FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationSat, 20 Nov 2010 18:14:50 +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/20/t1290276818jlf3r91c8p1d0p0.htm/, Retrieved Sat, 27 Apr 2024 07:25:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98283, Retrieved Sat, 27 Apr 2024 07:25:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact214

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [WS7 - popularitei...] [2010-11-20 16:26:09] [8ef49741e164ec6343c90c7935194465]
-   P       [Multiple Regression] [WS7 - Determinist...] [2010-11-20 18:14:50] [934c3727858e074bf543f25f5906ed72] [Current]
-    D        [Multiple Regression] [ws 7] [2010-11-23 20:49:21] [8214fe6d084e5ad7598b249a26cc9f06]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	13	14	13	3	1
12	12	8	13	5	1
15	10	12	16	6	1
12	9	7	12	6	1
10	10	10	11	5	1
12	12	7	12	3	1
15	13	16	18	8	1
9	12	11	11	4	1
12	12	14	14	4	1
11	6	6	9	4	1
11	5	16	14	6	1
11	12	11	12	6	1
15	11	16	11	5	1
7	14	12	12	4	1
11	14	7	13	6	1
11	12	13	11	4	1
10	12	11	12	6	1
14	11	15	16	6	1
10	11	7	9	4	2
6	7	9	11	4	2
11	9	7	13	2	2
15	11	14	15	7	2
11	11	15	10	5	2
12	12	7	11	4	2
14	12	15	13	6	2
15	11	17	16	6	2
9	11	15	15	7	2
13	8	14	14	5	2
13	9	14	14	6	2
16	12	8	14	4	2
13	10	8	8	4	2
12	10	14	13	7	2
14	12	14	15	7	2
11	8	8	13	4	3
9	12	11	11	4	3
16	11	16	15	6	3
12	12	10	15	6	3
10	7	8	9	5	3
13	11	14	13	6	3
16	11	16	16	7	3
14	12	13	13	6	3
15	9	5	11	3	3
5	15	8	12	3	3
8	11	10	12	4	3
11	11	8	12	6	3
16	11	13	14	7	3
17	11	15	14	5	3
9	15	6	8	4	3
9	11	12	13	5	3
13	12	16	16	6	3
10	12	5	13	6	3
6	9	15	11	6	4
12	12	12	14	5	4
8	12	8	13	4	4
14	13	13	13	5	4
12	11	14	13	5	4
11	9	12	12	4	4
16	9	16	16	6	4
8	11	10	15	2	4
15	11	15	15	8	4
7	12	8	12	3	4
16	12	16	14	6	4
14	9	19	12	6	4
16	11	14	15	6	4
9	9	6	12	5	4
14	12	13	13	5	4
11	12	15	12	6	4
13	12	7	12	5	4
15	12	13	13	6	5
5	14	4	5	2	5
15	11	14	13	5	5
13	12	13	13	5	5
11	11	11	14	5	5
11	6	14	17	6	5
12	10	12	13	6	5
12	12	15	13	6	5
12	13	14	12	5	5
12	8	13	13	5	5
14	12	8	14	4	5
6	12	6	11	2	5
7	12	7	12	4	5
14	6	13	12	6	5
14	11	13	16	6	5
10	10	11	12	5	5
13	12	5	12	3	5
12	13	12	12	6	5
9	11	8	10	4	6
12	7	11	15	5	6
16	11	14	15	8	6
10	11	9	12	4	6
14	11	10	16	6	6
10	11	13	15	6	6
16	12	16	16	7	6
15	10	16	13	6	6
12	11	11	12	5	6
10	12	8	11	4	6
8	7	4	13	6	6
8	13	7	10	3	6
11	8	14	15	5	6
13	12	11	13	6	6
16	11	17	16	7	6
16	12	15	15	7	6
14	14	17	18	6	6
11	10	5	13	3	6
4	10	4	10	2	6
14	13	10	16	8	6
9	10	11	13	3	7
14	11	15	15	8	7
8	10	10	14	3	7
8	7	9	15	4	7
11	10	12	14	5	7
12	8	15	13	7	7
11	12	7	13	6	7
14	12	13	15	6	7
15	12	12	16	7	7
16	11	14	14	6	7
16	12	14	14	6	7
11	12	8	16	6	7
14	12	15	14	6	7
14	11	12	12	4	7
12	12	12	13	4	7
14	11	16	12	5	7
8	11	9	12	4	7
13	13	15	14	6	7
16	12	15	14	6	7
12	12	6	14	5	7
16	12	14	16	8	7
12	12	15	13	6	7
11	8	10	14	5	7
4	8	6	4	4	7
16	12	14	16	8	7
15	11	12	13	6	7
10	12	8	16	4	7
13	13	11	15	6	7
15	12	13	14	6	7
12	12	9	13	4	7
14	11	15	14	6	7
7	12	13	12	3	8
19	12	15	15	6	8
12	10	14	14	5	8
12	11	16	13	4	8
13	12	14	14	6	8
15	12	14	16	4	8
8	10	10	6	4	8
12	12	10	13	4	8
10	13	4	13	6	8
8	12	8	14	5	8
10	15	15	15	6	8
15	11	16	14	6	8
16	12	12	15	8	9
13	11	12	13	7	10
16	12	15	16	7	10
9	11	9	12	4	14
14	10	12	15	6	14
14	11	14	12	6	14
12	11	11	14	2	14

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98283&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98283&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98283&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = + 0.184727929928006 + 0.102458915938333FindingFriends[t] + 0.241835431638646KnowingPeople[t] + 0.349828593066031Liked[t] + 0.637259261836312Celebrity[t] + 0.0988008593457308Date[t] -0.0064127230084315t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  +  0.184727929928006 +  0.102458915938333FindingFriends[t] +  0.241835431638646KnowingPeople[t] +  0.349828593066031Liked[t] +  0.637259261836312Celebrity[t] +  0.0988008593457308Date[t] -0.0064127230084315t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98283&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  +  0.184727929928006 +  0.102458915938333FindingFriends[t] +  0.241835431638646KnowingPeople[t] +  0.349828593066031Liked[t] +  0.637259261836312Celebrity[t] +  0.0988008593457308Date[t] -0.0064127230084315t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98283&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98283&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
Popularity[t] = + 0.184727929928006 + 0.102458915938333FindingFriends[t] + 0.241835431638646KnowingPeople[t] + 0.349828593066031Liked[t] + 0.637259261836312Celebrity[t] + 0.0988008593457308Date[t] -0.0064127230084315t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1847279299280061.4561140.12690.8992190.44961
FindingFriends0.1024589159383330.0977071.04860.2960430.148022
KnowingPeople0.2418354316386460.0618463.91030.000147e-05
Liked0.3498285930660310.097953.57150.0004780.000239
Celebrity0.6372592618363120.1581234.03028.9e-054.4e-05
Date0.09880085934573080.1967060.50230.6162150.308107
t-0.00641272300843150.011948-0.53670.5922470.296123

\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) & 0.184727929928006 & 1.456114 & 0.1269 & 0.899219 & 0.44961 \tabularnewline
FindingFriends & 0.102458915938333 & 0.097707 & 1.0486 & 0.296043 & 0.148022 \tabularnewline
KnowingPeople & 0.241835431638646 & 0.061846 & 3.9103 & 0.00014 & 7e-05 \tabularnewline
Liked & 0.349828593066031 & 0.09795 & 3.5715 & 0.000478 & 0.000239 \tabularnewline
Celebrity & 0.637259261836312 & 0.158123 & 4.0302 & 8.9e-05 & 4.4e-05 \tabularnewline
Date & 0.0988008593457308 & 0.196706 & 0.5023 & 0.616215 & 0.308107 \tabularnewline
t & -0.0064127230084315 & 0.011948 & -0.5367 & 0.592247 & 0.296123 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98283&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]0.184727929928006[/C][C]1.456114[/C][C]0.1269[/C][C]0.899219[/C][C]0.44961[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.102458915938333[/C][C]0.097707[/C][C]1.0486[/C][C]0.296043[/C][C]0.148022[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.241835431638646[/C][C]0.061846[/C][C]3.9103[/C][C]0.00014[/C][C]7e-05[/C][/ROW]
[ROW][C]Liked[/C][C]0.349828593066031[/C][C]0.09795[/C][C]3.5715[/C][C]0.000478[/C][C]0.000239[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.637259261836312[/C][C]0.158123[/C][C]4.0302[/C][C]8.9e-05[/C][C]4.4e-05[/C][/ROW]
[ROW][C]Date[/C][C]0.0988008593457308[/C][C]0.196706[/C][C]0.5023[/C][C]0.616215[/C][C]0.308107[/C][/ROW]
[ROW][C]t[/C][C]-0.0064127230084315[/C][C]0.011948[/C][C]-0.5367[/C][C]0.592247[/C][C]0.296123[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98283&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98283&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)0.1847279299280061.4561140.12690.8992190.44961
FindingFriends0.1024589159383330.0977071.04860.2960430.148022
KnowingPeople0.2418354316386460.0618463.91030.000147e-05
Liked0.3498285930660310.097953.57150.0004780.000239
Celebrity0.6372592618363120.1581234.03028.9e-054.4e-05
Date0.09880085934573080.1967060.50230.6162150.308107
t-0.00641272300843150.011948-0.53670.5922470.296123






Multiple Linear Regression - Regression Statistics
Multiple R0.707225438473912
R-squared0.500167820824616
Adjusted R-squared0.480040350522252
F-TEST (value)24.8500091323379
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.11754990906277
Sum Squared Residuals668.118624988388

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.707225438473912 \tabularnewline
R-squared & 0.500167820824616 \tabularnewline
Adjusted R-squared & 0.480040350522252 \tabularnewline
F-TEST (value) & 24.8500091323379 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.11754990906277 \tabularnewline
Sum Squared Residuals & 668.118624988388 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98283&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.707225438473912[/C][/ROW]
[ROW][C]R-squared[/C][C]0.500167820824616[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.480040350522252[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]24.8500091323379[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.11754990906277[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]668.118624988388[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98283&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98283&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 R0.707225438473912
R-squared0.500167820824616
Adjusted R-squared0.480040350522252
F-TEST (value)24.8500091323379
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.11754990906277
Sum Squared Residuals668.118624988388






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.45432751177201.54567248822804
21211.1689618066660.831038193334013
31513.61171801936991.38828198063014
41210.89435484996581.10564515003425
51010.7288194829093-0.728819482909259
6129.277128366254972.72287163374503
71516.8349613115104-1.83496131151042
8910.5190753155630-1.51907531556297
91212.2876546666686-0.287654666668564
10117.982662029590823.01733797040918
111113.3158061960333-2.31580619603329
121112.1177715402679-1.11777154026790
131512.23098920461202.76901079538798
14711.2771808340937-4.27718083409372
151111.6859380696307-0.685938069630715
161110.95144439477280.048555605227192
171012.0857079252257-2.08570792522574
181414.3434923850977-0.343492385097678
19108.777878393190981.22212160680902
2069.54495805583857-3.54495805583857
21118.684930963888952.31506903611105
221514.46223758954130.537762410458681
231111.6739988091688-0.673998809168759
24129.547930880219212.45206911978079
251413.45037732012460.54962267987537
261514.87466232365320.125337676346752
27914.6720094061378-5.67200940613781
281312.49203738693710.507962613062922
291313.2253428417033-0.225342841703291
301610.80077575300545.19922424699464
31138.490473639724084.50952636027592
321213.5959942573866-1.59599425738661
331414.4941565523869-0.494156552386905
341110.1142614634980.885738536501996
35910.5435335130268-1.54353351302678
361614.317671928211.68232807179001
371212.9627055313080-0.962705531308015
38109.224096545098140.775903454901863
391313.1151057097753-0.115105709775342
401615.27910889107860.720891108921396
411412.96290374805821.03709625194183
42158.102995852484586.89700414751543
4359.7866715130881-4.78667151308811
44810.4913532514399-2.49135325143995
451111.2757881888268-0.275788188826849
461613.81546907198002.18453092801998
471713.01820868857633.98179131142375
4898.508881924340850.491118075659151
49911.9300483545774-2.93004835457742
501314.6801813150963-1.68018131509631
511010.9640930648647-0.964093064864686
52612.4678015836414-6.46780158364138
531212.4554858308938-0.455485830893791
54810.4946435264284-2.49464352642844
551412.43712613938791.56287386061212
561212.4676310161414-0.467631016141426
571110.78554174307670.214458256923304
581614.42030364255961.57969635744041
59810.2689305211847-2.26893052118467
601515.2952505273873-0.295250527387342
6179.46266661046707-2.46266661046707
621614.00237231220881.99762768779120
631413.71443195016930.285568049830749
641613.75324568004232.24675431995765
6599.92048663101368-0.920486631013682
661412.26412727035681.73587272964320
671113.0288160793959-2.02881607939594
681310.45046064144202.54953935855797
691512.98094922251352.01905077748645
7055.65526965476048-0.65526965476048
711512.47024103036072.52975896963932
721312.32445179165190.67554820834806
731112.0817378824939-1.08173788249391
741113.9752819157442-2.97528191574416
751212.4957196209476-0.495719620947646
761213.4197310247318-1.41973102473182
771212.2868539311207-0.28685393112073
781211.87613978984800.123860210151980
791410.78295490362943.21704509637059
8067.96886701447297-1.96886701447297
8179.82863683984184-2.82863683984184
821411.93300173470792.06699826529209
831413.83819796365530.161802036344738
841011.2090818273308-1.20908182733078
85138.682055822694514.31794417730549
861212.3827278226039-0.382727822603868
8799.32868069070524-0.328680690705235
881212.0243408260259-0.0243408260258719
891615.06504784719560.934952152804356
901010.2509351394506-0.250935139450646
911413.16019074401760.839809255982396
921013.5294557228591-3.52945572285908
931615.33809606560730.66190393439274
941513.44002046968781.55997953031224
951211.33980164952210.660198350477908
96109.723253692633710.276746307366286
97810.2113803731837-2.21138037318372
9888.5839638760142-0.583963876014196
991112.7817660837874-1.78176608378740
1001312.39728480532060.602715194679391
1011615.42617079724010.573829202759879
1021614.68871753382671.31128246617330
1031415.783120023334-1.78312002333400
104118.803925706069412.19607429393059
10546.86893251038793-2.86893251038793
1061414.5434362544404-0.543436254440421
107910.3345009862217-1.33450098622172
1081415.2838424010198-1.28384240101982
109810.4296687016322-2.42966870163224
110810.8611316540725-2.86113165407251
1111112.1750326425653-1.17503264256529
1121213.6138983132027-1.61389831320273
1131111.4453785390021-0.445378539002147
1141413.58963559195770.41036440804235
1151514.32847529221290.671524707787085
1161613.36635806857512.63364193142493
1171613.46240426150502.53759573849503
1181112.7046361347967-1.70463613479673
1191413.69141424712680.308585752873246
1201410.88286060345943.11713939654063
1211211.32873538945530.6712646105447
1221412.47463614583341.5253638541666
123810.1381161395181-2.13811613951814
1241313.7618095480229-0.76180954802293
1251613.65293790907622.34706209092383
1261210.83274703948361.16725296051639
1271615.37245274122530.627547258774659
1281213.2838711469848-1.28387114698484
1291111.3710149332596-0.371014933259569
13046.26171529119994-2.26171529119994
1311615.34680184919160.653198150808385
1321512.43025504409682.56974495590315
1331011.3339267659976-1.33392676599763
1341313.0801691844501-0.0801691844500615
1351513.10513981571461.89486018428544
1361210.50703824941291.49296175058711
1371413.47352631703670.526473682963345
138710.573267534394-3.573267534394
1391914.01178923936994.98821076063011
1401212.5715353979438-0.571535397943801
1411212.1641645992487-0.164164599248651
1421313.4008870456399-0.400887045639915
1431512.81961298509092.18038701490908
14488.14265477299094-0.142654772990940
1451210.78996003332141.21003996667862
1461010.7095121600920-0.709512160092034
147811.2805515789296-3.28055157892957
1481014.261451480109-4.261451480109
1491513.73720993191991.26279006808015
1501614.58906237437961.41093762562044
1511313.2420751468102-0.242075146810151
1521615.11311341385410.88688658614592
153910.6373404646853-1.63734046468531
1541413.57797942352520.422020576474804
1551413.10821070053430.891789299465702
1561210.52691182139671.47308817860326

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.4543275117720 & 1.54567248822804 \tabularnewline
2 & 12 & 11.168961806666 & 0.831038193334013 \tabularnewline
3 & 15 & 13.6117180193699 & 1.38828198063014 \tabularnewline
4 & 12 & 10.8943548499658 & 1.10564515003425 \tabularnewline
5 & 10 & 10.7288194829093 & -0.728819482909259 \tabularnewline
6 & 12 & 9.27712836625497 & 2.72287163374503 \tabularnewline
7 & 15 & 16.8349613115104 & -1.83496131151042 \tabularnewline
8 & 9 & 10.5190753155630 & -1.51907531556297 \tabularnewline
9 & 12 & 12.2876546666686 & -0.287654666668564 \tabularnewline
10 & 11 & 7.98266202959082 & 3.01733797040918 \tabularnewline
11 & 11 & 13.3158061960333 & -2.31580619603329 \tabularnewline
12 & 11 & 12.1177715402679 & -1.11777154026790 \tabularnewline
13 & 15 & 12.2309892046120 & 2.76901079538798 \tabularnewline
14 & 7 & 11.2771808340937 & -4.27718083409372 \tabularnewline
15 & 11 & 11.6859380696307 & -0.685938069630715 \tabularnewline
16 & 11 & 10.9514443947728 & 0.048555605227192 \tabularnewline
17 & 10 & 12.0857079252257 & -2.08570792522574 \tabularnewline
18 & 14 & 14.3434923850977 & -0.343492385097678 \tabularnewline
19 & 10 & 8.77787839319098 & 1.22212160680902 \tabularnewline
20 & 6 & 9.54495805583857 & -3.54495805583857 \tabularnewline
21 & 11 & 8.68493096388895 & 2.31506903611105 \tabularnewline
22 & 15 & 14.4622375895413 & 0.537762410458681 \tabularnewline
23 & 11 & 11.6739988091688 & -0.673998809168759 \tabularnewline
24 & 12 & 9.54793088021921 & 2.45206911978079 \tabularnewline
25 & 14 & 13.4503773201246 & 0.54962267987537 \tabularnewline
26 & 15 & 14.8746623236532 & 0.125337676346752 \tabularnewline
27 & 9 & 14.6720094061378 & -5.67200940613781 \tabularnewline
28 & 13 & 12.4920373869371 & 0.507962613062922 \tabularnewline
29 & 13 & 13.2253428417033 & -0.225342841703291 \tabularnewline
30 & 16 & 10.8007757530054 & 5.19922424699464 \tabularnewline
31 & 13 & 8.49047363972408 & 4.50952636027592 \tabularnewline
32 & 12 & 13.5959942573866 & -1.59599425738661 \tabularnewline
33 & 14 & 14.4941565523869 & -0.494156552386905 \tabularnewline
34 & 11 & 10.114261463498 & 0.885738536501996 \tabularnewline
35 & 9 & 10.5435335130268 & -1.54353351302678 \tabularnewline
36 & 16 & 14.31767192821 & 1.68232807179001 \tabularnewline
37 & 12 & 12.9627055313080 & -0.962705531308015 \tabularnewline
38 & 10 & 9.22409654509814 & 0.775903454901863 \tabularnewline
39 & 13 & 13.1151057097753 & -0.115105709775342 \tabularnewline
40 & 16 & 15.2791088910786 & 0.720891108921396 \tabularnewline
41 & 14 & 12.9629037480582 & 1.03709625194183 \tabularnewline
42 & 15 & 8.10299585248458 & 6.89700414751543 \tabularnewline
43 & 5 & 9.7866715130881 & -4.78667151308811 \tabularnewline
44 & 8 & 10.4913532514399 & -2.49135325143995 \tabularnewline
45 & 11 & 11.2757881888268 & -0.275788188826849 \tabularnewline
46 & 16 & 13.8154690719800 & 2.18453092801998 \tabularnewline
47 & 17 & 13.0182086885763 & 3.98179131142375 \tabularnewline
48 & 9 & 8.50888192434085 & 0.491118075659151 \tabularnewline
49 & 9 & 11.9300483545774 & -2.93004835457742 \tabularnewline
50 & 13 & 14.6801813150963 & -1.68018131509631 \tabularnewline
51 & 10 & 10.9640930648647 & -0.964093064864686 \tabularnewline
52 & 6 & 12.4678015836414 & -6.46780158364138 \tabularnewline
53 & 12 & 12.4554858308938 & -0.455485830893791 \tabularnewline
54 & 8 & 10.4946435264284 & -2.49464352642844 \tabularnewline
55 & 14 & 12.4371261393879 & 1.56287386061212 \tabularnewline
56 & 12 & 12.4676310161414 & -0.467631016141426 \tabularnewline
57 & 11 & 10.7855417430767 & 0.214458256923304 \tabularnewline
58 & 16 & 14.4203036425596 & 1.57969635744041 \tabularnewline
59 & 8 & 10.2689305211847 & -2.26893052118467 \tabularnewline
60 & 15 & 15.2952505273873 & -0.295250527387342 \tabularnewline
61 & 7 & 9.46266661046707 & -2.46266661046707 \tabularnewline
62 & 16 & 14.0023723122088 & 1.99762768779120 \tabularnewline
63 & 14 & 13.7144319501693 & 0.285568049830749 \tabularnewline
64 & 16 & 13.7532456800423 & 2.24675431995765 \tabularnewline
65 & 9 & 9.92048663101368 & -0.920486631013682 \tabularnewline
66 & 14 & 12.2641272703568 & 1.73587272964320 \tabularnewline
67 & 11 & 13.0288160793959 & -2.02881607939594 \tabularnewline
68 & 13 & 10.4504606414420 & 2.54953935855797 \tabularnewline
69 & 15 & 12.9809492225135 & 2.01905077748645 \tabularnewline
70 & 5 & 5.65526965476048 & -0.65526965476048 \tabularnewline
71 & 15 & 12.4702410303607 & 2.52975896963932 \tabularnewline
72 & 13 & 12.3244517916519 & 0.67554820834806 \tabularnewline
73 & 11 & 12.0817378824939 & -1.08173788249391 \tabularnewline
74 & 11 & 13.9752819157442 & -2.97528191574416 \tabularnewline
75 & 12 & 12.4957196209476 & -0.495719620947646 \tabularnewline
76 & 12 & 13.4197310247318 & -1.41973102473182 \tabularnewline
77 & 12 & 12.2868539311207 & -0.28685393112073 \tabularnewline
78 & 12 & 11.8761397898480 & 0.123860210151980 \tabularnewline
79 & 14 & 10.7829549036294 & 3.21704509637059 \tabularnewline
80 & 6 & 7.96886701447297 & -1.96886701447297 \tabularnewline
81 & 7 & 9.82863683984184 & -2.82863683984184 \tabularnewline
82 & 14 & 11.9330017347079 & 2.06699826529209 \tabularnewline
83 & 14 & 13.8381979636553 & 0.161802036344738 \tabularnewline
84 & 10 & 11.2090818273308 & -1.20908182733078 \tabularnewline
85 & 13 & 8.68205582269451 & 4.31794417730549 \tabularnewline
86 & 12 & 12.3827278226039 & -0.382727822603868 \tabularnewline
87 & 9 & 9.32868069070524 & -0.328680690705235 \tabularnewline
88 & 12 & 12.0243408260259 & -0.0243408260258719 \tabularnewline
89 & 16 & 15.0650478471956 & 0.934952152804356 \tabularnewline
90 & 10 & 10.2509351394506 & -0.250935139450646 \tabularnewline
91 & 14 & 13.1601907440176 & 0.839809255982396 \tabularnewline
92 & 10 & 13.5294557228591 & -3.52945572285908 \tabularnewline
93 & 16 & 15.3380960656073 & 0.66190393439274 \tabularnewline
94 & 15 & 13.4400204696878 & 1.55997953031224 \tabularnewline
95 & 12 & 11.3398016495221 & 0.660198350477908 \tabularnewline
96 & 10 & 9.72325369263371 & 0.276746307366286 \tabularnewline
97 & 8 & 10.2113803731837 & -2.21138037318372 \tabularnewline
98 & 8 & 8.5839638760142 & -0.583963876014196 \tabularnewline
99 & 11 & 12.7817660837874 & -1.78176608378740 \tabularnewline
100 & 13 & 12.3972848053206 & 0.602715194679391 \tabularnewline
101 & 16 & 15.4261707972401 & 0.573829202759879 \tabularnewline
102 & 16 & 14.6887175338267 & 1.31128246617330 \tabularnewline
103 & 14 & 15.783120023334 & -1.78312002333400 \tabularnewline
104 & 11 & 8.80392570606941 & 2.19607429393059 \tabularnewline
105 & 4 & 6.86893251038793 & -2.86893251038793 \tabularnewline
106 & 14 & 14.5434362544404 & -0.543436254440421 \tabularnewline
107 & 9 & 10.3345009862217 & -1.33450098622172 \tabularnewline
108 & 14 & 15.2838424010198 & -1.28384240101982 \tabularnewline
109 & 8 & 10.4296687016322 & -2.42966870163224 \tabularnewline
110 & 8 & 10.8611316540725 & -2.86113165407251 \tabularnewline
111 & 11 & 12.1750326425653 & -1.17503264256529 \tabularnewline
112 & 12 & 13.6138983132027 & -1.61389831320273 \tabularnewline
113 & 11 & 11.4453785390021 & -0.445378539002147 \tabularnewline
114 & 14 & 13.5896355919577 & 0.41036440804235 \tabularnewline
115 & 15 & 14.3284752922129 & 0.671524707787085 \tabularnewline
116 & 16 & 13.3663580685751 & 2.63364193142493 \tabularnewline
117 & 16 & 13.4624042615050 & 2.53759573849503 \tabularnewline
118 & 11 & 12.7046361347967 & -1.70463613479673 \tabularnewline
119 & 14 & 13.6914142471268 & 0.308585752873246 \tabularnewline
120 & 14 & 10.8828606034594 & 3.11713939654063 \tabularnewline
121 & 12 & 11.3287353894553 & 0.6712646105447 \tabularnewline
122 & 14 & 12.4746361458334 & 1.5253638541666 \tabularnewline
123 & 8 & 10.1381161395181 & -2.13811613951814 \tabularnewline
124 & 13 & 13.7618095480229 & -0.76180954802293 \tabularnewline
125 & 16 & 13.6529379090762 & 2.34706209092383 \tabularnewline
126 & 12 & 10.8327470394836 & 1.16725296051639 \tabularnewline
127 & 16 & 15.3724527412253 & 0.627547258774659 \tabularnewline
128 & 12 & 13.2838711469848 & -1.28387114698484 \tabularnewline
129 & 11 & 11.3710149332596 & -0.371014933259569 \tabularnewline
130 & 4 & 6.26171529119994 & -2.26171529119994 \tabularnewline
131 & 16 & 15.3468018491916 & 0.653198150808385 \tabularnewline
132 & 15 & 12.4302550440968 & 2.56974495590315 \tabularnewline
133 & 10 & 11.3339267659976 & -1.33392676599763 \tabularnewline
134 & 13 & 13.0801691844501 & -0.0801691844500615 \tabularnewline
135 & 15 & 13.1051398157146 & 1.89486018428544 \tabularnewline
136 & 12 & 10.5070382494129 & 1.49296175058711 \tabularnewline
137 & 14 & 13.4735263170367 & 0.526473682963345 \tabularnewline
138 & 7 & 10.573267534394 & -3.573267534394 \tabularnewline
139 & 19 & 14.0117892393699 & 4.98821076063011 \tabularnewline
140 & 12 & 12.5715353979438 & -0.571535397943801 \tabularnewline
141 & 12 & 12.1641645992487 & -0.164164599248651 \tabularnewline
142 & 13 & 13.4008870456399 & -0.400887045639915 \tabularnewline
143 & 15 & 12.8196129850909 & 2.18038701490908 \tabularnewline
144 & 8 & 8.14265477299094 & -0.142654772990940 \tabularnewline
145 & 12 & 10.7899600333214 & 1.21003996667862 \tabularnewline
146 & 10 & 10.7095121600920 & -0.709512160092034 \tabularnewline
147 & 8 & 11.2805515789296 & -3.28055157892957 \tabularnewline
148 & 10 & 14.261451480109 & -4.261451480109 \tabularnewline
149 & 15 & 13.7372099319199 & 1.26279006808015 \tabularnewline
150 & 16 & 14.5890623743796 & 1.41093762562044 \tabularnewline
151 & 13 & 13.2420751468102 & -0.242075146810151 \tabularnewline
152 & 16 & 15.1131134138541 & 0.88688658614592 \tabularnewline
153 & 9 & 10.6373404646853 & -1.63734046468531 \tabularnewline
154 & 14 & 13.5779794235252 & 0.422020576474804 \tabularnewline
155 & 14 & 13.1082107005343 & 0.891789299465702 \tabularnewline
156 & 12 & 10.5269118213967 & 1.47308817860326 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98283&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]11.4543275117720[/C][C]1.54567248822804[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.168961806666[/C][C]0.831038193334013[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.6117180193699[/C][C]1.38828198063014[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.8943548499658[/C][C]1.10564515003425[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.7288194829093[/C][C]-0.728819482909259[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.27712836625497[/C][C]2.72287163374503[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.8349613115104[/C][C]-1.83496131151042[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.5190753155630[/C][C]-1.51907531556297[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.2876546666686[/C][C]-0.287654666668564[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.98266202959082[/C][C]3.01733797040918[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.3158061960333[/C][C]-2.31580619603329[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.1177715402679[/C][C]-1.11777154026790[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.2309892046120[/C][C]2.76901079538798[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.2771808340937[/C][C]-4.27718083409372[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.6859380696307[/C][C]-0.685938069630715[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.9514443947728[/C][C]0.048555605227192[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]12.0857079252257[/C][C]-2.08570792522574[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.3434923850977[/C][C]-0.343492385097678[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.77787839319098[/C][C]1.22212160680902[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.54495805583857[/C][C]-3.54495805583857[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.68493096388895[/C][C]2.31506903611105[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.4622375895413[/C][C]0.537762410458681[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.6739988091688[/C][C]-0.673998809168759[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.54793088021921[/C][C]2.45206911978079[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.4503773201246[/C][C]0.54962267987537[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.8746623236532[/C][C]0.125337676346752[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.6720094061378[/C][C]-5.67200940613781[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.4920373869371[/C][C]0.507962613062922[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.2253428417033[/C][C]-0.225342841703291[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]10.8007757530054[/C][C]5.19922424699464[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.49047363972408[/C][C]4.50952636027592[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5959942573866[/C][C]-1.59599425738661[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.4941565523869[/C][C]-0.494156552386905[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.114261463498[/C][C]0.885738536501996[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.5435335130268[/C][C]-1.54353351302678[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.31767192821[/C][C]1.68232807179001[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.9627055313080[/C][C]-0.962705531308015[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.22409654509814[/C][C]0.775903454901863[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]13.1151057097753[/C][C]-0.115105709775342[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.2791088910786[/C][C]0.720891108921396[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.9629037480582[/C][C]1.03709625194183[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.10299585248458[/C][C]6.89700414751543[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.7866715130881[/C][C]-4.78667151308811[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.4913532514399[/C][C]-2.49135325143995[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.2757881888268[/C][C]-0.275788188826849[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.8154690719800[/C][C]2.18453092801998[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]13.0182086885763[/C][C]3.98179131142375[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.50888192434085[/C][C]0.491118075659151[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.9300483545774[/C][C]-2.93004835457742[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.6801813150963[/C][C]-1.68018131509631[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.9640930648647[/C][C]-0.964093064864686[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.4678015836414[/C][C]-6.46780158364138[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.4554858308938[/C][C]-0.455485830893791[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.4946435264284[/C][C]-2.49464352642844[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]12.4371261393879[/C][C]1.56287386061212[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.4676310161414[/C][C]-0.467631016141426[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.7855417430767[/C][C]0.214458256923304[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.4203036425596[/C][C]1.57969635744041[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.2689305211847[/C][C]-2.26893052118467[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.2952505273873[/C][C]-0.295250527387342[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.46266661046707[/C][C]-2.46266661046707[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.0023723122088[/C][C]1.99762768779120[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.7144319501693[/C][C]0.285568049830749[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.7532456800423[/C][C]2.24675431995765[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]9.92048663101368[/C][C]-0.920486631013682[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.2641272703568[/C][C]1.73587272964320[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.0288160793959[/C][C]-2.02881607939594[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.4504606414420[/C][C]2.54953935855797[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]12.9809492225135[/C][C]2.01905077748645[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.65526965476048[/C][C]-0.65526965476048[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.4702410303607[/C][C]2.52975896963932[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.3244517916519[/C][C]0.67554820834806[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.0817378824939[/C][C]-1.08173788249391[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]13.9752819157442[/C][C]-2.97528191574416[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.4957196209476[/C][C]-0.495719620947646[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.4197310247318[/C][C]-1.41973102473182[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.2868539311207[/C][C]-0.28685393112073[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.8761397898480[/C][C]0.123860210151980[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.7829549036294[/C][C]3.21704509637059[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.96886701447297[/C][C]-1.96886701447297[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.82863683984184[/C][C]-2.82863683984184[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]11.9330017347079[/C][C]2.06699826529209[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.8381979636553[/C][C]0.161802036344738[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.2090818273308[/C][C]-1.20908182733078[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]8.68205582269451[/C][C]4.31794417730549[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.3827278226039[/C][C]-0.382727822603868[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.32868069070524[/C][C]-0.328680690705235[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.0243408260259[/C][C]-0.0243408260258719[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]15.0650478471956[/C][C]0.934952152804356[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.2509351394506[/C][C]-0.250935139450646[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.1601907440176[/C][C]0.839809255982396[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.5294557228591[/C][C]-3.52945572285908[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.3380960656073[/C][C]0.66190393439274[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.4400204696878[/C][C]1.55997953031224[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.3398016495221[/C][C]0.660198350477908[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.72325369263371[/C][C]0.276746307366286[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.2113803731837[/C][C]-2.21138037318372[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.5839638760142[/C][C]-0.583963876014196[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.7817660837874[/C][C]-1.78176608378740[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.3972848053206[/C][C]0.602715194679391[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.4261707972401[/C][C]0.573829202759879[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.6887175338267[/C][C]1.31128246617330[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.783120023334[/C][C]-1.78312002333400[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]8.80392570606941[/C][C]2.19607429393059[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]6.86893251038793[/C][C]-2.86893251038793[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.5434362544404[/C][C]-0.543436254440421[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.3345009862217[/C][C]-1.33450098622172[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.2838424010198[/C][C]-1.28384240101982[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.4296687016322[/C][C]-2.42966870163224[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.8611316540725[/C][C]-2.86113165407251[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]12.1750326425653[/C][C]-1.17503264256529[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.6138983132027[/C][C]-1.61389831320273[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.4453785390021[/C][C]-0.445378539002147[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.5896355919577[/C][C]0.41036440804235[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.3284752922129[/C][C]0.671524707787085[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.3663580685751[/C][C]2.63364193142493[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]13.4624042615050[/C][C]2.53759573849503[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.7046361347967[/C][C]-1.70463613479673[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.6914142471268[/C][C]0.308585752873246[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.8828606034594[/C][C]3.11713939654063[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.3287353894553[/C][C]0.6712646105447[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.4746361458334[/C][C]1.5253638541666[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.1381161395181[/C][C]-2.13811613951814[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]13.7618095480229[/C][C]-0.76180954802293[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.6529379090762[/C][C]2.34706209092383[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.8327470394836[/C][C]1.16725296051639[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.3724527412253[/C][C]0.627547258774659[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]13.2838711469848[/C][C]-1.28387114698484[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.3710149332596[/C][C]-0.371014933259569[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]6.26171529119994[/C][C]-2.26171529119994[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.3468018491916[/C][C]0.653198150808385[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.4302550440968[/C][C]2.56974495590315[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.3339267659976[/C][C]-1.33392676599763[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]13.0801691844501[/C][C]-0.0801691844500615[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.1051398157146[/C][C]1.89486018428544[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.5070382494129[/C][C]1.49296175058711[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.4735263170367[/C][C]0.526473682963345[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.573267534394[/C][C]-3.573267534394[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]14.0117892393699[/C][C]4.98821076063011[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]12.5715353979438[/C][C]-0.571535397943801[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.1641645992487[/C][C]-0.164164599248651[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.4008870456399[/C][C]-0.400887045639915[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.8196129850909[/C][C]2.18038701490908[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.14265477299094[/C][C]-0.142654772990940[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.7899600333214[/C][C]1.21003996667862[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.7095121600920[/C][C]-0.709512160092034[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.2805515789296[/C][C]-3.28055157892957[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.261451480109[/C][C]-4.261451480109[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.7372099319199[/C][C]1.26279006808015[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.5890623743796[/C][C]1.41093762562044[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.2420751468102[/C][C]-0.242075146810151[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.1131134138541[/C][C]0.88688658614592[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]10.6373404646853[/C][C]-1.63734046468531[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.5779794235252[/C][C]0.422020576474804[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]13.1082107005343[/C][C]0.891789299465702[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.5269118213967[/C][C]1.47308817860326[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98283&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98283&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
11311.45432751177201.54567248822804
21211.1689618066660.831038193334013
31513.61171801936991.38828198063014
41210.89435484996581.10564515003425
51010.7288194829093-0.728819482909259
6129.277128366254972.72287163374503
71516.8349613115104-1.83496131151042
8910.5190753155630-1.51907531556297
91212.2876546666686-0.287654666668564
10117.982662029590823.01733797040918
111113.3158061960333-2.31580619603329
121112.1177715402679-1.11777154026790
131512.23098920461202.76901079538798
14711.2771808340937-4.27718083409372
151111.6859380696307-0.685938069630715
161110.95144439477280.048555605227192
171012.0857079252257-2.08570792522574
181414.3434923850977-0.343492385097678
19108.777878393190981.22212160680902
2069.54495805583857-3.54495805583857
21118.684930963888952.31506903611105
221514.46223758954130.537762410458681
231111.6739988091688-0.673998809168759
24129.547930880219212.45206911978079
251413.45037732012460.54962267987537
261514.87466232365320.125337676346752
27914.6720094061378-5.67200940613781
281312.49203738693710.507962613062922
291313.2253428417033-0.225342841703291
301610.80077575300545.19922424699464
31138.490473639724084.50952636027592
321213.5959942573866-1.59599425738661
331414.4941565523869-0.494156552386905
341110.1142614634980.885738536501996
35910.5435335130268-1.54353351302678
361614.317671928211.68232807179001
371212.9627055313080-0.962705531308015
38109.224096545098140.775903454901863
391313.1151057097753-0.115105709775342
401615.27910889107860.720891108921396
411412.96290374805821.03709625194183
42158.102995852484586.89700414751543
4359.7866715130881-4.78667151308811
44810.4913532514399-2.49135325143995
451111.2757881888268-0.275788188826849
461613.81546907198002.18453092801998
471713.01820868857633.98179131142375
4898.508881924340850.491118075659151
49911.9300483545774-2.93004835457742
501314.6801813150963-1.68018131509631
511010.9640930648647-0.964093064864686
52612.4678015836414-6.46780158364138
531212.4554858308938-0.455485830893791
54810.4946435264284-2.49464352642844
551412.43712613938791.56287386061212
561212.4676310161414-0.467631016141426
571110.78554174307670.214458256923304
581614.42030364255961.57969635744041
59810.2689305211847-2.26893052118467
601515.2952505273873-0.295250527387342
6179.46266661046707-2.46266661046707
621614.00237231220881.99762768779120
631413.71443195016930.285568049830749
641613.75324568004232.24675431995765
6599.92048663101368-0.920486631013682
661412.26412727035681.73587272964320
671113.0288160793959-2.02881607939594
681310.45046064144202.54953935855797
691512.98094922251352.01905077748645
7055.65526965476048-0.65526965476048
711512.47024103036072.52975896963932
721312.32445179165190.67554820834806
731112.0817378824939-1.08173788249391
741113.9752819157442-2.97528191574416
751212.4957196209476-0.495719620947646
761213.4197310247318-1.41973102473182
771212.2868539311207-0.28685393112073
781211.87613978984800.123860210151980
791410.78295490362943.21704509637059
8067.96886701447297-1.96886701447297
8179.82863683984184-2.82863683984184
821411.93300173470792.06699826529209
831413.83819796365530.161802036344738
841011.2090818273308-1.20908182733078
85138.682055822694514.31794417730549
861212.3827278226039-0.382727822603868
8799.32868069070524-0.328680690705235
881212.0243408260259-0.0243408260258719
891615.06504784719560.934952152804356
901010.2509351394506-0.250935139450646
911413.16019074401760.839809255982396
921013.5294557228591-3.52945572285908
931615.33809606560730.66190393439274
941513.44002046968781.55997953031224
951211.33980164952210.660198350477908
96109.723253692633710.276746307366286
97810.2113803731837-2.21138037318372
9888.5839638760142-0.583963876014196
991112.7817660837874-1.78176608378740
1001312.39728480532060.602715194679391
1011615.42617079724010.573829202759879
1021614.68871753382671.31128246617330
1031415.783120023334-1.78312002333400
104118.803925706069412.19607429393059
10546.86893251038793-2.86893251038793
1061414.5434362544404-0.543436254440421
107910.3345009862217-1.33450098622172
1081415.2838424010198-1.28384240101982
109810.4296687016322-2.42966870163224
110810.8611316540725-2.86113165407251
1111112.1750326425653-1.17503264256529
1121213.6138983132027-1.61389831320273
1131111.4453785390021-0.445378539002147
1141413.58963559195770.41036440804235
1151514.32847529221290.671524707787085
1161613.36635806857512.63364193142493
1171613.46240426150502.53759573849503
1181112.7046361347967-1.70463613479673
1191413.69141424712680.308585752873246
1201410.88286060345943.11713939654063
1211211.32873538945530.6712646105447
1221412.47463614583341.5253638541666
123810.1381161395181-2.13811613951814
1241313.7618095480229-0.76180954802293
1251613.65293790907622.34706209092383
1261210.83274703948361.16725296051639
1271615.37245274122530.627547258774659
1281213.2838711469848-1.28387114698484
1291111.3710149332596-0.371014933259569
13046.26171529119994-2.26171529119994
1311615.34680184919160.653198150808385
1321512.43025504409682.56974495590315
1331011.3339267659976-1.33392676599763
1341313.0801691844501-0.0801691844500615
1351513.10513981571461.89486018428544
1361210.50703824941291.49296175058711
1371413.47352631703670.526473682963345
138710.573267534394-3.573267534394
1391914.01178923936994.98821076063011
1401212.5715353979438-0.571535397943801
1411212.1641645992487-0.164164599248651
1421313.4008870456399-0.400887045639915
1431512.81961298509092.18038701490908
14488.14265477299094-0.142654772990940
1451210.78996003332141.21003996667862
1461010.7095121600920-0.709512160092034
147811.2805515789296-3.28055157892957
1481014.261451480109-4.261451480109
1491513.73720993191991.26279006808015
1501614.58906237437961.41093762562044
1511313.2420751468102-0.242075146810151
1521615.11311341385410.88688658614592
153910.6373404646853-1.63734046468531
1541413.57797942352520.422020576474804
1551413.10821070053430.891789299465702
1561210.52691182139671.47308817860326






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.07385393424063790.1477078684812760.926146065759362
110.1114121685982580.2228243371965160.888587831401742
120.07345787413724060.1469157482744810.92654212586276
130.548827345484510.902345309030980.45117265451549
140.7186992086308220.5626015827383550.281300791369178
150.6520904123102280.6958191753795450.347909587689772
160.5761124921378210.8477750157243570.423887507862179
170.489204168649930.978408337299860.51079583135007
180.4518794925294630.9037589850589270.548120507470537
190.3660004165063050.732000833012610.633999583493695
200.5100387405013010.9799225189973990.489961259498699
210.5164266747214250.967146650557150.483573325278575
220.5458465860458270.9083068279083470.454153413954173
230.4723615361310520.9447230722621040.527638463868948
240.4779454316082210.9558908632164430.522054568391779
250.4347313659746490.8694627319492980.565268634025351
260.3743861511192350.748772302238470.625613848880765
270.6200735979705190.7598528040589610.379926402029481
280.5749546427581560.8500907144836880.425045357241844
290.5197605362760810.9604789274478380.480239463723919
300.7117639701334920.5764720597330170.288236029866508
310.8125705987111380.3748588025777250.187429401288862
320.7768257167972980.4463485664054050.223174283202702
330.7307830950400260.5384338099199480.269216904959974
340.6978619859844220.6042760280311560.302138014015578
350.695750871668920.608498256662160.30424912833108
360.7084453286808880.5831093426382230.291554671319112
370.6753947060822270.6492105878355460.324605293917773
380.6263349167561530.7473301664876940.373665083243847
390.5728974113800880.8542051772398240.427102588619912
400.5435509648210210.9128980703579580.456449035178979
410.5020915590297390.9958168819405220.497908440970261
420.7575288156648850.4849423686702310.242471184335115
430.9563070475383710.0873859049232570.0436929524616285
440.9666834209797180.06663315804056360.0333165790202818
450.9562902741612520.08741945167749670.0437097258387484
460.9612254468567140.07754910628657180.0387745531432859
470.9806509822552610.03869803548947740.0193490177447387
480.974216525457870.05156694908426220.0257834745421311
490.9821366416463710.03572671670725690.0178633583536284
500.9792101509228930.04157969815421410.0207898490771071
510.974466745752570.05106650849486110.0255332542474306
520.9975974613516140.0048050772967720.002402538648386
530.9965345544845240.006930891030952280.00346544551547614
540.9971213461018360.00575730779632820.0028786538981641
550.997104513935850.005790972128298580.00289548606414929
560.9959130703232510.008173859353497150.00408692967674857
570.9942292340735850.01154153185282980.00577076592641488
580.9935649807290860.01287003854182850.00643501927091424
590.995147676804760.009704646390480470.00485232319524023
600.9936622767108020.01267544657839690.00633772328919846
610.994486780541910.01102643891617780.00551321945808888
620.9950416877703480.009916624459304080.00495831222965204
630.9933549103728010.01329017925439750.00664508962719873
640.9937154061575680.01256918768486380.0062845938424319
650.9920741012968070.01585179740638680.00792589870319338
660.9912330134799510.01753397304009720.00876698652004858
670.9911936554924380.01761268901512420.00880634450756212
680.992408603297750.01518279340450070.00759139670225035
690.992622989007230.01475402198553770.00737701099276885
700.9900354594781920.01992908104361620.0099645405218081
710.9915123445940230.01697531081195310.00848765540597653
720.9887447120094480.02251057598110490.0112552879905524
730.9858755355293720.0282489289412560.014124464470628
740.9896839541107110.02063209177857820.0103160458892891
750.9860649847109670.02787003057806630.0139350152890332
760.9838140106574770.03237197868504520.0161859893425226
770.9787572305813240.04248553883735130.0212427694186757
780.971898413430230.05620317313953980.0281015865697699
790.9819288397198160.03614232056036870.0180711602801844
800.9811711890934850.03765762181302920.0188288109065146
810.9844656187665860.03106876246682740.0155343812334137
820.9855315805793760.0289368388412480.014468419420624
830.9805481128186120.03890377436277650.0194518871813882
840.9760251948548720.04794961029025640.0239748051451282
850.9936147428220520.01277051435589670.00638525717794837
860.9911861584938160.01762768301236880.00881384150618442
870.9879374134762950.02412517304741030.0120625865237051
880.9841687718519260.03166245629614730.0158312281480737
890.9802669954503850.03946600909922930.0197330045496147
900.9739334460187770.05213310796244690.0260665539812234
910.968947376731810.06210524653637970.0310526232681899
920.9814832067207420.03703358655851690.0185167932792585
930.9760770990135980.04784580197280390.0239229009864019
940.9728246090977620.05435078180447630.0271753909022381
950.965979035563560.0680419288728820.034020964436441
960.9575439971121530.08491200577569440.0424560028878472
970.9535973701117690.09280525977646260.0464026298882313
980.9410287167714980.1179425664570050.0589712832285023
990.9353320342401170.1293359315197650.0646679657598827
1000.9214983733308720.1570032533382570.0785016266691284
1010.9026602897059450.1946794205881110.0973397102940554
1020.8886871692377960.2226256615244080.111312830762204
1030.89189046225240.2162190754951990.108109537747600
1040.9289292049938920.1421415900122150.0710707950061076
1050.9254569164493070.1490861671013860.0745430835506932
1060.905625300271350.1887493994573010.0943746997286506
1070.885230909702820.2295381805943590.114769090297179
1080.8795482157603140.2409035684793710.120451784239686
1090.8769714482726470.2460571034547060.123028551727353
1100.8967147050313440.2065705899373120.103285294968656
1110.8888188319459750.2223623361080510.111181168054025
1120.9265296027254040.1469407945491920.073470397274596
1130.9048660085096630.1902679829806750.0951339914903373
1140.8827618407216510.2344763185566970.117238159278349
1150.8565786521910780.2868426956178440.143421347808922
1160.8473604826302370.3052790347395250.152639517369763
1170.8451600678784470.3096798642431050.154839932121553
1180.8462139050526980.3075721898946040.153786094947302
1190.8140406528364670.3719186943270660.185959347163533
1200.8513210199907520.2973579600184960.148678980009248
1210.8189905159203930.3620189681592150.181009484079607
1220.790452367248020.4190952655039610.209547632751980
1230.7825107808871970.4349784382256050.217489219112803
1240.7467063561374270.5065872877251460.253293643862573
1250.7341309093665210.5317381812669570.265869090633479
1260.7153981521943160.5692036956113680.284601847805684
1270.6572114164685910.6855771670628180.342788583531409
1280.6292789849081680.7414420301836650.370721015091832
1290.6362651289010090.7274697421979830.363734871098991
1300.6400845547008640.7198308905982720.359915445299136
1310.5945461467446810.8109077065106370.405453853255319
1320.5531033495610240.8937933008779530.446896650438976
1330.5436999016228740.9126001967542520.456300098377126
1340.471346782798360.942693565596720.52865321720164
1350.4143155955371650.828631191074330.585684404462835
1360.3937028356181140.7874056712362270.606297164381886
1370.3207904510305650.641580902061130.679209548969435
1380.4116151689132430.8232303378264860.588384831086757
1390.7713645714642730.4572708570714540.228635428535727
1400.7637751011990890.4724497976018230.236224898800911
1410.7096114088818370.5807771822363270.290388591118163
1420.6233492404007720.7533015191984560.376650759599228
1430.559951555234120.8800968895317610.440048444765880
1440.4346622858536390.8693245717072780.565337714146361
1450.6995806556278060.6008386887443870.300419344372194
1460.8492485876772470.3015028246455060.150751412322753

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0738539342406379 & 0.147707868481276 & 0.926146065759362 \tabularnewline
11 & 0.111412168598258 & 0.222824337196516 & 0.888587831401742 \tabularnewline
12 & 0.0734578741372406 & 0.146915748274481 & 0.92654212586276 \tabularnewline
13 & 0.54882734548451 & 0.90234530903098 & 0.45117265451549 \tabularnewline
14 & 0.718699208630822 & 0.562601582738355 & 0.281300791369178 \tabularnewline
15 & 0.652090412310228 & 0.695819175379545 & 0.347909587689772 \tabularnewline
16 & 0.576112492137821 & 0.847775015724357 & 0.423887507862179 \tabularnewline
17 & 0.48920416864993 & 0.97840833729986 & 0.51079583135007 \tabularnewline
18 & 0.451879492529463 & 0.903758985058927 & 0.548120507470537 \tabularnewline
19 & 0.366000416506305 & 0.73200083301261 & 0.633999583493695 \tabularnewline
20 & 0.510038740501301 & 0.979922518997399 & 0.489961259498699 \tabularnewline
21 & 0.516426674721425 & 0.96714665055715 & 0.483573325278575 \tabularnewline
22 & 0.545846586045827 & 0.908306827908347 & 0.454153413954173 \tabularnewline
23 & 0.472361536131052 & 0.944723072262104 & 0.527638463868948 \tabularnewline
24 & 0.477945431608221 & 0.955890863216443 & 0.522054568391779 \tabularnewline
25 & 0.434731365974649 & 0.869462731949298 & 0.565268634025351 \tabularnewline
26 & 0.374386151119235 & 0.74877230223847 & 0.625613848880765 \tabularnewline
27 & 0.620073597970519 & 0.759852804058961 & 0.379926402029481 \tabularnewline
28 & 0.574954642758156 & 0.850090714483688 & 0.425045357241844 \tabularnewline
29 & 0.519760536276081 & 0.960478927447838 & 0.480239463723919 \tabularnewline
30 & 0.711763970133492 & 0.576472059733017 & 0.288236029866508 \tabularnewline
31 & 0.812570598711138 & 0.374858802577725 & 0.187429401288862 \tabularnewline
32 & 0.776825716797298 & 0.446348566405405 & 0.223174283202702 \tabularnewline
33 & 0.730783095040026 & 0.538433809919948 & 0.269216904959974 \tabularnewline
34 & 0.697861985984422 & 0.604276028031156 & 0.302138014015578 \tabularnewline
35 & 0.69575087166892 & 0.60849825666216 & 0.30424912833108 \tabularnewline
36 & 0.708445328680888 & 0.583109342638223 & 0.291554671319112 \tabularnewline
37 & 0.675394706082227 & 0.649210587835546 & 0.324605293917773 \tabularnewline
38 & 0.626334916756153 & 0.747330166487694 & 0.373665083243847 \tabularnewline
39 & 0.572897411380088 & 0.854205177239824 & 0.427102588619912 \tabularnewline
40 & 0.543550964821021 & 0.912898070357958 & 0.456449035178979 \tabularnewline
41 & 0.502091559029739 & 0.995816881940522 & 0.497908440970261 \tabularnewline
42 & 0.757528815664885 & 0.484942368670231 & 0.242471184335115 \tabularnewline
43 & 0.956307047538371 & 0.087385904923257 & 0.0436929524616285 \tabularnewline
44 & 0.966683420979718 & 0.0666331580405636 & 0.0333165790202818 \tabularnewline
45 & 0.956290274161252 & 0.0874194516774967 & 0.0437097258387484 \tabularnewline
46 & 0.961225446856714 & 0.0775491062865718 & 0.0387745531432859 \tabularnewline
47 & 0.980650982255261 & 0.0386980354894774 & 0.0193490177447387 \tabularnewline
48 & 0.97421652545787 & 0.0515669490842622 & 0.0257834745421311 \tabularnewline
49 & 0.982136641646371 & 0.0357267167072569 & 0.0178633583536284 \tabularnewline
50 & 0.979210150922893 & 0.0415796981542141 & 0.0207898490771071 \tabularnewline
51 & 0.97446674575257 & 0.0510665084948611 & 0.0255332542474306 \tabularnewline
52 & 0.997597461351614 & 0.004805077296772 & 0.002402538648386 \tabularnewline
53 & 0.996534554484524 & 0.00693089103095228 & 0.00346544551547614 \tabularnewline
54 & 0.997121346101836 & 0.0057573077963282 & 0.0028786538981641 \tabularnewline
55 & 0.99710451393585 & 0.00579097212829858 & 0.00289548606414929 \tabularnewline
56 & 0.995913070323251 & 0.00817385935349715 & 0.00408692967674857 \tabularnewline
57 & 0.994229234073585 & 0.0115415318528298 & 0.00577076592641488 \tabularnewline
58 & 0.993564980729086 & 0.0128700385418285 & 0.00643501927091424 \tabularnewline
59 & 0.99514767680476 & 0.00970464639048047 & 0.00485232319524023 \tabularnewline
60 & 0.993662276710802 & 0.0126754465783969 & 0.00633772328919846 \tabularnewline
61 & 0.99448678054191 & 0.0110264389161778 & 0.00551321945808888 \tabularnewline
62 & 0.995041687770348 & 0.00991662445930408 & 0.00495831222965204 \tabularnewline
63 & 0.993354910372801 & 0.0132901792543975 & 0.00664508962719873 \tabularnewline
64 & 0.993715406157568 & 0.0125691876848638 & 0.0062845938424319 \tabularnewline
65 & 0.992074101296807 & 0.0158517974063868 & 0.00792589870319338 \tabularnewline
66 & 0.991233013479951 & 0.0175339730400972 & 0.00876698652004858 \tabularnewline
67 & 0.991193655492438 & 0.0176126890151242 & 0.00880634450756212 \tabularnewline
68 & 0.99240860329775 & 0.0151827934045007 & 0.00759139670225035 \tabularnewline
69 & 0.99262298900723 & 0.0147540219855377 & 0.00737701099276885 \tabularnewline
70 & 0.990035459478192 & 0.0199290810436162 & 0.0099645405218081 \tabularnewline
71 & 0.991512344594023 & 0.0169753108119531 & 0.00848765540597653 \tabularnewline
72 & 0.988744712009448 & 0.0225105759811049 & 0.0112552879905524 \tabularnewline
73 & 0.985875535529372 & 0.028248928941256 & 0.014124464470628 \tabularnewline
74 & 0.989683954110711 & 0.0206320917785782 & 0.0103160458892891 \tabularnewline
75 & 0.986064984710967 & 0.0278700305780663 & 0.0139350152890332 \tabularnewline
76 & 0.983814010657477 & 0.0323719786850452 & 0.0161859893425226 \tabularnewline
77 & 0.978757230581324 & 0.0424855388373513 & 0.0212427694186757 \tabularnewline
78 & 0.97189841343023 & 0.0562031731395398 & 0.0281015865697699 \tabularnewline
79 & 0.981928839719816 & 0.0361423205603687 & 0.0180711602801844 \tabularnewline
80 & 0.981171189093485 & 0.0376576218130292 & 0.0188288109065146 \tabularnewline
81 & 0.984465618766586 & 0.0310687624668274 & 0.0155343812334137 \tabularnewline
82 & 0.985531580579376 & 0.028936838841248 & 0.014468419420624 \tabularnewline
83 & 0.980548112818612 & 0.0389037743627765 & 0.0194518871813882 \tabularnewline
84 & 0.976025194854872 & 0.0479496102902564 & 0.0239748051451282 \tabularnewline
85 & 0.993614742822052 & 0.0127705143558967 & 0.00638525717794837 \tabularnewline
86 & 0.991186158493816 & 0.0176276830123688 & 0.00881384150618442 \tabularnewline
87 & 0.987937413476295 & 0.0241251730474103 & 0.0120625865237051 \tabularnewline
88 & 0.984168771851926 & 0.0316624562961473 & 0.0158312281480737 \tabularnewline
89 & 0.980266995450385 & 0.0394660090992293 & 0.0197330045496147 \tabularnewline
90 & 0.973933446018777 & 0.0521331079624469 & 0.0260665539812234 \tabularnewline
91 & 0.96894737673181 & 0.0621052465363797 & 0.0310526232681899 \tabularnewline
92 & 0.981483206720742 & 0.0370335865585169 & 0.0185167932792585 \tabularnewline
93 & 0.976077099013598 & 0.0478458019728039 & 0.0239229009864019 \tabularnewline
94 & 0.972824609097762 & 0.0543507818044763 & 0.0271753909022381 \tabularnewline
95 & 0.96597903556356 & 0.068041928872882 & 0.034020964436441 \tabularnewline
96 & 0.957543997112153 & 0.0849120057756944 & 0.0424560028878472 \tabularnewline
97 & 0.953597370111769 & 0.0928052597764626 & 0.0464026298882313 \tabularnewline
98 & 0.941028716771498 & 0.117942566457005 & 0.0589712832285023 \tabularnewline
99 & 0.935332034240117 & 0.129335931519765 & 0.0646679657598827 \tabularnewline
100 & 0.921498373330872 & 0.157003253338257 & 0.0785016266691284 \tabularnewline
101 & 0.902660289705945 & 0.194679420588111 & 0.0973397102940554 \tabularnewline
102 & 0.888687169237796 & 0.222625661524408 & 0.111312830762204 \tabularnewline
103 & 0.8918904622524 & 0.216219075495199 & 0.108109537747600 \tabularnewline
104 & 0.928929204993892 & 0.142141590012215 & 0.0710707950061076 \tabularnewline
105 & 0.925456916449307 & 0.149086167101386 & 0.0745430835506932 \tabularnewline
106 & 0.90562530027135 & 0.188749399457301 & 0.0943746997286506 \tabularnewline
107 & 0.88523090970282 & 0.229538180594359 & 0.114769090297179 \tabularnewline
108 & 0.879548215760314 & 0.240903568479371 & 0.120451784239686 \tabularnewline
109 & 0.876971448272647 & 0.246057103454706 & 0.123028551727353 \tabularnewline
110 & 0.896714705031344 & 0.206570589937312 & 0.103285294968656 \tabularnewline
111 & 0.888818831945975 & 0.222362336108051 & 0.111181168054025 \tabularnewline
112 & 0.926529602725404 & 0.146940794549192 & 0.073470397274596 \tabularnewline
113 & 0.904866008509663 & 0.190267982980675 & 0.0951339914903373 \tabularnewline
114 & 0.882761840721651 & 0.234476318556697 & 0.117238159278349 \tabularnewline
115 & 0.856578652191078 & 0.286842695617844 & 0.143421347808922 \tabularnewline
116 & 0.847360482630237 & 0.305279034739525 & 0.152639517369763 \tabularnewline
117 & 0.845160067878447 & 0.309679864243105 & 0.154839932121553 \tabularnewline
118 & 0.846213905052698 & 0.307572189894604 & 0.153786094947302 \tabularnewline
119 & 0.814040652836467 & 0.371918694327066 & 0.185959347163533 \tabularnewline
120 & 0.851321019990752 & 0.297357960018496 & 0.148678980009248 \tabularnewline
121 & 0.818990515920393 & 0.362018968159215 & 0.181009484079607 \tabularnewline
122 & 0.79045236724802 & 0.419095265503961 & 0.209547632751980 \tabularnewline
123 & 0.782510780887197 & 0.434978438225605 & 0.217489219112803 \tabularnewline
124 & 0.746706356137427 & 0.506587287725146 & 0.253293643862573 \tabularnewline
125 & 0.734130909366521 & 0.531738181266957 & 0.265869090633479 \tabularnewline
126 & 0.715398152194316 & 0.569203695611368 & 0.284601847805684 \tabularnewline
127 & 0.657211416468591 & 0.685577167062818 & 0.342788583531409 \tabularnewline
128 & 0.629278984908168 & 0.741442030183665 & 0.370721015091832 \tabularnewline
129 & 0.636265128901009 & 0.727469742197983 & 0.363734871098991 \tabularnewline
130 & 0.640084554700864 & 0.719830890598272 & 0.359915445299136 \tabularnewline
131 & 0.594546146744681 & 0.810907706510637 & 0.405453853255319 \tabularnewline
132 & 0.553103349561024 & 0.893793300877953 & 0.446896650438976 \tabularnewline
133 & 0.543699901622874 & 0.912600196754252 & 0.456300098377126 \tabularnewline
134 & 0.47134678279836 & 0.94269356559672 & 0.52865321720164 \tabularnewline
135 & 0.414315595537165 & 0.82863119107433 & 0.585684404462835 \tabularnewline
136 & 0.393702835618114 & 0.787405671236227 & 0.606297164381886 \tabularnewline
137 & 0.320790451030565 & 0.64158090206113 & 0.679209548969435 \tabularnewline
138 & 0.411615168913243 & 0.823230337826486 & 0.588384831086757 \tabularnewline
139 & 0.771364571464273 & 0.457270857071454 & 0.228635428535727 \tabularnewline
140 & 0.763775101199089 & 0.472449797601823 & 0.236224898800911 \tabularnewline
141 & 0.709611408881837 & 0.580777182236327 & 0.290388591118163 \tabularnewline
142 & 0.623349240400772 & 0.753301519198456 & 0.376650759599228 \tabularnewline
143 & 0.55995155523412 & 0.880096889531761 & 0.440048444765880 \tabularnewline
144 & 0.434662285853639 & 0.869324571707278 & 0.565337714146361 \tabularnewline
145 & 0.699580655627806 & 0.600838688744387 & 0.300419344372194 \tabularnewline
146 & 0.849248587677247 & 0.301502824645506 & 0.150751412322753 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98283&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.0738539342406379[/C][C]0.147707868481276[/C][C]0.926146065759362[/C][/ROW]
[ROW][C]11[/C][C]0.111412168598258[/C][C]0.222824337196516[/C][C]0.888587831401742[/C][/ROW]
[ROW][C]12[/C][C]0.0734578741372406[/C][C]0.146915748274481[/C][C]0.92654212586276[/C][/ROW]
[ROW][C]13[/C][C]0.54882734548451[/C][C]0.90234530903098[/C][C]0.45117265451549[/C][/ROW]
[ROW][C]14[/C][C]0.718699208630822[/C][C]0.562601582738355[/C][C]0.281300791369178[/C][/ROW]
[ROW][C]15[/C][C]0.652090412310228[/C][C]0.695819175379545[/C][C]0.347909587689772[/C][/ROW]
[ROW][C]16[/C][C]0.576112492137821[/C][C]0.847775015724357[/C][C]0.423887507862179[/C][/ROW]
[ROW][C]17[/C][C]0.48920416864993[/C][C]0.97840833729986[/C][C]0.51079583135007[/C][/ROW]
[ROW][C]18[/C][C]0.451879492529463[/C][C]0.903758985058927[/C][C]0.548120507470537[/C][/ROW]
[ROW][C]19[/C][C]0.366000416506305[/C][C]0.73200083301261[/C][C]0.633999583493695[/C][/ROW]
[ROW][C]20[/C][C]0.510038740501301[/C][C]0.979922518997399[/C][C]0.489961259498699[/C][/ROW]
[ROW][C]21[/C][C]0.516426674721425[/C][C]0.96714665055715[/C][C]0.483573325278575[/C][/ROW]
[ROW][C]22[/C][C]0.545846586045827[/C][C]0.908306827908347[/C][C]0.454153413954173[/C][/ROW]
[ROW][C]23[/C][C]0.472361536131052[/C][C]0.944723072262104[/C][C]0.527638463868948[/C][/ROW]
[ROW][C]24[/C][C]0.477945431608221[/C][C]0.955890863216443[/C][C]0.522054568391779[/C][/ROW]
[ROW][C]25[/C][C]0.434731365974649[/C][C]0.869462731949298[/C][C]0.565268634025351[/C][/ROW]
[ROW][C]26[/C][C]0.374386151119235[/C][C]0.74877230223847[/C][C]0.625613848880765[/C][/ROW]
[ROW][C]27[/C][C]0.620073597970519[/C][C]0.759852804058961[/C][C]0.379926402029481[/C][/ROW]
[ROW][C]28[/C][C]0.574954642758156[/C][C]0.850090714483688[/C][C]0.425045357241844[/C][/ROW]
[ROW][C]29[/C][C]0.519760536276081[/C][C]0.960478927447838[/C][C]0.480239463723919[/C][/ROW]
[ROW][C]30[/C][C]0.711763970133492[/C][C]0.576472059733017[/C][C]0.288236029866508[/C][/ROW]
[ROW][C]31[/C][C]0.812570598711138[/C][C]0.374858802577725[/C][C]0.187429401288862[/C][/ROW]
[ROW][C]32[/C][C]0.776825716797298[/C][C]0.446348566405405[/C][C]0.223174283202702[/C][/ROW]
[ROW][C]33[/C][C]0.730783095040026[/C][C]0.538433809919948[/C][C]0.269216904959974[/C][/ROW]
[ROW][C]34[/C][C]0.697861985984422[/C][C]0.604276028031156[/C][C]0.302138014015578[/C][/ROW]
[ROW][C]35[/C][C]0.69575087166892[/C][C]0.60849825666216[/C][C]0.30424912833108[/C][/ROW]
[ROW][C]36[/C][C]0.708445328680888[/C][C]0.583109342638223[/C][C]0.291554671319112[/C][/ROW]
[ROW][C]37[/C][C]0.675394706082227[/C][C]0.649210587835546[/C][C]0.324605293917773[/C][/ROW]
[ROW][C]38[/C][C]0.626334916756153[/C][C]0.747330166487694[/C][C]0.373665083243847[/C][/ROW]
[ROW][C]39[/C][C]0.572897411380088[/C][C]0.854205177239824[/C][C]0.427102588619912[/C][/ROW]
[ROW][C]40[/C][C]0.543550964821021[/C][C]0.912898070357958[/C][C]0.456449035178979[/C][/ROW]
[ROW][C]41[/C][C]0.502091559029739[/C][C]0.995816881940522[/C][C]0.497908440970261[/C][/ROW]
[ROW][C]42[/C][C]0.757528815664885[/C][C]0.484942368670231[/C][C]0.242471184335115[/C][/ROW]
[ROW][C]43[/C][C]0.956307047538371[/C][C]0.087385904923257[/C][C]0.0436929524616285[/C][/ROW]
[ROW][C]44[/C][C]0.966683420979718[/C][C]0.0666331580405636[/C][C]0.0333165790202818[/C][/ROW]
[ROW][C]45[/C][C]0.956290274161252[/C][C]0.0874194516774967[/C][C]0.0437097258387484[/C][/ROW]
[ROW][C]46[/C][C]0.961225446856714[/C][C]0.0775491062865718[/C][C]0.0387745531432859[/C][/ROW]
[ROW][C]47[/C][C]0.980650982255261[/C][C]0.0386980354894774[/C][C]0.0193490177447387[/C][/ROW]
[ROW][C]48[/C][C]0.97421652545787[/C][C]0.0515669490842622[/C][C]0.0257834745421311[/C][/ROW]
[ROW][C]49[/C][C]0.982136641646371[/C][C]0.0357267167072569[/C][C]0.0178633583536284[/C][/ROW]
[ROW][C]50[/C][C]0.979210150922893[/C][C]0.0415796981542141[/C][C]0.0207898490771071[/C][/ROW]
[ROW][C]51[/C][C]0.97446674575257[/C][C]0.0510665084948611[/C][C]0.0255332542474306[/C][/ROW]
[ROW][C]52[/C][C]0.997597461351614[/C][C]0.004805077296772[/C][C]0.002402538648386[/C][/ROW]
[ROW][C]53[/C][C]0.996534554484524[/C][C]0.00693089103095228[/C][C]0.00346544551547614[/C][/ROW]
[ROW][C]54[/C][C]0.997121346101836[/C][C]0.0057573077963282[/C][C]0.0028786538981641[/C][/ROW]
[ROW][C]55[/C][C]0.99710451393585[/C][C]0.00579097212829858[/C][C]0.00289548606414929[/C][/ROW]
[ROW][C]56[/C][C]0.995913070323251[/C][C]0.00817385935349715[/C][C]0.00408692967674857[/C][/ROW]
[ROW][C]57[/C][C]0.994229234073585[/C][C]0.0115415318528298[/C][C]0.00577076592641488[/C][/ROW]
[ROW][C]58[/C][C]0.993564980729086[/C][C]0.0128700385418285[/C][C]0.00643501927091424[/C][/ROW]
[ROW][C]59[/C][C]0.99514767680476[/C][C]0.00970464639048047[/C][C]0.00485232319524023[/C][/ROW]
[ROW][C]60[/C][C]0.993662276710802[/C][C]0.0126754465783969[/C][C]0.00633772328919846[/C][/ROW]
[ROW][C]61[/C][C]0.99448678054191[/C][C]0.0110264389161778[/C][C]0.00551321945808888[/C][/ROW]
[ROW][C]62[/C][C]0.995041687770348[/C][C]0.00991662445930408[/C][C]0.00495831222965204[/C][/ROW]
[ROW][C]63[/C][C]0.993354910372801[/C][C]0.0132901792543975[/C][C]0.00664508962719873[/C][/ROW]
[ROW][C]64[/C][C]0.993715406157568[/C][C]0.0125691876848638[/C][C]0.0062845938424319[/C][/ROW]
[ROW][C]65[/C][C]0.992074101296807[/C][C]0.0158517974063868[/C][C]0.00792589870319338[/C][/ROW]
[ROW][C]66[/C][C]0.991233013479951[/C][C]0.0175339730400972[/C][C]0.00876698652004858[/C][/ROW]
[ROW][C]67[/C][C]0.991193655492438[/C][C]0.0176126890151242[/C][C]0.00880634450756212[/C][/ROW]
[ROW][C]68[/C][C]0.99240860329775[/C][C]0.0151827934045007[/C][C]0.00759139670225035[/C][/ROW]
[ROW][C]69[/C][C]0.99262298900723[/C][C]0.0147540219855377[/C][C]0.00737701099276885[/C][/ROW]
[ROW][C]70[/C][C]0.990035459478192[/C][C]0.0199290810436162[/C][C]0.0099645405218081[/C][/ROW]
[ROW][C]71[/C][C]0.991512344594023[/C][C]0.0169753108119531[/C][C]0.00848765540597653[/C][/ROW]
[ROW][C]72[/C][C]0.988744712009448[/C][C]0.0225105759811049[/C][C]0.0112552879905524[/C][/ROW]
[ROW][C]73[/C][C]0.985875535529372[/C][C]0.028248928941256[/C][C]0.014124464470628[/C][/ROW]
[ROW][C]74[/C][C]0.989683954110711[/C][C]0.0206320917785782[/C][C]0.0103160458892891[/C][/ROW]
[ROW][C]75[/C][C]0.986064984710967[/C][C]0.0278700305780663[/C][C]0.0139350152890332[/C][/ROW]
[ROW][C]76[/C][C]0.983814010657477[/C][C]0.0323719786850452[/C][C]0.0161859893425226[/C][/ROW]
[ROW][C]77[/C][C]0.978757230581324[/C][C]0.0424855388373513[/C][C]0.0212427694186757[/C][/ROW]
[ROW][C]78[/C][C]0.97189841343023[/C][C]0.0562031731395398[/C][C]0.0281015865697699[/C][/ROW]
[ROW][C]79[/C][C]0.981928839719816[/C][C]0.0361423205603687[/C][C]0.0180711602801844[/C][/ROW]
[ROW][C]80[/C][C]0.981171189093485[/C][C]0.0376576218130292[/C][C]0.0188288109065146[/C][/ROW]
[ROW][C]81[/C][C]0.984465618766586[/C][C]0.0310687624668274[/C][C]0.0155343812334137[/C][/ROW]
[ROW][C]82[/C][C]0.985531580579376[/C][C]0.028936838841248[/C][C]0.014468419420624[/C][/ROW]
[ROW][C]83[/C][C]0.980548112818612[/C][C]0.0389037743627765[/C][C]0.0194518871813882[/C][/ROW]
[ROW][C]84[/C][C]0.976025194854872[/C][C]0.0479496102902564[/C][C]0.0239748051451282[/C][/ROW]
[ROW][C]85[/C][C]0.993614742822052[/C][C]0.0127705143558967[/C][C]0.00638525717794837[/C][/ROW]
[ROW][C]86[/C][C]0.991186158493816[/C][C]0.0176276830123688[/C][C]0.00881384150618442[/C][/ROW]
[ROW][C]87[/C][C]0.987937413476295[/C][C]0.0241251730474103[/C][C]0.0120625865237051[/C][/ROW]
[ROW][C]88[/C][C]0.984168771851926[/C][C]0.0316624562961473[/C][C]0.0158312281480737[/C][/ROW]
[ROW][C]89[/C][C]0.980266995450385[/C][C]0.0394660090992293[/C][C]0.0197330045496147[/C][/ROW]
[ROW][C]90[/C][C]0.973933446018777[/C][C]0.0521331079624469[/C][C]0.0260665539812234[/C][/ROW]
[ROW][C]91[/C][C]0.96894737673181[/C][C]0.0621052465363797[/C][C]0.0310526232681899[/C][/ROW]
[ROW][C]92[/C][C]0.981483206720742[/C][C]0.0370335865585169[/C][C]0.0185167932792585[/C][/ROW]
[ROW][C]93[/C][C]0.976077099013598[/C][C]0.0478458019728039[/C][C]0.0239229009864019[/C][/ROW]
[ROW][C]94[/C][C]0.972824609097762[/C][C]0.0543507818044763[/C][C]0.0271753909022381[/C][/ROW]
[ROW][C]95[/C][C]0.96597903556356[/C][C]0.068041928872882[/C][C]0.034020964436441[/C][/ROW]
[ROW][C]96[/C][C]0.957543997112153[/C][C]0.0849120057756944[/C][C]0.0424560028878472[/C][/ROW]
[ROW][C]97[/C][C]0.953597370111769[/C][C]0.0928052597764626[/C][C]0.0464026298882313[/C][/ROW]
[ROW][C]98[/C][C]0.941028716771498[/C][C]0.117942566457005[/C][C]0.0589712832285023[/C][/ROW]
[ROW][C]99[/C][C]0.935332034240117[/C][C]0.129335931519765[/C][C]0.0646679657598827[/C][/ROW]
[ROW][C]100[/C][C]0.921498373330872[/C][C]0.157003253338257[/C][C]0.0785016266691284[/C][/ROW]
[ROW][C]101[/C][C]0.902660289705945[/C][C]0.194679420588111[/C][C]0.0973397102940554[/C][/ROW]
[ROW][C]102[/C][C]0.888687169237796[/C][C]0.222625661524408[/C][C]0.111312830762204[/C][/ROW]
[ROW][C]103[/C][C]0.8918904622524[/C][C]0.216219075495199[/C][C]0.108109537747600[/C][/ROW]
[ROW][C]104[/C][C]0.928929204993892[/C][C]0.142141590012215[/C][C]0.0710707950061076[/C][/ROW]
[ROW][C]105[/C][C]0.925456916449307[/C][C]0.149086167101386[/C][C]0.0745430835506932[/C][/ROW]
[ROW][C]106[/C][C]0.90562530027135[/C][C]0.188749399457301[/C][C]0.0943746997286506[/C][/ROW]
[ROW][C]107[/C][C]0.88523090970282[/C][C]0.229538180594359[/C][C]0.114769090297179[/C][/ROW]
[ROW][C]108[/C][C]0.879548215760314[/C][C]0.240903568479371[/C][C]0.120451784239686[/C][/ROW]
[ROW][C]109[/C][C]0.876971448272647[/C][C]0.246057103454706[/C][C]0.123028551727353[/C][/ROW]
[ROW][C]110[/C][C]0.896714705031344[/C][C]0.206570589937312[/C][C]0.103285294968656[/C][/ROW]
[ROW][C]111[/C][C]0.888818831945975[/C][C]0.222362336108051[/C][C]0.111181168054025[/C][/ROW]
[ROW][C]112[/C][C]0.926529602725404[/C][C]0.146940794549192[/C][C]0.073470397274596[/C][/ROW]
[ROW][C]113[/C][C]0.904866008509663[/C][C]0.190267982980675[/C][C]0.0951339914903373[/C][/ROW]
[ROW][C]114[/C][C]0.882761840721651[/C][C]0.234476318556697[/C][C]0.117238159278349[/C][/ROW]
[ROW][C]115[/C][C]0.856578652191078[/C][C]0.286842695617844[/C][C]0.143421347808922[/C][/ROW]
[ROW][C]116[/C][C]0.847360482630237[/C][C]0.305279034739525[/C][C]0.152639517369763[/C][/ROW]
[ROW][C]117[/C][C]0.845160067878447[/C][C]0.309679864243105[/C][C]0.154839932121553[/C][/ROW]
[ROW][C]118[/C][C]0.846213905052698[/C][C]0.307572189894604[/C][C]0.153786094947302[/C][/ROW]
[ROW][C]119[/C][C]0.814040652836467[/C][C]0.371918694327066[/C][C]0.185959347163533[/C][/ROW]
[ROW][C]120[/C][C]0.851321019990752[/C][C]0.297357960018496[/C][C]0.148678980009248[/C][/ROW]
[ROW][C]121[/C][C]0.818990515920393[/C][C]0.362018968159215[/C][C]0.181009484079607[/C][/ROW]
[ROW][C]122[/C][C]0.79045236724802[/C][C]0.419095265503961[/C][C]0.209547632751980[/C][/ROW]
[ROW][C]123[/C][C]0.782510780887197[/C][C]0.434978438225605[/C][C]0.217489219112803[/C][/ROW]
[ROW][C]124[/C][C]0.746706356137427[/C][C]0.506587287725146[/C][C]0.253293643862573[/C][/ROW]
[ROW][C]125[/C][C]0.734130909366521[/C][C]0.531738181266957[/C][C]0.265869090633479[/C][/ROW]
[ROW][C]126[/C][C]0.715398152194316[/C][C]0.569203695611368[/C][C]0.284601847805684[/C][/ROW]
[ROW][C]127[/C][C]0.657211416468591[/C][C]0.685577167062818[/C][C]0.342788583531409[/C][/ROW]
[ROW][C]128[/C][C]0.629278984908168[/C][C]0.741442030183665[/C][C]0.370721015091832[/C][/ROW]
[ROW][C]129[/C][C]0.636265128901009[/C][C]0.727469742197983[/C][C]0.363734871098991[/C][/ROW]
[ROW][C]130[/C][C]0.640084554700864[/C][C]0.719830890598272[/C][C]0.359915445299136[/C][/ROW]
[ROW][C]131[/C][C]0.594546146744681[/C][C]0.810907706510637[/C][C]0.405453853255319[/C][/ROW]
[ROW][C]132[/C][C]0.553103349561024[/C][C]0.893793300877953[/C][C]0.446896650438976[/C][/ROW]
[ROW][C]133[/C][C]0.543699901622874[/C][C]0.912600196754252[/C][C]0.456300098377126[/C][/ROW]
[ROW][C]134[/C][C]0.47134678279836[/C][C]0.94269356559672[/C][C]0.52865321720164[/C][/ROW]
[ROW][C]135[/C][C]0.414315595537165[/C][C]0.82863119107433[/C][C]0.585684404462835[/C][/ROW]
[ROW][C]136[/C][C]0.393702835618114[/C][C]0.787405671236227[/C][C]0.606297164381886[/C][/ROW]
[ROW][C]137[/C][C]0.320790451030565[/C][C]0.64158090206113[/C][C]0.679209548969435[/C][/ROW]
[ROW][C]138[/C][C]0.411615168913243[/C][C]0.823230337826486[/C][C]0.588384831086757[/C][/ROW]
[ROW][C]139[/C][C]0.771364571464273[/C][C]0.457270857071454[/C][C]0.228635428535727[/C][/ROW]
[ROW][C]140[/C][C]0.763775101199089[/C][C]0.472449797601823[/C][C]0.236224898800911[/C][/ROW]
[ROW][C]141[/C][C]0.709611408881837[/C][C]0.580777182236327[/C][C]0.290388591118163[/C][/ROW]
[ROW][C]142[/C][C]0.623349240400772[/C][C]0.753301519198456[/C][C]0.376650759599228[/C][/ROW]
[ROW][C]143[/C][C]0.55995155523412[/C][C]0.880096889531761[/C][C]0.440048444765880[/C][/ROW]
[ROW][C]144[/C][C]0.434662285853639[/C][C]0.869324571707278[/C][C]0.565337714146361[/C][/ROW]
[ROW][C]145[/C][C]0.699580655627806[/C][C]0.600838688744387[/C][C]0.300419344372194[/C][/ROW]
[ROW][C]146[/C][C]0.849248587677247[/C][C]0.301502824645506[/C][C]0.150751412322753[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98283&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98283&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
100.07385393424063790.1477078684812760.926146065759362
110.1114121685982580.2228243371965160.888587831401742
120.07345787413724060.1469157482744810.92654212586276
130.548827345484510.902345309030980.45117265451549
140.7186992086308220.5626015827383550.281300791369178
150.6520904123102280.6958191753795450.347909587689772
160.5761124921378210.8477750157243570.423887507862179
170.489204168649930.978408337299860.51079583135007
180.4518794925294630.9037589850589270.548120507470537
190.3660004165063050.732000833012610.633999583493695
200.5100387405013010.9799225189973990.489961259498699
210.5164266747214250.967146650557150.483573325278575
220.5458465860458270.9083068279083470.454153413954173
230.4723615361310520.9447230722621040.527638463868948
240.4779454316082210.9558908632164430.522054568391779
250.4347313659746490.8694627319492980.565268634025351
260.3743861511192350.748772302238470.625613848880765
270.6200735979705190.7598528040589610.379926402029481
280.5749546427581560.8500907144836880.425045357241844
290.5197605362760810.9604789274478380.480239463723919
300.7117639701334920.5764720597330170.288236029866508
310.8125705987111380.3748588025777250.187429401288862
320.7768257167972980.4463485664054050.223174283202702
330.7307830950400260.5384338099199480.269216904959974
340.6978619859844220.6042760280311560.302138014015578
350.695750871668920.608498256662160.30424912833108
360.7084453286808880.5831093426382230.291554671319112
370.6753947060822270.6492105878355460.324605293917773
380.6263349167561530.7473301664876940.373665083243847
390.5728974113800880.8542051772398240.427102588619912
400.5435509648210210.9128980703579580.456449035178979
410.5020915590297390.9958168819405220.497908440970261
420.7575288156648850.4849423686702310.242471184335115
430.9563070475383710.0873859049232570.0436929524616285
440.9666834209797180.06663315804056360.0333165790202818
450.9562902741612520.08741945167749670.0437097258387484
460.9612254468567140.07754910628657180.0387745531432859
470.9806509822552610.03869803548947740.0193490177447387
480.974216525457870.05156694908426220.0257834745421311
490.9821366416463710.03572671670725690.0178633583536284
500.9792101509228930.04157969815421410.0207898490771071
510.974466745752570.05106650849486110.0255332542474306
520.9975974613516140.0048050772967720.002402538648386
530.9965345544845240.006930891030952280.00346544551547614
540.9971213461018360.00575730779632820.0028786538981641
550.997104513935850.005790972128298580.00289548606414929
560.9959130703232510.008173859353497150.00408692967674857
570.9942292340735850.01154153185282980.00577076592641488
580.9935649807290860.01287003854182850.00643501927091424
590.995147676804760.009704646390480470.00485232319524023
600.9936622767108020.01267544657839690.00633772328919846
610.994486780541910.01102643891617780.00551321945808888
620.9950416877703480.009916624459304080.00495831222965204
630.9933549103728010.01329017925439750.00664508962719873
640.9937154061575680.01256918768486380.0062845938424319
650.9920741012968070.01585179740638680.00792589870319338
660.9912330134799510.01753397304009720.00876698652004858
670.9911936554924380.01761268901512420.00880634450756212
680.992408603297750.01518279340450070.00759139670225035
690.992622989007230.01475402198553770.00737701099276885
700.9900354594781920.01992908104361620.0099645405218081
710.9915123445940230.01697531081195310.00848765540597653
720.9887447120094480.02251057598110490.0112552879905524
730.9858755355293720.0282489289412560.014124464470628
740.9896839541107110.02063209177857820.0103160458892891
750.9860649847109670.02787003057806630.0139350152890332
760.9838140106574770.03237197868504520.0161859893425226
770.9787572305813240.04248553883735130.0212427694186757
780.971898413430230.05620317313953980.0281015865697699
790.9819288397198160.03614232056036870.0180711602801844
800.9811711890934850.03765762181302920.0188288109065146
810.9844656187665860.03106876246682740.0155343812334137
820.9855315805793760.0289368388412480.014468419420624
830.9805481128186120.03890377436277650.0194518871813882
840.9760251948548720.04794961029025640.0239748051451282
850.9936147428220520.01277051435589670.00638525717794837
860.9911861584938160.01762768301236880.00881384150618442
870.9879374134762950.02412517304741030.0120625865237051
880.9841687718519260.03166245629614730.0158312281480737
890.9802669954503850.03946600909922930.0197330045496147
900.9739334460187770.05213310796244690.0260665539812234
910.968947376731810.06210524653637970.0310526232681899
920.9814832067207420.03703358655851690.0185167932792585
930.9760770990135980.04784580197280390.0239229009864019
940.9728246090977620.05435078180447630.0271753909022381
950.965979035563560.0680419288728820.034020964436441
960.9575439971121530.08491200577569440.0424560028878472
970.9535973701117690.09280525977646260.0464026298882313
980.9410287167714980.1179425664570050.0589712832285023
990.9353320342401170.1293359315197650.0646679657598827
1000.9214983733308720.1570032533382570.0785016266691284
1010.9026602897059450.1946794205881110.0973397102940554
1020.8886871692377960.2226256615244080.111312830762204
1030.89189046225240.2162190754951990.108109537747600
1040.9289292049938920.1421415900122150.0710707950061076
1050.9254569164493070.1490861671013860.0745430835506932
1060.905625300271350.1887493994573010.0943746997286506
1070.885230909702820.2295381805943590.114769090297179
1080.8795482157603140.2409035684793710.120451784239686
1090.8769714482726470.2460571034547060.123028551727353
1100.8967147050313440.2065705899373120.103285294968656
1110.8888188319459750.2223623361080510.111181168054025
1120.9265296027254040.1469407945491920.073470397274596
1130.9048660085096630.1902679829806750.0951339914903373
1140.8827618407216510.2344763185566970.117238159278349
1150.8565786521910780.2868426956178440.143421347808922
1160.8473604826302370.3052790347395250.152639517369763
1170.8451600678784470.3096798642431050.154839932121553
1180.8462139050526980.3075721898946040.153786094947302
1190.8140406528364670.3719186943270660.185959347163533
1200.8513210199907520.2973579600184960.148678980009248
1210.8189905159203930.3620189681592150.181009484079607
1220.790452367248020.4190952655039610.209547632751980
1230.7825107808871970.4349784382256050.217489219112803
1240.7467063561374270.5065872877251460.253293643862573
1250.7341309093665210.5317381812669570.265869090633479
1260.7153981521943160.5692036956113680.284601847805684
1270.6572114164685910.6855771670628180.342788583531409
1280.6292789849081680.7414420301836650.370721015091832
1290.6362651289010090.7274697421979830.363734871098991
1300.6400845547008640.7198308905982720.359915445299136
1310.5945461467446810.8109077065106370.405453853255319
1320.5531033495610240.8937933008779530.446896650438976
1330.5436999016228740.9126001967542520.456300098377126
1340.471346782798360.942693565596720.52865321720164
1350.4143155955371650.828631191074330.585684404462835
1360.3937028356181140.7874056712362270.606297164381886
1370.3207904510305650.641580902061130.679209548969435
1380.4116151689132430.8232303378264860.588384831086757
1390.7713645714642730.4572708570714540.228635428535727
1400.7637751011990890.4724497976018230.236224898800911
1410.7096114088818370.5807771822363270.290388591118163
1420.6233492404007720.7533015191984560.376650759599228
1430.559951555234120.8800968895317610.440048444765880
1440.4346622858536390.8693245717072780.565337714146361
1450.6995806556278060.6008386887443870.300419344372194
1460.8492485876772470.3015028246455060.150751412322753






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.0510948905109489NOK
5% type I error level420.306569343065693NOK
10% type I error level550.401459854014599NOK

\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 & 7 & 0.0510948905109489 & NOK \tabularnewline
5% type I error level & 42 & 0.306569343065693 & NOK \tabularnewline
10% type I error level & 55 & 0.401459854014599 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98283&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]7[/C][C]0.0510948905109489[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]42[/C][C]0.306569343065693[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]55[/C][C]0.401459854014599[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98283&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98283&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 level70.0510948905109489NOK
5% type I error level420.306569343065693NOK
10% type I error level550.401459854014599NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

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