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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 computationSat, 21 Nov 2009 02:10:54 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/21/t1258794747k1nj31fu7vu162i.htm/, Retrieved Sun, 28 Apr 2024 19:48:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58516, Retrieved Sun, 28 Apr 2024 19:48:09 +0000
QR Codes:

Original text written by user:WS 7 Multiple Regression analysis
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact184

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS 7 Multiple Reg...] [2009-11-19 18:53:22] [101f710c1bf3d900563184d79f7da6e1]
-   PD        [Multiple Regression] [WS 7 Multiple Reg...] [2009-11-21 09:10:54] [9b6f46453e60f88d91cef176fe926003] [Current]
-   P           [Multiple Regression] [WS Multiple Regre...] [2009-11-21 09:31:56] [101f710c1bf3d900563184d79f7da6e1]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14.5	14.8
14.3	14.7
15.3	16
14.4	15.4
13.7	15
14.2	15.5
13.5	15.1
11.9	11.7
14.6	16.3
15.6	16.7
14.1	15
14.9	14.9
14.2	14.6
14.6	15.3
17.2	17.9
15.4	16.4
14.3	15.4
17.5	17.9
14.5	15.9
14.4	13.9
16.6	17.8
16.7	17.9
16.6	17.4
16.9	16.7
15.7	16
16.4	16.6
18.4	19.1
16.9	17.8
16.5	17.2
18.3	18.6
15.1	16.3
15.7	15.1
18.1	19.2
16.8	17.7
18.9	19.1
19	18
18.1	17.5
17.8	17.8
21.5	21.1
17.1	17.2
18.7	19.4
19	19.8
16.4	17.6
16.9	16.2
18.6	19.5
19.3	19.9
19.4	20
17.6	17.3
18.6	18.9
18.1	18.6
20.4	21.4
18.1	18.6
19.6	19.8
19.9	20.8
19.2	19.6
17.8	17.7
19.2	19.8
22	22.2
21.1	20.7
19.5	17.9
22.2	20.9
20.9	21.2
22.2	21.4
23.5	23
21.5	21.3
24.3	23.9
22.8	22.4
20.3	18.3
23.7	22.8
23.3	22.3
19.6	17.8
18	16.4
17.3	16
16.8	16.4
18.2	17.7
16.5	16.6
16	16.2
18.4	18.3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58516&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]4 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=58516&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58516&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = -2.29101830970334 + 1.18227381282826X[t] -0.528396202113022M1[t] -1.09215623702356M2[t] -1.41384671982294M3[t] -1.40672834794418M4[t] -1.51707239523279M5[t] -1.67619740018946M6[t] -1.85649345552018M7[t] + 0.152145441079096M8[t] -1.98138135702689M9[t] -1.75420734980634M10[t] -1.10066825881478M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -2.29101830970334 +  1.18227381282826X[t] -0.528396202113022M1[t] -1.09215623702356M2[t] -1.41384671982294M3[t] -1.40672834794418M4[t] -1.51707239523279M5[t] -1.67619740018946M6[t] -1.85649345552018M7[t] +  0.152145441079096M8[t] -1.98138135702689M9[t] -1.75420734980634M10[t] -1.10066825881478M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58516&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -2.29101830970334 +  1.18227381282826X[t] -0.528396202113022M1[t] -1.09215623702356M2[t] -1.41384671982294M3[t] -1.40672834794418M4[t] -1.51707239523279M5[t] -1.67619740018946M6[t] -1.85649345552018M7[t] +  0.152145441079096M8[t] -1.98138135702689M9[t] -1.75420734980634M10[t] -1.10066825881478M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58516&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58516&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
Y[t] = -2.29101830970334 + 1.18227381282826X[t] -0.528396202113022M1[t] -1.09215623702356M2[t] -1.41384671982294M3[t] -1.40672834794418M4[t] -1.51707239523279M5[t] -1.67619740018946M6[t] -1.85649345552018M7[t] + 0.152145441079096M8[t] -1.98138135702689M9[t] -1.75420734980634M10[t] -1.10066825881478M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-2.291018309703340.577156-3.96950.0001839.1e-05
X1.182273812828260.03116637.935300
M1-0.5283962021130220.324777-1.6270.1085860.054293
M2-1.092156237023560.324961-3.36090.0013050.000652
M3-1.413846719822940.333002-4.24587.1e-053.5e-05
M4-1.406728347944180.326229-4.31215.6e-052.8e-05
M5-1.517072395232790.325948-4.65431.6e-058e-06
M6-1.676197400189460.3332-5.03064e-062e-06
M7-1.856493455520180.338322-5.48741e-060
M80.1521454410790960.3397710.44780.6557950.327897
M9-1.981381357026890.345001-5.743100
M10-1.754207349806340.346507-5.06254e-062e-06
M11-1.100668258814780.34011-3.23620.0019070.000954

\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) & -2.29101830970334 & 0.577156 & -3.9695 & 0.000183 & 9.1e-05 \tabularnewline
X & 1.18227381282826 & 0.031166 & 37.9353 & 0 & 0 \tabularnewline
M1 & -0.528396202113022 & 0.324777 & -1.627 & 0.108586 & 0.054293 \tabularnewline
M2 & -1.09215623702356 & 0.324961 & -3.3609 & 0.001305 & 0.000652 \tabularnewline
M3 & -1.41384671982294 & 0.333002 & -4.2458 & 7.1e-05 & 3.5e-05 \tabularnewline
M4 & -1.40672834794418 & 0.326229 & -4.3121 & 5.6e-05 & 2.8e-05 \tabularnewline
M5 & -1.51707239523279 & 0.325948 & -4.6543 & 1.6e-05 & 8e-06 \tabularnewline
M6 & -1.67619740018946 & 0.3332 & -5.0306 & 4e-06 & 2e-06 \tabularnewline
M7 & -1.85649345552018 & 0.338322 & -5.4874 & 1e-06 & 0 \tabularnewline
M8 & 0.152145441079096 & 0.339771 & 0.4478 & 0.655795 & 0.327897 \tabularnewline
M9 & -1.98138135702689 & 0.345001 & -5.7431 & 0 & 0 \tabularnewline
M10 & -1.75420734980634 & 0.346507 & -5.0625 & 4e-06 & 2e-06 \tabularnewline
M11 & -1.10066825881478 & 0.34011 & -3.2362 & 0.001907 & 0.000954 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58516&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]-2.29101830970334[/C][C]0.577156[/C][C]-3.9695[/C][C]0.000183[/C][C]9.1e-05[/C][/ROW]
[ROW][C]X[/C][C]1.18227381282826[/C][C]0.031166[/C][C]37.9353[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.528396202113022[/C][C]0.324777[/C][C]-1.627[/C][C]0.108586[/C][C]0.054293[/C][/ROW]
[ROW][C]M2[/C][C]-1.09215623702356[/C][C]0.324961[/C][C]-3.3609[/C][C]0.001305[/C][C]0.000652[/C][/ROW]
[ROW][C]M3[/C][C]-1.41384671982294[/C][C]0.333002[/C][C]-4.2458[/C][C]7.1e-05[/C][C]3.5e-05[/C][/ROW]
[ROW][C]M4[/C][C]-1.40672834794418[/C][C]0.326229[/C][C]-4.3121[/C][C]5.6e-05[/C][C]2.8e-05[/C][/ROW]
[ROW][C]M5[/C][C]-1.51707239523279[/C][C]0.325948[/C][C]-4.6543[/C][C]1.6e-05[/C][C]8e-06[/C][/ROW]
[ROW][C]M6[/C][C]-1.67619740018946[/C][C]0.3332[/C][C]-5.0306[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M7[/C][C]-1.85649345552018[/C][C]0.338322[/C][C]-5.4874[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]0.152145441079096[/C][C]0.339771[/C][C]0.4478[/C][C]0.655795[/C][C]0.327897[/C][/ROW]
[ROW][C]M9[/C][C]-1.98138135702689[/C][C]0.345001[/C][C]-5.7431[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]-1.75420734980634[/C][C]0.346507[/C][C]-5.0625[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M11[/C][C]-1.10066825881478[/C][C]0.34011[/C][C]-3.2362[/C][C]0.001907[/C][C]0.000954[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58516&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58516&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)-2.291018309703340.577156-3.96950.0001839.1e-05
X1.182273812828260.03116637.935300
M1-0.5283962021130220.324777-1.6270.1085860.054293
M2-1.092156237023560.324961-3.36090.0013050.000652
M3-1.413846719822940.333002-4.24587.1e-053.5e-05
M4-1.406728347944180.326229-4.31215.6e-052.8e-05
M5-1.517072395232790.325948-4.65431.6e-058e-06
M6-1.676197400189460.3332-5.03064e-062e-06
M7-1.856493455520180.338322-5.48741e-060
M80.1521454410790960.3397710.44780.6557950.327897
M9-1.981381357026890.345001-5.743100
M10-1.754207349806340.346507-5.06254e-062e-06
M11-1.100668258814780.34011-3.23620.0019070.000954






Multiple Linear Regression - Regression Statistics
Multiple R0.980495588337264
R-squared0.961371598748837
Adjusted R-squared0.954240201594776
F-TEST (value)134.808310066057
F-TEST (DF numerator)12
F-TEST (DF denominator)65
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.583743276693048
Sum Squared Residuals22.1491538504819

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.980495588337264 \tabularnewline
R-squared & 0.961371598748837 \tabularnewline
Adjusted R-squared & 0.954240201594776 \tabularnewline
F-TEST (value) & 134.808310066057 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 65 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.583743276693048 \tabularnewline
Sum Squared Residuals & 22.1491538504819 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58516&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.980495588337264[/C][/ROW]
[ROW][C]R-squared[/C][C]0.961371598748837[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.954240201594776[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]134.808310066057[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]65[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.583743276693048[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]22.1491538504819[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58516&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58516&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 R0.980495588337264
R-squared0.961371598748837
Adjusted R-squared0.954240201594776
F-TEST (value)134.808310066057
F-TEST (DF numerator)12
F-TEST (DF denominator)65
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.583743276693048
Sum Squared Residuals22.1491538504819






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
114.514.6782379180418-0.178237918041825
214.313.99625050184850.303749498151452
315.315.21151597572590.0884840242741036
414.414.5092700599077-0.109270059907701
513.713.9260164874878-0.226016487487795
614.214.3580283889452-0.158028388945250
713.513.7048228084832-0.204822808483219
811.911.69373074146640.206269258533592
914.614.9986634823704-0.398663482370433
1015.615.6987470147223-0.0987470147222814
1114.114.3424206239058-0.242420623905796
1214.915.3248615014378-0.424861501437754
1314.214.4417831554763-0.241783155476253
1414.614.7056147895455-0.105614789545495
1517.217.4578362200996-0.257836220099595
1615.415.6915438727360-0.29154387273596
1714.314.3989260126191-0.0989260126190978
1817.517.19548553973310.304514460266929
1914.514.6506418587458-0.150641858745832
2014.414.29473312968860.105266870311413
2116.616.7720742016128-0.172074201612825
2216.717.1174755901162-0.417475590116195
2316.617.1798777746936-0.57987777469362
2416.917.4529543645286-0.552954364528624
2515.716.0969664934358-0.39696649343582
2616.416.24257074622220.157429253777763
2718.418.8765647954935-0.476564795493512
2816.917.3467272106955-0.44672721069553
2916.516.5270188757100-0.0270188757099678
3018.318.02307720871290.276922791287143
3115.115.1235513838771-0.0235513838771379
3215.715.7134617050825-0.0134617050825008
3318.118.4272575395724-0.327257539572389
3416.816.8810208275505-0.0810208275505417
3518.919.1897432565017-0.289743256501670
361918.98991032120540.0100896787946367
3718.117.87037721267820.22962278732179
3817.817.66129932161610.138700678383852
3921.521.24111242115000.258887578849966
4017.116.63736292299860.462637077001431
4118.719.1280212639321-0.428021263932143
421919.4418057841068-0.44180578410677
4316.416.6605073405539-0.26050734055388
4416.917.0139628991936-0.113962899193588
4518.618.7819396834209-0.181939683420869
4619.319.4820232157727-0.182023215772716
4719.420.2537896880471-0.853789688047104
4817.618.1623186522256-0.56231865222558
4918.619.5255605506378-0.925560550637774
5018.118.6071183718788-0.507118371878757
5120.421.5957945649985-1.19579456499851
5218.118.2925462609581-0.192546260958137
5319.619.6009307890634-0.000930789063447662
5419.920.6240795969350-0.724079596935033
5519.219.02505496621040.174945033789598
5617.818.7873736184360-0.987373618435979
5719.219.13662182726930.0633781727306502
582222.2012529852777-0.201252985277720
5921.121.08138135702690.0186186429731163
6019.518.87168293992250.628317060077465
6122.221.89010817629430.309891823705701
6220.921.6810302852322-0.781030285232237
6322.221.59579456499850.60420543500149
6423.523.49455103740250.00544896259751321
6521.521.37434150830580.125658491694158
6624.324.28912841670260.0108715832973603
6722.822.33542164212950.464578357870471
6820.319.49673790613290.803262093867063
6923.722.68344326575411.01655673424587
7023.322.31948036656050.980519633439453
7119.617.65278729982491.94721270017507
721817.09827222068010.901727779319857
7317.316.09696649343581.20303350656418
7416.816.00611598365660.793884016343421
7518.217.22138145753390.978618542466057
7616.515.92799863530160.572001364698384
771615.34474506288170.655254937118294
7818.417.66839506486440.73160493513562

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14.5 & 14.6782379180418 & -0.178237918041825 \tabularnewline
2 & 14.3 & 13.9962505018485 & 0.303749498151452 \tabularnewline
3 & 15.3 & 15.2115159757259 & 0.0884840242741036 \tabularnewline
4 & 14.4 & 14.5092700599077 & -0.109270059907701 \tabularnewline
5 & 13.7 & 13.9260164874878 & -0.226016487487795 \tabularnewline
6 & 14.2 & 14.3580283889452 & -0.158028388945250 \tabularnewline
7 & 13.5 & 13.7048228084832 & -0.204822808483219 \tabularnewline
8 & 11.9 & 11.6937307414664 & 0.206269258533592 \tabularnewline
9 & 14.6 & 14.9986634823704 & -0.398663482370433 \tabularnewline
10 & 15.6 & 15.6987470147223 & -0.0987470147222814 \tabularnewline
11 & 14.1 & 14.3424206239058 & -0.242420623905796 \tabularnewline
12 & 14.9 & 15.3248615014378 & -0.424861501437754 \tabularnewline
13 & 14.2 & 14.4417831554763 & -0.241783155476253 \tabularnewline
14 & 14.6 & 14.7056147895455 & -0.105614789545495 \tabularnewline
15 & 17.2 & 17.4578362200996 & -0.257836220099595 \tabularnewline
16 & 15.4 & 15.6915438727360 & -0.29154387273596 \tabularnewline
17 & 14.3 & 14.3989260126191 & -0.0989260126190978 \tabularnewline
18 & 17.5 & 17.1954855397331 & 0.304514460266929 \tabularnewline
19 & 14.5 & 14.6506418587458 & -0.150641858745832 \tabularnewline
20 & 14.4 & 14.2947331296886 & 0.105266870311413 \tabularnewline
21 & 16.6 & 16.7720742016128 & -0.172074201612825 \tabularnewline
22 & 16.7 & 17.1174755901162 & -0.417475590116195 \tabularnewline
23 & 16.6 & 17.1798777746936 & -0.57987777469362 \tabularnewline
24 & 16.9 & 17.4529543645286 & -0.552954364528624 \tabularnewline
25 & 15.7 & 16.0969664934358 & -0.39696649343582 \tabularnewline
26 & 16.4 & 16.2425707462222 & 0.157429253777763 \tabularnewline
27 & 18.4 & 18.8765647954935 & -0.476564795493512 \tabularnewline
28 & 16.9 & 17.3467272106955 & -0.44672721069553 \tabularnewline
29 & 16.5 & 16.5270188757100 & -0.0270188757099678 \tabularnewline
30 & 18.3 & 18.0230772087129 & 0.276922791287143 \tabularnewline
31 & 15.1 & 15.1235513838771 & -0.0235513838771379 \tabularnewline
32 & 15.7 & 15.7134617050825 & -0.0134617050825008 \tabularnewline
33 & 18.1 & 18.4272575395724 & -0.327257539572389 \tabularnewline
34 & 16.8 & 16.8810208275505 & -0.0810208275505417 \tabularnewline
35 & 18.9 & 19.1897432565017 & -0.289743256501670 \tabularnewline
36 & 19 & 18.9899103212054 & 0.0100896787946367 \tabularnewline
37 & 18.1 & 17.8703772126782 & 0.22962278732179 \tabularnewline
38 & 17.8 & 17.6612993216161 & 0.138700678383852 \tabularnewline
39 & 21.5 & 21.2411124211500 & 0.258887578849966 \tabularnewline
40 & 17.1 & 16.6373629229986 & 0.462637077001431 \tabularnewline
41 & 18.7 & 19.1280212639321 & -0.428021263932143 \tabularnewline
42 & 19 & 19.4418057841068 & -0.44180578410677 \tabularnewline
43 & 16.4 & 16.6605073405539 & -0.26050734055388 \tabularnewline
44 & 16.9 & 17.0139628991936 & -0.113962899193588 \tabularnewline
45 & 18.6 & 18.7819396834209 & -0.181939683420869 \tabularnewline
46 & 19.3 & 19.4820232157727 & -0.182023215772716 \tabularnewline
47 & 19.4 & 20.2537896880471 & -0.853789688047104 \tabularnewline
48 & 17.6 & 18.1623186522256 & -0.56231865222558 \tabularnewline
49 & 18.6 & 19.5255605506378 & -0.925560550637774 \tabularnewline
50 & 18.1 & 18.6071183718788 & -0.507118371878757 \tabularnewline
51 & 20.4 & 21.5957945649985 & -1.19579456499851 \tabularnewline
52 & 18.1 & 18.2925462609581 & -0.192546260958137 \tabularnewline
53 & 19.6 & 19.6009307890634 & -0.000930789063447662 \tabularnewline
54 & 19.9 & 20.6240795969350 & -0.724079596935033 \tabularnewline
55 & 19.2 & 19.0250549662104 & 0.174945033789598 \tabularnewline
56 & 17.8 & 18.7873736184360 & -0.987373618435979 \tabularnewline
57 & 19.2 & 19.1366218272693 & 0.0633781727306502 \tabularnewline
58 & 22 & 22.2012529852777 & -0.201252985277720 \tabularnewline
59 & 21.1 & 21.0813813570269 & 0.0186186429731163 \tabularnewline
60 & 19.5 & 18.8716829399225 & 0.628317060077465 \tabularnewline
61 & 22.2 & 21.8901081762943 & 0.309891823705701 \tabularnewline
62 & 20.9 & 21.6810302852322 & -0.781030285232237 \tabularnewline
63 & 22.2 & 21.5957945649985 & 0.60420543500149 \tabularnewline
64 & 23.5 & 23.4945510374025 & 0.00544896259751321 \tabularnewline
65 & 21.5 & 21.3743415083058 & 0.125658491694158 \tabularnewline
66 & 24.3 & 24.2891284167026 & 0.0108715832973603 \tabularnewline
67 & 22.8 & 22.3354216421295 & 0.464578357870471 \tabularnewline
68 & 20.3 & 19.4967379061329 & 0.803262093867063 \tabularnewline
69 & 23.7 & 22.6834432657541 & 1.01655673424587 \tabularnewline
70 & 23.3 & 22.3194803665605 & 0.980519633439453 \tabularnewline
71 & 19.6 & 17.6527872998249 & 1.94721270017507 \tabularnewline
72 & 18 & 17.0982722206801 & 0.901727779319857 \tabularnewline
73 & 17.3 & 16.0969664934358 & 1.20303350656418 \tabularnewline
74 & 16.8 & 16.0061159836566 & 0.793884016343421 \tabularnewline
75 & 18.2 & 17.2213814575339 & 0.978618542466057 \tabularnewline
76 & 16.5 & 15.9279986353016 & 0.572001364698384 \tabularnewline
77 & 16 & 15.3447450628817 & 0.655254937118294 \tabularnewline
78 & 18.4 & 17.6683950648644 & 0.73160493513562 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58516&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]14.5[/C][C]14.6782379180418[/C][C]-0.178237918041825[/C][/ROW]
[ROW][C]2[/C][C]14.3[/C][C]13.9962505018485[/C][C]0.303749498151452[/C][/ROW]
[ROW][C]3[/C][C]15.3[/C][C]15.2115159757259[/C][C]0.0884840242741036[/C][/ROW]
[ROW][C]4[/C][C]14.4[/C][C]14.5092700599077[/C][C]-0.109270059907701[/C][/ROW]
[ROW][C]5[/C][C]13.7[/C][C]13.9260164874878[/C][C]-0.226016487487795[/C][/ROW]
[ROW][C]6[/C][C]14.2[/C][C]14.3580283889452[/C][C]-0.158028388945250[/C][/ROW]
[ROW][C]7[/C][C]13.5[/C][C]13.7048228084832[/C][C]-0.204822808483219[/C][/ROW]
[ROW][C]8[/C][C]11.9[/C][C]11.6937307414664[/C][C]0.206269258533592[/C][/ROW]
[ROW][C]9[/C][C]14.6[/C][C]14.9986634823704[/C][C]-0.398663482370433[/C][/ROW]
[ROW][C]10[/C][C]15.6[/C][C]15.6987470147223[/C][C]-0.0987470147222814[/C][/ROW]
[ROW][C]11[/C][C]14.1[/C][C]14.3424206239058[/C][C]-0.242420623905796[/C][/ROW]
[ROW][C]12[/C][C]14.9[/C][C]15.3248615014378[/C][C]-0.424861501437754[/C][/ROW]
[ROW][C]13[/C][C]14.2[/C][C]14.4417831554763[/C][C]-0.241783155476253[/C][/ROW]
[ROW][C]14[/C][C]14.6[/C][C]14.7056147895455[/C][C]-0.105614789545495[/C][/ROW]
[ROW][C]15[/C][C]17.2[/C][C]17.4578362200996[/C][C]-0.257836220099595[/C][/ROW]
[ROW][C]16[/C][C]15.4[/C][C]15.6915438727360[/C][C]-0.29154387273596[/C][/ROW]
[ROW][C]17[/C][C]14.3[/C][C]14.3989260126191[/C][C]-0.0989260126190978[/C][/ROW]
[ROW][C]18[/C][C]17.5[/C][C]17.1954855397331[/C][C]0.304514460266929[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]14.6506418587458[/C][C]-0.150641858745832[/C][/ROW]
[ROW][C]20[/C][C]14.4[/C][C]14.2947331296886[/C][C]0.105266870311413[/C][/ROW]
[ROW][C]21[/C][C]16.6[/C][C]16.7720742016128[/C][C]-0.172074201612825[/C][/ROW]
[ROW][C]22[/C][C]16.7[/C][C]17.1174755901162[/C][C]-0.417475590116195[/C][/ROW]
[ROW][C]23[/C][C]16.6[/C][C]17.1798777746936[/C][C]-0.57987777469362[/C][/ROW]
[ROW][C]24[/C][C]16.9[/C][C]17.4529543645286[/C][C]-0.552954364528624[/C][/ROW]
[ROW][C]25[/C][C]15.7[/C][C]16.0969664934358[/C][C]-0.39696649343582[/C][/ROW]
[ROW][C]26[/C][C]16.4[/C][C]16.2425707462222[/C][C]0.157429253777763[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]18.8765647954935[/C][C]-0.476564795493512[/C][/ROW]
[ROW][C]28[/C][C]16.9[/C][C]17.3467272106955[/C][C]-0.44672721069553[/C][/ROW]
[ROW][C]29[/C][C]16.5[/C][C]16.5270188757100[/C][C]-0.0270188757099678[/C][/ROW]
[ROW][C]30[/C][C]18.3[/C][C]18.0230772087129[/C][C]0.276922791287143[/C][/ROW]
[ROW][C]31[/C][C]15.1[/C][C]15.1235513838771[/C][C]-0.0235513838771379[/C][/ROW]
[ROW][C]32[/C][C]15.7[/C][C]15.7134617050825[/C][C]-0.0134617050825008[/C][/ROW]
[ROW][C]33[/C][C]18.1[/C][C]18.4272575395724[/C][C]-0.327257539572389[/C][/ROW]
[ROW][C]34[/C][C]16.8[/C][C]16.8810208275505[/C][C]-0.0810208275505417[/C][/ROW]
[ROW][C]35[/C][C]18.9[/C][C]19.1897432565017[/C][C]-0.289743256501670[/C][/ROW]
[ROW][C]36[/C][C]19[/C][C]18.9899103212054[/C][C]0.0100896787946367[/C][/ROW]
[ROW][C]37[/C][C]18.1[/C][C]17.8703772126782[/C][C]0.22962278732179[/C][/ROW]
[ROW][C]38[/C][C]17.8[/C][C]17.6612993216161[/C][C]0.138700678383852[/C][/ROW]
[ROW][C]39[/C][C]21.5[/C][C]21.2411124211500[/C][C]0.258887578849966[/C][/ROW]
[ROW][C]40[/C][C]17.1[/C][C]16.6373629229986[/C][C]0.462637077001431[/C][/ROW]
[ROW][C]41[/C][C]18.7[/C][C]19.1280212639321[/C][C]-0.428021263932143[/C][/ROW]
[ROW][C]42[/C][C]19[/C][C]19.4418057841068[/C][C]-0.44180578410677[/C][/ROW]
[ROW][C]43[/C][C]16.4[/C][C]16.6605073405539[/C][C]-0.26050734055388[/C][/ROW]
[ROW][C]44[/C][C]16.9[/C][C]17.0139628991936[/C][C]-0.113962899193588[/C][/ROW]
[ROW][C]45[/C][C]18.6[/C][C]18.7819396834209[/C][C]-0.181939683420869[/C][/ROW]
[ROW][C]46[/C][C]19.3[/C][C]19.4820232157727[/C][C]-0.182023215772716[/C][/ROW]
[ROW][C]47[/C][C]19.4[/C][C]20.2537896880471[/C][C]-0.853789688047104[/C][/ROW]
[ROW][C]48[/C][C]17.6[/C][C]18.1623186522256[/C][C]-0.56231865222558[/C][/ROW]
[ROW][C]49[/C][C]18.6[/C][C]19.5255605506378[/C][C]-0.925560550637774[/C][/ROW]
[ROW][C]50[/C][C]18.1[/C][C]18.6071183718788[/C][C]-0.507118371878757[/C][/ROW]
[ROW][C]51[/C][C]20.4[/C][C]21.5957945649985[/C][C]-1.19579456499851[/C][/ROW]
[ROW][C]52[/C][C]18.1[/C][C]18.2925462609581[/C][C]-0.192546260958137[/C][/ROW]
[ROW][C]53[/C][C]19.6[/C][C]19.6009307890634[/C][C]-0.000930789063447662[/C][/ROW]
[ROW][C]54[/C][C]19.9[/C][C]20.6240795969350[/C][C]-0.724079596935033[/C][/ROW]
[ROW][C]55[/C][C]19.2[/C][C]19.0250549662104[/C][C]0.174945033789598[/C][/ROW]
[ROW][C]56[/C][C]17.8[/C][C]18.7873736184360[/C][C]-0.987373618435979[/C][/ROW]
[ROW][C]57[/C][C]19.2[/C][C]19.1366218272693[/C][C]0.0633781727306502[/C][/ROW]
[ROW][C]58[/C][C]22[/C][C]22.2012529852777[/C][C]-0.201252985277720[/C][/ROW]
[ROW][C]59[/C][C]21.1[/C][C]21.0813813570269[/C][C]0.0186186429731163[/C][/ROW]
[ROW][C]60[/C][C]19.5[/C][C]18.8716829399225[/C][C]0.628317060077465[/C][/ROW]
[ROW][C]61[/C][C]22.2[/C][C]21.8901081762943[/C][C]0.309891823705701[/C][/ROW]
[ROW][C]62[/C][C]20.9[/C][C]21.6810302852322[/C][C]-0.781030285232237[/C][/ROW]
[ROW][C]63[/C][C]22.2[/C][C]21.5957945649985[/C][C]0.60420543500149[/C][/ROW]
[ROW][C]64[/C][C]23.5[/C][C]23.4945510374025[/C][C]0.00544896259751321[/C][/ROW]
[ROW][C]65[/C][C]21.5[/C][C]21.3743415083058[/C][C]0.125658491694158[/C][/ROW]
[ROW][C]66[/C][C]24.3[/C][C]24.2891284167026[/C][C]0.0108715832973603[/C][/ROW]
[ROW][C]67[/C][C]22.8[/C][C]22.3354216421295[/C][C]0.464578357870471[/C][/ROW]
[ROW][C]68[/C][C]20.3[/C][C]19.4967379061329[/C][C]0.803262093867063[/C][/ROW]
[ROW][C]69[/C][C]23.7[/C][C]22.6834432657541[/C][C]1.01655673424587[/C][/ROW]
[ROW][C]70[/C][C]23.3[/C][C]22.3194803665605[/C][C]0.980519633439453[/C][/ROW]
[ROW][C]71[/C][C]19.6[/C][C]17.6527872998249[/C][C]1.94721270017507[/C][/ROW]
[ROW][C]72[/C][C]18[/C][C]17.0982722206801[/C][C]0.901727779319857[/C][/ROW]
[ROW][C]73[/C][C]17.3[/C][C]16.0969664934358[/C][C]1.20303350656418[/C][/ROW]
[ROW][C]74[/C][C]16.8[/C][C]16.0061159836566[/C][C]0.793884016343421[/C][/ROW]
[ROW][C]75[/C][C]18.2[/C][C]17.2213814575339[/C][C]0.978618542466057[/C][/ROW]
[ROW][C]76[/C][C]16.5[/C][C]15.9279986353016[/C][C]0.572001364698384[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]15.3447450628817[/C][C]0.655254937118294[/C][/ROW]
[ROW][C]78[/C][C]18.4[/C][C]17.6683950648644[/C][C]0.73160493513562[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58516&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58516&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
114.514.6782379180418-0.178237918041825
214.313.99625050184850.303749498151452
315.315.21151597572590.0884840242741036
414.414.5092700599077-0.109270059907701
513.713.9260164874878-0.226016487487795
614.214.3580283889452-0.158028388945250
713.513.7048228084832-0.204822808483219
811.911.69373074146640.206269258533592
914.614.9986634823704-0.398663482370433
1015.615.6987470147223-0.0987470147222814
1114.114.3424206239058-0.242420623905796
1214.915.3248615014378-0.424861501437754
1314.214.4417831554763-0.241783155476253
1414.614.7056147895455-0.105614789545495
1517.217.4578362200996-0.257836220099595
1615.415.6915438727360-0.29154387273596
1714.314.3989260126191-0.0989260126190978
1817.517.19548553973310.304514460266929
1914.514.6506418587458-0.150641858745832
2014.414.29473312968860.105266870311413
2116.616.7720742016128-0.172074201612825
2216.717.1174755901162-0.417475590116195
2316.617.1798777746936-0.57987777469362
2416.917.4529543645286-0.552954364528624
2515.716.0969664934358-0.39696649343582
2616.416.24257074622220.157429253777763
2718.418.8765647954935-0.476564795493512
2816.917.3467272106955-0.44672721069553
2916.516.5270188757100-0.0270188757099678
3018.318.02307720871290.276922791287143
3115.115.1235513838771-0.0235513838771379
3215.715.7134617050825-0.0134617050825008
3318.118.4272575395724-0.327257539572389
3416.816.8810208275505-0.0810208275505417
3518.919.1897432565017-0.289743256501670
361918.98991032120540.0100896787946367
3718.117.87037721267820.22962278732179
3817.817.66129932161610.138700678383852
3921.521.24111242115000.258887578849966
4017.116.63736292299860.462637077001431
4118.719.1280212639321-0.428021263932143
421919.4418057841068-0.44180578410677
4316.416.6605073405539-0.26050734055388
4416.917.0139628991936-0.113962899193588
4518.618.7819396834209-0.181939683420869
4619.319.4820232157727-0.182023215772716
4719.420.2537896880471-0.853789688047104
4817.618.1623186522256-0.56231865222558
4918.619.5255605506378-0.925560550637774
5018.118.6071183718788-0.507118371878757
5120.421.5957945649985-1.19579456499851
5218.118.2925462609581-0.192546260958137
5319.619.6009307890634-0.000930789063447662
5419.920.6240795969350-0.724079596935033
5519.219.02505496621040.174945033789598
5617.818.7873736184360-0.987373618435979
5719.219.13662182726930.0633781727306502
582222.2012529852777-0.201252985277720
5921.121.08138135702690.0186186429731163
6019.518.87168293992250.628317060077465
6122.221.89010817629430.309891823705701
6220.921.6810302852322-0.781030285232237
6322.221.59579456499850.60420543500149
6423.523.49455103740250.00544896259751321
6521.521.37434150830580.125658491694158
6624.324.28912841670260.0108715832973603
6722.822.33542164212950.464578357870471
6820.319.49673790613290.803262093867063
6923.722.68344326575411.01655673424587
7023.322.31948036656050.980519633439453
7119.617.65278729982491.94721270017507
721817.09827222068010.901727779319857
7317.316.09696649343581.20303350656418
7416.816.00611598365660.793884016343421
7518.217.22138145753390.978618542466057
7616.515.92799863530160.572001364698384
771615.34474506288170.655254937118294
7818.417.66839506486440.73160493513562






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.009732492660854120.01946498532170820.990267507339146
170.003168137488616220.006336274977232440.996831862511384
180.01414625521808200.02829251043616410.985853744781918
190.004372359404155540.008744718808311080.995627640595844
200.001306298744187290.002612597488374590.998693701255813
210.0004778106317278270.0009556212634556530.999522189368272
220.0002533114489838210.0005066228979676420.999746688551016
230.0001262049862245390.0002524099724490780.999873795013775
243.99824145091164e-057.99648290182327e-050.99996001758549
251.37223613160377e-052.74447226320754e-050.999986277638684
264.04688491923357e-068.09376983846715e-060.99999595311508
272.45605398836595e-064.9121079767319e-060.999997543946012
288.3617539185452e-071.67235078370904e-060.999999163824608
293.94283250540627e-077.88566501081254e-070.99999960571675
302.16129265549682e-074.32258531099365e-070.999999783870734
317.79334161734906e-081.55866832346981e-070.999999922066584
322.01028992086117e-084.02057984172234e-080.9999999798971
336.03859113494223e-091.20771822698845e-080.999999993961409
342.42324259855201e-094.84648519710403e-090.999999997576757
351.09233485250410e-092.18466970500819e-090.999999998907665
363.4271187090009e-096.8542374180018e-090.999999996572881
376.68598564873209e-091.33719712974642e-080.999999993314014
381.76701960503517e-093.53403921007034e-090.99999999823298
391.59424344158696e-093.18848688317391e-090.999999998405757
401.16794497268976e-082.33588994537951e-080.99999998832055
411.02323293318461e-082.04646586636922e-080.99999998976767
422.21494860165548e-084.42989720331096e-080.999999977850514
431.23356324898483e-082.46712649796967e-080.999999987664367
444.63360642107868e-099.26721284215737e-090.999999995366394
452.52194452470515e-095.04388904941031e-090.999999997478055
461.24834160879368e-092.49668321758737e-090.999999998751658
477.94686583862144e-091.58937316772429e-080.999999992053134
481.18727878670707e-082.37455757341414e-080.999999988127212
493.25961854170414e-076.51923708340828e-070.999999674038146
504.26473317970132e-078.52946635940265e-070.999999573526682
514.44654527463367e-058.89309054926733e-050.999955534547254
522.87118929357833e-055.74237858715666e-050.999971288107064
531.70232491362704e-053.40464982725408e-050.999982976750864
546.35280041356182e-050.0001270560082712360.999936471995864
556.98944357101535e-050.0001397888714203070.99993010556429
560.003263358545111750.00652671709022350.996736641454888
570.02049708773639350.0409941754727870.979502912263607
580.06820101514469820.1364020302893960.931798984855302
590.6203298263986520.7593403472026950.379670173601348
600.5752398797783040.8495202404433910.424760120221696
610.4819902519126810.9639805038253620.518009748087319
620.9915505470677670.01689890586446590.00844945293223293

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.00973249266085412 & 0.0194649853217082 & 0.990267507339146 \tabularnewline
17 & 0.00316813748861622 & 0.00633627497723244 & 0.996831862511384 \tabularnewline
18 & 0.0141462552180820 & 0.0282925104361641 & 0.985853744781918 \tabularnewline
19 & 0.00437235940415554 & 0.00874471880831108 & 0.995627640595844 \tabularnewline
20 & 0.00130629874418729 & 0.00261259748837459 & 0.998693701255813 \tabularnewline
21 & 0.000477810631727827 & 0.000955621263455653 & 0.999522189368272 \tabularnewline
22 & 0.000253311448983821 & 0.000506622897967642 & 0.999746688551016 \tabularnewline
23 & 0.000126204986224539 & 0.000252409972449078 & 0.999873795013775 \tabularnewline
24 & 3.99824145091164e-05 & 7.99648290182327e-05 & 0.99996001758549 \tabularnewline
25 & 1.37223613160377e-05 & 2.74447226320754e-05 & 0.999986277638684 \tabularnewline
26 & 4.04688491923357e-06 & 8.09376983846715e-06 & 0.99999595311508 \tabularnewline
27 & 2.45605398836595e-06 & 4.9121079767319e-06 & 0.999997543946012 \tabularnewline
28 & 8.3617539185452e-07 & 1.67235078370904e-06 & 0.999999163824608 \tabularnewline
29 & 3.94283250540627e-07 & 7.88566501081254e-07 & 0.99999960571675 \tabularnewline
30 & 2.16129265549682e-07 & 4.32258531099365e-07 & 0.999999783870734 \tabularnewline
31 & 7.79334161734906e-08 & 1.55866832346981e-07 & 0.999999922066584 \tabularnewline
32 & 2.01028992086117e-08 & 4.02057984172234e-08 & 0.9999999798971 \tabularnewline
33 & 6.03859113494223e-09 & 1.20771822698845e-08 & 0.999999993961409 \tabularnewline
34 & 2.42324259855201e-09 & 4.84648519710403e-09 & 0.999999997576757 \tabularnewline
35 & 1.09233485250410e-09 & 2.18466970500819e-09 & 0.999999998907665 \tabularnewline
36 & 3.4271187090009e-09 & 6.8542374180018e-09 & 0.999999996572881 \tabularnewline
37 & 6.68598564873209e-09 & 1.33719712974642e-08 & 0.999999993314014 \tabularnewline
38 & 1.76701960503517e-09 & 3.53403921007034e-09 & 0.99999999823298 \tabularnewline
39 & 1.59424344158696e-09 & 3.18848688317391e-09 & 0.999999998405757 \tabularnewline
40 & 1.16794497268976e-08 & 2.33588994537951e-08 & 0.99999998832055 \tabularnewline
41 & 1.02323293318461e-08 & 2.04646586636922e-08 & 0.99999998976767 \tabularnewline
42 & 2.21494860165548e-08 & 4.42989720331096e-08 & 0.999999977850514 \tabularnewline
43 & 1.23356324898483e-08 & 2.46712649796967e-08 & 0.999999987664367 \tabularnewline
44 & 4.63360642107868e-09 & 9.26721284215737e-09 & 0.999999995366394 \tabularnewline
45 & 2.52194452470515e-09 & 5.04388904941031e-09 & 0.999999997478055 \tabularnewline
46 & 1.24834160879368e-09 & 2.49668321758737e-09 & 0.999999998751658 \tabularnewline
47 & 7.94686583862144e-09 & 1.58937316772429e-08 & 0.999999992053134 \tabularnewline
48 & 1.18727878670707e-08 & 2.37455757341414e-08 & 0.999999988127212 \tabularnewline
49 & 3.25961854170414e-07 & 6.51923708340828e-07 & 0.999999674038146 \tabularnewline
50 & 4.26473317970132e-07 & 8.52946635940265e-07 & 0.999999573526682 \tabularnewline
51 & 4.44654527463367e-05 & 8.89309054926733e-05 & 0.999955534547254 \tabularnewline
52 & 2.87118929357833e-05 & 5.74237858715666e-05 & 0.999971288107064 \tabularnewline
53 & 1.70232491362704e-05 & 3.40464982725408e-05 & 0.999982976750864 \tabularnewline
54 & 6.35280041356182e-05 & 0.000127056008271236 & 0.999936471995864 \tabularnewline
55 & 6.98944357101535e-05 & 0.000139788871420307 & 0.99993010556429 \tabularnewline
56 & 0.00326335854511175 & 0.0065267170902235 & 0.996736641454888 \tabularnewline
57 & 0.0204970877363935 & 0.040994175472787 & 0.979502912263607 \tabularnewline
58 & 0.0682010151446982 & 0.136402030289396 & 0.931798984855302 \tabularnewline
59 & 0.620329826398652 & 0.759340347202695 & 0.379670173601348 \tabularnewline
60 & 0.575239879778304 & 0.849520240443391 & 0.424760120221696 \tabularnewline
61 & 0.481990251912681 & 0.963980503825362 & 0.518009748087319 \tabularnewline
62 & 0.991550547067767 & 0.0168989058644659 & 0.00844945293223293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58516&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]16[/C][C]0.00973249266085412[/C][C]0.0194649853217082[/C][C]0.990267507339146[/C][/ROW]
[ROW][C]17[/C][C]0.00316813748861622[/C][C]0.00633627497723244[/C][C]0.996831862511384[/C][/ROW]
[ROW][C]18[/C][C]0.0141462552180820[/C][C]0.0282925104361641[/C][C]0.985853744781918[/C][/ROW]
[ROW][C]19[/C][C]0.00437235940415554[/C][C]0.00874471880831108[/C][C]0.995627640595844[/C][/ROW]
[ROW][C]20[/C][C]0.00130629874418729[/C][C]0.00261259748837459[/C][C]0.998693701255813[/C][/ROW]
[ROW][C]21[/C][C]0.000477810631727827[/C][C]0.000955621263455653[/C][C]0.999522189368272[/C][/ROW]
[ROW][C]22[/C][C]0.000253311448983821[/C][C]0.000506622897967642[/C][C]0.999746688551016[/C][/ROW]
[ROW][C]23[/C][C]0.000126204986224539[/C][C]0.000252409972449078[/C][C]0.999873795013775[/C][/ROW]
[ROW][C]24[/C][C]3.99824145091164e-05[/C][C]7.99648290182327e-05[/C][C]0.99996001758549[/C][/ROW]
[ROW][C]25[/C][C]1.37223613160377e-05[/C][C]2.74447226320754e-05[/C][C]0.999986277638684[/C][/ROW]
[ROW][C]26[/C][C]4.04688491923357e-06[/C][C]8.09376983846715e-06[/C][C]0.99999595311508[/C][/ROW]
[ROW][C]27[/C][C]2.45605398836595e-06[/C][C]4.9121079767319e-06[/C][C]0.999997543946012[/C][/ROW]
[ROW][C]28[/C][C]8.3617539185452e-07[/C][C]1.67235078370904e-06[/C][C]0.999999163824608[/C][/ROW]
[ROW][C]29[/C][C]3.94283250540627e-07[/C][C]7.88566501081254e-07[/C][C]0.99999960571675[/C][/ROW]
[ROW][C]30[/C][C]2.16129265549682e-07[/C][C]4.32258531099365e-07[/C][C]0.999999783870734[/C][/ROW]
[ROW][C]31[/C][C]7.79334161734906e-08[/C][C]1.55866832346981e-07[/C][C]0.999999922066584[/C][/ROW]
[ROW][C]32[/C][C]2.01028992086117e-08[/C][C]4.02057984172234e-08[/C][C]0.9999999798971[/C][/ROW]
[ROW][C]33[/C][C]6.03859113494223e-09[/C][C]1.20771822698845e-08[/C][C]0.999999993961409[/C][/ROW]
[ROW][C]34[/C][C]2.42324259855201e-09[/C][C]4.84648519710403e-09[/C][C]0.999999997576757[/C][/ROW]
[ROW][C]35[/C][C]1.09233485250410e-09[/C][C]2.18466970500819e-09[/C][C]0.999999998907665[/C][/ROW]
[ROW][C]36[/C][C]3.4271187090009e-09[/C][C]6.8542374180018e-09[/C][C]0.999999996572881[/C][/ROW]
[ROW][C]37[/C][C]6.68598564873209e-09[/C][C]1.33719712974642e-08[/C][C]0.999999993314014[/C][/ROW]
[ROW][C]38[/C][C]1.76701960503517e-09[/C][C]3.53403921007034e-09[/C][C]0.99999999823298[/C][/ROW]
[ROW][C]39[/C][C]1.59424344158696e-09[/C][C]3.18848688317391e-09[/C][C]0.999999998405757[/C][/ROW]
[ROW][C]40[/C][C]1.16794497268976e-08[/C][C]2.33588994537951e-08[/C][C]0.99999998832055[/C][/ROW]
[ROW][C]41[/C][C]1.02323293318461e-08[/C][C]2.04646586636922e-08[/C][C]0.99999998976767[/C][/ROW]
[ROW][C]42[/C][C]2.21494860165548e-08[/C][C]4.42989720331096e-08[/C][C]0.999999977850514[/C][/ROW]
[ROW][C]43[/C][C]1.23356324898483e-08[/C][C]2.46712649796967e-08[/C][C]0.999999987664367[/C][/ROW]
[ROW][C]44[/C][C]4.63360642107868e-09[/C][C]9.26721284215737e-09[/C][C]0.999999995366394[/C][/ROW]
[ROW][C]45[/C][C]2.52194452470515e-09[/C][C]5.04388904941031e-09[/C][C]0.999999997478055[/C][/ROW]
[ROW][C]46[/C][C]1.24834160879368e-09[/C][C]2.49668321758737e-09[/C][C]0.999999998751658[/C][/ROW]
[ROW][C]47[/C][C]7.94686583862144e-09[/C][C]1.58937316772429e-08[/C][C]0.999999992053134[/C][/ROW]
[ROW][C]48[/C][C]1.18727878670707e-08[/C][C]2.37455757341414e-08[/C][C]0.999999988127212[/C][/ROW]
[ROW][C]49[/C][C]3.25961854170414e-07[/C][C]6.51923708340828e-07[/C][C]0.999999674038146[/C][/ROW]
[ROW][C]50[/C][C]4.26473317970132e-07[/C][C]8.52946635940265e-07[/C][C]0.999999573526682[/C][/ROW]
[ROW][C]51[/C][C]4.44654527463367e-05[/C][C]8.89309054926733e-05[/C][C]0.999955534547254[/C][/ROW]
[ROW][C]52[/C][C]2.87118929357833e-05[/C][C]5.74237858715666e-05[/C][C]0.999971288107064[/C][/ROW]
[ROW][C]53[/C][C]1.70232491362704e-05[/C][C]3.40464982725408e-05[/C][C]0.999982976750864[/C][/ROW]
[ROW][C]54[/C][C]6.35280041356182e-05[/C][C]0.000127056008271236[/C][C]0.999936471995864[/C][/ROW]
[ROW][C]55[/C][C]6.98944357101535e-05[/C][C]0.000139788871420307[/C][C]0.99993010556429[/C][/ROW]
[ROW][C]56[/C][C]0.00326335854511175[/C][C]0.0065267170902235[/C][C]0.996736641454888[/C][/ROW]
[ROW][C]57[/C][C]0.0204970877363935[/C][C]0.040994175472787[/C][C]0.979502912263607[/C][/ROW]
[ROW][C]58[/C][C]0.0682010151446982[/C][C]0.136402030289396[/C][C]0.931798984855302[/C][/ROW]
[ROW][C]59[/C][C]0.620329826398652[/C][C]0.759340347202695[/C][C]0.379670173601348[/C][/ROW]
[ROW][C]60[/C][C]0.575239879778304[/C][C]0.849520240443391[/C][C]0.424760120221696[/C][/ROW]
[ROW][C]61[/C][C]0.481990251912681[/C][C]0.963980503825362[/C][C]0.518009748087319[/C][/ROW]
[ROW][C]62[/C][C]0.991550547067767[/C][C]0.0168989058644659[/C][C]0.00844945293223293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58516&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58516&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
160.009732492660854120.01946498532170820.990267507339146
170.003168137488616220.006336274977232440.996831862511384
180.01414625521808200.02829251043616410.985853744781918
190.004372359404155540.008744718808311080.995627640595844
200.001306298744187290.002612597488374590.998693701255813
210.0004778106317278270.0009556212634556530.999522189368272
220.0002533114489838210.0005066228979676420.999746688551016
230.0001262049862245390.0002524099724490780.999873795013775
243.99824145091164e-057.99648290182327e-050.99996001758549
251.37223613160377e-052.74447226320754e-050.999986277638684
264.04688491923357e-068.09376983846715e-060.99999595311508
272.45605398836595e-064.9121079767319e-060.999997543946012
288.3617539185452e-071.67235078370904e-060.999999163824608
293.94283250540627e-077.88566501081254e-070.99999960571675
302.16129265549682e-074.32258531099365e-070.999999783870734
317.79334161734906e-081.55866832346981e-070.999999922066584
322.01028992086117e-084.02057984172234e-080.9999999798971
336.03859113494223e-091.20771822698845e-080.999999993961409
342.42324259855201e-094.84648519710403e-090.999999997576757
351.09233485250410e-092.18466970500819e-090.999999998907665
363.4271187090009e-096.8542374180018e-090.999999996572881
376.68598564873209e-091.33719712974642e-080.999999993314014
381.76701960503517e-093.53403921007034e-090.99999999823298
391.59424344158696e-093.18848688317391e-090.999999998405757
401.16794497268976e-082.33588994537951e-080.99999998832055
411.02323293318461e-082.04646586636922e-080.99999998976767
422.21494860165548e-084.42989720331096e-080.999999977850514
431.23356324898483e-082.46712649796967e-080.999999987664367
444.63360642107868e-099.26721284215737e-090.999999995366394
452.52194452470515e-095.04388904941031e-090.999999997478055
461.24834160879368e-092.49668321758737e-090.999999998751658
477.94686583862144e-091.58937316772429e-080.999999992053134
481.18727878670707e-082.37455757341414e-080.999999988127212
493.25961854170414e-076.51923708340828e-070.999999674038146
504.26473317970132e-078.52946635940265e-070.999999573526682
514.44654527463367e-058.89309054926733e-050.999955534547254
522.87118929357833e-055.74237858715666e-050.999971288107064
531.70232491362704e-053.40464982725408e-050.999982976750864
546.35280041356182e-050.0001270560082712360.999936471995864
556.98944357101535e-050.0001397888714203070.99993010556429
560.003263358545111750.00652671709022350.996736641454888
570.02049708773639350.0409941754727870.979502912263607
580.06820101514469820.1364020302893960.931798984855302
590.6203298263986520.7593403472026950.379670173601348
600.5752398797783040.8495202404433910.424760120221696
610.4819902519126810.9639805038253620.518009748087319
620.9915505470677670.01689890586446590.00844945293223293






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level390.829787234042553NOK
5% type I error level430.914893617021277NOK
10% type I error level430.914893617021277NOK

\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 & 39 & 0.829787234042553 & NOK \tabularnewline
5% type I error level & 43 & 0.914893617021277 & NOK \tabularnewline
10% type I error level & 43 & 0.914893617021277 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58516&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]39[/C][C]0.829787234042553[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]43[/C][C]0.914893617021277[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]43[/C][C]0.914893617021277[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58516&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58516&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 level390.829787234042553NOK
5% type I error level430.914893617021277NOK
10% type I error level430.914893617021277NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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')
}