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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 20:52:04 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290545502az8zgx85nnzv49c.htm/, Retrieved Thu, 25 Apr 2024 14:41:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99648, Retrieved Thu, 25 Apr 2024 14:41:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [WS7 - Multiple re...] [2010-11-23 20:52:04] [ee4a783fb13f41eb2e9bc8a0c4f26279] [Current]
- R         [Multiple Regression] [] [2011-11-23 00:35:15] [19d77e37efa419fdc040c74a96874aff]
- R         [Multiple Regression] [] [2011-11-23 00:35:15] [19d77e37efa419fdc040c74a96874aff]
- R         [Multiple Regression] [] [2011-11-23 00:35:15] [19d77e37efa419fdc040c74a96874aff]
- R         [Multiple Regression] [] [2011-11-23 00:38:04] [19d77e37efa419fdc040c74a96874aff]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	13	14	13	3	2	1
12	12	8	13	5	1	1
15	10	12	16	6	0	1
12	9	7	12	6	3	1
10	10	10	11	5	3	1
12	12	7	12	3	1	1
15	13	16	18	8	3	1
9	12	11	11	4	1	1
12	12	14	14	4	4	1
11	6	6	9	4	0	1
11	5	16	14	6	3	1
11	12	11	12	6	2	1
15	11	16	11	5	4	1
7	14	12	12	4	3	1
11	14	7	13	6	1	1
11	12	13	11	4	1	1
10	12	11	12	6	2	1
14	11	15	16	6	3	1
10	11	7	9	4	1	2
6	7	9	11	4	1	2
11	9	7	13	2	2	2
15	11	14	15	7	3	2
11	11	15	10	5	4	2
12	12	7	11	4	2	2
14	12	15	13	6	1	2
15	11	17	16	6	2	2
9	11	15	15	7	2	2
13	8	14	14	5	4	2
13	9	14	14	6	2	2
16	12	8	14	4	3	2
13	10	8	8	4	3	2
12	10	14	13	7	3	2
14	12	14	15	7	4	2
11	8	8	13	4	2	3
9	12	11	11	4	2	3
16	11	16	15	6	4	3
12	12	10	15	6	3	3
10	7	8	9	5	4	3
13	11	14	13	6	2	3
16	11	16	16	7	5	3
14	12	13	13	6	3	3
15	9	5	11	3	1	3
5	15	8	12	3	1	3
8	11	10	12	4	1	3
11	11	8	12	6	2	3
16	11	13	14	7	3	3
17	11	15	14	5	9	3
9	15	6	8	4	0	3
9	11	12	13	5	0	3
13	12	16	16	6	2	3
10	12	5	13	6	2	3
6	9	15	11	6	3	4
12	12	12	14	5	1	4
8	12	8	13	4	2	4
14	13	13	13	5	0	4
12	11	14	13	5	5	4
11	9	12	12	4	2	4
16	9	16	16	6	4	4
8	11	10	15	2	3	4
15	11	15	15	8	0	4
7	12	8	12	3	0	4
16	12	16	14	6	4	4
14	9	19	12	6	1	4
16	11	14	15	6	1	4
9	9	6	12	5	4	4
14	12	13	13	5	2	4
11	12	15	12	6	4	4
13	12	7	12	5	1	4
15	12	13	13	6	4	5
5	14	4	5	2	2	5
15	11	14	13	5	5	5
13	12	13	13	5	4	5
11	11	11	14	5	4	5
11	6	14	17	6	4	5
12	10	12	13	6	4	5
12	12	15	13	6	3	5
12	13	14	12	5	3	5
12	8	13	13	5	3	5
14	12	8	14	4	2	5
6	12	6	11	2	1	5
7	12	7	12	4	1	5
14	6	13	12	6	5	5
14	11	13	16	6	4	5
10	10	11	12	5	2	5
13	12	5	12	3	3	5
12	13	12	12	6	2	5
9	11	8	10	4	2	6
12	7	11	15	5	2	6
16	11	14	15	8	2	6
10	11	9	12	4	3	6
14	11	10	16	6	2	6
10	11	13	15	6	3	6
16	12	16	16	7	4	6
15	10	16	13	6	3	6
12	11	11	12	5	3	6
10	12	8	11	4	0	6
8	7	4	13	6	1	6
8	13	7	10	3	2	6
11	8	14	15	5	2	6
13	12	11	13	6	3	6
16	11	17	16	7	4	6
16	12	15	15	7	4	6
14	14	17	18	6	1	6
11	10	5	13	3	2	6
4	10	4	10	2	2	6
14	13	10	16	8	3	6
9	10	11	13	3	3	7
14	11	15	15	8	3	7
8	10	10	14	3	1	7
8	7	9	15	4	1	7
11	10	12	14	5	1	7
12	8	15	13	7	1	7
11	12	7	13	6	0	7
14	12	13	15	6	1	7
15	12	12	16	7	3	7
16	11	14	14	6	3	7
16	12	14	14	6	0	7
11	12	8	16	6	2	7
14	12	15	14	6	5	7
14	11	12	12	4	2	7
12	12	12	13	4	3	7
14	11	16	12	5	3	7
8	11	9	12	4	5	7
13	13	15	14	6	4	7
16	12	15	14	6	4	7
12	12	6	14	5	0	7
16	12	14	16	8	3	7
12	12	15	13	6	0	7
11	8	10	14	5	2	7
4	8	6	4	4	0	7
16	12	14	16	8	6	7
15	11	12	13	6	3	7
10	12	8	16	4	1	7
13	13	11	15	6	6	7
15	12	13	14	6	2	7
12	12	9	13	4	1	7
14	11	15	14	6	3	7
7	12	13	12	3	1	8
19	12	15	15	6	2	8
12	10	14	14	5	4	8
12	11	16	13	4	1	8
13	12	14	14	6	2	8
15	12	14	16	4	0	8
8	10	10	6	4	5	8
12	12	10	13	4	2	8
10	13	4	13	6	1	8
8	12	8	14	5	1	8
10	15	15	15	6	4	8
15	11	16	14	6	3	8
16	12	12	15	8	0	9
13	11	12	13	7	3	10
16	12	15	16	7	3	10
9	11	9	12	4	0	14
14	10	12	15	6	2	14
14	11	14	12	6	5	14
12	11	11	14	2	2	14

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99648&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99648&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99648&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -0.0931214533089994 + 0.114501138766228FindingFriends[t] + 0.209268449968476KnowingPeople[t] + 0.358378633517791Liked[t] + 0.6165874587436Celebrity[t] + 0.213464699493470Sum_friends[t] + 0.104200617441747Day[t] -0.00667926178074984t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  -0.0931214533089994 +  0.114501138766228FindingFriends[t] +  0.209268449968476KnowingPeople[t] +  0.358378633517791Liked[t] +  0.6165874587436Celebrity[t] +  0.213464699493470Sum_friends[t] +  0.104200617441747Day[t] -0.00667926178074984t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99648&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  -0.0931214533089994 +  0.114501138766228FindingFriends[t] +  0.209268449968476KnowingPeople[t] +  0.358378633517791Liked[t] +  0.6165874587436Celebrity[t] +  0.213464699493470Sum_friends[t] +  0.104200617441747Day[t] -0.00667926178074984t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99648&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99648&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] = -0.0931214533089994 + 0.114501138766228FindingFriends[t] + 0.209268449968476KnowingPeople[t] + 0.358378633517791Liked[t] + 0.6165874587436Celebrity[t] + 0.213464699493470Sum_friends[t] + 0.104200617441747Day[t] -0.00667926178074984t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.09312145330899941.454348-0.0640.9490330.474516
FindingFriends0.1145011387662280.0972561.17730.2409570.120479
KnowingPeople0.2092684499684760.0641123.26410.0013640.000682
Liked0.3583786335177910.0973793.68030.0003260.000163
Celebrity0.61658745874360.1574413.91630.0001376.8e-05
Sum_friends0.2134646994934700.1207261.76820.0790930.039547
Day0.1042006174417470.1953410.53340.5945370.297269
t-0.006679261780749840.011864-0.5630.57430.28715

\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) & -0.0931214533089994 & 1.454348 & -0.064 & 0.949033 & 0.474516 \tabularnewline
FindingFriends & 0.114501138766228 & 0.097256 & 1.1773 & 0.240957 & 0.120479 \tabularnewline
KnowingPeople & 0.209268449968476 & 0.064112 & 3.2641 & 0.001364 & 0.000682 \tabularnewline
Liked & 0.358378633517791 & 0.097379 & 3.6803 & 0.000326 & 0.000163 \tabularnewline
Celebrity & 0.6165874587436 & 0.157441 & 3.9163 & 0.000137 & 6.8e-05 \tabularnewline
Sum_friends & 0.213464699493470 & 0.120726 & 1.7682 & 0.079093 & 0.039547 \tabularnewline
Day & 0.104200617441747 & 0.195341 & 0.5334 & 0.594537 & 0.297269 \tabularnewline
t & -0.00667926178074984 & 0.011864 & -0.563 & 0.5743 & 0.28715 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99648&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]-0.0931214533089994[/C][C]1.454348[/C][C]-0.064[/C][C]0.949033[/C][C]0.474516[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.114501138766228[/C][C]0.097256[/C][C]1.1773[/C][C]0.240957[/C][C]0.120479[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.209268449968476[/C][C]0.064112[/C][C]3.2641[/C][C]0.001364[/C][C]0.000682[/C][/ROW]
[ROW][C]Liked[/C][C]0.358378633517791[/C][C]0.097379[/C][C]3.6803[/C][C]0.000326[/C][C]0.000163[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.6165874587436[/C][C]0.157441[/C][C]3.9163[/C][C]0.000137[/C][C]6.8e-05[/C][/ROW]
[ROW][C]Sum_friends[/C][C]0.213464699493470[/C][C]0.120726[/C][C]1.7682[/C][C]0.079093[/C][C]0.039547[/C][/ROW]
[ROW][C]Day[/C][C]0.104200617441747[/C][C]0.195341[/C][C]0.5334[/C][C]0.594537[/C][C]0.297269[/C][/ROW]
[ROW][C]t[/C][C]-0.00667926178074984[/C][C]0.011864[/C][C]-0.563[/C][C]0.5743[/C][C]0.28715[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99648&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99648&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)-0.09312145330899941.454348-0.0640.9490330.474516
FindingFriends0.1145011387662280.0972561.17730.2409570.120479
KnowingPeople0.2092684499684760.0641123.26410.0013640.000682
Liked0.3583786335177910.0973793.68030.0003260.000163
Celebrity0.61658745874360.1574413.91630.0001376.8e-05
Sum_friends0.2134646994934700.1207261.76820.0790930.039547
Day0.1042006174417470.1953410.53340.5945370.297269
t-0.006679261780749840.011864-0.5630.57430.28715






Multiple Linear Regression - Regression Statistics
Multiple R0.714498470884513
R-squared0.510508064896307
Adjusted R-squared0.487356419317078
F-TEST (value)22.050616797379
F-TEST (DF numerator)7
F-TEST (DF denominator)148
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10259968841939
Sum Squared Residuals654.296966561714

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.714498470884513 \tabularnewline
R-squared & 0.510508064896307 \tabularnewline
Adjusted R-squared & 0.487356419317078 \tabularnewline
F-TEST (value) & 22.050616797379 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 148 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.10259968841939 \tabularnewline
Sum Squared Residuals & 654.296966561714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99648&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.714498470884513[/C][/ROW]
[ROW][C]R-squared[/C][C]0.510508064896307[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.487356419317078[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.050616797379[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]148[/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]2.10259968841939[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]654.296966561714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99648&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99648&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 R0.714498470884513
R-squared0.510508064896307
Adjusted R-squared0.487356419317078
F-TEST (value)22.050616797379
F-TEST (DF numerator)7
F-TEST (DF denominator)148
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.10259968841939
Sum Squared Residuals654.296966561714






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.35828701682061.64171298317940
21211.00120613445650.998793865543455
31513.08085705482071.91914294517927
41211.12021396884060.879786031159374
51010.8808751034701-0.880875103470149
6129.173667086360092.82633291363991
71516.8250435068735-1.82504350687353
8910.2555911878983-1.25559118789830
91212.5922472750568-0.592247275056757
10117.5786616153683.421338384632
111113.2156278980623-2.21562789806234
121112.0338923912738-1.03389239127376
131512.41101754729472.58898245270529
14711.4390943772195-4.43909437721946
151111.5506970176144-0.550697017614382
161110.62069399358920.37930600641075
171012.00049608237-2.00049608237001
181414.3633687152616-0.363368715261566
19108.617987720076081.38201227992392
2069.28859807020295-3.28859807020295
21118.789431235059562.21056876494044
221514.48979266083760.510207339162353
231111.8807784634427-0.880778463442691
24129.629314516467612.37068548353239
251413.03325033946400.96674966053602
261514.61920743890080.380792561099205
27914.4522001024089-5.4522001024089
281312.72812482234290.271875177657057
291313.0256047590851-0.0256047590850806
301611.08710799579844.91289200420157
31138.701154655378484.29884534462152
321213.5917416372283-1.59174163722834
331414.7442866195091-0.744286619509097
341110.1347436780410.865256321958994
35910.4971170541950-1.49711705419502
361614.51589775403571.48410224596428
371213.1546442317169-1.15464423171687
38109.603527815811160.396472184188844
391312.9336364027340.0663635972660046
401615.67761149866760.322388501332421
411413.03897526746370.961024732536281
42158.021195947383166.97880405261684
4359.687707501623-4.6877075016230
44810.2581480434579-2.25814804345788
451111.2795714987209-0.279571498720851
461613.86604391205512.13395608794487
471714.32551482968502.67448517031504
4898.205382517961260.794617482038739
49911.404790027259-2.40479002725901
501314.4683384624023-1.4683384624023
511011.0845703504149-1.08457035041494
52612.4279802219199-6.4279802219199
531212.1686180693552-0.168618069355242
54810.5633636149327-2.56336361493267
551411.90718580151722.09281419848281
561212.9480962096398-0.948096209639807
571110.67851757964780.321482420352153
581614.60253096828631.39746903171370
59810.5310501162415-2.53105011624149
601514.62984375828430.370156241715689
6179.11471329121909-2.11471329121909
621614.20256007042641.79743992957359
631413.12303137673640.876968623263592
641613.37414804319912.62585195680089
65910.4129896433215-1.41298964332153
661412.14614218214961.85385781785035
671113.2431380445186-2.2431380445186
681310.30532962576602.69467037423397
691513.27382187197971.72617812802031
7055.85242053591145-0.852420535911449
711512.95210790037032.04789209962970
721312.63719662789380.362803372106162
731112.4558579609277-1.45585796092770
741114.1962017145182-3.19620171451821
751212.7954755737943-0.795475573794256
761213.4321392399579-1.43213923995792
771212.3557265747135-0.355726574713532
781211.92565180265100.0743481973490444
791410.85896132137353.14103867862654
8067.91196964212172-1.91196964212172
8179.70611238131443-2.70611238131443
821412.35507070220831.64492929779175
831414.1409469688363-0.140946968836333
841011.1241982765507-1.1241982765507
85139.071200374497823.92879962550218
861212.2801990780000-0.280199077999961
8799.36171217173182-0.361712171731819
881211.93331433112410.0666856688758652
891614.86220735054451.13779264945548
901010.4811648028871-0.481164802887096
911413.13697874313970.863021256860287
921013.6131908972401-3.61319089724007
931615.53724891588580.462751084114163
941513.39637931778221.60362068221781
951211.48289285266390.517107147336102
96109.347549189002150.652450810997852
97810.0946873175326-2.09468731753260
9888.69138666096395-0.691386660963952
991112.6021489402075-1.60214894020754
1001312.53896377478780.461036225212233
1011615.57858213284210.421417867157914
1021614.90948847637281.09051152362718
1031415.3685027353908-1.36850273539084
104118.964406674597192.03559332540281
10547.05673560355099-3.05673560355099
1061414.7124317109416-0.712431710941592
107910.517644906001-1.51764490600101
1081415.2622351436140-1.26223514361397
109810.2264671670019-2.22646716700188
110810.6419821312154-2.64198213121536
1111111.8648204608645-0.864820460864535
1121213.1317405554262-1.13174055542617
1131111.0788660907255-0.078866090725452
1141413.25801949528460.741980504715392
1151514.44396727478370.556032725216287
1161613.40797904839452.59202095160549
1171612.87540682689963.12459317310043
1181112.7568035313305-1.75680353133049
1191414.1386402507739-0.138640250773902
1201410.79932821731833.20067178268169
1211211.47899342731500.521006572684956
1221412.45309565186781.54690434813222
123810.7918791805510-2.79187918055104
1241314.0062803811429-1.00628038114291
1251613.88509998059592.11490001940407
1261210.52455841238141.47544158761858
1271615.39894049209530.601059507904733
1281212.652824763762-0.652824763762011
1291111.3105192708351-0.310519270835101
13045.839463016272-1.83946301627200
1311616.0126175434527-0.0126175434526787
1321512.52419532644782.47580467355223
1331011.2099749876386-1.20997498763857
1341313.8877219959662-0.88772199596624
1351512.97284106386452.02715893613546
1361210.32406975171141.67593024828857
1371413.47698300096720.523016999032764
138710.2770195532042-3.27701955320419
1391913.82724016763805.17275983236197
1401212.8342534850819-0.8342534850819
1411211.74525207126250.254747928737472
1421313.2395552988095-0.239555298809516
1431512.28952898759022.71047101240979
14488.70031081069254-0.700310810692544
1451210.79089016258841.20910983741163
1461010.6628115577567-0.662811557756726
147811.1204961318578-3.12049613185784
1481014.5375596268969-4.53755962689691
1491513.71030092700851.28969907299154
1501614.03640907408641.96359092591364
1511313.3264786636824-0.326478663682361
1521615.13724179112660.862758208873362
153910.2535821517534-1.25358215175342
1541413.49544731813940.504552681860618
1551413.58706429298890.412935707011149
1561210.56259301488341.43740698511655

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.3582870168206 & 1.64171298317940 \tabularnewline
2 & 12 & 11.0012061344565 & 0.998793865543455 \tabularnewline
3 & 15 & 13.0808570548207 & 1.91914294517927 \tabularnewline
4 & 12 & 11.1202139688406 & 0.879786031159374 \tabularnewline
5 & 10 & 10.8808751034701 & -0.880875103470149 \tabularnewline
6 & 12 & 9.17366708636009 & 2.82633291363991 \tabularnewline
7 & 15 & 16.8250435068735 & -1.82504350687353 \tabularnewline
8 & 9 & 10.2555911878983 & -1.25559118789830 \tabularnewline
9 & 12 & 12.5922472750568 & -0.592247275056757 \tabularnewline
10 & 11 & 7.578661615368 & 3.421338384632 \tabularnewline
11 & 11 & 13.2156278980623 & -2.21562789806234 \tabularnewline
12 & 11 & 12.0338923912738 & -1.03389239127376 \tabularnewline
13 & 15 & 12.4110175472947 & 2.58898245270529 \tabularnewline
14 & 7 & 11.4390943772195 & -4.43909437721946 \tabularnewline
15 & 11 & 11.5506970176144 & -0.550697017614382 \tabularnewline
16 & 11 & 10.6206939935892 & 0.37930600641075 \tabularnewline
17 & 10 & 12.00049608237 & -2.00049608237001 \tabularnewline
18 & 14 & 14.3633687152616 & -0.363368715261566 \tabularnewline
19 & 10 & 8.61798772007608 & 1.38201227992392 \tabularnewline
20 & 6 & 9.28859807020295 & -3.28859807020295 \tabularnewline
21 & 11 & 8.78943123505956 & 2.21056876494044 \tabularnewline
22 & 15 & 14.4897926608376 & 0.510207339162353 \tabularnewline
23 & 11 & 11.8807784634427 & -0.880778463442691 \tabularnewline
24 & 12 & 9.62931451646761 & 2.37068548353239 \tabularnewline
25 & 14 & 13.0332503394640 & 0.96674966053602 \tabularnewline
26 & 15 & 14.6192074389008 & 0.380792561099205 \tabularnewline
27 & 9 & 14.4522001024089 & -5.4522001024089 \tabularnewline
28 & 13 & 12.7281248223429 & 0.271875177657057 \tabularnewline
29 & 13 & 13.0256047590851 & -0.0256047590850806 \tabularnewline
30 & 16 & 11.0871079957984 & 4.91289200420157 \tabularnewline
31 & 13 & 8.70115465537848 & 4.29884534462152 \tabularnewline
32 & 12 & 13.5917416372283 & -1.59174163722834 \tabularnewline
33 & 14 & 14.7442866195091 & -0.744286619509097 \tabularnewline
34 & 11 & 10.134743678041 & 0.865256321958994 \tabularnewline
35 & 9 & 10.4971170541950 & -1.49711705419502 \tabularnewline
36 & 16 & 14.5158977540357 & 1.48410224596428 \tabularnewline
37 & 12 & 13.1546442317169 & -1.15464423171687 \tabularnewline
38 & 10 & 9.60352781581116 & 0.396472184188844 \tabularnewline
39 & 13 & 12.933636402734 & 0.0663635972660046 \tabularnewline
40 & 16 & 15.6776114986676 & 0.322388501332421 \tabularnewline
41 & 14 & 13.0389752674637 & 0.961024732536281 \tabularnewline
42 & 15 & 8.02119594738316 & 6.97880405261684 \tabularnewline
43 & 5 & 9.687707501623 & -4.6877075016230 \tabularnewline
44 & 8 & 10.2581480434579 & -2.25814804345788 \tabularnewline
45 & 11 & 11.2795714987209 & -0.279571498720851 \tabularnewline
46 & 16 & 13.8660439120551 & 2.13395608794487 \tabularnewline
47 & 17 & 14.3255148296850 & 2.67448517031504 \tabularnewline
48 & 9 & 8.20538251796126 & 0.794617482038739 \tabularnewline
49 & 9 & 11.404790027259 & -2.40479002725901 \tabularnewline
50 & 13 & 14.4683384624023 & -1.4683384624023 \tabularnewline
51 & 10 & 11.0845703504149 & -1.08457035041494 \tabularnewline
52 & 6 & 12.4279802219199 & -6.4279802219199 \tabularnewline
53 & 12 & 12.1686180693552 & -0.168618069355242 \tabularnewline
54 & 8 & 10.5633636149327 & -2.56336361493267 \tabularnewline
55 & 14 & 11.9071858015172 & 2.09281419848281 \tabularnewline
56 & 12 & 12.9480962096398 & -0.948096209639807 \tabularnewline
57 & 11 & 10.6785175796478 & 0.321482420352153 \tabularnewline
58 & 16 & 14.6025309682863 & 1.39746903171370 \tabularnewline
59 & 8 & 10.5310501162415 & -2.53105011624149 \tabularnewline
60 & 15 & 14.6298437582843 & 0.370156241715689 \tabularnewline
61 & 7 & 9.11471329121909 & -2.11471329121909 \tabularnewline
62 & 16 & 14.2025600704264 & 1.79743992957359 \tabularnewline
63 & 14 & 13.1230313767364 & 0.876968623263592 \tabularnewline
64 & 16 & 13.3741480431991 & 2.62585195680089 \tabularnewline
65 & 9 & 10.4129896433215 & -1.41298964332153 \tabularnewline
66 & 14 & 12.1461421821496 & 1.85385781785035 \tabularnewline
67 & 11 & 13.2431380445186 & -2.2431380445186 \tabularnewline
68 & 13 & 10.3053296257660 & 2.69467037423397 \tabularnewline
69 & 15 & 13.2738218719797 & 1.72617812802031 \tabularnewline
70 & 5 & 5.85242053591145 & -0.852420535911449 \tabularnewline
71 & 15 & 12.9521079003703 & 2.04789209962970 \tabularnewline
72 & 13 & 12.6371966278938 & 0.362803372106162 \tabularnewline
73 & 11 & 12.4558579609277 & -1.45585796092770 \tabularnewline
74 & 11 & 14.1962017145182 & -3.19620171451821 \tabularnewline
75 & 12 & 12.7954755737943 & -0.795475573794256 \tabularnewline
76 & 12 & 13.4321392399579 & -1.43213923995792 \tabularnewline
77 & 12 & 12.3557265747135 & -0.355726574713532 \tabularnewline
78 & 12 & 11.9256518026510 & 0.0743481973490444 \tabularnewline
79 & 14 & 10.8589613213735 & 3.14103867862654 \tabularnewline
80 & 6 & 7.91196964212172 & -1.91196964212172 \tabularnewline
81 & 7 & 9.70611238131443 & -2.70611238131443 \tabularnewline
82 & 14 & 12.3550707022083 & 1.64492929779175 \tabularnewline
83 & 14 & 14.1409469688363 & -0.140946968836333 \tabularnewline
84 & 10 & 11.1241982765507 & -1.1241982765507 \tabularnewline
85 & 13 & 9.07120037449782 & 3.92879962550218 \tabularnewline
86 & 12 & 12.2801990780000 & -0.280199077999961 \tabularnewline
87 & 9 & 9.36171217173182 & -0.361712171731819 \tabularnewline
88 & 12 & 11.9333143311241 & 0.0666856688758652 \tabularnewline
89 & 16 & 14.8622073505445 & 1.13779264945548 \tabularnewline
90 & 10 & 10.4811648028871 & -0.481164802887096 \tabularnewline
91 & 14 & 13.1369787431397 & 0.863021256860287 \tabularnewline
92 & 10 & 13.6131908972401 & -3.61319089724007 \tabularnewline
93 & 16 & 15.5372489158858 & 0.462751084114163 \tabularnewline
94 & 15 & 13.3963793177822 & 1.60362068221781 \tabularnewline
95 & 12 & 11.4828928526639 & 0.517107147336102 \tabularnewline
96 & 10 & 9.34754918900215 & 0.652450810997852 \tabularnewline
97 & 8 & 10.0946873175326 & -2.09468731753260 \tabularnewline
98 & 8 & 8.69138666096395 & -0.691386660963952 \tabularnewline
99 & 11 & 12.6021489402075 & -1.60214894020754 \tabularnewline
100 & 13 & 12.5389637747878 & 0.461036225212233 \tabularnewline
101 & 16 & 15.5785821328421 & 0.421417867157914 \tabularnewline
102 & 16 & 14.9094884763728 & 1.09051152362718 \tabularnewline
103 & 14 & 15.3685027353908 & -1.36850273539084 \tabularnewline
104 & 11 & 8.96440667459719 & 2.03559332540281 \tabularnewline
105 & 4 & 7.05673560355099 & -3.05673560355099 \tabularnewline
106 & 14 & 14.7124317109416 & -0.712431710941592 \tabularnewline
107 & 9 & 10.517644906001 & -1.51764490600101 \tabularnewline
108 & 14 & 15.2622351436140 & -1.26223514361397 \tabularnewline
109 & 8 & 10.2264671670019 & -2.22646716700188 \tabularnewline
110 & 8 & 10.6419821312154 & -2.64198213121536 \tabularnewline
111 & 11 & 11.8648204608645 & -0.864820460864535 \tabularnewline
112 & 12 & 13.1317405554262 & -1.13174055542617 \tabularnewline
113 & 11 & 11.0788660907255 & -0.078866090725452 \tabularnewline
114 & 14 & 13.2580194952846 & 0.741980504715392 \tabularnewline
115 & 15 & 14.4439672747837 & 0.556032725216287 \tabularnewline
116 & 16 & 13.4079790483945 & 2.59202095160549 \tabularnewline
117 & 16 & 12.8754068268996 & 3.12459317310043 \tabularnewline
118 & 11 & 12.7568035313305 & -1.75680353133049 \tabularnewline
119 & 14 & 14.1386402507739 & -0.138640250773902 \tabularnewline
120 & 14 & 10.7993282173183 & 3.20067178268169 \tabularnewline
121 & 12 & 11.4789934273150 & 0.521006572684956 \tabularnewline
122 & 14 & 12.4530956518678 & 1.54690434813222 \tabularnewline
123 & 8 & 10.7918791805510 & -2.79187918055104 \tabularnewline
124 & 13 & 14.0062803811429 & -1.00628038114291 \tabularnewline
125 & 16 & 13.8850999805959 & 2.11490001940407 \tabularnewline
126 & 12 & 10.5245584123814 & 1.47544158761858 \tabularnewline
127 & 16 & 15.3989404920953 & 0.601059507904733 \tabularnewline
128 & 12 & 12.652824763762 & -0.652824763762011 \tabularnewline
129 & 11 & 11.3105192708351 & -0.310519270835101 \tabularnewline
130 & 4 & 5.839463016272 & -1.83946301627200 \tabularnewline
131 & 16 & 16.0126175434527 & -0.0126175434526787 \tabularnewline
132 & 15 & 12.5241953264478 & 2.47580467355223 \tabularnewline
133 & 10 & 11.2099749876386 & -1.20997498763857 \tabularnewline
134 & 13 & 13.8877219959662 & -0.88772199596624 \tabularnewline
135 & 15 & 12.9728410638645 & 2.02715893613546 \tabularnewline
136 & 12 & 10.3240697517114 & 1.67593024828857 \tabularnewline
137 & 14 & 13.4769830009672 & 0.523016999032764 \tabularnewline
138 & 7 & 10.2770195532042 & -3.27701955320419 \tabularnewline
139 & 19 & 13.8272401676380 & 5.17275983236197 \tabularnewline
140 & 12 & 12.8342534850819 & -0.8342534850819 \tabularnewline
141 & 12 & 11.7452520712625 & 0.254747928737472 \tabularnewline
142 & 13 & 13.2395552988095 & -0.239555298809516 \tabularnewline
143 & 15 & 12.2895289875902 & 2.71047101240979 \tabularnewline
144 & 8 & 8.70031081069254 & -0.700310810692544 \tabularnewline
145 & 12 & 10.7908901625884 & 1.20910983741163 \tabularnewline
146 & 10 & 10.6628115577567 & -0.662811557756726 \tabularnewline
147 & 8 & 11.1204961318578 & -3.12049613185784 \tabularnewline
148 & 10 & 14.5375596268969 & -4.53755962689691 \tabularnewline
149 & 15 & 13.7103009270085 & 1.28969907299154 \tabularnewline
150 & 16 & 14.0364090740864 & 1.96359092591364 \tabularnewline
151 & 13 & 13.3264786636824 & -0.326478663682361 \tabularnewline
152 & 16 & 15.1372417911266 & 0.862758208873362 \tabularnewline
153 & 9 & 10.2535821517534 & -1.25358215175342 \tabularnewline
154 & 14 & 13.4954473181394 & 0.504552681860618 \tabularnewline
155 & 14 & 13.5870642929889 & 0.412935707011149 \tabularnewline
156 & 12 & 10.5625930148834 & 1.43740698511655 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99648&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]11.3582870168206[/C][C]1.64171298317940[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.0012061344565[/C][C]0.998793865543455[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.0808570548207[/C][C]1.91914294517927[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.1202139688406[/C][C]0.879786031159374[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.8808751034701[/C][C]-0.880875103470149[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.17366708636009[/C][C]2.82633291363991[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.8250435068735[/C][C]-1.82504350687353[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.2555911878983[/C][C]-1.25559118789830[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.5922472750568[/C][C]-0.592247275056757[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.578661615368[/C][C]3.421338384632[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.2156278980623[/C][C]-2.21562789806234[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.0338923912738[/C][C]-1.03389239127376[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.4110175472947[/C][C]2.58898245270529[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.4390943772195[/C][C]-4.43909437721946[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.5506970176144[/C][C]-0.550697017614382[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.6206939935892[/C][C]0.37930600641075[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]12.00049608237[/C][C]-2.00049608237001[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.3633687152616[/C][C]-0.363368715261566[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.61798772007608[/C][C]1.38201227992392[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.28859807020295[/C][C]-3.28859807020295[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.78943123505956[/C][C]2.21056876494044[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.4897926608376[/C][C]0.510207339162353[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.8807784634427[/C][C]-0.880778463442691[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.62931451646761[/C][C]2.37068548353239[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.0332503394640[/C][C]0.96674966053602[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.6192074389008[/C][C]0.380792561099205[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.4522001024089[/C][C]-5.4522001024089[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.7281248223429[/C][C]0.271875177657057[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.0256047590851[/C][C]-0.0256047590850806[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]11.0871079957984[/C][C]4.91289200420157[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.70115465537848[/C][C]4.29884534462152[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5917416372283[/C][C]-1.59174163722834[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.7442866195091[/C][C]-0.744286619509097[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.134743678041[/C][C]0.865256321958994[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.4971170541950[/C][C]-1.49711705419502[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.5158977540357[/C][C]1.48410224596428[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]13.1546442317169[/C][C]-1.15464423171687[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.60352781581116[/C][C]0.396472184188844[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]12.933636402734[/C][C]0.0663635972660046[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.6776114986676[/C][C]0.322388501332421[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]13.0389752674637[/C][C]0.961024732536281[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.02119594738316[/C][C]6.97880405261684[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.687707501623[/C][C]-4.6877075016230[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.2581480434579[/C][C]-2.25814804345788[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.2795714987209[/C][C]-0.279571498720851[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.8660439120551[/C][C]2.13395608794487[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]14.3255148296850[/C][C]2.67448517031504[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.20538251796126[/C][C]0.794617482038739[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.404790027259[/C][C]-2.40479002725901[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.4683384624023[/C][C]-1.4683384624023[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.0845703504149[/C][C]-1.08457035041494[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]12.4279802219199[/C][C]-6.4279802219199[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.1686180693552[/C][C]-0.168618069355242[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.5633636149327[/C][C]-2.56336361493267[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]11.9071858015172[/C][C]2.09281419848281[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.9480962096398[/C][C]-0.948096209639807[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]10.6785175796478[/C][C]0.321482420352153[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.6025309682863[/C][C]1.39746903171370[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.5310501162415[/C][C]-2.53105011624149[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.6298437582843[/C][C]0.370156241715689[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.11471329121909[/C][C]-2.11471329121909[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]14.2025600704264[/C][C]1.79743992957359[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.1230313767364[/C][C]0.876968623263592[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.3741480431991[/C][C]2.62585195680089[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.4129896433215[/C][C]-1.41298964332153[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.1461421821496[/C][C]1.85385781785035[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.2431380445186[/C][C]-2.2431380445186[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.3053296257660[/C][C]2.69467037423397[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.2738218719797[/C][C]1.72617812802031[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.85242053591145[/C][C]-0.852420535911449[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.9521079003703[/C][C]2.04789209962970[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.6371966278938[/C][C]0.362803372106162[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.4558579609277[/C][C]-1.45585796092770[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.1962017145182[/C][C]-3.19620171451821[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.7954755737943[/C][C]-0.795475573794256[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.4321392399579[/C][C]-1.43213923995792[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.3557265747135[/C][C]-0.355726574713532[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.9256518026510[/C][C]0.0743481973490444[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]10.8589613213735[/C][C]3.14103867862654[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]7.91196964212172[/C][C]-1.91196964212172[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.70611238131443[/C][C]-2.70611238131443[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]12.3550707022083[/C][C]1.64492929779175[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]14.1409469688363[/C][C]-0.140946968836333[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.1241982765507[/C][C]-1.1241982765507[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]9.07120037449782[/C][C]3.92879962550218[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.2801990780000[/C][C]-0.280199077999961[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.36171217173182[/C][C]-0.361712171731819[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.9333143311241[/C][C]0.0666856688758652[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.8622073505445[/C][C]1.13779264945548[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.4811648028871[/C][C]-0.481164802887096[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.1369787431397[/C][C]0.863021256860287[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.6131908972401[/C][C]-3.61319089724007[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.5372489158858[/C][C]0.462751084114163[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.3963793177822[/C][C]1.60362068221781[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.4828928526639[/C][C]0.517107147336102[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.34754918900215[/C][C]0.652450810997852[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.0946873175326[/C][C]-2.09468731753260[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.69138666096395[/C][C]-0.691386660963952[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.6021489402075[/C][C]-1.60214894020754[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.5389637747878[/C][C]0.461036225212233[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.5785821328421[/C][C]0.421417867157914[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.9094884763728[/C][C]1.09051152362718[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.3685027353908[/C][C]-1.36850273539084[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]8.96440667459719[/C][C]2.03559332540281[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]7.05673560355099[/C][C]-3.05673560355099[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.7124317109416[/C][C]-0.712431710941592[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.517644906001[/C][C]-1.51764490600101[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.2622351436140[/C][C]-1.26223514361397[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.2264671670019[/C][C]-2.22646716700188[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.6419821312154[/C][C]-2.64198213121536[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.8648204608645[/C][C]-0.864820460864535[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.1317405554262[/C][C]-1.13174055542617[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.0788660907255[/C][C]-0.078866090725452[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.2580194952846[/C][C]0.741980504715392[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.4439672747837[/C][C]0.556032725216287[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.4079790483945[/C][C]2.59202095160549[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]12.8754068268996[/C][C]3.12459317310043[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.7568035313305[/C][C]-1.75680353133049[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.1386402507739[/C][C]-0.138640250773902[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.7993282173183[/C][C]3.20067178268169[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.4789934273150[/C][C]0.521006572684956[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.4530956518678[/C][C]1.54690434813222[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.7918791805510[/C][C]-2.79187918055104[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]14.0062803811429[/C][C]-1.00628038114291[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.8850999805959[/C][C]2.11490001940407[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.5245584123814[/C][C]1.47544158761858[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.3989404920953[/C][C]0.601059507904733[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]12.652824763762[/C][C]-0.652824763762011[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.3105192708351[/C][C]-0.310519270835101[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]5.839463016272[/C][C]-1.83946301627200[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]16.0126175434527[/C][C]-0.0126175434526787[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]12.5241953264478[/C][C]2.47580467355223[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.2099749876386[/C][C]-1.20997498763857[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]13.8877219959662[/C][C]-0.88772199596624[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]12.9728410638645[/C][C]2.02715893613546[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.3240697517114[/C][C]1.67593024828857[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]13.4769830009672[/C][C]0.523016999032764[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.2770195532042[/C][C]-3.27701955320419[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]13.8272401676380[/C][C]5.17275983236197[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]12.8342534850819[/C][C]-0.8342534850819[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]11.7452520712625[/C][C]0.254747928737472[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.2395552988095[/C][C]-0.239555298809516[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.2895289875902[/C][C]2.71047101240979[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.70031081069254[/C][C]-0.700310810692544[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]10.7908901625884[/C][C]1.20910983741163[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.6628115577567[/C][C]-0.662811557756726[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.1204961318578[/C][C]-3.12049613185784[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.5375596268969[/C][C]-4.53755962689691[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.7103009270085[/C][C]1.28969907299154[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]14.0364090740864[/C][C]1.96359092591364[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.3264786636824[/C][C]-0.326478663682361[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.1372417911266[/C][C]0.862758208873362[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]10.2535821517534[/C][C]-1.25358215175342[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.4954473181394[/C][C]0.504552681860618[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]13.5870642929889[/C][C]0.412935707011149[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.5625930148834[/C][C]1.43740698511655[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99648&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99648&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
11311.35828701682061.64171298317940
21211.00120613445650.998793865543455
31513.08085705482071.91914294517927
41211.12021396884060.879786031159374
51010.8808751034701-0.880875103470149
6129.173667086360092.82633291363991
71516.8250435068735-1.82504350687353
8910.2555911878983-1.25559118789830
91212.5922472750568-0.592247275056757
10117.5786616153683.421338384632
111113.2156278980623-2.21562789806234
121112.0338923912738-1.03389239127376
131512.41101754729472.58898245270529
14711.4390943772195-4.43909437721946
151111.5506970176144-0.550697017614382
161110.62069399358920.37930600641075
171012.00049608237-2.00049608237001
181414.3633687152616-0.363368715261566
19108.617987720076081.38201227992392
2069.28859807020295-3.28859807020295
21118.789431235059562.21056876494044
221514.48979266083760.510207339162353
231111.8807784634427-0.880778463442691
24129.629314516467612.37068548353239
251413.03325033946400.96674966053602
261514.61920743890080.380792561099205
27914.4522001024089-5.4522001024089
281312.72812482234290.271875177657057
291313.0256047590851-0.0256047590850806
301611.08710799579844.91289200420157
31138.701154655378484.29884534462152
321213.5917416372283-1.59174163722834
331414.7442866195091-0.744286619509097
341110.1347436780410.865256321958994
35910.4971170541950-1.49711705419502
361614.51589775403571.48410224596428
371213.1546442317169-1.15464423171687
38109.603527815811160.396472184188844
391312.9336364027340.0663635972660046
401615.67761149866760.322388501332421
411413.03897526746370.961024732536281
42158.021195947383166.97880405261684
4359.687707501623-4.6877075016230
44810.2581480434579-2.25814804345788
451111.2795714987209-0.279571498720851
461613.86604391205512.13395608794487
471714.32551482968502.67448517031504
4898.205382517961260.794617482038739
49911.404790027259-2.40479002725901
501314.4683384624023-1.4683384624023
511011.0845703504149-1.08457035041494
52612.4279802219199-6.4279802219199
531212.1686180693552-0.168618069355242
54810.5633636149327-2.56336361493267
551411.90718580151722.09281419848281
561212.9480962096398-0.948096209639807
571110.67851757964780.321482420352153
581614.60253096828631.39746903171370
59810.5310501162415-2.53105011624149
601514.62984375828430.370156241715689
6179.11471329121909-2.11471329121909
621614.20256007042641.79743992957359
631413.12303137673640.876968623263592
641613.37414804319912.62585195680089
65910.4129896433215-1.41298964332153
661412.14614218214961.85385781785035
671113.2431380445186-2.2431380445186
681310.30532962576602.69467037423397
691513.27382187197971.72617812802031
7055.85242053591145-0.852420535911449
711512.95210790037032.04789209962970
721312.63719662789380.362803372106162
731112.4558579609277-1.45585796092770
741114.1962017145182-3.19620171451821
751212.7954755737943-0.795475573794256
761213.4321392399579-1.43213923995792
771212.3557265747135-0.355726574713532
781211.92565180265100.0743481973490444
791410.85896132137353.14103867862654
8067.91196964212172-1.91196964212172
8179.70611238131443-2.70611238131443
821412.35507070220831.64492929779175
831414.1409469688363-0.140946968836333
841011.1241982765507-1.1241982765507
85139.071200374497823.92879962550218
861212.2801990780000-0.280199077999961
8799.36171217173182-0.361712171731819
881211.93331433112410.0666856688758652
891614.86220735054451.13779264945548
901010.4811648028871-0.481164802887096
911413.13697874313970.863021256860287
921013.6131908972401-3.61319089724007
931615.53724891588580.462751084114163
941513.39637931778221.60362068221781
951211.48289285266390.517107147336102
96109.347549189002150.652450810997852
97810.0946873175326-2.09468731753260
9888.69138666096395-0.691386660963952
991112.6021489402075-1.60214894020754
1001312.53896377478780.461036225212233
1011615.57858213284210.421417867157914
1021614.90948847637281.09051152362718
1031415.3685027353908-1.36850273539084
104118.964406674597192.03559332540281
10547.05673560355099-3.05673560355099
1061414.7124317109416-0.712431710941592
107910.517644906001-1.51764490600101
1081415.2622351436140-1.26223514361397
109810.2264671670019-2.22646716700188
110810.6419821312154-2.64198213121536
1111111.8648204608645-0.864820460864535
1121213.1317405554262-1.13174055542617
1131111.0788660907255-0.078866090725452
1141413.25801949528460.741980504715392
1151514.44396727478370.556032725216287
1161613.40797904839452.59202095160549
1171612.87540682689963.12459317310043
1181112.7568035313305-1.75680353133049
1191414.1386402507739-0.138640250773902
1201410.79932821731833.20067178268169
1211211.47899342731500.521006572684956
1221412.45309565186781.54690434813222
123810.7918791805510-2.79187918055104
1241314.0062803811429-1.00628038114291
1251613.88509998059592.11490001940407
1261210.52455841238141.47544158761858
1271615.39894049209530.601059507904733
1281212.652824763762-0.652824763762011
1291111.3105192708351-0.310519270835101
13045.839463016272-1.83946301627200
1311616.0126175434527-0.0126175434526787
1321512.52419532644782.47580467355223
1331011.2099749876386-1.20997498763857
1341313.8877219959662-0.88772199596624
1351512.97284106386452.02715893613546
1361210.32406975171141.67593024828857
1371413.47698300096720.523016999032764
138710.2770195532042-3.27701955320419
1391913.82724016763805.17275983236197
1401212.8342534850819-0.8342534850819
1411211.74525207126250.254747928737472
1421313.2395552988095-0.239555298809516
1431512.28952898759022.71047101240979
14488.70031081069254-0.700310810692544
1451210.79089016258841.20910983741163
1461010.6628115577567-0.662811557756726
147811.1204961318578-3.12049613185784
1481014.5375596268969-4.53755962689691
1491513.71030092700851.28969907299154
1501614.03640907408641.96359092591364
1511313.3264786636824-0.326478663682361
1521615.13724179112660.862758208873362
153910.2535821517534-1.25358215175342
1541413.49544731813940.504552681860618
1551413.58706429298890.412935707011149
1561210.56259301488341.43740698511655






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.2319584747297550.4639169494595110.768041525270245
120.1449268784994100.2898537569988190.85507312150059
130.6768478263210990.6463043473578020.323152173678901
140.8073181993361370.3853636013277260.192681800663863
150.7427820218024780.5144359563950440.257217978197522
160.6564143692218070.6871712615563850.343585630778193
170.5691292595775270.8617414808449470.430870740422473
180.5435395227044080.9129209545911840.456460477295592
190.453608716939860.907217433879720.54639128306014
200.5914348900930330.8171302198139350.408565109906967
210.6103375666608220.7793248666783550.389662433339178
220.6363470471424890.7273059057150230.363652952857511
230.5644095754790910.8711808490418180.435590424520909
240.5674442513545720.8651114972908560.432555748645428
250.5218915715124870.9562168569750260.478108428487513
260.4564734427289110.9129468854578230.543526557271089
270.6938932297488370.6122135405023260.306106770251163
280.6504963336102830.6990073327794330.349503666389717
290.5966517173438250.806696565312350.403348282656175
300.7587721327316230.4824557345367550.241227867268377
310.8366734460293590.3266531079412820.163326553970641
320.8031135085395840.3937729829208320.196886491460416
330.7590393768953890.4819212462092220.240960623104611
340.7286910718496080.5426178563007830.271308928150392
350.7213769451524270.5572461096951460.278623054847573
360.7198323164941010.5603353670117980.280167683505899
370.6935478961783770.6129042076432460.306452103821623
380.6452524397324750.709495120535050.354747560267525
390.5968964568718790.8062070862562430.403103543128121
400.5536627193147780.8926745613704440.446337280685222
410.5138071823535110.9723856352929780.486192817646489
420.7908096310402280.4183807379195430.209190368959772
430.9575481789638050.08490364207239070.0424518210361953
440.9610054888882020.07798902222359580.0389945111117979
450.948781088740270.1024378225194590.0512189112597295
460.955660657867870.08867868426425970.0443393421321299
470.9552245153391780.0895509693216450.0447754846608225
480.9452516322537930.1094967354924140.0547483677462069
490.9427397396916950.114520520616610.057260260308305
500.9316304234485710.1367391531028580.068369576551429
510.9235814689922570.1528370620154860.0764185310077431
520.9881598475417860.02368030491642820.0118401524582141
530.9846222344529020.03075553109419700.0153777655470985
540.9876017303194760.02479653936104850.0123982696805243
550.9921896529579870.01562069408402690.00781034704201347
560.9896348987410580.02073020251788490.0103651012589424
570.9859361410521050.02812771789579030.0140638589478951
580.9842134220869230.03157315582615480.0157865779130774
590.9892847733593410.02143045328131740.0107152266406587
600.9884596493009830.02308070139803370.0115403506990168
610.9883191446167540.02336171076649280.0116808553832464
620.9887439274879760.02251214502404840.0112560725120242
630.9873524917003880.02529501659922420.0126475082996121
640.989822895124790.02035420975041950.0101771048752097
650.9886495271764450.02270094564710960.0113504728235548
660.9879518045146280.02409639097074380.0120481954853719
670.9883478927985830.02330421440283440.0116521072014172
680.990574911589240.01885017682151920.00942508841075962
690.9900789493311990.01984210133760250.00992105066880124
700.9869745531686440.02605089366271250.0130254468313562
710.9873788704735060.02524225905298820.0126211295264941
720.9831882897789630.03362342044207480.0168117102210374
730.98007770065040.03984459869919860.0199222993495993
740.9860153341751650.02796933164967070.0139846658248354
750.9815516481873150.03689670362537070.0184483518126854
760.9784707077825160.04305858443496830.0215292922174841
770.9719950173170360.05600996536592750.0280049826829638
780.9633421745961010.07331565080779850.0366578254038992
790.9761693473976830.04766130520463320.0238306526023166
800.9743585991756780.05128280164864330.0256414008243217
810.9774080009230240.04518399815395250.0225919990769763
820.9771642896836990.0456714206326030.0228357103163015
830.9697342846648830.06053143067023330.0302657153351166
840.9626228150805280.07475436983894480.0373771849194724
850.9879650254467460.02406994910650810.0120349745532540
860.983786048791810.032427902416380.01621395120819
870.9782640535604810.04347189287903780.0217359464395189
880.9719500995470730.05609980090585360.0280499004529268
890.9669478428138830.06610431437223350.0330521571861167
900.9574459635708760.08510807285824720.0425540364291236
910.9503476572705780.09930468545884440.0496523427294222
920.9692205841614410.06155883167711710.0307794158385585
930.9606494664071380.07870106718572320.0393505335928616
940.9566776399742890.08664472005142280.0433223600257114
950.9465688480127750.1068623039744510.0534311519872253
960.9352303554882080.1295392890235850.0647696445117925
970.9279499091593940.1441001816812130.0720500908406064
980.9105084665982970.1789830668034070.0894915334017033
990.9003599976074060.1992800047851870.0996400023925936
1000.8808113267370120.2383773465259770.119188673262988
1010.8549988827912850.2900022344174290.145001117208715
1020.8347835645990550.330432870801890.165216435400945
1030.8365794191650060.3268411616699870.163420580834994
1040.8889876678322380.2220246643355230.111012332167762
1050.886192320307170.2276153593856580.113807679692829
1060.8595331036058050.2809337927883900.140466896394195
1070.8325014232972840.3349971534054310.167498576702715
1080.824718882415220.3505622351695620.175281117584781
1090.8169834303579430.3660331392841130.183016569642057
1100.8370562682068560.3258874635862870.162943731793144
1110.826141327456820.3477173450863590.173858672543179
1120.8834128902021030.2331742195957940.116587109797897
1130.8545885416733370.2908229166533260.145411458326663
1140.8310827584707820.3378344830584360.168917241529218
1150.796939253680710.406121492638580.20306074631929
1160.7854261921374450.4291476157251090.214573807862555
1170.7868210977770570.4263578044458850.213178902222943
1180.7898540323373840.4202919353252320.210145967662616
1190.7485818672777720.5028362654444550.251418132722228
1200.7939435068637470.4121129862725070.206056493136253
1210.754339191950160.4913216160996810.245660808049840
1220.71970700262170.5605859947565980.280292997378299
1230.7173725452581420.5652549094837160.282627454741858
1240.6773157905095250.6453684189809510.322684209490475
1250.6601114978508250.6797770042983510.339888502149175
1260.6414794305415310.7170411389169370.358520569458469
1270.5775341883056950.844931623388610.422465811694305
1280.5463987431385030.9072025137229940.453601256861497
1290.5488826448340610.9022347103318770.451117355165939
1300.5637784200328040.8724431599343930.436221579967196
1310.5052317859481850.989536428103630.494768214051815
1320.4604197437356380.9208394874712760.539580256264362
1330.4448596174385330.8897192348770660.555140382561467
1340.3723994550882290.7447989101764590.62760054491177
1350.3196238039361120.6392476078722240.680376196063888
1360.3016831267821120.6033662535642230.698316873217888
1370.2345382900343890.4690765800687780.765461709965611
1380.3218849580678440.6437699161356870.678115041932156
1390.7053278970489770.5893442059020470.294672102951023
1400.6616925817549830.6766148364900350.338307418245017
1410.5946868672859130.8106262654281750.405313132714087
1420.4931566723027130.9863133446054270.506843327697287
1430.423069683224760.846139366449520.57693031677524
1440.2971134936674150.594226987334830.702886506332585
1450.5276348464315870.9447303071368270.472365153568413

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.231958474729755 & 0.463916949459511 & 0.768041525270245 \tabularnewline
12 & 0.144926878499410 & 0.289853756998819 & 0.85507312150059 \tabularnewline
13 & 0.676847826321099 & 0.646304347357802 & 0.323152173678901 \tabularnewline
14 & 0.807318199336137 & 0.385363601327726 & 0.192681800663863 \tabularnewline
15 & 0.742782021802478 & 0.514435956395044 & 0.257217978197522 \tabularnewline
16 & 0.656414369221807 & 0.687171261556385 & 0.343585630778193 \tabularnewline
17 & 0.569129259577527 & 0.861741480844947 & 0.430870740422473 \tabularnewline
18 & 0.543539522704408 & 0.912920954591184 & 0.456460477295592 \tabularnewline
19 & 0.45360871693986 & 0.90721743387972 & 0.54639128306014 \tabularnewline
20 & 0.591434890093033 & 0.817130219813935 & 0.408565109906967 \tabularnewline
21 & 0.610337566660822 & 0.779324866678355 & 0.389662433339178 \tabularnewline
22 & 0.636347047142489 & 0.727305905715023 & 0.363652952857511 \tabularnewline
23 & 0.564409575479091 & 0.871180849041818 & 0.435590424520909 \tabularnewline
24 & 0.567444251354572 & 0.865111497290856 & 0.432555748645428 \tabularnewline
25 & 0.521891571512487 & 0.956216856975026 & 0.478108428487513 \tabularnewline
26 & 0.456473442728911 & 0.912946885457823 & 0.543526557271089 \tabularnewline
27 & 0.693893229748837 & 0.612213540502326 & 0.306106770251163 \tabularnewline
28 & 0.650496333610283 & 0.699007332779433 & 0.349503666389717 \tabularnewline
29 & 0.596651717343825 & 0.80669656531235 & 0.403348282656175 \tabularnewline
30 & 0.758772132731623 & 0.482455734536755 & 0.241227867268377 \tabularnewline
31 & 0.836673446029359 & 0.326653107941282 & 0.163326553970641 \tabularnewline
32 & 0.803113508539584 & 0.393772982920832 & 0.196886491460416 \tabularnewline
33 & 0.759039376895389 & 0.481921246209222 & 0.240960623104611 \tabularnewline
34 & 0.728691071849608 & 0.542617856300783 & 0.271308928150392 \tabularnewline
35 & 0.721376945152427 & 0.557246109695146 & 0.278623054847573 \tabularnewline
36 & 0.719832316494101 & 0.560335367011798 & 0.280167683505899 \tabularnewline
37 & 0.693547896178377 & 0.612904207643246 & 0.306452103821623 \tabularnewline
38 & 0.645252439732475 & 0.70949512053505 & 0.354747560267525 \tabularnewline
39 & 0.596896456871879 & 0.806207086256243 & 0.403103543128121 \tabularnewline
40 & 0.553662719314778 & 0.892674561370444 & 0.446337280685222 \tabularnewline
41 & 0.513807182353511 & 0.972385635292978 & 0.486192817646489 \tabularnewline
42 & 0.790809631040228 & 0.418380737919543 & 0.209190368959772 \tabularnewline
43 & 0.957548178963805 & 0.0849036420723907 & 0.0424518210361953 \tabularnewline
44 & 0.961005488888202 & 0.0779890222235958 & 0.0389945111117979 \tabularnewline
45 & 0.94878108874027 & 0.102437822519459 & 0.0512189112597295 \tabularnewline
46 & 0.95566065786787 & 0.0886786842642597 & 0.0443393421321299 \tabularnewline
47 & 0.955224515339178 & 0.089550969321645 & 0.0447754846608225 \tabularnewline
48 & 0.945251632253793 & 0.109496735492414 & 0.0547483677462069 \tabularnewline
49 & 0.942739739691695 & 0.11452052061661 & 0.057260260308305 \tabularnewline
50 & 0.931630423448571 & 0.136739153102858 & 0.068369576551429 \tabularnewline
51 & 0.923581468992257 & 0.152837062015486 & 0.0764185310077431 \tabularnewline
52 & 0.988159847541786 & 0.0236803049164282 & 0.0118401524582141 \tabularnewline
53 & 0.984622234452902 & 0.0307555310941970 & 0.0153777655470985 \tabularnewline
54 & 0.987601730319476 & 0.0247965393610485 & 0.0123982696805243 \tabularnewline
55 & 0.992189652957987 & 0.0156206940840269 & 0.00781034704201347 \tabularnewline
56 & 0.989634898741058 & 0.0207302025178849 & 0.0103651012589424 \tabularnewline
57 & 0.985936141052105 & 0.0281277178957903 & 0.0140638589478951 \tabularnewline
58 & 0.984213422086923 & 0.0315731558261548 & 0.0157865779130774 \tabularnewline
59 & 0.989284773359341 & 0.0214304532813174 & 0.0107152266406587 \tabularnewline
60 & 0.988459649300983 & 0.0230807013980337 & 0.0115403506990168 \tabularnewline
61 & 0.988319144616754 & 0.0233617107664928 & 0.0116808553832464 \tabularnewline
62 & 0.988743927487976 & 0.0225121450240484 & 0.0112560725120242 \tabularnewline
63 & 0.987352491700388 & 0.0252950165992242 & 0.0126475082996121 \tabularnewline
64 & 0.98982289512479 & 0.0203542097504195 & 0.0101771048752097 \tabularnewline
65 & 0.988649527176445 & 0.0227009456471096 & 0.0113504728235548 \tabularnewline
66 & 0.987951804514628 & 0.0240963909707438 & 0.0120481954853719 \tabularnewline
67 & 0.988347892798583 & 0.0233042144028344 & 0.0116521072014172 \tabularnewline
68 & 0.99057491158924 & 0.0188501768215192 & 0.00942508841075962 \tabularnewline
69 & 0.990078949331199 & 0.0198421013376025 & 0.00992105066880124 \tabularnewline
70 & 0.986974553168644 & 0.0260508936627125 & 0.0130254468313562 \tabularnewline
71 & 0.987378870473506 & 0.0252422590529882 & 0.0126211295264941 \tabularnewline
72 & 0.983188289778963 & 0.0336234204420748 & 0.0168117102210374 \tabularnewline
73 & 0.9800777006504 & 0.0398445986991986 & 0.0199222993495993 \tabularnewline
74 & 0.986015334175165 & 0.0279693316496707 & 0.0139846658248354 \tabularnewline
75 & 0.981551648187315 & 0.0368967036253707 & 0.0184483518126854 \tabularnewline
76 & 0.978470707782516 & 0.0430585844349683 & 0.0215292922174841 \tabularnewline
77 & 0.971995017317036 & 0.0560099653659275 & 0.0280049826829638 \tabularnewline
78 & 0.963342174596101 & 0.0733156508077985 & 0.0366578254038992 \tabularnewline
79 & 0.976169347397683 & 0.0476613052046332 & 0.0238306526023166 \tabularnewline
80 & 0.974358599175678 & 0.0512828016486433 & 0.0256414008243217 \tabularnewline
81 & 0.977408000923024 & 0.0451839981539525 & 0.0225919990769763 \tabularnewline
82 & 0.977164289683699 & 0.045671420632603 & 0.0228357103163015 \tabularnewline
83 & 0.969734284664883 & 0.0605314306702333 & 0.0302657153351166 \tabularnewline
84 & 0.962622815080528 & 0.0747543698389448 & 0.0373771849194724 \tabularnewline
85 & 0.987965025446746 & 0.0240699491065081 & 0.0120349745532540 \tabularnewline
86 & 0.98378604879181 & 0.03242790241638 & 0.01621395120819 \tabularnewline
87 & 0.978264053560481 & 0.0434718928790378 & 0.0217359464395189 \tabularnewline
88 & 0.971950099547073 & 0.0560998009058536 & 0.0280499004529268 \tabularnewline
89 & 0.966947842813883 & 0.0661043143722335 & 0.0330521571861167 \tabularnewline
90 & 0.957445963570876 & 0.0851080728582472 & 0.0425540364291236 \tabularnewline
91 & 0.950347657270578 & 0.0993046854588444 & 0.0496523427294222 \tabularnewline
92 & 0.969220584161441 & 0.0615588316771171 & 0.0307794158385585 \tabularnewline
93 & 0.960649466407138 & 0.0787010671857232 & 0.0393505335928616 \tabularnewline
94 & 0.956677639974289 & 0.0866447200514228 & 0.0433223600257114 \tabularnewline
95 & 0.946568848012775 & 0.106862303974451 & 0.0534311519872253 \tabularnewline
96 & 0.935230355488208 & 0.129539289023585 & 0.0647696445117925 \tabularnewline
97 & 0.927949909159394 & 0.144100181681213 & 0.0720500908406064 \tabularnewline
98 & 0.910508466598297 & 0.178983066803407 & 0.0894915334017033 \tabularnewline
99 & 0.900359997607406 & 0.199280004785187 & 0.0996400023925936 \tabularnewline
100 & 0.880811326737012 & 0.238377346525977 & 0.119188673262988 \tabularnewline
101 & 0.854998882791285 & 0.290002234417429 & 0.145001117208715 \tabularnewline
102 & 0.834783564599055 & 0.33043287080189 & 0.165216435400945 \tabularnewline
103 & 0.836579419165006 & 0.326841161669987 & 0.163420580834994 \tabularnewline
104 & 0.888987667832238 & 0.222024664335523 & 0.111012332167762 \tabularnewline
105 & 0.88619232030717 & 0.227615359385658 & 0.113807679692829 \tabularnewline
106 & 0.859533103605805 & 0.280933792788390 & 0.140466896394195 \tabularnewline
107 & 0.832501423297284 & 0.334997153405431 & 0.167498576702715 \tabularnewline
108 & 0.82471888241522 & 0.350562235169562 & 0.175281117584781 \tabularnewline
109 & 0.816983430357943 & 0.366033139284113 & 0.183016569642057 \tabularnewline
110 & 0.837056268206856 & 0.325887463586287 & 0.162943731793144 \tabularnewline
111 & 0.82614132745682 & 0.347717345086359 & 0.173858672543179 \tabularnewline
112 & 0.883412890202103 & 0.233174219595794 & 0.116587109797897 \tabularnewline
113 & 0.854588541673337 & 0.290822916653326 & 0.145411458326663 \tabularnewline
114 & 0.831082758470782 & 0.337834483058436 & 0.168917241529218 \tabularnewline
115 & 0.79693925368071 & 0.40612149263858 & 0.20306074631929 \tabularnewline
116 & 0.785426192137445 & 0.429147615725109 & 0.214573807862555 \tabularnewline
117 & 0.786821097777057 & 0.426357804445885 & 0.213178902222943 \tabularnewline
118 & 0.789854032337384 & 0.420291935325232 & 0.210145967662616 \tabularnewline
119 & 0.748581867277772 & 0.502836265444455 & 0.251418132722228 \tabularnewline
120 & 0.793943506863747 & 0.412112986272507 & 0.206056493136253 \tabularnewline
121 & 0.75433919195016 & 0.491321616099681 & 0.245660808049840 \tabularnewline
122 & 0.7197070026217 & 0.560585994756598 & 0.280292997378299 \tabularnewline
123 & 0.717372545258142 & 0.565254909483716 & 0.282627454741858 \tabularnewline
124 & 0.677315790509525 & 0.645368418980951 & 0.322684209490475 \tabularnewline
125 & 0.660111497850825 & 0.679777004298351 & 0.339888502149175 \tabularnewline
126 & 0.641479430541531 & 0.717041138916937 & 0.358520569458469 \tabularnewline
127 & 0.577534188305695 & 0.84493162338861 & 0.422465811694305 \tabularnewline
128 & 0.546398743138503 & 0.907202513722994 & 0.453601256861497 \tabularnewline
129 & 0.548882644834061 & 0.902234710331877 & 0.451117355165939 \tabularnewline
130 & 0.563778420032804 & 0.872443159934393 & 0.436221579967196 \tabularnewline
131 & 0.505231785948185 & 0.98953642810363 & 0.494768214051815 \tabularnewline
132 & 0.460419743735638 & 0.920839487471276 & 0.539580256264362 \tabularnewline
133 & 0.444859617438533 & 0.889719234877066 & 0.555140382561467 \tabularnewline
134 & 0.372399455088229 & 0.744798910176459 & 0.62760054491177 \tabularnewline
135 & 0.319623803936112 & 0.639247607872224 & 0.680376196063888 \tabularnewline
136 & 0.301683126782112 & 0.603366253564223 & 0.698316873217888 \tabularnewline
137 & 0.234538290034389 & 0.469076580068778 & 0.765461709965611 \tabularnewline
138 & 0.321884958067844 & 0.643769916135687 & 0.678115041932156 \tabularnewline
139 & 0.705327897048977 & 0.589344205902047 & 0.294672102951023 \tabularnewline
140 & 0.661692581754983 & 0.676614836490035 & 0.338307418245017 \tabularnewline
141 & 0.594686867285913 & 0.810626265428175 & 0.405313132714087 \tabularnewline
142 & 0.493156672302713 & 0.986313344605427 & 0.506843327697287 \tabularnewline
143 & 0.42306968322476 & 0.84613936644952 & 0.57693031677524 \tabularnewline
144 & 0.297113493667415 & 0.59422698733483 & 0.702886506332585 \tabularnewline
145 & 0.527634846431587 & 0.944730307136827 & 0.472365153568413 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99648&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]11[/C][C]0.231958474729755[/C][C]0.463916949459511[/C][C]0.768041525270245[/C][/ROW]
[ROW][C]12[/C][C]0.144926878499410[/C][C]0.289853756998819[/C][C]0.85507312150059[/C][/ROW]
[ROW][C]13[/C][C]0.676847826321099[/C][C]0.646304347357802[/C][C]0.323152173678901[/C][/ROW]
[ROW][C]14[/C][C]0.807318199336137[/C][C]0.385363601327726[/C][C]0.192681800663863[/C][/ROW]
[ROW][C]15[/C][C]0.742782021802478[/C][C]0.514435956395044[/C][C]0.257217978197522[/C][/ROW]
[ROW][C]16[/C][C]0.656414369221807[/C][C]0.687171261556385[/C][C]0.343585630778193[/C][/ROW]
[ROW][C]17[/C][C]0.569129259577527[/C][C]0.861741480844947[/C][C]0.430870740422473[/C][/ROW]
[ROW][C]18[/C][C]0.543539522704408[/C][C]0.912920954591184[/C][C]0.456460477295592[/C][/ROW]
[ROW][C]19[/C][C]0.45360871693986[/C][C]0.90721743387972[/C][C]0.54639128306014[/C][/ROW]
[ROW][C]20[/C][C]0.591434890093033[/C][C]0.817130219813935[/C][C]0.408565109906967[/C][/ROW]
[ROW][C]21[/C][C]0.610337566660822[/C][C]0.779324866678355[/C][C]0.389662433339178[/C][/ROW]
[ROW][C]22[/C][C]0.636347047142489[/C][C]0.727305905715023[/C][C]0.363652952857511[/C][/ROW]
[ROW][C]23[/C][C]0.564409575479091[/C][C]0.871180849041818[/C][C]0.435590424520909[/C][/ROW]
[ROW][C]24[/C][C]0.567444251354572[/C][C]0.865111497290856[/C][C]0.432555748645428[/C][/ROW]
[ROW][C]25[/C][C]0.521891571512487[/C][C]0.956216856975026[/C][C]0.478108428487513[/C][/ROW]
[ROW][C]26[/C][C]0.456473442728911[/C][C]0.912946885457823[/C][C]0.543526557271089[/C][/ROW]
[ROW][C]27[/C][C]0.693893229748837[/C][C]0.612213540502326[/C][C]0.306106770251163[/C][/ROW]
[ROW][C]28[/C][C]0.650496333610283[/C][C]0.699007332779433[/C][C]0.349503666389717[/C][/ROW]
[ROW][C]29[/C][C]0.596651717343825[/C][C]0.80669656531235[/C][C]0.403348282656175[/C][/ROW]
[ROW][C]30[/C][C]0.758772132731623[/C][C]0.482455734536755[/C][C]0.241227867268377[/C][/ROW]
[ROW][C]31[/C][C]0.836673446029359[/C][C]0.326653107941282[/C][C]0.163326553970641[/C][/ROW]
[ROW][C]32[/C][C]0.803113508539584[/C][C]0.393772982920832[/C][C]0.196886491460416[/C][/ROW]
[ROW][C]33[/C][C]0.759039376895389[/C][C]0.481921246209222[/C][C]0.240960623104611[/C][/ROW]
[ROW][C]34[/C][C]0.728691071849608[/C][C]0.542617856300783[/C][C]0.271308928150392[/C][/ROW]
[ROW][C]35[/C][C]0.721376945152427[/C][C]0.557246109695146[/C][C]0.278623054847573[/C][/ROW]
[ROW][C]36[/C][C]0.719832316494101[/C][C]0.560335367011798[/C][C]0.280167683505899[/C][/ROW]
[ROW][C]37[/C][C]0.693547896178377[/C][C]0.612904207643246[/C][C]0.306452103821623[/C][/ROW]
[ROW][C]38[/C][C]0.645252439732475[/C][C]0.70949512053505[/C][C]0.354747560267525[/C][/ROW]
[ROW][C]39[/C][C]0.596896456871879[/C][C]0.806207086256243[/C][C]0.403103543128121[/C][/ROW]
[ROW][C]40[/C][C]0.553662719314778[/C][C]0.892674561370444[/C][C]0.446337280685222[/C][/ROW]
[ROW][C]41[/C][C]0.513807182353511[/C][C]0.972385635292978[/C][C]0.486192817646489[/C][/ROW]
[ROW][C]42[/C][C]0.790809631040228[/C][C]0.418380737919543[/C][C]0.209190368959772[/C][/ROW]
[ROW][C]43[/C][C]0.957548178963805[/C][C]0.0849036420723907[/C][C]0.0424518210361953[/C][/ROW]
[ROW][C]44[/C][C]0.961005488888202[/C][C]0.0779890222235958[/C][C]0.0389945111117979[/C][/ROW]
[ROW][C]45[/C][C]0.94878108874027[/C][C]0.102437822519459[/C][C]0.0512189112597295[/C][/ROW]
[ROW][C]46[/C][C]0.95566065786787[/C][C]0.0886786842642597[/C][C]0.0443393421321299[/C][/ROW]
[ROW][C]47[/C][C]0.955224515339178[/C][C]0.089550969321645[/C][C]0.0447754846608225[/C][/ROW]
[ROW][C]48[/C][C]0.945251632253793[/C][C]0.109496735492414[/C][C]0.0547483677462069[/C][/ROW]
[ROW][C]49[/C][C]0.942739739691695[/C][C]0.11452052061661[/C][C]0.057260260308305[/C][/ROW]
[ROW][C]50[/C][C]0.931630423448571[/C][C]0.136739153102858[/C][C]0.068369576551429[/C][/ROW]
[ROW][C]51[/C][C]0.923581468992257[/C][C]0.152837062015486[/C][C]0.0764185310077431[/C][/ROW]
[ROW][C]52[/C][C]0.988159847541786[/C][C]0.0236803049164282[/C][C]0.0118401524582141[/C][/ROW]
[ROW][C]53[/C][C]0.984622234452902[/C][C]0.0307555310941970[/C][C]0.0153777655470985[/C][/ROW]
[ROW][C]54[/C][C]0.987601730319476[/C][C]0.0247965393610485[/C][C]0.0123982696805243[/C][/ROW]
[ROW][C]55[/C][C]0.992189652957987[/C][C]0.0156206940840269[/C][C]0.00781034704201347[/C][/ROW]
[ROW][C]56[/C][C]0.989634898741058[/C][C]0.0207302025178849[/C][C]0.0103651012589424[/C][/ROW]
[ROW][C]57[/C][C]0.985936141052105[/C][C]0.0281277178957903[/C][C]0.0140638589478951[/C][/ROW]
[ROW][C]58[/C][C]0.984213422086923[/C][C]0.0315731558261548[/C][C]0.0157865779130774[/C][/ROW]
[ROW][C]59[/C][C]0.989284773359341[/C][C]0.0214304532813174[/C][C]0.0107152266406587[/C][/ROW]
[ROW][C]60[/C][C]0.988459649300983[/C][C]0.0230807013980337[/C][C]0.0115403506990168[/C][/ROW]
[ROW][C]61[/C][C]0.988319144616754[/C][C]0.0233617107664928[/C][C]0.0116808553832464[/C][/ROW]
[ROW][C]62[/C][C]0.988743927487976[/C][C]0.0225121450240484[/C][C]0.0112560725120242[/C][/ROW]
[ROW][C]63[/C][C]0.987352491700388[/C][C]0.0252950165992242[/C][C]0.0126475082996121[/C][/ROW]
[ROW][C]64[/C][C]0.98982289512479[/C][C]0.0203542097504195[/C][C]0.0101771048752097[/C][/ROW]
[ROW][C]65[/C][C]0.988649527176445[/C][C]0.0227009456471096[/C][C]0.0113504728235548[/C][/ROW]
[ROW][C]66[/C][C]0.987951804514628[/C][C]0.0240963909707438[/C][C]0.0120481954853719[/C][/ROW]
[ROW][C]67[/C][C]0.988347892798583[/C][C]0.0233042144028344[/C][C]0.0116521072014172[/C][/ROW]
[ROW][C]68[/C][C]0.99057491158924[/C][C]0.0188501768215192[/C][C]0.00942508841075962[/C][/ROW]
[ROW][C]69[/C][C]0.990078949331199[/C][C]0.0198421013376025[/C][C]0.00992105066880124[/C][/ROW]
[ROW][C]70[/C][C]0.986974553168644[/C][C]0.0260508936627125[/C][C]0.0130254468313562[/C][/ROW]
[ROW][C]71[/C][C]0.987378870473506[/C][C]0.0252422590529882[/C][C]0.0126211295264941[/C][/ROW]
[ROW][C]72[/C][C]0.983188289778963[/C][C]0.0336234204420748[/C][C]0.0168117102210374[/C][/ROW]
[ROW][C]73[/C][C]0.9800777006504[/C][C]0.0398445986991986[/C][C]0.0199222993495993[/C][/ROW]
[ROW][C]74[/C][C]0.986015334175165[/C][C]0.0279693316496707[/C][C]0.0139846658248354[/C][/ROW]
[ROW][C]75[/C][C]0.981551648187315[/C][C]0.0368967036253707[/C][C]0.0184483518126854[/C][/ROW]
[ROW][C]76[/C][C]0.978470707782516[/C][C]0.0430585844349683[/C][C]0.0215292922174841[/C][/ROW]
[ROW][C]77[/C][C]0.971995017317036[/C][C]0.0560099653659275[/C][C]0.0280049826829638[/C][/ROW]
[ROW][C]78[/C][C]0.963342174596101[/C][C]0.0733156508077985[/C][C]0.0366578254038992[/C][/ROW]
[ROW][C]79[/C][C]0.976169347397683[/C][C]0.0476613052046332[/C][C]0.0238306526023166[/C][/ROW]
[ROW][C]80[/C][C]0.974358599175678[/C][C]0.0512828016486433[/C][C]0.0256414008243217[/C][/ROW]
[ROW][C]81[/C][C]0.977408000923024[/C][C]0.0451839981539525[/C][C]0.0225919990769763[/C][/ROW]
[ROW][C]82[/C][C]0.977164289683699[/C][C]0.045671420632603[/C][C]0.0228357103163015[/C][/ROW]
[ROW][C]83[/C][C]0.969734284664883[/C][C]0.0605314306702333[/C][C]0.0302657153351166[/C][/ROW]
[ROW][C]84[/C][C]0.962622815080528[/C][C]0.0747543698389448[/C][C]0.0373771849194724[/C][/ROW]
[ROW][C]85[/C][C]0.987965025446746[/C][C]0.0240699491065081[/C][C]0.0120349745532540[/C][/ROW]
[ROW][C]86[/C][C]0.98378604879181[/C][C]0.03242790241638[/C][C]0.01621395120819[/C][/ROW]
[ROW][C]87[/C][C]0.978264053560481[/C][C]0.0434718928790378[/C][C]0.0217359464395189[/C][/ROW]
[ROW][C]88[/C][C]0.971950099547073[/C][C]0.0560998009058536[/C][C]0.0280499004529268[/C][/ROW]
[ROW][C]89[/C][C]0.966947842813883[/C][C]0.0661043143722335[/C][C]0.0330521571861167[/C][/ROW]
[ROW][C]90[/C][C]0.957445963570876[/C][C]0.0851080728582472[/C][C]0.0425540364291236[/C][/ROW]
[ROW][C]91[/C][C]0.950347657270578[/C][C]0.0993046854588444[/C][C]0.0496523427294222[/C][/ROW]
[ROW][C]92[/C][C]0.969220584161441[/C][C]0.0615588316771171[/C][C]0.0307794158385585[/C][/ROW]
[ROW][C]93[/C][C]0.960649466407138[/C][C]0.0787010671857232[/C][C]0.0393505335928616[/C][/ROW]
[ROW][C]94[/C][C]0.956677639974289[/C][C]0.0866447200514228[/C][C]0.0433223600257114[/C][/ROW]
[ROW][C]95[/C][C]0.946568848012775[/C][C]0.106862303974451[/C][C]0.0534311519872253[/C][/ROW]
[ROW][C]96[/C][C]0.935230355488208[/C][C]0.129539289023585[/C][C]0.0647696445117925[/C][/ROW]
[ROW][C]97[/C][C]0.927949909159394[/C][C]0.144100181681213[/C][C]0.0720500908406064[/C][/ROW]
[ROW][C]98[/C][C]0.910508466598297[/C][C]0.178983066803407[/C][C]0.0894915334017033[/C][/ROW]
[ROW][C]99[/C][C]0.900359997607406[/C][C]0.199280004785187[/C][C]0.0996400023925936[/C][/ROW]
[ROW][C]100[/C][C]0.880811326737012[/C][C]0.238377346525977[/C][C]0.119188673262988[/C][/ROW]
[ROW][C]101[/C][C]0.854998882791285[/C][C]0.290002234417429[/C][C]0.145001117208715[/C][/ROW]
[ROW][C]102[/C][C]0.834783564599055[/C][C]0.33043287080189[/C][C]0.165216435400945[/C][/ROW]
[ROW][C]103[/C][C]0.836579419165006[/C][C]0.326841161669987[/C][C]0.163420580834994[/C][/ROW]
[ROW][C]104[/C][C]0.888987667832238[/C][C]0.222024664335523[/C][C]0.111012332167762[/C][/ROW]
[ROW][C]105[/C][C]0.88619232030717[/C][C]0.227615359385658[/C][C]0.113807679692829[/C][/ROW]
[ROW][C]106[/C][C]0.859533103605805[/C][C]0.280933792788390[/C][C]0.140466896394195[/C][/ROW]
[ROW][C]107[/C][C]0.832501423297284[/C][C]0.334997153405431[/C][C]0.167498576702715[/C][/ROW]
[ROW][C]108[/C][C]0.82471888241522[/C][C]0.350562235169562[/C][C]0.175281117584781[/C][/ROW]
[ROW][C]109[/C][C]0.816983430357943[/C][C]0.366033139284113[/C][C]0.183016569642057[/C][/ROW]
[ROW][C]110[/C][C]0.837056268206856[/C][C]0.325887463586287[/C][C]0.162943731793144[/C][/ROW]
[ROW][C]111[/C][C]0.82614132745682[/C][C]0.347717345086359[/C][C]0.173858672543179[/C][/ROW]
[ROW][C]112[/C][C]0.883412890202103[/C][C]0.233174219595794[/C][C]0.116587109797897[/C][/ROW]
[ROW][C]113[/C][C]0.854588541673337[/C][C]0.290822916653326[/C][C]0.145411458326663[/C][/ROW]
[ROW][C]114[/C][C]0.831082758470782[/C][C]0.337834483058436[/C][C]0.168917241529218[/C][/ROW]
[ROW][C]115[/C][C]0.79693925368071[/C][C]0.40612149263858[/C][C]0.20306074631929[/C][/ROW]
[ROW][C]116[/C][C]0.785426192137445[/C][C]0.429147615725109[/C][C]0.214573807862555[/C][/ROW]
[ROW][C]117[/C][C]0.786821097777057[/C][C]0.426357804445885[/C][C]0.213178902222943[/C][/ROW]
[ROW][C]118[/C][C]0.789854032337384[/C][C]0.420291935325232[/C][C]0.210145967662616[/C][/ROW]
[ROW][C]119[/C][C]0.748581867277772[/C][C]0.502836265444455[/C][C]0.251418132722228[/C][/ROW]
[ROW][C]120[/C][C]0.793943506863747[/C][C]0.412112986272507[/C][C]0.206056493136253[/C][/ROW]
[ROW][C]121[/C][C]0.75433919195016[/C][C]0.491321616099681[/C][C]0.245660808049840[/C][/ROW]
[ROW][C]122[/C][C]0.7197070026217[/C][C]0.560585994756598[/C][C]0.280292997378299[/C][/ROW]
[ROW][C]123[/C][C]0.717372545258142[/C][C]0.565254909483716[/C][C]0.282627454741858[/C][/ROW]
[ROW][C]124[/C][C]0.677315790509525[/C][C]0.645368418980951[/C][C]0.322684209490475[/C][/ROW]
[ROW][C]125[/C][C]0.660111497850825[/C][C]0.679777004298351[/C][C]0.339888502149175[/C][/ROW]
[ROW][C]126[/C][C]0.641479430541531[/C][C]0.717041138916937[/C][C]0.358520569458469[/C][/ROW]
[ROW][C]127[/C][C]0.577534188305695[/C][C]0.84493162338861[/C][C]0.422465811694305[/C][/ROW]
[ROW][C]128[/C][C]0.546398743138503[/C][C]0.907202513722994[/C][C]0.453601256861497[/C][/ROW]
[ROW][C]129[/C][C]0.548882644834061[/C][C]0.902234710331877[/C][C]0.451117355165939[/C][/ROW]
[ROW][C]130[/C][C]0.563778420032804[/C][C]0.872443159934393[/C][C]0.436221579967196[/C][/ROW]
[ROW][C]131[/C][C]0.505231785948185[/C][C]0.98953642810363[/C][C]0.494768214051815[/C][/ROW]
[ROW][C]132[/C][C]0.460419743735638[/C][C]0.920839487471276[/C][C]0.539580256264362[/C][/ROW]
[ROW][C]133[/C][C]0.444859617438533[/C][C]0.889719234877066[/C][C]0.555140382561467[/C][/ROW]
[ROW][C]134[/C][C]0.372399455088229[/C][C]0.744798910176459[/C][C]0.62760054491177[/C][/ROW]
[ROW][C]135[/C][C]0.319623803936112[/C][C]0.639247607872224[/C][C]0.680376196063888[/C][/ROW]
[ROW][C]136[/C][C]0.301683126782112[/C][C]0.603366253564223[/C][C]0.698316873217888[/C][/ROW]
[ROW][C]137[/C][C]0.234538290034389[/C][C]0.469076580068778[/C][C]0.765461709965611[/C][/ROW]
[ROW][C]138[/C][C]0.321884958067844[/C][C]0.643769916135687[/C][C]0.678115041932156[/C][/ROW]
[ROW][C]139[/C][C]0.705327897048977[/C][C]0.589344205902047[/C][C]0.294672102951023[/C][/ROW]
[ROW][C]140[/C][C]0.661692581754983[/C][C]0.676614836490035[/C][C]0.338307418245017[/C][/ROW]
[ROW][C]141[/C][C]0.594686867285913[/C][C]0.810626265428175[/C][C]0.405313132714087[/C][/ROW]
[ROW][C]142[/C][C]0.493156672302713[/C][C]0.986313344605427[/C][C]0.506843327697287[/C][/ROW]
[ROW][C]143[/C][C]0.42306968322476[/C][C]0.84613936644952[/C][C]0.57693031677524[/C][/ROW]
[ROW][C]144[/C][C]0.297113493667415[/C][C]0.59422698733483[/C][C]0.702886506332585[/C][/ROW]
[ROW][C]145[/C][C]0.527634846431587[/C][C]0.944730307136827[/C][C]0.472365153568413[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99648&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99648&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
110.2319584747297550.4639169494595110.768041525270245
120.1449268784994100.2898537569988190.85507312150059
130.6768478263210990.6463043473578020.323152173678901
140.8073181993361370.3853636013277260.192681800663863
150.7427820218024780.5144359563950440.257217978197522
160.6564143692218070.6871712615563850.343585630778193
170.5691292595775270.8617414808449470.430870740422473
180.5435395227044080.9129209545911840.456460477295592
190.453608716939860.907217433879720.54639128306014
200.5914348900930330.8171302198139350.408565109906967
210.6103375666608220.7793248666783550.389662433339178
220.6363470471424890.7273059057150230.363652952857511
230.5644095754790910.8711808490418180.435590424520909
240.5674442513545720.8651114972908560.432555748645428
250.5218915715124870.9562168569750260.478108428487513
260.4564734427289110.9129468854578230.543526557271089
270.6938932297488370.6122135405023260.306106770251163
280.6504963336102830.6990073327794330.349503666389717
290.5966517173438250.806696565312350.403348282656175
300.7587721327316230.4824557345367550.241227867268377
310.8366734460293590.3266531079412820.163326553970641
320.8031135085395840.3937729829208320.196886491460416
330.7590393768953890.4819212462092220.240960623104611
340.7286910718496080.5426178563007830.271308928150392
350.7213769451524270.5572461096951460.278623054847573
360.7198323164941010.5603353670117980.280167683505899
370.6935478961783770.6129042076432460.306452103821623
380.6452524397324750.709495120535050.354747560267525
390.5968964568718790.8062070862562430.403103543128121
400.5536627193147780.8926745613704440.446337280685222
410.5138071823535110.9723856352929780.486192817646489
420.7908096310402280.4183807379195430.209190368959772
430.9575481789638050.08490364207239070.0424518210361953
440.9610054888882020.07798902222359580.0389945111117979
450.948781088740270.1024378225194590.0512189112597295
460.955660657867870.08867868426425970.0443393421321299
470.9552245153391780.0895509693216450.0447754846608225
480.9452516322537930.1094967354924140.0547483677462069
490.9427397396916950.114520520616610.057260260308305
500.9316304234485710.1367391531028580.068369576551429
510.9235814689922570.1528370620154860.0764185310077431
520.9881598475417860.02368030491642820.0118401524582141
530.9846222344529020.03075553109419700.0153777655470985
540.9876017303194760.02479653936104850.0123982696805243
550.9921896529579870.01562069408402690.00781034704201347
560.9896348987410580.02073020251788490.0103651012589424
570.9859361410521050.02812771789579030.0140638589478951
580.9842134220869230.03157315582615480.0157865779130774
590.9892847733593410.02143045328131740.0107152266406587
600.9884596493009830.02308070139803370.0115403506990168
610.9883191446167540.02336171076649280.0116808553832464
620.9887439274879760.02251214502404840.0112560725120242
630.9873524917003880.02529501659922420.0126475082996121
640.989822895124790.02035420975041950.0101771048752097
650.9886495271764450.02270094564710960.0113504728235548
660.9879518045146280.02409639097074380.0120481954853719
670.9883478927985830.02330421440283440.0116521072014172
680.990574911589240.01885017682151920.00942508841075962
690.9900789493311990.01984210133760250.00992105066880124
700.9869745531686440.02605089366271250.0130254468313562
710.9873788704735060.02524225905298820.0126211295264941
720.9831882897789630.03362342044207480.0168117102210374
730.98007770065040.03984459869919860.0199222993495993
740.9860153341751650.02796933164967070.0139846658248354
750.9815516481873150.03689670362537070.0184483518126854
760.9784707077825160.04305858443496830.0215292922174841
770.9719950173170360.05600996536592750.0280049826829638
780.9633421745961010.07331565080779850.0366578254038992
790.9761693473976830.04766130520463320.0238306526023166
800.9743585991756780.05128280164864330.0256414008243217
810.9774080009230240.04518399815395250.0225919990769763
820.9771642896836990.0456714206326030.0228357103163015
830.9697342846648830.06053143067023330.0302657153351166
840.9626228150805280.07475436983894480.0373771849194724
850.9879650254467460.02406994910650810.0120349745532540
860.983786048791810.032427902416380.01621395120819
870.9782640535604810.04347189287903780.0217359464395189
880.9719500995470730.05609980090585360.0280499004529268
890.9669478428138830.06610431437223350.0330521571861167
900.9574459635708760.08510807285824720.0425540364291236
910.9503476572705780.09930468545884440.0496523427294222
920.9692205841614410.06155883167711710.0307794158385585
930.9606494664071380.07870106718572320.0393505335928616
940.9566776399742890.08664472005142280.0433223600257114
950.9465688480127750.1068623039744510.0534311519872253
960.9352303554882080.1295392890235850.0647696445117925
970.9279499091593940.1441001816812130.0720500908406064
980.9105084665982970.1789830668034070.0894915334017033
990.9003599976074060.1992800047851870.0996400023925936
1000.8808113267370120.2383773465259770.119188673262988
1010.8549988827912850.2900022344174290.145001117208715
1020.8347835645990550.330432870801890.165216435400945
1030.8365794191650060.3268411616699870.163420580834994
1040.8889876678322380.2220246643355230.111012332167762
1050.886192320307170.2276153593856580.113807679692829
1060.8595331036058050.2809337927883900.140466896394195
1070.8325014232972840.3349971534054310.167498576702715
1080.824718882415220.3505622351695620.175281117584781
1090.8169834303579430.3660331392841130.183016569642057
1100.8370562682068560.3258874635862870.162943731793144
1110.826141327456820.3477173450863590.173858672543179
1120.8834128902021030.2331742195957940.116587109797897
1130.8545885416733370.2908229166533260.145411458326663
1140.8310827584707820.3378344830584360.168917241529218
1150.796939253680710.406121492638580.20306074631929
1160.7854261921374450.4291476157251090.214573807862555
1170.7868210977770570.4263578044458850.213178902222943
1180.7898540323373840.4202919353252320.210145967662616
1190.7485818672777720.5028362654444550.251418132722228
1200.7939435068637470.4121129862725070.206056493136253
1210.754339191950160.4913216160996810.245660808049840
1220.71970700262170.5605859947565980.280292997378299
1230.7173725452581420.5652549094837160.282627454741858
1240.6773157905095250.6453684189809510.322684209490475
1250.6601114978508250.6797770042983510.339888502149175
1260.6414794305415310.7170411389169370.358520569458469
1270.5775341883056950.844931623388610.422465811694305
1280.5463987431385030.9072025137229940.453601256861497
1290.5488826448340610.9022347103318770.451117355165939
1300.5637784200328040.8724431599343930.436221579967196
1310.5052317859481850.989536428103630.494768214051815
1320.4604197437356380.9208394874712760.539580256264362
1330.4448596174385330.8897192348770660.555140382561467
1340.3723994550882290.7447989101764590.62760054491177
1350.3196238039361120.6392476078722240.680376196063888
1360.3016831267821120.6033662535642230.698316873217888
1370.2345382900343890.4690765800687780.765461709965611
1380.3218849580678440.6437699161356870.678115041932156
1390.7053278970489770.5893442059020470.294672102951023
1400.6616925817549830.6766148364900350.338307418245017
1410.5946868672859130.8106262654281750.405313132714087
1420.4931566723027130.9863133446054270.506843327697287
1430.423069683224760.846139366449520.57693031677524
1440.2971134936674150.594226987334830.702886506332585
1450.5276348464315870.9447303071368270.472365153568413






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level310.229629629629630NOK
10% type I error level470.348148148148148NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 31 & 0.229629629629630 & NOK \tabularnewline
10% type I error level & 47 & 0.348148148148148 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99648&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.229629629629630[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]47[/C][C]0.348148148148148[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99648&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99648&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level310.229629629629630NOK
10% type I error level470.348148148148148NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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