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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 13 Dec 2009 05:17:00 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/13/t126070668080304f6m9bx4enh.htm/, Retrieved Sun, 28 Apr 2024 17:18:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=67237, Retrieved Sun, 28 Apr 2024 17:18:22 +0000
QR Codes:

Original text written by user:Paper: Y(t-4)
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [shw7: Multiple li...] [2009-11-19 18:19:23] [3c8b83428ce260cd44df892bb7619588]
-   P         [Multiple Regression] [Paper: Y(t-4)] [2009-12-13 12:17:00] [7a39e26d7a09dd77604df90cb29f8d39] [Current]
-               [Multiple Regression] [Y(t-4)] [2009-12-17 17:12:35] [1433a524809eda02c3198b3ae6eebb69]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.7905	0.313	0.7744	0.779	0.7775	0.7461
0.7719	0.364	0.7905	0.7744	0.779	0.7775
0.7811	0.363	0.7719	0.7905	0.7744	0.779
0.7557	-0.155	0.7811	0.7719	0.7905	0.7744
0.7637	0.052	0.7557	0.7811	0.7719	0.7905
0.7595	0.568	0.7637	0.7557	0.7811	0.7719
0.7471	0.668	0.7595	0.7637	0.7557	0.7811
0.7615	1.378	0.7471	0.7595	0.7637	0.7557
0.7487	0.252	0.7615	0.7471	0.7595	0.7637
0.7389	-0.402	0.7487	0.7615	0.7471	0.7595
0.7337	-0.05	0.7389	0.7487	0.7615	0.7471
0.751	0.555	0.7337	0.7389	0.7487	0.7615
0.7382	0.05	0.751	0.7337	0.7389	0.7487
0.7159	0.15	0.7382	0.751	0.7337	0.7389
0.7542	0.45	0.7159	0.7382	0.751	0.7337
0.7636	0.299	0.7542	0.7159	0.7382	0.751
0.7433	0.199	0.7636	0.7542	0.7159	0.7382
0.7658	0.496	0.7433	0.7636	0.7542	0.7159
0.7627	0.444	0.7658	0.7433	0.7636	0.7542
0.748	-0.393	0.7627	0.7658	0.7433	0.7636
0.7692	-0.444	0.748	0.7627	0.7658	0.7433
0.785	0.198	0.7692	0.748	0.7627	0.7658
0.7913	0.494	0.785	0.7692	0.748	0.7627
0.772	0.133	0.7913	0.785	0.7692	0.748
0.788	0.388	0.772	0.7913	0.785	0.7692
0.807	0.484	0.788	0.772	0.7913	0.785
0.8268	0.278	0.807	0.788	0.772	0.7913
0.8244	0.369	0.8268	0.807	0.788	0.772
0.8487	0.165	0.8244	0.8268	0.807	0.788
0.8572	0.155	0.8487	0.8244	0.8268	0.807
0.8214	0.087	0.8572	0.8487	0.8244	0.8268
0.8827	0.414	0.8214	0.8572	0.8487	0.8244
0.9216	0.36	0.8827	0.8214	0.8572	0.8487
0.8865	0.975	0.9216	0.8827	0.8214	0.8572
0.8816	0.27	0.8865	0.9216	0.8827	0.8214
0.8884	0.359	0.8816	0.8865	0.9216	0.8827
0.9466	0.169	0.8884	0.8816	0.8865	0.9216
0.918	0.381	0.9466	0.8884	0.8816	0.8865
0.9337	0.154	0.918	0.9466	0.8884	0.8816
0.9559	0.486	0.9337	0.918	0.9466	0.8884
0.9626	0.925	0.9559	0.9337	0.918	0.9466
0.9434	0.728	0.9626	0.9559	0.9337	0.918
0.8639	-0.014	0.9434	0.9626	0.9559	0.9337
0.7996	0.046	0.8639	0.9434	0.9626	0.9559
0.668	-0.819	0.7996	0.8639	0.9434	0.9626
0.6572	-1.674	0.668	0.7996	0.8639	0.9434
0.6928	-0.788	0.6572	0.668	0.7996	0.8639
0.6438	0.279	0.6928	0.6572	0.668	0.7996
0.6454	0.396	0.6438	0.6928	0.6572	0.668
0.6873	-0.141	0.6454	0.6438	0.6928	0.6572
0.7265	-0.019	0.6873	0.6454	0.6438	0.6928
0.7912	0.099	0.7265	0.6873	0.6454	0.6438
0.8114	0.742	0.7912	0.7265	0.6873	0.6454
0.8281	0.005	0.8114	0.7912	0.7265	0.6873
0.8393	0.448	0.8281	0.8114	0.7912	0.7265

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67237&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67237&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67237&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
USDOLLAR[t] = + 0.139527755525393 + 0.0131669901810773Amerikaanse_inflatie[t] + 1.04991533179280`Y[t-1]`[t] -0.239723305758452`Y[t-2]`[t] + 0.288489458849696`Y[t-3]`[t] -0.287960825487985`Y[t-4]`[t] + 0.000313710754245402t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
USDOLLAR[t] =  +  0.139527755525393 +  0.0131669901810773Amerikaanse_inflatie[t] +  1.04991533179280`Y[t-1]`[t] -0.239723305758452`Y[t-2]`[t] +  0.288489458849696`Y[t-3]`[t] -0.287960825487985`Y[t-4]`[t] +  0.000313710754245402t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67237&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]USDOLLAR[t] =  +  0.139527755525393 +  0.0131669901810773Amerikaanse_inflatie[t] +  1.04991533179280`Y[t-1]`[t] -0.239723305758452`Y[t-2]`[t] +  0.288489458849696`Y[t-3]`[t] -0.287960825487985`Y[t-4]`[t] +  0.000313710754245402t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67237&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67237&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
USDOLLAR[t] = + 0.139527755525393 + 0.0131669901810773Amerikaanse_inflatie[t] + 1.04991533179280`Y[t-1]`[t] -0.239723305758452`Y[t-2]`[t] + 0.288489458849696`Y[t-3]`[t] -0.287960825487985`Y[t-4]`[t] + 0.000313710754245402t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.1395277555253930.0472512.95290.0048620.002431
Amerikaanse_inflatie0.01316699018107730.0124821.05490.2967750.148387
`Y[t-1]`1.049915331792800.1578156.652800
`Y[t-2]`-0.2397233057584520.208727-1.14850.2564540.128227
`Y[t-3]`0.2884894588496960.2083111.38490.1724870.086244
`Y[t-4]`-0.2879608254879850.136952-2.10260.0407670.020384
t0.0003137107542454020.0002821.11420.2707340.135367

\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) & 0.139527755525393 & 0.047251 & 2.9529 & 0.004862 & 0.002431 \tabularnewline
Amerikaanse_inflatie & 0.0131669901810773 & 0.012482 & 1.0549 & 0.296775 & 0.148387 \tabularnewline
`Y[t-1]` & 1.04991533179280 & 0.157815 & 6.6528 & 0 & 0 \tabularnewline
`Y[t-2]` & -0.239723305758452 & 0.208727 & -1.1485 & 0.256454 & 0.128227 \tabularnewline
`Y[t-3]` & 0.288489458849696 & 0.208311 & 1.3849 & 0.172487 & 0.086244 \tabularnewline
`Y[t-4]` & -0.287960825487985 & 0.136952 & -2.1026 & 0.040767 & 0.020384 \tabularnewline
t & 0.000313710754245402 & 0.000282 & 1.1142 & 0.270734 & 0.135367 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67237&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]0.139527755525393[/C][C]0.047251[/C][C]2.9529[/C][C]0.004862[/C][C]0.002431[/C][/ROW]
[ROW][C]Amerikaanse_inflatie[/C][C]0.0131669901810773[/C][C]0.012482[/C][C]1.0549[/C][C]0.296775[/C][C]0.148387[/C][/ROW]
[ROW][C]`Y[t-1]`[/C][C]1.04991533179280[/C][C]0.157815[/C][C]6.6528[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Y[t-2]`[/C][C]-0.239723305758452[/C][C]0.208727[/C][C]-1.1485[/C][C]0.256454[/C][C]0.128227[/C][/ROW]
[ROW][C]`Y[t-3]`[/C][C]0.288489458849696[/C][C]0.208311[/C][C]1.3849[/C][C]0.172487[/C][C]0.086244[/C][/ROW]
[ROW][C]`Y[t-4]`[/C][C]-0.287960825487985[/C][C]0.136952[/C][C]-2.1026[/C][C]0.040767[/C][C]0.020384[/C][/ROW]
[ROW][C]t[/C][C]0.000313710754245402[/C][C]0.000282[/C][C]1.1142[/C][C]0.270734[/C][C]0.135367[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67237&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67237&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)0.1395277555253930.0472512.95290.0048620.002431
Amerikaanse_inflatie0.01316699018107730.0124821.05490.2967750.148387
`Y[t-1]`1.049915331792800.1578156.652800
`Y[t-2]`-0.2397233057584520.208727-1.14850.2564540.128227
`Y[t-3]`0.2884894588496960.2083111.38490.1724870.086244
`Y[t-4]`-0.2879608254879850.136952-2.10260.0407670.020384
t0.0003137107542454020.0002821.11420.2707340.135367






Multiple Linear Regression - Regression Statistics
Multiple R0.930948624007176
R-squared0.866665340540855
Adjusted R-squared0.849998508108461
F-TEST (value)51.9994032493198
F-TEST (DF numerator)6
F-TEST (DF denominator)48
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0309802869114829
Sum Squared Residuals0.0460693525016543

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.930948624007176 \tabularnewline
R-squared & 0.866665340540855 \tabularnewline
Adjusted R-squared & 0.849998508108461 \tabularnewline
F-TEST (value) & 51.9994032493198 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0309802869114829 \tabularnewline
Sum Squared Residuals & 0.0460693525016543 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67237&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.930948624007176[/C][/ROW]
[ROW][C]R-squared[/C][C]0.866665340540855[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.849998508108461[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]51.9994032493198[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0309802869114829[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0460693525016543[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67237&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67237&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 R0.930948624007176
R-squared0.866665340540855
Adjusted R-squared0.849998508108461
F-TEST (value)51.9994032493198
F-TEST (DF numerator)6
F-TEST (DF denominator)48
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0309802869114829
Sum Squared Residuals0.0460693525016543






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.79050.7797256943198760.0107743056801237
20.77190.79010804988966-0.0182080498896601
30.78110.7652616305107270.0158383694892728
40.75570.7788422149755-0.0231422149755000
50.76370.7430061156317530.0206938843682472
60.75950.772612462315535-0.0131124623155349
70.74710.757938569399019-0.0108385693990188
80.76150.765210851589976-0.00371085158997645
90.74870.765274538838477-0.0165745388384771
100.73890.7377182723417420.00118172765825810
110.73370.743171014145352-0.00947101414535184
120.7510.7405011816699560.0104988183300437
130.73820.754434360682236-0.0162343606822362
140.71590.739800511921784-0.0239005119217841
150.75420.7302079300757190.0239920699242812
160.76360.765416624884481-0.00181662488448086
170.74330.76235402176282-0.0190540217628200
180.76580.7604823209736480.00531767902635243
190.76270.780283727607709-0.0175837276077088
200.7480.752364987898034-0.00436498789803381
210.76920.7496531866050660.0195468133949344
220.7850.7768288067883050.00817119321169461
230.79130.7891983583103190.00210164168968107
240.7720.797934624630793-0.0259346246307934
250.7880.7782856591008140.00971434089918577
260.8070.7985564085703090.00844359142969119
270.82680.8048885380028070.0219114619971932
280.82440.83280750089713-0.00840750089713013
290.84870.8240427539144520.0246572460855478
300.85720.8505539088642240.00664609113577566
310.82140.846677269230563-0.0252772692305634
320.88270.8193733686281110.063326631371889
330.92160.8873726784384940.0342273215615064
340.88650.909155166274381-0.0226551662743812
350.88160.882002285590992-0.000402285590992284
360.88840.880327802724530.0080721972754696
370.94660.8651261976816720.0814738023183285
380.9180.936400090811753-0.0184000908117533
390.93370.8931181562755470.0405818437244526
400.95590.9359750179154830.019924982084517
410.96260.9366033832581120.0259966167418883
420.94340.948800736394756-0.00540073639475557
430.86390.91946350094194-0.0555635009419402
440.79960.837241798748545-0.037641798748545
450.6680.770246174828995-0.102246174828995
460.65720.6191413957455740.0380586042544255
470.69280.6556725746769640.0371274253230362
480.64380.690552129762691-0.0467521297626909
490.64540.667206335903917-0.0218063359039167
500.68730.6872558810942754.41189057245569e-05
510.72650.708396470892510.0181035291074904
520.79120.7559478245661910.0352521754338087
530.81140.834887249393154-0.0234872493931535
540.82810.830438308402549-0.00233830840254906
550.83930.856653374700077-0.0173533747000768

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.7905 & 0.779725694319876 & 0.0107743056801237 \tabularnewline
2 & 0.7719 & 0.79010804988966 & -0.0182080498896601 \tabularnewline
3 & 0.7811 & 0.765261630510727 & 0.0158383694892728 \tabularnewline
4 & 0.7557 & 0.7788422149755 & -0.0231422149755000 \tabularnewline
5 & 0.7637 & 0.743006115631753 & 0.0206938843682472 \tabularnewline
6 & 0.7595 & 0.772612462315535 & -0.0131124623155349 \tabularnewline
7 & 0.7471 & 0.757938569399019 & -0.0108385693990188 \tabularnewline
8 & 0.7615 & 0.765210851589976 & -0.00371085158997645 \tabularnewline
9 & 0.7487 & 0.765274538838477 & -0.0165745388384771 \tabularnewline
10 & 0.7389 & 0.737718272341742 & 0.00118172765825810 \tabularnewline
11 & 0.7337 & 0.743171014145352 & -0.00947101414535184 \tabularnewline
12 & 0.751 & 0.740501181669956 & 0.0104988183300437 \tabularnewline
13 & 0.7382 & 0.754434360682236 & -0.0162343606822362 \tabularnewline
14 & 0.7159 & 0.739800511921784 & -0.0239005119217841 \tabularnewline
15 & 0.7542 & 0.730207930075719 & 0.0239920699242812 \tabularnewline
16 & 0.7636 & 0.765416624884481 & -0.00181662488448086 \tabularnewline
17 & 0.7433 & 0.76235402176282 & -0.0190540217628200 \tabularnewline
18 & 0.7658 & 0.760482320973648 & 0.00531767902635243 \tabularnewline
19 & 0.7627 & 0.780283727607709 & -0.0175837276077088 \tabularnewline
20 & 0.748 & 0.752364987898034 & -0.00436498789803381 \tabularnewline
21 & 0.7692 & 0.749653186605066 & 0.0195468133949344 \tabularnewline
22 & 0.785 & 0.776828806788305 & 0.00817119321169461 \tabularnewline
23 & 0.7913 & 0.789198358310319 & 0.00210164168968107 \tabularnewline
24 & 0.772 & 0.797934624630793 & -0.0259346246307934 \tabularnewline
25 & 0.788 & 0.778285659100814 & 0.00971434089918577 \tabularnewline
26 & 0.807 & 0.798556408570309 & 0.00844359142969119 \tabularnewline
27 & 0.8268 & 0.804888538002807 & 0.0219114619971932 \tabularnewline
28 & 0.8244 & 0.83280750089713 & -0.00840750089713013 \tabularnewline
29 & 0.8487 & 0.824042753914452 & 0.0246572460855478 \tabularnewline
30 & 0.8572 & 0.850553908864224 & 0.00664609113577566 \tabularnewline
31 & 0.8214 & 0.846677269230563 & -0.0252772692305634 \tabularnewline
32 & 0.8827 & 0.819373368628111 & 0.063326631371889 \tabularnewline
33 & 0.9216 & 0.887372678438494 & 0.0342273215615064 \tabularnewline
34 & 0.8865 & 0.909155166274381 & -0.0226551662743812 \tabularnewline
35 & 0.8816 & 0.882002285590992 & -0.000402285590992284 \tabularnewline
36 & 0.8884 & 0.88032780272453 & 0.0080721972754696 \tabularnewline
37 & 0.9466 & 0.865126197681672 & 0.0814738023183285 \tabularnewline
38 & 0.918 & 0.936400090811753 & -0.0184000908117533 \tabularnewline
39 & 0.9337 & 0.893118156275547 & 0.0405818437244526 \tabularnewline
40 & 0.9559 & 0.935975017915483 & 0.019924982084517 \tabularnewline
41 & 0.9626 & 0.936603383258112 & 0.0259966167418883 \tabularnewline
42 & 0.9434 & 0.948800736394756 & -0.00540073639475557 \tabularnewline
43 & 0.8639 & 0.91946350094194 & -0.0555635009419402 \tabularnewline
44 & 0.7996 & 0.837241798748545 & -0.037641798748545 \tabularnewline
45 & 0.668 & 0.770246174828995 & -0.102246174828995 \tabularnewline
46 & 0.6572 & 0.619141395745574 & 0.0380586042544255 \tabularnewline
47 & 0.6928 & 0.655672574676964 & 0.0371274253230362 \tabularnewline
48 & 0.6438 & 0.690552129762691 & -0.0467521297626909 \tabularnewline
49 & 0.6454 & 0.667206335903917 & -0.0218063359039167 \tabularnewline
50 & 0.6873 & 0.687255881094275 & 4.41189057245569e-05 \tabularnewline
51 & 0.7265 & 0.70839647089251 & 0.0181035291074904 \tabularnewline
52 & 0.7912 & 0.755947824566191 & 0.0352521754338087 \tabularnewline
53 & 0.8114 & 0.834887249393154 & -0.0234872493931535 \tabularnewline
54 & 0.8281 & 0.830438308402549 & -0.00233830840254906 \tabularnewline
55 & 0.8393 & 0.856653374700077 & -0.0173533747000768 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67237&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]0.7905[/C][C]0.779725694319876[/C][C]0.0107743056801237[/C][/ROW]
[ROW][C]2[/C][C]0.7719[/C][C]0.79010804988966[/C][C]-0.0182080498896601[/C][/ROW]
[ROW][C]3[/C][C]0.7811[/C][C]0.765261630510727[/C][C]0.0158383694892728[/C][/ROW]
[ROW][C]4[/C][C]0.7557[/C][C]0.7788422149755[/C][C]-0.0231422149755000[/C][/ROW]
[ROW][C]5[/C][C]0.7637[/C][C]0.743006115631753[/C][C]0.0206938843682472[/C][/ROW]
[ROW][C]6[/C][C]0.7595[/C][C]0.772612462315535[/C][C]-0.0131124623155349[/C][/ROW]
[ROW][C]7[/C][C]0.7471[/C][C]0.757938569399019[/C][C]-0.0108385693990188[/C][/ROW]
[ROW][C]8[/C][C]0.7615[/C][C]0.765210851589976[/C][C]-0.00371085158997645[/C][/ROW]
[ROW][C]9[/C][C]0.7487[/C][C]0.765274538838477[/C][C]-0.0165745388384771[/C][/ROW]
[ROW][C]10[/C][C]0.7389[/C][C]0.737718272341742[/C][C]0.00118172765825810[/C][/ROW]
[ROW][C]11[/C][C]0.7337[/C][C]0.743171014145352[/C][C]-0.00947101414535184[/C][/ROW]
[ROW][C]12[/C][C]0.751[/C][C]0.740501181669956[/C][C]0.0104988183300437[/C][/ROW]
[ROW][C]13[/C][C]0.7382[/C][C]0.754434360682236[/C][C]-0.0162343606822362[/C][/ROW]
[ROW][C]14[/C][C]0.7159[/C][C]0.739800511921784[/C][C]-0.0239005119217841[/C][/ROW]
[ROW][C]15[/C][C]0.7542[/C][C]0.730207930075719[/C][C]0.0239920699242812[/C][/ROW]
[ROW][C]16[/C][C]0.7636[/C][C]0.765416624884481[/C][C]-0.00181662488448086[/C][/ROW]
[ROW][C]17[/C][C]0.7433[/C][C]0.76235402176282[/C][C]-0.0190540217628200[/C][/ROW]
[ROW][C]18[/C][C]0.7658[/C][C]0.760482320973648[/C][C]0.00531767902635243[/C][/ROW]
[ROW][C]19[/C][C]0.7627[/C][C]0.780283727607709[/C][C]-0.0175837276077088[/C][/ROW]
[ROW][C]20[/C][C]0.748[/C][C]0.752364987898034[/C][C]-0.00436498789803381[/C][/ROW]
[ROW][C]21[/C][C]0.7692[/C][C]0.749653186605066[/C][C]0.0195468133949344[/C][/ROW]
[ROW][C]22[/C][C]0.785[/C][C]0.776828806788305[/C][C]0.00817119321169461[/C][/ROW]
[ROW][C]23[/C][C]0.7913[/C][C]0.789198358310319[/C][C]0.00210164168968107[/C][/ROW]
[ROW][C]24[/C][C]0.772[/C][C]0.797934624630793[/C][C]-0.0259346246307934[/C][/ROW]
[ROW][C]25[/C][C]0.788[/C][C]0.778285659100814[/C][C]0.00971434089918577[/C][/ROW]
[ROW][C]26[/C][C]0.807[/C][C]0.798556408570309[/C][C]0.00844359142969119[/C][/ROW]
[ROW][C]27[/C][C]0.8268[/C][C]0.804888538002807[/C][C]0.0219114619971932[/C][/ROW]
[ROW][C]28[/C][C]0.8244[/C][C]0.83280750089713[/C][C]-0.00840750089713013[/C][/ROW]
[ROW][C]29[/C][C]0.8487[/C][C]0.824042753914452[/C][C]0.0246572460855478[/C][/ROW]
[ROW][C]30[/C][C]0.8572[/C][C]0.850553908864224[/C][C]0.00664609113577566[/C][/ROW]
[ROW][C]31[/C][C]0.8214[/C][C]0.846677269230563[/C][C]-0.0252772692305634[/C][/ROW]
[ROW][C]32[/C][C]0.8827[/C][C]0.819373368628111[/C][C]0.063326631371889[/C][/ROW]
[ROW][C]33[/C][C]0.9216[/C][C]0.887372678438494[/C][C]0.0342273215615064[/C][/ROW]
[ROW][C]34[/C][C]0.8865[/C][C]0.909155166274381[/C][C]-0.0226551662743812[/C][/ROW]
[ROW][C]35[/C][C]0.8816[/C][C]0.882002285590992[/C][C]-0.000402285590992284[/C][/ROW]
[ROW][C]36[/C][C]0.8884[/C][C]0.88032780272453[/C][C]0.0080721972754696[/C][/ROW]
[ROW][C]37[/C][C]0.9466[/C][C]0.865126197681672[/C][C]0.0814738023183285[/C][/ROW]
[ROW][C]38[/C][C]0.918[/C][C]0.936400090811753[/C][C]-0.0184000908117533[/C][/ROW]
[ROW][C]39[/C][C]0.9337[/C][C]0.893118156275547[/C][C]0.0405818437244526[/C][/ROW]
[ROW][C]40[/C][C]0.9559[/C][C]0.935975017915483[/C][C]0.019924982084517[/C][/ROW]
[ROW][C]41[/C][C]0.9626[/C][C]0.936603383258112[/C][C]0.0259966167418883[/C][/ROW]
[ROW][C]42[/C][C]0.9434[/C][C]0.948800736394756[/C][C]-0.00540073639475557[/C][/ROW]
[ROW][C]43[/C][C]0.8639[/C][C]0.91946350094194[/C][C]-0.0555635009419402[/C][/ROW]
[ROW][C]44[/C][C]0.7996[/C][C]0.837241798748545[/C][C]-0.037641798748545[/C][/ROW]
[ROW][C]45[/C][C]0.668[/C][C]0.770246174828995[/C][C]-0.102246174828995[/C][/ROW]
[ROW][C]46[/C][C]0.6572[/C][C]0.619141395745574[/C][C]0.0380586042544255[/C][/ROW]
[ROW][C]47[/C][C]0.6928[/C][C]0.655672574676964[/C][C]0.0371274253230362[/C][/ROW]
[ROW][C]48[/C][C]0.6438[/C][C]0.690552129762691[/C][C]-0.0467521297626909[/C][/ROW]
[ROW][C]49[/C][C]0.6454[/C][C]0.667206335903917[/C][C]-0.0218063359039167[/C][/ROW]
[ROW][C]50[/C][C]0.6873[/C][C]0.687255881094275[/C][C]4.41189057245569e-05[/C][/ROW]
[ROW][C]51[/C][C]0.7265[/C][C]0.70839647089251[/C][C]0.0181035291074904[/C][/ROW]
[ROW][C]52[/C][C]0.7912[/C][C]0.755947824566191[/C][C]0.0352521754338087[/C][/ROW]
[ROW][C]53[/C][C]0.8114[/C][C]0.834887249393154[/C][C]-0.0234872493931535[/C][/ROW]
[ROW][C]54[/C][C]0.8281[/C][C]0.830438308402549[/C][C]-0.00233830840254906[/C][/ROW]
[ROW][C]55[/C][C]0.8393[/C][C]0.856653374700077[/C][C]-0.0173533747000768[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67237&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67237&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
10.79050.7797256943198760.0107743056801237
20.77190.79010804988966-0.0182080498896601
30.78110.7652616305107270.0158383694892728
40.75570.7788422149755-0.0231422149755000
50.76370.7430061156317530.0206938843682472
60.75950.772612462315535-0.0131124623155349
70.74710.757938569399019-0.0108385693990188
80.76150.765210851589976-0.00371085158997645
90.74870.765274538838477-0.0165745388384771
100.73890.7377182723417420.00118172765825810
110.73370.743171014145352-0.00947101414535184
120.7510.7405011816699560.0104988183300437
130.73820.754434360682236-0.0162343606822362
140.71590.739800511921784-0.0239005119217841
150.75420.7302079300757190.0239920699242812
160.76360.765416624884481-0.00181662488448086
170.74330.76235402176282-0.0190540217628200
180.76580.7604823209736480.00531767902635243
190.76270.780283727607709-0.0175837276077088
200.7480.752364987898034-0.00436498789803381
210.76920.7496531866050660.0195468133949344
220.7850.7768288067883050.00817119321169461
230.79130.7891983583103190.00210164168968107
240.7720.797934624630793-0.0259346246307934
250.7880.7782856591008140.00971434089918577
260.8070.7985564085703090.00844359142969119
270.82680.8048885380028070.0219114619971932
280.82440.83280750089713-0.00840750089713013
290.84870.8240427539144520.0246572460855478
300.85720.8505539088642240.00664609113577566
310.82140.846677269230563-0.0252772692305634
320.88270.8193733686281110.063326631371889
330.92160.8873726784384940.0342273215615064
340.88650.909155166274381-0.0226551662743812
350.88160.882002285590992-0.000402285590992284
360.88840.880327802724530.0080721972754696
370.94660.8651261976816720.0814738023183285
380.9180.936400090811753-0.0184000908117533
390.93370.8931181562755470.0405818437244526
400.95590.9359750179154830.019924982084517
410.96260.9366033832581120.0259966167418883
420.94340.948800736394756-0.00540073639475557
430.86390.91946350094194-0.0555635009419402
440.79960.837241798748545-0.037641798748545
450.6680.770246174828995-0.102246174828995
460.65720.6191413957455740.0380586042544255
470.69280.6556725746769640.0371274253230362
480.64380.690552129762691-0.0467521297626909
490.64540.667206335903917-0.0218063359039167
500.68730.6872558810942754.41189057245569e-05
510.72650.708396470892510.0181035291074904
520.79120.7559478245661910.0352521754338087
530.81140.834887249393154-0.0234872493931535
540.82810.830438308402549-0.00233830840254906
550.83930.856653374700077-0.0173533747000768






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.01487207861273710.02974415722547430.985127921387263
110.00343654488799830.00687308977599660.996563455112002
120.002746459677696760.005492919355393520.997253540322303
130.0009297724504556360.001859544900911270.999070227549544
140.0003896660454311580.0007793320908623160.999610333954569
150.0006866724270901720.001373344854180340.99931332757291
160.005639739974937330.01127947994987470.994360260025063
170.002910149235246180.005820298470492360.997089850764754
180.001303108431746080.002606216863492160.998696891568254
190.0005267287494879520.001053457498975900.999473271250512
200.0002068966384500050.0004137932769000110.99979310336155
210.0001036976668949410.0002073953337898820.999896302333105
225.70047851863195e-050.0001140095703726390.999942995214814
232.18351707291133e-054.36703414582265e-050.99997816482927
243.44205933863158e-056.88411867726315e-050.999965579406614
251.23135334506723e-052.46270669013445e-050.99998768646655
264.4286370856544e-068.8572741713088e-060.999995571362914
274.26316253765911e-068.52632507531822e-060.999995736837462
281.88595790230237e-063.77191580460475e-060.999998114042098
297.4244804893096e-071.48489609786192e-060.999999257551951
302.67996315944247e-075.35992631888494e-070.999999732003684
314.92154902185338e-069.84309804370676e-060.999995078450978
324.80042281665100e-069.60084563330199e-060.999995199577183
334.72741963992569e-069.45483927985138e-060.99999527258036
344.49525095946404e-068.99050191892808e-060.99999550474904
356.40247680715861e-061.28049536143172e-050.999993597523193
361.46747030998700e-052.93494061997401e-050.9999853252969
376.32555535813644e-050.0001265111071627290.999936744446419
389.13370277169951e-050.0001826740554339900.999908662972283
394.17358140832399e-058.34716281664798e-050.999958264185917
401.77411773282186e-053.54823546564373e-050.999982258822672
417.57318102174488e-050.0001514636204348980.999924268189783
420.0007961521876027970.001592304375205590.999203847812397
430.01199612034264870.02399224068529740.988003879657351
440.5572706811082810.8854586377834390.442729318891719
450.6036682120193670.7926635759612660.396331787980633

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0148720786127371 & 0.0297441572254743 & 0.985127921387263 \tabularnewline
11 & 0.0034365448879983 & 0.0068730897759966 & 0.996563455112002 \tabularnewline
12 & 0.00274645967769676 & 0.00549291935539352 & 0.997253540322303 \tabularnewline
13 & 0.000929772450455636 & 0.00185954490091127 & 0.999070227549544 \tabularnewline
14 & 0.000389666045431158 & 0.000779332090862316 & 0.999610333954569 \tabularnewline
15 & 0.000686672427090172 & 0.00137334485418034 & 0.99931332757291 \tabularnewline
16 & 0.00563973997493733 & 0.0112794799498747 & 0.994360260025063 \tabularnewline
17 & 0.00291014923524618 & 0.00582029847049236 & 0.997089850764754 \tabularnewline
18 & 0.00130310843174608 & 0.00260621686349216 & 0.998696891568254 \tabularnewline
19 & 0.000526728749487952 & 0.00105345749897590 & 0.999473271250512 \tabularnewline
20 & 0.000206896638450005 & 0.000413793276900011 & 0.99979310336155 \tabularnewline
21 & 0.000103697666894941 & 0.000207395333789882 & 0.999896302333105 \tabularnewline
22 & 5.70047851863195e-05 & 0.000114009570372639 & 0.999942995214814 \tabularnewline
23 & 2.18351707291133e-05 & 4.36703414582265e-05 & 0.99997816482927 \tabularnewline
24 & 3.44205933863158e-05 & 6.88411867726315e-05 & 0.999965579406614 \tabularnewline
25 & 1.23135334506723e-05 & 2.46270669013445e-05 & 0.99998768646655 \tabularnewline
26 & 4.4286370856544e-06 & 8.8572741713088e-06 & 0.999995571362914 \tabularnewline
27 & 4.26316253765911e-06 & 8.52632507531822e-06 & 0.999995736837462 \tabularnewline
28 & 1.88595790230237e-06 & 3.77191580460475e-06 & 0.999998114042098 \tabularnewline
29 & 7.4244804893096e-07 & 1.48489609786192e-06 & 0.999999257551951 \tabularnewline
30 & 2.67996315944247e-07 & 5.35992631888494e-07 & 0.999999732003684 \tabularnewline
31 & 4.92154902185338e-06 & 9.84309804370676e-06 & 0.999995078450978 \tabularnewline
32 & 4.80042281665100e-06 & 9.60084563330199e-06 & 0.999995199577183 \tabularnewline
33 & 4.72741963992569e-06 & 9.45483927985138e-06 & 0.99999527258036 \tabularnewline
34 & 4.49525095946404e-06 & 8.99050191892808e-06 & 0.99999550474904 \tabularnewline
35 & 6.40247680715861e-06 & 1.28049536143172e-05 & 0.999993597523193 \tabularnewline
36 & 1.46747030998700e-05 & 2.93494061997401e-05 & 0.9999853252969 \tabularnewline
37 & 6.32555535813644e-05 & 0.000126511107162729 & 0.999936744446419 \tabularnewline
38 & 9.13370277169951e-05 & 0.000182674055433990 & 0.999908662972283 \tabularnewline
39 & 4.17358140832399e-05 & 8.34716281664798e-05 & 0.999958264185917 \tabularnewline
40 & 1.77411773282186e-05 & 3.54823546564373e-05 & 0.999982258822672 \tabularnewline
41 & 7.57318102174488e-05 & 0.000151463620434898 & 0.999924268189783 \tabularnewline
42 & 0.000796152187602797 & 0.00159230437520559 & 0.999203847812397 \tabularnewline
43 & 0.0119961203426487 & 0.0239922406852974 & 0.988003879657351 \tabularnewline
44 & 0.557270681108281 & 0.885458637783439 & 0.442729318891719 \tabularnewline
45 & 0.603668212019367 & 0.792663575961266 & 0.396331787980633 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67237&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]0.0148720786127371[/C][C]0.0297441572254743[/C][C]0.985127921387263[/C][/ROW]
[ROW][C]11[/C][C]0.0034365448879983[/C][C]0.0068730897759966[/C][C]0.996563455112002[/C][/ROW]
[ROW][C]12[/C][C]0.00274645967769676[/C][C]0.00549291935539352[/C][C]0.997253540322303[/C][/ROW]
[ROW][C]13[/C][C]0.000929772450455636[/C][C]0.00185954490091127[/C][C]0.999070227549544[/C][/ROW]
[ROW][C]14[/C][C]0.000389666045431158[/C][C]0.000779332090862316[/C][C]0.999610333954569[/C][/ROW]
[ROW][C]15[/C][C]0.000686672427090172[/C][C]0.00137334485418034[/C][C]0.99931332757291[/C][/ROW]
[ROW][C]16[/C][C]0.00563973997493733[/C][C]0.0112794799498747[/C][C]0.994360260025063[/C][/ROW]
[ROW][C]17[/C][C]0.00291014923524618[/C][C]0.00582029847049236[/C][C]0.997089850764754[/C][/ROW]
[ROW][C]18[/C][C]0.00130310843174608[/C][C]0.00260621686349216[/C][C]0.998696891568254[/C][/ROW]
[ROW][C]19[/C][C]0.000526728749487952[/C][C]0.00105345749897590[/C][C]0.999473271250512[/C][/ROW]
[ROW][C]20[/C][C]0.000206896638450005[/C][C]0.000413793276900011[/C][C]0.99979310336155[/C][/ROW]
[ROW][C]21[/C][C]0.000103697666894941[/C][C]0.000207395333789882[/C][C]0.999896302333105[/C][/ROW]
[ROW][C]22[/C][C]5.70047851863195e-05[/C][C]0.000114009570372639[/C][C]0.999942995214814[/C][/ROW]
[ROW][C]23[/C][C]2.18351707291133e-05[/C][C]4.36703414582265e-05[/C][C]0.99997816482927[/C][/ROW]
[ROW][C]24[/C][C]3.44205933863158e-05[/C][C]6.88411867726315e-05[/C][C]0.999965579406614[/C][/ROW]
[ROW][C]25[/C][C]1.23135334506723e-05[/C][C]2.46270669013445e-05[/C][C]0.99998768646655[/C][/ROW]
[ROW][C]26[/C][C]4.4286370856544e-06[/C][C]8.8572741713088e-06[/C][C]0.999995571362914[/C][/ROW]
[ROW][C]27[/C][C]4.26316253765911e-06[/C][C]8.52632507531822e-06[/C][C]0.999995736837462[/C][/ROW]
[ROW][C]28[/C][C]1.88595790230237e-06[/C][C]3.77191580460475e-06[/C][C]0.999998114042098[/C][/ROW]
[ROW][C]29[/C][C]7.4244804893096e-07[/C][C]1.48489609786192e-06[/C][C]0.999999257551951[/C][/ROW]
[ROW][C]30[/C][C]2.67996315944247e-07[/C][C]5.35992631888494e-07[/C][C]0.999999732003684[/C][/ROW]
[ROW][C]31[/C][C]4.92154902185338e-06[/C][C]9.84309804370676e-06[/C][C]0.999995078450978[/C][/ROW]
[ROW][C]32[/C][C]4.80042281665100e-06[/C][C]9.60084563330199e-06[/C][C]0.999995199577183[/C][/ROW]
[ROW][C]33[/C][C]4.72741963992569e-06[/C][C]9.45483927985138e-06[/C][C]0.99999527258036[/C][/ROW]
[ROW][C]34[/C][C]4.49525095946404e-06[/C][C]8.99050191892808e-06[/C][C]0.99999550474904[/C][/ROW]
[ROW][C]35[/C][C]6.40247680715861e-06[/C][C]1.28049536143172e-05[/C][C]0.999993597523193[/C][/ROW]
[ROW][C]36[/C][C]1.46747030998700e-05[/C][C]2.93494061997401e-05[/C][C]0.9999853252969[/C][/ROW]
[ROW][C]37[/C][C]6.32555535813644e-05[/C][C]0.000126511107162729[/C][C]0.999936744446419[/C][/ROW]
[ROW][C]38[/C][C]9.13370277169951e-05[/C][C]0.000182674055433990[/C][C]0.999908662972283[/C][/ROW]
[ROW][C]39[/C][C]4.17358140832399e-05[/C][C]8.34716281664798e-05[/C][C]0.999958264185917[/C][/ROW]
[ROW][C]40[/C][C]1.77411773282186e-05[/C][C]3.54823546564373e-05[/C][C]0.999982258822672[/C][/ROW]
[ROW][C]41[/C][C]7.57318102174488e-05[/C][C]0.000151463620434898[/C][C]0.999924268189783[/C][/ROW]
[ROW][C]42[/C][C]0.000796152187602797[/C][C]0.00159230437520559[/C][C]0.999203847812397[/C][/ROW]
[ROW][C]43[/C][C]0.0119961203426487[/C][C]0.0239922406852974[/C][C]0.988003879657351[/C][/ROW]
[ROW][C]44[/C][C]0.557270681108281[/C][C]0.885458637783439[/C][C]0.442729318891719[/C][/ROW]
[ROW][C]45[/C][C]0.603668212019367[/C][C]0.792663575961266[/C][C]0.396331787980633[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67237&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67237&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.01487207861273710.02974415722547430.985127921387263
110.00343654488799830.00687308977599660.996563455112002
120.002746459677696760.005492919355393520.997253540322303
130.0009297724504556360.001859544900911270.999070227549544
140.0003896660454311580.0007793320908623160.999610333954569
150.0006866724270901720.001373344854180340.99931332757291
160.005639739974937330.01127947994987470.994360260025063
170.002910149235246180.005820298470492360.997089850764754
180.001303108431746080.002606216863492160.998696891568254
190.0005267287494879520.001053457498975900.999473271250512
200.0002068966384500050.0004137932769000110.99979310336155
210.0001036976668949410.0002073953337898820.999896302333105
225.70047851863195e-050.0001140095703726390.999942995214814
232.18351707291133e-054.36703414582265e-050.99997816482927
243.44205933863158e-056.88411867726315e-050.999965579406614
251.23135334506723e-052.46270669013445e-050.99998768646655
264.4286370856544e-068.8572741713088e-060.999995571362914
274.26316253765911e-068.52632507531822e-060.999995736837462
281.88595790230237e-063.77191580460475e-060.999998114042098
297.4244804893096e-071.48489609786192e-060.999999257551951
302.67996315944247e-075.35992631888494e-070.999999732003684
314.92154902185338e-069.84309804370676e-060.999995078450978
324.80042281665100e-069.60084563330199e-060.999995199577183
334.72741963992569e-069.45483927985138e-060.99999527258036
344.49525095946404e-068.99050191892808e-060.99999550474904
356.40247680715861e-061.28049536143172e-050.999993597523193
361.46747030998700e-052.93494061997401e-050.9999853252969
376.32555535813644e-050.0001265111071627290.999936744446419
389.13370277169951e-050.0001826740554339900.999908662972283
394.17358140832399e-058.34716281664798e-050.999958264185917
401.77411773282186e-053.54823546564373e-050.999982258822672
417.57318102174488e-050.0001514636204348980.999924268189783
420.0007961521876027970.001592304375205590.999203847812397
430.01199612034264870.02399224068529740.988003879657351
440.5572706811082810.8854586377834390.442729318891719
450.6036682120193670.7926635759612660.396331787980633






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level310.861111111111111NOK
5% type I error level340.944444444444444NOK
10% type I error level340.944444444444444NOK

\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 & 31 & 0.861111111111111 & NOK \tabularnewline
5% type I error level & 34 & 0.944444444444444 & NOK \tabularnewline
10% type I error level & 34 & 0.944444444444444 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67237&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]31[/C][C]0.861111111111111[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]34[/C][C]0.944444444444444[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.944444444444444[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67237&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67237&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 level310.861111111111111NOK
5% type I error level340.944444444444444NOK
10% type I error level340.944444444444444NOK


Figures (Output of Computation)

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

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