.121301446523529
730.8650236760894670.2699526478210660.134976323910533
740.9090266526531550.181946694693690.0909733473468451
750.8916866593235970.2166266813528060.108313340676403
760.8772503424671220.2454993150657560.122749657532878
770.8547807671876850.2904384656246300.145219232812315
780.8289334137108880.3421331725782230.171066586289112
790.8392689513483240.3214620973033520.160731048651676
800.8803873144759780.2392253710480440.119612685524022
810.8597702957631910.2804594084736170.140229704236809
820.8597787317144190.2804425365711620.140221268285581
830.8316036097465860.3367927805068290.168396390253414
840.8136249852597140.3727500294805710.186375014740286
850.8787175060080670.2425649879838660.121282493991933
860.8587075354971890.2825849290056230.141292464502811
870.8329948596328230.3340102807343540.167005140367177
880.8054808128876280.3890383742247440.194519187112372
890.7996267275962450.4007465448075090.200373272403755
900.7666700296236220.4666599407527560.233329970376378
910.7367104198895390.5265791602209220.263289580110461
920.8043168187606530.3913663624786940.195683181239347
930.773837813558490.452324372883020.22616218644151
940.7489349409603450.5021301180793110.251065059039655
950.7131598524687030.5736802950625940.286840147531297
960.6842064018154910.6315871963690180.315793598184509
970.6444614223316750.711077155336650.355538577668325
980.6125259577425740.7749480845148530.387474042257426
990.6569059998155740.6861880003688530.343094000184426
1000.6204711757955130.7590576484089730.379528824204486
1010.5794534535223510.8410930929552970.420546546477649
1020.570288775939930.859422448120140.42971122406007
1030.5596173406829540.8807653186340930.440382659317046
1040.5905257540632680.8189484918734650.409474245936732
1050.5502121612281720.8995756775436560.449787838771828
1060.5733347057328570.8533305885342850.426665294267143
1070.5274531809665740.9450936380668530.472546819033426
1080.6544628492677410.6910743014645170.345537150732259
1090.7858802080437710.4282395839124570.214119791956229
1100.7809245430413820.4381509139172360.219075456958618
1110.7608572257650210.4782855484699580.239142774234979
1120.7207926998617370.5584146002765270.279207300138263
1130.6735971970008130.6528056059983750.326402802999187
1140.6312833106412120.7374333787175750.368716689358788
1150.6528300805657790.6943398388684420.347169919434221
1160.6569388953960430.6861222092079130.343061104603957
1170.6042889618114930.7914220763770140.395711038188507
1180.6217306969970760.7565386060058480.378269303002924
1190.5649586586614690.8700826826770630.435041341338531
1200.5151037707916670.9697924584166660.484896229208333
1210.5593397276884920.8813205446230160.440660272311508
1220.5079946048611840.9840107902776330.492005395138816
1230.5078159879716290.9843680240567420.492184012028371
1240.5023943503686670.9952112992626670.497605649631333
1250.4513425245161460.9026850490322910.548657475483854
1260.4009057169021070.8018114338042140.599094283097893
1270.3813185691208240.7626371382416480.618681430879176
1280.3279626144770130.6559252289540250.672037385522987
1290.2768126649133210.5536253298266420.723187335086679
1300.3256686404414610.6513372808829230.674331359558539
1310.3248660108708840.6497320217417680.675133989129116
1320.2664261518804350.5328523037608690.733573848119565
1330.2414851905437750.482970381087550.758514809456225
1340.2008946921049400.4017893842098810.79910530789506
1350.1516823381698980.3033646763397970.848317661830101
1360.3043184966160070.6086369932320130.695681503383993
1370.6181237409076240.7637525181847520.381876259092376
1380.5556180266577750.888763946684450.444381973342225
1390.6229287166641130.7541425666717740.377071283335887
1400.5901920641554110.8196158716891790.409807935844589
1410.5557013251985050.888597349602990.444298674801495
1420.5121789754842870.9756420490314260.487821024515713
1430.3947529864469040.7895059728938070.605247013553096
1440.4616181247161590.9232362494323190.538381875283841
1450.3887497141774490.7774994283548980.611250285822551
1460.7589015246878660.4821969506242690.241098475312134

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.122295528643141 & 0.244591057286282 & 0.877704471356859 \tabularnewline
9 & 0.0480362566872007 & 0.0960725133744013 & 0.9519637433128 \tabularnewline
10 & 0.0397219524321037 & 0.0794439048642075 & 0.960278047567896 \tabularnewline
11 & 0.0410006855100272 & 0.0820013710200545 & 0.958999314489973 \tabularnewline
12 & 0.0182749768316880 & 0.0365499536633759 & 0.981725023168312 \tabularnewline
13 & 0.0518876561373771 & 0.103775312274754 & 0.948112343862623 \tabularnewline
14 & 0.350265460647852 & 0.700530921295705 & 0.649734539352148 \tabularnewline
15 & 0.262635888502977 & 0.525271777005953 & 0.737364111497023 \tabularnewline
16 & 0.211599397603859 & 0.423198795207719 & 0.78840060239614 \tabularnewline
17 & 0.159451252941621 & 0.318902505883242 & 0.840548747058379 \tabularnewline
18 & 0.111887894198079 & 0.223775788396159 & 0.88811210580192 \tabularnewline
19 & 0.0760020904624192 & 0.152004180924838 & 0.92399790953758 \tabularnewline
20 & 0.133114101885191 & 0.266228203770382 & 0.866885898114809 \tabularnewline
21 & 0.106130865003053 & 0.212261730006106 & 0.893869134996947 \tabularnewline
22 & 0.103622766471158 & 0.207245532942315 & 0.896377233528842 \tabularnewline
23 & 0.0736758489767836 & 0.147351697953567 & 0.926324151023216 \tabularnewline
24 & 0.0941172663080095 & 0.188234532616019 & 0.90588273369199 \tabularnewline
25 & 0.0824674982851736 & 0.164934996570347 & 0.917532501714826 \tabularnewline
26 & 0.0601833469515753 & 0.120366693903151 & 0.939816653048425 \tabularnewline
27 & 0.202952211524517 & 0.405904423049033 & 0.797047788475483 \tabularnewline
28 & 0.158389011064893 & 0.316778022129786 & 0.841610988935107 \tabularnewline
29 & 0.151701962357905 & 0.303403924715811 & 0.848298037642095 \tabularnewline
30 & 0.278195983767821 & 0.556391967535642 & 0.721804016232179 \tabularnewline
31 & 0.398535105750248 & 0.797070211500495 & 0.601464894249752 \tabularnewline
32 & 0.342933830852711 & 0.685867661705422 & 0.657066169147289 \tabularnewline
33 & 0.304704332912259 & 0.609408665824519 & 0.695295667087741 \tabularnewline
34 & 0.286798207921088 & 0.573596415842175 & 0.713201792078912 \tabularnewline
35 & 0.287432631573007 & 0.574865263146013 & 0.712567368426993 \tabularnewline
36 & 0.275241456258855 & 0.55048291251771 & 0.724758543741145 \tabularnewline
37 & 0.254429080914762 & 0.508858161829525 & 0.745570919085238 \tabularnewline
38 & 0.219460618096459 & 0.438921236192918 & 0.780539381903541 \tabularnewline
39 & 0.180977130327486 & 0.361954260654973 & 0.819022869672514 \tabularnewline
40 & 0.153109892553383 & 0.306219785106767 & 0.846890107446617 \tabularnewline
41 & 0.1326557916651 & 0.2653115833302 & 0.8673442083349 \tabularnewline
42 & 0.292963712875834 & 0.585927425751668 & 0.707036287124166 \tabularnewline
43 & 0.5817129369753 & 0.836574126049399 & 0.418287063024699 \tabularnewline
44 & 0.607652639865722 & 0.784694720268555 & 0.392347360134277 \tabularnewline
45 & 0.561614015547539 & 0.876771968904922 & 0.438385984452461 \tabularnewline
46 & 0.59866072181978 & 0.80267855636044 & 0.40133927818022 \tabularnewline
47 & 0.638378490082785 & 0.72324301983443 & 0.361621509917215 \tabularnewline
48 & 0.612648012662566 & 0.774703974674868 & 0.387351987337434 \tabularnewline
49 & 0.588247236376275 & 0.823505527247451 & 0.411752763623725 \tabularnewline
50 & 0.557381650314967 & 0.885236699370066 & 0.442618349685033 \tabularnewline
51 & 0.514487082754772 & 0.971025834490456 & 0.485512917245228 \tabularnewline
52 & 0.910659095783542 & 0.178681808432916 & 0.089340904216458 \tabularnewline
53 & 0.89381268654461 & 0.212374626910780 & 0.106187313455390 \tabularnewline
54 & 0.915186564420892 & 0.169626871158215 & 0.0848134355791076 \tabularnewline
55 & 0.905114543143451 & 0.189770913713099 & 0.0948854568565493 \tabularnewline
56 & 0.884196155100995 & 0.231607689798011 & 0.115803844899005 \tabularnewline
57 & 0.861807858614069 & 0.276384282771863 & 0.138192141385931 \tabularnewline
58 & 0.836192466996715 & 0.327615066006569 & 0.163807533003285 \tabularnewline
59 & 0.899065337980423 & 0.201869324039153 & 0.100934662019577 \tabularnewline
60 & 0.877749944340806 & 0.244500111318388 & 0.122250055659194 \tabularnewline
61 & 0.902236779760657 & 0.195526440478686 & 0.0977632202393432 \tabularnewline
62 & 0.8892069763304 & 0.221586047339202 & 0.110793023669601 \tabularnewline
63 & 0.869328653228496 & 0.261342693543009 & 0.130671346771504 \tabularnewline
64 & 0.877216306091028 & 0.245567387817944 & 0.122783693908972 \tabularnewline
65 & 0.88033124929142 & 0.239337501417159 & 0.119668750708580 \tabularnewline
66 & 0.866353436604929 & 0.267293126790142 & 0.133646563395071 \tabularnewline
67 & 0.861627761946789 & 0.276744476106422 & 0.138372238053211 \tabularnewline
68 & 0.912061630616419 & 0.175876738767162 & 0.0879383693835812 \tabularnewline
69 & 0.916856386836403 & 0.166287226327194 & 0.083143613163597 \tabularnewline
70 & 0.899848763080267 & 0.200302473839466 & 0.100151236919733 \tabularnewline
71 & 0.900684849228096 & 0.198630301543808 & 0.0993151507719041 \tabularnewline
72 & 0.878698553476471 & 0.242602893047057 & 0.121301446523529 \tabularnewline
73 & 0.865023676089467 & 0.269952647821066 & 0.134976323910533 \tabularnewline
74 & 0.909026652653155 & 0.18194669469369 & 0.0909733473468451 \tabularnewline
75 & 0.891686659323597 & 0.216626681352806 & 0.108313340676403 \tabularnewline
76 & 0.877250342467122 & 0.245499315065756 & 0.122749657532878 \tabularnewline
77 & 0.854780767187685 & 0.290438465624630 & 0.145219232812315 \tabularnewline
78 & 0.828933413710888 & 0.342133172578223 & 0.171066586289112 \tabularnewline
79 & 0.839268951348324 & 0.321462097303352 & 0.160731048651676 \tabularnewline
80 & 0.880387314475978 & 0.239225371048044 & 0.119612685524022 \tabularnewline
81 & 0.859770295763191 & 0.280459408473617 & 0.140229704236809 \tabularnewline
82 & 0.859778731714419 & 0.280442536571162 & 0.140221268285581 \tabularnewline
83 & 0.831603609746586 & 0.336792780506829 & 0.168396390253414 \tabularnewline
84 & 0.813624985259714 & 0.372750029480571 & 0.186375014740286 \tabularnewline
85 & 0.878717506008067 & 0.242564987983866 & 0.121282493991933 \tabularnewline
86 & 0.858707535497189 & 0.282584929005623 & 0.141292464502811 \tabularnewline
87 & 0.832994859632823 & 0.334010280734354 & 0.167005140367177 \tabularnewline
88 & 0.805480812887628 & 0.389038374224744 & 0.194519187112372 \tabularnewline
89 & 0.799626727596245 & 0.400746544807509 & 0.200373272403755 \tabularnewline
90 & 0.766670029623622 & 0.466659940752756 & 0.233329970376378 \tabularnewline
91 & 0.736710419889539 & 0.526579160220922 & 0.263289580110461 \tabularnewline
92 & 0.804316818760653 & 0.391366362478694 & 0.195683181239347 \tabularnewline
93 & 0.77383781355849 & 0.45232437288302 & 0.22616218644151 \tabularnewline
94 & 0.748934940960345 & 0.502130118079311 & 0.251065059039655 \tabularnewline
95 & 0.713159852468703 & 0.573680295062594 & 0.286840147531297 \tabularnewline
96 & 0.684206401815491 & 0.631587196369018 & 0.315793598184509 \tabularnewline
97 & 0.644461422331675 & 0.71107715533665 & 0.355538577668325 \tabularnewline
98 & 0.612525957742574 & 0.774948084514853 & 0.387474042257426 \tabularnewline
99 & 0.656905999815574 & 0.686188000368853 & 0.343094000184426 \tabularnewline
100 & 0.620471175795513 & 0.759057648408973 & 0.379528824204486 \tabularnewline
101 & 0.579453453522351 & 0.841093092955297 & 0.420546546477649 \tabularnewline
102 & 0.57028877593993 & 0.85942244812014 & 0.42971122406007 \tabularnewline
103 & 0.559617340682954 & 0.880765318634093 & 0.440382659317046 \tabularnewline
104 & 0.590525754063268 & 0.818948491873465 & 0.409474245936732 \tabularnewline
105 & 0.550212161228172 & 0.899575677543656 & 0.449787838771828 \tabularnewline
106 & 0.573334705732857 & 0.853330588534285 & 0.426665294267143 \tabularnewline
107 & 0.527453180966574 & 0.945093638066853 & 0.472546819033426 \tabularnewline
108 & 0.654462849267741 & 0.691074301464517 & 0.345537150732259 \tabularnewline
109 & 0.785880208043771 & 0.428239583912457 & 0.214119791956229 \tabularnewline
110 & 0.780924543041382 & 0.438150913917236 & 0.219075456958618 \tabularnewline
111 & 0.760857225765021 & 0.478285548469958 & 0.239142774234979 \tabularnewline
112 & 0.720792699861737 & 0.558414600276527 & 0.279207300138263 \tabularnewline
113 & 0.673597197000813 & 0.652805605998375 & 0.326402802999187 \tabularnewline
114 & 0.631283310641212 & 0.737433378717575 & 0.368716689358788 \tabularnewline
115 & 0.652830080565779 & 0.694339838868442 & 0.347169919434221 \tabularnewline
116 & 0.656938895396043 & 0.686122209207913 & 0.343061104603957 \tabularnewline
117 & 0.604288961811493 & 0.791422076377014 & 0.395711038188507 \tabularnewline
118 & 0.621730696997076 & 0.756538606005848 & 0.378269303002924 \tabularnewline
119 & 0.564958658661469 & 0.870082682677063 & 0.435041341338531 \tabularnewline
120 & 0.515103770791667 & 0.969792458416666 & 0.484896229208333 \tabularnewline
121 & 0.559339727688492 & 0.881320544623016 & 0.440660272311508 \tabularnewline
122 & 0.507994604861184 & 0.984010790277633 & 0.492005395138816 \tabularnewline
123 & 0.507815987971629 & 0.984368024056742 & 0.492184012028371 \tabularnewline
124 & 0.502394350368667 & 0.995211299262667 & 0.497605649631333 \tabularnewline
125 & 0.451342524516146 & 0.902685049032291 & 0.548657475483854 \tabularnewline
126 & 0.400905716902107 & 0.801811433804214 & 0.599094283097893 \tabularnewline
127 & 0.381318569120824 & 0.762637138241648 & 0.618681430879176 \tabularnewline
128 & 0.327962614477013 & 0.655925228954025 & 0.672037385522987 \tabularnewline
129 & 0.276812664913321 & 0.553625329826642 & 0.723187335086679 \tabularnewline
130 & 0.325668640441461 & 0.651337280882923 & 0.674331359558539 \tabularnewline
131 & 0.324866010870884 & 0.649732021741768 & 0.675133989129116 \tabularnewline
132 & 0.266426151880435 & 0.532852303760869 & 0.733573848119565 \tabularnewline
133 & 0.241485190543775 & 0.48297038108755 & 0.758514809456225 \tabularnewline
134 & 0.200894692104940 & 0.401789384209881 & 0.79910530789506 \tabularnewline
135 & 0.151682338169898 & 0.303364676339797 & 0.848317661830101 \tabularnewline
136 & 0.304318496616007 & 0.608636993232013 & 0.695681503383993 \tabularnewline
137 & 0.618123740907624 & 0.763752518184752 & 0.381876259092376 \tabularnewline
138 & 0.555618026657775 & 0.88876394668445 & 0.444381973342225 \tabularnewline
139 & 0.622928716664113 & 0.754142566671774 & 0.377071283335887 \tabularnewline
140 & 0.590192064155411 & 0.819615871689179 & 0.409807935844589 \tabularnewline
141 & 0.555701325198505 & 0.88859734960299 & 0.444298674801495 \tabularnewline
142 & 0.512178975484287 & 0.975642049031426 & 0.487821024515713 \tabularnewline
143 & 0.394752986446904 & 0.789505972893807 & 0.605247013553096 \tabularnewline
144 & 0.461618124716159 & 0.923236249432319 & 0.538381875283841 \tabularnewline
145 & 0.388749714177449 & 0.777499428354898 & 0.611250285822551 \tabularnewline
146 & 0.758901524687866 & 0.482196950624269 & 0.241098475312134 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108365&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]8[/C][C]0.122295528643141[/C][C]0.244591057286282[/C][C]0.877704471356859[/C][/ROW]
[ROW][C]9[/C][C]0.0480362566872007[/C][C]0.0960725133744013[/C][C]0.9519637433128[/C][/ROW]
[ROW][C]10[/C][C]0.0397219524321037[/C][C]0.0794439048642075[/C][C]0.960278047567896[/C][/ROW]
[ROW][C]11[/C][C]0.0410006855100272[/C][C]0.0820013710200545[/C][C]0.958999314489973[/C][/ROW]
[ROW][C]12[/C][C]0.0182749768316880[/C][C]0.0365499536633759[/C][C]0.981725023168312[/C][/ROW]
[ROW][C]13[/C][C]0.0518876561373771[/C][C]0.103775312274754[/C][C]0.948112343862623[/C][/ROW]
[ROW][C]14[/C][C]0.350265460647852[/C][C]0.700530921295705[/C][C]0.649734539352148[/C][/ROW]
[ROW][C]15[/C][C]0.262635888502977[/C][C]0.525271777005953[/C][C]0.737364111497023[/C][/ROW]
[ROW][C]16[/C][C]0.211599397603859[/C][C]0.423198795207719[/C][C]0.78840060239614[/C][/ROW]
[ROW][C]17[/C][C]0.159451252941621[/C][C]0.318902505883242[/C][C]0.840548747058379[/C][/ROW]
[ROW][C]18[/C][C]0.111887894198079[/C][C]0.223775788396159[/C][C]0.88811210580192[/C][/ROW]
[ROW][C]19[/C][C]0.0760020904624192[/C][C]0.152004180924838[/C][C]0.92399790953758[/C][/ROW]
[ROW][C]20[/C][C]0.133114101885191[/C][C]0.266228203770382[/C][C]0.866885898114809[/C][/ROW]
[ROW][C]21[/C][C]0.106130865003053[/C][C]0.212261730006106[/C][C]0.893869134996947[/C][/ROW]
[ROW][C]22[/C][C]0.103622766471158[/C][C]0.207245532942315[/C][C]0.896377233528842[/C][/ROW]
[ROW][C]23[/C][C]0.0736758489767836[/C][C]0.147351697953567[/C][C]0.926324151023216[/C][/ROW]
[ROW][C]24[/C][C]0.0941172663080095[/C][C]0.188234532616019[/C][C]0.90588273369199[/C][/ROW]
[ROW][C]25[/C][C]0.0824674982851736[/C][C]0.164934996570347[/C][C]0.917532501714826[/C][/ROW]
[ROW][C]26[/C][C]0.0601833469515753[/C][C]0.120366693903151[/C][C]0.939816653048425[/C][/ROW]
[ROW][C]27[/C][C]0.202952211524517[/C][C]0.405904423049033[/C][C]0.797047788475483[/C][/ROW]
[ROW][C]28[/C][C]0.158389011064893[/C][C]0.316778022129786[/C][C]0.841610988935107[/C][/ROW]
[ROW][C]29[/C][C]0.151701962357905[/C][C]0.303403924715811[/C][C]0.848298037642095[/C][/ROW]
[ROW][C]30[/C][C]0.278195983767821[/C][C]0.556391967535642[/C][C]0.721804016232179[/C][/ROW]
[ROW][C]31[/C][C]0.398535105750248[/C][C]0.797070211500495[/C][C]0.601464894249752[/C][/ROW]
[ROW][C]32[/C][C]0.342933830852711[/C][C]0.685867661705422[/C][C]0.657066169147289[/C][/ROW]
[ROW][C]33[/C][C]0.304704332912259[/C][C]0.609408665824519[/C][C]0.695295667087741[/C][/ROW]
[ROW][C]34[/C][C]0.286798207921088[/C][C]0.573596415842175[/C][C]0.713201792078912[/C][/ROW]
[ROW][C]35[/C][C]0.287432631573007[/C][C]0.574865263146013[/C][C]0.712567368426993[/C][/ROW]
[ROW][C]36[/C][C]0.275241456258855[/C][C]0.55048291251771[/C][C]0.724758543741145[/C][/ROW]
[ROW][C]37[/C][C]0.254429080914762[/C][C]0.508858161829525[/C][C]0.745570919085238[/C][/ROW]
[ROW][C]38[/C][C]0.219460618096459[/C][C]0.438921236192918[/C][C]0.780539381903541[/C][/ROW]
[ROW][C]39[/C][C]0.180977130327486[/C][C]0.361954260654973[/C][C]0.819022869672514[/C][/ROW]
[ROW][C]40[/C][C]0.153109892553383[/C][C]0.306219785106767[/C][C]0.846890107446617[/C][/ROW]
[ROW][C]41[/C][C]0.1326557916651[/C][C]0.2653115833302[/C][C]0.8673442083349[/C][/ROW]
[ROW][C]42[/C][C]0.292963712875834[/C][C]0.585927425751668[/C][C]0.707036287124166[/C][/ROW]
[ROW][C]43[/C][C]0.5817129369753[/C][C]0.836574126049399[/C][C]0.418287063024699[/C][/ROW]
[ROW][C]44[/C][C]0.607652639865722[/C][C]0.784694720268555[/C][C]0.392347360134277[/C][/ROW]
[ROW][C]45[/C][C]0.561614015547539[/C][C]0.876771968904922[/C][C]0.438385984452461[/C][/ROW]
[ROW][C]46[/C][C]0.59866072181978[/C][C]0.80267855636044[/C][C]0.40133927818022[/C][/ROW]
[ROW][C]47[/C][C]0.638378490082785[/C][C]0.72324301983443[/C][C]0.361621509917215[/C][/ROW]
[ROW][C]48[/C][C]0.612648012662566[/C][C]0.774703974674868[/C][C]0.387351987337434[/C][/ROW]
[ROW][C]49[/C][C]0.588247236376275[/C][C]0.823505527247451[/C][C]0.411752763623725[/C][/ROW]
[ROW][C]50[/C][C]0.557381650314967[/C][C]0.885236699370066[/C][C]0.442618349685033[/C][/ROW]
[ROW][C]51[/C][C]0.514487082754772[/C][C]0.971025834490456[/C][C]0.485512917245228[/C][/ROW]
[ROW][C]52[/C][C]0.910659095783542[/C][C]0.178681808432916[/C][C]0.089340904216458[/C][/ROW]
[ROW][C]53[/C][C]0.89381268654461[/C][C]0.212374626910780[/C][C]0.106187313455390[/C][/ROW]
[ROW][C]54[/C][C]0.915186564420892[/C][C]0.169626871158215[/C][C]0.0848134355791076[/C][/ROW]
[ROW][C]55[/C][C]0.905114543143451[/C][C]0.189770913713099[/C][C]0.0948854568565493[/C][/ROW]
[ROW][C]56[/C][C]0.884196155100995[/C][C]0.231607689798011[/C][C]0.115803844899005[/C][/ROW]
[ROW][C]57[/C][C]0.861807858614069[/C][C]0.276384282771863[/C][C]0.138192141385931[/C][/ROW]
[ROW][C]58[/C][C]0.836192466996715[/C][C]0.327615066006569[/C][C]0.163807533003285[/C][/ROW]
[ROW][C]59[/C][C]0.899065337980423[/C][C]0.201869324039153[/C][C]0.100934662019577[/C][/ROW]
[ROW][C]60[/C][C]0.877749944340806[/C][C]0.244500111318388[/C][C]0.122250055659194[/C][/ROW]
[ROW][C]61[/C][C]0.902236779760657[/C][C]0.195526440478686[/C][C]0.0977632202393432[/C][/ROW]
[ROW][C]62[/C][C]0.8892069763304[/C][C]0.221586047339202[/C][C]0.110793023669601[/C][/ROW]
[ROW][C]63[/C][C]0.869328653228496[/C][C]0.261342693543009[/C][C]0.130671346771504[/C][/ROW]
[ROW][C]64[/C][C]0.877216306091028[/C][C]0.245567387817944[/C][C]0.122783693908972[/C][/ROW]
[ROW][C]65[/C][C]0.88033124929142[/C][C]0.239337501417159[/C][C]0.119668750708580[/C][/ROW]
[ROW][C]66[/C][C]0.866353436604929[/C][C]0.267293126790142[/C][C]0.133646563395071[/C][/ROW]
[ROW][C]67[/C][C]0.861627761946789[/C][C]0.276744476106422[/C][C]0.138372238053211[/C][/ROW]
[ROW][C]68[/C][C]0.912061630616419[/C][C]0.175876738767162[/C][C]0.0879383693835812[/C][/ROW]
[ROW][C]69[/C][C]0.916856386836403[/C][C]0.166287226327194[/C][C]0.083143613163597[/C][/ROW]
[ROW][C]70[/C][C]0.899848763080267[/C][C]0.200302473839466[/C][C]0.100151236919733[/C][/ROW]
[ROW][C]71[/C][C]0.900684849228096[/C][C]0.198630301543808[/C][C]0.0993151507719041[/C][/ROW]
[ROW][C]72[/C][C]0.878698553476471[/C][C]0.242602893047057[/C][C]0.121301446523529[/C][/ROW]
[ROW][C]73[/C][C]0.865023676089467[/C][C]0.269952647821066[/C][C]0.134976323910533[/C][/ROW]
[ROW][C]74[/C][C]0.909026652653155[/C][C]0.18194669469369[/C][C]0.0909733473468451[/C][/ROW]
[ROW][C]75[/C][C]0.891686659323597[/C][C]0.216626681352806[/C][C]0.108313340676403[/C][/ROW]
[ROW][C]76[/C][C]0.877250342467122[/C][C]0.245499315065756[/C][C]0.122749657532878[/C][/ROW]
[ROW][C]77[/C][C]0.854780767187685[/C][C]0.290438465624630[/C][C]0.145219232812315[/C][/ROW]
[ROW][C]78[/C][C]0.828933413710888[/C][C]0.342133172578223[/C][C]0.171066586289112[/C][/ROW]
[ROW][C]79[/C][C]0.839268951348324[/C][C]0.321462097303352[/C][C]0.160731048651676[/C][/ROW]
[ROW][C]80[/C][C]0.880387314475978[/C][C]0.239225371048044[/C][C]0.119612685524022[/C][/ROW]
[ROW][C]81[/C][C]0.859770295763191[/C][C]0.280459408473617[/C][C]0.140229704236809[/C][/ROW]
[ROW][C]82[/C][C]0.859778731714419[/C][C]0.280442536571162[/C][C]0.140221268285581[/C][/ROW]
[ROW][C]83[/C][C]0.831603609746586[/C][C]0.336792780506829[/C][C]0.168396390253414[/C][/ROW]
[ROW][C]84[/C][C]0.813624985259714[/C][C]0.372750029480571[/C][C]0.186375014740286[/C][/ROW]
[ROW][C]85[/C][C]0.878717506008067[/C][C]0.242564987983866[/C][C]0.121282493991933[/C][/ROW]
[ROW][C]86[/C][C]0.858707535497189[/C][C]0.282584929005623[/C][C]0.141292464502811[/C][/ROW]
[ROW][C]87[/C][C]0.832994859632823[/C][C]0.334010280734354[/C][C]0.167005140367177[/C][/ROW]
[ROW][C]88[/C][C]0.805480812887628[/C][C]0.389038374224744[/C][C]0.194519187112372[/C][/ROW]
[ROW][C]89[/C][C]0.799626727596245[/C][C]0.400746544807509[/C][C]0.200373272403755[/C][/ROW]
[ROW][C]90[/C][C]0.766670029623622[/C][C]0.466659940752756[/C][C]0.233329970376378[/C][/ROW]
[ROW][C]91[/C][C]0.736710419889539[/C][C]0.526579160220922[/C][C]0.263289580110461[/C][/ROW]
[ROW][C]92[/C][C]0.804316818760653[/C][C]0.391366362478694[/C][C]0.195683181239347[/C][/ROW]
[ROW][C]93[/C][C]0.77383781355849[/C][C]0.45232437288302[/C][C]0.22616218644151[/C][/ROW]
[ROW][C]94[/C][C]0.748934940960345[/C][C]0.502130118079311[/C][C]0.251065059039655[/C][/ROW]
[ROW][C]95[/C][C]0.713159852468703[/C][C]0.573680295062594[/C][C]0.286840147531297[/C][/ROW]
[ROW][C]96[/C][C]0.684206401815491[/C][C]0.631587196369018[/C][C]0.315793598184509[/C][/ROW]
[ROW][C]97[/C][C]0.644461422331675[/C][C]0.71107715533665[/C][C]0.355538577668325[/C][/ROW]
[ROW][C]98[/C][C]0.612525957742574[/C][C]0.774948084514853[/C][C]0.387474042257426[/C][/ROW]
[ROW][C]99[/C][C]0.656905999815574[/C][C]0.686188000368853[/C][C]0.343094000184426[/C][/ROW]
[ROW][C]100[/C][C]0.620471175795513[/C][C]0.759057648408973[/C][C]0.379528824204486[/C][/ROW]
[ROW][C]101[/C][C]0.579453453522351[/C][C]0.841093092955297[/C][C]0.420546546477649[/C][/ROW]
[ROW][C]102[/C][C]0.57028877593993[/C][C]0.85942244812014[/C][C]0.42971122406007[/C][/ROW]
[ROW][C]103[/C][C]0.559617340682954[/C][C]0.880765318634093[/C][C]0.440382659317046[/C][/ROW]
[ROW][C]104[/C][C]0.590525754063268[/C][C]0.818948491873465[/C][C]0.409474245936732[/C][/ROW]
[ROW][C]105[/C][C]0.550212161228172[/C][C]0.899575677543656[/C][C]0.449787838771828[/C][/ROW]
[ROW][C]106[/C][C]0.573334705732857[/C][C]0.853330588534285[/C][C]0.426665294267143[/C][/ROW]
[ROW][C]107[/C][C]0.527453180966574[/C][C]0.945093638066853[/C][C]0.472546819033426[/C][/ROW]
[ROW][C]108[/C][C]0.654462849267741[/C][C]0.691074301464517[/C][C]0.345537150732259[/C][/ROW]
[ROW][C]109[/C][C]0.785880208043771[/C][C]0.428239583912457[/C][C]0.214119791956229[/C][/ROW]
[ROW][C]110[/C][C]0.780924543041382[/C][C]0.438150913917236[/C][C]0.219075456958618[/C][/ROW]
[ROW][C]111[/C][C]0.760857225765021[/C][C]0.478285548469958[/C][C]0.239142774234979[/C][/ROW]
[ROW][C]112[/C][C]0.720792699861737[/C][C]0.558414600276527[/C][C]0.279207300138263[/C][/ROW]
[ROW][C]113[/C][C]0.673597197000813[/C][C]0.652805605998375[/C][C]0.326402802999187[/C][/ROW]
[ROW][C]114[/C][C]0.631283310641212[/C][C]0.737433378717575[/C][C]0.368716689358788[/C][/ROW]
[ROW][C]115[/C][C]0.652830080565779[/C][C]0.694339838868442[/C][C]0.347169919434221[/C][/ROW]
[ROW][C]116[/C][C]0.656938895396043[/C][C]0.686122209207913[/C][C]0.343061104603957[/C][/ROW]
[ROW][C]117[/C][C]0.604288961811493[/C][C]0.791422076377014[/C][C]0.395711038188507[/C][/ROW]
[ROW][C]118[/C][C]0.621730696997076[/C][C]0.756538606005848[/C][C]0.378269303002924[/C][/ROW]
[ROW][C]119[/C][C]0.564958658661469[/C][C]0.870082682677063[/C][C]0.435041341338531[/C][/ROW]
[ROW][C]120[/C][C]0.515103770791667[/C][C]0.969792458416666[/C][C]0.484896229208333[/C][/ROW]
[ROW][C]121[/C][C]0.559339727688492[/C][C]0.881320544623016[/C][C]0.440660272311508[/C][/ROW]
[ROW][C]122[/C][C]0.507994604861184[/C][C]0.984010790277633[/C][C]0.492005395138816[/C][/ROW]
[ROW][C]123[/C][C]0.507815987971629[/C][C]0.984368024056742[/C][C]0.492184012028371[/C][/ROW]
[ROW][C]124[/C][C]0.502394350368667[/C][C]0.995211299262667[/C][C]0.497605649631333[/C][/ROW]
[ROW][C]125[/C][C]0.451342524516146[/C][C]0.902685049032291[/C][C]0.548657475483854[/C][/ROW]
[ROW][C]126[/C][C]0.400905716902107[/C][C]0.801811433804214[/C][C]0.599094283097893[/C][/ROW]
[ROW][C]127[/C][C]0.381318569120824[/C][C]0.762637138241648[/C][C]0.618681430879176[/C][/ROW]
[ROW][C]128[/C][C]0.327962614477013[/C][C]0.655925228954025[/C][C]0.672037385522987[/C][/ROW]
[ROW][C]129[/C][C]0.276812664913321[/C][C]0.553625329826642[/C][C]0.723187335086679[/C][/ROW]
[ROW][C]130[/C][C]0.325668640441461[/C][C]0.651337280882923[/C][C]0.674331359558539[/C][/ROW]
[ROW][C]131[/C][C]0.324866010870884[/C][C]0.649732021741768[/C][C]0.675133989129116[/C][/ROW]
[ROW][C]132[/C][C]0.266426151880435[/C][C]0.532852303760869[/C][C]0.733573848119565[/C][/ROW]
[ROW][C]133[/C][C]0.241485190543775[/C][C]0.48297038108755[/C][C]0.758514809456225[/C][/ROW]
[ROW][C]134[/C][C]0.200894692104940[/C][C]0.401789384209881[/C][C]0.79910530789506[/C][/ROW]
[ROW][C]135[/C][C]0.151682338169898[/C][C]0.303364676339797[/C][C]0.848317661830101[/C][/ROW]
[ROW][C]136[/C][C]0.304318496616007[/C][C]0.608636993232013[/C][C]0.695681503383993[/C][/ROW]
[ROW][C]137[/C][C]0.618123740907624[/C][C]0.763752518184752[/C][C]0.381876259092376[/C][/ROW]
[ROW][C]138[/C][C]0.555618026657775[/C][C]0.88876394668445[/C][C]0.444381973342225[/C][/ROW]
[ROW][C]139[/C][C]0.622928716664113[/C][C]0.754142566671774[/C][C]0.377071283335887[/C][/ROW]
[ROW][C]140[/C][C]0.590192064155411[/C][C]0.819615871689179[/C][C]0.409807935844589[/C][/ROW]
[ROW][C]141[/C][C]0.555701325198505[/C][C]0.88859734960299[/C][C]0.444298674801495[/C][/ROW]
[ROW][C]142[/C][C]0.512178975484287[/C][C]0.975642049031426[/C][C]0.487821024515713[/C][/ROW]
[ROW][C]143[/C][C]0.394752986446904[/C][C]0.789505972893807[/C][C]0.605247013553096[/C][/ROW]
[ROW][C]144[/C][C]0.461618124716159[/C][C]0.923236249432319[/C][C]0.538381875283841[/C][/ROW]
[ROW][C]145[/C][C]0.388749714177449[/C][C]0.777499428354898[/C][C]0.611250285822551[/C][/ROW]
[ROW][C]146[/C][C]0.758901524687866[/C][C]0.482196950624269[/C][C]0.241098475312134[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108365&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108365&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1222955286431410.2445910572862820.877704471356859
90.04803625668720070.09607251337440130.9519637433128
100.03972195243210370.07944390486420750.960278047567896
110.04100068551002720.08200137102005450.958999314489973
120.01827497683168800.03654995366337590.981725023168312
130.05188765613737710.1037753122747540.948112343862623
140.3502654606478520.7005309212957050.649734539352148
150.2626358885029770.5252717770059530.737364111497023
160.2115993976038590.4231987952077190.78840060239614
170.1594512529416210.3189025058832420.840548747058379
180.1118878941980790.2237757883961590.88811210580192
190.07600209046241920.1520041809248380.92399790953758
200.1331141018851910.2662282037703820.866885898114809
210.1061308650030530.2122617300061060.893869134996947
220.1036227664711580.2072455329423150.896377233528842
230.07367584897678360.1473516979535670.926324151023216
240.09411726630800950.1882345326160190.90588273369199
250.08246749828517360.1649349965703470.917532501714826
260.06018334695157530.1203666939031510.939816653048425
270.2029522115245170.4059044230490330.797047788475483
280.1583890110648930.3167780221297860.841610988935107
290.1517019623579050.3034039247158110.848298037642095
300.2781959837678210.5563919675356420.721804016232179
310.3985351057502480.7970702115004950.601464894249752
320.3429338308527110.6858676617054220.657066169147289
330.3047043329122590.6094086658245190.695295667087741
340.2867982079210880.5735964158421750.713201792078912
350.2874326315730070.5748652631460130.712567368426993
360.2752414562588550.550482912517710.724758543741145
370.2544290809147620.5088581618295250.745570919085238
380.2194606180964590.4389212361929180.780539381903541
390.1809771303274860.3619542606549730.819022869672514
400.1531098925533830.3062197851067670.846890107446617
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420.2929637128758340.5859274257516680.707036287124166
430.58171293697530.8365741260493990.418287063024699
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450.5616140155475390.8767719689049220.438385984452461
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480.6126480126625660.7747039746748680.387351987337434
490.5882472363762750.8235055272474510.411752763623725
500.5573816503149670.8852366993700660.442618349685033
510.5144870827547720.9710258344904560.485512917245228
520.9106590957835420.1786818084329160.089340904216458
530.893812686544610.2123746269107800.106187313455390
540.9151865644208920.1696268711582150.0848134355791076
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560.8841961551009950.2316076897980110.115803844899005
570.8618078586140690.2763842827718630.138192141385931
580.8361924669967150.3276150660065690.163807533003285
590.8990653379804230.2018693240391530.100934662019577
600.8777499443408060.2445001113183880.122250055659194
610.9022367797606570.1955264404786860.0977632202393432
620.88920697633040.2215860473392020.110793023669601
630.8693286532284960.2613426935430090.130671346771504
640.8772163060910280.2455673878179440.122783693908972
650.880331249291420.2393375014171590.119668750708580
660.8663534366049290.2672931267901420.133646563395071
670.8616277619467890.2767444761064220.138372238053211
680.9120616306164190.1758767387671620.0879383693835812
690.9168563868364030.1662872263271940.083143613163597
700.8998487630802670.2003024738394660.100151236919733
710.9006848492280960.1986303015438080.0993151507719041
720.8786985534764710.2426028930470570.121301446523529
730.8650236760894670.2699526478210660.134976323910533
740.9090266526531550.181946694693690.0909733473468451
750.8916866593235970.2166266813528060.108313340676403
760.8772503424671220.2454993150657560.122749657532878
770.8547807671876850.2904384656246300.145219232812315
780.8289334137108880.3421331725782230.171066586289112
790.8392689513483240.3214620973033520.160731048651676
800.8803873144759780.2392253710480440.119612685524022
810.8597702957631910.2804594084736170.140229704236809
820.8597787317144190.2804425365711620.140221268285581
830.8316036097465860.3367927805068290.168396390253414
840.8136249852597140.3727500294805710.186375014740286
850.8787175060080670.2425649879838660.121282493991933
860.8587075354971890.2825849290056230.141292464502811
870.8329948596328230.3340102807343540.167005140367177
880.8054808128876280.3890383742247440.194519187112372
890.7996267275962450.4007465448075090.200373272403755
900.7666700296236220.4666599407527560.233329970376378
910.7367104198895390.5265791602209220.263289580110461
920.8043168187606530.3913663624786940.195683181239347
930.773837813558490.452324372883020.22616218644151
940.7489349409603450.5021301180793110.251065059039655
950.7131598524687030.5736802950625940.286840147531297
960.6842064018154910.6315871963690180.315793598184509
970.6444614223316750.711077155336650.355538577668325
980.6125259577425740.7749480845148530.387474042257426
990.6569059998155740.6861880003688530.343094000184426
1000.6204711757955130.7590576484089730.379528824204486
1010.5794534535223510.8410930929552970.420546546477649
1020.570288775939930.859422448120140.42971122406007
1030.5596173406829540.8807653186340930.440382659317046
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1050.5502121612281720.8995756775436560.449787838771828
1060.5733347057328570.8533305885342850.426665294267143
1070.5274531809665740.9450936380668530.472546819033426
1080.6544628492677410.6910743014645170.345537150732259
1090.7858802080437710.4282395839124570.214119791956229
1100.7809245430413820.4381509139172360.219075456958618
1110.7608572257650210.4782855484699580.239142774234979
1120.7207926998617370.5584146002765270.279207300138263
1130.6735971970008130.6528056059983750.326402802999187
1140.6312833106412120.7374333787175750.368716689358788
1150.6528300805657790.6943398388684420.347169919434221
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1200.5151037707916670.9697924584166660.484896229208333
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1460.7589015246878660.4821969506242690.241098475312134






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00719424460431655OK
10% type I error level40.0287769784172662OK

\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 & 1 & 0.00719424460431655 & OK \tabularnewline
10% type I error level & 4 & 0.0287769784172662 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108365&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]1[/C][C]0.00719424460431655[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0287769784172662[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108365&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108365&T=6

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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 level10.00719424460431655OK
10% type I error level40.0287769784172662OK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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