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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sat, 04 Dec 2010 10:49:43 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/04/t1291460061rpxste7vddihbat.htm/, Retrieved Sun, 05 May 2024 00:09:45 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105088, Retrieved Sun, 05 May 2024 00:09:45 +0000

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130

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] [247f085ab5b7724f755ad01dc754a3e8]
-    D        [Multiple Regression] [Paper invoer VS c...] [2010-12-04 10:49:43] [9d72585f2b7b60ae977d4816136e1c95] [Current]

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17972385,83	0	17637387,4	15213975,95	16471559,62	14731798,37
16896235,55	0	17972385,83	17637387,4	15213975,95	16471559,62
16697955,94	0	16896235,55	17972385,83	17637387,4	15213975,95
19691579,52	0	16697955,94	16896235,55	17972385,83	17637387,4
15930700,75	0	19691579,52	16697955,94	16896235,55	17972385,83
17444615,98	0	15930700,75	19691579,52	16697955,94	16896235,55
17699369,88	0	17444615,98	15930700,75	19691579,52	16697955,94
15189796,81	0	17699369,88	17444615,98	15930700,75	19691579,52
15672722,75	0	15189796,81	17699369,88	17444615,98	15930700,75
17180794,3	0	15672722,75	15189796,81	17699369,88	17444615,98
17664893,45	0	17180794,3	15672722,75	15189796,81	17699369,88
17862884,98	0	17664893,45	17180794,3	15672722,75	15189796,81
16162288,88	0	17862884,98	17664893,45	17180794,3	15672722,75
17463628,82	0	16162288,88	17862884,98	17664893,45	17180794,3
16772112,17	0	17463628,82	16162288,88	17862884,98	17664893,45
19106861,48	0	16772112,17	17463628,82	16162288,88	17862884,98
16721314,25	0	19106861,48	16772112,17	17463628,82	16162288,88
18161267,85	0	16721314,25	19106861,48	16772112,17	17463628,82
18509941,2	0	18161267,85	16721314,25	19106861,48	16772112,17
17802737,97	0	18509941,2	18161267,85	16721314,25	19106861,48
16409869,75	0	17802737,97	18509941,2	18161267,85	16721314,25
17967742,04	0	16409869,75	17802737,97	18509941,2	18161267,85
20286602,27	0	17967742,04	16409869,75	17802737,97	18509941,2
19537280,81	0	20286602,27	17967742,04	16409869,75	17802737,97
18021889,62	0	19537280,81	20286602,27	17967742,04	16409869,75
20194317,23	0	18021889,62	19537280,81	20286602,27	17967742,04
19049596,62	0	20194317,23	18021889,62	19537280,81	20286602,27
20244720,94	0	19049596,62	20194317,23	18021889,62	19537280,81
21473302,24	0	20244720,94	19049596,62	20194317,23	18021889,62
19673603,19	0	21473302,24	20244720,94	19049596,62	20194317,23
21053177,29	0	19673603,19	21473302,24	20244720,94	19049596,62
20159479,84	0	21053177,29	19673603,19	21473302,24	20244720,94
18203628,31	0	20159479,84	21053177,29	19673603,19	21473302,24
21289464,94	0	18203628,31	20159479,84	21053177,29	19673603,19
20432335,71	1	21289464,94	18203628,31	20159479,84	21053177,29
17180395,07	1	20432335,71	21289464,94	18203628,31	20159479,84
15816786,32	1	17180395,07	20432335,71	21289464,94	18203628,31
15071819,75	1	15816786,32	17180395,07	20432335,71	21289464,94
14521120,61	1	15071819,75	15816786,32	17180395,07	20432335,71
15668789,39	1	14521120,61	15071819,75	15816786,32	17180395,07
14346884,11	1	15668789,39	14521120,61	15071819,75	15816786,32
13881008,13	1	14346884,11	15668789,39	14521120,61	15071819,75
15465943,69	1	13881008,13	14346884,11	15668789,39	14521120,61
14238232,92	1	15465943,69	13881008,13	14346884,11	15668789,39
13557713,21	1	14238232,92	15465943,69	13881008,13	14346884,11
16127590,29	1	13557713,21	14238232,92	15465943,69	13881008,13
16793894,2	1	16127590,29	13557713,21	14238232,92	15465943,69
16014007,43	1	16793894,2	16127590,29	13557713,21	14238232,92
16867867,15	1	16014007,43	16793894,2	16127590,29	13557713,21
16014583,21	1	16867867,15	16014007,43	16793894,2	16127590,29
15878594,85	1	16014583,21	16867867,15	16014007,43	16793894,2
18664899,14	1	15878594,85	16014583,21	16867867,15	16014007,43
17962530,06	1	18664899,14	15878594,85	16014583,21	16867867,15
17332692,2	1	17962530,06	18664899,14	15878594,85	16014583,21
19542066,35	1	17332692,2	17962530,06	18664899,14	15878594,85
17203555,19	1	19542066,35	17332692,2	17962530,06	18664899,14

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105088&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105088&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105088&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 7058102.37766021 -2793503.20246403X[t] + 0.360299173377357Y1[t] + 0.152048144010289Y2[t] + 0.3745587444819Y3[t] -0.320617214160464Y4[t] -1277764.43976877M1[t] -301214.211190674M2[t] -613984.12257938M3[t] + 1702120.12956651M4[t] -697110.854102996M5[t] -310757.834273430M6[t] + 31032.2778017763M7[t] -794467.94169392M8[t] -1913826.67528654M9[t] + 447318.9919382M10[t] + 1871094.16661258M11[t] + 62686.6577060315t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  7058102.37766021 -2793503.20246403X[t] +  0.360299173377357Y1[t] +  0.152048144010289Y2[t] +  0.3745587444819Y3[t] -0.320617214160464Y4[t] -1277764.43976877M1[t] -301214.211190674M2[t] -613984.12257938M3[t] +  1702120.12956651M4[t] -697110.854102996M5[t] -310757.834273430M6[t] +  31032.2778017763M7[t] -794467.94169392M8[t] -1913826.67528654M9[t] +  447318.9919382M10[t] +  1871094.16661258M11[t] +  62686.6577060315t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105088&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  7058102.37766021 -2793503.20246403X[t] +  0.360299173377357Y1[t] +  0.152048144010289Y2[t] +  0.3745587444819Y3[t] -0.320617214160464Y4[t] -1277764.43976877M1[t] -301214.211190674M2[t] -613984.12257938M3[t] +  1702120.12956651M4[t] -697110.854102996M5[t] -310757.834273430M6[t] +  31032.2778017763M7[t] -794467.94169392M8[t] -1913826.67528654M9[t] +  447318.9919382M10[t] +  1871094.16661258M11[t] +  62686.6577060315t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105088&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105088&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 7058102.37766021 -2793503.20246403X[t] + 0.360299173377357Y1[t] + 0.152048144010289Y2[t] + 0.3745587444819Y3[t] -0.320617214160464Y4[t] -1277764.43976877M1[t] -301214.211190674M2[t] -613984.12257938M3[t] + 1702120.12956651M4[t] -697110.854102996M5[t] -310757.834273430M6[t] + 31032.2778017763M7[t] -794467.94169392M8[t] -1913826.67528654M9[t] + 447318.9919382M10[t] + 1871094.16661258M11[t] + 62686.6577060315t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	7058102.37766021	1705473.86239	4.1385	0.000187	9.3e-05
X	-2793503.20246403	626762.642767	-4.457	7.1e-05	3.6e-05
Y1	0.360299173377357	0.126369	2.8512	0.007004	0.003502
Y2	0.152048144010289	0.122565	1.2405	0.222379	0.111189
Y3	0.3745587444819	0.120063	3.1197	0.003448	0.001724
Y4	-0.320617214160464	0.110861	-2.8921	0.0063	0.00315
M1	-1277764.43976877	647489.781113	-1.9734	0.055753	0.027877
M2	-301214.211190674	675546.515769	-0.4459	0.658213	0.329106
M3	-613984.12257938	674823.388649	-0.9098	0.36864	0.18432
M4	1702120.12956651	668428.172544	2.5465	0.015057	0.007528
M5	-697110.854102996	621690.910914	-1.1213	0.26919	0.134595
M6	-310757.834273430	612045.124272	-0.5077	0.614573	0.307286
M7	31032.2778017763	721473.942765	0.043	0.965917	0.482958
M8	-794467.94169392	637796.661994	-1.2456	0.220521	0.11026
M9	-1913826.67528654	693698.412609	-2.7589	0.008872	0.004436
M10	447318.9919382	820443.485288	0.5452	0.588791	0.294395
M11	1871094.16661258	683553.738046	2.7373	0.009371	0.004685
t	62686.6577060315	16390.407887	3.8246	0.000473	0.000237

\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) & 7058102.37766021 & 1705473.86239 & 4.1385 & 0.000187 & 9.3e-05 \tabularnewline
X & -2793503.20246403 & 626762.642767 & -4.457 & 7.1e-05 & 3.6e-05 \tabularnewline
Y1 & 0.360299173377357 & 0.126369 & 2.8512 & 0.007004 & 0.003502 \tabularnewline
Y2 & 0.152048144010289 & 0.122565 & 1.2405 & 0.222379 & 0.111189 \tabularnewline
Y3 & 0.3745587444819 & 0.120063 & 3.1197 & 0.003448 & 0.001724 \tabularnewline
Y4 & -0.320617214160464 & 0.110861 & -2.8921 & 0.0063 & 0.00315 \tabularnewline
M1 & -1277764.43976877 & 647489.781113 & -1.9734 & 0.055753 & 0.027877 \tabularnewline
M2 & -301214.211190674 & 675546.515769 & -0.4459 & 0.658213 & 0.329106 \tabularnewline
M3 & -613984.12257938 & 674823.388649 & -0.9098 & 0.36864 & 0.18432 \tabularnewline
M4 & 1702120.12956651 & 668428.172544 & 2.5465 & 0.015057 & 0.007528 \tabularnewline
M5 & -697110.854102996 & 621690.910914 & -1.1213 & 0.26919 & 0.134595 \tabularnewline
M6 & -310757.834273430 & 612045.124272 & -0.5077 & 0.614573 & 0.307286 \tabularnewline
M7 & 31032.2778017763 & 721473.942765 & 0.043 & 0.965917 & 0.482958 \tabularnewline
M8 & -794467.94169392 & 637796.661994 & -1.2456 & 0.220521 & 0.11026 \tabularnewline
M9 & -1913826.67528654 & 693698.412609 & -2.7589 & 0.008872 & 0.004436 \tabularnewline
M10 & 447318.9919382 & 820443.485288 & 0.5452 & 0.588791 & 0.294395 \tabularnewline
M11 & 1871094.16661258 & 683553.738046 & 2.7373 & 0.009371 & 0.004685 \tabularnewline
t & 62686.6577060315 & 16390.407887 & 3.8246 & 0.000473 & 0.000237 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105088&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]7058102.37766021[/C][C]1705473.86239[/C][C]4.1385[/C][C]0.000187[/C][C]9.3e-05[/C][/ROW]
[ROW][C]X[/C][C]-2793503.20246403[/C][C]626762.642767[/C][C]-4.457[/C][C]7.1e-05[/C][C]3.6e-05[/C][/ROW]
[ROW][C]Y1[/C][C]0.360299173377357[/C][C]0.126369[/C][C]2.8512[/C][C]0.007004[/C][C]0.003502[/C][/ROW]
[ROW][C]Y2[/C][C]0.152048144010289[/C][C]0.122565[/C][C]1.2405[/C][C]0.222379[/C][C]0.111189[/C][/ROW]
[ROW][C]Y3[/C][C]0.3745587444819[/C][C]0.120063[/C][C]3.1197[/C][C]0.003448[/C][C]0.001724[/C][/ROW]
[ROW][C]Y4[/C][C]-0.320617214160464[/C][C]0.110861[/C][C]-2.8921[/C][C]0.0063[/C][C]0.00315[/C][/ROW]
[ROW][C]M1[/C][C]-1277764.43976877[/C][C]647489.781113[/C][C]-1.9734[/C][C]0.055753[/C][C]0.027877[/C][/ROW]
[ROW][C]M2[/C][C]-301214.211190674[/C][C]675546.515769[/C][C]-0.4459[/C][C]0.658213[/C][C]0.329106[/C][/ROW]
[ROW][C]M3[/C][C]-613984.12257938[/C][C]674823.388649[/C][C]-0.9098[/C][C]0.36864[/C][C]0.18432[/C][/ROW]
[ROW][C]M4[/C][C]1702120.12956651[/C][C]668428.172544[/C][C]2.5465[/C][C]0.015057[/C][C]0.007528[/C][/ROW]
[ROW][C]M5[/C][C]-697110.854102996[/C][C]621690.910914[/C][C]-1.1213[/C][C]0.26919[/C][C]0.134595[/C][/ROW]
[ROW][C]M6[/C][C]-310757.834273430[/C][C]612045.124272[/C][C]-0.5077[/C][C]0.614573[/C][C]0.307286[/C][/ROW]
[ROW][C]M7[/C][C]31032.2778017763[/C][C]721473.942765[/C][C]0.043[/C][C]0.965917[/C][C]0.482958[/C][/ROW]
[ROW][C]M8[/C][C]-794467.94169392[/C][C]637796.661994[/C][C]-1.2456[/C][C]0.220521[/C][C]0.11026[/C][/ROW]
[ROW][C]M9[/C][C]-1913826.67528654[/C][C]693698.412609[/C][C]-2.7589[/C][C]0.008872[/C][C]0.004436[/C][/ROW]
[ROW][C]M10[/C][C]447318.9919382[/C][C]820443.485288[/C][C]0.5452[/C][C]0.588791[/C][C]0.294395[/C][/ROW]
[ROW][C]M11[/C][C]1871094.16661258[/C][C]683553.738046[/C][C]2.7373[/C][C]0.009371[/C][C]0.004685[/C][/ROW]
[ROW][C]t[/C][C]62686.6577060315[/C][C]16390.407887[/C][C]3.8246[/C][C]0.000473[/C][C]0.000237[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105088&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105088&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	7058102.37766021	1705473.86239	4.1385	0.000187	9.3e-05
X	-2793503.20246403	626762.642767	-4.457	7.1e-05	3.6e-05
Y1	0.360299173377357	0.126369	2.8512	0.007004	0.003502
Y2	0.152048144010289	0.122565	1.2405	0.222379	0.111189
Y3	0.3745587444819	0.120063	3.1197	0.003448	0.001724
Y4	-0.320617214160464	0.110861	-2.8921	0.0063	0.00315
M1	-1277764.43976877	647489.781113	-1.9734	0.055753	0.027877
M2	-301214.211190674	675546.515769	-0.4459	0.658213	0.329106
M3	-613984.12257938	674823.388649	-0.9098	0.36864	0.18432
M4	1702120.12956651	668428.172544	2.5465	0.015057	0.007528
M5	-697110.854102996	621690.910914	-1.1213	0.26919	0.134595
M6	-310757.834273430	612045.124272	-0.5077	0.614573	0.307286
M7	31032.2778017763	721473.942765	0.043	0.965917	0.482958
M8	-794467.94169392	637796.661994	-1.2456	0.220521	0.11026
M9	-1913826.67528654	693698.412609	-2.7589	0.008872	0.004436
M10	447318.9919382	820443.485288	0.5452	0.588791	0.294395
M11	1871094.16661258	683553.738046	2.7373	0.009371	0.004685
t	62686.6577060315	16390.407887	3.8246	0.000473	0.000237

Multiple Linear Regression - Regression Statistics
Multiple R	0.936477260038661
R-squared	0.876989658569518
Adjusted R-squared	0.821958716350618
F-TEST (value)	15.9363009828373
F-TEST (DF numerator)	17
F-TEST (DF denominator)	38
p-value	2.46369591394568e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	822642.663004327
Sum Squared Residuals	25716156137804.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.936477260038661 \tabularnewline
R-squared & 0.876989658569518 \tabularnewline
Adjusted R-squared & 0.821958716350618 \tabularnewline
F-TEST (value) & 15.9363009828373 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 38 \tabularnewline
p-value & 2.46369591394568e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 822642.663004327 \tabularnewline
Sum Squared Residuals & 25716156137804.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105088&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.936477260038661[/C][/ROW]
[ROW][C]R-squared[/C][C]0.876989658569518[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.821958716350618[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.9363009828373[/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]2.46369591394568e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]822642.663004327[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]25716156137804.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105088&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105088&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.936477260038661
R-squared	0.876989658569518
Adjusted R-squared	0.821958716350618
F-TEST (value)	15.9363009828373
F-TEST (DF numerator)	17
F-TEST (DF denominator)	38
p-value	2.46369591394568e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	822642.663004327
Sum Squared Residuals	25716156137804.4

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	17972385.83	15957316.0405312	2015069.78946881
2	16896235.55	16456891.4315774	439344.118422616
3	16697955.94	17180920.9340329	-482964.994032918
4	19691579.52	18673134.3750347	1018445.14496525
5	15930700.75	16874554.3425273	-943853.592527275
6	17444615.98	16694492.3602775	750123.619722504
7	17699369.88	18257456.6449242	-558086.764924161
8	15189796.81	15448141.4209251	-258344.610925058
9	15672722.75	15298859.7618952	373862.988104766
10	17180794.3	17125146.9936770	55647.3063230449
11	17664893.45	18206732.7277884	-541839.277788402
12	17862884.98	17787541.6828714	75343.2971285835
13	16162288.88	17127433.4795061	-965144.599506103
14	17463628.82	17281861.1120908	181767.707909166
15	16772112.17	17161026.0204014	-388913.850401367
16	19106861.48	18788077.7426223	318783.737377711
17	16721314.25	18220266.4779752	-1498952.22797515
18	18161267.85	17488544.1653530	672723.684646968
19	18509941.2	19145329.9102478	-635388.710247796
20	17802737.97	17084996.9456764	717741.02432356
21	16409869.75	17130729.9860034	-720860.236003403
22	17967742.04	18614108.8445430	-646366.804542972
23	20286602.27	20073407.9154846	213194.354515382
24	19537280.81	19042385.9786602	494894.831339799
25	18021889.62	18939998.9055509	-918109.285550902
26	20194317.23	19688377.3642148	505939.865785151
27	19049596.62	18966474.1525072	83122.4674927592
28	20244720.94	20935779.0964159	-691058.156415871
29	21473302.24	20155346.6906513	1317955.54934873
30	19673603.19	20103476.8269737	-429873.636973670
31	21053177.29	20860988.4209119	192188.869088106
32	20159479.84	20398591.8058036	-239111.965803580
33	18203628.31	18161685.6284733	41942.6815267043
34	21289464.94	20838687.2638710	450777.676129021
35	20432335.71	19569029.2861691	863306.423830858
36	17180395.07	17474948.049778	-294552.979777997
37	15816786.32	16740780.5973217	-923994.277321695
38	15071819.75	15483841.2475146	-412021.497514609
39	14521120.61	13814780.5575235	706340.05247648
40	15668789.39	16413760.8053831	-744971.415383076
41	14346884.11	14565150.5055092	-218266.395509183
42	13881008.13	14744989.6391656	-863981.509165582
43	15465943.69	15387051.4355003	78892.2544997095
44	14238232.92	14261359.7187137	-23126.7987136971
45	13557713.21	13252658.6436281	305054.566371932
46	16127590.29	15987648.4679091	139941.822090906
47	16793894.2	17328555.7005578	-534661.50055784
48	16014007.43	16289692.5786904	-275685.148690384
49	16867867.15	16075688.7770901	792178.372909895
50	16014583.21	16729613.4046023	-715030.194602325
51	15878594.85	15796178.5255350	82416.324465046
52	18664899.14	18566098.450544	98800.6894559813
53	17962530.06	16619413.3933371	1343116.66666288
54	17332692.2	17461684.3582302	-128992.158230219
55	19542066.35	18619671.9984159	922394.351584143
56	17203555.19	17400712.8388812	-197157.648881226

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 17972385.83 & 15957316.0405312 & 2015069.78946881 \tabularnewline
2 & 16896235.55 & 16456891.4315774 & 439344.118422616 \tabularnewline
3 & 16697955.94 & 17180920.9340329 & -482964.994032918 \tabularnewline
4 & 19691579.52 & 18673134.3750347 & 1018445.14496525 \tabularnewline
5 & 15930700.75 & 16874554.3425273 & -943853.592527275 \tabularnewline
6 & 17444615.98 & 16694492.3602775 & 750123.619722504 \tabularnewline
7 & 17699369.88 & 18257456.6449242 & -558086.764924161 \tabularnewline
8 & 15189796.81 & 15448141.4209251 & -258344.610925058 \tabularnewline
9 & 15672722.75 & 15298859.7618952 & 373862.988104766 \tabularnewline
10 & 17180794.3 & 17125146.9936770 & 55647.3063230449 \tabularnewline
11 & 17664893.45 & 18206732.7277884 & -541839.277788402 \tabularnewline
12 & 17862884.98 & 17787541.6828714 & 75343.2971285835 \tabularnewline
13 & 16162288.88 & 17127433.4795061 & -965144.599506103 \tabularnewline
14 & 17463628.82 & 17281861.1120908 & 181767.707909166 \tabularnewline
15 & 16772112.17 & 17161026.0204014 & -388913.850401367 \tabularnewline
16 & 19106861.48 & 18788077.7426223 & 318783.737377711 \tabularnewline
17 & 16721314.25 & 18220266.4779752 & -1498952.22797515 \tabularnewline
18 & 18161267.85 & 17488544.1653530 & 672723.684646968 \tabularnewline
19 & 18509941.2 & 19145329.9102478 & -635388.710247796 \tabularnewline
20 & 17802737.97 & 17084996.9456764 & 717741.02432356 \tabularnewline
21 & 16409869.75 & 17130729.9860034 & -720860.236003403 \tabularnewline
22 & 17967742.04 & 18614108.8445430 & -646366.804542972 \tabularnewline
23 & 20286602.27 & 20073407.9154846 & 213194.354515382 \tabularnewline
24 & 19537280.81 & 19042385.9786602 & 494894.831339799 \tabularnewline
25 & 18021889.62 & 18939998.9055509 & -918109.285550902 \tabularnewline
26 & 20194317.23 & 19688377.3642148 & 505939.865785151 \tabularnewline
27 & 19049596.62 & 18966474.1525072 & 83122.4674927592 \tabularnewline
28 & 20244720.94 & 20935779.0964159 & -691058.156415871 \tabularnewline
29 & 21473302.24 & 20155346.6906513 & 1317955.54934873 \tabularnewline
30 & 19673603.19 & 20103476.8269737 & -429873.636973670 \tabularnewline
31 & 21053177.29 & 20860988.4209119 & 192188.869088106 \tabularnewline
32 & 20159479.84 & 20398591.8058036 & -239111.965803580 \tabularnewline
33 & 18203628.31 & 18161685.6284733 & 41942.6815267043 \tabularnewline
34 & 21289464.94 & 20838687.2638710 & 450777.676129021 \tabularnewline
35 & 20432335.71 & 19569029.2861691 & 863306.423830858 \tabularnewline
36 & 17180395.07 & 17474948.049778 & -294552.979777997 \tabularnewline
37 & 15816786.32 & 16740780.5973217 & -923994.277321695 \tabularnewline
38 & 15071819.75 & 15483841.2475146 & -412021.497514609 \tabularnewline
39 & 14521120.61 & 13814780.5575235 & 706340.05247648 \tabularnewline
40 & 15668789.39 & 16413760.8053831 & -744971.415383076 \tabularnewline
41 & 14346884.11 & 14565150.5055092 & -218266.395509183 \tabularnewline
42 & 13881008.13 & 14744989.6391656 & -863981.509165582 \tabularnewline
43 & 15465943.69 & 15387051.4355003 & 78892.2544997095 \tabularnewline
44 & 14238232.92 & 14261359.7187137 & -23126.7987136971 \tabularnewline
45 & 13557713.21 & 13252658.6436281 & 305054.566371932 \tabularnewline
46 & 16127590.29 & 15987648.4679091 & 139941.822090906 \tabularnewline
47 & 16793894.2 & 17328555.7005578 & -534661.50055784 \tabularnewline
48 & 16014007.43 & 16289692.5786904 & -275685.148690384 \tabularnewline
49 & 16867867.15 & 16075688.7770901 & 792178.372909895 \tabularnewline
50 & 16014583.21 & 16729613.4046023 & -715030.194602325 \tabularnewline
51 & 15878594.85 & 15796178.5255350 & 82416.324465046 \tabularnewline
52 & 18664899.14 & 18566098.450544 & 98800.6894559813 \tabularnewline
53 & 17962530.06 & 16619413.3933371 & 1343116.66666288 \tabularnewline
54 & 17332692.2 & 17461684.3582302 & -128992.158230219 \tabularnewline
55 & 19542066.35 & 18619671.9984159 & 922394.351584143 \tabularnewline
56 & 17203555.19 & 17400712.8388812 & -197157.648881226 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105088&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]17972385.83[/C][C]15957316.0405312[/C][C]2015069.78946881[/C][/ROW]
[ROW][C]2[/C][C]16896235.55[/C][C]16456891.4315774[/C][C]439344.118422616[/C][/ROW]
[ROW][C]3[/C][C]16697955.94[/C][C]17180920.9340329[/C][C]-482964.994032918[/C][/ROW]
[ROW][C]4[/C][C]19691579.52[/C][C]18673134.3750347[/C][C]1018445.14496525[/C][/ROW]
[ROW][C]5[/C][C]15930700.75[/C][C]16874554.3425273[/C][C]-943853.592527275[/C][/ROW]
[ROW][C]6[/C][C]17444615.98[/C][C]16694492.3602775[/C][C]750123.619722504[/C][/ROW]
[ROW][C]7[/C][C]17699369.88[/C][C]18257456.6449242[/C][C]-558086.764924161[/C][/ROW]
[ROW][C]8[/C][C]15189796.81[/C][C]15448141.4209251[/C][C]-258344.610925058[/C][/ROW]
[ROW][C]9[/C][C]15672722.75[/C][C]15298859.7618952[/C][C]373862.988104766[/C][/ROW]
[ROW][C]10[/C][C]17180794.3[/C][C]17125146.9936770[/C][C]55647.3063230449[/C][/ROW]
[ROW][C]11[/C][C]17664893.45[/C][C]18206732.7277884[/C][C]-541839.277788402[/C][/ROW]
[ROW][C]12[/C][C]17862884.98[/C][C]17787541.6828714[/C][C]75343.2971285835[/C][/ROW]
[ROW][C]13[/C][C]16162288.88[/C][C]17127433.4795061[/C][C]-965144.599506103[/C][/ROW]
[ROW][C]14[/C][C]17463628.82[/C][C]17281861.1120908[/C][C]181767.707909166[/C][/ROW]
[ROW][C]15[/C][C]16772112.17[/C][C]17161026.0204014[/C][C]-388913.850401367[/C][/ROW]
[ROW][C]16[/C][C]19106861.48[/C][C]18788077.7426223[/C][C]318783.737377711[/C][/ROW]
[ROW][C]17[/C][C]16721314.25[/C][C]18220266.4779752[/C][C]-1498952.22797515[/C][/ROW]
[ROW][C]18[/C][C]18161267.85[/C][C]17488544.1653530[/C][C]672723.684646968[/C][/ROW]
[ROW][C]19[/C][C]18509941.2[/C][C]19145329.9102478[/C][C]-635388.710247796[/C][/ROW]
[ROW][C]20[/C][C]17802737.97[/C][C]17084996.9456764[/C][C]717741.02432356[/C][/ROW]
[ROW][C]21[/C][C]16409869.75[/C][C]17130729.9860034[/C][C]-720860.236003403[/C][/ROW]
[ROW][C]22[/C][C]17967742.04[/C][C]18614108.8445430[/C][C]-646366.804542972[/C][/ROW]
[ROW][C]23[/C][C]20286602.27[/C][C]20073407.9154846[/C][C]213194.354515382[/C][/ROW]
[ROW][C]24[/C][C]19537280.81[/C][C]19042385.9786602[/C][C]494894.831339799[/C][/ROW]
[ROW][C]25[/C][C]18021889.62[/C][C]18939998.9055509[/C][C]-918109.285550902[/C][/ROW]
[ROW][C]26[/C][C]20194317.23[/C][C]19688377.3642148[/C][C]505939.865785151[/C][/ROW]
[ROW][C]27[/C][C]19049596.62[/C][C]18966474.1525072[/C][C]83122.4674927592[/C][/ROW]
[ROW][C]28[/C][C]20244720.94[/C][C]20935779.0964159[/C][C]-691058.156415871[/C][/ROW]
[ROW][C]29[/C][C]21473302.24[/C][C]20155346.6906513[/C][C]1317955.54934873[/C][/ROW]
[ROW][C]30[/C][C]19673603.19[/C][C]20103476.8269737[/C][C]-429873.636973670[/C][/ROW]
[ROW][C]31[/C][C]21053177.29[/C][C]20860988.4209119[/C][C]192188.869088106[/C][/ROW]
[ROW][C]32[/C][C]20159479.84[/C][C]20398591.8058036[/C][C]-239111.965803580[/C][/ROW]
[ROW][C]33[/C][C]18203628.31[/C][C]18161685.6284733[/C][C]41942.6815267043[/C][/ROW]
[ROW][C]34[/C][C]21289464.94[/C][C]20838687.2638710[/C][C]450777.676129021[/C][/ROW]
[ROW][C]35[/C][C]20432335.71[/C][C]19569029.2861691[/C][C]863306.423830858[/C][/ROW]
[ROW][C]36[/C][C]17180395.07[/C][C]17474948.049778[/C][C]-294552.979777997[/C][/ROW]
[ROW][C]37[/C][C]15816786.32[/C][C]16740780.5973217[/C][C]-923994.277321695[/C][/ROW]
[ROW][C]38[/C][C]15071819.75[/C][C]15483841.2475146[/C][C]-412021.497514609[/C][/ROW]
[ROW][C]39[/C][C]14521120.61[/C][C]13814780.5575235[/C][C]706340.05247648[/C][/ROW]
[ROW][C]40[/C][C]15668789.39[/C][C]16413760.8053831[/C][C]-744971.415383076[/C][/ROW]
[ROW][C]41[/C][C]14346884.11[/C][C]14565150.5055092[/C][C]-218266.395509183[/C][/ROW]
[ROW][C]42[/C][C]13881008.13[/C][C]14744989.6391656[/C][C]-863981.509165582[/C][/ROW]
[ROW][C]43[/C][C]15465943.69[/C][C]15387051.4355003[/C][C]78892.2544997095[/C][/ROW]
[ROW][C]44[/C][C]14238232.92[/C][C]14261359.7187137[/C][C]-23126.7987136971[/C][/ROW]
[ROW][C]45[/C][C]13557713.21[/C][C]13252658.6436281[/C][C]305054.566371932[/C][/ROW]
[ROW][C]46[/C][C]16127590.29[/C][C]15987648.4679091[/C][C]139941.822090906[/C][/ROW]
[ROW][C]47[/C][C]16793894.2[/C][C]17328555.7005578[/C][C]-534661.50055784[/C][/ROW]
[ROW][C]48[/C][C]16014007.43[/C][C]16289692.5786904[/C][C]-275685.148690384[/C][/ROW]
[ROW][C]49[/C][C]16867867.15[/C][C]16075688.7770901[/C][C]792178.372909895[/C][/ROW]
[ROW][C]50[/C][C]16014583.21[/C][C]16729613.4046023[/C][C]-715030.194602325[/C][/ROW]
[ROW][C]51[/C][C]15878594.85[/C][C]15796178.5255350[/C][C]82416.324465046[/C][/ROW]
[ROW][C]52[/C][C]18664899.14[/C][C]18566098.450544[/C][C]98800.6894559813[/C][/ROW]
[ROW][C]53[/C][C]17962530.06[/C][C]16619413.3933371[/C][C]1343116.66666288[/C][/ROW]
[ROW][C]54[/C][C]17332692.2[/C][C]17461684.3582302[/C][C]-128992.158230219[/C][/ROW]
[ROW][C]55[/C][C]19542066.35[/C][C]18619671.9984159[/C][C]922394.351584143[/C][/ROW]
[ROW][C]56[/C][C]17203555.19[/C][C]17400712.8388812[/C][C]-197157.648881226[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105088&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105088&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	17972385.83	15957316.0405312	2015069.78946881
2	16896235.55	16456891.4315774	439344.118422616
3	16697955.94	17180920.9340329	-482964.994032918
4	19691579.52	18673134.3750347	1018445.14496525
5	15930700.75	16874554.3425273	-943853.592527275
6	17444615.98	16694492.3602775	750123.619722504
7	17699369.88	18257456.6449242	-558086.764924161
8	15189796.81	15448141.4209251	-258344.610925058
9	15672722.75	15298859.7618952	373862.988104766
10	17180794.3	17125146.9936770	55647.3063230449
11	17664893.45	18206732.7277884	-541839.277788402
12	17862884.98	17787541.6828714	75343.2971285835
13	16162288.88	17127433.4795061	-965144.599506103
14	17463628.82	17281861.1120908	181767.707909166
15	16772112.17	17161026.0204014	-388913.850401367
16	19106861.48	18788077.7426223	318783.737377711
17	16721314.25	18220266.4779752	-1498952.22797515
18	18161267.85	17488544.1653530	672723.684646968
19	18509941.2	19145329.9102478	-635388.710247796
20	17802737.97	17084996.9456764	717741.02432356
21	16409869.75	17130729.9860034	-720860.236003403
22	17967742.04	18614108.8445430	-646366.804542972
23	20286602.27	20073407.9154846	213194.354515382
24	19537280.81	19042385.9786602	494894.831339799
25	18021889.62	18939998.9055509	-918109.285550902
26	20194317.23	19688377.3642148	505939.865785151
27	19049596.62	18966474.1525072	83122.4674927592
28	20244720.94	20935779.0964159	-691058.156415871
29	21473302.24	20155346.6906513	1317955.54934873
30	19673603.19	20103476.8269737	-429873.636973670
31	21053177.29	20860988.4209119	192188.869088106
32	20159479.84	20398591.8058036	-239111.965803580
33	18203628.31	18161685.6284733	41942.6815267043
34	21289464.94	20838687.2638710	450777.676129021
35	20432335.71	19569029.2861691	863306.423830858
36	17180395.07	17474948.049778	-294552.979777997
37	15816786.32	16740780.5973217	-923994.277321695
38	15071819.75	15483841.2475146	-412021.497514609
39	14521120.61	13814780.5575235	706340.05247648
40	15668789.39	16413760.8053831	-744971.415383076
41	14346884.11	14565150.5055092	-218266.395509183
42	13881008.13	14744989.6391656	-863981.509165582
43	15465943.69	15387051.4355003	78892.2544997095
44	14238232.92	14261359.7187137	-23126.7987136971
45	13557713.21	13252658.6436281	305054.566371932
46	16127590.29	15987648.4679091	139941.822090906
47	16793894.2	17328555.7005578	-534661.50055784
48	16014007.43	16289692.5786904	-275685.148690384
49	16867867.15	16075688.7770901	792178.372909895
50	16014583.21	16729613.4046023	-715030.194602325
51	15878594.85	15796178.5255350	82416.324465046
52	18664899.14	18566098.450544	98800.6894559813
53	17962530.06	16619413.3933371	1343116.66666288
54	17332692.2	17461684.3582302	-128992.158230219
55	19542066.35	18619671.9984159	922394.351584143
56	17203555.19	17400712.8388812	-197157.648881226

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.82997349703611	0.340053005927782	0.170026502963891
22	0.83314792584259	0.333704148314821	0.166852074157411
23	0.787621552744803	0.424756894510394	0.212378447255197
24	0.757448683521364	0.485102632957272	0.242551316478636
25	0.663480249894047	0.673039500211906	0.336519750105953
26	0.666354737186291	0.667290525627417	0.333645262813709
27	0.632807094776176	0.734385810447648	0.367192905223824
28	0.511944058955326	0.976111882089348	0.488055941044674
29	0.879802087010773	0.240395825978454	0.120197912989227
30	0.898077144022206	0.203845711955589	0.101922855977794
31	0.891500414035321	0.216999171929357	0.108499585964679
32	0.818936660411113	0.362126679177775	0.181063339588887
33	0.812199403684684	0.375601192630632	0.187800596315316
34	0.685847504404318	0.628304991191365	0.314152495595682
35	0.610865026455625	0.77826994708875	0.389134973544375

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.82997349703611 & 0.340053005927782 & 0.170026502963891 \tabularnewline
22 & 0.83314792584259 & 0.333704148314821 & 0.166852074157411 \tabularnewline
23 & 0.787621552744803 & 0.424756894510394 & 0.212378447255197 \tabularnewline
24 & 0.757448683521364 & 0.485102632957272 & 0.242551316478636 \tabularnewline
25 & 0.663480249894047 & 0.673039500211906 & 0.336519750105953 \tabularnewline
26 & 0.666354737186291 & 0.667290525627417 & 0.333645262813709 \tabularnewline
27 & 0.632807094776176 & 0.734385810447648 & 0.367192905223824 \tabularnewline
28 & 0.511944058955326 & 0.976111882089348 & 0.488055941044674 \tabularnewline
29 & 0.879802087010773 & 0.240395825978454 & 0.120197912989227 \tabularnewline
30 & 0.898077144022206 & 0.203845711955589 & 0.101922855977794 \tabularnewline
31 & 0.891500414035321 & 0.216999171929357 & 0.108499585964679 \tabularnewline
32 & 0.818936660411113 & 0.362126679177775 & 0.181063339588887 \tabularnewline
33 & 0.812199403684684 & 0.375601192630632 & 0.187800596315316 \tabularnewline
34 & 0.685847504404318 & 0.628304991191365 & 0.314152495595682 \tabularnewline
35 & 0.610865026455625 & 0.77826994708875 & 0.389134973544375 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105088&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]0.82997349703611[/C][C]0.340053005927782[/C][C]0.170026502963891[/C][/ROW]
[ROW][C]22[/C][C]0.83314792584259[/C][C]0.333704148314821[/C][C]0.166852074157411[/C][/ROW]
[ROW][C]23[/C][C]0.787621552744803[/C][C]0.424756894510394[/C][C]0.212378447255197[/C][/ROW]
[ROW][C]24[/C][C]0.757448683521364[/C][C]0.485102632957272[/C][C]0.242551316478636[/C][/ROW]
[ROW][C]25[/C][C]0.663480249894047[/C][C]0.673039500211906[/C][C]0.336519750105953[/C][/ROW]
[ROW][C]26[/C][C]0.666354737186291[/C][C]0.667290525627417[/C][C]0.333645262813709[/C][/ROW]
[ROW][C]27[/C][C]0.632807094776176[/C][C]0.734385810447648[/C][C]0.367192905223824[/C][/ROW]
[ROW][C]28[/C][C]0.511944058955326[/C][C]0.976111882089348[/C][C]0.488055941044674[/C][/ROW]
[ROW][C]29[/C][C]0.879802087010773[/C][C]0.240395825978454[/C][C]0.120197912989227[/C][/ROW]
[ROW][C]30[/C][C]0.898077144022206[/C][C]0.203845711955589[/C][C]0.101922855977794[/C][/ROW]
[ROW][C]31[/C][C]0.891500414035321[/C][C]0.216999171929357[/C][C]0.108499585964679[/C][/ROW]
[ROW][C]32[/C][C]0.818936660411113[/C][C]0.362126679177775[/C][C]0.181063339588887[/C][/ROW]
[ROW][C]33[/C][C]0.812199403684684[/C][C]0.375601192630632[/C][C]0.187800596315316[/C][/ROW]
[ROW][C]34[/C][C]0.685847504404318[/C][C]0.628304991191365[/C][C]0.314152495595682[/C][/ROW]
[ROW][C]35[/C][C]0.610865026455625[/C][C]0.77826994708875[/C][C]0.389134973544375[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105088&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105088&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.82997349703611	0.340053005927782	0.170026502963891
22	0.83314792584259	0.333704148314821	0.166852074157411
23	0.787621552744803	0.424756894510394	0.212378447255197
24	0.757448683521364	0.485102632957272	0.242551316478636
25	0.663480249894047	0.673039500211906	0.336519750105953
26	0.666354737186291	0.667290525627417	0.333645262813709
27	0.632807094776176	0.734385810447648	0.367192905223824
28	0.511944058955326	0.976111882089348	0.488055941044674
29	0.879802087010773	0.240395825978454	0.120197912989227
30	0.898077144022206	0.203845711955589	0.101922855977794
31	0.891500414035321	0.216999171929357	0.108499585964679
32	0.818936660411113	0.362126679177775	0.181063339588887
33	0.812199403684684	0.375601192630632	0.187800596315316
34	0.685847504404318	0.628304991191365	0.314152495595682
35	0.610865026455625	0.77826994708875	0.389134973544375

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\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=105088&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=105088&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105088&T=6

As an alternative you can also use a 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 tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

PNG link

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PDF link

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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code