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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 17:50:24 +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/t1290362653dl98v8ep6tt1mv2.htm/, Retrieved Sat, 27 Apr 2024 17:01:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98395, Retrieved Sat, 27 Apr 2024 17:01:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact211

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] [8b27277f7b82c0354d659d066108e38e] [Current]
-   PD      [Multiple Regression] [workshop 7] [2010-11-21 18:15:20] [65eb19f81eab2b6e672eafaed2a27190]
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 time10 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98395&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98395&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98395&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
KnowingPeople[t] = + 0.725680871464287 -0.0675593352860248FindingFriends[t] + 0.387096838191507Popularity[t] + 0.27399933524668Liked[t] + 0.670538732124844`Celebrity `[t] -0.00187723512719696t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
KnowingPeople[t] =  +  0.725680871464287 -0.0675593352860248FindingFriends[t] +  0.387096838191507Popularity[t] +  0.27399933524668Liked[t] +  0.670538732124844`Celebrity
`[t] -0.00187723512719696t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98395&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]KnowingPeople[t] =  +  0.725680871464287 -0.0675593352860248FindingFriends[t] +  0.387096838191507Popularity[t] +  0.27399933524668Liked[t] +  0.670538732124844`Celebrity
`[t] -0.00187723512719696t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98395&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98395&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
KnowingPeople[t] = + 0.725680871464287 -0.0675593352860248FindingFriends[t] + 0.387096838191507Popularity[t] + 0.27399933524668Liked[t] + 0.670538732124844`Celebrity `[t] -0.00187723512719696t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.7256808714642871.8032420.40240.687940.34397
FindingFriends-0.06755933528602480.122209-0.55280.5812120.290606
Popularity0.3870968381915070.0980043.94980.000126e-05
Liked0.273999335246680.1264032.16770.0317620.015881
`Celebrity `0.6705387321248440.2004073.34590.0010370.000518
t-0.001877235127196960.004821-0.38940.6975250.348762

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.725680871464287 & 1.803242 & 0.4024 & 0.68794 & 0.34397 \tabularnewline
FindingFriends & -0.0675593352860248 & 0.122209 & -0.5528 & 0.581212 & 0.290606 \tabularnewline
Popularity & 0.387096838191507 & 0.098004 & 3.9498 & 0.00012 & 6e-05 \tabularnewline
Liked & 0.27399933524668 & 0.126403 & 2.1677 & 0.031762 & 0.015881 \tabularnewline
`Celebrity
` & 0.670538732124844 & 0.200407 & 3.3459 & 0.001037 & 0.000518 \tabularnewline
t & -0.00187723512719696 & 0.004821 & -0.3894 & 0.697525 & 0.348762 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98395&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.725680871464287[/C][C]1.803242[/C][C]0.4024[/C][C]0.68794[/C][C]0.34397[/C][/ROW]
[ROW][C]FindingFriends[/C][C]-0.0675593352860248[/C][C]0.122209[/C][C]-0.5528[/C][C]0.581212[/C][C]0.290606[/C][/ROW]
[ROW][C]Popularity[/C][C]0.387096838191507[/C][C]0.098004[/C][C]3.9498[/C][C]0.00012[/C][C]6e-05[/C][/ROW]
[ROW][C]Liked[/C][C]0.27399933524668[/C][C]0.126403[/C][C]2.1677[/C][C]0.031762[/C][C]0.015881[/C][/ROW]
[ROW][C]`Celebrity
`[/C][C]0.670538732124844[/C][C]0.200407[/C][C]3.3459[/C][C]0.001037[/C][C]0.000518[/C][/ROW]
[ROW][C]t[/C][C]-0.00187723512719696[/C][C]0.004821[/C][C]-0.3894[/C][C]0.697525[/C][C]0.348762[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98395&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98395&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.7256808714642871.8032420.40240.687940.34397
FindingFriends-0.06755933528602480.122209-0.55280.5812120.290606
Popularity0.3870968381915070.0980043.94980.000126e-05
Liked0.273999335246680.1264032.16770.0317620.015881
`Celebrity `0.6705387321248440.2004073.34590.0010370.000518
t-0.001877235127196960.004821-0.38940.6975250.348762






Multiple Linear Regression - Regression Statistics
Multiple R0.653361596228413
R-squared0.426881375426139
Adjusted R-squared0.407777421273677
F-TEST (value)22.3451842492579
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.66394071596264
Sum Squared Residuals1064.48702072453

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.653361596228413 \tabularnewline
R-squared & 0.426881375426139 \tabularnewline
Adjusted R-squared & 0.407777421273677 \tabularnewline
F-TEST (value) & 22.3451842492579 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 1.11022302462516e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.66394071596264 \tabularnewline
Sum Squared Residuals & 1064.48702072453 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98395&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.653361596228413[/C][/ROW]
[ROW][C]R-squared[/C][C]0.426881375426139[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.407777421273677[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.3451842492579[/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]1.11022302462516e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.66394071596264[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1064.48702072453[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98395&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98395&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 R0.653361596228413
R-squared0.426881375426139
Adjusted R-squared0.407777421273677
F-TEST (value)22.3451842492579
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.66394071596264
Sum Squared Residuals1064.48702072453






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11410.45139872868973.54860127131033
2811.4710614549067-3.47106145490674
31214.2581301427910-2.25813014279098
4712.0665243873886-5.06652438738857
51010.2783560732208-0.278356073220814
679.84847571490158-2.84847571490158
71615.93701933116720.0629806688328206
8119.079970126950831.92002987304917
91411.06138141213822.93861858786181
1069.70776667430223-3.70776667430223
111612.48452291494423.51547708505585
121111.4617316623214-0.461731662321422
131612.13126304787483.86873695212525
14128.433393704479263.56660629552074
15711.5949806216145-4.59498062161446
16139.839145922316273.16085407768373
171111.0652486484939-0.0652486484939305
181513.77531544240551.22468455759449
1978.96597804353583-1.96597804353583
2098.233949467280070.766050532719934
2179.23935895878203-2.23935895878203
221414.5514427369664-0.551442736966389
231510.29010400859014.70989599140993
24710.2112248794902-3.2112248794902
251512.87261745548912.12738254451094
261714.14739439957942.85260560042056
271512.21947553218142.78052446781864
281412.35358685618191.6464131438181
291412.95468901789351.04531098210648
30812.5703468272331-4.57034682723309
3189.89830173662333-1.89830173662333
321412.89094053591261.10905946408744
331414.0761369770897-0.0761369770896918
34810.6235917016642-2.62359170166418
35119.02928477851651.97071522148349
361614.24171955125231.75828044874770
371012.6238956280730-2.62389562807305
3889.87108664938803-1.87108664938803
391412.52679866080281.47320133919718
401615.17874867811500.821251321884969
411312.84258169345390.157418306546089
42510.8708644355084-5.8708644355084
4386.866662141996671.13333785800333
44108.966851494712941.03314850528706
45811.4673422384099-3.46734223840995
461314.6194865968585-1.61948659685849
471513.66362873567311.33637126432689
4867.98020471026484-1.98020471026484
491210.28910022464001.71089977536002
501613.26058774485772.73941225514233
51511.2754219894159-6.27542198941592
52159.37983673688745.6201632631126
531211.64932179866640.350678201333629
5489.15451914340162-1.15451914340162
551312.07820233426230.921797665737714
561411.43725009332412.56274990667588
571210.23885662320591.76114337679405
581614.60953838427271.39046161572731
59108.419613509295331.58038649070466
601515.1506465342577-0.150646534257749
6187.809743591948210.190256408051787
621613.85135276741252.14864723258753
631912.72996119126706.27003880873303
641414.1891569676908-0.189156967690781
65610.1201837979302-4.1201837979302
661312.12511208314910.874887916850856
671511.35848373032563.64151626967441
68711.4602614394566-4.46026143945656
691313.1771159480839-0.177115948083903
7044.29500204999677-0.295002049996772
711412.57038208099071.42961791900931
721311.72675183419451.27324816580554
731111.2922395932169-0.292239593216950
741413.12069577238480.87930422761524
751212.1396806933183-0.139680693318250
761512.0026847876192.99731521238100
771410.98871014983433.01128985016574
781311.59862892638391.40137107361614
79811.7041686296174-3.70416862961742
8066.44244121896844-0.44244121896844
8178.44273762152912-1.44273762152912
821312.89697172970830.103028270291697
831313.6532951591377-0.653295159137705
841110.40405383341890.59594616658106
85510.0872709780445-5.08727097804453
861211.64235376581430.35764623418567
8788.72522855194161-0.725228551941614
881112.1954145808913-1.19541458089128
891415.4833035537605-1.48330355376054
9099.65469235524489-0.65469235524489
911013.6382772781201-3.63827727812013
921311.81401335498021.18598664501978
931615.01169588108760.988304118912432
941613.26530374047602.73469625952397
951111.0900385881168-0.0900385881167627
9689.301870273949-1.30187027394900
97410.7526721736120-6.75267217361196
9877.51182472465405-0.511824724654047
991411.72010882101462.27989117898542
1001112.3447279827578-1.34472798275778
1011715.06423733535601.93576266464398
1021514.72080142969610.279198570303885
1031713.96107112122913.03892887877095
10459.6865278400635-4.6865278400635
10545.48243599973087-1.48243599973087
1061014.8160775448898-4.81607754488981
107118.906702458298892.09329754170110
1081514.67344240996080.326557590039212
109108.789850485099681.21014951490032
11099.93518932320208-0.935189323202077
1111211.28846399366950.71153600633051
1121512.87588039630892.12411960369114
113711.5461302497212-4.54613024972121
1141313.2535421996619-0.253542199661895
1151214.5832998700977-2.58329987009773
1161413.81754140582990.182458594170141
1171413.74810483541660.251895164583363
118812.3587420798253-4.35874207982527
1191512.97015668877922.02984331122077
1201211.1467626541950.85323734580499
1211210.57713174264551.42286825735455
1221611.81354691606554.18645308393454
12398.818549919664380.181450080335623
1241512.50611433966572.49388566033429
1251513.73308695439911.26691304560094
126611.512283634381-5.512283634381
1271415.6184086188877-1.61840861888772
1281511.90506856100483.09493143899524
1291011.389792431952-1.38979243195199
13065.26770524489260.732294755107398
1311415.6108996783789-1.61089967837893
1321213.1264094703565-1.12640947035652
133810.6024092504761-2.60240925047612
1341112.7613413236404-1.76134132364042
1351313.3272177649356-0.327217764935585
136910.5489732157375-1.5489732157375
1371513.00392579177571.99607420822429
138137.665196487154055.33480351284595
1391515.1420955124395-0.142095512439505
1401411.62112101317232.37887898682771
1411610.60714637538755.39285362461246
1421412.53988344266221.46011655733781
1431412.51912109016171.48087890983832
144107.202691305799182.79730869420082
1451010.5320780995927-0.532078099592727
146411.0295253170462-7.02952531704618
14789.92447434394383-1.92447434394383
1481511.43865084671313.5613491532869
1491613.36849580844092.63150419155915
1501215.3012328757155-3.30123287571551
1511212.9870870586816-0.987087058681607
1521514.90093900858290.099060991417052
15399.14932970403997-0.149329704039975
1541213.3135714651461-1.31357146514607
1551412.42213688899281.57786311100720
156119.511909719476581.48809028052342

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 10.4513987286897 & 3.54860127131033 \tabularnewline
2 & 8 & 11.4710614549067 & -3.47106145490674 \tabularnewline
3 & 12 & 14.2581301427910 & -2.25813014279098 \tabularnewline
4 & 7 & 12.0665243873886 & -5.06652438738857 \tabularnewline
5 & 10 & 10.2783560732208 & -0.278356073220814 \tabularnewline
6 & 7 & 9.84847571490158 & -2.84847571490158 \tabularnewline
7 & 16 & 15.9370193311672 & 0.0629806688328206 \tabularnewline
8 & 11 & 9.07997012695083 & 1.92002987304917 \tabularnewline
9 & 14 & 11.0613814121382 & 2.93861858786181 \tabularnewline
10 & 6 & 9.70776667430223 & -3.70776667430223 \tabularnewline
11 & 16 & 12.4845229149442 & 3.51547708505585 \tabularnewline
12 & 11 & 11.4617316623214 & -0.461731662321422 \tabularnewline
13 & 16 & 12.1312630478748 & 3.86873695212525 \tabularnewline
14 & 12 & 8.43339370447926 & 3.56660629552074 \tabularnewline
15 & 7 & 11.5949806216145 & -4.59498062161446 \tabularnewline
16 & 13 & 9.83914592231627 & 3.16085407768373 \tabularnewline
17 & 11 & 11.0652486484939 & -0.0652486484939305 \tabularnewline
18 & 15 & 13.7753154424055 & 1.22468455759449 \tabularnewline
19 & 7 & 8.96597804353583 & -1.96597804353583 \tabularnewline
20 & 9 & 8.23394946728007 & 0.766050532719934 \tabularnewline
21 & 7 & 9.23935895878203 & -2.23935895878203 \tabularnewline
22 & 14 & 14.5514427369664 & -0.551442736966389 \tabularnewline
23 & 15 & 10.2901040085901 & 4.70989599140993 \tabularnewline
24 & 7 & 10.2112248794902 & -3.2112248794902 \tabularnewline
25 & 15 & 12.8726174554891 & 2.12738254451094 \tabularnewline
26 & 17 & 14.1473943995794 & 2.85260560042056 \tabularnewline
27 & 15 & 12.2194755321814 & 2.78052446781864 \tabularnewline
28 & 14 & 12.3535868561819 & 1.6464131438181 \tabularnewline
29 & 14 & 12.9546890178935 & 1.04531098210648 \tabularnewline
30 & 8 & 12.5703468272331 & -4.57034682723309 \tabularnewline
31 & 8 & 9.89830173662333 & -1.89830173662333 \tabularnewline
32 & 14 & 12.8909405359126 & 1.10905946408744 \tabularnewline
33 & 14 & 14.0761369770897 & -0.0761369770896918 \tabularnewline
34 & 8 & 10.6235917016642 & -2.62359170166418 \tabularnewline
35 & 11 & 9.0292847785165 & 1.97071522148349 \tabularnewline
36 & 16 & 14.2417195512523 & 1.75828044874770 \tabularnewline
37 & 10 & 12.6238956280730 & -2.62389562807305 \tabularnewline
38 & 8 & 9.87108664938803 & -1.87108664938803 \tabularnewline
39 & 14 & 12.5267986608028 & 1.47320133919718 \tabularnewline
40 & 16 & 15.1787486781150 & 0.821251321884969 \tabularnewline
41 & 13 & 12.8425816934539 & 0.157418306546089 \tabularnewline
42 & 5 & 10.8708644355084 & -5.8708644355084 \tabularnewline
43 & 8 & 6.86666214199667 & 1.13333785800333 \tabularnewline
44 & 10 & 8.96685149471294 & 1.03314850528706 \tabularnewline
45 & 8 & 11.4673422384099 & -3.46734223840995 \tabularnewline
46 & 13 & 14.6194865968585 & -1.61948659685849 \tabularnewline
47 & 15 & 13.6636287356731 & 1.33637126432689 \tabularnewline
48 & 6 & 7.98020471026484 & -1.98020471026484 \tabularnewline
49 & 12 & 10.2891002246400 & 1.71089977536002 \tabularnewline
50 & 16 & 13.2605877448577 & 2.73941225514233 \tabularnewline
51 & 5 & 11.2754219894159 & -6.27542198941592 \tabularnewline
52 & 15 & 9.3798367368874 & 5.6201632631126 \tabularnewline
53 & 12 & 11.6493217986664 & 0.350678201333629 \tabularnewline
54 & 8 & 9.15451914340162 & -1.15451914340162 \tabularnewline
55 & 13 & 12.0782023342623 & 0.921797665737714 \tabularnewline
56 & 14 & 11.4372500933241 & 2.56274990667588 \tabularnewline
57 & 12 & 10.2388566232059 & 1.76114337679405 \tabularnewline
58 & 16 & 14.6095383842727 & 1.39046161572731 \tabularnewline
59 & 10 & 8.41961350929533 & 1.58038649070466 \tabularnewline
60 & 15 & 15.1506465342577 & -0.150646534257749 \tabularnewline
61 & 8 & 7.80974359194821 & 0.190256408051787 \tabularnewline
62 & 16 & 13.8513527674125 & 2.14864723258753 \tabularnewline
63 & 19 & 12.7299611912670 & 6.27003880873303 \tabularnewline
64 & 14 & 14.1891569676908 & -0.189156967690781 \tabularnewline
65 & 6 & 10.1201837979302 & -4.1201837979302 \tabularnewline
66 & 13 & 12.1251120831491 & 0.874887916850856 \tabularnewline
67 & 15 & 11.3584837303256 & 3.64151626967441 \tabularnewline
68 & 7 & 11.4602614394566 & -4.46026143945656 \tabularnewline
69 & 13 & 13.1771159480839 & -0.177115948083903 \tabularnewline
70 & 4 & 4.29500204999677 & -0.295002049996772 \tabularnewline
71 & 14 & 12.5703820809907 & 1.42961791900931 \tabularnewline
72 & 13 & 11.7267518341945 & 1.27324816580554 \tabularnewline
73 & 11 & 11.2922395932169 & -0.292239593216950 \tabularnewline
74 & 14 & 13.1206957723848 & 0.87930422761524 \tabularnewline
75 & 12 & 12.1396806933183 & -0.139680693318250 \tabularnewline
76 & 15 & 12.002684787619 & 2.99731521238100 \tabularnewline
77 & 14 & 10.9887101498343 & 3.01128985016574 \tabularnewline
78 & 13 & 11.5986289263839 & 1.40137107361614 \tabularnewline
79 & 8 & 11.7041686296174 & -3.70416862961742 \tabularnewline
80 & 6 & 6.44244121896844 & -0.44244121896844 \tabularnewline
81 & 7 & 8.44273762152912 & -1.44273762152912 \tabularnewline
82 & 13 & 12.8969717297083 & 0.103028270291697 \tabularnewline
83 & 13 & 13.6532951591377 & -0.653295159137705 \tabularnewline
84 & 11 & 10.4040538334189 & 0.59594616658106 \tabularnewline
85 & 5 & 10.0872709780445 & -5.08727097804453 \tabularnewline
86 & 12 & 11.6423537658143 & 0.35764623418567 \tabularnewline
87 & 8 & 8.72522855194161 & -0.725228551941614 \tabularnewline
88 & 11 & 12.1954145808913 & -1.19541458089128 \tabularnewline
89 & 14 & 15.4833035537605 & -1.48330355376054 \tabularnewline
90 & 9 & 9.65469235524489 & -0.65469235524489 \tabularnewline
91 & 10 & 13.6382772781201 & -3.63827727812013 \tabularnewline
92 & 13 & 11.8140133549802 & 1.18598664501978 \tabularnewline
93 & 16 & 15.0116958810876 & 0.988304118912432 \tabularnewline
94 & 16 & 13.2653037404760 & 2.73469625952397 \tabularnewline
95 & 11 & 11.0900385881168 & -0.0900385881167627 \tabularnewline
96 & 8 & 9.301870273949 & -1.30187027394900 \tabularnewline
97 & 4 & 10.7526721736120 & -6.75267217361196 \tabularnewline
98 & 7 & 7.51182472465405 & -0.511824724654047 \tabularnewline
99 & 14 & 11.7201088210146 & 2.27989117898542 \tabularnewline
100 & 11 & 12.3447279827578 & -1.34472798275778 \tabularnewline
101 & 17 & 15.0642373353560 & 1.93576266464398 \tabularnewline
102 & 15 & 14.7208014296961 & 0.279198570303885 \tabularnewline
103 & 17 & 13.9610711212291 & 3.03892887877095 \tabularnewline
104 & 5 & 9.6865278400635 & -4.6865278400635 \tabularnewline
105 & 4 & 5.48243599973087 & -1.48243599973087 \tabularnewline
106 & 10 & 14.8160775448898 & -4.81607754488981 \tabularnewline
107 & 11 & 8.90670245829889 & 2.09329754170110 \tabularnewline
108 & 15 & 14.6734424099608 & 0.326557590039212 \tabularnewline
109 & 10 & 8.78985048509968 & 1.21014951490032 \tabularnewline
110 & 9 & 9.93518932320208 & -0.935189323202077 \tabularnewline
111 & 12 & 11.2884639936695 & 0.71153600633051 \tabularnewline
112 & 15 & 12.8758803963089 & 2.12411960369114 \tabularnewline
113 & 7 & 11.5461302497212 & -4.54613024972121 \tabularnewline
114 & 13 & 13.2535421996619 & -0.253542199661895 \tabularnewline
115 & 12 & 14.5832998700977 & -2.58329987009773 \tabularnewline
116 & 14 & 13.8175414058299 & 0.182458594170141 \tabularnewline
117 & 14 & 13.7481048354166 & 0.251895164583363 \tabularnewline
118 & 8 & 12.3587420798253 & -4.35874207982527 \tabularnewline
119 & 15 & 12.9701566887792 & 2.02984331122077 \tabularnewline
120 & 12 & 11.146762654195 & 0.85323734580499 \tabularnewline
121 & 12 & 10.5771317426455 & 1.42286825735455 \tabularnewline
122 & 16 & 11.8135469160655 & 4.18645308393454 \tabularnewline
123 & 9 & 8.81854991966438 & 0.181450080335623 \tabularnewline
124 & 15 & 12.5061143396657 & 2.49388566033429 \tabularnewline
125 & 15 & 13.7330869543991 & 1.26691304560094 \tabularnewline
126 & 6 & 11.512283634381 & -5.512283634381 \tabularnewline
127 & 14 & 15.6184086188877 & -1.61840861888772 \tabularnewline
128 & 15 & 11.9050685610048 & 3.09493143899524 \tabularnewline
129 & 10 & 11.389792431952 & -1.38979243195199 \tabularnewline
130 & 6 & 5.2677052448926 & 0.732294755107398 \tabularnewline
131 & 14 & 15.6108996783789 & -1.61089967837893 \tabularnewline
132 & 12 & 13.1264094703565 & -1.12640947035652 \tabularnewline
133 & 8 & 10.6024092504761 & -2.60240925047612 \tabularnewline
134 & 11 & 12.7613413236404 & -1.76134132364042 \tabularnewline
135 & 13 & 13.3272177649356 & -0.327217764935585 \tabularnewline
136 & 9 & 10.5489732157375 & -1.5489732157375 \tabularnewline
137 & 15 & 13.0039257917757 & 1.99607420822429 \tabularnewline
138 & 13 & 7.66519648715405 & 5.33480351284595 \tabularnewline
139 & 15 & 15.1420955124395 & -0.142095512439505 \tabularnewline
140 & 14 & 11.6211210131723 & 2.37887898682771 \tabularnewline
141 & 16 & 10.6071463753875 & 5.39285362461246 \tabularnewline
142 & 14 & 12.5398834426622 & 1.46011655733781 \tabularnewline
143 & 14 & 12.5191210901617 & 1.48087890983832 \tabularnewline
144 & 10 & 7.20269130579918 & 2.79730869420082 \tabularnewline
145 & 10 & 10.5320780995927 & -0.532078099592727 \tabularnewline
146 & 4 & 11.0295253170462 & -7.02952531704618 \tabularnewline
147 & 8 & 9.92447434394383 & -1.92447434394383 \tabularnewline
148 & 15 & 11.4386508467131 & 3.5613491532869 \tabularnewline
149 & 16 & 13.3684958084409 & 2.63150419155915 \tabularnewline
150 & 12 & 15.3012328757155 & -3.30123287571551 \tabularnewline
151 & 12 & 12.9870870586816 & -0.987087058681607 \tabularnewline
152 & 15 & 14.9009390085829 & 0.099060991417052 \tabularnewline
153 & 9 & 9.14932970403997 & -0.149329704039975 \tabularnewline
154 & 12 & 13.3135714651461 & -1.31357146514607 \tabularnewline
155 & 14 & 12.4221368889928 & 1.57786311100720 \tabularnewline
156 & 11 & 9.51190971947658 & 1.48809028052342 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98395&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]14[/C][C]10.4513987286897[/C][C]3.54860127131033[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]11.4710614549067[/C][C]-3.47106145490674[/C][/ROW]
[ROW][C]3[/C][C]12[/C][C]14.2581301427910[/C][C]-2.25813014279098[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]12.0665243873886[/C][C]-5.06652438738857[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.2783560732208[/C][C]-0.278356073220814[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]9.84847571490158[/C][C]-2.84847571490158[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]15.9370193311672[/C][C]0.0629806688328206[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]9.07997012695083[/C][C]1.92002987304917[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]11.0613814121382[/C][C]2.93861858786181[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]9.70776667430223[/C][C]-3.70776667430223[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]12.4845229149442[/C][C]3.51547708505585[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]11.4617316623214[/C][C]-0.461731662321422[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]12.1312630478748[/C][C]3.86873695212525[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]8.43339370447926[/C][C]3.56660629552074[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]11.5949806216145[/C][C]-4.59498062161446[/C][/ROW]
[ROW][C]16[/C][C]13[/C][C]9.83914592231627[/C][C]3.16085407768373[/C][/ROW]
[ROW][C]17[/C][C]11[/C][C]11.0652486484939[/C][C]-0.0652486484939305[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]13.7753154424055[/C][C]1.22468455759449[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]8.96597804353583[/C][C]-1.96597804353583[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]8.23394946728007[/C][C]0.766050532719934[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]9.23935895878203[/C][C]-2.23935895878203[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]14.5514427369664[/C][C]-0.551442736966389[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]10.2901040085901[/C][C]4.70989599140993[/C][/ROW]
[ROW][C]24[/C][C]7[/C][C]10.2112248794902[/C][C]-3.2112248794902[/C][/ROW]
[ROW][C]25[/C][C]15[/C][C]12.8726174554891[/C][C]2.12738254451094[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]14.1473943995794[/C][C]2.85260560042056[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]12.2194755321814[/C][C]2.78052446781864[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]12.3535868561819[/C][C]1.6464131438181[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.9546890178935[/C][C]1.04531098210648[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]12.5703468272331[/C][C]-4.57034682723309[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]9.89830173662333[/C][C]-1.89830173662333[/C][/ROW]
[ROW][C]32[/C][C]14[/C][C]12.8909405359126[/C][C]1.10905946408744[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.0761369770897[/C][C]-0.0761369770896918[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]10.6235917016642[/C][C]-2.62359170166418[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]9.0292847785165[/C][C]1.97071522148349[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.2417195512523[/C][C]1.75828044874770[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.6238956280730[/C][C]-2.62389562807305[/C][/ROW]
[ROW][C]38[/C][C]8[/C][C]9.87108664938803[/C][C]-1.87108664938803[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]12.5267986608028[/C][C]1.47320133919718[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.1787486781150[/C][C]0.821251321884969[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]12.8425816934539[/C][C]0.157418306546089[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]10.8708644355084[/C][C]-5.8708644355084[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]6.86666214199667[/C][C]1.13333785800333[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]8.96685149471294[/C][C]1.03314850528706[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]11.4673422384099[/C][C]-3.46734223840995[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]14.6194865968585[/C][C]-1.61948659685849[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]13.6636287356731[/C][C]1.33637126432689[/C][/ROW]
[ROW][C]48[/C][C]6[/C][C]7.98020471026484[/C][C]-1.98020471026484[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]10.2891002246400[/C][C]1.71089977536002[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]13.2605877448577[/C][C]2.73941225514233[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]11.2754219894159[/C][C]-6.27542198941592[/C][/ROW]
[ROW][C]52[/C][C]15[/C][C]9.3798367368874[/C][C]5.6201632631126[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]11.6493217986664[/C][C]0.350678201333629[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]9.15451914340162[/C][C]-1.15451914340162[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]12.0782023342623[/C][C]0.921797665737714[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]11.4372500933241[/C][C]2.56274990667588[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]10.2388566232059[/C][C]1.76114337679405[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.6095383842727[/C][C]1.39046161572731[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]8.41961350929533[/C][C]1.58038649070466[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.1506465342577[/C][C]-0.150646534257749[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]7.80974359194821[/C][C]0.190256408051787[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.8513527674125[/C][C]2.14864723258753[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]12.7299611912670[/C][C]6.27003880873303[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.1891569676908[/C][C]-0.189156967690781[/C][/ROW]
[ROW][C]65[/C][C]6[/C][C]10.1201837979302[/C][C]-4.1201837979302[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.1251120831491[/C][C]0.874887916850856[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]11.3584837303256[/C][C]3.64151626967441[/C][/ROW]
[ROW][C]68[/C][C]7[/C][C]11.4602614394566[/C][C]-4.46026143945656[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]13.1771159480839[/C][C]-0.177115948083903[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]4.29500204999677[/C][C]-0.295002049996772[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]12.5703820809907[/C][C]1.42961791900931[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]11.7267518341945[/C][C]1.27324816580554[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]11.2922395932169[/C][C]-0.292239593216950[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]13.1206957723848[/C][C]0.87930422761524[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.1396806933183[/C][C]-0.139680693318250[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]12.002684787619[/C][C]2.99731521238100[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]10.9887101498343[/C][C]3.01128985016574[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]11.5986289263839[/C][C]1.40137107361614[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]11.7041686296174[/C][C]-3.70416862961742[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]6.44244121896844[/C][C]-0.44244121896844[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]8.44273762152912[/C][C]-1.44273762152912[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]12.8969717297083[/C][C]0.103028270291697[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]13.6532951591377[/C][C]-0.653295159137705[/C][/ROW]
[ROW][C]84[/C][C]11[/C][C]10.4040538334189[/C][C]0.59594616658106[/C][/ROW]
[ROW][C]85[/C][C]5[/C][C]10.0872709780445[/C][C]-5.08727097804453[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]11.6423537658143[/C][C]0.35764623418567[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]8.72522855194161[/C][C]-0.725228551941614[/C][/ROW]
[ROW][C]88[/C][C]11[/C][C]12.1954145808913[/C][C]-1.19541458089128[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]15.4833035537605[/C][C]-1.48330355376054[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]9.65469235524489[/C][C]-0.65469235524489[/C][/ROW]
[ROW][C]91[/C][C]10[/C][C]13.6382772781201[/C][C]-3.63827727812013[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]11.8140133549802[/C][C]1.18598664501978[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.0116958810876[/C][C]0.988304118912432[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]13.2653037404760[/C][C]2.73469625952397[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]11.0900385881168[/C][C]-0.0900385881167627[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]9.301870273949[/C][C]-1.30187027394900[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]10.7526721736120[/C][C]-6.75267217361196[/C][/ROW]
[ROW][C]98[/C][C]7[/C][C]7.51182472465405[/C][C]-0.511824724654047[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.7201088210146[/C][C]2.27989117898542[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]12.3447279827578[/C][C]-1.34472798275778[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.0642373353560[/C][C]1.93576266464398[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]14.7208014296961[/C][C]0.279198570303885[/C][/ROW]
[ROW][C]103[/C][C]17[/C][C]13.9610711212291[/C][C]3.03892887877095[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]9.6865278400635[/C][C]-4.6865278400635[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]5.48243599973087[/C][C]-1.48243599973087[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]14.8160775448898[/C][C]-4.81607754488981[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]8.90670245829889[/C][C]2.09329754170110[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]14.6734424099608[/C][C]0.326557590039212[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]8.78985048509968[/C][C]1.21014951490032[/C][/ROW]
[ROW][C]110[/C][C]9[/C][C]9.93518932320208[/C][C]-0.935189323202077[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.2884639936695[/C][C]0.71153600633051[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.8758803963089[/C][C]2.12411960369114[/C][/ROW]
[ROW][C]113[/C][C]7[/C][C]11.5461302497212[/C][C]-4.54613024972121[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]13.2535421996619[/C][C]-0.253542199661895[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.5832998700977[/C][C]-2.58329987009773[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]13.8175414058299[/C][C]0.182458594170141[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.7481048354166[/C][C]0.251895164583363[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]12.3587420798253[/C][C]-4.35874207982527[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]12.9701566887792[/C][C]2.02984331122077[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.146762654195[/C][C]0.85323734580499[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]10.5771317426455[/C][C]1.42286825735455[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]11.8135469160655[/C][C]4.18645308393454[/C][/ROW]
[ROW][C]123[/C][C]9[/C][C]8.81854991966438[/C][C]0.181450080335623[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]12.5061143396657[/C][C]2.49388566033429[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]13.7330869543991[/C][C]1.26691304560094[/C][/ROW]
[ROW][C]126[/C][C]6[/C][C]11.512283634381[/C][C]-5.512283634381[/C][/ROW]
[ROW][C]127[/C][C]14[/C][C]15.6184086188877[/C][C]-1.61840861888772[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]11.9050685610048[/C][C]3.09493143899524[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]11.389792431952[/C][C]-1.38979243195199[/C][/ROW]
[ROW][C]130[/C][C]6[/C][C]5.2677052448926[/C][C]0.732294755107398[/C][/ROW]
[ROW][C]131[/C][C]14[/C][C]15.6108996783789[/C][C]-1.61089967837893[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]13.1264094703565[/C][C]-1.12640947035652[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]10.6024092504761[/C][C]-2.60240925047612[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]12.7613413236404[/C][C]-1.76134132364042[/C][/ROW]
[ROW][C]135[/C][C]13[/C][C]13.3272177649356[/C][C]-0.327217764935585[/C][/ROW]
[ROW][C]136[/C][C]9[/C][C]10.5489732157375[/C][C]-1.5489732157375[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]13.0039257917757[/C][C]1.99607420822429[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]7.66519648715405[/C][C]5.33480351284595[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]15.1420955124395[/C][C]-0.142095512439505[/C][/ROW]
[ROW][C]140[/C][C]14[/C][C]11.6211210131723[/C][C]2.37887898682771[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]10.6071463753875[/C][C]5.39285362461246[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]12.5398834426622[/C][C]1.46011655733781[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]12.5191210901617[/C][C]1.48087890983832[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]7.20269130579918[/C][C]2.79730869420082[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]10.5320780995927[/C][C]-0.532078099592727[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]11.0295253170462[/C][C]-7.02952531704618[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]9.92447434394383[/C][C]-1.92447434394383[/C][/ROW]
[ROW][C]148[/C][C]15[/C][C]11.4386508467131[/C][C]3.5613491532869[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]13.3684958084409[/C][C]2.63150419155915[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]15.3012328757155[/C][C]-3.30123287571551[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]12.9870870586816[/C][C]-0.987087058681607[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]14.9009390085829[/C][C]0.099060991417052[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.14932970403997[/C][C]-0.149329704039975[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.3135714651461[/C][C]-1.31357146514607[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.4221368889928[/C][C]1.57786311100720[/C][/ROW]
[ROW][C]156[/C][C]11[/C][C]9.51190971947658[/C][C]1.48809028052342[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98395&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98395&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
11410.45139872868973.54860127131033
2811.4710614549067-3.47106145490674
31214.2581301427910-2.25813014279098
4712.0665243873886-5.06652438738857
51010.2783560732208-0.278356073220814
679.84847571490158-2.84847571490158
71615.93701933116720.0629806688328206
8119.079970126950831.92002987304917
91411.06138141213822.93861858786181
1069.70776667430223-3.70776667430223
111612.48452291494423.51547708505585
121111.4617316623214-0.461731662321422
131612.13126304787483.86873695212525
14128.433393704479263.56660629552074
15711.5949806216145-4.59498062161446
16139.839145922316273.16085407768373
171111.0652486484939-0.0652486484939305
181513.77531544240551.22468455759449
1978.96597804353583-1.96597804353583
2098.233949467280070.766050532719934
2179.23935895878203-2.23935895878203
221414.5514427369664-0.551442736966389
231510.29010400859014.70989599140993
24710.2112248794902-3.2112248794902
251512.87261745548912.12738254451094
261714.14739439957942.85260560042056
271512.21947553218142.78052446781864
281412.35358685618191.6464131438181
291412.95468901789351.04531098210648
30812.5703468272331-4.57034682723309
3189.89830173662333-1.89830173662333
321412.89094053591261.10905946408744
331414.0761369770897-0.0761369770896918
34810.6235917016642-2.62359170166418
35119.02928477851651.97071522148349
361614.24171955125231.75828044874770
371012.6238956280730-2.62389562807305
3889.87108664938803-1.87108664938803
391412.52679866080281.47320133919718
401615.17874867811500.821251321884969
411312.84258169345390.157418306546089
42510.8708644355084-5.8708644355084
4386.866662141996671.13333785800333
44108.966851494712941.03314850528706
45811.4673422384099-3.46734223840995
461314.6194865968585-1.61948659685849
471513.66362873567311.33637126432689
4867.98020471026484-1.98020471026484
491210.28910022464001.71089977536002
501613.26058774485772.73941225514233
51511.2754219894159-6.27542198941592
52159.37983673688745.6201632631126
531211.64932179866640.350678201333629
5489.15451914340162-1.15451914340162
551312.07820233426230.921797665737714
561411.43725009332412.56274990667588
571210.23885662320591.76114337679405
581614.60953838427271.39046161572731
59108.419613509295331.58038649070466
601515.1506465342577-0.150646534257749
6187.809743591948210.190256408051787
621613.85135276741252.14864723258753
631912.72996119126706.27003880873303
641414.1891569676908-0.189156967690781
65610.1201837979302-4.1201837979302
661312.12511208314910.874887916850856
671511.35848373032563.64151626967441
68711.4602614394566-4.46026143945656
691313.1771159480839-0.177115948083903
7044.29500204999677-0.295002049996772
711412.57038208099071.42961791900931
721311.72675183419451.27324816580554
731111.2922395932169-0.292239593216950
741413.12069577238480.87930422761524
751212.1396806933183-0.139680693318250
761512.0026847876192.99731521238100
771410.98871014983433.01128985016574
781311.59862892638391.40137107361614
79811.7041686296174-3.70416862961742
8066.44244121896844-0.44244121896844
8178.44273762152912-1.44273762152912
821312.89697172970830.103028270291697
831313.6532951591377-0.653295159137705
841110.40405383341890.59594616658106
85510.0872709780445-5.08727097804453
861211.64235376581430.35764623418567
8788.72522855194161-0.725228551941614
881112.1954145808913-1.19541458089128
891415.4833035537605-1.48330355376054
9099.65469235524489-0.65469235524489
911013.6382772781201-3.63827727812013
921311.81401335498021.18598664501978
931615.01169588108760.988304118912432
941613.26530374047602.73469625952397
951111.0900385881168-0.0900385881167627
9689.301870273949-1.30187027394900
97410.7526721736120-6.75267217361196
9877.51182472465405-0.511824724654047
991411.72010882101462.27989117898542
1001112.3447279827578-1.34472798275778
1011715.06423733535601.93576266464398
1021514.72080142969610.279198570303885
1031713.96107112122913.03892887877095
10459.6865278400635-4.6865278400635
10545.48243599973087-1.48243599973087
1061014.8160775448898-4.81607754488981
107118.906702458298892.09329754170110
1081514.67344240996080.326557590039212
109108.789850485099681.21014951490032
11099.93518932320208-0.935189323202077
1111211.28846399366950.71153600633051
1121512.87588039630892.12411960369114
113711.5461302497212-4.54613024972121
1141313.2535421996619-0.253542199661895
1151214.5832998700977-2.58329987009773
1161413.81754140582990.182458594170141
1171413.74810483541660.251895164583363
118812.3587420798253-4.35874207982527
1191512.97015668877922.02984331122077
1201211.1467626541950.85323734580499
1211210.57713174264551.42286825735455
1221611.81354691606554.18645308393454
12398.818549919664380.181450080335623
1241512.50611433966572.49388566033429
1251513.73308695439911.26691304560094
126611.512283634381-5.512283634381
1271415.6184086188877-1.61840861888772
1281511.90506856100483.09493143899524
1291011.389792431952-1.38979243195199
13065.26770524489260.732294755107398
1311415.6108996783789-1.61089967837893
1321213.1264094703565-1.12640947035652
133810.6024092504761-2.60240925047612
1341112.7613413236404-1.76134132364042
1351313.3272177649356-0.327217764935585
136910.5489732157375-1.5489732157375
1371513.00392579177571.99607420822429
138137.665196487154055.33480351284595
1391515.1420955124395-0.142095512439505
1401411.62112101317232.37887898682771
1411610.60714637538755.39285362461246
1421412.53988344266221.46011655733781
1431412.51912109016171.48087890983832
144107.202691305799182.79730869420082
1451010.5320780995927-0.532078099592727
146411.0295253170462-7.02952531704618
14789.92447434394383-1.92447434394383
1481511.43865084671313.5613491532869
1491613.36849580844092.63150419155915
1501215.3012328757155-3.30123287571551
1511212.9870870586816-0.987087058681607
1521514.90093900858290.099060991417052
15399.14932970403997-0.149329704039975
1541213.3135714651461-1.31357146514607
1551412.42213688899281.57786311100720
156119.511909719476581.48809028052342






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6729937495750810.6540125008498370.327006250424919
100.6424956550479360.7150086899041290.357504344952064
110.508496084242660.983007831514680.49150391575734
120.4289002789919590.8578005579839180.571099721008041
130.775398321535920.4492033569281590.224601678464079
140.7115979448382650.5768041103234690.288402055161735
150.909063239877340.1818735202453190.0909367601226594
160.8738435652011180.2523128695977640.126156434798882
170.8240576627318020.3518846745363970.175942337268198
180.8105644210139170.3788711579721650.189435578986083
190.8079249986708450.384150002658310.192075001329155
200.772750701215810.4544985975683810.227249298784190
210.8926919662149020.2146160675701960.107308033785098
220.8560851099326380.2878297801347230.143914890067362
230.9039594193991310.1920811612017380.096040580600869
240.9275997543610270.1448004912779450.0724002456389724
250.9115490143147160.1769019713705670.0884509856852835
260.8945058254547540.2109883490904920.105494174545246
270.8712487312519110.2575025374961770.128751268748088
280.8372298871877490.3255402256245020.162770112812251
290.7975038097428950.404992380514210.202496190257105
300.8863549147467920.2272901705064160.113645085253208
310.861978219695750.2760435606085010.138021780304251
320.8279023867875470.3441952264249050.172097613212453
330.7923348963300180.4153302073399630.207665103669982
340.8037969138011740.3924061723976530.196203086198826
350.7706032459234230.4587935081531540.229396754076577
360.7416520321802470.5166959356395050.258347967819753
370.7721714000004520.4556571999990960.227828599999548
380.7448095901314990.5103808197370020.255190409868501
390.7086819630795240.5826360738409520.291318036920476
400.6619486744423020.6761026511153970.338051325557699
410.6102281422743410.7795437154513190.389771857725659
420.7338434905463860.5323130189072270.266156509453614
430.6993090095870140.6013819808259720.300690990412986
440.6547605518126960.6904788963746080.345239448187304
450.6927268852682720.6145462294634560.307273114731728
460.6567761115493870.6864477769012260.343223888450613
470.6436087558173730.7127824883652530.356391244182627
480.6136708557547040.7726582884905920.386329144245296
490.5778938773207810.8442122453584390.422106122679219
500.5673142686556290.8653714626887420.432685731344371
510.7823615236650340.4352769526699330.217638476334966
520.8807978701017720.2384042597964560.119202129898228
530.8544386199484920.2911227601030170.145561380051508
540.8347145121049770.3305709757900460.165285487895023
550.8109903839231160.3780192321537670.189009616076884
560.8084706480055430.3830587039889140.191529351994457
570.7874851091284710.4250297817430580.212514890871529
580.7569173489869210.4861653020261570.243082651013079
590.728652606348770.542694787302460.27134739365123
600.6887175550377580.6225648899244840.311282444962242
610.6488965629620730.7022068740758550.351103437037927
620.6356458282426560.7287083435146880.364354171757344
630.8070194117786250.3859611764427490.192980588221375
640.774291209121670.451417581756660.22570879087833
650.831428350040050.33714329991990.16857164995995
660.8026027451479450.394794509704110.197397254852055
670.8324677166342080.3350645667315840.167532283365792
680.8814614405535340.2370771188929320.118538559446466
690.8563278746926530.2873442506146940.143672125307347
700.8297317776907640.3405364446184720.170268222309236
710.805518609228030.3889627815439390.194481390771970
720.778343174736870.4433136505262610.221656825263130
730.7466574991363320.5066850017273370.253342500863668
740.7268403518195170.5463192963609670.273159648180483
750.6889990984709060.6220018030581870.311000901529093
760.7080060397757240.5839879204485520.291993960224276
770.7259680852698860.5480638294602290.274031914730114
780.6996153744320040.6007692511359920.300384625567996
790.7414910677418140.5170178645163720.258508932258186
800.703834887912490.5923302241750210.296165112087511
810.6771062889007810.6457874221984380.322893711099219
820.6334693136281380.7330613727437240.366530686371862
830.5959250270951570.8081499458096860.404074972904843
840.558110608287760.883778783424480.44188939171224
850.7074072202285970.5851855595428060.292592779771403
860.6685501567530920.6628996864938160.331449843246908
870.6276094390721750.744781121855650.372390560927825
880.5897286017918030.8205427964163950.410271398208197
890.5577971339170570.8844057321658860.442202866082943
900.5129863330748710.9740273338502580.487013666925129
910.54742422561970.90515154876060.4525757743803
920.5390245091951570.9219509816096870.460975490804843
930.5039530088167590.9920939823664820.496046991183241
940.5070700887924870.9858598224150270.492929911207513
950.4585867371293050.917173474258610.541413262870695
960.4237665889373720.8475331778747440.576233411062628
970.6235438467588560.7529123064822880.376456153241144
980.5809058401503390.8381883196993230.419094159849661
990.5818867706024180.8362264587951640.418113229397582
1000.543945241728950.91210951654210.45605475827105
1010.5347129002782240.9305741994435530.465287099721776
1020.4880754916493750.976150983298750.511924508350625
1030.5715825898046180.8568348203907640.428417410195382
1040.7178603955484970.5642792089030070.282139604451503
1050.7050685885750370.5898628228499260.294931411424963
1060.748153711800750.5036925763985020.251846288199251
1070.7244146591050420.5511706817899170.275585340894958
1080.7038109042316390.5923781915367220.296189095768361
1090.6669825754105070.6660348491789860.333017424589493
1100.6186659955203910.7626680089592170.381334004479608
1110.5769970701544090.8460058596911810.423002929845591
1120.6337926872417860.7324146255164280.366207312758214
1130.6921354602211240.6157290795577530.307864539778876
1140.6430668969261940.7138662061476120.356933103073806
1150.608406039046130.783187921907740.39159396095387
1160.5555082043603590.8889835912792830.444491795639641
1170.502563296519680.994873406960640.49743670348032
1180.5222558469509640.9554883060980710.477744153049036
1190.4983848655333840.9967697310667670.501615134466616
1200.4610544725420740.9221089450841470.538945527457926
1210.4110359873098520.8220719746197040.588964012690148
1220.4388687488593650.8777374977187310.561131251140635
1230.3808389496985850.7616778993971690.619161050301415
1240.3842359317151240.7684718634302490.615764068284876
1250.339713312088710.679426624177420.66028668791129
1260.5349922167725010.9300155664549980.465007783227499
1270.4750546059494010.9501092118988020.524945394050599
1280.5166955812491970.9666088375016070.483304418750803
1290.4553800649483020.9107601298966040.544619935051698
1300.3996884268403680.7993768536807350.600311573159632
1310.3403609041177790.6807218082355580.659639095882221
1320.2953249895339310.5906499790678620.704675010466069
1330.3137728463527290.6275456927054570.686227153647271
1340.2782415258638580.5564830517277170.721758474136142
1350.2282789514617750.456557902923550.771721048538225
1360.2987177162799230.5974354325598470.701282283720077
1370.239709071401850.47941814280370.76029092859815
1380.2570508820208550.514101764041710.742949117979145
1390.2547863319828260.5095726639656520.745213668017174
1400.2112340439008980.4224680878017960.788765956099102
1410.2952770237952570.5905540475905140.704722976204743
1420.2642379389116510.5284758778233010.73576206108835
1430.2007501986634410.4015003973268830.799249801336559
1440.2167330515590740.4334661031181480.783266948440926
1450.1414985464579490.2829970929158980.858501453542051
1460.660013931173890.679972137652220.33998606882611
1470.5452941360727530.9094117278544930.454705863927247

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.672993749575081 & 0.654012500849837 & 0.327006250424919 \tabularnewline
10 & 0.642495655047936 & 0.715008689904129 & 0.357504344952064 \tabularnewline
11 & 0.50849608424266 & 0.98300783151468 & 0.49150391575734 \tabularnewline
12 & 0.428900278991959 & 0.857800557983918 & 0.571099721008041 \tabularnewline
13 & 0.77539832153592 & 0.449203356928159 & 0.224601678464079 \tabularnewline
14 & 0.711597944838265 & 0.576804110323469 & 0.288402055161735 \tabularnewline
15 & 0.90906323987734 & 0.181873520245319 & 0.0909367601226594 \tabularnewline
16 & 0.873843565201118 & 0.252312869597764 & 0.126156434798882 \tabularnewline
17 & 0.824057662731802 & 0.351884674536397 & 0.175942337268198 \tabularnewline
18 & 0.810564421013917 & 0.378871157972165 & 0.189435578986083 \tabularnewline
19 & 0.807924998670845 & 0.38415000265831 & 0.192075001329155 \tabularnewline
20 & 0.77275070121581 & 0.454498597568381 & 0.227249298784190 \tabularnewline
21 & 0.892691966214902 & 0.214616067570196 & 0.107308033785098 \tabularnewline
22 & 0.856085109932638 & 0.287829780134723 & 0.143914890067362 \tabularnewline
23 & 0.903959419399131 & 0.192081161201738 & 0.096040580600869 \tabularnewline
24 & 0.927599754361027 & 0.144800491277945 & 0.0724002456389724 \tabularnewline
25 & 0.911549014314716 & 0.176901971370567 & 0.0884509856852835 \tabularnewline
26 & 0.894505825454754 & 0.210988349090492 & 0.105494174545246 \tabularnewline
27 & 0.871248731251911 & 0.257502537496177 & 0.128751268748088 \tabularnewline
28 & 0.837229887187749 & 0.325540225624502 & 0.162770112812251 \tabularnewline
29 & 0.797503809742895 & 0.40499238051421 & 0.202496190257105 \tabularnewline
30 & 0.886354914746792 & 0.227290170506416 & 0.113645085253208 \tabularnewline
31 & 0.86197821969575 & 0.276043560608501 & 0.138021780304251 \tabularnewline
32 & 0.827902386787547 & 0.344195226424905 & 0.172097613212453 \tabularnewline
33 & 0.792334896330018 & 0.415330207339963 & 0.207665103669982 \tabularnewline
34 & 0.803796913801174 & 0.392406172397653 & 0.196203086198826 \tabularnewline
35 & 0.770603245923423 & 0.458793508153154 & 0.229396754076577 \tabularnewline
36 & 0.741652032180247 & 0.516695935639505 & 0.258347967819753 \tabularnewline
37 & 0.772171400000452 & 0.455657199999096 & 0.227828599999548 \tabularnewline
38 & 0.744809590131499 & 0.510380819737002 & 0.255190409868501 \tabularnewline
39 & 0.708681963079524 & 0.582636073840952 & 0.291318036920476 \tabularnewline
40 & 0.661948674442302 & 0.676102651115397 & 0.338051325557699 \tabularnewline
41 & 0.610228142274341 & 0.779543715451319 & 0.389771857725659 \tabularnewline
42 & 0.733843490546386 & 0.532313018907227 & 0.266156509453614 \tabularnewline
43 & 0.699309009587014 & 0.601381980825972 & 0.300690990412986 \tabularnewline
44 & 0.654760551812696 & 0.690478896374608 & 0.345239448187304 \tabularnewline
45 & 0.692726885268272 & 0.614546229463456 & 0.307273114731728 \tabularnewline
46 & 0.656776111549387 & 0.686447776901226 & 0.343223888450613 \tabularnewline
47 & 0.643608755817373 & 0.712782488365253 & 0.356391244182627 \tabularnewline
48 & 0.613670855754704 & 0.772658288490592 & 0.386329144245296 \tabularnewline
49 & 0.577893877320781 & 0.844212245358439 & 0.422106122679219 \tabularnewline
50 & 0.567314268655629 & 0.865371462688742 & 0.432685731344371 \tabularnewline
51 & 0.782361523665034 & 0.435276952669933 & 0.217638476334966 \tabularnewline
52 & 0.880797870101772 & 0.238404259796456 & 0.119202129898228 \tabularnewline
53 & 0.854438619948492 & 0.291122760103017 & 0.145561380051508 \tabularnewline
54 & 0.834714512104977 & 0.330570975790046 & 0.165285487895023 \tabularnewline
55 & 0.810990383923116 & 0.378019232153767 & 0.189009616076884 \tabularnewline
56 & 0.808470648005543 & 0.383058703988914 & 0.191529351994457 \tabularnewline
57 & 0.787485109128471 & 0.425029781743058 & 0.212514890871529 \tabularnewline
58 & 0.756917348986921 & 0.486165302026157 & 0.243082651013079 \tabularnewline
59 & 0.72865260634877 & 0.54269478730246 & 0.27134739365123 \tabularnewline
60 & 0.688717555037758 & 0.622564889924484 & 0.311282444962242 \tabularnewline
61 & 0.648896562962073 & 0.702206874075855 & 0.351103437037927 \tabularnewline
62 & 0.635645828242656 & 0.728708343514688 & 0.364354171757344 \tabularnewline
63 & 0.807019411778625 & 0.385961176442749 & 0.192980588221375 \tabularnewline
64 & 0.77429120912167 & 0.45141758175666 & 0.22570879087833 \tabularnewline
65 & 0.83142835004005 & 0.3371432999199 & 0.16857164995995 \tabularnewline
66 & 0.802602745147945 & 0.39479450970411 & 0.197397254852055 \tabularnewline
67 & 0.832467716634208 & 0.335064566731584 & 0.167532283365792 \tabularnewline
68 & 0.881461440553534 & 0.237077118892932 & 0.118538559446466 \tabularnewline
69 & 0.856327874692653 & 0.287344250614694 & 0.143672125307347 \tabularnewline
70 & 0.829731777690764 & 0.340536444618472 & 0.170268222309236 \tabularnewline
71 & 0.80551860922803 & 0.388962781543939 & 0.194481390771970 \tabularnewline
72 & 0.77834317473687 & 0.443313650526261 & 0.221656825263130 \tabularnewline
73 & 0.746657499136332 & 0.506685001727337 & 0.253342500863668 \tabularnewline
74 & 0.726840351819517 & 0.546319296360967 & 0.273159648180483 \tabularnewline
75 & 0.688999098470906 & 0.622001803058187 & 0.311000901529093 \tabularnewline
76 & 0.708006039775724 & 0.583987920448552 & 0.291993960224276 \tabularnewline
77 & 0.725968085269886 & 0.548063829460229 & 0.274031914730114 \tabularnewline
78 & 0.699615374432004 & 0.600769251135992 & 0.300384625567996 \tabularnewline
79 & 0.741491067741814 & 0.517017864516372 & 0.258508932258186 \tabularnewline
80 & 0.70383488791249 & 0.592330224175021 & 0.296165112087511 \tabularnewline
81 & 0.677106288900781 & 0.645787422198438 & 0.322893711099219 \tabularnewline
82 & 0.633469313628138 & 0.733061372743724 & 0.366530686371862 \tabularnewline
83 & 0.595925027095157 & 0.808149945809686 & 0.404074972904843 \tabularnewline
84 & 0.55811060828776 & 0.88377878342448 & 0.44188939171224 \tabularnewline
85 & 0.707407220228597 & 0.585185559542806 & 0.292592779771403 \tabularnewline
86 & 0.668550156753092 & 0.662899686493816 & 0.331449843246908 \tabularnewline
87 & 0.627609439072175 & 0.74478112185565 & 0.372390560927825 \tabularnewline
88 & 0.589728601791803 & 0.820542796416395 & 0.410271398208197 \tabularnewline
89 & 0.557797133917057 & 0.884405732165886 & 0.442202866082943 \tabularnewline
90 & 0.512986333074871 & 0.974027333850258 & 0.487013666925129 \tabularnewline
91 & 0.5474242256197 & 0.9051515487606 & 0.4525757743803 \tabularnewline
92 & 0.539024509195157 & 0.921950981609687 & 0.460975490804843 \tabularnewline
93 & 0.503953008816759 & 0.992093982366482 & 0.496046991183241 \tabularnewline
94 & 0.507070088792487 & 0.985859822415027 & 0.492929911207513 \tabularnewline
95 & 0.458586737129305 & 0.91717347425861 & 0.541413262870695 \tabularnewline
96 & 0.423766588937372 & 0.847533177874744 & 0.576233411062628 \tabularnewline
97 & 0.623543846758856 & 0.752912306482288 & 0.376456153241144 \tabularnewline
98 & 0.580905840150339 & 0.838188319699323 & 0.419094159849661 \tabularnewline
99 & 0.581886770602418 & 0.836226458795164 & 0.418113229397582 \tabularnewline
100 & 0.54394524172895 & 0.9121095165421 & 0.45605475827105 \tabularnewline
101 & 0.534712900278224 & 0.930574199443553 & 0.465287099721776 \tabularnewline
102 & 0.488075491649375 & 0.97615098329875 & 0.511924508350625 \tabularnewline
103 & 0.571582589804618 & 0.856834820390764 & 0.428417410195382 \tabularnewline
104 & 0.717860395548497 & 0.564279208903007 & 0.282139604451503 \tabularnewline
105 & 0.705068588575037 & 0.589862822849926 & 0.294931411424963 \tabularnewline
106 & 0.74815371180075 & 0.503692576398502 & 0.251846288199251 \tabularnewline
107 & 0.724414659105042 & 0.551170681789917 & 0.275585340894958 \tabularnewline
108 & 0.703810904231639 & 0.592378191536722 & 0.296189095768361 \tabularnewline
109 & 0.666982575410507 & 0.666034849178986 & 0.333017424589493 \tabularnewline
110 & 0.618665995520391 & 0.762668008959217 & 0.381334004479608 \tabularnewline
111 & 0.576997070154409 & 0.846005859691181 & 0.423002929845591 \tabularnewline
112 & 0.633792687241786 & 0.732414625516428 & 0.366207312758214 \tabularnewline
113 & 0.692135460221124 & 0.615729079557753 & 0.307864539778876 \tabularnewline
114 & 0.643066896926194 & 0.713866206147612 & 0.356933103073806 \tabularnewline
115 & 0.60840603904613 & 0.78318792190774 & 0.39159396095387 \tabularnewline
116 & 0.555508204360359 & 0.888983591279283 & 0.444491795639641 \tabularnewline
117 & 0.50256329651968 & 0.99487340696064 & 0.49743670348032 \tabularnewline
118 & 0.522255846950964 & 0.955488306098071 & 0.477744153049036 \tabularnewline
119 & 0.498384865533384 & 0.996769731066767 & 0.501615134466616 \tabularnewline
120 & 0.461054472542074 & 0.922108945084147 & 0.538945527457926 \tabularnewline
121 & 0.411035987309852 & 0.822071974619704 & 0.588964012690148 \tabularnewline
122 & 0.438868748859365 & 0.877737497718731 & 0.561131251140635 \tabularnewline
123 & 0.380838949698585 & 0.761677899397169 & 0.619161050301415 \tabularnewline
124 & 0.384235931715124 & 0.768471863430249 & 0.615764068284876 \tabularnewline
125 & 0.33971331208871 & 0.67942662417742 & 0.66028668791129 \tabularnewline
126 & 0.534992216772501 & 0.930015566454998 & 0.465007783227499 \tabularnewline
127 & 0.475054605949401 & 0.950109211898802 & 0.524945394050599 \tabularnewline
128 & 0.516695581249197 & 0.966608837501607 & 0.483304418750803 \tabularnewline
129 & 0.455380064948302 & 0.910760129896604 & 0.544619935051698 \tabularnewline
130 & 0.399688426840368 & 0.799376853680735 & 0.600311573159632 \tabularnewline
131 & 0.340360904117779 & 0.680721808235558 & 0.659639095882221 \tabularnewline
132 & 0.295324989533931 & 0.590649979067862 & 0.704675010466069 \tabularnewline
133 & 0.313772846352729 & 0.627545692705457 & 0.686227153647271 \tabularnewline
134 & 0.278241525863858 & 0.556483051727717 & 0.721758474136142 \tabularnewline
135 & 0.228278951461775 & 0.45655790292355 & 0.771721048538225 \tabularnewline
136 & 0.298717716279923 & 0.597435432559847 & 0.701282283720077 \tabularnewline
137 & 0.23970907140185 & 0.4794181428037 & 0.76029092859815 \tabularnewline
138 & 0.257050882020855 & 0.51410176404171 & 0.742949117979145 \tabularnewline
139 & 0.254786331982826 & 0.509572663965652 & 0.745213668017174 \tabularnewline
140 & 0.211234043900898 & 0.422468087801796 & 0.788765956099102 \tabularnewline
141 & 0.295277023795257 & 0.590554047590514 & 0.704722976204743 \tabularnewline
142 & 0.264237938911651 & 0.528475877823301 & 0.73576206108835 \tabularnewline
143 & 0.200750198663441 & 0.401500397326883 & 0.799249801336559 \tabularnewline
144 & 0.216733051559074 & 0.433466103118148 & 0.783266948440926 \tabularnewline
145 & 0.141498546457949 & 0.282997092915898 & 0.858501453542051 \tabularnewline
146 & 0.66001393117389 & 0.67997213765222 & 0.33998606882611 \tabularnewline
147 & 0.545294136072753 & 0.909411727854493 & 0.454705863927247 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98395&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]0.672993749575081[/C][C]0.654012500849837[/C][C]0.327006250424919[/C][/ROW]
[ROW][C]10[/C][C]0.642495655047936[/C][C]0.715008689904129[/C][C]0.357504344952064[/C][/ROW]
[ROW][C]11[/C][C]0.50849608424266[/C][C]0.98300783151468[/C][C]0.49150391575734[/C][/ROW]
[ROW][C]12[/C][C]0.428900278991959[/C][C]0.857800557983918[/C][C]0.571099721008041[/C][/ROW]
[ROW][C]13[/C][C]0.77539832153592[/C][C]0.449203356928159[/C][C]0.224601678464079[/C][/ROW]
[ROW][C]14[/C][C]0.711597944838265[/C][C]0.576804110323469[/C][C]0.288402055161735[/C][/ROW]
[ROW][C]15[/C][C]0.90906323987734[/C][C]0.181873520245319[/C][C]0.0909367601226594[/C][/ROW]
[ROW][C]16[/C][C]0.873843565201118[/C][C]0.252312869597764[/C][C]0.126156434798882[/C][/ROW]
[ROW][C]17[/C][C]0.824057662731802[/C][C]0.351884674536397[/C][C]0.175942337268198[/C][/ROW]
[ROW][C]18[/C][C]0.810564421013917[/C][C]0.378871157972165[/C][C]0.189435578986083[/C][/ROW]
[ROW][C]19[/C][C]0.807924998670845[/C][C]0.38415000265831[/C][C]0.192075001329155[/C][/ROW]
[ROW][C]20[/C][C]0.77275070121581[/C][C]0.454498597568381[/C][C]0.227249298784190[/C][/ROW]
[ROW][C]21[/C][C]0.892691966214902[/C][C]0.214616067570196[/C][C]0.107308033785098[/C][/ROW]
[ROW][C]22[/C][C]0.856085109932638[/C][C]0.287829780134723[/C][C]0.143914890067362[/C][/ROW]
[ROW][C]23[/C][C]0.903959419399131[/C][C]0.192081161201738[/C][C]0.096040580600869[/C][/ROW]
[ROW][C]24[/C][C]0.927599754361027[/C][C]0.144800491277945[/C][C]0.0724002456389724[/C][/ROW]
[ROW][C]25[/C][C]0.911549014314716[/C][C]0.176901971370567[/C][C]0.0884509856852835[/C][/ROW]
[ROW][C]26[/C][C]0.894505825454754[/C][C]0.210988349090492[/C][C]0.105494174545246[/C][/ROW]
[ROW][C]27[/C][C]0.871248731251911[/C][C]0.257502537496177[/C][C]0.128751268748088[/C][/ROW]
[ROW][C]28[/C][C]0.837229887187749[/C][C]0.325540225624502[/C][C]0.162770112812251[/C][/ROW]
[ROW][C]29[/C][C]0.797503809742895[/C][C]0.40499238051421[/C][C]0.202496190257105[/C][/ROW]
[ROW][C]30[/C][C]0.886354914746792[/C][C]0.227290170506416[/C][C]0.113645085253208[/C][/ROW]
[ROW][C]31[/C][C]0.86197821969575[/C][C]0.276043560608501[/C][C]0.138021780304251[/C][/ROW]
[ROW][C]32[/C][C]0.827902386787547[/C][C]0.344195226424905[/C][C]0.172097613212453[/C][/ROW]
[ROW][C]33[/C][C]0.792334896330018[/C][C]0.415330207339963[/C][C]0.207665103669982[/C][/ROW]
[ROW][C]34[/C][C]0.803796913801174[/C][C]0.392406172397653[/C][C]0.196203086198826[/C][/ROW]
[ROW][C]35[/C][C]0.770603245923423[/C][C]0.458793508153154[/C][C]0.229396754076577[/C][/ROW]
[ROW][C]36[/C][C]0.741652032180247[/C][C]0.516695935639505[/C][C]0.258347967819753[/C][/ROW]
[ROW][C]37[/C][C]0.772171400000452[/C][C]0.455657199999096[/C][C]0.227828599999548[/C][/ROW]
[ROW][C]38[/C][C]0.744809590131499[/C][C]0.510380819737002[/C][C]0.255190409868501[/C][/ROW]
[ROW][C]39[/C][C]0.708681963079524[/C][C]0.582636073840952[/C][C]0.291318036920476[/C][/ROW]
[ROW][C]40[/C][C]0.661948674442302[/C][C]0.676102651115397[/C][C]0.338051325557699[/C][/ROW]
[ROW][C]41[/C][C]0.610228142274341[/C][C]0.779543715451319[/C][C]0.389771857725659[/C][/ROW]
[ROW][C]42[/C][C]0.733843490546386[/C][C]0.532313018907227[/C][C]0.266156509453614[/C][/ROW]
[ROW][C]43[/C][C]0.699309009587014[/C][C]0.601381980825972[/C][C]0.300690990412986[/C][/ROW]
[ROW][C]44[/C][C]0.654760551812696[/C][C]0.690478896374608[/C][C]0.345239448187304[/C][/ROW]
[ROW][C]45[/C][C]0.692726885268272[/C][C]0.614546229463456[/C][C]0.307273114731728[/C][/ROW]
[ROW][C]46[/C][C]0.656776111549387[/C][C]0.686447776901226[/C][C]0.343223888450613[/C][/ROW]
[ROW][C]47[/C][C]0.643608755817373[/C][C]0.712782488365253[/C][C]0.356391244182627[/C][/ROW]
[ROW][C]48[/C][C]0.613670855754704[/C][C]0.772658288490592[/C][C]0.386329144245296[/C][/ROW]
[ROW][C]49[/C][C]0.577893877320781[/C][C]0.844212245358439[/C][C]0.422106122679219[/C][/ROW]
[ROW][C]50[/C][C]0.567314268655629[/C][C]0.865371462688742[/C][C]0.432685731344371[/C][/ROW]
[ROW][C]51[/C][C]0.782361523665034[/C][C]0.435276952669933[/C][C]0.217638476334966[/C][/ROW]
[ROW][C]52[/C][C]0.880797870101772[/C][C]0.238404259796456[/C][C]0.119202129898228[/C][/ROW]
[ROW][C]53[/C][C]0.854438619948492[/C][C]0.291122760103017[/C][C]0.145561380051508[/C][/ROW]
[ROW][C]54[/C][C]0.834714512104977[/C][C]0.330570975790046[/C][C]0.165285487895023[/C][/ROW]
[ROW][C]55[/C][C]0.810990383923116[/C][C]0.378019232153767[/C][C]0.189009616076884[/C][/ROW]
[ROW][C]56[/C][C]0.808470648005543[/C][C]0.383058703988914[/C][C]0.191529351994457[/C][/ROW]
[ROW][C]57[/C][C]0.787485109128471[/C][C]0.425029781743058[/C][C]0.212514890871529[/C][/ROW]
[ROW][C]58[/C][C]0.756917348986921[/C][C]0.486165302026157[/C][C]0.243082651013079[/C][/ROW]
[ROW][C]59[/C][C]0.72865260634877[/C][C]0.54269478730246[/C][C]0.27134739365123[/C][/ROW]
[ROW][C]60[/C][C]0.688717555037758[/C][C]0.622564889924484[/C][C]0.311282444962242[/C][/ROW]
[ROW][C]61[/C][C]0.648896562962073[/C][C]0.702206874075855[/C][C]0.351103437037927[/C][/ROW]
[ROW][C]62[/C][C]0.635645828242656[/C][C]0.728708343514688[/C][C]0.364354171757344[/C][/ROW]
[ROW][C]63[/C][C]0.807019411778625[/C][C]0.385961176442749[/C][C]0.192980588221375[/C][/ROW]
[ROW][C]64[/C][C]0.77429120912167[/C][C]0.45141758175666[/C][C]0.22570879087833[/C][/ROW]
[ROW][C]65[/C][C]0.83142835004005[/C][C]0.3371432999199[/C][C]0.16857164995995[/C][/ROW]
[ROW][C]66[/C][C]0.802602745147945[/C][C]0.39479450970411[/C][C]0.197397254852055[/C][/ROW]
[ROW][C]67[/C][C]0.832467716634208[/C][C]0.335064566731584[/C][C]0.167532283365792[/C][/ROW]
[ROW][C]68[/C][C]0.881461440553534[/C][C]0.237077118892932[/C][C]0.118538559446466[/C][/ROW]
[ROW][C]69[/C][C]0.856327874692653[/C][C]0.287344250614694[/C][C]0.143672125307347[/C][/ROW]
[ROW][C]70[/C][C]0.829731777690764[/C][C]0.340536444618472[/C][C]0.170268222309236[/C][/ROW]
[ROW][C]71[/C][C]0.80551860922803[/C][C]0.388962781543939[/C][C]0.194481390771970[/C][/ROW]
[ROW][C]72[/C][C]0.77834317473687[/C][C]0.443313650526261[/C][C]0.221656825263130[/C][/ROW]
[ROW][C]73[/C][C]0.746657499136332[/C][C]0.506685001727337[/C][C]0.253342500863668[/C][/ROW]
[ROW][C]74[/C][C]0.726840351819517[/C][C]0.546319296360967[/C][C]0.273159648180483[/C][/ROW]
[ROW][C]75[/C][C]0.688999098470906[/C][C]0.622001803058187[/C][C]0.311000901529093[/C][/ROW]
[ROW][C]76[/C][C]0.708006039775724[/C][C]0.583987920448552[/C][C]0.291993960224276[/C][/ROW]
[ROW][C]77[/C][C]0.725968085269886[/C][C]0.548063829460229[/C][C]0.274031914730114[/C][/ROW]
[ROW][C]78[/C][C]0.699615374432004[/C][C]0.600769251135992[/C][C]0.300384625567996[/C][/ROW]
[ROW][C]79[/C][C]0.741491067741814[/C][C]0.517017864516372[/C][C]0.258508932258186[/C][/ROW]
[ROW][C]80[/C][C]0.70383488791249[/C][C]0.592330224175021[/C][C]0.296165112087511[/C][/ROW]
[ROW][C]81[/C][C]0.677106288900781[/C][C]0.645787422198438[/C][C]0.322893711099219[/C][/ROW]
[ROW][C]82[/C][C]0.633469313628138[/C][C]0.733061372743724[/C][C]0.366530686371862[/C][/ROW]
[ROW][C]83[/C][C]0.595925027095157[/C][C]0.808149945809686[/C][C]0.404074972904843[/C][/ROW]
[ROW][C]84[/C][C]0.55811060828776[/C][C]0.88377878342448[/C][C]0.44188939171224[/C][/ROW]
[ROW][C]85[/C][C]0.707407220228597[/C][C]0.585185559542806[/C][C]0.292592779771403[/C][/ROW]
[ROW][C]86[/C][C]0.668550156753092[/C][C]0.662899686493816[/C][C]0.331449843246908[/C][/ROW]
[ROW][C]87[/C][C]0.627609439072175[/C][C]0.74478112185565[/C][C]0.372390560927825[/C][/ROW]
[ROW][C]88[/C][C]0.589728601791803[/C][C]0.820542796416395[/C][C]0.410271398208197[/C][/ROW]
[ROW][C]89[/C][C]0.557797133917057[/C][C]0.884405732165886[/C][C]0.442202866082943[/C][/ROW]
[ROW][C]90[/C][C]0.512986333074871[/C][C]0.974027333850258[/C][C]0.487013666925129[/C][/ROW]
[ROW][C]91[/C][C]0.5474242256197[/C][C]0.9051515487606[/C][C]0.4525757743803[/C][/ROW]
[ROW][C]92[/C][C]0.539024509195157[/C][C]0.921950981609687[/C][C]0.460975490804843[/C][/ROW]
[ROW][C]93[/C][C]0.503953008816759[/C][C]0.992093982366482[/C][C]0.496046991183241[/C][/ROW]
[ROW][C]94[/C][C]0.507070088792487[/C][C]0.985859822415027[/C][C]0.492929911207513[/C][/ROW]
[ROW][C]95[/C][C]0.458586737129305[/C][C]0.91717347425861[/C][C]0.541413262870695[/C][/ROW]
[ROW][C]96[/C][C]0.423766588937372[/C][C]0.847533177874744[/C][C]0.576233411062628[/C][/ROW]
[ROW][C]97[/C][C]0.623543846758856[/C][C]0.752912306482288[/C][C]0.376456153241144[/C][/ROW]
[ROW][C]98[/C][C]0.580905840150339[/C][C]0.838188319699323[/C][C]0.419094159849661[/C][/ROW]
[ROW][C]99[/C][C]0.581886770602418[/C][C]0.836226458795164[/C][C]0.418113229397582[/C][/ROW]
[ROW][C]100[/C][C]0.54394524172895[/C][C]0.9121095165421[/C][C]0.45605475827105[/C][/ROW]
[ROW][C]101[/C][C]0.534712900278224[/C][C]0.930574199443553[/C][C]0.465287099721776[/C][/ROW]
[ROW][C]102[/C][C]0.488075491649375[/C][C]0.97615098329875[/C][C]0.511924508350625[/C][/ROW]
[ROW][C]103[/C][C]0.571582589804618[/C][C]0.856834820390764[/C][C]0.428417410195382[/C][/ROW]
[ROW][C]104[/C][C]0.717860395548497[/C][C]0.564279208903007[/C][C]0.282139604451503[/C][/ROW]
[ROW][C]105[/C][C]0.705068588575037[/C][C]0.589862822849926[/C][C]0.294931411424963[/C][/ROW]
[ROW][C]106[/C][C]0.74815371180075[/C][C]0.503692576398502[/C][C]0.251846288199251[/C][/ROW]
[ROW][C]107[/C][C]0.724414659105042[/C][C]0.551170681789917[/C][C]0.275585340894958[/C][/ROW]
[ROW][C]108[/C][C]0.703810904231639[/C][C]0.592378191536722[/C][C]0.296189095768361[/C][/ROW]
[ROW][C]109[/C][C]0.666982575410507[/C][C]0.666034849178986[/C][C]0.333017424589493[/C][/ROW]
[ROW][C]110[/C][C]0.618665995520391[/C][C]0.762668008959217[/C][C]0.381334004479608[/C][/ROW]
[ROW][C]111[/C][C]0.576997070154409[/C][C]0.846005859691181[/C][C]0.423002929845591[/C][/ROW]
[ROW][C]112[/C][C]0.633792687241786[/C][C]0.732414625516428[/C][C]0.366207312758214[/C][/ROW]
[ROW][C]113[/C][C]0.692135460221124[/C][C]0.615729079557753[/C][C]0.307864539778876[/C][/ROW]
[ROW][C]114[/C][C]0.643066896926194[/C][C]0.713866206147612[/C][C]0.356933103073806[/C][/ROW]
[ROW][C]115[/C][C]0.60840603904613[/C][C]0.78318792190774[/C][C]0.39159396095387[/C][/ROW]
[ROW][C]116[/C][C]0.555508204360359[/C][C]0.888983591279283[/C][C]0.444491795639641[/C][/ROW]
[ROW][C]117[/C][C]0.50256329651968[/C][C]0.99487340696064[/C][C]0.49743670348032[/C][/ROW]
[ROW][C]118[/C][C]0.522255846950964[/C][C]0.955488306098071[/C][C]0.477744153049036[/C][/ROW]
[ROW][C]119[/C][C]0.498384865533384[/C][C]0.996769731066767[/C][C]0.501615134466616[/C][/ROW]
[ROW][C]120[/C][C]0.461054472542074[/C][C]0.922108945084147[/C][C]0.538945527457926[/C][/ROW]
[ROW][C]121[/C][C]0.411035987309852[/C][C]0.822071974619704[/C][C]0.588964012690148[/C][/ROW]
[ROW][C]122[/C][C]0.438868748859365[/C][C]0.877737497718731[/C][C]0.561131251140635[/C][/ROW]
[ROW][C]123[/C][C]0.380838949698585[/C][C]0.761677899397169[/C][C]0.619161050301415[/C][/ROW]
[ROW][C]124[/C][C]0.384235931715124[/C][C]0.768471863430249[/C][C]0.615764068284876[/C][/ROW]
[ROW][C]125[/C][C]0.33971331208871[/C][C]0.67942662417742[/C][C]0.66028668791129[/C][/ROW]
[ROW][C]126[/C][C]0.534992216772501[/C][C]0.930015566454998[/C][C]0.465007783227499[/C][/ROW]
[ROW][C]127[/C][C]0.475054605949401[/C][C]0.950109211898802[/C][C]0.524945394050599[/C][/ROW]
[ROW][C]128[/C][C]0.516695581249197[/C][C]0.966608837501607[/C][C]0.483304418750803[/C][/ROW]
[ROW][C]129[/C][C]0.455380064948302[/C][C]0.910760129896604[/C][C]0.544619935051698[/C][/ROW]
[ROW][C]130[/C][C]0.399688426840368[/C][C]0.799376853680735[/C][C]0.600311573159632[/C][/ROW]
[ROW][C]131[/C][C]0.340360904117779[/C][C]0.680721808235558[/C][C]0.659639095882221[/C][/ROW]
[ROW][C]132[/C][C]0.295324989533931[/C][C]0.590649979067862[/C][C]0.704675010466069[/C][/ROW]
[ROW][C]133[/C][C]0.313772846352729[/C][C]0.627545692705457[/C][C]0.686227153647271[/C][/ROW]
[ROW][C]134[/C][C]0.278241525863858[/C][C]0.556483051727717[/C][C]0.721758474136142[/C][/ROW]
[ROW][C]135[/C][C]0.228278951461775[/C][C]0.45655790292355[/C][C]0.771721048538225[/C][/ROW]
[ROW][C]136[/C][C]0.298717716279923[/C][C]0.597435432559847[/C][C]0.701282283720077[/C][/ROW]
[ROW][C]137[/C][C]0.23970907140185[/C][C]0.4794181428037[/C][C]0.76029092859815[/C][/ROW]
[ROW][C]138[/C][C]0.257050882020855[/C][C]0.51410176404171[/C][C]0.742949117979145[/C][/ROW]
[ROW][C]139[/C][C]0.254786331982826[/C][C]0.509572663965652[/C][C]0.745213668017174[/C][/ROW]
[ROW][C]140[/C][C]0.211234043900898[/C][C]0.422468087801796[/C][C]0.788765956099102[/C][/ROW]
[ROW][C]141[/C][C]0.295277023795257[/C][C]0.590554047590514[/C][C]0.704722976204743[/C][/ROW]
[ROW][C]142[/C][C]0.264237938911651[/C][C]0.528475877823301[/C][C]0.73576206108835[/C][/ROW]
[ROW][C]143[/C][C]0.200750198663441[/C][C]0.401500397326883[/C][C]0.799249801336559[/C][/ROW]
[ROW][C]144[/C][C]0.216733051559074[/C][C]0.433466103118148[/C][C]0.783266948440926[/C][/ROW]
[ROW][C]145[/C][C]0.141498546457949[/C][C]0.282997092915898[/C][C]0.858501453542051[/C][/ROW]
[ROW][C]146[/C][C]0.66001393117389[/C][C]0.67997213765222[/C][C]0.33998606882611[/C][/ROW]
[ROW][C]147[/C][C]0.545294136072753[/C][C]0.909411727854493[/C][C]0.454705863927247[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98395&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98395&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
90.6729937495750810.6540125008498370.327006250424919
100.6424956550479360.7150086899041290.357504344952064
110.508496084242660.983007831514680.49150391575734
120.4289002789919590.8578005579839180.571099721008041
130.775398321535920.4492033569281590.224601678464079
140.7115979448382650.5768041103234690.288402055161735
150.909063239877340.1818735202453190.0909367601226594
160.8738435652011180.2523128695977640.126156434798882
170.8240576627318020.3518846745363970.175942337268198
180.8105644210139170.3788711579721650.189435578986083
190.8079249986708450.384150002658310.192075001329155
200.772750701215810.4544985975683810.227249298784190
210.8926919662149020.2146160675701960.107308033785098
220.8560851099326380.2878297801347230.143914890067362
230.9039594193991310.1920811612017380.096040580600869
240.9275997543610270.1448004912779450.0724002456389724
250.9115490143147160.1769019713705670.0884509856852835
260.8945058254547540.2109883490904920.105494174545246
270.8712487312519110.2575025374961770.128751268748088
280.8372298871877490.3255402256245020.162770112812251
290.7975038097428950.404992380514210.202496190257105
300.8863549147467920.2272901705064160.113645085253208
310.861978219695750.2760435606085010.138021780304251
320.8279023867875470.3441952264249050.172097613212453
330.7923348963300180.4153302073399630.207665103669982
340.8037969138011740.3924061723976530.196203086198826
350.7706032459234230.4587935081531540.229396754076577
360.7416520321802470.5166959356395050.258347967819753
370.7721714000004520.4556571999990960.227828599999548
380.7448095901314990.5103808197370020.255190409868501
390.7086819630795240.5826360738409520.291318036920476
400.6619486744423020.6761026511153970.338051325557699
410.6102281422743410.7795437154513190.389771857725659
420.7338434905463860.5323130189072270.266156509453614
430.6993090095870140.6013819808259720.300690990412986
440.6547605518126960.6904788963746080.345239448187304
450.6927268852682720.6145462294634560.307273114731728
460.6567761115493870.6864477769012260.343223888450613
470.6436087558173730.7127824883652530.356391244182627
480.6136708557547040.7726582884905920.386329144245296
490.5778938773207810.8442122453584390.422106122679219
500.5673142686556290.8653714626887420.432685731344371
510.7823615236650340.4352769526699330.217638476334966
520.8807978701017720.2384042597964560.119202129898228
530.8544386199484920.2911227601030170.145561380051508
540.8347145121049770.3305709757900460.165285487895023
550.8109903839231160.3780192321537670.189009616076884
560.8084706480055430.3830587039889140.191529351994457
570.7874851091284710.4250297817430580.212514890871529
580.7569173489869210.4861653020261570.243082651013079
590.728652606348770.542694787302460.27134739365123
600.6887175550377580.6225648899244840.311282444962242
610.6488965629620730.7022068740758550.351103437037927
620.6356458282426560.7287083435146880.364354171757344
630.8070194117786250.3859611764427490.192980588221375
640.774291209121670.451417581756660.22570879087833
650.831428350040050.33714329991990.16857164995995
660.8026027451479450.394794509704110.197397254852055
670.8324677166342080.3350645667315840.167532283365792
680.8814614405535340.2370771188929320.118538559446466
690.8563278746926530.2873442506146940.143672125307347
700.8297317776907640.3405364446184720.170268222309236
710.805518609228030.3889627815439390.194481390771970
720.778343174736870.4433136505262610.221656825263130
730.7466574991363320.5066850017273370.253342500863668
740.7268403518195170.5463192963609670.273159648180483
750.6889990984709060.6220018030581870.311000901529093
760.7080060397757240.5839879204485520.291993960224276
770.7259680852698860.5480638294602290.274031914730114
780.6996153744320040.6007692511359920.300384625567996
790.7414910677418140.5170178645163720.258508932258186
800.703834887912490.5923302241750210.296165112087511
810.6771062889007810.6457874221984380.322893711099219
820.6334693136281380.7330613727437240.366530686371862
830.5959250270951570.8081499458096860.404074972904843
840.558110608287760.883778783424480.44188939171224
850.7074072202285970.5851855595428060.292592779771403
860.6685501567530920.6628996864938160.331449843246908
870.6276094390721750.744781121855650.372390560927825
880.5897286017918030.8205427964163950.410271398208197
890.5577971339170570.8844057321658860.442202866082943
900.5129863330748710.9740273338502580.487013666925129
910.54742422561970.90515154876060.4525757743803
920.5390245091951570.9219509816096870.460975490804843
930.5039530088167590.9920939823664820.496046991183241
940.5070700887924870.9858598224150270.492929911207513
950.4585867371293050.917173474258610.541413262870695
960.4237665889373720.8475331778747440.576233411062628
970.6235438467588560.7529123064822880.376456153241144
980.5809058401503390.8381883196993230.419094159849661
990.5818867706024180.8362264587951640.418113229397582
1000.543945241728950.91210951654210.45605475827105
1010.5347129002782240.9305741994435530.465287099721776
1020.4880754916493750.976150983298750.511924508350625
1030.5715825898046180.8568348203907640.428417410195382
1040.7178603955484970.5642792089030070.282139604451503
1050.7050685885750370.5898628228499260.294931411424963
1060.748153711800750.5036925763985020.251846288199251
1070.7244146591050420.5511706817899170.275585340894958
1080.7038109042316390.5923781915367220.296189095768361
1090.6669825754105070.6660348491789860.333017424589493
1100.6186659955203910.7626680089592170.381334004479608
1110.5769970701544090.8460058596911810.423002929845591
1120.6337926872417860.7324146255164280.366207312758214
1130.6921354602211240.6157290795577530.307864539778876
1140.6430668969261940.7138662061476120.356933103073806
1150.608406039046130.783187921907740.39159396095387
1160.5555082043603590.8889835912792830.444491795639641
1170.502563296519680.994873406960640.49743670348032
1180.5222558469509640.9554883060980710.477744153049036
1190.4983848655333840.9967697310667670.501615134466616
1200.4610544725420740.9221089450841470.538945527457926
1210.4110359873098520.8220719746197040.588964012690148
1220.4388687488593650.8777374977187310.561131251140635
1230.3808389496985850.7616778993971690.619161050301415
1240.3842359317151240.7684718634302490.615764068284876
1250.339713312088710.679426624177420.66028668791129
1260.5349922167725010.9300155664549980.465007783227499
1270.4750546059494010.9501092118988020.524945394050599
1280.5166955812491970.9666088375016070.483304418750803
1290.4553800649483020.9107601298966040.544619935051698
1300.3996884268403680.7993768536807350.600311573159632
1310.3403609041177790.6807218082355580.659639095882221
1320.2953249895339310.5906499790678620.704675010466069
1330.3137728463527290.6275456927054570.686227153647271
1340.2782415258638580.5564830517277170.721758474136142
1350.2282789514617750.456557902923550.771721048538225
1360.2987177162799230.5974354325598470.701282283720077
1370.239709071401850.47941814280370.76029092859815
1380.2570508820208550.514101764041710.742949117979145
1390.2547863319828260.5095726639656520.745213668017174
1400.2112340439008980.4224680878017960.788765956099102
1410.2952770237952570.5905540475905140.704722976204743
1420.2642379389116510.5284758778233010.73576206108835
1430.2007501986634410.4015003973268830.799249801336559
1440.2167330515590740.4334661031181480.783266948440926
1450.1414985464579490.2829970929158980.858501453542051
1460.660013931173890.679972137652220.33998606882611
1470.5452941360727530.9094117278544930.454705863927247






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98395&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98395&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98395&T=6

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


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

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


Figures (Output of Computation)

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

Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}