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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 computationFri, 26 Nov 2010 13:29:37 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/26/t1290778626c3l9i648hpdgykd.htm/, Retrieved Sat, 04 May 2024 05:35:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=101866, Retrieved Sat, 04 May 2024 05:35:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114

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] [W8] [2010-11-26 13:29:37] [9d72585f2b7b60ae977d4816136e1c95] [Current]
-    D        [Multiple Regression] [Paper invoer VS c...] [2010-12-04 10:49:43] [247f085ab5b7724f755ad01dc754a3e8]
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Dataset

Dataseries X:
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Histogram
Boxplots 
16571771,60	17972385,83	17535166,38	16198106,13	17487530,67	13768040,14
16198892,67	16896235,55	16571771,60	17535166,38	16198106,13	17487530,67
16554237,93	16697955,94	16198892,67	16571771,60	17535166,38	16198106,13
19554176,37	19691579,52	16554237,93	16198892,67	16571771,60	17535166,38
15903762,33	15930700,75	19554176,37	16554237,93	16198892,67	16571771,60
18003781,65	17444615,98	15903762,33	19554176,37	16554237,93	16198892,67
18329610,38	17699369,88	18003781,65	15903762,33	19554176,37	16554237,93
16260733,42	15189796,81	18329610,38	18003781,65	15903762,33	19554176,37
14851949,20	15672722,75	16260733,42	18329610,38	18003781,65	15903762,33
18174068,44	17180794,3	14851949,20	16260733,42	18329610,38	18003781,65
18406552,23	17664893,45	18174068,44	14851949,20	16260733,42	18329610,38
18466459,42	17862884,98	18406552,23	18174068,44	14851949,20	16260733,42
16016524,60	16162288,88	18466459,42	18406552,23	18174068,44	14851949,20
17428458,32	17463628,82	16016524,60	18466459,42	18406552,23	18174068,44
17167191,42	16772112,17	17428458,32	16016524,60	18466459,42	18406552,23
19629987,60	19106861,48	17167191,42	17428458,32	16016524,60	18466459,42
17183629,01	16721314,25	19629987,60	17167191,42	17428458,32	16016524,60
18344657,85	18161267,85	17183629,01	19629987,60	17167191,42	17428458,32
19301440,71	18509941,2	18344657,85	17183629,01	19629987,60	17167191,42
18147463,68	17802737,97	19301440,71	18344657,85	17183629,01	19629987,60
16192909,22	16409869,75	18147463,68	19301440,71	18344657,85	17183629,01
18374420,60	17967742,04	16192909,22	18147463,68	19301440,71	18344657,85
20515191,95	20286602,27	18374420,60	16192909,22	18147463,68	19301440,71
18957217,20	19537280,81	20515191,95	18374420,60	16192909,22	18147463,68
16471529,53	18021889,62	18957217,20	20515191,95	18374420,60	16192909,22
18746813,27	20194317,23	16471529,53	18957217,20	20515191,95	18374420,60
19009453,59	19049596,62	18746813,27	16471529,53	18957217,20	20515191,95
19211178,55	20244720,94	19009453,59	18746813,27	16471529,53	18957217,20
20547653,75	21473302,24	19211178,55	19009453,59	18746813,27	16471529,53
19325754,03	19673603,19	20547653,75	19211178,55	19009453,59	18746813,27
20605542,58	21053177,29	19325754,03	20547653,75	19211178,55	19009453,59
20056915,06	20159479,84	20605542,58	19325754,03	20547653,75	19211178,55
16141449,72	18203628,31	20056915,06	20605542,58	19325754,03	20547653,75
20359793,22	21289464,94	16141449,72	20056915,06	20605542,58	19325754,03
19711553,27	20432335,71	20359793,22	16141449,72	20056915,06	20605542,58
15638580,70	17180395,07	19711553,27	20359793,22	16141449,72	20056915,06
14384486,00	15816786,32	15638580,70	19711553,27	20359793,22	16141449,72
13855616,12	15071819,75	14384486,00	15638580,70	19711553,27	20359793,22
14308336,46	14521120,61	13855616,12	14384486,00	15638580,70	19711553,27
15290621,44	15668789,39	14308336,46	13855616,12	14384486,00	15638580,70
14423755,53	14346884,11	15290621,44	14308336,46	13855616,12	14384486,00
13779681,49	13881008,13	14423755,53	15290621,44	14308336,46	13855616,12
15686348,94	15465943,69	13779681,49	14423755,53	15290621,44	14308336,46
14733828,17	14238232,92	15686348,94	13779681,49	14423755,53	15290621,44
12522497,94	13557713,21	14733828,17	15686348,94	13779681,49	14423755,53
16189383,57	16127590,29	12522497,94	14733828,17	15686348,94	13779681,49
16059123,25	16793894,2	16189383,57	12522497,94	14733828,17	15686348,94
16007123,26	16014007,43	16059123,25	16189383,57	12522497,94	14733828,17
15806842,33	16867867,15	16007123,26	16059123,25	16189383,57	12522497,94
15159951,13	16014583,21	15806842,33	16007123,26	16059123,25	16189383,57
15692144,17	15878594,85	15159951,13	15806842,33	16007123,26	16059123,25
18908869,11	18664899,14	15692144,17	15159951,13	15806842,33	16007123,26
16969881,42	17962530,06	18908869,11	15692144,17	15159951,13	15806842,33
16997477,78	17332692,2	16969881,42	18908869,11	15692144,17	15159951,13
19858875,65	19542066,35	16997477,78	16969881,42	18908869,11	15692144,17
17681170,13	17203555,19	19858875,65	16997477,78	16969881,42	18908869,11

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101866&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101866&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101866&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 time7 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 3151494.74893363 + 0.86246309155318X[t] + 0.057069425232684Y1[t] -0.00910064194217095Y2[t] + 0.0398947625434009Y3[t] -0.119766534283845Y4[t] -1281242.59489156M1[t] -490303.731102045M2[t] + 268741.195930662M3[t] + 399800.043282565M4[t] -201692.974633398M5[t] + 235204.245288726M6[t] + 645793.772386501M7[t] + 822791.718607045M8[t] -988516.699854688M9[t] + 617633.42020277M10[t] + 439029.687609985M11[t] -17613.1437160194t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  3151494.74893363 +  0.86246309155318X[t] +  0.057069425232684Y1[t] -0.00910064194217095Y2[t] +  0.0398947625434009Y3[t] -0.119766534283845Y4[t] -1281242.59489156M1[t] -490303.731102045M2[t] +  268741.195930662M3[t] +  399800.043282565M4[t] -201692.974633398M5[t] +  235204.245288726M6[t] +  645793.772386501M7[t] +  822791.718607045M8[t] -988516.699854688M9[t] +  617633.42020277M10[t] +  439029.687609985M11[t] -17613.1437160194t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101866&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  3151494.74893363 +  0.86246309155318X[t] +  0.057069425232684Y1[t] -0.00910064194217095Y2[t] +  0.0398947625434009Y3[t] -0.119766534283845Y4[t] -1281242.59489156M1[t] -490303.731102045M2[t] +  268741.195930662M3[t] +  399800.043282565M4[t] -201692.974633398M5[t] +  235204.245288726M6[t] +  645793.772386501M7[t] +  822791.718607045M8[t] -988516.699854688M9[t] +  617633.42020277M10[t] +  439029.687609985M11[t] -17613.1437160194t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101866&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101866&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 3151494.74893363 + 0.86246309155318X[t] + 0.057069425232684Y1[t] -0.00910064194217095Y2[t] + 0.0398947625434009Y3[t] -0.119766534283845Y4[t] -1281242.59489156M1[t] -490303.731102045M2[t] + 268741.195930662M3[t] + 399800.043282565M4[t] -201692.974633398M5[t] + 235204.245288726M6[t] + 645793.772386501M7[t] + 822791.718607045M8[t] -988516.699854688M9[t] + 617633.42020277M10[t] + 439029.687609985M11[t] -17613.1437160194t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3151494.74893363935594.4911073.36840.0017440.000872
X0.862463091553180.07858510.974900
Y10.0570694252326840.0802470.71120.4813210.240661
Y2-0.009100641942170950.079353-0.11470.9092980.454649
Y30.03989476254340090.0782270.510.6130080.306504
Y4-0.1197665342838450.074535-1.60690.1163650.058182
M1-1281242.59489156509065.081879-2.51690.016180.00809
M2-490303.731102045480760.953342-1.01980.3142490.157125
M3268741.195930662466154.1490580.57650.5676720.283836
M4399800.043282565440200.2415130.90820.3694850.184742
M5-201692.974633398386083.800141-0.52240.6044190.30221
M6235204.245288726387701.9512210.60670.5476830.273841
M7645793.772386501465962.1424961.38590.1738460.086923
M8822791.718607045379989.3407582.16530.0367090.018355
M9-988516.699854688443508.823958-2.22890.0318150.015907
M10617633.42020277548277.7121821.12650.2670190.133509
M11439029.687609985518014.2625620.84750.402010.201005
t-17613.14371601944517.678382-3.89870.0003810.00019

\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) & 3151494.74893363 & 935594.491107 & 3.3684 & 0.001744 & 0.000872 \tabularnewline
X & 0.86246309155318 & 0.078585 & 10.9749 & 0 & 0 \tabularnewline
Y1 & 0.057069425232684 & 0.080247 & 0.7112 & 0.481321 & 0.240661 \tabularnewline
Y2 & -0.00910064194217095 & 0.079353 & -0.1147 & 0.909298 & 0.454649 \tabularnewline
Y3 & 0.0398947625434009 & 0.078227 & 0.51 & 0.613008 & 0.306504 \tabularnewline
Y4 & -0.119766534283845 & 0.074535 & -1.6069 & 0.116365 & 0.058182 \tabularnewline
M1 & -1281242.59489156 & 509065.081879 & -2.5169 & 0.01618 & 0.00809 \tabularnewline
M2 & -490303.731102045 & 480760.953342 & -1.0198 & 0.314249 & 0.157125 \tabularnewline
M3 & 268741.195930662 & 466154.149058 & 0.5765 & 0.567672 & 0.283836 \tabularnewline
M4 & 399800.043282565 & 440200.241513 & 0.9082 & 0.369485 & 0.184742 \tabularnewline
M5 & -201692.974633398 & 386083.800141 & -0.5224 & 0.604419 & 0.30221 \tabularnewline
M6 & 235204.245288726 & 387701.951221 & 0.6067 & 0.547683 & 0.273841 \tabularnewline
M7 & 645793.772386501 & 465962.142496 & 1.3859 & 0.173846 & 0.086923 \tabularnewline
M8 & 822791.718607045 & 379989.340758 & 2.1653 & 0.036709 & 0.018355 \tabularnewline
M9 & -988516.699854688 & 443508.823958 & -2.2289 & 0.031815 & 0.015907 \tabularnewline
M10 & 617633.42020277 & 548277.712182 & 1.1265 & 0.267019 & 0.133509 \tabularnewline
M11 & 439029.687609985 & 518014.262562 & 0.8475 & 0.40201 & 0.201005 \tabularnewline
t & -17613.1437160194 & 4517.678382 & -3.8987 & 0.000381 & 0.00019 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101866&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]3151494.74893363[/C][C]935594.491107[/C][C]3.3684[/C][C]0.001744[/C][C]0.000872[/C][/ROW]
[ROW][C]X[/C][C]0.86246309155318[/C][C]0.078585[/C][C]10.9749[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y1[/C][C]0.057069425232684[/C][C]0.080247[/C][C]0.7112[/C][C]0.481321[/C][C]0.240661[/C][/ROW]
[ROW][C]Y2[/C][C]-0.00910064194217095[/C][C]0.079353[/C][C]-0.1147[/C][C]0.909298[/C][C]0.454649[/C][/ROW]
[ROW][C]Y3[/C][C]0.0398947625434009[/C][C]0.078227[/C][C]0.51[/C][C]0.613008[/C][C]0.306504[/C][/ROW]
[ROW][C]Y4[/C][C]-0.119766534283845[/C][C]0.074535[/C][C]-1.6069[/C][C]0.116365[/C][C]0.058182[/C][/ROW]
[ROW][C]M1[/C][C]-1281242.59489156[/C][C]509065.081879[/C][C]-2.5169[/C][C]0.01618[/C][C]0.00809[/C][/ROW]
[ROW][C]M2[/C][C]-490303.731102045[/C][C]480760.953342[/C][C]-1.0198[/C][C]0.314249[/C][C]0.157125[/C][/ROW]
[ROW][C]M3[/C][C]268741.195930662[/C][C]466154.149058[/C][C]0.5765[/C][C]0.567672[/C][C]0.283836[/C][/ROW]
[ROW][C]M4[/C][C]399800.043282565[/C][C]440200.241513[/C][C]0.9082[/C][C]0.369485[/C][C]0.184742[/C][/ROW]
[ROW][C]M5[/C][C]-201692.974633398[/C][C]386083.800141[/C][C]-0.5224[/C][C]0.604419[/C][C]0.30221[/C][/ROW]
[ROW][C]M6[/C][C]235204.245288726[/C][C]387701.951221[/C][C]0.6067[/C][C]0.547683[/C][C]0.273841[/C][/ROW]
[ROW][C]M7[/C][C]645793.772386501[/C][C]465962.142496[/C][C]1.3859[/C][C]0.173846[/C][C]0.086923[/C][/ROW]
[ROW][C]M8[/C][C]822791.718607045[/C][C]379989.340758[/C][C]2.1653[/C][C]0.036709[/C][C]0.018355[/C][/ROW]
[ROW][C]M9[/C][C]-988516.699854688[/C][C]443508.823958[/C][C]-2.2289[/C][C]0.031815[/C][C]0.015907[/C][/ROW]
[ROW][C]M10[/C][C]617633.42020277[/C][C]548277.712182[/C][C]1.1265[/C][C]0.267019[/C][C]0.133509[/C][/ROW]
[ROW][C]M11[/C][C]439029.687609985[/C][C]518014.262562[/C][C]0.8475[/C][C]0.40201[/C][C]0.201005[/C][/ROW]
[ROW][C]t[/C][C]-17613.1437160194[/C][C]4517.678382[/C][C]-3.8987[/C][C]0.000381[/C][C]0.00019[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101866&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101866&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)3151494.74893363935594.4911073.36840.0017440.000872
X0.862463091553180.07858510.974900
Y10.0570694252326840.0802470.71120.4813210.240661
Y2-0.009100641942170950.079353-0.11470.9092980.454649
Y30.03989476254340090.0782270.510.6130080.306504
Y4-0.1197665342838450.074535-1.60690.1163650.058182
M1-1281242.59489156509065.081879-2.51690.016180.00809
M2-490303.731102045480760.953342-1.01980.3142490.157125
M3268741.195930662466154.1490580.57650.5676720.283836
M4399800.043282565440200.2415130.90820.3694850.184742
M5-201692.974633398386083.800141-0.52240.6044190.30221
M6235204.245288726387701.9512210.60670.5476830.273841
M7645793.772386501465962.1424961.38590.1738460.086923
M8822791.718607045379989.3407582.16530.0367090.018355
M9-988516.699854688443508.823958-2.22890.0318150.015907
M10617633.42020277548277.7121821.12650.2670190.133509
M11439029.687609985518014.2625620.84750.402010.201005
t-17613.14371601944517.678382-3.89870.0003810.00019






Multiple Linear Regression - Regression Statistics
Multiple R0.97944652981867
R-squared0.959315504773834
Adjusted R-squared0.941114546383181
F-TEST (value)52.7068676376117
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation485956.855387267
Sum Squared Residuals8973854481319.48

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.97944652981867 \tabularnewline
R-squared & 0.959315504773834 \tabularnewline
Adjusted R-squared & 0.941114546383181 \tabularnewline
F-TEST (value) & 52.7068676376117 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 38 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 485956.855387267 \tabularnewline
Sum Squared Residuals & 8973854481319.48 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101866&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.97944652981867[/C][/ROW]
[ROW][C]R-squared[/C][C]0.959315504773834[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.941114546383181[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]52.7068676376117[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]38[/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]485956.855387267[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8973854481319.48[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101866&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101866&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 R0.97944652981867
R-squared0.959315504773834
Adjusted R-squared0.941114546383181
F-TEST (value)52.7068676376117
F-TEST (DF numerator)17
F-TEST (DF denominator)38
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation485956.855387267
Sum Squared Residuals8973854481319.48






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116571771.617255177.5905915-683405.990591523
216198892.6716536303.1443226-337410.474322625
316554237.9317301985.2164883-747747.28648832
419554176.3719822424.0769997-268247.706999724
515903762.3316228176.2145419-324413.884541911
618003781.6517776362.7355703227418.914429663
718329610.3818619246.3232642-289635.943264194
816260733.4216108775.6804458151957.739554234
914851949.215096302.2582609-244353.058260939
1018174068.4417685411.7125495488656.727450484
1118406552.2317987564.0292963418988.200703702
1218466459.4217876294.9950413590164.424958676
1316016524.615413301.3655188603223.234481239
1417428458.3216780019.3652301648438.954769853
1517167191.4217002463.9914871164727.428512923
1619629987.618996870.5035644633117.096435603
1717183629.0116812994.9249657370634.08503428
1818344657.8518132634.9406470212022.909352959
1919301440.7119044395.6015091257045.108490907
2018147463.6818245323.2018766-97859.5218765806
2116192909.2215479850.9243100713058.295689977
2218374420.618210070.1089061164350.491093864
2320515191.9519995441.6895382519750.260461832
2418957217.219055087.5352568-97870.3352567868
2516471529.5316661988.8011975-190459.271197479
2618746813.2719005408.4648715-258595.194871476
2719009453.5918593483.6626872415969.927312781
2819211178.5519819389.4885690-608210.93856897
2920547653.7520657485.5872439-109831.837243946
3019325754.0319337006.8269402-11252.7969402468
3120605542.5820814511.3051512-208968.725151206
3220056915.0620316430.3722358-259515.312235764
3316141449.7216548889.8962452-407440.176245162
3420359793.2220777786.1336861-417992.91368611
3519711553.2719943535.4150014-231982.145001435
3615638580.716516330.1473086-877749.447308581
371438448614452100.9127655-67614.9127655282
3813855616.1214017338.9027664-161722.782766369
3914308336.4614180190.9487047128145.511295329
4015290621.4415751882.1601039-461260.720103876
4114423755.5314173719.3150554250036.214944576
4213779681.4914214193.6732223-434512.183222282
4315686348.9415930217.8604883-243868.920488343
4414733828.1714993193.0620991-259364.892099093
4512522497.9412583763.0011839-61265.0611838757
4616189383.5716424397.8748582-235014.304858239
4716059123.2516765879.5661641-706756.316164099
4816007123.2615621667.9023933385455.357606691
4915806842.3315468585.3899267338256.940073290
5015159951.1315050661.6328094109289.497190618
5115692144.1715653239.750632738904.4193672878
5218908869.1118204266.8407630704602.269236968
5316969881.4217156305.998193-186424.578192998
5416997477.7816991154.62362016323.15637990719
5519858875.6519373447.1695872485428.480412837
5617681170.1317216388.1433428464781.986657203

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 16571771.6 & 17255177.5905915 & -683405.990591523 \tabularnewline
2 & 16198892.67 & 16536303.1443226 & -337410.474322625 \tabularnewline
3 & 16554237.93 & 17301985.2164883 & -747747.28648832 \tabularnewline
4 & 19554176.37 & 19822424.0769997 & -268247.706999724 \tabularnewline
5 & 15903762.33 & 16228176.2145419 & -324413.884541911 \tabularnewline
6 & 18003781.65 & 17776362.7355703 & 227418.914429663 \tabularnewline
7 & 18329610.38 & 18619246.3232642 & -289635.943264194 \tabularnewline
8 & 16260733.42 & 16108775.6804458 & 151957.739554234 \tabularnewline
9 & 14851949.2 & 15096302.2582609 & -244353.058260939 \tabularnewline
10 & 18174068.44 & 17685411.7125495 & 488656.727450484 \tabularnewline
11 & 18406552.23 & 17987564.0292963 & 418988.200703702 \tabularnewline
12 & 18466459.42 & 17876294.9950413 & 590164.424958676 \tabularnewline
13 & 16016524.6 & 15413301.3655188 & 603223.234481239 \tabularnewline
14 & 17428458.32 & 16780019.3652301 & 648438.954769853 \tabularnewline
15 & 17167191.42 & 17002463.9914871 & 164727.428512923 \tabularnewline
16 & 19629987.6 & 18996870.5035644 & 633117.096435603 \tabularnewline
17 & 17183629.01 & 16812994.9249657 & 370634.08503428 \tabularnewline
18 & 18344657.85 & 18132634.9406470 & 212022.909352959 \tabularnewline
19 & 19301440.71 & 19044395.6015091 & 257045.108490907 \tabularnewline
20 & 18147463.68 & 18245323.2018766 & -97859.5218765806 \tabularnewline
21 & 16192909.22 & 15479850.9243100 & 713058.295689977 \tabularnewline
22 & 18374420.6 & 18210070.1089061 & 164350.491093864 \tabularnewline
23 & 20515191.95 & 19995441.6895382 & 519750.260461832 \tabularnewline
24 & 18957217.2 & 19055087.5352568 & -97870.3352567868 \tabularnewline
25 & 16471529.53 & 16661988.8011975 & -190459.271197479 \tabularnewline
26 & 18746813.27 & 19005408.4648715 & -258595.194871476 \tabularnewline
27 & 19009453.59 & 18593483.6626872 & 415969.927312781 \tabularnewline
28 & 19211178.55 & 19819389.4885690 & -608210.93856897 \tabularnewline
29 & 20547653.75 & 20657485.5872439 & -109831.837243946 \tabularnewline
30 & 19325754.03 & 19337006.8269402 & -11252.7969402468 \tabularnewline
31 & 20605542.58 & 20814511.3051512 & -208968.725151206 \tabularnewline
32 & 20056915.06 & 20316430.3722358 & -259515.312235764 \tabularnewline
33 & 16141449.72 & 16548889.8962452 & -407440.176245162 \tabularnewline
34 & 20359793.22 & 20777786.1336861 & -417992.91368611 \tabularnewline
35 & 19711553.27 & 19943535.4150014 & -231982.145001435 \tabularnewline
36 & 15638580.7 & 16516330.1473086 & -877749.447308581 \tabularnewline
37 & 14384486 & 14452100.9127655 & -67614.9127655282 \tabularnewline
38 & 13855616.12 & 14017338.9027664 & -161722.782766369 \tabularnewline
39 & 14308336.46 & 14180190.9487047 & 128145.511295329 \tabularnewline
40 & 15290621.44 & 15751882.1601039 & -461260.720103876 \tabularnewline
41 & 14423755.53 & 14173719.3150554 & 250036.214944576 \tabularnewline
42 & 13779681.49 & 14214193.6732223 & -434512.183222282 \tabularnewline
43 & 15686348.94 & 15930217.8604883 & -243868.920488343 \tabularnewline
44 & 14733828.17 & 14993193.0620991 & -259364.892099093 \tabularnewline
45 & 12522497.94 & 12583763.0011839 & -61265.0611838757 \tabularnewline
46 & 16189383.57 & 16424397.8748582 & -235014.304858239 \tabularnewline
47 & 16059123.25 & 16765879.5661641 & -706756.316164099 \tabularnewline
48 & 16007123.26 & 15621667.9023933 & 385455.357606691 \tabularnewline
49 & 15806842.33 & 15468585.3899267 & 338256.940073290 \tabularnewline
50 & 15159951.13 & 15050661.6328094 & 109289.497190618 \tabularnewline
51 & 15692144.17 & 15653239.7506327 & 38904.4193672878 \tabularnewline
52 & 18908869.11 & 18204266.8407630 & 704602.269236968 \tabularnewline
53 & 16969881.42 & 17156305.998193 & -186424.578192998 \tabularnewline
54 & 16997477.78 & 16991154.6236201 & 6323.15637990719 \tabularnewline
55 & 19858875.65 & 19373447.1695872 & 485428.480412837 \tabularnewline
56 & 17681170.13 & 17216388.1433428 & 464781.986657203 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101866&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]16571771.6[/C][C]17255177.5905915[/C][C]-683405.990591523[/C][/ROW]
[ROW][C]2[/C][C]16198892.67[/C][C]16536303.1443226[/C][C]-337410.474322625[/C][/ROW]
[ROW][C]3[/C][C]16554237.93[/C][C]17301985.2164883[/C][C]-747747.28648832[/C][/ROW]
[ROW][C]4[/C][C]19554176.37[/C][C]19822424.0769997[/C][C]-268247.706999724[/C][/ROW]
[ROW][C]5[/C][C]15903762.33[/C][C]16228176.2145419[/C][C]-324413.884541911[/C][/ROW]
[ROW][C]6[/C][C]18003781.65[/C][C]17776362.7355703[/C][C]227418.914429663[/C][/ROW]
[ROW][C]7[/C][C]18329610.38[/C][C]18619246.3232642[/C][C]-289635.943264194[/C][/ROW]
[ROW][C]8[/C][C]16260733.42[/C][C]16108775.6804458[/C][C]151957.739554234[/C][/ROW]
[ROW][C]9[/C][C]14851949.2[/C][C]15096302.2582609[/C][C]-244353.058260939[/C][/ROW]
[ROW][C]10[/C][C]18174068.44[/C][C]17685411.7125495[/C][C]488656.727450484[/C][/ROW]
[ROW][C]11[/C][C]18406552.23[/C][C]17987564.0292963[/C][C]418988.200703702[/C][/ROW]
[ROW][C]12[/C][C]18466459.42[/C][C]17876294.9950413[/C][C]590164.424958676[/C][/ROW]
[ROW][C]13[/C][C]16016524.6[/C][C]15413301.3655188[/C][C]603223.234481239[/C][/ROW]
[ROW][C]14[/C][C]17428458.32[/C][C]16780019.3652301[/C][C]648438.954769853[/C][/ROW]
[ROW][C]15[/C][C]17167191.42[/C][C]17002463.9914871[/C][C]164727.428512923[/C][/ROW]
[ROW][C]16[/C][C]19629987.6[/C][C]18996870.5035644[/C][C]633117.096435603[/C][/ROW]
[ROW][C]17[/C][C]17183629.01[/C][C]16812994.9249657[/C][C]370634.08503428[/C][/ROW]
[ROW][C]18[/C][C]18344657.85[/C][C]18132634.9406470[/C][C]212022.909352959[/C][/ROW]
[ROW][C]19[/C][C]19301440.71[/C][C]19044395.6015091[/C][C]257045.108490907[/C][/ROW]
[ROW][C]20[/C][C]18147463.68[/C][C]18245323.2018766[/C][C]-97859.5218765806[/C][/ROW]
[ROW][C]21[/C][C]16192909.22[/C][C]15479850.9243100[/C][C]713058.295689977[/C][/ROW]
[ROW][C]22[/C][C]18374420.6[/C][C]18210070.1089061[/C][C]164350.491093864[/C][/ROW]
[ROW][C]23[/C][C]20515191.95[/C][C]19995441.6895382[/C][C]519750.260461832[/C][/ROW]
[ROW][C]24[/C][C]18957217.2[/C][C]19055087.5352568[/C][C]-97870.3352567868[/C][/ROW]
[ROW][C]25[/C][C]16471529.53[/C][C]16661988.8011975[/C][C]-190459.271197479[/C][/ROW]
[ROW][C]26[/C][C]18746813.27[/C][C]19005408.4648715[/C][C]-258595.194871476[/C][/ROW]
[ROW][C]27[/C][C]19009453.59[/C][C]18593483.6626872[/C][C]415969.927312781[/C][/ROW]
[ROW][C]28[/C][C]19211178.55[/C][C]19819389.4885690[/C][C]-608210.93856897[/C][/ROW]
[ROW][C]29[/C][C]20547653.75[/C][C]20657485.5872439[/C][C]-109831.837243946[/C][/ROW]
[ROW][C]30[/C][C]19325754.03[/C][C]19337006.8269402[/C][C]-11252.7969402468[/C][/ROW]
[ROW][C]31[/C][C]20605542.58[/C][C]20814511.3051512[/C][C]-208968.725151206[/C][/ROW]
[ROW][C]32[/C][C]20056915.06[/C][C]20316430.3722358[/C][C]-259515.312235764[/C][/ROW]
[ROW][C]33[/C][C]16141449.72[/C][C]16548889.8962452[/C][C]-407440.176245162[/C][/ROW]
[ROW][C]34[/C][C]20359793.22[/C][C]20777786.1336861[/C][C]-417992.91368611[/C][/ROW]
[ROW][C]35[/C][C]19711553.27[/C][C]19943535.4150014[/C][C]-231982.145001435[/C][/ROW]
[ROW][C]36[/C][C]15638580.7[/C][C]16516330.1473086[/C][C]-877749.447308581[/C][/ROW]
[ROW][C]37[/C][C]14384486[/C][C]14452100.9127655[/C][C]-67614.9127655282[/C][/ROW]
[ROW][C]38[/C][C]13855616.12[/C][C]14017338.9027664[/C][C]-161722.782766369[/C][/ROW]
[ROW][C]39[/C][C]14308336.46[/C][C]14180190.9487047[/C][C]128145.511295329[/C][/ROW]
[ROW][C]40[/C][C]15290621.44[/C][C]15751882.1601039[/C][C]-461260.720103876[/C][/ROW]
[ROW][C]41[/C][C]14423755.53[/C][C]14173719.3150554[/C][C]250036.214944576[/C][/ROW]
[ROW][C]42[/C][C]13779681.49[/C][C]14214193.6732223[/C][C]-434512.183222282[/C][/ROW]
[ROW][C]43[/C][C]15686348.94[/C][C]15930217.8604883[/C][C]-243868.920488343[/C][/ROW]
[ROW][C]44[/C][C]14733828.17[/C][C]14993193.0620991[/C][C]-259364.892099093[/C][/ROW]
[ROW][C]45[/C][C]12522497.94[/C][C]12583763.0011839[/C][C]-61265.0611838757[/C][/ROW]
[ROW][C]46[/C][C]16189383.57[/C][C]16424397.8748582[/C][C]-235014.304858239[/C][/ROW]
[ROW][C]47[/C][C]16059123.25[/C][C]16765879.5661641[/C][C]-706756.316164099[/C][/ROW]
[ROW][C]48[/C][C]16007123.26[/C][C]15621667.9023933[/C][C]385455.357606691[/C][/ROW]
[ROW][C]49[/C][C]15806842.33[/C][C]15468585.3899267[/C][C]338256.940073290[/C][/ROW]
[ROW][C]50[/C][C]15159951.13[/C][C]15050661.6328094[/C][C]109289.497190618[/C][/ROW]
[ROW][C]51[/C][C]15692144.17[/C][C]15653239.7506327[/C][C]38904.4193672878[/C][/ROW]
[ROW][C]52[/C][C]18908869.11[/C][C]18204266.8407630[/C][C]704602.269236968[/C][/ROW]
[ROW][C]53[/C][C]16969881.42[/C][C]17156305.998193[/C][C]-186424.578192998[/C][/ROW]
[ROW][C]54[/C][C]16997477.78[/C][C]16991154.6236201[/C][C]6323.15637990719[/C][/ROW]
[ROW][C]55[/C][C]19858875.65[/C][C]19373447.1695872[/C][C]485428.480412837[/C][/ROW]
[ROW][C]56[/C][C]17681170.13[/C][C]17216388.1433428[/C][C]464781.986657203[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101866&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101866&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
116571771.617255177.5905915-683405.990591523
216198892.6716536303.1443226-337410.474322625
316554237.9317301985.2164883-747747.28648832
419554176.3719822424.0769997-268247.706999724
515903762.3316228176.2145419-324413.884541911
618003781.6517776362.7355703227418.914429663
718329610.3818619246.3232642-289635.943264194
816260733.4216108775.6804458151957.739554234
914851949.215096302.2582609-244353.058260939
1018174068.4417685411.7125495488656.727450484
1118406552.2317987564.0292963418988.200703702
1218466459.4217876294.9950413590164.424958676
1316016524.615413301.3655188603223.234481239
1417428458.3216780019.3652301648438.954769853
1517167191.4217002463.9914871164727.428512923
1619629987.618996870.5035644633117.096435603
1717183629.0116812994.9249657370634.08503428
1818344657.8518132634.9406470212022.909352959
1919301440.7119044395.6015091257045.108490907
2018147463.6818245323.2018766-97859.5218765806
2116192909.2215479850.9243100713058.295689977
2218374420.618210070.1089061164350.491093864
2320515191.9519995441.6895382519750.260461832
2418957217.219055087.5352568-97870.3352567868
2516471529.5316661988.8011975-190459.271197479
2618746813.2719005408.4648715-258595.194871476
2719009453.5918593483.6626872415969.927312781
2819211178.5519819389.4885690-608210.93856897
2920547653.7520657485.5872439-109831.837243946
3019325754.0319337006.8269402-11252.7969402468
3120605542.5820814511.3051512-208968.725151206
3220056915.0620316430.3722358-259515.312235764
3316141449.7216548889.8962452-407440.176245162
3420359793.2220777786.1336861-417992.91368611
3519711553.2719943535.4150014-231982.145001435
3615638580.716516330.1473086-877749.447308581
371438448614452100.9127655-67614.9127655282
3813855616.1214017338.9027664-161722.782766369
3914308336.4614180190.9487047128145.511295329
4015290621.4415751882.1601039-461260.720103876
4114423755.5314173719.3150554250036.214944576
4213779681.4914214193.6732223-434512.183222282
4315686348.9415930217.8604883-243868.920488343
4414733828.1714993193.0620991-259364.892099093
4512522497.9412583763.0011839-61265.0611838757
4616189383.5716424397.8748582-235014.304858239
4716059123.2516765879.5661641-706756.316164099
4816007123.2615621667.9023933385455.357606691
4915806842.3315468585.3899267338256.940073290
5015159951.1315050661.6328094109289.497190618
5115692144.1715653239.750632738904.4193672878
5218908869.1118204266.8407630704602.269236968
5316969881.4217156305.998193-186424.578192998
5416997477.7816991154.62362016323.15637990719
5519858875.6519373447.1695872485428.480412837
5617681170.1317216388.1433428464781.986657203






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.3305386605915700.6610773211831390.66946133940843
220.4738820731602610.9477641463205230.526117926839738
230.5557128859354670.8885742281290660.444287114064533
240.5420428249840140.9159143500319730.457957175015986
250.586317100353990.8273657992920190.413682899646010
260.5566790981959490.8866418036081030.443320901804051
270.5433759626086880.9132480747826250.456624037391312
280.6041700635215130.7916598729569730.395829936478487
290.5414988656358150.9170022687283710.458501134364185
300.5206830065775820.9586339868448350.479316993422418
310.4385472029132050.877094405826410.561452797086795
320.358531974665080.717063949330160.64146802533492
330.2599881096898950.5199762193797890.740011890310105
340.2059480075287030.4118960150574050.794051992471297
350.4339922250502890.8679844501005770.566007774949711

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.330538660591570 & 0.661077321183139 & 0.66946133940843 \tabularnewline
22 & 0.473882073160261 & 0.947764146320523 & 0.526117926839738 \tabularnewline
23 & 0.555712885935467 & 0.888574228129066 & 0.444287114064533 \tabularnewline
24 & 0.542042824984014 & 0.915914350031973 & 0.457957175015986 \tabularnewline
25 & 0.58631710035399 & 0.827365799292019 & 0.413682899646010 \tabularnewline
26 & 0.556679098195949 & 0.886641803608103 & 0.443320901804051 \tabularnewline
27 & 0.543375962608688 & 0.913248074782625 & 0.456624037391312 \tabularnewline
28 & 0.604170063521513 & 0.791659872956973 & 0.395829936478487 \tabularnewline
29 & 0.541498865635815 & 0.917002268728371 & 0.458501134364185 \tabularnewline
30 & 0.520683006577582 & 0.958633986844835 & 0.479316993422418 \tabularnewline
31 & 0.438547202913205 & 0.87709440582641 & 0.561452797086795 \tabularnewline
32 & 0.35853197466508 & 0.71706394933016 & 0.64146802533492 \tabularnewline
33 & 0.259988109689895 & 0.519976219379789 & 0.740011890310105 \tabularnewline
34 & 0.205948007528703 & 0.411896015057405 & 0.794051992471297 \tabularnewline
35 & 0.433992225050289 & 0.867984450100577 & 0.566007774949711 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101866&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]21[/C][C]0.330538660591570[/C][C]0.661077321183139[/C][C]0.66946133940843[/C][/ROW]
[ROW][C]22[/C][C]0.473882073160261[/C][C]0.947764146320523[/C][C]0.526117926839738[/C][/ROW]
[ROW][C]23[/C][C]0.555712885935467[/C][C]0.888574228129066[/C][C]0.444287114064533[/C][/ROW]
[ROW][C]24[/C][C]0.542042824984014[/C][C]0.915914350031973[/C][C]0.457957175015986[/C][/ROW]
[ROW][C]25[/C][C]0.58631710035399[/C][C]0.827365799292019[/C][C]0.413682899646010[/C][/ROW]
[ROW][C]26[/C][C]0.556679098195949[/C][C]0.886641803608103[/C][C]0.443320901804051[/C][/ROW]
[ROW][C]27[/C][C]0.543375962608688[/C][C]0.913248074782625[/C][C]0.456624037391312[/C][/ROW]
[ROW][C]28[/C][C]0.604170063521513[/C][C]0.791659872956973[/C][C]0.395829936478487[/C][/ROW]
[ROW][C]29[/C][C]0.541498865635815[/C][C]0.917002268728371[/C][C]0.458501134364185[/C][/ROW]
[ROW][C]30[/C][C]0.520683006577582[/C][C]0.958633986844835[/C][C]0.479316993422418[/C][/ROW]
[ROW][C]31[/C][C]0.438547202913205[/C][C]0.87709440582641[/C][C]0.561452797086795[/C][/ROW]
[ROW][C]32[/C][C]0.35853197466508[/C][C]0.71706394933016[/C][C]0.64146802533492[/C][/ROW]
[ROW][C]33[/C][C]0.259988109689895[/C][C]0.519976219379789[/C][C]0.740011890310105[/C][/ROW]
[ROW][C]34[/C][C]0.205948007528703[/C][C]0.411896015057405[/C][C]0.794051992471297[/C][/ROW]
[ROW][C]35[/C][C]0.433992225050289[/C][C]0.867984450100577[/C][C]0.566007774949711[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101866&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101866&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
210.3305386605915700.6610773211831390.66946133940843
220.4738820731602610.9477641463205230.526117926839738
230.5557128859354670.8885742281290660.444287114064533
240.5420428249840140.9159143500319730.457957175015986
250.586317100353990.8273657992920190.413682899646010
260.5566790981959490.8866418036081030.443320901804051
270.5433759626086880.9132480747826250.456624037391312
280.6041700635215130.7916598729569730.395829936478487
290.5414988656358150.9170022687283710.458501134364185
300.5206830065775820.9586339868448350.479316993422418
310.4385472029132050.877094405826410.561452797086795
320.358531974665080.717063949330160.64146802533492
330.2599881096898950.5199762193797890.740011890310105
340.2059480075287030.4118960150574050.794051992471297
350.4339922250502890.8679844501005770.566007774949711






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101866&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101866&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101866&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}