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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 01 Dec 2008 12:40:33 -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/2008/Dec/01/t1228160540wzhsxthogjxmlh4.htm/, Retrieved Sun, 15 Jun 2025 07:52:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27255, Retrieved Sun, 15 Jun 2025 07:52:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact308

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [q3] [2008-11-23 15:48:09] [c5a66f1c8528a963efc2b82a8519f117]
-   P   [Multiple Regression] [q3a] [2008-11-23 15:53:10] [c5a66f1c8528a963efc2b82a8519f117]
-   P     [Multiple Regression] [q3a] [2008-11-23 16:20:20] [c5a66f1c8528a963efc2b82a8519f117]
-    D      [Multiple Regression] [Q3 - a] [2008-11-23 18:07:18] [c5a66f1c8528a963efc2b82a8519f117]
F             [Multiple Regression] [Q3 - 5 peaks] [2008-11-23 18:24:38] [a0d819c22534897f04a2f0b92f1eb36a]
-    D            [Multiple Regression] [verbetering Q3 - ...] [2008-12-01 19:40:33] [5f3e73ccf1ddc75508eed47fa51813d3] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1515	0
1510	0
1225	0
1577	0
1417	0
1224	0
1693	0
1633	0
1639	0
1914	0
1586	0
1552	0
2081	0
1500	0
1437	0
1470	0
1849	0
1387	0
1592	0
1589	0
1798	0
1935	0
1887	0
2027	0
2080	0
1556	0
1682	0
1785	0
1869	0
1781	0
2082	1
2570	1
1862	0
1936	0
1504	0
1765	0
1607	0
1577	0
1493	0
1615	0
1700	0
1335	0
1523	0
1623	0
1540	0
1637	0
1524	0
1419	0
1821	0
1593	0
1357	0
1263	0
1750	0
1405	0
1393	0
1639	0
1679	0
1551	0
1744	0
1429	0
1784	0

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27255&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27255&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27255&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
Gebouwen[t] = + 1624.71186440678 + 701.288135593221Dummy[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Gebouwen[t] =  +  1624.71186440678 +  701.288135593221Dummy[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27255&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Gebouwen[t] =  +  1624.71186440678 +  701.288135593221Dummy[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27255&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27255&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
Gebouwen[t] = + 1624.71186440678 + 701.288135593221Dummy[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1624.7118644067826.76120460.711500
Dummy701.288135593221147.7935814.74511.4e-057e-06

\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) & 1624.71186440678 & 26.761204 & 60.7115 & 0 & 0 \tabularnewline
Dummy & 701.288135593221 & 147.793581 & 4.7451 & 1.4e-05 & 7e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27255&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]1624.71186440678[/C][C]26.761204[/C][C]60.7115[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy[/C][C]701.288135593221[/C][C]147.793581[/C][C]4.7451[/C][C]1.4e-05[/C][C]7e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27255&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27255&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)1624.7118644067826.76120460.711500
Dummy701.288135593221147.7935814.74511.4e-057e-06






Multiple Linear Regression - Regression Statistics
Multiple R0.525558164934626
R-squared0.276211384729452
Adjusted R-squared0.263943781080798
F-TEST (value)22.5155126168241
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value1.36637341620061e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation205.556710047803
Sum Squared Residuals2492960.10169491

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.525558164934626 \tabularnewline
R-squared & 0.276211384729452 \tabularnewline
Adjusted R-squared & 0.263943781080798 \tabularnewline
F-TEST (value) & 22.5155126168241 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 1.36637341620061e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 205.556710047803 \tabularnewline
Sum Squared Residuals & 2492960.10169491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27255&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.525558164934626[/C][/ROW]
[ROW][C]R-squared[/C][C]0.276211384729452[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.263943781080798[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.5155126168241[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]1.36637341620061e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]205.556710047803[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2492960.10169491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27255&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27255&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 R0.525558164934626
R-squared0.276211384729452
Adjusted R-squared0.263943781080798
F-TEST (value)22.5155126168241
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value1.36637341620061e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation205.556710047803
Sum Squared Residuals2492960.10169491






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
115151624.71186440677-109.711864406772
215101624.71186440678-114.711864406782
312251624.71186440678-399.71186440678
415771624.71186440678-47.7118644067798
514171624.71186440678-207.711864406780
612241624.71186440678-400.71186440678
716931624.7118644067868.2881355932202
816331624.711864406788.28813559322024
916391624.7118644067814.2881355932202
1019141624.71186440678289.28813559322
1115861624.71186440678-38.7118644067798
1215521624.71186440678-72.7118644067798
1320811624.71186440678456.28813559322
1415001624.71186440678-124.711864406780
1514371624.71186440678-187.711864406780
1614701624.71186440678-154.711864406780
1718491624.71186440678224.288135593220
1813871624.71186440678-237.71186440678
1915921624.71186440678-32.7118644067798
2015891624.71186440678-35.7118644067798
2117981624.71186440678173.288135593220
2219351624.71186440678310.28813559322
2318871624.71186440678262.28813559322
2420271624.71186440678402.28813559322
2520801624.71186440678455.28813559322
2615561624.71186440678-68.7118644067798
2716821624.7118644067857.2881355932202
2817851624.71186440678160.288135593220
2918691624.71186440678244.28813559322
3017811624.71186440678156.288135593220
3120822326-244
3225702326244
3318621624.71186440678237.28813559322
3419361624.71186440678311.28813559322
3515041624.71186440678-120.711864406780
3617651624.71186440678140.288135593220
3716071624.71186440678-17.7118644067798
3815771624.71186440678-47.7118644067798
3914931624.71186440678-131.711864406780
4016151624.71186440678-9.71186440677976
4117001624.7118644067875.2881355932202
4213351624.71186440678-289.71186440678
4315231624.71186440678-101.711864406780
4416231624.71186440678-1.71186440677976
4515401624.71186440678-84.7118644067798
4616371624.7118644067812.2881355932202
4715241624.71186440678-100.711864406780
4814191624.71186440678-205.711864406780
4918211624.71186440678196.288135593220
5015931624.71186440678-31.7118644067798
5113571624.71186440678-267.71186440678
5212631624.71186440678-361.71186440678
5317501624.71186440678125.288135593220
5414051624.71186440678-219.711864406780
5513931624.71186440678-231.711864406780
5616391624.7118644067814.2881355932202
5716791624.7118644067854.2881355932202
5815511624.71186440678-73.7118644067798
5917441624.71186440678119.288135593220
6014291624.71186440678-195.711864406780
6117841624.71186440678159.288135593220

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1515 & 1624.71186440677 & -109.711864406772 \tabularnewline
2 & 1510 & 1624.71186440678 & -114.711864406782 \tabularnewline
3 & 1225 & 1624.71186440678 & -399.71186440678 \tabularnewline
4 & 1577 & 1624.71186440678 & -47.7118644067798 \tabularnewline
5 & 1417 & 1624.71186440678 & -207.711864406780 \tabularnewline
6 & 1224 & 1624.71186440678 & -400.71186440678 \tabularnewline
7 & 1693 & 1624.71186440678 & 68.2881355932202 \tabularnewline
8 & 1633 & 1624.71186440678 & 8.28813559322024 \tabularnewline
9 & 1639 & 1624.71186440678 & 14.2881355932202 \tabularnewline
10 & 1914 & 1624.71186440678 & 289.28813559322 \tabularnewline
11 & 1586 & 1624.71186440678 & -38.7118644067798 \tabularnewline
12 & 1552 & 1624.71186440678 & -72.7118644067798 \tabularnewline
13 & 2081 & 1624.71186440678 & 456.28813559322 \tabularnewline
14 & 1500 & 1624.71186440678 & -124.711864406780 \tabularnewline
15 & 1437 & 1624.71186440678 & -187.711864406780 \tabularnewline
16 & 1470 & 1624.71186440678 & -154.711864406780 \tabularnewline
17 & 1849 & 1624.71186440678 & 224.288135593220 \tabularnewline
18 & 1387 & 1624.71186440678 & -237.71186440678 \tabularnewline
19 & 1592 & 1624.71186440678 & -32.7118644067798 \tabularnewline
20 & 1589 & 1624.71186440678 & -35.7118644067798 \tabularnewline
21 & 1798 & 1624.71186440678 & 173.288135593220 \tabularnewline
22 & 1935 & 1624.71186440678 & 310.28813559322 \tabularnewline
23 & 1887 & 1624.71186440678 & 262.28813559322 \tabularnewline
24 & 2027 & 1624.71186440678 & 402.28813559322 \tabularnewline
25 & 2080 & 1624.71186440678 & 455.28813559322 \tabularnewline
26 & 1556 & 1624.71186440678 & -68.7118644067798 \tabularnewline
27 & 1682 & 1624.71186440678 & 57.2881355932202 \tabularnewline
28 & 1785 & 1624.71186440678 & 160.288135593220 \tabularnewline
29 & 1869 & 1624.71186440678 & 244.28813559322 \tabularnewline
30 & 1781 & 1624.71186440678 & 156.288135593220 \tabularnewline
31 & 2082 & 2326 & -244 \tabularnewline
32 & 2570 & 2326 & 244 \tabularnewline
33 & 1862 & 1624.71186440678 & 237.28813559322 \tabularnewline
34 & 1936 & 1624.71186440678 & 311.28813559322 \tabularnewline
35 & 1504 & 1624.71186440678 & -120.711864406780 \tabularnewline
36 & 1765 & 1624.71186440678 & 140.288135593220 \tabularnewline
37 & 1607 & 1624.71186440678 & -17.7118644067798 \tabularnewline
38 & 1577 & 1624.71186440678 & -47.7118644067798 \tabularnewline
39 & 1493 & 1624.71186440678 & -131.711864406780 \tabularnewline
40 & 1615 & 1624.71186440678 & -9.71186440677976 \tabularnewline
41 & 1700 & 1624.71186440678 & 75.2881355932202 \tabularnewline
42 & 1335 & 1624.71186440678 & -289.71186440678 \tabularnewline
43 & 1523 & 1624.71186440678 & -101.711864406780 \tabularnewline
44 & 1623 & 1624.71186440678 & -1.71186440677976 \tabularnewline
45 & 1540 & 1624.71186440678 & -84.7118644067798 \tabularnewline
46 & 1637 & 1624.71186440678 & 12.2881355932202 \tabularnewline
47 & 1524 & 1624.71186440678 & -100.711864406780 \tabularnewline
48 & 1419 & 1624.71186440678 & -205.711864406780 \tabularnewline
49 & 1821 & 1624.71186440678 & 196.288135593220 \tabularnewline
50 & 1593 & 1624.71186440678 & -31.7118644067798 \tabularnewline
51 & 1357 & 1624.71186440678 & -267.71186440678 \tabularnewline
52 & 1263 & 1624.71186440678 & -361.71186440678 \tabularnewline
53 & 1750 & 1624.71186440678 & 125.288135593220 \tabularnewline
54 & 1405 & 1624.71186440678 & -219.711864406780 \tabularnewline
55 & 1393 & 1624.71186440678 & -231.711864406780 \tabularnewline
56 & 1639 & 1624.71186440678 & 14.2881355932202 \tabularnewline
57 & 1679 & 1624.71186440678 & 54.2881355932202 \tabularnewline
58 & 1551 & 1624.71186440678 & -73.7118644067798 \tabularnewline
59 & 1744 & 1624.71186440678 & 119.288135593220 \tabularnewline
60 & 1429 & 1624.71186440678 & -195.711864406780 \tabularnewline
61 & 1784 & 1624.71186440678 & 159.288135593220 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27255&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]1515[/C][C]1624.71186440677[/C][C]-109.711864406772[/C][/ROW]
[ROW][C]2[/C][C]1510[/C][C]1624.71186440678[/C][C]-114.711864406782[/C][/ROW]
[ROW][C]3[/C][C]1225[/C][C]1624.71186440678[/C][C]-399.71186440678[/C][/ROW]
[ROW][C]4[/C][C]1577[/C][C]1624.71186440678[/C][C]-47.7118644067798[/C][/ROW]
[ROW][C]5[/C][C]1417[/C][C]1624.71186440678[/C][C]-207.711864406780[/C][/ROW]
[ROW][C]6[/C][C]1224[/C][C]1624.71186440678[/C][C]-400.71186440678[/C][/ROW]
[ROW][C]7[/C][C]1693[/C][C]1624.71186440678[/C][C]68.2881355932202[/C][/ROW]
[ROW][C]8[/C][C]1633[/C][C]1624.71186440678[/C][C]8.28813559322024[/C][/ROW]
[ROW][C]9[/C][C]1639[/C][C]1624.71186440678[/C][C]14.2881355932202[/C][/ROW]
[ROW][C]10[/C][C]1914[/C][C]1624.71186440678[/C][C]289.28813559322[/C][/ROW]
[ROW][C]11[/C][C]1586[/C][C]1624.71186440678[/C][C]-38.7118644067798[/C][/ROW]
[ROW][C]12[/C][C]1552[/C][C]1624.71186440678[/C][C]-72.7118644067798[/C][/ROW]
[ROW][C]13[/C][C]2081[/C][C]1624.71186440678[/C][C]456.28813559322[/C][/ROW]
[ROW][C]14[/C][C]1500[/C][C]1624.71186440678[/C][C]-124.711864406780[/C][/ROW]
[ROW][C]15[/C][C]1437[/C][C]1624.71186440678[/C][C]-187.711864406780[/C][/ROW]
[ROW][C]16[/C][C]1470[/C][C]1624.71186440678[/C][C]-154.711864406780[/C][/ROW]
[ROW][C]17[/C][C]1849[/C][C]1624.71186440678[/C][C]224.288135593220[/C][/ROW]
[ROW][C]18[/C][C]1387[/C][C]1624.71186440678[/C][C]-237.71186440678[/C][/ROW]
[ROW][C]19[/C][C]1592[/C][C]1624.71186440678[/C][C]-32.7118644067798[/C][/ROW]
[ROW][C]20[/C][C]1589[/C][C]1624.71186440678[/C][C]-35.7118644067798[/C][/ROW]
[ROW][C]21[/C][C]1798[/C][C]1624.71186440678[/C][C]173.288135593220[/C][/ROW]
[ROW][C]22[/C][C]1935[/C][C]1624.71186440678[/C][C]310.28813559322[/C][/ROW]
[ROW][C]23[/C][C]1887[/C][C]1624.71186440678[/C][C]262.28813559322[/C][/ROW]
[ROW][C]24[/C][C]2027[/C][C]1624.71186440678[/C][C]402.28813559322[/C][/ROW]
[ROW][C]25[/C][C]2080[/C][C]1624.71186440678[/C][C]455.28813559322[/C][/ROW]
[ROW][C]26[/C][C]1556[/C][C]1624.71186440678[/C][C]-68.7118644067798[/C][/ROW]
[ROW][C]27[/C][C]1682[/C][C]1624.71186440678[/C][C]57.2881355932202[/C][/ROW]
[ROW][C]28[/C][C]1785[/C][C]1624.71186440678[/C][C]160.288135593220[/C][/ROW]
[ROW][C]29[/C][C]1869[/C][C]1624.71186440678[/C][C]244.28813559322[/C][/ROW]
[ROW][C]30[/C][C]1781[/C][C]1624.71186440678[/C][C]156.288135593220[/C][/ROW]
[ROW][C]31[/C][C]2082[/C][C]2326[/C][C]-244[/C][/ROW]
[ROW][C]32[/C][C]2570[/C][C]2326[/C][C]244[/C][/ROW]
[ROW][C]33[/C][C]1862[/C][C]1624.71186440678[/C][C]237.28813559322[/C][/ROW]
[ROW][C]34[/C][C]1936[/C][C]1624.71186440678[/C][C]311.28813559322[/C][/ROW]
[ROW][C]35[/C][C]1504[/C][C]1624.71186440678[/C][C]-120.711864406780[/C][/ROW]
[ROW][C]36[/C][C]1765[/C][C]1624.71186440678[/C][C]140.288135593220[/C][/ROW]
[ROW][C]37[/C][C]1607[/C][C]1624.71186440678[/C][C]-17.7118644067798[/C][/ROW]
[ROW][C]38[/C][C]1577[/C][C]1624.71186440678[/C][C]-47.7118644067798[/C][/ROW]
[ROW][C]39[/C][C]1493[/C][C]1624.71186440678[/C][C]-131.711864406780[/C][/ROW]
[ROW][C]40[/C][C]1615[/C][C]1624.71186440678[/C][C]-9.71186440677976[/C][/ROW]
[ROW][C]41[/C][C]1700[/C][C]1624.71186440678[/C][C]75.2881355932202[/C][/ROW]
[ROW][C]42[/C][C]1335[/C][C]1624.71186440678[/C][C]-289.71186440678[/C][/ROW]
[ROW][C]43[/C][C]1523[/C][C]1624.71186440678[/C][C]-101.711864406780[/C][/ROW]
[ROW][C]44[/C][C]1623[/C][C]1624.71186440678[/C][C]-1.71186440677976[/C][/ROW]
[ROW][C]45[/C][C]1540[/C][C]1624.71186440678[/C][C]-84.7118644067798[/C][/ROW]
[ROW][C]46[/C][C]1637[/C][C]1624.71186440678[/C][C]12.2881355932202[/C][/ROW]
[ROW][C]47[/C][C]1524[/C][C]1624.71186440678[/C][C]-100.711864406780[/C][/ROW]
[ROW][C]48[/C][C]1419[/C][C]1624.71186440678[/C][C]-205.711864406780[/C][/ROW]
[ROW][C]49[/C][C]1821[/C][C]1624.71186440678[/C][C]196.288135593220[/C][/ROW]
[ROW][C]50[/C][C]1593[/C][C]1624.71186440678[/C][C]-31.7118644067798[/C][/ROW]
[ROW][C]51[/C][C]1357[/C][C]1624.71186440678[/C][C]-267.71186440678[/C][/ROW]
[ROW][C]52[/C][C]1263[/C][C]1624.71186440678[/C][C]-361.71186440678[/C][/ROW]
[ROW][C]53[/C][C]1750[/C][C]1624.71186440678[/C][C]125.288135593220[/C][/ROW]
[ROW][C]54[/C][C]1405[/C][C]1624.71186440678[/C][C]-219.711864406780[/C][/ROW]
[ROW][C]55[/C][C]1393[/C][C]1624.71186440678[/C][C]-231.711864406780[/C][/ROW]
[ROW][C]56[/C][C]1639[/C][C]1624.71186440678[/C][C]14.2881355932202[/C][/ROW]
[ROW][C]57[/C][C]1679[/C][C]1624.71186440678[/C][C]54.2881355932202[/C][/ROW]
[ROW][C]58[/C][C]1551[/C][C]1624.71186440678[/C][C]-73.7118644067798[/C][/ROW]
[ROW][C]59[/C][C]1744[/C][C]1624.71186440678[/C][C]119.288135593220[/C][/ROW]
[ROW][C]60[/C][C]1429[/C][C]1624.71186440678[/C][C]-195.711864406780[/C][/ROW]
[ROW][C]61[/C][C]1784[/C][C]1624.71186440678[/C][C]159.288135593220[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27255&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27255&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
115151624.71186440677-109.711864406772
215101624.71186440678-114.711864406782
312251624.71186440678-399.71186440678
415771624.71186440678-47.7118644067798
514171624.71186440678-207.711864406780
612241624.71186440678-400.71186440678
716931624.7118644067868.2881355932202
816331624.711864406788.28813559322024
916391624.7118644067814.2881355932202
1019141624.71186440678289.28813559322
1115861624.71186440678-38.7118644067798
1215521624.71186440678-72.7118644067798
1320811624.71186440678456.28813559322
1415001624.71186440678-124.711864406780
1514371624.71186440678-187.711864406780
1614701624.71186440678-154.711864406780
1718491624.71186440678224.288135593220
1813871624.71186440678-237.71186440678
1915921624.71186440678-32.7118644067798
2015891624.71186440678-35.7118644067798
2117981624.71186440678173.288135593220
2219351624.71186440678310.28813559322
2318871624.71186440678262.28813559322
2420271624.71186440678402.28813559322
2520801624.71186440678455.28813559322
2615561624.71186440678-68.7118644067798
2716821624.7118644067857.2881355932202
2817851624.71186440678160.288135593220
2918691624.71186440678244.28813559322
3017811624.71186440678156.288135593220
3120822326-244
3225702326244
3318621624.71186440678237.28813559322
3419361624.71186440678311.28813559322
3515041624.71186440678-120.711864406780
3617651624.71186440678140.288135593220
3716071624.71186440678-17.7118644067798
3815771624.71186440678-47.7118644067798
3914931624.71186440678-131.711864406780
4016151624.71186440678-9.71186440677976
4117001624.7118644067875.2881355932202
4213351624.71186440678-289.71186440678
4315231624.71186440678-101.711864406780
4416231624.71186440678-1.71186440677976
4515401624.71186440678-84.7118644067798
4616371624.7118644067812.2881355932202
4715241624.71186440678-100.711864406780
4814191624.71186440678-205.711864406780
4918211624.71186440678196.288135593220
5015931624.71186440678-31.7118644067798
5113571624.71186440678-267.71186440678
5212631624.71186440678-361.71186440678
5317501624.71186440678125.288135593220
5414051624.71186440678-219.711864406780
5513931624.71186440678-231.711864406780
5616391624.7118644067814.2881355932202
5716791624.7118644067854.2881355932202
5815511624.71186440678-73.7118644067798
5917441624.71186440678119.288135593220
6014291624.71186440678-195.711864406780
6117841624.71186440678159.288135593220






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.3858791081783410.7717582163566810.614120891821659
60.4398032274892780.8796064549785570.560196772510722
70.5330778027690460.9338443944619080.466922197230954
80.484848075084660.969696150169320.51515192491534
90.4279850842558230.8559701685116450.572014915744177
100.6939277753338010.6121444493323990.306072224666199
110.600267009315670.7994659813686590.399732990684329
120.5017846475953730.9964307048092550.498215352404627
130.8585717053770380.2828565892459230.141428294622962
140.8130574671724860.3738850656550280.186942532827514
150.782571992362790.434856015274420.21742800763721
160.736638375159550.52672324968090.26336162484045
170.7668822532917350.466235493416530.233117746708265
180.7657182818776550.4685634362446910.234281718122345
190.7001152783646520.5997694432706950.299884721635348
200.6283115134371890.7433769731256220.371688486562811
210.6207462357969250.758507528406150.379253764203075
220.7187483595787070.5625032808425850.281251640421293
230.7570199895729890.4859600208540220.242980010427011
240.883847612133490.2323047757330190.116152387866510
250.970402861723680.05919427655263990.0295971382763200
260.957016345056910.08596730988617980.0429836549430899
270.9384836277788480.1230327444423030.0615163722211517
280.9291000272625710.1417999454748570.0708999727374285
290.9405094810997130.1189810378005750.0594905189002874
300.9327700046051950.1344599907896110.0672299953948053
310.9530196468710180.09396070625796480.0469803531289824
320.9530329256854860.09393414862902750.0469670743145137
330.964667010583760.07066597883248050.0353329894162403
340.9871856211141270.02562875777174660.0128143788858733
350.9816300651950450.03673986960991020.0183699348049551
360.981159892141060.03768021571788050.0188401078589403
370.970733874826340.05853225034732140.0292661251736607
380.9554509565931150.0890980868137710.0445490434068855
390.9395954519823690.1208090960352620.0604045480176312
400.9130370547624230.1739258904751540.0869629452375772
410.893849551398350.2123008972033010.106150448601650
420.9176532047264440.1646935905471110.0823467952735556
430.8841190820147520.2317618359704970.115880917985249
440.8391285988024450.3217428023951090.160871401197555
450.781731428681030.4365371426379410.218268571318971
460.7173786037636170.5652427924727660.282621396236383
470.6401003237546090.7197993524907830.359899676245391
480.6057056852137740.7885886295724520.394294314786226
490.656423696100630.687152607798740.34357630389937
500.5602905192248180.8794189615503630.439709480775182
510.5656754390528650.868649121894270.434324560947135
520.7400268450314650.519946309937070.259973154968535
530.7043530916657610.5912938166684770.295646908334239
540.6964808340652770.6070383318694470.303519165934723
550.7569764529531620.4860470940936760.243023547046838
560.5929850405687040.814029918862590.407014959431295

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.385879108178341 & 0.771758216356681 & 0.614120891821659 \tabularnewline
6 & 0.439803227489278 & 0.879606454978557 & 0.560196772510722 \tabularnewline
7 & 0.533077802769046 & 0.933844394461908 & 0.466922197230954 \tabularnewline
8 & 0.48484807508466 & 0.96969615016932 & 0.51515192491534 \tabularnewline
9 & 0.427985084255823 & 0.855970168511645 & 0.572014915744177 \tabularnewline
10 & 0.693927775333801 & 0.612144449332399 & 0.306072224666199 \tabularnewline
11 & 0.60026700931567 & 0.799465981368659 & 0.399732990684329 \tabularnewline
12 & 0.501784647595373 & 0.996430704809255 & 0.498215352404627 \tabularnewline
13 & 0.858571705377038 & 0.282856589245923 & 0.141428294622962 \tabularnewline
14 & 0.813057467172486 & 0.373885065655028 & 0.186942532827514 \tabularnewline
15 & 0.78257199236279 & 0.43485601527442 & 0.21742800763721 \tabularnewline
16 & 0.73663837515955 & 0.5267232496809 & 0.26336162484045 \tabularnewline
17 & 0.766882253291735 & 0.46623549341653 & 0.233117746708265 \tabularnewline
18 & 0.765718281877655 & 0.468563436244691 & 0.234281718122345 \tabularnewline
19 & 0.700115278364652 & 0.599769443270695 & 0.299884721635348 \tabularnewline
20 & 0.628311513437189 & 0.743376973125622 & 0.371688486562811 \tabularnewline
21 & 0.620746235796925 & 0.75850752840615 & 0.379253764203075 \tabularnewline
22 & 0.718748359578707 & 0.562503280842585 & 0.281251640421293 \tabularnewline
23 & 0.757019989572989 & 0.485960020854022 & 0.242980010427011 \tabularnewline
24 & 0.88384761213349 & 0.232304775733019 & 0.116152387866510 \tabularnewline
25 & 0.97040286172368 & 0.0591942765526399 & 0.0295971382763200 \tabularnewline
26 & 0.95701634505691 & 0.0859673098861798 & 0.0429836549430899 \tabularnewline
27 & 0.938483627778848 & 0.123032744442303 & 0.0615163722211517 \tabularnewline
28 & 0.929100027262571 & 0.141799945474857 & 0.0708999727374285 \tabularnewline
29 & 0.940509481099713 & 0.118981037800575 & 0.0594905189002874 \tabularnewline
30 & 0.932770004605195 & 0.134459990789611 & 0.0672299953948053 \tabularnewline
31 & 0.953019646871018 & 0.0939607062579648 & 0.0469803531289824 \tabularnewline
32 & 0.953032925685486 & 0.0939341486290275 & 0.0469670743145137 \tabularnewline
33 & 0.96466701058376 & 0.0706659788324805 & 0.0353329894162403 \tabularnewline
34 & 0.987185621114127 & 0.0256287577717466 & 0.0128143788858733 \tabularnewline
35 & 0.981630065195045 & 0.0367398696099102 & 0.0183699348049551 \tabularnewline
36 & 0.98115989214106 & 0.0376802157178805 & 0.0188401078589403 \tabularnewline
37 & 0.97073387482634 & 0.0585322503473214 & 0.0292661251736607 \tabularnewline
38 & 0.955450956593115 & 0.089098086813771 & 0.0445490434068855 \tabularnewline
39 & 0.939595451982369 & 0.120809096035262 & 0.0604045480176312 \tabularnewline
40 & 0.913037054762423 & 0.173925890475154 & 0.0869629452375772 \tabularnewline
41 & 0.89384955139835 & 0.212300897203301 & 0.106150448601650 \tabularnewline
42 & 0.917653204726444 & 0.164693590547111 & 0.0823467952735556 \tabularnewline
43 & 0.884119082014752 & 0.231761835970497 & 0.115880917985249 \tabularnewline
44 & 0.839128598802445 & 0.321742802395109 & 0.160871401197555 \tabularnewline
45 & 0.78173142868103 & 0.436537142637941 & 0.218268571318971 \tabularnewline
46 & 0.717378603763617 & 0.565242792472766 & 0.282621396236383 \tabularnewline
47 & 0.640100323754609 & 0.719799352490783 & 0.359899676245391 \tabularnewline
48 & 0.605705685213774 & 0.788588629572452 & 0.394294314786226 \tabularnewline
49 & 0.65642369610063 & 0.68715260779874 & 0.34357630389937 \tabularnewline
50 & 0.560290519224818 & 0.879418961550363 & 0.439709480775182 \tabularnewline
51 & 0.565675439052865 & 0.86864912189427 & 0.434324560947135 \tabularnewline
52 & 0.740026845031465 & 0.51994630993707 & 0.259973154968535 \tabularnewline
53 & 0.704353091665761 & 0.591293816668477 & 0.295646908334239 \tabularnewline
54 & 0.696480834065277 & 0.607038331869447 & 0.303519165934723 \tabularnewline
55 & 0.756976452953162 & 0.486047094093676 & 0.243023547046838 \tabularnewline
56 & 0.592985040568704 & 0.81402991886259 & 0.407014959431295 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27255&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]5[/C][C]0.385879108178341[/C][C]0.771758216356681[/C][C]0.614120891821659[/C][/ROW]
[ROW][C]6[/C][C]0.439803227489278[/C][C]0.879606454978557[/C][C]0.560196772510722[/C][/ROW]
[ROW][C]7[/C][C]0.533077802769046[/C][C]0.933844394461908[/C][C]0.466922197230954[/C][/ROW]
[ROW][C]8[/C][C]0.48484807508466[/C][C]0.96969615016932[/C][C]0.51515192491534[/C][/ROW]
[ROW][C]9[/C][C]0.427985084255823[/C][C]0.855970168511645[/C][C]0.572014915744177[/C][/ROW]
[ROW][C]10[/C][C]0.693927775333801[/C][C]0.612144449332399[/C][C]0.306072224666199[/C][/ROW]
[ROW][C]11[/C][C]0.60026700931567[/C][C]0.799465981368659[/C][C]0.399732990684329[/C][/ROW]
[ROW][C]12[/C][C]0.501784647595373[/C][C]0.996430704809255[/C][C]0.498215352404627[/C][/ROW]
[ROW][C]13[/C][C]0.858571705377038[/C][C]0.282856589245923[/C][C]0.141428294622962[/C][/ROW]
[ROW][C]14[/C][C]0.813057467172486[/C][C]0.373885065655028[/C][C]0.186942532827514[/C][/ROW]
[ROW][C]15[/C][C]0.78257199236279[/C][C]0.43485601527442[/C][C]0.21742800763721[/C][/ROW]
[ROW][C]16[/C][C]0.73663837515955[/C][C]0.5267232496809[/C][C]0.26336162484045[/C][/ROW]
[ROW][C]17[/C][C]0.766882253291735[/C][C]0.46623549341653[/C][C]0.233117746708265[/C][/ROW]
[ROW][C]18[/C][C]0.765718281877655[/C][C]0.468563436244691[/C][C]0.234281718122345[/C][/ROW]
[ROW][C]19[/C][C]0.700115278364652[/C][C]0.599769443270695[/C][C]0.299884721635348[/C][/ROW]
[ROW][C]20[/C][C]0.628311513437189[/C][C]0.743376973125622[/C][C]0.371688486562811[/C][/ROW]
[ROW][C]21[/C][C]0.620746235796925[/C][C]0.75850752840615[/C][C]0.379253764203075[/C][/ROW]
[ROW][C]22[/C][C]0.718748359578707[/C][C]0.562503280842585[/C][C]0.281251640421293[/C][/ROW]
[ROW][C]23[/C][C]0.757019989572989[/C][C]0.485960020854022[/C][C]0.242980010427011[/C][/ROW]
[ROW][C]24[/C][C]0.88384761213349[/C][C]0.232304775733019[/C][C]0.116152387866510[/C][/ROW]
[ROW][C]25[/C][C]0.97040286172368[/C][C]0.0591942765526399[/C][C]0.0295971382763200[/C][/ROW]
[ROW][C]26[/C][C]0.95701634505691[/C][C]0.0859673098861798[/C][C]0.0429836549430899[/C][/ROW]
[ROW][C]27[/C][C]0.938483627778848[/C][C]0.123032744442303[/C][C]0.0615163722211517[/C][/ROW]
[ROW][C]28[/C][C]0.929100027262571[/C][C]0.141799945474857[/C][C]0.0708999727374285[/C][/ROW]
[ROW][C]29[/C][C]0.940509481099713[/C][C]0.118981037800575[/C][C]0.0594905189002874[/C][/ROW]
[ROW][C]30[/C][C]0.932770004605195[/C][C]0.134459990789611[/C][C]0.0672299953948053[/C][/ROW]
[ROW][C]31[/C][C]0.953019646871018[/C][C]0.0939607062579648[/C][C]0.0469803531289824[/C][/ROW]
[ROW][C]32[/C][C]0.953032925685486[/C][C]0.0939341486290275[/C][C]0.0469670743145137[/C][/ROW]
[ROW][C]33[/C][C]0.96466701058376[/C][C]0.0706659788324805[/C][C]0.0353329894162403[/C][/ROW]
[ROW][C]34[/C][C]0.987185621114127[/C][C]0.0256287577717466[/C][C]0.0128143788858733[/C][/ROW]
[ROW][C]35[/C][C]0.981630065195045[/C][C]0.0367398696099102[/C][C]0.0183699348049551[/C][/ROW]
[ROW][C]36[/C][C]0.98115989214106[/C][C]0.0376802157178805[/C][C]0.0188401078589403[/C][/ROW]
[ROW][C]37[/C][C]0.97073387482634[/C][C]0.0585322503473214[/C][C]0.0292661251736607[/C][/ROW]
[ROW][C]38[/C][C]0.955450956593115[/C][C]0.089098086813771[/C][C]0.0445490434068855[/C][/ROW]
[ROW][C]39[/C][C]0.939595451982369[/C][C]0.120809096035262[/C][C]0.0604045480176312[/C][/ROW]
[ROW][C]40[/C][C]0.913037054762423[/C][C]0.173925890475154[/C][C]0.0869629452375772[/C][/ROW]
[ROW][C]41[/C][C]0.89384955139835[/C][C]0.212300897203301[/C][C]0.106150448601650[/C][/ROW]
[ROW][C]42[/C][C]0.917653204726444[/C][C]0.164693590547111[/C][C]0.0823467952735556[/C][/ROW]
[ROW][C]43[/C][C]0.884119082014752[/C][C]0.231761835970497[/C][C]0.115880917985249[/C][/ROW]
[ROW][C]44[/C][C]0.839128598802445[/C][C]0.321742802395109[/C][C]0.160871401197555[/C][/ROW]
[ROW][C]45[/C][C]0.78173142868103[/C][C]0.436537142637941[/C][C]0.218268571318971[/C][/ROW]
[ROW][C]46[/C][C]0.717378603763617[/C][C]0.565242792472766[/C][C]0.282621396236383[/C][/ROW]
[ROW][C]47[/C][C]0.640100323754609[/C][C]0.719799352490783[/C][C]0.359899676245391[/C][/ROW]
[ROW][C]48[/C][C]0.605705685213774[/C][C]0.788588629572452[/C][C]0.394294314786226[/C][/ROW]
[ROW][C]49[/C][C]0.65642369610063[/C][C]0.68715260779874[/C][C]0.34357630389937[/C][/ROW]
[ROW][C]50[/C][C]0.560290519224818[/C][C]0.879418961550363[/C][C]0.439709480775182[/C][/ROW]
[ROW][C]51[/C][C]0.565675439052865[/C][C]0.86864912189427[/C][C]0.434324560947135[/C][/ROW]
[ROW][C]52[/C][C]0.740026845031465[/C][C]0.51994630993707[/C][C]0.259973154968535[/C][/ROW]
[ROW][C]53[/C][C]0.704353091665761[/C][C]0.591293816668477[/C][C]0.295646908334239[/C][/ROW]
[ROW][C]54[/C][C]0.696480834065277[/C][C]0.607038331869447[/C][C]0.303519165934723[/C][/ROW]
[ROW][C]55[/C][C]0.756976452953162[/C][C]0.486047094093676[/C][C]0.243023547046838[/C][/ROW]
[ROW][C]56[/C][C]0.592985040568704[/C][C]0.81402991886259[/C][C]0.407014959431295[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27255&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27255&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
50.3858791081783410.7717582163566810.614120891821659
60.4398032274892780.8796064549785570.560196772510722
70.5330778027690460.9338443944619080.466922197230954
80.484848075084660.969696150169320.51515192491534
90.4279850842558230.8559701685116450.572014915744177
100.6939277753338010.6121444493323990.306072224666199
110.600267009315670.7994659813686590.399732990684329
120.5017846475953730.9964307048092550.498215352404627
130.8585717053770380.2828565892459230.141428294622962
140.8130574671724860.3738850656550280.186942532827514
150.782571992362790.434856015274420.21742800763721
160.736638375159550.52672324968090.26336162484045
170.7668822532917350.466235493416530.233117746708265
180.7657182818776550.4685634362446910.234281718122345
190.7001152783646520.5997694432706950.299884721635348
200.6283115134371890.7433769731256220.371688486562811
210.6207462357969250.758507528406150.379253764203075
220.7187483595787070.5625032808425850.281251640421293
230.7570199895729890.4859600208540220.242980010427011
240.883847612133490.2323047757330190.116152387866510
250.970402861723680.05919427655263990.0295971382763200
260.957016345056910.08596730988617980.0429836549430899
270.9384836277788480.1230327444423030.0615163722211517
280.9291000272625710.1417999454748570.0708999727374285
290.9405094810997130.1189810378005750.0594905189002874
300.9327700046051950.1344599907896110.0672299953948053
310.9530196468710180.09396070625796480.0469803531289824
320.9530329256854860.09393414862902750.0469670743145137
330.964667010583760.07066597883248050.0353329894162403
340.9871856211141270.02562875777174660.0128143788858733
350.9816300651950450.03673986960991020.0183699348049551
360.981159892141060.03768021571788050.0188401078589403
370.970733874826340.05853225034732140.0292661251736607
380.9554509565931150.0890980868137710.0445490434068855
390.9395954519823690.1208090960352620.0604045480176312
400.9130370547624230.1739258904751540.0869629452375772
410.893849551398350.2123008972033010.106150448601650
420.9176532047264440.1646935905471110.0823467952735556
430.8841190820147520.2317618359704970.115880917985249
440.8391285988024450.3217428023951090.160871401197555
450.781731428681030.4365371426379410.218268571318971
460.7173786037636170.5652427924727660.282621396236383
470.6401003237546090.7197993524907830.359899676245391
480.6057056852137740.7885886295724520.394294314786226
490.656423696100630.687152607798740.34357630389937
500.5602905192248180.8794189615503630.439709480775182
510.5656754390528650.868649121894270.434324560947135
520.7400268450314650.519946309937070.259973154968535
530.7043530916657610.5912938166684770.295646908334239
540.6964808340652770.6070383318694470.303519165934723
550.7569764529531620.4860470940936760.243023547046838
560.5929850405687040.814029918862590.407014959431295






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0576923076923077NOK
10% type I error level100.192307692307692NOK

\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 & 3 & 0.0576923076923077 & NOK \tabularnewline
10% type I error level & 10 & 0.192307692307692 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27255&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]3[/C][C]0.0576923076923077[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.192307692307692[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27255&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27255&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 level30.0576923076923077NOK
10% type I error level100.192307692307692NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')
}