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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 21 Nov 2010 09:20:47 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/21/t1290331228z46z0xbp7kdaeke.htm/, Retrieved Mon, 10 Nov 2025 01:10:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98325, Retrieved Mon, 10 Nov 2025 01:10:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact428

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [W7 month] [2010-11-19 11:08:20] [56d90b683fcd93137645f9226b43c62b]
-   PD      [Multiple Regression] [W7 Trend zonder m...] [2010-11-21 09:20:47] [59f7d3e7fcb6374015f4e6b9053b0f01] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
24	14	11	12	24	26
25	11	7	8	25	23
17	6	17	8	30	25
18	12	10	8	19	23
18	8	12	9	22	19
16	10	12	7	22	29
20	10	11	4	25	25
16	11	11	11	23	21
18	16	12	7	17	22
17	11	13	7	21	25
23	13	14	12	19	24
30	12	16	10	19	18
23	8	11	10	15	22
18	12	10	8	16	15
15	11	11	8	23	22
12	4	15	4	27	28
21	9	9	9	22	20
15	8	11	8	14	12
20	8	17	7	22	24
31	14	17	11	23	20
27	15	11	9	23	21
34	16	18	11	21	20
21	9	14	13	19	21
31	14	10	8	18	23
19	11	11	8	20	28
16	8	15	9	23	24
20	9	15	6	25	24
21	9	13	9	19	24
22	9	16	9	24	23
17	9	13	6	22	23
24	10	9	6	25	29
25	16	18	16	26	24
26	11	18	5	29	18
25	8	12	7	32	25
17	9	17	9	25	21
32	16	9	6	29	26
33	11	9	6	28	22
13	16	12	5	17	22
32	12	18	12	28	22
25	12	12	7	29	23
29	14	18	10	26	30
22	9	14	9	25	23
18	10	15	8	14	17
17	9	16	5	25	23
20	10	10	8	26	23
15	12	11	8	20	25
20	14	14	10	18	24
33	14	9	6	32	24
29	10	12	8	25	23
23	14	17	7	25	21
26	16	5	4	23	24
18	9	12	8	21	24
20	10	12	8	20	28
11	6	6	4	15	16
28	8	24	20	30	20
26	13	12	8	24	29
22	10	12	8	26	27
17	8	14	6	24	22
12	7	7	4	22	28
14	15	13	8	14	16
17	9	12	9	24	25
21	10	13	6	24	24
19	12	14	7	24	28
18	13	8	9	24	24
10	10	11	5	19	23
29	11	9	5	31	30
31	8	11	8	22	24
19	9	13	8	27	21
9	13	10	6	19	25
20	11	11	8	25	25
28	8	12	7	20	22
19	9	9	7	21	23
30	9	15	9	27	26
29	15	18	11	23	23
26	9	15	6	25	25
23	10	12	8	20	21
13	14	13	6	21	25
21	12	14	9	22	24
19	12	10	8	23	29
28	11	13	6	25	22
23	14	13	10	25	27
18	6	11	8	17	26
21	12	13	8	19	22
20	8	16	10	25	24
23	14	8	5	19	27
21	11	16	7	20	24
21	10	11	5	26	24
15	14	9	8	23	29
28	12	16	14	27	22
19	10	12	7	17	21
26	14	14	8	17	24
10	5	8	6	19	24
16	11	9	5	17	23
22	10	15	6	22	20
19	9	11	10	21	27
31	10	21	12	32	26
31	16	14	9	21	25
29	13	18	12	21	21
19	9	12	7	18	21
22	10	13	8	18	19
23	10	15	10	23	21
15	7	12	6	19	21
20	9	19	10	20	16
18	8	15	10	21	22
23	14	11	10	20	29
25	14	11	5	17	15
21	8	10	7	18	17
24	9	13	10	19	15
25	14	15	11	22	21
17	14	12	6	15	21
13	8	12	7	14	19
28	8	16	12	18	24
21	8	9	11	24	20
25	7	18	11	35	17
9	6	8	11	29	23
16	8	13	5	21	24
19	6	17	8	25	14
17	11	9	6	20	19
25	14	15	9	22	24
20	11	8	4	13	13
29	11	7	4	26	22
14	11	12	7	17	16
22	14	14	11	25	19
15	8	6	6	20	25
19	20	8	7	19	25
20	11	17	8	21	23
15	8	10	4	22	24
20	11	11	8	24	26
18	10	14	9	21	26
33	14	11	8	26	25
22	11	13	11	24	18
16	9	12	8	16	21
17	9	11	5	23	26
16	8	9	4	18	23
21	10	12	8	16	23
26	13	20	10	26	22
18	13	12	6	19	20
18	12	13	9	21	13
17	8	12	9	21	24
22	13	12	13	22	15
30	14	9	9	23	14
30	12	15	10	29	22
24	14	24	20	21	10
21	15	7	5	21	24
21	13	17	11	23	22
29	16	11	6	27	24
31	9	17	9	25	19
20	9	11	7	21	20
16	9	12	9	10	13
22	8	14	10	20	20
20	7	11	9	26	22
28	16	16	8	24	24
38	11	21	7	29	29
22	9	14	6	19	12
20	11	20	13	24	20
17	9	13	6	19	21
28	14	11	8	24	24
22	13	15	10	22	22
31	16	19	16	17	20

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98325&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98325&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98325&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Twijfel[t] = + 7.28456727859203 + 0.247876363312639Zorgen[t] -0.106836322758769VerOud[t] + 0.148172353972168KritOud[t] -0.191083348051682PersStand[t] + 0.113319775964761Org[t] + 0.00141604344161365t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Twijfel[t] =  +  7.28456727859203 +  0.247876363312639Zorgen[t] -0.106836322758769VerOud[t] +  0.148172353972168KritOud[t] -0.191083348051682PersStand[t] +  0.113319775964761Org[t] +  0.00141604344161365t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98325&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Twijfel[t] =  +  7.28456727859203 +  0.247876363312639Zorgen[t] -0.106836322758769VerOud[t] +  0.148172353972168KritOud[t] -0.191083348051682PersStand[t] +  0.113319775964761Org[t] +  0.00141604344161365t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98325&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98325&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
Twijfel[t] = + 7.28456727859203 + 0.247876363312639Zorgen[t] -0.106836322758769VerOud[t] + 0.148172353972168KritOud[t] -0.191083348051682PersStand[t] + 0.113319775964761Org[t] + 0.00141604344161365t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.284567278592031.6971854.29213.1e-051.6e-05
Zorgen0.2478763633126390.0402746.154700
VerOud-0.1068363227587690.074214-1.43960.1520430.076022
KritOud0.1481723539721680.0931711.59030.113840.05692
PersStand-0.1910833480516820.05704-3.350.0010190.00051
Org0.1133197759647610.0579181.95660.0522310.026115
t0.001416043441613650.0044470.31840.7506230.375311

\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) & 7.28456727859203 & 1.697185 & 4.2921 & 3.1e-05 & 1.6e-05 \tabularnewline
Zorgen & 0.247876363312639 & 0.040274 & 6.1547 & 0 & 0 \tabularnewline
VerOud & -0.106836322758769 & 0.074214 & -1.4396 & 0.152043 & 0.076022 \tabularnewline
KritOud & 0.148172353972168 & 0.093171 & 1.5903 & 0.11384 & 0.05692 \tabularnewline
PersStand & -0.191083348051682 & 0.05704 & -3.35 & 0.001019 & 0.00051 \tabularnewline
Org & 0.113319775964761 & 0.057918 & 1.9566 & 0.052231 & 0.026115 \tabularnewline
t & 0.00141604344161365 & 0.004447 & 0.3184 & 0.750623 & 0.375311 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98325&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]7.28456727859203[/C][C]1.697185[/C][C]4.2921[/C][C]3.1e-05[/C][C]1.6e-05[/C][/ROW]
[ROW][C]Zorgen[/C][C]0.247876363312639[/C][C]0.040274[/C][C]6.1547[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]VerOud[/C][C]-0.106836322758769[/C][C]0.074214[/C][C]-1.4396[/C][C]0.152043[/C][C]0.076022[/C][/ROW]
[ROW][C]KritOud[/C][C]0.148172353972168[/C][C]0.093171[/C][C]1.5903[/C][C]0.11384[/C][C]0.05692[/C][/ROW]
[ROW][C]PersStand[/C][C]-0.191083348051682[/C][C]0.05704[/C][C]-3.35[/C][C]0.001019[/C][C]0.00051[/C][/ROW]
[ROW][C]Org[/C][C]0.113319775964761[/C][C]0.057918[/C][C]1.9566[/C][C]0.052231[/C][C]0.026115[/C][/ROW]
[ROW][C]t[/C][C]0.00141604344161365[/C][C]0.004447[/C][C]0.3184[/C][C]0.750623[/C][C]0.375311[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98325&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98325&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)7.284567278592031.6971854.29213.1e-051.6e-05
Zorgen0.2478763633126390.0402746.154700
VerOud-0.1068363227587690.074214-1.43960.1520430.076022
KritOud0.1481723539721680.0931711.59030.113840.05692
PersStand-0.1910833480516820.05704-3.350.0010190.00051
Org0.1133197759647610.0579181.95660.0522310.026115
t0.001416043441613650.0044470.31840.7506230.375311






Multiple Linear Regression - Regression Statistics
Multiple R0.489953106743654
R-squared0.240054046807758
Adjusted R-squared0.210056180234380
F-TEST (value)8.00237064261089
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value1.63423508947602e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.48907037510704
Sum Squared Residuals941.711642499795

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.489953106743654 \tabularnewline
R-squared & 0.240054046807758 \tabularnewline
Adjusted R-squared & 0.210056180234380 \tabularnewline
F-TEST (value) & 8.00237064261089 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 1.63423508947602e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.48907037510704 \tabularnewline
Sum Squared Residuals & 941.711642499795 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98325&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.489953106743654[/C][/ROW]
[ROW][C]R-squared[/C][C]0.240054046807758[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.210056180234380[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.00237064261089[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]1.63423508947602e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.48907037510704[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]941.711642499795[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98325&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98325&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 R0.489953106743654
R-squared0.240054046807758
Adjusted R-squared0.210056180234380
F-TEST (value)8.00237064261089
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value1.63423508947602e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.48907037510704
Sum Squared Residuals941.711642499795






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11412.19819856069981.80180143930024
21111.7511041666546-0.751104166654634
367.97236888767856-1.97236888767856
41210.84479283038321.15520716961683
589.75417943426532-1.75417943426532
61010.0966958027849-0.0966958027849393
7109.725407412305280.274592587694715
8119.701412072545841.29858792745416
91610.75887496824015.24112503175986
10119.98120426129791.01879573870210
111312.37275085185600.627249148143973
121212.9193654292357-0.919365429235675
13812.9374410393484-4.93744103934843
141210.52564510123631.47435489876373
15119.132250727372751.86774927262725
1647.28558823753454-3.28558823753454
17910.9486297897437-1.94862978974368
1889.72305123051512-1.72305123051512
19810.0058293271588-2.00582932715883
201412.68221233101741.31778766898258
211512.15011592578152.84988407421849
221613.70400388118322.29599611881684
23911.702203672608-2.702203672608
241414.2865897703314-0.286589770331441
251111.3910853149831-0.391085314983056
2689.34317018340976-1.34317018340976
2799.50940792208206-0.509407922082058
28911.5633901245804-2.56339012458045
29910.4234370468352-1.42343704683522
3099.4436298761768-0.443629876176804
311011.7141943654755-1.71419436547548
321611.72600117924704.27399882075298
33119.09222899236381.90777100763619
34810.0031197045880-2.00311970458801
3598.667992268181730.332007731818266
361612.59999276908373.40000723091634
371112.5870894200305-1.58708942003055
38169.26421370353946.7357862964606
391212.2695523626052-0.269552362605226
401210.35822645746321.64177354253678
411412.52113555542761.47886444457235
42910.4644349090421-1.46443490904205
431010.6413349952821-0.64133499528211
4498.421523117955870.578476882044125
451010.0610199017528-0.0610199017528414
461210.08935744611211.91064255388790
471411.57483796592352.42516203407645
481412.06497205761111.93502794238891
491012.2749820478672-2.27498204786719
50149.880146391737444.11985360826256
511612.18483636030333.81516363969666
52910.4302433499245-1.43024334992449
531011.5717745719021-1.57177457190211
5468.98621129487519-2.98621129487519
55811.2362582516123-3.23625825161234
561312.41226726586080.587732734139183
571010.8133716080190-0.813371608018989
5888.88095629771509-0.880956297715092
5979.15658542785248-2.15658542785248
60159.774255150091765.22574484990824
6199.88335346336826-0.883353463368259
621010.2116017994204-0.211601799420394
631210.21188025130921.78811974869083
641310.44950347207612.55049652792395
65108.396807189145221.60319281085478
661111.8217850361777-0.82178503617766
67813.5896296993201-5.58962969932009
6899.1074806693398-0.107480669339806
69138.63624322825954.3637567717405
701110.40730756501560.592692434984383
71812.7521832505915-4.75218325059153
72910.7654574204088-1.76545742040878
73912.3422994712653-3.34229947126534
741512.49604895537482.50395104462521
75911.1779559631201-2.17795596312011
761011.5547342292438-1.55473422924381
77148.93640136466335.0635986353367
781210.95410592974731.04589407025269
791211.11445771539870.885542284601323
801111.5545022245767-0.55450222457671
811411.47582474716762.52417525283239
82611.5705339200679-5.57053392006792
831211.26646060796750.733539392032494
84810.0759754913839-2.07597549138394
851412.42130885317721.57869114682285
861110.83758361992170.162416380078289
87109.930336480902740.0696635190972592
881410.24253297588143.75746702411859
891212.0499497829489-0.0499497829489087
901011.0081310743587-1.00813107435873
911413.01914069733770.980859302662273
9259.01704146028203-4.01704146028203
931110.51955392700710.480446072992858
941010.2200064995915-0.220006499591453
95911.4821499398239-2.48214993982391
961011.4708272188406-1.47082721884057
971613.76417751228082.23582248771919
981312.83373349611950.166266503880472
99910.8297921172816-1.82979211728157
1001011.3895337299450-1.38953372994498
1011010.9927210107971-0.99272101079714
10279.503279092332-2.503279092332
10399.83122988103862-0.831229881038616
104810.2530737966269-2.25307379662691
1051412.90553872747181.09446127252820
1061411.64861890832622.35138109167375
107811.0972667330982-3.09726673309825
108911.5485970601368-2.54859706013678
1091411.83905778697922.16094221302082
1101410.77469355869693.22530644130308
11189.8972202989823-1.89722029898230
112813.7325637585563-5.73256375855635
113810.9987479719796-2.99874797197956
11478.58826640738003-1.58826640738003
11567.51844260950576-1.51844260950576
11689.4737640188872-1.47376401888721
11768.33844977129384-2.33844977129384
118119.92447458231821.07552541768180
1191411.89683284134532.10316715865474
1201111.1390921545270-0.139092154526987
1211112.0140262495521-1.01402624955210
122119.247463768103361.75253623189664
1231410.42220003189813.57779996810195
124810.4376457404075-2.43764574040748
1252011.55615029360608.44384970639404
1261110.38328190147060.616718098529425
12789.22271739968478-1.22271739968478
1281110.79384120864570.206158791354348
1291010.7004179553129-0.700417955312895
1301413.52357954652510.476420453474937
1311110.61812827427670.381871725723348
132910.6632315109924-1.66323151099243
13399.80385862205098-0.80385862205098
134810.2383560060895-2.23835600608945
1351012.13350100981-2.13350100980999
1361310.79179973920742.20820026079259
1371310.18314992676162.81685007323836
138129.34684158150432.65315841849570
139810.4537351200044-2.45373512000441
1401311.07626106416341.92373893583664
1411412.48410444247731.51589555752272
1421211.75273302274640.247266977253558
1431410.95591699404133.04408300595869
1441511.39381298836823.60618701163180
1451310.60709368002232.39290631997775
1461611.95398295637954.04601704362046
147912.0702186680899-3.07021866808989
148910.5673211118722-1.56732111187224
149911.0754184840640-2.07541848406404
150811.3809973670726-3.38099736707262
151710.1391367618125-3.13913676181253
1521612.05001599202213.94998400797787
1531113.4590238403895-2.45902384038951
154910.078497265284-1.078497265284
155119.931490590812581.06850940918742
15699.96866184204565-0.968661842045649
1571412.59127782302401.40872217697596
1581311.12996224767291.87003775232708
1591614.55273158205511.44726841794489

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 12.1981985606998 & 1.80180143930024 \tabularnewline
2 & 11 & 11.7511041666546 & -0.751104166654634 \tabularnewline
3 & 6 & 7.97236888767856 & -1.97236888767856 \tabularnewline
4 & 12 & 10.8447928303832 & 1.15520716961683 \tabularnewline
5 & 8 & 9.75417943426532 & -1.75417943426532 \tabularnewline
6 & 10 & 10.0966958027849 & -0.0966958027849393 \tabularnewline
7 & 10 & 9.72540741230528 & 0.274592587694715 \tabularnewline
8 & 11 & 9.70141207254584 & 1.29858792745416 \tabularnewline
9 & 16 & 10.7588749682401 & 5.24112503175986 \tabularnewline
10 & 11 & 9.9812042612979 & 1.01879573870210 \tabularnewline
11 & 13 & 12.3727508518560 & 0.627249148143973 \tabularnewline
12 & 12 & 12.9193654292357 & -0.919365429235675 \tabularnewline
13 & 8 & 12.9374410393484 & -4.93744103934843 \tabularnewline
14 & 12 & 10.5256451012363 & 1.47435489876373 \tabularnewline
15 & 11 & 9.13225072737275 & 1.86774927262725 \tabularnewline
16 & 4 & 7.28558823753454 & -3.28558823753454 \tabularnewline
17 & 9 & 10.9486297897437 & -1.94862978974368 \tabularnewline
18 & 8 & 9.72305123051512 & -1.72305123051512 \tabularnewline
19 & 8 & 10.0058293271588 & -2.00582932715883 \tabularnewline
20 & 14 & 12.6822123310174 & 1.31778766898258 \tabularnewline
21 & 15 & 12.1501159257815 & 2.84988407421849 \tabularnewline
22 & 16 & 13.7040038811832 & 2.29599611881684 \tabularnewline
23 & 9 & 11.702203672608 & -2.702203672608 \tabularnewline
24 & 14 & 14.2865897703314 & -0.286589770331441 \tabularnewline
25 & 11 & 11.3910853149831 & -0.391085314983056 \tabularnewline
26 & 8 & 9.34317018340976 & -1.34317018340976 \tabularnewline
27 & 9 & 9.50940792208206 & -0.509407922082058 \tabularnewline
28 & 9 & 11.5633901245804 & -2.56339012458045 \tabularnewline
29 & 9 & 10.4234370468352 & -1.42343704683522 \tabularnewline
30 & 9 & 9.4436298761768 & -0.443629876176804 \tabularnewline
31 & 10 & 11.7141943654755 & -1.71419436547548 \tabularnewline
32 & 16 & 11.7260011792470 & 4.27399882075298 \tabularnewline
33 & 11 & 9.0922289923638 & 1.90777100763619 \tabularnewline
34 & 8 & 10.0031197045880 & -2.00311970458801 \tabularnewline
35 & 9 & 8.66799226818173 & 0.332007731818266 \tabularnewline
36 & 16 & 12.5999927690837 & 3.40000723091634 \tabularnewline
37 & 11 & 12.5870894200305 & -1.58708942003055 \tabularnewline
38 & 16 & 9.2642137035394 & 6.7357862964606 \tabularnewline
39 & 12 & 12.2695523626052 & -0.269552362605226 \tabularnewline
40 & 12 & 10.3582264574632 & 1.64177354253678 \tabularnewline
41 & 14 & 12.5211355554276 & 1.47886444457235 \tabularnewline
42 & 9 & 10.4644349090421 & -1.46443490904205 \tabularnewline
43 & 10 & 10.6413349952821 & -0.64133499528211 \tabularnewline
44 & 9 & 8.42152311795587 & 0.578476882044125 \tabularnewline
45 & 10 & 10.0610199017528 & -0.0610199017528414 \tabularnewline
46 & 12 & 10.0893574461121 & 1.91064255388790 \tabularnewline
47 & 14 & 11.5748379659235 & 2.42516203407645 \tabularnewline
48 & 14 & 12.0649720576111 & 1.93502794238891 \tabularnewline
49 & 10 & 12.2749820478672 & -2.27498204786719 \tabularnewline
50 & 14 & 9.88014639173744 & 4.11985360826256 \tabularnewline
51 & 16 & 12.1848363603033 & 3.81516363969666 \tabularnewline
52 & 9 & 10.4302433499245 & -1.43024334992449 \tabularnewline
53 & 10 & 11.5717745719021 & -1.57177457190211 \tabularnewline
54 & 6 & 8.98621129487519 & -2.98621129487519 \tabularnewline
55 & 8 & 11.2362582516123 & -3.23625825161234 \tabularnewline
56 & 13 & 12.4122672658608 & 0.587732734139183 \tabularnewline
57 & 10 & 10.8133716080190 & -0.813371608018989 \tabularnewline
58 & 8 & 8.88095629771509 & -0.880956297715092 \tabularnewline
59 & 7 & 9.15658542785248 & -2.15658542785248 \tabularnewline
60 & 15 & 9.77425515009176 & 5.22574484990824 \tabularnewline
61 & 9 & 9.88335346336826 & -0.883353463368259 \tabularnewline
62 & 10 & 10.2116017994204 & -0.211601799420394 \tabularnewline
63 & 12 & 10.2118802513092 & 1.78811974869083 \tabularnewline
64 & 13 & 10.4495034720761 & 2.55049652792395 \tabularnewline
65 & 10 & 8.39680718914522 & 1.60319281085478 \tabularnewline
66 & 11 & 11.8217850361777 & -0.82178503617766 \tabularnewline
67 & 8 & 13.5896296993201 & -5.58962969932009 \tabularnewline
68 & 9 & 9.1074806693398 & -0.107480669339806 \tabularnewline
69 & 13 & 8.6362432282595 & 4.3637567717405 \tabularnewline
70 & 11 & 10.4073075650156 & 0.592692434984383 \tabularnewline
71 & 8 & 12.7521832505915 & -4.75218325059153 \tabularnewline
72 & 9 & 10.7654574204088 & -1.76545742040878 \tabularnewline
73 & 9 & 12.3422994712653 & -3.34229947126534 \tabularnewline
74 & 15 & 12.4960489553748 & 2.50395104462521 \tabularnewline
75 & 9 & 11.1779559631201 & -2.17795596312011 \tabularnewline
76 & 10 & 11.5547342292438 & -1.55473422924381 \tabularnewline
77 & 14 & 8.9364013646633 & 5.0635986353367 \tabularnewline
78 & 12 & 10.9541059297473 & 1.04589407025269 \tabularnewline
79 & 12 & 11.1144577153987 & 0.885542284601323 \tabularnewline
80 & 11 & 11.5545022245767 & -0.55450222457671 \tabularnewline
81 & 14 & 11.4758247471676 & 2.52417525283239 \tabularnewline
82 & 6 & 11.5705339200679 & -5.57053392006792 \tabularnewline
83 & 12 & 11.2664606079675 & 0.733539392032494 \tabularnewline
84 & 8 & 10.0759754913839 & -2.07597549138394 \tabularnewline
85 & 14 & 12.4213088531772 & 1.57869114682285 \tabularnewline
86 & 11 & 10.8375836199217 & 0.162416380078289 \tabularnewline
87 & 10 & 9.93033648090274 & 0.0696635190972592 \tabularnewline
88 & 14 & 10.2425329758814 & 3.75746702411859 \tabularnewline
89 & 12 & 12.0499497829489 & -0.0499497829489087 \tabularnewline
90 & 10 & 11.0081310743587 & -1.00813107435873 \tabularnewline
91 & 14 & 13.0191406973377 & 0.980859302662273 \tabularnewline
92 & 5 & 9.01704146028203 & -4.01704146028203 \tabularnewline
93 & 11 & 10.5195539270071 & 0.480446072992858 \tabularnewline
94 & 10 & 10.2200064995915 & -0.220006499591453 \tabularnewline
95 & 9 & 11.4821499398239 & -2.48214993982391 \tabularnewline
96 & 10 & 11.4708272188406 & -1.47082721884057 \tabularnewline
97 & 16 & 13.7641775122808 & 2.23582248771919 \tabularnewline
98 & 13 & 12.8337334961195 & 0.166266503880472 \tabularnewline
99 & 9 & 10.8297921172816 & -1.82979211728157 \tabularnewline
100 & 10 & 11.3895337299450 & -1.38953372994498 \tabularnewline
101 & 10 & 10.9927210107971 & -0.99272101079714 \tabularnewline
102 & 7 & 9.503279092332 & -2.503279092332 \tabularnewline
103 & 9 & 9.83122988103862 & -0.831229881038616 \tabularnewline
104 & 8 & 10.2530737966269 & -2.25307379662691 \tabularnewline
105 & 14 & 12.9055387274718 & 1.09446127252820 \tabularnewline
106 & 14 & 11.6486189083262 & 2.35138109167375 \tabularnewline
107 & 8 & 11.0972667330982 & -3.09726673309825 \tabularnewline
108 & 9 & 11.5485970601368 & -2.54859706013678 \tabularnewline
109 & 14 & 11.8390577869792 & 2.16094221302082 \tabularnewline
110 & 14 & 10.7746935586969 & 3.22530644130308 \tabularnewline
111 & 8 & 9.8972202989823 & -1.89722029898230 \tabularnewline
112 & 8 & 13.7325637585563 & -5.73256375855635 \tabularnewline
113 & 8 & 10.9987479719796 & -2.99874797197956 \tabularnewline
114 & 7 & 8.58826640738003 & -1.58826640738003 \tabularnewline
115 & 6 & 7.51844260950576 & -1.51844260950576 \tabularnewline
116 & 8 & 9.4737640188872 & -1.47376401888721 \tabularnewline
117 & 6 & 8.33844977129384 & -2.33844977129384 \tabularnewline
118 & 11 & 9.9244745823182 & 1.07552541768180 \tabularnewline
119 & 14 & 11.8968328413453 & 2.10316715865474 \tabularnewline
120 & 11 & 11.1390921545270 & -0.139092154526987 \tabularnewline
121 & 11 & 12.0140262495521 & -1.01402624955210 \tabularnewline
122 & 11 & 9.24746376810336 & 1.75253623189664 \tabularnewline
123 & 14 & 10.4222000318981 & 3.57779996810195 \tabularnewline
124 & 8 & 10.4376457404075 & -2.43764574040748 \tabularnewline
125 & 20 & 11.5561502936060 & 8.44384970639404 \tabularnewline
126 & 11 & 10.3832819014706 & 0.616718098529425 \tabularnewline
127 & 8 & 9.22271739968478 & -1.22271739968478 \tabularnewline
128 & 11 & 10.7938412086457 & 0.206158791354348 \tabularnewline
129 & 10 & 10.7004179553129 & -0.700417955312895 \tabularnewline
130 & 14 & 13.5235795465251 & 0.476420453474937 \tabularnewline
131 & 11 & 10.6181282742767 & 0.381871725723348 \tabularnewline
132 & 9 & 10.6632315109924 & -1.66323151099243 \tabularnewline
133 & 9 & 9.80385862205098 & -0.80385862205098 \tabularnewline
134 & 8 & 10.2383560060895 & -2.23835600608945 \tabularnewline
135 & 10 & 12.13350100981 & -2.13350100980999 \tabularnewline
136 & 13 & 10.7917997392074 & 2.20820026079259 \tabularnewline
137 & 13 & 10.1831499267616 & 2.81685007323836 \tabularnewline
138 & 12 & 9.3468415815043 & 2.65315841849570 \tabularnewline
139 & 8 & 10.4537351200044 & -2.45373512000441 \tabularnewline
140 & 13 & 11.0762610641634 & 1.92373893583664 \tabularnewline
141 & 14 & 12.4841044424773 & 1.51589555752272 \tabularnewline
142 & 12 & 11.7527330227464 & 0.247266977253558 \tabularnewline
143 & 14 & 10.9559169940413 & 3.04408300595869 \tabularnewline
144 & 15 & 11.3938129883682 & 3.60618701163180 \tabularnewline
145 & 13 & 10.6070936800223 & 2.39290631997775 \tabularnewline
146 & 16 & 11.9539829563795 & 4.04601704362046 \tabularnewline
147 & 9 & 12.0702186680899 & -3.07021866808989 \tabularnewline
148 & 9 & 10.5673211118722 & -1.56732111187224 \tabularnewline
149 & 9 & 11.0754184840640 & -2.07541848406404 \tabularnewline
150 & 8 & 11.3809973670726 & -3.38099736707262 \tabularnewline
151 & 7 & 10.1391367618125 & -3.13913676181253 \tabularnewline
152 & 16 & 12.0500159920221 & 3.94998400797787 \tabularnewline
153 & 11 & 13.4590238403895 & -2.45902384038951 \tabularnewline
154 & 9 & 10.078497265284 & -1.078497265284 \tabularnewline
155 & 11 & 9.93149059081258 & 1.06850940918742 \tabularnewline
156 & 9 & 9.96866184204565 & -0.968661842045649 \tabularnewline
157 & 14 & 12.5912778230240 & 1.40872217697596 \tabularnewline
158 & 13 & 11.1299622476729 & 1.87003775232708 \tabularnewline
159 & 16 & 14.5527315820551 & 1.44726841794489 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98325&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]14[/C][C]12.1981985606998[/C][C]1.80180143930024[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.7511041666546[/C][C]-0.751104166654634[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]7.97236888767856[/C][C]-1.97236888767856[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.8447928303832[/C][C]1.15520716961683[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]9.75417943426532[/C][C]-1.75417943426532[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]10.0966958027849[/C][C]-0.0966958027849393[/C][/ROW]
[ROW][C]7[/C][C]10[/C][C]9.72540741230528[/C][C]0.274592587694715[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]9.70141207254584[/C][C]1.29858792745416[/C][/ROW]
[ROW][C]9[/C][C]16[/C][C]10.7588749682401[/C][C]5.24112503175986[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]9.9812042612979[/C][C]1.01879573870210[/C][/ROW]
[ROW][C]11[/C][C]13[/C][C]12.3727508518560[/C][C]0.627249148143973[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]12.9193654292357[/C][C]-0.919365429235675[/C][/ROW]
[ROW][C]13[/C][C]8[/C][C]12.9374410393484[/C][C]-4.93744103934843[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]10.5256451012363[/C][C]1.47435489876373[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]9.13225072737275[/C][C]1.86774927262725[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]7.28558823753454[/C][C]-3.28558823753454[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]10.9486297897437[/C][C]-1.94862978974368[/C][/ROW]
[ROW][C]18[/C][C]8[/C][C]9.72305123051512[/C][C]-1.72305123051512[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]10.0058293271588[/C][C]-2.00582932715883[/C][/ROW]
[ROW][C]20[/C][C]14[/C][C]12.6822123310174[/C][C]1.31778766898258[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]12.1501159257815[/C][C]2.84988407421849[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]13.7040038811832[/C][C]2.29599611881684[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]11.702203672608[/C][C]-2.702203672608[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.2865897703314[/C][C]-0.286589770331441[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]11.3910853149831[/C][C]-0.391085314983056[/C][/ROW]
[ROW][C]26[/C][C]8[/C][C]9.34317018340976[/C][C]-1.34317018340976[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]9.50940792208206[/C][C]-0.509407922082058[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]11.5633901245804[/C][C]-2.56339012458045[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.4234370468352[/C][C]-1.42343704683522[/C][/ROW]
[ROW][C]30[/C][C]9[/C][C]9.4436298761768[/C][C]-0.443629876176804[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]11.7141943654755[/C][C]-1.71419436547548[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]11.7260011792470[/C][C]4.27399882075298[/C][/ROW]
[ROW][C]33[/C][C]11[/C][C]9.0922289923638[/C][C]1.90777100763619[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]10.0031197045880[/C][C]-2.00311970458801[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]8.66799226818173[/C][C]0.332007731818266[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]12.5999927690837[/C][C]3.40000723091634[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]12.5870894200305[/C][C]-1.58708942003055[/C][/ROW]
[ROW][C]38[/C][C]16[/C][C]9.2642137035394[/C][C]6.7357862964606[/C][/ROW]
[ROW][C]39[/C][C]12[/C][C]12.2695523626052[/C][C]-0.269552362605226[/C][/ROW]
[ROW][C]40[/C][C]12[/C][C]10.3582264574632[/C][C]1.64177354253678[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]12.5211355554276[/C][C]1.47886444457235[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]10.4644349090421[/C][C]-1.46443490904205[/C][/ROW]
[ROW][C]43[/C][C]10[/C][C]10.6413349952821[/C][C]-0.64133499528211[/C][/ROW]
[ROW][C]44[/C][C]9[/C][C]8.42152311795587[/C][C]0.578476882044125[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.0610199017528[/C][C]-0.0610199017528414[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]10.0893574461121[/C][C]1.91064255388790[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]11.5748379659235[/C][C]2.42516203407645[/C][/ROW]
[ROW][C]48[/C][C]14[/C][C]12.0649720576111[/C][C]1.93502794238891[/C][/ROW]
[ROW][C]49[/C][C]10[/C][C]12.2749820478672[/C][C]-2.27498204786719[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]9.88014639173744[/C][C]4.11985360826256[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]12.1848363603033[/C][C]3.81516363969666[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]10.4302433499245[/C][C]-1.43024334992449[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]11.5717745719021[/C][C]-1.57177457190211[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]8.98621129487519[/C][C]-2.98621129487519[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]11.2362582516123[/C][C]-3.23625825161234[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]12.4122672658608[/C][C]0.587732734139183[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]10.8133716080190[/C][C]-0.813371608018989[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]8.88095629771509[/C][C]-0.880956297715092[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]9.15658542785248[/C][C]-2.15658542785248[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]9.77425515009176[/C][C]5.22574484990824[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]9.88335346336826[/C][C]-0.883353463368259[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]10.2116017994204[/C][C]-0.211601799420394[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]10.2118802513092[/C][C]1.78811974869083[/C][/ROW]
[ROW][C]64[/C][C]13[/C][C]10.4495034720761[/C][C]2.55049652792395[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]8.39680718914522[/C][C]1.60319281085478[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]11.8217850361777[/C][C]-0.82178503617766[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]13.5896296993201[/C][C]-5.58962969932009[/C][/ROW]
[ROW][C]68[/C][C]9[/C][C]9.1074806693398[/C][C]-0.107480669339806[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]8.6362432282595[/C][C]4.3637567717405[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]10.4073075650156[/C][C]0.592692434984383[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]12.7521832505915[/C][C]-4.75218325059153[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]10.7654574204088[/C][C]-1.76545742040878[/C][/ROW]
[ROW][C]73[/C][C]9[/C][C]12.3422994712653[/C][C]-3.34229947126534[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]12.4960489553748[/C][C]2.50395104462521[/C][/ROW]
[ROW][C]75[/C][C]9[/C][C]11.1779559631201[/C][C]-2.17795596312011[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]11.5547342292438[/C][C]-1.55473422924381[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]8.9364013646633[/C][C]5.0635986353367[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]10.9541059297473[/C][C]1.04589407025269[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.1144577153987[/C][C]0.885542284601323[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]11.5545022245767[/C][C]-0.55450222457671[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]11.4758247471676[/C][C]2.52417525283239[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]11.5705339200679[/C][C]-5.57053392006792[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.2664606079675[/C][C]0.733539392032494[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]10.0759754913839[/C][C]-2.07597549138394[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]12.4213088531772[/C][C]1.57869114682285[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]10.8375836199217[/C][C]0.162416380078289[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]9.93033648090274[/C][C]0.0696635190972592[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]10.2425329758814[/C][C]3.75746702411859[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]12.0499497829489[/C][C]-0.0499497829489087[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]11.0081310743587[/C][C]-1.00813107435873[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.0191406973377[/C][C]0.980859302662273[/C][/ROW]
[ROW][C]92[/C][C]5[/C][C]9.01704146028203[/C][C]-4.01704146028203[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]10.5195539270071[/C][C]0.480446072992858[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]10.2200064995915[/C][C]-0.220006499591453[/C][/ROW]
[ROW][C]95[/C][C]9[/C][C]11.4821499398239[/C][C]-2.48214993982391[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.4708272188406[/C][C]-1.47082721884057[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.7641775122808[/C][C]2.23582248771919[/C][/ROW]
[ROW][C]98[/C][C]13[/C][C]12.8337334961195[/C][C]0.166266503880472[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]10.8297921172816[/C][C]-1.82979211728157[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]11.3895337299450[/C][C]-1.38953372994498[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.9927210107971[/C][C]-0.99272101079714[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]9.503279092332[/C][C]-2.503279092332[/C][/ROW]
[ROW][C]103[/C][C]9[/C][C]9.83122988103862[/C][C]-0.831229881038616[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]10.2530737966269[/C][C]-2.25307379662691[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]12.9055387274718[/C][C]1.09446127252820[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]11.6486189083262[/C][C]2.35138109167375[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]11.0972667330982[/C][C]-3.09726673309825[/C][/ROW]
[ROW][C]108[/C][C]9[/C][C]11.5485970601368[/C][C]-2.54859706013678[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]11.8390577869792[/C][C]2.16094221302082[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]10.7746935586969[/C][C]3.22530644130308[/C][/ROW]
[ROW][C]111[/C][C]8[/C][C]9.8972202989823[/C][C]-1.89722029898230[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]13.7325637585563[/C][C]-5.73256375855635[/C][/ROW]
[ROW][C]113[/C][C]8[/C][C]10.9987479719796[/C][C]-2.99874797197956[/C][/ROW]
[ROW][C]114[/C][C]7[/C][C]8.58826640738003[/C][C]-1.58826640738003[/C][/ROW]
[ROW][C]115[/C][C]6[/C][C]7.51844260950576[/C][C]-1.51844260950576[/C][/ROW]
[ROW][C]116[/C][C]8[/C][C]9.4737640188872[/C][C]-1.47376401888721[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]8.33844977129384[/C][C]-2.33844977129384[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]9.9244745823182[/C][C]1.07552541768180[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]11.8968328413453[/C][C]2.10316715865474[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]11.1390921545270[/C][C]-0.139092154526987[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]12.0140262495521[/C][C]-1.01402624955210[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]9.24746376810336[/C][C]1.75253623189664[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]10.4222000318981[/C][C]3.57779996810195[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]10.4376457404075[/C][C]-2.43764574040748[/C][/ROW]
[ROW][C]125[/C][C]20[/C][C]11.5561502936060[/C][C]8.44384970639404[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]10.3832819014706[/C][C]0.616718098529425[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]9.22271739968478[/C][C]-1.22271739968478[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]10.7938412086457[/C][C]0.206158791354348[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]10.7004179553129[/C][C]-0.700417955312895[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]13.5235795465251[/C][C]0.476420453474937[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]10.6181282742767[/C][C]0.381871725723348[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]10.6632315109924[/C][C]-1.66323151099243[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]9.80385862205098[/C][C]-0.80385862205098[/C][/ROW]
[ROW][C]134[/C][C]8[/C][C]10.2383560060895[/C][C]-2.23835600608945[/C][/ROW]
[ROW][C]135[/C][C]10[/C][C]12.13350100981[/C][C]-2.13350100980999[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]10.7917997392074[/C][C]2.20820026079259[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]10.1831499267616[/C][C]2.81685007323836[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]9.3468415815043[/C][C]2.65315841849570[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]10.4537351200044[/C][C]-2.45373512000441[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]11.0762610641634[/C][C]1.92373893583664[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]12.4841044424773[/C][C]1.51589555752272[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.7527330227464[/C][C]0.247266977253558[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]10.9559169940413[/C][C]3.04408300595869[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]11.3938129883682[/C][C]3.60618701163180[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]10.6070936800223[/C][C]2.39290631997775[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]11.9539829563795[/C][C]4.04601704362046[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]12.0702186680899[/C][C]-3.07021866808989[/C][/ROW]
[ROW][C]148[/C][C]9[/C][C]10.5673211118722[/C][C]-1.56732111187224[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]11.0754184840640[/C][C]-2.07541848406404[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]11.3809973670726[/C][C]-3.38099736707262[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]10.1391367618125[/C][C]-3.13913676181253[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]12.0500159920221[/C][C]3.94998400797787[/C][/ROW]
[ROW][C]153[/C][C]11[/C][C]13.4590238403895[/C][C]-2.45902384038951[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]10.078497265284[/C][C]-1.078497265284[/C][/ROW]
[ROW][C]155[/C][C]11[/C][C]9.93149059081258[/C][C]1.06850940918742[/C][/ROW]
[ROW][C]156[/C][C]9[/C][C]9.96866184204565[/C][C]-0.968661842045649[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]12.5912778230240[/C][C]1.40872217697596[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.1299622476729[/C][C]1.87003775232708[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.5527315820551[/C][C]1.44726841794489[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98325&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98325&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
11412.19819856069981.80180143930024
21111.7511041666546-0.751104166654634
367.97236888767856-1.97236888767856
41210.84479283038321.15520716961683
589.75417943426532-1.75417943426532
61010.0966958027849-0.0966958027849393
7109.725407412305280.274592587694715
8119.701412072545841.29858792745416
91610.75887496824015.24112503175986
10119.98120426129791.01879573870210
111312.37275085185600.627249148143973
121212.9193654292357-0.919365429235675
13812.9374410393484-4.93744103934843
141210.52564510123631.47435489876373
15119.132250727372751.86774927262725
1647.28558823753454-3.28558823753454
17910.9486297897437-1.94862978974368
1889.72305123051512-1.72305123051512
19810.0058293271588-2.00582932715883
201412.68221233101741.31778766898258
211512.15011592578152.84988407421849
221613.70400388118322.29599611881684
23911.702203672608-2.702203672608
241414.2865897703314-0.286589770331441
251111.3910853149831-0.391085314983056
2689.34317018340976-1.34317018340976
2799.50940792208206-0.509407922082058
28911.5633901245804-2.56339012458045
29910.4234370468352-1.42343704683522
3099.4436298761768-0.443629876176804
311011.7141943654755-1.71419436547548
321611.72600117924704.27399882075298
33119.09222899236381.90777100763619
34810.0031197045880-2.00311970458801
3598.667992268181730.332007731818266
361612.59999276908373.40000723091634
371112.5870894200305-1.58708942003055
38169.26421370353946.7357862964606
391212.2695523626052-0.269552362605226
401210.35822645746321.64177354253678
411412.52113555542761.47886444457235
42910.4644349090421-1.46443490904205
431010.6413349952821-0.64133499528211
4498.421523117955870.578476882044125
451010.0610199017528-0.0610199017528414
461210.08935744611211.91064255388790
471411.57483796592352.42516203407645
481412.06497205761111.93502794238891
491012.2749820478672-2.27498204786719
50149.880146391737444.11985360826256
511612.18483636030333.81516363969666
52910.4302433499245-1.43024334992449
531011.5717745719021-1.57177457190211
5468.98621129487519-2.98621129487519
55811.2362582516123-3.23625825161234
561312.41226726586080.587732734139183
571010.8133716080190-0.813371608018989
5888.88095629771509-0.880956297715092
5979.15658542785248-2.15658542785248
60159.774255150091765.22574484990824
6199.88335346336826-0.883353463368259
621010.2116017994204-0.211601799420394
631210.21188025130921.78811974869083
641310.44950347207612.55049652792395
65108.396807189145221.60319281085478
661111.8217850361777-0.82178503617766
67813.5896296993201-5.58962969932009
6899.1074806693398-0.107480669339806
69138.63624322825954.3637567717405
701110.40730756501560.592692434984383
71812.7521832505915-4.75218325059153
72910.7654574204088-1.76545742040878
73912.3422994712653-3.34229947126534
741512.49604895537482.50395104462521
75911.1779559631201-2.17795596312011
761011.5547342292438-1.55473422924381
77148.93640136466335.0635986353367
781210.95410592974731.04589407025269
791211.11445771539870.885542284601323
801111.5545022245767-0.55450222457671
811411.47582474716762.52417525283239
82611.5705339200679-5.57053392006792
831211.26646060796750.733539392032494
84810.0759754913839-2.07597549138394
851412.42130885317721.57869114682285
861110.83758361992170.162416380078289
87109.930336480902740.0696635190972592
881410.24253297588143.75746702411859
891212.0499497829489-0.0499497829489087
901011.0081310743587-1.00813107435873
911413.01914069733770.980859302662273
9259.01704146028203-4.01704146028203
931110.51955392700710.480446072992858
941010.2200064995915-0.220006499591453
95911.4821499398239-2.48214993982391
961011.4708272188406-1.47082721884057
971613.76417751228082.23582248771919
981312.83373349611950.166266503880472
99910.8297921172816-1.82979211728157
1001011.3895337299450-1.38953372994498
1011010.9927210107971-0.99272101079714
10279.503279092332-2.503279092332
10399.83122988103862-0.831229881038616
104810.2530737966269-2.25307379662691
1051412.90553872747181.09446127252820
1061411.64861890832622.35138109167375
107811.0972667330982-3.09726673309825
108911.5485970601368-2.54859706013678
1091411.83905778697922.16094221302082
1101410.77469355869693.22530644130308
11189.8972202989823-1.89722029898230
112813.7325637585563-5.73256375855635
113810.9987479719796-2.99874797197956
11478.58826640738003-1.58826640738003
11567.51844260950576-1.51844260950576
11689.4737640188872-1.47376401888721
11768.33844977129384-2.33844977129384
118119.92447458231821.07552541768180
1191411.89683284134532.10316715865474
1201111.1390921545270-0.139092154526987
1211112.0140262495521-1.01402624955210
122119.247463768103361.75253623189664
1231410.42220003189813.57779996810195
124810.4376457404075-2.43764574040748
1252011.55615029360608.44384970639404
1261110.38328190147060.616718098529425
12789.22271739968478-1.22271739968478
1281110.79384120864570.206158791354348
1291010.7004179553129-0.700417955312895
1301413.52357954652510.476420453474937
1311110.61812827427670.381871725723348
132910.6632315109924-1.66323151099243
13399.80385862205098-0.80385862205098
134810.2383560060895-2.23835600608945
1351012.13350100981-2.13350100980999
1361310.79179973920742.20820026079259
1371310.18314992676162.81685007323836
138129.34684158150432.65315841849570
139810.4537351200044-2.45373512000441
1401311.07626106416341.92373893583664
1411412.48410444247731.51589555752272
1421211.75273302274640.247266977253558
1431410.95591699404133.04408300595869
1441511.39381298836823.60618701163180
1451310.60709368002232.39290631997775
1461611.95398295637954.04601704362046
147912.0702186680899-3.07021866808989
148910.5673211118722-1.56732111187224
149911.0754184840640-2.07541848406404
150811.3809973670726-3.38099736707262
151710.1391367618125-3.13913676181253
1521612.05001599202213.94998400797787
1531113.4590238403895-2.45902384038951
154910.078497265284-1.078497265284
155119.931490590812581.06850940918742
15699.96866184204565-0.968661842045649
1571412.59127782302401.40872217697596
1581311.12996224767291.87003775232708
1591614.55273158205511.44726841794489






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3474227910729670.6948455821459340.652577208927033
110.4650418723611420.9300837447222830.534958127638858
120.3464958847238390.6929917694476790.653504115276161
130.8247635004881430.3504729990237140.175236499511857
140.7633542600904810.4732914798190380.236645739909519
150.7187443094747460.5625113810505070.281255690525254
160.7081502377176040.5836995245647920.291849762282396
170.6275972965152710.7448054069694580.372402703484729
180.607628981836640.7847420363267210.392371018163361
190.5276541753583790.9446916492832410.472345824641621
200.6031666373496950.793666725300610.396833362650305
210.6454199429392780.7091601141214440.354580057060722
220.6159397562948490.7681204874103020.384060243705151
230.6022762981331520.7954474037336960.397723701866848
240.5393113437891380.9213773124217230.460688656210862
250.4692160855323770.9384321710647540.530783914467623
260.401527501668150.80305500333630.59847249833185
270.3372684424737630.6745368849475270.662731557526237
280.3072441071509030.6144882143018060.692755892849097
290.2531293869080260.5062587738160530.746870613091974
300.2087547741488770.4175095482977550.791245225851123
310.1724851141485190.3449702282970380.827514885851481
320.2836469540440870.5672939080881730.716353045955913
330.2609655295007150.5219310590014290.739034470499286
340.2458752954795500.4917505909591010.75412470452045
350.2037825467421860.4075650934843720.796217453257814
360.2367412713997090.4734825427994190.763258728600291
370.2268649806066580.4537299612133160.773135019393342
380.6224549990316070.7550900019367860.377545000968393
390.5725858853829410.8548282292341180.427414114617059
400.532790552888480.934418894223040.46720944711152
410.4884043412590920.9768086825181830.511595658740908
420.4643531581772560.9287063163545110.535646841822744
430.4219509033270670.8439018066541350.578049096672933
440.3706772736489560.7413545472979110.629322726351044
450.3211738050642890.6423476101285770.678826194935712
460.2937006821843490.5874013643686990.706299317815651
470.2747358889093260.5494717778186520.725264111090674
480.2487038266674640.4974076533349280.751296173332536
490.2624001200424460.5248002400848920.737599879957554
500.3180589272216050.6361178544432090.681941072778395
510.3542762985145950.708552597029190.645723701485405
520.3434501080948280.6869002161896560.656549891905172
530.3295783923593880.6591567847187750.670421607640612
540.3571969968421390.7143939936842780.642803003157861
550.3772812998130750.7545625996261490.622718700186925
560.333095780466580.666191560933160.66690421953342
570.2938366998200830.5876733996401650.706163300179917
580.2567729297020720.5135458594041440.743227070297928
590.2415007437473120.4830014874946230.758499256252688
600.3896325171626280.7792650343252570.610367482837372
610.3463801638607320.6927603277214630.653619836139268
620.3045151356600150.6090302713200310.695484864339985
630.2837137889240710.5674275778481420.716286211075929
640.2933225156900810.5866450313801610.70667748430992
650.2686565349241050.537313069848210.731343465075895
660.2373162804413960.4746325608827910.762683719558604
670.4206837294160010.8413674588320030.579316270583999
680.3752324505630490.7504649011260980.624767549436951
690.4729918139539950.945983627907990.527008186046005
700.4309976003208690.8619952006417380.569002399679131
710.5496009846815150.900798030636970.450399015318485
720.5225497256283760.9549005487432480.477450274371624
730.5460922644073280.9078154711853440.453907735592672
740.5544398261950760.8911203476098480.445560173804924
750.5360622128069810.9278755743860380.463937787193019
760.5029307705266990.9941384589466020.497069229473301
770.6671005698892840.6657988602214310.332899430110716
780.6361209758790890.7277580482418220.363879024120911
790.6000957529962450.799808494007510.399904247003755
800.5547954170006030.8904091659987930.445204582999397
810.5647032874486290.8705934251027420.435296712551371
820.7194968068134110.5610063863731780.280503193186589
830.6859938336574440.6280123326851130.314006166342556
840.6630912800543840.6738174398912310.336908719945616
850.6402617578037180.7194764843925640.359738242196282
860.5997695611970950.800460877605810.400230438802905
870.5567924180801840.8864151638396310.443207581919816
880.6439900015172810.7120199969654370.356009998482719
890.5993237849137620.8013524301724760.400676215086238
900.5573479413793640.8853041172412720.442652058620636
910.524091709823960.951816580352080.47590829017604
920.5686905768514520.8626188462970970.431309423148548
930.5317953528604640.9364092942790730.468204647139537
940.4877584357864910.9755168715729820.512241564213509
950.4704154417799750.9408308835599510.529584558220024
960.4289976119482090.8579952238964180.571002388051791
970.4291752510095580.8583505020191170.570824748990442
980.3849512939724880.7699025879449750.615048706027512
990.3514277690126010.7028555380252020.648572230987399
1000.312597229087630.625194458175260.68740277091237
1010.2719547829929680.5439095659859360.728045217007032
1020.2538156372058030.5076312744116070.746184362794197
1030.2162299412098560.4324598824197120.783770058790144
1040.1967946862863670.3935893725727340.803205313713633
1050.1727958910115430.3455917820230870.827204108988457
1060.1806143943058680.3612287886117360.819385605694132
1070.1811123084749270.3622246169498540.818887691525073
1080.1740472857390550.348094571478110.825952714260945
1090.1712347479559270.3424694959118550.828765252044073
1100.2166968328439420.4333936656878850.783303167156058
1110.1878010508135640.3756021016271280.812198949186436
1120.3729959383908790.7459918767817590.62700406160912
1130.4352384152836980.8704768305673970.564761584716302
1140.4042444673650980.8084889347301970.595755532634902
1150.3984469675541070.7968939351082150.601553032445893
1160.3576691145687110.7153382291374220.642330885431289
1170.3609396650714900.7218793301429790.63906033492851
1180.3182320550279420.6364641100558840.681767944972058
1190.2889468628865540.5778937257731090.711053137113446
1200.2434425076345240.4868850152690480.756557492365476
1210.2293675164852380.4587350329704760.770632483514762
1220.2060182840497640.4120365680995280.793981715950236
1230.2105542737512770.4211085475025550.789445726248722
1240.2280425726496750.456085145299350.771957427350325
1250.7593833856445030.4812332287109930.240616614355497
1260.7197674885085980.5604650229828040.280232511491402
1270.6678587601880570.6642824796238850.332141239811943
1280.6079094823114480.7841810353771030.392090517688552
1290.5453568218868090.9092863562263820.454643178113191
1300.4827111591435750.965422318287150.517288840856425
1310.4274796185084620.8549592370169230.572520381491538
1320.3745502317405340.7491004634810690.625449768259466
1330.3149158451766990.6298316903533980.685084154823301
1340.2832495688231460.5664991376462920.716750431176854
1350.2857470133107890.5714940266215770.714252986689211
1360.2413916320097830.4827832640195660.758608367990217
1370.2502191054817340.5004382109634670.749780894518266
1380.2575096343976140.5150192687952280.742490365602386
1390.2874910538702670.5749821077405340.712508946129733
1400.2271000846657210.4542001693314420.772899915334279
1410.1730739454720570.3461478909441140.826926054527943
1420.1318546451282400.2637092902564800.86814535487176
1430.1370435207832350.274087041566470.862956479216765
1440.1275009475054220.2550018950108450.872499052494577
1450.1441704330532890.2883408661065770.855829566946711
1460.4157264734924090.8314529469848170.584273526507591
1470.3052196080630060.6104392161260110.694780391936994
1480.2117380849059670.4234761698119330.788261915094033
1490.1234477108621820.2468954217243630.876552289137818

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.347422791072967 & 0.694845582145934 & 0.652577208927033 \tabularnewline
11 & 0.465041872361142 & 0.930083744722283 & 0.534958127638858 \tabularnewline
12 & 0.346495884723839 & 0.692991769447679 & 0.653504115276161 \tabularnewline
13 & 0.824763500488143 & 0.350472999023714 & 0.175236499511857 \tabularnewline
14 & 0.763354260090481 & 0.473291479819038 & 0.236645739909519 \tabularnewline
15 & 0.718744309474746 & 0.562511381050507 & 0.281255690525254 \tabularnewline
16 & 0.708150237717604 & 0.583699524564792 & 0.291849762282396 \tabularnewline
17 & 0.627597296515271 & 0.744805406969458 & 0.372402703484729 \tabularnewline
18 & 0.60762898183664 & 0.784742036326721 & 0.392371018163361 \tabularnewline
19 & 0.527654175358379 & 0.944691649283241 & 0.472345824641621 \tabularnewline
20 & 0.603166637349695 & 0.79366672530061 & 0.396833362650305 \tabularnewline
21 & 0.645419942939278 & 0.709160114121444 & 0.354580057060722 \tabularnewline
22 & 0.615939756294849 & 0.768120487410302 & 0.384060243705151 \tabularnewline
23 & 0.602276298133152 & 0.795447403733696 & 0.397723701866848 \tabularnewline
24 & 0.539311343789138 & 0.921377312421723 & 0.460688656210862 \tabularnewline
25 & 0.469216085532377 & 0.938432171064754 & 0.530783914467623 \tabularnewline
26 & 0.40152750166815 & 0.8030550033363 & 0.59847249833185 \tabularnewline
27 & 0.337268442473763 & 0.674536884947527 & 0.662731557526237 \tabularnewline
28 & 0.307244107150903 & 0.614488214301806 & 0.692755892849097 \tabularnewline
29 & 0.253129386908026 & 0.506258773816053 & 0.746870613091974 \tabularnewline
30 & 0.208754774148877 & 0.417509548297755 & 0.791245225851123 \tabularnewline
31 & 0.172485114148519 & 0.344970228297038 & 0.827514885851481 \tabularnewline
32 & 0.283646954044087 & 0.567293908088173 & 0.716353045955913 \tabularnewline
33 & 0.260965529500715 & 0.521931059001429 & 0.739034470499286 \tabularnewline
34 & 0.245875295479550 & 0.491750590959101 & 0.75412470452045 \tabularnewline
35 & 0.203782546742186 & 0.407565093484372 & 0.796217453257814 \tabularnewline
36 & 0.236741271399709 & 0.473482542799419 & 0.763258728600291 \tabularnewline
37 & 0.226864980606658 & 0.453729961213316 & 0.773135019393342 \tabularnewline
38 & 0.622454999031607 & 0.755090001936786 & 0.377545000968393 \tabularnewline
39 & 0.572585885382941 & 0.854828229234118 & 0.427414114617059 \tabularnewline
40 & 0.53279055288848 & 0.93441889422304 & 0.46720944711152 \tabularnewline
41 & 0.488404341259092 & 0.976808682518183 & 0.511595658740908 \tabularnewline
42 & 0.464353158177256 & 0.928706316354511 & 0.535646841822744 \tabularnewline
43 & 0.421950903327067 & 0.843901806654135 & 0.578049096672933 \tabularnewline
44 & 0.370677273648956 & 0.741354547297911 & 0.629322726351044 \tabularnewline
45 & 0.321173805064289 & 0.642347610128577 & 0.678826194935712 \tabularnewline
46 & 0.293700682184349 & 0.587401364368699 & 0.706299317815651 \tabularnewline
47 & 0.274735888909326 & 0.549471777818652 & 0.725264111090674 \tabularnewline
48 & 0.248703826667464 & 0.497407653334928 & 0.751296173332536 \tabularnewline
49 & 0.262400120042446 & 0.524800240084892 & 0.737599879957554 \tabularnewline
50 & 0.318058927221605 & 0.636117854443209 & 0.681941072778395 \tabularnewline
51 & 0.354276298514595 & 0.70855259702919 & 0.645723701485405 \tabularnewline
52 & 0.343450108094828 & 0.686900216189656 & 0.656549891905172 \tabularnewline
53 & 0.329578392359388 & 0.659156784718775 & 0.670421607640612 \tabularnewline
54 & 0.357196996842139 & 0.714393993684278 & 0.642803003157861 \tabularnewline
55 & 0.377281299813075 & 0.754562599626149 & 0.622718700186925 \tabularnewline
56 & 0.33309578046658 & 0.66619156093316 & 0.66690421953342 \tabularnewline
57 & 0.293836699820083 & 0.587673399640165 & 0.706163300179917 \tabularnewline
58 & 0.256772929702072 & 0.513545859404144 & 0.743227070297928 \tabularnewline
59 & 0.241500743747312 & 0.483001487494623 & 0.758499256252688 \tabularnewline
60 & 0.389632517162628 & 0.779265034325257 & 0.610367482837372 \tabularnewline
61 & 0.346380163860732 & 0.692760327721463 & 0.653619836139268 \tabularnewline
62 & 0.304515135660015 & 0.609030271320031 & 0.695484864339985 \tabularnewline
63 & 0.283713788924071 & 0.567427577848142 & 0.716286211075929 \tabularnewline
64 & 0.293322515690081 & 0.586645031380161 & 0.70667748430992 \tabularnewline
65 & 0.268656534924105 & 0.53731306984821 & 0.731343465075895 \tabularnewline
66 & 0.237316280441396 & 0.474632560882791 & 0.762683719558604 \tabularnewline
67 & 0.420683729416001 & 0.841367458832003 & 0.579316270583999 \tabularnewline
68 & 0.375232450563049 & 0.750464901126098 & 0.624767549436951 \tabularnewline
69 & 0.472991813953995 & 0.94598362790799 & 0.527008186046005 \tabularnewline
70 & 0.430997600320869 & 0.861995200641738 & 0.569002399679131 \tabularnewline
71 & 0.549600984681515 & 0.90079803063697 & 0.450399015318485 \tabularnewline
72 & 0.522549725628376 & 0.954900548743248 & 0.477450274371624 \tabularnewline
73 & 0.546092264407328 & 0.907815471185344 & 0.453907735592672 \tabularnewline
74 & 0.554439826195076 & 0.891120347609848 & 0.445560173804924 \tabularnewline
75 & 0.536062212806981 & 0.927875574386038 & 0.463937787193019 \tabularnewline
76 & 0.502930770526699 & 0.994138458946602 & 0.497069229473301 \tabularnewline
77 & 0.667100569889284 & 0.665798860221431 & 0.332899430110716 \tabularnewline
78 & 0.636120975879089 & 0.727758048241822 & 0.363879024120911 \tabularnewline
79 & 0.600095752996245 & 0.79980849400751 & 0.399904247003755 \tabularnewline
80 & 0.554795417000603 & 0.890409165998793 & 0.445204582999397 \tabularnewline
81 & 0.564703287448629 & 0.870593425102742 & 0.435296712551371 \tabularnewline
82 & 0.719496806813411 & 0.561006386373178 & 0.280503193186589 \tabularnewline
83 & 0.685993833657444 & 0.628012332685113 & 0.314006166342556 \tabularnewline
84 & 0.663091280054384 & 0.673817439891231 & 0.336908719945616 \tabularnewline
85 & 0.640261757803718 & 0.719476484392564 & 0.359738242196282 \tabularnewline
86 & 0.599769561197095 & 0.80046087760581 & 0.400230438802905 \tabularnewline
87 & 0.556792418080184 & 0.886415163839631 & 0.443207581919816 \tabularnewline
88 & 0.643990001517281 & 0.712019996965437 & 0.356009998482719 \tabularnewline
89 & 0.599323784913762 & 0.801352430172476 & 0.400676215086238 \tabularnewline
90 & 0.557347941379364 & 0.885304117241272 & 0.442652058620636 \tabularnewline
91 & 0.52409170982396 & 0.95181658035208 & 0.47590829017604 \tabularnewline
92 & 0.568690576851452 & 0.862618846297097 & 0.431309423148548 \tabularnewline
93 & 0.531795352860464 & 0.936409294279073 & 0.468204647139537 \tabularnewline
94 & 0.487758435786491 & 0.975516871572982 & 0.512241564213509 \tabularnewline
95 & 0.470415441779975 & 0.940830883559951 & 0.529584558220024 \tabularnewline
96 & 0.428997611948209 & 0.857995223896418 & 0.571002388051791 \tabularnewline
97 & 0.429175251009558 & 0.858350502019117 & 0.570824748990442 \tabularnewline
98 & 0.384951293972488 & 0.769902587944975 & 0.615048706027512 \tabularnewline
99 & 0.351427769012601 & 0.702855538025202 & 0.648572230987399 \tabularnewline
100 & 0.31259722908763 & 0.62519445817526 & 0.68740277091237 \tabularnewline
101 & 0.271954782992968 & 0.543909565985936 & 0.728045217007032 \tabularnewline
102 & 0.253815637205803 & 0.507631274411607 & 0.746184362794197 \tabularnewline
103 & 0.216229941209856 & 0.432459882419712 & 0.783770058790144 \tabularnewline
104 & 0.196794686286367 & 0.393589372572734 & 0.803205313713633 \tabularnewline
105 & 0.172795891011543 & 0.345591782023087 & 0.827204108988457 \tabularnewline
106 & 0.180614394305868 & 0.361228788611736 & 0.819385605694132 \tabularnewline
107 & 0.181112308474927 & 0.362224616949854 & 0.818887691525073 \tabularnewline
108 & 0.174047285739055 & 0.34809457147811 & 0.825952714260945 \tabularnewline
109 & 0.171234747955927 & 0.342469495911855 & 0.828765252044073 \tabularnewline
110 & 0.216696832843942 & 0.433393665687885 & 0.783303167156058 \tabularnewline
111 & 0.187801050813564 & 0.375602101627128 & 0.812198949186436 \tabularnewline
112 & 0.372995938390879 & 0.745991876781759 & 0.62700406160912 \tabularnewline
113 & 0.435238415283698 & 0.870476830567397 & 0.564761584716302 \tabularnewline
114 & 0.404244467365098 & 0.808488934730197 & 0.595755532634902 \tabularnewline
115 & 0.398446967554107 & 0.796893935108215 & 0.601553032445893 \tabularnewline
116 & 0.357669114568711 & 0.715338229137422 & 0.642330885431289 \tabularnewline
117 & 0.360939665071490 & 0.721879330142979 & 0.63906033492851 \tabularnewline
118 & 0.318232055027942 & 0.636464110055884 & 0.681767944972058 \tabularnewline
119 & 0.288946862886554 & 0.577893725773109 & 0.711053137113446 \tabularnewline
120 & 0.243442507634524 & 0.486885015269048 & 0.756557492365476 \tabularnewline
121 & 0.229367516485238 & 0.458735032970476 & 0.770632483514762 \tabularnewline
122 & 0.206018284049764 & 0.412036568099528 & 0.793981715950236 \tabularnewline
123 & 0.210554273751277 & 0.421108547502555 & 0.789445726248722 \tabularnewline
124 & 0.228042572649675 & 0.45608514529935 & 0.771957427350325 \tabularnewline
125 & 0.759383385644503 & 0.481233228710993 & 0.240616614355497 \tabularnewline
126 & 0.719767488508598 & 0.560465022982804 & 0.280232511491402 \tabularnewline
127 & 0.667858760188057 & 0.664282479623885 & 0.332141239811943 \tabularnewline
128 & 0.607909482311448 & 0.784181035377103 & 0.392090517688552 \tabularnewline
129 & 0.545356821886809 & 0.909286356226382 & 0.454643178113191 \tabularnewline
130 & 0.482711159143575 & 0.96542231828715 & 0.517288840856425 \tabularnewline
131 & 0.427479618508462 & 0.854959237016923 & 0.572520381491538 \tabularnewline
132 & 0.374550231740534 & 0.749100463481069 & 0.625449768259466 \tabularnewline
133 & 0.314915845176699 & 0.629831690353398 & 0.685084154823301 \tabularnewline
134 & 0.283249568823146 & 0.566499137646292 & 0.716750431176854 \tabularnewline
135 & 0.285747013310789 & 0.571494026621577 & 0.714252986689211 \tabularnewline
136 & 0.241391632009783 & 0.482783264019566 & 0.758608367990217 \tabularnewline
137 & 0.250219105481734 & 0.500438210963467 & 0.749780894518266 \tabularnewline
138 & 0.257509634397614 & 0.515019268795228 & 0.742490365602386 \tabularnewline
139 & 0.287491053870267 & 0.574982107740534 & 0.712508946129733 \tabularnewline
140 & 0.227100084665721 & 0.454200169331442 & 0.772899915334279 \tabularnewline
141 & 0.173073945472057 & 0.346147890944114 & 0.826926054527943 \tabularnewline
142 & 0.131854645128240 & 0.263709290256480 & 0.86814535487176 \tabularnewline
143 & 0.137043520783235 & 0.27408704156647 & 0.862956479216765 \tabularnewline
144 & 0.127500947505422 & 0.255001895010845 & 0.872499052494577 \tabularnewline
145 & 0.144170433053289 & 0.288340866106577 & 0.855829566946711 \tabularnewline
146 & 0.415726473492409 & 0.831452946984817 & 0.584273526507591 \tabularnewline
147 & 0.305219608063006 & 0.610439216126011 & 0.694780391936994 \tabularnewline
148 & 0.211738084905967 & 0.423476169811933 & 0.788261915094033 \tabularnewline
149 & 0.123447710862182 & 0.246895421724363 & 0.876552289137818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98325&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.347422791072967[/C][C]0.694845582145934[/C][C]0.652577208927033[/C][/ROW]
[ROW][C]11[/C][C]0.465041872361142[/C][C]0.930083744722283[/C][C]0.534958127638858[/C][/ROW]
[ROW][C]12[/C][C]0.346495884723839[/C][C]0.692991769447679[/C][C]0.653504115276161[/C][/ROW]
[ROW][C]13[/C][C]0.824763500488143[/C][C]0.350472999023714[/C][C]0.175236499511857[/C][/ROW]
[ROW][C]14[/C][C]0.763354260090481[/C][C]0.473291479819038[/C][C]0.236645739909519[/C][/ROW]
[ROW][C]15[/C][C]0.718744309474746[/C][C]0.562511381050507[/C][C]0.281255690525254[/C][/ROW]
[ROW][C]16[/C][C]0.708150237717604[/C][C]0.583699524564792[/C][C]0.291849762282396[/C][/ROW]
[ROW][C]17[/C][C]0.627597296515271[/C][C]0.744805406969458[/C][C]0.372402703484729[/C][/ROW]
[ROW][C]18[/C][C]0.60762898183664[/C][C]0.784742036326721[/C][C]0.392371018163361[/C][/ROW]
[ROW][C]19[/C][C]0.527654175358379[/C][C]0.944691649283241[/C][C]0.472345824641621[/C][/ROW]
[ROW][C]20[/C][C]0.603166637349695[/C][C]0.79366672530061[/C][C]0.396833362650305[/C][/ROW]
[ROW][C]21[/C][C]0.645419942939278[/C][C]0.709160114121444[/C][C]0.354580057060722[/C][/ROW]
[ROW][C]22[/C][C]0.615939756294849[/C][C]0.768120487410302[/C][C]0.384060243705151[/C][/ROW]
[ROW][C]23[/C][C]0.602276298133152[/C][C]0.795447403733696[/C][C]0.397723701866848[/C][/ROW]
[ROW][C]24[/C][C]0.539311343789138[/C][C]0.921377312421723[/C][C]0.460688656210862[/C][/ROW]
[ROW][C]25[/C][C]0.469216085532377[/C][C]0.938432171064754[/C][C]0.530783914467623[/C][/ROW]
[ROW][C]26[/C][C]0.40152750166815[/C][C]0.8030550033363[/C][C]0.59847249833185[/C][/ROW]
[ROW][C]27[/C][C]0.337268442473763[/C][C]0.674536884947527[/C][C]0.662731557526237[/C][/ROW]
[ROW][C]28[/C][C]0.307244107150903[/C][C]0.614488214301806[/C][C]0.692755892849097[/C][/ROW]
[ROW][C]29[/C][C]0.253129386908026[/C][C]0.506258773816053[/C][C]0.746870613091974[/C][/ROW]
[ROW][C]30[/C][C]0.208754774148877[/C][C]0.417509548297755[/C][C]0.791245225851123[/C][/ROW]
[ROW][C]31[/C][C]0.172485114148519[/C][C]0.344970228297038[/C][C]0.827514885851481[/C][/ROW]
[ROW][C]32[/C][C]0.283646954044087[/C][C]0.567293908088173[/C][C]0.716353045955913[/C][/ROW]
[ROW][C]33[/C][C]0.260965529500715[/C][C]0.521931059001429[/C][C]0.739034470499286[/C][/ROW]
[ROW][C]34[/C][C]0.245875295479550[/C][C]0.491750590959101[/C][C]0.75412470452045[/C][/ROW]
[ROW][C]35[/C][C]0.203782546742186[/C][C]0.407565093484372[/C][C]0.796217453257814[/C][/ROW]
[ROW][C]36[/C][C]0.236741271399709[/C][C]0.473482542799419[/C][C]0.763258728600291[/C][/ROW]
[ROW][C]37[/C][C]0.226864980606658[/C][C]0.453729961213316[/C][C]0.773135019393342[/C][/ROW]
[ROW][C]38[/C][C]0.622454999031607[/C][C]0.755090001936786[/C][C]0.377545000968393[/C][/ROW]
[ROW][C]39[/C][C]0.572585885382941[/C][C]0.854828229234118[/C][C]0.427414114617059[/C][/ROW]
[ROW][C]40[/C][C]0.53279055288848[/C][C]0.93441889422304[/C][C]0.46720944711152[/C][/ROW]
[ROW][C]41[/C][C]0.488404341259092[/C][C]0.976808682518183[/C][C]0.511595658740908[/C][/ROW]
[ROW][C]42[/C][C]0.464353158177256[/C][C]0.928706316354511[/C][C]0.535646841822744[/C][/ROW]
[ROW][C]43[/C][C]0.421950903327067[/C][C]0.843901806654135[/C][C]0.578049096672933[/C][/ROW]
[ROW][C]44[/C][C]0.370677273648956[/C][C]0.741354547297911[/C][C]0.629322726351044[/C][/ROW]
[ROW][C]45[/C][C]0.321173805064289[/C][C]0.642347610128577[/C][C]0.678826194935712[/C][/ROW]
[ROW][C]46[/C][C]0.293700682184349[/C][C]0.587401364368699[/C][C]0.706299317815651[/C][/ROW]
[ROW][C]47[/C][C]0.274735888909326[/C][C]0.549471777818652[/C][C]0.725264111090674[/C][/ROW]
[ROW][C]48[/C][C]0.248703826667464[/C][C]0.497407653334928[/C][C]0.751296173332536[/C][/ROW]
[ROW][C]49[/C][C]0.262400120042446[/C][C]0.524800240084892[/C][C]0.737599879957554[/C][/ROW]
[ROW][C]50[/C][C]0.318058927221605[/C][C]0.636117854443209[/C][C]0.681941072778395[/C][/ROW]
[ROW][C]51[/C][C]0.354276298514595[/C][C]0.70855259702919[/C][C]0.645723701485405[/C][/ROW]
[ROW][C]52[/C][C]0.343450108094828[/C][C]0.686900216189656[/C][C]0.656549891905172[/C][/ROW]
[ROW][C]53[/C][C]0.329578392359388[/C][C]0.659156784718775[/C][C]0.670421607640612[/C][/ROW]
[ROW][C]54[/C][C]0.357196996842139[/C][C]0.714393993684278[/C][C]0.642803003157861[/C][/ROW]
[ROW][C]55[/C][C]0.377281299813075[/C][C]0.754562599626149[/C][C]0.622718700186925[/C][/ROW]
[ROW][C]56[/C][C]0.33309578046658[/C][C]0.66619156093316[/C][C]0.66690421953342[/C][/ROW]
[ROW][C]57[/C][C]0.293836699820083[/C][C]0.587673399640165[/C][C]0.706163300179917[/C][/ROW]
[ROW][C]58[/C][C]0.256772929702072[/C][C]0.513545859404144[/C][C]0.743227070297928[/C][/ROW]
[ROW][C]59[/C][C]0.241500743747312[/C][C]0.483001487494623[/C][C]0.758499256252688[/C][/ROW]
[ROW][C]60[/C][C]0.389632517162628[/C][C]0.779265034325257[/C][C]0.610367482837372[/C][/ROW]
[ROW][C]61[/C][C]0.346380163860732[/C][C]0.692760327721463[/C][C]0.653619836139268[/C][/ROW]
[ROW][C]62[/C][C]0.304515135660015[/C][C]0.609030271320031[/C][C]0.695484864339985[/C][/ROW]
[ROW][C]63[/C][C]0.283713788924071[/C][C]0.567427577848142[/C][C]0.716286211075929[/C][/ROW]
[ROW][C]64[/C][C]0.293322515690081[/C][C]0.586645031380161[/C][C]0.70667748430992[/C][/ROW]
[ROW][C]65[/C][C]0.268656534924105[/C][C]0.53731306984821[/C][C]0.731343465075895[/C][/ROW]
[ROW][C]66[/C][C]0.237316280441396[/C][C]0.474632560882791[/C][C]0.762683719558604[/C][/ROW]
[ROW][C]67[/C][C]0.420683729416001[/C][C]0.841367458832003[/C][C]0.579316270583999[/C][/ROW]
[ROW][C]68[/C][C]0.375232450563049[/C][C]0.750464901126098[/C][C]0.624767549436951[/C][/ROW]
[ROW][C]69[/C][C]0.472991813953995[/C][C]0.94598362790799[/C][C]0.527008186046005[/C][/ROW]
[ROW][C]70[/C][C]0.430997600320869[/C][C]0.861995200641738[/C][C]0.569002399679131[/C][/ROW]
[ROW][C]71[/C][C]0.549600984681515[/C][C]0.90079803063697[/C][C]0.450399015318485[/C][/ROW]
[ROW][C]72[/C][C]0.522549725628376[/C][C]0.954900548743248[/C][C]0.477450274371624[/C][/ROW]
[ROW][C]73[/C][C]0.546092264407328[/C][C]0.907815471185344[/C][C]0.453907735592672[/C][/ROW]
[ROW][C]74[/C][C]0.554439826195076[/C][C]0.891120347609848[/C][C]0.445560173804924[/C][/ROW]
[ROW][C]75[/C][C]0.536062212806981[/C][C]0.927875574386038[/C][C]0.463937787193019[/C][/ROW]
[ROW][C]76[/C][C]0.502930770526699[/C][C]0.994138458946602[/C][C]0.497069229473301[/C][/ROW]
[ROW][C]77[/C][C]0.667100569889284[/C][C]0.665798860221431[/C][C]0.332899430110716[/C][/ROW]
[ROW][C]78[/C][C]0.636120975879089[/C][C]0.727758048241822[/C][C]0.363879024120911[/C][/ROW]
[ROW][C]79[/C][C]0.600095752996245[/C][C]0.79980849400751[/C][C]0.399904247003755[/C][/ROW]
[ROW][C]80[/C][C]0.554795417000603[/C][C]0.890409165998793[/C][C]0.445204582999397[/C][/ROW]
[ROW][C]81[/C][C]0.564703287448629[/C][C]0.870593425102742[/C][C]0.435296712551371[/C][/ROW]
[ROW][C]82[/C][C]0.719496806813411[/C][C]0.561006386373178[/C][C]0.280503193186589[/C][/ROW]
[ROW][C]83[/C][C]0.685993833657444[/C][C]0.628012332685113[/C][C]0.314006166342556[/C][/ROW]
[ROW][C]84[/C][C]0.663091280054384[/C][C]0.673817439891231[/C][C]0.336908719945616[/C][/ROW]
[ROW][C]85[/C][C]0.640261757803718[/C][C]0.719476484392564[/C][C]0.359738242196282[/C][/ROW]
[ROW][C]86[/C][C]0.599769561197095[/C][C]0.80046087760581[/C][C]0.400230438802905[/C][/ROW]
[ROW][C]87[/C][C]0.556792418080184[/C][C]0.886415163839631[/C][C]0.443207581919816[/C][/ROW]
[ROW][C]88[/C][C]0.643990001517281[/C][C]0.712019996965437[/C][C]0.356009998482719[/C][/ROW]
[ROW][C]89[/C][C]0.599323784913762[/C][C]0.801352430172476[/C][C]0.400676215086238[/C][/ROW]
[ROW][C]90[/C][C]0.557347941379364[/C][C]0.885304117241272[/C][C]0.442652058620636[/C][/ROW]
[ROW][C]91[/C][C]0.52409170982396[/C][C]0.95181658035208[/C][C]0.47590829017604[/C][/ROW]
[ROW][C]92[/C][C]0.568690576851452[/C][C]0.862618846297097[/C][C]0.431309423148548[/C][/ROW]
[ROW][C]93[/C][C]0.531795352860464[/C][C]0.936409294279073[/C][C]0.468204647139537[/C][/ROW]
[ROW][C]94[/C][C]0.487758435786491[/C][C]0.975516871572982[/C][C]0.512241564213509[/C][/ROW]
[ROW][C]95[/C][C]0.470415441779975[/C][C]0.940830883559951[/C][C]0.529584558220024[/C][/ROW]
[ROW][C]96[/C][C]0.428997611948209[/C][C]0.857995223896418[/C][C]0.571002388051791[/C][/ROW]
[ROW][C]97[/C][C]0.429175251009558[/C][C]0.858350502019117[/C][C]0.570824748990442[/C][/ROW]
[ROW][C]98[/C][C]0.384951293972488[/C][C]0.769902587944975[/C][C]0.615048706027512[/C][/ROW]
[ROW][C]99[/C][C]0.351427769012601[/C][C]0.702855538025202[/C][C]0.648572230987399[/C][/ROW]
[ROW][C]100[/C][C]0.31259722908763[/C][C]0.62519445817526[/C][C]0.68740277091237[/C][/ROW]
[ROW][C]101[/C][C]0.271954782992968[/C][C]0.543909565985936[/C][C]0.728045217007032[/C][/ROW]
[ROW][C]102[/C][C]0.253815637205803[/C][C]0.507631274411607[/C][C]0.746184362794197[/C][/ROW]
[ROW][C]103[/C][C]0.216229941209856[/C][C]0.432459882419712[/C][C]0.783770058790144[/C][/ROW]
[ROW][C]104[/C][C]0.196794686286367[/C][C]0.393589372572734[/C][C]0.803205313713633[/C][/ROW]
[ROW][C]105[/C][C]0.172795891011543[/C][C]0.345591782023087[/C][C]0.827204108988457[/C][/ROW]
[ROW][C]106[/C][C]0.180614394305868[/C][C]0.361228788611736[/C][C]0.819385605694132[/C][/ROW]
[ROW][C]107[/C][C]0.181112308474927[/C][C]0.362224616949854[/C][C]0.818887691525073[/C][/ROW]
[ROW][C]108[/C][C]0.174047285739055[/C][C]0.34809457147811[/C][C]0.825952714260945[/C][/ROW]
[ROW][C]109[/C][C]0.171234747955927[/C][C]0.342469495911855[/C][C]0.828765252044073[/C][/ROW]
[ROW][C]110[/C][C]0.216696832843942[/C][C]0.433393665687885[/C][C]0.783303167156058[/C][/ROW]
[ROW][C]111[/C][C]0.187801050813564[/C][C]0.375602101627128[/C][C]0.812198949186436[/C][/ROW]
[ROW][C]112[/C][C]0.372995938390879[/C][C]0.745991876781759[/C][C]0.62700406160912[/C][/ROW]
[ROW][C]113[/C][C]0.435238415283698[/C][C]0.870476830567397[/C][C]0.564761584716302[/C][/ROW]
[ROW][C]114[/C][C]0.404244467365098[/C][C]0.808488934730197[/C][C]0.595755532634902[/C][/ROW]
[ROW][C]115[/C][C]0.398446967554107[/C][C]0.796893935108215[/C][C]0.601553032445893[/C][/ROW]
[ROW][C]116[/C][C]0.357669114568711[/C][C]0.715338229137422[/C][C]0.642330885431289[/C][/ROW]
[ROW][C]117[/C][C]0.360939665071490[/C][C]0.721879330142979[/C][C]0.63906033492851[/C][/ROW]
[ROW][C]118[/C][C]0.318232055027942[/C][C]0.636464110055884[/C][C]0.681767944972058[/C][/ROW]
[ROW][C]119[/C][C]0.288946862886554[/C][C]0.577893725773109[/C][C]0.711053137113446[/C][/ROW]
[ROW][C]120[/C][C]0.243442507634524[/C][C]0.486885015269048[/C][C]0.756557492365476[/C][/ROW]
[ROW][C]121[/C][C]0.229367516485238[/C][C]0.458735032970476[/C][C]0.770632483514762[/C][/ROW]
[ROW][C]122[/C][C]0.206018284049764[/C][C]0.412036568099528[/C][C]0.793981715950236[/C][/ROW]
[ROW][C]123[/C][C]0.210554273751277[/C][C]0.421108547502555[/C][C]0.789445726248722[/C][/ROW]
[ROW][C]124[/C][C]0.228042572649675[/C][C]0.45608514529935[/C][C]0.771957427350325[/C][/ROW]
[ROW][C]125[/C][C]0.759383385644503[/C][C]0.481233228710993[/C][C]0.240616614355497[/C][/ROW]
[ROW][C]126[/C][C]0.719767488508598[/C][C]0.560465022982804[/C][C]0.280232511491402[/C][/ROW]
[ROW][C]127[/C][C]0.667858760188057[/C][C]0.664282479623885[/C][C]0.332141239811943[/C][/ROW]
[ROW][C]128[/C][C]0.607909482311448[/C][C]0.784181035377103[/C][C]0.392090517688552[/C][/ROW]
[ROW][C]129[/C][C]0.545356821886809[/C][C]0.909286356226382[/C][C]0.454643178113191[/C][/ROW]
[ROW][C]130[/C][C]0.482711159143575[/C][C]0.96542231828715[/C][C]0.517288840856425[/C][/ROW]
[ROW][C]131[/C][C]0.427479618508462[/C][C]0.854959237016923[/C][C]0.572520381491538[/C][/ROW]
[ROW][C]132[/C][C]0.374550231740534[/C][C]0.749100463481069[/C][C]0.625449768259466[/C][/ROW]
[ROW][C]133[/C][C]0.314915845176699[/C][C]0.629831690353398[/C][C]0.685084154823301[/C][/ROW]
[ROW][C]134[/C][C]0.283249568823146[/C][C]0.566499137646292[/C][C]0.716750431176854[/C][/ROW]
[ROW][C]135[/C][C]0.285747013310789[/C][C]0.571494026621577[/C][C]0.714252986689211[/C][/ROW]
[ROW][C]136[/C][C]0.241391632009783[/C][C]0.482783264019566[/C][C]0.758608367990217[/C][/ROW]
[ROW][C]137[/C][C]0.250219105481734[/C][C]0.500438210963467[/C][C]0.749780894518266[/C][/ROW]
[ROW][C]138[/C][C]0.257509634397614[/C][C]0.515019268795228[/C][C]0.742490365602386[/C][/ROW]
[ROW][C]139[/C][C]0.287491053870267[/C][C]0.574982107740534[/C][C]0.712508946129733[/C][/ROW]
[ROW][C]140[/C][C]0.227100084665721[/C][C]0.454200169331442[/C][C]0.772899915334279[/C][/ROW]
[ROW][C]141[/C][C]0.173073945472057[/C][C]0.346147890944114[/C][C]0.826926054527943[/C][/ROW]
[ROW][C]142[/C][C]0.131854645128240[/C][C]0.263709290256480[/C][C]0.86814535487176[/C][/ROW]
[ROW][C]143[/C][C]0.137043520783235[/C][C]0.27408704156647[/C][C]0.862956479216765[/C][/ROW]
[ROW][C]144[/C][C]0.127500947505422[/C][C]0.255001895010845[/C][C]0.872499052494577[/C][/ROW]
[ROW][C]145[/C][C]0.144170433053289[/C][C]0.288340866106577[/C][C]0.855829566946711[/C][/ROW]
[ROW][C]146[/C][C]0.415726473492409[/C][C]0.831452946984817[/C][C]0.584273526507591[/C][/ROW]
[ROW][C]147[/C][C]0.305219608063006[/C][C]0.610439216126011[/C][C]0.694780391936994[/C][/ROW]
[ROW][C]148[/C][C]0.211738084905967[/C][C]0.423476169811933[/C][C]0.788261915094033[/C][/ROW]
[ROW][C]149[/C][C]0.123447710862182[/C][C]0.246895421724363[/C][C]0.876552289137818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98325&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98325&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.3474227910729670.6948455821459340.652577208927033
110.4650418723611420.9300837447222830.534958127638858
120.3464958847238390.6929917694476790.653504115276161
130.8247635004881430.3504729990237140.175236499511857
140.7633542600904810.4732914798190380.236645739909519
150.7187443094747460.5625113810505070.281255690525254
160.7081502377176040.5836995245647920.291849762282396
170.6275972965152710.7448054069694580.372402703484729
180.607628981836640.7847420363267210.392371018163361
190.5276541753583790.9446916492832410.472345824641621
200.6031666373496950.793666725300610.396833362650305
210.6454199429392780.7091601141214440.354580057060722
220.6159397562948490.7681204874103020.384060243705151
230.6022762981331520.7954474037336960.397723701866848
240.5393113437891380.9213773124217230.460688656210862
250.4692160855323770.9384321710647540.530783914467623
260.401527501668150.80305500333630.59847249833185
270.3372684424737630.6745368849475270.662731557526237
280.3072441071509030.6144882143018060.692755892849097
290.2531293869080260.5062587738160530.746870613091974
300.2087547741488770.4175095482977550.791245225851123
310.1724851141485190.3449702282970380.827514885851481
320.2836469540440870.5672939080881730.716353045955913
330.2609655295007150.5219310590014290.739034470499286
340.2458752954795500.4917505909591010.75412470452045
350.2037825467421860.4075650934843720.796217453257814
360.2367412713997090.4734825427994190.763258728600291
370.2268649806066580.4537299612133160.773135019393342
380.6224549990316070.7550900019367860.377545000968393
390.5725858853829410.8548282292341180.427414114617059
400.532790552888480.934418894223040.46720944711152
410.4884043412590920.9768086825181830.511595658740908
420.4643531581772560.9287063163545110.535646841822744
430.4219509033270670.8439018066541350.578049096672933
440.3706772736489560.7413545472979110.629322726351044
450.3211738050642890.6423476101285770.678826194935712
460.2937006821843490.5874013643686990.706299317815651
470.2747358889093260.5494717778186520.725264111090674
480.2487038266674640.4974076533349280.751296173332536
490.2624001200424460.5248002400848920.737599879957554
500.3180589272216050.6361178544432090.681941072778395
510.3542762985145950.708552597029190.645723701485405
520.3434501080948280.6869002161896560.656549891905172
530.3295783923593880.6591567847187750.670421607640612
540.3571969968421390.7143939936842780.642803003157861
550.3772812998130750.7545625996261490.622718700186925
560.333095780466580.666191560933160.66690421953342
570.2938366998200830.5876733996401650.706163300179917
580.2567729297020720.5135458594041440.743227070297928
590.2415007437473120.4830014874946230.758499256252688
600.3896325171626280.7792650343252570.610367482837372
610.3463801638607320.6927603277214630.653619836139268
620.3045151356600150.6090302713200310.695484864339985
630.2837137889240710.5674275778481420.716286211075929
640.2933225156900810.5866450313801610.70667748430992
650.2686565349241050.537313069848210.731343465075895
660.2373162804413960.4746325608827910.762683719558604
670.4206837294160010.8413674588320030.579316270583999
680.3752324505630490.7504649011260980.624767549436951
690.4729918139539950.945983627907990.527008186046005
700.4309976003208690.8619952006417380.569002399679131
710.5496009846815150.900798030636970.450399015318485
720.5225497256283760.9549005487432480.477450274371624
730.5460922644073280.9078154711853440.453907735592672
740.5544398261950760.8911203476098480.445560173804924
750.5360622128069810.9278755743860380.463937787193019
760.5029307705266990.9941384589466020.497069229473301
770.6671005698892840.6657988602214310.332899430110716
780.6361209758790890.7277580482418220.363879024120911
790.6000957529962450.799808494007510.399904247003755
800.5547954170006030.8904091659987930.445204582999397
810.5647032874486290.8705934251027420.435296712551371
820.7194968068134110.5610063863731780.280503193186589
830.6859938336574440.6280123326851130.314006166342556
840.6630912800543840.6738174398912310.336908719945616
850.6402617578037180.7194764843925640.359738242196282
860.5997695611970950.800460877605810.400230438802905
870.5567924180801840.8864151638396310.443207581919816
880.6439900015172810.7120199969654370.356009998482719
890.5993237849137620.8013524301724760.400676215086238
900.5573479413793640.8853041172412720.442652058620636
910.524091709823960.951816580352080.47590829017604
920.5686905768514520.8626188462970970.431309423148548
930.5317953528604640.9364092942790730.468204647139537
940.4877584357864910.9755168715729820.512241564213509
950.4704154417799750.9408308835599510.529584558220024
960.4289976119482090.8579952238964180.571002388051791
970.4291752510095580.8583505020191170.570824748990442
980.3849512939724880.7699025879449750.615048706027512
990.3514277690126010.7028555380252020.648572230987399
1000.312597229087630.625194458175260.68740277091237
1010.2719547829929680.5439095659859360.728045217007032
1020.2538156372058030.5076312744116070.746184362794197
1030.2162299412098560.4324598824197120.783770058790144
1040.1967946862863670.3935893725727340.803205313713633
1050.1727958910115430.3455917820230870.827204108988457
1060.1806143943058680.3612287886117360.819385605694132
1070.1811123084749270.3622246169498540.818887691525073
1080.1740472857390550.348094571478110.825952714260945
1090.1712347479559270.3424694959118550.828765252044073
1100.2166968328439420.4333936656878850.783303167156058
1110.1878010508135640.3756021016271280.812198949186436
1120.3729959383908790.7459918767817590.62700406160912
1130.4352384152836980.8704768305673970.564761584716302
1140.4042444673650980.8084889347301970.595755532634902
1150.3984469675541070.7968939351082150.601553032445893
1160.3576691145687110.7153382291374220.642330885431289
1170.3609396650714900.7218793301429790.63906033492851
1180.3182320550279420.6364641100558840.681767944972058
1190.2889468628865540.5778937257731090.711053137113446
1200.2434425076345240.4868850152690480.756557492365476
1210.2293675164852380.4587350329704760.770632483514762
1220.2060182840497640.4120365680995280.793981715950236
1230.2105542737512770.4211085475025550.789445726248722
1240.2280425726496750.456085145299350.771957427350325
1250.7593833856445030.4812332287109930.240616614355497
1260.7197674885085980.5604650229828040.280232511491402
1270.6678587601880570.6642824796238850.332141239811943
1280.6079094823114480.7841810353771030.392090517688552
1290.5453568218868090.9092863562263820.454643178113191
1300.4827111591435750.965422318287150.517288840856425
1310.4274796185084620.8549592370169230.572520381491538
1320.3745502317405340.7491004634810690.625449768259466
1330.3149158451766990.6298316903533980.685084154823301
1340.2832495688231460.5664991376462920.716750431176854
1350.2857470133107890.5714940266215770.714252986689211
1360.2413916320097830.4827832640195660.758608367990217
1370.2502191054817340.5004382109634670.749780894518266
1380.2575096343976140.5150192687952280.742490365602386
1390.2874910538702670.5749821077405340.712508946129733
1400.2271000846657210.4542001693314420.772899915334279
1410.1730739454720570.3461478909441140.826926054527943
1420.1318546451282400.2637092902564800.86814535487176
1430.1370435207832350.274087041566470.862956479216765
1440.1275009475054220.2550018950108450.872499052494577
1450.1441704330532890.2883408661065770.855829566946711
1460.4157264734924090.8314529469848170.584273526507591
1470.3052196080630060.6104392161260110.694780391936994
1480.2117380849059670.4234761698119330.788261915094033
1490.1234477108621820.2468954217243630.876552289137818






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98325&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98325&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98325&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}