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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 computationMon, 22 Nov 2010 23:28:04 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290468604873adt0tcuyhbuw.htm/, Retrieved Thu, 25 Apr 2024 11:50:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98798, Retrieved Thu, 25 Apr 2024 11:50:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact177

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [Workshop7, Mini-t...] [2010-11-22 22:12:50] [d946de7cca328fbcf207448a112523ab]
-   P     [Multiple Regression] [Workshop7, Mini-t...] [2010-11-22 22:25:18] [d946de7cca328fbcf207448a112523ab]
-   PD        [Multiple Regression] [Workshop7, Mini-t...] [2010-11-22 23:28:04] [99c051a77087383325372ff23bc64341] [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 time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98798&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98798&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98798&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 14.1670937733770 -0.00374346986010199Expect[t] -0.0356071628680675Criticism[t] -0.230056642873884Concerns[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  14.1670937733770 -0.00374346986010199Expect[t] -0.0356071628680675Criticism[t] -0.230056642873884Concerns[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98798&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  14.1670937733770 -0.00374346986010199Expect[t] -0.0356071628680675Criticism[t] -0.230056642873884Concerns[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98798&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98798&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
Depression[t] = + 14.1670937733770 -0.00374346986010199Expect[t] -0.0356071628680675Criticism[t] -0.230056642873884Concerns[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.16709377337701.19413411.863900
Expect-0.003743469860101990.184582-0.02030.9838450.491923
Criticism-0.03560716286806750.233426-0.15250.8789580.439479
Concerns-0.2300566428738840.211821-1.08610.2791250.139562

\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) & 14.1670937733770 & 1.194134 & 11.8639 & 0 & 0 \tabularnewline
Expect & -0.00374346986010199 & 0.184582 & -0.0203 & 0.983845 & 0.491923 \tabularnewline
Criticism & -0.0356071628680675 & 0.233426 & -0.1525 & 0.878958 & 0.439479 \tabularnewline
Concerns & -0.230056642873884 & 0.211821 & -1.0861 & 0.279125 & 0.139562 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98798&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]14.1670937733770[/C][C]1.194134[/C][C]11.8639[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Expect[/C][C]-0.00374346986010199[/C][C]0.184582[/C][C]-0.0203[/C][C]0.983845[/C][C]0.491923[/C][/ROW]
[ROW][C]Criticism[/C][C]-0.0356071628680675[/C][C]0.233426[/C][C]-0.1525[/C][C]0.878958[/C][C]0.439479[/C][/ROW]
[ROW][C]Concerns[/C][C]-0.230056642873884[/C][C]0.211821[/C][C]-1.0861[/C][C]0.279125[/C][C]0.139562[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98798&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98798&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)14.16709377337701.19413411.863900
Expect-0.003743469860101990.184582-0.02030.9838450.491923
Criticism-0.03560716286806750.233426-0.15250.8789580.439479
Concerns-0.2300566428738840.211821-1.08610.2791250.139562






Multiple Linear Regression - Regression Statistics
Multiple R0.099685842877191
R-squared0.009937267270136
Adjusted R-squared-0.0092252372343129
F-TEST (value)0.518578731074994
F-TEST (DF numerator)3
F-TEST (DF denominator)155
p-value0.67010300974329
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.15987814050084
Sum Squared Residuals1547.64862873633

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.099685842877191 \tabularnewline
R-squared & 0.009937267270136 \tabularnewline
Adjusted R-squared & -0.0092252372343129 \tabularnewline
F-TEST (value) & 0.518578731074994 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 0.67010300974329 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.15987814050084 \tabularnewline
Sum Squared Residuals & 1547.64862873633 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98798&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.099685842877191[/C][/ROW]
[ROW][C]R-squared[/C][C]0.009937267270136[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0092252372343129[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.518578731074994[/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]0.67010300974329[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.15987814050084[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1547.64862873633[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98798&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98798&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 R0.099685842877191
R-squared0.009937267270136
Adjusted R-squared-0.0092252372343129
F-TEST (value)0.518578731074994
F-TEST (DF numerator)3
F-TEST (DF denominator)155
p-value0.67010300974329
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.15987814050084
Sum Squared Residuals1547.64862873633






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11212.7066598054202-0.706659805420156
21112.7324480430155-1.73244804301554
31413.12323151803050.876768481969459
41213.0857212011086-1.08572120110865
52113.06417414981457.9358258501855
61213.2010062355471-1.20100623554709
72213.05383886905028.94616113094978
81113.131663644741-2.131663644741
91013.0997813126826-3.09978131268258
101313.1485220391848-0.14852203918478
111012.7516570620623-2.75165706206229
12812.424632392187-4.424632392187
131512.79287942972052.20712057027949
141413.08572120110860.914278798891355
151013.2379874169041-3.23798741690410
161413.45355218721680.54644781278321
171412.91565140387921.08434859612084
181113.2379874169041-2.23798741690410
191012.9891977151678-2.98919771516781
201312.35434461439070.645655385609337
21712.6059325989968-5.60593259899679
221412.19833492873511.80166507126489
231212.8350784034928-0.83507840349277
241412.42085750320311.57914249679688
251113.0332370047463-2.03323700474634
26913.1597838678889-4.15978386788886
271113.0107447664619-2.01074476646195
281512.90816446415902.09183553584104
291412.85193679793651.14806320206345
301313.1663256206188-0.166325620618814
31912.8172247638845-3.8172247638845
321512.56943030731272.43056969268729
331012.7146572416548-2.71465724165482
341112.7408929497993-1.74089294979932
351313.1054279365965-0.105427936596509
36812.4077239395690-4.40772393956899
372012.35711147813677.64288852186327
381213.3907513491407-1.39075134914065
391012.2840568365943-2.28405683659432
401012.7408929497993-2.74089294979932
41912.4738019501879-3.47380195018789
421412.85568026779671.14431973220335
43813.0763625264584-5.07636252645839
441413.17851399726270.821486002737306
451112.9844962782441-1.98449627824414
461313.2379874169041-0.237987416904096
47912.9414021756559-3.94140217565586
481112.3571114781367-1.35711147813673
491512.52063952263632.47936047736373
501112.8350597644423-1.83505976444231
511012.7567933771795-2.75679337717952
521413.08197773124850.918022268751457
531812.98075280838405.01924719161597
541413.52331082944820.476689170551758
551112.3351481876995-1.33514818769950
561212.6724769069330-0.67247690693303
571312.87722731909080.122772680909214
58913.1644538856888-4.16445388568876
591013.4685260666572-3.4685260666572
601513.28485640847621.71514359152375
612013.11478661124686.88521338875324
621212.9615752084611-0.961575208461057
631213.0454253813902-1.04542538139022
641413.07166108953470.928338910465287
651313.5467610347962-0.546761034796208
661112.5796654717285-1.57966547172852
671712.41898576827314.58101423172693
681213.0294935348862-1.02949353488624
691313.5814416497245-0.58144164972448
701412.98262454331411.01737545668592
711312.58905556550260.410944434497446
721513.05478405604051.94521594395952
731312.44430770855110.555692291448914
741012.4559983687539-2.45599836875386
751112.7024688650109-1.70246886501094
761912.82661485765856.17338514234147
771313.3710760327766-0.37107603277657
781712.90629272922894.09370727077109
791313.0351087396764-0.0351087396763905
80912.6049874120065-3.60498741200654
811112.7891359598604-1.78913595986041
821013.0838494661786-3.08384946617859
83912.925968045593-3.92596804559299
841212.9376587057958-0.937658705795762
851212.8875125416808-0.887512541680837
861312.93815642223690.0618435777631296
871312.98312225975520.0168777402448073
881213.2417308867642-1.24173088676420
891512.45694355574412.54305644425589
902213.04916885125038.95083114874968
911312.66873343707290.331266562927073
921513.53457265815231.46542734184767
931313.2422286032053-0.242228603205306
941512.90721927716872.0927807228313
951012.9976298418783-2.99762984187827
961112.3290540932364-1.32905409323642
971612.39556698204893.60443301795112
981112.4381947873198-1.43819478731983
991113.0491688512503-2.04916885125032
1001012.8753555841607-2.87535558416074
1011012.7853924900003-2.78539249000031
1021613.27172284484212.72827715515789
1031212.9320435010056-0.93204350100561
1041113.0407553635903-2.04075536359032
1051612.79287942972053.20712057027949
1061912.77837184759746.22162815240256
1071112.9493868318172-1.94938683181718
1081612.73852349842823.26147650157184
1091512.66406341927302.33593658072697
1102413.168197355548910.8318026444511
1111413.35514418627260.644855813727413
1121512.49255071861222.50744928138782
1131112.8800442410111-1.88004424101109
1141512.65844821448292.34155178551712
1151213.4961672124144-1.49616721241441
1161013.2347416634851-3.2347416634851
1171413.02200659516600.977993404833966
1181313.1738125603390-0.173812560339018
119912.6996705821411-3.6996705821411
1201513.05945407384041.94054592615963
1211512.60121252302272.39878747697734
1221413.30453172484030.695468275159667
1231112.8200731049286-1.82007310492858
124813.2829532544224-5.28295325442242
1251113.0566557909705-2.05665579097053
1261112.9713941337338-1.97139413373378
127813.3110734775703-5.31107347757028
1281012.9826245433141-2.98262454331408
1291113.0604306799544-2.06043067995441
1301312.31776084540860.68223915459144
1311112.8219448398586-1.82194483985863
1322013.18320265411306.81679734588695
1331013.1878726719129-3.18787267191295
1341513.26003218463931.73996781536066
1351212.9278397805230-0.92783978052304
1361412.62189586462461.37810413537545
1372313.11758489411669.88241510588339
1381413.06230241488450.937697585115542
1391613.11478661124682.88521338875324
1401112.7882094119206-1.78820941192062
1411212.4555381181314-0.455538118131392
1421012.4265041271171-2.42650412711705
1431412.53989859985731.46010140014274
1441212.9906091994754-0.990609199475397
1451212.8650703615707-0.865070361570685
1461112.5581184204344-1.55811842043438
1471212.3899517772587-0.38995177725873
1481313.0004281247481-0.000428124748118555
1491113.1653990726790-2.16539907267902
1501912.83787668636266.16212331363738
1511212.9648209618801-0.96482096188005
1521712.56376504434834.43623495565168
153912.0614842039521-3.06148420395207
1541212.9090910120988-0.909091012098752
1551912.87676102177356.12323897822654
1561813.16632562061884.83367437938119
1571512.57312371899862.42687628100143
1581412.83600495143261.16399504856743
1591112.2615832373604-1.26158323736039

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 12.7066598054202 & -0.706659805420156 \tabularnewline
2 & 11 & 12.7324480430155 & -1.73244804301554 \tabularnewline
3 & 14 & 13.1232315180305 & 0.876768481969459 \tabularnewline
4 & 12 & 13.0857212011086 & -1.08572120110865 \tabularnewline
5 & 21 & 13.0641741498145 & 7.9358258501855 \tabularnewline
6 & 12 & 13.2010062355471 & -1.20100623554709 \tabularnewline
7 & 22 & 13.0538388690502 & 8.94616113094978 \tabularnewline
8 & 11 & 13.131663644741 & -2.131663644741 \tabularnewline
9 & 10 & 13.0997813126826 & -3.09978131268258 \tabularnewline
10 & 13 & 13.1485220391848 & -0.14852203918478 \tabularnewline
11 & 10 & 12.7516570620623 & -2.75165706206229 \tabularnewline
12 & 8 & 12.424632392187 & -4.424632392187 \tabularnewline
13 & 15 & 12.7928794297205 & 2.20712057027949 \tabularnewline
14 & 14 & 13.0857212011086 & 0.914278798891355 \tabularnewline
15 & 10 & 13.2379874169041 & -3.23798741690410 \tabularnewline
16 & 14 & 13.4535521872168 & 0.54644781278321 \tabularnewline
17 & 14 & 12.9156514038792 & 1.08434859612084 \tabularnewline
18 & 11 & 13.2379874169041 & -2.23798741690410 \tabularnewline
19 & 10 & 12.9891977151678 & -2.98919771516781 \tabularnewline
20 & 13 & 12.3543446143907 & 0.645655385609337 \tabularnewline
21 & 7 & 12.6059325989968 & -5.60593259899679 \tabularnewline
22 & 14 & 12.1983349287351 & 1.80166507126489 \tabularnewline
23 & 12 & 12.8350784034928 & -0.83507840349277 \tabularnewline
24 & 14 & 12.4208575032031 & 1.57914249679688 \tabularnewline
25 & 11 & 13.0332370047463 & -2.03323700474634 \tabularnewline
26 & 9 & 13.1597838678889 & -4.15978386788886 \tabularnewline
27 & 11 & 13.0107447664619 & -2.01074476646195 \tabularnewline
28 & 15 & 12.9081644641590 & 2.09183553584104 \tabularnewline
29 & 14 & 12.8519367979365 & 1.14806320206345 \tabularnewline
30 & 13 & 13.1663256206188 & -0.166325620618814 \tabularnewline
31 & 9 & 12.8172247638845 & -3.8172247638845 \tabularnewline
32 & 15 & 12.5694303073127 & 2.43056969268729 \tabularnewline
33 & 10 & 12.7146572416548 & -2.71465724165482 \tabularnewline
34 & 11 & 12.7408929497993 & -1.74089294979932 \tabularnewline
35 & 13 & 13.1054279365965 & -0.105427936596509 \tabularnewline
36 & 8 & 12.4077239395690 & -4.40772393956899 \tabularnewline
37 & 20 & 12.3571114781367 & 7.64288852186327 \tabularnewline
38 & 12 & 13.3907513491407 & -1.39075134914065 \tabularnewline
39 & 10 & 12.2840568365943 & -2.28405683659432 \tabularnewline
40 & 10 & 12.7408929497993 & -2.74089294979932 \tabularnewline
41 & 9 & 12.4738019501879 & -3.47380195018789 \tabularnewline
42 & 14 & 12.8556802677967 & 1.14431973220335 \tabularnewline
43 & 8 & 13.0763625264584 & -5.07636252645839 \tabularnewline
44 & 14 & 13.1785139972627 & 0.821486002737306 \tabularnewline
45 & 11 & 12.9844962782441 & -1.98449627824414 \tabularnewline
46 & 13 & 13.2379874169041 & -0.237987416904096 \tabularnewline
47 & 9 & 12.9414021756559 & -3.94140217565586 \tabularnewline
48 & 11 & 12.3571114781367 & -1.35711147813673 \tabularnewline
49 & 15 & 12.5206395226363 & 2.47936047736373 \tabularnewline
50 & 11 & 12.8350597644423 & -1.83505976444231 \tabularnewline
51 & 10 & 12.7567933771795 & -2.75679337717952 \tabularnewline
52 & 14 & 13.0819777312485 & 0.918022268751457 \tabularnewline
53 & 18 & 12.9807528083840 & 5.01924719161597 \tabularnewline
54 & 14 & 13.5233108294482 & 0.476689170551758 \tabularnewline
55 & 11 & 12.3351481876995 & -1.33514818769950 \tabularnewline
56 & 12 & 12.6724769069330 & -0.67247690693303 \tabularnewline
57 & 13 & 12.8772273190908 & 0.122772680909214 \tabularnewline
58 & 9 & 13.1644538856888 & -4.16445388568876 \tabularnewline
59 & 10 & 13.4685260666572 & -3.4685260666572 \tabularnewline
60 & 15 & 13.2848564084762 & 1.71514359152375 \tabularnewline
61 & 20 & 13.1147866112468 & 6.88521338875324 \tabularnewline
62 & 12 & 12.9615752084611 & -0.961575208461057 \tabularnewline
63 & 12 & 13.0454253813902 & -1.04542538139022 \tabularnewline
64 & 14 & 13.0716610895347 & 0.928338910465287 \tabularnewline
65 & 13 & 13.5467610347962 & -0.546761034796208 \tabularnewline
66 & 11 & 12.5796654717285 & -1.57966547172852 \tabularnewline
67 & 17 & 12.4189857682731 & 4.58101423172693 \tabularnewline
68 & 12 & 13.0294935348862 & -1.02949353488624 \tabularnewline
69 & 13 & 13.5814416497245 & -0.58144164972448 \tabularnewline
70 & 14 & 12.9826245433141 & 1.01737545668592 \tabularnewline
71 & 13 & 12.5890555655026 & 0.410944434497446 \tabularnewline
72 & 15 & 13.0547840560405 & 1.94521594395952 \tabularnewline
73 & 13 & 12.4443077085511 & 0.555692291448914 \tabularnewline
74 & 10 & 12.4559983687539 & -2.45599836875386 \tabularnewline
75 & 11 & 12.7024688650109 & -1.70246886501094 \tabularnewline
76 & 19 & 12.8266148576585 & 6.17338514234147 \tabularnewline
77 & 13 & 13.3710760327766 & -0.37107603277657 \tabularnewline
78 & 17 & 12.9062927292289 & 4.09370727077109 \tabularnewline
79 & 13 & 13.0351087396764 & -0.0351087396763905 \tabularnewline
80 & 9 & 12.6049874120065 & -3.60498741200654 \tabularnewline
81 & 11 & 12.7891359598604 & -1.78913595986041 \tabularnewline
82 & 10 & 13.0838494661786 & -3.08384946617859 \tabularnewline
83 & 9 & 12.925968045593 & -3.92596804559299 \tabularnewline
84 & 12 & 12.9376587057958 & -0.937658705795762 \tabularnewline
85 & 12 & 12.8875125416808 & -0.887512541680837 \tabularnewline
86 & 13 & 12.9381564222369 & 0.0618435777631296 \tabularnewline
87 & 13 & 12.9831222597552 & 0.0168777402448073 \tabularnewline
88 & 12 & 13.2417308867642 & -1.24173088676420 \tabularnewline
89 & 15 & 12.4569435557441 & 2.54305644425589 \tabularnewline
90 & 22 & 13.0491688512503 & 8.95083114874968 \tabularnewline
91 & 13 & 12.6687334370729 & 0.331266562927073 \tabularnewline
92 & 15 & 13.5345726581523 & 1.46542734184767 \tabularnewline
93 & 13 & 13.2422286032053 & -0.242228603205306 \tabularnewline
94 & 15 & 12.9072192771687 & 2.0927807228313 \tabularnewline
95 & 10 & 12.9976298418783 & -2.99762984187827 \tabularnewline
96 & 11 & 12.3290540932364 & -1.32905409323642 \tabularnewline
97 & 16 & 12.3955669820489 & 3.60443301795112 \tabularnewline
98 & 11 & 12.4381947873198 & -1.43819478731983 \tabularnewline
99 & 11 & 13.0491688512503 & -2.04916885125032 \tabularnewline
100 & 10 & 12.8753555841607 & -2.87535558416074 \tabularnewline
101 & 10 & 12.7853924900003 & -2.78539249000031 \tabularnewline
102 & 16 & 13.2717228448421 & 2.72827715515789 \tabularnewline
103 & 12 & 12.9320435010056 & -0.93204350100561 \tabularnewline
104 & 11 & 13.0407553635903 & -2.04075536359032 \tabularnewline
105 & 16 & 12.7928794297205 & 3.20712057027949 \tabularnewline
106 & 19 & 12.7783718475974 & 6.22162815240256 \tabularnewline
107 & 11 & 12.9493868318172 & -1.94938683181718 \tabularnewline
108 & 16 & 12.7385234984282 & 3.26147650157184 \tabularnewline
109 & 15 & 12.6640634192730 & 2.33593658072697 \tabularnewline
110 & 24 & 13.1681973555489 & 10.8318026444511 \tabularnewline
111 & 14 & 13.3551441862726 & 0.644855813727413 \tabularnewline
112 & 15 & 12.4925507186122 & 2.50744928138782 \tabularnewline
113 & 11 & 12.8800442410111 & -1.88004424101109 \tabularnewline
114 & 15 & 12.6584482144829 & 2.34155178551712 \tabularnewline
115 & 12 & 13.4961672124144 & -1.49616721241441 \tabularnewline
116 & 10 & 13.2347416634851 & -3.2347416634851 \tabularnewline
117 & 14 & 13.0220065951660 & 0.977993404833966 \tabularnewline
118 & 13 & 13.1738125603390 & -0.173812560339018 \tabularnewline
119 & 9 & 12.6996705821411 & -3.6996705821411 \tabularnewline
120 & 15 & 13.0594540738404 & 1.94054592615963 \tabularnewline
121 & 15 & 12.6012125230227 & 2.39878747697734 \tabularnewline
122 & 14 & 13.3045317248403 & 0.695468275159667 \tabularnewline
123 & 11 & 12.8200731049286 & -1.82007310492858 \tabularnewline
124 & 8 & 13.2829532544224 & -5.28295325442242 \tabularnewline
125 & 11 & 13.0566557909705 & -2.05665579097053 \tabularnewline
126 & 11 & 12.9713941337338 & -1.97139413373378 \tabularnewline
127 & 8 & 13.3110734775703 & -5.31107347757028 \tabularnewline
128 & 10 & 12.9826245433141 & -2.98262454331408 \tabularnewline
129 & 11 & 13.0604306799544 & -2.06043067995441 \tabularnewline
130 & 13 & 12.3177608454086 & 0.68223915459144 \tabularnewline
131 & 11 & 12.8219448398586 & -1.82194483985863 \tabularnewline
132 & 20 & 13.1832026541130 & 6.81679734588695 \tabularnewline
133 & 10 & 13.1878726719129 & -3.18787267191295 \tabularnewline
134 & 15 & 13.2600321846393 & 1.73996781536066 \tabularnewline
135 & 12 & 12.9278397805230 & -0.92783978052304 \tabularnewline
136 & 14 & 12.6218958646246 & 1.37810413537545 \tabularnewline
137 & 23 & 13.1175848941166 & 9.88241510588339 \tabularnewline
138 & 14 & 13.0623024148845 & 0.937697585115542 \tabularnewline
139 & 16 & 13.1147866112468 & 2.88521338875324 \tabularnewline
140 & 11 & 12.7882094119206 & -1.78820941192062 \tabularnewline
141 & 12 & 12.4555381181314 & -0.455538118131392 \tabularnewline
142 & 10 & 12.4265041271171 & -2.42650412711705 \tabularnewline
143 & 14 & 12.5398985998573 & 1.46010140014274 \tabularnewline
144 & 12 & 12.9906091994754 & -0.990609199475397 \tabularnewline
145 & 12 & 12.8650703615707 & -0.865070361570685 \tabularnewline
146 & 11 & 12.5581184204344 & -1.55811842043438 \tabularnewline
147 & 12 & 12.3899517772587 & -0.38995177725873 \tabularnewline
148 & 13 & 13.0004281247481 & -0.000428124748118555 \tabularnewline
149 & 11 & 13.1653990726790 & -2.16539907267902 \tabularnewline
150 & 19 & 12.8378766863626 & 6.16212331363738 \tabularnewline
151 & 12 & 12.9648209618801 & -0.96482096188005 \tabularnewline
152 & 17 & 12.5637650443483 & 4.43623495565168 \tabularnewline
153 & 9 & 12.0614842039521 & -3.06148420395207 \tabularnewline
154 & 12 & 12.9090910120988 & -0.909091012098752 \tabularnewline
155 & 19 & 12.8767610217735 & 6.12323897822654 \tabularnewline
156 & 18 & 13.1663256206188 & 4.83367437938119 \tabularnewline
157 & 15 & 12.5731237189986 & 2.42687628100143 \tabularnewline
158 & 14 & 12.8360049514326 & 1.16399504856743 \tabularnewline
159 & 11 & 12.2615832373604 & -1.26158323736039 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98798&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]12[/C][C]12.7066598054202[/C][C]-0.706659805420156[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]12.7324480430155[/C][C]-1.73244804301554[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]13.1232315180305[/C][C]0.876768481969459[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.0857212011086[/C][C]-1.08572120110865[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]13.0641741498145[/C][C]7.9358258501855[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]13.2010062355471[/C][C]-1.20100623554709[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]13.0538388690502[/C][C]8.94616113094978[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]13.131663644741[/C][C]-2.131663644741[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]13.0997813126826[/C][C]-3.09978131268258[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]13.1485220391848[/C][C]-0.14852203918478[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]12.7516570620623[/C][C]-2.75165706206229[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]12.424632392187[/C][C]-4.424632392187[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.7928794297205[/C][C]2.20712057027949[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]13.0857212011086[/C][C]0.914278798891355[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]13.2379874169041[/C][C]-3.23798741690410[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.4535521872168[/C][C]0.54644781278321[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.9156514038792[/C][C]1.08434859612084[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]13.2379874169041[/C][C]-2.23798741690410[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.9891977151678[/C][C]-2.98919771516781[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]12.3543446143907[/C][C]0.645655385609337[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]12.6059325989968[/C][C]-5.60593259899679[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]12.1983349287351[/C][C]1.80166507126489[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.8350784034928[/C][C]-0.83507840349277[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]12.4208575032031[/C][C]1.57914249679688[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]13.0332370047463[/C][C]-2.03323700474634[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]13.1597838678889[/C][C]-4.15978386788886[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]13.0107447664619[/C][C]-2.01074476646195[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]12.9081644641590[/C][C]2.09183553584104[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.8519367979365[/C][C]1.14806320206345[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]13.1663256206188[/C][C]-0.166325620618814[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]12.8172247638845[/C][C]-3.8172247638845[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]12.5694303073127[/C][C]2.43056969268729[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]12.7146572416548[/C][C]-2.71465724165482[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]12.7408929497993[/C][C]-1.74089294979932[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]13.1054279365965[/C][C]-0.105427936596509[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]12.4077239395690[/C][C]-4.40772393956899[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]12.3571114781367[/C][C]7.64288852186327[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]13.3907513491407[/C][C]-1.39075134914065[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]12.2840568365943[/C][C]-2.28405683659432[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.7408929497993[/C][C]-2.74089294979932[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.4738019501879[/C][C]-3.47380195018789[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]12.8556802677967[/C][C]1.14431973220335[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]13.0763625264584[/C][C]-5.07636252645839[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.1785139972627[/C][C]0.821486002737306[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]12.9844962782441[/C][C]-1.98449627824414[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]13.2379874169041[/C][C]-0.237987416904096[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.9414021756559[/C][C]-3.94140217565586[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.3571114781367[/C][C]-1.35711147813673[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]12.5206395226363[/C][C]2.47936047736373[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.8350597644423[/C][C]-1.83505976444231[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]12.7567933771795[/C][C]-2.75679337717952[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.0819777312485[/C][C]0.918022268751457[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]12.9807528083840[/C][C]5.01924719161597[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]13.5233108294482[/C][C]0.476689170551758[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]12.3351481876995[/C][C]-1.33514818769950[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.6724769069330[/C][C]-0.67247690693303[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.8772273190908[/C][C]0.122772680909214[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]13.1644538856888[/C][C]-4.16445388568876[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.4685260666572[/C][C]-3.4685260666572[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]13.2848564084762[/C][C]1.71514359152375[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]13.1147866112468[/C][C]6.88521338875324[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.9615752084611[/C][C]-0.961575208461057[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]13.0454253813902[/C][C]-1.04542538139022[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.0716610895347[/C][C]0.928338910465287[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]13.5467610347962[/C][C]-0.546761034796208[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]12.5796654717285[/C][C]-1.57966547172852[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]12.4189857682731[/C][C]4.58101423172693[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.0294935348862[/C][C]-1.02949353488624[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]13.5814416497245[/C][C]-0.58144164972448[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.9826245433141[/C][C]1.01737545668592[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]12.5890555655026[/C][C]0.410944434497446[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]13.0547840560405[/C][C]1.94521594395952[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]12.4443077085511[/C][C]0.555692291448914[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]12.4559983687539[/C][C]-2.45599836875386[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.7024688650109[/C][C]-1.70246886501094[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]12.8266148576585[/C][C]6.17338514234147[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]13.3710760327766[/C][C]-0.37107603277657[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]12.9062927292289[/C][C]4.09370727077109[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]13.0351087396764[/C][C]-0.0351087396763905[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]12.6049874120065[/C][C]-3.60498741200654[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]12.7891359598604[/C][C]-1.78913595986041[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]13.0838494661786[/C][C]-3.08384946617859[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.925968045593[/C][C]-3.92596804559299[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.9376587057958[/C][C]-0.937658705795762[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.8875125416808[/C][C]-0.887512541680837[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.9381564222369[/C][C]0.0618435777631296[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]12.9831222597552[/C][C]0.0168777402448073[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]13.2417308867642[/C][C]-1.24173088676420[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]12.4569435557441[/C][C]2.54305644425589[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]13.0491688512503[/C][C]8.95083114874968[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]12.6687334370729[/C][C]0.331266562927073[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.5345726581523[/C][C]1.46542734184767[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]13.2422286032053[/C][C]-0.242228603205306[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.9072192771687[/C][C]2.0927807228313[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]12.9976298418783[/C][C]-2.99762984187827[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]12.3290540932364[/C][C]-1.32905409323642[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]12.3955669820489[/C][C]3.60443301795112[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.4381947873198[/C][C]-1.43819478731983[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]13.0491688512503[/C][C]-2.04916885125032[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.8753555841607[/C][C]-2.87535558416074[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]12.7853924900003[/C][C]-2.78539249000031[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.2717228448421[/C][C]2.72827715515789[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]12.9320435010056[/C][C]-0.93204350100561[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.0407553635903[/C][C]-2.04075536359032[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]12.7928794297205[/C][C]3.20712057027949[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]12.7783718475974[/C][C]6.22162815240256[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]12.9493868318172[/C][C]-1.94938683181718[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]12.7385234984282[/C][C]3.26147650157184[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]12.6640634192730[/C][C]2.33593658072697[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]13.1681973555489[/C][C]10.8318026444511[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]13.3551441862726[/C][C]0.644855813727413[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.4925507186122[/C][C]2.50744928138782[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.8800442410111[/C][C]-1.88004424101109[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]12.6584482144829[/C][C]2.34155178551712[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]13.4961672124144[/C][C]-1.49616721241441[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]13.2347416634851[/C][C]-3.2347416634851[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.0220065951660[/C][C]0.977993404833966[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.1738125603390[/C][C]-0.173812560339018[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]12.6996705821411[/C][C]-3.6996705821411[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]13.0594540738404[/C][C]1.94054592615963[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]12.6012125230227[/C][C]2.39878747697734[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.3045317248403[/C][C]0.695468275159667[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]12.8200731049286[/C][C]-1.82007310492858[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]13.2829532544224[/C][C]-5.28295325442242[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]13.0566557909705[/C][C]-2.05665579097053[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]12.9713941337338[/C][C]-1.97139413373378[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]13.3110734775703[/C][C]-5.31107347757028[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]12.9826245433141[/C][C]-2.98262454331408[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]13.0604306799544[/C][C]-2.06043067995441[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.3177608454086[/C][C]0.68223915459144[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]12.8219448398586[/C][C]-1.82194483985863[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]13.1832026541130[/C][C]6.81679734588695[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]13.1878726719129[/C][C]-3.18787267191295[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.2600321846393[/C][C]1.73996781536066[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.9278397805230[/C][C]-0.92783978052304[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]12.6218958646246[/C][C]1.37810413537545[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]13.1175848941166[/C][C]9.88241510588339[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.0623024148845[/C][C]0.937697585115542[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]13.1147866112468[/C][C]2.88521338875324[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]12.7882094119206[/C][C]-1.78820941192062[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.4555381181314[/C][C]-0.455538118131392[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.4265041271171[/C][C]-2.42650412711705[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]12.5398985998573[/C][C]1.46010140014274[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]12.9906091994754[/C][C]-0.990609199475397[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]12.8650703615707[/C][C]-0.865070361570685[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]12.5581184204344[/C][C]-1.55811842043438[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]12.3899517772587[/C][C]-0.38995177725873[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]13.0004281247481[/C][C]-0.000428124748118555[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]13.1653990726790[/C][C]-2.16539907267902[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]12.8378766863626[/C][C]6.16212331363738[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]12.9648209618801[/C][C]-0.96482096188005[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]12.5637650443483[/C][C]4.43623495565168[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]12.0614842039521[/C][C]-3.06148420395207[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]12.9090910120988[/C][C]-0.909091012098752[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]12.8767610217735[/C][C]6.12323897822654[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]13.1663256206188[/C][C]4.83367437938119[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]12.5731237189986[/C][C]2.42687628100143[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]12.8360049514326[/C][C]1.16399504856743[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]12.2615832373604[/C][C]-1.26158323736039[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98798&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98798&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
11212.7066598054202-0.706659805420156
21112.7324480430155-1.73244804301554
31413.12323151803050.876768481969459
41213.0857212011086-1.08572120110865
52113.06417414981457.9358258501855
61213.2010062355471-1.20100623554709
72213.05383886905028.94616113094978
81113.131663644741-2.131663644741
91013.0997813126826-3.09978131268258
101313.1485220391848-0.14852203918478
111012.7516570620623-2.75165706206229
12812.424632392187-4.424632392187
131512.79287942972052.20712057027949
141413.08572120110860.914278798891355
151013.2379874169041-3.23798741690410
161413.45355218721680.54644781278321
171412.91565140387921.08434859612084
181113.2379874169041-2.23798741690410
191012.9891977151678-2.98919771516781
201312.35434461439070.645655385609337
21712.6059325989968-5.60593259899679
221412.19833492873511.80166507126489
231212.8350784034928-0.83507840349277
241412.42085750320311.57914249679688
251113.0332370047463-2.03323700474634
26913.1597838678889-4.15978386788886
271113.0107447664619-2.01074476646195
281512.90816446415902.09183553584104
291412.85193679793651.14806320206345
301313.1663256206188-0.166325620618814
31912.8172247638845-3.8172247638845
321512.56943030731272.43056969268729
331012.7146572416548-2.71465724165482
341112.7408929497993-1.74089294979932
351313.1054279365965-0.105427936596509
36812.4077239395690-4.40772393956899
372012.35711147813677.64288852186327
381213.3907513491407-1.39075134914065
391012.2840568365943-2.28405683659432
401012.7408929497993-2.74089294979932
41912.4738019501879-3.47380195018789
421412.85568026779671.14431973220335
43813.0763625264584-5.07636252645839
441413.17851399726270.821486002737306
451112.9844962782441-1.98449627824414
461313.2379874169041-0.237987416904096
47912.9414021756559-3.94140217565586
481112.3571114781367-1.35711147813673
491512.52063952263632.47936047736373
501112.8350597644423-1.83505976444231
511012.7567933771795-2.75679337717952
521413.08197773124850.918022268751457
531812.98075280838405.01924719161597
541413.52331082944820.476689170551758
551112.3351481876995-1.33514818769950
561212.6724769069330-0.67247690693303
571312.87722731909080.122772680909214
58913.1644538856888-4.16445388568876
591013.4685260666572-3.4685260666572
601513.28485640847621.71514359152375
612013.11478661124686.88521338875324
621212.9615752084611-0.961575208461057
631213.0454253813902-1.04542538139022
641413.07166108953470.928338910465287
651313.5467610347962-0.546761034796208
661112.5796654717285-1.57966547172852
671712.41898576827314.58101423172693
681213.0294935348862-1.02949353488624
691313.5814416497245-0.58144164972448
701412.98262454331411.01737545668592
711312.58905556550260.410944434497446
721513.05478405604051.94521594395952
731312.44430770855110.555692291448914
741012.4559983687539-2.45599836875386
751112.7024688650109-1.70246886501094
761912.82661485765856.17338514234147
771313.3710760327766-0.37107603277657
781712.90629272922894.09370727077109
791313.0351087396764-0.0351087396763905
80912.6049874120065-3.60498741200654
811112.7891359598604-1.78913595986041
821013.0838494661786-3.08384946617859
83912.925968045593-3.92596804559299
841212.9376587057958-0.937658705795762
851212.8875125416808-0.887512541680837
861312.93815642223690.0618435777631296
871312.98312225975520.0168777402448073
881213.2417308867642-1.24173088676420
891512.45694355574412.54305644425589
902213.04916885125038.95083114874968
911312.66873343707290.331266562927073
921513.53457265815231.46542734184767
931313.2422286032053-0.242228603205306
941512.90721927716872.0927807228313
951012.9976298418783-2.99762984187827
961112.3290540932364-1.32905409323642
971612.39556698204893.60443301795112
981112.4381947873198-1.43819478731983
991113.0491688512503-2.04916885125032
1001012.8753555841607-2.87535558416074
1011012.7853924900003-2.78539249000031
1021613.27172284484212.72827715515789
1031212.9320435010056-0.93204350100561
1041113.0407553635903-2.04075536359032
1051612.79287942972053.20712057027949
1061912.77837184759746.22162815240256
1071112.9493868318172-1.94938683181718
1081612.73852349842823.26147650157184
1091512.66406341927302.33593658072697
1102413.168197355548910.8318026444511
1111413.35514418627260.644855813727413
1121512.49255071861222.50744928138782
1131112.8800442410111-1.88004424101109
1141512.65844821448292.34155178551712
1151213.4961672124144-1.49616721241441
1161013.2347416634851-3.2347416634851
1171413.02200659516600.977993404833966
1181313.1738125603390-0.173812560339018
119912.6996705821411-3.6996705821411
1201513.05945407384041.94054592615963
1211512.60121252302272.39878747697734
1221413.30453172484030.695468275159667
1231112.8200731049286-1.82007310492858
124813.2829532544224-5.28295325442242
1251113.0566557909705-2.05665579097053
1261112.9713941337338-1.97139413373378
127813.3110734775703-5.31107347757028
1281012.9826245433141-2.98262454331408
1291113.0604306799544-2.06043067995441
1301312.31776084540860.68223915459144
1311112.8219448398586-1.82194483985863
1322013.18320265411306.81679734588695
1331013.1878726719129-3.18787267191295
1341513.26003218463931.73996781536066
1351212.9278397805230-0.92783978052304
1361412.62189586462461.37810413537545
1372313.11758489411669.88241510588339
1381413.06230241488450.937697585115542
1391613.11478661124682.88521338875324
1401112.7882094119206-1.78820941192062
1411212.4555381181314-0.455538118131392
1421012.4265041271171-2.42650412711705
1431412.53989859985731.46010140014274
1441212.9906091994754-0.990609199475397
1451212.8650703615707-0.865070361570685
1461112.5581184204344-1.55811842043438
1471212.3899517772587-0.38995177725873
1481313.0004281247481-0.000428124748118555
1491113.1653990726790-2.16539907267902
1501912.83787668636266.16212331363738
1511212.9648209618801-0.96482096188005
1521712.56376504434834.43623495565168
153912.0614842039521-3.06148420395207
1541212.9090910120988-0.909091012098752
1551912.87676102177356.12323897822654
1561813.16632562061884.83367437938119
1571512.57312371899862.42687628100143
1581412.83600495143261.16399504856743
1591112.2615832373604-1.26158323736039






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.9791533205914760.04169335881704890.0208466794085244
80.9550738715948180.08985225681036410.0449261284051821
90.9726045167650930.05479096646981380.0273954832349069
100.955773034385010.08845393122998130.0442269656149907
110.9343001510670050.1313996978659910.0656998489329953
120.9316738627787140.1366522744425720.0683261372212861
130.9243819994108650.1512360011782700.0756180005891351
140.8872345959729230.2255308080541540.112765404027077
150.9007537802016130.1984924395967740.0992462197983872
160.8735362400472570.2529275199054870.126463759952743
170.8298652071461010.3402695857077970.170134792853899
180.803756644099930.392486711800140.19624335590007
190.7836918619677790.4326162760644420.216308138032221
200.7548697727473960.4902604545052080.245130227252604
210.8462375933815580.3075248132368840.153762406618442
220.8383720962918860.3232558074162280.161627903708114
230.802254962711690.395490074576620.19774503728831
240.756327627365950.4873447452681010.243672372634051
250.7218133506557170.5563732986885650.278186649344283
260.7216503687074740.5566992625850530.278349631292526
270.6944922179857780.6110155640284440.305507782014222
280.674732222828210.650535554343580.32526777717179
290.6341627137564690.7316745724870620.365837286243531
300.5758084690071310.8483830619857380.424191530992869
310.6287410013915610.7425179972168780.371258998608439
320.6401706831869070.7196586336261860.359829316813093
330.6222283260575240.7555433478849520.377771673942476
340.5764608137834730.8470783724330550.423539186216527
350.5197997380851840.9604005238296310.480200261914816
360.5351774396932020.9296451206135970.464822560306798
370.7894002592285320.4211994815429350.210599740771468
380.7522040027635230.4955919944729550.247795997236477
390.7249171696082260.5501656607835480.275082830391774
400.7077149615228950.584570076954210.292285038477105
410.7007958349813280.5984083300373440.299204165018672
420.6650297017036460.6699405965927080.334970298296354
430.712299826072660.5754003478546810.287700173927340
440.6759983456199040.6480033087601920.324001654380096
450.6426451201333450.714709759733310.357354879866655
460.5942644806728370.8114710386543260.405735519327163
470.602843914886610.7943121702267790.397156085113389
480.5621937210759740.8756125578480530.437806278924026
490.548896595162850.90220680967430.45110340483715
500.5099496817977770.9801006364044470.490050318202223
510.4992031902887460.9984063805774920.500796809711254
520.4591291495859070.9182582991718150.540870850414093
530.5493589757633910.9012820484732170.450641024236609
540.5016474746905650.996705050618870.498352525309435
550.4592191657594860.9184383315189720.540780834240514
560.4124736936112070.8249473872224140.587526306388793
570.3669123705619120.7338247411238230.633087629438088
580.390711297419380.781422594838760.60928870258062
590.3948127410412230.7896254820824460.605187258958777
600.3701622049037720.7403244098075430.629837795096228
610.5581932537170760.8836134925658470.441806746282924
620.5147142844055670.9705714311888660.485285715594433
630.4720672634271440.9441345268542880.527932736572856
640.4292966856807190.8585933713614380.570703314319281
650.3854807236681480.7709614473362950.614519276331852
660.3499453644487020.6998907288974030.650054635551298
670.4067223752360960.8134447504721920.593277624763904
680.3663574995577520.7327149991155050.633642500442248
690.3257259588139150.651451917627830.674274041186085
700.2897726993055520.5795453986111030.710227300694448
710.252562442424080.505124884848160.74743755757592
720.2303015331433220.4606030662866440.769698466856678
730.1986687866619720.3973375733239450.801331213338028
740.1835772410804610.3671544821609210.81642275891954
750.1625624422255180.3251248844510350.837437557774482
760.2598602743225680.5197205486451360.740139725677432
770.2262987043457420.4525974086914850.773701295654257
780.2524133613377710.5048267226755420.747586638662229
790.2169009300924590.4338018601849170.783099069907541
800.2258377163878870.4516754327757730.774162283612113
810.2023206482040360.4046412964080730.797679351795963
820.2012264148524890.4024528297049780.79877358514751
830.2199849404482150.439969880896430.780015059551785
840.1915121116150660.3830242232301320.808487888384934
850.1639653695164370.3279307390328740.836034630483563
860.1397825531451130.2795651062902250.860217446854887
870.1164691926448360.2329383852896720.883530807355164
880.09871786861330820.1974357372266160.901282131386692
890.09341517872992620.1868303574598520.906584821270074
900.2967354436417430.5934708872834850.703264556358257
910.258701148363890.517402296727780.74129885163611
920.2282580910823500.4565161821647010.77174190891765
930.1951900450448910.3903800900897830.804809954955109
940.1766610229669750.3533220459339510.823338977033025
950.1724872923978000.3449745847955990.8275127076022
960.1513229812284890.3026459624569780.848677018771511
970.1587100265380320.3174200530760650.841289973461968
980.1378256064987860.2756512129975720.862174393501214
990.1250346290784510.2500692581569020.874965370921549
1000.1227420158300170.2454840316600330.877257984169983
1010.1195643799145540.2391287598291070.880435620085446
1020.1100893415252120.2201786830504250.889910658474788
1030.0954409757084330.1908819514168660.904559024291567
1040.08732742025515130.1746548405103030.912672579744849
1050.08813597626068170.1762719525213630.911864023739318
1060.1490309279303020.2980618558606050.850969072069698
1070.1318224998125960.2636449996251920.868177500187404
1080.1322141316118160.2644282632236330.867785868388184
1090.1192340401182300.2384680802364610.88076595988177
1100.5139456385995750.972108722800850.486054361400425
1110.4642846921299670.9285693842599340.535715307870033
1120.4437815279298710.8875630558597420.556218472070129
1130.4045138116829440.809027623365890.595486188317056
1140.374873398555240.749746797110480.62512660144476
1150.3360308187473680.6720616374947350.663969181252632
1160.3535508152039630.7071016304079270.646449184796037
1170.3085296458157140.6170592916314270.691470354184286
1180.26368589927330.52737179854660.7363141007267
1190.2842326318241760.5684652636483520.715767368175824
1200.2572006704540500.5144013409081010.74279932954595
1210.2664697716225590.5329395432451170.733530228377441
1220.2242378663702530.4484757327405060.775762133629747
1230.1991482617570370.3982965235140750.800851738242963
1240.2592146352233070.5184292704466130.740785364776694
1250.2318302937018440.4636605874036880.768169706298156
1260.2296695255250860.4593390510501720.770330474474914
1270.3949141474747000.7898282949494010.6050858525253
1280.4147197500910260.8294395001820530.585280249908974
1290.4418368523120310.8836737046240630.558163147687969
1300.424197131064680.848394262129360.57580286893532
1310.3965674251886750.793134850377350.603432574811325
1320.4933627803698420.9867255607396830.506637219630158
1330.6170029584622680.7659940830754640.382997041537732
1340.560569890109840.878860219780320.43943010989016
1350.5266279910976050.946744017804790.473372008902395
1360.4624610620237420.9249221240474850.537538937976258
1370.8208617086318570.3582765827362860.179138291368143
1380.7713276407371680.4573447185256640.228672359262832
1390.7238092530083280.5523814939833450.276190746991673
1400.6850266457110850.629946708577830.314973354288915
1410.626356644251240.747286711497520.37364335574876
1420.5742351852514570.8515296294970860.425764814748543
1430.5040974223597140.9918051552805720.495902577640286
1440.4216070105058720.8432140210117440.578392989494128
1450.4183467887352050.836693577470410.581653211264795
1460.329981454575490.659962909150980.67001854542451
1470.2468733204930410.4937466409860810.753126679506959
1480.1822634741509350.3645269483018710.817736525849065
1490.3248326550922490.6496653101844980.675167344907751
1500.3719208757648990.7438417515297990.6280791242351
1510.400335009051720.800670018103440.59966499094828
1520.4758085397580620.9516170795161240.524191460241938

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.979153320591476 & 0.0416933588170489 & 0.0208466794085244 \tabularnewline
8 & 0.955073871594818 & 0.0898522568103641 & 0.0449261284051821 \tabularnewline
9 & 0.972604516765093 & 0.0547909664698138 & 0.0273954832349069 \tabularnewline
10 & 0.95577303438501 & 0.0884539312299813 & 0.0442269656149907 \tabularnewline
11 & 0.934300151067005 & 0.131399697865991 & 0.0656998489329953 \tabularnewline
12 & 0.931673862778714 & 0.136652274442572 & 0.0683261372212861 \tabularnewline
13 & 0.924381999410865 & 0.151236001178270 & 0.0756180005891351 \tabularnewline
14 & 0.887234595972923 & 0.225530808054154 & 0.112765404027077 \tabularnewline
15 & 0.900753780201613 & 0.198492439596774 & 0.0992462197983872 \tabularnewline
16 & 0.873536240047257 & 0.252927519905487 & 0.126463759952743 \tabularnewline
17 & 0.829865207146101 & 0.340269585707797 & 0.170134792853899 \tabularnewline
18 & 0.80375664409993 & 0.39248671180014 & 0.19624335590007 \tabularnewline
19 & 0.783691861967779 & 0.432616276064442 & 0.216308138032221 \tabularnewline
20 & 0.754869772747396 & 0.490260454505208 & 0.245130227252604 \tabularnewline
21 & 0.846237593381558 & 0.307524813236884 & 0.153762406618442 \tabularnewline
22 & 0.838372096291886 & 0.323255807416228 & 0.161627903708114 \tabularnewline
23 & 0.80225496271169 & 0.39549007457662 & 0.19774503728831 \tabularnewline
24 & 0.75632762736595 & 0.487344745268101 & 0.243672372634051 \tabularnewline
25 & 0.721813350655717 & 0.556373298688565 & 0.278186649344283 \tabularnewline
26 & 0.721650368707474 & 0.556699262585053 & 0.278349631292526 \tabularnewline
27 & 0.694492217985778 & 0.611015564028444 & 0.305507782014222 \tabularnewline
28 & 0.67473222282821 & 0.65053555434358 & 0.32526777717179 \tabularnewline
29 & 0.634162713756469 & 0.731674572487062 & 0.365837286243531 \tabularnewline
30 & 0.575808469007131 & 0.848383061985738 & 0.424191530992869 \tabularnewline
31 & 0.628741001391561 & 0.742517997216878 & 0.371258998608439 \tabularnewline
32 & 0.640170683186907 & 0.719658633626186 & 0.359829316813093 \tabularnewline
33 & 0.622228326057524 & 0.755543347884952 & 0.377771673942476 \tabularnewline
34 & 0.576460813783473 & 0.847078372433055 & 0.423539186216527 \tabularnewline
35 & 0.519799738085184 & 0.960400523829631 & 0.480200261914816 \tabularnewline
36 & 0.535177439693202 & 0.929645120613597 & 0.464822560306798 \tabularnewline
37 & 0.789400259228532 & 0.421199481542935 & 0.210599740771468 \tabularnewline
38 & 0.752204002763523 & 0.495591994472955 & 0.247795997236477 \tabularnewline
39 & 0.724917169608226 & 0.550165660783548 & 0.275082830391774 \tabularnewline
40 & 0.707714961522895 & 0.58457007695421 & 0.292285038477105 \tabularnewline
41 & 0.700795834981328 & 0.598408330037344 & 0.299204165018672 \tabularnewline
42 & 0.665029701703646 & 0.669940596592708 & 0.334970298296354 \tabularnewline
43 & 0.71229982607266 & 0.575400347854681 & 0.287700173927340 \tabularnewline
44 & 0.675998345619904 & 0.648003308760192 & 0.324001654380096 \tabularnewline
45 & 0.642645120133345 & 0.71470975973331 & 0.357354879866655 \tabularnewline
46 & 0.594264480672837 & 0.811471038654326 & 0.405735519327163 \tabularnewline
47 & 0.60284391488661 & 0.794312170226779 & 0.397156085113389 \tabularnewline
48 & 0.562193721075974 & 0.875612557848053 & 0.437806278924026 \tabularnewline
49 & 0.54889659516285 & 0.9022068096743 & 0.45110340483715 \tabularnewline
50 & 0.509949681797777 & 0.980100636404447 & 0.490050318202223 \tabularnewline
51 & 0.499203190288746 & 0.998406380577492 & 0.500796809711254 \tabularnewline
52 & 0.459129149585907 & 0.918258299171815 & 0.540870850414093 \tabularnewline
53 & 0.549358975763391 & 0.901282048473217 & 0.450641024236609 \tabularnewline
54 & 0.501647474690565 & 0.99670505061887 & 0.498352525309435 \tabularnewline
55 & 0.459219165759486 & 0.918438331518972 & 0.540780834240514 \tabularnewline
56 & 0.412473693611207 & 0.824947387222414 & 0.587526306388793 \tabularnewline
57 & 0.366912370561912 & 0.733824741123823 & 0.633087629438088 \tabularnewline
58 & 0.39071129741938 & 0.78142259483876 & 0.60928870258062 \tabularnewline
59 & 0.394812741041223 & 0.789625482082446 & 0.605187258958777 \tabularnewline
60 & 0.370162204903772 & 0.740324409807543 & 0.629837795096228 \tabularnewline
61 & 0.558193253717076 & 0.883613492565847 & 0.441806746282924 \tabularnewline
62 & 0.514714284405567 & 0.970571431188866 & 0.485285715594433 \tabularnewline
63 & 0.472067263427144 & 0.944134526854288 & 0.527932736572856 \tabularnewline
64 & 0.429296685680719 & 0.858593371361438 & 0.570703314319281 \tabularnewline
65 & 0.385480723668148 & 0.770961447336295 & 0.614519276331852 \tabularnewline
66 & 0.349945364448702 & 0.699890728897403 & 0.650054635551298 \tabularnewline
67 & 0.406722375236096 & 0.813444750472192 & 0.593277624763904 \tabularnewline
68 & 0.366357499557752 & 0.732714999115505 & 0.633642500442248 \tabularnewline
69 & 0.325725958813915 & 0.65145191762783 & 0.674274041186085 \tabularnewline
70 & 0.289772699305552 & 0.579545398611103 & 0.710227300694448 \tabularnewline
71 & 0.25256244242408 & 0.50512488484816 & 0.74743755757592 \tabularnewline
72 & 0.230301533143322 & 0.460603066286644 & 0.769698466856678 \tabularnewline
73 & 0.198668786661972 & 0.397337573323945 & 0.801331213338028 \tabularnewline
74 & 0.183577241080461 & 0.367154482160921 & 0.81642275891954 \tabularnewline
75 & 0.162562442225518 & 0.325124884451035 & 0.837437557774482 \tabularnewline
76 & 0.259860274322568 & 0.519720548645136 & 0.740139725677432 \tabularnewline
77 & 0.226298704345742 & 0.452597408691485 & 0.773701295654257 \tabularnewline
78 & 0.252413361337771 & 0.504826722675542 & 0.747586638662229 \tabularnewline
79 & 0.216900930092459 & 0.433801860184917 & 0.783099069907541 \tabularnewline
80 & 0.225837716387887 & 0.451675432775773 & 0.774162283612113 \tabularnewline
81 & 0.202320648204036 & 0.404641296408073 & 0.797679351795963 \tabularnewline
82 & 0.201226414852489 & 0.402452829704978 & 0.79877358514751 \tabularnewline
83 & 0.219984940448215 & 0.43996988089643 & 0.780015059551785 \tabularnewline
84 & 0.191512111615066 & 0.383024223230132 & 0.808487888384934 \tabularnewline
85 & 0.163965369516437 & 0.327930739032874 & 0.836034630483563 \tabularnewline
86 & 0.139782553145113 & 0.279565106290225 & 0.860217446854887 \tabularnewline
87 & 0.116469192644836 & 0.232938385289672 & 0.883530807355164 \tabularnewline
88 & 0.0987178686133082 & 0.197435737226616 & 0.901282131386692 \tabularnewline
89 & 0.0934151787299262 & 0.186830357459852 & 0.906584821270074 \tabularnewline
90 & 0.296735443641743 & 0.593470887283485 & 0.703264556358257 \tabularnewline
91 & 0.25870114836389 & 0.51740229672778 & 0.74129885163611 \tabularnewline
92 & 0.228258091082350 & 0.456516182164701 & 0.77174190891765 \tabularnewline
93 & 0.195190045044891 & 0.390380090089783 & 0.804809954955109 \tabularnewline
94 & 0.176661022966975 & 0.353322045933951 & 0.823338977033025 \tabularnewline
95 & 0.172487292397800 & 0.344974584795599 & 0.8275127076022 \tabularnewline
96 & 0.151322981228489 & 0.302645962456978 & 0.848677018771511 \tabularnewline
97 & 0.158710026538032 & 0.317420053076065 & 0.841289973461968 \tabularnewline
98 & 0.137825606498786 & 0.275651212997572 & 0.862174393501214 \tabularnewline
99 & 0.125034629078451 & 0.250069258156902 & 0.874965370921549 \tabularnewline
100 & 0.122742015830017 & 0.245484031660033 & 0.877257984169983 \tabularnewline
101 & 0.119564379914554 & 0.239128759829107 & 0.880435620085446 \tabularnewline
102 & 0.110089341525212 & 0.220178683050425 & 0.889910658474788 \tabularnewline
103 & 0.095440975708433 & 0.190881951416866 & 0.904559024291567 \tabularnewline
104 & 0.0873274202551513 & 0.174654840510303 & 0.912672579744849 \tabularnewline
105 & 0.0881359762606817 & 0.176271952521363 & 0.911864023739318 \tabularnewline
106 & 0.149030927930302 & 0.298061855860605 & 0.850969072069698 \tabularnewline
107 & 0.131822499812596 & 0.263644999625192 & 0.868177500187404 \tabularnewline
108 & 0.132214131611816 & 0.264428263223633 & 0.867785868388184 \tabularnewline
109 & 0.119234040118230 & 0.238468080236461 & 0.88076595988177 \tabularnewline
110 & 0.513945638599575 & 0.97210872280085 & 0.486054361400425 \tabularnewline
111 & 0.464284692129967 & 0.928569384259934 & 0.535715307870033 \tabularnewline
112 & 0.443781527929871 & 0.887563055859742 & 0.556218472070129 \tabularnewline
113 & 0.404513811682944 & 0.80902762336589 & 0.595486188317056 \tabularnewline
114 & 0.37487339855524 & 0.74974679711048 & 0.62512660144476 \tabularnewline
115 & 0.336030818747368 & 0.672061637494735 & 0.663969181252632 \tabularnewline
116 & 0.353550815203963 & 0.707101630407927 & 0.646449184796037 \tabularnewline
117 & 0.308529645815714 & 0.617059291631427 & 0.691470354184286 \tabularnewline
118 & 0.2636858992733 & 0.5273717985466 & 0.7363141007267 \tabularnewline
119 & 0.284232631824176 & 0.568465263648352 & 0.715767368175824 \tabularnewline
120 & 0.257200670454050 & 0.514401340908101 & 0.74279932954595 \tabularnewline
121 & 0.266469771622559 & 0.532939543245117 & 0.733530228377441 \tabularnewline
122 & 0.224237866370253 & 0.448475732740506 & 0.775762133629747 \tabularnewline
123 & 0.199148261757037 & 0.398296523514075 & 0.800851738242963 \tabularnewline
124 & 0.259214635223307 & 0.518429270446613 & 0.740785364776694 \tabularnewline
125 & 0.231830293701844 & 0.463660587403688 & 0.768169706298156 \tabularnewline
126 & 0.229669525525086 & 0.459339051050172 & 0.770330474474914 \tabularnewline
127 & 0.394914147474700 & 0.789828294949401 & 0.6050858525253 \tabularnewline
128 & 0.414719750091026 & 0.829439500182053 & 0.585280249908974 \tabularnewline
129 & 0.441836852312031 & 0.883673704624063 & 0.558163147687969 \tabularnewline
130 & 0.42419713106468 & 0.84839426212936 & 0.57580286893532 \tabularnewline
131 & 0.396567425188675 & 0.79313485037735 & 0.603432574811325 \tabularnewline
132 & 0.493362780369842 & 0.986725560739683 & 0.506637219630158 \tabularnewline
133 & 0.617002958462268 & 0.765994083075464 & 0.382997041537732 \tabularnewline
134 & 0.56056989010984 & 0.87886021978032 & 0.43943010989016 \tabularnewline
135 & 0.526627991097605 & 0.94674401780479 & 0.473372008902395 \tabularnewline
136 & 0.462461062023742 & 0.924922124047485 & 0.537538937976258 \tabularnewline
137 & 0.820861708631857 & 0.358276582736286 & 0.179138291368143 \tabularnewline
138 & 0.771327640737168 & 0.457344718525664 & 0.228672359262832 \tabularnewline
139 & 0.723809253008328 & 0.552381493983345 & 0.276190746991673 \tabularnewline
140 & 0.685026645711085 & 0.62994670857783 & 0.314973354288915 \tabularnewline
141 & 0.62635664425124 & 0.74728671149752 & 0.37364335574876 \tabularnewline
142 & 0.574235185251457 & 0.851529629497086 & 0.425764814748543 \tabularnewline
143 & 0.504097422359714 & 0.991805155280572 & 0.495902577640286 \tabularnewline
144 & 0.421607010505872 & 0.843214021011744 & 0.578392989494128 \tabularnewline
145 & 0.418346788735205 & 0.83669357747041 & 0.581653211264795 \tabularnewline
146 & 0.32998145457549 & 0.65996290915098 & 0.67001854542451 \tabularnewline
147 & 0.246873320493041 & 0.493746640986081 & 0.753126679506959 \tabularnewline
148 & 0.182263474150935 & 0.364526948301871 & 0.817736525849065 \tabularnewline
149 & 0.324832655092249 & 0.649665310184498 & 0.675167344907751 \tabularnewline
150 & 0.371920875764899 & 0.743841751529799 & 0.6280791242351 \tabularnewline
151 & 0.40033500905172 & 0.80067001810344 & 0.59966499094828 \tabularnewline
152 & 0.475808539758062 & 0.951617079516124 & 0.524191460241938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98798&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]0.979153320591476[/C][C]0.0416933588170489[/C][C]0.0208466794085244[/C][/ROW]
[ROW][C]8[/C][C]0.955073871594818[/C][C]0.0898522568103641[/C][C]0.0449261284051821[/C][/ROW]
[ROW][C]9[/C][C]0.972604516765093[/C][C]0.0547909664698138[/C][C]0.0273954832349069[/C][/ROW]
[ROW][C]10[/C][C]0.95577303438501[/C][C]0.0884539312299813[/C][C]0.0442269656149907[/C][/ROW]
[ROW][C]11[/C][C]0.934300151067005[/C][C]0.131399697865991[/C][C]0.0656998489329953[/C][/ROW]
[ROW][C]12[/C][C]0.931673862778714[/C][C]0.136652274442572[/C][C]0.0683261372212861[/C][/ROW]
[ROW][C]13[/C][C]0.924381999410865[/C][C]0.151236001178270[/C][C]0.0756180005891351[/C][/ROW]
[ROW][C]14[/C][C]0.887234595972923[/C][C]0.225530808054154[/C][C]0.112765404027077[/C][/ROW]
[ROW][C]15[/C][C]0.900753780201613[/C][C]0.198492439596774[/C][C]0.0992462197983872[/C][/ROW]
[ROW][C]16[/C][C]0.873536240047257[/C][C]0.252927519905487[/C][C]0.126463759952743[/C][/ROW]
[ROW][C]17[/C][C]0.829865207146101[/C][C]0.340269585707797[/C][C]0.170134792853899[/C][/ROW]
[ROW][C]18[/C][C]0.80375664409993[/C][C]0.39248671180014[/C][C]0.19624335590007[/C][/ROW]
[ROW][C]19[/C][C]0.783691861967779[/C][C]0.432616276064442[/C][C]0.216308138032221[/C][/ROW]
[ROW][C]20[/C][C]0.754869772747396[/C][C]0.490260454505208[/C][C]0.245130227252604[/C][/ROW]
[ROW][C]21[/C][C]0.846237593381558[/C][C]0.307524813236884[/C][C]0.153762406618442[/C][/ROW]
[ROW][C]22[/C][C]0.838372096291886[/C][C]0.323255807416228[/C][C]0.161627903708114[/C][/ROW]
[ROW][C]23[/C][C]0.80225496271169[/C][C]0.39549007457662[/C][C]0.19774503728831[/C][/ROW]
[ROW][C]24[/C][C]0.75632762736595[/C][C]0.487344745268101[/C][C]0.243672372634051[/C][/ROW]
[ROW][C]25[/C][C]0.721813350655717[/C][C]0.556373298688565[/C][C]0.278186649344283[/C][/ROW]
[ROW][C]26[/C][C]0.721650368707474[/C][C]0.556699262585053[/C][C]0.278349631292526[/C][/ROW]
[ROW][C]27[/C][C]0.694492217985778[/C][C]0.611015564028444[/C][C]0.305507782014222[/C][/ROW]
[ROW][C]28[/C][C]0.67473222282821[/C][C]0.65053555434358[/C][C]0.32526777717179[/C][/ROW]
[ROW][C]29[/C][C]0.634162713756469[/C][C]0.731674572487062[/C][C]0.365837286243531[/C][/ROW]
[ROW][C]30[/C][C]0.575808469007131[/C][C]0.848383061985738[/C][C]0.424191530992869[/C][/ROW]
[ROW][C]31[/C][C]0.628741001391561[/C][C]0.742517997216878[/C][C]0.371258998608439[/C][/ROW]
[ROW][C]32[/C][C]0.640170683186907[/C][C]0.719658633626186[/C][C]0.359829316813093[/C][/ROW]
[ROW][C]33[/C][C]0.622228326057524[/C][C]0.755543347884952[/C][C]0.377771673942476[/C][/ROW]
[ROW][C]34[/C][C]0.576460813783473[/C][C]0.847078372433055[/C][C]0.423539186216527[/C][/ROW]
[ROW][C]35[/C][C]0.519799738085184[/C][C]0.960400523829631[/C][C]0.480200261914816[/C][/ROW]
[ROW][C]36[/C][C]0.535177439693202[/C][C]0.929645120613597[/C][C]0.464822560306798[/C][/ROW]
[ROW][C]37[/C][C]0.789400259228532[/C][C]0.421199481542935[/C][C]0.210599740771468[/C][/ROW]
[ROW][C]38[/C][C]0.752204002763523[/C][C]0.495591994472955[/C][C]0.247795997236477[/C][/ROW]
[ROW][C]39[/C][C]0.724917169608226[/C][C]0.550165660783548[/C][C]0.275082830391774[/C][/ROW]
[ROW][C]40[/C][C]0.707714961522895[/C][C]0.58457007695421[/C][C]0.292285038477105[/C][/ROW]
[ROW][C]41[/C][C]0.700795834981328[/C][C]0.598408330037344[/C][C]0.299204165018672[/C][/ROW]
[ROW][C]42[/C][C]0.665029701703646[/C][C]0.669940596592708[/C][C]0.334970298296354[/C][/ROW]
[ROW][C]43[/C][C]0.71229982607266[/C][C]0.575400347854681[/C][C]0.287700173927340[/C][/ROW]
[ROW][C]44[/C][C]0.675998345619904[/C][C]0.648003308760192[/C][C]0.324001654380096[/C][/ROW]
[ROW][C]45[/C][C]0.642645120133345[/C][C]0.71470975973331[/C][C]0.357354879866655[/C][/ROW]
[ROW][C]46[/C][C]0.594264480672837[/C][C]0.811471038654326[/C][C]0.405735519327163[/C][/ROW]
[ROW][C]47[/C][C]0.60284391488661[/C][C]0.794312170226779[/C][C]0.397156085113389[/C][/ROW]
[ROW][C]48[/C][C]0.562193721075974[/C][C]0.875612557848053[/C][C]0.437806278924026[/C][/ROW]
[ROW][C]49[/C][C]0.54889659516285[/C][C]0.9022068096743[/C][C]0.45110340483715[/C][/ROW]
[ROW][C]50[/C][C]0.509949681797777[/C][C]0.980100636404447[/C][C]0.490050318202223[/C][/ROW]
[ROW][C]51[/C][C]0.499203190288746[/C][C]0.998406380577492[/C][C]0.500796809711254[/C][/ROW]
[ROW][C]52[/C][C]0.459129149585907[/C][C]0.918258299171815[/C][C]0.540870850414093[/C][/ROW]
[ROW][C]53[/C][C]0.549358975763391[/C][C]0.901282048473217[/C][C]0.450641024236609[/C][/ROW]
[ROW][C]54[/C][C]0.501647474690565[/C][C]0.99670505061887[/C][C]0.498352525309435[/C][/ROW]
[ROW][C]55[/C][C]0.459219165759486[/C][C]0.918438331518972[/C][C]0.540780834240514[/C][/ROW]
[ROW][C]56[/C][C]0.412473693611207[/C][C]0.824947387222414[/C][C]0.587526306388793[/C][/ROW]
[ROW][C]57[/C][C]0.366912370561912[/C][C]0.733824741123823[/C][C]0.633087629438088[/C][/ROW]
[ROW][C]58[/C][C]0.39071129741938[/C][C]0.78142259483876[/C][C]0.60928870258062[/C][/ROW]
[ROW][C]59[/C][C]0.394812741041223[/C][C]0.789625482082446[/C][C]0.605187258958777[/C][/ROW]
[ROW][C]60[/C][C]0.370162204903772[/C][C]0.740324409807543[/C][C]0.629837795096228[/C][/ROW]
[ROW][C]61[/C][C]0.558193253717076[/C][C]0.883613492565847[/C][C]0.441806746282924[/C][/ROW]
[ROW][C]62[/C][C]0.514714284405567[/C][C]0.970571431188866[/C][C]0.485285715594433[/C][/ROW]
[ROW][C]63[/C][C]0.472067263427144[/C][C]0.944134526854288[/C][C]0.527932736572856[/C][/ROW]
[ROW][C]64[/C][C]0.429296685680719[/C][C]0.858593371361438[/C][C]0.570703314319281[/C][/ROW]
[ROW][C]65[/C][C]0.385480723668148[/C][C]0.770961447336295[/C][C]0.614519276331852[/C][/ROW]
[ROW][C]66[/C][C]0.349945364448702[/C][C]0.699890728897403[/C][C]0.650054635551298[/C][/ROW]
[ROW][C]67[/C][C]0.406722375236096[/C][C]0.813444750472192[/C][C]0.593277624763904[/C][/ROW]
[ROW][C]68[/C][C]0.366357499557752[/C][C]0.732714999115505[/C][C]0.633642500442248[/C][/ROW]
[ROW][C]69[/C][C]0.325725958813915[/C][C]0.65145191762783[/C][C]0.674274041186085[/C][/ROW]
[ROW][C]70[/C][C]0.289772699305552[/C][C]0.579545398611103[/C][C]0.710227300694448[/C][/ROW]
[ROW][C]71[/C][C]0.25256244242408[/C][C]0.50512488484816[/C][C]0.74743755757592[/C][/ROW]
[ROW][C]72[/C][C]0.230301533143322[/C][C]0.460603066286644[/C][C]0.769698466856678[/C][/ROW]
[ROW][C]73[/C][C]0.198668786661972[/C][C]0.397337573323945[/C][C]0.801331213338028[/C][/ROW]
[ROW][C]74[/C][C]0.183577241080461[/C][C]0.367154482160921[/C][C]0.81642275891954[/C][/ROW]
[ROW][C]75[/C][C]0.162562442225518[/C][C]0.325124884451035[/C][C]0.837437557774482[/C][/ROW]
[ROW][C]76[/C][C]0.259860274322568[/C][C]0.519720548645136[/C][C]0.740139725677432[/C][/ROW]
[ROW][C]77[/C][C]0.226298704345742[/C][C]0.452597408691485[/C][C]0.773701295654257[/C][/ROW]
[ROW][C]78[/C][C]0.252413361337771[/C][C]0.504826722675542[/C][C]0.747586638662229[/C][/ROW]
[ROW][C]79[/C][C]0.216900930092459[/C][C]0.433801860184917[/C][C]0.783099069907541[/C][/ROW]
[ROW][C]80[/C][C]0.225837716387887[/C][C]0.451675432775773[/C][C]0.774162283612113[/C][/ROW]
[ROW][C]81[/C][C]0.202320648204036[/C][C]0.404641296408073[/C][C]0.797679351795963[/C][/ROW]
[ROW][C]82[/C][C]0.201226414852489[/C][C]0.402452829704978[/C][C]0.79877358514751[/C][/ROW]
[ROW][C]83[/C][C]0.219984940448215[/C][C]0.43996988089643[/C][C]0.780015059551785[/C][/ROW]
[ROW][C]84[/C][C]0.191512111615066[/C][C]0.383024223230132[/C][C]0.808487888384934[/C][/ROW]
[ROW][C]85[/C][C]0.163965369516437[/C][C]0.327930739032874[/C][C]0.836034630483563[/C][/ROW]
[ROW][C]86[/C][C]0.139782553145113[/C][C]0.279565106290225[/C][C]0.860217446854887[/C][/ROW]
[ROW][C]87[/C][C]0.116469192644836[/C][C]0.232938385289672[/C][C]0.883530807355164[/C][/ROW]
[ROW][C]88[/C][C]0.0987178686133082[/C][C]0.197435737226616[/C][C]0.901282131386692[/C][/ROW]
[ROW][C]89[/C][C]0.0934151787299262[/C][C]0.186830357459852[/C][C]0.906584821270074[/C][/ROW]
[ROW][C]90[/C][C]0.296735443641743[/C][C]0.593470887283485[/C][C]0.703264556358257[/C][/ROW]
[ROW][C]91[/C][C]0.25870114836389[/C][C]0.51740229672778[/C][C]0.74129885163611[/C][/ROW]
[ROW][C]92[/C][C]0.228258091082350[/C][C]0.456516182164701[/C][C]0.77174190891765[/C][/ROW]
[ROW][C]93[/C][C]0.195190045044891[/C][C]0.390380090089783[/C][C]0.804809954955109[/C][/ROW]
[ROW][C]94[/C][C]0.176661022966975[/C][C]0.353322045933951[/C][C]0.823338977033025[/C][/ROW]
[ROW][C]95[/C][C]0.172487292397800[/C][C]0.344974584795599[/C][C]0.8275127076022[/C][/ROW]
[ROW][C]96[/C][C]0.151322981228489[/C][C]0.302645962456978[/C][C]0.848677018771511[/C][/ROW]
[ROW][C]97[/C][C]0.158710026538032[/C][C]0.317420053076065[/C][C]0.841289973461968[/C][/ROW]
[ROW][C]98[/C][C]0.137825606498786[/C][C]0.275651212997572[/C][C]0.862174393501214[/C][/ROW]
[ROW][C]99[/C][C]0.125034629078451[/C][C]0.250069258156902[/C][C]0.874965370921549[/C][/ROW]
[ROW][C]100[/C][C]0.122742015830017[/C][C]0.245484031660033[/C][C]0.877257984169983[/C][/ROW]
[ROW][C]101[/C][C]0.119564379914554[/C][C]0.239128759829107[/C][C]0.880435620085446[/C][/ROW]
[ROW][C]102[/C][C]0.110089341525212[/C][C]0.220178683050425[/C][C]0.889910658474788[/C][/ROW]
[ROW][C]103[/C][C]0.095440975708433[/C][C]0.190881951416866[/C][C]0.904559024291567[/C][/ROW]
[ROW][C]104[/C][C]0.0873274202551513[/C][C]0.174654840510303[/C][C]0.912672579744849[/C][/ROW]
[ROW][C]105[/C][C]0.0881359762606817[/C][C]0.176271952521363[/C][C]0.911864023739318[/C][/ROW]
[ROW][C]106[/C][C]0.149030927930302[/C][C]0.298061855860605[/C][C]0.850969072069698[/C][/ROW]
[ROW][C]107[/C][C]0.131822499812596[/C][C]0.263644999625192[/C][C]0.868177500187404[/C][/ROW]
[ROW][C]108[/C][C]0.132214131611816[/C][C]0.264428263223633[/C][C]0.867785868388184[/C][/ROW]
[ROW][C]109[/C][C]0.119234040118230[/C][C]0.238468080236461[/C][C]0.88076595988177[/C][/ROW]
[ROW][C]110[/C][C]0.513945638599575[/C][C]0.97210872280085[/C][C]0.486054361400425[/C][/ROW]
[ROW][C]111[/C][C]0.464284692129967[/C][C]0.928569384259934[/C][C]0.535715307870033[/C][/ROW]
[ROW][C]112[/C][C]0.443781527929871[/C][C]0.887563055859742[/C][C]0.556218472070129[/C][/ROW]
[ROW][C]113[/C][C]0.404513811682944[/C][C]0.80902762336589[/C][C]0.595486188317056[/C][/ROW]
[ROW][C]114[/C][C]0.37487339855524[/C][C]0.74974679711048[/C][C]0.62512660144476[/C][/ROW]
[ROW][C]115[/C][C]0.336030818747368[/C][C]0.672061637494735[/C][C]0.663969181252632[/C][/ROW]
[ROW][C]116[/C][C]0.353550815203963[/C][C]0.707101630407927[/C][C]0.646449184796037[/C][/ROW]
[ROW][C]117[/C][C]0.308529645815714[/C][C]0.617059291631427[/C][C]0.691470354184286[/C][/ROW]
[ROW][C]118[/C][C]0.2636858992733[/C][C]0.5273717985466[/C][C]0.7363141007267[/C][/ROW]
[ROW][C]119[/C][C]0.284232631824176[/C][C]0.568465263648352[/C][C]0.715767368175824[/C][/ROW]
[ROW][C]120[/C][C]0.257200670454050[/C][C]0.514401340908101[/C][C]0.74279932954595[/C][/ROW]
[ROW][C]121[/C][C]0.266469771622559[/C][C]0.532939543245117[/C][C]0.733530228377441[/C][/ROW]
[ROW][C]122[/C][C]0.224237866370253[/C][C]0.448475732740506[/C][C]0.775762133629747[/C][/ROW]
[ROW][C]123[/C][C]0.199148261757037[/C][C]0.398296523514075[/C][C]0.800851738242963[/C][/ROW]
[ROW][C]124[/C][C]0.259214635223307[/C][C]0.518429270446613[/C][C]0.740785364776694[/C][/ROW]
[ROW][C]125[/C][C]0.231830293701844[/C][C]0.463660587403688[/C][C]0.768169706298156[/C][/ROW]
[ROW][C]126[/C][C]0.229669525525086[/C][C]0.459339051050172[/C][C]0.770330474474914[/C][/ROW]
[ROW][C]127[/C][C]0.394914147474700[/C][C]0.789828294949401[/C][C]0.6050858525253[/C][/ROW]
[ROW][C]128[/C][C]0.414719750091026[/C][C]0.829439500182053[/C][C]0.585280249908974[/C][/ROW]
[ROW][C]129[/C][C]0.441836852312031[/C][C]0.883673704624063[/C][C]0.558163147687969[/C][/ROW]
[ROW][C]130[/C][C]0.42419713106468[/C][C]0.84839426212936[/C][C]0.57580286893532[/C][/ROW]
[ROW][C]131[/C][C]0.396567425188675[/C][C]0.79313485037735[/C][C]0.603432574811325[/C][/ROW]
[ROW][C]132[/C][C]0.493362780369842[/C][C]0.986725560739683[/C][C]0.506637219630158[/C][/ROW]
[ROW][C]133[/C][C]0.617002958462268[/C][C]0.765994083075464[/C][C]0.382997041537732[/C][/ROW]
[ROW][C]134[/C][C]0.56056989010984[/C][C]0.87886021978032[/C][C]0.43943010989016[/C][/ROW]
[ROW][C]135[/C][C]0.526627991097605[/C][C]0.94674401780479[/C][C]0.473372008902395[/C][/ROW]
[ROW][C]136[/C][C]0.462461062023742[/C][C]0.924922124047485[/C][C]0.537538937976258[/C][/ROW]
[ROW][C]137[/C][C]0.820861708631857[/C][C]0.358276582736286[/C][C]0.179138291368143[/C][/ROW]
[ROW][C]138[/C][C]0.771327640737168[/C][C]0.457344718525664[/C][C]0.228672359262832[/C][/ROW]
[ROW][C]139[/C][C]0.723809253008328[/C][C]0.552381493983345[/C][C]0.276190746991673[/C][/ROW]
[ROW][C]140[/C][C]0.685026645711085[/C][C]0.62994670857783[/C][C]0.314973354288915[/C][/ROW]
[ROW][C]141[/C][C]0.62635664425124[/C][C]0.74728671149752[/C][C]0.37364335574876[/C][/ROW]
[ROW][C]142[/C][C]0.574235185251457[/C][C]0.851529629497086[/C][C]0.425764814748543[/C][/ROW]
[ROW][C]143[/C][C]0.504097422359714[/C][C]0.991805155280572[/C][C]0.495902577640286[/C][/ROW]
[ROW][C]144[/C][C]0.421607010505872[/C][C]0.843214021011744[/C][C]0.578392989494128[/C][/ROW]
[ROW][C]145[/C][C]0.418346788735205[/C][C]0.83669357747041[/C][C]0.581653211264795[/C][/ROW]
[ROW][C]146[/C][C]0.32998145457549[/C][C]0.65996290915098[/C][C]0.67001854542451[/C][/ROW]
[ROW][C]147[/C][C]0.246873320493041[/C][C]0.493746640986081[/C][C]0.753126679506959[/C][/ROW]
[ROW][C]148[/C][C]0.182263474150935[/C][C]0.364526948301871[/C][C]0.817736525849065[/C][/ROW]
[ROW][C]149[/C][C]0.324832655092249[/C][C]0.649665310184498[/C][C]0.675167344907751[/C][/ROW]
[ROW][C]150[/C][C]0.371920875764899[/C][C]0.743841751529799[/C][C]0.6280791242351[/C][/ROW]
[ROW][C]151[/C][C]0.40033500905172[/C][C]0.80067001810344[/C][C]0.59966499094828[/C][/ROW]
[ROW][C]152[/C][C]0.475808539758062[/C][C]0.951617079516124[/C][C]0.524191460241938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98798&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98798&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
70.9791533205914760.04169335881704890.0208466794085244
80.9550738715948180.08985225681036410.0449261284051821
90.9726045167650930.05479096646981380.0273954832349069
100.955773034385010.08845393122998130.0442269656149907
110.9343001510670050.1313996978659910.0656998489329953
120.9316738627787140.1366522744425720.0683261372212861
130.9243819994108650.1512360011782700.0756180005891351
140.8872345959729230.2255308080541540.112765404027077
150.9007537802016130.1984924395967740.0992462197983872
160.8735362400472570.2529275199054870.126463759952743
170.8298652071461010.3402695857077970.170134792853899
180.803756644099930.392486711800140.19624335590007
190.7836918619677790.4326162760644420.216308138032221
200.7548697727473960.4902604545052080.245130227252604
210.8462375933815580.3075248132368840.153762406618442
220.8383720962918860.3232558074162280.161627903708114
230.802254962711690.395490074576620.19774503728831
240.756327627365950.4873447452681010.243672372634051
250.7218133506557170.5563732986885650.278186649344283
260.7216503687074740.5566992625850530.278349631292526
270.6944922179857780.6110155640284440.305507782014222
280.674732222828210.650535554343580.32526777717179
290.6341627137564690.7316745724870620.365837286243531
300.5758084690071310.8483830619857380.424191530992869
310.6287410013915610.7425179972168780.371258998608439
320.6401706831869070.7196586336261860.359829316813093
330.6222283260575240.7555433478849520.377771673942476
340.5764608137834730.8470783724330550.423539186216527
350.5197997380851840.9604005238296310.480200261914816
360.5351774396932020.9296451206135970.464822560306798
370.7894002592285320.4211994815429350.210599740771468
380.7522040027635230.4955919944729550.247795997236477
390.7249171696082260.5501656607835480.275082830391774
400.7077149615228950.584570076954210.292285038477105
410.7007958349813280.5984083300373440.299204165018672
420.6650297017036460.6699405965927080.334970298296354
430.712299826072660.5754003478546810.287700173927340
440.6759983456199040.6480033087601920.324001654380096
450.6426451201333450.714709759733310.357354879866655
460.5942644806728370.8114710386543260.405735519327163
470.602843914886610.7943121702267790.397156085113389
480.5621937210759740.8756125578480530.437806278924026
490.548896595162850.90220680967430.45110340483715
500.5099496817977770.9801006364044470.490050318202223
510.4992031902887460.9984063805774920.500796809711254
520.4591291495859070.9182582991718150.540870850414093
530.5493589757633910.9012820484732170.450641024236609
540.5016474746905650.996705050618870.498352525309435
550.4592191657594860.9184383315189720.540780834240514
560.4124736936112070.8249473872224140.587526306388793
570.3669123705619120.7338247411238230.633087629438088
580.390711297419380.781422594838760.60928870258062
590.3948127410412230.7896254820824460.605187258958777
600.3701622049037720.7403244098075430.629837795096228
610.5581932537170760.8836134925658470.441806746282924
620.5147142844055670.9705714311888660.485285715594433
630.4720672634271440.9441345268542880.527932736572856
640.4292966856807190.8585933713614380.570703314319281
650.3854807236681480.7709614473362950.614519276331852
660.3499453644487020.6998907288974030.650054635551298
670.4067223752360960.8134447504721920.593277624763904
680.3663574995577520.7327149991155050.633642500442248
690.3257259588139150.651451917627830.674274041186085
700.2897726993055520.5795453986111030.710227300694448
710.252562442424080.505124884848160.74743755757592
720.2303015331433220.4606030662866440.769698466856678
730.1986687866619720.3973375733239450.801331213338028
740.1835772410804610.3671544821609210.81642275891954
750.1625624422255180.3251248844510350.837437557774482
760.2598602743225680.5197205486451360.740139725677432
770.2262987043457420.4525974086914850.773701295654257
780.2524133613377710.5048267226755420.747586638662229
790.2169009300924590.4338018601849170.783099069907541
800.2258377163878870.4516754327757730.774162283612113
810.2023206482040360.4046412964080730.797679351795963
820.2012264148524890.4024528297049780.79877358514751
830.2199849404482150.439969880896430.780015059551785
840.1915121116150660.3830242232301320.808487888384934
850.1639653695164370.3279307390328740.836034630483563
860.1397825531451130.2795651062902250.860217446854887
870.1164691926448360.2329383852896720.883530807355164
880.09871786861330820.1974357372266160.901282131386692
890.09341517872992620.1868303574598520.906584821270074
900.2967354436417430.5934708872834850.703264556358257
910.258701148363890.517402296727780.74129885163611
920.2282580910823500.4565161821647010.77174190891765
930.1951900450448910.3903800900897830.804809954955109
940.1766610229669750.3533220459339510.823338977033025
950.1724872923978000.3449745847955990.8275127076022
960.1513229812284890.3026459624569780.848677018771511
970.1587100265380320.3174200530760650.841289973461968
980.1378256064987860.2756512129975720.862174393501214
990.1250346290784510.2500692581569020.874965370921549
1000.1227420158300170.2454840316600330.877257984169983
1010.1195643799145540.2391287598291070.880435620085446
1020.1100893415252120.2201786830504250.889910658474788
1030.0954409757084330.1908819514168660.904559024291567
1040.08732742025515130.1746548405103030.912672579744849
1050.08813597626068170.1762719525213630.911864023739318
1060.1490309279303020.2980618558606050.850969072069698
1070.1318224998125960.2636449996251920.868177500187404
1080.1322141316118160.2644282632236330.867785868388184
1090.1192340401182300.2384680802364610.88076595988177
1100.5139456385995750.972108722800850.486054361400425
1110.4642846921299670.9285693842599340.535715307870033
1120.4437815279298710.8875630558597420.556218472070129
1130.4045138116829440.809027623365890.595486188317056
1140.374873398555240.749746797110480.62512660144476
1150.3360308187473680.6720616374947350.663969181252632
1160.3535508152039630.7071016304079270.646449184796037
1170.3085296458157140.6170592916314270.691470354184286
1180.26368589927330.52737179854660.7363141007267
1190.2842326318241760.5684652636483520.715767368175824
1200.2572006704540500.5144013409081010.74279932954595
1210.2664697716225590.5329395432451170.733530228377441
1220.2242378663702530.4484757327405060.775762133629747
1230.1991482617570370.3982965235140750.800851738242963
1240.2592146352233070.5184292704466130.740785364776694
1250.2318302937018440.4636605874036880.768169706298156
1260.2296695255250860.4593390510501720.770330474474914
1270.3949141474747000.7898282949494010.6050858525253
1280.4147197500910260.8294395001820530.585280249908974
1290.4418368523120310.8836737046240630.558163147687969
1300.424197131064680.848394262129360.57580286893532
1310.3965674251886750.793134850377350.603432574811325
1320.4933627803698420.9867255607396830.506637219630158
1330.6170029584622680.7659940830754640.382997041537732
1340.560569890109840.878860219780320.43943010989016
1350.5266279910976050.946744017804790.473372008902395
1360.4624610620237420.9249221240474850.537538937976258
1370.8208617086318570.3582765827362860.179138291368143
1380.7713276407371680.4573447185256640.228672359262832
1390.7238092530083280.5523814939833450.276190746991673
1400.6850266457110850.629946708577830.314973354288915
1410.626356644251240.747286711497520.37364335574876
1420.5742351852514570.8515296294970860.425764814748543
1430.5040974223597140.9918051552805720.495902577640286
1440.4216070105058720.8432140210117440.578392989494128
1450.4183467887352050.836693577470410.581653211264795
1460.329981454575490.659962909150980.67001854542451
1470.2468733204930410.4937466409860810.753126679506959
1480.1822634741509350.3645269483018710.817736525849065
1490.3248326550922490.6496653101844980.675167344907751
1500.3719208757648990.7438417515297990.6280791242351
1510.400335009051720.800670018103440.59966499094828
1520.4758085397580620.9516170795161240.524191460241938






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00684931506849315OK
10% type I error level40.0273972602739726OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00684931506849315 & OK \tabularnewline
10% type I error level & 4 & 0.0273972602739726 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98798&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00684931506849315[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0273972602739726[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98798&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98798&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 level10.00684931506849315OK
10% type I error level40.0273972602739726OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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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 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}