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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 21 Nov 2010 18:15:20 +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/21/t1290363256vo1leyp9a0gvz3o.htm/, Retrieved Sat, 27 Apr 2024 17:16:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98396, Retrieved Sat, 27 Apr 2024 17:16:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact199

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [workshop 7] [2010-11-21 17:50:24] [65eb19f81eab2b6e672eafaed2a27190]
-   PD      [Multiple Regression] [workshop 7] [2010-11-21 18:15:20] [8b27277f7b82c0354d659d066108e38e] [Current]
F   P         [Multiple Regression] [WS7 Celebrity] [2010-11-21 18:23:11] [65eb19f81eab2b6e672eafaed2a27190]
F   PD          [Multiple Regression] [WS7 Celebrity aan...] [2010-11-22 11:35:59] [65eb19f81eab2b6e672eafaed2a27190]
-   PD          [Multiple Regression] [Workshop 7 - Doub...] [2010-11-22 17:46:53] [1429a1a14191a86916b95357f6de790b]
-    D            [Multiple Regression] [Workshop 7 - Doub...] [2010-11-22 18:43:58] [1429a1a14191a86916b95357f6de790b]
-    D              [Multiple Regression] [Workshop 7 - Adju...] [2010-11-23 12:04:06] [1429a1a14191a86916b95357f6de790b]
-    D                [Multiple Regression] [] [2011-11-19 17:50:25] [e21b9c93af4eb9605ecfaf58a559e5ab]
- RM                    [Multiple Regression] [] [2011-11-22 22:42:42] [74be16979710d4c4e7c6647856088456]
-    D              [Multiple Regression] [] [2011-11-19 17:00:50] [e21b9c93af4eb9605ecfaf58a559e5ab]
- RM                  [Multiple Regression] [] [2011-11-22 22:35:20] [74be16979710d4c4e7c6647856088456]
- R                 [Multiple Regression] [Workshop 7 - Doub...] [2011-11-22 19:05:21] [74be16979710d4c4e7c6647856088456]
-   PD              [Multiple Regression] [WS7 Computation1] [2011-11-24 20:39:28] [3dd791303389e75e672968b227170a72]
-   PD              [Multiple Regression] [WS7 Computation 2] [2011-11-24 21:28:34] [3dd791303389e75e672968b227170a72]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	13	14	13	3
12	12	8	13	5
10	15	12	16	6
9	12	7	12	6
10	10	10	11	5
12	12	7	12	3
13	15	16	18	8
12	9	11	11	4
12	12	14	14	4
6	11	6	9	4
5	11	16	14	6
12	11	11	12	6
11	15	16	11	5
14	7	12	12	4
14	11	7	13	6
12	11	13	11	4
12	10	11	12	6
11	14	15	16	6
11	10	7	9	4
7	6	9	11	4
9	11	7	13	2
11	15	14	15	7
11	11	15	10	5
12	12	7	11	4
12	14	15	13	6
11	15	17	16	6
11	9	15	15	7
8	13	14	14	5
9	13	14	14	6
12	16	8	14	4
10	13	8	8	4
10	12	14	13	7
12	14	14	15	7
8	11	8	13	4
12	9	11	11	4
11	16	16	15	6
12	12	10	15	6
7	10	8	9	5
11	13	14	13	6
11	16	16	16	7
12	14	13	13	6
9	15	5	11	3
15	5	8	12	3
11	8	10	12	4
11	11	8	12	6
11	16	13	14	7
11	17	15	14	5
15	9	6	8	4
11	9	12	13	5
12	13	16	16	6
12	10	5	13	6
9	6	15	11	6
12	12	12	14	5
12	8	8	13	4
13	14	13	13	5
11	12	14	13	5
9	11	12	12	4
9	16	16	16	6
11	8	10	15	2
11	15	15	15	8
12	7	8	12	3
12	16	16	14	6
9	14	19	12	6
11	16	14	15	6
9	9	6	12	5
12	14	13	13	5
12	11	15	12	6
12	13	7	12	5
12	15	13	13	6
14	5	4	5	2
11	15	14	13	5
12	13	13	13	5
11	11	11	14	5
6	11	14	17	6
10	12	12	13	6
12	12	15	13	6
13	12	14	12	5
8	12	13	13	5
12	14	8	14	4
12	6	6	11	2
12	7	7	12	4
6	14	13	12	6
11	14	13	16	6
10	10	11	12	5
12	13	5	12	3
13	12	12	12	6
11	9	8	10	4
7	12	11	15	5
11	16	14	15	8
11	10	9	12	4
11	14	10	16	6
11	10	13	15	6
12	16	16	16	7
10	15	16	13	6
11	12	11	12	5
12	10	8	11	4
7	8	4	13	6
13	8	7	10	3
8	11	14	15	5
12	13	11	13	6
11	16	17	16	7
12	16	15	15	7
14	14	17	18	6
10	11	5	13	3
10	4	4	10	2
13	14	10	16	8
10	9	11	13	3
11	14	15	15	8
10	8	10	14	3
7	8	9	15	4
10	11	12	14	5
8	12	15	13	7
12	11	7	13	6
12	14	13	15	6
12	15	12	16	7
11	16	14	14	6
12	16	14	14	6
12	11	8	16	6
12	14	15	14	6
11	14	12	12	4
12	12	12	13	4
11	14	16	12	5
11	8	9	12	4
13	13	15	14	6
12	16	15	14	6
12	12	6	14	5
12	16	14	16	8
12	12	15	13	6
8	11	10	14	5
8	4	6	4	4
12	16	14	16	8
11	15	12	13	6
12	10	8	16	4
13	13	11	15	6
12	15	13	14	6
12	12	9	13	4
11	14	15	14	6
12	7	13	12	3
12	19	15	15	6
10	12	14	14	5
11	12	16	13	4
12	13	14	14	6
12	15	14	16	4
10	8	10	6	4
12	12	10	13	4
13	10	4	13	6
12	8	8	14	5
15	10	15	15	6
11	15	16	14	6
12	16	12	15	8
11	13	12	13	7
12	16	15	16	7
11	9	9	12	4
10	14	12	15	6
11	14	14	12	6
11	12	11	14	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98396&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98396&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98396&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
FindingFriends[t] = + 9.9004545250637 + 0.0682003253664285Popularity[t] -0.0300954894917058KnowingPeople[t] + 0.0519923282704495Liked[t] -0.0593588957098072Celebrity[t] + 0.00368806807662014t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
FindingFriends[t] =  +  9.9004545250637 +  0.0682003253664285Popularity[t] -0.0300954894917058KnowingPeople[t] +  0.0519923282704495Liked[t] -0.0593588957098072Celebrity[t] +  0.00368806807662014t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98396&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]FindingFriends[t] =  +  9.9004545250637 +  0.0682003253664285Popularity[t] -0.0300954894917058KnowingPeople[t] +  0.0519923282704495Liked[t] -0.0593588957098072Celebrity[t] +  0.00368806807662014t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98396&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98396&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
FindingFriends[t] = + 9.9004545250637 + 0.0682003253664285Popularity[t] -0.0300954894917058KnowingPeople[t] + 0.0519923282704495Liked[t] -0.0593588957098072Celebrity[t] + 0.00368806807662014t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.90045452506370.89253711.092500
Popularity0.06820032536642850.0685030.99560.3210550.160528
KnowingPeople-0.03009548949170580.05444-0.55280.5812120.290606
Liked0.05199232827044950.0855720.60760.544380.27219
Celebrity-0.05935889570980720.138575-0.42840.669010.334505
t0.003688068076620140.0032051.15070.2516790.12584

\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) & 9.9004545250637 & 0.892537 & 11.0925 & 0 & 0 \tabularnewline
Popularity & 0.0682003253664285 & 0.068503 & 0.9956 & 0.321055 & 0.160528 \tabularnewline
KnowingPeople & -0.0300954894917058 & 0.05444 & -0.5528 & 0.581212 & 0.290606 \tabularnewline
Liked & 0.0519923282704495 & 0.085572 & 0.6076 & 0.54438 & 0.27219 \tabularnewline
Celebrity & -0.0593588957098072 & 0.138575 & -0.4284 & 0.66901 & 0.334505 \tabularnewline
t & 0.00368806807662014 & 0.003205 & 1.1507 & 0.251679 & 0.12584 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98396&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]9.9004545250637[/C][C]0.892537[/C][C]11.0925[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Popularity[/C][C]0.0682003253664285[/C][C]0.068503[/C][C]0.9956[/C][C]0.321055[/C][C]0.160528[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]-0.0300954894917058[/C][C]0.05444[/C][C]-0.5528[/C][C]0.581212[/C][C]0.290606[/C][/ROW]
[ROW][C]Liked[/C][C]0.0519923282704495[/C][C]0.085572[/C][C]0.6076[/C][C]0.54438[/C][C]0.27219[/C][/ROW]
[ROW][C]Celebrity[/C][C]-0.0593588957098072[/C][C]0.138575[/C][C]-0.4284[/C][C]0.66901[/C][C]0.334505[/C][/ROW]
[ROW][C]t[/C][C]0.00368806807662014[/C][C]0.003205[/C][C]1.1507[/C][C]0.251679[/C][C]0.12584[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98396&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98396&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.90045452506370.89253711.092500
Popularity0.06820032536642850.0685030.99560.3210550.160528
KnowingPeople-0.03009548949170580.05444-0.55280.5812120.290606
Liked0.05199232827044950.0855720.60760.544380.27219
Celebrity-0.05935889570980720.138575-0.42840.669010.334505
t0.003688068076620140.0032051.15070.2516790.12584






Multiple Linear Regression - Regression Statistics
Multiple R0.154362735607863
R-squared0.0238278541443429
Adjusted R-squared-0.00871121738417924
F-TEST (value)0.732284389966589
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0.600311992850885
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.77800336030926
Sum Squared Residuals474.194392390652

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.154362735607863 \tabularnewline
R-squared & 0.0238278541443429 \tabularnewline
Adjusted R-squared & -0.00871121738417924 \tabularnewline
F-TEST (value) & 0.732284389966589 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0.600311992850885 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.77800336030926 \tabularnewline
Sum Squared Residuals & 474.194392390652 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98396&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.154362735607863[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0238278541443429[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00871121738417924[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.732284389966589[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C]0.600311992850885[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.77800336030926[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]474.194392390652[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98396&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98396&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.154362735607863
R-squared0.0238278541443429
Adjusted R-squared-0.00871121738417924
F-TEST (value)0.732284389966589
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0.600311992850885
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.77800336030926
Sum Squared Residuals474.194392390652






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11310.86723355040642.13276644959356
21210.86457643864721.13542356135276
31011.0491016139579-1.04910161395786
4910.7906968403119-1.79069684031193
51010.5750643566199-0.575064356619933
61210.97614966359461.02385033640541
71310.92873879341882.07126120658119
81210.54719164170151.45280835829853
91210.82117120221361.17882879778640
10610.7374612195052-4.73746121950520
11510.5814382425974-5.58143824259739
121210.63161910159161.36838089840836
131110.76499759111480.235002408885198
141410.45481623820713.54518376179293
151410.81505759205883.18494240794123
161210.65290585806391.34709414193613
171210.58185911660831.41814088339169
181110.94593584126560.0540641587343793
191110.67235801733660.327641982663360
20710.4470384615050-3.44703846150503
21911.0746215833577-2.07462158335772
221110.94763270445000.0523672955500213
231110.50718013163650.492819868363454
241210.93118366499351.06881633500650
251210.81577533299061.18422466700939
261110.98344973226160.0165502677384013
271110.52677560314280.473224396857197
28810.900085925326-2.90008592532601
29910.8444150976928-1.84441509769282
301211.35199487023860.648005129761425
311010.8391279925932-0.839127992593212
321010.675927752576-0.675927752575995
331210.92000112792641.07999887207363
34810.9737531874425-2.97375318744246
351210.64676947977021.35323052022979
361111.0666338996155-0.0666338996154848
371210.97809360317661.02190639682337
38710.6529769255909-3.65297692559091
391110.82930345018860.170696549811428
401111.0740196044826-0.0740196044826077
411210.93497540119991.06502459880005
42911.3217197411652-2.32171974116516
431510.60511041537284.39488958462717
441110.69384958485550.306150415144482
451110.84361181659520.156388183404779
461111.0824498248765-0.0824498248765467
471111.2128650307558-0.212865030755798
481510.68921482741354.31078517258655
491110.71293270418230.287067295817722
501210.96565820485931.03434179514067
511210.93981869643411.06018130356592
52910.2657659115870-1.26576591158703
531210.98427828085851.01572171914151
541210.84291357287561.15708642712442
551311.04596724998241.95403275001756
561110.88315917783450.116840822165507
57910.8862044669675-1.88620446696745
58911.1997637255716-2.19976372557158
591111.0238653822358-0.0238653822357834
601110.99832490616000.00167509383996898
611210.80789629148491.19210370851515
621211.11053134133720.889468658662841
63910.7835476336649-1.78354763366491
641111.2300907847443-0.23009078474426
65910.9005224020880-1.90052240208799
661211.08653599882530.913464001174743
671210.71408088783891.28591911216108
681211.15429241829190.845707581708146
691211.10644163271170.893558367288261
701410.52048280922513.47951719077494
711111.1430811750831-0.143081175083081
721211.04046408191860.95953591808145
731111.0199348065162-0.019934806516174
74611.0299544952192-5.02995449521922
751010.9540645545639-0.95406455456388
761210.86746615416541.13253384583462
771310.90861627917312.09138372082693
78810.9943921650118-2.99439216501184
791211.39630955526010.603690444739895
801210.87732680599701.12267319400303
811210.85239424679921.14760575320085
82611.0341938640709-5.03419386407092
831111.2458512452293-0.245851245229341
841010.8883185734517-0.888318573451668
851211.39589834599740.604101654002578
861310.94264097513632.05735902486375
871110.87684315995910.123156840040875
88711.1954484813024-4.19544848130236
891111.2035746952401-0.203574695240149
901111.0299968566046-0.0299968566046075
911111.3656422583174-0.365642258317420
921110.95425022818280.045749771817241
931211.26948721254350.730512787456525
941011.1083568661521-1.10835686615213
951111.0652879730273-0.0652879730273464
961211.03022842628560.969771573714416
97711.0031646667175-4.00316466671746
981310.93866596863702.06133403136297
99811.0775304363036-3.07753043630363
1001211.14456207133750.855437928662482
1011111.2688962676647-0.268896267664730
1021211.28078298645430.719217013545687
1031411.30321530533582.69678469466418
1041011.3815633169908-1.38156331699080
1051010.8413265078926-0.841326507892583
1061311.30224548804711.69775451195289
1071011.0756539335376-1.07565393353757
1081111.1071518484714-0.107151848471369
1091011.0969175620865-1.09691756208653
110711.1233345522155-4.1233345522155
1111011.1299859039360-1.12998590393603
112810.9408777092139-2.9408777092139
1131211.17648826356750.823511736432454
1141211.30818902733410.691810972665884
1151211.40280634282950.597193657170487
1161111.3698779964581-0.369877996458058
1171211.37356606453470.626433935465322
1181211.32081009927030.67918990072971
1191211.21444606046340.785553939536644
1201111.3231537318938-0.323153731893808
1211211.24243347750800.757566522491979
1221111.1507890143704-0.150789014370418
1231111.0153024524002-0.0153024524002152
1241311.16468607548001.83531392451997
1251211.37297511965590.627024880344067
1261211.4340801874020.565919812598001
1271211.39571361042220.604286389577836
1281211.05924569414960.94075430585037
129811.2565621082986-3.25656210829861
130810.4426654697824-2.44266546978236
1311211.41046588272860.589534117271355
1321111.3688854110305-0.368885411030514
1331211.42664858647280.573351413527223
1341311.37394104248351.62605895751650
1351211.40184645403910.598153545960882
1361211.38804096713240.61195903286756
1371111.2808312858425-0.280831285842518
1381210.94140008592611.05859991407393
1391211.68120137709840.318798622901650
1401011.2449492245410-1.24494922454103
1411111.1958128810736-0.195812881073601
1421211.26116679035090.738833209649104
1431211.62395795712090.376042042879113
1441010.7507024228948-0.750702422894835
1451211.39113809033030.608861909669684
1461311.32028065320471.6797193467953
1471211.17853733656190.821462663438104
1481511.10059106149013.89940893850992
1491111.3631929386367-0.363192938636682
1501211.48873782689740.511262173102611
1511111.2431991580436-0.243199158043632
1521211.51717871855580.482821281444231
1531111.1941448200652-0.194144820065248
1541011.4858072398906-1.48580723989063
1551111.2733273441725-0.273327344172487
1561111.5723214693715-0.572321469371496

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 10.8672335504064 & 2.13276644959356 \tabularnewline
2 & 12 & 10.8645764386472 & 1.13542356135276 \tabularnewline
3 & 10 & 11.0491016139579 & -1.04910161395786 \tabularnewline
4 & 9 & 10.7906968403119 & -1.79069684031193 \tabularnewline
5 & 10 & 10.5750643566199 & -0.575064356619933 \tabularnewline
6 & 12 & 10.9761496635946 & 1.02385033640541 \tabularnewline
7 & 13 & 10.9287387934188 & 2.07126120658119 \tabularnewline
8 & 12 & 10.5471916417015 & 1.45280835829853 \tabularnewline
9 & 12 & 10.8211712022136 & 1.17882879778640 \tabularnewline
10 & 6 & 10.7374612195052 & -4.73746121950520 \tabularnewline
11 & 5 & 10.5814382425974 & -5.58143824259739 \tabularnewline
12 & 12 & 10.6316191015916 & 1.36838089840836 \tabularnewline
13 & 11 & 10.7649975911148 & 0.235002408885198 \tabularnewline
14 & 14 & 10.4548162382071 & 3.54518376179293 \tabularnewline
15 & 14 & 10.8150575920588 & 3.18494240794123 \tabularnewline
16 & 12 & 10.6529058580639 & 1.34709414193613 \tabularnewline
17 & 12 & 10.5818591166083 & 1.41814088339169 \tabularnewline
18 & 11 & 10.9459358412656 & 0.0540641587343793 \tabularnewline
19 & 11 & 10.6723580173366 & 0.327641982663360 \tabularnewline
20 & 7 & 10.4470384615050 & -3.44703846150503 \tabularnewline
21 & 9 & 11.0746215833577 & -2.07462158335772 \tabularnewline
22 & 11 & 10.9476327044500 & 0.0523672955500213 \tabularnewline
23 & 11 & 10.5071801316365 & 0.492819868363454 \tabularnewline
24 & 12 & 10.9311836649935 & 1.06881633500650 \tabularnewline
25 & 12 & 10.8157753329906 & 1.18422466700939 \tabularnewline
26 & 11 & 10.9834497322616 & 0.0165502677384013 \tabularnewline
27 & 11 & 10.5267756031428 & 0.473224396857197 \tabularnewline
28 & 8 & 10.900085925326 & -2.90008592532601 \tabularnewline
29 & 9 & 10.8444150976928 & -1.84441509769282 \tabularnewline
30 & 12 & 11.3519948702386 & 0.648005129761425 \tabularnewline
31 & 10 & 10.8391279925932 & -0.839127992593212 \tabularnewline
32 & 10 & 10.675927752576 & -0.675927752575995 \tabularnewline
33 & 12 & 10.9200011279264 & 1.07999887207363 \tabularnewline
34 & 8 & 10.9737531874425 & -2.97375318744246 \tabularnewline
35 & 12 & 10.6467694797702 & 1.35323052022979 \tabularnewline
36 & 11 & 11.0666338996155 & -0.0666338996154848 \tabularnewline
37 & 12 & 10.9780936031766 & 1.02190639682337 \tabularnewline
38 & 7 & 10.6529769255909 & -3.65297692559091 \tabularnewline
39 & 11 & 10.8293034501886 & 0.170696549811428 \tabularnewline
40 & 11 & 11.0740196044826 & -0.0740196044826077 \tabularnewline
41 & 12 & 10.9349754011999 & 1.06502459880005 \tabularnewline
42 & 9 & 11.3217197411652 & -2.32171974116516 \tabularnewline
43 & 15 & 10.6051104153728 & 4.39488958462717 \tabularnewline
44 & 11 & 10.6938495848555 & 0.306150415144482 \tabularnewline
45 & 11 & 10.8436118165952 & 0.156388183404779 \tabularnewline
46 & 11 & 11.0824498248765 & -0.0824498248765467 \tabularnewline
47 & 11 & 11.2128650307558 & -0.212865030755798 \tabularnewline
48 & 15 & 10.6892148274135 & 4.31078517258655 \tabularnewline
49 & 11 & 10.7129327041823 & 0.287067295817722 \tabularnewline
50 & 12 & 10.9656582048593 & 1.03434179514067 \tabularnewline
51 & 12 & 10.9398186964341 & 1.06018130356592 \tabularnewline
52 & 9 & 10.2657659115870 & -1.26576591158703 \tabularnewline
53 & 12 & 10.9842782808585 & 1.01572171914151 \tabularnewline
54 & 12 & 10.8429135728756 & 1.15708642712442 \tabularnewline
55 & 13 & 11.0459672499824 & 1.95403275001756 \tabularnewline
56 & 11 & 10.8831591778345 & 0.116840822165507 \tabularnewline
57 & 9 & 10.8862044669675 & -1.88620446696745 \tabularnewline
58 & 9 & 11.1997637255716 & -2.19976372557158 \tabularnewline
59 & 11 & 11.0238653822358 & -0.0238653822357834 \tabularnewline
60 & 11 & 10.9983249061600 & 0.00167509383996898 \tabularnewline
61 & 12 & 10.8078962914849 & 1.19210370851515 \tabularnewline
62 & 12 & 11.1105313413372 & 0.889468658662841 \tabularnewline
63 & 9 & 10.7835476336649 & -1.78354763366491 \tabularnewline
64 & 11 & 11.2300907847443 & -0.23009078474426 \tabularnewline
65 & 9 & 10.9005224020880 & -1.90052240208799 \tabularnewline
66 & 12 & 11.0865359988253 & 0.913464001174743 \tabularnewline
67 & 12 & 10.7140808878389 & 1.28591911216108 \tabularnewline
68 & 12 & 11.1542924182919 & 0.845707581708146 \tabularnewline
69 & 12 & 11.1064416327117 & 0.893558367288261 \tabularnewline
70 & 14 & 10.5204828092251 & 3.47951719077494 \tabularnewline
71 & 11 & 11.1430811750831 & -0.143081175083081 \tabularnewline
72 & 12 & 11.0404640819186 & 0.95953591808145 \tabularnewline
73 & 11 & 11.0199348065162 & -0.019934806516174 \tabularnewline
74 & 6 & 11.0299544952192 & -5.02995449521922 \tabularnewline
75 & 10 & 10.9540645545639 & -0.95406455456388 \tabularnewline
76 & 12 & 10.8674661541654 & 1.13253384583462 \tabularnewline
77 & 13 & 10.9086162791731 & 2.09138372082693 \tabularnewline
78 & 8 & 10.9943921650118 & -2.99439216501184 \tabularnewline
79 & 12 & 11.3963095552601 & 0.603690444739895 \tabularnewline
80 & 12 & 10.8773268059970 & 1.12267319400303 \tabularnewline
81 & 12 & 10.8523942467992 & 1.14760575320085 \tabularnewline
82 & 6 & 11.0341938640709 & -5.03419386407092 \tabularnewline
83 & 11 & 11.2458512452293 & -0.245851245229341 \tabularnewline
84 & 10 & 10.8883185734517 & -0.888318573451668 \tabularnewline
85 & 12 & 11.3958983459974 & 0.604101654002578 \tabularnewline
86 & 13 & 10.9426409751363 & 2.05735902486375 \tabularnewline
87 & 11 & 10.8768431599591 & 0.123156840040875 \tabularnewline
88 & 7 & 11.1954484813024 & -4.19544848130236 \tabularnewline
89 & 11 & 11.2035746952401 & -0.203574695240149 \tabularnewline
90 & 11 & 11.0299968566046 & -0.0299968566046075 \tabularnewline
91 & 11 & 11.3656422583174 & -0.365642258317420 \tabularnewline
92 & 11 & 10.9542502281828 & 0.045749771817241 \tabularnewline
93 & 12 & 11.2694872125435 & 0.730512787456525 \tabularnewline
94 & 10 & 11.1083568661521 & -1.10835686615213 \tabularnewline
95 & 11 & 11.0652879730273 & -0.0652879730273464 \tabularnewline
96 & 12 & 11.0302284262856 & 0.969771573714416 \tabularnewline
97 & 7 & 11.0031646667175 & -4.00316466671746 \tabularnewline
98 & 13 & 10.9386659686370 & 2.06133403136297 \tabularnewline
99 & 8 & 11.0775304363036 & -3.07753043630363 \tabularnewline
100 & 12 & 11.1445620713375 & 0.855437928662482 \tabularnewline
101 & 11 & 11.2688962676647 & -0.268896267664730 \tabularnewline
102 & 12 & 11.2807829864543 & 0.719217013545687 \tabularnewline
103 & 14 & 11.3032153053358 & 2.69678469466418 \tabularnewline
104 & 10 & 11.3815633169908 & -1.38156331699080 \tabularnewline
105 & 10 & 10.8413265078926 & -0.841326507892583 \tabularnewline
106 & 13 & 11.3022454880471 & 1.69775451195289 \tabularnewline
107 & 10 & 11.0756539335376 & -1.07565393353757 \tabularnewline
108 & 11 & 11.1071518484714 & -0.107151848471369 \tabularnewline
109 & 10 & 11.0969175620865 & -1.09691756208653 \tabularnewline
110 & 7 & 11.1233345522155 & -4.1233345522155 \tabularnewline
111 & 10 & 11.1299859039360 & -1.12998590393603 \tabularnewline
112 & 8 & 10.9408777092139 & -2.9408777092139 \tabularnewline
113 & 12 & 11.1764882635675 & 0.823511736432454 \tabularnewline
114 & 12 & 11.3081890273341 & 0.691810972665884 \tabularnewline
115 & 12 & 11.4028063428295 & 0.597193657170487 \tabularnewline
116 & 11 & 11.3698779964581 & -0.369877996458058 \tabularnewline
117 & 12 & 11.3735660645347 & 0.626433935465322 \tabularnewline
118 & 12 & 11.3208100992703 & 0.67918990072971 \tabularnewline
119 & 12 & 11.2144460604634 & 0.785553939536644 \tabularnewline
120 & 11 & 11.3231537318938 & -0.323153731893808 \tabularnewline
121 & 12 & 11.2424334775080 & 0.757566522491979 \tabularnewline
122 & 11 & 11.1507890143704 & -0.150789014370418 \tabularnewline
123 & 11 & 11.0153024524002 & -0.0153024524002152 \tabularnewline
124 & 13 & 11.1646860754800 & 1.83531392451997 \tabularnewline
125 & 12 & 11.3729751196559 & 0.627024880344067 \tabularnewline
126 & 12 & 11.434080187402 & 0.565919812598001 \tabularnewline
127 & 12 & 11.3957136104222 & 0.604286389577836 \tabularnewline
128 & 12 & 11.0592456941496 & 0.94075430585037 \tabularnewline
129 & 8 & 11.2565621082986 & -3.25656210829861 \tabularnewline
130 & 8 & 10.4426654697824 & -2.44266546978236 \tabularnewline
131 & 12 & 11.4104658827286 & 0.589534117271355 \tabularnewline
132 & 11 & 11.3688854110305 & -0.368885411030514 \tabularnewline
133 & 12 & 11.4266485864728 & 0.573351413527223 \tabularnewline
134 & 13 & 11.3739410424835 & 1.62605895751650 \tabularnewline
135 & 12 & 11.4018464540391 & 0.598153545960882 \tabularnewline
136 & 12 & 11.3880409671324 & 0.61195903286756 \tabularnewline
137 & 11 & 11.2808312858425 & -0.280831285842518 \tabularnewline
138 & 12 & 10.9414000859261 & 1.05859991407393 \tabularnewline
139 & 12 & 11.6812013770984 & 0.318798622901650 \tabularnewline
140 & 10 & 11.2449492245410 & -1.24494922454103 \tabularnewline
141 & 11 & 11.1958128810736 & -0.195812881073601 \tabularnewline
142 & 12 & 11.2611667903509 & 0.738833209649104 \tabularnewline
143 & 12 & 11.6239579571209 & 0.376042042879113 \tabularnewline
144 & 10 & 10.7507024228948 & -0.750702422894835 \tabularnewline
145 & 12 & 11.3911380903303 & 0.608861909669684 \tabularnewline
146 & 13 & 11.3202806532047 & 1.6797193467953 \tabularnewline
147 & 12 & 11.1785373365619 & 0.821462663438104 \tabularnewline
148 & 15 & 11.1005910614901 & 3.89940893850992 \tabularnewline
149 & 11 & 11.3631929386367 & -0.363192938636682 \tabularnewline
150 & 12 & 11.4887378268974 & 0.511262173102611 \tabularnewline
151 & 11 & 11.2431991580436 & -0.243199158043632 \tabularnewline
152 & 12 & 11.5171787185558 & 0.482821281444231 \tabularnewline
153 & 11 & 11.1941448200652 & -0.194144820065248 \tabularnewline
154 & 10 & 11.4858072398906 & -1.48580723989063 \tabularnewline
155 & 11 & 11.2733273441725 & -0.273327344172487 \tabularnewline
156 & 11 & 11.5723214693715 & -0.572321469371496 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98396&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]10.8672335504064[/C][C]2.13276644959356[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.8645764386472[/C][C]1.13542356135276[/C][/ROW]
[ROW][C]3[/C][C]10[/C][C]11.0491016139579[/C][C]-1.04910161395786[/C][/ROW]
[ROW][C]4[/C][C]9[/C][C]10.7906968403119[/C][C]-1.79069684031193[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.5750643566199[/C][C]-0.575064356619933[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.9761496635946[/C][C]1.02385033640541[/C][/ROW]
[ROW][C]7[/C][C]13[/C][C]10.9287387934188[/C][C]2.07126120658119[/C][/ROW]
[ROW][C]8[/C][C]12[/C][C]10.5471916417015[/C][C]1.45280835829853[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.8211712022136[/C][C]1.17882879778640[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]10.7374612195052[/C][C]-4.73746121950520[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]10.5814382425974[/C][C]-5.58143824259739[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]10.6316191015916[/C][C]1.36838089840836[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]10.7649975911148[/C][C]0.235002408885198[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]10.4548162382071[/C][C]3.54518376179293[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]10.8150575920588[/C][C]3.18494240794123[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]10.6529058580639[/C][C]1.34709414193613[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]10.5818591166083[/C][C]1.41814088339169[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]10.9459358412656[/C][C]0.0540641587343793[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]10.6723580173366[/C][C]0.327641982663360[/C][/ROW]
[ROW][C]20[/C][C]7[/C][C]10.4470384615050[/C][C]-3.44703846150503[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]11.0746215833577[/C][C]-2.07462158335772[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]10.9476327044500[/C][C]0.0523672955500213[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]10.5071801316365[/C][C]0.492819868363454[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]10.9311836649935[/C][C]1.06881633500650[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]10.8157753329906[/C][C]1.18422466700939[/C][/ROW]
[ROW][C]26[/C][C]11[/C][C]10.9834497322616[/C][C]0.0165502677384013[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]10.5267756031428[/C][C]0.473224396857197[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]10.900085925326[/C][C]-2.90008592532601[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.8444150976928[/C][C]-1.84441509769282[/C][/ROW]
[ROW][C]30[/C][C]12[/C][C]11.3519948702386[/C][C]0.648005129761425[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]10.8391279925932[/C][C]-0.839127992593212[/C][/ROW]
[ROW][C]32[/C][C]10[/C][C]10.675927752576[/C][C]-0.675927752575995[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]10.9200011279264[/C][C]1.07999887207363[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]10.9737531874425[/C][C]-2.97375318744246[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.6467694797702[/C][C]1.35323052022979[/C][/ROW]
[ROW][C]36[/C][C]11[/C][C]11.0666338996155[/C][C]-0.0666338996154848[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]10.9780936031766[/C][C]1.02190639682337[/C][/ROW]
[ROW][C]38[/C][C]7[/C][C]10.6529769255909[/C][C]-3.65297692559091[/C][/ROW]
[ROW][C]39[/C][C]11[/C][C]10.8293034501886[/C][C]0.170696549811428[/C][/ROW]
[ROW][C]40[/C][C]11[/C][C]11.0740196044826[/C][C]-0.0740196044826077[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]10.9349754011999[/C][C]1.06502459880005[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]11.3217197411652[/C][C]-2.32171974116516[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]10.6051104153728[/C][C]4.39488958462717[/C][/ROW]
[ROW][C]44[/C][C]11[/C][C]10.6938495848555[/C][C]0.306150415144482[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]10.8436118165952[/C][C]0.156388183404779[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]11.0824498248765[/C][C]-0.0824498248765467[/C][/ROW]
[ROW][C]47[/C][C]11[/C][C]11.2128650307558[/C][C]-0.212865030755798[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]10.6892148274135[/C][C]4.31078517258655[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]10.7129327041823[/C][C]0.287067295817722[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.9656582048593[/C][C]1.03434179514067[/C][/ROW]
[ROW][C]51[/C][C]12[/C][C]10.9398186964341[/C][C]1.06018130356592[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]10.2657659115870[/C][C]-1.26576591158703[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]10.9842782808585[/C][C]1.01572171914151[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]10.8429135728756[/C][C]1.15708642712442[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]11.0459672499824[/C][C]1.95403275001756[/C][/ROW]
[ROW][C]56[/C][C]11[/C][C]10.8831591778345[/C][C]0.116840822165507[/C][/ROW]
[ROW][C]57[/C][C]9[/C][C]10.8862044669675[/C][C]-1.88620446696745[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]11.1997637255716[/C][C]-2.19976372557158[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]11.0238653822358[/C][C]-0.0238653822357834[/C][/ROW]
[ROW][C]60[/C][C]11[/C][C]10.9983249061600[/C][C]0.00167509383996898[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]10.8078962914849[/C][C]1.19210370851515[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.1105313413372[/C][C]0.889468658662841[/C][/ROW]
[ROW][C]63[/C][C]9[/C][C]10.7835476336649[/C][C]-1.78354763366491[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]11.2300907847443[/C][C]-0.23009078474426[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.9005224020880[/C][C]-1.90052240208799[/C][/ROW]
[ROW][C]66[/C][C]12[/C][C]11.0865359988253[/C][C]0.913464001174743[/C][/ROW]
[ROW][C]67[/C][C]12[/C][C]10.7140808878389[/C][C]1.28591911216108[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]11.1542924182919[/C][C]0.845707581708146[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]11.1064416327117[/C][C]0.893558367288261[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]10.5204828092251[/C][C]3.47951719077494[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]11.1430811750831[/C][C]-0.143081175083081[/C][/ROW]
[ROW][C]72[/C][C]12[/C][C]11.0404640819186[/C][C]0.95953591808145[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]11.0199348065162[/C][C]-0.019934806516174[/C][/ROW]
[ROW][C]74[/C][C]6[/C][C]11.0299544952192[/C][C]-5.02995449521922[/C][/ROW]
[ROW][C]75[/C][C]10[/C][C]10.9540645545639[/C][C]-0.95406455456388[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]10.8674661541654[/C][C]1.13253384583462[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.9086162791731[/C][C]2.09138372082693[/C][/ROW]
[ROW][C]78[/C][C]8[/C][C]10.9943921650118[/C][C]-2.99439216501184[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.3963095552601[/C][C]0.603690444739895[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]10.8773268059970[/C][C]1.12267319400303[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]10.8523942467992[/C][C]1.14760575320085[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]11.0341938640709[/C][C]-5.03419386407092[/C][/ROW]
[ROW][C]83[/C][C]11[/C][C]11.2458512452293[/C][C]-0.245851245229341[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]10.8883185734517[/C][C]-0.888318573451668[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]11.3958983459974[/C][C]0.604101654002578[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]10.9426409751363[/C][C]2.05735902486375[/C][/ROW]
[ROW][C]87[/C][C]11[/C][C]10.8768431599591[/C][C]0.123156840040875[/C][/ROW]
[ROW][C]88[/C][C]7[/C][C]11.1954484813024[/C][C]-4.19544848130236[/C][/ROW]
[ROW][C]89[/C][C]11[/C][C]11.2035746952401[/C][C]-0.203574695240149[/C][/ROW]
[ROW][C]90[/C][C]11[/C][C]11.0299968566046[/C][C]-0.0299968566046075[/C][/ROW]
[ROW][C]91[/C][C]11[/C][C]11.3656422583174[/C][C]-0.365642258317420[/C][/ROW]
[ROW][C]92[/C][C]11[/C][C]10.9542502281828[/C][C]0.045749771817241[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.2694872125435[/C][C]0.730512787456525[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]11.1083568661521[/C][C]-1.10835686615213[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]11.0652879730273[/C][C]-0.0652879730273464[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]11.0302284262856[/C][C]0.969771573714416[/C][/ROW]
[ROW][C]97[/C][C]7[/C][C]11.0031646667175[/C][C]-4.00316466671746[/C][/ROW]
[ROW][C]98[/C][C]13[/C][C]10.9386659686370[/C][C]2.06133403136297[/C][/ROW]
[ROW][C]99[/C][C]8[/C][C]11.0775304363036[/C][C]-3.07753043630363[/C][/ROW]
[ROW][C]100[/C][C]12[/C][C]11.1445620713375[/C][C]0.855437928662482[/C][/ROW]
[ROW][C]101[/C][C]11[/C][C]11.2688962676647[/C][C]-0.268896267664730[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]11.2807829864543[/C][C]0.719217013545687[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]11.3032153053358[/C][C]2.69678469466418[/C][/ROW]
[ROW][C]104[/C][C]10[/C][C]11.3815633169908[/C][C]-1.38156331699080[/C][/ROW]
[ROW][C]105[/C][C]10[/C][C]10.8413265078926[/C][C]-0.841326507892583[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]11.3022454880471[/C][C]1.69775451195289[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]11.0756539335376[/C][C]-1.07565393353757[/C][/ROW]
[ROW][C]108[/C][C]11[/C][C]11.1071518484714[/C][C]-0.107151848471369[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]11.0969175620865[/C][C]-1.09691756208653[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]11.1233345522155[/C][C]-4.1233345522155[/C][/ROW]
[ROW][C]111[/C][C]10[/C][C]11.1299859039360[/C][C]-1.12998590393603[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]10.9408777092139[/C][C]-2.9408777092139[/C][/ROW]
[ROW][C]113[/C][C]12[/C][C]11.1764882635675[/C][C]0.823511736432454[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]11.3081890273341[/C][C]0.691810972665884[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]11.4028063428295[/C][C]0.597193657170487[/C][/ROW]
[ROW][C]116[/C][C]11[/C][C]11.3698779964581[/C][C]-0.369877996458058[/C][/ROW]
[ROW][C]117[/C][C]12[/C][C]11.3735660645347[/C][C]0.626433935465322[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]11.3208100992703[/C][C]0.67918990072971[/C][/ROW]
[ROW][C]119[/C][C]12[/C][C]11.2144460604634[/C][C]0.785553939536644[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]11.3231537318938[/C][C]-0.323153731893808[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.2424334775080[/C][C]0.757566522491979[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]11.1507890143704[/C][C]-0.150789014370418[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]11.0153024524002[/C][C]-0.0153024524002152[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]11.1646860754800[/C][C]1.83531392451997[/C][/ROW]
[ROW][C]125[/C][C]12[/C][C]11.3729751196559[/C][C]0.627024880344067[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]11.434080187402[/C][C]0.565919812598001[/C][/ROW]
[ROW][C]127[/C][C]12[/C][C]11.3957136104222[/C][C]0.604286389577836[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]11.0592456941496[/C][C]0.94075430585037[/C][/ROW]
[ROW][C]129[/C][C]8[/C][C]11.2565621082986[/C][C]-3.25656210829861[/C][/ROW]
[ROW][C]130[/C][C]8[/C][C]10.4426654697824[/C][C]-2.44266546978236[/C][/ROW]
[ROW][C]131[/C][C]12[/C][C]11.4104658827286[/C][C]0.589534117271355[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]11.3688854110305[/C][C]-0.368885411030514[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]11.4266485864728[/C][C]0.573351413527223[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]11.3739410424835[/C][C]1.62605895751650[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]11.4018464540391[/C][C]0.598153545960882[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]11.3880409671324[/C][C]0.61195903286756[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]11.2808312858425[/C][C]-0.280831285842518[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]10.9414000859261[/C][C]1.05859991407393[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]11.6812013770984[/C][C]0.318798622901650[/C][/ROW]
[ROW][C]140[/C][C]10[/C][C]11.2449492245410[/C][C]-1.24494922454103[/C][/ROW]
[ROW][C]141[/C][C]11[/C][C]11.1958128810736[/C][C]-0.195812881073601[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.2611667903509[/C][C]0.738833209649104[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]11.6239579571209[/C][C]0.376042042879113[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]10.7507024228948[/C][C]-0.750702422894835[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.3911380903303[/C][C]0.608861909669684[/C][/ROW]
[ROW][C]146[/C][C]13[/C][C]11.3202806532047[/C][C]1.6797193467953[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]11.1785373365619[/C][C]0.821462663438104[/C][/ROW]
[ROW][C]148[/C][C]15[/C][C]11.1005910614901[/C][C]3.89940893850992[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]11.3631929386367[/C][C]-0.363192938636682[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]11.4887378268974[/C][C]0.511262173102611[/C][/ROW]
[ROW][C]151[/C][C]11[/C][C]11.2431991580436[/C][C]-0.243199158043632[/C][/ROW]
[ROW][C]152[/C][C]12[/C][C]11.5171787185558[/C][C]0.482821281444231[/C][/ROW]
[ROW][C]153[/C][C]11[/C][C]11.1941448200652[/C][C]-0.194144820065248[/C][/ROW]
[ROW][C]154[/C][C]10[/C][C]11.4858072398906[/C][C]-1.48580723989063[/C][/ROW]
[ROW][C]155[/C][C]11[/C][C]11.2733273441725[/C][C]-0.273327344172487[/C][/ROW]
[ROW][C]156[/C][C]11[/C][C]11.5723214693715[/C][C]-0.572321469371496[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98396&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98396&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11310.86723355040642.13276644959356
21210.86457643864721.13542356135276
31011.0491016139579-1.04910161395786
4910.7906968403119-1.79069684031193
51010.5750643566199-0.575064356619933
61210.97614966359461.02385033640541
71310.92873879341882.07126120658119
81210.54719164170151.45280835829853
91210.82117120221361.17882879778640
10610.7374612195052-4.73746121950520
11510.5814382425974-5.58143824259739
121210.63161910159161.36838089840836
131110.76499759111480.235002408885198
141410.45481623820713.54518376179293
151410.81505759205883.18494240794123
161210.65290585806391.34709414193613
171210.58185911660831.41814088339169
181110.94593584126560.0540641587343793
191110.67235801733660.327641982663360
20710.4470384615050-3.44703846150503
21911.0746215833577-2.07462158335772
221110.94763270445000.0523672955500213
231110.50718013163650.492819868363454
241210.93118366499351.06881633500650
251210.81577533299061.18422466700939
261110.98344973226160.0165502677384013
271110.52677560314280.473224396857197
28810.900085925326-2.90008592532601
29910.8444150976928-1.84441509769282
301211.35199487023860.648005129761425
311010.8391279925932-0.839127992593212
321010.675927752576-0.675927752575995
331210.92000112792641.07999887207363
34810.9737531874425-2.97375318744246
351210.64676947977021.35323052022979
361111.0666338996155-0.0666338996154848
371210.97809360317661.02190639682337
38710.6529769255909-3.65297692559091
391110.82930345018860.170696549811428
401111.0740196044826-0.0740196044826077
411210.93497540119991.06502459880005
42911.3217197411652-2.32171974116516
431510.60511041537284.39488958462717
441110.69384958485550.306150415144482
451110.84361181659520.156388183404779
461111.0824498248765-0.0824498248765467
471111.2128650307558-0.212865030755798
481510.68921482741354.31078517258655
491110.71293270418230.287067295817722
501210.96565820485931.03434179514067
511210.93981869643411.06018130356592
52910.2657659115870-1.26576591158703
531210.98427828085851.01572171914151
541210.84291357287561.15708642712442
551311.04596724998241.95403275001756
561110.88315917783450.116840822165507
57910.8862044669675-1.88620446696745
58911.1997637255716-2.19976372557158
591111.0238653822358-0.0238653822357834
601110.99832490616000.00167509383996898
611210.80789629148491.19210370851515
621211.11053134133720.889468658662841
63910.7835476336649-1.78354763366491
641111.2300907847443-0.23009078474426
65910.9005224020880-1.90052240208799
661211.08653599882530.913464001174743
671210.71408088783891.28591911216108
681211.15429241829190.845707581708146
691211.10644163271170.893558367288261
701410.52048280922513.47951719077494
711111.1430811750831-0.143081175083081
721211.04046408191860.95953591808145
731111.0199348065162-0.019934806516174
74611.0299544952192-5.02995449521922
751010.9540645545639-0.95406455456388
761210.86746615416541.13253384583462
771310.90861627917312.09138372082693
78810.9943921650118-2.99439216501184
791211.39630955526010.603690444739895
801210.87732680599701.12267319400303
811210.85239424679921.14760575320085
82611.0341938640709-5.03419386407092
831111.2458512452293-0.245851245229341
841010.8883185734517-0.888318573451668
851211.39589834599740.604101654002578
861310.94264097513632.05735902486375
871110.87684315995910.123156840040875
88711.1954484813024-4.19544848130236
891111.2035746952401-0.203574695240149
901111.0299968566046-0.0299968566046075
911111.3656422583174-0.365642258317420
921110.95425022818280.045749771817241
931211.26948721254350.730512787456525
941011.1083568661521-1.10835686615213
951111.0652879730273-0.0652879730273464
961211.03022842628560.969771573714416
97711.0031646667175-4.00316466671746
981310.93866596863702.06133403136297
99811.0775304363036-3.07753043630363
1001211.14456207133750.855437928662482
1011111.2688962676647-0.268896267664730
1021211.28078298645430.719217013545687
1031411.30321530533582.69678469466418
1041011.3815633169908-1.38156331699080
1051010.8413265078926-0.841326507892583
1061311.30224548804711.69775451195289
1071011.0756539335376-1.07565393353757
1081111.1071518484714-0.107151848471369
1091011.0969175620865-1.09691756208653
110711.1233345522155-4.1233345522155
1111011.1299859039360-1.12998590393603
112810.9408777092139-2.9408777092139
1131211.17648826356750.823511736432454
1141211.30818902733410.691810972665884
1151211.40280634282950.597193657170487
1161111.3698779964581-0.369877996458058
1171211.37356606453470.626433935465322
1181211.32081009927030.67918990072971
1191211.21444606046340.785553939536644
1201111.3231537318938-0.323153731893808
1211211.24243347750800.757566522491979
1221111.1507890143704-0.150789014370418
1231111.0153024524002-0.0153024524002152
1241311.16468607548001.83531392451997
1251211.37297511965590.627024880344067
1261211.4340801874020.565919812598001
1271211.39571361042220.604286389577836
1281211.05924569414960.94075430585037
129811.2565621082986-3.25656210829861
130810.4426654697824-2.44266546978236
1311211.41046588272860.589534117271355
1321111.3688854110305-0.368885411030514
1331211.42664858647280.573351413527223
1341311.37394104248351.62605895751650
1351211.40184645403910.598153545960882
1361211.38804096713240.61195903286756
1371111.2808312858425-0.280831285842518
1381210.94140008592611.05859991407393
1391211.68120137709840.318798622901650
1401011.2449492245410-1.24494922454103
1411111.1958128810736-0.195812881073601
1421211.26116679035090.738833209649104
1431211.62395795712090.376042042879113
1441010.7507024228948-0.750702422894835
1451211.39113809033030.608861909669684
1461311.32028065320471.6797193467953
1471211.17853733656190.821462663438104
1481511.10059106149013.89940893850992
1491111.3631929386367-0.363192938636682
1501211.48873782689740.511262173102611
1511111.2431991580436-0.243199158043632
1521211.51717871855580.482821281444231
1531111.1941448200652-0.194144820065248
1541011.4858072398906-1.48580723989063
1551111.2733273441725-0.273327344172487
1561111.5723214693715-0.572321469371496






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3395671070572350.679134214114470.660432892942765
100.2049894134155760.4099788268311520.795010586584424
110.9021846686041270.1956306627917470.0978153313958733
120.989712130600950.02057573879810060.0102878693990503
130.9922117465458880.01557650690822320.00778825345411158
140.9959919288860740.008016142227851460.00400807111392573
150.9971437136327730.005712572734454730.00285628636722737
160.9950187024066480.009962595186703020.00498129759335151
170.9920849000950780.01583019980984470.00791509990492237
180.992406983699170.01518603260166010.00759301630083003
190.9874208938906230.02515821221875380.0125791061093769
200.9983391292075520.003321741584896280.00166087079244814
210.998845752463610.002308495072778160.00115424753638908
220.997994533867130.004010932265741440.00200546613287072
230.9968344898363170.006331020327365460.00316551016368273
240.9959534752025280.008093049594944710.00404652479747236
250.9941743968395070.01165120632098630.00582560316049316
260.9913755381821240.01724892363575150.00862446181787577
270.987098534185470.02580293162906040.0129014658145302
280.991899368827080.01620126234584140.00810063117292069
290.9905922265222570.01881554695548670.00940777347774336
300.9877378572858630.02452428542827440.0122621427141372
310.9825723967250270.0348552065499460.017427603274973
320.9755112971816740.0489774056366520.024488702818326
330.9703213924055980.05935721518880360.0296786075944018
340.9760289533534680.04794209329306440.0239710466465322
350.975952190686020.04809561862796130.0240478093139807
360.9668413605203350.06631727895933050.0331586394796652
370.9612154676013170.07756906479736640.0387845323986832
380.9764074699371310.04718506012573810.0235925300628690
390.968642136816180.06271572636764190.0313578631838209
400.9578185123502010.08436297529959780.0421814876497989
410.952201708219970.0955965835600610.0477982917800305
420.953476012207460.09304797558507850.0465239877925392
430.9884397423834080.02312051523318440.0115602576165922
440.9840371376895350.03192572462092960.0159628623104648
450.9786212031649470.04275759367010570.0213787968350528
460.971700749842420.05659850031515990.0282992501575799
470.9627798480969170.07444030380616530.0372201519030827
480.9912136781614460.01757264367710810.00878632183855404
490.9882231144947840.02355377101043250.0117768855052163
500.9852394620507020.02952107589859650.0147605379492983
510.9813473420171320.0373053159657350.0186526579828675
520.9802362043648050.03952759127039040.0197637956351952
530.9754433729669880.04911325406602440.0245566270330122
540.9706609266101490.05867814677970230.0293390733898512
550.9714871912735290.05702561745294210.0285128087264710
560.9632140150769010.07357196984619710.0367859849230986
570.965278750161450.06944249967710050.0347212498385502
580.9689696852161740.06206062956765160.0310303147838258
590.9617661637378130.07646767252437360.0382338362621868
600.9507748205948770.09845035881024660.0492251794051233
610.944503227303830.1109935453923420.0554967726961708
620.935261560539760.1294768789204810.0647384394602403
630.930150712069450.1396985758610990.0698492879305494
640.912707394672650.1745852106547010.0872926053273505
650.9155051373599360.1689897252801290.0844948626400644
660.9025319797291240.1949360405417520.097468020270876
670.8965409904598420.2069180190803160.103459009540158
680.8789909104684320.2420181790631360.121009089531568
690.861779363350650.2764412732986990.138220636649349
700.9249721207362120.1500557585275760.0750278792637881
710.9068925842138860.1862148315722280.0931074157861142
720.8954218765559550.2091562468880900.104578123444045
730.8757364868065050.2485270263869900.124263513193495
740.9732046682674150.05359066346516910.0267953317325845
750.9665707999429480.06685840011410420.0334292000570521
760.9633878749408760.0732242501182490.0366121250591245
770.971809283002230.05638143399554020.0281907169977701
780.9806703363444280.03865932731114460.0193296636555723
790.9759051461710430.04818970765791330.0240948538289566
800.975563835744490.04887232851102130.0244361642555106
810.9757504509456740.0484990981086510.0242495490543255
820.9968791818002340.006241636399531120.00312081819976556
830.9955346120346880.00893077593062450.00446538796531225
840.993921490755030.01215701848994050.00607850924497023
850.992439947929810.01512010414037810.00756005207018907
860.9948571541450120.01028569170997660.0051428458549883
870.9936422863022610.01271542739547780.00635771369773889
880.9986969504485330.00260609910293380.0013030495514669
890.998099244933330.003801510133339850.00190075506666993
900.9973446501633430.00531069967331460.0026553498366573
910.9961836822433930.007632635513214050.00381631775660703
920.9946064758263350.01078704834732990.00539352417366496
930.9930495500293180.01390089994136440.0069504499706822
940.9910387028827490.01792259423450220.0089612971172511
950.9877311132930120.02453777341397540.0122688867069877
960.9876958126195060.02460837476098840.0123041873804942
970.9967512796384990.006497440723002850.00324872036150143
980.99883617351630.002327652967401710.00116382648370085
990.9995792092814570.000841581437085470.000420790718542735
1000.9994997133171560.001000573365687030.000500286682843515
1010.9992617633979720.001476473204056310.000738236602028157
1020.9989625757589260.002074848482147720.00103742424107386
1030.9994669880316670.001066023936666110.000533011968333054
1040.9992282330303140.001543533939372670.000771766969686336
1050.9988910771601610.002217845679677910.00110892283983896
1060.9989144374846350.002171125030730290.00108556251536514
1070.9983587883339010.003282423332197760.00164121166609888
1080.9974728534904340.005054293019131320.00252714650956566
1090.9963179141911230.007364171617754070.00368208580887703
1100.9998270602534540.0003458794930915140.000172939746545757
1110.9998107991368280.000378401726344810.000189200863172405
1120.9999886385514622.27228970768488e-051.13614485384244e-05
1130.9999809117564653.81764870706583e-051.90882435353291e-05
1140.999964971633817.00567323781508e-053.50283661890754e-05
1150.9999377767880620.0001244464238752256.22232119376127e-05
1160.9998927438153540.0002145123692915640.000107256184645782
1170.9998190969121020.0003618061757957430.000180903087897872
1180.9997064955941460.0005870088117087920.000293504405854396
1190.999495497218340.001009005563319600.000504502781659801
1200.9991328210425620.001734357914875490.000867178957437744
1210.9987300198086950.00253996038261010.00126998019130505
1220.997844929765320.004310140469361230.00215507023468062
1230.9964464888453420.007107022309316870.00355351115465844
1240.996185706777530.007628586444938150.00381429322246907
1250.9946748346371630.01065033072567410.00532516536283704
1260.992340866103130.01531826779374090.00765913389687046
1270.9878278637327580.02434427253448430.0121721362672422
1280.9830331591110040.03393368177799180.0169668408889959
1290.9992272257922480.001545548415504080.00077277420775204
1300.9994956453560460.001008709287908680.000504354643954341
1310.999106396633190.001787206733621460.00089360336681073
1320.9983827984057570.003234403188485120.00161720159424256
1330.9980121772440920.003975645511816060.00198782275590803
1340.9963856216694670.00722875666106570.00361437833053285
1350.9932095282185430.01358094356291450.00679047178145724
1360.987973652602930.02405269479413880.0120263473970694
1370.9825718348371740.03485633032565240.0174281651628262
1380.9693139890477680.06137202190446440.0306860109522322
1390.9619204002630930.07615919947381490.0380795997369075
1400.9842749844692640.03145003106147150.0157250155307357
1410.9789130051861020.04217398962779650.0210869948138982
1420.964632523590580.07073495281883830.0353674764094191
1430.9373160569229940.1253678861540120.0626839430770062
1440.8929993240260270.2140013519479460.107000675973973
1450.8097960622229320.3804078755541370.190203937777068
1460.8448966742566760.3102066514866470.155103325743324
1470.7301605444373830.5396789111252340.269839455562617

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.339567107057235 & 0.67913421411447 & 0.660432892942765 \tabularnewline
10 & 0.204989413415576 & 0.409978826831152 & 0.795010586584424 \tabularnewline
11 & 0.902184668604127 & 0.195630662791747 & 0.0978153313958733 \tabularnewline
12 & 0.98971213060095 & 0.0205757387981006 & 0.0102878693990503 \tabularnewline
13 & 0.992211746545888 & 0.0155765069082232 & 0.00778825345411158 \tabularnewline
14 & 0.995991928886074 & 0.00801614222785146 & 0.00400807111392573 \tabularnewline
15 & 0.997143713632773 & 0.00571257273445473 & 0.00285628636722737 \tabularnewline
16 & 0.995018702406648 & 0.00996259518670302 & 0.00498129759335151 \tabularnewline
17 & 0.992084900095078 & 0.0158301998098447 & 0.00791509990492237 \tabularnewline
18 & 0.99240698369917 & 0.0151860326016601 & 0.00759301630083003 \tabularnewline
19 & 0.987420893890623 & 0.0251582122187538 & 0.0125791061093769 \tabularnewline
20 & 0.998339129207552 & 0.00332174158489628 & 0.00166087079244814 \tabularnewline
21 & 0.99884575246361 & 0.00230849507277816 & 0.00115424753638908 \tabularnewline
22 & 0.99799453386713 & 0.00401093226574144 & 0.00200546613287072 \tabularnewline
23 & 0.996834489836317 & 0.00633102032736546 & 0.00316551016368273 \tabularnewline
24 & 0.995953475202528 & 0.00809304959494471 & 0.00404652479747236 \tabularnewline
25 & 0.994174396839507 & 0.0116512063209863 & 0.00582560316049316 \tabularnewline
26 & 0.991375538182124 & 0.0172489236357515 & 0.00862446181787577 \tabularnewline
27 & 0.98709853418547 & 0.0258029316290604 & 0.0129014658145302 \tabularnewline
28 & 0.99189936882708 & 0.0162012623458414 & 0.00810063117292069 \tabularnewline
29 & 0.990592226522257 & 0.0188155469554867 & 0.00940777347774336 \tabularnewline
30 & 0.987737857285863 & 0.0245242854282744 & 0.0122621427141372 \tabularnewline
31 & 0.982572396725027 & 0.034855206549946 & 0.017427603274973 \tabularnewline
32 & 0.975511297181674 & 0.048977405636652 & 0.024488702818326 \tabularnewline
33 & 0.970321392405598 & 0.0593572151888036 & 0.0296786075944018 \tabularnewline
34 & 0.976028953353468 & 0.0479420932930644 & 0.0239710466465322 \tabularnewline
35 & 0.97595219068602 & 0.0480956186279613 & 0.0240478093139807 \tabularnewline
36 & 0.966841360520335 & 0.0663172789593305 & 0.0331586394796652 \tabularnewline
37 & 0.961215467601317 & 0.0775690647973664 & 0.0387845323986832 \tabularnewline
38 & 0.976407469937131 & 0.0471850601257381 & 0.0235925300628690 \tabularnewline
39 & 0.96864213681618 & 0.0627157263676419 & 0.0313578631838209 \tabularnewline
40 & 0.957818512350201 & 0.0843629752995978 & 0.0421814876497989 \tabularnewline
41 & 0.95220170821997 & 0.095596583560061 & 0.0477982917800305 \tabularnewline
42 & 0.95347601220746 & 0.0930479755850785 & 0.0465239877925392 \tabularnewline
43 & 0.988439742383408 & 0.0231205152331844 & 0.0115602576165922 \tabularnewline
44 & 0.984037137689535 & 0.0319257246209296 & 0.0159628623104648 \tabularnewline
45 & 0.978621203164947 & 0.0427575936701057 & 0.0213787968350528 \tabularnewline
46 & 0.97170074984242 & 0.0565985003151599 & 0.0282992501575799 \tabularnewline
47 & 0.962779848096917 & 0.0744403038061653 & 0.0372201519030827 \tabularnewline
48 & 0.991213678161446 & 0.0175726436771081 & 0.00878632183855404 \tabularnewline
49 & 0.988223114494784 & 0.0235537710104325 & 0.0117768855052163 \tabularnewline
50 & 0.985239462050702 & 0.0295210758985965 & 0.0147605379492983 \tabularnewline
51 & 0.981347342017132 & 0.037305315965735 & 0.0186526579828675 \tabularnewline
52 & 0.980236204364805 & 0.0395275912703904 & 0.0197637956351952 \tabularnewline
53 & 0.975443372966988 & 0.0491132540660244 & 0.0245566270330122 \tabularnewline
54 & 0.970660926610149 & 0.0586781467797023 & 0.0293390733898512 \tabularnewline
55 & 0.971487191273529 & 0.0570256174529421 & 0.0285128087264710 \tabularnewline
56 & 0.963214015076901 & 0.0735719698461971 & 0.0367859849230986 \tabularnewline
57 & 0.96527875016145 & 0.0694424996771005 & 0.0347212498385502 \tabularnewline
58 & 0.968969685216174 & 0.0620606295676516 & 0.0310303147838258 \tabularnewline
59 & 0.961766163737813 & 0.0764676725243736 & 0.0382338362621868 \tabularnewline
60 & 0.950774820594877 & 0.0984503588102466 & 0.0492251794051233 \tabularnewline
61 & 0.94450322730383 & 0.110993545392342 & 0.0554967726961708 \tabularnewline
62 & 0.93526156053976 & 0.129476878920481 & 0.0647384394602403 \tabularnewline
63 & 0.93015071206945 & 0.139698575861099 & 0.0698492879305494 \tabularnewline
64 & 0.91270739467265 & 0.174585210654701 & 0.0872926053273505 \tabularnewline
65 & 0.915505137359936 & 0.168989725280129 & 0.0844948626400644 \tabularnewline
66 & 0.902531979729124 & 0.194936040541752 & 0.097468020270876 \tabularnewline
67 & 0.896540990459842 & 0.206918019080316 & 0.103459009540158 \tabularnewline
68 & 0.878990910468432 & 0.242018179063136 & 0.121009089531568 \tabularnewline
69 & 0.86177936335065 & 0.276441273298699 & 0.138220636649349 \tabularnewline
70 & 0.924972120736212 & 0.150055758527576 & 0.0750278792637881 \tabularnewline
71 & 0.906892584213886 & 0.186214831572228 & 0.0931074157861142 \tabularnewline
72 & 0.895421876555955 & 0.209156246888090 & 0.104578123444045 \tabularnewline
73 & 0.875736486806505 & 0.248527026386990 & 0.124263513193495 \tabularnewline
74 & 0.973204668267415 & 0.0535906634651691 & 0.0267953317325845 \tabularnewline
75 & 0.966570799942948 & 0.0668584001141042 & 0.0334292000570521 \tabularnewline
76 & 0.963387874940876 & 0.073224250118249 & 0.0366121250591245 \tabularnewline
77 & 0.97180928300223 & 0.0563814339955402 & 0.0281907169977701 \tabularnewline
78 & 0.980670336344428 & 0.0386593273111446 & 0.0193296636555723 \tabularnewline
79 & 0.975905146171043 & 0.0481897076579133 & 0.0240948538289566 \tabularnewline
80 & 0.97556383574449 & 0.0488723285110213 & 0.0244361642555106 \tabularnewline
81 & 0.975750450945674 & 0.048499098108651 & 0.0242495490543255 \tabularnewline
82 & 0.996879181800234 & 0.00624163639953112 & 0.00312081819976556 \tabularnewline
83 & 0.995534612034688 & 0.0089307759306245 & 0.00446538796531225 \tabularnewline
84 & 0.99392149075503 & 0.0121570184899405 & 0.00607850924497023 \tabularnewline
85 & 0.99243994792981 & 0.0151201041403781 & 0.00756005207018907 \tabularnewline
86 & 0.994857154145012 & 0.0102856917099766 & 0.0051428458549883 \tabularnewline
87 & 0.993642286302261 & 0.0127154273954778 & 0.00635771369773889 \tabularnewline
88 & 0.998696950448533 & 0.0026060991029338 & 0.0013030495514669 \tabularnewline
89 & 0.99809924493333 & 0.00380151013333985 & 0.00190075506666993 \tabularnewline
90 & 0.997344650163343 & 0.0053106996733146 & 0.0026553498366573 \tabularnewline
91 & 0.996183682243393 & 0.00763263551321405 & 0.00381631775660703 \tabularnewline
92 & 0.994606475826335 & 0.0107870483473299 & 0.00539352417366496 \tabularnewline
93 & 0.993049550029318 & 0.0139008999413644 & 0.0069504499706822 \tabularnewline
94 & 0.991038702882749 & 0.0179225942345022 & 0.0089612971172511 \tabularnewline
95 & 0.987731113293012 & 0.0245377734139754 & 0.0122688867069877 \tabularnewline
96 & 0.987695812619506 & 0.0246083747609884 & 0.0123041873804942 \tabularnewline
97 & 0.996751279638499 & 0.00649744072300285 & 0.00324872036150143 \tabularnewline
98 & 0.9988361735163 & 0.00232765296740171 & 0.00116382648370085 \tabularnewline
99 & 0.999579209281457 & 0.00084158143708547 & 0.000420790718542735 \tabularnewline
100 & 0.999499713317156 & 0.00100057336568703 & 0.000500286682843515 \tabularnewline
101 & 0.999261763397972 & 0.00147647320405631 & 0.000738236602028157 \tabularnewline
102 & 0.998962575758926 & 0.00207484848214772 & 0.00103742424107386 \tabularnewline
103 & 0.999466988031667 & 0.00106602393666611 & 0.000533011968333054 \tabularnewline
104 & 0.999228233030314 & 0.00154353393937267 & 0.000771766969686336 \tabularnewline
105 & 0.998891077160161 & 0.00221784567967791 & 0.00110892283983896 \tabularnewline
106 & 0.998914437484635 & 0.00217112503073029 & 0.00108556251536514 \tabularnewline
107 & 0.998358788333901 & 0.00328242333219776 & 0.00164121166609888 \tabularnewline
108 & 0.997472853490434 & 0.00505429301913132 & 0.00252714650956566 \tabularnewline
109 & 0.996317914191123 & 0.00736417161775407 & 0.00368208580887703 \tabularnewline
110 & 0.999827060253454 & 0.000345879493091514 & 0.000172939746545757 \tabularnewline
111 & 0.999810799136828 & 0.00037840172634481 & 0.000189200863172405 \tabularnewline
112 & 0.999988638551462 & 2.27228970768488e-05 & 1.13614485384244e-05 \tabularnewline
113 & 0.999980911756465 & 3.81764870706583e-05 & 1.90882435353291e-05 \tabularnewline
114 & 0.99996497163381 & 7.00567323781508e-05 & 3.50283661890754e-05 \tabularnewline
115 & 0.999937776788062 & 0.000124446423875225 & 6.22232119376127e-05 \tabularnewline
116 & 0.999892743815354 & 0.000214512369291564 & 0.000107256184645782 \tabularnewline
117 & 0.999819096912102 & 0.000361806175795743 & 0.000180903087897872 \tabularnewline
118 & 0.999706495594146 & 0.000587008811708792 & 0.000293504405854396 \tabularnewline
119 & 0.99949549721834 & 0.00100900556331960 & 0.000504502781659801 \tabularnewline
120 & 0.999132821042562 & 0.00173435791487549 & 0.000867178957437744 \tabularnewline
121 & 0.998730019808695 & 0.0025399603826101 & 0.00126998019130505 \tabularnewline
122 & 0.99784492976532 & 0.00431014046936123 & 0.00215507023468062 \tabularnewline
123 & 0.996446488845342 & 0.00710702230931687 & 0.00355351115465844 \tabularnewline
124 & 0.99618570677753 & 0.00762858644493815 & 0.00381429322246907 \tabularnewline
125 & 0.994674834637163 & 0.0106503307256741 & 0.00532516536283704 \tabularnewline
126 & 0.99234086610313 & 0.0153182677937409 & 0.00765913389687046 \tabularnewline
127 & 0.987827863732758 & 0.0243442725344843 & 0.0121721362672422 \tabularnewline
128 & 0.983033159111004 & 0.0339336817779918 & 0.0169668408889959 \tabularnewline
129 & 0.999227225792248 & 0.00154554841550408 & 0.00077277420775204 \tabularnewline
130 & 0.999495645356046 & 0.00100870928790868 & 0.000504354643954341 \tabularnewline
131 & 0.99910639663319 & 0.00178720673362146 & 0.00089360336681073 \tabularnewline
132 & 0.998382798405757 & 0.00323440318848512 & 0.00161720159424256 \tabularnewline
133 & 0.998012177244092 & 0.00397564551181606 & 0.00198782275590803 \tabularnewline
134 & 0.996385621669467 & 0.0072287566610657 & 0.00361437833053285 \tabularnewline
135 & 0.993209528218543 & 0.0135809435629145 & 0.00679047178145724 \tabularnewline
136 & 0.98797365260293 & 0.0240526947941388 & 0.0120263473970694 \tabularnewline
137 & 0.982571834837174 & 0.0348563303256524 & 0.0174281651628262 \tabularnewline
138 & 0.969313989047768 & 0.0613720219044644 & 0.0306860109522322 \tabularnewline
139 & 0.961920400263093 & 0.0761591994738149 & 0.0380795997369075 \tabularnewline
140 & 0.984274984469264 & 0.0314500310614715 & 0.0157250155307357 \tabularnewline
141 & 0.978913005186102 & 0.0421739896277965 & 0.0210869948138982 \tabularnewline
142 & 0.96463252359058 & 0.0707349528188383 & 0.0353674764094191 \tabularnewline
143 & 0.937316056922994 & 0.125367886154012 & 0.0626839430770062 \tabularnewline
144 & 0.892999324026027 & 0.214001351947946 & 0.107000675973973 \tabularnewline
145 & 0.809796062222932 & 0.380407875554137 & 0.190203937777068 \tabularnewline
146 & 0.844896674256676 & 0.310206651486647 & 0.155103325743324 \tabularnewline
147 & 0.730160544437383 & 0.539678911125234 & 0.269839455562617 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98396&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]9[/C][C]0.339567107057235[/C][C]0.67913421411447[/C][C]0.660432892942765[/C][/ROW]
[ROW][C]10[/C][C]0.204989413415576[/C][C]0.409978826831152[/C][C]0.795010586584424[/C][/ROW]
[ROW][C]11[/C][C]0.902184668604127[/C][C]0.195630662791747[/C][C]0.0978153313958733[/C][/ROW]
[ROW][C]12[/C][C]0.98971213060095[/C][C]0.0205757387981006[/C][C]0.0102878693990503[/C][/ROW]
[ROW][C]13[/C][C]0.992211746545888[/C][C]0.0155765069082232[/C][C]0.00778825345411158[/C][/ROW]
[ROW][C]14[/C][C]0.995991928886074[/C][C]0.00801614222785146[/C][C]0.00400807111392573[/C][/ROW]
[ROW][C]15[/C][C]0.997143713632773[/C][C]0.00571257273445473[/C][C]0.00285628636722737[/C][/ROW]
[ROW][C]16[/C][C]0.995018702406648[/C][C]0.00996259518670302[/C][C]0.00498129759335151[/C][/ROW]
[ROW][C]17[/C][C]0.992084900095078[/C][C]0.0158301998098447[/C][C]0.00791509990492237[/C][/ROW]
[ROW][C]18[/C][C]0.99240698369917[/C][C]0.0151860326016601[/C][C]0.00759301630083003[/C][/ROW]
[ROW][C]19[/C][C]0.987420893890623[/C][C]0.0251582122187538[/C][C]0.0125791061093769[/C][/ROW]
[ROW][C]20[/C][C]0.998339129207552[/C][C]0.00332174158489628[/C][C]0.00166087079244814[/C][/ROW]
[ROW][C]21[/C][C]0.99884575246361[/C][C]0.00230849507277816[/C][C]0.00115424753638908[/C][/ROW]
[ROW][C]22[/C][C]0.99799453386713[/C][C]0.00401093226574144[/C][C]0.00200546613287072[/C][/ROW]
[ROW][C]23[/C][C]0.996834489836317[/C][C]0.00633102032736546[/C][C]0.00316551016368273[/C][/ROW]
[ROW][C]24[/C][C]0.995953475202528[/C][C]0.00809304959494471[/C][C]0.00404652479747236[/C][/ROW]
[ROW][C]25[/C][C]0.994174396839507[/C][C]0.0116512063209863[/C][C]0.00582560316049316[/C][/ROW]
[ROW][C]26[/C][C]0.991375538182124[/C][C]0.0172489236357515[/C][C]0.00862446181787577[/C][/ROW]
[ROW][C]27[/C][C]0.98709853418547[/C][C]0.0258029316290604[/C][C]0.0129014658145302[/C][/ROW]
[ROW][C]28[/C][C]0.99189936882708[/C][C]0.0162012623458414[/C][C]0.00810063117292069[/C][/ROW]
[ROW][C]29[/C][C]0.990592226522257[/C][C]0.0188155469554867[/C][C]0.00940777347774336[/C][/ROW]
[ROW][C]30[/C][C]0.987737857285863[/C][C]0.0245242854282744[/C][C]0.0122621427141372[/C][/ROW]
[ROW][C]31[/C][C]0.982572396725027[/C][C]0.034855206549946[/C][C]0.017427603274973[/C][/ROW]
[ROW][C]32[/C][C]0.975511297181674[/C][C]0.048977405636652[/C][C]0.024488702818326[/C][/ROW]
[ROW][C]33[/C][C]0.970321392405598[/C][C]0.0593572151888036[/C][C]0.0296786075944018[/C][/ROW]
[ROW][C]34[/C][C]0.976028953353468[/C][C]0.0479420932930644[/C][C]0.0239710466465322[/C][/ROW]
[ROW][C]35[/C][C]0.97595219068602[/C][C]0.0480956186279613[/C][C]0.0240478093139807[/C][/ROW]
[ROW][C]36[/C][C]0.966841360520335[/C][C]0.0663172789593305[/C][C]0.0331586394796652[/C][/ROW]
[ROW][C]37[/C][C]0.961215467601317[/C][C]0.0775690647973664[/C][C]0.0387845323986832[/C][/ROW]
[ROW][C]38[/C][C]0.976407469937131[/C][C]0.0471850601257381[/C][C]0.0235925300628690[/C][/ROW]
[ROW][C]39[/C][C]0.96864213681618[/C][C]0.0627157263676419[/C][C]0.0313578631838209[/C][/ROW]
[ROW][C]40[/C][C]0.957818512350201[/C][C]0.0843629752995978[/C][C]0.0421814876497989[/C][/ROW]
[ROW][C]41[/C][C]0.95220170821997[/C][C]0.095596583560061[/C][C]0.0477982917800305[/C][/ROW]
[ROW][C]42[/C][C]0.95347601220746[/C][C]0.0930479755850785[/C][C]0.0465239877925392[/C][/ROW]
[ROW][C]43[/C][C]0.988439742383408[/C][C]0.0231205152331844[/C][C]0.0115602576165922[/C][/ROW]
[ROW][C]44[/C][C]0.984037137689535[/C][C]0.0319257246209296[/C][C]0.0159628623104648[/C][/ROW]
[ROW][C]45[/C][C]0.978621203164947[/C][C]0.0427575936701057[/C][C]0.0213787968350528[/C][/ROW]
[ROW][C]46[/C][C]0.97170074984242[/C][C]0.0565985003151599[/C][C]0.0282992501575799[/C][/ROW]
[ROW][C]47[/C][C]0.962779848096917[/C][C]0.0744403038061653[/C][C]0.0372201519030827[/C][/ROW]
[ROW][C]48[/C][C]0.991213678161446[/C][C]0.0175726436771081[/C][C]0.00878632183855404[/C][/ROW]
[ROW][C]49[/C][C]0.988223114494784[/C][C]0.0235537710104325[/C][C]0.0117768855052163[/C][/ROW]
[ROW][C]50[/C][C]0.985239462050702[/C][C]0.0295210758985965[/C][C]0.0147605379492983[/C][/ROW]
[ROW][C]51[/C][C]0.981347342017132[/C][C]0.037305315965735[/C][C]0.0186526579828675[/C][/ROW]
[ROW][C]52[/C][C]0.980236204364805[/C][C]0.0395275912703904[/C][C]0.0197637956351952[/C][/ROW]
[ROW][C]53[/C][C]0.975443372966988[/C][C]0.0491132540660244[/C][C]0.0245566270330122[/C][/ROW]
[ROW][C]54[/C][C]0.970660926610149[/C][C]0.0586781467797023[/C][C]0.0293390733898512[/C][/ROW]
[ROW][C]55[/C][C]0.971487191273529[/C][C]0.0570256174529421[/C][C]0.0285128087264710[/C][/ROW]
[ROW][C]56[/C][C]0.963214015076901[/C][C]0.0735719698461971[/C][C]0.0367859849230986[/C][/ROW]
[ROW][C]57[/C][C]0.96527875016145[/C][C]0.0694424996771005[/C][C]0.0347212498385502[/C][/ROW]
[ROW][C]58[/C][C]0.968969685216174[/C][C]0.0620606295676516[/C][C]0.0310303147838258[/C][/ROW]
[ROW][C]59[/C][C]0.961766163737813[/C][C]0.0764676725243736[/C][C]0.0382338362621868[/C][/ROW]
[ROW][C]60[/C][C]0.950774820594877[/C][C]0.0984503588102466[/C][C]0.0492251794051233[/C][/ROW]
[ROW][C]61[/C][C]0.94450322730383[/C][C]0.110993545392342[/C][C]0.0554967726961708[/C][/ROW]
[ROW][C]62[/C][C]0.93526156053976[/C][C]0.129476878920481[/C][C]0.0647384394602403[/C][/ROW]
[ROW][C]63[/C][C]0.93015071206945[/C][C]0.139698575861099[/C][C]0.0698492879305494[/C][/ROW]
[ROW][C]64[/C][C]0.91270739467265[/C][C]0.174585210654701[/C][C]0.0872926053273505[/C][/ROW]
[ROW][C]65[/C][C]0.915505137359936[/C][C]0.168989725280129[/C][C]0.0844948626400644[/C][/ROW]
[ROW][C]66[/C][C]0.902531979729124[/C][C]0.194936040541752[/C][C]0.097468020270876[/C][/ROW]
[ROW][C]67[/C][C]0.896540990459842[/C][C]0.206918019080316[/C][C]0.103459009540158[/C][/ROW]
[ROW][C]68[/C][C]0.878990910468432[/C][C]0.242018179063136[/C][C]0.121009089531568[/C][/ROW]
[ROW][C]69[/C][C]0.86177936335065[/C][C]0.276441273298699[/C][C]0.138220636649349[/C][/ROW]
[ROW][C]70[/C][C]0.924972120736212[/C][C]0.150055758527576[/C][C]0.0750278792637881[/C][/ROW]
[ROW][C]71[/C][C]0.906892584213886[/C][C]0.186214831572228[/C][C]0.0931074157861142[/C][/ROW]
[ROW][C]72[/C][C]0.895421876555955[/C][C]0.209156246888090[/C][C]0.104578123444045[/C][/ROW]
[ROW][C]73[/C][C]0.875736486806505[/C][C]0.248527026386990[/C][C]0.124263513193495[/C][/ROW]
[ROW][C]74[/C][C]0.973204668267415[/C][C]0.0535906634651691[/C][C]0.0267953317325845[/C][/ROW]
[ROW][C]75[/C][C]0.966570799942948[/C][C]0.0668584001141042[/C][C]0.0334292000570521[/C][/ROW]
[ROW][C]76[/C][C]0.963387874940876[/C][C]0.073224250118249[/C][C]0.0366121250591245[/C][/ROW]
[ROW][C]77[/C][C]0.97180928300223[/C][C]0.0563814339955402[/C][C]0.0281907169977701[/C][/ROW]
[ROW][C]78[/C][C]0.980670336344428[/C][C]0.0386593273111446[/C][C]0.0193296636555723[/C][/ROW]
[ROW][C]79[/C][C]0.975905146171043[/C][C]0.0481897076579133[/C][C]0.0240948538289566[/C][/ROW]
[ROW][C]80[/C][C]0.97556383574449[/C][C]0.0488723285110213[/C][C]0.0244361642555106[/C][/ROW]
[ROW][C]81[/C][C]0.975750450945674[/C][C]0.048499098108651[/C][C]0.0242495490543255[/C][/ROW]
[ROW][C]82[/C][C]0.996879181800234[/C][C]0.00624163639953112[/C][C]0.00312081819976556[/C][/ROW]
[ROW][C]83[/C][C]0.995534612034688[/C][C]0.0089307759306245[/C][C]0.00446538796531225[/C][/ROW]
[ROW][C]84[/C][C]0.99392149075503[/C][C]0.0121570184899405[/C][C]0.00607850924497023[/C][/ROW]
[ROW][C]85[/C][C]0.99243994792981[/C][C]0.0151201041403781[/C][C]0.00756005207018907[/C][/ROW]
[ROW][C]86[/C][C]0.994857154145012[/C][C]0.0102856917099766[/C][C]0.0051428458549883[/C][/ROW]
[ROW][C]87[/C][C]0.993642286302261[/C][C]0.0127154273954778[/C][C]0.00635771369773889[/C][/ROW]
[ROW][C]88[/C][C]0.998696950448533[/C][C]0.0026060991029338[/C][C]0.0013030495514669[/C][/ROW]
[ROW][C]89[/C][C]0.99809924493333[/C][C]0.00380151013333985[/C][C]0.00190075506666993[/C][/ROW]
[ROW][C]90[/C][C]0.997344650163343[/C][C]0.0053106996733146[/C][C]0.0026553498366573[/C][/ROW]
[ROW][C]91[/C][C]0.996183682243393[/C][C]0.00763263551321405[/C][C]0.00381631775660703[/C][/ROW]
[ROW][C]92[/C][C]0.994606475826335[/C][C]0.0107870483473299[/C][C]0.00539352417366496[/C][/ROW]
[ROW][C]93[/C][C]0.993049550029318[/C][C]0.0139008999413644[/C][C]0.0069504499706822[/C][/ROW]
[ROW][C]94[/C][C]0.991038702882749[/C][C]0.0179225942345022[/C][C]0.0089612971172511[/C][/ROW]
[ROW][C]95[/C][C]0.987731113293012[/C][C]0.0245377734139754[/C][C]0.0122688867069877[/C][/ROW]
[ROW][C]96[/C][C]0.987695812619506[/C][C]0.0246083747609884[/C][C]0.0123041873804942[/C][/ROW]
[ROW][C]97[/C][C]0.996751279638499[/C][C]0.00649744072300285[/C][C]0.00324872036150143[/C][/ROW]
[ROW][C]98[/C][C]0.9988361735163[/C][C]0.00232765296740171[/C][C]0.00116382648370085[/C][/ROW]
[ROW][C]99[/C][C]0.999579209281457[/C][C]0.00084158143708547[/C][C]0.000420790718542735[/C][/ROW]
[ROW][C]100[/C][C]0.999499713317156[/C][C]0.00100057336568703[/C][C]0.000500286682843515[/C][/ROW]
[ROW][C]101[/C][C]0.999261763397972[/C][C]0.00147647320405631[/C][C]0.000738236602028157[/C][/ROW]
[ROW][C]102[/C][C]0.998962575758926[/C][C]0.00207484848214772[/C][C]0.00103742424107386[/C][/ROW]
[ROW][C]103[/C][C]0.999466988031667[/C][C]0.00106602393666611[/C][C]0.000533011968333054[/C][/ROW]
[ROW][C]104[/C][C]0.999228233030314[/C][C]0.00154353393937267[/C][C]0.000771766969686336[/C][/ROW]
[ROW][C]105[/C][C]0.998891077160161[/C][C]0.00221784567967791[/C][C]0.00110892283983896[/C][/ROW]
[ROW][C]106[/C][C]0.998914437484635[/C][C]0.00217112503073029[/C][C]0.00108556251536514[/C][/ROW]
[ROW][C]107[/C][C]0.998358788333901[/C][C]0.00328242333219776[/C][C]0.00164121166609888[/C][/ROW]
[ROW][C]108[/C][C]0.997472853490434[/C][C]0.00505429301913132[/C][C]0.00252714650956566[/C][/ROW]
[ROW][C]109[/C][C]0.996317914191123[/C][C]0.00736417161775407[/C][C]0.00368208580887703[/C][/ROW]
[ROW][C]110[/C][C]0.999827060253454[/C][C]0.000345879493091514[/C][C]0.000172939746545757[/C][/ROW]
[ROW][C]111[/C][C]0.999810799136828[/C][C]0.00037840172634481[/C][C]0.000189200863172405[/C][/ROW]
[ROW][C]112[/C][C]0.999988638551462[/C][C]2.27228970768488e-05[/C][C]1.13614485384244e-05[/C][/ROW]
[ROW][C]113[/C][C]0.999980911756465[/C][C]3.81764870706583e-05[/C][C]1.90882435353291e-05[/C][/ROW]
[ROW][C]114[/C][C]0.99996497163381[/C][C]7.00567323781508e-05[/C][C]3.50283661890754e-05[/C][/ROW]
[ROW][C]115[/C][C]0.999937776788062[/C][C]0.000124446423875225[/C][C]6.22232119376127e-05[/C][/ROW]
[ROW][C]116[/C][C]0.999892743815354[/C][C]0.000214512369291564[/C][C]0.000107256184645782[/C][/ROW]
[ROW][C]117[/C][C]0.999819096912102[/C][C]0.000361806175795743[/C][C]0.000180903087897872[/C][/ROW]
[ROW][C]118[/C][C]0.999706495594146[/C][C]0.000587008811708792[/C][C]0.000293504405854396[/C][/ROW]
[ROW][C]119[/C][C]0.99949549721834[/C][C]0.00100900556331960[/C][C]0.000504502781659801[/C][/ROW]
[ROW][C]120[/C][C]0.999132821042562[/C][C]0.00173435791487549[/C][C]0.000867178957437744[/C][/ROW]
[ROW][C]121[/C][C]0.998730019808695[/C][C]0.0025399603826101[/C][C]0.00126998019130505[/C][/ROW]
[ROW][C]122[/C][C]0.99784492976532[/C][C]0.00431014046936123[/C][C]0.00215507023468062[/C][/ROW]
[ROW][C]123[/C][C]0.996446488845342[/C][C]0.00710702230931687[/C][C]0.00355351115465844[/C][/ROW]
[ROW][C]124[/C][C]0.99618570677753[/C][C]0.00762858644493815[/C][C]0.00381429322246907[/C][/ROW]
[ROW][C]125[/C][C]0.994674834637163[/C][C]0.0106503307256741[/C][C]0.00532516536283704[/C][/ROW]
[ROW][C]126[/C][C]0.99234086610313[/C][C]0.0153182677937409[/C][C]0.00765913389687046[/C][/ROW]
[ROW][C]127[/C][C]0.987827863732758[/C][C]0.0243442725344843[/C][C]0.0121721362672422[/C][/ROW]
[ROW][C]128[/C][C]0.983033159111004[/C][C]0.0339336817779918[/C][C]0.0169668408889959[/C][/ROW]
[ROW][C]129[/C][C]0.999227225792248[/C][C]0.00154554841550408[/C][C]0.00077277420775204[/C][/ROW]
[ROW][C]130[/C][C]0.999495645356046[/C][C]0.00100870928790868[/C][C]0.000504354643954341[/C][/ROW]
[ROW][C]131[/C][C]0.99910639663319[/C][C]0.00178720673362146[/C][C]0.00089360336681073[/C][/ROW]
[ROW][C]132[/C][C]0.998382798405757[/C][C]0.00323440318848512[/C][C]0.00161720159424256[/C][/ROW]
[ROW][C]133[/C][C]0.998012177244092[/C][C]0.00397564551181606[/C][C]0.00198782275590803[/C][/ROW]
[ROW][C]134[/C][C]0.996385621669467[/C][C]0.0072287566610657[/C][C]0.00361437833053285[/C][/ROW]
[ROW][C]135[/C][C]0.993209528218543[/C][C]0.0135809435629145[/C][C]0.00679047178145724[/C][/ROW]
[ROW][C]136[/C][C]0.98797365260293[/C][C]0.0240526947941388[/C][C]0.0120263473970694[/C][/ROW]
[ROW][C]137[/C][C]0.982571834837174[/C][C]0.0348563303256524[/C][C]0.0174281651628262[/C][/ROW]
[ROW][C]138[/C][C]0.969313989047768[/C][C]0.0613720219044644[/C][C]0.0306860109522322[/C][/ROW]
[ROW][C]139[/C][C]0.961920400263093[/C][C]0.0761591994738149[/C][C]0.0380795997369075[/C][/ROW]
[ROW][C]140[/C][C]0.984274984469264[/C][C]0.0314500310614715[/C][C]0.0157250155307357[/C][/ROW]
[ROW][C]141[/C][C]0.978913005186102[/C][C]0.0421739896277965[/C][C]0.0210869948138982[/C][/ROW]
[ROW][C]142[/C][C]0.96463252359058[/C][C]0.0707349528188383[/C][C]0.0353674764094191[/C][/ROW]
[ROW][C]143[/C][C]0.937316056922994[/C][C]0.125367886154012[/C][C]0.0626839430770062[/C][/ROW]
[ROW][C]144[/C][C]0.892999324026027[/C][C]0.214001351947946[/C][C]0.107000675973973[/C][/ROW]
[ROW][C]145[/C][C]0.809796062222932[/C][C]0.380407875554137[/C][C]0.190203937777068[/C][/ROW]
[ROW][C]146[/C][C]0.844896674256676[/C][C]0.310206651486647[/C][C]0.155103325743324[/C][/ROW]
[ROW][C]147[/C][C]0.730160544437383[/C][C]0.539678911125234[/C][C]0.269839455562617[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98396&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98396&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3395671070572350.679134214114470.660432892942765
100.2049894134155760.4099788268311520.795010586584424
110.9021846686041270.1956306627917470.0978153313958733
120.989712130600950.02057573879810060.0102878693990503
130.9922117465458880.01557650690822320.00778825345411158
140.9959919288860740.008016142227851460.00400807111392573
150.9971437136327730.005712572734454730.00285628636722737
160.9950187024066480.009962595186703020.00498129759335151
170.9920849000950780.01583019980984470.00791509990492237
180.992406983699170.01518603260166010.00759301630083003
190.9874208938906230.02515821221875380.0125791061093769
200.9983391292075520.003321741584896280.00166087079244814
210.998845752463610.002308495072778160.00115424753638908
220.997994533867130.004010932265741440.00200546613287072
230.9968344898363170.006331020327365460.00316551016368273
240.9959534752025280.008093049594944710.00404652479747236
250.9941743968395070.01165120632098630.00582560316049316
260.9913755381821240.01724892363575150.00862446181787577
270.987098534185470.02580293162906040.0129014658145302
280.991899368827080.01620126234584140.00810063117292069
290.9905922265222570.01881554695548670.00940777347774336
300.9877378572858630.02452428542827440.0122621427141372
310.9825723967250270.0348552065499460.017427603274973
320.9755112971816740.0489774056366520.024488702818326
330.9703213924055980.05935721518880360.0296786075944018
340.9760289533534680.04794209329306440.0239710466465322
350.975952190686020.04809561862796130.0240478093139807
360.9668413605203350.06631727895933050.0331586394796652
370.9612154676013170.07756906479736640.0387845323986832
380.9764074699371310.04718506012573810.0235925300628690
390.968642136816180.06271572636764190.0313578631838209
400.9578185123502010.08436297529959780.0421814876497989
410.952201708219970.0955965835600610.0477982917800305
420.953476012207460.09304797558507850.0465239877925392
430.9884397423834080.02312051523318440.0115602576165922
440.9840371376895350.03192572462092960.0159628623104648
450.9786212031649470.04275759367010570.0213787968350528
460.971700749842420.05659850031515990.0282992501575799
470.9627798480969170.07444030380616530.0372201519030827
480.9912136781614460.01757264367710810.00878632183855404
490.9882231144947840.02355377101043250.0117768855052163
500.9852394620507020.02952107589859650.0147605379492983
510.9813473420171320.0373053159657350.0186526579828675
520.9802362043648050.03952759127039040.0197637956351952
530.9754433729669880.04911325406602440.0245566270330122
540.9706609266101490.05867814677970230.0293390733898512
550.9714871912735290.05702561745294210.0285128087264710
560.9632140150769010.07357196984619710.0367859849230986
570.965278750161450.06944249967710050.0347212498385502
580.9689696852161740.06206062956765160.0310303147838258
590.9617661637378130.07646767252437360.0382338362621868
600.9507748205948770.09845035881024660.0492251794051233
610.944503227303830.1109935453923420.0554967726961708
620.935261560539760.1294768789204810.0647384394602403
630.930150712069450.1396985758610990.0698492879305494
640.912707394672650.1745852106547010.0872926053273505
650.9155051373599360.1689897252801290.0844948626400644
660.9025319797291240.1949360405417520.097468020270876
670.8965409904598420.2069180190803160.103459009540158
680.8789909104684320.2420181790631360.121009089531568
690.861779363350650.2764412732986990.138220636649349
700.9249721207362120.1500557585275760.0750278792637881
710.9068925842138860.1862148315722280.0931074157861142
720.8954218765559550.2091562468880900.104578123444045
730.8757364868065050.2485270263869900.124263513193495
740.9732046682674150.05359066346516910.0267953317325845
750.9665707999429480.06685840011410420.0334292000570521
760.9633878749408760.0732242501182490.0366121250591245
770.971809283002230.05638143399554020.0281907169977701
780.9806703363444280.03865932731114460.0193296636555723
790.9759051461710430.04818970765791330.0240948538289566
800.975563835744490.04887232851102130.0244361642555106
810.9757504509456740.0484990981086510.0242495490543255
820.9968791818002340.006241636399531120.00312081819976556
830.9955346120346880.00893077593062450.00446538796531225
840.993921490755030.01215701848994050.00607850924497023
850.992439947929810.01512010414037810.00756005207018907
860.9948571541450120.01028569170997660.0051428458549883
870.9936422863022610.01271542739547780.00635771369773889
880.9986969504485330.00260609910293380.0013030495514669
890.998099244933330.003801510133339850.00190075506666993
900.9973446501633430.00531069967331460.0026553498366573
910.9961836822433930.007632635513214050.00381631775660703
920.9946064758263350.01078704834732990.00539352417366496
930.9930495500293180.01390089994136440.0069504499706822
940.9910387028827490.01792259423450220.0089612971172511
950.9877311132930120.02453777341397540.0122688867069877
960.9876958126195060.02460837476098840.0123041873804942
970.9967512796384990.006497440723002850.00324872036150143
980.99883617351630.002327652967401710.00116382648370085
990.9995792092814570.000841581437085470.000420790718542735
1000.9994997133171560.001000573365687030.000500286682843515
1010.9992617633979720.001476473204056310.000738236602028157
1020.9989625757589260.002074848482147720.00103742424107386
1030.9994669880316670.001066023936666110.000533011968333054
1040.9992282330303140.001543533939372670.000771766969686336
1050.9988910771601610.002217845679677910.00110892283983896
1060.9989144374846350.002171125030730290.00108556251536514
1070.9983587883339010.003282423332197760.00164121166609888
1080.9974728534904340.005054293019131320.00252714650956566
1090.9963179141911230.007364171617754070.00368208580887703
1100.9998270602534540.0003458794930915140.000172939746545757
1110.9998107991368280.000378401726344810.000189200863172405
1120.9999886385514622.27228970768488e-051.13614485384244e-05
1130.9999809117564653.81764870706583e-051.90882435353291e-05
1140.999964971633817.00567323781508e-053.50283661890754e-05
1150.9999377767880620.0001244464238752256.22232119376127e-05
1160.9998927438153540.0002145123692915640.000107256184645782
1170.9998190969121020.0003618061757957430.000180903087897872
1180.9997064955941460.0005870088117087920.000293504405854396
1190.999495497218340.001009005563319600.000504502781659801
1200.9991328210425620.001734357914875490.000867178957437744
1210.9987300198086950.00253996038261010.00126998019130505
1220.997844929765320.004310140469361230.00215507023468062
1230.9964464888453420.007107022309316870.00355351115465844
1240.996185706777530.007628586444938150.00381429322246907
1250.9946748346371630.01065033072567410.00532516536283704
1260.992340866103130.01531826779374090.00765913389687046
1270.9878278637327580.02434427253448430.0121721362672422
1280.9830331591110040.03393368177799180.0169668408889959
1290.9992272257922480.001545548415504080.00077277420775204
1300.9994956453560460.001008709287908680.000504354643954341
1310.999106396633190.001787206733621460.00089360336681073
1320.9983827984057570.003234403188485120.00161720159424256
1330.9980121772440920.003975645511816060.00198782275590803
1340.9963856216694670.00722875666106570.00361437833053285
1350.9932095282185430.01358094356291450.00679047178145724
1360.987973652602930.02405269479413880.0120263473970694
1370.9825718348371740.03485633032565240.0174281651628262
1380.9693139890477680.06137202190446440.0306860109522322
1390.9619204002630930.07615919947381490.0380795997369075
1400.9842749844692640.03145003106147150.0157250155307357
1410.9789130051861020.04217398962779650.0210869948138982
1420.964632523590580.07073495281883830.0353674764094191
1430.9373160569229940.1253678861540120.0626839430770062
1440.8929993240260270.2140013519479460.107000675973973
1450.8097960622229320.3804078755541370.190203937777068
1460.8448966742566760.3102066514866470.155103325743324
1470.7301605444373830.5396789111252340.269839455562617






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level480.345323741007194NOK
5% type I error level950.683453237410072NOK
10% type I error level1180.848920863309353NOK

\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 & 48 & 0.345323741007194 & NOK \tabularnewline
5% type I error level & 95 & 0.683453237410072 & NOK \tabularnewline
10% type I error level & 118 & 0.848920863309353 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98396&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]48[/C][C]0.345323741007194[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]95[/C][C]0.683453237410072[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]118[/C][C]0.848920863309353[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98396&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98396&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level480.345323741007194NOK
5% type I error level950.683453237410072NOK
10% type I error level1180.848920863309353NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}