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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 computationTue, 22 Nov 2011 12:05:34 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321981590ho49jfu6mwd1gqd.htm/, Retrieved Fri, 26 Apr 2024 23:10:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146312, Retrieved Fri, 26 Apr 2024 23:10:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact72

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-22 17:05:34] [3b32143baae8ca4a077b118800e50af3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5,5	6	5,33	12
3,5	4	5,56	11
8,5	4	3,78	14
5	4	4,00	12
6	4,5	4,00	21
6	3,5	3,56	12
5,5	2	4,44	22
5,5	5,5	3,56	11
6	3,5	4,00	10
6,5	3,5	3,78	13
7	6	5,11	10
8	5	6,67	8
5,5	5	5,11	15
5	4	4,00	14
5,5	4	3,33	10
7,5	2	2,67	14
4,5	4,5	4,67	14
5,5	4	3,33	11
8,5	3,5	4,44	10
8,5	5,5	6,89	13
5,5	4,5	6,00	7
9	5,5	7,56	14
7	6,5	4,67	12
5	4	6,89	14
5,5	4	4,22	11
7,5	4,5	3,56	9
7,5	3	4,44	11
6,5	4,5	4,67	15
8	4,5	4,89	14
6,5	3	3,78	13
4,5	3	5,33	9
9	8	5,56	15
9	2,5	5,78	10
6	3,5	5,56	11
8,5	4,5	3,78	13
4,5	3	7,11	8
4,5	3	7,33	20
6	2,5	2,89	12
9	6	7,11	10
6	3,5	5,56	10
9	5	6,44	9
7	4,5	4,89	14
7,5	4	4,00	8
8	2,5	3,78	14
5	4	4,44	11
5,5	4	3,33	13
7	5	4,44	9
4,5	3	7,33	11
6	4	6,44	15
8,5	3,5	5,11	11
2,5	2	5,78	10
6	4	4,00	14
6	4	4,44	18
3	2	2,44	14
12	10	6,22	11
6	4	5,78	12
6	4	4,89	13
7	3	3,78	9
3,5	2	2,67	10
6,5	4	3,11	15
6	4,5	3,78	20
6,5	3	4,67	12
7	3,5	4,22	12
4	4,5	4,00	14
5,5	2,5	2,22	13
4,5	2,5	6,44	11
5,5	4	6,89	17
6,5	4	4,22	12
5	3	2,00	13
5,5	4	4,44	14
6	3,5	6,22	13
4,5	3,5	4,22	15
7,5	4,5	6,67	13
9	5,5	6,44	10
7,5	3	5,78	11
6	4	5,11	19
6,5	3	2,89	13
7	4,5	4,67	17
5	4	4,22	13
6,5	3	6,22	9
6,5	5	5,11	11
5,5	4	4,00	10
6,5	4	4,67	9
8	5	4,44	12
4	2,5	5,11	12
8	3,5	4,67	13
5,5	2,5	4,67	13
4,5	4	3,33	12
8	7	6,22	15
6	3,5	4,22	22
7	4	5,78	13
4	3	2,22	15
4,5	2,5	3,56	13
7,5	3	4,89	15
5,5	5	4,22	10
10,5	6	6,89	11
7	4,5	6,89	16
9	6	6,44	11
6	3,5	4,22	11
6,5	4	4,89	10
7,5	5	5,11	10
6	3	3,33	16
9,5	5	4,44	12
7,5	5	4,00	11
5,5	5	5,11	16
5,5	2,5	5,56	19
5	3,5	4,67	11
6,5	5	5,33	16
7,5	5,5	5,56	15
6	3	3,78	24
6	3,5	2,89	14
8	6	6,22	15
4,5	5,5	4,67	11
9	5,5	5,56	15
4	5,5	2,00	12
6,5	2,5	3,56	10
8,5	4	4,22	14
4,5	3	3,78	13
7,5	4,5	5,56	9
4	2	4,44	15
3,5	2	6,44	15
6	3,5	3,11	14
7	5,5	4,89	11
3	3	3,33	8
4	3,5	4,22	11
8,5	4	4,44	11
5	2	3,33	8
5,5	4	4,44	10
7	4,5	4,00	11
5,5	4	7,33	13
6,5	5,5	4,89	11
6	4	3,56	20
5,5	2,5	3,78	10
4,5	2	3,56	15
6	4	4,67	12
10	5	5,78	14
6	3	4,00	23
6,5	4,5	4,00	14
6	4,5	3,78	16
6	6,5	4,89	11
4,5	4,5	6,67	12
7,5	5	6,67	10
12	10	5,33	14
3,5	2,5	4,67	12
8,5	5,5	4,67	12
5,5	3	6,44	11
8,5	4,5	6,89	12
5,5	3,5	4,44	13
6	4,5	3,56	11
7	5	4,89	19
5,5	4,5	4,44	12
8	4	6,22	17
10,5	3,5	8,44	9
7	3	4,89	12
10	6,5	4,44	19
6,5	3	3,78	18
5,5	4	6,22	15
7,5	5	4,89	14
9,5	8	6,89	11

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146312&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146312&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146312&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 time8 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Intercept[t] = + 2.55290742861106 + 0.69169552328498Expect[t] + 0.216573604373535Critisism[t] -0.000708867555636805Concerns[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Intercept[t] =  +  2.55290742861106 +  0.69169552328498Expect[t] +  0.216573604373535Critisism[t] -0.000708867555636805Concerns[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146312&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Intercept[t] =  +  2.55290742861106 +  0.69169552328498Expect[t] +  0.216573604373535Critisism[t] -0.000708867555636805Concerns[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146312&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146312&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
Intercept[t] = + 2.55290742861106 + 0.69169552328498Expect[t] + 0.216573604373535Critisism[t] -0.000708867555636805Concerns[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.552907428611060.6878763.71130.0002870.000143
Expect0.691695523284980.0850458.133300
Critisism0.2165736043735350.0908752.38320.0183740.009187
Concerns-0.0007088675556368050.034953-0.02030.9838450.491923

\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) & 2.55290742861106 & 0.687876 & 3.7113 & 0.000287 & 0.000143 \tabularnewline
Expect & 0.69169552328498 & 0.085045 & 8.1333 & 0 & 0 \tabularnewline
Critisism & 0.216573604373535 & 0.090875 & 2.3832 & 0.018374 & 0.009187 \tabularnewline
Concerns & -0.000708867555636805 & 0.034953 & -0.0203 & 0.983845 & 0.491923 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146312&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]2.55290742861106[/C][C]0.687876[/C][C]3.7113[/C][C]0.000287[/C][C]0.000143[/C][/ROW]
[ROW][C]Expect[/C][C]0.69169552328498[/C][C]0.085045[/C][C]8.1333[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Critisism[/C][C]0.216573604373535[/C][C]0.090875[/C][C]2.3832[/C][C]0.018374[/C][C]0.009187[/C][/ROW]
[ROW][C]Concerns[/C][C]-0.000708867555636805[/C][C]0.034953[/C][C]-0.0203[/C][C]0.983845[/C][C]0.491923[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146312&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146312&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)2.552907428611060.6878763.71130.0002870.000143
Expect0.691695523284980.0850458.133300
Critisism0.2165736043735350.0908752.38320.0183740.009187
Concerns-0.0007088675556368050.034953-0.02030.9838450.491923






Multiple Linear Regression - Regression Statistics
Multiple R0.612388905419877
R-squared0.375020171481355
Adjusted R-squared0.362923787703574
F-TEST (value)31.0026681007118
F-TEST (DF numerator)3
F-TEST (DF denominator)155
p-value8.88178419700125e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.37504120899082
Sum Squared Residuals293.064440595554

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.612388905419877 \tabularnewline
R-squared & 0.375020171481355 \tabularnewline
Adjusted R-squared & 0.362923787703574 \tabularnewline
F-TEST (value) & 31.0026681007118 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 8.88178419700125e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.37504120899082 \tabularnewline
Sum Squared Residuals & 293.064440595554 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146312&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.612388905419877[/C][/ROW]
[ROW][C]R-squared[/C][C]0.375020171481355[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.362923787703574[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]31.0026681007118[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]8.88178419700125e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.37504120899082[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]293.064440595554[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146312&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146312&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 R0.612388905419877
R-squared0.375020171481355
Adjusted R-squared0.362923787703574
F-TEST (value)31.0026681007118
F-TEST (DF numerator)3
F-TEST (DF denominator)155
p-value8.88178419700125e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.37504120899082
Sum Squared Residuals293.064440595554






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15.57.84891146896424-2.34891146896424
23.56.51604121895583-3.01604121895583
38.56.128413600504032.37158639949597
456.17747752857748-1.17747752857748
566.51694548221924-0.516945482219236
665.736337381010630.263662618989368
75.54.88229019237550.617709807624496
85.57.12043729513623-1.62043729513623
965.833047502046260.166952497953739
106.55.783274706417170.716725293582827
1177.80268301111333-0.802683011113334
1287.450260045762340.549739954237658
135.57.10744315005017-1.60744315005017
1456.1760597934662-1.1760597934662
155.56.03379094875848-0.533790948758483
167.54.504625853079442.99537414692056
174.56.66701187003896-2.16701187003896
185.56.03308208120285-0.533082081202846
198.55.928339887970622.57166011202938
208.57.840209662588830.659790337411174
215.56.96001683674522-1.46001683674522
2297.984605109963461.01539489003654
2378.0518206517202-1.0518206517202
2456.80195751010572-1.80195751010572
255.56.22583258909529-0.725832589095292
267.56.430159506962521.06984049303748
277.55.581783258772491.91821674122751
286.56.66630300248332-0.166303002483325
2986.714658063001141.28534193699886
306.55.437426944774681.06257305522532
314.55.77595150177621-1.27595150177621
3299.2799878418732-0.279987841873201
3395.526852994546173.47314700545383
3466.17019345731334-0.170193457313338
358.56.474970229702152.02502977029785
364.56.16216138511674-1.66216138511674
374.56.20130116741127-1.70130116741127
3864.899537542795381.10046245720462
3998.23583021986040.764169780139596
4066.17090232486897-0.170902324868975
4197.399739249200791.60026075079921
4276.714658063001140.285341936998861
437.56.180312998800021.31968700119998
4485.090870315576562.90912968442344
4556.27347878205747-1.27347878205747
465.56.03166434609157-0.531664346091572
4776.966592040453720.0334079595462771
484.56.207680975412-1.707680975412
4966.70379052058199-0.703790520581991
508.56.072735335345252.42726466465475
512.55.18100523290368-2.68100523290368
5266.1760597934662-0.176059793466204
5366.26851670916801-0.268516709168012
5434.45481392407353-1.45481392407353
551210.80915293755221.19084706244776
5666.56297854436237-0.562978544362369
5766.36951916891429-0.369519168914286
5875.440262414997231.55973758500277
593.54.50746132330199-1.00746132330199
606.55.982600418018120.517399581981879
6166.4700081568127-0.470008156812695
626.55.630886320222770.869113679777235
6375.879275959897161.12072404010284
6446.52190755510869-2.52190755510869
655.54.753724360309480.746275639690521
664.55.66908270587707-1.16908270587707
675.56.79983090743881-1.29983090743881
686.56.225123721539650.274876278460345
6955.05192592898979-0.0519259289897912
705.56.27135217939056-0.771352179390559
7166.3117143010886-0.311714301088597
724.55.87714935723025-1.37714935723025
737.57.100867946341670.399132053658332
7497.744878143287651.25512185671235
757.55.871991888633031.62800811136697
7666.41291215654264-0.412912156542643
776.55.244676436882241.25532356311776
7876.664885267372050.335114732627949
7956.22441485398402-1.22441485398402
806.55.968702009668650.531297990331346
816.57.11027862027272-0.610278620272718
825.56.17889526368875-0.678895263688751
836.56.324708446174660.175291553825344
8486.964465437786811.03553456221319
8545.38033094450463-1.38033094450463
8685.976025214309622.02397478569038
875.55.284329691024640.215670308975362
884.56.03237321364721-1.53237321364721
8988.73123089747475-0.731230897474754
9065.87218728434080.127812715659203
9176.562269676806730.437730323193268
9245.0981543868407-1.0981543868407
934.55.04393299017002-0.543932990170015
947.55.676405910518031.82359408948197
955.56.91823697993591-1.41823697993591
9610.58.187475159342592.31252484065741
9777.14638753663693-0.146387536636935
9898.09001703737450.909982962625501
9965.87998482745280.120015172547199
1006.56.37164577158120.128354228418803
1017.57.110987487828350.389012512171645
10265.337842220139680.662157779860318
1039.56.964465437786812.53553456221319
1047.56.86988191941810.630118080581906
1055.57.10673428249453-1.60673428249453
1065.55.472826993583260.0271730064167366
10755.97744294942089-0.977442949420892
1086.57.15438047545671-0.654380475456711
1097.57.55074903366075-0.0507490336607508
11065.429629401662680.570370598337322
11165.589815330969090.41018466903091
11288.03953537418977-0.0395353741897737
1134.57.36083399599085-2.86083399599085
11497.550749033660751.44925096633925
11546.78187360475788-2.78187360475788
1166.55.046059592836931.45394040716307
1178.56.223705986428382.27629401357162
1184.55.43742694477468-0.937426944774683
1197.56.86330671570960.636693284290408
12044.88725226526496-0.887252265264962
1213.55.32039947401203-1.82039947401203
12265.637461523931270.362538476068732
12377.40848018895303-0.40848018895303
12435.34351316058478-2.34351316058478
12545.8799848274528-1.8799848274528
1268.56.273478782057472.22652121794253
12754.65181763729980.348182362700204
1285.56.2741876496131-0.774187649613106
12976.52403415777560.475965842224396
1305.56.89795876358571-1.39795876358571
1316.57.40848018895303-0.90848018895303
13266.07651420220803-0.0765142022080275
1335.55.09370578579910.406294214200897
1344.54.69666749341625-0.196667493416252
13566.32258184350774-0.322581843507745
136107.253256332536082.74674366746392
13765.477984462180490.522015537819508
1386.56.52190755510869-0.0219075551086936
13966.47284362703524-0.472843627035242
14068.10017571223801-2.10017571223801
1414.57.1015768138973-2.6015768138973
1427.57.448842310651070.0511576893489317
1431210.61427582699291.38572417300712
1443.55.28503855858028-1.78503855858028
1458.57.360125128435221.13987487156478
1465.56.01493046751956-0.514930467519559
1478.57.149223006859481.35077699314052
1485.55.92621328530371-0.426213285303706
14966.42874177185125-0.428741771851249
15077.05696148686545-0.0569614868654453
1515.56.61861767614432-1.11861767614432
15286.654726592508541.34527340749146
15310.56.795343173020393.70465682697961
15475.678532513184941.32146748681506
155107.997046649824832.00295335017517
1566.55.43388260699651.0661173930035
1575.56.65614432761981-1.15614432761981
1587.57.060505824643630.43949417535637
1599.59.57086620591255-0.0708662059125495

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5.5 & 7.84891146896424 & -2.34891146896424 \tabularnewline
2 & 3.5 & 6.51604121895583 & -3.01604121895583 \tabularnewline
3 & 8.5 & 6.12841360050403 & 2.37158639949597 \tabularnewline
4 & 5 & 6.17747752857748 & -1.17747752857748 \tabularnewline
5 & 6 & 6.51694548221924 & -0.516945482219236 \tabularnewline
6 & 6 & 5.73633738101063 & 0.263662618989368 \tabularnewline
7 & 5.5 & 4.8822901923755 & 0.617709807624496 \tabularnewline
8 & 5.5 & 7.12043729513623 & -1.62043729513623 \tabularnewline
9 & 6 & 5.83304750204626 & 0.166952497953739 \tabularnewline
10 & 6.5 & 5.78327470641717 & 0.716725293582827 \tabularnewline
11 & 7 & 7.80268301111333 & -0.802683011113334 \tabularnewline
12 & 8 & 7.45026004576234 & 0.549739954237658 \tabularnewline
13 & 5.5 & 7.10744315005017 & -1.60744315005017 \tabularnewline
14 & 5 & 6.1760597934662 & -1.1760597934662 \tabularnewline
15 & 5.5 & 6.03379094875848 & -0.533790948758483 \tabularnewline
16 & 7.5 & 4.50462585307944 & 2.99537414692056 \tabularnewline
17 & 4.5 & 6.66701187003896 & -2.16701187003896 \tabularnewline
18 & 5.5 & 6.03308208120285 & -0.533082081202846 \tabularnewline
19 & 8.5 & 5.92833988797062 & 2.57166011202938 \tabularnewline
20 & 8.5 & 7.84020966258883 & 0.659790337411174 \tabularnewline
21 & 5.5 & 6.96001683674522 & -1.46001683674522 \tabularnewline
22 & 9 & 7.98460510996346 & 1.01539489003654 \tabularnewline
23 & 7 & 8.0518206517202 & -1.0518206517202 \tabularnewline
24 & 5 & 6.80195751010572 & -1.80195751010572 \tabularnewline
25 & 5.5 & 6.22583258909529 & -0.725832589095292 \tabularnewline
26 & 7.5 & 6.43015950696252 & 1.06984049303748 \tabularnewline
27 & 7.5 & 5.58178325877249 & 1.91821674122751 \tabularnewline
28 & 6.5 & 6.66630300248332 & -0.166303002483325 \tabularnewline
29 & 8 & 6.71465806300114 & 1.28534193699886 \tabularnewline
30 & 6.5 & 5.43742694477468 & 1.06257305522532 \tabularnewline
31 & 4.5 & 5.77595150177621 & -1.27595150177621 \tabularnewline
32 & 9 & 9.2799878418732 & -0.279987841873201 \tabularnewline
33 & 9 & 5.52685299454617 & 3.47314700545383 \tabularnewline
34 & 6 & 6.17019345731334 & -0.170193457313338 \tabularnewline
35 & 8.5 & 6.47497022970215 & 2.02502977029785 \tabularnewline
36 & 4.5 & 6.16216138511674 & -1.66216138511674 \tabularnewline
37 & 4.5 & 6.20130116741127 & -1.70130116741127 \tabularnewline
38 & 6 & 4.89953754279538 & 1.10046245720462 \tabularnewline
39 & 9 & 8.2358302198604 & 0.764169780139596 \tabularnewline
40 & 6 & 6.17090232486897 & -0.170902324868975 \tabularnewline
41 & 9 & 7.39973924920079 & 1.60026075079921 \tabularnewline
42 & 7 & 6.71465806300114 & 0.285341936998861 \tabularnewline
43 & 7.5 & 6.18031299880002 & 1.31968700119998 \tabularnewline
44 & 8 & 5.09087031557656 & 2.90912968442344 \tabularnewline
45 & 5 & 6.27347878205747 & -1.27347878205747 \tabularnewline
46 & 5.5 & 6.03166434609157 & -0.531664346091572 \tabularnewline
47 & 7 & 6.96659204045372 & 0.0334079595462771 \tabularnewline
48 & 4.5 & 6.207680975412 & -1.707680975412 \tabularnewline
49 & 6 & 6.70379052058199 & -0.703790520581991 \tabularnewline
50 & 8.5 & 6.07273533534525 & 2.42726466465475 \tabularnewline
51 & 2.5 & 5.18100523290368 & -2.68100523290368 \tabularnewline
52 & 6 & 6.1760597934662 & -0.176059793466204 \tabularnewline
53 & 6 & 6.26851670916801 & -0.268516709168012 \tabularnewline
54 & 3 & 4.45481392407353 & -1.45481392407353 \tabularnewline
55 & 12 & 10.8091529375522 & 1.19084706244776 \tabularnewline
56 & 6 & 6.56297854436237 & -0.562978544362369 \tabularnewline
57 & 6 & 6.36951916891429 & -0.369519168914286 \tabularnewline
58 & 7 & 5.44026241499723 & 1.55973758500277 \tabularnewline
59 & 3.5 & 4.50746132330199 & -1.00746132330199 \tabularnewline
60 & 6.5 & 5.98260041801812 & 0.517399581981879 \tabularnewline
61 & 6 & 6.4700081568127 & -0.470008156812695 \tabularnewline
62 & 6.5 & 5.63088632022277 & 0.869113679777235 \tabularnewline
63 & 7 & 5.87927595989716 & 1.12072404010284 \tabularnewline
64 & 4 & 6.52190755510869 & -2.52190755510869 \tabularnewline
65 & 5.5 & 4.75372436030948 & 0.746275639690521 \tabularnewline
66 & 4.5 & 5.66908270587707 & -1.16908270587707 \tabularnewline
67 & 5.5 & 6.79983090743881 & -1.29983090743881 \tabularnewline
68 & 6.5 & 6.22512372153965 & 0.274876278460345 \tabularnewline
69 & 5 & 5.05192592898979 & -0.0519259289897912 \tabularnewline
70 & 5.5 & 6.27135217939056 & -0.771352179390559 \tabularnewline
71 & 6 & 6.3117143010886 & -0.311714301088597 \tabularnewline
72 & 4.5 & 5.87714935723025 & -1.37714935723025 \tabularnewline
73 & 7.5 & 7.10086794634167 & 0.399132053658332 \tabularnewline
74 & 9 & 7.74487814328765 & 1.25512185671235 \tabularnewline
75 & 7.5 & 5.87199188863303 & 1.62800811136697 \tabularnewline
76 & 6 & 6.41291215654264 & -0.412912156542643 \tabularnewline
77 & 6.5 & 5.24467643688224 & 1.25532356311776 \tabularnewline
78 & 7 & 6.66488526737205 & 0.335114732627949 \tabularnewline
79 & 5 & 6.22441485398402 & -1.22441485398402 \tabularnewline
80 & 6.5 & 5.96870200966865 & 0.531297990331346 \tabularnewline
81 & 6.5 & 7.11027862027272 & -0.610278620272718 \tabularnewline
82 & 5.5 & 6.17889526368875 & -0.678895263688751 \tabularnewline
83 & 6.5 & 6.32470844617466 & 0.175291553825344 \tabularnewline
84 & 8 & 6.96446543778681 & 1.03553456221319 \tabularnewline
85 & 4 & 5.38033094450463 & -1.38033094450463 \tabularnewline
86 & 8 & 5.97602521430962 & 2.02397478569038 \tabularnewline
87 & 5.5 & 5.28432969102464 & 0.215670308975362 \tabularnewline
88 & 4.5 & 6.03237321364721 & -1.53237321364721 \tabularnewline
89 & 8 & 8.73123089747475 & -0.731230897474754 \tabularnewline
90 & 6 & 5.8721872843408 & 0.127812715659203 \tabularnewline
91 & 7 & 6.56226967680673 & 0.437730323193268 \tabularnewline
92 & 4 & 5.0981543868407 & -1.0981543868407 \tabularnewline
93 & 4.5 & 5.04393299017002 & -0.543932990170015 \tabularnewline
94 & 7.5 & 5.67640591051803 & 1.82359408948197 \tabularnewline
95 & 5.5 & 6.91823697993591 & -1.41823697993591 \tabularnewline
96 & 10.5 & 8.18747515934259 & 2.31252484065741 \tabularnewline
97 & 7 & 7.14638753663693 & -0.146387536636935 \tabularnewline
98 & 9 & 8.0900170373745 & 0.909982962625501 \tabularnewline
99 & 6 & 5.8799848274528 & 0.120015172547199 \tabularnewline
100 & 6.5 & 6.3716457715812 & 0.128354228418803 \tabularnewline
101 & 7.5 & 7.11098748782835 & 0.389012512171645 \tabularnewline
102 & 6 & 5.33784222013968 & 0.662157779860318 \tabularnewline
103 & 9.5 & 6.96446543778681 & 2.53553456221319 \tabularnewline
104 & 7.5 & 6.8698819194181 & 0.630118080581906 \tabularnewline
105 & 5.5 & 7.10673428249453 & -1.60673428249453 \tabularnewline
106 & 5.5 & 5.47282699358326 & 0.0271730064167366 \tabularnewline
107 & 5 & 5.97744294942089 & -0.977442949420892 \tabularnewline
108 & 6.5 & 7.15438047545671 & -0.654380475456711 \tabularnewline
109 & 7.5 & 7.55074903366075 & -0.0507490336607508 \tabularnewline
110 & 6 & 5.42962940166268 & 0.570370598337322 \tabularnewline
111 & 6 & 5.58981533096909 & 0.41018466903091 \tabularnewline
112 & 8 & 8.03953537418977 & -0.0395353741897737 \tabularnewline
113 & 4.5 & 7.36083399599085 & -2.86083399599085 \tabularnewline
114 & 9 & 7.55074903366075 & 1.44925096633925 \tabularnewline
115 & 4 & 6.78187360475788 & -2.78187360475788 \tabularnewline
116 & 6.5 & 5.04605959283693 & 1.45394040716307 \tabularnewline
117 & 8.5 & 6.22370598642838 & 2.27629401357162 \tabularnewline
118 & 4.5 & 5.43742694477468 & -0.937426944774683 \tabularnewline
119 & 7.5 & 6.8633067157096 & 0.636693284290408 \tabularnewline
120 & 4 & 4.88725226526496 & -0.887252265264962 \tabularnewline
121 & 3.5 & 5.32039947401203 & -1.82039947401203 \tabularnewline
122 & 6 & 5.63746152393127 & 0.362538476068732 \tabularnewline
123 & 7 & 7.40848018895303 & -0.40848018895303 \tabularnewline
124 & 3 & 5.34351316058478 & -2.34351316058478 \tabularnewline
125 & 4 & 5.8799848274528 & -1.8799848274528 \tabularnewline
126 & 8.5 & 6.27347878205747 & 2.22652121794253 \tabularnewline
127 & 5 & 4.6518176372998 & 0.348182362700204 \tabularnewline
128 & 5.5 & 6.2741876496131 & -0.774187649613106 \tabularnewline
129 & 7 & 6.5240341577756 & 0.475965842224396 \tabularnewline
130 & 5.5 & 6.89795876358571 & -1.39795876358571 \tabularnewline
131 & 6.5 & 7.40848018895303 & -0.90848018895303 \tabularnewline
132 & 6 & 6.07651420220803 & -0.0765142022080275 \tabularnewline
133 & 5.5 & 5.0937057857991 & 0.406294214200897 \tabularnewline
134 & 4.5 & 4.69666749341625 & -0.196667493416252 \tabularnewline
135 & 6 & 6.32258184350774 & -0.322581843507745 \tabularnewline
136 & 10 & 7.25325633253608 & 2.74674366746392 \tabularnewline
137 & 6 & 5.47798446218049 & 0.522015537819508 \tabularnewline
138 & 6.5 & 6.52190755510869 & -0.0219075551086936 \tabularnewline
139 & 6 & 6.47284362703524 & -0.472843627035242 \tabularnewline
140 & 6 & 8.10017571223801 & -2.10017571223801 \tabularnewline
141 & 4.5 & 7.1015768138973 & -2.6015768138973 \tabularnewline
142 & 7.5 & 7.44884231065107 & 0.0511576893489317 \tabularnewline
143 & 12 & 10.6142758269929 & 1.38572417300712 \tabularnewline
144 & 3.5 & 5.28503855858028 & -1.78503855858028 \tabularnewline
145 & 8.5 & 7.36012512843522 & 1.13987487156478 \tabularnewline
146 & 5.5 & 6.01493046751956 & -0.514930467519559 \tabularnewline
147 & 8.5 & 7.14922300685948 & 1.35077699314052 \tabularnewline
148 & 5.5 & 5.92621328530371 & -0.426213285303706 \tabularnewline
149 & 6 & 6.42874177185125 & -0.428741771851249 \tabularnewline
150 & 7 & 7.05696148686545 & -0.0569614868654453 \tabularnewline
151 & 5.5 & 6.61861767614432 & -1.11861767614432 \tabularnewline
152 & 8 & 6.65472659250854 & 1.34527340749146 \tabularnewline
153 & 10.5 & 6.79534317302039 & 3.70465682697961 \tabularnewline
154 & 7 & 5.67853251318494 & 1.32146748681506 \tabularnewline
155 & 10 & 7.99704664982483 & 2.00295335017517 \tabularnewline
156 & 6.5 & 5.4338826069965 & 1.0661173930035 \tabularnewline
157 & 5.5 & 6.65614432761981 & -1.15614432761981 \tabularnewline
158 & 7.5 & 7.06050582464363 & 0.43949417535637 \tabularnewline
159 & 9.5 & 9.57086620591255 & -0.0708662059125495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146312&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]5.5[/C][C]7.84891146896424[/C][C]-2.34891146896424[/C][/ROW]
[ROW][C]2[/C][C]3.5[/C][C]6.51604121895583[/C][C]-3.01604121895583[/C][/ROW]
[ROW][C]3[/C][C]8.5[/C][C]6.12841360050403[/C][C]2.37158639949597[/C][/ROW]
[ROW][C]4[/C][C]5[/C][C]6.17747752857748[/C][C]-1.17747752857748[/C][/ROW]
[ROW][C]5[/C][C]6[/C][C]6.51694548221924[/C][C]-0.516945482219236[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]5.73633738101063[/C][C]0.263662618989368[/C][/ROW]
[ROW][C]7[/C][C]5.5[/C][C]4.8822901923755[/C][C]0.617709807624496[/C][/ROW]
[ROW][C]8[/C][C]5.5[/C][C]7.12043729513623[/C][C]-1.62043729513623[/C][/ROW]
[ROW][C]9[/C][C]6[/C][C]5.83304750204626[/C][C]0.166952497953739[/C][/ROW]
[ROW][C]10[/C][C]6.5[/C][C]5.78327470641717[/C][C]0.716725293582827[/C][/ROW]
[ROW][C]11[/C][C]7[/C][C]7.80268301111333[/C][C]-0.802683011113334[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]7.45026004576234[/C][C]0.549739954237658[/C][/ROW]
[ROW][C]13[/C][C]5.5[/C][C]7.10744315005017[/C][C]-1.60744315005017[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]6.1760597934662[/C][C]-1.1760597934662[/C][/ROW]
[ROW][C]15[/C][C]5.5[/C][C]6.03379094875848[/C][C]-0.533790948758483[/C][/ROW]
[ROW][C]16[/C][C]7.5[/C][C]4.50462585307944[/C][C]2.99537414692056[/C][/ROW]
[ROW][C]17[/C][C]4.5[/C][C]6.66701187003896[/C][C]-2.16701187003896[/C][/ROW]
[ROW][C]18[/C][C]5.5[/C][C]6.03308208120285[/C][C]-0.533082081202846[/C][/ROW]
[ROW][C]19[/C][C]8.5[/C][C]5.92833988797062[/C][C]2.57166011202938[/C][/ROW]
[ROW][C]20[/C][C]8.5[/C][C]7.84020966258883[/C][C]0.659790337411174[/C][/ROW]
[ROW][C]21[/C][C]5.5[/C][C]6.96001683674522[/C][C]-1.46001683674522[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]7.98460510996346[/C][C]1.01539489003654[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]8.0518206517202[/C][C]-1.0518206517202[/C][/ROW]
[ROW][C]24[/C][C]5[/C][C]6.80195751010572[/C][C]-1.80195751010572[/C][/ROW]
[ROW][C]25[/C][C]5.5[/C][C]6.22583258909529[/C][C]-0.725832589095292[/C][/ROW]
[ROW][C]26[/C][C]7.5[/C][C]6.43015950696252[/C][C]1.06984049303748[/C][/ROW]
[ROW][C]27[/C][C]7.5[/C][C]5.58178325877249[/C][C]1.91821674122751[/C][/ROW]
[ROW][C]28[/C][C]6.5[/C][C]6.66630300248332[/C][C]-0.166303002483325[/C][/ROW]
[ROW][C]29[/C][C]8[/C][C]6.71465806300114[/C][C]1.28534193699886[/C][/ROW]
[ROW][C]30[/C][C]6.5[/C][C]5.43742694477468[/C][C]1.06257305522532[/C][/ROW]
[ROW][C]31[/C][C]4.5[/C][C]5.77595150177621[/C][C]-1.27595150177621[/C][/ROW]
[ROW][C]32[/C][C]9[/C][C]9.2799878418732[/C][C]-0.279987841873201[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]5.52685299454617[/C][C]3.47314700545383[/C][/ROW]
[ROW][C]34[/C][C]6[/C][C]6.17019345731334[/C][C]-0.170193457313338[/C][/ROW]
[ROW][C]35[/C][C]8.5[/C][C]6.47497022970215[/C][C]2.02502977029785[/C][/ROW]
[ROW][C]36[/C][C]4.5[/C][C]6.16216138511674[/C][C]-1.66216138511674[/C][/ROW]
[ROW][C]37[/C][C]4.5[/C][C]6.20130116741127[/C][C]-1.70130116741127[/C][/ROW]
[ROW][C]38[/C][C]6[/C][C]4.89953754279538[/C][C]1.10046245720462[/C][/ROW]
[ROW][C]39[/C][C]9[/C][C]8.2358302198604[/C][C]0.764169780139596[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]6.17090232486897[/C][C]-0.170902324868975[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]7.39973924920079[/C][C]1.60026075079921[/C][/ROW]
[ROW][C]42[/C][C]7[/C][C]6.71465806300114[/C][C]0.285341936998861[/C][/ROW]
[ROW][C]43[/C][C]7.5[/C][C]6.18031299880002[/C][C]1.31968700119998[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]5.09087031557656[/C][C]2.90912968442344[/C][/ROW]
[ROW][C]45[/C][C]5[/C][C]6.27347878205747[/C][C]-1.27347878205747[/C][/ROW]
[ROW][C]46[/C][C]5.5[/C][C]6.03166434609157[/C][C]-0.531664346091572[/C][/ROW]
[ROW][C]47[/C][C]7[/C][C]6.96659204045372[/C][C]0.0334079595462771[/C][/ROW]
[ROW][C]48[/C][C]4.5[/C][C]6.207680975412[/C][C]-1.707680975412[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]6.70379052058199[/C][C]-0.703790520581991[/C][/ROW]
[ROW][C]50[/C][C]8.5[/C][C]6.07273533534525[/C][C]2.42726466465475[/C][/ROW]
[ROW][C]51[/C][C]2.5[/C][C]5.18100523290368[/C][C]-2.68100523290368[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]6.1760597934662[/C][C]-0.176059793466204[/C][/ROW]
[ROW][C]53[/C][C]6[/C][C]6.26851670916801[/C][C]-0.268516709168012[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]4.45481392407353[/C][C]-1.45481392407353[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]10.8091529375522[/C][C]1.19084706244776[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]6.56297854436237[/C][C]-0.562978544362369[/C][/ROW]
[ROW][C]57[/C][C]6[/C][C]6.36951916891429[/C][C]-0.369519168914286[/C][/ROW]
[ROW][C]58[/C][C]7[/C][C]5.44026241499723[/C][C]1.55973758500277[/C][/ROW]
[ROW][C]59[/C][C]3.5[/C][C]4.50746132330199[/C][C]-1.00746132330199[/C][/ROW]
[ROW][C]60[/C][C]6.5[/C][C]5.98260041801812[/C][C]0.517399581981879[/C][/ROW]
[ROW][C]61[/C][C]6[/C][C]6.4700081568127[/C][C]-0.470008156812695[/C][/ROW]
[ROW][C]62[/C][C]6.5[/C][C]5.63088632022277[/C][C]0.869113679777235[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]5.87927595989716[/C][C]1.12072404010284[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]6.52190755510869[/C][C]-2.52190755510869[/C][/ROW]
[ROW][C]65[/C][C]5.5[/C][C]4.75372436030948[/C][C]0.746275639690521[/C][/ROW]
[ROW][C]66[/C][C]4.5[/C][C]5.66908270587707[/C][C]-1.16908270587707[/C][/ROW]
[ROW][C]67[/C][C]5.5[/C][C]6.79983090743881[/C][C]-1.29983090743881[/C][/ROW]
[ROW][C]68[/C][C]6.5[/C][C]6.22512372153965[/C][C]0.274876278460345[/C][/ROW]
[ROW][C]69[/C][C]5[/C][C]5.05192592898979[/C][C]-0.0519259289897912[/C][/ROW]
[ROW][C]70[/C][C]5.5[/C][C]6.27135217939056[/C][C]-0.771352179390559[/C][/ROW]
[ROW][C]71[/C][C]6[/C][C]6.3117143010886[/C][C]-0.311714301088597[/C][/ROW]
[ROW][C]72[/C][C]4.5[/C][C]5.87714935723025[/C][C]-1.37714935723025[/C][/ROW]
[ROW][C]73[/C][C]7.5[/C][C]7.10086794634167[/C][C]0.399132053658332[/C][/ROW]
[ROW][C]74[/C][C]9[/C][C]7.74487814328765[/C][C]1.25512185671235[/C][/ROW]
[ROW][C]75[/C][C]7.5[/C][C]5.87199188863303[/C][C]1.62800811136697[/C][/ROW]
[ROW][C]76[/C][C]6[/C][C]6.41291215654264[/C][C]-0.412912156542643[/C][/ROW]
[ROW][C]77[/C][C]6.5[/C][C]5.24467643688224[/C][C]1.25532356311776[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]6.66488526737205[/C][C]0.335114732627949[/C][/ROW]
[ROW][C]79[/C][C]5[/C][C]6.22441485398402[/C][C]-1.22441485398402[/C][/ROW]
[ROW][C]80[/C][C]6.5[/C][C]5.96870200966865[/C][C]0.531297990331346[/C][/ROW]
[ROW][C]81[/C][C]6.5[/C][C]7.11027862027272[/C][C]-0.610278620272718[/C][/ROW]
[ROW][C]82[/C][C]5.5[/C][C]6.17889526368875[/C][C]-0.678895263688751[/C][/ROW]
[ROW][C]83[/C][C]6.5[/C][C]6.32470844617466[/C][C]0.175291553825344[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]6.96446543778681[/C][C]1.03553456221319[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]5.38033094450463[/C][C]-1.38033094450463[/C][/ROW]
[ROW][C]86[/C][C]8[/C][C]5.97602521430962[/C][C]2.02397478569038[/C][/ROW]
[ROW][C]87[/C][C]5.5[/C][C]5.28432969102464[/C][C]0.215670308975362[/C][/ROW]
[ROW][C]88[/C][C]4.5[/C][C]6.03237321364721[/C][C]-1.53237321364721[/C][/ROW]
[ROW][C]89[/C][C]8[/C][C]8.73123089747475[/C][C]-0.731230897474754[/C][/ROW]
[ROW][C]90[/C][C]6[/C][C]5.8721872843408[/C][C]0.127812715659203[/C][/ROW]
[ROW][C]91[/C][C]7[/C][C]6.56226967680673[/C][C]0.437730323193268[/C][/ROW]
[ROW][C]92[/C][C]4[/C][C]5.0981543868407[/C][C]-1.0981543868407[/C][/ROW]
[ROW][C]93[/C][C]4.5[/C][C]5.04393299017002[/C][C]-0.543932990170015[/C][/ROW]
[ROW][C]94[/C][C]7.5[/C][C]5.67640591051803[/C][C]1.82359408948197[/C][/ROW]
[ROW][C]95[/C][C]5.5[/C][C]6.91823697993591[/C][C]-1.41823697993591[/C][/ROW]
[ROW][C]96[/C][C]10.5[/C][C]8.18747515934259[/C][C]2.31252484065741[/C][/ROW]
[ROW][C]97[/C][C]7[/C][C]7.14638753663693[/C][C]-0.146387536636935[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]8.0900170373745[/C][C]0.909982962625501[/C][/ROW]
[ROW][C]99[/C][C]6[/C][C]5.8799848274528[/C][C]0.120015172547199[/C][/ROW]
[ROW][C]100[/C][C]6.5[/C][C]6.3716457715812[/C][C]0.128354228418803[/C][/ROW]
[ROW][C]101[/C][C]7.5[/C][C]7.11098748782835[/C][C]0.389012512171645[/C][/ROW]
[ROW][C]102[/C][C]6[/C][C]5.33784222013968[/C][C]0.662157779860318[/C][/ROW]
[ROW][C]103[/C][C]9.5[/C][C]6.96446543778681[/C][C]2.53553456221319[/C][/ROW]
[ROW][C]104[/C][C]7.5[/C][C]6.8698819194181[/C][C]0.630118080581906[/C][/ROW]
[ROW][C]105[/C][C]5.5[/C][C]7.10673428249453[/C][C]-1.60673428249453[/C][/ROW]
[ROW][C]106[/C][C]5.5[/C][C]5.47282699358326[/C][C]0.0271730064167366[/C][/ROW]
[ROW][C]107[/C][C]5[/C][C]5.97744294942089[/C][C]-0.977442949420892[/C][/ROW]
[ROW][C]108[/C][C]6.5[/C][C]7.15438047545671[/C][C]-0.654380475456711[/C][/ROW]
[ROW][C]109[/C][C]7.5[/C][C]7.55074903366075[/C][C]-0.0507490336607508[/C][/ROW]
[ROW][C]110[/C][C]6[/C][C]5.42962940166268[/C][C]0.570370598337322[/C][/ROW]
[ROW][C]111[/C][C]6[/C][C]5.58981533096909[/C][C]0.41018466903091[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]8.03953537418977[/C][C]-0.0395353741897737[/C][/ROW]
[ROW][C]113[/C][C]4.5[/C][C]7.36083399599085[/C][C]-2.86083399599085[/C][/ROW]
[ROW][C]114[/C][C]9[/C][C]7.55074903366075[/C][C]1.44925096633925[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]6.78187360475788[/C][C]-2.78187360475788[/C][/ROW]
[ROW][C]116[/C][C]6.5[/C][C]5.04605959283693[/C][C]1.45394040716307[/C][/ROW]
[ROW][C]117[/C][C]8.5[/C][C]6.22370598642838[/C][C]2.27629401357162[/C][/ROW]
[ROW][C]118[/C][C]4.5[/C][C]5.43742694477468[/C][C]-0.937426944774683[/C][/ROW]
[ROW][C]119[/C][C]7.5[/C][C]6.8633067157096[/C][C]0.636693284290408[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]4.88725226526496[/C][C]-0.887252265264962[/C][/ROW]
[ROW][C]121[/C][C]3.5[/C][C]5.32039947401203[/C][C]-1.82039947401203[/C][/ROW]
[ROW][C]122[/C][C]6[/C][C]5.63746152393127[/C][C]0.362538476068732[/C][/ROW]
[ROW][C]123[/C][C]7[/C][C]7.40848018895303[/C][C]-0.40848018895303[/C][/ROW]
[ROW][C]124[/C][C]3[/C][C]5.34351316058478[/C][C]-2.34351316058478[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]5.8799848274528[/C][C]-1.8799848274528[/C][/ROW]
[ROW][C]126[/C][C]8.5[/C][C]6.27347878205747[/C][C]2.22652121794253[/C][/ROW]
[ROW][C]127[/C][C]5[/C][C]4.6518176372998[/C][C]0.348182362700204[/C][/ROW]
[ROW][C]128[/C][C]5.5[/C][C]6.2741876496131[/C][C]-0.774187649613106[/C][/ROW]
[ROW][C]129[/C][C]7[/C][C]6.5240341577756[/C][C]0.475965842224396[/C][/ROW]
[ROW][C]130[/C][C]5.5[/C][C]6.89795876358571[/C][C]-1.39795876358571[/C][/ROW]
[ROW][C]131[/C][C]6.5[/C][C]7.40848018895303[/C][C]-0.90848018895303[/C][/ROW]
[ROW][C]132[/C][C]6[/C][C]6.07651420220803[/C][C]-0.0765142022080275[/C][/ROW]
[ROW][C]133[/C][C]5.5[/C][C]5.0937057857991[/C][C]0.406294214200897[/C][/ROW]
[ROW][C]134[/C][C]4.5[/C][C]4.69666749341625[/C][C]-0.196667493416252[/C][/ROW]
[ROW][C]135[/C][C]6[/C][C]6.32258184350774[/C][C]-0.322581843507745[/C][/ROW]
[ROW][C]136[/C][C]10[/C][C]7.25325633253608[/C][C]2.74674366746392[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]5.47798446218049[/C][C]0.522015537819508[/C][/ROW]
[ROW][C]138[/C][C]6.5[/C][C]6.52190755510869[/C][C]-0.0219075551086936[/C][/ROW]
[ROW][C]139[/C][C]6[/C][C]6.47284362703524[/C][C]-0.472843627035242[/C][/ROW]
[ROW][C]140[/C][C]6[/C][C]8.10017571223801[/C][C]-2.10017571223801[/C][/ROW]
[ROW][C]141[/C][C]4.5[/C][C]7.1015768138973[/C][C]-2.6015768138973[/C][/ROW]
[ROW][C]142[/C][C]7.5[/C][C]7.44884231065107[/C][C]0.0511576893489317[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]10.6142758269929[/C][C]1.38572417300712[/C][/ROW]
[ROW][C]144[/C][C]3.5[/C][C]5.28503855858028[/C][C]-1.78503855858028[/C][/ROW]
[ROW][C]145[/C][C]8.5[/C][C]7.36012512843522[/C][C]1.13987487156478[/C][/ROW]
[ROW][C]146[/C][C]5.5[/C][C]6.01493046751956[/C][C]-0.514930467519559[/C][/ROW]
[ROW][C]147[/C][C]8.5[/C][C]7.14922300685948[/C][C]1.35077699314052[/C][/ROW]
[ROW][C]148[/C][C]5.5[/C][C]5.92621328530371[/C][C]-0.426213285303706[/C][/ROW]
[ROW][C]149[/C][C]6[/C][C]6.42874177185125[/C][C]-0.428741771851249[/C][/ROW]
[ROW][C]150[/C][C]7[/C][C]7.05696148686545[/C][C]-0.0569614868654453[/C][/ROW]
[ROW][C]151[/C][C]5.5[/C][C]6.61861767614432[/C][C]-1.11861767614432[/C][/ROW]
[ROW][C]152[/C][C]8[/C][C]6.65472659250854[/C][C]1.34527340749146[/C][/ROW]
[ROW][C]153[/C][C]10.5[/C][C]6.79534317302039[/C][C]3.70465682697961[/C][/ROW]
[ROW][C]154[/C][C]7[/C][C]5.67853251318494[/C][C]1.32146748681506[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]7.99704664982483[/C][C]2.00295335017517[/C][/ROW]
[ROW][C]156[/C][C]6.5[/C][C]5.4338826069965[/C][C]1.0661173930035[/C][/ROW]
[ROW][C]157[/C][C]5.5[/C][C]6.65614432761981[/C][C]-1.15614432761981[/C][/ROW]
[ROW][C]158[/C][C]7.5[/C][C]7.06050582464363[/C][C]0.43949417535637[/C][/ROW]
[ROW][C]159[/C][C]9.5[/C][C]9.57086620591255[/C][C]-0.0708662059125495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146312&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146312&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
15.57.84891146896424-2.34891146896424
23.56.51604121895583-3.01604121895583
38.56.128413600504032.37158639949597
456.17747752857748-1.17747752857748
566.51694548221924-0.516945482219236
665.736337381010630.263662618989368
75.54.88229019237550.617709807624496
85.57.12043729513623-1.62043729513623
965.833047502046260.166952497953739
106.55.783274706417170.716725293582827
1177.80268301111333-0.802683011113334
1287.450260045762340.549739954237658
135.57.10744315005017-1.60744315005017
1456.1760597934662-1.1760597934662
155.56.03379094875848-0.533790948758483
167.54.504625853079442.99537414692056
174.56.66701187003896-2.16701187003896
185.56.03308208120285-0.533082081202846
198.55.928339887970622.57166011202938
208.57.840209662588830.659790337411174
215.56.96001683674522-1.46001683674522
2297.984605109963461.01539489003654
2378.0518206517202-1.0518206517202
2456.80195751010572-1.80195751010572
255.56.22583258909529-0.725832589095292
267.56.430159506962521.06984049303748
277.55.581783258772491.91821674122751
286.56.66630300248332-0.166303002483325
2986.714658063001141.28534193699886
306.55.437426944774681.06257305522532
314.55.77595150177621-1.27595150177621
3299.2799878418732-0.279987841873201
3395.526852994546173.47314700545383
3466.17019345731334-0.170193457313338
358.56.474970229702152.02502977029785
364.56.16216138511674-1.66216138511674
374.56.20130116741127-1.70130116741127
3864.899537542795381.10046245720462
3998.23583021986040.764169780139596
4066.17090232486897-0.170902324868975
4197.399739249200791.60026075079921
4276.714658063001140.285341936998861
437.56.180312998800021.31968700119998
4485.090870315576562.90912968442344
4556.27347878205747-1.27347878205747
465.56.03166434609157-0.531664346091572
4776.966592040453720.0334079595462771
484.56.207680975412-1.707680975412
4966.70379052058199-0.703790520581991
508.56.072735335345252.42726466465475
512.55.18100523290368-2.68100523290368
5266.1760597934662-0.176059793466204
5366.26851670916801-0.268516709168012
5434.45481392407353-1.45481392407353
551210.80915293755221.19084706244776
5666.56297854436237-0.562978544362369
5766.36951916891429-0.369519168914286
5875.440262414997231.55973758500277
593.54.50746132330199-1.00746132330199
606.55.982600418018120.517399581981879
6166.4700081568127-0.470008156812695
626.55.630886320222770.869113679777235
6375.879275959897161.12072404010284
6446.52190755510869-2.52190755510869
655.54.753724360309480.746275639690521
664.55.66908270587707-1.16908270587707
675.56.79983090743881-1.29983090743881
686.56.225123721539650.274876278460345
6955.05192592898979-0.0519259289897912
705.56.27135217939056-0.771352179390559
7166.3117143010886-0.311714301088597
724.55.87714935723025-1.37714935723025
737.57.100867946341670.399132053658332
7497.744878143287651.25512185671235
757.55.871991888633031.62800811136697
7666.41291215654264-0.412912156542643
776.55.244676436882241.25532356311776
7876.664885267372050.335114732627949
7956.22441485398402-1.22441485398402
806.55.968702009668650.531297990331346
816.57.11027862027272-0.610278620272718
825.56.17889526368875-0.678895263688751
836.56.324708446174660.175291553825344
8486.964465437786811.03553456221319
8545.38033094450463-1.38033094450463
8685.976025214309622.02397478569038
875.55.284329691024640.215670308975362
884.56.03237321364721-1.53237321364721
8988.73123089747475-0.731230897474754
9065.87218728434080.127812715659203
9176.562269676806730.437730323193268
9245.0981543868407-1.0981543868407
934.55.04393299017002-0.543932990170015
947.55.676405910518031.82359408948197
955.56.91823697993591-1.41823697993591
9610.58.187475159342592.31252484065741
9777.14638753663693-0.146387536636935
9898.09001703737450.909982962625501
9965.87998482745280.120015172547199
1006.56.37164577158120.128354228418803
1017.57.110987487828350.389012512171645
10265.337842220139680.662157779860318
1039.56.964465437786812.53553456221319
1047.56.86988191941810.630118080581906
1055.57.10673428249453-1.60673428249453
1065.55.472826993583260.0271730064167366
10755.97744294942089-0.977442949420892
1086.57.15438047545671-0.654380475456711
1097.57.55074903366075-0.0507490336607508
11065.429629401662680.570370598337322
11165.589815330969090.41018466903091
11288.03953537418977-0.0395353741897737
1134.57.36083399599085-2.86083399599085
11497.550749033660751.44925096633925
11546.78187360475788-2.78187360475788
1166.55.046059592836931.45394040716307
1178.56.223705986428382.27629401357162
1184.55.43742694477468-0.937426944774683
1197.56.86330671570960.636693284290408
12044.88725226526496-0.887252265264962
1213.55.32039947401203-1.82039947401203
12265.637461523931270.362538476068732
12377.40848018895303-0.40848018895303
12435.34351316058478-2.34351316058478
12545.8799848274528-1.8799848274528
1268.56.273478782057472.22652121794253
12754.65181763729980.348182362700204
1285.56.2741876496131-0.774187649613106
12976.52403415777560.475965842224396
1305.56.89795876358571-1.39795876358571
1316.57.40848018895303-0.90848018895303
13266.07651420220803-0.0765142022080275
1335.55.09370578579910.406294214200897
1344.54.69666749341625-0.196667493416252
13566.32258184350774-0.322581843507745
136107.253256332536082.74674366746392
13765.477984462180490.522015537819508
1386.56.52190755510869-0.0219075551086936
13966.47284362703524-0.472843627035242
14068.10017571223801-2.10017571223801
1414.57.1015768138973-2.6015768138973
1427.57.448842310651070.0511576893489317
1431210.61427582699291.38572417300712
1443.55.28503855858028-1.78503855858028
1458.57.360125128435221.13987487156478
1465.56.01493046751956-0.514930467519559
1478.57.149223006859481.35077699314052
1485.55.92621328530371-0.426213285303706
14966.42874177185125-0.428741771851249
15077.05696148686545-0.0569614868654453
1515.56.61861767614432-1.11861767614432
15286.654726592508541.34527340749146
15310.56.795343173020393.70465682697961
15475.678532513184941.32146748681506
155107.997046649824832.00295335017517
1566.55.43388260699651.0661173930035
1575.56.65614432761981-1.15614432761981
1587.57.060505824643630.43949417535637
1599.59.57086620591255-0.0708662059125495






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.6786423832152410.6427152335695180.321357616784759
80.653821856162520.692356287674960.34617814383748
90.5258284156204280.9483431687591430.474171584379572
100.4058960068081180.8117920136162360.594103993191882
110.4815939465572140.9631878931144280.518406053442786
120.7360188641730780.5279622716538450.263981135826922
130.6695527852608650.660894429478270.330447214739135
140.6220506810657160.7558986378685690.377949318934284
150.5428156157821980.9143687684356050.457184384217802
160.5929661882908650.8140676234182690.407033811709135
170.605199738370850.7896005232583010.39480026162915
180.5413673099140120.9172653801719750.458632690085988
190.6690081212191360.6619837575617270.330991878780864
200.7489406761632120.5021186476735760.251059323836788
210.7447197216356740.5105605567286520.255280278364326
220.7857699205484940.4284601589030120.214230079451506
230.7581565497273560.4836869005452880.241843450272644
240.7987056135666650.402588772866670.201294386433335
250.7600334058168110.4799331883663770.239966594183189
260.754378528829260.491242942341480.24562147117074
270.7542818411823140.4914363176353730.245718158817686
280.7040790927289960.5918418145420070.295920907271004
290.718676655457170.5626466890856590.281323344542829
300.6746098240349230.6507803519301530.325390175965077
310.709556978713350.5808860425733010.29044302128665
320.714348754875490.5713024902490210.285651245124511
330.8619782429110440.2760435141779130.138021757088956
340.8319405210741220.3361189578517550.168059478925878
350.8708062834833350.2583874330333310.129193716516665
360.8880853365903650.2238293268192710.111914663409635
370.895963232868620.2080735342627610.10403676713138
380.8767074741297530.2465850517404940.123292525870247
390.8864807801361290.2270384397277430.113519219863871
400.8595602952092350.280879409581530.140439704790765
410.8824312030806270.2351375938387460.117568796919373
420.8565863664593630.2868272670812740.143413633540637
430.8449353308364980.3101293383270040.155064669163502
440.9029768878496920.1940462243006170.0970231121503083
450.9042778086938620.1914443826122750.0957221913061377
460.8885532196232240.2228935607535520.111446780376776
470.8627663837695570.2744672324608860.137233616230443
480.876208172050720.2475836558985590.12379182794928
490.8540820911677040.2918358176645910.145917908832296
500.89890658127460.20218683745080.1010934187254
510.955539645502410.08892070899518160.0444603544975908
520.9436319524671190.1127360950657630.0563680475328813
530.9291059111023280.1417881777953440.0708940888976721
540.9442615504429450.1114768991141110.0557384495570553
550.949120531919440.1017589361611210.0508794680805603
560.9373776369937330.1252447260125330.0626223630062665
570.9223978659881640.1552042680236720.077602134011836
580.9238637856595340.1522724286809310.0761362143404655
590.9218536173794470.1562927652411060.0781463826205531
600.9057609360067420.1884781279865160.094239063993258
610.886626889945710.2267462201085790.113373110054289
620.8721339935864720.2557320128270560.127866006413528
630.8627872966040660.2744254067918680.137212703395934
640.9140560715449820.1718878569100360.085943928455018
650.9017031692533760.1965936614932480.0982968307466241
660.8948535504471660.2102928991056690.105146449552834
670.8936121463162330.2127757073675350.106387853683767
680.8718760810078120.2562478379843750.128123918992188
690.851296143114190.297407713771620.14870385688581
700.8311861890626170.3376276218747670.168813810937383
710.8039061511365420.3921876977269160.196093848863458
720.8024207013430370.3951585973139260.197579298656963
730.7765092251609750.446981549678050.223490774839025
740.7722031544297940.4555936911404130.227796845570206
750.7857315696090930.4285368607818150.214268430390907
760.7563356915446150.487328616910770.243664308455385
770.7565082366014690.4869835267970610.243491763398531
780.7227290692610440.5545418614779130.277270930738956
790.7135395062304840.5729209875390320.286460493769516
800.6784529202591840.6430941594816330.321547079740816
810.6442748350899480.7114503298201040.355725164910052
820.6117773174770340.7764453650459320.388222682522966
830.5686706860187950.862658627962410.431329313981205
840.5516700095615420.8966599808769160.448329990438458
850.5525995998085930.8948008003828140.447400400191407
860.6095892798388880.7808214403222240.390410720161112
870.5658138219723260.8683723560553490.434186178027674
880.5725406133832070.8549187732335860.427459386616793
890.5484536026250140.9030927947499720.451546397374986
900.5054002392217630.9891995215564750.494599760778237
910.4638143793247860.9276287586495720.536185620675214
920.4428424208459640.8856848416919280.557157579154036
930.4029424764284670.8058849528569350.597057523571533
940.4379422054077730.8758844108155470.562057794592227
950.4359065069039410.8718130138078820.564093493096059
960.5113013605902010.9773972788195970.488698639409798
970.470380888061230.940761776122460.52961911193877
980.440727704743270.881455409486540.55927229525673
990.3959989755061790.7919979510123570.604001024493821
1000.3520421069275310.7040842138550620.647957893072469
1010.3129663242698750.6259326485397510.687033675730125
1020.2833827867945620.5667655735891240.716617213205438
1030.3954947619885650.790989523977130.604505238011435
1040.3662586181530940.7325172363061870.633741381846906
1050.3886262722927580.7772525445855160.611373727707242
1060.3468825511660040.6937651023320090.653117448833996
1070.3194562219338910.6389124438677820.680543778066109
1080.2914379313018890.5828758626037780.708562068698111
1090.2522828487403670.5045656974807330.747717151259633
1100.2193425117777830.4386850235555660.780657488222217
1110.1920763886278530.3841527772557050.807923611372147
1120.1629593927763670.3259187855527330.837040607223633
1130.2711844305149940.5423688610299870.728815569485006
1140.2631813611255680.5263627222511350.736818638874432
1150.3680858107336660.7361716214673320.631914189266334
1160.4010653717388430.8021307434776860.598934628261157
1170.4967860488077080.9935720976154170.503213951192292
1180.4580672500914820.9161345001829640.541932749908518
1190.4207816662498270.8415633324996540.579218333750173
1200.3839282270597320.7678564541194640.616071772940268
1210.4458007606507040.8916015213014080.554199239349296
1220.4026800373504040.8053600747008070.597319962649596
1230.3522445557503840.7044891115007680.647755444249616
1240.3924910206433420.7849820412866840.607508979356658
1250.4252667145061830.8505334290123650.574733285493817
1260.5286705699077880.9426588601844240.471329430092212
1270.5046917885224530.9906164229550940.495308211477547
1280.4519309375707870.9038618751415750.548069062429213
1290.4134345133407910.8268690266815830.586565486659209
1300.4875210969725520.9750421939451030.512478903027448
1310.4436882252455190.8873764504910380.556311774754481
1320.3855279855325350.771055971065070.614472014467465
1330.3715356740184030.7430713480368050.628464325981597
1340.3141217341764720.6282434683529440.685878265823528
1350.2578091153958110.5156182307916210.742190884604189
1360.3644973753281760.7289947506563510.635502624671824
1370.3056544165024670.6113088330049340.694345583497533
1380.2489341833785910.4978683667571810.751065816621409
1390.1977861204739860.3955722409479730.802213879526014
1400.24099755655910.4819951131181990.7590024434409
1410.5548562159629870.8902875680740260.445143784037013
1420.4928715947378310.9857431894756620.507128405262169
1430.4266822336438540.8533644672877070.573317766356146
1440.4886319108760810.9772638217521620.511368089123919
1450.4665703393839420.9331406787678840.533429660616058
1460.4955347641078090.9910695282156190.504465235892191
1470.4006969380585380.8013938761170750.599303061941462
1480.3237590556659060.6475181113318120.676240944334094
1490.2296412028564090.4592824057128180.770358797143591
1500.1705471287823440.3410942575646870.829452871217656
1510.1501689385346060.3003378770692110.849831061465394
1520.08183239906844310.1636647981368860.918167600931557

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.678642383215241 & 0.642715233569518 & 0.321357616784759 \tabularnewline
8 & 0.65382185616252 & 0.69235628767496 & 0.34617814383748 \tabularnewline
9 & 0.525828415620428 & 0.948343168759143 & 0.474171584379572 \tabularnewline
10 & 0.405896006808118 & 0.811792013616236 & 0.594103993191882 \tabularnewline
11 & 0.481593946557214 & 0.963187893114428 & 0.518406053442786 \tabularnewline
12 & 0.736018864173078 & 0.527962271653845 & 0.263981135826922 \tabularnewline
13 & 0.669552785260865 & 0.66089442947827 & 0.330447214739135 \tabularnewline
14 & 0.622050681065716 & 0.755898637868569 & 0.377949318934284 \tabularnewline
15 & 0.542815615782198 & 0.914368768435605 & 0.457184384217802 \tabularnewline
16 & 0.592966188290865 & 0.814067623418269 & 0.407033811709135 \tabularnewline
17 & 0.60519973837085 & 0.789600523258301 & 0.39480026162915 \tabularnewline
18 & 0.541367309914012 & 0.917265380171975 & 0.458632690085988 \tabularnewline
19 & 0.669008121219136 & 0.661983757561727 & 0.330991878780864 \tabularnewline
20 & 0.748940676163212 & 0.502118647673576 & 0.251059323836788 \tabularnewline
21 & 0.744719721635674 & 0.510560556728652 & 0.255280278364326 \tabularnewline
22 & 0.785769920548494 & 0.428460158903012 & 0.214230079451506 \tabularnewline
23 & 0.758156549727356 & 0.483686900545288 & 0.241843450272644 \tabularnewline
24 & 0.798705613566665 & 0.40258877286667 & 0.201294386433335 \tabularnewline
25 & 0.760033405816811 & 0.479933188366377 & 0.239966594183189 \tabularnewline
26 & 0.75437852882926 & 0.49124294234148 & 0.24562147117074 \tabularnewline
27 & 0.754281841182314 & 0.491436317635373 & 0.245718158817686 \tabularnewline
28 & 0.704079092728996 & 0.591841814542007 & 0.295920907271004 \tabularnewline
29 & 0.71867665545717 & 0.562646689085659 & 0.281323344542829 \tabularnewline
30 & 0.674609824034923 & 0.650780351930153 & 0.325390175965077 \tabularnewline
31 & 0.70955697871335 & 0.580886042573301 & 0.29044302128665 \tabularnewline
32 & 0.71434875487549 & 0.571302490249021 & 0.285651245124511 \tabularnewline
33 & 0.861978242911044 & 0.276043514177913 & 0.138021757088956 \tabularnewline
34 & 0.831940521074122 & 0.336118957851755 & 0.168059478925878 \tabularnewline
35 & 0.870806283483335 & 0.258387433033331 & 0.129193716516665 \tabularnewline
36 & 0.888085336590365 & 0.223829326819271 & 0.111914663409635 \tabularnewline
37 & 0.89596323286862 & 0.208073534262761 & 0.10403676713138 \tabularnewline
38 & 0.876707474129753 & 0.246585051740494 & 0.123292525870247 \tabularnewline
39 & 0.886480780136129 & 0.227038439727743 & 0.113519219863871 \tabularnewline
40 & 0.859560295209235 & 0.28087940958153 & 0.140439704790765 \tabularnewline
41 & 0.882431203080627 & 0.235137593838746 & 0.117568796919373 \tabularnewline
42 & 0.856586366459363 & 0.286827267081274 & 0.143413633540637 \tabularnewline
43 & 0.844935330836498 & 0.310129338327004 & 0.155064669163502 \tabularnewline
44 & 0.902976887849692 & 0.194046224300617 & 0.0970231121503083 \tabularnewline
45 & 0.904277808693862 & 0.191444382612275 & 0.0957221913061377 \tabularnewline
46 & 0.888553219623224 & 0.222893560753552 & 0.111446780376776 \tabularnewline
47 & 0.862766383769557 & 0.274467232460886 & 0.137233616230443 \tabularnewline
48 & 0.87620817205072 & 0.247583655898559 & 0.12379182794928 \tabularnewline
49 & 0.854082091167704 & 0.291835817664591 & 0.145917908832296 \tabularnewline
50 & 0.8989065812746 & 0.2021868374508 & 0.1010934187254 \tabularnewline
51 & 0.95553964550241 & 0.0889207089951816 & 0.0444603544975908 \tabularnewline
52 & 0.943631952467119 & 0.112736095065763 & 0.0563680475328813 \tabularnewline
53 & 0.929105911102328 & 0.141788177795344 & 0.0708940888976721 \tabularnewline
54 & 0.944261550442945 & 0.111476899114111 & 0.0557384495570553 \tabularnewline
55 & 0.94912053191944 & 0.101758936161121 & 0.0508794680805603 \tabularnewline
56 & 0.937377636993733 & 0.125244726012533 & 0.0626223630062665 \tabularnewline
57 & 0.922397865988164 & 0.155204268023672 & 0.077602134011836 \tabularnewline
58 & 0.923863785659534 & 0.152272428680931 & 0.0761362143404655 \tabularnewline
59 & 0.921853617379447 & 0.156292765241106 & 0.0781463826205531 \tabularnewline
60 & 0.905760936006742 & 0.188478127986516 & 0.094239063993258 \tabularnewline
61 & 0.88662688994571 & 0.226746220108579 & 0.113373110054289 \tabularnewline
62 & 0.872133993586472 & 0.255732012827056 & 0.127866006413528 \tabularnewline
63 & 0.862787296604066 & 0.274425406791868 & 0.137212703395934 \tabularnewline
64 & 0.914056071544982 & 0.171887856910036 & 0.085943928455018 \tabularnewline
65 & 0.901703169253376 & 0.196593661493248 & 0.0982968307466241 \tabularnewline
66 & 0.894853550447166 & 0.210292899105669 & 0.105146449552834 \tabularnewline
67 & 0.893612146316233 & 0.212775707367535 & 0.106387853683767 \tabularnewline
68 & 0.871876081007812 & 0.256247837984375 & 0.128123918992188 \tabularnewline
69 & 0.85129614311419 & 0.29740771377162 & 0.14870385688581 \tabularnewline
70 & 0.831186189062617 & 0.337627621874767 & 0.168813810937383 \tabularnewline
71 & 0.803906151136542 & 0.392187697726916 & 0.196093848863458 \tabularnewline
72 & 0.802420701343037 & 0.395158597313926 & 0.197579298656963 \tabularnewline
73 & 0.776509225160975 & 0.44698154967805 & 0.223490774839025 \tabularnewline
74 & 0.772203154429794 & 0.455593691140413 & 0.227796845570206 \tabularnewline
75 & 0.785731569609093 & 0.428536860781815 & 0.214268430390907 \tabularnewline
76 & 0.756335691544615 & 0.48732861691077 & 0.243664308455385 \tabularnewline
77 & 0.756508236601469 & 0.486983526797061 & 0.243491763398531 \tabularnewline
78 & 0.722729069261044 & 0.554541861477913 & 0.277270930738956 \tabularnewline
79 & 0.713539506230484 & 0.572920987539032 & 0.286460493769516 \tabularnewline
80 & 0.678452920259184 & 0.643094159481633 & 0.321547079740816 \tabularnewline
81 & 0.644274835089948 & 0.711450329820104 & 0.355725164910052 \tabularnewline
82 & 0.611777317477034 & 0.776445365045932 & 0.388222682522966 \tabularnewline
83 & 0.568670686018795 & 0.86265862796241 & 0.431329313981205 \tabularnewline
84 & 0.551670009561542 & 0.896659980876916 & 0.448329990438458 \tabularnewline
85 & 0.552599599808593 & 0.894800800382814 & 0.447400400191407 \tabularnewline
86 & 0.609589279838888 & 0.780821440322224 & 0.390410720161112 \tabularnewline
87 & 0.565813821972326 & 0.868372356055349 & 0.434186178027674 \tabularnewline
88 & 0.572540613383207 & 0.854918773233586 & 0.427459386616793 \tabularnewline
89 & 0.548453602625014 & 0.903092794749972 & 0.451546397374986 \tabularnewline
90 & 0.505400239221763 & 0.989199521556475 & 0.494599760778237 \tabularnewline
91 & 0.463814379324786 & 0.927628758649572 & 0.536185620675214 \tabularnewline
92 & 0.442842420845964 & 0.885684841691928 & 0.557157579154036 \tabularnewline
93 & 0.402942476428467 & 0.805884952856935 & 0.597057523571533 \tabularnewline
94 & 0.437942205407773 & 0.875884410815547 & 0.562057794592227 \tabularnewline
95 & 0.435906506903941 & 0.871813013807882 & 0.564093493096059 \tabularnewline
96 & 0.511301360590201 & 0.977397278819597 & 0.488698639409798 \tabularnewline
97 & 0.47038088806123 & 0.94076177612246 & 0.52961911193877 \tabularnewline
98 & 0.44072770474327 & 0.88145540948654 & 0.55927229525673 \tabularnewline
99 & 0.395998975506179 & 0.791997951012357 & 0.604001024493821 \tabularnewline
100 & 0.352042106927531 & 0.704084213855062 & 0.647957893072469 \tabularnewline
101 & 0.312966324269875 & 0.625932648539751 & 0.687033675730125 \tabularnewline
102 & 0.283382786794562 & 0.566765573589124 & 0.716617213205438 \tabularnewline
103 & 0.395494761988565 & 0.79098952397713 & 0.604505238011435 \tabularnewline
104 & 0.366258618153094 & 0.732517236306187 & 0.633741381846906 \tabularnewline
105 & 0.388626272292758 & 0.777252544585516 & 0.611373727707242 \tabularnewline
106 & 0.346882551166004 & 0.693765102332009 & 0.653117448833996 \tabularnewline
107 & 0.319456221933891 & 0.638912443867782 & 0.680543778066109 \tabularnewline
108 & 0.291437931301889 & 0.582875862603778 & 0.708562068698111 \tabularnewline
109 & 0.252282848740367 & 0.504565697480733 & 0.747717151259633 \tabularnewline
110 & 0.219342511777783 & 0.438685023555566 & 0.780657488222217 \tabularnewline
111 & 0.192076388627853 & 0.384152777255705 & 0.807923611372147 \tabularnewline
112 & 0.162959392776367 & 0.325918785552733 & 0.837040607223633 \tabularnewline
113 & 0.271184430514994 & 0.542368861029987 & 0.728815569485006 \tabularnewline
114 & 0.263181361125568 & 0.526362722251135 & 0.736818638874432 \tabularnewline
115 & 0.368085810733666 & 0.736171621467332 & 0.631914189266334 \tabularnewline
116 & 0.401065371738843 & 0.802130743477686 & 0.598934628261157 \tabularnewline
117 & 0.496786048807708 & 0.993572097615417 & 0.503213951192292 \tabularnewline
118 & 0.458067250091482 & 0.916134500182964 & 0.541932749908518 \tabularnewline
119 & 0.420781666249827 & 0.841563332499654 & 0.579218333750173 \tabularnewline
120 & 0.383928227059732 & 0.767856454119464 & 0.616071772940268 \tabularnewline
121 & 0.445800760650704 & 0.891601521301408 & 0.554199239349296 \tabularnewline
122 & 0.402680037350404 & 0.805360074700807 & 0.597319962649596 \tabularnewline
123 & 0.352244555750384 & 0.704489111500768 & 0.647755444249616 \tabularnewline
124 & 0.392491020643342 & 0.784982041286684 & 0.607508979356658 \tabularnewline
125 & 0.425266714506183 & 0.850533429012365 & 0.574733285493817 \tabularnewline
126 & 0.528670569907788 & 0.942658860184424 & 0.471329430092212 \tabularnewline
127 & 0.504691788522453 & 0.990616422955094 & 0.495308211477547 \tabularnewline
128 & 0.451930937570787 & 0.903861875141575 & 0.548069062429213 \tabularnewline
129 & 0.413434513340791 & 0.826869026681583 & 0.586565486659209 \tabularnewline
130 & 0.487521096972552 & 0.975042193945103 & 0.512478903027448 \tabularnewline
131 & 0.443688225245519 & 0.887376450491038 & 0.556311774754481 \tabularnewline
132 & 0.385527985532535 & 0.77105597106507 & 0.614472014467465 \tabularnewline
133 & 0.371535674018403 & 0.743071348036805 & 0.628464325981597 \tabularnewline
134 & 0.314121734176472 & 0.628243468352944 & 0.685878265823528 \tabularnewline
135 & 0.257809115395811 & 0.515618230791621 & 0.742190884604189 \tabularnewline
136 & 0.364497375328176 & 0.728994750656351 & 0.635502624671824 \tabularnewline
137 & 0.305654416502467 & 0.611308833004934 & 0.694345583497533 \tabularnewline
138 & 0.248934183378591 & 0.497868366757181 & 0.751065816621409 \tabularnewline
139 & 0.197786120473986 & 0.395572240947973 & 0.802213879526014 \tabularnewline
140 & 0.2409975565591 & 0.481995113118199 & 0.7590024434409 \tabularnewline
141 & 0.554856215962987 & 0.890287568074026 & 0.445143784037013 \tabularnewline
142 & 0.492871594737831 & 0.985743189475662 & 0.507128405262169 \tabularnewline
143 & 0.426682233643854 & 0.853364467287707 & 0.573317766356146 \tabularnewline
144 & 0.488631910876081 & 0.977263821752162 & 0.511368089123919 \tabularnewline
145 & 0.466570339383942 & 0.933140678767884 & 0.533429660616058 \tabularnewline
146 & 0.495534764107809 & 0.991069528215619 & 0.504465235892191 \tabularnewline
147 & 0.400696938058538 & 0.801393876117075 & 0.599303061941462 \tabularnewline
148 & 0.323759055665906 & 0.647518111331812 & 0.676240944334094 \tabularnewline
149 & 0.229641202856409 & 0.459282405712818 & 0.770358797143591 \tabularnewline
150 & 0.170547128782344 & 0.341094257564687 & 0.829452871217656 \tabularnewline
151 & 0.150168938534606 & 0.300337877069211 & 0.849831061465394 \tabularnewline
152 & 0.0818323990684431 & 0.163664798136886 & 0.918167600931557 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146312&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]7[/C][C]0.678642383215241[/C][C]0.642715233569518[/C][C]0.321357616784759[/C][/ROW]
[ROW][C]8[/C][C]0.65382185616252[/C][C]0.69235628767496[/C][C]0.34617814383748[/C][/ROW]
[ROW][C]9[/C][C]0.525828415620428[/C][C]0.948343168759143[/C][C]0.474171584379572[/C][/ROW]
[ROW][C]10[/C][C]0.405896006808118[/C][C]0.811792013616236[/C][C]0.594103993191882[/C][/ROW]
[ROW][C]11[/C][C]0.481593946557214[/C][C]0.963187893114428[/C][C]0.518406053442786[/C][/ROW]
[ROW][C]12[/C][C]0.736018864173078[/C][C]0.527962271653845[/C][C]0.263981135826922[/C][/ROW]
[ROW][C]13[/C][C]0.669552785260865[/C][C]0.66089442947827[/C][C]0.330447214739135[/C][/ROW]
[ROW][C]14[/C][C]0.622050681065716[/C][C]0.755898637868569[/C][C]0.377949318934284[/C][/ROW]
[ROW][C]15[/C][C]0.542815615782198[/C][C]0.914368768435605[/C][C]0.457184384217802[/C][/ROW]
[ROW][C]16[/C][C]0.592966188290865[/C][C]0.814067623418269[/C][C]0.407033811709135[/C][/ROW]
[ROW][C]17[/C][C]0.60519973837085[/C][C]0.789600523258301[/C][C]0.39480026162915[/C][/ROW]
[ROW][C]18[/C][C]0.541367309914012[/C][C]0.917265380171975[/C][C]0.458632690085988[/C][/ROW]
[ROW][C]19[/C][C]0.669008121219136[/C][C]0.661983757561727[/C][C]0.330991878780864[/C][/ROW]
[ROW][C]20[/C][C]0.748940676163212[/C][C]0.502118647673576[/C][C]0.251059323836788[/C][/ROW]
[ROW][C]21[/C][C]0.744719721635674[/C][C]0.510560556728652[/C][C]0.255280278364326[/C][/ROW]
[ROW][C]22[/C][C]0.785769920548494[/C][C]0.428460158903012[/C][C]0.214230079451506[/C][/ROW]
[ROW][C]23[/C][C]0.758156549727356[/C][C]0.483686900545288[/C][C]0.241843450272644[/C][/ROW]
[ROW][C]24[/C][C]0.798705613566665[/C][C]0.40258877286667[/C][C]0.201294386433335[/C][/ROW]
[ROW][C]25[/C][C]0.760033405816811[/C][C]0.479933188366377[/C][C]0.239966594183189[/C][/ROW]
[ROW][C]26[/C][C]0.75437852882926[/C][C]0.49124294234148[/C][C]0.24562147117074[/C][/ROW]
[ROW][C]27[/C][C]0.754281841182314[/C][C]0.491436317635373[/C][C]0.245718158817686[/C][/ROW]
[ROW][C]28[/C][C]0.704079092728996[/C][C]0.591841814542007[/C][C]0.295920907271004[/C][/ROW]
[ROW][C]29[/C][C]0.71867665545717[/C][C]0.562646689085659[/C][C]0.281323344542829[/C][/ROW]
[ROW][C]30[/C][C]0.674609824034923[/C][C]0.650780351930153[/C][C]0.325390175965077[/C][/ROW]
[ROW][C]31[/C][C]0.70955697871335[/C][C]0.580886042573301[/C][C]0.29044302128665[/C][/ROW]
[ROW][C]32[/C][C]0.71434875487549[/C][C]0.571302490249021[/C][C]0.285651245124511[/C][/ROW]
[ROW][C]33[/C][C]0.861978242911044[/C][C]0.276043514177913[/C][C]0.138021757088956[/C][/ROW]
[ROW][C]34[/C][C]0.831940521074122[/C][C]0.336118957851755[/C][C]0.168059478925878[/C][/ROW]
[ROW][C]35[/C][C]0.870806283483335[/C][C]0.258387433033331[/C][C]0.129193716516665[/C][/ROW]
[ROW][C]36[/C][C]0.888085336590365[/C][C]0.223829326819271[/C][C]0.111914663409635[/C][/ROW]
[ROW][C]37[/C][C]0.89596323286862[/C][C]0.208073534262761[/C][C]0.10403676713138[/C][/ROW]
[ROW][C]38[/C][C]0.876707474129753[/C][C]0.246585051740494[/C][C]0.123292525870247[/C][/ROW]
[ROW][C]39[/C][C]0.886480780136129[/C][C]0.227038439727743[/C][C]0.113519219863871[/C][/ROW]
[ROW][C]40[/C][C]0.859560295209235[/C][C]0.28087940958153[/C][C]0.140439704790765[/C][/ROW]
[ROW][C]41[/C][C]0.882431203080627[/C][C]0.235137593838746[/C][C]0.117568796919373[/C][/ROW]
[ROW][C]42[/C][C]0.856586366459363[/C][C]0.286827267081274[/C][C]0.143413633540637[/C][/ROW]
[ROW][C]43[/C][C]0.844935330836498[/C][C]0.310129338327004[/C][C]0.155064669163502[/C][/ROW]
[ROW][C]44[/C][C]0.902976887849692[/C][C]0.194046224300617[/C][C]0.0970231121503083[/C][/ROW]
[ROW][C]45[/C][C]0.904277808693862[/C][C]0.191444382612275[/C][C]0.0957221913061377[/C][/ROW]
[ROW][C]46[/C][C]0.888553219623224[/C][C]0.222893560753552[/C][C]0.111446780376776[/C][/ROW]
[ROW][C]47[/C][C]0.862766383769557[/C][C]0.274467232460886[/C][C]0.137233616230443[/C][/ROW]
[ROW][C]48[/C][C]0.87620817205072[/C][C]0.247583655898559[/C][C]0.12379182794928[/C][/ROW]
[ROW][C]49[/C][C]0.854082091167704[/C][C]0.291835817664591[/C][C]0.145917908832296[/C][/ROW]
[ROW][C]50[/C][C]0.8989065812746[/C][C]0.2021868374508[/C][C]0.1010934187254[/C][/ROW]
[ROW][C]51[/C][C]0.95553964550241[/C][C]0.0889207089951816[/C][C]0.0444603544975908[/C][/ROW]
[ROW][C]52[/C][C]0.943631952467119[/C][C]0.112736095065763[/C][C]0.0563680475328813[/C][/ROW]
[ROW][C]53[/C][C]0.929105911102328[/C][C]0.141788177795344[/C][C]0.0708940888976721[/C][/ROW]
[ROW][C]54[/C][C]0.944261550442945[/C][C]0.111476899114111[/C][C]0.0557384495570553[/C][/ROW]
[ROW][C]55[/C][C]0.94912053191944[/C][C]0.101758936161121[/C][C]0.0508794680805603[/C][/ROW]
[ROW][C]56[/C][C]0.937377636993733[/C][C]0.125244726012533[/C][C]0.0626223630062665[/C][/ROW]
[ROW][C]57[/C][C]0.922397865988164[/C][C]0.155204268023672[/C][C]0.077602134011836[/C][/ROW]
[ROW][C]58[/C][C]0.923863785659534[/C][C]0.152272428680931[/C][C]0.0761362143404655[/C][/ROW]
[ROW][C]59[/C][C]0.921853617379447[/C][C]0.156292765241106[/C][C]0.0781463826205531[/C][/ROW]
[ROW][C]60[/C][C]0.905760936006742[/C][C]0.188478127986516[/C][C]0.094239063993258[/C][/ROW]
[ROW][C]61[/C][C]0.88662688994571[/C][C]0.226746220108579[/C][C]0.113373110054289[/C][/ROW]
[ROW][C]62[/C][C]0.872133993586472[/C][C]0.255732012827056[/C][C]0.127866006413528[/C][/ROW]
[ROW][C]63[/C][C]0.862787296604066[/C][C]0.274425406791868[/C][C]0.137212703395934[/C][/ROW]
[ROW][C]64[/C][C]0.914056071544982[/C][C]0.171887856910036[/C][C]0.085943928455018[/C][/ROW]
[ROW][C]65[/C][C]0.901703169253376[/C][C]0.196593661493248[/C][C]0.0982968307466241[/C][/ROW]
[ROW][C]66[/C][C]0.894853550447166[/C][C]0.210292899105669[/C][C]0.105146449552834[/C][/ROW]
[ROW][C]67[/C][C]0.893612146316233[/C][C]0.212775707367535[/C][C]0.106387853683767[/C][/ROW]
[ROW][C]68[/C][C]0.871876081007812[/C][C]0.256247837984375[/C][C]0.128123918992188[/C][/ROW]
[ROW][C]69[/C][C]0.85129614311419[/C][C]0.29740771377162[/C][C]0.14870385688581[/C][/ROW]
[ROW][C]70[/C][C]0.831186189062617[/C][C]0.337627621874767[/C][C]0.168813810937383[/C][/ROW]
[ROW][C]71[/C][C]0.803906151136542[/C][C]0.392187697726916[/C][C]0.196093848863458[/C][/ROW]
[ROW][C]72[/C][C]0.802420701343037[/C][C]0.395158597313926[/C][C]0.197579298656963[/C][/ROW]
[ROW][C]73[/C][C]0.776509225160975[/C][C]0.44698154967805[/C][C]0.223490774839025[/C][/ROW]
[ROW][C]74[/C][C]0.772203154429794[/C][C]0.455593691140413[/C][C]0.227796845570206[/C][/ROW]
[ROW][C]75[/C][C]0.785731569609093[/C][C]0.428536860781815[/C][C]0.214268430390907[/C][/ROW]
[ROW][C]76[/C][C]0.756335691544615[/C][C]0.48732861691077[/C][C]0.243664308455385[/C][/ROW]
[ROW][C]77[/C][C]0.756508236601469[/C][C]0.486983526797061[/C][C]0.243491763398531[/C][/ROW]
[ROW][C]78[/C][C]0.722729069261044[/C][C]0.554541861477913[/C][C]0.277270930738956[/C][/ROW]
[ROW][C]79[/C][C]0.713539506230484[/C][C]0.572920987539032[/C][C]0.286460493769516[/C][/ROW]
[ROW][C]80[/C][C]0.678452920259184[/C][C]0.643094159481633[/C][C]0.321547079740816[/C][/ROW]
[ROW][C]81[/C][C]0.644274835089948[/C][C]0.711450329820104[/C][C]0.355725164910052[/C][/ROW]
[ROW][C]82[/C][C]0.611777317477034[/C][C]0.776445365045932[/C][C]0.388222682522966[/C][/ROW]
[ROW][C]83[/C][C]0.568670686018795[/C][C]0.86265862796241[/C][C]0.431329313981205[/C][/ROW]
[ROW][C]84[/C][C]0.551670009561542[/C][C]0.896659980876916[/C][C]0.448329990438458[/C][/ROW]
[ROW][C]85[/C][C]0.552599599808593[/C][C]0.894800800382814[/C][C]0.447400400191407[/C][/ROW]
[ROW][C]86[/C][C]0.609589279838888[/C][C]0.780821440322224[/C][C]0.390410720161112[/C][/ROW]
[ROW][C]87[/C][C]0.565813821972326[/C][C]0.868372356055349[/C][C]0.434186178027674[/C][/ROW]
[ROW][C]88[/C][C]0.572540613383207[/C][C]0.854918773233586[/C][C]0.427459386616793[/C][/ROW]
[ROW][C]89[/C][C]0.548453602625014[/C][C]0.903092794749972[/C][C]0.451546397374986[/C][/ROW]
[ROW][C]90[/C][C]0.505400239221763[/C][C]0.989199521556475[/C][C]0.494599760778237[/C][/ROW]
[ROW][C]91[/C][C]0.463814379324786[/C][C]0.927628758649572[/C][C]0.536185620675214[/C][/ROW]
[ROW][C]92[/C][C]0.442842420845964[/C][C]0.885684841691928[/C][C]0.557157579154036[/C][/ROW]
[ROW][C]93[/C][C]0.402942476428467[/C][C]0.805884952856935[/C][C]0.597057523571533[/C][/ROW]
[ROW][C]94[/C][C]0.437942205407773[/C][C]0.875884410815547[/C][C]0.562057794592227[/C][/ROW]
[ROW][C]95[/C][C]0.435906506903941[/C][C]0.871813013807882[/C][C]0.564093493096059[/C][/ROW]
[ROW][C]96[/C][C]0.511301360590201[/C][C]0.977397278819597[/C][C]0.488698639409798[/C][/ROW]
[ROW][C]97[/C][C]0.47038088806123[/C][C]0.94076177612246[/C][C]0.52961911193877[/C][/ROW]
[ROW][C]98[/C][C]0.44072770474327[/C][C]0.88145540948654[/C][C]0.55927229525673[/C][/ROW]
[ROW][C]99[/C][C]0.395998975506179[/C][C]0.791997951012357[/C][C]0.604001024493821[/C][/ROW]
[ROW][C]100[/C][C]0.352042106927531[/C][C]0.704084213855062[/C][C]0.647957893072469[/C][/ROW]
[ROW][C]101[/C][C]0.312966324269875[/C][C]0.625932648539751[/C][C]0.687033675730125[/C][/ROW]
[ROW][C]102[/C][C]0.283382786794562[/C][C]0.566765573589124[/C][C]0.716617213205438[/C][/ROW]
[ROW][C]103[/C][C]0.395494761988565[/C][C]0.79098952397713[/C][C]0.604505238011435[/C][/ROW]
[ROW][C]104[/C][C]0.366258618153094[/C][C]0.732517236306187[/C][C]0.633741381846906[/C][/ROW]
[ROW][C]105[/C][C]0.388626272292758[/C][C]0.777252544585516[/C][C]0.611373727707242[/C][/ROW]
[ROW][C]106[/C][C]0.346882551166004[/C][C]0.693765102332009[/C][C]0.653117448833996[/C][/ROW]
[ROW][C]107[/C][C]0.319456221933891[/C][C]0.638912443867782[/C][C]0.680543778066109[/C][/ROW]
[ROW][C]108[/C][C]0.291437931301889[/C][C]0.582875862603778[/C][C]0.708562068698111[/C][/ROW]
[ROW][C]109[/C][C]0.252282848740367[/C][C]0.504565697480733[/C][C]0.747717151259633[/C][/ROW]
[ROW][C]110[/C][C]0.219342511777783[/C][C]0.438685023555566[/C][C]0.780657488222217[/C][/ROW]
[ROW][C]111[/C][C]0.192076388627853[/C][C]0.384152777255705[/C][C]0.807923611372147[/C][/ROW]
[ROW][C]112[/C][C]0.162959392776367[/C][C]0.325918785552733[/C][C]0.837040607223633[/C][/ROW]
[ROW][C]113[/C][C]0.271184430514994[/C][C]0.542368861029987[/C][C]0.728815569485006[/C][/ROW]
[ROW][C]114[/C][C]0.263181361125568[/C][C]0.526362722251135[/C][C]0.736818638874432[/C][/ROW]
[ROW][C]115[/C][C]0.368085810733666[/C][C]0.736171621467332[/C][C]0.631914189266334[/C][/ROW]
[ROW][C]116[/C][C]0.401065371738843[/C][C]0.802130743477686[/C][C]0.598934628261157[/C][/ROW]
[ROW][C]117[/C][C]0.496786048807708[/C][C]0.993572097615417[/C][C]0.503213951192292[/C][/ROW]
[ROW][C]118[/C][C]0.458067250091482[/C][C]0.916134500182964[/C][C]0.541932749908518[/C][/ROW]
[ROW][C]119[/C][C]0.420781666249827[/C][C]0.841563332499654[/C][C]0.579218333750173[/C][/ROW]
[ROW][C]120[/C][C]0.383928227059732[/C][C]0.767856454119464[/C][C]0.616071772940268[/C][/ROW]
[ROW][C]121[/C][C]0.445800760650704[/C][C]0.891601521301408[/C][C]0.554199239349296[/C][/ROW]
[ROW][C]122[/C][C]0.402680037350404[/C][C]0.805360074700807[/C][C]0.597319962649596[/C][/ROW]
[ROW][C]123[/C][C]0.352244555750384[/C][C]0.704489111500768[/C][C]0.647755444249616[/C][/ROW]
[ROW][C]124[/C][C]0.392491020643342[/C][C]0.784982041286684[/C][C]0.607508979356658[/C][/ROW]
[ROW][C]125[/C][C]0.425266714506183[/C][C]0.850533429012365[/C][C]0.574733285493817[/C][/ROW]
[ROW][C]126[/C][C]0.528670569907788[/C][C]0.942658860184424[/C][C]0.471329430092212[/C][/ROW]
[ROW][C]127[/C][C]0.504691788522453[/C][C]0.990616422955094[/C][C]0.495308211477547[/C][/ROW]
[ROW][C]128[/C][C]0.451930937570787[/C][C]0.903861875141575[/C][C]0.548069062429213[/C][/ROW]
[ROW][C]129[/C][C]0.413434513340791[/C][C]0.826869026681583[/C][C]0.586565486659209[/C][/ROW]
[ROW][C]130[/C][C]0.487521096972552[/C][C]0.975042193945103[/C][C]0.512478903027448[/C][/ROW]
[ROW][C]131[/C][C]0.443688225245519[/C][C]0.887376450491038[/C][C]0.556311774754481[/C][/ROW]
[ROW][C]132[/C][C]0.385527985532535[/C][C]0.77105597106507[/C][C]0.614472014467465[/C][/ROW]
[ROW][C]133[/C][C]0.371535674018403[/C][C]0.743071348036805[/C][C]0.628464325981597[/C][/ROW]
[ROW][C]134[/C][C]0.314121734176472[/C][C]0.628243468352944[/C][C]0.685878265823528[/C][/ROW]
[ROW][C]135[/C][C]0.257809115395811[/C][C]0.515618230791621[/C][C]0.742190884604189[/C][/ROW]
[ROW][C]136[/C][C]0.364497375328176[/C][C]0.728994750656351[/C][C]0.635502624671824[/C][/ROW]
[ROW][C]137[/C][C]0.305654416502467[/C][C]0.611308833004934[/C][C]0.694345583497533[/C][/ROW]
[ROW][C]138[/C][C]0.248934183378591[/C][C]0.497868366757181[/C][C]0.751065816621409[/C][/ROW]
[ROW][C]139[/C][C]0.197786120473986[/C][C]0.395572240947973[/C][C]0.802213879526014[/C][/ROW]
[ROW][C]140[/C][C]0.2409975565591[/C][C]0.481995113118199[/C][C]0.7590024434409[/C][/ROW]
[ROW][C]141[/C][C]0.554856215962987[/C][C]0.890287568074026[/C][C]0.445143784037013[/C][/ROW]
[ROW][C]142[/C][C]0.492871594737831[/C][C]0.985743189475662[/C][C]0.507128405262169[/C][/ROW]
[ROW][C]143[/C][C]0.426682233643854[/C][C]0.853364467287707[/C][C]0.573317766356146[/C][/ROW]
[ROW][C]144[/C][C]0.488631910876081[/C][C]0.977263821752162[/C][C]0.511368089123919[/C][/ROW]
[ROW][C]145[/C][C]0.466570339383942[/C][C]0.933140678767884[/C][C]0.533429660616058[/C][/ROW]
[ROW][C]146[/C][C]0.495534764107809[/C][C]0.991069528215619[/C][C]0.504465235892191[/C][/ROW]
[ROW][C]147[/C][C]0.400696938058538[/C][C]0.801393876117075[/C][C]0.599303061941462[/C][/ROW]
[ROW][C]148[/C][C]0.323759055665906[/C][C]0.647518111331812[/C][C]0.676240944334094[/C][/ROW]
[ROW][C]149[/C][C]0.229641202856409[/C][C]0.459282405712818[/C][C]0.770358797143591[/C][/ROW]
[ROW][C]150[/C][C]0.170547128782344[/C][C]0.341094257564687[/C][C]0.829452871217656[/C][/ROW]
[ROW][C]151[/C][C]0.150168938534606[/C][C]0.300337877069211[/C][C]0.849831061465394[/C][/ROW]
[ROW][C]152[/C][C]0.0818323990684431[/C][C]0.163664798136886[/C][C]0.918167600931557[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146312&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146312&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
70.6786423832152410.6427152335695180.321357616784759
80.653821856162520.692356287674960.34617814383748
90.5258284156204280.9483431687591430.474171584379572
100.4058960068081180.8117920136162360.594103993191882
110.4815939465572140.9631878931144280.518406053442786
120.7360188641730780.5279622716538450.263981135826922
130.6695527852608650.660894429478270.330447214739135
140.6220506810657160.7558986378685690.377949318934284
150.5428156157821980.9143687684356050.457184384217802
160.5929661882908650.8140676234182690.407033811709135
170.605199738370850.7896005232583010.39480026162915
180.5413673099140120.9172653801719750.458632690085988
190.6690081212191360.6619837575617270.330991878780864
200.7489406761632120.5021186476735760.251059323836788
210.7447197216356740.5105605567286520.255280278364326
220.7857699205484940.4284601589030120.214230079451506
230.7581565497273560.4836869005452880.241843450272644
240.7987056135666650.402588772866670.201294386433335
250.7600334058168110.4799331883663770.239966594183189
260.754378528829260.491242942341480.24562147117074
270.7542818411823140.4914363176353730.245718158817686
280.7040790927289960.5918418145420070.295920907271004
290.718676655457170.5626466890856590.281323344542829
300.6746098240349230.6507803519301530.325390175965077
310.709556978713350.5808860425733010.29044302128665
320.714348754875490.5713024902490210.285651245124511
330.8619782429110440.2760435141779130.138021757088956
340.8319405210741220.3361189578517550.168059478925878
350.8708062834833350.2583874330333310.129193716516665
360.8880853365903650.2238293268192710.111914663409635
370.895963232868620.2080735342627610.10403676713138
380.8767074741297530.2465850517404940.123292525870247
390.8864807801361290.2270384397277430.113519219863871
400.8595602952092350.280879409581530.140439704790765
410.8824312030806270.2351375938387460.117568796919373
420.8565863664593630.2868272670812740.143413633540637
430.8449353308364980.3101293383270040.155064669163502
440.9029768878496920.1940462243006170.0970231121503083
450.9042778086938620.1914443826122750.0957221913061377
460.8885532196232240.2228935607535520.111446780376776
470.8627663837695570.2744672324608860.137233616230443
480.876208172050720.2475836558985590.12379182794928
490.8540820911677040.2918358176645910.145917908832296
500.89890658127460.20218683745080.1010934187254
510.955539645502410.08892070899518160.0444603544975908
520.9436319524671190.1127360950657630.0563680475328813
530.9291059111023280.1417881777953440.0708940888976721
540.9442615504429450.1114768991141110.0557384495570553
550.949120531919440.1017589361611210.0508794680805603
560.9373776369937330.1252447260125330.0626223630062665
570.9223978659881640.1552042680236720.077602134011836
580.9238637856595340.1522724286809310.0761362143404655
590.9218536173794470.1562927652411060.0781463826205531
600.9057609360067420.1884781279865160.094239063993258
610.886626889945710.2267462201085790.113373110054289
620.8721339935864720.2557320128270560.127866006413528
630.8627872966040660.2744254067918680.137212703395934
640.9140560715449820.1718878569100360.085943928455018
650.9017031692533760.1965936614932480.0982968307466241
660.8948535504471660.2102928991056690.105146449552834
670.8936121463162330.2127757073675350.106387853683767
680.8718760810078120.2562478379843750.128123918992188
690.851296143114190.297407713771620.14870385688581
700.8311861890626170.3376276218747670.168813810937383
710.8039061511365420.3921876977269160.196093848863458
720.8024207013430370.3951585973139260.197579298656963
730.7765092251609750.446981549678050.223490774839025
740.7722031544297940.4555936911404130.227796845570206
750.7857315696090930.4285368607818150.214268430390907
760.7563356915446150.487328616910770.243664308455385
770.7565082366014690.4869835267970610.243491763398531
780.7227290692610440.5545418614779130.277270930738956
790.7135395062304840.5729209875390320.286460493769516
800.6784529202591840.6430941594816330.321547079740816
810.6442748350899480.7114503298201040.355725164910052
820.6117773174770340.7764453650459320.388222682522966
830.5686706860187950.862658627962410.431329313981205
840.5516700095615420.8966599808769160.448329990438458
850.5525995998085930.8948008003828140.447400400191407
860.6095892798388880.7808214403222240.390410720161112
870.5658138219723260.8683723560553490.434186178027674
880.5725406133832070.8549187732335860.427459386616793
890.5484536026250140.9030927947499720.451546397374986
900.5054002392217630.9891995215564750.494599760778237
910.4638143793247860.9276287586495720.536185620675214
920.4428424208459640.8856848416919280.557157579154036
930.4029424764284670.8058849528569350.597057523571533
940.4379422054077730.8758844108155470.562057794592227
950.4359065069039410.8718130138078820.564093493096059
960.5113013605902010.9773972788195970.488698639409798
970.470380888061230.940761776122460.52961911193877
980.440727704743270.881455409486540.55927229525673
990.3959989755061790.7919979510123570.604001024493821
1000.3520421069275310.7040842138550620.647957893072469
1010.3129663242698750.6259326485397510.687033675730125
1020.2833827867945620.5667655735891240.716617213205438
1030.3954947619885650.790989523977130.604505238011435
1040.3662586181530940.7325172363061870.633741381846906
1050.3886262722927580.7772525445855160.611373727707242
1060.3468825511660040.6937651023320090.653117448833996
1070.3194562219338910.6389124438677820.680543778066109
1080.2914379313018890.5828758626037780.708562068698111
1090.2522828487403670.5045656974807330.747717151259633
1100.2193425117777830.4386850235555660.780657488222217
1110.1920763886278530.3841527772557050.807923611372147
1120.1629593927763670.3259187855527330.837040607223633
1130.2711844305149940.5423688610299870.728815569485006
1140.2631813611255680.5263627222511350.736818638874432
1150.3680858107336660.7361716214673320.631914189266334
1160.4010653717388430.8021307434776860.598934628261157
1170.4967860488077080.9935720976154170.503213951192292
1180.4580672500914820.9161345001829640.541932749908518
1190.4207816662498270.8415633324996540.579218333750173
1200.3839282270597320.7678564541194640.616071772940268
1210.4458007606507040.8916015213014080.554199239349296
1220.4026800373504040.8053600747008070.597319962649596
1230.3522445557503840.7044891115007680.647755444249616
1240.3924910206433420.7849820412866840.607508979356658
1250.4252667145061830.8505334290123650.574733285493817
1260.5286705699077880.9426588601844240.471329430092212
1270.5046917885224530.9906164229550940.495308211477547
1280.4519309375707870.9038618751415750.548069062429213
1290.4134345133407910.8268690266815830.586565486659209
1300.4875210969725520.9750421939451030.512478903027448
1310.4436882252455190.8873764504910380.556311774754481
1320.3855279855325350.771055971065070.614472014467465
1330.3715356740184030.7430713480368050.628464325981597
1340.3141217341764720.6282434683529440.685878265823528
1350.2578091153958110.5156182307916210.742190884604189
1360.3644973753281760.7289947506563510.635502624671824
1370.3056544165024670.6113088330049340.694345583497533
1380.2489341833785910.4978683667571810.751065816621409
1390.1977861204739860.3955722409479730.802213879526014
1400.24099755655910.4819951131181990.7590024434409
1410.5548562159629870.8902875680740260.445143784037013
1420.4928715947378310.9857431894756620.507128405262169
1430.4266822336438540.8533644672877070.573317766356146
1440.4886319108760810.9772638217521620.511368089123919
1450.4665703393839420.9331406787678840.533429660616058
1460.4955347641078090.9910695282156190.504465235892191
1470.4006969380585380.8013938761170750.599303061941462
1480.3237590556659060.6475181113318120.676240944334094
1490.2296412028564090.4592824057128180.770358797143591
1500.1705471287823440.3410942575646870.829452871217656
1510.1501689385346060.3003378770692110.849831061465394
1520.08183239906844310.1636647981368860.918167600931557






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.00684931506849315OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.00684931506849315 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146312&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.00684931506849315[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146312&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146312&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.00684931506849315OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 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')
}