FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationWed, 18 Nov 2009 10:29:14 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/18/t1258565445a8959vf10siyn9q.htm/, Retrieved Wed, 19 Nov 2025 22:52:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57545, Retrieved Wed, 19 Nov 2025 22:52:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS7
Estimated Impact428

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [] [2009-11-18 17:29:14] [4563e36d4b7005634fe3557528d9fcab] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7357	4922	7862	8031	6820	7291
7213	4879	7357	7862	8031	6820
7079	4853	7213	7357	7862	8031
7012	4545	7079	7213	7357	7862
7319	4733	7012	7079	7213	7357
8148	5191	7319	7012	7079	7213
7599	4983	8148	7319	7012	7079
6908	4593	7599	8148	7319	7012
7878	4656	6908	7599	8148	7319
7407	4513	7878	6908	7599	8148
7911	4857	7407	7878	6908	7599
7323	4681	7911	7407	7878	6908
7179	4897	7323	7911	7407	7878
6758	4547	7179	7323	7911	7407
6934	4692	6758	7179	7323	7911
6696	4390	6934	6758	7179	7323
7688	5341	6696	6934	6758	7179
8296	5415	7688	6696	6934	6758
7697	4890	8296	7688	6696	6934
7907	5120	7697	8296	7688	6696
7592	4422	7907	7697	8296	7688
7710	4797	7592	7907	7697	8296
9011	5689	7710	7592	7907	7697
8225	5171	9011	7710	7592	7907
7733	4265	8225	9011	7710	7592
8062	5215	7733	8225	9011	7710
7859	4874	8062	7733	8225	9011
8221	4590	7859	8062	7733	8225
8330	4994	8221	7859	8062	7733
8868	4988	8330	8221	7859	8062
9053	5110	8868	8330	8221	7859
8811	5141	9053	8868	8330	8221
8120	4395	8811	9053	8868	8330
7953	4523	8120	8811	9053	8868
8878	5306	7953	8120	8811	9053
8601	5365	8878	7953	8120	8811
8361	5496	8601	8878	7953	8120
9116	5647	8361	8601	8878	7953
9310	5443	9116	8361	8601	8878
9891	5546	9310	9116	8361	8601
10147	5912	9891	9310	9116	8361
10317	5665	10147	9891	9310	9116
10682	5963	10317	10147	9891	9310
10276	5861	10682	10317	10147	9891
10614	5366	10276	10682	10317	10147
9413	5619	10614	10276	10682	10317
11068	6721	9413	10614	10276	10682
9772	6054	11068	9413	10614	10276
10350	6619	9772	11068	9413	10614
10541	6856	10350	9772	11068	9413
10049	6193	10541	10350	9772	11068
10714	6317	10049	10541	10350	9772
10759	6618	10714	10049	10541	10350
11684	6585	10759	10714	10049	10541
11462	6852	11684	10759	10714	10049
10485	6586	11462	11684	10759	10714
11056	6154	10485	11462	11684	10759
10184	6193	11056	10485	11462	11684
11082	7606	10184	11056	10485	11462
10554	6588	11082	10184	11056	10485
11315	7143	10554	11082	10184	11056
10847	7629	11315	10554	11082	10184
11104	7041	10847	11315	10554	11082
11026	7146	11104	10847	11315	10554
11073	7200	11026	11104	10847	11315
12073	7739	11073	11026	11104	10847
12328	7953	12073	11073	11026	11104
11172	7082	12328	12073	11073	11026

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57545&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]3 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=57545&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57545&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
UitEu[t] = + 1328.02804198041 + 0.462614678524535UitnietEU[t] + 0.247069152859301Y1[t] + 0.26469216676593Y2[t] + 0.0735500539413867Y3[t] -0.116615043335462Y4[t] -22.1976639065588M1[t] -89.166980723682M2[t] + 148.942807094104M3[t] + 303.475954632116M4[t] + 355.970057199878M5[t] + 834.309587042934M6[t] + 442.887565246856M7[t] -169.601359213052M8[t] + 423.025057139143M9[t] -24.1838037625282M10[t] + 686.388612891013M11[t] + 20.2978188290086t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
UitEu[t] =  +  1328.02804198041 +  0.462614678524535UitnietEU[t] +  0.247069152859301Y1[t] +  0.26469216676593Y2[t] +  0.0735500539413867Y3[t] -0.116615043335462Y4[t] -22.1976639065588M1[t] -89.166980723682M2[t] +  148.942807094104M3[t] +  303.475954632116M4[t] +  355.970057199878M5[t] +  834.309587042934M6[t] +  442.887565246856M7[t] -169.601359213052M8[t] +  423.025057139143M9[t] -24.1838037625282M10[t] +  686.388612891013M11[t] +  20.2978188290086t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57545&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]UitEu[t] =  +  1328.02804198041 +  0.462614678524535UitnietEU[t] +  0.247069152859301Y1[t] +  0.26469216676593Y2[t] +  0.0735500539413867Y3[t] -0.116615043335462Y4[t] -22.1976639065588M1[t] -89.166980723682M2[t] +  148.942807094104M3[t] +  303.475954632116M4[t] +  355.970057199878M5[t] +  834.309587042934M6[t] +  442.887565246856M7[t] -169.601359213052M8[t] +  423.025057139143M9[t] -24.1838037625282M10[t] +  686.388612891013M11[t] +  20.2978188290086t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57545&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57545&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
UitEu[t] = + 1328.02804198041 + 0.462614678524535UitnietEU[t] + 0.247069152859301Y1[t] + 0.26469216676593Y2[t] + 0.0735500539413867Y3[t] -0.116615043335462Y4[t] -22.1976639065588M1[t] -89.166980723682M2[t] + 148.942807094104M3[t] + 303.475954632116M4[t] + 355.970057199878M5[t] + 834.309587042934M6[t] + 442.887565246856M7[t] -169.601359213052M8[t] + 423.025057139143M9[t] -24.1838037625282M10[t] + 686.388612891013M11[t] + 20.2978188290086t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1328.02804198041702.7549941.88970.0645970.032298
UitnietEU0.4626146785245350.1239343.73280.0004850.000243
Y10.2470691528593010.1212922.0370.0469630.023482
Y20.264692166765930.1281052.06620.0440090.022004
Y30.07355005394138670.1281750.57380.568660.28433
Y4-0.1166150433354620.122405-0.95270.3453280.172664
M1-22.1976639065588285.232341-0.07780.9382790.46914
M2-89.166980723682257.743286-0.3460.730830.365415
M3148.942807094104241.971910.61550.5409920.270496
M4303.475954632116232.9069661.3030.1985460.099273
M5355.970057199878228.062131.56080.1248680.062434
M6834.309587042934230.7766563.61520.0006970.000348
M7442.887565246856230.4048441.92220.0602860.030143
M8-169.601359213052257.006612-0.65990.512340.25617
M9423.025057139143271.9870891.55530.1261790.06309
M10-24.1838037625282247.643326-0.09770.9225960.461298
M11686.388612891013281.1675852.44120.0182210.009111
t20.29781882900867.6135522.6660.0103110.005156

\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) & 1328.02804198041 & 702.754994 & 1.8897 & 0.064597 & 0.032298 \tabularnewline
UitnietEU & 0.462614678524535 & 0.123934 & 3.7328 & 0.000485 & 0.000243 \tabularnewline
Y1 & 0.247069152859301 & 0.121292 & 2.037 & 0.046963 & 0.023482 \tabularnewline
Y2 & 0.26469216676593 & 0.128105 & 2.0662 & 0.044009 & 0.022004 \tabularnewline
Y3 & 0.0735500539413867 & 0.128175 & 0.5738 & 0.56866 & 0.28433 \tabularnewline
Y4 & -0.116615043335462 & 0.122405 & -0.9527 & 0.345328 & 0.172664 \tabularnewline
M1 & -22.1976639065588 & 285.232341 & -0.0778 & 0.938279 & 0.46914 \tabularnewline
M2 & -89.166980723682 & 257.743286 & -0.346 & 0.73083 & 0.365415 \tabularnewline
M3 & 148.942807094104 & 241.97191 & 0.6155 & 0.540992 & 0.270496 \tabularnewline
M4 & 303.475954632116 & 232.906966 & 1.303 & 0.198546 & 0.099273 \tabularnewline
M5 & 355.970057199878 & 228.06213 & 1.5608 & 0.124868 & 0.062434 \tabularnewline
M6 & 834.309587042934 & 230.776656 & 3.6152 & 0.000697 & 0.000348 \tabularnewline
M7 & 442.887565246856 & 230.404844 & 1.9222 & 0.060286 & 0.030143 \tabularnewline
M8 & -169.601359213052 & 257.006612 & -0.6599 & 0.51234 & 0.25617 \tabularnewline
M9 & 423.025057139143 & 271.987089 & 1.5553 & 0.126179 & 0.06309 \tabularnewline
M10 & -24.1838037625282 & 247.643326 & -0.0977 & 0.922596 & 0.461298 \tabularnewline
M11 & 686.388612891013 & 281.167585 & 2.4412 & 0.018221 & 0.009111 \tabularnewline
t & 20.2978188290086 & 7.613552 & 2.666 & 0.010311 & 0.005156 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57545&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]1328.02804198041[/C][C]702.754994[/C][C]1.8897[/C][C]0.064597[/C][C]0.032298[/C][/ROW]
[ROW][C]UitnietEU[/C][C]0.462614678524535[/C][C]0.123934[/C][C]3.7328[/C][C]0.000485[/C][C]0.000243[/C][/ROW]
[ROW][C]Y1[/C][C]0.247069152859301[/C][C]0.121292[/C][C]2.037[/C][C]0.046963[/C][C]0.023482[/C][/ROW]
[ROW][C]Y2[/C][C]0.26469216676593[/C][C]0.128105[/C][C]2.0662[/C][C]0.044009[/C][C]0.022004[/C][/ROW]
[ROW][C]Y3[/C][C]0.0735500539413867[/C][C]0.128175[/C][C]0.5738[/C][C]0.56866[/C][C]0.28433[/C][/ROW]
[ROW][C]Y4[/C][C]-0.116615043335462[/C][C]0.122405[/C][C]-0.9527[/C][C]0.345328[/C][C]0.172664[/C][/ROW]
[ROW][C]M1[/C][C]-22.1976639065588[/C][C]285.232341[/C][C]-0.0778[/C][C]0.938279[/C][C]0.46914[/C][/ROW]
[ROW][C]M2[/C][C]-89.166980723682[/C][C]257.743286[/C][C]-0.346[/C][C]0.73083[/C][C]0.365415[/C][/ROW]
[ROW][C]M3[/C][C]148.942807094104[/C][C]241.97191[/C][C]0.6155[/C][C]0.540992[/C][C]0.270496[/C][/ROW]
[ROW][C]M4[/C][C]303.475954632116[/C][C]232.906966[/C][C]1.303[/C][C]0.198546[/C][C]0.099273[/C][/ROW]
[ROW][C]M5[/C][C]355.970057199878[/C][C]228.06213[/C][C]1.5608[/C][C]0.124868[/C][C]0.062434[/C][/ROW]
[ROW][C]M6[/C][C]834.309587042934[/C][C]230.776656[/C][C]3.6152[/C][C]0.000697[/C][C]0.000348[/C][/ROW]
[ROW][C]M7[/C][C]442.887565246856[/C][C]230.404844[/C][C]1.9222[/C][C]0.060286[/C][C]0.030143[/C][/ROW]
[ROW][C]M8[/C][C]-169.601359213052[/C][C]257.006612[/C][C]-0.6599[/C][C]0.51234[/C][C]0.25617[/C][/ROW]
[ROW][C]M9[/C][C]423.025057139143[/C][C]271.987089[/C][C]1.5553[/C][C]0.126179[/C][C]0.06309[/C][/ROW]
[ROW][C]M10[/C][C]-24.1838037625282[/C][C]247.643326[/C][C]-0.0977[/C][C]0.922596[/C][C]0.461298[/C][/ROW]
[ROW][C]M11[/C][C]686.388612891013[/C][C]281.167585[/C][C]2.4412[/C][C]0.018221[/C][C]0.009111[/C][/ROW]
[ROW][C]t[/C][C]20.2978188290086[/C][C]7.613552[/C][C]2.666[/C][C]0.010311[/C][C]0.005156[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57545&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57545&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)1328.02804198041702.7549941.88970.0645970.032298
UitnietEU0.4626146785245350.1239343.73280.0004850.000243
Y10.2470691528593010.1212922.0370.0469630.023482
Y20.264692166765930.1281052.06620.0440090.022004
Y30.07355005394138670.1281750.57380.568660.28433
Y4-0.1166150433354620.122405-0.95270.3453280.172664
M1-22.1976639065588285.232341-0.07780.9382790.46914
M2-89.166980723682257.743286-0.3460.730830.365415
M3148.942807094104241.971910.61550.5409920.270496
M4303.475954632116232.9069661.3030.1985460.099273
M5355.970057199878228.062131.56080.1248680.062434
M6834.309587042934230.7766563.61520.0006970.000348
M7442.887565246856230.4048441.92220.0602860.030143
M8-169.601359213052257.006612-0.65990.512340.25617
M9423.025057139143271.9870891.55530.1261790.06309
M10-24.1838037625282247.643326-0.09770.9225960.461298
M11686.388612891013281.1675852.44120.0182210.009111
t20.29781882900867.6135522.6660.0103110.005156






Multiple Linear Regression - Regression Statistics
Multiple R0.982961362758252
R-squared0.96621304067556
Adjusted R-squared0.95472547450525
F-TEST (value)84.1094646464612
F-TEST (DF numerator)17
F-TEST (DF denominator)50
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation335.87177104827
Sum Squared Residuals5640492.32935508

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.982961362758252 \tabularnewline
R-squared & 0.96621304067556 \tabularnewline
Adjusted R-squared & 0.95472547450525 \tabularnewline
F-TEST (value) & 84.1094646464612 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 50 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 335.87177104827 \tabularnewline
Sum Squared Residuals & 5640492.32935508 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57545&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.982961362758252[/C][/ROW]
[ROW][C]R-squared[/C][C]0.96621304067556[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.95472547450525[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]84.1094646464612[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]50[/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]335.87177104827[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5640492.32935508[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57545&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57545&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 R0.982961362758252
R-squared0.96621304067556
Adjusted R-squared0.95472547450525
F-TEST (value)84.1094646464612
F-TEST (DF numerator)17
F-TEST (DF denominator)50
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation335.87177104827
Sum Squared Residuals5640492.32935508






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
173577322.6892025990334.3107974009748
272137230.61717579098-17.6171757909846
370797154.09852197227-75.0985219722706
470127097.78639393959-85.7863939395869
573197253.8268804276165.1731195723882
681488029.39446563056118.605534369439
775997858.82579464077-259.82579464077
869087200.39688017798-292.396880177983
978787551.59723233938326.402767660618
1074076978.23423173706428.765768262944
1179118021.82431891594-110.824318915938
1273237426.0907311959-103.090731195901
1371797364.48517940662-185.485179406617
1467587056.67650446227-298.676504462271
1569347138.01004056845-204.010040568454
1666967163.15858042954-467.158580429539
1776887649.5084176044138.4915823955855
1882968426.51485917134-130.514859171344
1976978187.28146388406-490.28146388406
2079077815.1461829684691.8538170315371
2175927927.53559655479-335.535596554787
2277107536.90520214014173.094797859861
2390118711.50178065834299.498219341663
2482258110.78980057697114.210199423031
2577337885.34185658701-152.341856587009
2680628030.4762819765631.5237180234350
2778597872.662974711-13.6629747109992
2882218008.51285473512212.487145264884
2983308385.48019880515-55.4801988051456
3088688950.79395122953-82.7939512295247
3190538848.18136278212204.818637217880
3288118424.2458013767386.754198623304
3381208708.09569153524-588.095691535243
3479538056.48690599527-103.486905995268
3588788886.04870292889-8.04870292889158
3686018408.98530265846192.014697341540
3783618712.38821532068-351.388215320676
3891168690.46371904218425.536280957824
3993108849.26674162785460.733258372152
4098919334.17137550387556.828624496133
41101479854.69562853072292.304371469281
421031710382.3273563766-65.327356376565
431068210278.9345412208403.065458779167
44102769725.81012115537550.189878844625
451061410088.6927133518525.30728664822
4694139761.88875122688-348.888751226878
471106810722.8704495087345.129550491317
4897729911.42544639346-139.425446393460
491035010161.0173091432188.982690856759
501054110285.5304185084255.469581491643
511004910149.088007252-100.088007251992
521071410503.9264017425210.073598257548
531075910696.682347232662.3176527673875
541168411308.7317144751375.268285524856
551146211407.861131765554.1388682344847
561048510808.3661725801-323.366172580090
571105610984.078766218871.921233781192
581018410333.4849089007-149.484908900659
591108211607.7547479882-525.754747988151
601055410617.7087191752-63.7087191752088
611131510849.0782369434465.921763056568
621084711243.2359002196-396.235900219646
631110411171.8737138684-67.8737138684364
641102611452.4443936494-426.444393649438
651107311475.8065273995-402.806527399496
661207312288.2376531169-215.237653116862
671232812239.915705706788.0842942932987
681117211585.0348417414-413.034841741395

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7357 & 7322.68920259903 & 34.3107974009748 \tabularnewline
2 & 7213 & 7230.61717579098 & -17.6171757909846 \tabularnewline
3 & 7079 & 7154.09852197227 & -75.0985219722706 \tabularnewline
4 & 7012 & 7097.78639393959 & -85.7863939395869 \tabularnewline
5 & 7319 & 7253.82688042761 & 65.1731195723882 \tabularnewline
6 & 8148 & 8029.39446563056 & 118.605534369439 \tabularnewline
7 & 7599 & 7858.82579464077 & -259.82579464077 \tabularnewline
8 & 6908 & 7200.39688017798 & -292.396880177983 \tabularnewline
9 & 7878 & 7551.59723233938 & 326.402767660618 \tabularnewline
10 & 7407 & 6978.23423173706 & 428.765768262944 \tabularnewline
11 & 7911 & 8021.82431891594 & -110.824318915938 \tabularnewline
12 & 7323 & 7426.0907311959 & -103.090731195901 \tabularnewline
13 & 7179 & 7364.48517940662 & -185.485179406617 \tabularnewline
14 & 6758 & 7056.67650446227 & -298.676504462271 \tabularnewline
15 & 6934 & 7138.01004056845 & -204.010040568454 \tabularnewline
16 & 6696 & 7163.15858042954 & -467.158580429539 \tabularnewline
17 & 7688 & 7649.50841760441 & 38.4915823955855 \tabularnewline
18 & 8296 & 8426.51485917134 & -130.514859171344 \tabularnewline
19 & 7697 & 8187.28146388406 & -490.28146388406 \tabularnewline
20 & 7907 & 7815.14618296846 & 91.8538170315371 \tabularnewline
21 & 7592 & 7927.53559655479 & -335.535596554787 \tabularnewline
22 & 7710 & 7536.90520214014 & 173.094797859861 \tabularnewline
23 & 9011 & 8711.50178065834 & 299.498219341663 \tabularnewline
24 & 8225 & 8110.78980057697 & 114.210199423031 \tabularnewline
25 & 7733 & 7885.34185658701 & -152.341856587009 \tabularnewline
26 & 8062 & 8030.47628197656 & 31.5237180234350 \tabularnewline
27 & 7859 & 7872.662974711 & -13.6629747109992 \tabularnewline
28 & 8221 & 8008.51285473512 & 212.487145264884 \tabularnewline
29 & 8330 & 8385.48019880515 & -55.4801988051456 \tabularnewline
30 & 8868 & 8950.79395122953 & -82.7939512295247 \tabularnewline
31 & 9053 & 8848.18136278212 & 204.818637217880 \tabularnewline
32 & 8811 & 8424.2458013767 & 386.754198623304 \tabularnewline
33 & 8120 & 8708.09569153524 & -588.095691535243 \tabularnewline
34 & 7953 & 8056.48690599527 & -103.486905995268 \tabularnewline
35 & 8878 & 8886.04870292889 & -8.04870292889158 \tabularnewline
36 & 8601 & 8408.98530265846 & 192.014697341540 \tabularnewline
37 & 8361 & 8712.38821532068 & -351.388215320676 \tabularnewline
38 & 9116 & 8690.46371904218 & 425.536280957824 \tabularnewline
39 & 9310 & 8849.26674162785 & 460.733258372152 \tabularnewline
40 & 9891 & 9334.17137550387 & 556.828624496133 \tabularnewline
41 & 10147 & 9854.69562853072 & 292.304371469281 \tabularnewline
42 & 10317 & 10382.3273563766 & -65.327356376565 \tabularnewline
43 & 10682 & 10278.9345412208 & 403.065458779167 \tabularnewline
44 & 10276 & 9725.81012115537 & 550.189878844625 \tabularnewline
45 & 10614 & 10088.6927133518 & 525.30728664822 \tabularnewline
46 & 9413 & 9761.88875122688 & -348.888751226878 \tabularnewline
47 & 11068 & 10722.8704495087 & 345.129550491317 \tabularnewline
48 & 9772 & 9911.42544639346 & -139.425446393460 \tabularnewline
49 & 10350 & 10161.0173091432 & 188.982690856759 \tabularnewline
50 & 10541 & 10285.5304185084 & 255.469581491643 \tabularnewline
51 & 10049 & 10149.088007252 & -100.088007251992 \tabularnewline
52 & 10714 & 10503.9264017425 & 210.073598257548 \tabularnewline
53 & 10759 & 10696.6823472326 & 62.3176527673875 \tabularnewline
54 & 11684 & 11308.7317144751 & 375.268285524856 \tabularnewline
55 & 11462 & 11407.8611317655 & 54.1388682344847 \tabularnewline
56 & 10485 & 10808.3661725801 & -323.366172580090 \tabularnewline
57 & 11056 & 10984.0787662188 & 71.921233781192 \tabularnewline
58 & 10184 & 10333.4849089007 & -149.484908900659 \tabularnewline
59 & 11082 & 11607.7547479882 & -525.754747988151 \tabularnewline
60 & 10554 & 10617.7087191752 & -63.7087191752088 \tabularnewline
61 & 11315 & 10849.0782369434 & 465.921763056568 \tabularnewline
62 & 10847 & 11243.2359002196 & -396.235900219646 \tabularnewline
63 & 11104 & 11171.8737138684 & -67.8737138684364 \tabularnewline
64 & 11026 & 11452.4443936494 & -426.444393649438 \tabularnewline
65 & 11073 & 11475.8065273995 & -402.806527399496 \tabularnewline
66 & 12073 & 12288.2376531169 & -215.237653116862 \tabularnewline
67 & 12328 & 12239.9157057067 & 88.0842942932987 \tabularnewline
68 & 11172 & 11585.0348417414 & -413.034841741395 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57545&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]7357[/C][C]7322.68920259903[/C][C]34.3107974009748[/C][/ROW]
[ROW][C]2[/C][C]7213[/C][C]7230.61717579098[/C][C]-17.6171757909846[/C][/ROW]
[ROW][C]3[/C][C]7079[/C][C]7154.09852197227[/C][C]-75.0985219722706[/C][/ROW]
[ROW][C]4[/C][C]7012[/C][C]7097.78639393959[/C][C]-85.7863939395869[/C][/ROW]
[ROW][C]5[/C][C]7319[/C][C]7253.82688042761[/C][C]65.1731195723882[/C][/ROW]
[ROW][C]6[/C][C]8148[/C][C]8029.39446563056[/C][C]118.605534369439[/C][/ROW]
[ROW][C]7[/C][C]7599[/C][C]7858.82579464077[/C][C]-259.82579464077[/C][/ROW]
[ROW][C]8[/C][C]6908[/C][C]7200.39688017798[/C][C]-292.396880177983[/C][/ROW]
[ROW][C]9[/C][C]7878[/C][C]7551.59723233938[/C][C]326.402767660618[/C][/ROW]
[ROW][C]10[/C][C]7407[/C][C]6978.23423173706[/C][C]428.765768262944[/C][/ROW]
[ROW][C]11[/C][C]7911[/C][C]8021.82431891594[/C][C]-110.824318915938[/C][/ROW]
[ROW][C]12[/C][C]7323[/C][C]7426.0907311959[/C][C]-103.090731195901[/C][/ROW]
[ROW][C]13[/C][C]7179[/C][C]7364.48517940662[/C][C]-185.485179406617[/C][/ROW]
[ROW][C]14[/C][C]6758[/C][C]7056.67650446227[/C][C]-298.676504462271[/C][/ROW]
[ROW][C]15[/C][C]6934[/C][C]7138.01004056845[/C][C]-204.010040568454[/C][/ROW]
[ROW][C]16[/C][C]6696[/C][C]7163.15858042954[/C][C]-467.158580429539[/C][/ROW]
[ROW][C]17[/C][C]7688[/C][C]7649.50841760441[/C][C]38.4915823955855[/C][/ROW]
[ROW][C]18[/C][C]8296[/C][C]8426.51485917134[/C][C]-130.514859171344[/C][/ROW]
[ROW][C]19[/C][C]7697[/C][C]8187.28146388406[/C][C]-490.28146388406[/C][/ROW]
[ROW][C]20[/C][C]7907[/C][C]7815.14618296846[/C][C]91.8538170315371[/C][/ROW]
[ROW][C]21[/C][C]7592[/C][C]7927.53559655479[/C][C]-335.535596554787[/C][/ROW]
[ROW][C]22[/C][C]7710[/C][C]7536.90520214014[/C][C]173.094797859861[/C][/ROW]
[ROW][C]23[/C][C]9011[/C][C]8711.50178065834[/C][C]299.498219341663[/C][/ROW]
[ROW][C]24[/C][C]8225[/C][C]8110.78980057697[/C][C]114.210199423031[/C][/ROW]
[ROW][C]25[/C][C]7733[/C][C]7885.34185658701[/C][C]-152.341856587009[/C][/ROW]
[ROW][C]26[/C][C]8062[/C][C]8030.47628197656[/C][C]31.5237180234350[/C][/ROW]
[ROW][C]27[/C][C]7859[/C][C]7872.662974711[/C][C]-13.6629747109992[/C][/ROW]
[ROW][C]28[/C][C]8221[/C][C]8008.51285473512[/C][C]212.487145264884[/C][/ROW]
[ROW][C]29[/C][C]8330[/C][C]8385.48019880515[/C][C]-55.4801988051456[/C][/ROW]
[ROW][C]30[/C][C]8868[/C][C]8950.79395122953[/C][C]-82.7939512295247[/C][/ROW]
[ROW][C]31[/C][C]9053[/C][C]8848.18136278212[/C][C]204.818637217880[/C][/ROW]
[ROW][C]32[/C][C]8811[/C][C]8424.2458013767[/C][C]386.754198623304[/C][/ROW]
[ROW][C]33[/C][C]8120[/C][C]8708.09569153524[/C][C]-588.095691535243[/C][/ROW]
[ROW][C]34[/C][C]7953[/C][C]8056.48690599527[/C][C]-103.486905995268[/C][/ROW]
[ROW][C]35[/C][C]8878[/C][C]8886.04870292889[/C][C]-8.04870292889158[/C][/ROW]
[ROW][C]36[/C][C]8601[/C][C]8408.98530265846[/C][C]192.014697341540[/C][/ROW]
[ROW][C]37[/C][C]8361[/C][C]8712.38821532068[/C][C]-351.388215320676[/C][/ROW]
[ROW][C]38[/C][C]9116[/C][C]8690.46371904218[/C][C]425.536280957824[/C][/ROW]
[ROW][C]39[/C][C]9310[/C][C]8849.26674162785[/C][C]460.733258372152[/C][/ROW]
[ROW][C]40[/C][C]9891[/C][C]9334.17137550387[/C][C]556.828624496133[/C][/ROW]
[ROW][C]41[/C][C]10147[/C][C]9854.69562853072[/C][C]292.304371469281[/C][/ROW]
[ROW][C]42[/C][C]10317[/C][C]10382.3273563766[/C][C]-65.327356376565[/C][/ROW]
[ROW][C]43[/C][C]10682[/C][C]10278.9345412208[/C][C]403.065458779167[/C][/ROW]
[ROW][C]44[/C][C]10276[/C][C]9725.81012115537[/C][C]550.189878844625[/C][/ROW]
[ROW][C]45[/C][C]10614[/C][C]10088.6927133518[/C][C]525.30728664822[/C][/ROW]
[ROW][C]46[/C][C]9413[/C][C]9761.88875122688[/C][C]-348.888751226878[/C][/ROW]
[ROW][C]47[/C][C]11068[/C][C]10722.8704495087[/C][C]345.129550491317[/C][/ROW]
[ROW][C]48[/C][C]9772[/C][C]9911.42544639346[/C][C]-139.425446393460[/C][/ROW]
[ROW][C]49[/C][C]10350[/C][C]10161.0173091432[/C][C]188.982690856759[/C][/ROW]
[ROW][C]50[/C][C]10541[/C][C]10285.5304185084[/C][C]255.469581491643[/C][/ROW]
[ROW][C]51[/C][C]10049[/C][C]10149.088007252[/C][C]-100.088007251992[/C][/ROW]
[ROW][C]52[/C][C]10714[/C][C]10503.9264017425[/C][C]210.073598257548[/C][/ROW]
[ROW][C]53[/C][C]10759[/C][C]10696.6823472326[/C][C]62.3176527673875[/C][/ROW]
[ROW][C]54[/C][C]11684[/C][C]11308.7317144751[/C][C]375.268285524856[/C][/ROW]
[ROW][C]55[/C][C]11462[/C][C]11407.8611317655[/C][C]54.1388682344847[/C][/ROW]
[ROW][C]56[/C][C]10485[/C][C]10808.3661725801[/C][C]-323.366172580090[/C][/ROW]
[ROW][C]57[/C][C]11056[/C][C]10984.0787662188[/C][C]71.921233781192[/C][/ROW]
[ROW][C]58[/C][C]10184[/C][C]10333.4849089007[/C][C]-149.484908900659[/C][/ROW]
[ROW][C]59[/C][C]11082[/C][C]11607.7547479882[/C][C]-525.754747988151[/C][/ROW]
[ROW][C]60[/C][C]10554[/C][C]10617.7087191752[/C][C]-63.7087191752088[/C][/ROW]
[ROW][C]61[/C][C]11315[/C][C]10849.0782369434[/C][C]465.921763056568[/C][/ROW]
[ROW][C]62[/C][C]10847[/C][C]11243.2359002196[/C][C]-396.235900219646[/C][/ROW]
[ROW][C]63[/C][C]11104[/C][C]11171.8737138684[/C][C]-67.8737138684364[/C][/ROW]
[ROW][C]64[/C][C]11026[/C][C]11452.4443936494[/C][C]-426.444393649438[/C][/ROW]
[ROW][C]65[/C][C]11073[/C][C]11475.8065273995[/C][C]-402.806527399496[/C][/ROW]
[ROW][C]66[/C][C]12073[/C][C]12288.2376531169[/C][C]-215.237653116862[/C][/ROW]
[ROW][C]67[/C][C]12328[/C][C]12239.9157057067[/C][C]88.0842942932987[/C][/ROW]
[ROW][C]68[/C][C]11172[/C][C]11585.0348417414[/C][C]-413.034841741395[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57545&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57545&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
173577322.6892025990334.3107974009748
272137230.61717579098-17.6171757909846
370797154.09852197227-75.0985219722706
470127097.78639393959-85.7863939395869
573197253.8268804276165.1731195723882
681488029.39446563056118.605534369439
775997858.82579464077-259.82579464077
869087200.39688017798-292.396880177983
978787551.59723233938326.402767660618
1074076978.23423173706428.765768262944
1179118021.82431891594-110.824318915938
1273237426.0907311959-103.090731195901
1371797364.48517940662-185.485179406617
1467587056.67650446227-298.676504462271
1569347138.01004056845-204.010040568454
1666967163.15858042954-467.158580429539
1776887649.5084176044138.4915823955855
1882968426.51485917134-130.514859171344
1976978187.28146388406-490.28146388406
2079077815.1461829684691.8538170315371
2175927927.53559655479-335.535596554787
2277107536.90520214014173.094797859861
2390118711.50178065834299.498219341663
2482258110.78980057697114.210199423031
2577337885.34185658701-152.341856587009
2680628030.4762819765631.5237180234350
2778597872.662974711-13.6629747109992
2882218008.51285473512212.487145264884
2983308385.48019880515-55.4801988051456
3088688950.79395122953-82.7939512295247
3190538848.18136278212204.818637217880
3288118424.2458013767386.754198623304
3381208708.09569153524-588.095691535243
3479538056.48690599527-103.486905995268
3588788886.04870292889-8.04870292889158
3686018408.98530265846192.014697341540
3783618712.38821532068-351.388215320676
3891168690.46371904218425.536280957824
3993108849.26674162785460.733258372152
4098919334.17137550387556.828624496133
41101479854.69562853072292.304371469281
421031710382.3273563766-65.327356376565
431068210278.9345412208403.065458779167
44102769725.81012115537550.189878844625
451061410088.6927133518525.30728664822
4694139761.88875122688-348.888751226878
471106810722.8704495087345.129550491317
4897729911.42544639346-139.425446393460
491035010161.0173091432188.982690856759
501054110285.5304185084255.469581491643
511004910149.088007252-100.088007251992
521071410503.9264017425210.073598257548
531075910696.682347232662.3176527673875
541168411308.7317144751375.268285524856
551146211407.861131765554.1388682344847
561048510808.3661725801-323.366172580090
571105610984.078766218871.921233781192
581018410333.4849089007-149.484908900659
591108211607.7547479882-525.754747988151
601055410617.7087191752-63.7087191752088
611131510849.0782369434465.921763056568
621084711243.2359002196-396.235900219646
631110411171.8737138684-67.8737138684364
641102611452.4443936494-426.444393649438
651107311475.8065273995-402.806527399496
661207312288.2376531169-215.237653116862
671232812239.915705706788.0842942932987
681117211585.0348417414-413.034841741395






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.1396359694434830.2792719388869660.860364030556517
220.09266349273291050.1853269854658210.90733650726709
230.04078493745808680.08156987491617360.959215062541913
240.02834268062830620.05668536125661230.971657319371694
250.06022434384565120.1204486876913020.939775656154349
260.03342001423655500.06684002847310990.966579985763445
270.02333364816273240.04666729632546480.976666351837268
280.03140906666086770.06281813332173540.968590933339132
290.02352754279955040.04705508559910080.97647245720045
300.01566904005074770.03133808010149550.984330959949252
310.01651836139245380.03303672278490760.983481638607546
320.01031940605910990.02063881211821990.98968059394089
330.08965377319220970.1793075463844190.91034622680779
340.1072963442648430.2145926885296850.892703655735157
350.08363718388247640.1672743677649530.916362816117524
360.05591646402762530.1118329280552510.944083535972375
370.2922511227765160.5845022455530320.707748877223484
380.2843864401111250.568772880222250.715613559888875
390.3249332328540050.649866465708010.675066767145995
400.2365105019479150.473021003895830.763489498052085
410.2009261267099370.4018522534198730.799073873290064
420.3416778115590630.6833556231181250.658322188440937
430.5581362141433360.8837275717133270.441863785856664
440.4458231688251310.8916463376502620.554176831174869
450.3488939206303110.6977878412606230.651106079369689
460.4326795726414390.8653591452828770.567320427358561
470.4812045674286440.962409134857290.518795432571356

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.139635969443483 & 0.279271938886966 & 0.860364030556517 \tabularnewline
22 & 0.0926634927329105 & 0.185326985465821 & 0.90733650726709 \tabularnewline
23 & 0.0407849374580868 & 0.0815698749161736 & 0.959215062541913 \tabularnewline
24 & 0.0283426806283062 & 0.0566853612566123 & 0.971657319371694 \tabularnewline
25 & 0.0602243438456512 & 0.120448687691302 & 0.939775656154349 \tabularnewline
26 & 0.0334200142365550 & 0.0668400284731099 & 0.966579985763445 \tabularnewline
27 & 0.0233336481627324 & 0.0466672963254648 & 0.976666351837268 \tabularnewline
28 & 0.0314090666608677 & 0.0628181333217354 & 0.968590933339132 \tabularnewline
29 & 0.0235275427995504 & 0.0470550855991008 & 0.97647245720045 \tabularnewline
30 & 0.0156690400507477 & 0.0313380801014955 & 0.984330959949252 \tabularnewline
31 & 0.0165183613924538 & 0.0330367227849076 & 0.983481638607546 \tabularnewline
32 & 0.0103194060591099 & 0.0206388121182199 & 0.98968059394089 \tabularnewline
33 & 0.0896537731922097 & 0.179307546384419 & 0.91034622680779 \tabularnewline
34 & 0.107296344264843 & 0.214592688529685 & 0.892703655735157 \tabularnewline
35 & 0.0836371838824764 & 0.167274367764953 & 0.916362816117524 \tabularnewline
36 & 0.0559164640276253 & 0.111832928055251 & 0.944083535972375 \tabularnewline
37 & 0.292251122776516 & 0.584502245553032 & 0.707748877223484 \tabularnewline
38 & 0.284386440111125 & 0.56877288022225 & 0.715613559888875 \tabularnewline
39 & 0.324933232854005 & 0.64986646570801 & 0.675066767145995 \tabularnewline
40 & 0.236510501947915 & 0.47302100389583 & 0.763489498052085 \tabularnewline
41 & 0.200926126709937 & 0.401852253419873 & 0.799073873290064 \tabularnewline
42 & 0.341677811559063 & 0.683355623118125 & 0.658322188440937 \tabularnewline
43 & 0.558136214143336 & 0.883727571713327 & 0.441863785856664 \tabularnewline
44 & 0.445823168825131 & 0.891646337650262 & 0.554176831174869 \tabularnewline
45 & 0.348893920630311 & 0.697787841260623 & 0.651106079369689 \tabularnewline
46 & 0.432679572641439 & 0.865359145282877 & 0.567320427358561 \tabularnewline
47 & 0.481204567428644 & 0.96240913485729 & 0.518795432571356 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57545&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]0.139635969443483[/C][C]0.279271938886966[/C][C]0.860364030556517[/C][/ROW]
[ROW][C]22[/C][C]0.0926634927329105[/C][C]0.185326985465821[/C][C]0.90733650726709[/C][/ROW]
[ROW][C]23[/C][C]0.0407849374580868[/C][C]0.0815698749161736[/C][C]0.959215062541913[/C][/ROW]
[ROW][C]24[/C][C]0.0283426806283062[/C][C]0.0566853612566123[/C][C]0.971657319371694[/C][/ROW]
[ROW][C]25[/C][C]0.0602243438456512[/C][C]0.120448687691302[/C][C]0.939775656154349[/C][/ROW]
[ROW][C]26[/C][C]0.0334200142365550[/C][C]0.0668400284731099[/C][C]0.966579985763445[/C][/ROW]
[ROW][C]27[/C][C]0.0233336481627324[/C][C]0.0466672963254648[/C][C]0.976666351837268[/C][/ROW]
[ROW][C]28[/C][C]0.0314090666608677[/C][C]0.0628181333217354[/C][C]0.968590933339132[/C][/ROW]
[ROW][C]29[/C][C]0.0235275427995504[/C][C]0.0470550855991008[/C][C]0.97647245720045[/C][/ROW]
[ROW][C]30[/C][C]0.0156690400507477[/C][C]0.0313380801014955[/C][C]0.984330959949252[/C][/ROW]
[ROW][C]31[/C][C]0.0165183613924538[/C][C]0.0330367227849076[/C][C]0.983481638607546[/C][/ROW]
[ROW][C]32[/C][C]0.0103194060591099[/C][C]0.0206388121182199[/C][C]0.98968059394089[/C][/ROW]
[ROW][C]33[/C][C]0.0896537731922097[/C][C]0.179307546384419[/C][C]0.91034622680779[/C][/ROW]
[ROW][C]34[/C][C]0.107296344264843[/C][C]0.214592688529685[/C][C]0.892703655735157[/C][/ROW]
[ROW][C]35[/C][C]0.0836371838824764[/C][C]0.167274367764953[/C][C]0.916362816117524[/C][/ROW]
[ROW][C]36[/C][C]0.0559164640276253[/C][C]0.111832928055251[/C][C]0.944083535972375[/C][/ROW]
[ROW][C]37[/C][C]0.292251122776516[/C][C]0.584502245553032[/C][C]0.707748877223484[/C][/ROW]
[ROW][C]38[/C][C]0.284386440111125[/C][C]0.56877288022225[/C][C]0.715613559888875[/C][/ROW]
[ROW][C]39[/C][C]0.324933232854005[/C][C]0.64986646570801[/C][C]0.675066767145995[/C][/ROW]
[ROW][C]40[/C][C]0.236510501947915[/C][C]0.47302100389583[/C][C]0.763489498052085[/C][/ROW]
[ROW][C]41[/C][C]0.200926126709937[/C][C]0.401852253419873[/C][C]0.799073873290064[/C][/ROW]
[ROW][C]42[/C][C]0.341677811559063[/C][C]0.683355623118125[/C][C]0.658322188440937[/C][/ROW]
[ROW][C]43[/C][C]0.558136214143336[/C][C]0.883727571713327[/C][C]0.441863785856664[/C][/ROW]
[ROW][C]44[/C][C]0.445823168825131[/C][C]0.891646337650262[/C][C]0.554176831174869[/C][/ROW]
[ROW][C]45[/C][C]0.348893920630311[/C][C]0.697787841260623[/C][C]0.651106079369689[/C][/ROW]
[ROW][C]46[/C][C]0.432679572641439[/C][C]0.865359145282877[/C][C]0.567320427358561[/C][/ROW]
[ROW][C]47[/C][C]0.481204567428644[/C][C]0.96240913485729[/C][C]0.518795432571356[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57545&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57545&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
210.1396359694434830.2792719388869660.860364030556517
220.09266349273291050.1853269854658210.90733650726709
230.04078493745808680.08156987491617360.959215062541913
240.02834268062830620.05668536125661230.971657319371694
250.06022434384565120.1204486876913020.939775656154349
260.03342001423655500.06684002847310990.966579985763445
270.02333364816273240.04666729632546480.976666351837268
280.03140906666086770.06281813332173540.968590933339132
290.02352754279955040.04705508559910080.97647245720045
300.01566904005074770.03133808010149550.984330959949252
310.01651836139245380.03303672278490760.983481638607546
320.01031940605910990.02063881211821990.98968059394089
330.08965377319220970.1793075463844190.91034622680779
340.1072963442648430.2145926885296850.892703655735157
350.08363718388247640.1672743677649530.916362816117524
360.05591646402762530.1118329280552510.944083535972375
370.2922511227765160.5845022455530320.707748877223484
380.2843864401111250.568772880222250.715613559888875
390.3249332328540050.649866465708010.675066767145995
400.2365105019479150.473021003895830.763489498052085
410.2009261267099370.4018522534198730.799073873290064
420.3416778115590630.6833556231181250.658322188440937
430.5581362141433360.8837275717133270.441863785856664
440.4458231688251310.8916463376502620.554176831174869
450.3488939206303110.6977878412606230.651106079369689
460.4326795726414390.8653591452828770.567320427358561
470.4812045674286440.962409134857290.518795432571356






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.185185185185185NOK
10% type I error level90.333333333333333NOK

\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 & 5 & 0.185185185185185 & NOK \tabularnewline
10% type I error level & 9 & 0.333333333333333 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57545&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]5[/C][C]0.185185185185185[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57545&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57545&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 level50.185185185185185NOK
10% type I error level90.333333333333333NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
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')
}