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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 30 Nov 2010 17:13:46 +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/30/t12911372125dwa52b42q1yrar.htm/, Retrieved Fri, 29 Mar 2024 00:45:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103694, Retrieved Fri, 29 Mar 2024 00:45:46 +0000
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Original text written by user:enkel trend (geen month meer opgenomen)
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [mini tutorial wor...] [2010-11-30 17:13:46] [fba9c6aa004af59d8497d682e70ddad5] [Current]
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Dataseries X:
24	14	11	12	24	26
25	11	7	8	25	23
17	6	17	8	30	25
18	12	10	8	19	23
18	8	12	9	22	19
16	10	12	7	22	29
20	10	11	4	25	25
16	11	11	11	23	21
18	16	12	7	17	22
17	11	13	7	21	25
23	13	14	12	19	24
30	12	16	10	19	18
23	8	11	10	15	22
18	12	10	8	16	15
15	11	11	8	23	22
12	4	15	4	27	28
21	9	9	9	22	20
15	8	11	8	14	12
20	8	17	7	22	24
31	14	17	11	23	20
27	15	11	9	23	21
34	16	18	11	21	20
21	9	14	13	19	21
31	14	10	8	18	23
19	11	11	8	20	28
16	8	15	9	23	24
20	9	15	6	25	24
21	9	13	9	19	24
22	9	16	9	24	23
17	9	13	6	22	23
24	10	9	6	25	29
25	16	18	16	26	24
26	11	18	5	29	18
25	8	12	7	32	25
17	9	17	9	25	21
32	16	9	6	29	26
33	11	9	6	28	22
13	16	12	5	17	22
32	12	18	12	28	22
25	12	12	7	29	23
29	14	18	10	26	30
22	9	14	9	25	23
18	10	15	8	14	17
17	9	16	5	25	23
20	10	10	8	26	23
15	12	11	8	20	25
20	14	14	10	18	24
33	14	9	6	32	24
29	10	12	8	25	23
23	14	17	7	25	21
26	16	5	4	23	24
18	9	12	8	21	24
20	10	12	8	20	28
11	6	6	4	15	16
28	8	24	20	30	20
26	13	12	8	24	29
22	10	12	8	26	27
17	8	14	6	24	22
12	7	7	4	22	28
14	15	13	8	14	16
17	9	12	9	24	25
21	10	13	6	24	24
19	12	14	7	24	28
18	13	8	9	24	24
10	10	11	5	19	23
29	11	9	5	31	30
31	8	11	8	22	24
19	9	13	8	27	21
9	13	10	6	19	25
20	11	11	8	25	25
28	8	12	7	20	22
19	9	9	7	21	23
30	9	15	9	27	26
29	15	18	11	23	23
26	9	15	6	25	25
23	10	12	8	20	21
13	14	13	6	21	25
21	12	14	9	22	24
19	12	10	8	23	29
28	11	13	6	25	22
23	14	13	10	25	27
18	6	11	8	17	26
21	12	13	8	19	22
20	8	16	10	25	24
23	14	8	5	19	27
21	11	16	7	20	24
21	10	11	5	26	24
15	14	9	8	23	29
28	12	16	14	27	22
19	10	12	7	17	21
26	14	14	8	17	24
10	5	8	6	19	24
16	11	9	5	17	23
22	10	15	6	22	20
19	9	11	10	21	27
31	10	21	12	32	26
31	16	14	9	21	25
29	13	18	12	21	21
19	9	12	7	18	21
22	10	13	8	18	19
23	10	15	10	23	21
15	7	12	6	19	21
20	9	19	10	20	16
18	8	15	10	21	22
23	14	11	10	20	29
25	14	11	5	17	15
21	8	10	7	18	17
24	9	13	10	19	15
25	14	15	11	22	21
17	14	12	6	15	21
13	8	12	7	14	19
28	8	16	12	18	24
21	8	9	11	24	20
25	7	18	11	35	17
9	6	8	11	29	23
16	8	13	5	21	24
19	6	17	8	25	14
17	11	9	6	20	19
25	14	15	9	22	24
20	11	8	4	13	13
29	11	7	4	26	22
14	11	12	7	17	16
22	14	14	11	25	19
15	8	6	6	20	25
19	20	8	7	19	25
20	11	17	8	21	23
15	8	10	4	22	24
20	11	11	8	24	26
18	10	14	9	21	26
33	14	11	8	26	25
22	11	13	11	24	18
16	9	12	8	16	21
17	9	11	5	23	26
16	8	9	4	18	23
21	10	12	8	16	23
26	13	20	10	26	22
18	13	12	6	19	20
18	12	13	9	21	13
17	8	12	9	21	24
22	13	12	13	22	15
30	14	9	9	23	14
30	12	15	10	29	22
24	14	24	20	21	10
21	15	7	5	21	24
21	13	17	11	23	22
29	16	11	6	27	24
31	9	17	9	25	19
20	9	11	7	21	20
16	9	12	9	10	13
22	8	14	10	20	20
20	7	11	9	26	22
28	16	16	8	24	24
38	11	21	7	29	29
22	9	14	6	19	12
20	11	20	13	24	20
17	9	13	6	19	21
28	14	11	8	24	24
22	13	15	10	22	22
31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103694&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103694&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103694&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Multiple Linear Regression - Estimated Regression Equation
Parental.Expectations[t] = + 5.84398572246926 + 0.0895090012114705Concern.over.Mistakes[t] -0.12590016401359Doubts.about.actions[t] + 0.665601272609036Parental.Criticism[t] + 0.118116470483094Personal.Standards[t] -0.0820356250087838Organization[t] + 0.00239657305847149t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Parental.Expectations[t] =  +  5.84398572246926 +  0.0895090012114705Concern.over.Mistakes[t] -0.12590016401359Doubts.about.actions[t] +  0.665601272609036Parental.Criticism[t] +  0.118116470483094Personal.Standards[t] -0.0820356250087838Organization[t] +  0.00239657305847149t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103694&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Parental.Expectations[t] =  +  5.84398572246926 +  0.0895090012114705Concern.over.Mistakes[t] -0.12590016401359Doubts.about.actions[t] +  0.665601272609036Parental.Criticism[t] +  0.118116470483094Personal.Standards[t] -0.0820356250087838Organization[t] +  0.00239657305847149t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103694&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103694&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Parental.Expectations[t] = + 5.84398572246926 + 0.0895090012114705Concern.over.Mistakes[t] -0.12590016401359Doubts.about.actions[t] + 0.665601272609036Parental.Criticism[t] + 0.118116470483094Personal.Standards[t] -0.0820356250087838Organization[t] + 0.00239657305847149t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.843985722469261.8923913.08810.0023950.001197
Concern.over.Mistakes0.08950900121147050.0483231.85230.065920.03296
Doubts.about.actions-0.125900164013590.087456-1.43960.1520430.076022
Parental.Criticism0.6656012726090360.0865197.693100
Personal.Standards0.1181164704830940.0634461.86170.0645790.032289
Organization-0.08203562500878380.063311-1.29580.1970250.098512
t0.002396573058471490.0048260.49660.6201670.310084

\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) & 5.84398572246926 & 1.892391 & 3.0881 & 0.002395 & 0.001197 \tabularnewline
Concern.over.Mistakes & 0.0895090012114705 & 0.048323 & 1.8523 & 0.06592 & 0.03296 \tabularnewline
Doubts.about.actions & -0.12590016401359 & 0.087456 & -1.4396 & 0.152043 & 0.076022 \tabularnewline
Parental.Criticism & 0.665601272609036 & 0.086519 & 7.6931 & 0 & 0 \tabularnewline
Personal.Standards & 0.118116470483094 & 0.063446 & 1.8617 & 0.064579 & 0.032289 \tabularnewline
Organization & -0.0820356250087838 & 0.063311 & -1.2958 & 0.197025 & 0.098512 \tabularnewline
t & 0.00239657305847149 & 0.004826 & 0.4966 & 0.620167 & 0.310084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103694&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]5.84398572246926[/C][C]1.892391[/C][C]3.0881[/C][C]0.002395[/C][C]0.001197[/C][/ROW]
[ROW][C]Concern.over.Mistakes[/C][C]0.0895090012114705[/C][C]0.048323[/C][C]1.8523[/C][C]0.06592[/C][C]0.03296[/C][/ROW]
[ROW][C]Doubts.about.actions[/C][C]-0.12590016401359[/C][C]0.087456[/C][C]-1.4396[/C][C]0.152043[/C][C]0.076022[/C][/ROW]
[ROW][C]Parental.Criticism[/C][C]0.665601272609036[/C][C]0.086519[/C][C]7.6931[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Personal.Standards[/C][C]0.118116470483094[/C][C]0.063446[/C][C]1.8617[/C][C]0.064579[/C][C]0.032289[/C][/ROW]
[ROW][C]Organization[/C][C]-0.0820356250087838[/C][C]0.063311[/C][C]-1.2958[/C][C]0.197025[/C][C]0.098512[/C][/ROW]
[ROW][C]t[/C][C]0.00239657305847149[/C][C]0.004826[/C][C]0.4966[/C][C]0.620167[/C][C]0.310084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103694&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103694&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.843985722469261.8923913.08810.0023950.001197
Concern.over.Mistakes0.08950900121147050.0483231.85230.065920.03296
Doubts.about.actions-0.125900164013590.087456-1.43960.1520430.076022
Parental.Criticism0.6656012726090360.0865197.693100
Personal.Standards0.1181164704830940.0634461.86170.0645790.032289
Organization-0.08203562500878380.063311-1.29580.1970250.098512
t0.002396573058471490.0048260.49660.6201670.310084







Multiple Linear Regression - Regression Statistics
Multiple R0.639019153846926
R-squared0.408345478983241
Adjusted R-squared0.384990695258896
F-TEST (value)17.4844470324754
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value2.44249065417534e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.70203430228648
Sum Squared Residuals1109.75038435138

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.639019153846926 \tabularnewline
R-squared & 0.408345478983241 \tabularnewline
Adjusted R-squared & 0.384990695258896 \tabularnewline
F-TEST (value) & 17.4844470324754 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 2.44249065417534e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.70203430228648 \tabularnewline
Sum Squared Residuals & 1109.75038435138 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103694&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.639019153846926[/C][/ROW]
[ROW][C]R-squared[/C][C]0.408345478983241[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.384990695258896[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.4844470324754[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]2.44249065417534e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.70203430228648[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1109.75038435138[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103694&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103694&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.639019153846926
R-squared0.408345478983241
Adjusted R-squared0.384990695258896
F-TEST (value)17.4844470324754
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value2.44249065417534e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.70203430228648
Sum Squared Residuals1109.75038435138







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11114.9210803410871-3.92108034108708
2713.0925046624711-6.09250466247108
31713.43484114830363.56515885169636
41011.6361358131956-1.63613581319559
51213.4902262264019-1.49022622640187
61210.91024567370431.08975432629570
7119.956366345265971.04363365473403
81114.2259452167972-3.22594521679717
91210.32471943386711.67528056613286
101311.09346683268811.90653316731189
111414.554926132076-0.554926132076005
121614.4707970824631.52920291753701
131113.5496229211280-2.54962292112802
141011.9620371324013-1.96203713240128
151112.0743727841591-1.07437278415910
161510.00739054312184.99260945687818
17913.5795763177156-4.57957631771556
181112.2165710111153-1.21657101111528
191711.96141558138145.03858441861859
201715.30167424463891.69832575536110
211113.4068964786110-2.40689647861104
221815.08696112539692.91303887460312
231415.8199758100445-1.81997581004447
241012.4777674916039-2.47776749160385
251111.6098113580877-0.609811358087717
261513.0694746036461.930525396354
271511.54343614067583.45656385932416
281312.92344670987430.0765532901256699
291613.68797026156852.31202973843148
301311.00978506977631.99021493022365
31911.3749801486981-2.3749801486981
321817.89579206050390.104207939496122
331812.14214761764445.85785238235565
341213.544038263138-1.54403826313799
351713.53699241436273.46300758563734
36912.0662067965594-3.06620679655941
37912.9976392204493-3.99763922044934
38128.615472501287393.38452749871261
391816.78063083699541.21936916300456
401212.8645388840027-0.864538884002747
411814.04137616519633.95862383480374
421413.83724218181170.162757818188253
431511.88303388814043.11696611185962
441610.73208523143525.26791476856481
451012.9920289324247-2.99202893242469
461111.4223100984825-0.422310098482497
471412.79745657883181.20254342116819
48912.9546956639666-3.95469566396656
491213.6890797650787-1.68907976507872
501712.14929165222254.85070834777746
5159.6892712670686-4.68927126706859
521212.2830691280004-0.283069128000385
531211.89232456895000.107675431050021
5469.32418084441337-3.32418084441337
552422.68965502899551.31034497100449
561212.4492980602770-0.449298060277037
571212.8716633115142-0.871663311514151
581411.52105784540212.47894215459789
5979.14216034017986-2.14216034017986
601311.01827443022941.98172556977065
611213.1530443433647-1.15304434336469
621311.47280856443711.52719143556287
631411.38184557961942.61815442038062
64812.828178032706-4.828178032706
65119.321251270270651.67874872972935
66911.7415669730691-2.74156697306911
671113.7266513741233-2.72665137412326
681313.3657289960723-0.365728996072312
69109.365158091843760.634841908156238
701112.6438553743722-1.64385537437222
711212.7299476991651-0.729947699165077
72911.8369439427810-2.83694394278103
731514.61773402225600.382265977744037
741814.88006414833353.11993585166653
751512.11349002974252.88650997025749
761212.7902221279907-0.790222127990721
77139.852699458110013.14730054188999
781413.01992428220640.980075717793584
791011.8856399256721-1.88563992567208
801312.29879744445700.701202555543019
811313.7281754848096-0.728175484809557
821112.0961296798454-1.09612967984536
831312.17602771345800.823972286541977
841614.46834605945851.53165394054155
8589.7010565910997-1.7010565910997
861611.59756154450354.40243845549648
871111.1033545592561-0.103354559256068
88911.2973727503253-2.29737275032527
891617.7555095598081-1.75550955980811
901211.44578746190510.554212538094877
911411.99064078497222.00935921502779
92810.5990252105176-2.59902521051758
9399.5632762181969-0.563276218196896
941511.73091746258863.26908253741136
951113.5607264409179-2.56072644091785
962117.22385021004133.77614978995873
971413.25679643088580.743203569114158
981815.78282181142442.21717818857561
991211.71137325392810.28862674607195
1001312.68606918923390.313930810766054
1011514.53568841111990.464311588880138
1021211.06484249415880.93515750584118
1031914.45368343121064.54631656878938
1041514.02886488629010.971135113709867
1051113.0310396357798-2.03103963577984
1061110.67859718688980.321402813110241
1071012.3636065048675-2.36360650486749
1081314.7876214558746-1.78762145587455
1091514.77776314408220.222236855917843
110129.909266051022032.09073394897797
1111211.02058365545970.979416344540333
1121615.75590936662380.244090633376166
113915.5029829815267-6.50298298152667
1141817.5347037737850.465296226215003
115815.0299439185223-7.02994391852227
1161310.38652814750612.6134718524939
1171714.19887800207352.80112199792652
118911.0607927299636-2.06079272996361
1191513.22441945442241.77558054557755
12089.66830879116794-1.66830879116794
121711.2734798663308-4.27347986633081
1221211.35921075474920.640789245250814
1231415.0612088247332-1.06120882473319
124610.7816409078796-4.78164090787956
125810.1787563197468-2.17875631974678
1261712.46966883373184.53033116626818
127109.775896647811870.224103352188126
1281112.5827045162717-1.58270451627169
1291412.84323511208061.15676488791943
1301113.6916827520720-2.69168275207195
1311315.4220010557674-2.42200105576743
1321211.95130149286600.0486985071339539
1331110.46304041764660.536959582353384
13499.49175140350905-0.491751403509054
1351212.1160648040677-0.116064804067656
1362014.78270876620055.2172912337995
1371210.74388419576701.25611580423302
1381313.6794670666938-0.679467066693821
1391213.1935634194986-1.19356341949856
1401216.5328463645447-4.53284636454473
141914.6631617883371-5.66316178833711
1421515.6353737848601-0.635373784860085
1432421.54442448495362.45557551504643
144710.0198760511055-3.01987605110552
1451714.66798477882912.33201522117085
1461111.9891411384082-0.98914113840824
1471715.22260586388961.77739413611038
1481112.3546993714627-1.35469937146269
1491212.6052306846408-0.605230684640806
1501414.5430980313602-0.543098031360177
1511114.3714030662813-3.37140306628126
1521612.89086470931643.10913529068361
1532113.93265506932007.06734493067997
1541412.30254759873151.69745240126850
1552016.46763210194833.53236789805169
1561311.12147511371201.87852488628796
1571113.1546479026359-2.15464790263593
1581514.00493148670860.995068513291381
1591918.00230511188590.997694888114127

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11 & 14.9210803410871 & -3.92108034108708 \tabularnewline
2 & 7 & 13.0925046624711 & -6.09250466247108 \tabularnewline
3 & 17 & 13.4348411483036 & 3.56515885169636 \tabularnewline
4 & 10 & 11.6361358131956 & -1.63613581319559 \tabularnewline
5 & 12 & 13.4902262264019 & -1.49022622640187 \tabularnewline
6 & 12 & 10.9102456737043 & 1.08975432629570 \tabularnewline
7 & 11 & 9.95636634526597 & 1.04363365473403 \tabularnewline
8 & 11 & 14.2259452167972 & -3.22594521679717 \tabularnewline
9 & 12 & 10.3247194338671 & 1.67528056613286 \tabularnewline
10 & 13 & 11.0934668326881 & 1.90653316731189 \tabularnewline
11 & 14 & 14.554926132076 & -0.554926132076005 \tabularnewline
12 & 16 & 14.470797082463 & 1.52920291753701 \tabularnewline
13 & 11 & 13.5496229211280 & -2.54962292112802 \tabularnewline
14 & 10 & 11.9620371324013 & -1.96203713240128 \tabularnewline
15 & 11 & 12.0743727841591 & -1.07437278415910 \tabularnewline
16 & 15 & 10.0073905431218 & 4.99260945687818 \tabularnewline
17 & 9 & 13.5795763177156 & -4.57957631771556 \tabularnewline
18 & 11 & 12.2165710111153 & -1.21657101111528 \tabularnewline
19 & 17 & 11.9614155813814 & 5.03858441861859 \tabularnewline
20 & 17 & 15.3016742446389 & 1.69832575536110 \tabularnewline
21 & 11 & 13.4068964786110 & -2.40689647861104 \tabularnewline
22 & 18 & 15.0869611253969 & 2.91303887460312 \tabularnewline
23 & 14 & 15.8199758100445 & -1.81997581004447 \tabularnewline
24 & 10 & 12.4777674916039 & -2.47776749160385 \tabularnewline
25 & 11 & 11.6098113580877 & -0.609811358087717 \tabularnewline
26 & 15 & 13.069474603646 & 1.930525396354 \tabularnewline
27 & 15 & 11.5434361406758 & 3.45656385932416 \tabularnewline
28 & 13 & 12.9234467098743 & 0.0765532901256699 \tabularnewline
29 & 16 & 13.6879702615685 & 2.31202973843148 \tabularnewline
30 & 13 & 11.0097850697763 & 1.99021493022365 \tabularnewline
31 & 9 & 11.3749801486981 & -2.3749801486981 \tabularnewline
32 & 18 & 17.8957920605039 & 0.104207939496122 \tabularnewline
33 & 18 & 12.1421476176444 & 5.85785238235565 \tabularnewline
34 & 12 & 13.544038263138 & -1.54403826313799 \tabularnewline
35 & 17 & 13.5369924143627 & 3.46300758563734 \tabularnewline
36 & 9 & 12.0662067965594 & -3.06620679655941 \tabularnewline
37 & 9 & 12.9976392204493 & -3.99763922044934 \tabularnewline
38 & 12 & 8.61547250128739 & 3.38452749871261 \tabularnewline
39 & 18 & 16.7806308369954 & 1.21936916300456 \tabularnewline
40 & 12 & 12.8645388840027 & -0.864538884002747 \tabularnewline
41 & 18 & 14.0413761651963 & 3.95862383480374 \tabularnewline
42 & 14 & 13.8372421818117 & 0.162757818188253 \tabularnewline
43 & 15 & 11.8830338881404 & 3.11696611185962 \tabularnewline
44 & 16 & 10.7320852314352 & 5.26791476856481 \tabularnewline
45 & 10 & 12.9920289324247 & -2.99202893242469 \tabularnewline
46 & 11 & 11.4223100984825 & -0.422310098482497 \tabularnewline
47 & 14 & 12.7974565788318 & 1.20254342116819 \tabularnewline
48 & 9 & 12.9546956639666 & -3.95469566396656 \tabularnewline
49 & 12 & 13.6890797650787 & -1.68907976507872 \tabularnewline
50 & 17 & 12.1492916522225 & 4.85070834777746 \tabularnewline
51 & 5 & 9.6892712670686 & -4.68927126706859 \tabularnewline
52 & 12 & 12.2830691280004 & -0.283069128000385 \tabularnewline
53 & 12 & 11.8923245689500 & 0.107675431050021 \tabularnewline
54 & 6 & 9.32418084441337 & -3.32418084441337 \tabularnewline
55 & 24 & 22.6896550289955 & 1.31034497100449 \tabularnewline
56 & 12 & 12.4492980602770 & -0.449298060277037 \tabularnewline
57 & 12 & 12.8716633115142 & -0.871663311514151 \tabularnewline
58 & 14 & 11.5210578454021 & 2.47894215459789 \tabularnewline
59 & 7 & 9.14216034017986 & -2.14216034017986 \tabularnewline
60 & 13 & 11.0182744302294 & 1.98172556977065 \tabularnewline
61 & 12 & 13.1530443433647 & -1.15304434336469 \tabularnewline
62 & 13 & 11.4728085644371 & 1.52719143556287 \tabularnewline
63 & 14 & 11.3818455796194 & 2.61815442038062 \tabularnewline
64 & 8 & 12.828178032706 & -4.828178032706 \tabularnewline
65 & 11 & 9.32125127027065 & 1.67874872972935 \tabularnewline
66 & 9 & 11.7415669730691 & -2.74156697306911 \tabularnewline
67 & 11 & 13.7266513741233 & -2.72665137412326 \tabularnewline
68 & 13 & 13.3657289960723 & -0.365728996072312 \tabularnewline
69 & 10 & 9.36515809184376 & 0.634841908156238 \tabularnewline
70 & 11 & 12.6438553743722 & -1.64385537437222 \tabularnewline
71 & 12 & 12.7299476991651 & -0.729947699165077 \tabularnewline
72 & 9 & 11.8369439427810 & -2.83694394278103 \tabularnewline
73 & 15 & 14.6177340222560 & 0.382265977744037 \tabularnewline
74 & 18 & 14.8800641483335 & 3.11993585166653 \tabularnewline
75 & 15 & 12.1134900297425 & 2.88650997025749 \tabularnewline
76 & 12 & 12.7902221279907 & -0.790222127990721 \tabularnewline
77 & 13 & 9.85269945811001 & 3.14730054188999 \tabularnewline
78 & 14 & 13.0199242822064 & 0.980075717793584 \tabularnewline
79 & 10 & 11.8856399256721 & -1.88563992567208 \tabularnewline
80 & 13 & 12.2987974444570 & 0.701202555543019 \tabularnewline
81 & 13 & 13.7281754848096 & -0.728175484809557 \tabularnewline
82 & 11 & 12.0961296798454 & -1.09612967984536 \tabularnewline
83 & 13 & 12.1760277134580 & 0.823972286541977 \tabularnewline
84 & 16 & 14.4683460594585 & 1.53165394054155 \tabularnewline
85 & 8 & 9.7010565910997 & -1.7010565910997 \tabularnewline
86 & 16 & 11.5975615445035 & 4.40243845549648 \tabularnewline
87 & 11 & 11.1033545592561 & -0.103354559256068 \tabularnewline
88 & 9 & 11.2973727503253 & -2.29737275032527 \tabularnewline
89 & 16 & 17.7555095598081 & -1.75550955980811 \tabularnewline
90 & 12 & 11.4457874619051 & 0.554212538094877 \tabularnewline
91 & 14 & 11.9906407849722 & 2.00935921502779 \tabularnewline
92 & 8 & 10.5990252105176 & -2.59902521051758 \tabularnewline
93 & 9 & 9.5632762181969 & -0.563276218196896 \tabularnewline
94 & 15 & 11.7309174625886 & 3.26908253741136 \tabularnewline
95 & 11 & 13.5607264409179 & -2.56072644091785 \tabularnewline
96 & 21 & 17.2238502100413 & 3.77614978995873 \tabularnewline
97 & 14 & 13.2567964308858 & 0.743203569114158 \tabularnewline
98 & 18 & 15.7828218114244 & 2.21717818857561 \tabularnewline
99 & 12 & 11.7113732539281 & 0.28862674607195 \tabularnewline
100 & 13 & 12.6860691892339 & 0.313930810766054 \tabularnewline
101 & 15 & 14.5356884111199 & 0.464311588880138 \tabularnewline
102 & 12 & 11.0648424941588 & 0.93515750584118 \tabularnewline
103 & 19 & 14.4536834312106 & 4.54631656878938 \tabularnewline
104 & 15 & 14.0288648862901 & 0.971135113709867 \tabularnewline
105 & 11 & 13.0310396357798 & -2.03103963577984 \tabularnewline
106 & 11 & 10.6785971868898 & 0.321402813110241 \tabularnewline
107 & 10 & 12.3636065048675 & -2.36360650486749 \tabularnewline
108 & 13 & 14.7876214558746 & -1.78762145587455 \tabularnewline
109 & 15 & 14.7777631440822 & 0.222236855917843 \tabularnewline
110 & 12 & 9.90926605102203 & 2.09073394897797 \tabularnewline
111 & 12 & 11.0205836554597 & 0.979416344540333 \tabularnewline
112 & 16 & 15.7559093666238 & 0.244090633376166 \tabularnewline
113 & 9 & 15.5029829815267 & -6.50298298152667 \tabularnewline
114 & 18 & 17.534703773785 & 0.465296226215003 \tabularnewline
115 & 8 & 15.0299439185223 & -7.02994391852227 \tabularnewline
116 & 13 & 10.3865281475061 & 2.6134718524939 \tabularnewline
117 & 17 & 14.1988780020735 & 2.80112199792652 \tabularnewline
118 & 9 & 11.0607927299636 & -2.06079272996361 \tabularnewline
119 & 15 & 13.2244194544224 & 1.77558054557755 \tabularnewline
120 & 8 & 9.66830879116794 & -1.66830879116794 \tabularnewline
121 & 7 & 11.2734798663308 & -4.27347986633081 \tabularnewline
122 & 12 & 11.3592107547492 & 0.640789245250814 \tabularnewline
123 & 14 & 15.0612088247332 & -1.06120882473319 \tabularnewline
124 & 6 & 10.7816409078796 & -4.78164090787956 \tabularnewline
125 & 8 & 10.1787563197468 & -2.17875631974678 \tabularnewline
126 & 17 & 12.4696688337318 & 4.53033116626818 \tabularnewline
127 & 10 & 9.77589664781187 & 0.224103352188126 \tabularnewline
128 & 11 & 12.5827045162717 & -1.58270451627169 \tabularnewline
129 & 14 & 12.8432351120806 & 1.15676488791943 \tabularnewline
130 & 11 & 13.6916827520720 & -2.69168275207195 \tabularnewline
131 & 13 & 15.4220010557674 & -2.42200105576743 \tabularnewline
132 & 12 & 11.9513014928660 & 0.0486985071339539 \tabularnewline
133 & 11 & 10.4630404176466 & 0.536959582353384 \tabularnewline
134 & 9 & 9.49175140350905 & -0.491751403509054 \tabularnewline
135 & 12 & 12.1160648040677 & -0.116064804067656 \tabularnewline
136 & 20 & 14.7827087662005 & 5.2172912337995 \tabularnewline
137 & 12 & 10.7438841957670 & 1.25611580423302 \tabularnewline
138 & 13 & 13.6794670666938 & -0.679467066693821 \tabularnewline
139 & 12 & 13.1935634194986 & -1.19356341949856 \tabularnewline
140 & 12 & 16.5328463645447 & -4.53284636454473 \tabularnewline
141 & 9 & 14.6631617883371 & -5.66316178833711 \tabularnewline
142 & 15 & 15.6353737848601 & -0.635373784860085 \tabularnewline
143 & 24 & 21.5444244849536 & 2.45557551504643 \tabularnewline
144 & 7 & 10.0198760511055 & -3.01987605110552 \tabularnewline
145 & 17 & 14.6679847788291 & 2.33201522117085 \tabularnewline
146 & 11 & 11.9891411384082 & -0.98914113840824 \tabularnewline
147 & 17 & 15.2226058638896 & 1.77739413611038 \tabularnewline
148 & 11 & 12.3546993714627 & -1.35469937146269 \tabularnewline
149 & 12 & 12.6052306846408 & -0.605230684640806 \tabularnewline
150 & 14 & 14.5430980313602 & -0.543098031360177 \tabularnewline
151 & 11 & 14.3714030662813 & -3.37140306628126 \tabularnewline
152 & 16 & 12.8908647093164 & 3.10913529068361 \tabularnewline
153 & 21 & 13.9326550693200 & 7.06734493067997 \tabularnewline
154 & 14 & 12.3025475987315 & 1.69745240126850 \tabularnewline
155 & 20 & 16.4676321019483 & 3.53236789805169 \tabularnewline
156 & 13 & 11.1214751137120 & 1.87852488628796 \tabularnewline
157 & 11 & 13.1546479026359 & -2.15464790263593 \tabularnewline
158 & 15 & 14.0049314867086 & 0.995068513291381 \tabularnewline
159 & 19 & 18.0023051118859 & 0.997694888114127 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103694&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]11[/C][C]14.9210803410871[/C][C]-3.92108034108708[/C][/ROW]
[ROW][C]2[/C][C]7[/C][C]13.0925046624711[/C][C]-6.09250466247108[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]13.4348411483036[/C][C]3.56515885169636[/C][/ROW]
[ROW][C]4[/C][C]10[/C][C]11.6361358131956[/C][C]-1.63613581319559[/C][/ROW]
[ROW][C]5[/C][C]12[/C][C]13.4902262264019[/C][C]-1.49022622640187[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.9102456737043[/C][C]1.08975432629570[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]9.95636634526597[/C][C]1.04363365473403[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]14.2259452167972[/C][C]-3.22594521679717[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.3247194338671[/C][C]1.67528056613286[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]11.0934668326881[/C][C]1.90653316731189[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]14.554926132076[/C][C]-0.554926132076005[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]14.470797082463[/C][C]1.52920291753701[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]13.5496229211280[/C][C]-2.54962292112802[/C][/ROW]
[ROW][C]14[/C][C]10[/C][C]11.9620371324013[/C][C]-1.96203713240128[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]12.0743727841591[/C][C]-1.07437278415910[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]10.0073905431218[/C][C]4.99260945687818[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]13.5795763177156[/C][C]-4.57957631771556[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]12.2165710111153[/C][C]-1.21657101111528[/C][/ROW]
[ROW][C]19[/C][C]17[/C][C]11.9614155813814[/C][C]5.03858441861859[/C][/ROW]
[ROW][C]20[/C][C]17[/C][C]15.3016742446389[/C][C]1.69832575536110[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]13.4068964786110[/C][C]-2.40689647861104[/C][/ROW]
[ROW][C]22[/C][C]18[/C][C]15.0869611253969[/C][C]2.91303887460312[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]15.8199758100445[/C][C]-1.81997581004447[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]12.4777674916039[/C][C]-2.47776749160385[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]11.6098113580877[/C][C]-0.609811358087717[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]13.069474603646[/C][C]1.930525396354[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]11.5434361406758[/C][C]3.45656385932416[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.9234467098743[/C][C]0.0765532901256699[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]13.6879702615685[/C][C]2.31202973843148[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]11.0097850697763[/C][C]1.99021493022365[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.3749801486981[/C][C]-2.3749801486981[/C][/ROW]
[ROW][C]32[/C][C]18[/C][C]17.8957920605039[/C][C]0.104207939496122[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]12.1421476176444[/C][C]5.85785238235565[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]13.544038263138[/C][C]-1.54403826313799[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]13.5369924143627[/C][C]3.46300758563734[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]12.0662067965594[/C][C]-3.06620679655941[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]12.9976392204493[/C][C]-3.99763922044934[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]8.61547250128739[/C][C]3.38452749871261[/C][/ROW]
[ROW][C]39[/C][C]18[/C][C]16.7806308369954[/C][C]1.21936916300456[/C][/ROW]
[ROW][C]40[/C][C]12[/C][C]12.8645388840027[/C][C]-0.864538884002747[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]14.0413761651963[/C][C]3.95862383480374[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]13.8372421818117[/C][C]0.162757818188253[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]11.8830338881404[/C][C]3.11696611185962[/C][/ROW]
[ROW][C]44[/C][C]16[/C][C]10.7320852314352[/C][C]5.26791476856481[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]12.9920289324247[/C][C]-2.99202893242469[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]11.4223100984825[/C][C]-0.422310098482497[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]12.7974565788318[/C][C]1.20254342116819[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]12.9546956639666[/C][C]-3.95469566396656[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]13.6890797650787[/C][C]-1.68907976507872[/C][/ROW]
[ROW][C]50[/C][C]17[/C][C]12.1492916522225[/C][C]4.85070834777746[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]9.6892712670686[/C][C]-4.68927126706859[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]12.2830691280004[/C][C]-0.283069128000385[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]11.8923245689500[/C][C]0.107675431050021[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]9.32418084441337[/C][C]-3.32418084441337[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]22.6896550289955[/C][C]1.31034497100449[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.4492980602770[/C][C]-0.449298060277037[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]12.8716633115142[/C][C]-0.871663311514151[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]11.5210578454021[/C][C]2.47894215459789[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]9.14216034017986[/C][C]-2.14216034017986[/C][/ROW]
[ROW][C]60[/C][C]13[/C][C]11.0182744302294[/C][C]1.98172556977065[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]13.1530443433647[/C][C]-1.15304434336469[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]11.4728085644371[/C][C]1.52719143556287[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.3818455796194[/C][C]2.61815442038062[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]12.828178032706[/C][C]-4.828178032706[/C][/ROW]
[ROW][C]65[/C][C]11[/C][C]9.32125127027065[/C][C]1.67874872972935[/C][/ROW]
[ROW][C]66[/C][C]9[/C][C]11.7415669730691[/C][C]-2.74156697306911[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.7266513741233[/C][C]-2.72665137412326[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]13.3657289960723[/C][C]-0.365728996072312[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]9.36515809184376[/C][C]0.634841908156238[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]12.6438553743722[/C][C]-1.64385537437222[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]12.7299476991651[/C][C]-0.729947699165077[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]11.8369439427810[/C][C]-2.83694394278103[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.6177340222560[/C][C]0.382265977744037[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]14.8800641483335[/C][C]3.11993585166653[/C][/ROW]
[ROW][C]75[/C][C]15[/C][C]12.1134900297425[/C][C]2.88650997025749[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.7902221279907[/C][C]-0.790222127990721[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]9.85269945811001[/C][C]3.14730054188999[/C][/ROW]
[ROW][C]78[/C][C]14[/C][C]13.0199242822064[/C][C]0.980075717793584[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]11.8856399256721[/C][C]-1.88563992567208[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]12.2987974444570[/C][C]0.701202555543019[/C][/ROW]
[ROW][C]81[/C][C]13[/C][C]13.7281754848096[/C][C]-0.728175484809557[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]12.0961296798454[/C][C]-1.09612967984536[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]12.1760277134580[/C][C]0.823972286541977[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.4683460594585[/C][C]1.53165394054155[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]9.7010565910997[/C][C]-1.7010565910997[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]11.5975615445035[/C][C]4.40243845549648[/C][/ROW]
[ROW][C]87[/C][C]11[/C][C]11.1033545592561[/C][C]-0.103354559256068[/C][/ROW]
[ROW][C]88[/C][C]9[/C][C]11.2973727503253[/C][C]-2.29737275032527[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]17.7555095598081[/C][C]-1.75550955980811[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.4457874619051[/C][C]0.554212538094877[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]11.9906407849722[/C][C]2.00935921502779[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]10.5990252105176[/C][C]-2.59902521051758[/C][/ROW]
[ROW][C]93[/C][C]9[/C][C]9.5632762181969[/C][C]-0.563276218196896[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]11.7309174625886[/C][C]3.26908253741136[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]13.5607264409179[/C][C]-2.56072644091785[/C][/ROW]
[ROW][C]96[/C][C]21[/C][C]17.2238502100413[/C][C]3.77614978995873[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]13.2567964308858[/C][C]0.743203569114158[/C][/ROW]
[ROW][C]98[/C][C]18[/C][C]15.7828218114244[/C][C]2.21717818857561[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.7113732539281[/C][C]0.28862674607195[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.6860691892339[/C][C]0.313930810766054[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]14.5356884111199[/C][C]0.464311588880138[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]11.0648424941588[/C][C]0.93515750584118[/C][/ROW]
[ROW][C]103[/C][C]19[/C][C]14.4536834312106[/C][C]4.54631656878938[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.0288648862901[/C][C]0.971135113709867[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]13.0310396357798[/C][C]-2.03103963577984[/C][/ROW]
[ROW][C]106[/C][C]11[/C][C]10.6785971868898[/C][C]0.321402813110241[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]12.3636065048675[/C][C]-2.36360650486749[/C][/ROW]
[ROW][C]108[/C][C]13[/C][C]14.7876214558746[/C][C]-1.78762145587455[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]14.7777631440822[/C][C]0.222236855917843[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]9.90926605102203[/C][C]2.09073394897797[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.0205836554597[/C][C]0.979416344540333[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.7559093666238[/C][C]0.244090633376166[/C][/ROW]
[ROW][C]113[/C][C]9[/C][C]15.5029829815267[/C][C]-6.50298298152667[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]17.534703773785[/C][C]0.465296226215003[/C][/ROW]
[ROW][C]115[/C][C]8[/C][C]15.0299439185223[/C][C]-7.02994391852227[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]10.3865281475061[/C][C]2.6134718524939[/C][/ROW]
[ROW][C]117[/C][C]17[/C][C]14.1988780020735[/C][C]2.80112199792652[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]11.0607927299636[/C][C]-2.06079272996361[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]13.2244194544224[/C][C]1.77558054557755[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]9.66830879116794[/C][C]-1.66830879116794[/C][/ROW]
[ROW][C]121[/C][C]7[/C][C]11.2734798663308[/C][C]-4.27347986633081[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]11.3592107547492[/C][C]0.640789245250814[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]15.0612088247332[/C][C]-1.06120882473319[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]10.7816409078796[/C][C]-4.78164090787956[/C][/ROW]
[ROW][C]125[/C][C]8[/C][C]10.1787563197468[/C][C]-2.17875631974678[/C][/ROW]
[ROW][C]126[/C][C]17[/C][C]12.4696688337318[/C][C]4.53033116626818[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]9.77589664781187[/C][C]0.224103352188126[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]12.5827045162717[/C][C]-1.58270451627169[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]12.8432351120806[/C][C]1.15676488791943[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]13.6916827520720[/C][C]-2.69168275207195[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]15.4220010557674[/C][C]-2.42200105576743[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]11.9513014928660[/C][C]0.0486985071339539[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]10.4630404176466[/C][C]0.536959582353384[/C][/ROW]
[ROW][C]134[/C][C]9[/C][C]9.49175140350905[/C][C]-0.491751403509054[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.1160648040677[/C][C]-0.116064804067656[/C][/ROW]
[ROW][C]136[/C][C]20[/C][C]14.7827087662005[/C][C]5.2172912337995[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]10.7438841957670[/C][C]1.25611580423302[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]13.6794670666938[/C][C]-0.679467066693821[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.1935634194986[/C][C]-1.19356341949856[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]16.5328463645447[/C][C]-4.53284636454473[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]14.6631617883371[/C][C]-5.66316178833711[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]15.6353737848601[/C][C]-0.635373784860085[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]21.5444244849536[/C][C]2.45557551504643[/C][/ROW]
[ROW][C]144[/C][C]7[/C][C]10.0198760511055[/C][C]-3.01987605110552[/C][/ROW]
[ROW][C]145[/C][C]17[/C][C]14.6679847788291[/C][C]2.33201522117085[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]11.9891411384082[/C][C]-0.98914113840824[/C][/ROW]
[ROW][C]147[/C][C]17[/C][C]15.2226058638896[/C][C]1.77739413611038[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]12.3546993714627[/C][C]-1.35469937146269[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]12.6052306846408[/C][C]-0.605230684640806[/C][/ROW]
[ROW][C]150[/C][C]14[/C][C]14.5430980313602[/C][C]-0.543098031360177[/C][/ROW]
[ROW][C]151[/C][C]11[/C][C]14.3714030662813[/C][C]-3.37140306628126[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]12.8908647093164[/C][C]3.10913529068361[/C][/ROW]
[ROW][C]153[/C][C]21[/C][C]13.9326550693200[/C][C]7.06734493067997[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.3025475987315[/C][C]1.69745240126850[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]16.4676321019483[/C][C]3.53236789805169[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]11.1214751137120[/C][C]1.87852488628796[/C][/ROW]
[ROW][C]157[/C][C]11[/C][C]13.1546479026359[/C][C]-2.15464790263593[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]14.0049314867086[/C][C]0.995068513291381[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]18.0023051118859[/C][C]0.997694888114127[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103694&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103694&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11114.9210803410871-3.92108034108708
2713.0925046624711-6.09250466247108
31713.43484114830363.56515885169636
41011.6361358131956-1.63613581319559
51213.4902262264019-1.49022622640187
61210.91024567370431.08975432629570
7119.956366345265971.04363365473403
81114.2259452167972-3.22594521679717
91210.32471943386711.67528056613286
101311.09346683268811.90653316731189
111414.554926132076-0.554926132076005
121614.4707970824631.52920291753701
131113.5496229211280-2.54962292112802
141011.9620371324013-1.96203713240128
151112.0743727841591-1.07437278415910
161510.00739054312184.99260945687818
17913.5795763177156-4.57957631771556
181112.2165710111153-1.21657101111528
191711.96141558138145.03858441861859
201715.30167424463891.69832575536110
211113.4068964786110-2.40689647861104
221815.08696112539692.91303887460312
231415.8199758100445-1.81997581004447
241012.4777674916039-2.47776749160385
251111.6098113580877-0.609811358087717
261513.0694746036461.930525396354
271511.54343614067583.45656385932416
281312.92344670987430.0765532901256699
291613.68797026156852.31202973843148
301311.00978506977631.99021493022365
31911.3749801486981-2.3749801486981
321817.89579206050390.104207939496122
331812.14214761764445.85785238235565
341213.544038263138-1.54403826313799
351713.53699241436273.46300758563734
36912.0662067965594-3.06620679655941
37912.9976392204493-3.99763922044934
38128.615472501287393.38452749871261
391816.78063083699541.21936916300456
401212.8645388840027-0.864538884002747
411814.04137616519633.95862383480374
421413.83724218181170.162757818188253
431511.88303388814043.11696611185962
441610.73208523143525.26791476856481
451012.9920289324247-2.99202893242469
461111.4223100984825-0.422310098482497
471412.79745657883181.20254342116819
48912.9546956639666-3.95469566396656
491213.6890797650787-1.68907976507872
501712.14929165222254.85070834777746
5159.6892712670686-4.68927126706859
521212.2830691280004-0.283069128000385
531211.89232456895000.107675431050021
5469.32418084441337-3.32418084441337
552422.68965502899551.31034497100449
561212.4492980602770-0.449298060277037
571212.8716633115142-0.871663311514151
581411.52105784540212.47894215459789
5979.14216034017986-2.14216034017986
601311.01827443022941.98172556977065
611213.1530443433647-1.15304434336469
621311.47280856443711.52719143556287
631411.38184557961942.61815442038062
64812.828178032706-4.828178032706
65119.321251270270651.67874872972935
66911.7415669730691-2.74156697306911
671113.7266513741233-2.72665137412326
681313.3657289960723-0.365728996072312
69109.365158091843760.634841908156238
701112.6438553743722-1.64385537437222
711212.7299476991651-0.729947699165077
72911.8369439427810-2.83694394278103
731514.61773402225600.382265977744037
741814.88006414833353.11993585166653
751512.11349002974252.88650997025749
761212.7902221279907-0.790222127990721
77139.852699458110013.14730054188999
781413.01992428220640.980075717793584
791011.8856399256721-1.88563992567208
801312.29879744445700.701202555543019
811313.7281754848096-0.728175484809557
821112.0961296798454-1.09612967984536
831312.17602771345800.823972286541977
841614.46834605945851.53165394054155
8589.7010565910997-1.7010565910997
861611.59756154450354.40243845549648
871111.1033545592561-0.103354559256068
88911.2973727503253-2.29737275032527
891617.7555095598081-1.75550955980811
901211.44578746190510.554212538094877
911411.99064078497222.00935921502779
92810.5990252105176-2.59902521051758
9399.5632762181969-0.563276218196896
941511.73091746258863.26908253741136
951113.5607264409179-2.56072644091785
962117.22385021004133.77614978995873
971413.25679643088580.743203569114158
981815.78282181142442.21717818857561
991211.71137325392810.28862674607195
1001312.68606918923390.313930810766054
1011514.53568841111990.464311588880138
1021211.06484249415880.93515750584118
1031914.45368343121064.54631656878938
1041514.02886488629010.971135113709867
1051113.0310396357798-2.03103963577984
1061110.67859718688980.321402813110241
1071012.3636065048675-2.36360650486749
1081314.7876214558746-1.78762145587455
1091514.77776314408220.222236855917843
110129.909266051022032.09073394897797
1111211.02058365545970.979416344540333
1121615.75590936662380.244090633376166
113915.5029829815267-6.50298298152667
1141817.5347037737850.465296226215003
115815.0299439185223-7.02994391852227
1161310.38652814750612.6134718524939
1171714.19887800207352.80112199792652
118911.0607927299636-2.06079272996361
1191513.22441945442241.77558054557755
12089.66830879116794-1.66830879116794
121711.2734798663308-4.27347986633081
1221211.35921075474920.640789245250814
1231415.0612088247332-1.06120882473319
124610.7816409078796-4.78164090787956
125810.1787563197468-2.17875631974678
1261712.46966883373184.53033116626818
127109.775896647811870.224103352188126
1281112.5827045162717-1.58270451627169
1291412.84323511208061.15676488791943
1301113.6916827520720-2.69168275207195
1311315.4220010557674-2.42200105576743
1321211.95130149286600.0486985071339539
1331110.46304041764660.536959582353384
13499.49175140350905-0.491751403509054
1351212.1160648040677-0.116064804067656
1362014.78270876620055.2172912337995
1371210.74388419576701.25611580423302
1381313.6794670666938-0.679467066693821
1391213.1935634194986-1.19356341949856
1401216.5328463645447-4.53284636454473
141914.6631617883371-5.66316178833711
1421515.6353737848601-0.635373784860085
1432421.54442448495362.45557551504643
144710.0198760511055-3.01987605110552
1451714.66798477882912.33201522117085
1461111.9891411384082-0.98914113840824
1471715.22260586388961.77739413611038
1481112.3546993714627-1.35469937146269
1491212.6052306846408-0.605230684640806
1501414.5430980313602-0.543098031360177
1511114.3714030662813-3.37140306628126
1521612.89086470931643.10913529068361
1532113.93265506932007.06734493067997
1541412.30254759873151.69745240126850
1552016.46763210194833.53236789805169
1561311.12147511371201.87852488628796
1571113.1546479026359-2.15464790263593
1581514.00493148670860.995068513291381
1591918.00230511188590.997694888114127







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.5685567672945950.862886465410810.431443232705405
110.5881478258663750.823704348267250.411852174133625
120.6146633404593940.7706733190812110.385336659540606
130.6122101565599090.7755796868801820.387789843440091
140.5735876366778760.8528247266442480.426412363322124
150.6360412209590630.7279175580818730.363958779040937
160.5593670190677810.8812659618644380.440632980932219
170.7132071093083570.5735857813832870.286792890691643
180.6363065925473140.7273868149053710.363693407452686
190.6626307949591750.674738410081650.337369205040825
200.6142024564239770.7715950871520460.385797543576023
210.6533696275372550.693260744925490.346630372462745
220.6587421838333470.6825156323333070.341257816166653
230.6106820443634250.7786359112731510.389317955636575
240.7191999098545570.5616001802908870.280800090145443
250.7061223173048410.5877553653903170.293877682695159
260.6478815319027980.7042369361944050.352118468097202
270.5977422450502390.8045155098995220.402257754949761
280.5402405023789140.9195189952421710.459759497621086
290.4805921850204660.9611843700409310.519407814979534
300.4262231332001420.8524462664002850.573776866799858
310.5928803749931290.8142392500137410.407119625006871
320.538313306321370.923373387357260.46168669367863
330.5608530413830250.878293917233950.439146958616975
340.6658486094141270.6683027811717460.334151390585873
350.6444935085813960.7110129828372090.355506491418604
360.7298330872489220.5403338255021570.270166912751078
370.8061854515017610.3876290969964780.193814548498239
380.7813992387856260.4372015224287490.218600761214374
390.7502938130805660.4994123738388680.249706186919434
400.7319659548601880.5360680902796230.268034045139812
410.762262649378110.475474701243780.23773735062189
420.7291158241869430.5417683516261140.270884175813057
430.7029684378983050.5940631242033910.297031562101695
440.7404265653036210.5191468693927570.259573434696379
450.8184459490207750.3631081019584500.181554050979225
460.8095397723096090.3809204553807820.190460227690391
470.7733563332198430.4532873335603130.226643666780157
480.8228570485236230.3542859029527540.177142951476377
490.8079591287540090.3840817424919830.192040871245991
500.8495765867530710.3008468264938570.150423413246929
510.9162090006149440.1675819987701120.083790999385056
520.903157844270720.1936843114585600.0968421557292802
530.8818592051034170.2362815897931660.118140794896583
540.9185902407682280.1628195184635440.081409759231772
550.9005152127095330.1989695745809330.0994847872904667
560.8786533949823120.2426932100353760.121346605017688
570.8595056158104010.2809887683791980.140494384189599
580.8480702537751190.3038594924497620.151929746224881
590.8523847222445380.2952305555109230.147615277755462
600.8317084649139950.3365830701720110.168291535086005
610.813370426579540.3732591468409190.186629573420459
620.78886308636940.42227382726120.2111369136306
630.7816916502675240.4366166994649520.218308349732476
640.8636737373020640.2726525253958720.136326262697936
650.84779699066210.3044060186758000.152203009337900
660.8446411969486230.3107176061027540.155358803051377
670.8443726195890090.3112547608219830.155627380410991
680.8175749285527540.3648501428944920.182425071447246
690.792981400627210.414037198745580.20701859937279
700.7705146885110340.4589706229779320.229485311488966
710.7427355439494640.5145289121010730.257264456050536
720.7453204553359790.5093590893280430.254679544664021
730.7141166963735740.5717666072528520.285883303626426
740.7293836828771410.5412326342457180.270616317122859
750.7331857321997880.5336285356004240.266814267800212
760.6983759280372950.6032481439254090.301624071962705
770.7257762861004310.5484474277991380.274223713899569
780.6909315992567750.618136801486450.309068400743225
790.6678297631666580.6643404736666840.332170236833342
800.6268390250810210.7463219498379580.373160974918979
810.584460849725280.8310783005494390.415539150274719
820.548768565172630.9024628696547390.451231434827369
830.5056868999079110.9886262001841770.494313100092089
840.4726793024995210.9453586049990410.52732069750048
850.4438132076632660.8876264153265320.556186792336734
860.5205927479517030.9588145040965950.479407252048297
870.4764957433600370.9529914867200730.523504256639963
880.4583581527149020.9167163054298040.541641847285098
890.4360922610810630.8721845221621250.563907738918937
900.3912713201490890.7825426402981780.608728679850911
910.3694963032647340.7389926065294690.630503696735266
920.3647898116034880.7295796232069770.635210188396512
930.3227310818182440.6454621636364890.677268918181756
940.3429200968279730.6858401936559470.657079903172027
950.3379700996571110.6759401993142230.662029900342889
960.3714366890430110.7428733780860230.628563310956989
970.3302631351893190.6605262703786380.669736864810681
980.3084648028825510.6169296057651010.69153519711745
990.2682202929800010.5364405859600020.731779707019999
1000.2303106533655810.4606213067311620.769689346634419
1010.1973785451709420.3947570903418830.802621454829058
1020.1734476373741750.3468952747483490.826552362625825
1030.2482712570831830.4965425141663670.751728742916817
1040.2252346754784180.4504693509568370.774765324521582
1050.2030118131397500.4060236262794990.79698818686025
1060.1759049670645400.3518099341290810.82409503293546
1070.1651245116222900.3302490232445800.83487548837771
1080.1473552655030620.2947105310061240.852644734496938
1090.1220091253399450.2440182506798910.877990874660055
1100.1235514524136750.2471029048273510.876448547586325
1110.1107932002191020.2215864004382030.889206799780898
1120.0891952574293560.1783905148587120.910804742570644
1130.2116166195627390.4232332391254790.78838338043726
1140.1872704634427160.3745409268854320.812729536557284
1150.3898182056016550.779636411203310.610181794398345
1160.3979969443358340.7959938886716680.602003055664166
1170.4539404341671920.9078808683343840.546059565832808
1180.4123794342364820.8247588684729640.587620565763518
1190.3961801249889370.7923602499778740.603819875011063
1200.3630183840341830.7260367680683670.636981615965817
1210.375697362307630.751394724615260.62430263769237
1220.3725536830361600.7451073660723210.62744631696384
1230.3221889850542990.6443779701085990.6778110149457
1240.4082995218922620.8165990437845230.591700478107738
1250.3600549465428270.7201098930856540.639945053457173
1260.5006496962503150.998700607499370.499350303749685
1270.4538629738470970.9077259476941940.546137026152903
1280.4051681770585710.8103363541171420.594831822941429
1290.3543311421102830.7086622842205670.645668857889717
1300.3514696766855760.7029393533711520.648530323314424
1310.3200235005884730.6400470011769450.679976499411527
1320.2636552676204650.527310535240930.736344732379535
1330.2145076217910960.4290152435821910.785492378208904
1340.1683795156430710.3367590312861410.83162048435693
1350.1286572145436690.2573144290873390.87134278545633
1360.2558489335491630.5116978670983260.744151066450837
1370.3105530562230900.6211061124461810.68944694377691
1380.3130881347647830.6261762695295650.686911865235217
1390.2473497863242180.4946995726484360.752650213675782
1400.2484295799373890.4968591598747780.751570420062611
1410.4144364341598390.8288728683196770.585563565840161
1420.3682947785164820.7365895570329640.631705221483518
1430.2971531873033250.594306374606650.702846812696675
1440.2488994799308820.4977989598617640.751100520069118
1450.2758835516272990.5517671032545990.7241164483727
1460.2071661400043660.4143322800087320.792833859995634
1470.1349621109710920.2699242219421830.865037889028908
1480.08565552565604980.1713110513121000.91434447434395
1490.04293947039038270.08587894078076540.957060529609617

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.568556767294595 & 0.86288646541081 & 0.431443232705405 \tabularnewline
11 & 0.588147825866375 & 0.82370434826725 & 0.411852174133625 \tabularnewline
12 & 0.614663340459394 & 0.770673319081211 & 0.385336659540606 \tabularnewline
13 & 0.612210156559909 & 0.775579686880182 & 0.387789843440091 \tabularnewline
14 & 0.573587636677876 & 0.852824726644248 & 0.426412363322124 \tabularnewline
15 & 0.636041220959063 & 0.727917558081873 & 0.363958779040937 \tabularnewline
16 & 0.559367019067781 & 0.881265961864438 & 0.440632980932219 \tabularnewline
17 & 0.713207109308357 & 0.573585781383287 & 0.286792890691643 \tabularnewline
18 & 0.636306592547314 & 0.727386814905371 & 0.363693407452686 \tabularnewline
19 & 0.662630794959175 & 0.67473841008165 & 0.337369205040825 \tabularnewline
20 & 0.614202456423977 & 0.771595087152046 & 0.385797543576023 \tabularnewline
21 & 0.653369627537255 & 0.69326074492549 & 0.346630372462745 \tabularnewline
22 & 0.658742183833347 & 0.682515632333307 & 0.341257816166653 \tabularnewline
23 & 0.610682044363425 & 0.778635911273151 & 0.389317955636575 \tabularnewline
24 & 0.719199909854557 & 0.561600180290887 & 0.280800090145443 \tabularnewline
25 & 0.706122317304841 & 0.587755365390317 & 0.293877682695159 \tabularnewline
26 & 0.647881531902798 & 0.704236936194405 & 0.352118468097202 \tabularnewline
27 & 0.597742245050239 & 0.804515509899522 & 0.402257754949761 \tabularnewline
28 & 0.540240502378914 & 0.919518995242171 & 0.459759497621086 \tabularnewline
29 & 0.480592185020466 & 0.961184370040931 & 0.519407814979534 \tabularnewline
30 & 0.426223133200142 & 0.852446266400285 & 0.573776866799858 \tabularnewline
31 & 0.592880374993129 & 0.814239250013741 & 0.407119625006871 \tabularnewline
32 & 0.53831330632137 & 0.92337338735726 & 0.46168669367863 \tabularnewline
33 & 0.560853041383025 & 0.87829391723395 & 0.439146958616975 \tabularnewline
34 & 0.665848609414127 & 0.668302781171746 & 0.334151390585873 \tabularnewline
35 & 0.644493508581396 & 0.711012982837209 & 0.355506491418604 \tabularnewline
36 & 0.729833087248922 & 0.540333825502157 & 0.270166912751078 \tabularnewline
37 & 0.806185451501761 & 0.387629096996478 & 0.193814548498239 \tabularnewline
38 & 0.781399238785626 & 0.437201522428749 & 0.218600761214374 \tabularnewline
39 & 0.750293813080566 & 0.499412373838868 & 0.249706186919434 \tabularnewline
40 & 0.731965954860188 & 0.536068090279623 & 0.268034045139812 \tabularnewline
41 & 0.76226264937811 & 0.47547470124378 & 0.23773735062189 \tabularnewline
42 & 0.729115824186943 & 0.541768351626114 & 0.270884175813057 \tabularnewline
43 & 0.702968437898305 & 0.594063124203391 & 0.297031562101695 \tabularnewline
44 & 0.740426565303621 & 0.519146869392757 & 0.259573434696379 \tabularnewline
45 & 0.818445949020775 & 0.363108101958450 & 0.181554050979225 \tabularnewline
46 & 0.809539772309609 & 0.380920455380782 & 0.190460227690391 \tabularnewline
47 & 0.773356333219843 & 0.453287333560313 & 0.226643666780157 \tabularnewline
48 & 0.822857048523623 & 0.354285902952754 & 0.177142951476377 \tabularnewline
49 & 0.807959128754009 & 0.384081742491983 & 0.192040871245991 \tabularnewline
50 & 0.849576586753071 & 0.300846826493857 & 0.150423413246929 \tabularnewline
51 & 0.916209000614944 & 0.167581998770112 & 0.083790999385056 \tabularnewline
52 & 0.90315784427072 & 0.193684311458560 & 0.0968421557292802 \tabularnewline
53 & 0.881859205103417 & 0.236281589793166 & 0.118140794896583 \tabularnewline
54 & 0.918590240768228 & 0.162819518463544 & 0.081409759231772 \tabularnewline
55 & 0.900515212709533 & 0.198969574580933 & 0.0994847872904667 \tabularnewline
56 & 0.878653394982312 & 0.242693210035376 & 0.121346605017688 \tabularnewline
57 & 0.859505615810401 & 0.280988768379198 & 0.140494384189599 \tabularnewline
58 & 0.848070253775119 & 0.303859492449762 & 0.151929746224881 \tabularnewline
59 & 0.852384722244538 & 0.295230555510923 & 0.147615277755462 \tabularnewline
60 & 0.831708464913995 & 0.336583070172011 & 0.168291535086005 \tabularnewline
61 & 0.81337042657954 & 0.373259146840919 & 0.186629573420459 \tabularnewline
62 & 0.7888630863694 & 0.4222738272612 & 0.2111369136306 \tabularnewline
63 & 0.781691650267524 & 0.436616699464952 & 0.218308349732476 \tabularnewline
64 & 0.863673737302064 & 0.272652525395872 & 0.136326262697936 \tabularnewline
65 & 0.8477969906621 & 0.304406018675800 & 0.152203009337900 \tabularnewline
66 & 0.844641196948623 & 0.310717606102754 & 0.155358803051377 \tabularnewline
67 & 0.844372619589009 & 0.311254760821983 & 0.155627380410991 \tabularnewline
68 & 0.817574928552754 & 0.364850142894492 & 0.182425071447246 \tabularnewline
69 & 0.79298140062721 & 0.41403719874558 & 0.20701859937279 \tabularnewline
70 & 0.770514688511034 & 0.458970622977932 & 0.229485311488966 \tabularnewline
71 & 0.742735543949464 & 0.514528912101073 & 0.257264456050536 \tabularnewline
72 & 0.745320455335979 & 0.509359089328043 & 0.254679544664021 \tabularnewline
73 & 0.714116696373574 & 0.571766607252852 & 0.285883303626426 \tabularnewline
74 & 0.729383682877141 & 0.541232634245718 & 0.270616317122859 \tabularnewline
75 & 0.733185732199788 & 0.533628535600424 & 0.266814267800212 \tabularnewline
76 & 0.698375928037295 & 0.603248143925409 & 0.301624071962705 \tabularnewline
77 & 0.725776286100431 & 0.548447427799138 & 0.274223713899569 \tabularnewline
78 & 0.690931599256775 & 0.61813680148645 & 0.309068400743225 \tabularnewline
79 & 0.667829763166658 & 0.664340473666684 & 0.332170236833342 \tabularnewline
80 & 0.626839025081021 & 0.746321949837958 & 0.373160974918979 \tabularnewline
81 & 0.58446084972528 & 0.831078300549439 & 0.415539150274719 \tabularnewline
82 & 0.54876856517263 & 0.902462869654739 & 0.451231434827369 \tabularnewline
83 & 0.505686899907911 & 0.988626200184177 & 0.494313100092089 \tabularnewline
84 & 0.472679302499521 & 0.945358604999041 & 0.52732069750048 \tabularnewline
85 & 0.443813207663266 & 0.887626415326532 & 0.556186792336734 \tabularnewline
86 & 0.520592747951703 & 0.958814504096595 & 0.479407252048297 \tabularnewline
87 & 0.476495743360037 & 0.952991486720073 & 0.523504256639963 \tabularnewline
88 & 0.458358152714902 & 0.916716305429804 & 0.541641847285098 \tabularnewline
89 & 0.436092261081063 & 0.872184522162125 & 0.563907738918937 \tabularnewline
90 & 0.391271320149089 & 0.782542640298178 & 0.608728679850911 \tabularnewline
91 & 0.369496303264734 & 0.738992606529469 & 0.630503696735266 \tabularnewline
92 & 0.364789811603488 & 0.729579623206977 & 0.635210188396512 \tabularnewline
93 & 0.322731081818244 & 0.645462163636489 & 0.677268918181756 \tabularnewline
94 & 0.342920096827973 & 0.685840193655947 & 0.657079903172027 \tabularnewline
95 & 0.337970099657111 & 0.675940199314223 & 0.662029900342889 \tabularnewline
96 & 0.371436689043011 & 0.742873378086023 & 0.628563310956989 \tabularnewline
97 & 0.330263135189319 & 0.660526270378638 & 0.669736864810681 \tabularnewline
98 & 0.308464802882551 & 0.616929605765101 & 0.69153519711745 \tabularnewline
99 & 0.268220292980001 & 0.536440585960002 & 0.731779707019999 \tabularnewline
100 & 0.230310653365581 & 0.460621306731162 & 0.769689346634419 \tabularnewline
101 & 0.197378545170942 & 0.394757090341883 & 0.802621454829058 \tabularnewline
102 & 0.173447637374175 & 0.346895274748349 & 0.826552362625825 \tabularnewline
103 & 0.248271257083183 & 0.496542514166367 & 0.751728742916817 \tabularnewline
104 & 0.225234675478418 & 0.450469350956837 & 0.774765324521582 \tabularnewline
105 & 0.203011813139750 & 0.406023626279499 & 0.79698818686025 \tabularnewline
106 & 0.175904967064540 & 0.351809934129081 & 0.82409503293546 \tabularnewline
107 & 0.165124511622290 & 0.330249023244580 & 0.83487548837771 \tabularnewline
108 & 0.147355265503062 & 0.294710531006124 & 0.852644734496938 \tabularnewline
109 & 0.122009125339945 & 0.244018250679891 & 0.877990874660055 \tabularnewline
110 & 0.123551452413675 & 0.247102904827351 & 0.876448547586325 \tabularnewline
111 & 0.110793200219102 & 0.221586400438203 & 0.889206799780898 \tabularnewline
112 & 0.089195257429356 & 0.178390514858712 & 0.910804742570644 \tabularnewline
113 & 0.211616619562739 & 0.423233239125479 & 0.78838338043726 \tabularnewline
114 & 0.187270463442716 & 0.374540926885432 & 0.812729536557284 \tabularnewline
115 & 0.389818205601655 & 0.77963641120331 & 0.610181794398345 \tabularnewline
116 & 0.397996944335834 & 0.795993888671668 & 0.602003055664166 \tabularnewline
117 & 0.453940434167192 & 0.907880868334384 & 0.546059565832808 \tabularnewline
118 & 0.412379434236482 & 0.824758868472964 & 0.587620565763518 \tabularnewline
119 & 0.396180124988937 & 0.792360249977874 & 0.603819875011063 \tabularnewline
120 & 0.363018384034183 & 0.726036768068367 & 0.636981615965817 \tabularnewline
121 & 0.37569736230763 & 0.75139472461526 & 0.62430263769237 \tabularnewline
122 & 0.372553683036160 & 0.745107366072321 & 0.62744631696384 \tabularnewline
123 & 0.322188985054299 & 0.644377970108599 & 0.6778110149457 \tabularnewline
124 & 0.408299521892262 & 0.816599043784523 & 0.591700478107738 \tabularnewline
125 & 0.360054946542827 & 0.720109893085654 & 0.639945053457173 \tabularnewline
126 & 0.500649696250315 & 0.99870060749937 & 0.499350303749685 \tabularnewline
127 & 0.453862973847097 & 0.907725947694194 & 0.546137026152903 \tabularnewline
128 & 0.405168177058571 & 0.810336354117142 & 0.594831822941429 \tabularnewline
129 & 0.354331142110283 & 0.708662284220567 & 0.645668857889717 \tabularnewline
130 & 0.351469676685576 & 0.702939353371152 & 0.648530323314424 \tabularnewline
131 & 0.320023500588473 & 0.640047001176945 & 0.679976499411527 \tabularnewline
132 & 0.263655267620465 & 0.52731053524093 & 0.736344732379535 \tabularnewline
133 & 0.214507621791096 & 0.429015243582191 & 0.785492378208904 \tabularnewline
134 & 0.168379515643071 & 0.336759031286141 & 0.83162048435693 \tabularnewline
135 & 0.128657214543669 & 0.257314429087339 & 0.87134278545633 \tabularnewline
136 & 0.255848933549163 & 0.511697867098326 & 0.744151066450837 \tabularnewline
137 & 0.310553056223090 & 0.621106112446181 & 0.68944694377691 \tabularnewline
138 & 0.313088134764783 & 0.626176269529565 & 0.686911865235217 \tabularnewline
139 & 0.247349786324218 & 0.494699572648436 & 0.752650213675782 \tabularnewline
140 & 0.248429579937389 & 0.496859159874778 & 0.751570420062611 \tabularnewline
141 & 0.414436434159839 & 0.828872868319677 & 0.585563565840161 \tabularnewline
142 & 0.368294778516482 & 0.736589557032964 & 0.631705221483518 \tabularnewline
143 & 0.297153187303325 & 0.59430637460665 & 0.702846812696675 \tabularnewline
144 & 0.248899479930882 & 0.497798959861764 & 0.751100520069118 \tabularnewline
145 & 0.275883551627299 & 0.551767103254599 & 0.7241164483727 \tabularnewline
146 & 0.207166140004366 & 0.414332280008732 & 0.792833859995634 \tabularnewline
147 & 0.134962110971092 & 0.269924221942183 & 0.865037889028908 \tabularnewline
148 & 0.0856555256560498 & 0.171311051312100 & 0.91434447434395 \tabularnewline
149 & 0.0429394703903827 & 0.0858789407807654 & 0.957060529609617 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103694&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]10[/C][C]0.568556767294595[/C][C]0.86288646541081[/C][C]0.431443232705405[/C][/ROW]
[ROW][C]11[/C][C]0.588147825866375[/C][C]0.82370434826725[/C][C]0.411852174133625[/C][/ROW]
[ROW][C]12[/C][C]0.614663340459394[/C][C]0.770673319081211[/C][C]0.385336659540606[/C][/ROW]
[ROW][C]13[/C][C]0.612210156559909[/C][C]0.775579686880182[/C][C]0.387789843440091[/C][/ROW]
[ROW][C]14[/C][C]0.573587636677876[/C][C]0.852824726644248[/C][C]0.426412363322124[/C][/ROW]
[ROW][C]15[/C][C]0.636041220959063[/C][C]0.727917558081873[/C][C]0.363958779040937[/C][/ROW]
[ROW][C]16[/C][C]0.559367019067781[/C][C]0.881265961864438[/C][C]0.440632980932219[/C][/ROW]
[ROW][C]17[/C][C]0.713207109308357[/C][C]0.573585781383287[/C][C]0.286792890691643[/C][/ROW]
[ROW][C]18[/C][C]0.636306592547314[/C][C]0.727386814905371[/C][C]0.363693407452686[/C][/ROW]
[ROW][C]19[/C][C]0.662630794959175[/C][C]0.67473841008165[/C][C]0.337369205040825[/C][/ROW]
[ROW][C]20[/C][C]0.614202456423977[/C][C]0.771595087152046[/C][C]0.385797543576023[/C][/ROW]
[ROW][C]21[/C][C]0.653369627537255[/C][C]0.69326074492549[/C][C]0.346630372462745[/C][/ROW]
[ROW][C]22[/C][C]0.658742183833347[/C][C]0.682515632333307[/C][C]0.341257816166653[/C][/ROW]
[ROW][C]23[/C][C]0.610682044363425[/C][C]0.778635911273151[/C][C]0.389317955636575[/C][/ROW]
[ROW][C]24[/C][C]0.719199909854557[/C][C]0.561600180290887[/C][C]0.280800090145443[/C][/ROW]
[ROW][C]25[/C][C]0.706122317304841[/C][C]0.587755365390317[/C][C]0.293877682695159[/C][/ROW]
[ROW][C]26[/C][C]0.647881531902798[/C][C]0.704236936194405[/C][C]0.352118468097202[/C][/ROW]
[ROW][C]27[/C][C]0.597742245050239[/C][C]0.804515509899522[/C][C]0.402257754949761[/C][/ROW]
[ROW][C]28[/C][C]0.540240502378914[/C][C]0.919518995242171[/C][C]0.459759497621086[/C][/ROW]
[ROW][C]29[/C][C]0.480592185020466[/C][C]0.961184370040931[/C][C]0.519407814979534[/C][/ROW]
[ROW][C]30[/C][C]0.426223133200142[/C][C]0.852446266400285[/C][C]0.573776866799858[/C][/ROW]
[ROW][C]31[/C][C]0.592880374993129[/C][C]0.814239250013741[/C][C]0.407119625006871[/C][/ROW]
[ROW][C]32[/C][C]0.53831330632137[/C][C]0.92337338735726[/C][C]0.46168669367863[/C][/ROW]
[ROW][C]33[/C][C]0.560853041383025[/C][C]0.87829391723395[/C][C]0.439146958616975[/C][/ROW]
[ROW][C]34[/C][C]0.665848609414127[/C][C]0.668302781171746[/C][C]0.334151390585873[/C][/ROW]
[ROW][C]35[/C][C]0.644493508581396[/C][C]0.711012982837209[/C][C]0.355506491418604[/C][/ROW]
[ROW][C]36[/C][C]0.729833087248922[/C][C]0.540333825502157[/C][C]0.270166912751078[/C][/ROW]
[ROW][C]37[/C][C]0.806185451501761[/C][C]0.387629096996478[/C][C]0.193814548498239[/C][/ROW]
[ROW][C]38[/C][C]0.781399238785626[/C][C]0.437201522428749[/C][C]0.218600761214374[/C][/ROW]
[ROW][C]39[/C][C]0.750293813080566[/C][C]0.499412373838868[/C][C]0.249706186919434[/C][/ROW]
[ROW][C]40[/C][C]0.731965954860188[/C][C]0.536068090279623[/C][C]0.268034045139812[/C][/ROW]
[ROW][C]41[/C][C]0.76226264937811[/C][C]0.47547470124378[/C][C]0.23773735062189[/C][/ROW]
[ROW][C]42[/C][C]0.729115824186943[/C][C]0.541768351626114[/C][C]0.270884175813057[/C][/ROW]
[ROW][C]43[/C][C]0.702968437898305[/C][C]0.594063124203391[/C][C]0.297031562101695[/C][/ROW]
[ROW][C]44[/C][C]0.740426565303621[/C][C]0.519146869392757[/C][C]0.259573434696379[/C][/ROW]
[ROW][C]45[/C][C]0.818445949020775[/C][C]0.363108101958450[/C][C]0.181554050979225[/C][/ROW]
[ROW][C]46[/C][C]0.809539772309609[/C][C]0.380920455380782[/C][C]0.190460227690391[/C][/ROW]
[ROW][C]47[/C][C]0.773356333219843[/C][C]0.453287333560313[/C][C]0.226643666780157[/C][/ROW]
[ROW][C]48[/C][C]0.822857048523623[/C][C]0.354285902952754[/C][C]0.177142951476377[/C][/ROW]
[ROW][C]49[/C][C]0.807959128754009[/C][C]0.384081742491983[/C][C]0.192040871245991[/C][/ROW]
[ROW][C]50[/C][C]0.849576586753071[/C][C]0.300846826493857[/C][C]0.150423413246929[/C][/ROW]
[ROW][C]51[/C][C]0.916209000614944[/C][C]0.167581998770112[/C][C]0.083790999385056[/C][/ROW]
[ROW][C]52[/C][C]0.90315784427072[/C][C]0.193684311458560[/C][C]0.0968421557292802[/C][/ROW]
[ROW][C]53[/C][C]0.881859205103417[/C][C]0.236281589793166[/C][C]0.118140794896583[/C][/ROW]
[ROW][C]54[/C][C]0.918590240768228[/C][C]0.162819518463544[/C][C]0.081409759231772[/C][/ROW]
[ROW][C]55[/C][C]0.900515212709533[/C][C]0.198969574580933[/C][C]0.0994847872904667[/C][/ROW]
[ROW][C]56[/C][C]0.878653394982312[/C][C]0.242693210035376[/C][C]0.121346605017688[/C][/ROW]
[ROW][C]57[/C][C]0.859505615810401[/C][C]0.280988768379198[/C][C]0.140494384189599[/C][/ROW]
[ROW][C]58[/C][C]0.848070253775119[/C][C]0.303859492449762[/C][C]0.151929746224881[/C][/ROW]
[ROW][C]59[/C][C]0.852384722244538[/C][C]0.295230555510923[/C][C]0.147615277755462[/C][/ROW]
[ROW][C]60[/C][C]0.831708464913995[/C][C]0.336583070172011[/C][C]0.168291535086005[/C][/ROW]
[ROW][C]61[/C][C]0.81337042657954[/C][C]0.373259146840919[/C][C]0.186629573420459[/C][/ROW]
[ROW][C]62[/C][C]0.7888630863694[/C][C]0.4222738272612[/C][C]0.2111369136306[/C][/ROW]
[ROW][C]63[/C][C]0.781691650267524[/C][C]0.436616699464952[/C][C]0.218308349732476[/C][/ROW]
[ROW][C]64[/C][C]0.863673737302064[/C][C]0.272652525395872[/C][C]0.136326262697936[/C][/ROW]
[ROW][C]65[/C][C]0.8477969906621[/C][C]0.304406018675800[/C][C]0.152203009337900[/C][/ROW]
[ROW][C]66[/C][C]0.844641196948623[/C][C]0.310717606102754[/C][C]0.155358803051377[/C][/ROW]
[ROW][C]67[/C][C]0.844372619589009[/C][C]0.311254760821983[/C][C]0.155627380410991[/C][/ROW]
[ROW][C]68[/C][C]0.817574928552754[/C][C]0.364850142894492[/C][C]0.182425071447246[/C][/ROW]
[ROW][C]69[/C][C]0.79298140062721[/C][C]0.41403719874558[/C][C]0.20701859937279[/C][/ROW]
[ROW][C]70[/C][C]0.770514688511034[/C][C]0.458970622977932[/C][C]0.229485311488966[/C][/ROW]
[ROW][C]71[/C][C]0.742735543949464[/C][C]0.514528912101073[/C][C]0.257264456050536[/C][/ROW]
[ROW][C]72[/C][C]0.745320455335979[/C][C]0.509359089328043[/C][C]0.254679544664021[/C][/ROW]
[ROW][C]73[/C][C]0.714116696373574[/C][C]0.571766607252852[/C][C]0.285883303626426[/C][/ROW]
[ROW][C]74[/C][C]0.729383682877141[/C][C]0.541232634245718[/C][C]0.270616317122859[/C][/ROW]
[ROW][C]75[/C][C]0.733185732199788[/C][C]0.533628535600424[/C][C]0.266814267800212[/C][/ROW]
[ROW][C]76[/C][C]0.698375928037295[/C][C]0.603248143925409[/C][C]0.301624071962705[/C][/ROW]
[ROW][C]77[/C][C]0.725776286100431[/C][C]0.548447427799138[/C][C]0.274223713899569[/C][/ROW]
[ROW][C]78[/C][C]0.690931599256775[/C][C]0.61813680148645[/C][C]0.309068400743225[/C][/ROW]
[ROW][C]79[/C][C]0.667829763166658[/C][C]0.664340473666684[/C][C]0.332170236833342[/C][/ROW]
[ROW][C]80[/C][C]0.626839025081021[/C][C]0.746321949837958[/C][C]0.373160974918979[/C][/ROW]
[ROW][C]81[/C][C]0.58446084972528[/C][C]0.831078300549439[/C][C]0.415539150274719[/C][/ROW]
[ROW][C]82[/C][C]0.54876856517263[/C][C]0.902462869654739[/C][C]0.451231434827369[/C][/ROW]
[ROW][C]83[/C][C]0.505686899907911[/C][C]0.988626200184177[/C][C]0.494313100092089[/C][/ROW]
[ROW][C]84[/C][C]0.472679302499521[/C][C]0.945358604999041[/C][C]0.52732069750048[/C][/ROW]
[ROW][C]85[/C][C]0.443813207663266[/C][C]0.887626415326532[/C][C]0.556186792336734[/C][/ROW]
[ROW][C]86[/C][C]0.520592747951703[/C][C]0.958814504096595[/C][C]0.479407252048297[/C][/ROW]
[ROW][C]87[/C][C]0.476495743360037[/C][C]0.952991486720073[/C][C]0.523504256639963[/C][/ROW]
[ROW][C]88[/C][C]0.458358152714902[/C][C]0.916716305429804[/C][C]0.541641847285098[/C][/ROW]
[ROW][C]89[/C][C]0.436092261081063[/C][C]0.872184522162125[/C][C]0.563907738918937[/C][/ROW]
[ROW][C]90[/C][C]0.391271320149089[/C][C]0.782542640298178[/C][C]0.608728679850911[/C][/ROW]
[ROW][C]91[/C][C]0.369496303264734[/C][C]0.738992606529469[/C][C]0.630503696735266[/C][/ROW]
[ROW][C]92[/C][C]0.364789811603488[/C][C]0.729579623206977[/C][C]0.635210188396512[/C][/ROW]
[ROW][C]93[/C][C]0.322731081818244[/C][C]0.645462163636489[/C][C]0.677268918181756[/C][/ROW]
[ROW][C]94[/C][C]0.342920096827973[/C][C]0.685840193655947[/C][C]0.657079903172027[/C][/ROW]
[ROW][C]95[/C][C]0.337970099657111[/C][C]0.675940199314223[/C][C]0.662029900342889[/C][/ROW]
[ROW][C]96[/C][C]0.371436689043011[/C][C]0.742873378086023[/C][C]0.628563310956989[/C][/ROW]
[ROW][C]97[/C][C]0.330263135189319[/C][C]0.660526270378638[/C][C]0.669736864810681[/C][/ROW]
[ROW][C]98[/C][C]0.308464802882551[/C][C]0.616929605765101[/C][C]0.69153519711745[/C][/ROW]
[ROW][C]99[/C][C]0.268220292980001[/C][C]0.536440585960002[/C][C]0.731779707019999[/C][/ROW]
[ROW][C]100[/C][C]0.230310653365581[/C][C]0.460621306731162[/C][C]0.769689346634419[/C][/ROW]
[ROW][C]101[/C][C]0.197378545170942[/C][C]0.394757090341883[/C][C]0.802621454829058[/C][/ROW]
[ROW][C]102[/C][C]0.173447637374175[/C][C]0.346895274748349[/C][C]0.826552362625825[/C][/ROW]
[ROW][C]103[/C][C]0.248271257083183[/C][C]0.496542514166367[/C][C]0.751728742916817[/C][/ROW]
[ROW][C]104[/C][C]0.225234675478418[/C][C]0.450469350956837[/C][C]0.774765324521582[/C][/ROW]
[ROW][C]105[/C][C]0.203011813139750[/C][C]0.406023626279499[/C][C]0.79698818686025[/C][/ROW]
[ROW][C]106[/C][C]0.175904967064540[/C][C]0.351809934129081[/C][C]0.82409503293546[/C][/ROW]
[ROW][C]107[/C][C]0.165124511622290[/C][C]0.330249023244580[/C][C]0.83487548837771[/C][/ROW]
[ROW][C]108[/C][C]0.147355265503062[/C][C]0.294710531006124[/C][C]0.852644734496938[/C][/ROW]
[ROW][C]109[/C][C]0.122009125339945[/C][C]0.244018250679891[/C][C]0.877990874660055[/C][/ROW]
[ROW][C]110[/C][C]0.123551452413675[/C][C]0.247102904827351[/C][C]0.876448547586325[/C][/ROW]
[ROW][C]111[/C][C]0.110793200219102[/C][C]0.221586400438203[/C][C]0.889206799780898[/C][/ROW]
[ROW][C]112[/C][C]0.089195257429356[/C][C]0.178390514858712[/C][C]0.910804742570644[/C][/ROW]
[ROW][C]113[/C][C]0.211616619562739[/C][C]0.423233239125479[/C][C]0.78838338043726[/C][/ROW]
[ROW][C]114[/C][C]0.187270463442716[/C][C]0.374540926885432[/C][C]0.812729536557284[/C][/ROW]
[ROW][C]115[/C][C]0.389818205601655[/C][C]0.77963641120331[/C][C]0.610181794398345[/C][/ROW]
[ROW][C]116[/C][C]0.397996944335834[/C][C]0.795993888671668[/C][C]0.602003055664166[/C][/ROW]
[ROW][C]117[/C][C]0.453940434167192[/C][C]0.907880868334384[/C][C]0.546059565832808[/C][/ROW]
[ROW][C]118[/C][C]0.412379434236482[/C][C]0.824758868472964[/C][C]0.587620565763518[/C][/ROW]
[ROW][C]119[/C][C]0.396180124988937[/C][C]0.792360249977874[/C][C]0.603819875011063[/C][/ROW]
[ROW][C]120[/C][C]0.363018384034183[/C][C]0.726036768068367[/C][C]0.636981615965817[/C][/ROW]
[ROW][C]121[/C][C]0.37569736230763[/C][C]0.75139472461526[/C][C]0.62430263769237[/C][/ROW]
[ROW][C]122[/C][C]0.372553683036160[/C][C]0.745107366072321[/C][C]0.62744631696384[/C][/ROW]
[ROW][C]123[/C][C]0.322188985054299[/C][C]0.644377970108599[/C][C]0.6778110149457[/C][/ROW]
[ROW][C]124[/C][C]0.408299521892262[/C][C]0.816599043784523[/C][C]0.591700478107738[/C][/ROW]
[ROW][C]125[/C][C]0.360054946542827[/C][C]0.720109893085654[/C][C]0.639945053457173[/C][/ROW]
[ROW][C]126[/C][C]0.500649696250315[/C][C]0.99870060749937[/C][C]0.499350303749685[/C][/ROW]
[ROW][C]127[/C][C]0.453862973847097[/C][C]0.907725947694194[/C][C]0.546137026152903[/C][/ROW]
[ROW][C]128[/C][C]0.405168177058571[/C][C]0.810336354117142[/C][C]0.594831822941429[/C][/ROW]
[ROW][C]129[/C][C]0.354331142110283[/C][C]0.708662284220567[/C][C]0.645668857889717[/C][/ROW]
[ROW][C]130[/C][C]0.351469676685576[/C][C]0.702939353371152[/C][C]0.648530323314424[/C][/ROW]
[ROW][C]131[/C][C]0.320023500588473[/C][C]0.640047001176945[/C][C]0.679976499411527[/C][/ROW]
[ROW][C]132[/C][C]0.263655267620465[/C][C]0.52731053524093[/C][C]0.736344732379535[/C][/ROW]
[ROW][C]133[/C][C]0.214507621791096[/C][C]0.429015243582191[/C][C]0.785492378208904[/C][/ROW]
[ROW][C]134[/C][C]0.168379515643071[/C][C]0.336759031286141[/C][C]0.83162048435693[/C][/ROW]
[ROW][C]135[/C][C]0.128657214543669[/C][C]0.257314429087339[/C][C]0.87134278545633[/C][/ROW]
[ROW][C]136[/C][C]0.255848933549163[/C][C]0.511697867098326[/C][C]0.744151066450837[/C][/ROW]
[ROW][C]137[/C][C]0.310553056223090[/C][C]0.621106112446181[/C][C]0.68944694377691[/C][/ROW]
[ROW][C]138[/C][C]0.313088134764783[/C][C]0.626176269529565[/C][C]0.686911865235217[/C][/ROW]
[ROW][C]139[/C][C]0.247349786324218[/C][C]0.494699572648436[/C][C]0.752650213675782[/C][/ROW]
[ROW][C]140[/C][C]0.248429579937389[/C][C]0.496859159874778[/C][C]0.751570420062611[/C][/ROW]
[ROW][C]141[/C][C]0.414436434159839[/C][C]0.828872868319677[/C][C]0.585563565840161[/C][/ROW]
[ROW][C]142[/C][C]0.368294778516482[/C][C]0.736589557032964[/C][C]0.631705221483518[/C][/ROW]
[ROW][C]143[/C][C]0.297153187303325[/C][C]0.59430637460665[/C][C]0.702846812696675[/C][/ROW]
[ROW][C]144[/C][C]0.248899479930882[/C][C]0.497798959861764[/C][C]0.751100520069118[/C][/ROW]
[ROW][C]145[/C][C]0.275883551627299[/C][C]0.551767103254599[/C][C]0.7241164483727[/C][/ROW]
[ROW][C]146[/C][C]0.207166140004366[/C][C]0.414332280008732[/C][C]0.792833859995634[/C][/ROW]
[ROW][C]147[/C][C]0.134962110971092[/C][C]0.269924221942183[/C][C]0.865037889028908[/C][/ROW]
[ROW][C]148[/C][C]0.0856555256560498[/C][C]0.171311051312100[/C][C]0.91434447434395[/C][/ROW]
[ROW][C]149[/C][C]0.0429394703903827[/C][C]0.0858789407807654[/C][C]0.957060529609617[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103694&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103694&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.5685567672945950.862886465410810.431443232705405
110.5881478258663750.823704348267250.411852174133625
120.6146633404593940.7706733190812110.385336659540606
130.6122101565599090.7755796868801820.387789843440091
140.5735876366778760.8528247266442480.426412363322124
150.6360412209590630.7279175580818730.363958779040937
160.5593670190677810.8812659618644380.440632980932219
170.7132071093083570.5735857813832870.286792890691643
180.6363065925473140.7273868149053710.363693407452686
190.6626307949591750.674738410081650.337369205040825
200.6142024564239770.7715950871520460.385797543576023
210.6533696275372550.693260744925490.346630372462745
220.6587421838333470.6825156323333070.341257816166653
230.6106820443634250.7786359112731510.389317955636575
240.7191999098545570.5616001802908870.280800090145443
250.7061223173048410.5877553653903170.293877682695159
260.6478815319027980.7042369361944050.352118468097202
270.5977422450502390.8045155098995220.402257754949761
280.5402405023789140.9195189952421710.459759497621086
290.4805921850204660.9611843700409310.519407814979534
300.4262231332001420.8524462664002850.573776866799858
310.5928803749931290.8142392500137410.407119625006871
320.538313306321370.923373387357260.46168669367863
330.5608530413830250.878293917233950.439146958616975
340.6658486094141270.6683027811717460.334151390585873
350.6444935085813960.7110129828372090.355506491418604
360.7298330872489220.5403338255021570.270166912751078
370.8061854515017610.3876290969964780.193814548498239
380.7813992387856260.4372015224287490.218600761214374
390.7502938130805660.4994123738388680.249706186919434
400.7319659548601880.5360680902796230.268034045139812
410.762262649378110.475474701243780.23773735062189
420.7291158241869430.5417683516261140.270884175813057
430.7029684378983050.5940631242033910.297031562101695
440.7404265653036210.5191468693927570.259573434696379
450.8184459490207750.3631081019584500.181554050979225
460.8095397723096090.3809204553807820.190460227690391
470.7733563332198430.4532873335603130.226643666780157
480.8228570485236230.3542859029527540.177142951476377
490.8079591287540090.3840817424919830.192040871245991
500.8495765867530710.3008468264938570.150423413246929
510.9162090006149440.1675819987701120.083790999385056
520.903157844270720.1936843114585600.0968421557292802
530.8818592051034170.2362815897931660.118140794896583
540.9185902407682280.1628195184635440.081409759231772
550.9005152127095330.1989695745809330.0994847872904667
560.8786533949823120.2426932100353760.121346605017688
570.8595056158104010.2809887683791980.140494384189599
580.8480702537751190.3038594924497620.151929746224881
590.8523847222445380.2952305555109230.147615277755462
600.8317084649139950.3365830701720110.168291535086005
610.813370426579540.3732591468409190.186629573420459
620.78886308636940.42227382726120.2111369136306
630.7816916502675240.4366166994649520.218308349732476
640.8636737373020640.2726525253958720.136326262697936
650.84779699066210.3044060186758000.152203009337900
660.8446411969486230.3107176061027540.155358803051377
670.8443726195890090.3112547608219830.155627380410991
680.8175749285527540.3648501428944920.182425071447246
690.792981400627210.414037198745580.20701859937279
700.7705146885110340.4589706229779320.229485311488966
710.7427355439494640.5145289121010730.257264456050536
720.7453204553359790.5093590893280430.254679544664021
730.7141166963735740.5717666072528520.285883303626426
740.7293836828771410.5412326342457180.270616317122859
750.7331857321997880.5336285356004240.266814267800212
760.6983759280372950.6032481439254090.301624071962705
770.7257762861004310.5484474277991380.274223713899569
780.6909315992567750.618136801486450.309068400743225
790.6678297631666580.6643404736666840.332170236833342
800.6268390250810210.7463219498379580.373160974918979
810.584460849725280.8310783005494390.415539150274719
820.548768565172630.9024628696547390.451231434827369
830.5056868999079110.9886262001841770.494313100092089
840.4726793024995210.9453586049990410.52732069750048
850.4438132076632660.8876264153265320.556186792336734
860.5205927479517030.9588145040965950.479407252048297
870.4764957433600370.9529914867200730.523504256639963
880.4583581527149020.9167163054298040.541641847285098
890.4360922610810630.8721845221621250.563907738918937
900.3912713201490890.7825426402981780.608728679850911
910.3694963032647340.7389926065294690.630503696735266
920.3647898116034880.7295796232069770.635210188396512
930.3227310818182440.6454621636364890.677268918181756
940.3429200968279730.6858401936559470.657079903172027
950.3379700996571110.6759401993142230.662029900342889
960.3714366890430110.7428733780860230.628563310956989
970.3302631351893190.6605262703786380.669736864810681
980.3084648028825510.6169296057651010.69153519711745
990.2682202929800010.5364405859600020.731779707019999
1000.2303106533655810.4606213067311620.769689346634419
1010.1973785451709420.3947570903418830.802621454829058
1020.1734476373741750.3468952747483490.826552362625825
1030.2482712570831830.4965425141663670.751728742916817
1040.2252346754784180.4504693509568370.774765324521582
1050.2030118131397500.4060236262794990.79698818686025
1060.1759049670645400.3518099341290810.82409503293546
1070.1651245116222900.3302490232445800.83487548837771
1080.1473552655030620.2947105310061240.852644734496938
1090.1220091253399450.2440182506798910.877990874660055
1100.1235514524136750.2471029048273510.876448547586325
1110.1107932002191020.2215864004382030.889206799780898
1120.0891952574293560.1783905148587120.910804742570644
1130.2116166195627390.4232332391254790.78838338043726
1140.1872704634427160.3745409268854320.812729536557284
1150.3898182056016550.779636411203310.610181794398345
1160.3979969443358340.7959938886716680.602003055664166
1170.4539404341671920.9078808683343840.546059565832808
1180.4123794342364820.8247588684729640.587620565763518
1190.3961801249889370.7923602499778740.603819875011063
1200.3630183840341830.7260367680683670.636981615965817
1210.375697362307630.751394724615260.62430263769237
1220.3725536830361600.7451073660723210.62744631696384
1230.3221889850542990.6443779701085990.6778110149457
1240.4082995218922620.8165990437845230.591700478107738
1250.3600549465428270.7201098930856540.639945053457173
1260.5006496962503150.998700607499370.499350303749685
1270.4538629738470970.9077259476941940.546137026152903
1280.4051681770585710.8103363541171420.594831822941429
1290.3543311421102830.7086622842205670.645668857889717
1300.3514696766855760.7029393533711520.648530323314424
1310.3200235005884730.6400470011769450.679976499411527
1320.2636552676204650.527310535240930.736344732379535
1330.2145076217910960.4290152435821910.785492378208904
1340.1683795156430710.3367590312861410.83162048435693
1350.1286572145436690.2573144290873390.87134278545633
1360.2558489335491630.5116978670983260.744151066450837
1370.3105530562230900.6211061124461810.68944694377691
1380.3130881347647830.6261762695295650.686911865235217
1390.2473497863242180.4946995726484360.752650213675782
1400.2484295799373890.4968591598747780.751570420062611
1410.4144364341598390.8288728683196770.585563565840161
1420.3682947785164820.7365895570329640.631705221483518
1430.2971531873033250.594306374606650.702846812696675
1440.2488994799308820.4977989598617640.751100520069118
1450.2758835516272990.5517671032545990.7241164483727
1460.2071661400043660.4143322800087320.792833859995634
1470.1349621109710920.2699242219421830.865037889028908
1480.08565552565604980.1713110513121000.91434447434395
1490.04293947039038270.08587894078076540.957060529609617







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.00714285714285714OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.00714285714285714 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103694&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.00714285714285714[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103694&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103694&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.00714285714285714OK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}