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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 21:04:24 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356141995n6hx4jorxd9yad5.htm/, Retrieved Tue, 16 Apr 2024 14:51:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204441, Retrieved Tue, 16 Apr 2024 14:51:45 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-01 13:37:53] [b98453cac15ba1066b407e146608df68]
- RMPD  [Multiple Regression] [Paper Deel 5 Mult...] [2012-12-16 19:15:53] [86dcce9422b96d4554cb918e531c1d5d]
- R  D    [Multiple Regression] [Paper Deel 5 Mult...] [2012-12-16 19:54:44] [86dcce9422b96d4554cb918e531c1d5d]
- R P       [Multiple Regression] [Paper Deel 5 Mult...] [2012-12-19 18:07:34] [74be16979710d4c4e7c6647856088456]
-   P         [Multiple Regression] [Multiple Regressi...] [2012-12-19 20:22:16] [f4325a5733446a4ce20d70c276c6a563]
-  MP             [Multiple Regression] [Paper 2012 (deel5.7)] [2012-12-22 02:04:24] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 13 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204441&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204441&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204441&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 time13 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0301763693008489 + 0.0248249882036826Weeks_Treatment[t] -0.010950232649299UseLimit[t] + 0.234228256524765Used[t] + 0.0642435469513574Useful[t] -0.0242136418403001`Outcome\r`[t] + 0.000198533655196473t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  -0.0301763693008489 +  0.0248249882036826Weeks_Treatment[t] -0.010950232649299UseLimit[t] +  0.234228256524765Used[t] +  0.0642435469513574Useful[t] -0.0242136418403001`Outcome\r`[t] +  0.000198533655196473t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204441&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  -0.0301763693008489 +  0.0248249882036826Weeks_Treatment[t] -0.010950232649299UseLimit[t] +  0.234228256524765Used[t] +  0.0642435469513574Useful[t] -0.0242136418403001`Outcome\r`[t] +  0.000198533655196473t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204441&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204441&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
CorrectAnalysis[t] = -0.0301763693008489 + 0.0248249882036826Weeks_Treatment[t] -0.010950232649299UseLimit[t] + 0.234228256524765Used[t] + 0.0642435469513574Useful[t] -0.0242136418403001`Outcome\r`[t] + 0.000198533655196473t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.03017636930084890.049655-0.60770.5443130.272156
Weeks_Treatment0.02482498820368260.0141021.76030.0804310.040216
UseLimit-0.0109502326492990.042487-0.25770.7969750.398488
Used0.2342282565247650.0456825.12731e-060
Useful0.06424354695135740.0467411.37450.1713870.085694
`Outcome\r`-0.02421364184030010.040524-0.59750.5510880.275544
t0.0001985336551964730.0004560.43590.663580.33179

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.0301763693008489 & 0.049655 & -0.6077 & 0.544313 & 0.272156 \tabularnewline
Weeks_Treatment & 0.0248249882036826 & 0.014102 & 1.7603 & 0.080431 & 0.040216 \tabularnewline
UseLimit & -0.010950232649299 & 0.042487 & -0.2577 & 0.796975 & 0.398488 \tabularnewline
Used & 0.234228256524765 & 0.045682 & 5.1273 & 1e-06 & 0 \tabularnewline
Useful & 0.0642435469513574 & 0.046741 & 1.3745 & 0.171387 & 0.085694 \tabularnewline
`Outcome\r` & -0.0242136418403001 & 0.040524 & -0.5975 & 0.551088 & 0.275544 \tabularnewline
t & 0.000198533655196473 & 0.000456 & 0.4359 & 0.66358 & 0.33179 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204441&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.0301763693008489[/C][C]0.049655[/C][C]-0.6077[/C][C]0.544313[/C][C]0.272156[/C][/ROW]
[ROW][C]Weeks_Treatment[/C][C]0.0248249882036826[/C][C]0.014102[/C][C]1.7603[/C][C]0.080431[/C][C]0.040216[/C][/ROW]
[ROW][C]UseLimit[/C][C]-0.010950232649299[/C][C]0.042487[/C][C]-0.2577[/C][C]0.796975[/C][C]0.398488[/C][/ROW]
[ROW][C]Used[/C][C]0.234228256524765[/C][C]0.045682[/C][C]5.1273[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Useful[/C][C]0.0642435469513574[/C][C]0.046741[/C][C]1.3745[/C][C]0.171387[/C][C]0.085694[/C][/ROW]
[ROW][C]`Outcome\r`[/C][C]-0.0242136418403001[/C][C]0.040524[/C][C]-0.5975[/C][C]0.551088[/C][C]0.275544[/C][/ROW]
[ROW][C]t[/C][C]0.000198533655196473[/C][C]0.000456[/C][C]0.4359[/C][C]0.66358[/C][C]0.33179[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204441&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204441&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.03017636930084890.049655-0.60770.5443130.272156
Weeks_Treatment0.02482498820368260.0141021.76030.0804310.040216
UseLimit-0.0109502326492990.042487-0.25770.7969750.398488
Used0.2342282565247650.0456825.12731e-060
Useful0.06424354695135740.0467411.37450.1713870.085694
`Outcome\r`-0.02421364184030010.040524-0.59750.5510880.275544
t0.0001985336551964730.0004560.43590.663580.33179






Multiple Linear Regression - Regression Statistics
Multiple R0.481553903809642
R-squared0.231894162274306
Adjusted R-squared0.200542903591624
F-TEST (value)7.39664600459582
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value6.26110125634405e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.240450840966108
Sum Squared Residuals8.49904121743236

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.481553903809642 \tabularnewline
R-squared & 0.231894162274306 \tabularnewline
Adjusted R-squared & 0.200542903591624 \tabularnewline
F-TEST (value) & 7.39664600459582 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 6.26110125634405e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.240450840966108 \tabularnewline
Sum Squared Residuals & 8.49904121743236 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204441&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.481553903809642[/C][/ROW]
[ROW][C]R-squared[/C][C]0.231894162274306[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.200542903591624[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.39664600459582[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]6.26110125634405e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.240450840966108[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8.49904121743236[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204441&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204441&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 R0.481553903809642
R-squared0.231894162274306
Adjusted R-squared0.200542903591624
F-TEST (value)7.39664600459582
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value6.26110125634405e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.240450840966108
Sum Squared Residuals8.49904121743236






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.0341582426794786-0.0341582426794786
20-0.02977930199045620.0297793019904562
30-0.02958076833525960.0295807683352596
40-0.02938223468006320.0293822346800632
50-0.02918370102486660.0291837010248666
609.45050920880784e-05-9.45050920880784e-05
70-0.02878663371447370.0287866337144737
800.0707118527554531-0.0707118527554531
90-0.05260320824438090.0526032082443809
100-0.03914126539818330.0391412653981833
1100.0603572210717435-0.0603572210717435
120-0.02779396543849130.0277939654384913
1300.270876371692828-0.270876371692828
1400.0609528220373329-0.0609528220373329
1500.247059797162921-0.247059797162921
1600.346558283632848-0.346558283632848
1710.3600202264790450.639979773520955
1800.0617469566581188-0.0617469566581188
190-0.05061787169241610.0506178716924161
2010.3473524182536330.652647581746367
2100.0272861517603353-0.0272861517603353
2200.237499300099997-0.237499300099997
2300.0144198098797271-0.0144198098797271
2400.00366811088562458-0.00366811088562458
2500.284101539578258-0.284101539578258
2600.273457309210382-0.273457309210382
270-0.05997983510014340.0599798351001434
2800.209610829569418-0.209610829569418
290-0.04863253514045140.0486325351404514
3000.0400231873064026-0.0400231873064026
310-0.02402182598975830.0240218259897583
320-0.03477352498386090.0347735249838609
3300.029668555622693-0.029668555622693
3400.0516600859502612-0.0516600859502612
350-0.02322769136897240.0232276913689724
360-0.0230291577137760.023029157713776
3700.363990899582975-0.363990899582975
3800.187382524281082-0.187382524281082
3900.0175963483628707-0.0175963483628707
4000.141308476673098-0.141308476673098
4110.2522216721980290.747778327801971
4200.188176658901868-0.188176658901868
4300.00744025033435755-0.00744025033435755
4400.0669088316932271-0.0669088316932271
4500.0430011921343497-0.0430011921343497
4600.018986083949246-0.018986083949246
470-0.02084528750661480.0208452875066148
480-0.04486039569171840.0448603956917184
4900.0195816849148354-0.0195816849148354
500-0.02024968654102540.0202496865410254
5100.313477056453667-0.313477056453667
5210.3669689044109220.633031095589078
530-0.04386772741573610.0438677274157361
5410.2147727046045260.785227295395474
550-0.0192570182650430.019257018265043
5600.290256082889349-0.290256082889349
5700.255398210681173-0.255398210681173
580-0.04287505913975370.0428750591397537
590-0.04267652548455720.0426765254845572
6010.3443435318121930.655656468187807
6100.046070261991267-0.046070261991267
6200.280604520797455-0.280604520797455
630-0.01766874902347120.0176687490234712
6400.0466658629568564-0.0466658629568564
650-0.01727168171307830.0172716817130783
660-0.01707314805788180.0170731480578818
6710.3808971418881680.619102858111832
680-0.02762631339678790.0276263133967879
690-0.04069118893259250.0406911889325925
7000.21794924308767-0.21794924308767
710-0.01608047978189940.0160804797818994
720-0.04009558796700310.0400955879670031
7300.194331202212959-0.194331202212959
7400.207793145059156-0.207793145059156
750-0.03949998700141370.0394999870014137
7600.12424204641987-0.12424204641987
770-0.03910291969102070.0391029196910207
7800.259567417440299-0.259567417440299
7910.2948223569588680.705177643041132
8000.149249822880956-0.149249822880956
810-0.01409514322993470.0140951432299347
8200.185167772460428-0.185167772460428
830-0.01369807591954180.0136980759195418
8410.220728714260420.77927128573958
8500.0267288965019084-0.0267288965019084
860-0.02405270760325140.0240527076032514
870-0.0480678157883550.048067815788355
8800.236008950798972-0.236008950798972
890-0.01250687398836290.0125068739883629
900-0.03652198217346660.0365219821734666
9100.0521337402733874-0.0521337402733874
9200.0267884707352926-0.0267884707352926
9300.0415805749344813-0.0415805749344813
940-0.01151420571238060.0115142057123806
9500.0383343043501811-0.0383343043501811
960-0.03533078024228770.0353307802422877
9700.027781139011275-0.027781139011275
980-0.01072007109159470.0107200710915947
990-0.02147177008569720.0214717700856972
1000-0.03453664562150190.0345366456215019
1010-0.04528834461560440.0452883446156044
1020-0.009925936470808790.00992593647080879
1030-0.009727402815612310.00972740281561231
1040-0.009528869160415840.00952886916041584
10500.274547897426911-0.274547897426911
1060-0.00913180185002290.0091318018500229
1070-0.008933268194826420.00893326819482642
10800.264193265743202-0.264193265743202
1090-0.008536200884433480.00853620088443348
1100-0.0192878998785360.019287899878536
11100.329032413660148-0.329032413660148
11200.0417093764885211-0.0417093764885211
11300.226486190261118-0.226486190261118
11400.26538446767438-0.26538446767438
1150-0.01829523160255370.0182952316025537
1160-0.007146465298058170.00714646529805817
1170-0.04211180613246080.0421118061324608
1180-0.01769963063696420.0176996306369642
1190-0.006550864332468750.00655086433246875
1200-0.03056597251757240.0305659725175724
1210-0.01710402967137480.0171040296713748
1220-0.005955263366879330.00595526336687933
12300.267171270571149-0.267171270571149
12400.268699965579336-0.268699965579336
1250-0.029573304241590.02957330424159
12600.0444888476612717-0.0444888476612717
12700.0592809518604604-0.0592809518604604
1280-0.02897770327600060.0289777032760006
1290-0.004565527780504030.00456552778050403
1300-0.02858063596560770.0285806359656077
1310-0.01511869311941010.0151186931194101
1320-0.03913380130451380.0391338013045138
13300.219506630715748-0.219506630715748
1340-0.003572859504521660.00357285950452166
1350-0.003374325849325190.00337432584932519
1360-0.003175792194128720.00317579219412872
13700.260330670447592-0.260330670447592
13800.310179180510153-0.310179180510153
13900.0470697851788258-0.0470697851788258
1400-0.002381657573342830.00238165757334283
14110.2078314907663190.792168509233681
14200.257680000828881-0.257680000828881
1430-0.01273628925705240.0127362892570524
14400.0384423821585003-0.0384423821585003
14500.0628545576539969-0.0628545576539969
14600.024245878924901-0.024245878924901
14700.282886310945163-0.282886310945163
14800.0488565880755941-0.0488565880755941
1490-0.01154508732587360.0115450873258736
15000.0396335840896792-0.0396335840896792
1510-0.02441142920648180.0244114292064818
15210.2232787701644810.776721229835519
15310.2877208507710350.712279149228965
15400.223675837474874-0.223675837474874

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.0341582426794786 & -0.0341582426794786 \tabularnewline
2 & 0 & -0.0297793019904562 & 0.0297793019904562 \tabularnewline
3 & 0 & -0.0295807683352596 & 0.0295807683352596 \tabularnewline
4 & 0 & -0.0293822346800632 & 0.0293822346800632 \tabularnewline
5 & 0 & -0.0291837010248666 & 0.0291837010248666 \tabularnewline
6 & 0 & 9.45050920880784e-05 & -9.45050920880784e-05 \tabularnewline
7 & 0 & -0.0287866337144737 & 0.0287866337144737 \tabularnewline
8 & 0 & 0.0707118527554531 & -0.0707118527554531 \tabularnewline
9 & 0 & -0.0526032082443809 & 0.0526032082443809 \tabularnewline
10 & 0 & -0.0391412653981833 & 0.0391412653981833 \tabularnewline
11 & 0 & 0.0603572210717435 & -0.0603572210717435 \tabularnewline
12 & 0 & -0.0277939654384913 & 0.0277939654384913 \tabularnewline
13 & 0 & 0.270876371692828 & -0.270876371692828 \tabularnewline
14 & 0 & 0.0609528220373329 & -0.0609528220373329 \tabularnewline
15 & 0 & 0.247059797162921 & -0.247059797162921 \tabularnewline
16 & 0 & 0.346558283632848 & -0.346558283632848 \tabularnewline
17 & 1 & 0.360020226479045 & 0.639979773520955 \tabularnewline
18 & 0 & 0.0617469566581188 & -0.0617469566581188 \tabularnewline
19 & 0 & -0.0506178716924161 & 0.0506178716924161 \tabularnewline
20 & 1 & 0.347352418253633 & 0.652647581746367 \tabularnewline
21 & 0 & 0.0272861517603353 & -0.0272861517603353 \tabularnewline
22 & 0 & 0.237499300099997 & -0.237499300099997 \tabularnewline
23 & 0 & 0.0144198098797271 & -0.0144198098797271 \tabularnewline
24 & 0 & 0.00366811088562458 & -0.00366811088562458 \tabularnewline
25 & 0 & 0.284101539578258 & -0.284101539578258 \tabularnewline
26 & 0 & 0.273457309210382 & -0.273457309210382 \tabularnewline
27 & 0 & -0.0599798351001434 & 0.0599798351001434 \tabularnewline
28 & 0 & 0.209610829569418 & -0.209610829569418 \tabularnewline
29 & 0 & -0.0486325351404514 & 0.0486325351404514 \tabularnewline
30 & 0 & 0.0400231873064026 & -0.0400231873064026 \tabularnewline
31 & 0 & -0.0240218259897583 & 0.0240218259897583 \tabularnewline
32 & 0 & -0.0347735249838609 & 0.0347735249838609 \tabularnewline
33 & 0 & 0.029668555622693 & -0.029668555622693 \tabularnewline
34 & 0 & 0.0516600859502612 & -0.0516600859502612 \tabularnewline
35 & 0 & -0.0232276913689724 & 0.0232276913689724 \tabularnewline
36 & 0 & -0.023029157713776 & 0.023029157713776 \tabularnewline
37 & 0 & 0.363990899582975 & -0.363990899582975 \tabularnewline
38 & 0 & 0.187382524281082 & -0.187382524281082 \tabularnewline
39 & 0 & 0.0175963483628707 & -0.0175963483628707 \tabularnewline
40 & 0 & 0.141308476673098 & -0.141308476673098 \tabularnewline
41 & 1 & 0.252221672198029 & 0.747778327801971 \tabularnewline
42 & 0 & 0.188176658901868 & -0.188176658901868 \tabularnewline
43 & 0 & 0.00744025033435755 & -0.00744025033435755 \tabularnewline
44 & 0 & 0.0669088316932271 & -0.0669088316932271 \tabularnewline
45 & 0 & 0.0430011921343497 & -0.0430011921343497 \tabularnewline
46 & 0 & 0.018986083949246 & -0.018986083949246 \tabularnewline
47 & 0 & -0.0208452875066148 & 0.0208452875066148 \tabularnewline
48 & 0 & -0.0448603956917184 & 0.0448603956917184 \tabularnewline
49 & 0 & 0.0195816849148354 & -0.0195816849148354 \tabularnewline
50 & 0 & -0.0202496865410254 & 0.0202496865410254 \tabularnewline
51 & 0 & 0.313477056453667 & -0.313477056453667 \tabularnewline
52 & 1 & 0.366968904410922 & 0.633031095589078 \tabularnewline
53 & 0 & -0.0438677274157361 & 0.0438677274157361 \tabularnewline
54 & 1 & 0.214772704604526 & 0.785227295395474 \tabularnewline
55 & 0 & -0.019257018265043 & 0.019257018265043 \tabularnewline
56 & 0 & 0.290256082889349 & -0.290256082889349 \tabularnewline
57 & 0 & 0.255398210681173 & -0.255398210681173 \tabularnewline
58 & 0 & -0.0428750591397537 & 0.0428750591397537 \tabularnewline
59 & 0 & -0.0426765254845572 & 0.0426765254845572 \tabularnewline
60 & 1 & 0.344343531812193 & 0.655656468187807 \tabularnewline
61 & 0 & 0.046070261991267 & -0.046070261991267 \tabularnewline
62 & 0 & 0.280604520797455 & -0.280604520797455 \tabularnewline
63 & 0 & -0.0176687490234712 & 0.0176687490234712 \tabularnewline
64 & 0 & 0.0466658629568564 & -0.0466658629568564 \tabularnewline
65 & 0 & -0.0172716817130783 & 0.0172716817130783 \tabularnewline
66 & 0 & -0.0170731480578818 & 0.0170731480578818 \tabularnewline
67 & 1 & 0.380897141888168 & 0.619102858111832 \tabularnewline
68 & 0 & -0.0276263133967879 & 0.0276263133967879 \tabularnewline
69 & 0 & -0.0406911889325925 & 0.0406911889325925 \tabularnewline
70 & 0 & 0.21794924308767 & -0.21794924308767 \tabularnewline
71 & 0 & -0.0160804797818994 & 0.0160804797818994 \tabularnewline
72 & 0 & -0.0400955879670031 & 0.0400955879670031 \tabularnewline
73 & 0 & 0.194331202212959 & -0.194331202212959 \tabularnewline
74 & 0 & 0.207793145059156 & -0.207793145059156 \tabularnewline
75 & 0 & -0.0394999870014137 & 0.0394999870014137 \tabularnewline
76 & 0 & 0.12424204641987 & -0.12424204641987 \tabularnewline
77 & 0 & -0.0391029196910207 & 0.0391029196910207 \tabularnewline
78 & 0 & 0.259567417440299 & -0.259567417440299 \tabularnewline
79 & 1 & 0.294822356958868 & 0.705177643041132 \tabularnewline
80 & 0 & 0.149249822880956 & -0.149249822880956 \tabularnewline
81 & 0 & -0.0140951432299347 & 0.0140951432299347 \tabularnewline
82 & 0 & 0.185167772460428 & -0.185167772460428 \tabularnewline
83 & 0 & -0.0136980759195418 & 0.0136980759195418 \tabularnewline
84 & 1 & 0.22072871426042 & 0.77927128573958 \tabularnewline
85 & 0 & 0.0267288965019084 & -0.0267288965019084 \tabularnewline
86 & 0 & -0.0240527076032514 & 0.0240527076032514 \tabularnewline
87 & 0 & -0.048067815788355 & 0.048067815788355 \tabularnewline
88 & 0 & 0.236008950798972 & -0.236008950798972 \tabularnewline
89 & 0 & -0.0125068739883629 & 0.0125068739883629 \tabularnewline
90 & 0 & -0.0365219821734666 & 0.0365219821734666 \tabularnewline
91 & 0 & 0.0521337402733874 & -0.0521337402733874 \tabularnewline
92 & 0 & 0.0267884707352926 & -0.0267884707352926 \tabularnewline
93 & 0 & 0.0415805749344813 & -0.0415805749344813 \tabularnewline
94 & 0 & -0.0115142057123806 & 0.0115142057123806 \tabularnewline
95 & 0 & 0.0383343043501811 & -0.0383343043501811 \tabularnewline
96 & 0 & -0.0353307802422877 & 0.0353307802422877 \tabularnewline
97 & 0 & 0.027781139011275 & -0.027781139011275 \tabularnewline
98 & 0 & -0.0107200710915947 & 0.0107200710915947 \tabularnewline
99 & 0 & -0.0214717700856972 & 0.0214717700856972 \tabularnewline
100 & 0 & -0.0345366456215019 & 0.0345366456215019 \tabularnewline
101 & 0 & -0.0452883446156044 & 0.0452883446156044 \tabularnewline
102 & 0 & -0.00992593647080879 & 0.00992593647080879 \tabularnewline
103 & 0 & -0.00972740281561231 & 0.00972740281561231 \tabularnewline
104 & 0 & -0.00952886916041584 & 0.00952886916041584 \tabularnewline
105 & 0 & 0.274547897426911 & -0.274547897426911 \tabularnewline
106 & 0 & -0.0091318018500229 & 0.0091318018500229 \tabularnewline
107 & 0 & -0.00893326819482642 & 0.00893326819482642 \tabularnewline
108 & 0 & 0.264193265743202 & -0.264193265743202 \tabularnewline
109 & 0 & -0.00853620088443348 & 0.00853620088443348 \tabularnewline
110 & 0 & -0.019287899878536 & 0.019287899878536 \tabularnewline
111 & 0 & 0.329032413660148 & -0.329032413660148 \tabularnewline
112 & 0 & 0.0417093764885211 & -0.0417093764885211 \tabularnewline
113 & 0 & 0.226486190261118 & -0.226486190261118 \tabularnewline
114 & 0 & 0.26538446767438 & -0.26538446767438 \tabularnewline
115 & 0 & -0.0182952316025537 & 0.0182952316025537 \tabularnewline
116 & 0 & -0.00714646529805817 & 0.00714646529805817 \tabularnewline
117 & 0 & -0.0421118061324608 & 0.0421118061324608 \tabularnewline
118 & 0 & -0.0176996306369642 & 0.0176996306369642 \tabularnewline
119 & 0 & -0.00655086433246875 & 0.00655086433246875 \tabularnewline
120 & 0 & -0.0305659725175724 & 0.0305659725175724 \tabularnewline
121 & 0 & -0.0171040296713748 & 0.0171040296713748 \tabularnewline
122 & 0 & -0.00595526336687933 & 0.00595526336687933 \tabularnewline
123 & 0 & 0.267171270571149 & -0.267171270571149 \tabularnewline
124 & 0 & 0.268699965579336 & -0.268699965579336 \tabularnewline
125 & 0 & -0.02957330424159 & 0.02957330424159 \tabularnewline
126 & 0 & 0.0444888476612717 & -0.0444888476612717 \tabularnewline
127 & 0 & 0.0592809518604604 & -0.0592809518604604 \tabularnewline
128 & 0 & -0.0289777032760006 & 0.0289777032760006 \tabularnewline
129 & 0 & -0.00456552778050403 & 0.00456552778050403 \tabularnewline
130 & 0 & -0.0285806359656077 & 0.0285806359656077 \tabularnewline
131 & 0 & -0.0151186931194101 & 0.0151186931194101 \tabularnewline
132 & 0 & -0.0391338013045138 & 0.0391338013045138 \tabularnewline
133 & 0 & 0.219506630715748 & -0.219506630715748 \tabularnewline
134 & 0 & -0.00357285950452166 & 0.00357285950452166 \tabularnewline
135 & 0 & -0.00337432584932519 & 0.00337432584932519 \tabularnewline
136 & 0 & -0.00317579219412872 & 0.00317579219412872 \tabularnewline
137 & 0 & 0.260330670447592 & -0.260330670447592 \tabularnewline
138 & 0 & 0.310179180510153 & -0.310179180510153 \tabularnewline
139 & 0 & 0.0470697851788258 & -0.0470697851788258 \tabularnewline
140 & 0 & -0.00238165757334283 & 0.00238165757334283 \tabularnewline
141 & 1 & 0.207831490766319 & 0.792168509233681 \tabularnewline
142 & 0 & 0.257680000828881 & -0.257680000828881 \tabularnewline
143 & 0 & -0.0127362892570524 & 0.0127362892570524 \tabularnewline
144 & 0 & 0.0384423821585003 & -0.0384423821585003 \tabularnewline
145 & 0 & 0.0628545576539969 & -0.0628545576539969 \tabularnewline
146 & 0 & 0.024245878924901 & -0.024245878924901 \tabularnewline
147 & 0 & 0.282886310945163 & -0.282886310945163 \tabularnewline
148 & 0 & 0.0488565880755941 & -0.0488565880755941 \tabularnewline
149 & 0 & -0.0115450873258736 & 0.0115450873258736 \tabularnewline
150 & 0 & 0.0396335840896792 & -0.0396335840896792 \tabularnewline
151 & 0 & -0.0244114292064818 & 0.0244114292064818 \tabularnewline
152 & 1 & 0.223278770164481 & 0.776721229835519 \tabularnewline
153 & 1 & 0.287720850771035 & 0.712279149228965 \tabularnewline
154 & 0 & 0.223675837474874 & -0.223675837474874 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204441&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]0[/C][C]0.0341582426794786[/C][C]-0.0341582426794786[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]-0.0297793019904562[/C][C]0.0297793019904562[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.0295807683352596[/C][C]0.0295807683352596[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]-0.0293822346800632[/C][C]0.0293822346800632[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0291837010248666[/C][C]0.0291837010248666[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]9.45050920880784e-05[/C][C]-9.45050920880784e-05[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-0.0287866337144737[/C][C]0.0287866337144737[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.0707118527554531[/C][C]-0.0707118527554531[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.0526032082443809[/C][C]0.0526032082443809[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]-0.0391412653981833[/C][C]0.0391412653981833[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.0603572210717435[/C][C]-0.0603572210717435[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]-0.0277939654384913[/C][C]0.0277939654384913[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.270876371692828[/C][C]-0.270876371692828[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.0609528220373329[/C][C]-0.0609528220373329[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.247059797162921[/C][C]-0.247059797162921[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.346558283632848[/C][C]-0.346558283632848[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.360020226479045[/C][C]0.639979773520955[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.0617469566581188[/C][C]-0.0617469566581188[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.0506178716924161[/C][C]0.0506178716924161[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.347352418253633[/C][C]0.652647581746367[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0272861517603353[/C][C]-0.0272861517603353[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.237499300099997[/C][C]-0.237499300099997[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.0144198098797271[/C][C]-0.0144198098797271[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.00366811088562458[/C][C]-0.00366811088562458[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.284101539578258[/C][C]-0.284101539578258[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.273457309210382[/C][C]-0.273457309210382[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]-0.0599798351001434[/C][C]0.0599798351001434[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.209610829569418[/C][C]-0.209610829569418[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]-0.0486325351404514[/C][C]0.0486325351404514[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0400231873064026[/C][C]-0.0400231873064026[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]-0.0240218259897583[/C][C]0.0240218259897583[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]-0.0347735249838609[/C][C]0.0347735249838609[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.029668555622693[/C][C]-0.029668555622693[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.0516600859502612[/C][C]-0.0516600859502612[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]-0.0232276913689724[/C][C]0.0232276913689724[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-0.023029157713776[/C][C]0.023029157713776[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.363990899582975[/C][C]-0.363990899582975[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.187382524281082[/C][C]-0.187382524281082[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.0175963483628707[/C][C]-0.0175963483628707[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.141308476673098[/C][C]-0.141308476673098[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.252221672198029[/C][C]0.747778327801971[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.188176658901868[/C][C]-0.188176658901868[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.00744025033435755[/C][C]-0.00744025033435755[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.0669088316932271[/C][C]-0.0669088316932271[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0430011921343497[/C][C]-0.0430011921343497[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.018986083949246[/C][C]-0.018986083949246[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]-0.0208452875066148[/C][C]0.0208452875066148[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.0448603956917184[/C][C]0.0448603956917184[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.0195816849148354[/C][C]-0.0195816849148354[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]-0.0202496865410254[/C][C]0.0202496865410254[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.313477056453667[/C][C]-0.313477056453667[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.366968904410922[/C][C]0.633031095589078[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.0438677274157361[/C][C]0.0438677274157361[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.214772704604526[/C][C]0.785227295395474[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]-0.019257018265043[/C][C]0.019257018265043[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.290256082889349[/C][C]-0.290256082889349[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.255398210681173[/C][C]-0.255398210681173[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]-0.0428750591397537[/C][C]0.0428750591397537[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-0.0426765254845572[/C][C]0.0426765254845572[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.344343531812193[/C][C]0.655656468187807[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.046070261991267[/C][C]-0.046070261991267[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.280604520797455[/C][C]-0.280604520797455[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]-0.0176687490234712[/C][C]0.0176687490234712[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.0466658629568564[/C][C]-0.0466658629568564[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]-0.0172716817130783[/C][C]0.0172716817130783[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-0.0170731480578818[/C][C]0.0170731480578818[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.380897141888168[/C][C]0.619102858111832[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]-0.0276263133967879[/C][C]0.0276263133967879[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]-0.0406911889325925[/C][C]0.0406911889325925[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.21794924308767[/C][C]-0.21794924308767[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]-0.0160804797818994[/C][C]0.0160804797818994[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]-0.0400955879670031[/C][C]0.0400955879670031[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.194331202212959[/C][C]-0.194331202212959[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.207793145059156[/C][C]-0.207793145059156[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]-0.0394999870014137[/C][C]0.0394999870014137[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.12424204641987[/C][C]-0.12424204641987[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]-0.0391029196910207[/C][C]0.0391029196910207[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.259567417440299[/C][C]-0.259567417440299[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.294822356958868[/C][C]0.705177643041132[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.149249822880956[/C][C]-0.149249822880956[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]-0.0140951432299347[/C][C]0.0140951432299347[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.185167772460428[/C][C]-0.185167772460428[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]-0.0136980759195418[/C][C]0.0136980759195418[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.22072871426042[/C][C]0.77927128573958[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.0267288965019084[/C][C]-0.0267288965019084[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]-0.0240527076032514[/C][C]0.0240527076032514[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]-0.048067815788355[/C][C]0.048067815788355[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.236008950798972[/C][C]-0.236008950798972[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]-0.0125068739883629[/C][C]0.0125068739883629[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]-0.0365219821734666[/C][C]0.0365219821734666[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.0521337402733874[/C][C]-0.0521337402733874[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.0267884707352926[/C][C]-0.0267884707352926[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.0415805749344813[/C][C]-0.0415805749344813[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]-0.0115142057123806[/C][C]0.0115142057123806[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.0383343043501811[/C][C]-0.0383343043501811[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]-0.0353307802422877[/C][C]0.0353307802422877[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.027781139011275[/C][C]-0.027781139011275[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]-0.0107200710915947[/C][C]0.0107200710915947[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]-0.0214717700856972[/C][C]0.0214717700856972[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]-0.0345366456215019[/C][C]0.0345366456215019[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]-0.0452883446156044[/C][C]0.0452883446156044[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]-0.00992593647080879[/C][C]0.00992593647080879[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]-0.00972740281561231[/C][C]0.00972740281561231[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]-0.00952886916041584[/C][C]0.00952886916041584[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.274547897426911[/C][C]-0.274547897426911[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]-0.0091318018500229[/C][C]0.0091318018500229[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]-0.00893326819482642[/C][C]0.00893326819482642[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.264193265743202[/C][C]-0.264193265743202[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]-0.00853620088443348[/C][C]0.00853620088443348[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]-0.019287899878536[/C][C]0.019287899878536[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.329032413660148[/C][C]-0.329032413660148[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.0417093764885211[/C][C]-0.0417093764885211[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.226486190261118[/C][C]-0.226486190261118[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.26538446767438[/C][C]-0.26538446767438[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]-0.0182952316025537[/C][C]0.0182952316025537[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]-0.00714646529805817[/C][C]0.00714646529805817[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]-0.0421118061324608[/C][C]0.0421118061324608[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]-0.0176996306369642[/C][C]0.0176996306369642[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]-0.00655086433246875[/C][C]0.00655086433246875[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]-0.0305659725175724[/C][C]0.0305659725175724[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]-0.0171040296713748[/C][C]0.0171040296713748[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]-0.00595526336687933[/C][C]0.00595526336687933[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.267171270571149[/C][C]-0.267171270571149[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.268699965579336[/C][C]-0.268699965579336[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]-0.02957330424159[/C][C]0.02957330424159[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]0.0444888476612717[/C][C]-0.0444888476612717[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.0592809518604604[/C][C]-0.0592809518604604[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]-0.0289777032760006[/C][C]0.0289777032760006[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]-0.00456552778050403[/C][C]0.00456552778050403[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]-0.0285806359656077[/C][C]0.0285806359656077[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]-0.0151186931194101[/C][C]0.0151186931194101[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]-0.0391338013045138[/C][C]0.0391338013045138[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.219506630715748[/C][C]-0.219506630715748[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]-0.00357285950452166[/C][C]0.00357285950452166[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]-0.00337432584932519[/C][C]0.00337432584932519[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]-0.00317579219412872[/C][C]0.00317579219412872[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]0.260330670447592[/C][C]-0.260330670447592[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]0.310179180510153[/C][C]-0.310179180510153[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.0470697851788258[/C][C]-0.0470697851788258[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]-0.00238165757334283[/C][C]0.00238165757334283[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.207831490766319[/C][C]0.792168509233681[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.257680000828881[/C][C]-0.257680000828881[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]-0.0127362892570524[/C][C]0.0127362892570524[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.0384423821585003[/C][C]-0.0384423821585003[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.0628545576539969[/C][C]-0.0628545576539969[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]0.024245878924901[/C][C]-0.024245878924901[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.282886310945163[/C][C]-0.282886310945163[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.0488565880755941[/C][C]-0.0488565880755941[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]-0.0115450873258736[/C][C]0.0115450873258736[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.0396335840896792[/C][C]-0.0396335840896792[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]-0.0244114292064818[/C][C]0.0244114292064818[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]0.223278770164481[/C][C]0.776721229835519[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.287720850771035[/C][C]0.712279149228965[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.223675837474874[/C][C]-0.223675837474874[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204441&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204441&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
100.0341582426794786-0.0341582426794786
20-0.02977930199045620.0297793019904562
30-0.02958076833525960.0295807683352596
40-0.02938223468006320.0293822346800632
50-0.02918370102486660.0291837010248666
609.45050920880784e-05-9.45050920880784e-05
70-0.02878663371447370.0287866337144737
800.0707118527554531-0.0707118527554531
90-0.05260320824438090.0526032082443809
100-0.03914126539818330.0391412653981833
1100.0603572210717435-0.0603572210717435
120-0.02779396543849130.0277939654384913
1300.270876371692828-0.270876371692828
1400.0609528220373329-0.0609528220373329
1500.247059797162921-0.247059797162921
1600.346558283632848-0.346558283632848
1710.3600202264790450.639979773520955
1800.0617469566581188-0.0617469566581188
190-0.05061787169241610.0506178716924161
2010.3473524182536330.652647581746367
2100.0272861517603353-0.0272861517603353
2200.237499300099997-0.237499300099997
2300.0144198098797271-0.0144198098797271
2400.00366811088562458-0.00366811088562458
2500.284101539578258-0.284101539578258
2600.273457309210382-0.273457309210382
270-0.05997983510014340.0599798351001434
2800.209610829569418-0.209610829569418
290-0.04863253514045140.0486325351404514
3000.0400231873064026-0.0400231873064026
310-0.02402182598975830.0240218259897583
320-0.03477352498386090.0347735249838609
3300.029668555622693-0.029668555622693
3400.0516600859502612-0.0516600859502612
350-0.02322769136897240.0232276913689724
360-0.0230291577137760.023029157713776
3700.363990899582975-0.363990899582975
3800.187382524281082-0.187382524281082
3900.0175963483628707-0.0175963483628707
4000.141308476673098-0.141308476673098
4110.2522216721980290.747778327801971
4200.188176658901868-0.188176658901868
4300.00744025033435755-0.00744025033435755
4400.0669088316932271-0.0669088316932271
4500.0430011921343497-0.0430011921343497
4600.018986083949246-0.018986083949246
470-0.02084528750661480.0208452875066148
480-0.04486039569171840.0448603956917184
4900.0195816849148354-0.0195816849148354
500-0.02024968654102540.0202496865410254
5100.313477056453667-0.313477056453667
5210.3669689044109220.633031095589078
530-0.04386772741573610.0438677274157361
5410.2147727046045260.785227295395474
550-0.0192570182650430.019257018265043
5600.290256082889349-0.290256082889349
5700.255398210681173-0.255398210681173
580-0.04287505913975370.0428750591397537
590-0.04267652548455720.0426765254845572
6010.3443435318121930.655656468187807
6100.046070261991267-0.046070261991267
6200.280604520797455-0.280604520797455
630-0.01766874902347120.0176687490234712
6400.0466658629568564-0.0466658629568564
650-0.01727168171307830.0172716817130783
660-0.01707314805788180.0170731480578818
6710.3808971418881680.619102858111832
680-0.02762631339678790.0276263133967879
690-0.04069118893259250.0406911889325925
7000.21794924308767-0.21794924308767
710-0.01608047978189940.0160804797818994
720-0.04009558796700310.0400955879670031
7300.194331202212959-0.194331202212959
7400.207793145059156-0.207793145059156
750-0.03949998700141370.0394999870014137
7600.12424204641987-0.12424204641987
770-0.03910291969102070.0391029196910207
7800.259567417440299-0.259567417440299
7910.2948223569588680.705177643041132
8000.149249822880956-0.149249822880956
810-0.01409514322993470.0140951432299347
8200.185167772460428-0.185167772460428
830-0.01369807591954180.0136980759195418
8410.220728714260420.77927128573958
8500.0267288965019084-0.0267288965019084
860-0.02405270760325140.0240527076032514
870-0.0480678157883550.048067815788355
8800.236008950798972-0.236008950798972
890-0.01250687398836290.0125068739883629
900-0.03652198217346660.0365219821734666
9100.0521337402733874-0.0521337402733874
9200.0267884707352926-0.0267884707352926
9300.0415805749344813-0.0415805749344813
940-0.01151420571238060.0115142057123806
9500.0383343043501811-0.0383343043501811
960-0.03533078024228770.0353307802422877
9700.027781139011275-0.027781139011275
980-0.01072007109159470.0107200710915947
990-0.02147177008569720.0214717700856972
1000-0.03453664562150190.0345366456215019
1010-0.04528834461560440.0452883446156044
1020-0.009925936470808790.00992593647080879
1030-0.009727402815612310.00972740281561231
1040-0.009528869160415840.00952886916041584
10500.274547897426911-0.274547897426911
1060-0.00913180185002290.0091318018500229
1070-0.008933268194826420.00893326819482642
10800.264193265743202-0.264193265743202
1090-0.008536200884433480.00853620088443348
1100-0.0192878998785360.019287899878536
11100.329032413660148-0.329032413660148
11200.0417093764885211-0.0417093764885211
11300.226486190261118-0.226486190261118
11400.26538446767438-0.26538446767438
1150-0.01829523160255370.0182952316025537
1160-0.007146465298058170.00714646529805817
1170-0.04211180613246080.0421118061324608
1180-0.01769963063696420.0176996306369642
1190-0.006550864332468750.00655086433246875
1200-0.03056597251757240.0305659725175724
1210-0.01710402967137480.0171040296713748
1220-0.005955263366879330.00595526336687933
12300.267171270571149-0.267171270571149
12400.268699965579336-0.268699965579336
1250-0.029573304241590.02957330424159
12600.0444888476612717-0.0444888476612717
12700.0592809518604604-0.0592809518604604
1280-0.02897770327600060.0289777032760006
1290-0.004565527780504030.00456552778050403
1300-0.02858063596560770.0285806359656077
1310-0.01511869311941010.0151186931194101
1320-0.03913380130451380.0391338013045138
13300.219506630715748-0.219506630715748
1340-0.003572859504521660.00357285950452166
1350-0.003374325849325190.00337432584932519
1360-0.003175792194128720.00317579219412872
13700.260330670447592-0.260330670447592
13800.310179180510153-0.310179180510153
13900.0470697851788258-0.0470697851788258
1400-0.002381657573342830.00238165757334283
14110.2078314907663190.792168509233681
14200.257680000828881-0.257680000828881
1430-0.01273628925705240.0127362892570524
14400.0384423821585003-0.0384423821585003
14500.0628545576539969-0.0628545576539969
14600.024245878924901-0.024245878924901
14700.282886310945163-0.282886310945163
14800.0488565880755941-0.0488565880755941
1490-0.01154508732587360.0115450873258736
15000.0396335840896792-0.0396335840896792
1510-0.02441142920648180.0244114292064818
15210.2232787701644810.776721229835519
15310.2877208507710350.712279149228965
15400.223675837474874-0.223675837474874






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
10001
11001
12001
13001
14001
15001
16001
170.3950789202536970.7901578405073950.604921079746303
180.3217017206072570.6434034412145130.678298279392743
190.3180579742248560.6361159484497120.681942025775144
200.8116231069473810.3767537861052370.188376893052619
210.7601696241747240.4796607516505510.239830375825276
220.7500589602291470.4998820795417070.249941039770853
230.686738466918360.626523066163280.31326153308164
240.6188217089088420.7623565821823160.381178291091158
250.6095634572094670.7808730855810650.390436542790533
260.5977068543531180.8045862912937640.402293145646882
270.5670114422387610.8659771155224780.432988557761239
280.5096315427054830.9807369145890330.490368457294517
290.4588810963433890.9177621926867780.541118903656611
300.3995715813654110.7991431627308220.600428418634589
310.3433478290868490.6866956581736980.656652170913151
320.2897568116459250.579513623291850.710243188354075
330.2412356515255350.4824713030510710.758764348474465
340.1998757941006720.3997515882013440.800124205899328
350.1621703959362020.3243407918724050.837829604063798
360.1289083688230930.2578167376461860.871091631176907
370.1675620541382960.3351241082765910.832437945861704
380.1389783780662860.2779567561325720.861021621933714
390.1088738859378870.2177477718757740.891126114062113
400.09539431775081650.1907886355016330.904605682249183
410.5611399276302570.8777201447394860.438860072369743
420.5277571257675440.9444857484649120.472242874232456
430.4737474748564110.9474949497128210.526252525143589
440.421656082153160.8433121643063190.57834391784684
450.3710597487797580.7421194975595160.628940251220242
460.322417464280450.6448349285609010.67758253571955
470.2788587927111970.5577175854223940.721141207288803
480.2373064241464560.4746128482929110.762693575853544
490.1992637164499390.3985274328998780.800736283550061
500.1659629898693580.3319259797387160.834037010130642
510.1721277305508450.344255461101690.827872269449155
520.4398463895586890.8796927791173780.560153610441311
530.3916814211340520.7833628422681040.608318578865948
540.7952457104252510.4095085791494990.204754289574749
550.7583197267165640.4833605465668710.241680273283436
560.7806321416790420.4387357166419170.219367858320958
570.7888245038853630.4223509922292740.211175496114637
580.7519380751430220.4961238497139550.248061924856978
590.7117753382564940.5764493234870130.288224661743506
600.8899487967613320.2201024064773370.110051203238668
610.8696288356960080.2607423286079830.130371164303992
620.8843956117349530.2312087765300940.115604388265047
630.8588860871749290.2822278256501420.141113912825071
640.8342418447813660.3315163104372680.165758155218634
650.8022096182328870.3955807635342260.197790381767113
660.7667571339215970.4664857321568070.233242866078403
670.927874762810860.144250474378280.0721252371891402
680.9111968672603750.1776062654792510.0888031327396253
690.8904071445128040.2191857109743920.109592855487196
700.8909914503975290.2180170992049420.109008549602471
710.8668537062417070.2662925875165850.133146293758293
720.8394666176246130.3210667647507740.160533382375387
730.8349667628571480.3300664742857040.165033237142852
740.8318095392014420.3363809215971150.168190460798558
750.8002739378140070.3994521243719860.199726062185993
760.7850418912787270.4299162174425450.214958108721273
770.748971200961520.5020575980769610.25102879903848
780.7616809380527350.476638123894530.238319061947265
790.9777473185396430.04450536292071450.0222526814603573
800.9808965190791120.03820696184177570.0191034809208879
810.9745007627259120.05099847454817510.0254992372740876
820.974205614828520.05158877034296040.0257943851714802
830.9660589309890660.06788213802186740.0339410690109337
840.9988619222981320.002276155403736570.00113807770186829
850.9983707371399080.003258525720184940.00162926286009247
860.9976042808837270.004791438232546240.00239571911627312
870.9965586635331980.006882672933604680.00344133646680234
880.9959242927600840.008151414479832140.00407570723991607
890.9942002282307040.01159954353859170.00579977176929583
900.9919646247361180.01607075052776340.0080353752638817
910.9892225037706860.02155499245862880.0107774962293144
920.9872871256448780.02542574871024420.0127128743551221
930.9832117794248790.03357644115024250.0167882205751213
940.9773981180681890.0452037638636230.0226018819318115
950.9750622985277680.04987540294446450.0249377014722323
960.967622727814070.06475454437185920.0323772721859296
970.9662489014250.06750219715000010.033751098575
980.9561429296599170.08771414068016510.0438570703400825
990.9439910747206580.1120178505586830.0560089252793417
1000.9302876314044590.1394247371910830.0697123685955413
1010.9159815583099380.1680368833801240.0840184416900621
1020.8957166919835630.2085666160328740.104283308016437
1030.8721231603569550.255753679286090.127876839643045
1040.8450835440553020.3098329118893960.154916455944698
1050.8311807055632850.337638588873430.168819294436715
1060.7982466378583220.4035067242833560.201753362141678
1070.7618247513699270.4763504972601450.238175248630073
1080.7369375161462080.5261249677075850.263062483853792
1090.6944308381095860.6111383237808290.305569161890414
1100.6498068019168220.7003863961663560.350193198083178
1110.6282755201591190.7434489596817610.371724479840881
1120.6298172038152840.7403655923694310.370182796184716
1130.6181932580124520.7636134839750970.381806741987548
1140.5758439884570490.8483120230859020.424156011542951
1150.5251826454840410.9496347090319170.474817354515958
1160.469738691920590.939477383841180.53026130807941
1170.4259151838205130.8518303676410270.574084816179487
1180.3778555399956520.7557110799913030.622144460004348
1190.3248280459974840.6496560919949680.675171954002516
1200.2791058581759970.5582117163519950.720894141824003
1210.2394399425997290.4788798851994580.760560057400271
1220.1964403162877580.3928806325755160.803559683712242
1230.1635931910295660.3271863820591320.836406808970434
1240.1563512223118840.3127024446237690.843648777688116
1250.1226912758077810.2453825516155630.877308724192219
1260.1232062222383060.2464124444766120.876793777761694
1270.09655157149351250.1931031429870250.903448428506488
1280.07233964043274020.144679280865480.92766035956726
1290.05242518688650230.1048503737730050.947574813113498
1300.03717469033477420.07434938066954850.962825309665226
1310.02760682302880330.05521364605760660.972393176971197
1320.02304882763110020.04609765526220030.9769511723689
1330.02197140625668520.04394281251337050.978028593743315
1340.01385513322513830.02771026645027670.986144866774862
1350.00840251489808840.01680502979617680.991597485101912
1360.004936147862822270.009872295725644550.995063852137178
1370.006757388224755360.01351477644951070.993242611775245
1380.007072704927803940.01414540985560790.992927295072196
1390.005035892009739310.01007178401947860.994964107990261
1400.002536126454996740.005072252909993480.997463873545003
1410.05022904351573090.1004580870314620.949770956484269
1420.03009104942581530.06018209885163060.969908950574185
1430.01472776682656110.02945553365312220.985272233173439
1440.006552332861137520.0131046657222750.993447667138862

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0 & 0 & 1 \tabularnewline
11 & 0 & 0 & 1 \tabularnewline
12 & 0 & 0 & 1 \tabularnewline
13 & 0 & 0 & 1 \tabularnewline
14 & 0 & 0 & 1 \tabularnewline
15 & 0 & 0 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 0.395078920253697 & 0.790157840507395 & 0.604921079746303 \tabularnewline
18 & 0.321701720607257 & 0.643403441214513 & 0.678298279392743 \tabularnewline
19 & 0.318057974224856 & 0.636115948449712 & 0.681942025775144 \tabularnewline
20 & 0.811623106947381 & 0.376753786105237 & 0.188376893052619 \tabularnewline
21 & 0.760169624174724 & 0.479660751650551 & 0.239830375825276 \tabularnewline
22 & 0.750058960229147 & 0.499882079541707 & 0.249941039770853 \tabularnewline
23 & 0.68673846691836 & 0.62652306616328 & 0.31326153308164 \tabularnewline
24 & 0.618821708908842 & 0.762356582182316 & 0.381178291091158 \tabularnewline
25 & 0.609563457209467 & 0.780873085581065 & 0.390436542790533 \tabularnewline
26 & 0.597706854353118 & 0.804586291293764 & 0.402293145646882 \tabularnewline
27 & 0.567011442238761 & 0.865977115522478 & 0.432988557761239 \tabularnewline
28 & 0.509631542705483 & 0.980736914589033 & 0.490368457294517 \tabularnewline
29 & 0.458881096343389 & 0.917762192686778 & 0.541118903656611 \tabularnewline
30 & 0.399571581365411 & 0.799143162730822 & 0.600428418634589 \tabularnewline
31 & 0.343347829086849 & 0.686695658173698 & 0.656652170913151 \tabularnewline
32 & 0.289756811645925 & 0.57951362329185 & 0.710243188354075 \tabularnewline
33 & 0.241235651525535 & 0.482471303051071 & 0.758764348474465 \tabularnewline
34 & 0.199875794100672 & 0.399751588201344 & 0.800124205899328 \tabularnewline
35 & 0.162170395936202 & 0.324340791872405 & 0.837829604063798 \tabularnewline
36 & 0.128908368823093 & 0.257816737646186 & 0.871091631176907 \tabularnewline
37 & 0.167562054138296 & 0.335124108276591 & 0.832437945861704 \tabularnewline
38 & 0.138978378066286 & 0.277956756132572 & 0.861021621933714 \tabularnewline
39 & 0.108873885937887 & 0.217747771875774 & 0.891126114062113 \tabularnewline
40 & 0.0953943177508165 & 0.190788635501633 & 0.904605682249183 \tabularnewline
41 & 0.561139927630257 & 0.877720144739486 & 0.438860072369743 \tabularnewline
42 & 0.527757125767544 & 0.944485748464912 & 0.472242874232456 \tabularnewline
43 & 0.473747474856411 & 0.947494949712821 & 0.526252525143589 \tabularnewline
44 & 0.42165608215316 & 0.843312164306319 & 0.57834391784684 \tabularnewline
45 & 0.371059748779758 & 0.742119497559516 & 0.628940251220242 \tabularnewline
46 & 0.32241746428045 & 0.644834928560901 & 0.67758253571955 \tabularnewline
47 & 0.278858792711197 & 0.557717585422394 & 0.721141207288803 \tabularnewline
48 & 0.237306424146456 & 0.474612848292911 & 0.762693575853544 \tabularnewline
49 & 0.199263716449939 & 0.398527432899878 & 0.800736283550061 \tabularnewline
50 & 0.165962989869358 & 0.331925979738716 & 0.834037010130642 \tabularnewline
51 & 0.172127730550845 & 0.34425546110169 & 0.827872269449155 \tabularnewline
52 & 0.439846389558689 & 0.879692779117378 & 0.560153610441311 \tabularnewline
53 & 0.391681421134052 & 0.783362842268104 & 0.608318578865948 \tabularnewline
54 & 0.795245710425251 & 0.409508579149499 & 0.204754289574749 \tabularnewline
55 & 0.758319726716564 & 0.483360546566871 & 0.241680273283436 \tabularnewline
56 & 0.780632141679042 & 0.438735716641917 & 0.219367858320958 \tabularnewline
57 & 0.788824503885363 & 0.422350992229274 & 0.211175496114637 \tabularnewline
58 & 0.751938075143022 & 0.496123849713955 & 0.248061924856978 \tabularnewline
59 & 0.711775338256494 & 0.576449323487013 & 0.288224661743506 \tabularnewline
60 & 0.889948796761332 & 0.220102406477337 & 0.110051203238668 \tabularnewline
61 & 0.869628835696008 & 0.260742328607983 & 0.130371164303992 \tabularnewline
62 & 0.884395611734953 & 0.231208776530094 & 0.115604388265047 \tabularnewline
63 & 0.858886087174929 & 0.282227825650142 & 0.141113912825071 \tabularnewline
64 & 0.834241844781366 & 0.331516310437268 & 0.165758155218634 \tabularnewline
65 & 0.802209618232887 & 0.395580763534226 & 0.197790381767113 \tabularnewline
66 & 0.766757133921597 & 0.466485732156807 & 0.233242866078403 \tabularnewline
67 & 0.92787476281086 & 0.14425047437828 & 0.0721252371891402 \tabularnewline
68 & 0.911196867260375 & 0.177606265479251 & 0.0888031327396253 \tabularnewline
69 & 0.890407144512804 & 0.219185710974392 & 0.109592855487196 \tabularnewline
70 & 0.890991450397529 & 0.218017099204942 & 0.109008549602471 \tabularnewline
71 & 0.866853706241707 & 0.266292587516585 & 0.133146293758293 \tabularnewline
72 & 0.839466617624613 & 0.321066764750774 & 0.160533382375387 \tabularnewline
73 & 0.834966762857148 & 0.330066474285704 & 0.165033237142852 \tabularnewline
74 & 0.831809539201442 & 0.336380921597115 & 0.168190460798558 \tabularnewline
75 & 0.800273937814007 & 0.399452124371986 & 0.199726062185993 \tabularnewline
76 & 0.785041891278727 & 0.429916217442545 & 0.214958108721273 \tabularnewline
77 & 0.74897120096152 & 0.502057598076961 & 0.25102879903848 \tabularnewline
78 & 0.761680938052735 & 0.47663812389453 & 0.238319061947265 \tabularnewline
79 & 0.977747318539643 & 0.0445053629207145 & 0.0222526814603573 \tabularnewline
80 & 0.980896519079112 & 0.0382069618417757 & 0.0191034809208879 \tabularnewline
81 & 0.974500762725912 & 0.0509984745481751 & 0.0254992372740876 \tabularnewline
82 & 0.97420561482852 & 0.0515887703429604 & 0.0257943851714802 \tabularnewline
83 & 0.966058930989066 & 0.0678821380218674 & 0.0339410690109337 \tabularnewline
84 & 0.998861922298132 & 0.00227615540373657 & 0.00113807770186829 \tabularnewline
85 & 0.998370737139908 & 0.00325852572018494 & 0.00162926286009247 \tabularnewline
86 & 0.997604280883727 & 0.00479143823254624 & 0.00239571911627312 \tabularnewline
87 & 0.996558663533198 & 0.00688267293360468 & 0.00344133646680234 \tabularnewline
88 & 0.995924292760084 & 0.00815141447983214 & 0.00407570723991607 \tabularnewline
89 & 0.994200228230704 & 0.0115995435385917 & 0.00579977176929583 \tabularnewline
90 & 0.991964624736118 & 0.0160707505277634 & 0.0080353752638817 \tabularnewline
91 & 0.989222503770686 & 0.0215549924586288 & 0.0107774962293144 \tabularnewline
92 & 0.987287125644878 & 0.0254257487102442 & 0.0127128743551221 \tabularnewline
93 & 0.983211779424879 & 0.0335764411502425 & 0.0167882205751213 \tabularnewline
94 & 0.977398118068189 & 0.045203763863623 & 0.0226018819318115 \tabularnewline
95 & 0.975062298527768 & 0.0498754029444645 & 0.0249377014722323 \tabularnewline
96 & 0.96762272781407 & 0.0647545443718592 & 0.0323772721859296 \tabularnewline
97 & 0.966248901425 & 0.0675021971500001 & 0.033751098575 \tabularnewline
98 & 0.956142929659917 & 0.0877141406801651 & 0.0438570703400825 \tabularnewline
99 & 0.943991074720658 & 0.112017850558683 & 0.0560089252793417 \tabularnewline
100 & 0.930287631404459 & 0.139424737191083 & 0.0697123685955413 \tabularnewline
101 & 0.915981558309938 & 0.168036883380124 & 0.0840184416900621 \tabularnewline
102 & 0.895716691983563 & 0.208566616032874 & 0.104283308016437 \tabularnewline
103 & 0.872123160356955 & 0.25575367928609 & 0.127876839643045 \tabularnewline
104 & 0.845083544055302 & 0.309832911889396 & 0.154916455944698 \tabularnewline
105 & 0.831180705563285 & 0.33763858887343 & 0.168819294436715 \tabularnewline
106 & 0.798246637858322 & 0.403506724283356 & 0.201753362141678 \tabularnewline
107 & 0.761824751369927 & 0.476350497260145 & 0.238175248630073 \tabularnewline
108 & 0.736937516146208 & 0.526124967707585 & 0.263062483853792 \tabularnewline
109 & 0.694430838109586 & 0.611138323780829 & 0.305569161890414 \tabularnewline
110 & 0.649806801916822 & 0.700386396166356 & 0.350193198083178 \tabularnewline
111 & 0.628275520159119 & 0.743448959681761 & 0.371724479840881 \tabularnewline
112 & 0.629817203815284 & 0.740365592369431 & 0.370182796184716 \tabularnewline
113 & 0.618193258012452 & 0.763613483975097 & 0.381806741987548 \tabularnewline
114 & 0.575843988457049 & 0.848312023085902 & 0.424156011542951 \tabularnewline
115 & 0.525182645484041 & 0.949634709031917 & 0.474817354515958 \tabularnewline
116 & 0.46973869192059 & 0.93947738384118 & 0.53026130807941 \tabularnewline
117 & 0.425915183820513 & 0.851830367641027 & 0.574084816179487 \tabularnewline
118 & 0.377855539995652 & 0.755711079991303 & 0.622144460004348 \tabularnewline
119 & 0.324828045997484 & 0.649656091994968 & 0.675171954002516 \tabularnewline
120 & 0.279105858175997 & 0.558211716351995 & 0.720894141824003 \tabularnewline
121 & 0.239439942599729 & 0.478879885199458 & 0.760560057400271 \tabularnewline
122 & 0.196440316287758 & 0.392880632575516 & 0.803559683712242 \tabularnewline
123 & 0.163593191029566 & 0.327186382059132 & 0.836406808970434 \tabularnewline
124 & 0.156351222311884 & 0.312702444623769 & 0.843648777688116 \tabularnewline
125 & 0.122691275807781 & 0.245382551615563 & 0.877308724192219 \tabularnewline
126 & 0.123206222238306 & 0.246412444476612 & 0.876793777761694 \tabularnewline
127 & 0.0965515714935125 & 0.193103142987025 & 0.903448428506488 \tabularnewline
128 & 0.0723396404327402 & 0.14467928086548 & 0.92766035956726 \tabularnewline
129 & 0.0524251868865023 & 0.104850373773005 & 0.947574813113498 \tabularnewline
130 & 0.0371746903347742 & 0.0743493806695485 & 0.962825309665226 \tabularnewline
131 & 0.0276068230288033 & 0.0552136460576066 & 0.972393176971197 \tabularnewline
132 & 0.0230488276311002 & 0.0460976552622003 & 0.9769511723689 \tabularnewline
133 & 0.0219714062566852 & 0.0439428125133705 & 0.978028593743315 \tabularnewline
134 & 0.0138551332251383 & 0.0277102664502767 & 0.986144866774862 \tabularnewline
135 & 0.0084025148980884 & 0.0168050297961768 & 0.991597485101912 \tabularnewline
136 & 0.00493614786282227 & 0.00987229572564455 & 0.995063852137178 \tabularnewline
137 & 0.00675738822475536 & 0.0135147764495107 & 0.993242611775245 \tabularnewline
138 & 0.00707270492780394 & 0.0141454098556079 & 0.992927295072196 \tabularnewline
139 & 0.00503589200973931 & 0.0100717840194786 & 0.994964107990261 \tabularnewline
140 & 0.00253612645499674 & 0.00507225290999348 & 0.997463873545003 \tabularnewline
141 & 0.0502290435157309 & 0.100458087031462 & 0.949770956484269 \tabularnewline
142 & 0.0300910494258153 & 0.0601820988516306 & 0.969908950574185 \tabularnewline
143 & 0.0147277668265611 & 0.0294555336531222 & 0.985272233173439 \tabularnewline
144 & 0.00655233286113752 & 0.013104665722275 & 0.993447667138862 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204441&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[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]0.395078920253697[/C][C]0.790157840507395[/C][C]0.604921079746303[/C][/ROW]
[ROW][C]18[/C][C]0.321701720607257[/C][C]0.643403441214513[/C][C]0.678298279392743[/C][/ROW]
[ROW][C]19[/C][C]0.318057974224856[/C][C]0.636115948449712[/C][C]0.681942025775144[/C][/ROW]
[ROW][C]20[/C][C]0.811623106947381[/C][C]0.376753786105237[/C][C]0.188376893052619[/C][/ROW]
[ROW][C]21[/C][C]0.760169624174724[/C][C]0.479660751650551[/C][C]0.239830375825276[/C][/ROW]
[ROW][C]22[/C][C]0.750058960229147[/C][C]0.499882079541707[/C][C]0.249941039770853[/C][/ROW]
[ROW][C]23[/C][C]0.68673846691836[/C][C]0.62652306616328[/C][C]0.31326153308164[/C][/ROW]
[ROW][C]24[/C][C]0.618821708908842[/C][C]0.762356582182316[/C][C]0.381178291091158[/C][/ROW]
[ROW][C]25[/C][C]0.609563457209467[/C][C]0.780873085581065[/C][C]0.390436542790533[/C][/ROW]
[ROW][C]26[/C][C]0.597706854353118[/C][C]0.804586291293764[/C][C]0.402293145646882[/C][/ROW]
[ROW][C]27[/C][C]0.567011442238761[/C][C]0.865977115522478[/C][C]0.432988557761239[/C][/ROW]
[ROW][C]28[/C][C]0.509631542705483[/C][C]0.980736914589033[/C][C]0.490368457294517[/C][/ROW]
[ROW][C]29[/C][C]0.458881096343389[/C][C]0.917762192686778[/C][C]0.541118903656611[/C][/ROW]
[ROW][C]30[/C][C]0.399571581365411[/C][C]0.799143162730822[/C][C]0.600428418634589[/C][/ROW]
[ROW][C]31[/C][C]0.343347829086849[/C][C]0.686695658173698[/C][C]0.656652170913151[/C][/ROW]
[ROW][C]32[/C][C]0.289756811645925[/C][C]0.57951362329185[/C][C]0.710243188354075[/C][/ROW]
[ROW][C]33[/C][C]0.241235651525535[/C][C]0.482471303051071[/C][C]0.758764348474465[/C][/ROW]
[ROW][C]34[/C][C]0.199875794100672[/C][C]0.399751588201344[/C][C]0.800124205899328[/C][/ROW]
[ROW][C]35[/C][C]0.162170395936202[/C][C]0.324340791872405[/C][C]0.837829604063798[/C][/ROW]
[ROW][C]36[/C][C]0.128908368823093[/C][C]0.257816737646186[/C][C]0.871091631176907[/C][/ROW]
[ROW][C]37[/C][C]0.167562054138296[/C][C]0.335124108276591[/C][C]0.832437945861704[/C][/ROW]
[ROW][C]38[/C][C]0.138978378066286[/C][C]0.277956756132572[/C][C]0.861021621933714[/C][/ROW]
[ROW][C]39[/C][C]0.108873885937887[/C][C]0.217747771875774[/C][C]0.891126114062113[/C][/ROW]
[ROW][C]40[/C][C]0.0953943177508165[/C][C]0.190788635501633[/C][C]0.904605682249183[/C][/ROW]
[ROW][C]41[/C][C]0.561139927630257[/C][C]0.877720144739486[/C][C]0.438860072369743[/C][/ROW]
[ROW][C]42[/C][C]0.527757125767544[/C][C]0.944485748464912[/C][C]0.472242874232456[/C][/ROW]
[ROW][C]43[/C][C]0.473747474856411[/C][C]0.947494949712821[/C][C]0.526252525143589[/C][/ROW]
[ROW][C]44[/C][C]0.42165608215316[/C][C]0.843312164306319[/C][C]0.57834391784684[/C][/ROW]
[ROW][C]45[/C][C]0.371059748779758[/C][C]0.742119497559516[/C][C]0.628940251220242[/C][/ROW]
[ROW][C]46[/C][C]0.32241746428045[/C][C]0.644834928560901[/C][C]0.67758253571955[/C][/ROW]
[ROW][C]47[/C][C]0.278858792711197[/C][C]0.557717585422394[/C][C]0.721141207288803[/C][/ROW]
[ROW][C]48[/C][C]0.237306424146456[/C][C]0.474612848292911[/C][C]0.762693575853544[/C][/ROW]
[ROW][C]49[/C][C]0.199263716449939[/C][C]0.398527432899878[/C][C]0.800736283550061[/C][/ROW]
[ROW][C]50[/C][C]0.165962989869358[/C][C]0.331925979738716[/C][C]0.834037010130642[/C][/ROW]
[ROW][C]51[/C][C]0.172127730550845[/C][C]0.34425546110169[/C][C]0.827872269449155[/C][/ROW]
[ROW][C]52[/C][C]0.439846389558689[/C][C]0.879692779117378[/C][C]0.560153610441311[/C][/ROW]
[ROW][C]53[/C][C]0.391681421134052[/C][C]0.783362842268104[/C][C]0.608318578865948[/C][/ROW]
[ROW][C]54[/C][C]0.795245710425251[/C][C]0.409508579149499[/C][C]0.204754289574749[/C][/ROW]
[ROW][C]55[/C][C]0.758319726716564[/C][C]0.483360546566871[/C][C]0.241680273283436[/C][/ROW]
[ROW][C]56[/C][C]0.780632141679042[/C][C]0.438735716641917[/C][C]0.219367858320958[/C][/ROW]
[ROW][C]57[/C][C]0.788824503885363[/C][C]0.422350992229274[/C][C]0.211175496114637[/C][/ROW]
[ROW][C]58[/C][C]0.751938075143022[/C][C]0.496123849713955[/C][C]0.248061924856978[/C][/ROW]
[ROW][C]59[/C][C]0.711775338256494[/C][C]0.576449323487013[/C][C]0.288224661743506[/C][/ROW]
[ROW][C]60[/C][C]0.889948796761332[/C][C]0.220102406477337[/C][C]0.110051203238668[/C][/ROW]
[ROW][C]61[/C][C]0.869628835696008[/C][C]0.260742328607983[/C][C]0.130371164303992[/C][/ROW]
[ROW][C]62[/C][C]0.884395611734953[/C][C]0.231208776530094[/C][C]0.115604388265047[/C][/ROW]
[ROW][C]63[/C][C]0.858886087174929[/C][C]0.282227825650142[/C][C]0.141113912825071[/C][/ROW]
[ROW][C]64[/C][C]0.834241844781366[/C][C]0.331516310437268[/C][C]0.165758155218634[/C][/ROW]
[ROW][C]65[/C][C]0.802209618232887[/C][C]0.395580763534226[/C][C]0.197790381767113[/C][/ROW]
[ROW][C]66[/C][C]0.766757133921597[/C][C]0.466485732156807[/C][C]0.233242866078403[/C][/ROW]
[ROW][C]67[/C][C]0.92787476281086[/C][C]0.14425047437828[/C][C]0.0721252371891402[/C][/ROW]
[ROW][C]68[/C][C]0.911196867260375[/C][C]0.177606265479251[/C][C]0.0888031327396253[/C][/ROW]
[ROW][C]69[/C][C]0.890407144512804[/C][C]0.219185710974392[/C][C]0.109592855487196[/C][/ROW]
[ROW][C]70[/C][C]0.890991450397529[/C][C]0.218017099204942[/C][C]0.109008549602471[/C][/ROW]
[ROW][C]71[/C][C]0.866853706241707[/C][C]0.266292587516585[/C][C]0.133146293758293[/C][/ROW]
[ROW][C]72[/C][C]0.839466617624613[/C][C]0.321066764750774[/C][C]0.160533382375387[/C][/ROW]
[ROW][C]73[/C][C]0.834966762857148[/C][C]0.330066474285704[/C][C]0.165033237142852[/C][/ROW]
[ROW][C]74[/C][C]0.831809539201442[/C][C]0.336380921597115[/C][C]0.168190460798558[/C][/ROW]
[ROW][C]75[/C][C]0.800273937814007[/C][C]0.399452124371986[/C][C]0.199726062185993[/C][/ROW]
[ROW][C]76[/C][C]0.785041891278727[/C][C]0.429916217442545[/C][C]0.214958108721273[/C][/ROW]
[ROW][C]77[/C][C]0.74897120096152[/C][C]0.502057598076961[/C][C]0.25102879903848[/C][/ROW]
[ROW][C]78[/C][C]0.761680938052735[/C][C]0.47663812389453[/C][C]0.238319061947265[/C][/ROW]
[ROW][C]79[/C][C]0.977747318539643[/C][C]0.0445053629207145[/C][C]0.0222526814603573[/C][/ROW]
[ROW][C]80[/C][C]0.980896519079112[/C][C]0.0382069618417757[/C][C]0.0191034809208879[/C][/ROW]
[ROW][C]81[/C][C]0.974500762725912[/C][C]0.0509984745481751[/C][C]0.0254992372740876[/C][/ROW]
[ROW][C]82[/C][C]0.97420561482852[/C][C]0.0515887703429604[/C][C]0.0257943851714802[/C][/ROW]
[ROW][C]83[/C][C]0.966058930989066[/C][C]0.0678821380218674[/C][C]0.0339410690109337[/C][/ROW]
[ROW][C]84[/C][C]0.998861922298132[/C][C]0.00227615540373657[/C][C]0.00113807770186829[/C][/ROW]
[ROW][C]85[/C][C]0.998370737139908[/C][C]0.00325852572018494[/C][C]0.00162926286009247[/C][/ROW]
[ROW][C]86[/C][C]0.997604280883727[/C][C]0.00479143823254624[/C][C]0.00239571911627312[/C][/ROW]
[ROW][C]87[/C][C]0.996558663533198[/C][C]0.00688267293360468[/C][C]0.00344133646680234[/C][/ROW]
[ROW][C]88[/C][C]0.995924292760084[/C][C]0.00815141447983214[/C][C]0.00407570723991607[/C][/ROW]
[ROW][C]89[/C][C]0.994200228230704[/C][C]0.0115995435385917[/C][C]0.00579977176929583[/C][/ROW]
[ROW][C]90[/C][C]0.991964624736118[/C][C]0.0160707505277634[/C][C]0.0080353752638817[/C][/ROW]
[ROW][C]91[/C][C]0.989222503770686[/C][C]0.0215549924586288[/C][C]0.0107774962293144[/C][/ROW]
[ROW][C]92[/C][C]0.987287125644878[/C][C]0.0254257487102442[/C][C]0.0127128743551221[/C][/ROW]
[ROW][C]93[/C][C]0.983211779424879[/C][C]0.0335764411502425[/C][C]0.0167882205751213[/C][/ROW]
[ROW][C]94[/C][C]0.977398118068189[/C][C]0.045203763863623[/C][C]0.0226018819318115[/C][/ROW]
[ROW][C]95[/C][C]0.975062298527768[/C][C]0.0498754029444645[/C][C]0.0249377014722323[/C][/ROW]
[ROW][C]96[/C][C]0.96762272781407[/C][C]0.0647545443718592[/C][C]0.0323772721859296[/C][/ROW]
[ROW][C]97[/C][C]0.966248901425[/C][C]0.0675021971500001[/C][C]0.033751098575[/C][/ROW]
[ROW][C]98[/C][C]0.956142929659917[/C][C]0.0877141406801651[/C][C]0.0438570703400825[/C][/ROW]
[ROW][C]99[/C][C]0.943991074720658[/C][C]0.112017850558683[/C][C]0.0560089252793417[/C][/ROW]
[ROW][C]100[/C][C]0.930287631404459[/C][C]0.139424737191083[/C][C]0.0697123685955413[/C][/ROW]
[ROW][C]101[/C][C]0.915981558309938[/C][C]0.168036883380124[/C][C]0.0840184416900621[/C][/ROW]
[ROW][C]102[/C][C]0.895716691983563[/C][C]0.208566616032874[/C][C]0.104283308016437[/C][/ROW]
[ROW][C]103[/C][C]0.872123160356955[/C][C]0.25575367928609[/C][C]0.127876839643045[/C][/ROW]
[ROW][C]104[/C][C]0.845083544055302[/C][C]0.309832911889396[/C][C]0.154916455944698[/C][/ROW]
[ROW][C]105[/C][C]0.831180705563285[/C][C]0.33763858887343[/C][C]0.168819294436715[/C][/ROW]
[ROW][C]106[/C][C]0.798246637858322[/C][C]0.403506724283356[/C][C]0.201753362141678[/C][/ROW]
[ROW][C]107[/C][C]0.761824751369927[/C][C]0.476350497260145[/C][C]0.238175248630073[/C][/ROW]
[ROW][C]108[/C][C]0.736937516146208[/C][C]0.526124967707585[/C][C]0.263062483853792[/C][/ROW]
[ROW][C]109[/C][C]0.694430838109586[/C][C]0.611138323780829[/C][C]0.305569161890414[/C][/ROW]
[ROW][C]110[/C][C]0.649806801916822[/C][C]0.700386396166356[/C][C]0.350193198083178[/C][/ROW]
[ROW][C]111[/C][C]0.628275520159119[/C][C]0.743448959681761[/C][C]0.371724479840881[/C][/ROW]
[ROW][C]112[/C][C]0.629817203815284[/C][C]0.740365592369431[/C][C]0.370182796184716[/C][/ROW]
[ROW][C]113[/C][C]0.618193258012452[/C][C]0.763613483975097[/C][C]0.381806741987548[/C][/ROW]
[ROW][C]114[/C][C]0.575843988457049[/C][C]0.848312023085902[/C][C]0.424156011542951[/C][/ROW]
[ROW][C]115[/C][C]0.525182645484041[/C][C]0.949634709031917[/C][C]0.474817354515958[/C][/ROW]
[ROW][C]116[/C][C]0.46973869192059[/C][C]0.93947738384118[/C][C]0.53026130807941[/C][/ROW]
[ROW][C]117[/C][C]0.425915183820513[/C][C]0.851830367641027[/C][C]0.574084816179487[/C][/ROW]
[ROW][C]118[/C][C]0.377855539995652[/C][C]0.755711079991303[/C][C]0.622144460004348[/C][/ROW]
[ROW][C]119[/C][C]0.324828045997484[/C][C]0.649656091994968[/C][C]0.675171954002516[/C][/ROW]
[ROW][C]120[/C][C]0.279105858175997[/C][C]0.558211716351995[/C][C]0.720894141824003[/C][/ROW]
[ROW][C]121[/C][C]0.239439942599729[/C][C]0.478879885199458[/C][C]0.760560057400271[/C][/ROW]
[ROW][C]122[/C][C]0.196440316287758[/C][C]0.392880632575516[/C][C]0.803559683712242[/C][/ROW]
[ROW][C]123[/C][C]0.163593191029566[/C][C]0.327186382059132[/C][C]0.836406808970434[/C][/ROW]
[ROW][C]124[/C][C]0.156351222311884[/C][C]0.312702444623769[/C][C]0.843648777688116[/C][/ROW]
[ROW][C]125[/C][C]0.122691275807781[/C][C]0.245382551615563[/C][C]0.877308724192219[/C][/ROW]
[ROW][C]126[/C][C]0.123206222238306[/C][C]0.246412444476612[/C][C]0.876793777761694[/C][/ROW]
[ROW][C]127[/C][C]0.0965515714935125[/C][C]0.193103142987025[/C][C]0.903448428506488[/C][/ROW]
[ROW][C]128[/C][C]0.0723396404327402[/C][C]0.14467928086548[/C][C]0.92766035956726[/C][/ROW]
[ROW][C]129[/C][C]0.0524251868865023[/C][C]0.104850373773005[/C][C]0.947574813113498[/C][/ROW]
[ROW][C]130[/C][C]0.0371746903347742[/C][C]0.0743493806695485[/C][C]0.962825309665226[/C][/ROW]
[ROW][C]131[/C][C]0.0276068230288033[/C][C]0.0552136460576066[/C][C]0.972393176971197[/C][/ROW]
[ROW][C]132[/C][C]0.0230488276311002[/C][C]0.0460976552622003[/C][C]0.9769511723689[/C][/ROW]
[ROW][C]133[/C][C]0.0219714062566852[/C][C]0.0439428125133705[/C][C]0.978028593743315[/C][/ROW]
[ROW][C]134[/C][C]0.0138551332251383[/C][C]0.0277102664502767[/C][C]0.986144866774862[/C][/ROW]
[ROW][C]135[/C][C]0.0084025148980884[/C][C]0.0168050297961768[/C][C]0.991597485101912[/C][/ROW]
[ROW][C]136[/C][C]0.00493614786282227[/C][C]0.00987229572564455[/C][C]0.995063852137178[/C][/ROW]
[ROW][C]137[/C][C]0.00675738822475536[/C][C]0.0135147764495107[/C][C]0.993242611775245[/C][/ROW]
[ROW][C]138[/C][C]0.00707270492780394[/C][C]0.0141454098556079[/C][C]0.992927295072196[/C][/ROW]
[ROW][C]139[/C][C]0.00503589200973931[/C][C]0.0100717840194786[/C][C]0.994964107990261[/C][/ROW]
[ROW][C]140[/C][C]0.00253612645499674[/C][C]0.00507225290999348[/C][C]0.997463873545003[/C][/ROW]
[ROW][C]141[/C][C]0.0502290435157309[/C][C]0.100458087031462[/C][C]0.949770956484269[/C][/ROW]
[ROW][C]142[/C][C]0.0300910494258153[/C][C]0.0601820988516306[/C][C]0.969908950574185[/C][/ROW]
[ROW][C]143[/C][C]0.0147277668265611[/C][C]0.0294555336531222[/C][C]0.985272233173439[/C][/ROW]
[ROW][C]144[/C][C]0.00655233286113752[/C][C]0.013104665722275[/C][C]0.993447667138862[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204441&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204441&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
10001
11001
12001
13001
14001
15001
16001
170.3950789202536970.7901578405073950.604921079746303
180.3217017206072570.6434034412145130.678298279392743
190.3180579742248560.6361159484497120.681942025775144
200.8116231069473810.3767537861052370.188376893052619
210.7601696241747240.4796607516505510.239830375825276
220.7500589602291470.4998820795417070.249941039770853
230.686738466918360.626523066163280.31326153308164
240.6188217089088420.7623565821823160.381178291091158
250.6095634572094670.7808730855810650.390436542790533
260.5977068543531180.8045862912937640.402293145646882
270.5670114422387610.8659771155224780.432988557761239
280.5096315427054830.9807369145890330.490368457294517
290.4588810963433890.9177621926867780.541118903656611
300.3995715813654110.7991431627308220.600428418634589
310.3433478290868490.6866956581736980.656652170913151
320.2897568116459250.579513623291850.710243188354075
330.2412356515255350.4824713030510710.758764348474465
340.1998757941006720.3997515882013440.800124205899328
350.1621703959362020.3243407918724050.837829604063798
360.1289083688230930.2578167376461860.871091631176907
370.1675620541382960.3351241082765910.832437945861704
380.1389783780662860.2779567561325720.861021621933714
390.1088738859378870.2177477718757740.891126114062113
400.09539431775081650.1907886355016330.904605682249183
410.5611399276302570.8777201447394860.438860072369743
420.5277571257675440.9444857484649120.472242874232456
430.4737474748564110.9474949497128210.526252525143589
440.421656082153160.8433121643063190.57834391784684
450.3710597487797580.7421194975595160.628940251220242
460.322417464280450.6448349285609010.67758253571955
470.2788587927111970.5577175854223940.721141207288803
480.2373064241464560.4746128482929110.762693575853544
490.1992637164499390.3985274328998780.800736283550061
500.1659629898693580.3319259797387160.834037010130642
510.1721277305508450.344255461101690.827872269449155
520.4398463895586890.8796927791173780.560153610441311
530.3916814211340520.7833628422681040.608318578865948
540.7952457104252510.4095085791494990.204754289574749
550.7583197267165640.4833605465668710.241680273283436
560.7806321416790420.4387357166419170.219367858320958
570.7888245038853630.4223509922292740.211175496114637
580.7519380751430220.4961238497139550.248061924856978
590.7117753382564940.5764493234870130.288224661743506
600.8899487967613320.2201024064773370.110051203238668
610.8696288356960080.2607423286079830.130371164303992
620.8843956117349530.2312087765300940.115604388265047
630.8588860871749290.2822278256501420.141113912825071
640.8342418447813660.3315163104372680.165758155218634
650.8022096182328870.3955807635342260.197790381767113
660.7667571339215970.4664857321568070.233242866078403
670.927874762810860.144250474378280.0721252371891402
680.9111968672603750.1776062654792510.0888031327396253
690.8904071445128040.2191857109743920.109592855487196
700.8909914503975290.2180170992049420.109008549602471
710.8668537062417070.2662925875165850.133146293758293
720.8394666176246130.3210667647507740.160533382375387
730.8349667628571480.3300664742857040.165033237142852
740.8318095392014420.3363809215971150.168190460798558
750.8002739378140070.3994521243719860.199726062185993
760.7850418912787270.4299162174425450.214958108721273
770.748971200961520.5020575980769610.25102879903848
780.7616809380527350.476638123894530.238319061947265
790.9777473185396430.04450536292071450.0222526814603573
800.9808965190791120.03820696184177570.0191034809208879
810.9745007627259120.05099847454817510.0254992372740876
820.974205614828520.05158877034296040.0257943851714802
830.9660589309890660.06788213802186740.0339410690109337
840.9988619222981320.002276155403736570.00113807770186829
850.9983707371399080.003258525720184940.00162926286009247
860.9976042808837270.004791438232546240.00239571911627312
870.9965586635331980.006882672933604680.00344133646680234
880.9959242927600840.008151414479832140.00407570723991607
890.9942002282307040.01159954353859170.00579977176929583
900.9919646247361180.01607075052776340.0080353752638817
910.9892225037706860.02155499245862880.0107774962293144
920.9872871256448780.02542574871024420.0127128743551221
930.9832117794248790.03357644115024250.0167882205751213
940.9773981180681890.0452037638636230.0226018819318115
950.9750622985277680.04987540294446450.0249377014722323
960.967622727814070.06475454437185920.0323772721859296
970.9662489014250.06750219715000010.033751098575
980.9561429296599170.08771414068016510.0438570703400825
990.9439910747206580.1120178505586830.0560089252793417
1000.9302876314044590.1394247371910830.0697123685955413
1010.9159815583099380.1680368833801240.0840184416900621
1020.8957166919835630.2085666160328740.104283308016437
1030.8721231603569550.255753679286090.127876839643045
1040.8450835440553020.3098329118893960.154916455944698
1050.8311807055632850.337638588873430.168819294436715
1060.7982466378583220.4035067242833560.201753362141678
1070.7618247513699270.4763504972601450.238175248630073
1080.7369375161462080.5261249677075850.263062483853792
1090.6944308381095860.6111383237808290.305569161890414
1100.6498068019168220.7003863961663560.350193198083178
1110.6282755201591190.7434489596817610.371724479840881
1120.6298172038152840.7403655923694310.370182796184716
1130.6181932580124520.7636134839750970.381806741987548
1140.5758439884570490.8483120230859020.424156011542951
1150.5251826454840410.9496347090319170.474817354515958
1160.469738691920590.939477383841180.53026130807941
1170.4259151838205130.8518303676410270.574084816179487
1180.3778555399956520.7557110799913030.622144460004348
1190.3248280459974840.6496560919949680.675171954002516
1200.2791058581759970.5582117163519950.720894141824003
1210.2394399425997290.4788798851994580.760560057400271
1220.1964403162877580.3928806325755160.803559683712242
1230.1635931910295660.3271863820591320.836406808970434
1240.1563512223118840.3127024446237690.843648777688116
1250.1226912758077810.2453825516155630.877308724192219
1260.1232062222383060.2464124444766120.876793777761694
1270.09655157149351250.1931031429870250.903448428506488
1280.07233964043274020.144679280865480.92766035956726
1290.05242518688650230.1048503737730050.947574813113498
1300.03717469033477420.07434938066954850.962825309665226
1310.02760682302880330.05521364605760660.972393176971197
1320.02304882763110020.04609765526220030.9769511723689
1330.02197140625668520.04394281251337050.978028593743315
1340.01385513322513830.02771026645027670.986144866774862
1350.00840251489808840.01680502979617680.991597485101912
1360.004936147862822270.009872295725644550.995063852137178
1370.006757388224755360.01351477644951070.993242611775245
1380.007072704927803940.01414540985560790.992927295072196
1390.005035892009739310.01007178401947860.994964107990261
1400.002536126454996740.005072252909993480.997463873545003
1410.05022904351573090.1004580870314620.949770956484269
1420.03009104942581530.06018209885163060.969908950574185
1430.01472776682656110.02945553365312220.985272233173439
1440.006552332861137520.0131046657222750.993447667138862






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.103703703703704NOK
5% type I error level320.237037037037037NOK
10% type I error level410.303703703703704NOK

\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 & 14 & 0.103703703703704 & NOK \tabularnewline
5% type I error level & 32 & 0.237037037037037 & NOK \tabularnewline
10% type I error level & 41 & 0.303703703703704 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204441&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]14[/C][C]0.103703703703704[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.237037037037037[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]41[/C][C]0.303703703703704[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204441&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204441&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 level140.103703703703704NOK
5% type I error level320.237037037037037NOK
10% type I error level410.303703703703704NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}